title: ACCURATE COMPUTATION OF TRANSFER MAPS FOR REALISTIC BEAMLINE ELEMENTS FROM SURFACE DATA

abstract: The behavior of orbits in charged-particle beam transport systems, including both linear and circular accelerators as well as final focus sections and spectrometers, can depend sensitively on nonlinear fringe-field and high-order multipole effects in the various beam-line elements. The inclusion of these effects requires a detailed and realistic model of the interior and fringe fields, including their high spatial derivatives. A collection of surface fitting methods has been developed for extracting this information accurately from 3-dimensional field data on a grid, as provided by various 3-dimensional finite-element field codes. Based on these realistic field models, Lie or other methods may be used to compute accurate design orbits and accurate transfer maps about these orbits. This talk will provide a description of the methods used along with example applications. An exactly-soluble but numerically challenging model field is used to provide a rigorous collection of performance benchmarks. BACKGROUND For the design of high-performance storage or damping rings it is essential to have realistic electric and magnetic field information for the various beam-line elements, in order to compute accurate design orbits and high-order transfer maps about the design orbits. Realistic field data can be provided on a grid with the aid of various 3-dimensional finite element codes, sometimes spot checked against measured data. But the computation of high-order transfer maps based on this data poses a difficult problem: the calculation of high-order transfer maps requires a knowledge of high derivatives of the field data. The direct calculation of high derivatives based only on grid data is intolerably sensitive to noise (due to truncation or round-off) in the grid data. We will see that this problem can be overcome by the use of surface methods. The effect of numerical noise can be overcome by fitting onto a bounding surface far from the beam axis and continuing inward using the Maxwell equations. While the process of differentiation serves to amplify the effect of numerical noise, the process of continuing inward using the Maxwell equations is smoothing. This smoothing is related to the fact that harmonic functions take their extrema on boundaries. When using surface methods, all fits are made to such boundaries. Therefore if these fits are accurate, interior data based on these fits will be even more accurate.


title: Optimistic replication

abstract: Data replication is a key technology in distributed systems that enables higher availability and performance. This article surveys optimistic replication algorithms. They allow replica contents to diverge in the short term to support concurrent work practices and tolerate failures in low-quality communication links. The importance of such techniques is increasing as collaboration through wide-area and mobile networks becomes popular.Optimistic replication deploys algorithms not seen in traditional “pessimistic” systems. Instead of synchronous replica coordination, an optimistic algorithm propagates changes in the background, discovers conflicts after they happen, and reaches agreement on the final contents incrementally.We explore the solution space for optimistic replication algorithms. This article identifies key challenges facing optimistic replication systems---ordering operations, detecting and resolving conflicts, propagating changes efficiently, and bounding replica divergence---and provides a comprehensive survey of techniques developed for addressing these challenges.


title: State of the Art Control of Atari Games Using Shallow Reinforcement Learning

abstract: The recently introduced Deep Q-Networks (DQN) algorithm has gained attention as one of the first successful combinations of deep neural networks and reinforcement learning. Its promise was demonstrated in the Arcade Learning Environment (ALE), a challenging framework composed of dozens of Atari 2600 games used to evaluate general competency in AI. It achieved dramatically better results than earlier approaches, showing that its ability to learn good representations is quite robust and general. This paper attempts to understand the principles that underlie DQN's impressive performance and to better contextualize its success. We systematically evaluate the importance of key representational biases encoded by DQN's network by proposing simple linear representations that make use of these concepts. Incorporating these characteristics, we obtain a computationally practical feature set that achieves competitive performance to DQN in the ALE. Besides offering insight into the strengths and weaknesses of DQN, we provide a generic representation for the ALE, significantly reducing the burden of learning a representation for each game. Moreover, we also provide a simple, reproducible benchmark for the sake of comparison to future work in the ALE.


title: Reinforcement Learning approach for Real Time Strategy Games Battle city and S3

abstract: In this paper we proposed reinforcement learning algorithms with the generalized reward function. In our proposed method we use Q-learning and SARSA algorithms with generalised reward function to train the reinforcement learning agent. We evaluated the performance of our proposed algorithms on two real-time strategy games called BattleCity and S3. There are two main advantages of having such an approach as compared to other works in RTS. (1) We can ignore the concept of a simulator which is often game specific and is usually hard coded in any type of RTS games (2) our system can learn from interaction with any opponents and quickly change the strategy according to the opponents and do not need any human traces as used in previous works. Keywords : Reinforcement learning, Machine learning, Real time strategy, Artificial intelligence.


A Computational Semantic Analysis of Noun Compounds in Dutch


title: Theory of Mind for a Humanoid Robot

abstract: If we are to build human-like robots that can interact naturally with people, our robots must know not only about the properties of objects but also the properties of animate agents in the world. One of the fundamental social skills for humans is the attribution of beliefs, goals, and desires to other people. This set of skills has often been called a “theory of mind.” This paper presents the theories of Leslie (1994) and Baron-Cohen (1995) on the development of theory of mind in human children and discusses the potential application of both of these theories to building robots with similar capabilities. Initial implementation details and basic skills (such as finding faces and eyes and distinguishing animate from inanimate stimuli) are introduced. I further speculate on the usefulness of a robotic implementation in evaluating and comparing these two models.


title: Warranties for faster strong consistency

abstract: We present a new mechanism, warranties, to enable building distributed systems with linearizable transactions. A warranty is a time-limited assertion about one or more distributed objects. These assertions generalize optimistic concurrency control, improving throughput because clients holding warranties need not communicate to verify the warranty's assertion. Updates that might cause an active warranty to become false are delayed until the warranty expires, trading write latency for read latency. For workloads biased toward reads, warranties improve scalability and system throughput. Warranties can be expressed using language-level computations, and they integrate harmoniously into the programming model as a form of memoization. Experiments with some non-trivial programs demonstrate that warranties enable high performance despite the simple programming model.


title: Distributed Coordinate Descent Method for Learning with Big Data

abstract: In this paper we develop and analyze Hydra: HYbriD cooRdinAte descent method for solving loss minimization problems with big data. We initially partition the coordinates (features) and assign each partition to a different node of a cluster. At every iteration, each node picks a random subset of the coordinates from those it owns, independently from the other computers, and in parallel computes and applies updates to the selected coordinates based on a simple closed-form formula. We give bounds on the number of iterations sufficient to approximately solve the problem with high probability, and show how it depends on the data and on the partitioning. We perform numerical experiments with a LASSO instance described by a 3TB matrix.


title: Lowering the Barrier for Crowdsensing Application Development

abstract: Crowdsensing has the potential to support human-driven sensing and data collection at an unprecedented scale. While many organizers of data collection campaigns may have extensive domain knowledge, they do not necessarily have the skills required to develop robust software for crowdsensing. In this paper, we present Mobile Campaign Designer, a tool that simplifies the creation of mobile crowdsensing applications. Using Mobile Campaign Designer, an organizer is able to define parameters about their crowdsensing campaign, and the tool generates the source code and an executable for a tailored mobile application that embodies the current best practices in crowdsensing. An evaluation of the tool shows that users at all levels of technical expertise are capable of creating a crowdsensing application in an average of five minutes, and the generated applications are comparable in quality to existing crowdsensing applications.


title: Mendeley readership altmetrics for medical articles: An analysis of 45 fields

abstract: Medical research is highly funded and often expensive and so is particularly important to evaluate effectively. Nevertheless, citation counts may accrue too slowly for use in some formal and informal evaluations. It is therefore important to investigate whether alternative metrics could be used as substitutes. This article assesses whether one such altmetric, Mendeley readership counts, correlates strongly with citation counts across all medical fields, whether the relationship is stronger if student readers are excluded, and whether they are distributed similarly to citation counts. Based on a sample of 332,975 articles from 2009 in 45 medical fields in Scopus, citation counts correlated strongly about 0.7; 78% of articles had at least one reader with Mendeley readership counts from the new version 1 applications programming interface [API] in almost all fields, with one minor exception, and the correlations tended to decrease slightly when student readers were excluded. Readership followed either a lognormal or a hooked power law distribution, whereas citations always followed a hooked power law, showing that the two may have underlying differences.


title: Fluorescence lifetime measurements of fluoranthene, 1-naphthol, and napropamide in the presence of dissolved humic Acid.

abstract: The binding of hydrophobic organic solutes and nonionic pesticides with dissolved humic substances may significantly influence their behavior and fate in natural systems. The binding of fluoranthene, 1-naphthol, and napropamide [2-(1-naphthoxy)-N,N-diethylpropionamide] by an International Humic Substances Society reference soil humic acid in aqueous solutions was studied using fluorescence quenching and lifetime measurements. The quenching of fluoranthene, 1-naphthol, and napropamide fluorescence by the humic acid was adequately described by the Stern-Volmer equation. The effects of temperature ranging from 283 to 313 K on the quenching ratios was not significant, and the fluorescence lifetimes of these compounds did not shorten as a function of increasing humic acid concentrations


A science learning environment using a computational thinking approach


title: PADS: a domain-specific language for processing ad hoc data

abstract: PADS is a declarative data description language that allows data analysts to describe both the physical layout of ad hoc data sources and semantic properties of that data. From such descriptions, the PADS compiler generates libraries and tools for manipulating the data, including parsing routines, statistical profiling tools, translation programs to produce well-behaved formats such as Xml or those required for loading relational databases, and tools for running XQueries over raw PADS data sources. The descriptions are concise enough to serve as "living" documentation while flexible enough to describe most of the ASCII, binary, and Cobol formats that we have seen in practice. The generated parsing library provides for robust, application-specific error handling.


title: Statistical and Computational Guarantees of Lloyd's Algorithm and its Variants

abstract: Clustering is a fundamental problem in statistics and machine learning. Lloyd's algorithm, proposed in 1957, is still possibly the most widely used clustering algorithm in practice due to its simplicity and empirical performance. However, there has been little theoretical investigation on the statistical and computational guarantees of Lloyd's algorithm. This paper is an attempt to bridge this gap between practice and theory. We investigate the performance of Lloyd's algorithm on clustering sub-Gaussian mixtures. Under an appropriate initialization for labels or centers, we show that Lloyd's algorithm converges to an exponentially small clustering error after an order of $\log n$ iterations, where $n$ is the sample size. The error rate is shown to be minimax optimal. For the two-mixture case, we only require the initializer to be slightly better than random guess. In addition, we extend the Lloyd's algorithm and its analysis to community detection and crowdsourcing, two problems that have received a lot of attention recently in statistics and machine learning. Two variants of Lloyd's algorithm are proposed respectively for community detection and crowdsourcing. On the theoretical side, we provide statistical and computational guarantees of the two algorithms, and the results improve upon some previous signal-to-noise ratio conditions in literature for both problems. Experimental results on simulated and real data sets demonstrate competitive performance of our algorithms to the state-of-the-art methods.


title: Toward Conversational Human-Computer Interaction

abstract: The belief that humans will be able to interact with computers in conversational speech has long been a favorite subject in science fiction, reflecting the persistent belief that spoken dialogue would be the most natural and powerful user interface to computers. With recent improvements in computer technology and in speech and language processing, such systems are starting to appear feasible. There are significant technical problems that still need to be solved before speech-driven interfaces become truly conversational. This article describes the results of a 10-year effort building robust spoken dialogue systems at the University of Rochester.


A multiview recurrent neural network for 3 D mesh segmentation


title: UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring

abstract: In this study, a crop health monitoring system is developed by using state of the art technologies including wireless sensors and Unmanned Aerial Vehicles (UAVs). Conventionally data is collected from sensor nodes either by fixed base stations or mobile sinks. Mobile sinks are considered a better choice nowadays due to their improved network coverage and energy utilization. Usually, the mobile sink is used in two ways: either it goes for random walk to find the scattered nodes and collect data, or follows a pre-defined path established by the ground network/clusters. Neither of these options is suitable in our scenario due to the factors like dynamic data collection, the strict targeted area required to be scanned, unavailability of a large number of nodes, dynamic path of the UAV, and most importantly, none of these are known in advance. The contribution of this paper is the formation of dynamic runtime clusters of field sensors by considering the above mentioned factors. Furthermore a mechanism (Bayesian classifier) is defined to select best node as cluster head. The proposed system is validated through simulation results, lab and infield experiments using concept devices. The obtained results are encouraging, especially in terms of deployment time, energy, efficiency, throughput and ease of use.


A Study on Uncertainties in Software Project Management in Pakistan


title: A Modular IoT Platform for Real-Time Indoor Air Quality Monitoring

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


title: The recognition of Chinese caption text in news video using convolutional neural network

abstract: News video caption, which carries main contents of related news story, plays an important role in content-based video analysis and retrieval system. In this paper, the convolutional neural network (CNN) is used to the recognition of chinese caption text in news video. First, the color and edge feature are used for caption location. Then, the segmentation combined Otsu and K-means clustering algorithm is applied to the caption images before they are sent to CNN. It is worth mentioning that we present a method for generating and labeling training images automatically, which avoids the complex and time consuming data collection. Finally, two CNN models trained on different dataset are evaluated in our experiment. By using the baseline model, the recognition accuracy can achieve 93.3% in top-1 and 98.58% in top-5 on chinese caption texts collected from news video. We also show an improvement to 95% in top-1 accuracy by averaging the two CNN models. Experimental results suggest that CNN is competent to the challenging task of chinese character recognition.


title: An SLA-Based Approach to Manage Sensor Networks as-a-Service

abstract: The integration of sensing infrastructures into the Cloud gives a number of advantages in providing sensor data as a service over the Internet. Many solutions are now available in the literature, and most of them focus on modeling sensor networks as part of the infrastructure to be offered as a service (IaaS), directly managed by means of the Cloud tools that provide resource virtualization. We propose a different approach: sensor networks are modeled as providers that offer their resources to a Cloud application that runs independently from Cloud providers. Being offered as a Service, any user can negotiate with the provider his desired requirements in terms of operational parameters and non-functional features (i.e. security, dependability, etc). In particular, we propose a SLA-based approach for the specification and management of usage term guarantees related to the access and configuration of private sensor networks. To this end, a Cloud Sensing Brokering Platform is designed to illustrate the innovative way to integrate Cloud and Sensor Networks.


title: Qualidade de vida na asma pediátrica: revisão da literatura

abstract: OBJECTIVE: To analyze scientific articles that discuss the quality of life related to the health of children and adolescents with asthma, focusing the Pediatric Asthma Quality of Life Questionnaire (PAQLQ) evaluation tool. DATA SOURCES: Non-systematic review of the literature including articles in English, Portuguese and Spanish identified via Medline, SciELO and Lilacs, published between 1997 and 2009.The following descriptors were searched: "quality of life", "asthma pediatrics", "asthma quality of life" and "PAQLQ". DATA SYNTHESIS: The 26 articles were organized in two sections: studies on validity of PAQLQ (n=12) and the assessment of quality of life in asthmatic children using the PAQLQ (n=14). CONCLUSIONS: The assessment of quality of life should be incorporated in clinical evaluations considering that chronic illnesses may affect diverse dimensions of life. The PAQLQ is reliable and easy to use, being an appropriate tool to evaluate quality of life in children and adolescents with asthma.


title: Applications of Data Mining in Higher Education

abstract: Data analysis plays an important role for decision support irrespective of type of industry like any manufacturing unit and educations system. There are many domains in which data mining techniques plays an important role. This paper proposes the use of data mining techniques to improve the efficiency of higher education institution. If data mining techniques such as clustering, decision tree and association are applied to higher education processes, it would help to improve students performance, their life cycle management, selection of courses, to measure their retention rate and the grant fund management of an institution. This is an approach to examine the effect of using data mining techniques in higher education.


title: Complementary case-based reasoning and competitive fuzzy cognitive maps for advanced medical decisions

abstract: This paper presents a new hybrid modeling methodology suitable for complex decision making processes. It extends previous work on competitive fuzzy cognitive maps for medical decision support systems by complementing them with case based reasoning methods. The synergy of these methodologies is accomplished by a new proposed algorithm that leads to more dependable advanced medical decision support systems that are suitable to handle situations where the decisions are not clearly distinct. The methodology developed here is applied successfully to model and test two decision support systems, one a differential diagnosis problem from the speech pathology area for the diagnosis of language impairments and the other for decision making choices in external beam radiation therapy.


title: Position-Location Solutions by Taylor-Series Estimation

abstract: Taylor-series estimation gives a least-sum-squared-error solution to a set of simultaneous linearized algebraic equations. This method is useful in solving multimeasurement mixed-mode position-location problems typical of many navigational applications. While convergence is not proved, examples show that most problems do converge to the correct solution from reasonable initial guesses. The method also provides the statistical spread of the solution errors.


Cross-Domain Sentiment Classification via Topic-Related TrAdaBoost


title: Receiver Design for a Bionic Nervous System: Modeling the Dendritic Processing Power

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


title: Secure Image Steganography using Cryptography and Image Transposition

abstract: Information security is one of the most challenging problems in today's technological world. In order to secure the transmission of secret data over the public network (Internet), various schemes have been presented over the last decade. Steganography combined with cryptography, can be one of the best choices for solving this problem. This paper proposes a new steganographic method based on gray-level modification for true colour images using image transposition, secret key and cryptography. Both the secret key and secret information are initially encrypted using multiple encryption algorithms (bitxor operation, bits shuffling, and stego key-based encryption); these are, subsequently, hidden in the host image pixels. In addition, the input image is transposed before data hiding. Image transposition, bits shuffling, bitxoring, stego key-based encryption, and gray-level modification introduce five different security levels to the proposed scheme, making the data recovery extremely difficult for attackers. The proposed technique is evaluated by objective analysis using various image quality assessment metrics, producing promising results in terms of imperceptibility and security. Moreover, the high quality stego images and its minimal histogram changeability, also validate the effectiveness of the proposed approach.


title: Microbiota Controls the Homeostasis of Glial Cells in the Gut Lamina Propria

abstract: The intrinsic neural networks of the gastrointestinal tract are derived from dedicated neural crest progenitors that colonize the gut during embryogenesis and give rise to enteric neurons and glia. Here, we study how an essential subpopulation of enteric glial cells (EGCs) residing within the intestinal mucosa is integrated into the dynamic microenvironment of the alimentary tract. We find that under normal conditions colonization of the lamina propria by glial cells commences during early postnatal stages but reaches steady-state levels after weaning. By employing genetic lineage tracing, we provide evidence that in adult mice the network of mucosal EGCs is continuously renewed by incoming glial cells originating in the plexi of the gut wall. Finally, we demonstrate that both the initial colonization and homeostasis of glial cells in the intestinal mucosa are regulated by the indigenous gut microbiota.


title: Coherence regularized dictionary learning

abstract: Sparse representations over redundant learned dictionaries have shown to produce high quality results in various image processing tasks. An important characteristic of a learned dictionary is the mutual coherence of dictionary that affects its generalization performance and the optimality of sparse codes generated from it. In this paper, we present a dictionary learning model equipped with coherence regularization. For this model, two novel dictionary optimization algorithms based on group-wise minimization of inter- and intra-coherence penalties are proposed. Experimental results demonstrate that the proposed algorithms improve the generalization properties and sparse approximation performance of the trained dictionary compared to several incoherent dictionary learning methods.


title: Combined First and Second Order Total Variation Inpainting using Split Bregman

abstract: In this article we discuss the implementation of the combined first and second order total varia- tion inpainting that was introduced by Papafitsoros and Schonlieb. We describe the algorithm we use (split Bregman) in detail, and we give some examples that indicate the difference between pure first and pure second order total variation inpainting. Source Code We provide a source code for the algorithm written in C and an online demonstration, accessible on the article web page http://dx.doi.org/10.5201/ipol.2013.40.


title: DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI

abstract: Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for "pipeline" data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for "pipeline" data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest.


title: Robust Vision-Based Relative-Localization Approach Using an RGB-Depth Camera and LiDAR Sensor Fusion

abstract: This paper describes a robust vision-based relative-localization approach for a moving target based on an RGB-depth (RGB-D) camera and sensor measurements from two-dimensional (2-D) light detection and ranging (LiDAR). With the proposed approach, a target’s three-dimensional (3-D) and 2-D position information is measured with an RGB-D camera and LiDAR sensor, respectively, to find the location of a target by incorporating visual-tracking algorithms, depth information of the structured light sensor, and a low-level vision-LiDAR fusion algorithm, e.g., extrinsic calibration. To produce 2-D location measurements, both visual- and depth-tracking approaches are introduced, utilizing an adaptive color-based particle filter (ACPF) (for visual tracking) and an interacting multiple-model (IMM) estimator with intermittent observations from depth-image segmentation (for depth image tracking). The 2-D LiDAR data enhance location measurements by replacing results from both visual and depth tracking; through this procedure, multiple LiDAR location measurements for a target are generated. To deal with these multiple-location measurements, we propose a modified track-to-track fusion scheme. The proposed approach shows robust localization results, even when one of the trackers fails. The proposed approach was compared to position data from a Vicon motion-capture system as the ground truth. The results of this evaluation demonstrate the superiority and robustness of the proposed approach.


title: Coverage for robotics – A survey of recent results

abstract: This paper surveys recent results in coverage path planning, a new path planning approach that determines a path for a robot to pass over all points in its free space. Unlike conventional point-to-point path planning, coverage path planning enables applications such as robotic de-mining, snow removal, lawn mowing, car-body painting, machine milling, etc. This paper will focus on coverage path planning algorithms for mobile robots constrained to operate in the plane. These algorithms can be classified as either heuristic or complete. It is our conjecture that most complete algorithms use an exact cellular decomposition, either explicitly or implicitly, to achieve coverage. Therefore, this paper organizes the coverage algorithms into four categories: heuristic, approximate, partial-approximate and exact cellular decompositions. The final section describes some provably complete multi-robot coverage algorithms.


title: Auto-calibration of cylindrical multi-projector systems

abstract: In this paper we present a novel technique to calibrate multiple casually aligned projectors on a fiducial-free cylindrical curved surface using a single camera. We impose two priors to the cylindrical display: (a) cylinder is a vertically extruded surface; and (b) the aspect ratio of the rectangle formed by the four corners of the screen is known. Using these priors, we can estimate the display's 3D surface geometry and camera extrinsic parameters using a single image without any explicit display to camera correspondences. Using the estimated camera and display properties, we design a novel deterministic algorithm to recover the intrinsic and extrinsic parameters of each projector using a single projected pattern seen by the camera which is then used to register the images on the display from any arbitrary viewpoint making it appropriate for virtual reality systems. Finally, our method can be extended easily to handle sharp corners — making it suitable for the common CAVE like VR setup. To the best of our knowledge, this is the first method that can achieve accurate geometric auto-calibration of multiple projectors on a cylindrical display without performing an extensive stereo reconstruction.


title: 3D-e-Chem-VM: Structural Cheminformatics Research Infrastructure in a Freely Available Virtual Machine

abstract: 3D-e-Chem-VM is an open source, freely available Virtual Machine (http://3d-e-chem.github.io/3D-e-Chem-VM/) that integrates cheminformatics and bioinformatics tools for the analysis of protein–ligand interaction data. 3D-e-Chem-VM consists of software libraries, and database and workflow tools that can analyze and combine small molecule and protein structural information in a graphical programming environment. New chemical and biological data analytics tools and workflows have been developed for the efficient exploitation of structural and pharmacological protein–ligand interaction data from proteomewide databases (e.g., ChEMBLdb and PDB), as well as customized information systems focused on, e.g., G protein-coupled receptors (GPCRdb) and protein kinases (KLIFS). The integrated structural cheminformatics research infrastructure compiled in the 3D-e-Chem-VM enables the design of new approaches in virtual ligand screening (Chemdb4VS), ligand-based metabolism prediction (SyGMa), and structure-based protein b...


title: A model for smart agriculture using IoT

abstract: Climate changes and rainfall has been erratic over the past decade. Due to this in recent era, climate-smart methods called as smart agriculture is adopted by many Indian farmers. Smart agriculture is an automated and directed information technology implemented with the IOT (Internet of Things). IOT is developing rapidly and widely applied in all wireless environments. In this paper, sensor technology and wireless networks integration of IOT technology has been studied and reviewed based on the actual situation of agricultural system. A combined approach with internet and wireless communications, Remote Monitoring System (RMS) is proposed. Major objective is to collect real time data of agriculture production environment that provides easy access for agricultural facilities such as alerts through Short Massaging Service (SMS) and advices on weather pattern, crops etc.


title: Careers of Misconduct: The Structure of Prosecuted Professional Deviance Among Lawyers

abstract: We develop a career model of prosecuted misconduct among lawyers. This model combines labeling theory's emphasis on the role of sanctions in structuring deviant careers with a focus on the asynchronic interaction between life cycle changes among precariously positioned actors and system conditions. This conceptualization suggests that complaints of misconduct against inexperienced solo practitioners are more prone to prosecution because of social conceptions and lay theories about lawyer misconduct, perceived threats to the profession's public image, and the relative powerlessness of these lawyers to defend themselves, especially during economic recession. An event history analysis of misconduct complaints against lawyers in a Canadian province confirms that complaints against inexperienced solo practitioners are more likely to move to prosecution during an economic recession. Ourfindings encourage the application of career conceptualizations to other research on deviance.


title: Emotional and effortful control abilities in 42-month-old very preterm and full-term children

abstract: Abstract Background Very preterm (VP) infants are at greater risk for cognitive difficulties that may persist during school-age, adolescence and adulthood. Behavioral assessments report either effortful control (part of executive functions) or emotional reactivity/regulation impairments. Aims The aim of this study is to examine whether emotional recognition, reactivity, and regulation, as well as effortful control abilities are impaired in very preterm children at 42months of age, compared with their full-term peers, and to what extent emotional and effortful control difficulties are linked. Study design Children born very preterm (VP; n =41) and full-term (FT) aged-matched children ( n =47) participated in a series of specific neuropsychological tests assessing their level of emotional understanding, reactivity and regulation, as well as their attentional and effortful control abilities. Results VP children exhibited higher scores of frustration and fear, and were less accurate in naming facial expressions of emotions than their aged-matched peers. However, VP children and FT children equally performed when asked to choose emotional facial expression in social context, and when we assessed their selective attention skills. VP performed significantly lower than full terms on two tasks of inhibition when correcting for verbal skills. Moreover, significant correlations between cognitive capacities (effortful control) and emotional abilities were evidenced. Conclusions Compared to their FT peers, 42month-olds who were born very preterm are at higher risk of exhibiting specific emotional and effortful control difficulties. The results suggest that these difficulties are linked. Ongoing behavioral and emotional impairments starting at an early age in preterms highlight the need for early interventions based on a better understanding of the relationship between emotional and cognitive difficulties.


title: Measurement of self-reported pain intensity in children and adolescents.

abstract: Acute and chronic pain is a common experience in children and youth. A thorough assessment is fundamental to understand this experience and to assess and monitor treatment responses. The intensity of pain is the parameter most commonly assessed. In this article, we describe the different methods employed to assess pediatric pain intensity and review well-validated and commonly used self-report measures of pain. This review is based on the recent systematic reviews conducted for the Pediatric Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials Consensus Group and the Society of Pediatric Psychology. Amongst the several types of pediatric pain measures, self-report, when available, is regarded as the primary source of information about pain intensity, to be complemented by observation and knowledge of the context. There is a large number of self-report measures of pediatric pain intensity; and there is some agreement that professionals in the clinical and research practice should assess pain intensity using the Pieces of Hurt Tool, the Faces Pain Scale, the Oucher, or Visual Analogue Scales because these measures have shown to have sound psychometric properties and clinical utility. Despite the increased number of age-appropriate self-report measures of pediatric pain intensity over the last years, we report several research gaps and priorities of future research.


title: Aspect identification and sentiment analysis based on NLP

abstract: This paper proposes method for aspect identification and sentiment analysis. This paper deals with the problem of sentence level. We use semantic syntax tree to extract NP nodes, a method based on slack to identify aspect and mini-support function to narrow aspect; to analyze the sentiment for the aspect we use dictionary-based method. Experimental results show that the proposed approach achieves the state-of-the-art.


title: GCMDDBSCAN: Multi-density DBSCAN Based on Grid and Contribution

abstract: Multi Density DBSCAN (Density Based Spatial Clustering of Application with Noise) is an excellent density-based clustering algorithm, which extends DBSCAN algorithm so as to be able to discover the different densities clusters, and retains the advantage of separating noise and finding arbitrary shape clusters. But, because of great memory demand and low calculation efficiency, Multi Density DBSCAN can't deal with large database. Therefore, GCMDDBSCAN is proposed in this paper, and within it 'migration-coefficient' conception is introduced firstly. In GCMDDBSCAN, with the grid technique, the optimization effect of contribution and migration-coefficient, and the efficient SP-tree query index, the runtime is reduced a lot, and the capability of clustering large database is obviously enhanced, at the same time, the accuracy of clustering result is not degraded.


title: A bitonic sorting network with simpler flip interconnections

abstract: This paper presents the new scheme of interconnecting levels in a bitonic sorting network with simpler inter-level wiring. A parity technique which leads to the algorithm Construct-BSMF is introduced. Wiring simplification through the network is achieved wiring the N/2 even-parity keys straight through the network. N/2 odd-parity keys use flip interconnections. As a result, our interconnection scheme simplifies the inter-level wiring through the network and outperforms the perfect-shuffle interconnection scheme both in terms of cost and delay.


title: Automatic Knowledge Base Construction using Probabilistic Extraction, Deductive Reasoning, and Human Feedback

abstract: We envision an automatic knowledge base construction system consisting of three inter-related components. MADden is a knowledge extraction system applying statistical text analysis methods over database systems (DBMS) and massive parallel processing (MPP) frameworks; ProbKB performs probabilistic reasoning over the extracted knowledge to derive additional facts not existing in the original text corpus; CAMeL leverages human intelligence to reduce the uncertainty resulting from both the information extraction and probabilistic reasoning processes.


title: Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning

abstract: High-resolution Digital Surface Models (DSMs) from unmanned aerial vehicles (UAVs) imagery with accuracy better than 10 cm open new possibilities in geosciences and engineering. The accuracy of such DSMs depends on the number and distribution of ground control points (GCPs). Placing and measuring GCPs are often the most time-consuming on-site tasks in a UAV project. Safety or accessibility concerns may impede their proper placement, so either costlier techniques must be used, or a less accurate DSM is obtained. Photogrammetric blocks flown by drones with on-board receivers capable of RTK (real-time kinematic) positioning do not need GCPs, as camera stations at exposure time can be determined with cm-level accuracy, and used to georeference the block and control its deformations. This paper presents an experimental investigation on the repeatability of DSM generation from several blocks acquired with a RTK-enabled drone, where differential corrections were sent from a local master station or a network of Continuously Operating Reference Stations (CORS). Four different flights for each RTK mode were executed over a test field, according to the same flight plan. DSM generation was performed with three block control configurations: GCP only, camera stations only, and with camera stations and one GCP. The results show that irrespective of the RTK mode, the first and third configurations provide the best DSM inner consistency. The average range of the elevation discrepancies among the DSMs in such cases is about 6 cm (2.5 GSD, ground sampling density) for a 10-cm resolution DSM. Using camera stations only, the average range is almost twice as large (4.7 GSD). The average DSM accuracy, which was verified on checkpoints, turned out to be about 2.1 GSD with the first and third configurations, and 3.7 GSD with camera stations only.


Leader Efficacy Linking Leadership to Student Learning: The Contributions of


title: M.K.D. Impact of Media on Children and Adolescents: A 10-Year Review of the Research

abstract: ABSTRACT Objective To review the research literature published within the past 10 years regarding the impact of media on children and adolescents. Method Media categories researched with computer technology included television and movies, rock music and music videos, advertising, video games, and computers and the Internet. Results Research prior to 1990 documented that children learn behaviors and have their value systems shaped by media. Media research since has focused on content and viewing patterns. Conclusions The primary effects of media exposure are increased violent and aggressive behavior, increased high-risk behaviors, including alcohol and tobacco use, and accelerated onset of sexual activity. The newer forms of media have not been adequately studied, but concern is warranted through the logical extension of earlier research on other media forms and the amount of time the average child spends with increasingly sophisticated media.


title: Memory or Time: Performance Evaluation for Iterative Operation on Hadoop and Spark

abstract: Hadoop is a very popular general purpose framework for many different classes of data-intensive applications. However, it is not good for iterative operations because of the cost paid for the data reloading from disk at each iteration. As an emerging framework, Spark, which is designed to have a global cache mechanism, can achieve better performance in response time since the in-memory access over the distributed machines of cluster will proceed during the entire iterative process. Although the performance on time has been evaluated for Spark over Hadoop, the memory consumption, another system performance criteria, is not deeply analyzed in the literature. In this work, we conducted extensive experiments for iterative operations to compare the performance in both time and memory cost between Hadoop and Spark. We found that although Spark is in general faster than Hadoop in iterative operations, it has to pay for more memory consumption. Also, its speed advantage is weakened at the moment when the memory is not sufficient enough to store newly created intermediate results.


title: Ship Classification in TerraSAR-X Images With Feature Space Based Sparse Representation

abstract: Ship classification is the key step in maritime surveillance using synthetic aperture radar (SAR) imagery. In this letter, we develop a new ship classification method in TerraSAR-X images based on sparse representation in feature space, in which the sparse representation classification (SRC) method is exploited. In particular, to describe the ship more accurately and to reduce the dimension of the dictionary in SRC, we propose to employ a representative feature vector to construct the dictionary instead of utilizing the image pixels directly. By testing on a ship data set collected from TerraSAR-X images, we show that the proposed method is superior to traditional methods such as the template matching (TM), K-nearest neighbor (K-NN), Bayes and Support Vector Machines (SVM).


title: Echolocation by the long-eared bat,Plecotus phyllotis

abstract: The echolocating bat,Plecotus phyllotis (Vespertilionidae), uses long-CF/FM and FM sonar sounds in different situations. The CF component in long-CF/FM sounds occurs at 27 kHz and has a duration of 20 to 200 ms. The FM component sweeps down from 24 to 12 kHz, with a prominent second harmonic from 40 to 22 kHz. This second harmonic sweep is interrupted at 28 to 25 kHz, providing a notch in the spectrum of the FM component at the CF frequency. This notch probably permits isolation of CF and FM components in echoes for separate processing, thus avoiding mutual interference with the different kinds of target information the two components convey. The FM component is also used without the CF component as a sonar sound. Two other FM orientation sounds are used when the bat is in a confined space such as a room. One contains only the second and fourth harmonics of the 24 to 12 kHz fundamental sweep, while the other contains only the fifth harmonic. This bat's repertoire of sonar sounds closely matches the hearing capacities of the genus.


title: “Pokémon Go!” May Promote Walking, Discourage Sedentary Behavior in College Students

abstract: OBJECTIVE ::: To assess self-reported walking and sedentary behavior in young adults before and after downloading "Pokémon Go!". ::: ::: ::: MATERIALS AND METHODS ::: In September 2016, a sample of 358 (19.8 ± 2.1 years old, n = 187 females) college students who had downloaded "Pokémon Go!" on their cellular telephones (i.e., cell phones) were surveyed for weekly walking and sedentary behavior via the International Physical Activity Questionnaire. A single interview was administered to participants who estimated their walking and sedentary behavior at three time points: the week immediately preceding their download of "Pokémon Go!" (Baseline), the first week after downloading the game (Time 1), and the week the survey was completed (Time 2). Differences in self-reported physical activity and sedentary behavior across the three time points and across the two genders were compared via analyses of variance. ::: ::: ::: RESULTS ::: There was a significant main effect of time (F ≥ 49.3, P ≤ 0.001) for walking and sedentary behavior. Participants reported greater (t ≥ 9.5, P < 0.001) daily walking during Time 1 (218.6 ± 156.3 minutes) and Time 2 (182.7 ± 172.1 minutes) versus the baseline (108.5 ± 110.8 minutes). Walking behavior was also significantly greater (t = 4.1, P < 0.001) at Time 1 versus Time 2. Participants reported greater (t ≥ 6.5, P < 0.001) daily sedentary behavior during baseline (346.6 ± 201.3 minutes) versus both Time 1 (261.7 ± 172.4 minutes) and Time 2 (284.3 ± 175.4 minutes). Sedentary behavior was also significantly greater (t = 2.6, P = 0.03) at Time 2 versus Time 1. There were no effects of gender (F ≤ 1.8, P ≥ 0.17). ::: ::: ::: CONCLUSION ::: Playing "Pokémon Go!" was associated with increased self-reported walking and decreased sedentary behavior. Such games hold promise as technology that may promote physical activity and discourage sedentary behavior.


title: Electromagnetic design of an ultra-high speed switched reluctance machine over 1 million rpm

abstract: Ultra-high speed switched reluctance machines (SRM) are becoming more and more promising due to its robustness and simple geometry. This paper proposed a detailed design procedure for an ultra-high speed SRM, including dimension calculation, material selection, winding selection, windage torque calculation, drive system and control strategy. The ultimate goal is to build a machine that is electromagnetically good enough to rotate beyond 1,000,000 rpm. A Maxwell/Simplorer co-simulation model is built to validate the proposed design. Single pulse control is used to drive the machine. The machine is being built and will be finished in the near future.


title: Acoustic indices for biodiversity assessments: Analyses of bias based on simulated bird assemblages and recommendations for field surveys

abstract: Abstract Rapid acoustic surveys aim to estimate biodiversity based on the diversity of sounds produced by animal communities, and interest in this approach among conservation planners is increasing. Several indices of acoustic diversity have been proposed as proxies for species richness. However, in the field, the animal activity may be responsible for acoustic diversity to varying degrees. To evaluate how measures of acoustic diversity may depart from actual species richness, we selected seven acoustic indices and applied them to simulated recordings of bird assemblages under different field conditions. For a given sound, defined here as the song composition emitted by a species assemblage, we determined if the indices were i) only driven by animal sounds, ii) insensitive to species identity, and iii) independent of species evenness. Under the field conditions evaluated, none of the indices fulfilled the three criteria necessary for a perfect proxy of species richness. However, some indices may be appropriate as a measure of biodiversity under a more broad definition including phylogenetic and/or functional aspects of diversity. We provide recommendations for the application of these indices for biodiversity measurement under field conditions, such as the application of appropriate audio filters, the increase of the repetition rate of the recordings, and the identification of the main taxonomic groups occurring in the recorded communities.


title: Bounds for sorting by prefix reversal

abstract: For a permutation @s of the integers from 1 to n, let @?(@s) be the smallest number of prefix reversals that will transform @s to the identity permutation, and let @?(n) be the largest such @?(@s) for all @s in (the symmetric group) S"n. We show that @?(n)= =17n16 for n a multiple of 16. If, furthermore, each integer is required to participate in an even number of reversed prefixes, the corresponding function g(n) is shown to obey 3n2-1=


title: Methodology for Assessing Daylighting Design Strategies in Classroom with a Climate-Based Method

abstract: Considering the importance of daylight in the performance and well-being of the students, an investigation has been carried out in daylit classrooms. The objective was applying a methodology that integrates the daylight variations to know the annual lighting performance in typologies that resulted from passive design strategies in order to compare their performance. The context of the study was three zones of Chile: north, center and south. The study was done through a climate-based daylight modelling method using Radiance software. The interior illuminance was evaluated in relation to a target illuminance value (goal-oriented assessment), for which five intervals are defined: low, too low, in range, high and too high. The aim of the goal-oriented approach is to set a target range of values and assess the percentage of time over the year where each range is reached and the percentage of spaces in a temporal map within in range during the year. To see a compliance or non-compliance indicator, a category is proposed that considers the average annual illuminance “in range” over the year identifying which one is the most efficient. Finally, it is concluded that the information obtained is based on target ranges, which allows guiding the design decisions, effectively recognizing the annual performance.


title: Community-Enhanced De-anonymization of Online Social Networks

abstract: Online social network providers have become treasure troves of information for marketers and researchers. To profit from their data while honoring the privacy of their customers, social networking services share `anonymized' social network datasets, where, for example, identities of users are removed from the social network graph. However, by using external information such as a reference social graph (from the same network or another network with similar users), researchers have shown how such datasets can be de-anonymized. These approaches use `network alignment' techniques to map nodes from the reference graph into the anonymized graph and are often sensitive to larger network sizes, the number of seeds, and noise --- which may be added to preserve privacy. We propose a divide-and-conquer approach to strengthen the power of such algorithms. Our approach partitions the networks into `communities' and performs a two-stage mapping: first at the community level, and then for the entire network. Through extensive simulation on real-world social network datasets, we show how such community-aware network alignment improves de-anonymization performance under high levels of noise, large network sizes, and a low number of seeds. Even when nodes cannot be explicitly mapped, the community structure can be mapped between both networks, thus reducing the anonymity of users. For example, for our (real-world) Twitter dataset with 90,000 nodes, 20% noise, and 16 seeds, the state-of-the-art technique reduces anonymity by 0 bits, whereas our approach reduces anonymity by 9.71 bits (with 40% of nodes mapped).


title: Deep CCA based super vector for action recognition

abstract: Super vector based feature encoding methods have recently produced state-of-the-art performance in video based action recognition. Inspired by the idea of multi-view super vector (MVSV), we propose a novel global representation, deep canonical correlation analysis based super vector (DCCA-SV), which is composed of shared components and relatively independent components derived from a pair of descriptors. The shared parts are based on the representations learned by DCCA model and the independent parts are constructed by the first and second order statistics of the reconstruction errors. Compared with the existing feature encoding strategies, DCCA-SV takes advantages of deep learning models in describing complex data distribution. It can learn the complex nonlinear transformations of two views of data with a single model, which is more efficient in capturing the overall correlations between feature pairs. Furthermore, in DCCA-SV, there is no need to compute the inner product and matrix multiplication, which is more efficient in real applications. Experiments on Non-Human Primates' (NHPs') surveillance video action recognition dataset show that DCCA-SV achieves promising results compared with state-of-the-art methods.1


title: A robust algorithm for detecting people in overhead views

abstract: In this research a human detection system is proposed in which people are viewed from an overhead camera with a wide angle lens. Due to perspective change a person can have different orientations and sizes at different positions in the scene relative to the optical centre. We exploit this property of the overhead camera and develop a novel algorithm which uses the variable size bounding boxes with different orientations, with respect to the radial distance of the center of the image. In these overhead view images we neither used any assumption about the pose or the visibility of a person nor imposed any restriction about the environment. When compare the results of proposed algorithm with a standard histogram of oriented gradient (HOG) algorithm, we achieve not only a huge gain in overall detection rate but also a significant improvement in reducing spurious detections per image. On average, 9 false detections occur per image. A new algorithm is proposed where transforming the image patch containing a person to remove positional dependency and then applying the HOG algorithm eliminates 98% of the spurious detections in noisy images from an industrial assembly line and detects people with a 95% efficiency.


title: Overuse Lower Extremity Injuries in Sports

abstract: When athletes train harder the risk of injury increases, and there are several common overuse injuries to the lower extremity. Three of the most common lower extremity overuse injuries in sports are discussed including the diagnosis and treatments: medial tibal stress syndrome, iliotibial band syndrome, and stress fractures. The charge of sports medicine professionals is to identify and treat the cause of the injuries and not just treat the symptoms. Symptomatology is an excellent guide to healing and often the patient leads the physician to the proper diagnosis through an investigation of the athlete's training program, past injury history, dietary habits, choice of footwear, and training surface.


title: Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges

abstract: Robot navigation is an indispensable component of any mobile service robot. Many path planning algorithms generate a path which has many sharp or angular turns. Such paths are not fit for mobile robot as it has to slow down at these sharp turns. These robots could be carrying delicate, dangerous, or precious items and executing these sharp turns may not be feasible kinematically. On the contrary, smooth trajectories are often desired for robot motion and must be generated while considering the static and dynamic obstacles and other constraints like feasible curvature, robot and lane dimensions, and speed. The aim of this paper is to succinctly summarize and review the path smoothing techniques in robot navigation and discuss the challenges and future trends. Both autonomous mobile robots and autonomous vehicles (outdoor robots or self-driving cars) are discussed. The state-of-the-art algorithms are broadly classified into different categories and each approach is introduced briefly with necessary background, merits, and drawbacks. Finally, the paper discusses the current and future challenges in optimal trajectory generation and smoothing research.


title: Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport

abstract: Network-based cloud computing is rapidly expanding as an alternative to conventional office-based computing. As cloud computing becomes more widespread, the energy consumption of the network and computing resources that underpin the cloud will grow. This is happening at a time when there is increasing attention being paid to the need to manage energy consumption across the entire information and communications technology (ICT) sector. While data center energy use has received much attention recently, there has been less attention paid to the energy consumption of the transmission and switching networks that are key to connecting users to the cloud. In this paper, we present an analysis of energy consumption in cloud computing. The analysis considers both public and private clouds, and includes energy consumption in switching and transmission as well as data processing and data storage. We show that energy consumption in transport and switching can be a significant percentage of total energy consumption in cloud computing. Cloud computing can enable more energy-efficient use of computing power, especially when the computing tasks are of low intensity or infrequent. However, under some circum- stances cloud computing can consume more energy than conventional computing where each user performs all com- puting on their own personal computer (PC).


title: Photosensitive Graphene Transistors

abstract: High performance photodetectors play important roles in the development of innovative technologies in many fields, including medicine, display and imaging, military, optical communication, environment monitoring, security check, scientific research and industrial processing control. Graphene, the most fascinating two-dimensional material, has demonstrated promising applications in various types of photodetectors from terahertz to ultraviolet, due to its ultrahigh carrier mobility and light absorption in broad wavelength range. Graphene field effect transistors are recognized as a type of excellent transducers for photodetection thanks to the inherent amplification function of the transistors, the feasibility of miniaturization and the unique properties of graphene. In this review, we will introduce the applications of graphene transistors as photodetectors in different wavelength ranges including terahertz, infrared, visible, and ultraviolet, focusing on the device design, physics and photosensitive performance. Since the device properties are closely related to the quality of graphene, the devices based on graphene prepared with different methods will be addressed separately with a view to demonstrating more clearly their advantages and shortcomings in practical applications. It is expected that highly sensitive photodetectors based on graphene transistors will find important applications in many emerging areas especially flexible, wearable, printable or transparent electronics and high frequency communications.


title: Sound Event Recognition Using Auditory-Receptive-Field Binary Pattern and Hierarchical-Diving Deep Belief Network

abstract: Automatic sound event recognition SER has recently attracted renewed interest. Although practical SER system has many useful applications in everyday life, SER is challenging owing to the variations among sounds and noises in the real-world environment. This paper presents a novel feature extraction and classification method to solve the problem of SER. An audio–visual descriptor, called the auditory-receptive-field binary pattern, is designed based on the spectrogram image feature, the cepstral features, and the human auditory receptive field model. The extracted features are then fed into a classifier to perform event classification. The proposed classifier, called the hierarchical-diving deep belief network, is a deep neural network system that hierarchically learns the discriminative characteristics from physical feature representation to the abstract concept. The performance of our proposed system was verified using several experiments under various conditions. Using the RWCP dataset, the proposed system achieved a recognition rate of 99.27% for real-world sound data in 105 categories. Under noisy conditions, the developed system is very robust, with which it achieved 95.06% recognition rate with 0 dB signal-to-noise ratio. Using the TUT sound event dataset, the proposed system achieves error rates of 0.81 and 0.73 in sound event detection in home and residential area scenes. The experimental results reveal that the proposed system outperformed the other systems in this field.


title: The mixed workload CH-benCHmark

abstract: While standardized and widely used benchmarks address either operational or real-time Business Intelligence (BI) workloads, the lack of a hybrid benchmark led us to the definition of a new, complex, mixed workload benchmark, called mixed workload CH-benCHmark. This benchmark bridges the gap between the established single-workload suites of TPC-C for OLTP and TPC-H for OLAP, and executes a complex mixed workload: a transactional workload based on the order entry processing of TPC-C and a corresponding TPC-H-equivalent OLAP query suite run in parallel on the same tables in a single database system. As it is derived from these two most widely used TPC benchmarks, the CH-benCHmark produces results highly relevant to both hybrid and classic single-workload systems.


title: Education , politics and opinions about climate change evidence for interaction effects

abstract: U.S. public opinion regarding climate change has become increasingly polarized in recent years, as partisan think tanks and others worked to recast an originally scientific topic into a political wedge issue. Nominally “scientific” arguments against taking anthropogenic climate change seriously have been publicized to reach informed but ideologically receptive audiences. Reflecting the success of such arguments, polls have noted that concern about climate change increased with education among Democrats, but decreased with education among Republicans. These observations lead to the hypothesis that there exist interaction (non-additive) effects between education or knowledge and political orientation, net of other background factors, in predicting public concern about climate change. Two regional telephone surveys, conducted in New Hampshire (n = 541) and Michigan (n = 1, 008) in 2008, included identical climate-change questions that provide opportunities to test this hypothesis. Multivariate analysis of both surveys finds significant interactions. These empirical results fit with theoretical interpretations and several other recent studies. They suggest that the classically identified social bases of concern about the environment in general, and climate in particular, have shifted in recent years. Narrowcast media, including the many Web sites devoted to discrediting climate-change concerns, provide ideal conduits for channeling contrarian arguments to an audience predisposed to believe and electronically spread them further. Active-response Web sites by climate scientists could prove critical to counterbalancing contrarian arguments.


title: Offline Soft-Switched LED Driver Based on an Integrated Bridgeless Boost–Asymmetrical Half-Bridge Converter

abstract: This paper proposes an offline single-stage light-emitting diode (LED) driver based on a novel approach to reduce low-frequency ripple in LED arrays supplied from mains, while assuring high input power factor (PF). The proposal employs a series resonant dc–dc converter integrated into a bridgeless boost power factor correction (PFC) stage. The resonant stage works to reduce the low-frequency ripple transmitted from the output of the PFC stage to the LEDs with only small power losses. This solution achieves three main goals: 1) reduction of the bulk capacitance used at the dc bus, allowing for the use of long-life film capacitors; 2) increase in the overall efficiency of the LED driver owing to the low switching and conduction losses in the shared switching network; and 3) assuring high input PF. Experimental results of a driver without electrolytic capacitors for a 100-W street lighting LED luminaire are presented, showing overall efficiency in excess of 92%, input PF greater than 0.97, and line current harmonic content in compliance with the IEC 61000-3-2 standard.


title: 3D Human Pose Estimation from a Single Image via Distance Matrix Regression

abstract: This paper addresses the problem of 3D human pose estimation from a single image. We follow a standard two-step pipeline by first detecting the 2D position of the N body joints, and then using these observations to infer 3D pose. For the first step, we use a recent CNN-based detector. For the second step, most existing approaches perform 2N-to-3N regression of the Cartesian joint coordinates. We show that more precise pose estimates can be obtained by representing both the 2D and 3D human poses using NxN distance matrices, and formulating the problem as a 2D-to-3D distance matrix regression. For learning such a regressor we leverage on simple Neural Network architectures, which by construction, enforce positivity and symmetry of the predicted matrices. The approach has also the advantage to naturally handle missing observations and allowing to hypothesize the position of non-observed joints. Quantitative results on Humaneva and Human3.6M datasets demonstrate consistent performance gains over state-of-the-art. Qualitative evaluation on the images in-the-wild of the LSP dataset, using the regressor learned on Human3.6M, reveals very promising generalization results.


title: PRICE COMPARISON FOR INFRASTRUCTURE-AS-A-SERVICE

abstract: Today’s pricing of infrastructure-as-a-service is not transparent because some providers, such as Google, charge separately for each service characteristic (e.g., $50 per CPU or $15 per GB of memory per month) and let customers freely configure the service. In contrast, competitors like Amazon, Microsoft, and IBM only offer predefined bundles (e.g., 4 GB of memory, 400 GB of storage, and 2 CPUs for $140 per month). These different types of pricing plans make price comparisons very difficult. The aim of this study is to increase price transparency among providers by analyzing two price comparison methods. The first method is the well-known "hedonic pricing", which decomposes each provider's billing into the contributing values of the product s characteristics. The second is a newly developed method, called PriCo ("Pricing plan Comparison"), which in addition considers offers from competitive providers. We employ the two methods in an empirical study, which compares the pricing of the infrastructure-as-a-service providers Google, Microsoft, Amazon, IBM, and Terremark. The insights gained help customers to better identify the least expensive provider for their needs. The proposed methods also help providers to better understand and position their pricing in the market.


title: Voxel-based cortical thickness measurements in MRI

abstract: The thickness of the cerebral cortex can provide valuable information about normal and abnormal neuroanatomy. High resolution MRI together with powerful image processing techniques has made it possible to perform these measurements automatically over the whole brain. Here we present a method for automatically generating voxel-based cortical thickness (VBCT) maps. This technique results in maps where each voxel in the grey matter is assigned a thickness value. Sub-voxel measurements of thickness are possible using sub-sampling and interpolation of the image information. The method is applied to repeated MRI scans of a single subject from two MRI scanners to demonstrate its robustness and reproducibility. A simulated data set is used to show that small focal differences in thickness between two groups of subjects can be detected. We propose that the analysis of VBCT maps can provide results that are complementary to other anatomical analyses such as voxel-based morphometry.


title: Learning from Noisy Large-Scale Datasets with Minimal Supervision

abstract: We present an approach to effectively use millions of images with noisy annotations in conjunction with a small subset of cleanly-annotated images to learn powerful image representations. One common approach to combine clean and noisy data is to first pre-train a network using the large noisy dataset and then fine-tune with the clean dataset. We show this approach does not fully leverage the information contained in the clean set. Thus, we demonstrate how to use the clean annotations to reduce the noise in the large dataset before fine-tuning the network using both the clean set and the full set with reduced noise. The approach comprises a multi-task network that jointly learns to clean noisy annotations and to accurately classify images. We evaluate our approach on the recently released Open Images dataset, containing ~9 million images, multiple annotations per image and over 6000 unique classes. For the small clean set of annotations we use a quarter of the validation set with ~40k images. Our results demonstrate that the proposed approach clearly outperforms direct fine-tuning across all major categories of classes in the Open Image dataset. Further, our approach is particularly effective for a large number of classes with wide range of noise in annotations (20-80% false positive annotations).


title: Improved Rotor Flux Estimation at Low Speeds for Torque MRAS-Based Sensorless Induction Motor Drives

abstract: In this paper, an improved rotor flux estimation method for the Torque model reference adaptive schemes (TMRAS) sensorless induction machine drive is proposed to enhance its performance in low and zero speed conditions. The conventional TMRAS scheme uses an open loop flux estimator and a feedforward term, with basic low pass filters replacing the pure integrators. However, the performance of this estimation technique has drawbacks at very low speeds with incorrect flux estimation significantly affecting this inherently sensorless scheme. The performance of the proposed scheme is verified by both simulated and experimental testing for an indirect vector controlled 7.5-kW induction machine. Results show the effectiveness of the proposed estimator in the low- and zero-speed regions with improved robustness against motor parameter variation compared to the conventional method.


title: Empathic concern and the effect of stories in human-robot interaction

abstract: People have been shown to project lifelike attributes onto robots and to display behavior indicative of empathy in human-robot interaction. Our work explores the role of empathy by examining how humans respond to a simple robotic object when asked to strike it. We measure the effects of lifelike movement and stories on people's hesitation to strike the robot, and we evaluate the relationship between hesitation and people's trait empathy. Our results show that people with a certain type of high trait empathy (empathic concern) hesitate to strike the robots. We also find that high empathic concern and hesitation are more strongly related for robots with stories. This suggests that high trait empathy increases people's hesitation to strike a robot, and that stories may positively influence their empathic responses.


title: Classification of Monte Carlo tree search variants

abstract: Many variations of Monte Carlo tree search have been proposed and tested but relatively little comparison of these variants have occurred. In this study an Agent Case Embedding analysis and agglomorative hierarchical clustering was performed using eight variants of Monte Carlo Tree Search as agents and eight games as cases. This allowed us to compare the variant's abilities on each of the games to determine the type of games each are good at handling as well as which variants are similar to others. This comparison of variants exploits the ability of ACEs to compare different types of objects based on their behavior. By looking at the behavior of MCTS variants on a variety of games we obtain a good notion of the degree to which different MCTS variants exhibit different capabilities. A side effect of comparing MCTS variants with agent-case embeddings is that we also are able to compare the games used to test the MCTS variants.


title: Breast cancer classification using machine learning

abstract: During their life, among 8% of women are diagnosed with Breast cancer (BC), after lung cancer, BC is the second popular cause of death in both developed and undeveloped worlds. BC is characterized by the mutation of genes, constant pain, changes in the size, color(redness), skin texture of breasts. Classification of breast cancer leads pathologists to find a systematic and objective prognostic, generally the most frequent classification is binary (benign cancer/malign cancer). Today, Machine Learning (ML) techniques are being broadly used in the breast cancer classification problem. They provide high classification accuracy and effective diagnostic capabilities. In this paper, we present two different classifiers: Naive Bayes (NB) classifier and knearest neighbor (KNN) for breast cancer classification. We propose a comparison between the two new implementations and evaluate their accuracy using cross validation. Results show that KNN gives the highest accuracy (97.51%) with lowest error rate then NB classifier (96.19 %).


title: Alert prioritization in Intrusion Detection Systems

abstract: Intrusion Detection Systems (IDSs) are designed to monitor user and/or network activity and generate alerts whenever abnormal activities are detected. The number of these alerts can be very large; making the task of security analysts difficult to manage. Furthermore, IDS alert management techniques, such as clustering and correlation, suffer from involving unrelated alerts in their processes and consequently provide imprecise results. In this paper, we propose a fuzzy-logic based technique for scoring and prioritizing alerts generated by an IDS(1). In addition, we present an alert rescoring technique that leads to a further reduction of the number of alerts. The approach is validated using the 2000 DARPA intrusion detection scenario specific datasets and comparative results between the Snort IDS alert scoring and our scoring and prioritization scheme are presented.


title: Student analytics for productive teaching/learning

abstract: This paper presents an automated analytics system which monitors the students attending online lectures from a remote location and provides feedback to the teacher. The classroom videos are recorded and analyzed to identify the student trends, which might not be noticed by a teacher during class hours. Student behaviors are classified into five affective states: Active, Transcribing, Unavailing, Distracted and Transition. The student faces are tracked and optical flow of each student is calculated. Displacements and head motion of students are derived using a simple method and each student is automatically mapped to a particular affective state. The state transitions are provided to the educator as a feedback for assessment. Three different experiments were conducted with sessions from online courses, and was observed that the automated system efficiently differentiates the attention level of students and helped the teachers to improve their style of instruction. The engagement states also defined the height of student interest on a topic and classified the attentiveness automatically, without exploiting human effort.


title: Considering Pilot Protection in the Optimal Coordination of Distance and Directional Overcurrent Relays

abstract: The aim of the relay coordination is that protection systems detect and isolate the faulted part as fast and selective as possible. On the other hand, in order to reduce the fault clearing time, distance protection relays are usually equipped with pilot protection schemes. Such schemes can be considered in the Distance and Directional Over-Current Relays (D&DOCRs) coordination to achieve faster protection systems, while the selectivity is maintained. Therefore, in this paper, a new formulation is presented for the relay coordination problem considering pilot protection. In the proposed formulation, the selectivity constraints for the primary distance and backup overcurrent relays are defined based on the fault at the end of the transmission lines, rather than those at the end of the first zone of the primary distance relay. To solve this nonlinear optimization problem, a combination of Genetic Algorithm (GA) and Linear Programming (LP) is used as a Hybrid Genetic Algorithm (HGA). The proposed approach is tested on an 8-bus, the IEEE 14-bus and the IEEE 39-bus test systems. Simulation results indicate that considering the pilot protection in the D&DOCRS coordination, not only obtains feasible and effective solutions for the relay settings, but also reduces the overall operating time of the protection system.


title: Design of a Rectangular Microstrip Patch Antenna Using Inset Feed Technique

abstract: Today in the world of communication systems the most widely researched area is of wireless technology and a study of communication systems is incomplete without an understanding of the operation of the antennas. In the recent years of development in communication systems a need for the development of lightweight, compact and cost-effective antennas that are capable of maintaining high performance over a wide spectrum of frequencies. This technological trend has focused much effort into the design of a Micro strip patch antenna. In this work, the simulation tool of IE3D is used to study the performance and gain of the rectangular Microstrip patch antenna. The design and simulation of patch antennas is widely used in mobile cellular phones today, and our emphasis in this work is on optimization of a 2.4 GHz rectangular Microstrip patch antenna. The return loss and the various gain plots have been studied along with the radiation patterns.


title: Let CONAN tell you a story: Procedural quest generation

abstract: This work proposes an engine for the Creation Of Novel Adventure Narrative (CONAN), which is a procedural quest generator. It uses a planning approach to story generation. The engine is tested on its ability to create quests, which are sets of actions that must be performed in order to achieve a certain goal, usually for a reward. The engine takes in a world description represented as a set of facts, including characters, locations, and items, and generates quests according to the state of the world and the preferences of the characters. We evaluate quests through the classification of the motivations behind the quests, based on the sequences of actions required to complete the quests. We also compare different world descriptions and analyze the difference in motivations for the quests produced by the engine. Compared against human structural quest analysis, the current engine was found to be able to replicate the quest structures found in commercial video game quests.


title: Urinary tract anomalies associated with unicornuate uterus.

abstract: PURPOSE ::: We investigated the association between the various subclasses of the unicornuate uterus and urinary tract anomalies. ::: ::: ::: MATERIALS AND METHODS ::: We studied the urinary tract of 37 patients with a unicornuate uterus by urography and/or ultrasonography. Unicornuate uterus was diagnosed at laparoscopy or laparotomy. ::: ::: ::: RESULTS ::: A total of 15 patients (40.5%) had urinary tract anomalies, including ectopic kidney in 4, renal agenesis in 6, double renal pelvis in 2, horseshoe kidneys in 2 and unilateral medullary sponge kidney in 1. No differences were observed in the frequency of urinary anomalies among the various subclasses of unicornuate uterus. ::: ::: ::: CONCLUSIONS ::: Our results indicate the need to suspect a urinary tract anomaly, particularly of the kidney, in all women with a unicornuate uterus.


title: HANDLING DATA SKEW IN MAPREDUCE

abstract: MapReduce systems have become popular for processing large data sets and are increasingly being used in e-science applications. In contrast to simple application scenarios like word count, e-science applications involve complex computations which pose new challenges to MapReduce systems. In particular, (a) the runtime complexity of the reducer task is typically high, and (b) scientific data is often skewed. This leads to highly varying execution times for the reducers. Varying execution times result in low resource utilisation and high overall execution time since the next MapReduce cycle can only start after all reducers are done. In this paper we address the problem of efficiently processing MapReduce jobs with complex reducer tasks over skewed data. We define a new cost model that takes into account non-linear reducer tasks and we provide an algorithm to estimate the cost in a distributed environment. We propose two load balancing approaches, fine partitioning and dynamic fragmentation, that are based on our cost model and can deal with both skewed data and complex reduce tasks. Fine partitioning produces a fixed number of data partitions, dynamic fragmentation dynamically splits large partitions into smaller portions and replicates data if necessary. Our approaches can be seamlessly integrated into existing MapReduce systems like Hadoop. We empirically evaluate our solution on both synthetic data and real data from an e-science application.


title: Variations in recorded acoustic gunshot waveforms generated by small firearms.

abstract: Analysis of recorded acoustic gunshot signals to determine firearm waveform characteristics requires an understanding of the impulsive signal events, how the waveforms vary among different sources, and how the waveforms are affected by the environment and the recording system. This paper presents empirical results from waveforms produced by different small firearms and an analysis of their variations under different and controlled conditions. Acoustic signals were generated using multiple firearm makes and models firing different ammunition types. Simultaneous recordings from the microphones located at different distances from the source and at different azimuth angles (from the line-of-fire) were used to study source characteristics and sound propagation effects. The results indicate that recorded gunshot waveforms generally consist of multiple acoustic events, and these are observable depending on the received distance and azimuth angle. The source blast size, microphone distance, and microphone azimuth angle are the primary factors affecting the recorded muzzle blast characteristics. Ground or object reflections and ballistic shockwaves and their reflections can interfere with the muzzle blast waveform and its measurements. This experiment confirmed and quantified the wide range of correlation results between waveforms recorded from different source, microphone distance, and microphone angle configurations.


title: Face Recognition Under Varying Illumination

abstract: Face Recognition by a robot or machine is one of the challenging research topics in the recent years. It has become an active research area which crosscuts several disciplines such as image processing, pattern recognition, computer vision, neural networks and robotics. For many applications, the performances of face recognition systems in controlled environments have achieved a satisfactory level. However, there are still some challenging issues to address in face recognition under uncontrolled conditions. The variation in illumination is one of the main challenging problems that a practical face recognition system needs to deal with. It has been proven that in face recognition, differences caused by illumination variations are more significant than differences between individuals (Adini et al., 1997). Various methods have been proposed to solve the problem. These methods can be classified into three categories, named face and illumination modeling, illumination invariant feature extraction and preprocessing and normalization. In this chapter, an extensive and state-of-the-art study of existing approaches to handle illumination variations is presented. Several latest and representative approaches of each category are presented in detail, as well as the comparisons between them. Moreover, to deal with complex environment where illumination variations are coupled with other problems such as pose and expression variations, a good feature representation of human face should not only be illumination invariant, but also robust enough against pose and expression variations. Local binary pattern (LBP) is such a local texture descriptor. In this chapter, a detailed study of the LBP and its several important extensions is carried out, as well as its various combinations with other techniques to handle illumination invariant face recognition under a complex environment. By generalizing different strategies in handling illumination variations and evaluating their performances, several promising directions for future research have been suggested. This chapter is organized as follows. Several famous methods of face and illumination modeling are introduced in Section 2. In Section 3, latest and representative approaches of illumination invariant feature extraction are presented in detail. More attentions are paid on quotient-image-based methods. In Section 4, the normalization methods on discarding low frequency coefficients in various transformed domains are introduced with details. In Section 5, a detailed introduction of the LBP and its several important extensions is presented, as well as its various combinations with other face recognition techniques. In Section 6, comparisons between different methods and discussion of their advantages and disadvantages are presented. Finally, several promising directions as the conclusions are drawn in Section 7.


title: Realtime Depth Estimation and Obstacle Detection from Monocular Video

abstract: This paper deals with the detection of arbitrary static objects in traffic scenes from monocular video using structure from motion. A camera in a moving vehicle observes the road course ahead. The camera translation in depth is known. Many structure from motion algorithms were proposed for detecting moving or nearby objects. However, detecting stationary distant obstacles in the focus of expansion remains quite challenging due to very small subpixel motion between frames. In this work the scene depth is estimated from the scaling of supervised image regions. We generate obstacle hypotheses from these depth estimates in image space. A second step then performs testing of these by comparing with the counter hypothesis of a free driveway. The approach can detect obstacles already at distances of 50m and more with a standard focal length. This early detection allows driver warning and safety precaution in good time.


title: The IoT electric business model: Using blockchain technology for the internet of things

abstract: Nowadays, the development of traditional business models become more and more mature that people use them to guide various kinds of E-business activities. Internet of things (IoT), being an innovative revolution over the Internet, becomes a new platform for E-business. However, old business models could hardly fit for the E-business on the IoT. In this article, we 1) propose an IoT E-business model, which is specially designed for the IoT E-business; 2) redesign many elements in traditional E-business models; 3) realize the transaction of smart property and paid data on the IoT with the help of P2P trade based on the Blockchain and smart contract. We also experiment our design and make a comprehensive discuss.


title: Realities and challenges of nextgen air traffic management: the case of ADS-B

abstract: Air traffic is continuously increasing worldwide, with both manned and unmanned aircraft looking to coexist in the same airspace in the future. Next generation air traffic management systems are crucial in successfully handling this growth and improving the safety of billions of future passengers. The Automatic Dependent Surveillance Broadcast (ADS-B) system is a core part of this future. Unlike traditional radar systems, this technology empowers aircraft to automatically broadcast their locations and intents, providing enhanced situational awareness. This article discusses important issues with the current state of ADS-B as it is being rolled out. We report from our OpenSky sensor network in Central Europe, which is able to capture about 30 percent of the European commercial air traffic. We analyze the 1090 MHz communication channel to understand the current state and its behavior under the increasing traffic load. Furthermore, the article considers important security challenges faced by ADS-B. Our insights are intended to help identify open research issues, furthering new interest and developments in this field.


title: Low-Quality Product Review Detection in Opinion Summarization

abstract: Product reviews posted at online shopping sites vary greatly in quality. This paper addresses the problem of detecting lowquality product reviews. Three types of biases in the existing evaluation standard of product reviews are discovered. To assess the quality of product reviews, a set of specifications for judging the quality of reviews is first defined. A classificationbased approach is proposed to detect the low-quality reviews. We apply the proposed approach to enhance opinion summarization in a two-stage framework. Experimental results show that the proposed approach effectively (1) discriminates lowquality reviews from high-quality ones and (2) enhances the task of opinion summarization by detecting and filtering lowquality reviews.


title: Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach

abstract: Learning cooperative policies for multi-agent systems is often challenged by partial observability and a lack of coordination. In some settings, the structure of a problem allows a distributed solution with limited communication. Here, we consider a scenario where no communication is available, and instead we learn local policies for all agents that collectively mimic the solution to a centralized multi-agent static optimization problem. Our main contribution is an information theoretic framework based on rate distortion theory which facilitates analysis of how well the resulting fully decentralized policies are able to reconstruct the optimal solution. Moreover, this framework provides a natural extension that addresses which nodes an agent should communicate with to improve the performance of its individual policy.