title: Sonar-Based Detection and Tracking of a Diver for Underwater Human-Robot Interaction Scenarios

abstract: An underwater robotic assistant could help a human diver by illuminating work areas, fetching tools from the surface, or monitoring the diver's activity for abnormal behavior. However, in order for basic Underwater Human-Robot Interaction (UHRI) to be successful, the robotic assistant has to first be able to detect and track the diver. This paper discusses the detection and tracking of a diver with a high-frequency forward-looking sonar. The first step in the diver detection involves utilizing classical 2D image processing techniques to segment moving objects in the sonar image. The moving objects are then passed through a blob detection algorithm, and then the blob clusters are processed by the cluster classification process. Cluster classification is accomplished by matching observed cluster trajectories with trained Hidden Markov Models (HMM), which results in a cluster being classified as either a diver or clutter. Real-world results show that a moving diver can be autonomously distinguished from stationary objects in a noisy sonar image and tracked.


title: A Reassessment of the in vitro total protein content determination (TPC) with SIRC and 3T3 cells for the evaluation of the ocular irritation potential of shampoos: comparison with the in vivo Draize rabbit test

abstract: The aim of this work was to determine the correlation between results obtained from the Draize test and from the Total Protein Content Determination (TPC) to assess the ocular irritancy potential of 20 shampoos. For TPC, two established cell lines (SIRC and 3T3) were used. The concentration that induced 50% inhibition relative to controls (IC50) was calculated for each product. Among shampoos tested with SIRC, only one had a false positive result. However, for the 3T3, three false-negative results were found. Pearson coefficient related to the in vivo value of maximum average score (MAS) was -0.58 (p=0.007) with SIRC and -0.73 (p=0.007) with 3T3. These results showed that the TPC assay was capable to predict the ocular irritant potential of shampoos, and therefore was a promissory tool to be used as a preliminary assay for the detection of irritant products and to be part of a battery of screening tests to minimize the animal use in the Draize Test.


title: Efficient Scene Layout Aware Object Detection for Traffic Surveillance

abstract: We present an efficient scene layout aware object detection method for traffic surveillance. Given an input image, our approach first estimates its scene layout by transferring object annotations in a large dataset to the target image based on nonparametric label transfer. The transferred annotations are then integrated with object hypotheses generated by the state-of-the-art object detectors. We propose an approximate nearest neighbor search scheme for efficient inference in the scene layout estimation. Experiments verified that this simple and efficient approach provides consistent performance improvements to the state-of-the-art object detection baselines on all object categories in the TSWC-2017 localization challenge.


title: Ranking-based classification of heterogeneous information networks

abstract: It has been recently recognized that heterogeneous information networks composed of multiple types of nodes and links are prevalent in the real world. Both classification and ranking of the nodes (or data objects) in such networks are essential for network analysis. However, so far these approaches have generally been performed separately. In this paper, we combine ranking and classification in order to perform more accurate analysis of a heterogeneous information network. Our intuition is that highly ranked objects within a class should play more important roles in classification. On the other hand, class membership information is important for determining a quality ranking over a dataset. We believe it is therefore beneficial to integrate classification and ranking in a simultaneous, mutually enhancing process, and to this end, propose a novel ranking-based iterative classification framework, called RankClass. Specifically, we build a graph-based ranking model to iteratively compute the ranking distribution of the objects within each class. At each iteration, according to the current ranking results, the graph structure used in the ranking algorithm is adjusted so that the sub-network corresponding to the specific class is emphasized, while the rest of the network is weakened. As our experiments show, integrating ranking with classification not only generates more accurate classes than the state-of-art classification methods on networked data, but also provides meaningful ranking of objects within each class, serving as a more informative view of the data than traditional classification.


title: Reordering rules for English-Hindi SMT

abstract: Reordering is pre-processing stage for Statistical Machine Translation (SMT) system where the words of the source sentence are reordered as per the syntax of the target language. We are proposing a rich set of rules for better reordering. The idea is to facilitate the training process by better alignments and parallel phrase extraction for a phrase based SMT system. Reordering also helps the decoding process and hence improving the machine translation quality. We have observed significant improvements in the translation quality by using our approach over the baseline SMT. We have used BLEU, NIST, multi-reference word error rate, multi-reference position independent error rate for judging the improvements. We have exploited open source SMT toolkit MOSES to develop the system.


title: Investigating the Limits of Monte-Carlo Tree Search Methods in Computer Go

abstract: Monte-Carlo Tree Search methods have led to huge progress in computer Go. Still, program performance is uneven - most current Go programs are much stronger in some aspects of the game, such as local fighting and positional evaluation, than in other aspects. Well known weaknesses of many programs include (1) the handling of several simultaneous fights, including the two safe groups problem, and (2) dealing with coexistence in seki.


Assessment of reproduction and stress through hormone analysis in gorillas


An Approach to Risk Management for E-Commerce.


title: Four-arm 2nd- mode slot spiral antenna with simple single-port feed

abstract: The design and performance of a novel 4-arm equiangular slot spiral antenna operating in the 2nd mode are discussed in this letter. A straightforward feeding method consisting of a single vertical coaxial probe and no underlying mode forming network is proposed. Along with inherent simplicity, this feed enables design flexibility with respect to the slot-to-metal ratio, growth rate, and other spiral parameters. Numerical tools including an in-house finite element-boundary integral (FE-BI) code and Ansoft HFSS are used in designing the antenna. Good performance over an octave bandwidth is achieved, with axial ratio and WoW less than 3 dB, nominal gain of 3 dBic, and VSWR less than 1.6. Performance is verified with measurements. A method for increasing the bandwidth up to 3 : 1, which is the theoretical value determined by the excitation of the first higher order mode, is demonstrated computationally.


title: Representation-learning for anomaly detection in complex x-ray cargo imagery

abstract: Existing approaches to automated security image analysis focus on the detection of particular classes of threat. However, this mode of inspection is ineffectual when dealing with mature classes of threat, for which adversaries have refined effective concealment techniques. Furthermore, these methods may be unable to detect potential threats that have never been seen before. Therefore, in this paper, we investigate an anomaly detection framework, at X-ray image patch-level, based on: (i) image representations, and (ii) the detection of anomalies relative to those representations. We present encouraging preliminary results, using representations learnt using convolutional neural networks, as well as several contributions to a general-purpose anomaly detection algorithm based on decision-tree learning.


title: Relating Natural Language Text to Musical Passages

abstract: There is a vast body of musicological literature containing detailed analyses of musical works. These texts make frequent references to musical passages in scores by means of natural language phrases. Our long- term aim is to investigate whether these phrases can be linked automatically to the musical passages to which they refer. As a first step, we have organised for two years running a shared evaluation in which participants must develop software to identify passages in a MusicXML score based on a short noun phrase in English. In this paper, we present the rationale for this work, discuss the kind of references to musical passages which can occur in actual scholarly texts, describe the first two years of the evaluation and finally appraise the results to establish what progress we have made.


title: 3D geometry from planar parallax

abstract: Deriving 3D structure in a fixed object-centered coordinate system is an increasingly popular trend in shape from multiple views. For linear approximations to perspective projection (weak/para perspective), and for the case of image velocities, elegant linear methods have been devised for robust estimation. For reconstruction under arbitrary view transformations, linear projective methods using point correspondences have been suggested. In this paper, we formulate the problem of intrinsic 3D structure estimation through perspective projection using motion parallax, defined with respect to an arbitrary plane in the environment. It is shown that if an image coordinate system is warped using plane projective transformation with respect to a reference view, the residual image motion is dependent only on the epipoles and has a simple relation to the 3D structure. Our computational scheme avoids point/line correspondence and is based on hierarchical estimation and image warping working directly with spatio-temporal image intensities. >


title: Integration of Internal Control and Risk Management

abstract: The relationship between internal control and risk management is still an arguing problem. This paper examines the relationship from the view of the development of risk management theory and the evolution of internal control represented by the United States. It believes that internal control is integrated with risk management from the beginning to its perfection. However, the form of the integration varies along with social economic environment.


title: Free/Open Source Software Development: Recent Research Results and Methods

abstract: Abstract The focus of this chapter is to review what is known about free and open source software development (FOSSD) work practices, development processes, project and community dynamics, and other socio-technical relationships. It does not focus on specific properties or technical attributes of different FOSS systems, but it does seek to explore how FOSS is developed and evolved. The chapter provides a brief background on what FOSS is and how free software and open source software development efforts are similar and different. From there attention shifts to an extensive review of a set of empirical studies of FOSSD that articulate different levels of analysis. These characterize what has been analyzed in FOSSD studies across levels that examine why individuals participate; resources and capabilities supporting development activities; how cooperation, coordination, and control are realized in projects; alliance formation and inter-project social networking; FOSS as a multi-project software ecosystem, and FOSS as a social movement. Following this, the chapter reviews how different research methods are employed to examine different issues in FOSSD. These include reflective practice and industry polls, survey research, ethnographic studies, mining FOSS repositories, and multi-modal modeling and analysis of FOSSD processes and socio-technical networks. Finally, there is a discussion of limitations and constraints in the FOSSD studies so far, attention to emerging opportunities for future FOSSD studies, and then conclusions about what is known about FOSSD through the empirical studies reviewed here.


title: Investigating the Evolution of Bad Smells in Object-Oriented Code

abstract: Software design problems are known and perceived under many different terms such as bad smells, flaws, non-compliance to design principles, violation of heuristics, excessive metric values and antipatterns, signifying the importance of handling them in the construction and maintenance of software. Once a design problem is identified, it can be removed by applying an appropriate refactoring, improving in most cases several aspects of quality such as maintainability, comprehensibility and reusability. This paper, taking advantage of recent advances and tools in the identification of non-trivial bad smells, explores the presence and evolution of such problems by analyzing past versions of code. Several interesting questions can be investigated such as whether the number of problems increases with the passage of software generations, whether problems vanish by time or only by targeted human intervention, whether bad smells occur in the course of evolution of a module or exist right from the beginning and whether refactorings targeting at smell removal are frequent. In contrast to previous studies that investigate the application of refactorings in the history of a software project, we attempt to study the subject from the point of view of the problems themselves distinguishing deliberate maintenance activities from the removal of design problems as a side effect of software evolution. Results are discussed for two open-source systems and three bad smells.


title: STARDATA: A StarCraft AI Research Dataset

abstract: We release a dataset of 65646 StarCraft replays that contains 1535 million frames and 496 million player actions. We provide full game state data along with the original replays that can be viewed in StarCraft. The game state data was recorded every 3 frames which ensures suitability for a wide variety of machine learning tasks such as strategy classification, inverse reinforcement learning, imitation learning, forward modeling, partial information extraction, and others. We use TorchCraft to extract and store the data, which standardizes the data format for both reading from replays and reading directly from the game. Furthermore, the data can be used on different operating systems and platforms. The dataset contains valid, non-corrupted replays only and its quality and diversity was ensured by a number of heuristics. We illustrate the diversity of the data with various statistics and provide examples of tasks that benefit from the dataset. We make the dataset available at this https URL . En Taro Adun!


title: A Survey on Algorithms of Mining Frequent Subgraphs

abstract: Graphs are currently becoming more important in modeling and demonstrating information. In the recent years, graph mining is becoming an interesting field for various processes such as chemical compounds, protein structures, social networks and computer networks. One of the most important concepts in graph mining is to find frequent subgraphs. The major advantage of utilizing subgraphs is speeding up the search for similarities, finding graph specifications and graph classifications. In this article we classify the main algorithms in the graph mining field. Some fundamental algorithms are reviewed and categorized. Some issues for any algorithm are graph representation, search strategy, nature of input and completeness of output that are discussed in this article.


title: Development of a method for the estimation of the behavior of a CNG engine over the NEDC cycle and its application to quantify for the effect of hydrogen addition to methane operations

abstract: Abstract The current pollutant regulations and policies have set the need to consider the use of alternative fuels capable of complying with the emission limits still retaining appreciable engine performance. Natural gas has been considered as an effective alternative to gasoline but the drawbacks connected to its use forced the researchers to investigate into fuel additives, dual fuel solutions and innovative engine control strategies. The present work analyzes the use of hydrogen as an additive to CNG for a natural gas production engine of a C-segment vehicle and carries out a thorough investigation into the engine response over a selection of operating key points. The actual focus is set on the investigation into the vehicle as well as into the engine response and performance over driving cycles. Still, the simulation of real driving conditions would set the need to properly quantify for the effect of the hydrogen enriched blends on the full engine map over varying powers and speeds within the vehicle driving cycle. Such an approach often turns out to be too demanding in terms of time and costs and an alternative solution has been hereafter proposed by properly selecting a reduced number of operating points on the basis of the correspondent residence time and frequency over the NEDC. The selection has been performed by matching the actual engine map to the readings from the NEDC vehicle testing. Different selections have been considered and compared so as to assess for the one embedding the minimum number of working points. The key points are not meant to substitute the accomplishment of the NEDC cycle but are to be used as driving factors in the engine design so as to allow for detecting the optimal hardware and ECU configurations. The so considered engine key points have hence been extensively studied by reproducing the engine performance at the test bench and by performing a detailed heat release analysis. Different composition of the hydrogen–methane blends have been considered up to a 25% by volume of hydrogen in the mixture and specific attention has been paid to the main combustion parameters and to their optimization. As a matter of fact, such a study would allow for detecting major trends in the engine design and control strategies to compensate for the poor behavior of some of the considered points. As an example, low load and speed operations assessed for the need of a better control of the engine parameters to diminish the cylinder-to-cylinder as well as the cycle-to-cycle variability.


Deep neural networks for time series prediction with applications in ultra-short-term wind forecasting


title: & Research Article From Tacit Knowledge to Knowledge Management: Leveraging Invisible Assets

abstract: Within competitive advantage considerations, knowledge has emerged as one of the more strategic, although invisible, assets for organizations. This is notwithstanding a wider and specifically economistic and cognitive discounting of knowledge as a factor of production — largely ignoring the socially constructed and socially mediated nature of knowledge. Intellectual capabilities and knowledge/information transformations now have a central place within globalizing information economies. Constructing, transforming and commodifying knowledge and information require new organizational understandings and newer cognitive capabilities of strategic management praxis. Part of this cognitive awareness is a deliberate organizational designing for the role of symbolic analysts. As well, there is an emerging need for the Chief Knowledge Officer function going well beyond the Chief Information Officer requirements posited by an information technology-driven restructuring of routine processes, as compared with innovation creation capacities associated with critically non-routine functions within organizations discovered by Cranfield research. The paper considers neglected institutional and organizational dimensions to knowledge creation and knowledge conversion — it reviews the renewed importance of internal recruitment and socialization within institutions and details knowledge codification and application functions within knowledge-creating organizations. Knowledge management, as praxis, inevitably raises concerns about cognitive failure in leadership theory and praxis. Copyright # 2001 John Wiley & Sons, Ltd.


title: Are We There Yet? Research in Commercial Spoken Dialog Systems

abstract: In this paper we discuss the recent evolution of spoken dialog systems in commercial deployments. Yet based on a simple finite state machine design paradigm, dialog systems reached today a higher level of complexity. The availability of massive amounts of data during deployment led to the development of continuous optimization strategy pushing the design and development of spoken dialog applications from an art to science. At the same time new methods for evaluating the subjective caller experience are available. Finally we describe the inevitable evolution for spoken dialog applications from speech only to multimodal interaction.


title: Late colocutaneous fistula after mesh repair of an inguinal hernia

abstract: Tension free prosthetic mesh reinforcement has become the most popular type of inguinal hernia repair in the UK due to very low complication and recurrence rates [RCSE 1993, Amid 1995]. Polypropylene (Marlex) or nylon are the most commonly used prosthetic materials although metal meshes were used in years gone [Smith 1971, Preston 1973]. We report a patient who developed a colocutaneous fistula, presumably due to the intraperitoneal migration of a metal mesh, more than 20 years after open inguinal hernia repair. This complication has not previously been reported.


title: Mental skills training for sports: A brief review

abstract: Mental skills training for sport is reviewed in relation to general cognitive-somatic techniques. These techniques include mental rehearsal, mental imagery and visualization, visuo-motor behavior rehearsal, cognitive-behavior therapy, biofeedback, progressive muscle relaxation and meditation. It is concluded that the initial and continued ability to self-monitor, though enhanced by mental skills training, is fundamentally important for any implementation of cognitive-somatic therapy.


title: Chronology of the Grotte du Renne (France) and implications for the context of ornaments and human remains within the Châtelperronian.

abstract: There is extensive debate concerning the cognitive and behavioral adaptation of Neanderthals, especially in the period when the earliest anatomically modern humans dispersed into Western Europe, around 35,000-40,000 B.P. The site of the Grotte du Renne (at Arcy-sur-Cure) is of great importance because it provides the most persuasive evidence for behavioral complexity among Neanderthals. A range of ornaments and tools usually associated with modern human industries, such as the Aurignacian, were excavated from three of the Châtelperronian levels at the site, along with Neanderthal fossil remains (mainly teeth). This extremely rare occurrence has been taken to suggest that Neanderthals were the creators of these items. Whether Neanderthals independently achieved this level of behavioral complexity and whether this was culturally transmitted or mimicked via incoming modern humans has been contentious. At the heart of this discussion lies an assumption regarding the integrity of the excavated remains. One means of testing this is by radiocarbon dating; however, until recently, our ability to generate both accurate and precise results for this period has been compromised. A series of 31 accelerator mass spectrometry ultrafiltered dates on bones, antlers, artifacts, and teeth from six key archaeological levels shows an unexpected degree of variation. This suggests that some mixing of material may have occurred, which implies a more complex depositional history at the site and makes it difficult to be confident about the association of artifacts with human remains in the Châtelperronian levels.


title: Positive affect and the other side of coping.

abstract: Although research on coping over the past 30 years has produced convergent evidence about the functions of coping and the factors that influence it, psychologists still have a great deal to learn about how coping mechanisms affect diverse outcomes. One of the reasons more progress has not been made


title: DC analysis of current mode logic

abstract: A closed-form solution to finding the optimum signal swing for CML (current-mode logic) is illustrated, based on a few parameters of the minimum geometry transistor in a given technology. A simplified transistor model is used to develop the concept of noise margin optimized for both transistor and circuit parameters. It is shown that the voltage swing of the CML gate is not an arbitrary choice for the circuit designer but is deterministic. The effects of gate fan-in and series gating are then included as part of the closed-form solution, yielding an optimized set of parameters for defining all logic functions. Calculation of the maximum fan-out as well as bias regulators and calculation of the voltage drops in the power buses of chip layouts are treated. The procedure described has been used to develop a CML cell library for producing high-performance interface and networking circuits. >


title: Exploiting Memory Access Patterns to Improve Memory Performance in Data-Parallel Architectures

abstract: The introduction of General-Purpose computation on GPUs (GPGPUs) has changed the landscape for the future of parallel computing. At the core of this phenomenon are massively multithreaded, data-parallel architectures possessing impressive acceleration ratings, offering low-cost supercomputing together with attractive power budgets. Even given the numerous benefits provided by GPGPUs, there remain a number of barriers that delay wider adoption of these architectures. One major issue is the heterogeneous and distributed nature of the memory subsystem commonly found on data-parallel architectures. Application acceleration is highly dependent on being able to utilize the memory subsystem effectively so that all execution units remain busy. In this paper, we present techniques for enhancing the memory efficiency of applications on data-parallel architectures, based on the analysis and characterization of memory access patterns in loop bodies; we target vectorization via data transformation to benefit vector-based architectures (e.g., AMD GPUs) and algorithmic memory selection for scalar-based architectures (e.g., NVIDIA GPUs). We demonstrate the effectiveness of our proposed methods with kernels from a wide range of benchmark suites. For the benchmark kernels studied, we achieve consistent and significant performance improvements (up to 11.4× and 13.5× over baseline GPU implementations on each platform, respectively) by applying our proposed methodology.


title: What have we learnt about Loss Aversion and Endowment Effects? Still an anomaly?

abstract: This paper presents an insight into the theoretical and empirical literature of Loss Aversion and Endowment Effect. The definition and conceptualisation of both ideas is introduced in order to define a framework for further analysis. Their presence implies a radical change in some of the basic standard postulates of microeconomic foundation. These concepts robustly predict a divergence between Willingness to Accept and Willingness to Pay, even in a perfect-market framework and invalidate the standard assumptions of transitivity and reversibility of preferences under the neoclassical theory of consumer choice. Twenty years of successive positive evidence on Loss Aversion and Endowment Effect support the theoretical implications showed in this paper. I conclude that Loss Aversion and Endowment Effects truly matter and their existence must not be taken into account just as an anomaly or puzzle, but as part of a new theory in itself, leading to new questions and challenges for future economic research.


title: SDR-Based Resilient Wireless Communications

abstract: As the use of wireless technologies increases significantly due to ease of deployment, cost-effectiveness and the increase in bandwidth, there is a critical need to make the wireless communications secure, and resilient to attacks or faults (malicious or natural). Wireless communications are inherently prone to cyberattacks due to the open access to the medium. While current wireless protocols have addressed the privacy issues, they have failed to provide effective solutions against denial of service attacks, session hijacking and jamming attacks.In this paper, we present a resilient wireless communication architecture based on Moving Target Defense, and Software Defined Radios (SDRs). The approach achieves its resilient operations by randomly changing the runtime characteristics of the wireless communications channels between different wireless nodes to make it extremely difficult to succeed in launching attacks. The runtime characteristics that can be changed include packet size, network address, modulation type, and the operating frequency of the channel. In addition, the lifespan for each configuration will be random. To reduce the overhead in switching between two consecutive configurations, we use two radio channels that are selected at random from a finite set of potential channels, one will be designated as an active channel while the second acts as a standby channel. This will harden the wireless communications attacks because the attackers have no clue on what channels are currently being used to exploit existing vulnerability and launch an attack. The experimental results and evaluation show that our approach can tolerate a wide range of attacks (Jamming, DOS and session attacks) against wireless networks.


title: From Scratch to “Real” Programming

abstract: Computer science (CS) activities for young students are widely used, particularly visual programming environments. We investigated the use of the Scratch environment for teaching CS concepts to middle school students. In a previous article [Meerbaum-Salant et al. 2013], we reported on the extent to which the CS concepts were successfully learned. In this article, we look at the transition from studying CS with the visual Scratch environment in middle school to studying CS with a professional textual programming language (C# or Java) in secondary school. We found that the programming knowledge and experience of students who had learned Scratch greatly facilitated learning the more advanced material in secondary school: less time was needed to learn new topics, there were fewer learning difficulties, and they achieved higher cognitive levels of understanding of most concepts (although at the end of the teaching process, there were no significant differences in achievements compared to students who had not studied Scratch). Furthermore, there was increased enrollment in CS classes, and students were observed to display higher levels of motivation and self-efficacy. This research justifies teaching CS in general and visual programming in particular in middle schools.


title: Executive Function and the Frontal Lobes: A Meta-Analytic Review

abstract: Currently, there is debate among scholars regarding how to operationalize and measure executive functions. These functions generally are referred to as “supervisory” cognitive processes because they involve higher level organization and execution of complex thoughts and behavior. Although conceptualizations vary regarding what mental processes actually constitute the “executive function” construct, there has been a historical linkage of these “higher-level” processes with the frontal lobes. In fact, many investigators have used the term “frontal functions” synonymously with “executive functions” despite evidence that contradicts this synonymous usage. The current review provides a critical analysis of lesion and neuroimaging studies using three popular executive function measures (Wisconsin Card Sorting Test, Phonemic Verbal Fluency, and Stroop Color Word Interference Test) in order to examine the validity of the executive function construct in terms of its relation to activation and damage to the frontal lobes. Empirical lesion data are examined via meta-analysis procedures along with formula derivatives. Results reveal mixed evidence that does not support a one-to-one relationship between executive functions and frontal lobe activity. The paper concludes with a discussion of the implications of construing the validity of these neuropsychological tests in anatomical, rather than cognitive and behavioral, terms.


title: Hosting Virtual IoT Resources on Edge-Hosts with Blockchain

abstract: Current IoT systems tend to be cloud-centric which in turn introduces network latency and constrained interaction with sensors and actuators. This paper presents the idea of using restful micro-services called Virtual Resources. A Virtual Resource is a software-defined IoT management construct that enables multi-tenancy support and load distribution onto edge hosts. The paper presents a performance analysis of our Golang implementation of Virtual Resources in various settings.


title: Polymer assemblies for controlled delivery of bioactive molecules from surfaces.

abstract: Localized delivery of bioactive compounds from surfaces of biomedical devices affords significant therapeutic benefits, and often relies on the capability of surface coatings to provide spatial and temporal control over release rate. The layer-by-layer technique presents a unique means to construct surface coatings that can conform to a variety of biomaterial surfaces and serve as matrices enabling controlled delivery of bioactive molecules from surfaces. The versatility of layer-by-layer assembly enables construction of surface coatings of diverse chemistry and internal architecture with controlled release properties. This review focuses on recent developments in constructing such layered matrices using linear polymers, polymer nanoparticles and block copolymer micelles, including micelles with stimuli-responsive cores, as film building blocks and in controlling release rate of therapeutics from these matrices via degradation, application of pH, ionic strength, temperature, light, electric field and chemical or biological stimuli. Challenges and opportunities associated with fabrication of stratified multilayer films capable of multi-stage delivery of multiple drugs are also discussed.


Refugees or Migrant Workers? The Case of the European Volunteer Workers in Britain (1946–1951)


title: Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging

abstract: Environmental audio tagging aims to predict only the presence or absence of certain acoustic events in the interested acoustic scene. In this paper, we make contributions to audio tagging in two parts, respectively, acoustic modeling and feature learning. We propose to use a shrinking deep neural network DNN framework incorporating unsupervised feature learning to handle the multilabel classification task. For the acoustic modeling, a large set of contextual frames of the chunk are fed into the DNN to perform a multilabel classification for the expected tags, considering that only chunk or utterance level rather than frame-level labels are available. Dropout and background noise aware training are also adopted to improve the generalization capability of the DNNs. For the unsupervised feature learning, we propose to use a symmetric or asymmetric deep denoising auto-encoder syDAE or asyDAE to generate new data-driven features from the logarithmic Mel-filter banks features. The new features, which are smoothed against background noise and more compact with contextual information, can further improve the performance of the DNN baseline. Compared with the standard Gaussian mixture model baseline of the DCASE 2016 audio tagging challenge, our proposed method obtains a significant equal error rate EER reduction from 0.21 to 0.13 on the development set. The proposed asyDAE system can get a relative 6.7% EER reduction compared with the strong DNN baseline on the development set. Finally, the results also show that our approach obtains the state-of-the-art performance with 0.15 EER on the evaluation set of the DCASE 2016 audio tagging task while EER of the first prize of this challenge is 0.17.


title: Electronic markets and electronic hierarchies

abstract: By reducing the costs of coordination, information technology will lead to an overall shift toward proportionately more use of markets—rather than hierarchies—to coordinate economic activity.


title: Getting Even or Moving On? Power, Procedural Justice, and Types of Offense as Predictors of Revenge, Forgiveness, Reconciliation, and Avoidance in Organizations

abstract: A field study and an experimental study examined relationships among organizational variables and various responses of victims to perceived wrongdoing. Both studies showed that procedural justice climate moderates the effect of organizational variables on the victim’s revenge, forgiveness, reconciliation, or avoidance behaviors. In Study 1, a field study, absolute hierarchical status enhanced forgiveness and reconciliation, but only when perceptions of procedural justice climate were high; relative hierarchical status increased revenge, but only when perceptions of procedural justice climate were low. In Study 2, a laboratory experiment, victims were less likely to endorse vengeance or avoidance depending on the type of wrongdoing, but only when perceptions of procedural justice climate were high.


title: Proposal and experimental validation of a design strategy for a UAV with a passive rotating spherical shell

abstract: Unmanned aerial vehicles (UAVs) such as multicopters are expected to be used for inspection of aged infrastructure or for searching damaged buildings in the event of a disaster. However, in a confined space in such environments, UAVs suffer a high risk of falling as a result of contact with an obstacle.


title: Lexical normalization for social media text

abstract: Twitter provides access to large volumes of data in real time, but is notoriously noisy, hampering its utility for NLP. In this article, we target out-of-vocabulary words in short text messages and propose a method for identifying and normalizing lexical variants. Our method uses a classifier to detect lexical variants, and generates correction candidates based on morphophonemic similarity. Both word similarity and context are then exploited to select the most probable correction candidate for the word. The proposed method doesn't require any annotations, and achieves state-of-the-art performance over an SMS corpus and a novel dataset based on Twitter.


title: Hybrid Energy Storage with Supercapacitor for Cost-Efficient Data Center Power Shaving and Capping

abstract: Recent studies have proposed to dynamically reshape the power demand curve of a data center (i.e., power shaving) with energy storage devices, particularly uninterruptible power supply (UPS) batteries. Power shaving can be used to limit the peak power demand in a data center, in order to reduce both the power infrastructure investment (i.e., cap-ex ) and the electricity bills (i.e., op-ex ). However, power shaving requires the UPS batteries to be frequently charged/discharged, which is known to compromise the battery lifetime and availability. This paper presents a detailed quantitative study that explores different options to integrate supercapacitor (SC) with batteries for cost-efficient energy storage. Compared with batteries, SC allows more charge/discharge cycles and has a higher power density, which are desirable for fast power shaving. However, SC also has undesirable characteristics (e.g., relatively high self-discharging rate and cost). Therefore, we quantitatively compare three possible energy storage options (i.e., Battery-only , SC-only , and Battery+SC ) in detail, with different SC self-discharging rate assumptions. SC options ( SC-only and Battery+SC ) are shown to be more cost-efficient designs, saving the energy storage cost by 34 percent, on average, compared with Battery-only . For a 10 MW data center in a 10-year period, the savings can be converted to $3 M in total cost of ownership (TCO) reduction by allowing more servers to be deployed. In addition, we also propose the integration of energy storage with dynamic voltage and frequency scaling (DVFS) to cap the peak power demand (i.e., power capping). Specifically, we comparatively study four power capping algorithms and discuss their applicable scenarios. Finally, we introduce our proof-of-concept SC physical testbed and present preliminary hardware testing results.


title: Design considerations and experimental analysis of high-voltage SiC Schottky barrier rectifiers

abstract: Practical design of high-voltage SiC Schottky rectifiers requires an understanding of the device physics that affect the key performance parameters. Forward characteristics of SiC Schottky rectifiers follow thermionic emission theory and are relatively well understood. However, the reverse characteristics are not well understood and have not been experimentally investigated in-depth. In this paper we report the analysis and experimental results of both the forward and reverse characteristics of high-voltage SiC Schottky rectifiers. Ti and Ni Schottky rectifiers with boron implant edge termination were fabricated on n-type 4H SiC samples. Ni Schottky rectifiers fabricated on a 13-/spl mu/m thick 3.5/spl times/10/sup 15/ cm/sup -3/ epilayer have a current density of 100 A/cm/sup 2/ at approximately 2 V forward bias and a reverse leakage current density of less than 0.1 A/cm/sup 2/ at a reverse bias of 1720 V. The reverse leakage current is observed to depend on device area, Schottky barrier height, electric field at the metal-semiconductor interface, and temperature (a decreasing temperature dependence with increasing reverse bias). In addition. the reverse leakage current magnitude is larger and the electric field dependence is stronger than predicted by thermionic emission and image-force barrier height lowering. This suggests the reverse leakage current is due to a combination of thermionic field emission and field emission.


title: Hematopoietic Cell Transplant and Use of Massage for Improved Symptom Management: Results from a Pilot Randomized Control Trial

abstract: Background. Pediatric hematopoietic cell transplant (HCT) is a lifesaving treatment that often results in physical and psychological discomfort. An acupressure-massage intervention may improve symptom management in this setting. ::: Methods. This randomized controlled pilot trial compared a combined massage-acupressure intervention to usual care. Children were offered three practitioner-provided sessions per week throughout hospitalization. Parents were trained to provide additional acupressure as needed. Symptoms were assessed using nurses' reports and two questionnaires, the behavioral affective and somatic experiences scale and the Peds quality of life cancer module. ::: Results. We enrolled 23 children, ages 5 to 18. Children receiving the intervention reported fewer days of mucositis (Hedges' g effect size ), lower overall symptom burden (), feeling less tired and run-down (), having fewer moderate/severe symptoms of pain, nausea, and fatigue (), and less pain (). The intervention group showed trends toward increasing contentness/serenity () and decreasing depression (), but not decreased anxiety (). Differences were not statistically significant. ::: Discussion. Feasibility of studying massage-acupressure was established in children undergoing HCT. Larger studies are needed to test the efficacy of such interventions in reducing HCT-associated symptoms in children.


title: Klimatic: A Virtual Data Lake for Harvesting and Distribution of Geospatial Data

abstract: Many interesting geospatial datasets are publicly accessible on web sites and other online repositories. However, the sheer number of datasets and locations, plus a lack of support for cross-repository search, makes it difficult for researchers to discover and integrate relevant data. We describe here early results from a system, Klimatic, that aims to overcome these barriers to discovery and use by automating the tasks of crawling, indexing, integrating, and distributing geospatial data. Klimatic implements a scalable crawling and processing architecture that uses an elastic container-based model to locate and retrieve relevant datasets and to extract metadata from headers and within files to build a global index of known geospatial data. In so doing, we create an expansive geospatial virtual data lake that records the location, formats, and other characteristics of large numbers of geospatial datasets while also caching popular data subsets for rapid access. A flexible query interface allows users to request data that satisfy supplied type, spatial, temporal, and provider specifications; in processing such queries, the system uses interpolation and aggregation to combine data of different types, data formats, resolutions, and bounds. Klimatic has so far incorporated more than 10,000 datasets from over 120 sources and has been demonstrated to scale well with data size and query complexity.


title: Reweighted Low-Rank Tensor Completion and its Applications in Video Recovery

abstract: This paper focus on recovering multi-dimensional data called tensor from randomly corrupted incomplete observation. Inspired by reweighted $l_1$ norm minimization for sparsity enhancement, this paper proposes a reweighted singular value enhancement scheme to improve tensor low tubular rank in the tensor completion process. An efficient iterative decomposition scheme based on t-SVD is proposed which improves low-rank signal recovery significantly. The effectiveness of the proposed method is established by applying to video completion problem, and experimental results reveal that the algorithm outperforms its counterparts.


title: Text Data Mining: Theory and Methods

abstract: This paper provides the reader with a very brief introduction to some of the theory and methods of text data mining. The intent of this article is to introduce the reader to some of the current methodologies that are employed within this discipline area while at the same time making the reader aware of some of the interesting challenges that remain to be solved within the area. Finally, the articles serves as a very rudimentary tutorial on some of techniques while also providing the reader with a list of references for additional study.


title: Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions

abstract: In this paper, we present a novel method to fuse observations from an inertial measurement unit (IMU) and visual sensors, such that initial conditions of the inertial integration, including gravity estimation, can be recovered quickly and in a linear manner, thus removing any need for special initialization procedures. The algorithm is implemented using a graphical simultaneous localization and mapping like approach that guarantees constant time output. This paper discusses the technical aspects of the work, including observability and the ability for the system to estimate scale in real time. Results are presented of the system, estimating the platforms position, velocity, and attitude, as well as gravity vector and sensor alignment and calibration on-line in a built environment. This paper discusses the system setup, describing the real-time integration of the IMU data with either stereo or monocular vision data. We focus on human motion for the purposes of emulating high-dynamic motion, as well as to provide a localization system for future human-robot interaction.


title: Envy on Facebook: A Hidden Threat to Users' Life Satisfaction?

abstract: The wealth of social information presented on Facebook is astound- ing. While these affordances allow users to keep up-to-date, they also produce a basis for social comparison and envy on an unprecedented scale. Even though envy may endanger users' life satisfaction and lead to platform avoidance, no study exists uncovering this dynamics. To close this gap, we build on responses of 584 Facebook users collected as part of two independent studies. In study 1, we explore the scale, scope, and nature of envy incidents triggered by Face- book. In study 2, the role of envy feelings is examined as a mediator between intensity of passive following on Facebook and users' life satisfaction. Con- firming full mediation, we demonstrate that passive following exacerbates envy feelings, which decrease life satisfaction. From a provider's perspective, our findings signal that users frequently perceive Facebook as a stressful environ- ment, which may, in the long-run, endanger platform sustainability.


title: Behavioural and functional anatomical correlates of deception in humans

abstract: Brain activity in humans telling lies has yet to be elucidated. We developed an objective approach to its investigation, utilizing a computer-based interrogation and fMRI. Interrogatory questions probed recent episodic memory in 30 volunteers studied outside and 10 volunteers studied inside the MR scanner. In a counter-balanced design subjects answered specified questions both truthfully and with lies. Lying was associated with longer response times ( p , 0.001) and greater activity in bilateral ventrolateral prefrontal cortices ( p , 0.05, corrected). These findings were replicated using an alternative protocol. Ventrolateral prefrontal cortex may be engaged in generating lies or withholding the truth. NeuroReport 12:2849‐ 2853 & 2001 Lippincott Williams & Wilkins.


title: Longitudinal and lateral control for autonomous ground vehicles

abstract: Robust and stable control is a requirement for navigation of self-driving cars. Some approaches in the literature depend on a high number of parameters that are often difficult to estimate. A poor selection of these parameters often reduces considerably the efficiency of the control algorithms. In this paper we propose a simplified control system for autonomous vehicles that depends on a reduced number of parameters that can be easily set. This control system is composed of longitudinal and lateral controllers. The longitudinal controller is responsible for regulating the vehicle's cruise velocity while the lateral controller steers the vehicle's wheels for path tracking. Simulated and experimental tests have been carried out with the CaRINA II platform in the university campus with positive results.


title: Finding Syntax in Human Encephalography with Beam Search

abstract: Recurrent neural network grammars (RNNGs) are generative models of (tree,string) pairs that rely on neural networks to evaluate derivational choices. Parsing with them using beam search yields a variety of incremental complexity metrics such as word surprisal and parser action count. When used as regressors against human electrophysiological responses to naturalistic text, they derive two amplitude effects: an early peak and a P600-like later peak. By contrast, a non-syntactic neural language model yields no reliable effects. Model comparisons attribute the early peak to syntactic composition within the RNNG. This pattern of results recommends the RNNG+beam search combination as a mechanistic model of the syntactic processing that occurs during normal human language comprehension.


title: A Comparison of Subject and Institutional Repositories in Self-archiving Practices

abstract: The disciplinary culture theory presumes that if a scholar has been familiar with self-archiving through an existing subject-based repository, this scholar will be more enthusiastic about contributing his/her research to an institutional repository than one who has not had the experience. To test the theory, this article examines self-archiving practices of a group of physicists in both a subject repository and an institutional repository. It does not find a correlation between a disciplinary culture and self-archiving practices.


Technical preparation of karate competitor for kumite sports fight


title: Employability and Career Success: Bridging the Gap Between Theory and Reality

abstract: Employability is defined as the capacity to gain and retain formal employment, or find new employment if necessary. Reasons for unemployment are often attributed to economic factors, but psychological factors associated with employability also contribute to the problem. Consequently, industrial-organizational psychologists should be uniquely suited to contribute to policy solutions for enhancing employability. This review begins by surveying the most common research approach to employability—the study of career success—which psychologists believe is determined by cognitive abilities, personality, and educational achievement. Next, we review the literature concerning what employers actually want. This section highlights the importance of social skills (being rewarding to deal with) as a key determinant of employability. We conclude by proposing a model for understanding the psychological determinants of employability and for bridging the gap between what psychologists prescribe and what employers want.


title: Deanonymizing mobility traces: using social network as a side-channel

abstract: Location-based services, which employ data from smartphones, vehicles, etc., are growing in popularity. To reduce the threat that shared location data poses to a user's privacy, some services anonymize or obfuscate this data. In this paper, we show these methods can be effectively defeated: a set of location traces can be deanonymized given an easily obtained social network graph. The key idea of our approach is that a user may be identified by those she meets: a "contact graph" identifying meetings between anonymized users in a set of traces can be structurally correlated with a social network graph, thereby identifying anonymized users. We demonstrate the effectiveness of our approach using three real world datasets: University of St Andrews mobility trace and social network (27 nodes each), SmallBlue contact trace and Facebook social network (125 nodes), and Infocom 2006 bluetooth contact traces and conference attendees' DBLP social network (78 nodes). Our experiments show that 80% of users are identified precisely, while only 8% are identified incorrectly, with the remainder mapped to a small set of users.


title: Perceptual and Physiological Responses to Jackson Pollock's Fractals

abstract: Fractals have been very successful in quantifying the visual complexity exhibited by many natural patterns, and have captured the imagination of scientists and artists alike. Our research has shown that the poured patterns of the American abstract painter Jackson Pollock are also fractal. This discovery raises an intriguing possibility – are the visual characteristics of fractals responsible for the long-term appeal of Pollock’s work? To address this question, we have conducted ten years of scientific investigation of human response to fractals and here we present, for the first time, a review of this research that examines the inter-relationship between the various results. The investigations include eye-tracking, visual preference, skin conductance, and EEG measurement techniques. We discuss the artistic implications of the positive perceptual and physiological responses to fractal patterns.


title: A Modified Stripe-RGBW TFT-LCD with Image-Processing Engine for Mobile Phone Displays

abstract: This paper presents a modified stripe-RGBW (MS-RGBW) color filter structure to keep the same high resolution, and obtain a higher brightness in comparison with conventional RGB color filters. An image-processing engine is also designed to achieve sharp text image for thin-film-transistor (TFT) LCD with the MS-RGBW color filter. In MS-RGBW color filter structure, each pixel with three sub-pixels is the same area to that in the conventional RGB stripe color filter, and each row shifts two sub-pixels. The image-processing engine consists of two new algorithms: RGB-RGBW mapping algorithm and sub-pixel rendering algorithm. The RGB-RGBW mapping algorithm obtains a new RGBW image data without distortion in hue and saturation. The sub-pixel rendering algorithm transfers RGBW data into a MS-RGBW color filter structure in order to achieve sharp text image. Numerous simulation results are provided to illustrate the merits and performance of the proposed techniques. The usefulness of the proposed techniques for mobile phone displays is exemplified by conducting several experimental results on a 2.2-inch TFT LCD.


title: Accurate and Efficient Per-Flow Latency Measurement Without Probing and Time Stamping

abstract: With the growth in number and significance of the emerging applications that require extremely low latencies, network operators are facing increasing need to perform latency measurement on per-flow basis for network monitoring and troubleshooting. In this paper, we propose COLATE, the first per-flow latency measurement scheme that requires no probe packets and time stamping. Given a set of observation points, COLATE records packet timing information at each point so that later, for any two points, it can accurately estimate the average and the standard deviation of the latencies experienced by the packets of any flow in passing the two points. The key idea is that when recording packet timing information, COLATE purposely allows noise to be introduced for minimizing storage space, and when querying the latency of a target flow, COLATE uses statistical techniques to denoise and obtain an accurate latency estimate. COLATE is designed to be efficiently implementable on network middleboxes. In terms of processing overhead, COLATE performs only one hash and one memory update per packet. In terms of storage space, COLATE uses less than 0.1-b/packet, which means that, on a backbone link with half a million packets per second, using a 256-GB drive, COLATE can accumulate time stamps of packets traversing the link for over 1.5 years. We evaluated COLATE using three real traffic traces, namely, a backbone traffic trace, an enterprise network traffic trace, and a data center traffic trace. Results show that COLATE always achieves the required reliability for any given confidence interval.


title: Mesoniviridae: a proposed new family in the order Nidovirales formed by a single species of mosquito-borne viruses

abstract: Recently, two independent surveillance studies in Cote d’Ivoire and Vietnam, respectively, led to the discovery of two mosquito-borne viruses, Cavally virus and Nam Dinh virus, with genome and proteome properties typical for viruses of the order Nidovirales. Using a state-of-the-art approach, we show that the two insect nidoviruses are (i) sufficiently different from other nidoviruses to represent a new virus family, and (ii) related to each other closely enough to be placed in the same virus species. We propose to name this new family Mesoniviridae. Meso is derived from the Greek word “mesos” (in English “in the middle”) and refers to the distinctive genome size of these insect nidoviruses, which is intermediate between that of the families Arteriviridae and Coronaviridae, while ni is an abbreviation for “nido”. A taxonomic proposal to establish the new family Mesoniviridae, genus Alphamesonivirus, and species Alphamesonivirus 1 has been approved for consideration by the Executive Committee of the ICTV.


title: On Using Very Large Target Vocabulary for Neural Machine Translation

abstract: Neural machine translation, a recently proposed approach to machine translation based purely on neural networks, has shown promising results compared to the existing approaches such as phrase-based statistical machine translation. Despite its recent success, neural machine translation has its limitation in handling a larger vocabulary, as training complexity as well as decoding complexity increase proportionally to the number of target words. In this paper, we propose a method based on importance sampling that allows us to use a very large target vocabulary without increasing training complexity. We show that decoding can be efficiently done even with the model having a very large target vocabulary by selecting only a small subset of the whole target vocabulary. The models trained by the proposed approach are empirically found to outperform the baseline models with a small vocabulary as well as the LSTM-based neural machine translation models. Furthermore, when we use the ensemble of a few models with very large target vocabularies, we achieve the state-of-the-art translation performance (measured by BLEU) on the English!German translation and almost as high performance as state-of-the-art English!French translation system.


title: Using singular value decomposition approximation for collaborative filtering

abstract: Singular value decomposition (SVD), together with the expectation-maximization (EM) procedure, can be used to find a low-dimension model that maximizes the log-likelihood of observed ratings in recommendation systems. However, the computational cost of this approach is a major concern, since each iteration of the EM algorithm requires a new SVD computation. We present a novel algorithm that incorporates SVD approximation into the EM procedure to reduce the overall computational cost while maintaining accurate predictions. Furthermore, we propose a new framework for collaborating filtering in distributed recommendation systems that allows users to maintain their own rating profiles for privacy. A server periodically collects aggregate information from those users that are online to provide predictions for all users. Both theoretical analysis and experimental results show that this framework is effective and achieves almost the same prediction performance as that of centralized systems.


Multi-view Response Selection for Human-Computer Conversation


title: The benefit of interleaved mathematics practice is not limited to superficially similar kinds of problems

abstract: Most mathematics assignments consist of a group of problems requiring the same strategy. For example, a lesson on the quadratic formula is typically followed by a block of problems requiring students to use that formula, which means that students know the appropriate strategy before they read each problem. In an alternative approach, different kinds of problems appear in an interleaved order, which requires students to choose the strategy on the basis of the problem itself. In the classroom-based experiment reported here, grade 7 students (n = 140) received blocked or interleaved practice over a nine-week period, followed two weeks later by an unannounced test. The mean test scores were greater for material learned by interleaved practice rather than by blocked practice (72 % vs. 38 %, d = 1.05). This interleaving effect was observed even though the different kinds of problems were superficially dissimilar from each other, whereas previous interleaved mathematics studies had required students to learn nearly identical kinds of problems. We conclude that interleaving improves mathematics learning not only by improving discrimination between different kinds of problems, but also by strengthening the association between each kind of problem and its corresponding strategy.


title: A Smart Machine Vision System for PCB Inspection

abstract: In this paper, we present a smart machine vision (SMV) system for printed circuit board (PCB) inspection. It has advantages over the traditional manual inspection by its higher efficiency and accuracy. This SMV system consists of two modules, LIF (Learning Inspection Features) and OLI (On-Line Inspection). The LIF module automatically learns inspection features from the CAD files of a PCB board. The OLI module runs on-line to inspect PCB boards using a high-resolution 2-D sensor and the knowledge provided by the LIF components. Key algorithms developed for SMV are presented in the paper. The SMV system can be deployed on a manufacturing line with a much more affordable price comparing to other commercial inspection systems.


NewsVallum: Semantics-Aware Text and Image Processing for Fake News Detection system


title: Visualizing timelines: evolutionary summarization via iterative reinforcement between text and image streams

abstract: We present a novel graph-based framework for timeline summarization, the task of creating different summaries for different timestamps but for the same topic. Our work extends timeline summarization to a multimodal setting and creates timelines that are both textual and visual. Our approach exploits the fact that news documents are often accompanied by pictures and the two share some common content. Our model optimizes local summary creation and global timeline generation jointly following an iterative approach based on mutual reinforcement and co-ranking. In our algorithm, individual summaries are generated by taking into account the mutual dependencies between sentences and images, and are iteratively refined by considering how they contribute to the global timeline and its coherence. Experiments on real-world datasets show that the timelines produced by our model outperform several competitive baselines both in terms of ROUGE and when assessed by human evaluators.


title: Survey and evaluation of food recommendation systems and techniques

abstract: Recommending proper food is of paramount importance for person's sound health. It is also useful in revenue generation in restaurants by recommending varied choices of food or recommending restaurants depending on the type of food to customers. In this paper, we survey the existing food recommendation engines and compare the different recommendation algorithms — Content based filtering, collaborative filtering, hybrid techniques etc. Further, we would see the challenges and limitations of each technique.


Pricing for Data Markets


title: An Overview and Classification of Service Description Approaches in Automated Service Composition Research

abstract: In recent years, automated service composition has been a fervid research area in service computing. Within this area, service description plays a crucial role in terms of the development of a diverse number of automation schemes. In this paper, we provide an investigation and classification of the service description approaches that have been used in a diverse collection of automated service composition studies. To position the service description approaches used in automated service composition throughout the service description world and to clearly classify them, we propose five two-value dimensions. Using the proposed dimensions, we first perform a categorization and provide a simple introduction to current representative industrial service description standards. Subsequently, because we discovered that most of the studied automated composition approaches follow a tuple-based service description paradigm, an exhaustive classification and discussion of this paradigm is made, with the automated composition approaches adopting this paradigm as an example. Finally, we discuss issues that are currently relevant to the service description field and possible solutions. With this study, the reader can obtain a complete understanding of service description approaches used in automated service composition research, including their common formulation and assumptions.


title: Latent semantic indexing by self-organizing map

abstract: Abstract An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.


title: Development of grip amplified glove using bi-articular mechanism with pneumatic artificial rubber muscle

abstract: In this paper, a grip amplified glove using pneumatic artificial rubber muscles (PARMs) which are covered with a exoskeleton structure is developed. A bi-articular mechanism with a PARM that is suitable for bending finger is realized. The glove has totally 10 DOFs consist of four units. To achieve power-assist motion properly, the PI control, which is based on the pressure value from the balloon sensor, is performed. This sensor makes the applied part free from electricity. To evaluate the validity of glove, the skin surface electromyography (EMG) signals of muscles are measured during grasping. These results demonstrate that the effectiveness of the power amplified glove.


title: Speech Recognition Using Linear Predictive Coding and Artificial Neural Network for Controlling Movement of Mobile Robot

abstract: This paper describes about implementation of speech recognition system on a mobile robot for controlling movement of the robot. The methods used for speech recognition system are Linear Predictive Coding (LPC) and Artificial Neural Network (ANN). LPC method is used for extracting feature of a voice signal and ANN is used as the recognition method. Backpropagation method is used to train the ANN. Voice signals are sampled directly from the microphone and then they are processed using LPC method for extracting the features of voice signal. For each voice signal, LPC method produces 576 data. Then, these data become the input of the ANN. The ANN was trained by using 210 data training. This data training includes the pronunciation of the seven words used as the command, which are created from 30 different people. Experimental results show that the highest recognition rate that can be achieved by this system is 91.4%. This result is obtained by using 25 samples per word, 1 hidden layer, 5 neurons for each hidden layer, and learning rate 0.1.


title: Multiobjective evolutionary algorithms for dynamic social network clustering

abstract: The main focus of this paper is to propose integration of dynamic and multiobjective algorithms for graph clustering in dynamic environments under multiple objectives. The primary application is to multiobjective clustering in social networks which change over time. Social networks, typically represented by graphs, contain information about the relations (or interactions) among online materials (or people). A typical social network tends to expand over time, with newly added nodes and edges being incorporated into the existing graph. We reflect these characteristics of social networks based on real-world data, and propose a suitable dynamic multiobjective evolutionary algorithm. Several variants of the algorithm are proposed and compared. Since social networks change continuously, the immigrant schemes effectively used in previous dynamic optimisation give useful ideas for new algorithms. An adaptive integration of multiobjective evolutionary algorithms outperformed other algorithms in dynamic social networks.


title: Cognitive Bias for Universal Algorithmic Intelligence

abstract: Existing theoretical universal algorithmic intelligence models are not practically realizable. More pragmatic approach to artificial general intelligence is based on cognitive architectures, which are, however, non-universal in sense that they can construct and use models of the environment only from Turing-incomplete model spaces. We believe that the way to the real AGI consists in bridging the gap between these two approaches. This is possible if one considers cognitive functions as a "cognitive bias" (priors and search heuristics) that should be incorporated into the models of universal algorithmic intelligence without violating their universality. Earlier reported results suiting this approach and its overall feasibility are discussed on the example of perception, planning, knowledge representation, attention, theory of mind, language, and some others.


title: Gaussian Processes for Underdetermined Source Separation

abstract: Gaussian process (GP) models are very popular for machine learning and regression and they are widely used to account for spatial or temporal relationships between multivariate random variables. In this paper, we propose a general formulation of underdetermined source separation as a problem involving GP regression. The advantage of the proposed unified view is first to describe the different underdetermined source separation problems as particular cases of a more general framework. Second, it provides a flexible means to include a variety of prior information concerning the sources such as smoothness, local stationarity or periodicity through the use of adequate covariance functions. Third, given the model, it provides an optimal solution in the minimum mean squared error (MMSE) sense to the source separation problem. In order to make the GP models tractable for very large signals, we introduce framing as a GP approximation and we show that computations for regularly sampled and locally stationary GPs can be done very efficiently in the frequency domain. These findings establish a deep connection between GP and nonnegative tensor factorizations (NTF) with the Itakura-Saito distance and lead to effective methods to learn GP hyperparameters for very large and regularly sampled signals.


Speeding up Reinforcement Learning-based Information Extraction Training using Asynchronous Methods


Chromatographic profiling to determine the extent of structural changes in arrowroot starch ( Marantha arundinacea L . ) modification


title: Design of a hopping mechanism using a voice coil actuator: Linear elastic actuator in parallel (LEAP)

abstract: Among legged robots, hopping and running robots are useful because they can traverse terrain at high speeds and are a benchmark platform for locomotion actuators; if an actuator can power a hopping robot, it can power a walking robot. We aim to create a hopping mechanism for a small-scale, one-legged, untethered hopping robot. A parallel-elastic actuator is an efficient way to do this, and enables the actuator to directly inject energy into the spring, but requires a high-speed, low-inertia actuator. Voice coil actuators are electrically-powered direct-drive translational motors that have very low moving inertia, low friction, can produce force at high speeds, and have a linear force output. These qualities make them ideal candidate motors for a linear elastic actuator in parallel (“LEAP”). Here, we derive an electromechanical model of the LEAP mechanism, develop a simple bang-bang hopping controller, and simulate hopping with a range of spring parameters to find an optimal spring stiffness that maximizes hopping height. We detail our implemented design, and characterize its performance through a series of experiments. We test our robot with different spring stiffnesses, and demonstrate hopping at a maximum steady-state of 3.5 cm ground-clearance (approx. 20% leg length). Our results suggest that the LEAP mechanism may serve the weight-bearing functions of a robot leg.


Culture-sensitive Mathematics: The Walpole Island Experience


Unsupervised acquisition of entailment relations from the Web


title: A Performance Study of 14-Channel and 5-Channel EEG Systems for Real-Time Control of Unmanned Aerial Vehicles (UAVs)

abstract: Brain-computer interface (BCI), an actively re-searched multi-disciplinary domain, has completely trans-formed the approach to robotic control problems. Researchers have focused on developing algorithms that optimize robotic movement to achieve desired trajectories, and it's a general understanding that route optimization problems are difficult to solve mathematically. Humans, on the other hand, tend to optimize their day-to-day activities intuitively. In order to achieve the desired results, the brain exploits a multi-level filtering approach, where the macro features are weighted in the first layer and the microfeatures in further layers. This optimization inside the brain interestingly, leave distinct traces in electroencephalography (EEG) plots. Based on the observations, we propose to use artificial neural networks to classify the EEG data, which intuitively should give a high classification rate, because the human brain also exploits a network of neurons to classify auditory (time-series) and visual (spatial) data. In this paper, we discuss the performances of 14- channel and 5-channel EEG headsets for robotic applications. Data is acquired from 20 subjects corresponding to four different tasks. Using neural nets, we have been successfully able to classify the EEG input into four different classes. We get an overall classification accuracy of 98.8% for 14-channel and 84.5% 5-channel system. As a real-time demonstration of the interface, the predicted class number is sent to a multi-rotor via a wireless link as an appropriate velocity command.


title: Effects of interleukin-2 (IL-2) on human plasma lipid, lipoprotein, and C-reactive protein

abstract: Six patients with confirmed malignant disease received four consecutive weekly cycles of human recombinant interleukin-2 (IL-2) 4 days/week, continuous iv. infusion, 3 X 10(6) U/m2/day. Plasma cholesterol decreased a mean of 7% within 24 hours after IL-2 infusion and decreased by 33% within 4 days. Plasma cholesterol was significantly lower than baseline concentration by day 21 (-21%), and day 25 (-41%) was significantly lower than day 21. Decreased plasma cholesterol was the result of decreased HDL and LDL cholesterol concentrations. Plasma triglyceride demonstrated a mean increase of 46% after 4 days of therapy and remained greater than baseline concentrations at all time points analyzed. Apolipoprotein AI and AII decreased concomitantly with HDL-cholesterol concentrations, whereas apolipoprotein B after an initial mean decrease of 17% during the first cycle was not significantly different from baseline during the fourth cycle. Apolipoprotein E and Lp(a) were not significantly affected by IL-2 treatment. Plasma C-reactive protein (CRP) increased by 79% within 24 hours of therapy, increased by 254% on day 4, then decreased to baseline concentrations by day 21 after 3 days off of IL-2. Day 25 CRP was elevated compared to both baseline and day 21 concentrations. IL-2 induced plasma lipoprotein changes may be due in part to the induction of interferon gamma.


title: Deep GrabCut for Object Selection

abstract: Most previous bounding-box-based segmentation methods assume the bounding box tightly covers the object of interest. However it is common that a rectangle input could be too large or too small. In this paper, we propose a novel segmentation approach that uses a rectangle as a soft constraint by transforming it into an Euclidean distance map. A convolutional encoder-decoder network is trained end-to-end by concatenating images with these distance maps as inputs and predicting the object masks as outputs. Our approach gets accurate segmentation results given sloppy rectangles while being general for both interactive segmentation and instance segmentation. We show our network extends to curve-based input without retraining. We further apply our network to instance-level semantic segmentation and resolve any overlap using a conditional random field. Experiments on benchmark datasets demonstrate the effectiveness of the proposed approaches.


title: Cognitively Engaging Chronic Physical Activity, but Not Aerobic Exercise, Affects Executive Functions in Primary School Children: A Group-Randomized Controlled Trial

abstract: Although the positive effects of different kinds of physical activity (PA) on cognitive functioning have already been demonstrated in a variety of studies, the role of cognitive engagement in promoting children’s executive functions is still unclear. The aim of the present study was therefore to investigate the effects of two qualitatively different chronic PA interventions on executive functions in primary school children. 181 children aged between 10 and 12 years were assigned to either a 6-week physical education program with a high level of physical exertion and high cognitive engagement (team games), a physical education program with high physical exertion but low cognitive engagement (aerobic exercise), or to a physical education program with both low physical exertion and low cognitive engagement (control condition). Executive functions (updating, inhibition, shifting) and aerobic fitness (multistage 20-meter shuttle run test) were measured before and after the respective condition. Results revealed that both interventions (team games and aerobic exercise) have a positive impact on children’s aerobic fitness (4-5 % increase in estimated VO2max). Importantly, an improvement in shifting performance was found only in the team games and not in the aerobic exercise or control condition. Thus, the inclusion of cognitive engagement in PA seems to be the most promising type of chronic intervention to enhance executive functions in children, providing further evidence for the importance of the qualitative aspects of PA.


title: Semantic Role Labeling Using Different Syntactic Views

abstract: Semantic role labeling is the process of annotating the predicate-argument structure in text with semantic labels. In this paper we present a state-of-the-art baseline semantic role labeling system based on Support Vector Machine classifiers. We show improvements on this system by: i) adding new features including features extracted from dependency parses, ii) performing feature selection and calibration and iii) combining parses obtained from semantic parsers trained using different syntactic views. Error analysis of the baseline system showed that approximately half of the argument identification errors resulted from parse errors in which there was no syntactic constituent that aligned with the correct argument. In order to address this problem, we combined semantic parses from a Minipar syntactic parse and from a chunked syntactic representation with our original baseline system which was based on Charniak parses. All of the reported techniques resulted in performance improvements.


title: Collaborative Computing for Heterogeneous Integrated Systems

abstract: Computing systems today typically employ, in addition to powerful CPUs, various types of specialized devices such as Graphics Processing Units (GPUs) and Field-Programmable Gate Arrays (FPGAs). Such heterogeneous systems are evolving towards tighter integration of devices for improved performance and reduced energy consumption. Compared to traditional use of GPUs and FPGAs as offload accelerators, this tight integration enables close collaboration between processors which is important for better utilization of system resources and higher performance. Programming interfaces are also adapting rapidly to tightly integrated heterogeneous platforms by introducing features such as shared virtual memory, memory coherence, and system-wide atomics, making collaborative computing among different devices even more practical. In this paper, we survey current integrated heterogeneous systems and corresponding collaboration techniques. We evaluate the impact of collaborative computing on two heterogeneous integrated systems, CPU-GPU and CPU-FPGA, using OpenCL. Finally, we discuss the limitation of OpenCL and envision what suitable programming languages for collaborative computing will look like.


title: Academic stress and Internet addiction from general strain theory framework

abstract: Academic stress influenced negative emotions.Academic stress influenced Internet addiction.Negative emotions affected Internet addiction.Negative emotions fully mediated the associations. The goal of this study was to examine the mediating role of negative emotions in the link between academic stress and Internet addiction among Korean adolescents. We attempted to extend the general strain theory to Internet addiction by exploring psychological pathways from academic stress to Internet addiction using a national and longitudinal panel study. A total of 512 adolescents completed self-reported scales for academic stress, negative emotions, and Internet addiction. We found that academic stress was positively associated with negative emotions and Internet addiction, and negative emotions were positively associated with Internet addiction. Further, the results of structural equation modeling revealed that adolescents' academic stress had indirectly influenced Internet addiction through negative emotions. The results of this study suggest that adolescents who experience academic stress might be at risk for Internet addiction, particularly when accompanied with negative emotions. These findings provided significant implications for counselors and policymakers to prevent adolescents' Internet addiction, and extended the general strain theory to Internet addiction which is typically applicable to deviant behavior.


title: Seismotectonics Considered Artificial Neural Network Earthquake Prediction in Northeast Seismic Region of China

abstract: It is well known that earthquakes are a regional event, strongly controlled by local geological structures and circumstances. Reducing the research area can reduce the influence of other irrelevant seismotectonics. A new sub region- dividing scheme, considering the seismotectonics influence, was applied for the artificial neural network (ANN) earth- quake prediction model in the northeast seismic region of China (NSRC). The improved set of input parameters and pre- diction time duration are also discussed in this work. The new dividing scheme improved the prediction accuracy for dif- ferent prediction time frames. Three different research regions were analyzed as an earthquake data source for the ANN model under different prediction time duration frames. The results show: (1) dividing the research region into smaller sub- regions can improve the prediction accuracies in NSRC, (2) larger research regions need shorter prediction durations to obtain better performance, (3) different areas have different sets of input parameters in NSRC, and (4) the dividing scheme, considering the seismotectonics frame of the region, yields better results.


title: Algorithms for Reinforcement Learning

abstract: Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective.What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming.We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.


title: Lung Nodule Classification Using Deep Features in CT Images

abstract: Early detection of lung cancer can help in a sharp decrease in the lung cancer mortality rate, which accounts for more than 17% percent of the total cancer related deaths. A large number of cases are encountered by radiologists on a daily basis for initial diagnosis. Computer-aided diagnosis (CAD) systems can assist radiologists by offering a "second opinion" and making the whole process faster. We propose a CAD system which uses deep features extracted from an auto encoder to classify lung nodules as either malignant or benign. We use 4303 instances containing 4323 nodules from the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset to obtain an overall accuracy of 75.01% with a sensitivity of 83.35% and false positive of 0.39/patient over a 10 fold cross validation.


title: Pointer Networks

abstract: We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. Such problems cannot be trivially addressed by existent approaches such as sequence-to-sequence [1] and Neural Turing Machines [2], because the number of target classes in each step of the output depends on the length of the input, which is variable. Problems such as sorting variable sized sequences, and various combinatorial optimization problems belong to this class. Our model solves the problem of variable size output dictionaries using a recently proposed mechanism of neural attention. It differs from the previous attention attempts in that, instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, it uses attention as a pointer to select a member of the input sequence as the output. We call this architecture a Pointer Net (Ptr-Net). We show Ptr-Nets can be used to learn approximate solutions to three challenging geometric problems - finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem - using training examples alone. Ptr-Nets not only improve over sequence-to-sequence with input attention, but also allow us to generalize to variable size output dictionaries. We show that the learnt models generalize beyond the maximum lengths they were trained on. We hope our results on these tasks will encourage a broader exploration of neural learning for discrete problems.


title: Privacy-Preserving Similarity Joins Over Encrypted Data

abstract: Similarity search on high-dimensional data has been intensively studied for data processing and analytics. Despite its broad applicability, data security and privacy concerns along the trend of data outsourcing have not been fully addressed. In this paper, we investigate privacy-preserving similarity join queries, i.e., a pivotal primitive of similarity search that finds pairwise similar data points across two data sets. We start from locality-sensitive hashing and searchable symmetric encryption, i.e., the most practical techniques for similarity search and encrypted search, respectively. However, the immediate combination of two techniques discloses the distribution of the query set, which is exploitable to compromise the confidentiality of queries. To enhance the security, we propose the frequency hiding query scheme, which allows the server to see the flattened query distribution only. To improve the scalability, we further design the result sharing query scheme, which processes a small portion of query points and shares the results with other nearby points. Besides, we set up a strict constraint to carefully select query points to achieve “as-strong-as-possible” guarantees. We formalize the leakage functions in the context of similarity joins, and conduct rigorous security analysis. We implement and evaluate the proposed query schemes on Azure cloud. Experimental results indicate that they have different tradeoffs on security, efficiency, and accuracy, which can flexibly be used for different deployment scenarios.


title: Theoretical and structural analysis of the active site of the transcriptional regulators LasR and TraR, using molecular docking methodology for identifying potential analogues of acyl homoserine lactones (AHLs) with anti-quorum sensing activity.

abstract: In the present study the homology of transcriptional receptors LuxR type were evaluated using as point of reference the receptors TraR and LasR of the bacterial types Agrobacterium tumefaciens and Pseudomonas aureginosa respectively. A series of alignments were performed in order to demonstrate that the active site of the protein is conserved in wide range of gram negative bacteria. Moreover, some docking calculations were carried out for analogs of the acyl homoserin lactones (AHLs) and regulatory proteins LasR and TraR, to understand the complex microenvironment in which the ligands are exposed. The molecular alignments show clearly that there are preserved motifs in the residues (Y53, Y61, W57, D70, W85 to TraR, Y56, Y64, W60, D73, W88 to LasR) analyzed, which may serve as site-specific targets for the development of potential antagonists. In this study was found that the anti-quorum sensing activity of the AHLs molecular analogs appears to depend on; the structure of the lactone ring and on appropriate combination of absolute and relative stereochemistry of the carbonyl (C=O) and amide (NH(2)) groups of the side chain of these AHLs molecular analogs, in combination with the interactions with the conserved amino acids (D73, W60, Y56, S129 to LasR and D70, W57, Y53 to TraR) of the LuxR type protein family.


Artificial Neural Networks in Auditing: State of the Art


title: OntoRama: Browsing RDF ontologies using a hyperbolic-style browser

abstract: This paper presents a Java-based hyperbolic-style browser designed to render RDF files as structured ontological maps. The program was motivated by the need to browse the content of a web-accessible ontology server: WEB KB-2. The ontology server contains descriptions of over 74,500 object types derived from the WordNet 1.7 lexical database and can be accessed using RDF syntax. Such a structure creates complications for hyperbolic-style displays. In WEB KB-2 there are 140 stable ontology link types and a hyperbolic display needs to filter and iconify the view so different link relations can be distinguished in multi-link views. Our browsing tool, OntoRama, is therefore motivated by two possibly interfering aims: the first to display up to 10 times the number of nodes in a hyperbolic-style view than using a conventional graphics display; secondly, to render the ontology with multiple links comprehensible in that view.


title: Instagram Use, Loneliness, and Social Comparison Orientation: Interact and Browse on Social Media, But Don't Compare

abstract: Abstract Ever since the emergence of social networking sites (SNSs), it has remained a question without a conclusive answer whether SNSs make people more or less lonely. To achieve a better understanding, researchers need to move beyond studying overall SNS usage. In addition, it is necessary to attend to personal attributes as potential moderators. Given that SNSs provide rich opportunities for social comparison, one highly relevant personality trait would be social comparison orientation (SCO), and yet this personal attribute has been understudied in social media research. Drawing on literature of psychosocial implications of social media use and SCO, this study explored associations between loneliness and various Instagram activities and the role of SCO in this context. A total of 208 undergraduate students attending a U.S. mid-southern university completed a self-report survey (Mage = 19.43, SD = 1.35; 78 percent female; 57 percent White). Findings showed that Instagram interaction and Instagram brows...


title: Security trends and advances in manufacturing systems in the era of industry 4.0

abstract: The next industrial revolution will incorporate various enabling technologies. These technologies will make the product lifecycle of the manufacturing system efficient, decentralized, and well-connected. However, these technologies have various security issues, and when integrated in the product lifecycle of manufacturing systems can pose various challenges for maintaining the security requirements such as confidentiality, integrity, and availability. In this paper, we will present the various trends and advances in the security of the product lifecycle of the manufacturing system while highlighting the roles played by the major enabling components of Industry 4.0.


title: “Gestational Gigantomastia”: A Review Article and Case Presentation of a New Surgical Management Option

abstract: BACKGROUND ::: Gestational gigantomastia is a nightmare to pregnant women. The currently available surgical intervention is either reduction mammoplasty or simple mastectomy. Reduction mammoplasty carries high risk of recurrence. Simple mastectomy is a mutilating option for a benign condition. ::: ::: ::: METHODS ::: A thorough literature research was performed for all reported gestational gigantomastia cases. In addition, this study presents a case that was diagnosed and treated at the authors' center. ::: ::: ::: RESULTS ::: The patients' age mean age was 26.8 years. Surgical intervention is the only currently available curative option. The authors were able to introduce an alternative surgical technique: bilateral subcutaneous mastectomy (BSCM) with latissimus dorsi muscle flaps (LDF) and free nipple areola complex grafting (FNAG). ::: ::: ::: CONCLUSION ::: Despite being a benign condition, gestational gigantomastia could turn to be a catastrophe. BSCM with LDF and FNAG represents an excellent alternative breast saving surgical option. It offers the advantage of restoring normal breast shape with no fear of future recurrences.


title: DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo

abstract: In this paper, we introduce a local image descriptor, DAISY, which is very efficient to compute densely. We also present an EM-based algorithm to compute dense depth and occlusion maps from wide-baseline image pairs using this descriptor. This yields much better results in wide-baseline situations than the pixel and correlation-based algorithms that are commonly used in narrow-baseline stereo. Also, using a descriptor makes our algorithm robust against many photometric and geometric transformations. Our descriptor is inspired from earlier ones such as SIFT and GLOH but can be computed much faster for our purposes. Unlike SURF, which can also be computed efficiently at every pixel, it does not introduce artifacts that degrade the matching performance when used densely. It is important to note that our approach is the first algorithm that attempts to estimate dense depth maps from wide-baseline image pairs, and we show that it is a good one at that with many experiments for depth estimation accuracy, occlusion detection, and comparing it against other descriptors on laser-scanned ground truth scenes. We also tested our approach on a variety of indoor and outdoor scenes with different photometric and geometric transformations and our experiments support our claim to being robust against these.