Knowledge management technologies and applications - literature review from 1995 to 2002. This paper surveys knowledge management (KM) development using a literature review and classification of articles from 1995 to 2002 with keyword index in order to explore how KM technologies and applications have developed in this period. Based on the scope of 234 articles of knowledge management applications, this paper surveys and classifies KM technologies using the seven categories as: KM framework, knowledge-based systems, data mining, information and communication technology, artificial intelligence/expert systems, database technology, and modeling, together with their applications for different research and problem domains. Some discussion is presented, indicating future development for knowledge management technologies and applications as the followings: (1) KM technologies tend to develop towards expert orientation, and KM applications development is a problem-oriented domain. (2) Different social studies methodologies, such as statistical method, are suggested to implement in KM as another kind of technology. (3) Integration of qualitative and quantitative methods, and integration of KM technologies studies may broaden our horizon on this subject. (4) The ability to continually change and obtain new understanding is the power of KM technologies and will be the application of future works. q 2003 Elsevier Science Ltd. All rights reserved.

Question: What is the scope of knowledge management technologies and applications?


Boosting the margin: A new explanation for the effectiveness of voting methods. One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show that techniques used in the analysis of Vapnik's support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins of the training examples. Finally, we compare our explanation to those based on the bias-variance decomposition.

Question: What is boosting?


Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields. We present an approach to efficiently detect the 2D pose of multiple people in an image. The approach uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. The architecture encodes global context, allowing a greedy bottom-up parsing step that maintains high accuracy while achieving realtime performance, irrespective of the number of people in the image. The architecture is designed to jointly learn part locations and their association via two branches of the same sequential prediction process. Our method placed first in the inaugural COCO 2016 keypoints challenge, and significantly exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.

Question: How many people are in the image?


A visual tool for ontology alignment to enable geospatial interoperability. In distributed geospatial applications with heterogeneous databases, an ontology-driven approach to data integration relies on the alignment of the concepts of a global ontology that describe the domain, with the concepts of the ontologies that describe the data in the distributed databases. Once the alignment between the global ontology and each distributed ontology is established, agreements that encode a variety of mappings between concepts are derived. In this way, users can potentially query hundreds of geospatial databases using a single query. Using our approach, querying can be easily extended to new data sources and, therefore, to new regions. In this paper, we describe the AgreementMaker, a tool that displays the ontologies, supports several mapping layers visually, presents automatically generated mappings, and finally produces the agreements. r 2007 Elsevier Ltd. All rights reserved.

Question: What is a tool for ontology alignment?


A 5-DOF rotation-symmetric parallel manipulator with one unconstrained tool rotation. This paper introduces a novel 5-DOF parallel manipulator with a rotation-symmetric arm system. The manipulator is unorthodox since one degree of freedom of its manipulated platform is unconstrained. Such a manipulator is still useful in a wide range of applications utilizing a rotation-symmetric tool. The manipulator workspace is analyzed for singularities and collisions. The rotation-symmetric arm system leads to a large positional workspace in relation to the footprint of the manipulator. With careful choice of structural parameters, the rotational workspace of the tool is also sizeable.

Question: What is a 5-DOF parallel manipulator?


A study of injection locking and pulling in oscillators. Injection locking characteristics of oscillators are derived and a graphical analysis is presented that describes injection pulling in time and frequency domains. An identity obtained from phase and envelope equations is used to express the requisite oscillator nonlinearity and interpret phase noise reduction. The behavior of phase-locked oscillators under injection pulling is also formulated.

Question: What is injection locking?


Alternative placement of bispectral index electrode for monitoring depth of anesthesia during neurosurgery.. In neurosurgery in particular, the recommended placement of electrodes for monitoring depth of anesthesia during surgery sometimes conflicts with the surgical site or patient positioning. Therefore, we proposed this study to evaluate the agreement and correlation of bispectral index values recorded from the usual frontal area and the alternate, post-auricular areas in neurosurgery patients. Thirty-four patients scheduled for neurosurgery under general anesthesia were included. Bispectral index (BIS) sensors were placed at both the frontal and post-auricular areas. The anesthesia given was clinically adjusted according to the frontal (standard) BIS reading. The BIS values and impedance were recorded;Pearson's correlation and Bland-Altman plots were analyzed. The bias±2SD for the electrode placement before, during, and post-anesthesia were 0±23.32, 1.5±10.69, and 2.1±13.52, while the limits of agreement were -23.3 to 23.3, -12.2 to 9.2, and -17.7 to 13.5, respectively. The correlation coefficient between frontal- and post-auricular-area electrodes was 0.74 with a p-value<0.001.The post-auricular placement of a BIS electrode is a practical alternative to frontal lobe placement. Nevertheless, proper electrode location is important to minimize error.

Question: What is the correlation between the frontal and post-auricular placement of a BIS electrode?


Segmentation of color image using adaptive thresholding and masking with watershed algorithm. The individualization of an object from a digital image is a common problem in the field of image processing. We propose a modified version of the watershed algorithm for image segmentation. We have presented an adaptive masking and a threshoding mechanism over each color channel to overcome over segmentation problem, before combining the segmentation from each channel into the final one. Our proposed method ensures accuracy and quality of the 10 different kinds of color images. The experimental results are obtained using image quality assessment (IQA) metrics such as PSNR, MSE, PSNRRGB and Color Image Quality Measure (CQM) based on reversible YUV color transformation. Consequently, the proposed modified watershed approach can enhance the image segmentation performance. Similarly, it is worth noticing that our proposed MWS approach is faster than many other segmentation algorithms, which makes it appropriate for real-time application. According to the visual and quantitative verification, the proposed algorithm is performing better than three other algorithms.

Question: How to segment a color image?


Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data. Most commonly, this task is performed by thresholding a per-pixel reconstruction error based on an `-distance. This procedure, however, leads to large residuals whenever the reconstruction includes slight localization inaccuracies around edges. It also fails to reveal defective regions that have been visually altered when intensity values stay roughly consistent. We show that these problems prevent these approaches from being applied to complex real-world scenarios and that they cannot be easily avoided by employing more elaborate architectures such as variational or feature matching autoencoders. We propose to use a perceptual loss function based on structural similarity that examines inter-dependencies between local image regions, taking into account luminance, contrast, and structural information, instead of simply comparing single pixel values. It achieves significant performance gains on a challenging real-world dataset of nanofibrous materials and a novel dataset of two woven fabrics over state-of-the-art approaches for unsupervised defect segmentation that use per-pixel reconstruction

Question: What is the difference between a convolutional autoencoder and a variational autoencoder?


On-line cursive script recognition using time-delay neural networks and hidden Markov models. We present a writer-independent system for online handwriting recognition that can handle a variety of writing styles including cursive script and handprinting. The input to our system contains the pen trajectory information, encoded as a time-ordered sequence of feature vectors. A time-delay neural network is used to estimate a posteriori probabilities for characters in a word. A hidden Markov model segments the word in a way that optimizes the global word score, using a dictionary in the process. A geometrical normalization scheme and a fast but efficient dictionary search are also presented. Trained on 20 k words from 59 writers, using a 25 k-word dictionary, our system reached recognition rates of 89% for characters and 80% for words on test data from a disjoint set of writers.

Question: How to recognize handwriting?


Developer Behavior and Sentiment from Data Mining Open Source Repositories. Developer sentiment may wax and wane as a project progresses. Open-source projects that attract and retain developers tend to be successful. It may be possible to predict project success, in part, if one can measure developer behavior and sentiment -- projects with active, happy developers are more likely to succeed. We have analyzed GitHub.com projects in an attempt to model these concepts. We have data mined 124 projects from GitHub.com. The projects were automatically mined using sequence mining methods to derive a behavioral model of developer activities. The projects were also mined for developer sentiment. Finally, a regression model shows how sentiment varies with behavioral differences -- a change in behavior is correlated with a change in sentiment. The relationship between sentiment and success is not directly explored, herein. This research project is a preliminary step in a larger research project aimed at understanding and monitoring FLOSS projects using a process modeling approach.

Question: What is the relationship between developer behavior and success?


Challenging Complexity of Maximum Common Subgraph Detection Algorithms: A Performance Analysis of Three Algorithms on a Wide Database of Graphs. Graphs are an extremely general and powerful data structure. In pattern recognition and computer vision, graphs are used to represent patterns to be recognized or classified. Detection of maximum common subgraph (MCS) is useful for matching, comparing and evaluate the similarity of patterns. MCS is a well known NP-complete problem for which optimal and suboptimal algorithms are known from the literature. Nevertheless, until now no effort has been done for characterizing their performance. The lack of a large database of graphs makes the task of comparing the performance of different graph matching algorithms difficult, and often the selection of an algorithm is made on the basis of a few experimental results available. In this paper, three optimal and well-known algorithms for maximum common subgraph detection are described. Moreover a large database containing various categories of pairs of graphs (e.g. random graphs, meshes, bounded valence graphs), is presented, and the performance of the three algorithms is evaluated on this database. Article Type Communicated by Submitted Revised Regular Paper U. Brandes September 2005 January 2007 D. Conte et al., Maximum Common Subgraph, JGAA, 11(1) 99–143 (2007) 100

Question: What is the maximum common subgraph of two graphs?


Good Question! Statistical Ranking for Question Generation. We address the challenge of automatically generating questions from reading materials for educational practice and assessment. Our approach is to overgenerate questions, then rank them. We use manually written rules to perform a sequence of general purpose syntactic transformations (e.g., subject-auxiliary inversion) to turn declarative sentences into questions. These questions are then ranked by a logistic regression model trained on a small, tailored dataset consisting of labeled output from our system. Experimental results show that ranking nearly doubles the percentage of questions rated as acceptable by annotators, from 27% of all questions to 52% of the top ranked 20% of questions.

Question: How do we generate questions from reading materials?


Multivalued logics: a uniform approach to reasoning in artificial intelligence. This paper describes a uniform formalization of much of the current work in artificial intelligence on inference systems. We show that many of these systems, including first-order theorem provers, assumption-based truth maintenance systems (ATMSS), and unimplemented formal systems such as default logic or circumscription, can be subsumed under a single general framework. We begin by defining this framework, which is based on a mathematical structure known as a bilattice. We present a formal definition of inference using this structure and show that this definition generalizes work involving ATMSS and some simple nonmonotonic logics. Following the theoretical description, we describe a constructive approach to inference in this setting; the resulting generalization of both conventional inference and ATMSS is achieved without incurring any substantial computational overhead. We show that our approach can also be used to implement a default reasoner, and discuss a combination of default and ATMS methods that enables us to formally describe an "incremental" default reasoning system. This incremental system does not need to perform consistency checks before drawing tentative conclusions, but can instead adjust its beliefs when a default premise or conclusion is overturned in the face of convincing contradictory evidence. The system is therefore much more computationally viable than earlier approaches. Finally, we discuss the implementation of our ideas. We begin by considering general issues that need to be addressed when implementing a multivalued approach such as that we are proposing, and then turn to specific examples showing the results of an existing implementation. This single implementation is used to solve a digital simulation task using first-order logic, a diagnostic task using ATMSS as suggested by de Kleer and Williams, a problem in default reasoning as in Reiter's default logic

Question: What is the difference between a first-order logic and a multivalued logic?


Design and Optimization of Printed Spiral Coils for Efficient Transcutaneous Inductive Power Transmission. The next generation of implantable high-power neuroprosthetic devices such as visual prostheses and brain computer interfaces are going to be powered by transcutaneous inductive power links formed between a pair of printed spiral coils (PSC) that are batch-fabricated using micromachining technology. Optimizing the power efficiency of the wireless link is imperative to minimize the size of the external energy source, heating dissipation in the tissue, and interference with other devices. Previous design methodologies for coils made of 1-D filaments are not comprehensive and accurate enough to consider all geometrical aspects of PSCs with planar 3-D conductors as well as design constraints imposed by implantable device application and fabrication technology. We have outlined the theoretical foundation of optimal power transmission efficiency in an inductive link, and combined it with semi-empirical models to predict parasitic components in PSCs. We have used this foundation to devise an iterative PSC design methodology that starts with a set of realistic design constraints and ends with the optimal PSC pair geometries. We have executed this procedure on two design examples at 1 and 5 MHz achieving power transmission efficiencies of 41.2% and 85.8%, respectively, at 10-mm spacing. All results are verified with simulations using a commercial field solver (HFSS) as well as measurements using PSCs fabricated on printed circuit boards.

Question: What is the optimal power transmission efficiency of a transcutaneous inductive link?


60GHz high-gain low-noise amplifiers with a common-gate inductive feedback in 65nm CMOS. In this paper, a novel design technique of common-gate inductive feedback is presented for millimeter-wave low-noise amplifiers (LNAs). For this technique, by adopting a gate inductor at the common-gate transistor of the cascode stage, the gain of the LNA can be enhanced even under a wideband operation. Using a 65nm CMOS process, transmission-line-based and spiral-inductor-based LNAs are fabricated for demonstration. With a dc power consumption of 33.6 mW from a 1.2-V supply voltage, the transmission-line-based LNA exhibits a gain of 20.6 dB and a noise figure of 5.4 dB at 60 GHz while the 3dB bandwidth is 14.1 GHz. As for the spiral-inductor-based LNA, consuming a dc power of 28.8 mW from a 1.2-V supply voltage, the circuit shows a gain of 18.0 dB and a noise figure of 4.5 dB at 60 GHz while the 3dB bandwidth is 12.2 GHz.

Question: What is the noise figure of a 60GHz LNA?


Using Argument Mining to Assess the Argumentation Quality of Essays. Argument mining aims to determine the argumentative structure of texts. Although it is said to be crucial for future applications such as writing support systems, the benefit of its output has rarely been evaluated. This paper puts the analysis of the output into the focus. In particular, we investigate to what extent the mined structure can be leveraged to assess the argumentation quality of persuasive essays. We find insightful statistical patterns in the structure of essays. From these, we derive novel features that we evaluate in four argumentation-related essay scoring tasks. Our results reveal the benefit of argument mining for assessing argumentation quality. Among others, we improve the state of the art in scoring an essay's organization and its argument strength.

Question: What is argument mining?


Structured light in scattering media. Virtually all structured light methods assume that the scene and the sources are immersed in pure air and that light is neither scattered nor absorbed. Recently, however, structured lighting has found growing application in underwater and aerial imaging, where scattering effects cannot be ignored. In this paper, we present a comprehensive analysis of two representative methods - light stripe range scanning and photometric stereo - in the presence of scattering. For both methods, we derive physical models for the appearances of a surface immersed in a scattering medium. Based on these models, we present results on (a) the condition for object detectability in light striping and (b) the number of sources required for photometric stereo. In both cases, we demonstrate that while traditional methods fail when scattering is significant, our methods accurately recover the scene (depths, normals, albedos) as well as the properties of the medium. These results are in turn used to restore the appearances of scenes as if they were captured in clear air. Although we have focused on light striping and photometric stereo, our approach can also be extended to other methods such as grid coding, gated and active polarization imaging.

Question: What is the difference between a scattering medium and a clear medium?


Rank Correlation for Low-Rate DDoS Attack Detection: An Empirical Evaluation. A low-rate distributed denial of service (DDoS) attack has the ability to obscure its traffic because it is very similar to legitimate traffic. It can easily evade current detection mechanisms. Rank correlation measures can quantify significant differences between attack traffic and legitimate traffic based on their rank values. In this paper, we use two rank correlation measures, namely, Spearmen Rank Correlation (SRC) and Partial Rank Correlation (PRC) to detect low-rate DDoS attacks. These measures are empirically evaluated using three real-life datasets. Experimental results show that both measures can effectively discriminate legitimate traffic from attack traffic. We find that PRC performs better than SRC in detection of lowrate DDoS attacks in terms of spacing between malicious and legitimate traffic.

Question: What is the difference between SRC and PRC?


HeNet: A Deep Learning Approach on Intel$^\circledR$ Processor Trace for Effective Exploit Detection. This paper presents HeNet, a hierarchical ensemble neural network, applied to classify hardware-generated control flow traces for malware detection. Deep learning-based malware detection has so far focused on analyzing executable files and runtime API calls. Static code analysis approaches face challenges due to obfuscated code and adversarial perturbations. Behavioral data collected during execution is more difficult to obfuscate but recent research has shown successful attacks against API call based malware classifiers. We investigate control flow based characterization of a program execution to build robust deep learning malware classifiers. HeNet consists of a low-level behavior model and a toplevel ensemble model. The low-level model is a per-application behavior model, trained via transfer learning on a time-series of images generated from control flow trace of an execution. We use Intel R © Processor Trace enabled processor for low overhead execution tracing and design a lightweight image conversion and segmentation of the control flow trace. The top-level ensemble model aggregates the behavior classification of all the trace segments and detects an attack. The use of hardware trace adds portability to our system and the use of deep learning eliminates the manual effort of feature engineering. We evaluate HeNet against real-world exploitations of PDF readers. HeNet achieves 100% accuracy and 0% false positive on test set, and higher classification accuracy compared to classical machine learning algorithms.

Question: What is a deep learning approach for malware detection?


A critical review of blockchain and its current applications. Blockchain technology has been known as a digital currency platform since the emergence of Bitcoin, the first and the largest of the cryptocurrencies. Hitherto, it is used for the decentralization of markets more generally, not exclusively for the decentralization of money and payments. The decentralized transaction ledger of blockchain could be employed to register, confirm, and send all kinds of contracts to other parties in the network. In this paper, we thoroughly review state-of-the-art blockchain-related applications emerged in the literature. A number of published works were carefully included based on their contributions to the blockchain's body of knowledge. Several remarks are explored and discussed in the last section of the paper.

Question: What is a blockchain?


Scientific workflow management and the Kepler system. Many scientific disciplines are now data and information driven, and new scientific knowledge is often gained by scientists putting together data analysis and knowledge discovery "pipelines". A related trend is that more and more scientific communities realize the benefits of sharing their data and computational services, and are thus contributing to a distributed data and computational community infrastructure (a.k.a. "the Grid"). However, this infrastructure is only a means to an end and scientists ideally should be bothered little with its existence. The goal is for scientists to focus on development and use of what we call scientific workflows. These are networks of analytical steps that may involve, e.g., database access and querying steps, data analysis and mining steps, and many other steps including computationally intensive jobs on high performance cluster computers. In this paper we describe characteristics of and requirements for scientific workflows as identified in a number of our application projects. We then elaborate on Kepler, a particular scientific workflow system, currently under development across a number of scientific data management projects. We describe some key features of Kepler and its underlying Ptolemy ii system, planned extensions, and areas of future research. Kepler is a communitydriven, open source project, and we always welcome related projects and new contributors to join. ∗Work supported by NSF/ITR 0225676 (SEEK), DOE SciDAC DE-FC02-01ER25486 (SDM), NSF/ITR CCR-00225610 (Chess), NSF/ITR 0225673 (GEON), NIH/NCRR 1R24 RR019701-01 Biomedical Informatics Research Network Coordinating Center (BIRN-CC), NSF/ITR 0325963 (ROADNet), NSF/DBI-0078296 (Resurgence) †San Diego Supercomputer Center, UC San Diego; ?Dept. of Computer Science & Genome Center, UC Davis; ‡National Center for Ecological Analysis and Synthesis, UC Santa Barbara; and §Department of Electrical Engineering and Compute

Question: What is the scientific workflow management and the Kepler system?


A generative theory of similarity. We argue that similarity judgments are inferences about generative processes, and that two objects appear similar when they are likely to have been generated by the same process. We describe a formal model based on this idea and show how featural and spatial models emerge as special cases. We compare our approach to the transformational approach, and present an experiment where our model performs better than a transformational model. Every object is the outcome of a generative process. An animal grows from a fertilized egg into an adult, a city develops from a settlement into a metropolis, and an artifact is assembled from a pile of raw materials according to the plan of its designer. Observations like these motivate the generative approach, which proposes that an object may be understood by thinking about the process that generated it. The promise of the approach is that apparently complex objects may be produced by simple processes, an insight that has proved productive across disciplines including biology [18], physics [21], and architecture [1]. To give two celebrated examples from biology, the shape of a pinecone and the markings on a cheetah's tail can be generated by remarkably simple processes of growth. These patterns can be characterized much more compactly by describing their causal history than by attempting to describe them directly. Leyton has argued that the generative approach provides a general framework for understanding cognition. Applications of the approach can be found in generative theories of perception [12], memory [12], language [3], categorization [2], and music [11]. This paper offers a generative theory of similarity, a notion often invoked by models of high-level cognition. We argue that two objects are similar to the extent that they seem to have been generated by the same underlying process. The literature on similarity covers settings that extend from the comparison of simple stimuli like tones and

Question: What is the generative approach?


Toward a Theory of Situation Awareness in Dynamic Systems. Situational awareness has become an increasingly salient factor contributing to flight safety and operational performance, and the research has burgeoned to cope with the human performance challenges associated with the installation of advanced avionics systems in modern aircraft. The systematic study and application of situational awareness has also extended beyond the cockpit to include air traffic controllers and personnel operating within other complex, high consequence work domains. This volume offers a collection of essays that have made important contributions to situational awareness research and practice. To this end, it provides unique access to key readings that address the conceptual development of situational awareness, methods for its assessment, and applications to enhance situational awareness through training and design.

Question: What is the difference between a theory and a theory of a theory?


Testing using Log File Analysis: Tools, Methods, and Issues. Large software systems often keep log files of events. Such log files can be analyzed to check whether a run of a program reveals faults in the system. We discuss how such log files can be used in software testing. We present a framework for automatically analyzing log files, and describe a language for specifying analyzer programs and an implementation of that language. The language permits compositional, compact specifications of software, which act as test oracles; we discuss the use and efficacy of these oracles for unitand system-level testing in various settings. We explore methodological issues such as efficiency and logging policies, and the scope and limitations of the framework. We conclude that testing using log file analysis constitutes a useful methodology for software verification, somewhere between current testing practice and formal verification methodologies.

Question: What is the difference between unit and system testing?


Heart rate variability and its relation to prefrontal cognitive function: the effects of training and detraining. The aim of the present study was to investigate the relationship between physical fitness, heart rate variability (HRV) and cognitive function in 37 male sailors from the Royal Norwegian Navy. All subjects participated in an 8-week training program, after which the subjects completed the initial cognitive testing (pre-test). The subjects were assigned into a detrained group (DG) and a trained group (TG) based on their application for further duty. The DG withdrew from the training program for 4 weeks after which all subjects then completed the cognitive testing again (post-test). Physical fitness, measured as maximum oxygen consumption (V̇O2max), resting HRV, and cognitive function, measured using a continuous performance task (CPT) and a working memory test (WMT), were recorded during the pre-test and the post-test, and the data presented as the means and standard deviations. The results showed no between-group differences in V̇O2max or HRV at the pre-test. The DG showed a significant decrease in V̇O2max from the pre- to the post-test and a lower resting HRV than the TG on the post-test. Whereas there were no between-group differences on the CPT or WMT at the pre-test, the TG had faster reaction times and more true positive responses on tests of executive function at the post-test compared to the pre-test. The DG showed faster reaction times on non-executive tasks at the post-test compared to the pre-test. The results are discussed within a neurovisceral integration framework linking parasympathetic outflow to the heart to prefrontal neural functions.

Question: What is the relationship between physical fitness, heart rate variability and cognitive function?


Learning Content-rich Diffusion Network Embedding 1. Information networks are ubiquitous in the real world, while embedding, as a kind of network representation, has received attention from many researchers because of its effectiveness in preserving the semantics of the network and its broad application including classification, link prediction etc. Previously, many methods have been proposed to learn network embedding from non-attributed and static networks. Networks are treated simply as nodes and links. However, information is not merely encoded in the structure, nodes itself may have their intrinsic attributes and little research have been done to incorporate this information. Furthermore, outside information will also diffuse on the network over the time and for a specific time we can have a different diffusion structure. In this paper, we will be introducing the idea of diffusion network, and we propose two models for embedding learning that aim to efficiently capture the rich content of nodes as well as that of the diffusion. We then evaluate the quality of our embedding by conducting node classification experiments, the result of which shows that our method for generating embedding outperforms other baselines.

Question: What is the difference between a diffusion network and a static network?


Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods. Direct search methods are best known as unconstrained optimization techniques that do not explicitly use derivatives. Direct search methods were formally proposed and widely applied in the 1960s but fell out of favor with the mathematical optimization community by the early 1970s because they lacked coherent mathematical analysis. Nonetheless, users remained loyal to these methods, most of which were easy to program, some of which were reliable. In the past fifteen years, these methods have seen a revival due, in part, to the appearance of mathematical analysis, as well as to interest in parallel and distributed computing. This review begins by briefly summarizing the history of direct search methods and considering the special properties of problems for which they are well suited. Our focus then turns to a broad class of methods for which we provide a unifying framework that lends itself to a variety of convergence results. The underlying principles allow generalization to handle bound constraints and linear constraints. We also discuss extensions to problems with nonlinear constraints.

Question: What is the history of direct search methods?


Shilling attacks against recommender systems: a comprehensive survey. Online vendors employ collaborative filtering algorithms to provide recommendations to their customers so that they can increase their sales and profits. Although recommendation schemes are successful in e-commerce sites, they are vulnerable to shilling or profile injection attacks. On one hand, online shopping sites utilize collaborative filtering schemes to enhance their competitive edge over other companies. On the other hand, malicious users and/or competing vendors might decide to insert fake profiles into the user-item matrices in such a way so that they can affect the predicted ratings on behalf of their advantages. In the past decade, various studies have been conducted to scrutinize different shilling attacks strategies, profile injection attack types, shilling attack detection schemes, robust algorithms proposed to overcome such attacks, and evaluate them with respect to accuracy, cost/benefit, and overall performance. Due to their popularity and importance, we survey about shilling attacks in collaborative filtering algorithms. Giving an overall picture about various shilling attack types by introducing new classification attributes is imperative for further research. Explaining shilling attack detection schemes in detail and robust algorithms proposed so far might open a lead to develop new detection schemes and enhance such robust algorithms further, even propose new ones. Thus, we describe various attack types and introduce new dimensions for attack classification. Detailed description of the proposed detection and robust recommendation algorithms are given. Moreover, we briefly explain evaluation of the proposed schemes. We conclude the paper by discussing various open questions.

Question: What is a recommender system?


An Integral Model to Provide Reactive and Proactive Services in an Academic CSIRT Based on Business Intelligence. Cyber-attacks have increased in severity and complexity. That requires, that the CERT/CSIRT research and develops new security tools. Therefore, our study focuses on the design of an integral model based on Business Intelligence (BI), which provides reactive and proactive services in a CSIRT, in order to alert and reduce any suspicious or malicious activity on information systems and data networks. To achieve this purpose, a solution has been assembled, that generates information stores, being compiled from a continuous network transmission of several internal and external sources of an organization. However, it contemplates a data warehouse, which is focused like a correlator of logs, being formed by the information of feeds with diverse formats. Furthermore, it analyzed attack detection and port scanning, obtained from sensors such as Snort and Passive Vulnerability Scanner, which are stored in a database, where the logs have been generated by the systems. With such inputs, we designed and implemented BI systems using the phases of the Ralph Kimball methodology, ETL and OLAP processes. In addition, a software application has been implemented using the SCRUM methodology, which allowed to link the obtained logs to the BI system for visualization in dynamic dashboards, with the purpose of generating early alerts and constructing complex queries using the user interface through objects structures. The results demonstrate, that this solution has generated early warnings based on the level of criticality and level of sensitivity of malware and vulnerabilities as well as monitoring efficiency, increasing the level of security of member institutions.

Question: What is the Ralph Kimball methodology?


Tree Adjoining Grammar Based Parser for a Hindi Text-to-Scene Conversion System. We have developed Preksha: a Hindi text visualizer. Preksha generates a 3D scene by understanding a given Hindi text. We propose the Earley's parsing algorithm using Tree adjoining grammar (TAG) for Hindi language understanding. A brief overview of the TAG formalism and the Earley's parsing algorithm is presented. Preksha's Hindi language parsing is explainedwith the Lisp notation. The maincontribution of this paper is to apply TAG parsing for morphologically-richfree-order language Hindi for automatic text visualization (ATV).

Question: What is the lisp notation?


Process discovery from event data: Relating models and logs through abstractions. Event data are collected in logistics, manufacturing, finance, healthcare, customer relationship management, e-learning, e-government, and many other domains. The events found in these domains typically refer to activities executed by resources at particular times and for a particular case (i.e., process instances). Process mining techniques are able to exploit such data. In this article, we focus on process discovery. However, process mining also includes conformance checking, performance analysis, decision mining, organizational mining, predictions, recommendations, etc. These techniques help to diagnose problems and improve processes. All process mining techniques involve both event data and process models. Therefore, a typical first step is to automatically learn a control-flow model from the event data. This is very challenging, but in recent years many powerful discovery techniques have been developed. It is not easy to compare these techniques since they use different representations and make different assumptions. Users often need to resort to trying different algorithms in an ad-hoc manner. Developers of new techniques are often trying to solve specific instances of a more general problem. Therefore, we aim to unify existing approaches by focusing on log and model abstractions. These abstractions link observed and modeled behavior: Concrete behaviors recorded in event logs are related to possible behaviors represented by process models. Hence, such behavioral abstractions provide an "interface" between both. We discuss four discovery approaches involving three abstractions and different types of process models (Petri nets, block-structured models, and declarative models). The goal is to provide a comprehensive understanding of process discovery and show how to develop new techniques. Examples illustrate the different approaches and pointers to software are given. The discussio

Question: What is process discovery?


Receiver Design for Downlink Non-Orthogonal Multiple Access (NOMA). Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for further cellular enhancements toward 5G. Compared to orthogonal multiple access (OMA) such as orthogonal frequency-division multiple access (OFDMA), large performance gains were confirmed via system-level simulations. However, NOMA link-level simulations and the design of the receiver remain of great importance to validate NOMA performance gains. In this paper, we evaluate downlink NOMA link-level performance with multiple receiver designs and propose a novel NOMA transmitter and receiver design, where the signals of multi-users are jointly modulated at transmitter side and detected at receiver side. The predominant advantage of the proposed scheme is that at receiver side interference cancellation to the interference signal is not needed, thus low complexity is achieved. The performances of codeword-level SIC, symbol-level SIC and the proposed receiver are evaluated and compared with ideal SIC. Simulation results show that compared with ideal SIC, downlink NOMA link-level performance depends on actual receiver design and the difference in the power ratio split between the cell edge user and cell center user. In particular, it is shown that codeword-level SIC and the proposed receiver can both provide a good performance even when the power ratio difference between the cell center user and cell edge user is small and with real channel estimation.

Question: What is the difference between the proposed receiver and ideal SIC?


Low voltage sub-nanosecond pulsed current driver IC for high-resolution LIDAR applications. This paper introduces a new low voltage sub-nanosecond monolithic pulsed current driver for light detection and ranging (LIDAR) applications. Unique architecture based on a controlled current source and Vernier activation sequence, combined with a monolithic implementation that allows operation with low input voltage levels, high-resolution pulse width and sub-nanosecond rise and fall times. An on-chip low voltage pulsed driver sub-nanosecond prototype has been implemented in a TS 0.18-μm 5V-gated power management process. It incorporates an integrated wide range sesnseFET based current sensor and a rail-to-rail comparator for current regulation. To characterize the avalanche capabilities of the integrated lateral MOSFET power devices required for the driver IC, a separate line of investigation has been carried out. Several lateral diffused MOS (LDMOS) power devices have been custom designed and experimentally evaluated for a life-cycle performance characterization. Post-layout analysis of the power driver IC is in a good agreement with the theoretical predictions. For a 5V input voltage, rise and fall times of the laser pulse light output are on the order of hundreds of picoseconds, with currents up to 5A. To validate the concept of high-resolution pulse width generation and short fall time, a discrete prototype has been constructed and experimentally tested.

Question: What is a LIDAR?


A qualitative evaluation of evolution of a learning analytics tool. New systems are designed with improved quality or features. However, not every new system lives to its pre-development expectations when tested with end-users. The evaluation of the first version of our LOCO-Analyst tool for providing teachers with feedback on students learning activities and performance led to the enhancing the visualization features of the tool. The second evaluation of the improved tool allowed us to see how the improvements affect the user perceptions of the tool. Here, we present the qualitative results of our two evaluations. The results show that educators find the kinds of feedback implemented in the tool informative and they value the mix of textual and graphical representations of different kinds of feedback provided by the tool.

Question: What is the qualitative evaluation of evolution of a learning analytics tool?


Towards a New Structural Model of the Sense of Humor: Preliminary Findings. In this article some formal, content-related and procedural considerations towards the sense of humor are articulated and the analysis of both everyday humor behavior and of comic styles leads to the initial proposal of a four factormodel of humor (4FMH). This model is tested in a new dataset and it is also examined whether two forms of comic styles (benevolent humor and moral mockery) do fit in. The model seems to be robust but further studies on the structure of the sense of humor as a personality trait are required.

Question: What is the sense of humor?


Four-Dimensional Discrete-time Lotka-Volterra Models with an Application to Ecology. This paper presents a study of the two-predators-two-preys discrete-time Lotka-Volterra model with selfinhibition terms for preys with direct applications to ecological problems. Parameters in the model are modified so that each of them has its own biological meaning, enabling more intuitive interpretation of biological conditions to the model . Moreover, the modified version is applicable to simulate a part of a large ecosystem, not only a closed predator-prey system with four species. An easy graphical method of analysis of the conditions on parameters ensuring long persistence under coexistence is presented. As an application, it is explained that a predator specie who feed on relatively small number of preys compared to the other predator species must be selective on the available preys in order for long persistence of the ecosystem. This may be regarded as a theoretical explanation of the existence of flush-pursuer birds, those who uses highly specialized hunting strategy and cross-adapts to the ecosystem relative to the ordinary bird species.

Question: What is the difference between a predator and a prey?


De-anonymizing scale-free social networks by percolation graph matching. We address the problem of social network de-anonymization when relationships between people are described by scale-free graphs. In particular, we propose a rigorous, asymptotic mathematical analysis of the network de-anonymization problem while capturing the impact of power-law node degree distribution, which is a fundamental and quite ubiquitous feature of many complex systems such as social networks. By applying bootstrap percolation and a novel graph slicing technique, we prove that large inhomogeneities in the node degree lead to a dramatic reduction of the initial set of nodes that must be known a priori (the seeds) in order to successfully identify all other users. We characterize the size of this set when seeds are selected using different criteria, and we show that their number can be as small as n% for any small ε > 0. Our results are validated through simulation experiments on real social network graphs.

Question: How to de-anonymize social networks?


Low numeracy and dyscalculia : identification and intervention. One important factor in the failure to learn arithmetic in the normal way is an endogenous core deficit in the sense of number. This has been associated with low numeracy in general (e.g. Halberda et al. in Nature 455:665–668, 2008) and with dyscalculia more specifically (e.g. Landerl et al. in Cognition 93:99–125, 2004). Here, we describe straightforward ways of identifying this deficit, and offer some new ways of strengthening the sense of number using learning technologies.

Question: What is dyscalculia?


Context and behavioral processes in extinction.. This article provides a selective review and integration of the behavioral literature on Pavlovian extinction. The first part reviews evidence that extinction does not destroy the original learning, but instead generates new learning that is especially context-dependent. The second part examines insights provided by research on several related behavioral phenomena (the interference paradigms, conditioned inhibition, and inhibition despite reinforcement). The final part examines four potential causes of extinction: the discrimination of a new reinforcement rate, generalization decrement, response inhibition, and violation of a reinforcer expectation. The data are consistent with behavioral models that emphasize the role of generalization decrement and expectation violation, but would be more so if those models were expanded to better accommodate the finding that extinction involves a context-modulated form of inhibitory learning.

Question: What is the context of extinction?


Fossils, genes and the evolution of animal limbs. The morphological and functional evolution of appendages has played a crucial role in the adaptive radiation of tetrapods, arthropods and winged insects. The origin and diversification of fins, wings and other structures, long a focus of palaeontology, can now be approached through developmental genetics. Modifications of appendage number and architecture in each phylum are correlated with regulatory changes in specific patterning genes. Although their respective evolutionary histories are unique, vertebrate, insect and other animal appendages are organized by a similar genetic regulatory system that may have been established in a common ancestor.

Question: What is the origin of animal appendages?


Birds of a ' ' bad ' ' feather flock together : The Dark Triad and mate choice. Previous research on the Dark Triad traits (i.e., Machiavellianism, psychopathy, and narcissism) has focused solely on the role the Dark Triad traits played in mate choice of actors. The current study (N = 336) extends this by manipulating the apparent levels of Dark Triad traits in targets and correlating mate choice in these targets with individual differences in the Dark Triad traits in actors. As expected, both sexes preferred partners low in the Dark Triad traits for long-term mating, while those high in these traits were preferred for one-night stands. However, women high in psychopathy considered the Dark Triad traits in potential male partners more physically attractive and desirable for an one-night stand, as well as a potential husband. Men who were high on psychopathy were likewise attracted to psychopathy in potential mothers. Our findings are discussed from an evolutionary personality paradigm. 2015 Elsevier Ltd. All rights reserved.

Question: What is the dark triad?


Data-driven Automated Induction of Prerequisite Structure Graphs. With the growing popularity of MOOCs and sharp trend of digitalizing education, there is a huge amount of free digital educational material on the web along with the activity logs of large number of participating students. However, this data is largely unstructured and there is hardly any information about the relationship between material from different sources. We propose a generic algorithm to use educational material and student activity data from heterogeneous sources to create a Prerequisite Structure Graph (PSG). A PSG is a directed acyclic graph, where the nodes are educational units and the edges specify the pairwise ordering of the units in effective teaching by instructors or for effective learning by students. We propose an unsupervised approach utilizing both text content and student data, which outperforms to supervised methods (utilizing only text content) on the task of estimating a PSG.

Question: What is a prerequisite structure graph?


Convolutional Neural Network Information Fusion based on Dempster-Shafer Theory for Urban Scene Understanding. Dempster-Shafer theory provides a sensor fusion framework that autonomously accounts for obstacle occlusion in dynamic, urban environments. However, to discern static and moving obstacles, the Dempster-Shafer approach requires manual tuning of parameters dependent on the situation and sensor types. The proposed methodology utilizes a deep fully convolutional neural network to improve the robust performance of the information fusion algorithm in distinguishing static and moving obstacles from navigable space. The image-like spatial structure of probabilistic occupancy allows a semantic segmentation framework to discern classes for individual grid cells. A subset of the KITTI LIDAR tracking dataset in combination with semantic map data was used for the information fusion task. The probabilistic occupancy grid output of the Dempster-Shafer information fusion algorithm was provided as input to the neural network. The network then learned an offset from the original DST result to improve semantic labeling performance. The proposed framework outperformed the baseline approach in the mean intersection over union metric reaching 0.546 and 0.531 in the validation and test sets respectively. However, little improvement was achieved in discerning moving and static cells due to the limited dataset size. To improve model performance in future work, the dataset will be expanded to facilitate more effective learning, and temporal data will be fed through individual convolutional networks prior to being merged in channels as input to the main network.

Question: How does the dempster shafer theory work?


Analysis of waveguide slot-based structures using wide-band equivalent-circuit model. Analysis of geometrically complicated waveguide-based slotted arrays and filters is performed using a simple equivalent-circuit model. First, the circuit parameters (inductance and capacitance) of a simple waveguide slot-coupler problem are obtained through moment-method (MoM) analysis. The values of the lumped LC elements are virtually constant over the frequency range of interest (the X-band) for specific waveguide and slot dimensions. Based on the equivalent-circuit model of a single slot of two coupled waveguides, more complicated structures are then analyzed, such as slot coupler arrays and slot-based waveguide filters. The scattering parameters of these structures are obtained through circuit analysis, and are verified using the MoM and finite-difference time-domain method. Excellent agreement is observed over a wide band of frequencies and is confirmed by experimental results.

Question: How to analyze a slot-based waveguide structure?


Two (Note) Heads Are Better Than One: Pen-Based Multimodal Interaction with Music Scores. Digitizing early music sources requires new ways of dealing with musical documents. Assuming that current technologies cannot guarantee a perfect automatic transcription, our intention is to develop an interactive system in which user and software collaborate to complete the task. Since conventional score post-editing might be tedious, the user is allowed to interact using an electronic pen. Although this provides a more ergonomic interface, this interaction must be decoded as well. In our framework, the user traces the symbols using the electronic pen over a digital surface, which provides both the underlying image (offline data) and the drawing made by the e-pen (online data) to improve classification. Applying this methodology over 70 scores of the target musical archive, a dataset of 10 230 bimodal samples of 30 different symbols was obtained and made available for research purposes. This paper presents experimental results on classification over this dataset, in which symbols are recognized by combining the two modalities. This combination of modes has demonstrated its good performance, decreasing the error rate of using each modality separately and achieving an almost error-free performance.

Question: How many different symbols are there?


Terpene synthases of oregano (Origanum vulgare L.) and their roles in the pathway and regulation of terpene biosynthesis. The aroma, flavor and pharmaceutical value of cultivated oregano (Origanum vulgare L.) is a consequence of its essential oil which consists mostly of monoterpenes and sesquiterpenes. To investigate the biosynthetic pathway to oregano terpenes and its regulation, we identified and characterized seven terpene synthases, key enzymes of terpene biosynthesis, from two cultivars of O. vulgare. Heterologous expression of these enzymes showed that each forms multiple mono- or sesquiterpene products and together they are responsible for the direct production of almost all terpenes found in O. vulgare essential oil. The correlation of essential oil composition with relative and absolute terpene synthase transcript concentrations in different lines of O. vulgare demonstrated that monoterpene synthase activity is predominantly regulated on the level of transcription and that the phenolic monoterpene alcohol thymol is derived from γ-terpinene, a product of a single monoterpene synthase. The combination of heterologously-expressed terpene synthases for in vitro assays resulted in blends of mono- and sesquiterpene products that strongly resemble those found in vivo, indicating that terpene synthase expression levels directly control the composition of the essential oil. These results will facilitate metabolic engineering and directed breeding of O. vulgare cultivars with higher quantity of essential oil and improved oil composition.

Question: What is the difference between monoterpenes and sesquiterpenes?


The use of socially assistive robots in the design of intelligent cognitive therapies for people with dementia. Currently the 2 percent growth rate for the world's older population exceeds the 1.2 rate for the world's population as a whole. By 2050, the number of individuals over the age 85 is projected to be three times more than there is today. Most of these individuals will need physical, emotional, and cognitive assistance. In this paper, we present a new adaptive robotic system based on the socially assistive robotics (SAR) technology that tries to provide a customized help protocol through motivation, encouragements, and companionship to users suffering from cognitive changes related to aging and/or Alzheimer's disease. Our results show that this approach can engage the patients and keep them interested in interacting with the robot, which, in turn, increases their positive behavior.

Question: What is the use of socially assistive robots in the design of intelligent cognitive therapies for people with dementia?


Multi-View Dimensionality Reduction via Canonical Correlation Analysis. We analyze the multi-view regression problem where we have two views X = (X, X) of the input data and a target variableY of interest. We provide sufficient conditions under which we can reduce the dimensionality of X (via a projection) without loosing predictive power of Y . Crucially, this projection can be computed via a Canonical Correlation Analysis only on the unlabeled data. The algorithmic template is as f ollows: with unlabeled data, perform CCA and construct a certain projection; with the labeled data, do least squa res regression in this lower dimensional space. We show how, under certain natural assumptions, the number o f labeled samples could be significantly reduced (in comparison to the single view setting) — in particular, we show how this dimen sionality reduction does not loose predictive power ofY (thus it only introduces little bias but could drastically reduce the variance). We explore two separate assumptions under which this is possible and show how, under either assumption alone, dimensionality reduction could reduce the labeled sample complexity. The two assumptions we consider are a conditional independenceassumption and a redundancyassumption. The typical conditional independence assumption is that conditioned onY the viewsX andX are independent — we relax this assumption to: conditioned on some hidden state H the viewsX andX are independent. Under the redundancy assumption, we show that the best predictor from each view is roughly as good as the best predictor u sing both views.

Question: What is the difference between the two assumptions?


Identification of Refused Bequest Code Smells. Accumulated technical debt can be alleviated by means of refactoring application aiming at architectural improvement. A prerequisite for wide scale refactoring application is the automated identification of the corresponding refactoring opportunities, or code smells. One of the major architectural problems that has received limited attention is the so called 'Refused Bequest' which refers to inappropriate use of inheritance in object-oriented systems. This code smell occurs when subclasses do not take advantage of the inherited behavior, implying that replacement by delegation should be used instead. In this paper we propose a technique for the identification of Refused Bequest code smells whose major novelty lies in the intentional introduction of errors in the inherited methods. The essence of inheritance is evaluated by exercising the system's functionality through the corresponding unit tests in order to reveal whether inherited methods are actually employed by clients. Based on the results of this approach and other structural information, an indication of the smell strength on a 'thermometer' is obtained. The proposed approach has been implemented as an Eclipse plug in.

Question: How to identify code smells?


Opinion mining from student feedback data using supervised learning algorithms. This paper explores opinion mining using supervised learning algorithms to find the polarity of the student feedback based on pre-defined features of teaching and learning. The study conducted involves the application of a combination of machine learning and natural language processing techniques on student feedback data gathered from module evaluation survey results of Middle East College, Oman. In addition to providing a step by step explanation of the process of implementation of opinion mining from student comments using the open source data analytics tool Rapid Miner, this paper also presents a comparative performance study of the algorithms like SVM, Naïve Bayes, K Nearest Neighbor and Neural Network classifier. The data set extracted from the survey is subjected to data preprocessing which is then used to train the algorithms for binomial classification. The trained models are also capable of predicting the polarity of the student comments based on extracted features like examination, teaching etc. The results are compared to find the better performance with respect to various evaluation criteria for the different algorithms.

Question: What is opinion mining?


TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients. We present TimeSpan, an exploratory visualization tool designed to gain a better understanding of the temporal aspects of the stroke treatment process. Working with stroke experts, we seek to provide a tool to help improve outcomes for stroke victims. Time is of critical importance in the treatment of acute ischemic stroke patients. Every minute that the artery stays blocked, an estimated 1.9 million neurons and 12 km of myelinated axons are destroyed. Consequently, there is a critical need for efficiency of stroke treatment processes. Optimizing time to treatment requires a deep understanding of interval times. Stroke health care professionals must analyze the impact of procedures, events, and patient attributes on time-ultimately, to save lives and improve quality of life after stroke. First, we interviewed eight domain experts, and closely collaborated with two of them to inform the design of TimeSpan. We classify the analytical tasks which a visualization tool should support and extract design goals from the interviews and field observations. Based on these tasks and the understanding gained from the collaboration, we designed TimeSpan, a web-based tool for exploring multi-dimensional and temporal stroke data. We describe how TimeSpan incorporates factors from stacked bar graphs, line charts, histograms, and a matrix visualization to create an interactive hybrid view of temporal data. From feedback collected from domain experts in a focus group session, we reflect on the lessons we learned from abstracting the tasks and iteratively designing TimeSpan.

Question: What is the time it takes to treat a stroke?


A Model of Type Theory in Cubical Sets. We present a model of type theory with dependent product, sum, and identity, in cubical sets. We describe a universe and explain how to transform an equivalence between two types in an equality. We also explain how to model propositional truncation and the circle. While not expressed internally in type theory, the model is expressed in a constructive metalogic. Thus it is a step towards a computational interpretation of Voevodsky's Univalence Axiom.

Question: What is a model of type theory in cubical sets?


Web scraping technologies in an API world. Web services are the de facto standard in biomedical data integration. However, there are data integration scenarios that cannot be fully covered by Web services. A number of Web databases and tools do not support Web services, and existing Web services do not cover for all possible user data demands. As a consequence, Web data scraping, one of the oldest techniques for extracting Web contents, is still in position to offer a valid and valuable service to a wide range of bioinformatics applications, ranging from simple extraction robots to online meta-servers. This article reviews existing scraping frameworks and tools, identifying their strengths and limitations in terms of extraction capabilities. The main focus is set on showing how straightforward it is today to set up a data scraping pipeline, with minimal programming effort, and answer a number of practical needs. For exemplification purposes, we introduce a biomedical data extraction scenario where the desired data sources, well-known in clinical microbiology and similar domains, do not offer programmatic interfaces yet. Moreover, we describe the operation of WhichGenes and PathJam, two bioinformatics meta-servers that use scraping as means to cope with gene set enrichment analysis.

Question: What is a meta-server?


Safety and Efficacy of Selective Neurectomy of the Gastrocnemius Muscle for Calf Reduction in 300 Cases. Liposuction alone is not always sufficient to correct the shape of the lower leg, and muscle reduction may be necessary. To assess the outcomes of a new technique of selective neurectomy of the gastrocnemius muscle to correct calf hypertrophy. Between October 2007 and May 2010, 300 patients underwent neurectomy of the medial and lateral heads of the gastrocnemius muscle at the Department of Cosmetic and Plastic Surgery, the Second People's Hospital of Guangdong Province (Guangzhou, China) to correct the shape of their lower legs. Follow-up data from these 300 patients were analyzed retrospectively. Cosmetic results were evaluated independently by the surgeon, the patient, and a third party. Preoperative and postoperative calf circumferences were compared. The Fugl-Meyer motor function assessment was evaluated 3 months after surgery. The average reduction in calf circumference was 3.2 ± 1.2 cm. The Fugl-Meyer scores were normal in all patients both before and 3 months after surgery. A normal calf shape was achieved in all patients. Six patients complained of fatigue while walking and four of scar pigmentation, but in all cases, this resolved within 6 months. Calf asymmetry was observed in only two patients. The present series suggests that neurectomy of the medial and lateral heads of the gastrocnemius muscle may be safe and effective for correcting the shape of the calves. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Question: What is the efficacy of selective neurectomy of the gastrocnemius muscle for calf reduction?


Reality mining based on Social Network Analysis. Data Mining is the extraction of hidden predictive information from large databases. The process of discovering interesting, useful, nontrivial patterns from large spatial datasets is called spatial data mining. When time gets associated it becomes spatio temporal data mining. The study of spatio temporal data mining is of great concern for the study of mobile phone sensed data. Reality Mining is defined as the study of human social behavior based on mobile phone sensed data. It is based on the data collected by sensors in mobile phones, security cameras, RFID readers, etc. All allow for the measurement of human physical and social activity. In this paper Netviz, Gephi and Weka tools have been used to convert and analyze the Facebook. Further, analysis of a reality mining dataset is also presented.

Question: What is reality mining?


A Framework for Dynamic Image Sampling Based on Supervised Learning. Sparse sampling schemes can broadly be classified into two main categories: static sampling where the sampling pattern is predetermined, and dynamic sampling where each new measurement location is selected based on information obtained from previous measurements. Dynamic sampling methods are particularly appropriate for pointwise imaging methods, in which pixels are measured sequentially in arbitrary order. Examples of pointwise imaging schemes include certain implementations of atomic force microscopy, electron back scatter diffraction, and synchrotron X-ray imaging. In these pointwise imaging applications, dynamic sparse sampling methods have the potential to dramatically reduce the number of measurements required to achieve a desired level of fidelity. However, the existing dynamic sampling methods tend to be computationally expensive and are, therefore, too slow for many practical applications. In this paper, we present a framework for dynamic sampling based on machine learning techniques, which we call a supervised learning approach for dynamic sampling (SLADS). In each step of SLADS, the objective is to find the pixel that maximizes the expected reduction in distortion (ERD) given previous measurements. SLADS is fast because we use a simple regression function to compute the ERD, and it is accurate because the regression function is trained using datasets that are representative of the specific application. In addition, we introduce an approximate method to terminate dynamic sampling at a desired level of distortion. We then extend our algorithm to incorporate multiple measurements at each step, which we call groupwise SLADS. Finally, we present results on computationally generated synthetic data and experimentally collected data to demonstrate a dramatic improvement over state-of-the-art static sampling methods

Question: What is the difference between a framework and a framework for dynamic image sampling?


Object tracking with de-autocorrelation scheme for a dynamic occupancy gridmap system. Autonomous driving poses unique challenges for vehicle environment perception in complex driving environments. Due to the uncertain nature of the vehicle environment and imperfection of any perception framework, multiple stages of estimation might be necessary to achieve the desired performance. However, it is highly possible that the estimation of one stage might result in output estimates with significant auto/cross-correlation, which would pass to another stage. In such situations, a decorrelation procedure is required. We present an object tracking approach taking into consideration the auto-correlation (of the velocity components) introduced by an upfront dynamic occupancy gridmap system. More specifically, we use a linear state-space system to approximately "reconstruct" the nonlinear estimation/mapping procedure for the purpose of auto-correlation quantification. We focus on demonstrating an estimation improvement of the proposed decorrelation tracker over a direct Kalman filter (i.e., KF that ignores the auto-correlation). For this we recorded several scenarios of different target motion behaviors. It is shown that the decorrelation tracker indeed introduces noticable estimation improvement in particularly velocity space if the target moves in a relatively smooth manner.

Question: How to track a moving object?


ENHANCING THE TOURISM EXPERIENCE THROUGH MOBILE AUGMENTED REALITY: CHALLENGES AND PROSPECTS. The paper discusses the use of Augmented Reality (AR) applications for the needs of tourism. It describes the technology's evolution from pilot applications into commercial mobile applications. We address the technical aspects of mobile AR applications development, emphasizing on the technologies that render the delivery of augmented reality content possible and experientially superior. We examine the state of the art, providing an analysis concerning the development and the objectives of each application. Acknowledging the various technological limitations hindering AR's substantial end-user adoption, the paper proposes a model for developing AR mobile applications for the field of tourism, aiming to release AR's full potential within the field.

Question: What is the state of the art of mobile augmented reality?


20 years of the SMART protein domain annotation resource. SMART (Simple Modular Architecture Research Tool) is a web resource (http://smart.embl.de) for the identification and annotation of protein domains and the analysis of protein domain architectures. SMART version 8 contains manually curated models for more than 1300 protein domains, with approximately 100 new models added since our last update article (1). The underlying protein databases were synchronized with UniProt (2), Ensembl (3) and STRING (4), doubling the total number of annotated domains and other protein features to more than 200 million. In its 20th year, the SMART analysis results pages have been streamlined again and its information sources have been updated. SMART's vector based display engine has been extended to all protein schematics in SMART and rewritten to use the latest web technologies. The internal full text search engine has been redesigned and updated, resulting in greatly increased search speed.

Question: What is the SMART protein domain annotation resource?


Ordering-Based Search: A Simple and Effective Algorithm for Learning Bayesian Networks. One of the basic tasks for Bayesian networks (BNs) is that of learning a network structure from data. The BN-learning problem is NPhard, so the standard solution is heuristic search. Many approaches have been proposed for this task, but only a very small number outperform the baseline of greedy hill-climbing with tabu lists; moreover, many of the proposed algorithms are quite complex and hard to implement. In this paper, we propose a very simple and easy-toimplement method for addressing this task. Our approach is based on the well-known fact that the best network (of bounded in-degree) consistent with a given node ordering can be found very efficiently. We therefore propose a search not over the space of structures, but over the space of orderings, selecting for each ordering the best network consistent with it. This search space is much smaller, makes more global search steps, has a lower branching factor, and avoids costly acyclicity checks. We present results for this algorithm on both synthetic and real data sets, evaluating both the score of the network found and in the running time. We show that orderingbased search outperforms the standard baseline, and is competitive with recent algorithms that are much harder to implement.

Question: What is the best algorithm for learning Bayesian networks?


Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets. Despite the recent achievements in machine learning, we are still very far from achieving real artificial intelligence. In this paper, we discuss the limitations of standard deep learning approaches and show that some of these limitations can be overcome by learning how to grow the complexity of a model in a structured way. Specifically, we study the simplest sequence prediction problems that are beyond the scope of what is learnable with standard recurrent networks, algorithmically generated sequences which can only be learned by models which have the capacity to count and to memorize sequences. We show that some basic algorithms can be learned from sequential data using a recurrent network associated with a trainable memory.

Question: What is the simplest sequence prediction problem?


Golden section search over hyper-rectangle: a direct search method. Abstract: This paper generalises the golden section optimal search method to higher dimensional optimisation problem. The method is applicable to a strict quasi-convex function of N-variables over an N-dimensional hyper rectangle. An algorithm is proposed in N-dimension. The algorithm is illustrated graphically in two dimensions and verified through several test functions in higher dimension using MATLAB.

Question: What is a golden section search?


Fast Image Recovery Using Variable Splitting and Constrained Optimization. We propose a new fast algorithm for solving one of the standard formulations of image restoration and reconstruction which consists of an unconstrained optimization problem where the objective includes an l2 data-fidelity term and a nonsmooth regularizer. This formulation allows both wavelet-based (with orthogonal or frame-based representations) regularization or total-variation regularization. Our approach is based on a variable splitting to obtain an equivalent constrained optimization formulation, which is then addressed with an augmented Lagrangian method. The proposed algorithm is an instance of the so-called alternating direction method of multipliers, for which convergence has been proved. Experiments on a set of image restoration and reconstruction benchmark problems show that the proposed algorithm is faster than the current state of the art methods.

Question: How to recover an image from a corrupted one?


An Intelligent Tutoring System for Entity Relationship Modelling. The paper presents KERMIT, a Knowledge-based Entity Relationship Modelling Intelligent Tutor. KERMIT is a problem-solving environment for the university-level students, in which they can practise conceptual database design using the Entity-Relationship data model. KERMIT uses Constraint-Based Modelling (CBM) to model the domain knowledge and generate student models. We have used CBM previous in tutors that teach SQL and English punctuation rules. The research presented in this paper is significant because we show that CBM can be used to support students learning design tasks, which are very different from domains we dealt with in earlier tutors. The paper describes the system's architecture and functionality. The system observes students' actions and adapts to their knowledge and learning abilities. KERMIT has been evaluated in the context of genuine teaching activities. We present the results of two evaluation studies with students taking database courses, which show that KERMIT is an effective system. The students have enjoyed the system's adaptability and found it a valuable asset to their learning.

Question: What is the difference between a tutor and an intelligent tutoring system?


Efficient and robust computation of an approximated medial axis. The medial axis can be viewed as a compact representation for an arbitrary model; it is an essential geometric structure in many applications. A number of practical algorithms for its computation have been aimed at speeding up its computation and at addressing its instabilities. In this paper we propose a new algorithm to compute the medial axis with arbitrary precision. It exhibits several desirable properties not previously combined in a practical and efficient algorithm. First, it allows for a tradeoff between computation time and accuracy, making it well-suited for applications in which an approximation of the medial axis suffices, but computational efficiency is of particular concern. Second, it is output sensitive: the computation complexity of the algorithm does not depend on the size of the representation of a model, but on the size of the representation of the resulting medial axis. Third, the densities of the approximated medial axis points in different areas are adaptive to local free space volumes, based on the assumption that a coarser approximation in wide open area can still suffice the requirements of the applications. We present theoretical results, bounding the error introduced by the approximation process. The algorithm has been implemented and experimental results are presented that illustrate its computational efficiency and robustness.

Question: What is the medial axis?


Robust camera calibration of soccer video using genetic algorithm. This paper proposes an exact and robust genetic algorithm-based method for the calibration of soccer camera. According to the FIFA official soccer field layout we defined a field model for the soccer court. Camera calibration is done through finding the homography transform between the field model and the input frame which is followed with DLT decomposition. The intersection of lines in the field model and input frame form feature points and by means of a genetic algorithm, we found the correspondence between those points. Our algorithm was applied to a couple of soccer video frames and the achieved results demonstrate its robustness, accuracy and high performance.

Question: How to calibrate a camera?


Total Recall: System Support for Automated Availability Management. Goal: Highly available data storage in large-scale distributed systems in which * Hosts are transiently inaccessible * Individual host failures are common Current peer-to-peer systems are prime examples * Highly dynamic, challenging environment * hosts join and leave frequently in short-term * Hosts leave permanently over long-term * Workload varies in terms of popularity, access patterns, file size These systems require automated availability management.

Question: What is the goal of the document?


A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain. Tumor segmentation from magnetic resonance imaging (MRI) data is an important but time consuming manual task performed by medical experts. Automating this process is a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. MRI is an advanced medical imaging technique providing rich information about the human soft-tissue anatomy. There are different brain tumor detection and segmentation methods to detect and segment a brain tumor from MRI images. These detection and segmentation approaches are reviewed with an importance placed on enlightening the advantages and drawbacks of these methods for brain tumor detection and segmentation. The use of MRI image detection and segmentation in different procedures are also described. Here a brief review of different segmentation for detection of brain tumor from MRI of brain has been discussed.

Question: What is a brain tumor?


Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature. We propose an approach for extracting relations between entities from biomedical literature based solely on shallow linguistic information. We use a combination of kernel functions to integrate two different information sources: (i) the whole sentence where the relation appears, and (ii) the local contexts around the interacting entities. We performed experiments on extracting gene and protein interactions from two different data sets. The results show that our approach outperforms most of the previous methods based on syntactic and semantic information.

Question: What is the name of the approach?


RFID in robot-assisted indoor navigation for the visually impaired. We describe how radio frequency identification (RFID) can be used in robot-assisted indoor navigation for the visually impaired. We present a robotic guide for the visually impaired that was deployed and tested both with and without visually unpaired participants in two indoor environments. We describe how we modified the standard potential fields algorithms to achieve navigation at moderate walking speeds and to avoid oscillation in narrow spaces. The experiments illustrate that passive RFID tags deployed in the environment can act as reliable stimuli that trigger local navigation behaviors to achieve global navigation objectives.

Question: How can RFID be used in robot-assisted indoor navigation for the visually impaired?


Transliteration for Resource-Scarce Languages. Today, parallel corpus-based systems dominate the transliteration landscape. But the resource-scarce languages do not enjoy the luxury of large parallel transliteration corpus. For these languages, rule-based transliteration is the only viable option. In this article, we show that by properly harnessing the monolingual resources in conjunction with manually created rule base, one can achieve reasonable transliteration performance. We achieve this performance by exploiting the power of Character Sequence Modeling (CSM), which requires only monolingual resources. We present the results of our rule-based system for Hindi to English, English to Hindi, and Persian to English transliteration tasks. We also perform extrinsic evaluation of transliteration systems in the context of Cross Lingual Information Retrieval. Another important contribution of our work is to explain the widely varying accuracy numbers reported in transliteration literature, in terms of the entropy of the language pairs and the datasets involved.

Question: What is the difference between a transliteration system and a transliteration engine?


Fast userspace packet processing. In recent years, we have witnessed the emergence of high speed packet I/O frameworks, bringing unprecedented network performance to userspace. Using the Click modular router, we first review and quantitatively compare several such packet I/O frameworks, showing their superiority to kernel-based forwarding. We then reconsider the issue of software packet processing, in the context of modern commodity hardware with hardware multi-queues, multi-core processors and non-uniform memory access. Through a combination of existing techniques and improvements of our own, we derive modern general principles for the design of software packet processors. Our implementation of a fast packet processor framework, integrating a faster Click with both Netmap and DPDK, exhibits up-to about 2.3x speed-up compared to other software implementations, when used as an IP router.

Question: What is the fastest packet processor framework?


Clustering of contingency table and mixture model. Basing cluster analysis on mixture models has become a classical and powerful approach. It enables some classical criteria such as the well-known k-means criterion to be explained. To classify the rows or the columns of a contingency table, an adapted version of k-means known as Mndki2, which uses the chi-square distance, can be used. Unfortunately, this simple, effective method which can be used jointly with correspondence analysis based on the same representation of the data, cannot be associated with a mixture model in the same way as the classical k-means algorithm. In this paper we show that the Mndki2 algorithm can be viewed as an approximation of a classifying version of the EM algorithm for a mixture of multinomial distributions. A comparison of the algorithms belonging in this context are experimentally investigated using Monte Carlo simulations. 2006 Elsevier B.V. All rights reserved.

Question: How to cluster contingency table?


Modern Microwave Ferrites. Microwave ferrites are ubiquitous in systems that send, receive, and manipulate electromagnetic signals across very high frequency to quasi-optical frequency bands. In this paper, modern microwave ferrites are reviewed including spinel, garnet, and hexaferrite systems as thin and thick films, powders and compacts, and metamaterials. Their fundamental properties and utility are examined in the context of high frequency applications ranging from the VHF to millimeter-wave bands. Perspective and outlook of advances in theory, processing, and devices occurring in the science and engineering communities since the year 2000 are presented and discussed.

Question: What is a microwave ferrite?


Data Mining and Knowledge Discovery : Applications , Techniques , Challenges and Process Models in Healthcare. Many healthcare leaders find themselves overwhelmed with data, but lack the information they need to make right decisions. Knowledge Discovery in Databases (KDD) can help organizations turn their data into information. Organizations that take advantage of KDD techniques will find that they can lower the healthcare costs while improving healthcare quality by using fast and better clinical decision making. In this paper, a review study is done on existing data mining and knowledge discovery techniques, applications and process models that are applicable to healthcare environments. The challenges for applying data mining techniques in healthcare environment will also be discussed.

Question: What is the difference between data mining and knowledge discovery?


Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now able to classify objects in images with near-human-level performance, questions naturally arise as to what differences remain between computer and human vision. A recent study [30] revealed that changing an image (e.g. of a lion) in a way imperceptible to humans can cause a DNN to label the image as something else entirely (e.g. mislabeling a lion a library). Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99.99% confidence (e.g. labeling with certainty that white noise static is a lion). Specifically, we take convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and then find images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class. It is possible to produce images totally unrecognizable to human eyes that DNNs believe with near certainty are familiar objects, which we call "fooling images" (more generally, fooling examples). Our results shed light on interesting differences between human vision and current DNNs, and raise questions about the generality of DNN computer vision.

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


Detecting Review Spammer Groups via Bipartite Graph Projection. Online product reviews play an important role in E-commerce websites because most customers read and rely on them when making purchases. For the sake of profit or reputation, review spammers deliberately write fake reviews to promote or demote target products, some even fraudulently work in groups to try and control the sentiment about a product. To detect such spammer groups, previous work exploits frequent itemset mining (FIM) to generate candidate spammer groups, which can only find tightly coupled groups, i.e. each reviewer in the group reviews every target product. In this paper, we present the loose spammer group detection problem, i.e. each group member is not required to review every target product. We solve this problem using bipartite graph projection. We propose a set of group spam indicators to measure the spamicity of a loose spammer group, and design a novel algorithm to identify highly suspicious loose spammer groups in a divide and conquer manner. Experimental results show that our method not only can find loose spammer groups with high precision and recall, but also can generate more meaningful candidate spammer groups than FIM, thus it can also be used as an alternative preprocessing tool for existing FIM-based approaches.

Question: How to detect spammer groups?


Deep Learning for Natural Language Processing. 1Student,Dept. of Computer Engineering, VESIT, Maharashtra, India ---------------------------------------------------------------------------***-------------------------------------------------------------------Abstract Deep Learning has come into existence as a new area for research in Machine Learning. It aims to act like a human brain, having the ability to learn and process from complex data and also tries solving intricate tasks as well. Due to this capability, its been used in various fields like text, sound, images etc. Natural language process has started to being impacted by the deep learning techniques. This research paper highlights Deep Learning's recent developments and applications in Natural Language Processing.

Question: What is deep learning?


Modelling language evolution: Examples and predictions.. We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

Question: What is the difference between a rule-based model and an equation-based model?


Efficient Subspace Segmentation via Quadratic Programming. We explore in this paper efficient algorithmic solutions to robust subspace segmentation. We propose the SSQP, namely Subspace Segmentation via Quadratic Programming, to partition data drawn from multiple subspaces into multiple clusters. The basic idea of SSQP is to express each datum as the linear combination of other data regularized by an overall term targeting zero reconstruction coefficients over vectors from different subspaces. The derived coefficient matrix by solving a quadratic programming problem is taken as an affinity matrix, upon which spectral clustering is applied to obtain the ultimate segmentation result. Similar to sparse subspace clustering (SCC) and low-rank representation (LRR), SSQP is robust to data noises as validated by experiments on toy data. Experiments on Hopkins 155 database show that SSQP can achieve competitive accuracy as SCC and LRR in segmenting affine subspaces, while experimental results on the Extended Yale Face Database B demonstrate SSQP's superiority over SCC and LRR. Beyond segmentation accuracy, all experiments show that SSQP is much faster than both SSC and LRR in the practice of subspace segmentation.

Question: What is the difference between subspace clustering and subspace segmentation?


An Algorithm for Training Polynomial Networks. We consider deep neural networks, in which the output of each node is a quadratic function of its inputs. Similar to other deep architectures, these networks can compactly represent any function on a finite training set. The main goal of this paper is the derivation of an efficient layer-by-layer algorithm for training such networks, which we denote as the Basis Learner. The algorithm is a universal learner in the sense that the training error is guaranteed to decrease at every iteration, and can eventually reach zero under mild conditions. We present practical implementations of this algorithm, as well as preliminary experimental results. We also compare our deep architecture to other shallow architectures for learning polynomials, in particular kernel learning.

Question: What is a universal learner?