query: High-fidelity simulation for evaluating robotic vision performance

positive: Design and use paradigms for Gazebo, an open-source multi-robot simulator Simulators have played a critical role in robotics research as tools for quick and efficient testing of new concepts, strategies, and algorithms. To date, most simulators have been restricted to 2D worlds, and few have matured to the point where they are both highly capable and easily adaptable. Gazebo is designed to fill this niche by creating a 3D dynamic multi-robot environment capable of recreating the complex worlds that would be encountered by the next generation of mobile robots. Its open source status, fine grained control, and high fidelity place Gazebo in a unique position to become more than just a stepping stone between the drawing board and real hardware: data visualization, simulation of remote environments, and even reverse engineering of blackbox systems are all possible applications. Gazebo is developed in cooperation with the Player and Stage projects (Gerkey, B. P., et al., July 2003), (Gerkey, B. P., et al., May 2001), (Vaughan, R. T., et al., Oct. 2003), and is available from http://playerstage.sourceforge.net/gazebo/ gazebo.html.

positive: Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM In the last years several direct (i.e. featureless) monocular SLAM approaches have appeared showing impressive semi-dense or dense scene reconstructions. These works have questioned the need of features, in which consolidated SLAM techniques of the last decade were based. In this paper we present a novel feature-based monocular SLAM system that is more robust, gives more accurate camera poses, and obtains comparable or better semi-dense reconstructions than the current state of the art. Our semi-dense mapping operates over keyframes, optimized by local bundle adjustment, allowing to obtain accurate triangulations from wide baselines. Our novel method to search correspondences, the measurement fusion and the inter-keyframe depth consistency tests allow to obtain clean reconstructions with very few outliers. Against the current trend in direct SLAM, our experiments show that by decoupling the semi-dense reconstruction from the trajectory computation, the results obtained are better. This opens the discussion on the benefits of features even if a semi-dense reconstruction is desired.

positive: SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

positive: A Naturalistic Open Source Movie for Optical Flow Evaluation Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects. Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail. We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed. To validate the use of synthetic data, we compare the imageand flow-statistics of Sintel to those of real films and videos and show that they are similar. The data set, metrics, and evaluation website are publicly available.

positive: ImageNet Large Scale Visual Recognition Challenge

negative: Can you fool AI with adversarial examples on a visual Turing test? Deep learning has achieved impressive results in many areas of Computer Vision and Natural Language Processing. Among others, Visual Question Answering (VQA), also referred to a visual Turing test, is considered one of the most compelling problems, and recent deep learning models have reported significant progress in vision and language modeling. Although Artificial Intelligence (AI) is getting closer to passing the visual Turing test, at the same time the existence of adversarial examples to deep learning systems may hinder the practical application of such systems. In this work, we conduct the first extensive study on adversarial examples for VQA systems. In particular, we focus on generating targeted adversarial examples for a VQA system while the target is considered to be a question-answer pair. Our evaluation shows that the success rate of whether a targeted adversarial example can be generated is mostly dependent on the choice of the target question-answer pair, and less on the choice of images to which the question refers. We also report the language prior phenomenon of a VQA model, which can explain why targeted adversarial examples are hard to generate for some question-answer targets. We also demonstrate that a compositional VQA architecture is slightly more resilient to adversarial attacks than a non-compositional one. Our study sheds new light on how to build deep vision and language resilient models robust against adversarial examples.

negative: Designing a mobile game to thwarts malicious IT threats: A phishing threat avoidance perspective Phishing is an online identity theft, which aims to steal sensitive information such as username, password and online banking details from victims. To prevent this, phishing education needs to be considered. Game based education is becoming more and more popular. This paper introduces a mobile game prototype for the android platform based on a story, which simplifies and exaggerates real life. The elements of a game design framework for avoiding phishing attacks were used to address the game design issues and game design principles were used as a set of guidelines for structuring and presenting information. The overall mobile game design was aimed to enhance the user’s avoidance behaviour through motivation to protect themselves against phishing threats. The prototype mobile game design was presented on MIT App Inventor Emulator.

negative: Fibonacci Exposure Bracketing for High Dynamic Range Imaging Exposure bracketing for high dynamic range (HDR) imaging involves capturing several images of the scene at different exposures. If either the camera or the scene moves during capture, the captured images must be registered. Large exposure differences between bracketed images lead to inaccurate registration, resulting in artifacts such as ghosting (multiple copies of scene objects) and blur. We present two techniques, one for image capture (Fibonacci exposure bracketing) and one for image registration (generalized registration), to prevent such motion-related artifacts. Fibonacci bracketing involves capturing a sequence of images such that each exposure time is the sum of the previous N(N > 1) exposures. Generalized registration involves estimating motion between sums of contiguous sets of frames, instead of between individual frames. Together, the two techniques ensure that motion is always estimated between frames of the same total exposure time. This results in HDR images and videos which have both a large dynamic range and minimal motion-related artifacts. We show, by results for several real-world indoor and outdoor scenes, that the proposed approach significantly outperforms several existing bracketing schemes.

negative: Harvesting Mid-level Visual Concepts from Large-Scale Internet Images Obtaining effective mid-level representations has become an increasingly important task in computer vision. In this paper, we propose a fully automatic algorithm which harvests visual concepts from a large number of Internet images (more than a quarter of a million) using text-based queries. Existing approaches to visual concept learning from Internet images either rely on strong supervision with detailed manual annotations or learn image-level classifiers only. Here, we take the advantage of having massive well organized Google and Bing image data, visual concepts (around 14, 000) are automatically exploited from images using word-based queries. Using the learned visual concepts, we show state-of-the-art performances on a variety of benchmark datasets, which demonstrate the effectiveness of the learned mid-level representations: being able to generalize well to general natural images. Our method shows significant improvement over the competing systems in image classification, including those with strong supervision.

negative: Viewpoint-independent book spine segmentation We propose a method to precisely segment books on bookshelves in images taken from general viewpoints. The proposed segmentation algorithm overcomes difficulties due to text and texture on book spines, various book orientations under perspective projection, and book proximity. A shape dependent active contour is used as a first step to establish a set of book spine candidates. A subset of these candidates are selected using spatial constraints on the assembly of spine candidates by formulating the selection problem as the maximal weighted independent set (MWIS) of a graph. The segmented book spines may be used by recognition systems (e.g., library automation), or rendered in computer graphics applications. We also propose a novel application that uses the segmented book spines to assist users in bookshelf reorganization or to modify the image to create a bookshelf with a tidier look. Our method was successfully tested on challenging sets of images.

negative: An improved fuzzy clustering method using modified Fukuyama-Sugeno cluster validity index The objective of clustering algorithms is to group similar patterns in one class and dissimilar patterns in disjoint classes. This article proposes a novel algorithm for fuzzy partitional clustering with an aim to minimize a composite objective function, defined using the Fukuyama-Sugeno cluster validity index. The optimization of this objective function tries to minimize the separation between clusters of a data set and maximize the compactness of a certain cluster. But in certain cases, such as a data set having overlapping clusters, this approach leads to poor clustering results. Thus we introduce a new parameter in the objective function which enables us to yield more accurate clustering results. The algorithm has been validated with some artificial and real world datasets.

negative: American Sign Language Translation through Sensory Glove ; SignSpeak To make a communication bridge, a highly accurate, cost effective and an independent glove was designed for deaf/mute people to enable them to communicate. The glove translates the sign language gestures into speech according to the American Sign Language Standard. The glove contained flex and contact sensors to detect the movements of the fingers and bending of the palm. In addition an accelerometer was built in the glove to measure the acceleration produced by the changing positions of the hand. Principal Component Analysis (PCA) was used to train the glove into recognizing various gestures, and later classify the gestures into alphabets in real time. The glove then established a Bluetooth link with an Android phone, which was used to display the received letters and words and convert the text into speech. The glove was found to have an accuracy of 92%.

negative: Secure Conjunctive Keyword Search over Encrypted Data We study the setting in which a user stores encrypted documents (e.g. e-mails) on an untrusted server. In order to retrieve documents satisfying a certain search criterion, the user gives the server a capability that allows the server to identify exactly those documents. Work in this area has largely focused on search criteria consisting of a single keyword. If the user is actually interested in documents containing each of several keywords (conjunctive keyword search) the user must either give the server capabilities for each of the keywords individually and rely on an intersection calculation (by either the server or the user) to determine the correct set of documents, or alternatively, the user may store additional information on the server to facilitate such searches. Neither solution is desirable; the former enables the server to learn which documents match each individual keyword of the conjunctive search and the latter results in exponential storage if the user allows for searches on every set of keywords. We define a security model for conjunctive keyword search over encrypted data and present the first schemes for conducting such searches securely. We propose first a scheme for which the communication cost is linear in the number of documents, but that cost can be incurred “offline” before the conjunctive query is asked. The security of this scheme relies on the Decisional Diffie-Hellman (DDH) assumption. We propose a second scheme whose communication cost is on the order of the number of keyword fields and whose security relies on a new hardness assumption.

negative: How to Enhance Cloud Architectures to Enable Cross-Federation: Towards Interoperable Storage Providers Small/medium cloud storage providers can hardly compete with the biggest cloud players such as Google, Amazon, Dropbox, etc. As a consequence, the cloud storage market depends on such mega-providers and each small/medium provider cannot face alone the challenge of Big Data storage. A possible solution consists in establishing stronger partnerships among small-medium providers where they can borrow/lend resources each other, according to the rules of the federated cloud ecosystem they belong to. According to such an approach, the challenge consists in creating federated cloud ecosystems able to compete with mega-provides and one of the major problems for the achievement of such an ecosystem is the management of inter-domain communications. In this paper, we propose an architecture addressing such an issue. In particular, we present and test a solution integrating the CLEVER Message Oriented Middleware (MOM) with the Hadoop Distribute File System (HDFS), i.e., one of the major massive storage solutions currently available on the market.

negative: Systematic review of bankruptcy prediction models: Towards a framework for tool selection The bankruptcy prediction research domain continues to evolve with many new different predictive models developed using various tools. Yet many of the tools are used with the wrong data conditions or for the wrong situation. Using the Web of Science, Business Source Complete and Engineering Village databases, a systematic review of 49 journal articles published between 2010 and 2015 was carried out. This review shows how eight popular and promising tools perform based on 13 key criteria within the bankruptcy prediction models research area. These tools include two statistical tools: multiple discriminant analysis and Logistic regression; and six artificial intelligence tools: artificial neural network, support vector machines, rough sets, case based reasoning, decision tree and genetic algorithm. The 13 criteria identified include accuracy, result transparency, fully deterministic output, data size capability, data dispersion, variable selection method required, variable types applicable, and more. Overall, it was found that no single tool is predominantly better than other tools in relation to the 13 identified criteria. A tabular and a diagrammatic framework are provided as guidelines for the selection of tools that best fit different situations. It is concluded that an overall better performance model can only be found by informed integration of tools to form a hybrid model. This paper contributes towards a thorough understanding of the features of the tools used to develop bankruptcy prediction models and their related shortcomings.

negative:
Monitoring and depth of strategy use in computer-based learning environments for science and history. BACKGROUND
Self-regulated learning (SRL) models position metacognitive monitoring as central to SRL processing and predictive of student learning outcomes (Winne & Hadwin, 2008; Zimmerman, 2000). A body of research evidence also indicates that depth of strategy use, ranging from surface to deep processing, is predictive of learning performance.

AIMS
In this study, we investigated the relationships among the frequency of metacognitive monitoring and the utilization of deep and surface-level strategies, and the connections between these SRL processes and learning outcomes across two academic domains, science and history.

SAMPLE
This was a secondary data analysis of two studies. The first study sample was 170 undergraduate students from a University in the south-eastern United States. The second study sample consisted of 40 US high school students in the same area.

METHODS
We collected think-aloud protocol SRL and knowledge measure data and conducted both structural equation modelling and path analysis to investigate our research questions.

RESULTS
Findings showed across both studies and two distinct academic domains, students who enacted more frequent monitoring also enacted more frequent deep strategies resulting in better performance on academic evaluations.

CONCLUSIONS
These findings suggest the importance of measuring not only what depth of strategies learners use, but also the degree to which they monitor their learning. Attention to both is needed in research and practice.

negative: Question Type Classification Using a Part-of-Speech Hierarchy Question type (or answer type) classification is the task of determining the correct type of the answer expected to a given query. This is often done by defining or discovering syntactic patterns that represent the structure of typical queries of each type, and classify a given query according to which pattern they satisfy. In this paper, we combine the idea of using informer spans as patterns with our own part-of-speech hierarchy in order to propose both a new approach to pattern-based question type classification and a new way of discovering the informers to be used as patterns. We show experimentally that using our part-of-speech hierarchy greatly improves type classification results, and allows our system to learn valid new informers.

negative: Dissociation between face perception and face memory in adults, but not children, with developmental prosopagnosia Cognitive models propose that face recognition is accomplished through a series of discrete stages, including perceptual representation of facial structure, and encoding and retrieval of facial information. This implies that impaired face recognition can result from failures of face perception, face memory, or both. Studies of acquired prosopagnosia, autism spectrum disorders, and the development of normal face recognition support the idea that face perception and face memory are distinct processes, yet this distinction has received little attention in developmental prosopagnosia (DP). To address this issue, we tested the face perception and face memory of children and adults with DP. By definition, face memory is impaired in DP, so memory deficits were present in all participants. However, we found that all children, but only half of the adults had impaired face perception. Thus, results from adults indicate that face perception and face memory are dissociable, while the results from children provide no evidence for this division. Importantly, our findings raise the possibility that DP is qualitatively different in childhood versus adulthood. We discuss theoretical explanations for this developmental pattern and conclude that longitudinal studies are necessary to better understand the developmental trajectory of face perception and face memory deficits in DP.

negative: CryptoDL : Towards Deep Learning over Encrypted Data With increasing growth of cloud services, machine learning services can be run on cloud providers’ infrastructure where training and deploying machine learning models are performed on cloud servers. However, machine learning solutions require access to the raw data, which can create potential security and privacy risks. In this work, we take the first steps towards developing theoretical foundation for implementing deep learning algorithms in encrypted domain and propose a method to adopt deep neural networks (NN) within the practical limitations of current homomorphic encryption schemes. We first design two methods for approximation of activation functions with low degree polynomials. Then, we train NNs with the generated polynomials and analyze the performance of the trained models. Finally, we run the low degree polynomials over encrypted values to estimate the computation costs and time.

negative:
The public health risks of media violence: a meta-analytic review. OBJECTIVE
To conduct a meta-analytic review of studies that examine the impact of violent media on aggressive behavior and to determine whether this effect could be explained through methodological problems inherent in this research field.

STUDY DESIGN
A detailed literature search identified peer-reviewed articles addressing media violence effects. Effect sizes were calculated for all studies. Effect sizes were adjusted for observed publication bias.

RESULTS
Publication bias was a problem for studies of aggressive behavior, and methodological problems such as the use of poor aggression measures inflated effect size. Once corrected for publication bias, studies of media violence effects provided little support for the hypothesis that media violence is associated with higher aggression. The corrected overall effect size for all studies was r = .08.

CONCLUSIONS
Results from the current analysis do not support the conclusion that media violence leads to aggressive behavior. It cannot be concluded at this time that media violence presents a significant public health risk.

negative: A UHF class E2 DC/DC converter using GaN HEMTs In this paper, the design of a class E2 resonant DC/DC converter, operating at UHF band, is proposed. Combining the use of GaN HEMT devices, both for the inverter and the synchronous rectifier, with high Q lumped-element multi-harmonic matching networks, a peak efficiency value of 72% has been obtained at 780 MHz with a 10.3 W output power. By means of a Pulse Width Modulation (PWM) over the gate driving envelope, the output voltage may be controlled while keeping low switching losses, with an estimated small-signal bandwidth (BW) and a slew rate of 11 MHz and 630 V/µSeg, respectively.

negative:
Vaping on Instagram: cloud chasing, hand checks and product placement. INTRODUCTION
This study documented images posted on Instagram of electronic cigarettes (e-cigarette) and vaping (activity associated with e-cigarette use). Although e-cigarettes have been studied on Twitter, few studies have focused on Instagram, despite having 500 million users. Instagram's emphasis on images warranted investigation of e-cigarettes, as past tobacco industry strategies demonstrated that images could be used to mislead in advertisements, or normalise tobacco-related behaviours. Findings should prove informative to tobacco control policies in the future.

METHODS
3 months of publicly available data were collected from Instagram, including images and associated metadata (n=2208). Themes of images were classified as (1) activity, for example, a person blowing vapour; (2) product, for example, a personal photo of an e-cigarette device; (3) advertisement; (4) text, for example, 'meme' or image containing mostly text and (5) other. User endorsement (likes) of each type of image was recorded. Caption text was analysed to explore different trends in vaping and e-cigarette-related text.

RESULTS
Analyses found that advertisement-themed images were most common (29%), followed by product (28%), and activity (18%). Likes were more likely to accompany activity and product-themed images compared with advertisement or text-themed images (p<0.01). Vaping-related text greatly outnumbered e-cigarette-related text in the image captions.

CONCLUSIONS
Instagram affords its users the ability to post images of e-cigarette-related behaviours and gives advertisers the opportunity to display their product. Future research should incorporate novel data streams to improve public health surveillance, survey development and educational campaigns.

negative: Making Scheduling "Cool": Temperature-Aware Workload Placement in Data Centers Trends towards consolidation and higher-density computing configurations make the problem of heat management one of the critical challenges in emerging data centers. Conventional approaches to addressing this problem have focused at the facilities level to develop new cooling technologies or optimize the delivery of cooling. In contrast to these approaches, our paper explores an alternate dimension to address this problem, namely a systems-level solution to control the heat generation through temperatureaware workload placement. We first examine a theoretic thermodynamic formulation that uses information about steady state hot spots and cold spots in the data center and develop real-world scheduling algorithms. Based on the insights from these results, we develop an alternate approach. Our new approach leverages the non-intuitive observation that the source of cooling inefficiencies can often be in locations spatially uncorrelated with its manifested consequences; this enables additional energy savings. Overall, our results demonstrate up to a factor of two reduction in annual data center cooling costs over location-agnostic workload distribution, purely through software optimizations without the need for any costly capital investment.

negative: Calorie restriction extends Saccharomyces cerevisiae lifespan by increasing respiration Calorie restriction (CR) extends lifespan in a wide spectrum of organisms and is the only regimen known to lengthen the lifespan of mammals. We established a model of CR in budding yeast Saccharomyces cerevisiae. In this system, lifespan can be extended by limiting glucose or by reducing the activity of the glucose-sensing cyclic-AMP-dependent kinase (PKA). Lifespan extension in a mutant with reduced PKA activity requires Sir2 and NAD (nicotinamide adenine dinucleotide). In this study we explore how CR activates Sir2 to extend lifespan. Here we show that the shunting of carbon metabolism toward the mitochondrial tricarboxylic acid cycle and the concomitant increase in respiration play a central part in this process. We discuss how this metabolic strategy may apply to CR in animals.

negative: Internet of Things: A review to support IoT architecture's design Internet of Things (IoT) is a state-of-the-art field that is affecting every aspect of human life. This paper presents a state-of-the-art overview of IoT challenges, available standards, and popular architectures. The aim is to guide future researchers in the field of IoT by highlighting the main research gaps. Moreover, it provides research recommendations to help in designing future IoT architectures that fit IoT environment characteristics.

negative: Scrutinizing WPA2 Password Generating Algorithms in Wireless Routers A wireless router is a networking device that enables a user to set up a wireless connection to the Internet. A router can offer a secure channel by cryptographic means which provides authenticity and confidentiality. Nowadays, almost all routers use a secure channel by default that is based on Wi-Fi Protected Access II (WPA2). This is a security protocol which is believed not to be susceptible to practical key recovery attacks. However, the passwords should have sufficient entropy to avert bruteforce attacks. In this paper, we compose a strategy on how to reverse-engineer embedded routers. Furthermore, we describe a procedure that can instantly gather a complete wireless authentication trace which enables an offline password recovery attack. Finally, we present a number of use cases where we identify extremely weak password generating algorithms in various routers which are massively deployed in The Netherlands. The algorithms are used to generate the default WPA2 password. Such a password is loaded during device initialization and hardware reset. Users that did not explicitly change their wireless password are most likely vulnerable to practical attacks which can recover their password within minutes. A stolen password allows an adversary to abuse someone else’s internet connection, for instance compromising the firewall, making a fraudulent transaction or performing other criminal activities. Together with the Dutch National Cyber Security Centre we have initiated a responsible disclosure procedure. However, since these routers are also used by many other companies in various countries, our findings seem to relate an international industry wide security issue.

negative: Sampling-oscilloscope measurement of a microwave mixer with single-digit phase accuracy We describe a straightforward method of separately characterizing up- and down-conversion in microwave mixers using a sampling oscilloscope. The method mismatch-corrects the results, determines both magnitude and phase, and uses a novel time-base correction scheme to improve the accuracy of the measurements. We estimate our measurement accuracy to be on the order of a tenth of a decibel in magnitude and a few degrees in phase. We use the method to characterize the magnitude and phase reciprocity of a microwave mixer.

negative: Feedforward neural network and ANFIS-based approaches to forecasting the off-cam energy characteristics of Kaplan turbine The determination of the energy characteristics of a Kaplan hydraulic turbine is based on numerous measuring points during extensive and expensive experimental model tests in laboratory and on-site prototype tests at the hydropower plant. The results of those experimental researches are valuable insofar as they are detailed and comprehensive. In order to reduce the number of modes, in which the double-regulated turbine has to be tested with the aim of obtaining the off-cam energy characteristics in unknown operating modes, the application of contemporary artificial neural networks models is presented in the paper. The rationalization of the turbine test conditions may not be at the expense of the quality of the obtained characteristics. Two types of neural networks, feedforward neural networks and adaptive network-based fuzzy inference system with different partitioning methods, were used. The reliability of applied method was considered by analyzing and validating the predicted turbine energy parameters with the results obtained in the highly sophisticated laboratory.

negative: Developing a data quality framework for asset management in engineering organisations Data Quality (DQ) is seen as critical to effective business decision-making. However, maintaining DQ is often acknowledged as problematic. Asset data is the key enabler in gaining control of assets. The quality asset data provides the foundation for effective Asset Management (AM). Researches have indicated that achieving AM DQ is the key challenge engineering organisations face today. This paper investigates the DQ issues emerging from the unique nature of engineering AM data. It presents an exploratory research of a large scale national-wide DQ survey on how Australian engineering organisations address DQ issues, and proposes an AM specific DQ framework.

negative: Maintenance scheduling of geographically distributed assets with prognostics information Maintenance scheduling for high value assets has been studied for decades and is still a crucial area of research with new technological advancements. The main dilemma of maintenance scheduling is to avoid failures while preventing unnecessary maintenance. The technological advancements in real time monitoring and computational science make tracking asset health and forecasting asset failures possible. The usage and maintenance of assets can be planned more efficiently with the forecasted failure probability and remaining useful life (i.e., prognostic information). The prognostic information is time sensitive. Geographically distributed assets such as off-shore wind farms and railway switches add another complexity to the maintenance scheduling problem with the required time of travel to reach these assets. Thus, the travel time between geographically distributed assets should be incorporated in the maintenance scheduling when one technician (or team) is responsible for the maintenance of multiple assets. This paper presents a methodology to schedule the maintenance of geographically distributed assets using their prognostic information. Genetic Algorithm based solution incorporating the daily work duration of the maintenance team is also presented in the paper. © 2015 Elsevier B.V. All rights reserved.

negative: Slip-aware Model Predictive optimal control for Path following Traditional control and planning algorithms for wheeled mobile robots (WMR) either totally ignore or make simplifying assumptions about the effects of wheel slip on the motion. While this approach works reasonably well in practice on benign terrain, it fails very quickly when the WMR is deployed in terrain that induces significant wheel slip. We contribute a novel control framework that predictively corrects for the wheel slip to effectively minimize path following errors. Our framework, the Receding Horizon Model Predictive Path Follower (RHMPPF), specifically addresses the problem of path following in challenging environments where the wheel slip substantially affects the vehicle mobility. We formulate the solution to the problem as an optimal controller that utilizes a slip-aware model predictive component to effectively correct the controls generated by a strictly geometric pure-pursuit path follower. We present extensive experimental validation of our approach using a simulated 6-wheel skid-steered robot in a high-fidelity data-driven simulator, and on a real 4-wheel skid-steered robot. Our results show substantial improvement in the path following performance in both simulation and real world experiments.

negative: Higher-Order Inference for Multi-class Log-Supermodular Models Higher-order models have been shown to be very useful for a plethora of computer vision tasks. However, existing techniques have focused mainly on MAP inference. In this paper, we present the first efficient approach towards approximate Bayesian marginal inference in a general class of high-order, multi-label attractive models, where previous techniques slow down exponentially with the order (clique size). We formalize this task as performing inference in log-supermodular models under partition constraints, and present an efficient variational inference technique. The resulting optimization problems are convex and yield bounds on the partition function. We also obtain a fully factorized approximation to the posterior, which can be used in lieu of the true complicated distribution. We empirically demonstrate the performance of our approach by comparing it to traditional inference methods on a challenging high-fidelity multi-label image segmentation dataset. We obtain state-of-the-art classification accuracy for MAP inference, and substantially improved ROC curves using the approximate marginals.

negative:
Balancing traffic load in wireless networks with curveball routing We address the problem of balancing the traffic load in multi-hop wireless networks. We consider a point-to-point communicating network with a uniform distribution of source-sink pairs. When routing along shortest paths, the nodes that are centrally located forward a disproportionate amount of traffic. This translates into increased congestion and energy consumption. However, the maximum load can be decreased if the packets follow curved paths. We show that the optimum such routing scheme can be expressed in terms of geometric optics and computed by linear programming. We then propose a practical solution, which we call Curveball Routing which achieves results not much worse than the optimum.
 We evaluate our solution at three levels of fidelity: a Java high-level simulator, the ns2 simulator, and the Intel Mirage Sensor Network Testbed. Simulation results using the high-level simulator show that our solution successfully avoids the crowded center of the network, and reduces the maximum load by up to 40%. At the same time, the increase of the expected path length is minimal, i.e., only 8% on average. Simulation results using the ns2 simulator show that our solution can increase throughput on moderately loaded networks by up to 15%, while testbed results show a reduction in peak energy usage by up to 25%. Our prototype suggests that our solution is easily deployable.

negative: An airborne experimental test platform: From theory to flight This paper provides an overview of the experimental flight test platform developed by the University of Minnesota Unmanned Aerial Vehicle Research Group. Key components of the current infrastructure are highlighted, including the flight test system, high-fidelity nonlinear simulations, software-and hardware-in-the-loop simulations, and the real-time flight software. Recent flight control research and educational applications are described to showcase the advanced capabilities of the platform. A view towards future expansion of the platform is given in the context of upcoming research projects.

negative: The state of the art in underwater acoustic telemetry Progress in underwater acoustic telemetry since 1982 is reviewed within a framework of six current research areas: (1) underwater channel physics, channel simulations, and measurements; (2) receiver structures; (3) diversity exploitation; (4) error control coding; (5) networked systems; and (6) alternative modulation strategies. Advances in each of these areas as well as perspectives on the future challenges facing them are presented. A primary thesis of this paper is that increased integration of high-fidelity channel models into ongoing underwater telemetry research is needed if the performance envelope (defined in terms of range, rate, and channel complexity) of underwater modems is to expand.

negative: Knowledge-Based Integrated Aircraft Design An Applied Approach from Design to Concept Demonstration The design and development of new aircraft are becoming increasingly expensive and time-consuming. To assist the design process in reducing the development cost, time, and late design changes, the conceptual design needs enhancement using new tools and methods. Integration of several disciplines in the conceptual design as one entity enables the design process to be kept intact at every step and a high understanding of the aircraft concepts obtained at early stages. This thesis presents a Knowledge-Based Engineering (KBE) approach and integration of several disciplines in a holistic approach for use in aircraft conceptual design. KBE allows the reuse of obtained aircrafts’ data, information, and knowledge to gain more awareness and a better understanding of the concept under consideration at early stages of design. For this purpose, Knowledge-Based (KB) methodologies are investigated for enhanced geometrical representation and to enable variable fidelity tools and Multidisciplinary Design Optimization (MDO). The geometry parameterization techniques are qualitative approaches that produce quantitative results in terms of both robustness and flexibility of the design parameterization. The information/parameters from all tools/disciplines and the design intent of the generated concepts are saved and shared via a central database. The integrated framework facilitates multi-fidelity analysis, combining low-fidelity models with high-fidelity models for a quick estimation, enabling a rapid analysis and enhancing the time for a MDO process. The geometry is further propagated to other disciplines [Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA)] for analysis. This is possible with an automated streamlined process (for CFD, FEM, system simulation) to analyze and increase knowledge early in the design process. Several processes were studied to streamline the geometry for

negative: The role of physical embodiment in human-robot interaction Autonomous robots are agents with physical bodies that share our environment. In this work, we test the hypothesis that physical embodiment has a measurable effect on performance and perception of social interactions. Support of this hypothesis would suggest fundamental differences between virtual agents and robots from a social standpoint and have significant implications for human-robot interaction. We measure task performance and perception of a robot's social abilities in a structured but open-ended task based on the Towers of Hanoi puzzle. Our experiment compares aspects of embodiment by evaluating: (1) the difference between a physical robot and a simulated one; (2) the effect of physical presence through a co-located robot versus a remote tele-present robot. We present data from a pilot study with 12 subjects showing interesting differences in perception of remote physical robot's and simulated agent's attention to the task, and task enjoyment


query: External Knowledge and Query Strategies in Active Learning: a Study in Clinical Information Extraction

positive: 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks: a concept extraction task focused on the extraction of medical concepts from patient reports; an assertion classification task focused on assigning assertion types for medical problem concepts; and a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. i2b2 and the VA provided an annotated reference standard corpus for the three tasks. Using this reference standard, 22 systems were developed for concept extraction, 21 for assertion classification, and 16 for relation classification. These systems showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations. Depending on the task, the rule-based systems can either provide input for machine learning or post-process the output of machine learning. Ensembles of classifiers, information from unlabeled data, and external knowledge sources can help when the training data are inadequate.

positive: Heterogenous Uncertainty Sampling for Supervised Learning Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this heterogeneous approach, the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger.

positive: Application of information technology: MedEx: a medication information extraction system for clinical narratives Medication information is one of the most important types of clinical data in electronic medical records. It is critical for healthcare safety and quality, as well as for clinical research that uses electronic medical record data. However, medication data are often recorded in clinical notes as free-text. As such, they are not accessible to other computerized applications that rely on coded data. We describe a new natural language processing system (MedEx), which extracts medication information from clinical notes. MedEx was initially developed using discharge summaries. An evaluation using a data set of 50 discharge summaries showed it performed well on identifying not only drug names (F-measure 93.2%), but also signature information, such as strength, route, and frequency, with F-measures of 94.5%, 93.9%, and 96.0% respectively. We then applied MedEx unchanged to outpatient clinic visit notes. It performed similarly with F-measures over 90% on a set of 25 clinic visit notes.

positive: Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program The UMLS Metathesaurus, the largest thesaurus in the biomedical domain, provides a representation of biomedical knowledge consisting of concepts classified by semantic type and both hierarchical and non-hierarchical relationships among the concepts. This knowledge has proved useful for many applications including decision support systems, management of patient records, information retrieval (IR) and data mining. Gaining effective access to the knowledge is critical to the success of these applications. This paper describes MetaMap, a program developed at the National Library of Medicine (NLM) to map biomedical text to the Metathesaurus or, equivalently, to discover Metathesaurus concepts referred to in text. MetaMap uses a knowledge intensive approach based on symbolic, natural language processing (NLP) and computational linguistic techniques. Besides being applied for both IR and data mining applications, MetaMap is one of the foundations of NLM's Indexing Initiative System which is being applied to both semi-automatic and fully automatic indexing of the biomedical literature at the library.

positive: Information-Based Objective Functions for Active Data Selection Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed that measure the expected informativeness of candidate measurements. Three alternative specifications of what we want to gain information about lead to three different criteria for data selection. All these criteria depend on the assumption that the hypothesis space is correct, which may prove to be their main weakness.

negative:
The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data BACKGROUND
Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies.

NEW METHODS
We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model-based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps - from flexible read-in of data formats to GLM regressor/contrast creation - without any manual intervention.

RESULTS
We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N=35).

COMPARISON WITH EXISTING METHODS
The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data.

CONCLUSIONS
Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.

negative: Eratosthenes sieve based key-frame extraction technique for event summarization in videos The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. It is a herculean task to manage access to video content in real time where humongous amount of audiovisual recorded data is generated every second. In this paper we propose an Eratosthenes Sieve based key-frame extraction approach for video summarization (VS) which can work better for real-time applications. Here, Eratosthenes Sieve is used to generate sets of all Prime number frames and nonprime number frames up to total N frames of a video. k-means clustering procedure is employed on these sets to extract the key–frames quickly. Here, the challenge is to find the optimal set of clusters, achieved by employing Davies-Bouldin Index (DBI). DBI a cluster validation technique which allows users with free parameter based VS approach to choose the desired number of key-frames without incurring additional computational costs. Moreover, our proposed approach includes likes of both local and global perspective videos. The method strongly enhances clustering procedure performance trough engagement of Eratosthenes Sieve. Qualitative and quantitative evaluation and complexity computation are done in order to compare the performances of the proposed model and state-of-the-art models. Experimental results on two benchmark datasets with various types of videos exhibit that the proposed methods outperform the state-of-the-art models on F-measure.

negative: Quantifying the total wetted surface area of the world fleet: a first step in determining the potential extent of ships’ biofouling Ships’ hulls can transport aquatic nuisance species, but there is little quantitative information about the magnitude of vessel biofouling on a global or regional scale. There does not exist a robust method to estimate the wetted surface area (WSA) of a particular fleet of ships, especially across the diversity of possible vessel types. An estimate of the total WSA of ship arrivals into a port or region is essential to determine the potential scope of biofouling and to inform management strategies to reduce the future invasions. Multiple statistical models were developed so commonly available ships’ parameters could be used to estimate the WSA for a given set of fleet data. Using individual ship characteristics and publicly available data from  ~120,000 active commercial ships in the world fleet, the method results in a total global minimum WSA estimate of approximately 325 × 106 m2. The size of the global fleet employed here is greater than the commonly cited vessel number of approximately 80,000–90,000, as we include ships <100 gross tons. Over 190,000 vessels were initially identified, representing a WSA of 571 × 106 m2, but active status of only 120,000 vessels could be confirmed. Reliable data were unavailable on the operating status of many additional and especially smaller vessels. This approach, along with a contemporary and comprehensive estimate of global WSA, when combined with knowledge of the different operational profiles of ships that may reduce biofouling (port residence times, steaming speeds, extent of antifouling coatings, cleaning frequency, etc.), can inform current numerical models and risk assessments of bioinvasions.

negative: Documenting Software Architectures: Views and Beyond * This author is with the Software Engineering Institute at Carnegie Mellon University. ** This author is with the School of Computer Science at Carnegie Mellon University. Software architecture has emerged as a foundational concept for the successful development of large, complex systems. Signs that the field is maturing to become an engineering discipline include textbooks on the subject; the work in repeatable design, exemplified by architectural styles and patterns; robust methods for evaluating software architectures; international conferences devoted to it; and recognition of architecture as a professional practice. However, treatment of architecture to date has largely concentrated on its design, and to a lesser extent, its validation. But effectively documenting an architecture is as important as crafting it, because if the architecture is not understood (or worse, misunderstood) it cannot meet its goals as the unifying vision for system and software development. Three years ago, researchers at the Software Engineering Institute and the Carnegie Mellon School of Computer Science set out to answer the question: “How should you document an architecture so that others can successfully use it, maintain it, and build a system from it?” The result of that work is an approach we loosely call “views and beyond.” [1] Modern software architecture practice embraces the concept of architectural views. A view is a representation of a set of system elements and relations associated with them. Modern systems are more than complex enough to make it difficult to grasp them all at once. Instead, we restrict our attention at any one moment to one (or a small number) of the software system’s structures, which we represent as views. Some authors prescribe a fixed set of views with which to engineer and communicate an architecture. Rational’s Unified Process, for example, is based on Kruchten’s 4+1 view approach to software [4]. The Siemens Four Views model [3] is another example. A recent trend, however, is to recognize that architects should produce whatever views are useful for the system at hand. IEEE 1471 exemplifies this philosophy; it holds that an architecture description consists of a set of views, each of which conforms to a viewpoint, which in turn is a realization of the concerns of one or more stakeholders. This philosophy about views leads to the fundamental principle of the views-and-beyond approach:

negative: VeTrack: Real Time Vehicle Tracking in Uninstrumented Indoor Environments Although location awareness and turn-by-turn instructions are prevalent outdoors due to GPS, we are back into the darkness in uninstrumented indoor environments such as underground parking structures. We get confused, disoriented when driving in these mazes, and frequently forget where we parked, ending up circling back and forth upon return.In this paper, we propose VeTrack, a smartphone-only system that tracks the vehicle's location in real time using the phone's inertial sensors. It does not require any environment instrumentation or cloud backend. It uses a novel "shadow" tracing method to accurately estimate the vehicle's trajectories despite arbitrary phone/vehicle poses and frequent disturbances. We develop algorithms in a Sequential Monte Carlo framework to represent vehicle states probabilistically, and harness constraints by the garage map and detected landmarks to robustly infer the vehicle location. We also find landmark (e.g., speed bumps, turns) recognition methods reliable against noises, disturbances from bumpy rides and even hand-held movements. We implement a highly efficient prototype and conduct extensive experiments in multiple parking structures of different sizes and structures, with multiple vehicles and drivers. We find that VeTrack can estimate the vehicle's real time location with almost negligible latency, with error of 2-4 parking spaces at 80-percentile.

negative: Prediction and Simulation of Human Mobility Following Natural Disasters In recent decades, the frequency and intensity of natural disasters has increased significantly, and this trend is expected to continue. Therefore, understanding and predicting human behavior and mobility during a disaster will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, such research is very difficult to perform owing to the uniqueness of various disasters and the unavailability of reliable and large-scale human mobility data. In this study, we collect big and heterogeneous data (e.g., GPS records of 1.6 million users1 over 3 years, data on earthquakes that have occurred in Japan over 4 years, news report data, and transportation network data) to study human mobility following natural disasters. An empirical analysis is conducted to explore the basic laws governing human mobility following disasters, and an effective human mobility model is developed to predict and simulate population movements. The experimental results demonstrate the efficiency of our model, and they suggest that human mobility following disasters can be significantly more predictable and be more easily simulated than previously thought.

negative: Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is based on two scoring functions that operate by learning low-dimensional embeddings of words and of entities and relationships from a knowledge base. We empirically show on New York Times articles aligned with Freebase relations that our approach is able to efficiently use the extra information provided by a large subset of Freebase data (4M entities, 23k relationships) to improve over existing methods that rely on text features alone.

negative: Clarithromycin and Tetracycline Binding to Soil Humic Acid in the Absence and Presence of Calcium. Numerous ionizable organic micropollutants contain positively charged moieties at pH values typical of environmental systems. Describing organic cation and zwitterion interaction with dissolved natural organic matter requires explicit consideration of the pH-dependent speciation of both sorbate and sorbent. We studied the pH-, ionic strength-, and concentration-dependent binding of relatively large, organic cations and zwitterions (viz., the antibiotics clarithromycin and tetracycline) to dissolved humic acid in the absence and presence of Ca(2+) and evaluated the ability of the NICA-Donnan model to describe the data. Clarithromycin interaction with dissolved humic acid was well described by the model including the competitive effect of Ca(2+) on clarithromycin binding over a wide range of solution conditions by considering only the binding of the cationic species to low proton-affinity sites in humic acid. Tetracycline possesses multiple ionizable moieties and forms complexes with Ca(2+). An excellent fit to experimental data was achieved by considering tetracycline cation interaction with both low and high proton-affinity sites of humic acid and zwitterion interaction with high proton-affinity sites. In contrast to clarithromycin, tetracycline binding to humic acid increased in the presence of Ca(2+), especially under alkaline conditions. Model calculations indicate that this increase is due to electrostatic interaction of positively charged tetracycline-Ca complexes with humic acid rather than due to the formation of ternary complexes, except at very low TC concentrations.

negative: Survey of clustering algorithms for MANET Many clustering schemes have been proposed for ad hoc networks. A systematic classification of these clustering schemes enables one to better understand and make improvements. In mobile ad hoc networks, the movement of the network nodes may quickly change the topology resulting in the increase of the overhead message in topology maintenance. Protocols try to keep the number of nodes in a cluster around a pre-defined threshold to facilitate the optimal operation of the medium access control protocol. The clusterhead election is invoked on-demand, and is aimed to reduce the computation and communication costs. A large variety of approaches for ad hoc clustering have been developed by researchers which focus on different performance metrics. This paper presents a survey of different clustering schemes.

negative: Maximum energy harvesting from medium voltage electric-field energy using power line insulators One essential requirement for a modern Smart Grid is continuous monitoring of the grid variables. This task is efficiently achieved using self-powered electronic devices deployed throughout the grid network. Within the vicinity of the grid power lines, the high voltages of these lines can provide a source of energy for such loads using capacitive coupling. This is known as Electric-Field Energy Harvesting (EFEH). However, the reported strategies that use this principle have not explored how to maximise their power transfer, which is essential for practical systems since the available energy from EFEH is invariably quite small. In this paper, a novel approach for optimum energy harvesting is proposed using the parasitic capacitance of medium-voltage power line insulators. A follow-on power electronic conversion system is then used to maintain the load conditions that maximise the extracted energy. The system performance has been analysed using theoretical studies and computer simulations to identify the operating conditions that maximise the extracted energy. Results obtained so far suggest that up to 100 mW can be harvested from 22 kV grid feeders, using only the capacitive coupling that exists between the line insulator harvester and the feeder conductor.

negative: Efficient reinforcement learning using Gaussian processes IX

negative: Learning Sparsely Used Overcomplete Dictionaries We consider the problem of learning sparsely used overcomplete dictionaries, where each observation is a sparse combination of elements from an unknown overcomplete dictionary. We establish exact recovery when the dictionary elements are mutually incoherent. Our method consists of a clustering-based initialization step, which provides an approximate estimate of the true dictionary with guaranteed accuracy. This estimate is then refined via an iterative algorithm with the following alternating steps: 1) estimation of the dictionary coefficients for each observation through l1 minimization, given the dictionary estimate, and 2) estimation of the dictionary elements through least squares, given the coefficient estimates. We establish that, under a set of sufficient conditions, our method converges at a linear rate to the true dictionary as well as the true coefficients for each observation.

negative: Fuzzy controlled fast charging system for lithium-ion batteries A DSP is adopted to construct a fuzzy controlled lithium-ion battery charging system. By using this intelligent charging system, the data collection, calculation and peripheral circuit control are performed for the battery charging status. According to the lithium-ion battery charging specifications, two hours are required for battery charging. A fuzzy logic controller (FLC) is constructed by using the battery protection cell voltage and voltage difference among the batteries. They are used as the input variable to shorten the charging and equalizing time and assure that the battery will be operated within the safety voltage range.

negative: A Particle Swarm Optimization ( PSO )-based Heuristic for Scheduling Work fl ow Applications in Cloud Computing Environments Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the ‘execution time’. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based scheduling heuristic for data intensive applications that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We analyze the cost savings when using PSO as compared to using existing ‘Best Resource Selection’ (BRS) algorithm. Our results show that we can achieve: a) as much as 3 times cost savings as compared to BRS, b) good distribution of workload onto resources, when using PSO based scheduling heuristic.

negative: 3D Face Recognition Using Shapes of Facial Curves Recognition of human beings using shapes of their full facial surfaces is a difficult problem. Our approach is to approximate a facial surface using a collection of (closed) facial curves, and to compare surfaces by comparing their corresponding curves. The differences between shapes of curves are quantified using lengths of geodesic paths between them on a pre-defined curve shape space. The metric for comparing facial surfaces is a composition of the metric involving individual facial curves. These ideas are demonstrated in the context of face recognition using the nearest-neighbor classifier

negative: Community-Affiliation Graph Model for Overlapping Network Community Detection One of the main organizing principles in real-world networks is that of network communities, where sets of nodes organize into densely linked clusters. Communities in networks often overlap as nodes can belong to multiple communities at once. Identifying such overlapping communities is crucial for the understanding the structure as well as the function of real-world networks. Even though community structure in networks has been widely studied in the past, practically all research makes an implicit assumption that overlaps between communities are less densely connected than the non-overlapping parts themselves. Here we validate this assumption on 6 large scale social, collaboration and information networks where nodes explicitly state their community memberships. By examining such ground-truth communities we find that the community overlaps are more densely connected than the non-overlapping parts, which is in sharp contrast to the conventional wisdom that community overlaps are more sparsely connected than the communities themselves. Practically all existing community detection methods fail to detect communities with dense overlaps. We propose Community-Affiliation Graph Model, a model-based community detection method that builds on bipartite node-community affiliation networks. Our method successfully captures overlapping, non-overlapping as well as hierarchically nested communities, and identifies relevant communities more accurately than the state-of-the-art methods in networks ranging from biological to social and information networks.

negative: The head direction signal: origins and sensory-motor integration. Navigation first requires accurate perception of one's spatial orientation within the environment, which consists of knowledge about location and directional heading. Cells within several limbic system areas of the mammalian brain discharge allocentrically as a function of the animal's directional heading, independent of the animal's location and ongoing behavior. These cells are referred to as head direction (HD) cells and are believed to encode the animal's perceived directional heading with respect to its environment. Although HD cells are found in several areas, the principal circuit for generating this signal originates in the dorsal tegmental nucleus and projects serially, with some reciprocal connections, to the lateral mammillary nucleus --> anterodorsal thalamus --> PoS, and terminates in the entorhinal cortex. HD cells receive multimodal information about landmarks and self-generated movements. Vestibular information appears critical for generating the directional signal, but motor/proprioceptive and landmark information are important for updating it.

negative: A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm Missing values in datasets should be extracted from the datasets or should be estimated before they are used for classification, association rules or clustering in the preprocessing stage of data mining. In this study, we utilize a fuzzy c-means clustering hybrid approach that combines support vector regression and a genetic algorithm. In this method, the fuzzy clustering parameters, cluster size and weighting factor are optimized and missing values are estimated. The proposed novel hybrid method yields sufficient and sensible imputation performance results. The results are compared with those of fuzzy c-means genetic algorithm imputation, support vector regression genetic algorithm imputation and zero imputation. 2013 Elsevier Inc. All rights reserved.

negative: Task driven perceptual organization for extraction of rooftop polygons A new method for extracting planar polygonal rooftops in monocular aerial imagery is proposed. Structural features are extracted and hierarchically related using perceptual grouping techniques. Top-down feature veri cation is used so that features, and links between the features, are veri ed with local information in the image and weighed in a graph. Cycles in the graph correspond to possible building rooftop hypotheses. Virtual features are hypothesized for the perceptual completion of partially occluded rooftops. Extraction of the \best" grouping of features into a building rooftop hypothesis is posed as a graph search problem. The maximally weighted, independent set of cycles in the graph is extracted as the nal set of roof

negative: Didn't you see my message?: predicting attentiveness to mobile instant messages Mobile instant messaging (e.g., via SMS or WhatsApp) often goes along with an expectation of high attentiveness, i.e., that the receiver will notice and read the message within a few minutes. Hence, existing instant messaging services for mobile phones share indicators of availability, such as the last time the user has been online. However, in this paper we not only provide evidence that these cues create social pressure, but that they are also weak predictors of attentiveness. As remedy, we propose to share a machine-computed prediction of whether the user will view a message within the next few minutes or not. For two weeks, we collected behavioral data from 24 users of mobile instant messaging services. By the means of machine-learning techniques, we identified that simple features extracted from the phone, such as the user's interaction with the notification center, the screen activity, the proximity sensor, and the ringer mode, are strong predictors of how quickly the user will attend to the messages. With seven automatically selected features our model predicts whether a phone user will view a message within a few minutes with 70.6% accuracy and a precision for fast attendance of 81.2%

negative: Conjugated linoleic acid and disease prevention: a review of current knowledge. Conjugated linoleic acid (CLA), a derivative of a fatty acid linoleic acid (LA), has been reported to decrease tumorigenesis in animals. CLA is unique because unlike most antioxidants which are components of plant products, it is present in food from animal sources such as dairy foods and meats. CLA concentrations in dairy products typically range from 2.9 to 8.92 mg/g fat of which the 9-cis, 11-trans isomer makes up to 73% to 93% of the total CLA. Low concentrations of CLA are found in human blood and tissues. In vitro results suggest that CLA is cytotoxic to MCF-7 cells and it inhibits the proliferation of human malignant melanoma and colorectal cancer cells. In animal studies, CLA has inhibited the development of mouse epidermal tumors, mouse forestomach cancer and rat mammary cancer. Hamsters fed CLA collectively had significantly reduced levels of plasma total cholesterol, non-high-density lipoprotein cholesterol, (combined very-low and low-density lipoprotein) and triglycerides with no effect on high-density lipoprotein cholesterol, as compared to controls. Dietary CLA modulated certain aspects of the immune defense but had no obvious effect on the growth of an established, aggressive mammary tumor in mice. It is now thought that CLA itself may not have anti-oxidant capabilities but may produce substances which protect cells from the detrimental effects of peroxides. There is, however, insufficient evidence from human epidemiological data, and very few of the animal studies have shown a dose-response relationship with the quantity of CLA feed and the extent of tumor growth. Further research with tumor models is needed to test the efficacy and utility of CLA in cancer and other disease prevention and form the basis of evaluating its effect in humans by observational studies and clinical trials.

negative: Gore Galore: Literary Theory and Computer Games Computer games have not been adequately theorized within the humanities. In this paper a brief history of computer games is presented as a starting point for developing a topology of games and a theory of computer games as rhetorical artifacts suitable for critical study. The paper addresses the question of why games should be treated seriously and suggests a theoretical approach based on Backhtin’s poetics of the novel where the experience of time and space (the chronotope) provides a framework of questions for discussing computer games.

negative: Qualitative Methods in Research on Teaching This chapter reviews basic issues of theory and method in approaches to research on teaching that are alternatively called ethnographic, qualitative, participant observational, case study, symbolic interactionist, phenomenological, constructivist, or interpretive. These approaches are all slightly different, but each bears strong family resemblance to the others. The set of related approaches is relatively new in the field of research on teaching. The approaches have emerged as significant in the decade of the 1960s in England and in the 1970s in the United States, Australia, New Zealand, and Germany. Because interest in these approaches is so recent, the previous editions of the Handbook of Research on Teaching do not contain a chapter devoted to participant observational research. Accordingly, this chapter attempts to describe r~s and their theoretical _nresup~s in considerable detail and does not attempt an exhaustive review of the rapidly growing literature in the field. Such a review will be appropriate for the next edition of this handbook. From this point on I will use the term interpretive to refer to the whole family of approaches to participant observational research. I adopt this term for three reasons: (a) It is more inclusive than many of the others (e.g., ethnography, case study); (b) W Blake

negative: Breast disorders in pregnant and lactating women. The breast undergoes extensive changes during pregnancy and lactation that can create diagnostic challenges. This article reviews the anatomy of the breast, breast changes associated with pregnancy and lactation, and breast imaging techniques for pregnant and lactating women. Various benign breast conditions in this patient population also are discussed, such as lactating adenomas, galactoceles, and granulomatous mastitis. Finally, pregnancy-associated breast cancer is presented, including its epidemiology, diagnosis, staging, treatment, and prognosis.

negative: A traffic-aware street lighting scheme for Smart Cities using autonomous networked sensors Street lighting is a ubiquitous utility, but sustaining its operation presents a heavy financial and environmental burden. Many schemes have been proposed which selectively dim lights to improve energy efficiency, but little consideration has been given to the usefulness of the resultant street lighting system. This paper proposes a real-time adaptive lighting scheme, which detects the presence of vehicles and pedestrians and dynamically adjusts their brightness to the optimal level. This improves the energy efficiency of street lighting and its usefulness; a streetlight utility model is presented to evaluate this. The proposed scheme is simulated using an environment modelling a road network, its users, and a networked communication system and considers a real streetlight topology from a residential area. The proposed scheme achieves similar or improved utility to existing schemes, while consuming as little as 1-2% of the energy required by conventional and state-of-the-art techniques.

negative: Persuasive messages on information system acceptance: A theoretical extension of elaboration likelihood model and social influence theory Firms invest millions of dollars in the introduction of new information systems for long-term benefit. If employees are not willing to accept a new information system, such investments may be wasted. Employee acceptance of a new information system is in part determined by external influences. However, previous research has neglected the paths of persuasive strategies and external social influences on information system acceptance. Linkages between persuasive strategies and external social influences are also scarce. By integrating social influence theory and an elaboration likelihood model, this study explores the influence of persuasive messages (source credibility and argument quality) on social influence, affective response and cognitive response. This study also investigates the interrelationships among affective response, cognitive response and behavior intention. Furthermore, the moderating roles of social influences on the impact of affective response and cognitive response on behavior intention are identified. 2012 Elsevier Ltd. All rights reserved.

negative: Ontology based Semantic Search Engine We live in a connected age when every entity is linked to each other through numerous relationships thus forming complex networks, information networks etc., In this meshy scenario, storing analyzing, managing extracting information is going to be highly challenging task. Information retrieval happened to be an activity that only a few people engaged in. due to the advent of search engines and communication facilities, millions of people are engaged in information retrieval and extraction process. The information retrieval deals with finding a set of documents relevant to the user query. Commercial search engines like Google deals with key word search which is based on Boolean logical queries. The major disadvantage of this kind of keyword search is that it returns a lot of irrelevant information to the users which results in low precision. Nowadays the field of information retrieval is moving towards semantic level from syntactic level. These semantic search engines are based on the concept of ontology which gives a Meta data representation of concepts. In this paper we focus on the retrieval mechanisms related to it. This includes various phases in which the ontology is designed first and then the indexing and retrieval phases work. Key wordsOntology, semantic web, Information retrieval, Query builder I.INTRODUCTION In this digital age each and every concept is linked to each other through various relationships forming the complex information web. By passing through the web we face some sort of challenges in storing and extracting the information from them. We therefore rely on search engines to overcome those difficulties. Any way these search engines facilitate only keyword search which does not returns accurate results to the external user. Aiming to solve the limitations of keyword based models, the idea of semantic search, understood as searching by meanings rather than by literals has been the focus of a wide body of research in the information retrieval and the semantic web communities Semantic search has been present in the information retrieval since the early eighties. Some of these approaches are based on the statistical methods that study the co-occurrence of terms that capture and exploit tough and fuzzy conceptualizations. Other Information retrieval approaches apply linguistic algorithms modeled on human language processing structures and taxonomies, where the level of conceptualization is often shallow and sparse, especially the level of relations, which are commonly at the core of expressing user needs and finding the answers. Ontologies are used to represent knowledge in a conceptual manner that can be distributed among various applications. The ontology has its application in information retrieval where it exploits the knowledge bases enhancing the semantic search, steering in one hand the use of fully fledged ontologies in the semantic based perspective, and on the other the consideration of unstructured content as target search space. In other words, this work explores the use of semantic information to support more expensive queries and more accurate results, while the retrieval problem is formulated in a way that is consistent with the Information retrieval field, thus drawing benefit from the state of art in this area and enabling more realistic and applicable approaches. II. RELEATED WORK A. Semantic web Current World Wide Web (WWW) is a huge library of interlinked documents that are transferred by computers and presented to people. It has grown from hypertext systems, but the difference is that anyone can contribute to it. This also means that the quality of information or even the persistence of documents cannot be generally guaranteed. Current WWW contains a lot of information and knowledge, but machines usually serve only to deliver and present the content of documents describing the knowledge. People have to connect all the sources of relevant information and interpret them themselves Semantic web is an effort to enhance current web so that computers can process the information presented on WWW, interpret and connect it, to help humans to find required knowledge. In the same way as WWW is a huge distributed hypertext system, semantic web is intended to form a huge distributed knowledge based system. The focus of semantic web is to share data instead of documents. In other words, it is a project that should provide a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. It is a collaborative effort led by World Wide Web Consortium (W3C). The semantic web has got its peak in recent years by the use of explicit metadata representation of information in the web. The metadata representation is embedded in web page using RDF to enhance the visualization of results. Semantic Web can be seen as a huge engineering solution... but it is more than that. We will find that as it becomes easier to publish data in a repurposable form, so more people will want to publish data, and there will be a knock-on or domino effect. N.Vanjulavalli et al IJCSET |August 2012 | Vol 2, Issue 8, 1349-1353 ISSN:2231-0711 Available online at www.ijcset.net 1349 We may find that a large number of Semantic Web applications can be used for a variety of different tasks, increasing the modularity of applications on the Web. But enough subjective reasoning. onto how this will be accomplished. The Semantic Web is generally built on syntaxes which use URIs to represent data, usually in triples based structures: i.e. many triples of URI data that can be held in databases, or interchanged on the world Wide Web using a set of particular syntaxes developed especially for the task. These syntaxes are called "Resource Description Framework" syntaxes. Once information is in RDF form, it becomes easy to process it, since RDF is a generic format, which already has many parsers. XML RDF is quite a verbose specification, and it can take some getting used to (for example, to learn XML RDF properly, you need to understand a little about XML and namespaces beforehand...), but let's take a quick look at an example of XML RDF right now:<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdfsyntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:foaf="http://xmlns.com/0.1/foaf/" > <rdf:Description rdf:about=""> <dc:creator rdf:parseType="Resource"> <foaf:name>Sean B. Palmer</foaf:name> </dc:creator> <dc:title>The Semantic Web: An Introduction</dc:title>

negative: For better or for worse: neural systems supporting the cognitive down- and up-regulation of negative emotion Functional neuroimaging studies examining the neural bases of the cognitive control of emotion have found increased prefrontal and decreased amygdala activation for the reduction or down-regulation of negative emotion. It is unknown, however, (1) whether the same neural systems underlie the enhancement or up-regulation of emotion, and (2) whether altering the nature of the regulatory strategy alters the neural systems mediating the regulation. To address these questions using functional magnetic resonance imaging (fMRI), participants up- and down-regulated negative emotion either by focusing internally on the self-relevance of aversive scenes or by focusing externally on alternative meanings for pictured actions and their situational contexts. Results indicated (1a) that both up- and down-regulating negative emotion recruited prefrontal and anterior cingulate regions implicated in cognitive control, (1b) that amygdala activation was modulated up or down in accord with the regulatory goal, and (1c) that up-regulation uniquely recruited regions of left rostromedial PFC implicated in the retrieval of emotion knowledge, whereas down-regulation uniquely recruited regions of right lateral and orbital PFC implicated in behavioral inhibition. Results also indicated that (2) self-focused regulation recruited medial prefrontal regions implicated in internally focused processing, whereas situation-focused regulation recruited lateral prefrontal regions implicated in externally focused processing. These data suggest that both common and distinct neural systems support various forms of reappraisal and that which particular prefrontal systems modulate the amygdala in different ways depends on the regulatory goal and strategy employed.

negative:
A systematic review of implementation strategies for assessment, prevention, and management of ICU delirium and their effect on clinical outcomes INTRODUCTION
Despite recommendations from professional societies and patient safety organizations, the majority of ICU patients worldwide are not routinely monitored for delirium, thus preventing timely prevention and management. The purpose of this systematic review is to summarize what types of implementation strategies have been tested to improve ICU clinicians' ability to effectively assess, prevent and treat delirium and to evaluate the effect of these strategies on clinical outcomes.

METHOD
We searched PubMed, Embase, PsychINFO, Cochrane and CINAHL (January 2000 and April 2014) for studies on implementation strategies that included delirium-oriented interventions in adult ICU patients. Studies were suitable for inclusion if implementation strategies' efficacy, in terms of a clinical outcome, or process outcome was described.

RESULTS
We included 21 studies, all including process measures, while 9 reported both process measures and clinical outcomes. Some individual strategies such as "audit and feedback" and "tailored interventions" may be important to establish clinical outcome improvements, but otherwise robust data on effectiveness of specific implementation strategies were scarce. Successful implementation interventions were frequently reported to change process measures, such as improvements in adherence to delirium screening with up to 92%, but relating process measures to outcome changes was generally not possible. In meta-analyses, reduced mortality and ICU length of stay reduction were statistically more likely with implementation programs that employed more (six or more) rather than less implementation strategies and when a framework was used that either integrated current evidence on pain, agitation and delirium management (PAD) or when a strategy of early awakening, breathing, delirium screening and early exercise (ABCDE bundle) was employed. Using implementation strategies aimed at organizational change, next to behavioral change, was also associated with reduced mortality.

CONCLUSION
Our findings may indicate that multi-component implementation programs with a higher number of strategies targeting ICU delirium assessment, prevention and treatment and integrated within PAD or ABCDE bundle have the potential to improve clinical outcomes. However, prospective confirmation of these findings is needed to inform the most effective implementation practice with regard to integrated delirium management and such research should clearly delineate effective practice change from improvements in clinical outcomes.

negative: Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or/and speech production knowledge, and, then modeling the acoustic features with an ANN. Recent advances in machine learning techniques, more specifically in the field of image processing and text processing, have shown that such divide and conquer strategy (i.e., separating feature extraction and modeling steps) may not be necessary. Motivated from these studies, in the framework of convolutional neural networks (CNNs), this paper investigates a novel approach, where the input to the ANN is raw speech signal and the output is phoneme class conditional probability estimates. On TIMIT phoneme recognition task, we study different ANN architectures to show the benefit of CNNs and compare the proposed approach against conventional approach where, spectral-based feature MFCC is extracted and modeled by a multilayer perceptron. Our studies show that the proposed approach can yield comparable or better phoneme recognition performance when compared to the conventional approach. It indicates that CNNs can learn features relevant for phoneme classification automatically from the raw speech signal.

negative: Classification of Artefacts in EEG Signal Recordings and Overview of Removing Techniques EEG is a record of brain activity from various sites of the brain. Artefacts are unwanted noise signals in an EEG record. Classification of artefacts based on source of its generation like physiological artefacts and external artefacts. Body of the subjects are main source of Physiological artifacts, while external artefacts are from outside the body due to the environment or measuring devices. Recognition, identification and elimination of artifacts is an important process to minimize the chance of misinterpretation of EEG, not only for clinical and non-clinical fields such as brain computer interface, intelligent control system robotics etc. This paper classifies the artefacts from the database collected at Dr. R. N. Cooper Mun. General Hospital Mumbai India.

negative: In the mood: engaging teenagers in psychotherapy using mobile phones Mental illness is a significant and growing problem throughout the world. Many mental health problems have their root in childhood, and early intervention is recommended. Engaging young people in psychotherapeutic activities is challenging, and treatment adherence is often poor. This paper presents a series of studies carried out as part of the development of a mobile and online symptom tracking tool for adolescents with mental health problems. Teenagers use the system to record symptoms on their mobile phones and can view this information in a clinical setting with a therapist. We focus on a clinical pilot of the system with ten users in public mental health clinics. As well as indicating that the mobile diary tool can increase client adherence to therapeutic activities, the study yields insight into the factors influencing the success of the design and informs the design of other systems to be used as adjuncts to therapy.