ASR-based corrective feedback on pronunciation: does it really work?. We studied a group of immigrants who were following regular, teacher-fronted Dutch classes, and who were assigned to three groups using either a) Dutch CAPT, an ASR-based Computer Assisted Pronunciation Training (CAPT) system that provides feedback on a number of Dutch speech sounds that are problematic for L2 learners b) a CAPT system without feedback c) no CAPT system. Participants were tested before and after the training. The results show that the ASR-based feedback was effective in correcting the errors addressed in the training.

Question: Does ASR-based corrective feedback on pronunciation really work?


Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory. Very high resolution satellite images provide valuable information to researchers. Among these, urban-area boundaries and building locations play crucial roles. For a human expert, manually extracting this valuable information is tedious. One possible solution to extract this information is using automated techniques. Unfortunately, the solution is not straightforward if standard image processing and pattern recognition techniques are used. Therefore, to detect the urban area and buildings in satellite images, we propose the use of scale invariant feature transform (SIFT) and graph theoretical tools. SIFT keypoints are powerful in detecting objects under various imaging conditions. However, SIFT is not sufficient for detecting urban areas and buildings alone. Therefore, we formalize the problem in terms of graph theory. In forming the graph, we represent each keypoint as a vertex of the graph. The unary and binary relationships between these vertices (such as spatial distance and intensity values) lead to the edges of the graph. Based on this formalism, we extract the urban area using a novel multiple subgraph matching method. Then, we extract separate buildings in the urban area using a novel graph cut method. We form a diverse and representative test set using panchromatic 1-m-resolution Ikonos imagery. By extensive testings, we report very promising results on automatically detecting urban areas and buildings.

Question: How to detect urban areas and buildings in satellite images?


Algorithmic Design of Low-Power Variable-Stiffness Mechanisms. Compliant actuators enabling low-power stiffness adaptation are missing ingredients and key enablers of next generation robotic systems. One of the key components of these actuators is the mechanism implementing stiffness adaptation that requires sophisticated control and nontrivial mechanical design. However, despite recent advances in controlling these systems, their design remains experience based and not well understood. In this paper, we present an optimization-based computational framework for the design of intrinsically low-power compliant variable stiffness mechanisms. The core ingredient of this framework is the mathematical formulation of the design problem—provided by a constrained nonlinear parameter optimization—which is computationally solved here to identify optimal variable stiffness designs. We show the basic capability of this formulation in finding parameters for variable stiffness mechanisms that require the least power by design. Further, we demonstrate the generality of this method in cross-comparing mechanisms with different kinematic topology to identify the one that requires the least power by design.

Question: What is the mathematical formulation of the design problem?


USING CAMERA-EQUIPPED MOBILE PHONES FOR INTERACTING WITH REAL-WORLD OBJECTS. The idea described in this paper is to use the built-in cameras of consumer mobile phones as sensors for 2-dimensional visual codes. Such codes can be attached to physical objects in order to retrieve object-related information and functionality. They are also suitable for display on electronic screens. The proposed visual code system allows the simultaneous detection of multiple codes, introduces a position-independent coordinate system, and provides the phone's orientation as a parameter. The ability to detect objects in the user's vicinity offers a natural way of interaction and strengthens the role of mobile phones in a large number of application scenarios. We describe the hardware requirements, the design of a suitable visual code, a lightweight recognition algorithm, and present some example applications.

Question: How does the camera on a mobile phone work?


Machine learning in the Internet of Things: A semantic-enhanced approach. Novel Internet of Things (IoT) applications and services rely on an intelligent understanding of the environment leveraging data gathered via heterogeneous sensors and micro-devices. Though increasingly effective, Machine Learning (ML) techniques generally do not go beyond classification of events with opaque labels, lacking machine-understandable representation and explanation of taxonomies. This paper proposes a framework for semantic-enhanced data mining on sensor streams, amenable to resource-constrained pervasive contexts. It merges an ontology-based characterization of data distributions with non-standard reasoning for a fine-grained event detection. The typical classification problem of ML is treated as a resource discovery by exploiting semantic matchmaking. Outputs of classification are endowed with computer-processable descriptions in standard Semantic Web languages, while explanation of matchmaking outcomes motivates confidence on results. A case study on road and traffic analysis has allowed to validate the proposal and achieve an assessment with respect to state-of-the-art ML algorithms.

Question: What is a semantic-enhanced approach?


SparkClouds: Visualizing Trends in Tag Clouds. Tag clouds have proliferated over the web over the last decade. They provide a visual summary of a collection of texts by visually depicting the tag frequency by font size. In use, tag clouds can evolve as the associated data source changes over time. Interesting discussions around tag clouds often include a series of tag clouds and consider how they evolve over time. However, since tag clouds do not explicitly represent trends or support comparisons, the cognitive demands placed on the person for perceiving trends in multiple tag clouds are high. In this paper, we introduce SparkClouds, which integrate sparklines into a tag cloud to convey trends between multiple tag clouds. We present results from a controlled study that compares SparkClouds with two traditional trend visualizations-multiple line graphs and stacked bar charts-as well as Parallel Tag Clouds. Results show that SparkClouds' ability to show trends compares favourably to the alternative visualizations.

Question: How do you compare sparklines to other visualizations?


Deep learning of binary hash codes for fast image retrieval. Approximate nearest neighbor search is an efficient strategy for large-scale image retrieval. Encouraged by the recent advances in convolutional neural networks (CNNs), we propose an effective deep learning framework to generate binary hash codes for fast image retrieval. Our idea is that when the data labels are available, binary codes can be learned by employing a hidden layer for representing the latent concepts that dominate the class labels. The utilization of the CNN also allows for learning image representations. Unlike other supervised methods that require pair-wised inputs for binary code learning, our method learns hash codes and image representations in a point-wised manner, making it suitable for large-scale datasets. Experimental results show that our method outperforms several state-of-the-art hashing algorithms on the CIFAR-10 and MNIST datasets. We further demonstrate its scalability and efficacy on a large-scale dataset of 1 million clothing images.

Question: How to learn binary hash codes for fast image retrieval?


Toward normative expert systems: Part I. The Pathfinder project.. Pathfinder is an expert system that assists surgical pathologists with the diagnosis of lymph-node diseases. The program is one of a growing number of normative expert systems that use probability and decision theory to acquire, represent, manipulate, and explain uncertain medical knowledge. In this article, we describe Pathfinder and our research in uncertain-reasoning paradigms that was stimulated by the development of the program. We discuss limitations with early decision-theoretic methods for reasoning under uncertainty and our initial attempts to use non-decision-theoretic methods. Then, we describe experimental and theoretical results that directed us to return to reasoning methods based in probability and decision theory.

Question: What is a normative expert system?


"One of the greatest medical success stories:" Physicians and nurses' small stories about vaccine knowledge and anxieties.. In recent years, the Canadian province of Alberta experienced outbreaks of measles, mumps, pertussis, and influenza. Even so, the dominant cultural narrative maintains that vaccines are safe, effective, and necessary to maintain population health. Many vaccine supporters have expressed anxieties that stories contradicting this narrative have lowered herd immunity levels because they frighten the public into avoiding vaccination. As such, vaccine policies often emphasize educating parents and the public about the importance and safety of vaccination. These policies rely on health professionals to encourage vaccine uptake and assume that all professionals support vaccination. Health professionals, however, are socially positioned between vaccine experts (such as immunologists) and non-experts (the wider public). In this article, I discuss health professionals' anxieties about the potential risks associated with vaccination and with the limitations of Alberta's immunisation program. Specifically, I address the question: If medical knowledge overwhelmingly supports vaccination, then why do some professionals continue to question certain vaccines? To investigate this topic, I interviewed twenty-seven physicians and seven nurses. With stock images and small stories that interviewees shared about their vaccine anxieties, I challenge the common assumption that all health professionals support vaccines uncritically. All interviewees provided generic statements that supported vaccination and Alberta's immunisation program, but they expressed anxieties when I asked for details. I found that their anxieties reflected nuances that the culturally dominant vaccine narrative overlooks. Particularly, they critiqued the influence that pharmaceutical companies, the perceived newness of specific vaccines, and the limitations of medical knowledge and vaccine schedu

Question: What are the limitations of medical knowledge?


Compact Personalized Models for Neural Machine Translation. We propose and compare methods for gradientbased domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality by encouraging structured sparsity in the set of offset tensors during learning via group lasso regularization. We evaluate this technique for both batch and incremental adaptation across multiple data sets and language pairs. Our system architecture—combining a state-of-the-art self-attentive model with compact domain adaptation—provides high quality personalized machine translation that is both space and time efficient.

Question: How to train a neural machine translation model?


Improving code review effectiveness through reviewer recommendations. Effectively performing code review increases the quality of software and reduces occurrence of defects. However, this requires reviewers with experiences and deep understandings of system code. Manual selection of such reviewers can be a costly and time-consuming task. To reduce this cost, we propose a reviewer recommendation algorithm determining file path similarity called FPS algorithm. Using three OSS projects as case studies, FPS algorithm was accurate up to 77.97%, which significantly outperformed the previous approach.

Question: How to improve code review effectiveness through reviewer recommendations?


Information Bottleneck Inspired Method For Chat Text Segmentation. We present a novel technique for segmenting chat conversations using the information bottleneck method (Tishby et al., 2000), augmented with sequential continuity constraints. Furthermore, we utilize critical non-textual clues such as time between two consecutive posts and people mentions within the posts. To ascertain the effectiveness of the proposed method, we have collected data from public Slack conversations and Fresco, a proprietary platform deployed inside our organization. Experiments demonstrate that the proposed method yields an absolute (relative) improvement of as high as 3.23% (11.25%). To facilitate future research, we are releasing manual annotations for segmentation on public Slack conversations.

Question: How does the proposed method work?


The processing-speed theory of adult age differences in cognition.. A theory is proposed to account for some of the age-related differences reported in measures of Type A or fluid cognition. The central hypothesis in the theory is that increased age in adulthood is associated with a decrease in the speed with which many processing operations can be executed and that this reduction in speed leads to impairments in cognitive functioning because of what are termed the limited time mechanism and the simultaneity mechanism. That is, cognitive performance is degraded when processing is slow because relevant operations cannot be successfully executed (limited time) and because the products of early processing may no longer be available when later processing is complete (simultaneity). Several types of evidence, such as the discovery of considerable shared age-related variance across various measures of speed and large attenuation of the age-related influences on cognitive measures after statistical control of measures of speed, are consistent with this theory.

Question: What is the processing-speed theory of adult age differences in cognition?


Miniaturized Wilkinson power dividers utilizing capacitive loading. The authors report the miniaturization of a planar Wilkinson power divider by capacitive loading of the quarter wave transmission lines employed in conventional Wilkinson power dividers. Reduction of the transmission line segments from /spl lambda//4 to between /spl lambda//5 and /spl lambda//12 are reported here. The input and output lines at the three ports and the lines comprising the divider itself are coplanar waveguide (CPW) and asymmetric coplanar stripline (ACPS), respectively. The 10 GHz power dividers are fabricated on high resistivity silicon (HRS) and alumina wafers. These miniaturized dividers are 74% smaller than conventional Wilkinson power dividers, and have a return loss better than +30 dB and an insertion loss less than 0.55 dB. Design equations and a discussion about the effect of parasitic reactance on the isolation are presented for the first time.

Question: What is a miniaturized Wilkinson power divider?


BLINKS: ranked keyword searches on graphs. Query processing over graph-structured data is enjoying a growing number of applications. A top-k keyword search query on a graph finds the top k answers according to some ranking criteria, where each answer is a substructure of the graph containing all query keywords. Current techniques for supporting such queries on general graphs suffer from several drawbacks, e.g., poor worst-case performance, not taking full advantage of indexes, and high memory requirements. To address these problems, we propose BLINKS, a bi-level indexing and query processing scheme for top-k keyword search on graphs. BLINKS follows a search strategy with provable performance bounds, while additionally exploiting a bi-level index for pruning and accelerating the search. To reduce the index space, BLINKS partitions a data graph into blocks: The bi-level index stores summary information at the block level to initiate and guide search among blocks, and more detailed information for each block to accelerate search within blocks. Our experiments show that BLINKS offers orders-of-magnitude performance improvement over existing approaches.

Question: What is the best way to search a graph?


Privacy-Preserving Multiple Linear Regression of Vertically Partitioned Real Medical Datasets. This paper studies the feasibility of privacy-preservingdata mining in epidemiological study. As for the data-miningalgorithm, we focus to a linear multiple regression thatcan be used to identify the most significant factorsamong many possible variables, such as the historyof many diseases. We try to identify the linear model to estimate a lengthof hospital stay from distributed dataset related tothe patient and the disease information. In this paper, we have done experiment usingthe real medical dataset related to stroke andattempt to apply multiple regression with sixpredictors of age, sex, the medical scales, e.g., Japan Coma Scale, and the modified Rankin Scale. Our contributions of this paper include(1) to propose a practical privacy-preserving protocols for linear multiple regressionwith vertically partitioned datasets, and(2) to show the feasibility of the proposed system usingthe real medical dataset distributed into two parties, the hospital who knows the technical details of diseasesduring the patients are in the hospital, and the local government who knows the residence even afterthe patients left hospital. (3) to show the accuracy and the performance of thePPDM system which allows us to estimate the expectedprocessing time with arbitrary number of predictors.

Question: What is the difference between the Canadian Armed Forces and the Canadian Army?


Trust-Aware Collaborative Filtering for Recommender Systems. Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust values among users, termed "web of trust", allows a twofold enhancement of Recommender Systems. Firstly, the filtering process can be informed by the reputation of users which can be computed by propagating trust. Secondly, the trust metrics can help to solve a problem associated with the usual method of similarity assessment, its reduced computability. An empirical evaluation on Epinions.com dataset shows that trust propagation allows to increase the coverage of Recommender Systems while preserving the quality of predictions. The greatest improuvements are achieved for new users, who provided few ratings.

Question: What is the difference between trust and reputation?


The child sexual abuse accommodation syndrome.. Child victims of sexual abuse face secondary trauma in the crisis of discovery. Their attempts to reconcile their private experiences with the realities of the outer world are assaulted by the disbelief, blame and rejection they experience from adults. The normal coping behavior of the child contradicts the entrenched beliefs and expectations typically held by adults, stigmatizing the child with charges of lying, manipulating or imagining from parents, courts and clinicians. Such abandonment by the very adults most crucial to the child's protection and recovery drives the child deeper into self-blame, self-hate, alienation and revictimization. In contrast, the advocacy of an empathic clinician within a supportive treatment network can provide vital credibility and endorsement for the child. Evaluation of the responses of normal children to sexual assault provides clear evidence that societal definitions of "normal" victim behavior are inappropriate and procrustean, serving adults as mythic insulators against the child's pain. Within this climate of prejudice, the sequential survival options available to the victim further alienate the child from any hope of outside credibility or acceptance. Ironically, the child's inevitable choice of the "wrong" options reinforces and perpetuates the prejudicial myths. The most typical reactions of children are classified in this paper as the child sexual abuse accommodation syndrome. The syndrome is composed of five categories, of which two define basic childhood vulnerability and three are sequentially contingent on sexual assault: (1) secrecy, (2) helplessness, (3) entrapment and accommodation, (4) delayed, unconvincing disclosure, and (5) retraction. The accommodation syndrome is proposed as a simple and logical model for use by clinicians to improve understanding and acceptance of the child's position in the complex and controversial dynamics of sexual victimization. Application

Question: What is the child sexual abuse accommodation syndrome?


Adaptive postfiltering for quality enhancement of coded speech. AbstrucfAn adaptive postfiltering algorithm for enhancing the perceptual quality of coded speech is presented. The posffilter consists of a long-term postfilter section in cascade with a shortterm postfilter section and includes spectral tilt compensation and automatic gain control. The long-term section emphasizes pitch harmonics and attenuates the spectral valleys between pitch harmonics. The short-term section, on the other hand, emphasizes speech formants and attenuates the spectral valleys between formants. Both filter sections have poles and zeros. Unlike earlier postfilters that often introduced a substantial amount of muffling to the output speech, our postfilter significantly reduces this effect by minimizing the spectral tilt in its frequency response. As a result, this postlilter achieves noticeable noise reduction while introducing only minimal distortion in speech. The complexity of the postfilter is quite low. Variations of this postlilter are now being used in several national and international speech coding standards. This paper presents for the first time a complete description of our original postfiltering algorithm and the underlying ideas that motivated its development.

Question: What is the complexity of the postfilter?


Application of Fuzzy Graph Coloring in Traffic Light Problem. Let G= (V,E,σ,μ) be a simple connected undirected fuzzy graph. In this paper, we use a fuzzy graph model to represent a traffic network of a city and discuss a method to find the different type of accidental zones in a traffic flows. This paper is based on fuzzy coloring of fuzzy graphs and fuzziness of vertices. Using this approach, we can minimize the total waiting time of a traffic flow. It will help to reduce the traffic jam of a road.

Question: What is a fuzzy graph?


Quasi-Cyclic LDPC Codes: Influence of Proto- and Tanner-Graph Structure on Minimum Hamming Distance Upper Bounds. Quasi-cyclic (QC) low-density parity-check (LDPC) codes are an important instance of proto-graph-based LDPC codes. In this paper we present upper bounds on the minimum Hamming distance of QC LDPC codes and study how these upper bounds depend on graph structure parameters (like variable degrees, check node degrees, girth) of the Tanner graph and of the underlying proto-graph. Moreover, for several classes of proto-graphs we present explicit QC LDPC code constructions that achieve (or come close to) the respective minimum Hamming distance upper bounds. Because of the tight algebraic connection between QC codes and convolutional codes, we can state similar results for the free Hamming distance of convolutional codes. In fact, some QC code statements are established by first proving the corresponding convolutional code statements and then using a result by Tanner that says that the minimum Hamming distance of a QC code is upper bounded by the free Hamming distance of the convolutional code that is obtained by "unwrapping" the QC code.

Question: What is the free Hamming distance of a convolutional code?


Adapting recommender systems to the requirements of personal health record systems. In the future many people in industrialized countries will manage their personal health data electronically in centralized, reliable and trusted repositories - so-called personal health record systems (PHR). At this stage PHR systems still fail to satisfy the individual medical information needs of their users. Personalized recommendations could solve this problem.  A first approach of integrating recommender system (RS) methodology into personal health records - termed health recommender system (HRS) - is presented. By exploitation of existing semantic networks like Wikipedia a health graph data structure is obtained. The data kept within such a graph represent health related concepts and are used to compute semantic distances among pairs of such concepts.  A ranking procedure based on the health graph is outlined which enables a match between entries of a PHR system and health information artifacts. This way a PHR user will obtain individualized health information he might be interested in.

Question: How to integrate recommender systems into personal health records?


Okapi at TREC-3. During the course of TREC{1 the low-level search functions were split o into a separate Basic Search System (BSS) [2], but retrieval and ranking of documents was still done using the \classical" probabilistic model of Robertson and Sparck Jones[7] with no account taken of document length or term frequency within document or query. Four runs were submitted to NIST for evaluation: automatic ad hoc, automatic routing, manual ad hoc and manual ad hoc with feedback. The results were undistinguished, although not among the worst. Of the ad hoc runs, the manual was better than the automatic (in which only the CONCEPTS elds of the topics were used), and feedback appeared bene cial.

Question: What is the best way to search for documents?


Clinical practice guidelines for diagnosis of autism spectrum disorder in adults and children in the UK: a narrative review. BACKGROUND Research suggests that diagnostic procedures for Autism Spectrum Disorder are not consistent across practice and that diagnostic rates can be affected by contextual and social drivers. The purpose of this review was to consider how the content of clinical practice guidelines shapes diagnoses of Autism Spectrum Disorder in the UK; and investigate where, within those guidelines, social factors and influences are considered.   METHODS We electronically searched multiple databases (NICE Evidence Base; TRIP; Social Policy and Practice; US National Guidelines Clearinghouse; HMIC; The Cochrane Library; Embase; Global health; Ovid; PsychARTICLES; PsychINFO) and relevant web sources (government, professional and regional NHS websites) for clinical practice guidelines. We extracted details of key diagnostic elements such as assessment process and diagnostic tools. A qualitative narrative analysis was conducted to identify social factors and influences.   RESULTS Twenty-one documents were found and analysed. Guidelines varied in recommendations for use of diagnostic tools and assessment procedures. Although multidisciplinary assessment was identified as the 'ideal' assessment, some guidelines suggested in practice one experienced healthcare professional was sufficient. Social factors in operational, interactional and contextual areas added complexity to guidelines but there were few concrete recommendations as to how these factors should be operationalized for best diagnostic outcomes.   CONCLUSION Although individual guidelines appeared to present a coherent and systematic assessment process, they varied enough in their recommendations to make the choices available to healthcare professionals particularly complex and confusing. We recommend a more explicit acknowledgement of social factors in clinical practice guidelines with advice about how

Question: How to diagnose autism spectrum disorder in adults and children in the UK?


New cardinality estimation algorithms for HyperLogLog sketches. This paper presents new methods to estimate the cardinalities of data sets recorded by HyperLogLog sketches. A theoretically motivated extension to the original estimator is presented that eliminates the bias for small and large cardinalities. Based on the maximum likelihood principle a second unbiased method is derived together with a robust and efficient numerical algorithm to calculate the estimate. The maximum likelihood approach can also be applied to more than a single HyperLogLog sketch. In particular, it is shown that it gives more precise cardinality estimates for union, intersection, or relative complements of two sets that are both represented by HyperLogLog sketches compared to the conventional technique using the inclusion-exclusion principle. All the new methods are demonstrated and verified by extensive simulations.

Question: What is the cardinality of a set?


Question: What is the language?


SmartPort: A Platform for Sensor Data Monitoring in a Seaport Based on FIWARE. Seaport monitoring and management is a significant research area, in which infrastructure automatically collects big data sets that lead the organization in its multiple activities. Thus, this problem is heavily related to the fields of data acquisition, transfer, storage, big data analysis and information visualization. Las Palmas de Gran Canaria port is a good example of how a seaport generates big data volumes through a network of sensors. They are placed on meteorological stations and maritime buoys, registering environmental parameters. Likewise, the Automatic Identification System (AIS) registers several dynamic parameters about the tracked vessels. However, such an amount of data is useless without a system that enables a meaningful visualization and helps make decisions. In this work, we present SmartPort, a platform that offers a distributed architecture for the collection of the port sensors' data and a rich Internet application that allows the user to explore the geolocated data. The presented SmartPort tool is a representative, promising and inspiring approach to manage and develop a smart system. It covers a demanding need for big data analysis and visualization utilities for managing complex infrastructures, such as a seaport.

Question: What is a seaport?


A comparative analysis of machine learning techniques for student retention management. Student retention is an essential part of many enrollment management systems. It affects university rankings, school reputation, and financial wellbeing. Student retention has become one of the most important priorities for decision makers in higher education institutions. Improving student retention starts with a thorough understanding of the reasons behind the attrition. Such an understanding is the basis for accurately predicting at-risk students and appropriately intervening to retain them. In this study, using five years of institutional data along with several data mining techniques (both individuals as well as ensembles), we developed analytical models to predict and to explain the reasons behind freshmen student attrition. The comparative analyses results showed that the ensembles performed better than individual models, while the balanced dataset produced better prediction results than the unbalanced dataset. The sensitivity analysis of the Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.

Question: What is the difference between a balanced and an unbalanced dataset?


Exploring trajectory-driven local geographic topics in foursquare. The location based social networking services (LBSNSs) are becoming very popular today. In LBSNSs, such as Foursquare, users can explore their places of interests around their current locations, check in at these places to share their locations with their friends, etc. These check-ins contain rich information and imply human mobility patterns; thus, they can greatly facilitate mining and analysis of local geographic topics driven by users' trajectories. The local geographic topics indicate the potential and intrinsic relations among the locations in accordance with users' trajectories. These relations are useful for users in both location and friend recommendations. In this paper, we focus on exploring the local geographic topics through check-ins in Pittsburgh area in Foursquare. We use the Latent Dirichlet Allocation (LDA) model to discover the local geographic topics from the checkins. We also compare the local geographic topics on weekdays with those at weekends. Our results show that LDA works well in finding the related places of interests.

Question: What is the LDA model?


A survey of hardware Trojan threat and defense. Hardware Trojans (HTs) can be implanted in security-weak parts of a chip with various means to steal the internal sensitive data or modify original functionality, which may lead to huge economic losses and great harm to society. Therefore, it is very important to analyze the specific HT threats existing in the whole life cycle of integrated circuits (ICs), and perform protection against hardware Trojans. In this paper, we elaborate an IC market model to illustrate the potential HT threats faced by the parties involved in the model. Then we categorize the recent research advances in the countermeasures against HT attacks. Finally, the challenges and prospects for HT defense are illuminated. & 2016 Elsevier B.V. All rights reserved.

Question: What is the market model of hardware Trojans?


An Algebra and Equivalences to Transform Graph Patterns in Neo4j. Modern query optimizers of relational database systems embody more than three decades of research and practice in the area of data management and processing. Key advances include algebraic query transformation, intelligent search space pruning, and modular optimizer architectures. Surprisingly, many of these contributions seem to have been overlooked in the emerging field of graph databases so far. In particular, we believe that query optimization based on a general graph algebra and its equivalences can greatly improve on the current state of the art. Although some graph algebras have already been proposed, they have often been developed in a context, in which a relational database system is used as a backend to process graph data. As a consequence, these algebras are typically tightly coupled to the relational algebra, making them unsuitable for native graph databases. While we support the approach of extending the relational algebra, we argue that graph-specific operations should be defined at a higher level, independent of the database backend. In this paper, we introduce such a general graph algebra and corresponding equivalences. We demonstrate how it can be used to optimize Cypher queries in the setting of the Neo4j native graph database.

Question: What is the graph algebra?


Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective. Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users to efficiently solve real-world artificial intelligence and data mining problems. Dramatic advances in big data analytics has led to a wide variety of interactive model analysis tasks. In this paper, we present a comprehensive analysis and interpretation of this rapidly developing area. Specifically, we classify the relevant work into three categories: understanding, diagnosis, and refinement. Each category is exemplified by recent influential work. Possible future research opportunities are also explored and discussed.

Question: What is the difference between understanding, diagnosis, and refinement?


School Leadership and Cyberbullying—A Multilevel Analysis. Cyberbullying is a relatively new form of bullying, with both similarities and differences to traditional bullying. While earlier research has examined associations between school-contextual characteristics and traditional bullying, fewer studies have focused on the links to students' involvement in cyberbullying behavior. The aim of the present study is to assess whether school-contextual conditions in terms of teachers' ratings of the school leadership are associated with the occurrence of cyberbullying victimization and perpetration among students. The data are derived from two separate data collections performed in 2016: The Stockholm School Survey conducted among students in the second grade of upper secondary school (ages 17-18 years) in Stockholm municipality, and the Stockholm Teacher Survey which was carried out among teachers in the same schools. The data include information from 6067 students distributed across 58 schools, linked with school-contextual information based on reports from 1251 teachers. Cyberbullying victimization and perpetration are measured by students' self-reports. Teachers' ratings of the school leadership are captured by an index based on 10 items; the mean value of this index was aggregated to the school level. Results from binary logistic multilevel regression models show that high teacher ratings of the school leadership are associated with less cyberbullying victimization and perpetration. We conclude that a strong school leadership potentially prevents cyberbullying behavior among students.

Question: What is cyberbullying?


An improved IEEE 802.16 WiMAX module for the ns-3 simulator. IEEE 802.16 WiMAX is a promising new wireless technology for providing broadband ubiquitous network access. As more and more researchers and industrials are interested in simulating such networks, a number of WiMAX simulators have been emerged in the networking community. One of the most recent WiMAX simulator is the one developed for ns-3. This module provides a standard compliant and well designed implementation of the standard and benefits from the major enhancements provided by ns-3 (compared to other network simulators) which has all the capabilities of becoming the leading network simulator in near future. However, this WiMAX module still lacks some important features which motivated this work. In this paper, we first provide a snapshot of existing WiMAX simulators available in the public domain, while highlighting their limitations. Then, we describe the new features and enhancements we have integrated within the ns-3 WiMAX module, and in particular: a realistic and scalable physical model, an IP packet classifier for the convergence sub-layer, efficient uplink and downlink schedulers, support for multicast traffic and pcap packet tracing functionality. The new design of the physical layer has improved the simulation time by several magnitude orders while still providing a realistic implementation of the standard. Furthermore, the IP classifier has enabled the simulation of an unlimited number of service flows per subscriber station, while the proposed schedulers improve the management of the QoS requirements for the different service flows.

Question: What is the difference between WiMAX and WiMAX?


Solving traveling salesman problems via artificial intelligent search techniques. The traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics and combinatorial optimization. It is also considered as the class of the NPcomplete combinatorial optimization problems. By literatures, many algorithms and approaches have been launched to solve such the TSP. However, no current algorithms that can provide the exactly optimal solution of the TSP problem are available. This paper proposes the application of AI search techniques to solve the TSP problems. Three AI search methods, i.e. genetic algorithms (GA), tabu search (TS), and adaptive tabu search (ATS), are conducted. They are tested against ten benchmark real-world TSP problems. As results compared with the exactly optimal solutions, the AI search techniques can provide very satisfactory solutions for all TSP problems. Key-Words: Traveling Salesman Problem, Genetic Algorithm, Tabu Search, Adaptive Tabu Search

Question: What is the traveling salesman problem?


The Structure and Dynamics of Co-Citation Clusters: A Multiple-Perspective Co-Citation Analysis. A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Co-citation networks are decomposed into co-citation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a co-citation cluster's members and their citers. The generic method is applied to a three-part analysis of the field of Information Science as defined by 12 journals published between 1996 and 2008: 1) a comparative author co-citation analysis (ACA), 2) a progressive ACA of a time series of co-citation networks, and 3) a progressive document co-citation analysis (DCA). Results show that the multipleperspective method increases the interpretability and accountability of both ACA and DCA networks.

Question: What is the structure and dynamics of co-citation clusters?


Oboe: auto-tuning video ABR algorithms to network conditions. Most content providers are interested in providing good video delivery QoE for all users, not just on average. State-of-the-art ABR algorithms like BOLA and MPC rely on parameters that are sensitive to network conditions, so may perform poorly for some users and/or videos. In this paper, we propose a technique called Oboe to auto-tune these parameters to different network conditions. Oboe pre-computes, for a given ABR algorithm, the best possible parameters for different network conditions, then dynamically adapts the parameters at run-time for the current network conditions. Using testbed experiments, we show that Oboe significantly improves BOLA, MPC, and a commercially deployed ABR. Oboe also betters a recently proposed reinforcement learning based ABR, Pensieve, by 24% on average on a composite QoE metric, in part because it is able to better specialize ABR behavior across different network states.

Question: How does oboe work?


Please Biofeed the Zombies: Enhancing the Gameplay and Display of a Horror Game Using Biofeedback. This paper describes an investigation into how real-time but low-cost biometric information can be interpreted by computer games to enhance gameplay without fundamentally changing it. We adapted a cheap sensor, (the Lightstone mediation sensor device by Wild Divine), to record and transfer biometric information about the player (via sensors that clip over their fingers) into a commercial game engine, Half-Life 2. During game play, the computer game was dynamically modified by the player's biometric information to increase the cinematically augmented "horror" affordances. These included dynamic changes in the game shaders, screen shake, and the creation of new spawning points for the game's non-playing characters (zombies), all these features were driven by the player's biometric data. To evaluate the usefulness of this biofeedback device, we compared it against a control group of players who also had sensors clipped on their fingers, but for the second group the gameplay was not modified by the biometric information of the players. While the evaluation results indicate biometric data can improve the situated feeling of horror, there are many design issues that will need to be investigated by future research, and the judicious selection of theme and appropriate interaction is vital. Author

Question: How to enhance the horror of a game?


Procrastination and cramming: how adept students ace the system.. Clear power differentials between teacher and pupil and the assignment of well-delineated tasks within specified time constraints characterize the academic system. Most university students are supremely adept at working within that structure. Many students who outwardly adapt to the system, however, engage in an intense and private ritual that comprises five aspects: calculated procrastination, preparatory anxiety, climactic cramming, nick-of-time deadline-making, and a secret, if often uncelebrated, victory. These adept students often find it difficult to admit others into their efficient program of academic survival. Although such behaviors are adaptive for school rhythms and expectations, these students may also try to impose them onto personal relationships, including those that are psychotherapeutic. The students' tendency to transfer their longstanding habits of procrastination and cramming to the workplace after graduation is less problematic. The argument is made that school rhythms and expectations shape the workplace, not vice versa. Methods of addressing the troublesome aspects of these work styles are suggested. Rather than attempting to change existing work patterns, the therapist can identify the underlying psychodynamic drama and its attendant manifestations that are becoming problematic.

Question: How adept are students at the system?


Unmanned surface vehicles: An overview of developments and challenges.

Question: What is an unmanned surface vehicle?


What is Gab: A Bastion of Free Speech or an Alt-Right Echo Chamber. Over the past few years, a number of new "fringe" communities, like 4chan or certain subreddits, have gained traction on the Web at a rapid pace. However, more often than not, little is known about how they evolve or what kind of activities they attract, despite recent research has shown that they influence how false information reaches mainstream communities. This motivates the need to monitor these communities and analyze their impact on the Web's information ecosystem. In August 2016, a new social network called Gab was created as an alternative to Twitter. It positions itself as putting "people and free speech first", welcoming users banned or suspended from other social networks. In this paper, we provide, to the best of our knowledge, the first characterization of Gab. We collect and analyze 22M posts produced by 336K users between August 2016 and January 2018, finding that Gab is predominantly used for the dissemination and discussion of news and world events, and that it attracts alt-right users, conspiracy theorists, and other trolls. We also measure the prevalence of hate speech on the platform, finding it to be much higher than Twitter, but lower than 4chan's Politically Incorrect board.

Question: What is Gab: A Bastion of Free Speech or an Alt-Right Echo Chamber?


Gesture Recognition: A Survey. With increasing use of computers in our daily lives, lately there has been a rapid increase in the efforts to develop a better human computer interaction interface. The need of easy to use and advance types of human-computer interaction with natural interfaces is more than ever. In the present framework, the UI (User Interface) of a computer allows user to interact with electronic devices with graphical icons and visual indicators, which is still inconvenient and not suitable for working in virtual environments. An interface which allow user to communicate through gestures is the next step in the direction of advance human computer interface. In the present paper author explore different aspects of gesture recognition techniques.

Question: What is gesture recognition?


Word Channel Based Multiscale Pedestrian Detection without Image Resizing and Using Only One Classifier. Most pedestrian detection approaches that achieve high accuracy and precision rate and that can be used for realtime applications are based on histograms of gradient orientations. Usually multiscale detection is attained by resizing the image several times and by recomputing the image features or using multiple classifiers for different scales. In this paper we present a pedestrian detection approach that uses the same classifier for all pedestrian scales based on image features computed for a single scale. We go beyond the low level pixel-wise gradient orientation bins and use higher level visual words organized into Word Channels. Boosting is used to learn classification features from the integral Word Channels. The proposed approach is evaluated on multiple datasets and achieves outstanding results on the INRIA and Caltech-USA benchmarks. By using a GPU implementation we achieve a classification rate of over 10 million bounding boxes per second and a 16 FPS rate for multiscale detection in a 640×480 image.

Question: What is the difference between a word channel and a histogram of gradient orientations?


Divide and conquer: neuroevolution for multiclass classification. Neuroevolution is a powerful and general technique for evolving the structure and weights of artificial neural networks. Though neuroevolutionary approaches such as NeuroEvolution of Augmenting Topologies (NEAT) have been successfully applied to various problems including classification, regression, and reinforcement learning problems, little work has explored application of these techniques to larger-scale multiclass classification problems. In this paper, NEAT is evaluated in several multiclass classification problems, and then extended via two ensemble approaches: One-vs-All and One-vs-One. These approaches decompose multiclass classification problems into a set of binary classification problems, in which each binary problem is solved by an instance of NEAT. These ensemble models exhibit reduced variance and increasingly superior accuracy as the number of classes increases. Additionally, higher accuracy is achieved early in training, even when artificially constrained for the sake of fair comparison with standard NEAT. However, because the approach can be trivially distributed, it can be applied quickly at large scale to solve real problems. In fact, these approaches are incorporated into DarwinTM, an enterprise automatic machine learning solution that also incorporates various other algorithmic enhancements to NEAT. The resulting complete system has proven robust to a wide variety of client datasets.

Question: What is the largest neural network?


Development of a capacitive-type 6-axis force-torque sensor. Force sensing is a crucial task for robots, especially when end effectors such as fingers and hands need to interact with unknown environments; to sense such forces, a force-torque (F/T) sensor is an essential component. In this paper, we propose a small-sized 6-axis F/T sensor with a novel arrangement of 12 transducers using the force transducer we have previously developed. The copper beryllium used in our sensor reduces hysteresis in each transducer. Additionally, the sensor provides digital output via I2C bus to reduce the susceptibility to noise, and reduce the number of required wires. Sensor characteristics such as its sensitivity, signal-to-noise ratio, linearity, and hysteresis are determined. More importantly, we showed that our sensor can detect and measure the 6-axis F/T.

Question: How to measure 6-axis force and torque?


Routing Attacks and Countermeasures in the RPL-Based Internet of Things. The Routing Protocol for Low-Power and Lossy Networks (RPL) is a novel routing protocol standardized for constrained environments such as 6LoWPAN networks. Providing security in IPv6/RPL connected 6LoWPANs is challenging because the devices are connected to the untrusted Internet and are resource constrained, the communication links are lossy, and the devices use a set of novel IoT technologies such as RPL, 6LoWPAN, and CoAP/CoAPs. In this paper we provide a comprehensive analysis of IoT technologies and their new security capabilities that can be exploited by attackers or IDSs. One of the major contributions in this paper is our implementation and demonstration of well-known routing attacks against 6LoWPAN networks running RPL as a routing protocol. We implement these attacks in the RPL implementation in the Contiki operating system and demonstrate these attacks in the Cooja simulator. Furthermore, we highlight novel security features in the IPv6 protocol and exemplify the use of these features for intrusion detection in the IoT by implementing a lightweight heartbeat protocol.

Question: What is the difference between 6LoWPAN and RPL?


Hierarchical Attention Networks for Document Classification. We propose a hierarchical attention network for document classification. Our model has two distinctive characteristics: (i) it has a hierarchical structure that mirrors the hierarchical structure of documents; (ii) it has two levels of attention mechanisms applied at the wordand sentence-level, enabling it to attend differentially to more and less important content when constructing the document representation. Experiments conducted on six large scale text classification tasks demonstrate that the proposed architecture outperform previous methods by a substantial margin. Visualization of the attention layers illustrates that the model selects qualitatively informative words and sentences.

Question: What is the difference between a hierarchical attention network and a convolutional neural network?


A fast-transient LDO based on buffered flipped voltage follower. In this work, the analysis of the flipped voltage follower (FVF) based single-transistor-control (STC) LDO is given. Two evolved versions of FVF, cascaded FVF (CAFVF) and level shifted FVF (LSFVF), are studied. Then, a buffered FVF (BFVF) for LDO application is proposed, combining the virtues of both CAFVF and LSFVF structures. It alleviates the minimum loading requirement of FVF and the simulation result shows that it has faster transient response and better load regulation.

Question: What is a LDO?


SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones. Photos obtained via crowdsourcing can be used in many critical applications. Due to the limitations of communication bandwidth, storage and processing capability, it is a challenge to transfer the huge amount of crowdsourced photos. To address this problem, we propose a framework, called SmartPhoto, to quantify the quality (utility) of crowdsourced photos based on the accessible geographical and geometrical information (called metadata) including the smartphone's orientation, position and all related parameters of the built-in camera. From the metadata, we can infer where and how the photo is taken, and then only transmit the most useful photos. Three optimization problems regarding the tradeoffs between photo utility and resource constraints, namely the Max-Utility problem, the online Max-Utility problem and the Min-Selection problem, are studied. Efficient algorithms are proposed and their performance bounds are theoretically proved. We have implemented SmartPhoto in a testbed using Android based smartphones, and proposed techniques to improve the accuracy of the collected metadata by reducing sensor reading errors and solving object occlusion issues. Results based on real implementations and extensive simulations demonstrate the effectiveness of the proposed algorithms.

Question: How to improve the accuracy of the collected metadata?


Studying the Effects of Task Notification Policies on Participation and Outcomes in On-the-go Crowdsourcing. Recent years have seen the growth of physical crowdsourcing systems (e.g., Uber; TaskRabbit) that motivate large numbers of people to provide new and improved physical tasking and delivery services on-demand. In these systems, opportunistically relying on people to make convenient contributions may lead to incomplete solutions, while directing people to do inconvenient tasks requires high incentives. To increase people's willingness to participate and reduce the need to incentivize participation, we study on-the-go crowdsourcing as an alternative approach that suggests tasks along people's existing routes that are conveniently on their way. We explore as a first step in this paper the design of task notification policies that decide when, where, and to whom to suggest tasks. Situating our work in the context of practical problems such as package delivery and lost-and-found searches, we conducted controlled experiments that show how small changes in task notification policy can influence individual participation and actions in significant ways that in turn affect system outcomes. We discuss the implications of our findings on the design of future on-the-go crowdsourcing technologies.

Question: How can we design a task notification policy that motivates people to participate in on-the-go crowdsourcing?


Biconvex Relaxation for Semidefinite Programming in Computer Vision. References [1] Wang, P., Shen, C., van den Hengel, A., A fast semidefinite approach to solving binary quadratic problems. CVPR 2013. [2] Candes, E.J., Li, X., Soltanolkotabi, M., Phase retrieval via wirtinger flow: Theory and algorithms. IEEE Transaction on Information Theory, 2015. [3] Zheng, Q., Lafferty, J., A convergent gradient descent algorithm for rank minimization and semidefinite programming from random linear measurements. NIPS 2015. [4] Netrapalli, P., Jain, P., Sanghavi, S., Phase retrieval using alternating minimization. NIPS 2013. Many important problems requires solving for low rank SDPs with PSD constraint matrices.

Question: What is the rank of the matrix in the example?


Walking on Minimax Paths for k-NN Search. Link-based dissimilarity measures, such as shortest path or Euclidean commute time distance, base their distance on paths between nodes of a weighted graph. These measures are known to be better suited to data manifold with nonconvex-shaped clusters, compared to Euclidean distance, so that k-nearest neighbor (NN) search is improved in such metric spaces. In this paper we present a new link-based dissimilarity measure based on minimax paths between nodes. Two main benefits of minimax path-based dissimilarity measure are: (1) only a subset of paths is considered to make it scalable, while Euclidean commute time distance considers all possible paths; (2) it better captures nonconvex-shaped cluster structure, compared to shortest path distance. We define the total cost assigned to a path between nodes as Lp norm of intermediate costs of edges involving the path, showing that minimax path emerges from our Lp norm over paths framework. We also define minimax distance as the intermediate cost of the longest edge on the minimax path, then present a greedy algorithm to compute k smallest minimax distances between a query and N data points in O(logN + k log k) time. Numerical experiments demonstrate that our minimax kNN algorithm reduce the search time by several orders of magnitude, compared to existing methods, while the quality of k-NN search is significantly improved over Euclidean distance. Introduction Given a set of N data points X = {x1, . . . ,xN}, k-nearest neighbor (k-NN) search in metric spaces involves finding k closest points in the dataset X to a query xq . Dissimilarity measure defines distance duv between two data points (or nodes of a weighted graph) xu and xv in the corresponding metric space, and the performance of k-NN search depends on distance metric. Euclidean distance ‖xu − xv‖2 is the most popular measure for k-NN search but it does not work well when data points X lie on a curved manifold with nonconvex-shap

Question: What is the minimax distance?


Leakage Current Analytical Model and Application in Single-Phase Transformerless Photovoltaic Grid-Connected Inverter. Due to the characteristics of low cost and high efficiency, the transformerless photovoltaic (PV) grid-connected inverters have been popularized in the application of solar electric generation system in residential market. Unfortunately, the leakage current through the stray capacitors between the PV array and the ground is harmful. This paper focuses on the leakage current suppressing method, in which all common-mode paths are considered. First, the common-mode analytical model at switching frequency is developed, and the rules of eliminating switching frequency common-mode source are summarized based on this model. The existing full-bridge- and half-bridge-type converters have been analyzed by using the developed model and rules, and then, a new full-bridge-type converter structure and a compensation strategy for half-bridge-type inverter have been presented finally.

Question: What is the common-mode analytical model?


Requirements engineering and the creative process in the video game industry. The software engineering process in video game development is not clearly understood, hindering the development of reliable practices and processes for this field. An investigation of factors leading to success or failure in video game development suggests that many failures can be traced to problems with the transition from preproduction to production. Three examples, drawn from real video games, illustrate specific problems: 1) how to transform documentation from its preproduction form to a form that can be used as a basis for production;, 2) how to identify implied information in preproduction documents; and 3) how to apply domain knowledge without hindering the creative process. We identify 3 levels of implication and show that there is a strong correlation between experience and the ability to identify issues at each level. The accumulated evidence clearly identifies the need to extend traditional requirements engineering techniques to support the creative process in video game development.

Question: How to transform documentation from its preproduction form to a form that can be used as a basis for production?


Centralities in Large Networks: Algorithms and Observations. Node centrality measures are important in a large number of graph applications, from search and ranking to social and biological network analysis. In this paper we study node centrality for very large graphs, up to billions of nodes and edges. Various definitions for centrality have been proposed, ranging from very simple (e.g., node degree) to more elaborate. However, measuring centrality in billion-scale graphs poses several challenges. Many of the "traditional" definitions such as closeness and betweenness were not designed with scalability in mind. Therefore, it is very difficult, if not impossible, to compute them both accurately and efficiently. In this paper, we propose centrality measures suitable for very large graphs, as well as scalable methods to effectively compute them. More specifically, we propose effective closeness and LINERANK which are designed for billion-scale graphs. We also develop algorithms to compute the proposed centrality measures in MAPREDUCE, a modern paradigm for large-scale, distributed data processing. We present extensive experimental results on both synthetic and real datasets, which demonstrate the scalability of our approach to very large graphs, as well as interesting findings and anomalies.

Question: What is the largest graph in the world?


3D face landmark labelling. Most 3D face processing systems require feature detection and localisation, for example to crop, register, analyse or recognise faces. The three features often used in the literature are the tip of the nose, and the two inner corner of the eyes. Failure to localise these landmarks can cause the system to fail and they become very difficult to detect under large pose variations or when occlusion is present. In this paper, we present a proof-of-concept for a face labelling system capable of overcoming this problem, as a larger number of landmarks are employed. A set of points containing hand-placed landmarks is used as input data. The aim here is to retrieve the landmark's labels when some part of the face is missing. By using graph matching techniques to reduce the number of candidates, and translation and unit-quaternion clustering to determine a final correspondence, we evaluate the accuracy at which landmarks can be retrieved under changes in expression, orientation and in the presence of occlusions.

Question: How to localise landmarks in a 3D face?


Mobile Phone Use and Sleep Quality and Length in College Students. Proper sleep length and quality are essential for physical and mental health and have found to be related to a variety of negative outcomes (Brown, Buboltz, &Soper, 2002). College is a time,a transition for individuals where they begin laying a foundation for their future and acquiring sufficient sleep is of great value. College students are recognized as one of the most sleep-deprived groups, but also, as one of the most technologically-oriented population. Due to this combination, college students' sleep habits and mobile phone use habits havebegun to receiveattention. The purpose of this study was to examine the relationship between sleep quality/length and mobile phone use among college students. Three hundred and fifty college students voluntarily participated by completing the Sleep Quality Index (SQI), The Sleep Habits Survey,the Mobile Phone Problem Use Scale(MPPUS), the SMS Problem Use Scale (SMS-PUDQ) and the Mini IPIP. Results indicate that various aspects of mobile phone use such as problem mobile phone use, addictive text messaging, problematic texting, and pathological texting are related to sleep quality, but not sleep length. Additionally, extraverted individuals were found to engage in greater mobile phone use and problematic text messaging.

Question: What is the relationship between sleep quality and mobile phone use?


Single-label and multi-label conceptor classifiers in pre-trained neural networks. Training large neural network models from scratch is not feasible due to over-fitting on small datasets and too much time consumed on large datasets. To address this, transfer learning, namely utilizing the feature extracting capacity learned by large models, becomes a hot spot in neural network community. At the classifying stage of pre-trained neural network model, either a linear SVM classifier or a Softmax classifier is employed and that is the only trained part of the whole model. In this paper, inspired by transfer learning, we propose a classifier based on conceptors called Multi-label Conceptor Classifier (MCC) to deal with multi-label classification in pre-trained neural networks. When no multi-label sample exists, MCC equates to Fast Conceptor Classifier, a fast single-label classifier proposed in our previous work, thus being applicable to single-label classification. Moreover, by introducing a random search algorithm, we further improve the performance of MCC on single-label datasets Caltech-101 and Caltech-256, where it achieves state-of-the-art results. Also, its evaluations with pre-trained rather than fine-tuning neural networks are investigated on multi-label dataset PASCAL VOC-2007, where it achieves comparable results.

Question: What is the difference between a single-label classifier and a multi-label classifier?


Pocket reflectometry. We present a simple, fast solution for reflectance acquisition using tools that fit into a pocket. Our method captures video of a flat target surface from a fixed video camera lit by a hand-held, moving, linear light source. After processing, we obtain an SVBRDF.  We introduce a BRDF chart, analogous to a color "checker" chart, which arranges a set of known-BRDF reference tiles over a small card. A sequence of light responses from the chart tiles as well as from points on the target is captured and matched to reconstruct the target's appearance.  We develop a new algorithm for BRDF reconstruction which works directly on these LDR responses, without knowing the light or camera position, or acquiring HDR lighting. It compensates for spatial variation caused by the local (finite distance) camera and light position by warping responses over time to align them to a specular reference. After alignment, we find an optimal linear combination of the Lambertian and purely specular reference responses to match each target point's response. The same weights are then applied to the corresponding (known) reference BRDFs to reconstruct the target point's BRDF. We extend the basic algorithm to also recover varying surface normals by adding two spherical caps for diffuse and specular references to the BRDF chart.  We demonstrate convincing results obtained after less than 30 seconds of data capture, using commercial mobile phone cameras in a casual environment.

Question: How to reconstruct a reflectance from a video?


Training a Fully Convolutional Neural Network to Route Integrated Circuits. We present a deep, fully convolutional neural network that learns to route a circuit layout 'net' with appropriate choice of metal tracks and wire class combinations. Inputs to the network are the encoded layouts containing spatial location of pins to be routed. After 15 fully convolutional stages followed by a score comparator, the network outputs 8 layout layers (corresponding to 4 route layers, 3 via layers and an identity-mapped pin layer) which are then decoded to obtain the routed layouts. We formulate this as a binary segmentation problem on a per-pixel per-layer basis, where the network is trained to correctly classify pixels in each layout layer to be 'on' or 'off'. To demonstrate learnability of layout design rules, we train the network on a dataset of 50,000 train and 10,000 validation samples that we generate based on certain pre-defined layout constraints. Precision, recall and F1 score metrics are used to track the training progress. Our network achieves F1 ≈ 97% on the train set and F1 ≈ 92% on the validation set. We use PyTorch for implementing our model. Code is made publicly available1.

Question: How to train a neural network to route an integrated circuit?


StreamOp: An Innovative Middleware for Supporting Data Management and Query Functionalities over Sensor Network Streams Efficiently. During the last decade, a lot of progress has happened in the field of autonomous embedded devices (also known as motes) and wireless sensor networks. In this context, we envision next-generation systems to have autonomous database management functionalities on each mote, for supporting easy management of data on flash disks, adequate querying capabilities, and ubiquitous computing with no-effort. Following this major vision, in this paper we describe application scenarios, middleware approach and data management algorithms of a novel system, called Stream Operation (StreamOp), which effectively and efficiently realizes the depicted challenges. In particular, StreamOp supports heterogeneity, i.e. It works on top of different platforms, efficiency, i.e. It returns responses quickly, and autonomy, i.e. It saves battery power. We show how StreamOp provides these features, along with some experimental results that clearly validate our main assumptions.

Question: StreamOp: An Innovative Middleware for Supporting Data Management and Query Functionalities over Sensor Network Streams Efficiently.


Animal-assisted therapy and loneliness in nursing homes: use of robotic versus living dogs.. Loneliness is a common problem in long-term care facilities (LTCF) and previous work has shown that animal-assisted therapy (AAT) can to some degree reverse loneliness. Here, we compared the ability of a living dog (Dog) and a robotic dog (AIBO) to treat loneliness in elderly patients living in LTCF. In comparison with a control group not receiving AAT, both the Dog and AIBO groups had statistically significant improvements in their levels of loneliness. As measured by a modified Lexington Attachment to Pets Scale (MLAPS), residents showed high levels of attachment to both the dog and AIBO. Subscale analysis showed that the AIBO group scored lower than the living dog on "animal rights/animal welfare" but not on "general attachment" or "people substituting." However, MLAPS measures did not correlate with changes in loneliness, showing that attachment was not the mechanism by which AAT decreases loneliness. We conclude that interactive robotic dogs can reduce loneliness in residents of LTCF and that residents become attached to these robots. However, level of attachment does not explain the decrease in loneliness associated with AAT conducted with either a living or robotic dog.

Question: What is animal-assisted therapy?


Recent and robust query auto-completion. Query auto-completion (QAC) is a common interactive feature that assists users in formulating queries by providing completion suggestions as they type. In order for QAC to minimise the user's cognitive and physical effort, it must: (i) suggest the user's intended query after minimal input keystrokes, and (ii) rank the user's intended query highly in completion suggestions. Typically, QAC approaches rank completion suggestions by their past popularity. Accordingly, QAC is usually very effective for previously seen and consistently popular queries. Users are increasingly turning to search engines to find out about unpredictable emerging and ongoing events and phenomena, often using previously unseen or unpopular queries. Consequently, QAC must be both robust and time-sensitive -- that is, able to sufficiently rank both consistently and recently popular queries in completion suggestions. To address this trade-off, we propose several practical completion suggestion ranking approaches, including: (i) a sliding window of query popularity evidence from the past 2-28 days, (ii) the query popularity distribution in the last N queries observed with a given prefix, and (iii) short-range query popularity prediction based on recently observed trends. Using real-time simulation experiments, we extensively investigated the parameters necessary to maximise QAC effectiveness for three openly available query log datasets with prefixes of 2-5 characters: MSN and AOL (both English), and Sogou 2008 (Chinese). Optimal parameters vary for each query log, capturing the differing temporal dynamics and querying distributions. Results demonstrate consistent and language-independent improvements of up to 9.2% over a non-temporal QAC baseline for all query logs with prefix lengths of 2-3 characters. This work is an important step towards more effective QAC approaches.

Question: What is the canadian military?


Unpacking Spear Phishing Susceptibility. We report the results of a field experiment where we sent to over 1200 university students an email or a Facebook message with a link to (non-existing) party pictures from a non-existing person, and later asked them about the reasons for their link clicking behavior. We registered a significant difference in clicking rates: 20% of email versus 42.5% of Facebook recipients clicked. The most frequently reported reason for clicking was curiosity (34%), followed by the explanations that the message fit recipient's expectations (27%). Moreover, 16% thought that they might know the sender. These results show that people's decisional heuristics are relatively easy to misuse in a targeted attack, making defense especially challenging.

Question: What is the difference between email and Facebook?


Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience. This work evaluates the feasibility of a motor imagery-based optical brain-computer interface (BCI) for humanoid robot control. The functional near-infrared spectroscopy (fNIRS) based BCI-robot system developed in this study operates through a high-level control mechanism where user specifies a target action through the BCI and the robot performs the set of micro operations necessary to fulfill the identified goal. For the evaluation of the system, four motor imagery tasks (left hand, right hand, left foot, and right foot) were mapped to operational commands (turn left, turn right, walk forward, walk backward) that were sent to the robot in real time to direct the robot navigating a small room. An ecologically valid offline analysis with minimal preprocessing shows that seven subjects could achieve an average accuracy of 32.5 %. This was increased to 43.6 % just by including calibration data from the same day of the robot control using the same cap setup, indicating that day-of calibration following the initial training may be important for BCI control.

Question: How does the system work?


Active Appearance Models Revisited. Active Appearance Models (AAMs) and the closely related concepts of Morphable Models and Active Blobs are generative models of a certain visual phenomenon. Although linear in both shape and appearance, overall, AAMs are nonlinear parametric models in terms of the pixel intensities. Fitting an AAM to an image consists of minimising the error between the input image and the closest model instance; i.e. solving a nonlinear optimisation problem. We propose an efficient fitting algorithm for AAMs based on the inverse compositional image alignment algorithm. We show that the effects of appearance variation during fitting can be precomputed ("projected out") using this algorithm and how it can be extended to include a global shape normalising warp, typically a 2D similarity transformation. We evaluate our algorithm to determine which of its novel aspects improve AAM fitting performance.

Question: What is the difference between a morphable model and an active appearance model?


Adaptive Computer-Based Training Increases on the Job Performance of X-Ray Screeners. Due to severe terrorist attacks in recent years, aviation security issues have moved into the focus of politicians as well as the general public. Effective screening of passenger bags using state-of-the-art X-ray screening systems is essential to prevent terrorist attacks. The performance of the screening process depends critically on the security personnel, because they decide whether bags are OK or whether they might contain a prohibited item. Screening X-ray images of passenger bags for dangerous and prohibited items effectively and efficiently is a demanding object recognition task. Effectiveness of computer-based training (CBT) on X-ray detection performance was assessed using computer-based tests and on the job performance measures using threat image projection (TIP). It was found that adaptive CBT is a powerful tool to increase detection performance and efficiency of screeners in X-ray image interpretation. Moreover, the results of training could be generalized to the real life situation as shown in the increased detection performance in TIP not only for trained items, but also for new (untrained) items. These results illustrate that CBT is a very useful tool to increase airport security from a human factors perspective.

Question: What is the difference between adaptive and non-adaptive training?


Watermarking, Tamper-Proofing, and Obfuscation-Tools for Software Protection. We identify three types of attack on the intellectual property contained in software, and three corresponding technical defenses. A potent defense against reverse engineering is obfuscation, a process that renders software unintelligible but still functional. A defense against software piracy is watermarking, a process that makes it possible to determine the origin of software. A defense against tampering is tamper-proofing, so that unauthorized modifications to software (for example to remove a watermark) will result in non-functional code. We briefly survey the available technology for each type of defense.

Question: What is the difference between watermarking, tamper-proofing, and obfuscation?


Implementing it Service Management: A Case Study Focussing on Critical Success Factors. Queensland Health, a large Australian government agency, implemented a centralised IT service management model based on the ITIL framework. This paper presents an in-depth case study of the implementation. It sheds light on the challenges and breakthroughs, confirms a set of factors that contributed to the project's success and offers a learning opportunity for other organisations. The study indicates that the commitment of senior management is crucial to the project's success as is a project champion and the recognition of the need for an appropriate change management strategy to transform the organisational culture to a service-oriented focus. Maintaining close and forthright relationships with multiple vendors facilitates technology transfer to in-house staff while a benefits realisation plan is a valuable tool for tracking and communicating tangible and intangible project benefits to the project stakeholders. An effective project governance and execution process further contributes to the implementation success.

Question: What is the difference between a case study and a case study?


Local Edge-Preserving Multiscale Decomposition for High Dynamic Range Image Tone Mapping. A novel filter is proposed for edge-preserving decomposition of an image. It is different from previous filters in its locally adaptive property. The filtered image contains local means everywhere and preserves local salient edges. Comparisons are made between our filtered result and the results of three other methods. A detailed analysis is also made on the behavior of the filter. A multiscale decomposition with this filter is proposed for manipulating a high dynamic range image, which has three detail layers and one base layer. The multiscale decomposition with the filter addresses three assumptions: 1) the base layer preserves local means everywhere; 2) every scale's salient edges are relatively large gradients in a local window; and 3) all of the nonzero gradient information belongs to the detail layer. An effective function is also proposed for compressing the detail layers. The reproduced image gives a good visualization. Experimental results on real images demonstrate that our algorithm is especially effective at preserving or enhancing local details.

Question: What is the difference between the edge-preserving decomposition and the edge-preserving decomposition?


Synthesis of graphene-based nanosheets via chemical reduction of exfoliated graphite oxide. Reduction of a colloidal suspension of exfoliated graphene oxide sheets in water with hydrazine hydrate results in their aggregation and subsequent formation of a high-surface-area carbon material which consists of thin graphene-based sheets. The reduced material was characterized by elemental analysis, thermo-gravimetric analysis, scanning electron microscopy, X-ray photoelectron spectroscopy, NMR spectroscopy, Raman spectroscopy, and by electrical conductivity measurements. 2007 Elsevier Ltd. All rights reserved.

Question: What is the chemical formula of graphene?


DIFusion: Fast Skip-Scan with Zero Space Overhead. Scan is a crucial operation in main-memory column-stores. It scans a column and returns a result bit vector indicating which records satisfy a filter predicate. ByteSlice is an in-memory data layout that chops data into multiple bytes and exploits early-stop capability by high-order bytes comparisons. As column widths are usually not multiples of byte, the last-byte of ByteSlice is padded with 0's, wasting memory bandwidth and computation power. To fully leverage the resources, we propose to weave a secondary index into the vacant bits (i.e., bits originally padded with 0's), forming our new layout coined DIFusion (Data Index Fusion). DIFusion enables skip-scan, a new fast scan that inherits the early-stopping capability from ByteSlice and at the same time possesses the data-skipping ability of index with zero space overhead. Empirical results show that skip-scan on DIFusion outperforms scan on ByteSlice.

Question: What is the data structure of DIFusion?


A Fast-Response Pseudo-PWM Buck Converter With PLL-Based Hysteresis Control. Hysteresis voltage-mode control is a simple and fast control scheme for switched-mode power converters. However, it is well-known that the switching frequency of a switched-mode power converter with hysteresis control depends on many factors such as loading current and delay of the controller which vary from time to time. It results in a wide noise spectrum and leads to difficulty in shielding electro-magnetic interference. In this work, a phase-lock loop (PLL) is utilized to control the hysteresis level of the comparator used in the controller, while not interfering with the intrinsic behavior of the hysteresis controller. Some design techniques are used to solve the integration problem and to improve the settling speed of the PLL. Moreover, different control modes are implemented. A buck converter with proposed control scheme is fabricated using a commercial 0.35-μ m CMOS technology. The chip area is 1900 μm × 2200 μm. The switching frequency is locked to 1 MHz, and the measured frequency deviation is within ±1%. The measured load transient response between 160 and 360 mA is 5 μ s only.

Question: How to design a fast-response pseudo-pwm buck converter with pll-based hysteresis control?


Different Feature Selection for Sentiment Classification. Sentiment Analysis (SA) research has increased tremendously in recent times .Sentiment analysis means to extract opinion of users from review documents. Sentiment classification using Machine learning (ML ) methods faces the problem of high dimensionality of feature vector. Therefore, a feature selection method is required to eliminate the irrelevant and noisy features from the feature vector for efficient working of ML algorithms Rough set theory provides an important concept for feature reduction called reduct. The cost of reduct set computation is highly influenced by the attribute size of the dataset where the problem of finding reducts has been proven as NPhard problems.Different feature selection are applied on different data set, Experimental results show that mRMR is better compared to IG for sentiment classification, Hybrid feature selection method based on the RST and Information Gain (IG) is better compared to the previous methods. Proposed methods are evaluated on four standard datasets viz. Movie review, Product (book, DVD, and electronics) reviewed datasets, and Experimental results show that hybrid feature selection method outperforms than feature selection methods for sentimental classification.

Question: What is the difference between feature selection and feature reduction?


The activation of attentional networks. Alerting, orienting, and executive control are widely thought to be relatively independent aspects of attention that are linked to separable brain regions. However, neuroimaging studies have yet to examine evidence for the anatomical separability of these three aspects of attention in the same subjects performing the same task. The attention network test (ANT) examines the effects of cues and targets within a single reaction time task to provide a means of exploring the efficiency of the alerting, orienting, and executive control networks involved in attention. It also provides an opportunity to examine the brain activity of these three networks as they operate in a single integrated task. We used event-related functional magnetic resonance imaging (fMRI) to explore the brain areas involved in the three attention systems targeted by the ANT. The alerting contrast showed strong thalamic involvement and activation of anterior and posterior cortical sites. As expected, the orienting contrast activated parietal sites and frontal eye fields. The executive control network contrast showed activation of the anterior cingulate along with several other brain areas. With some exceptions, activation patterns of these three networks within this single task are consistent with previous fMRI studies that have been studied in separate tasks. Overall, the fMRI results suggest that the functional contrasts within this single task differentially activate three separable anatomical networks related to the components of attention.

Question: What is the difference between the alerting, orienting, and executive control networks?


Psychological Needs as a Predictor of Cyber bullying : a Preliminary Report on College Students. Recent surveys show that cyber bullying is a pervasive problem in North America. Many news stories have reported cyber bullying incidents around the world. Reports on the prevalence of cyber bullying and victimization as a result of cyber bullying increase yearly. Although we know what cyber bullying is it is important that we learn more about the psychological eff ects of it. Th erefore, the aim of the current study is to investigate the relationship between psychological needs and cyber bullying. Participants of the study included 666 undergraduate students (231 males and 435 females) from 15 programs in the Faculty of Education at Selcuk University, Turkey. Questions about demographics, engagement in and exposure to cyber bullying, and the Adjective Check List were administered. 22.5% of the students reported engaging in cyber bullying at least one time, and 55.3% of the students reported being victims of cyber bullying at least once in their lifetime. Males reported more cyber bullying behavior than females. Results indicate that aggression and succorance positively predict cyber bullying wheras intraception negatively predict it. In addition, endurance and aff iliation negatively predict cyber victimization. Only the need for change was found as a positive, but weak predictor of cyber victimization. In light of these findings, aggression and intraception should be investigated further in future research on cyber bullying.

Question: What is the relationship between psychological needs and cyber bullying?


Faster Dynamic Programming for Markov Decision Processes. OF THESIS FASTER DYNAMIC PROGRAMMING FOR MARKOV DECISION PROCESSES Markov decision processes (MDPs) are a general framework used by Artificial Intelligence (AI) researchers to model decision theoretic planning problems. Solving real world MDPs has been a major and challenging research topic in the AI literature. This paper discusses two main groups of approaches in solving MDPs. The first group of approaches combines the strategies of heuristic search and dynamic programming to expedite the convergence process. The second makes use of graphical structures in MDPs to decrease the effort of classic dynamic programming algorithms. Two new algorithms proposed by the author, MBLAO* and TVI, are described here.

Question: What is the difference between the two groups of approaches?


Diagram Rewriting for Orthogonal Matrices: A Study of Critical Peaks. One of the rules, which is similar to the Yang-Baxter equatio n, nvolves a map h : [0, π[ → [0, π[. To study the algebraic properties of h, we use the confluence of critical peaks in our rewrite system, and we introduce parametric diagramsdescribing the calculation of angles of rotations generated b y rewriting. In particular, h satisfies thetetrahedron equation(also called Zamolodchikov equation).

Question: What is the tetrahedron equation?


Secrets of optical flow estimation and their principles. The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that "classical" flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. Moreover, we find that while median filtering of intermediate flow fields during optimization is a key to recent performance gains, it leads to higher energy solutions. To understand the principles behind this phenomenon, we derive a new objective that formalizes the median filtering heuristic. This objective includes a nonlocal term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that ranks at the top of the Middlebury benchmark.

Question: What is the middlebury optical flow benchmark?


Scalp dermoscopy of androgenetic alopecia in Asian people.. Although dermoscopy is used mainly for diagnosing pigmented skin lesions, this device has been reported to be useful in observing alopecia areata and frontal fibrosing alopecia. Herein, we investigated the dermoscopic features and their incidence of androgenetic alopecia (AGA; n = 50 men) and female AGA (FAGA; n = 10 women) in Asian people. More than 20% hair diameter diversity (HDD), which reportedly is an early sign of AGA and corresponds to hair follicle miniaturization, was observed in the affected area of all AGA and FAGA cases, suggesting that HDD is an essential feature to diagnose AGA and FAGA. Peripilar signs, corresponding to perifollicular pigmentation, were seen in 66% (33/50) of AGA and 20% (2/10) of FAGA women. This incidence in the present study was lower than previously reported in white subjects possibly because the Asian skin color conceals slight peripilar pigmentation. Yellow dots were observed in 26% (13/50) of AGA and 10% (1/10) of FAGA cases and the number of yellow dots in AGA and FAGA was limited to 10 on the overall hair loss area. Yellow dots possibly indicate the coincidence of AGA and enlargement of the sebaceous glands caused by common end-organ hypersensitivity to androgen. In conclusion, dermoscopy is useful to diagnose AGA and FAGA and provides insights into the pathogenesis of AGA.

Question: What is the incidence of androgenetic alopecia in Asian people?


Reducing Uncertainty of Low-Sampling-Rate Trajectories. The increasing availability of GPS-embedded mobile devices has given rise to a new spectrum of location-based services, which have accumulated a huge collection of location trajectories. In practice, a large portion of these trajectories are of low-sampling-rate. For instance, the time interval between consecutive GPS points of some trajectories can be several minutes or even hours. With such a low sampling rate, most details of their movement are lost, which makes them difficult to process effectively. In this work, we investigate how to reduce the uncertainty in such kind of trajectories. Specifically, given a low-sampling-rate trajectory, we aim to infer its possible routes. The methodology adopted in our work is to take full advantage of the rich information extracted from the historical trajectories. We propose a systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process. To validate the effectiveness of the system, we apply our solution to the map-matching problem which is an important application scenario of this work, and conduct extensive experiments on a real taxi trajectory dataset. The experiment results demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories.

Question: How to reduce uncertainty of low-sampling-rate trajectories?


A process study of computer-aided translation. We investigate novel types of assistance for human translators, based on statistical machine translation methods. We developed the computer-aided tool Caitra that makes suggestions for sentence completion, shows word and phrase translation options, and allows postediting of machine translation output. We carried out a study of the translation process that involved non-professional translators that were native in either French or English and recorded their interaction with the tool. Users translated 192 sentences from French news stories into English. Most translators were faster and better when assisted by our tool. A detailed examination of the logs also provides insight into the human translation process, such as time spent on different activities and length of pauses.

Question: What is a process study?


Common regions of the human frontal lobe recruited by diverse cognitive demands. Though many neuroscientific methods have been brought to bear in the search for functional specializations within prefrontal cortex, little consensus has emerged. To assess the contribution of functional neuroimaging, this article reviews patterns of frontal-lobe activation associated with a broad range of different cognitive demands, including aspects of perception, response selection, executive control, working memory, episodic memory and problem solving. The results show a striking regularity: for many demands, there is a similar recruitment of mid-dorsolateral, mid-ventrolateral and dorsal anterior cingulate cortex. Much of the remainder of frontal cortex, including most of the medial and orbital surfaces, is largely insensitive to these demands. Undoubtedly, these results provide strong evidence for regional specialization of function within prefrontal cortex. This specialization, however, takes an unexpected form: a specific frontal-lobe network that is consistently recruited for solution of diverse cognitive problems.

Question: What is the function of the frontal lobe?


PUF-FSM: A Controlled Strong PUF. Existing strong controlled physical unclonable function (PUF) designs are built to resist modeling attacks and they deal with noisy PUF responses by exploiting error correction logic. These designs are burdened by the costs of the error correction logic and information shown to leak through the associated helper data for assisting error corrections; leaving the design vulnerable to fault attacks or reliability-based attacks. We present a hybrid PUF–finite state machine (PUF-FSM) construction to realize a controlled strong PUF. The PUF-FSM design removes the need for error correction logic and related computation, storage of the helper data and loading it on-chip by only employing error-free responses judiciously determined on demand in the absence of the underlying PUF—an Arbiter PUF—with a large challenge response pair space. The PUF-FSM demonstrates improved security, especially to reliability-based attacks and is able to support a range of applications from authentication to more advanced cryptographic applications built upon shared keys. We experimentally validate the practicability of the PUF-FSM.

Question: What is a controlled strong PUF?