title: A Survey on Visual-Based Localization: On the Benefit of Heterogeneous Data

abstract: Abstract We are surrounded by plenty of information about our environment. From these multiple sources, numerous data could be extracted: set of images, 3D model, coloured points cloud... When classical localization devices failed ( e.g. GPS sensor in cluttered environments), aforementioned data could be used within a localization framework. This is called Visual Based Localization (VBL). Due to numerous data types that can be collected from a scene, VBL encompasses a large amount of different methods. This paper presents a survey about recent methods that localize a visual acquisition system according to a known environment. We start by categorizing VBL methods into two distinct families: indirect and direct localization systems. As the localization environment is almost always dynamic, we pay special attention to methods designed to handle appearances changes occurring in a scene. Thereafter, we highlight methods exploiting heterogeneous types of data. Finally, we conclude the paper with a discussion on promising trends that could permit to a localization system to reach high precision pose estimation within an area as large as possible.


title: GRAPH MINING APPLICATIONS TO SOCIAL NETWORK ANALYSIS

abstract: The prosperity of Web 2.0 and social media brings about many diverse social networks of unprecedented scales, which present new challenges for more effec- tive graph-mining techniques. In this chapter, we present some graph patterns that are commonly observed in large-scale social networks. As most networks demonstrate strong community structures, one basic task in social network anal- ysis is community detection which uncovers the group membership of actors in a network. We categorize and survey representative graph mining approaches and evaluation strategies for community detection. We then present and discuss some research issues for future exploration.


title: Comparison of Named Entity Recognition tools for raw OCR text

abstract: This short paper analyses an experiment comparing the efficacy of several Named Entity Recognition (NER) tools at extracting entities directly from the output of an optical character recognition (OCR) workflow. The authors present how they first created a set of test data, consisting of raw and corrected OCR output manually annotated with people, locations, and organizations. They then ran each of the NER tools against both raw and corrected OCR output, comparing the precision, recall, and F1 score against the manually annotated data.


title: Theory Application by School-Based Occupational Therapists

abstract: OBJECTIVES ::: Recent literature indicates that there is an inconsistent use of theory to guide clinical actions by occupational therapists, including those working in pediatrics. The purpose of this study was to describe school-based therapists' theory application by collecting information about what frames of reference they used and why. ::: ::: ::: METHOD ::: Of the 72 school-based therapists in the mid-Atlantic states who agreed to respond to a questionnaire, 51 (70.8%) returned the questionnaire. Information about demographics, what frames of reference were used, and why they were used was obtained from the questionnaire. ::: ::: ::: RESULTS ::: Respondents reported using a multitheoretical approach, with sensory integration theory and neurodevelopmental theory being the predominate frames of reference applied but not the only ones used. The frames of reference were used on the basis of several factors, including the children's needs and the respondent's education. ::: ::: ::: CONCLUSION ::: Formal and continuing education seems to have a great effect on school-based occupational therapists as they develop their personal conceptual frameworks.


title: Global Software Development Challenges: A Case Study on Temporal, Geographical and Socio-Cultural Distance

abstract: Global software development (GSD) is a phenomenon that is receiving considerable interest from companies all over the world. In GSD, stakeholders from different national and organizational cultures are involved in developing software and the many benefits include access to a large labour pool, cost advantage and round-the-clock development. However, GSD is technologically and organizationally complex and presents a variety of challenges to be managed by the software development team. In particular, temporal, geographical and socio-cultural distances impose problems not experienced in traditional systems development. In this paper, we present findings from a case study in which we explore the particular challenges associated with managing GSD. Our study also reveals some of the solutions that are used to deal with these challenges. We do so by empirical investigation at three US based GSD companies operating in Ireland. Based on qualitative interviews we present challenges related to temporal, geographical and socio-cultural distance.


title: Airport capacity: representation, estimation, optimization

abstract: A major goal of air traffic management is to strategically control the flow of traffic so that the demand at an airport meets but does not exceed the operational capacity. This work considers the major aspects of airport operational capacities relevant to the strategic management of air traffic. A representation of airport capacity that properly reflects an airport's operational limits is discussed. A method is presented for estimating practical airport capacities under various operational conditions. A technique is proposed for optimizing the available airport capacity to best satisfy the expected traffic demand. The optimization is achieved by considering arrival and departure operations as interdependent processes and by strategically allocating the airport capacity between arrivals and departures. The underlying mathematical model is presented, as well as numerical examples illustrating the benefits when solving airport congestion problems. >


title: Availability: A heuristic for judging frequency and probability☆☆☆

abstract: Abstract This paper explores a judgmental heuristic in which a person evaluates the frequency of classes or the probability of events by availability, i.e., by the ease with which relevant instances come to mind. In general, availability is correlated with ecological frequency, but it is also affected by other factors. Consequently, the reliance on the availability heuristic leads to systematic biases. Such biases are demonstrated in the judged frequency of classes of words, of combinatorial outcomes, and of repeated events. The phenomenon of illusory correlation is explained as an availability bias. The effects of the availability of incidents and scenarios on subjective probability are discussed.


title: Group Recommender Systems

abstract: Group recommender systems provide suggestions in contexts in which people operate in groups. The goal of this tutorial is to provide the RecSys audience with an overview on group recommendation. We will first formally introduce the problem of producing recommendations to groups, then present a survey based on the tasks performed by these systems. We will also analyze challenging topics like their evaluation, and present emerging aspects and techniques in this area. The tutorial will end with a summary that highlights open issues and research challenges.


title: STRATEGIC KNOWLEDGE MANAGEMENT, INNOVATION AND PERFORMANCE

abstract: Challenge can balance organizational innovativeness and flexibility with the disciplines that turn innovative pursuits into tangible business advantage. Our aim is to spread knowledge involving a certain subject of the results of knowledge management (KM) strategies on firm’s innovation and incorporated in performance. This study consisted of 195 Iranian organizations and structural equations modeling, results show that both KM strategies influences on innovation and organizational performance directly and indirectly. Thus, one of the main final decisions of our research is that KM has been found as a significant mechanism to increase innovation and incorporated in performance. In addition, both codification and personalization strategies have a positive influence on financial results. Managers can use these findings as a dispute to negotiate with and influence to stakeholders about the goodness of performing KM projects. Our research can contribute to professional, since it furnishes organizations with new perception and findings which managers can translate into their own companies. Our final decision may help academics and managers in designing KM strategic schedules in orderto obtain higher innovation, effectiveness, and efficiency and profit capacity.


title: Unsupervised Learning of Long-Term Motion Dynamics for Videos

abstract: We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework, we propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows. We argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations. We demonstrate the effectiveness of our learned temporal representations on activity classification across multiple modalities and datasets such as NTU RGB+D and MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, depth, and RGB-D videos.


title: Torque ripple and back EMF harmonic reduction of IPMSM with asymmetrical stator design

abstract: This paper suggests asymmetric stator teeth design to reduce torque ripple and back EMF Total Harmonic Distortion(THD) for Interior Permanent Magnet Synchronous Machine(IPMSM). IPMSM which has 8 poles, 12 slots is analyzed in this study. From changing design parameter in stator structure, 8 comparison models are analyzed. Analysis of proposed method is carried out using Finite Element Method(FEM). Suggested method has advantage to reduce torque ripple and back electromotive force(EMF) harmonics without average torque decrease. Comparison between reference model and comparison models applying proposed method proceeds to verify advantage of this method.


title: Torque Ripple Reduction in Interior Permanent Magnet Synchronous Machines Using Stators With Odd Number of Slots Per Pole Pair

abstract: This paper develops analytical principles for torque ripple reduction in interior permanent magnet (IPM) synchronous machines. The significance of slot harmonics and the benefits of stators with odd number of slots per pole pair are highlighted. Based on these valuable analytical insights, this paper proposes coordination of the selection of stators with odd number of slots per pole pair and IPM rotors with multiple layers of flux barriers in order to reduce torque ripple. The effectiveness of using stators with odd number of slots per pole pair in reducing torque ripple is validated by applying a finite-element-based Monte Carlo optimization method to four IPM machine topologies, which are combinations of two stator topologies (even or odd number of slots per pole pair) and two IPM rotor topologies (one- or two-layer). It is demonstrated that the torque ripple can be reduced to less than 5% by selecting a stator with an odd number of slots per pole pair and the IPM rotor with optimized barrier configurations, without using stator/rotor skewing or rotor pole shaping.


title: Deep learning methods for subject text classification of articles

abstract: This work presents a method of classification of text documents using deep neural network with LSTM (long short-term memory) units. We have tested different approaches to build feature vectors, which represent documents to be classified: we used feature vectors constructed as sequences of words included in the documents, or, alternatively, we first converted words into vector representations using word2vec tool and used sequences of these vector representations as features of documents. We evaluated feasibility of this approach for the task of subject classification of documents using a collection of Wikipedia articles representing 7 subject categories. Our experiments show that the approach based on an LSTM network with documents represented as sequences of words coded into word2vec vectors outperformed a standard, bag-of-word approach with documents represented as frequency-of-words feature vectors.


title: Analysis on physiological properties of the heavy panicle type of indica-japonica inter-subspecific hybrid rice Yongyou 6.

abstract: 【Objective】 This study was conducted to reveal the relationship between heavy panicle,super-high-yield and physiological properties of indica-japonica inter-subspecific hybrid rice Yongyou 6.【Method】 A continuing experiment was conducted to evaluate the temperature and light response characteristic,source-translocation-sink characteristic and other physiological properties with the heavy type of indica-japonica inter-subspecific hybrid rice variety Yongyou 6,the light panicle type of conventional late japonica rice variety Xiushui 63,and the middle panicle type of late indica hybrid rice variety,Shanyou 63 during 2003-2008.【Result】 The results showed that compared to conventional late japonica rice Xiushui 63(light panicle type,CK1) and late indica hybrid rice Shanyou 63(middle panicle type,CK2),the heavy panicle type of indica-japonica inter-subspecific bybrid rice Yongyou 6 have many outstanding physiological properties as follows:(1) Strong source that characterized with larger LAI,longer functional period of leaf,higher chlorophyll content of leaf,higher photosynthetic rate of functional leaves,higher specific leaf weight and grain-leaf ratio;(2) Efficient flow that characterized with higher output,output rate and translocation rate of dry matter in single stem and sheath of rice;(3) Greater sink that characterized with increased gross spikelet number,effective spikelet number per unit area,higher yield capacity and heavier single panicle weight(about 5 g);(4) Stronger root system that characterized with developed secondary roots,more roots,higher root activity and higher root/shoot ratio which enhanced the lodging resistance and yield.【Conclusion】 In order to increase the yield of new indica-japonica inter-subspecific hybrid rice combinations,the breeders have to cooperate with researchers of relevant subjects at the early stage during the parents selection,especially hybrid F1 selection,to evaluate the source-translocation-sink characteristic and root/shoot ratio of parents and new combinations.


title: Learning Important Spatial Pooling Regions for Scene Classification

abstract: We address the false response influence problem when learning and applying discriminative parts to construct the mid-level representation in scene classification. It is often caused by the complexity of latent image structure when convolving part filters with input images. This problem makes mid-level representation, even after pooling, not distinct enough to classify input data correctly to categories. Our solution is to learn important spatial pooling regions along with their appearance. The experiments show that this new framework suppresses false response and produces improved results on several datasets, including MIT-Indoor, 15-Scene, and UIUC 8-Sport. When combined with global image features, our method achieves state-of-the-art performance on these datasets.


title: Automatic hole-filling of CAD models with feature-preserving

abstract: In this paper, we propose an automatic hole-filling method, particularly for recovering missing feature curves and corners. We first extract the feature vertices around a hole of a CAD model and classify them into different feature sets. These feature sets are then automatically paired, using ordered double normals, Gaussian mapping and convex/concave analysis, to produce missing feature curves. Additionally, by minimizing a newly defined energy, the missing corners can be efficiently recovered as well. The hole is consequently divided into simple sub-holes according to the produced feature curves and recovered corners. Finally, each sub-hole is filled by a modified advancing front method individually. The experiments show that our approach is simple, efficient, and suitable for CAD systems.


Title Design and realization of a smart battery management system


title: Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature

abstract: In the era of precision medicine, cancer therapy can be tailored to an individual patient based on the genomic profile of a tumour. Despite the ever-increasing abundance of cancer genomic data, linking mutation profiles to drug efficacy remains a challenge. Herein, we report Cancer Drug Response profile scan (CDRscan) a novel deep learning model that predicts anticancer drug responsiveness based on a large-scale drug screening assay data encompassing genomic profiles of 787 human cancer cell lines and structural profiles of 244 drugs. CDRscan employs a two-step convolution architecture, where the genomic mutational fingerprints of cell lines and the molecular fingerprints of drugs are processed individually, then merged by ‘virtual docking’, an in silico modelling of drug treatment. Analysis of the goodness-of-fit between observed and predicted drug response revealed a high prediction accuracy of CDRscan (R2 > 0.84; AUROC > 0.98). We applied CDRscan to 1,487 approved drugs and identified 14 oncology and 23 non-oncology drugs having new potential cancer indications. This, to our knowledge, is the first-time application of a deep learning model in predicting the feasibility of drug repurposing. By further clinical validation, CDRscan is expected to allow selection of the most effective anticancer drugs for the genomic profile of the individual patient.


title: Investigation of symmetrical split ring resonator (SSRR) couplings for high sensitivity material characterization

abstract: In this paper an investigation of enhanced coupled methods based on planar symmetrical split ring resonator (SSRR) is presented for characterizing the properties of materials. The designed SSRR sensor demonstrates significant improvement in resonant frequency and reflection coefficients when the coupling gap between the feedlines and rings of the sensors is enhanced. The simulated results show that the SSRR achieves narrow resonance with higher Q value of 407. The SSRR sensor can be used for bio -sensing, food industry, quality control, and medicine and pharmacy applications. Experimentally, there is a good agreement in the results of simulation and measurement with possibility of 1.4 % typical error in shifting of the resonance frequency.


title: Concurrent Hierarchical Reinforcement Learning for RoboCup Keepaway

abstract: RoboCup Keepaway, originated from the RoboCup soccer simulation 2D challenge, has been widely used as a machine learning benchmark. In this paper, we present a concurrent hierarchical reinforcement learning approach to RoboCup Keepaway. Following the idea of hierarchies of abstract machines (HAMs), we write a partial policy as a HAM from the perspective of a single keeper, run multiple instances of the HAM, and use reinforcement learning to learn the optimal completion of the resulting joint HAM. Furthermore, we apply the idea of exploiting the intrinsic internal transitions within the HAM structure for more efficient learning. Experimental results confirm that the concurrent HAM approaches outperform the state of the art significantly on the very complex RoboCup Keepaway domain.


title: An Ontology for Secure Socio-Technical Systems

abstract: Security is often compromised by exploiting vulnerabilities in the interface between the organization and the information systems that support it. This reveals the necessity of modeling and analyzing information systems together with the organizational setting where they will operate. In this chapter we address this problem by presenting a modeling language tailored to analyze the problem of security at an organizational level. This language proposes a set of concepts founded on the notions of permission, delegation, and trust. The chapter also presents a semantics for these concepts, based on Datalog. A case study from the bank domain is employed to illustrate the proposed language.


title: Efficient logging for enterprise workloads on column-oriented in-memory databases

abstract: The introduction of a 64 bit address space in commodity operating systems and the constant drop in hardware prices made large capacities of main memory in the order of terabytes technically feasible and economically viable. Especially column-oriented in-memory databases are a promising platform to improve data management for enterprise applications. As in-memory databases hold the primary persistence in volatile memory, some form of recovery mechanism is required to prevent potential data loss in case of failures. Two desirable characteristics of any recovery mechanism are (1) that it has a minimal impact on the running system, and (2) that the system recovers quickly and without any data loss after a failure. This paper introduces an efficient logging mechanism for dictionary-compressed column structures that addresses these two characteristics by (1) reducing the overall log size by writing dictionary-compressed values and (2) allowing for parallel writing and reading of log files. We demonstrate the efficiency of our logging approach by comparing the resulting log-file size with traditional logical logging on a workload produced by a productive enterprise system.


title: BouldAR: using augmented reality to support collaborative boulder training

abstract: Nowadays smartphones are ubiquitous and - to some extent - already used to support sports training, e.g. runners or bikers track their trip with a gps-enabled smartphone. But recent mobile technology has powerful processors that allow even more complex tasks like image or graphics processing. In this work we address the question on how mobile technology can be used for collaborative boulder training. More specifically, we present a mobile augmented reality application to support various parts of boulder training. The proposed approach also incorporates sharing and other social features. Thus our solution supports collaborative training by providing an intuitive way to create, share and define goals and challenges together with friends. Furthermore we propose a novel method of trackable generation for augmented reality. Synthetically generated images of climbing walls are used as trackables for real, existing walls.


title: Trends in Social Media : Persistence and Decay

abstract: Social media generates a prodigious wealth of real-time content at an incessant rate. From all the content that people create and share, only a few topics manage to attract enough attention to rise to the top and become temporal trends which are displayed to users. The question of what factors cause the formation and persistence of trends is an important one that has not been answered yet. In this paper, we conduct an intensive study of trending topics on Twitter and provide a theoretical basis for the formation, persistence and decay of trends. We also demonstrate empirically how factors such as user activity and number of followers do not contribute strongly to trend creation and its propagation. In fact, we find that the resonance of the content with the users of the social network plays a major role in causing trends.


title: User cooperation diversity—Part I: System description

abstract: Mobile users' data rate and quality of service are limited by the fact that, within the duration of any given call, they experience severe variations in signal attenuation, thereby necessitating the use of some type of diversity. In this two-part paper, we propose a new form of spatial diversity, in which diversity gains are achieved via the cooperation of mobile users. Part I describes the user cooperation strategy, while Part II (see ibid., p.1939-48) focuses on implementation issues and performance analysis. Results show that, even though the interuser channel is noisy, cooperation leads not only to an increase in capacity for both users but also to a more robust system, where users' achievable rates are less susceptible to channel variations.


title: Transcranial direct current stimulation : State of the art 2008

abstract: Summary Effects of weak electrical currents on brain and neuronal function were first described decades ago. Recently, DC polarization of the brain was reintroduced as a noninvasive technique to alter cortical activity in humans. Beyond this, transcranial direct current stimulation (tDCS) of different cortical areas has been shown, in various studies, to result in modifications of perceptual, cognitive, and behavioral functions. Moreover, preliminary data suggest that it can induce beneficial effects in brain disorders. Brain stimulation with weak direct currents is a promising tool in human neuroscience and neurobehavioral research. To facilitate and standardize future tDCS studies, we offer this overview of the state of the art for tDCS.


title: What's Wrong with the diffusion of innovation theory? The case of a complex and networked technology

abstract: This paper examines the usefulness of the diffusion of innovation research in developing theoretical accounts of the adoption of complex and networked IT solutions. We contrast six conjectures underlying DOI research with field data obtained from the study of the diffusion of EDI. Our analysis shows that DOI based analyses miss some important facets in the diffusion of complex technologies. We suggest that complex IT solutions should be understood as socially constructed and learning intensive artifacts, which can be adopted for varying reasons within volatile diffusion arenas. Therefore DOI researchers should carefully recognize the complex, networked, and learning intensive features of technology; understand the role of institutional regimes, focus on process features (including histories) and key players in the diffusion arena, develop multi-layered theories that factor out mappings between different layers and locales, use multiple perspectives including political models, institutional models and theories of team behavior, and apply varying time scales while crafting accounts of what happened and why. In general the paper calls for a need to develop DOI theories at the site by using multiple levels of analysis.


title: Semantic Web User Interfaces: A Systematic Mapping Study and Review

abstract: Context: Exploration of user interfaces has attracted a considerable attention in Semantic Web research in the last several years. However, the overall impact of these research eorts and the level of the maturity of their results are still unclear. Objective: This study aims at surveying existing research on Semantic Web user interfaces in order to synthesise their results, assess the level of the maturity of the research results, and identify needs for future research. Method: This paper reports the results of a systematic literature review and mapping study on Semantic Web user interfaces published between 2003 and 2011. After collecting a corpus of 87 papers, the collected papers are analysed through four main dimensions: general information such as sources, types and time of publication; covered research topics; quality of evaluations; and nally, used research methods and reported rigour of their use. Results: Based on only the obtained data, the paper provides a high level overview of the works reviewed, a detailed quality assessment of papers involving human subjects and a discussion of the identied gaps in the analysed papers and draws promising venues for future research. Conclusion: We arrive at two main conclusions: important gaps exist which hinder a wide adoption of Semantic Web technologies (e.g. lack user interfaces based on semantic technologies for social and mobile applications) and experimental research hardly used any proven theories and instruments specically designed for investigating technology acceptance.


A SUMO-based Semantic Analysis for Knowledge Extraction


title: Feature Selection for SVMs

abstract: We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-life problems of face recognition, pedestrian detection and analyzing DNA microarray data.


title: Human Pose Forecasting via Deep Markov Models

abstract: Human pose forecasting is an important problem in computer vision with applications to human-robot interaction, visual surveillance, and autonomous driving. Usually, forecasting algorithms use 3D skeleton sequences and are trained to forecast for a few milliseconds into the future. Long-range forecasting is challenging due to the difficulty of estimating how long a person continues an activity. To this end, our contributions are threefold: (i) we propose a generative framework for poses using variational autoencoders based on Deep Markov Models (DMMs); (ii) we evaluate our pose forecasts using a pose-based action classifier, which we argue better reflects the subjective quality of pose forecasts than distance in coordinate space; (iii) last, for evaluation of the new model, we introduce a 480,000-frame video dataset called Ikea Furniture Assembly (Ikea FA), which depicts humans repeatedly assembling and disassembling furniture. We demonstrate promising results for our approach on both Ikea FA and the existing NTU RGB+D dataset.


title: Delay-dependent stability and stabilization of neutral time-delay systems

abstract: This paper is concerned with the problem of stability and stabilization of neutral time-delay systems. A new delay-dependent stability condition is derived in terms of linear matrix inequality by constructing a new Lyapunov functional and using some integral inequalities without introducing any free-weighting matrices. On the basis of the obtained stability condition, a stabilizing method is also proposed. Using an iterative algorithm, the state feedback controller can be obtained. Numerical examples illustrate that the proposed methods are effective and lead to less conservative results. Copyright © 2008 John Wiley & Sons, Ltd.


title: Functional overlap of the Arabidopsis leaf and root microbiota

abstract: Roots and leaves of healthy plants host taxonomically structured bacterial assemblies, and members of these communities contribute to plant growth and health. We established Arabidopsis leaf- and root-derived microbiota culture collections representing the majority of bacterial species that are reproducibly detectable by culture-independent community sequencing. We found an extensive taxonomic overlap between the leaf and root microbiota. Genome drafts of 400 isolates revealed a large overlap of genome-encoded functional capabilities between leaf- and root-derived bacteria with few significant differences at the level of individual functional categories. Using defined bacterial communities and a gnotobiotic Arabidopsis plant system we show that the isolates form assemblies resembling natural microbiota on their cognate host organs, but are also capable of ectopic leaf or root colonization. While this raises the possibility of reciprocal relocation between root and leaf microbiota members, genome information and recolonization experiments also provide evidence for microbiota specialization to their respective niche.


title: Invisible Pixels Are Dead, Long Live Invisible Pixels!

abstract: Privacy has deteriorated in the world wide web ever since the 1990s. The tracking of browsing habits by different third-parties has been at the center of this deterioration. Web cookies and so-called web beacons have been the classical ways to implement third-party tracking. Due to the introduction of more sophisticated technical tracking solutions and other fundamental transformations, the use of classical image-based web beacons might be expected to have lost their appeal. According to a sample of over thirty thousand images collected from popular websites, this paper shows that such an assumption is a fallacy: classical 1 x 1 images are still commonly used for third-party tracking in the contemporary world wide web. While it seems that ad-blockers are unable to fully block these classical image-based tracking beacons, the paper further demonstrates that even limited information can be used to accurately classify the third-party 1 x 1 images from other images. An average classification accuracy of 0.956 is reached in the empirical experiment. With these results the paper contributes to the ongoing attempts to better understand the lack of privacy in the world wide web, and the means by which the situation might be eventually improved.


title: Adversarial Attacks on Deep-Learning Based Radio Signal Classification

abstract: Deep learning (DL), despite its enormous success in many computer vision and language processing applications, is exceedingly vulnerable to adversarial attacks. We consider the use of DL for radio signal (modulation) classification tasks, and present practical methods for the crafting of white-box and universal black-box adversarial attacks in that application. We show that these attacks can considerably reduce the classification performance, with extremely small perturbations of the input. In particular, these attacks are significantly more powerful than classical jamming attacks, which raises significant security and robustness concerns in the use of DL-based algorithms for the wireless physical layer.


Causal Effect Identification by Adjustment under Confounding and Selection Biases.


title: Embodied Construction Grammar in Simulation-Based Language Understanding

abstract: We present Embodied Construction Grammar, a formalism for linguistic analysis designed specifically for integration into a simulation-based model of language understanding. As in other construction grammars, linguistic constructions serve to map between phonological forms and conceptual representations. In the model we describe, however, conceptual representations are also constrained to be grounded in the body’s perceptual and motor systems, and more precisely to parameteri ze mental simulations using those systems. Understanding an utterance thus involves at least two distinct processes: analysis to determine which constructions the utterance instantiates, and simulation according to the parameters specified by those constructions. In this chapter, we outline a constru ction formalism that is both representationally adequate for these purposes and specified precisely enough for use in a computational architecture.


title: Mathematical Foundations for a Compositional Distributional Model of Meaning

abstract: We propose a mathematical framework for a unification of the distributional theory of meaning in terms of vector space models, and a compositional theory for grammatical types, for which we rely on the algebra of Pregroups, introduced by Lambek. This mathematical framework enables us to compute the meaning of a well-typed sentence from the meanings of its constituents. Concretely, the type reductions of Pregroups are `lifted' to morphisms in a category, a procedure that transforms meanings of constituents into a meaning of the (well-typed) whole. Importantly, meanings of whole sentences live in a single space, independent of the grammatical structure of the sentence. Hence the inner-product can be used to compare meanings of arbitrary sentences, as it is for comparing the meanings of words in the distributional model. The mathematical structure we employ admits a purely diagrammatic calculus which exposes how the information flows between the words in a sentence in order to make up the meaning of the whole sentence. A variation of our `categorical model' which involves constraining the scalars of the vector spaces to the semiring of Booleans results in a Montague-style Boolean-valued semantics.


title: Improved Texture Networks: Maximizing Quality and Diversity in Feed-Forward Stylization and Texture Synthesis

abstract: The recent work of Gatys et al., who characterized the style of an image by the statistics of convolutional neural network filters, ignited a renewed interest in the texture generation and image stylization problems. While their image generation technique uses a slow optimization process, recently several authors have proposed to learn generator neural networks that can produce similar outputs in one quick forward pass. While generator networks are promising, they are still inferior in visual quality and diversity compared to generation-by-optimization. In this work, we advance them in two significant ways. First, we introduce an instance normalization module to replace batch normalization with significant improvements to the quality of image stylization. Second, we improve diversity by introducing a new learning formulation that encourages generators to sample unbiasedly from the Julesz texture ensemble, which is the equivalence class of all images characterized by certain filter responses. Together, these two improvements take feed forward texture synthesis and image stylization much closer to the quality of generation-via-optimization, while retaining the speed advantage.


title: Compact wideband directional antenna with three-dimensional structure for microwave-based head imaging systems

abstract: Microwave-based head imaging systems demand wideband antennas with compact size. To ensure high scattered signals from the intended target point, the antennas need to have directional radiation patterns. Designing an antenna by addressing all these issues is challenging as the head imaging system utilizes low microwave frequencies (around 1-3 GHz) to maintain high signal penetration in the lossy human head. This paper reports a coplanar waveguide-fed design that overcomes all these limitations. The antenna is constructed in a three-dimensional manner by combining a dipole antenna with a folded parasitic structure printed on two substrate blocks. The antenna mechanism is explained with the description of its design parameters. The antenna covers 101% fractional bandwidth with a stable gain, good radiation efficiency and well directional radiation characteristics. The overall volume of the antenna is 0.24 x 0.1 x 0.05 lambda(m) where lambda(m) is the wavelength at the lowest frequency of operation.


title: URSA: A System for Uniform Reduction to SAT

abstract: There are a huge number of problems, from various areas, being solved by ::: reducing them to SAT. However, for many applications, translation into SAT is ::: performed by specialized, problem-specific tools. In this paper we describe a ::: new system for uniform solving of a wide class of problems by reducing them to ::: SAT. The system uses a new specification language URSA that combines imperative ::: and declarative programming paradigms. The reduction to SAT is defined ::: precisely by the semantics of the specification language. The domain of the ::: approach is wide (e.g., many NP-complete problems can be simply specified and ::: then solved by the system) and there are problems easily solvable by the ::: proposed system, while they can be hardly solved by using other programming ::: languages or constraint programming systems. So, the system can be seen not ::: only as a tool for solving problems by reducing them to SAT, but also as a ::: general-purpose constraint solving system (for finite domains). In this paper, ::: we also describe an open-source implementation of the described approach. The ::: performed experiments suggest that the system is competitive to ::: state-of-the-art related modelling systems.


title: Kronecker Determinantal Point Processes

abstract: Determinantal Point Processes (DPPs) are probabilistic models over all subsets a ground set of N items. They have recently gained prominence in several applications that rely on diverse subsets. However, their applicability to large problems is still limited due to O(N^3) complexity of core tasks such as sampling and learning. We enable efficient sampling and learning for DPPs by introducing KronDPP, a DPP model whose kernel matrix decomposes as a tensor product of multiple smaller kernel matrices. This decomposition immediately enables fast exact sampling. But contrary to what one may expect, leveraging the Kronecker product structure for speeding up DPP learning turns out to be more difficult. We overcome this challenge, and derive batch and stochastic optimization algorithms for efficiently learning the parameters of a KronDPP.


title: Sub 1 V CMOS bandgap reference design techniques: a survey

abstract: This paper presents a review of constraints, limitation factors and challenges to implement sub 1 V CMOS bandgap voltage reference (BVR) circuits in today's and future submicron technology. Moreover, we provide insight analysis of BVR circuit architectures a designer can relay upon when building CMOS voltage reference.


title: Mild hypoxia is associated with quantitative EEG changes, but not with dissociative symptoms

abstract: Backround and aims. Hypoxia at altitude may lead to mental changes resembling dissociative symptoms. This study examined whether hypoxia precipitates dissociative states in normal subjects and whether quantitative electro- encephalographic (EEG) changes occur. Methods. Dissociative symptoms and EEG changes were examined in a hypobaric chamber. Results. No dissociation was noted. EEG slowing accompanied hypoxia, replicating previous findings.


title: Teleoperation of a mobile robot with model-predictive obstacle avoidance control

abstract: This paper presents a mixed teleoperation/autonomous control approach for navigation and obstacle avoidance in mobile robots. The proposed method builds on an earlier general control framework that systematically combines teleoperation and autonomous control subtasks. This paper considers a scenario in which the user teleoperates a mobile robot while being assisted by an autonomous subtask designed to help avoid collisions with obstacles in the robot task environment. The autonomous subtask control commands are generated by formulating and solving a constrained optimization problem over a rolling horizon window of time into the future. The effectiveness of the proposed model-predictive control obstacle avoidance scheme is demonstrated in teleoperation experiments with a mobile robot using sonar measurements for obstacle localization.


title: FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

abstract: Fully convolutional models for dense prediction have proven successful for a wide range of visual tasks. Such models perform well in a supervised setting, but performance can be surprisingly poor under domain shifts that appear mild to a human observer. For example, training on one city and testing on another in a different geographic region and/or weather condition may result in significantly degraded performance due to pixel-level distribution shift. In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems. Our method consists of both global and category specific adaptation techniques. Global domain alignment is performed using a novel semantic segmentation network with fully convolutional domain adversarial learning. This initially adapted space then enables category specific adaptation through a generalization of constrained weak learning, with explicit transfer of the spatial layout from the source to the target domains. Our approach outperforms baselines across different settings on multiple large-scale datasets, including adapting across various real city environments, different synthetic sub-domains, from simulated to real environments, and on a novel large-scale dash-cam dataset.


title: Event relation acquisition using dependency patterns and confidence-weighted co-occurrence statistics

abstract: Event relation knowledge is important for deep language understanding and inference. Previous work has established automatic acquisition methods of event relations that focus on common sense knowledge acquisition from large-scale unlabeled corpus. However, in the case of domain-specific knowledge acquisition, such a method can not acquire much knowledge due to the limited amount of available knowledge sources. We propose an coverage-oriented acquisition method of event relations. The proposed method utilizes various patterns of dependency structures co-occurring with event relations than the existing method relying only on direct dependency relations between events. Experimental results show that the proposed method can acquire a larger amount of positive relation instances while keeping higher precision compared with the existing method and the proposed method also performs well for small sizes of corpora.


title: Gazetracker: software designed to facilitate eye movement analysis

abstract: The Eye-gaze Response Interface Computer Aid (ERICA) is a computer system developed at the University of Virginia that tracks eye movement. Originally developed as a means to allow individuals with disabilities to communicate, ERICA was then expanded to provide methods for experimenters to analyze eye movements. This paper describes an application called Gaze Tracker™ that facilitates the analysis of a test subject's eye movements and pupil response to visual stimuli, such as still images or dynamic software applications that the test subject interacts with (for example, Internet Explorer).


title: Design of Wireless Sensor Network-Based Greenhouse Environment Monitoring and Automatic Control System

abstract: In view of the characteristics of greenhouse environment monitoring system , a system scheme based on wireless sensor network (WSN) is presented, which adopts Atmega128L chip and CC2530 that is a low power RF chip from TI to design the sink node and sensor nodes in the WSN. The monitoring and management center can control the temperature and humidity of the greenhouse, measure the carbon dioxide content, and collect the information about intensity of illumination, and so on. And the system adopts multilevel energy memory. It combines energy management with energy transfer, which makes the energy collected by solar energy batteries be used reasonably. Therefore, the self-managing energy supply system is established. In addition, the nodes deployment method and time synchronization problem are analyzed in detail. The system can solve the problem of complex cabling with the advantages of low power consumption, low cost, good robustness, extended flexible and high reliability. An effective tool is provided for monitoring and analysis decision-making of the greenhouse environment.


title: Event-based approach to money laundering data analysis and visualization

abstract: Crime specific event patterns are crucial in detecting potential relationships among suspects in criminal networks. However, current link analysis tools commonly used in detection do not utilize such patterns for detecting various types of crimes. These analysis tools usually provide generic functions for all types of crimes and heavily rely on the user's expertise on the domain knowledge of the crime for successful detection. As a result, they are less effective in detecting patterns in certain crimes. In addition, substantial effort is also required for analyzing vast amount of crime data and visualizing the structural views of the entire criminal network. In order to alleviate these problems, an event-based approach to money laundering data analysis and visualization is proposed in this paper. The effectiveness of the proposed method is demonstrated on a money laundering case from Taiwan.


title: Mobile traffic forecasting for maximizing 5G network slicing resource utilization

abstract: The emerging network slicing paradigm for 5G provides new business opportunities by enabling multi-tenancy support. At the same time, new technical challenges are introduced, as novel resource allocation algorithms are required to accommodate different business models. In particular, infrastructure providers need to implement radically new admission control policies to decide on network slices requests depending on their Service Level Agreements (SLA). When implementing such admission control policies, infrastructure providers may apply forecasting techniques in order to adjust the allocated slice resources so as to optimize the network utilization while meeting network slices' SLAs. This paper focuses on the design of three key network slicing building blocks responsible for (i) traffic analysis and prediction per network slice, (ii) admission control decisions for network slice requests, and (iii) adaptive correction of the forecasted load based on measured deviations. Our results show very substantial potential gains in terms of system utilization as well as a trade-off between conservative forecasting configurations versus more aggressive ones (higher gains, SLA risk).


title: A Softbot-Based Interface to the Internet

abstract: The Internet Softbot (software robot) is a fullyimplemented AI agent developed at the University of Washington (Etzioni, Lcsh, & Segal 1993). The softbot uses a UNIX shell and the World-Wide Web to interact with a wide range of internet resources. The softbot’s effectors include ftp, telnet, mail, and numerous file manipulation commaslds. Its sensors include internet facilities such as archie, gopher, netfind, and many more. The softbot is designed to incorporate new facilities into its repertoirc as they become available. The softbot’s "added value" is three-fold. First, it provides an integrated and expressive interface to the internet. Second, the softbot dynamically chooses which facilities to invoke, and in what sequence. For example, the softbot might use netfind to determine David McAllester’s e-mail address. Since it knows that netfind requires a person’s institution as input, the softbot would first search bibliographic databases for a technical report by McAllester which would reveal his institutkm, and then feed that information to netfind. Third, the softbot fluidly backtracks from one facility to another based on information collected at run time. As a result., the softbot’s behavior changes in response to transient system conditions (e.g., the UUCP gateway is down). In this article, we focus on the ideas underlying the softbot-based interface.


ltrasensitive WO 3 gas sensors for NO 2 detection in air and low xygen environment


title: Digital Camera Calibration, Relative Orientation and Essential Matrix Parameters

abstract: The fundamental matrix, based on the co-planarity condition, even though it is very interesting for theoretical issues, it does not allow finding the camera calibration parameters, and the base and rotation parameters altogether. In this work we present an easy calibration method for calculating the internal parameters: pixel dimensions and image center pixel coordinates. We show that the method is slightly easier if the camera rotation angles, in relation with the general referential system, are small. The accuracy of the four calibration parameters are evaluated by simulations. In addition, a method to improve the accuracy is explained. When the calibration parameters are known, the fundamental matrix can be reduced to the essential matrix. In order to find the relative orientation parameters in stereo vision, there is also presented a new method to extract the base and the camera rotation by means of the essential matrix. The proposed method is simple to implement. We also include a simpler method for the relative orientation when the relative rotation angles between the two cameras are small.


title: A vision-guided autonomous quadrotor in an air-ground multi-robot system

abstract: In this paper, a vision-guided autonomous quadrotor in an air-ground multi-robot system has been proposed. This quadrotor is equipped with a monocular camera, IMUs and a flight computer, which enables autonomous flights. Two complementary pose/motion estimation methods, respectively marker-based and optical-flow-based, are developed by considering different altitudes in a flight. To achieve smooth take-off, stable tracking and safe landing with respect to a moving ground robot and desired trajectories, appropriate controllers are designed. Additionally, data synchronization and time delay compensation are applied to improve the system performance. Real-time experiments are conducted in both indoor and outdoor environments.


title: Lessons Learned from Distributed Agile Software Projects: A Case-Based Analysis

abstract: Agile software development in a distributed setting is challenging. The teams involved in the process face difficulties in communication, personnel selection, work culture, and knowledge management. The shortcomings associated with working in different time zones and the inabilities to develop trusting relationships between developers are well known. Companies often take recourse to agile software development methods in a distributed environment in search of reduced cost, higher efficiency, increased flexibility, and good customization. However, it is not clear whether agile methods can be successfully followed and their benefits realized in a distributed setting. This paper revisits and synthesizes the lessons learnt from twelve case studies detailing successful implementation of distributed agile software projects. The cases are analyzed from the perspective of the agile manifesto to determine how closely they follow its values and principles and to what extent they realize the benefits of the agile methodology. The cases lead to the discovery of disparate and innovative solutions adopted by different companies for overcoming the challenges of distributed agile software development. Some solutions are commonplace and others are unique and their combination in the context of the challenges is enlightening. The list of solutions can suitably guide companies that plan to adopt the agile methodology in distributed software development environments in future.


title: KEY ISSUES IN E-PROCUREMENT: PROCUREMENT IMPLEMENTATION AND OPERATION IN THE PUBLIC SECTOR

abstract: This paper presents the analysis from a study into the key lessons learned from e-procurement implementation across a range of UK public sector organisations. The literature relating to e-procurement implementation and operation is reviewed, identifying five main themes addressed by the current literature: impact on cost efficiency; the impact on the form and nature of supplier transaction; e-procurement system implementation; broader IT infrastructure issues; and the behavioural and relational impact of eprocurement. The research carried out was intended to explore the perceptions and reflections of both 'early' and 'late' adopters of e-procurement. Seven key lessons are drawn from the study and presented here. We conclude by proposing areas for further research, including the need for research into failed eprocurement projects.


title: A Comparison of String Distance Metrics for Name-Matching Tasks

abstract: Using an open-source, Java toolkit of name-matching methods, we experimentally compare string distance metrics on the task of matching entity names. We investigate a number of different metrics proposed by different communities, including edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods. Overall, the best-performing method is a hybrid scheme combining a TFIDF weighting scheme, which is widely used in information retrieval, with the Jaro-Winkler string-distance scheme, which was developed in the probabilistic record linkage community.


title: Prenatal diagnosis of congenital heart disease: A review of current knowledge

abstract: Abstract This article reviews important features to improve the diagnosis of congenital heart disease (CHD) by applying ultrasound in prenatal cardiac screening. As low and high-risk pregnancies for CHD are subject to routine obstetric ultrasound, the diagnosis of structural heart defects represents a challenge that involves a team of specialists and subspecialists on fetal ultrasonography. In this review, the images highlight normal anatomy of the heart as well as pathologic cases consistent with cardiac malposition and isomerism, septal defects, pulmonary stenosis/atresia, aortic malformations, hypoplastic left ventricle, conotruncal anomalies, tricuspid dysplasia, and Ebstein’s anomaly, and univentricular heart, among other congenital cardiovascular defects. Anatomical details of most CHD in fetuses were provided by two-dimensional (2D) ultrasound with higher quality imaging, enhancing diagnostic accuracy in a variety of CHD. Moreover, the accuracy of the cardiac defects in obstetrics ultrasound improves the outcome of most CHD, providing planned delivery, aided genetic counseling, and perinatal management.


title: Walking assist device with bodyweight support system

abstract: We aim at realization of a “user-friendly walking assist device” to support the life of the elderly, the work operation of production, and others. Based on a biomedical engineering analysis, we developed a new walking assist device with a concept to reduce the floor reaction force of the user of the device. The device comprises an axis layout in which expansion and contraction mechanisms disposed on the right and left are connected to a seat at a single point, a device layout in which the device is disposed along the inner side of the user's legs, and a mechanism that utilizes a circular arc-shaped rail. With these original layouts and mechanisms, we achieved a walking assist device that can be put on and taken off easily without the need to fasten the device to a user's body, and that can always maintain the assist force vector in the direction from the center of pressure of floor reaction force to the center of gravity of the user's body by using only two actuators. The device can reduce the load on leg muscles and joints in various movements and postures. It was confirmed that the device has an assist effect of reducing the user's leg muscle activities as well as the user's total body energy consumption when the user climbs stairs or performs a squat exercise.


title: Bright Lights and the Pastoral Idyll Ideas of Community Underlying Management Education Methodologies

abstract: The idea of community underlies much of the more participative, if not experiential, approaches to management education and development. More recently the idea has entered the conceptual vocabulary of the `learning organization'. The appeal of community lies in its implied promise of solidarity, belonging, and a sense of personal significance. But these promises can mask darker tendencies towards coercion and the assimilation of differences. This article offers a critique of the notion of community. It proposes an alternative basis for a management pedagogy in which differences are the grounds for debate, dispute and understanding, rather than the reason for assimilation or exclusion.


title: The Robotic Social Attributes Scale (RoSAS): Development and Validation

abstract: Accurately measuring perceptions of robots has become increasingly important as technological progress permits more frequent and extensive interaction between people and robots. Across four studies, we develop and validate a scale to measure social perception of robots. Drawing from the Godspeed Scale and from the psychological literature on social perception, we develop an 18-item scale (The Robotic Social Attribute Scale; RoSAS) to measure people's judgments of the social attributes of robots. Factor analyses reveal three underlying scale dimensions-warmth, competence, and discomfort. We then validate the RoSAS and show that the discomfort dimension does not reflect a concern with unfamiliarity. Using images of robots that systematically vary in their machineness and gender-typicality, we show that the application of these social attributes to robots varies based on their appearance.


title: Sequential Clique Optimization for Video Object Segmentation

abstract: A novel algorithm to segment out objects in a video sequence is proposed in this work. First, we extract object instances in each frame. Then, we select a visually important object instance in each frame to construct the salient object track through the sequence. This can be formulated as finding the maximal weight clique in a complete k-partite graph, which is NP hard. Therefore, we develop the sequential clique optimization (SCO) technique to efficiently determine the cliques corresponding to salient object tracks. We convert these tracks into video object segmentation results. Experimental results show that the proposed algorithm significantly outperforms the state-of-the-art video object segmentation and video salient object detection algorithms on recent benchmark datasets.


title: Real-Time Hair Rendering Using Sequential Adversarial Networks

abstract: We present an adversarial network for rendering photorealistic hair as an alternative to conventional computer graphics pipelines. Our deep learning approach does not require low-level parameter tuning nor ad-hoc asset design. Our method simply takes a strand-based 3D hair model as input and provides intuitive user-control for color and lighting through reference images. To handle the diversity of hairstyles and its appearance complexity, we disentangle hair structure, color, and illumination properties using a sequential GAN architecture and a semi-supervised training approach. We also introduce an intermediate edge activation map to orientation field conversion step to ensure a successful CG-to-photoreal transition, while preserving the hair structures of the original input data. As we only require a feed-forward pass through the network, our rendering performs in real-time. We demonstrate the synthesis of photorealistic hair images on a wide range of intricate hairstyles and compare our technique with state-of-the-art hair rendering methods.


title: Bias and variance approximation in value function estimates

abstract: We consider a finite-state, finite-action, infinite-horizon, discounted reward Markov decision process and study the bias and variance in the value function estimates that result from empirical estimates of the model parameters. We provide closed-form approximations for the bias and variance, which can then be used to derive confidence intervals around the value function estimates. We illustrate and validate our findings using a large database describing the transaction and mailing histories for customers of a mail-order catalog firm.


title: A Visual Analytics Framework for Identifying Topic Drivers in Media Events

abstract: Media data has been the subject of large scale analysis with applications of text mining being used to provide overviews of media themes and information flows. Such information extracted from media articles has also shown its contextual value of being integrated with other data, such as criminal records and stock market pricing. In this work, we explore linking textual media data with curated secondary textual data sources through user-guided semantic lexical matching for identifying relationships and data links. In this manner, critical information can be identified and used to annotate media timelines in order to provide a more detailed overview of events that may be driving media topics and frames. These linked events are further analyzed through an application of causality modeling to model temporal drivers between the data series. Such causal links are then annotated through automatic entity extraction which enables the analyst to explore persons, locations, and organizations that may be pertinent to the media topic of interest. To demonstrate the proposed framework, two media datasets and an armed conflict event dataset are explored.


title: Classification of Rice Leaf Diseases Based onMorphological Changes

abstract: Rice is widely cultivated economical crop in the world. During cultivation the earliest and accurate diagnosis of the rice plant diseases able to reduce the damage, resulting environment protection and better return. In the work, an automated system has been developed to classify the leaf brown spot and the leaf blast diseases of rice plant based on the morphological changes of the plants caused by the diseases. Radial distribution of the hue from the center to the boundary of the spot images has been used as features to classify the diseases by Bayes' and SVM Classifier. The system has been validated using 1000 test spot images of infected rice leaves collected from the field, gives 79.5% and 68.1% accuracies for Bayes' and SVM Classifier based system respectively. blast and the brown spot diseases based on the morphological changes caused by the diseases. The accuracy of the proposed system is compared with the existing classifier, which shows comparable results. The paper, is organized in four Section II describes the process of image acquisition and feature extraction. Section III describes the classification process. Section IV represents results and discussion and the paper is concluded in section V.


title: A double-tier MEC-NFV architecture: Design and optimisation

abstract: The emerging requirements for 5th generation (5G) mobile networks lead to a complete network paradigm refurbishment by leveraging on Network Function Virtualisation (NFV) and Mobile Edge Computing (MEC). While NFV adds flexibility by allowing network functions to be dynamically deployed and inter-connected, MEC brings intelligence at the edge of the mobile network, reduces latency and enhances the offered capacity. This paper analyses the compound effect of simultaneously considering virtual network functions and MEC applications deployed on the same network infrastructure. The proposed architecture aligns and integrates the MEC system with the NFV Management and Orchestration (MANO) by introducing a management subsystem that enriches the MANO with application-oriented orchestration capabilities. This opens new opportunities on jointly managing applications and virtual network functions by allowing MANO-related operations to be triggered by virtual applications runtime processes or MEC services.


title: An Airborne Radar Power Supply With Contactless Transfer of Energy—Part I: Rotating Transformer

abstract: Reliability and precision are key requirements for electronic systems in aerospace applications. Transferring electrical energy from a stationary to a moving device involves wearable parts such as slip rings and brushes. This paper examines the possibility of using a rotating transformer for contactless transfer of energy from the base to the revolving platform of an airborne radar system. The first part of the series focuses on the magnetic interface, investigating its electrical properties and their association with the core and windings geometry. The reader will gain an understanding of the merits and limitations of this technology and will be able to assess its suitability for other applications. The effects of the increased leakage and reduced magnetizing inductances of the transformer are investigated, and two winding layouts are proposed and characterized by measurements and finite-element analysis. Some equations are presented along with practical guidelines on designing a rotating transformer with a 0.25-2-mm air gap. The transformer voltage gain and efficiency plots are introduced as performance-assessment tools. The impact of the air-gap stray flux on the winding conduction losses is shown, and some electromagnetic-compatibility considerations are presented. Finally, a mechanical layout for a 1-kW rotating transformer is proposed.


title: Phage Response to CRISPR-Encoded Resistance in Streptococcus thermophilus

abstract: Clustered regularly interspaced short palindromic repeats (CRISPR) and their associated genes are linked to a mechanism of acquired resistance against bacteriophages. Bacteria can integrate short stretches of phage-derived sequences (spacers) within CRISPR loci to become phage resistant. In this study, we further characterized the efficiency of CRISPR1 as a phage resistance mechanism in Streptococcus thermophilus. First, we show that CRISPR1 is distinct from previously known phage defense systems and is effective against the two main groups of S. thermophilus phages. Analyses of 30 bacteriophage-insensitive mutants of S. thermophilus indicate that the addition of one new spacer in CRISPR1 is the most frequent outcome of a phage challenge and that the iterative addition of spacers increases the overall phage resistance of the host. The added new spacers have a size of between 29 to 31 nucleotides, with 30 being by far the most frequent. Comparative analysis of 39 newly acquired spacers with the complete genomic sequences of the wild-type phages 2972, 858, and DT1 demonstrated that the newly added spacer must be identical to a region (named proto-spacer) in the phage genome to confer a phage resistance phenotype. Moreover, we found a CRISPR1-specific sequence (NNAGAAW) located downstream of the proto-spacer region that is important for the phage resistance phenotype. Finally, we show through the analyses of 20 mutant phages that virulent phages are rapidly evolving through single nucleotide mutations as well as deletions, in response to CRISPR1.


title: Activity forecasting

abstract: We address the task of inferring the future actions of people from noisy visual input. We denote this task activity forecasting. To achieve accurate activity forecasting, our approach models the effect of the physical environment on the choice of human actions. This is accomplished by the use of state-of-the-art semantic scene understanding combined with ideas from optimal control theory. Our unified model also integrates several other key elements of activity analysis, namely, destination forecasting, sequence smoothing and transfer learning. As proof-of-concept, we focus on the domain of trajectory-based activity analysis from visual input. Experimental results demonstrate that our model accurately predicts distributions over future actions of individuals. We show how the same techniques can improve the results of tracking algorithms by leveraging information about likely goals and trajectories.


title: Classes for Fast Maximum Entropy Training

abstract: Maximum entropy models are considered by many to be one of the most promising avenues of language modeling research. Unfortunately, long training times make maximum entropy research difficult. We present a speedup technique: we change the form of the model to use classes. Our speedup works by creating two maximum entropy models, the first of which predicts the class of each word, and the second of which predicts the word itself. This factoring of the model leads to fewer nonzero indicator functions, and faster normalization, achieving speedups of up to a factor of 35 over one of the best previous techniques. It also results in typically slightly lower perplexities. The same trick can be used to speed training of other machine learning techniques, e.g. neural networks, applied to any problem with a large number of outputs, such as language modeling.


title: Thinking ahead: The role and roots of prediction in language comprehension

abstract: Reviewed are studies using event-related potentials to examine when and how sentence context information is used during language comprehension. Results suggest that, when it can, the brain uses context to predict features of likely upcoming items. However, although prediction seems important for comprehension, it also appears susceptible to age-related deterioration and can be associated with processing costs. The brain may address this trade-off by employing multiple processing strategies, distributed across the two cerebral hemispheres. In particular, left hemisphere language processing seems to be oriented toward prediction and the use of top-down cues, whereas right hemisphere comprehension is more bottom-up, biased toward the veridical maintenance of information. Such asymmetries may arise, in turn, because language comprehension mechanisms are integrated with language production mechanisms only in the left hemisphere (the PARLO framework).


title: Positive Psychology in Clinical Practice

abstract: Ke yW ords well being, strength(s), meaning, flow, happiness ■ Abstract Positive psychology is the scientific study of positive experiences and positive individual traits, and the institutions that facilitate their development. A field concerned with well-being and optimal functioning, positive psychology aims to broaden the focus of clinical psychology beyond suffering and its direct alleviation. Our proposed conceptual framework parses happiness into three domains: pleasure, en- gagement, and meaning. For each of these constructs, there are now valid and practical assessment tools appropriate for the clinical setting. Additionally, mounting evidence demonstrates the efficacy and effectiveness of positive interventions aimed at culti- vating pleasure, engagement, and meaning. We contend that positive interventions are justifiable in their own right. Positive interventions may also usefully supplement direct attempts to prevent and treat psychopathology and, indeed, may covertly be a central component of good psychotherapy as it is done now.


title: An overall view of knowledge management challenges for global business

abstract: Purpose – The purpose of this paper is to offer an overall view of knowledge management challenges for global business via discussing the challenges and proposing theoretical and managerial implications.Design/methodology/approach – Based on a comprehensive literature review, the paper identifies six main knowledge management challenges faced by global business today. Then, the challenges are discussed in relation to managerial practice.Findings – The paper argues that developing a working definition of knowledge, dealing with tacit knowledge and utilization of information technology, adaptation to cultural complexity, attention to human resources, developing new organizational structures, and coping with increased competition are the main knowledge management challenges faced by global business today.Practical implications – Suggested implications include a more significant managerial emphasis on considering and dealing with the knowledge management challenges in a holistic manner, taking into account al...


title: Neural Networks in Business: Techniques and Applications for the Operations Researcher

abstract: Abstract This paper presents an overview of the different types of neural network models which are applicable when solving business problems. The history of neural networks in business is outlined, leading to a discussion of the current applications in business including data mining, as well as the current research directions. The role of neural networks as a modern operations research tool is discussed. Scope and purpose Neural networks are becoming increasingly popular in business. Many organisations are investing in neural network and data mining solutions to problems which have traditionally fallen under the responsibility of operations research. This article provides an overview for the operations research reader of the basic neural network techniques, as well as their historical and current use in business. The paper is intended as an introductory article for the remainder of this special issue on neural networks in business.


title: Low-Speed and Standstill Operation of a Sensorless Direct Torque and Flux Controlled IPM Synchronous Motor Drive

abstract: This paper investigates a sensorless direct torque and flux control scheme for interior permanent magnet synchronous motor drives at low speeds, including standstill. Closed-loop control of both torque and stator flux linkage are achieved by using two proportional-integral controllers. The reference voltage vectors are generated by a space vector modulation unit. A high-frequency signal injection technique is adopted at low speeds to obtain the rotor position information. A hybrid current-voltage model flux observer with a smooth handover algorithm estimates the stator flux linkage over the whole speed range. Experimental results confirm the effectiveness of the proposed method.


Experiences of Harvesting Web Resources in Engineering Using Automatic Classification


title: A Multimodal, Full-Surround Vehicular Testbed for Naturalistic Studies and Benchmarking: Design, Calibration and Deployment

abstract: Recent progress in autonomous and semi-autonomous driving has been made possible in part through an assortment of sensors that provide the intelligent agent with an enhanced perception of its surroundings. It has been clear for quite some while now that for intelligent vehicles to function effectively in all situations and conditions, a fusion of different sensor technologies is essential. Consequently, the availability of synchronized multi-sensory data streams are necessary to promote the development of fusion based algorithms for low, mid and high level semantic tasks. In this paper, we provide a comprehensive description of LISA-A: our heavily sensorized, full-surround testbed capable of providing high quality data from a slew of synchronized and calibrated sensors such as cameras, LIDARs, radars, and the IMU/GPS. The vehicle has recorded over 100 hours of real world data for a very diverse set of weather, traffic and daylight conditions. All captured data is accurately calibrated and synchronized using timestamps, and stored safely in high performance servers mounted inside the vehicle itself. Details on the testbed instrumentation, sensor layout, sensor outputs, calibration and synchronization are described in this paper.


title: A new weight initialization method for sigmoidal feedforward artificial neural networks

abstract: Initial weight choice has been recognized to be an important aspect of the training methodology for sigmoidal feedforward neural networks. In this paper, a new mechanism for weight initialization is proposed. The mechanism distributes the initial input to output weights in a manner that all weights (including thresholds) leading into a hidden layer are uniformly distributed in a region and the center of the region from which the weights are sampled are such that no region overlaps for two distinct hidden nodes. The proposed method is compared against random weight initialization routines on five function approximation tasks using the Resilient Backpropagation (RPROP) algorithm for training. The proposed method is shown to lead to about twice as fast convergence to a pre-specifled goal for training as compared to any of the random weight initialization methods. Moreover, it is shown that at least for these problems the networks reach a deeper minima of the error functional during training and generalizes better than the networks trained whose weights were initialized by random weight initialization methods.


Inclusive Smart Cities and Digital Health


title: Angrier Birds: Bayesian reinforcement learning

abstract: We train a reinforcement learner to play a simplified version of the game Angry Birds. The learner is provided with a game state in a manner similar to the output that could be produced by computer vision algorithms. We improve on the efficiency of regular e-greedy Q-Learning with linear function approximation through more systematic exploration in Randomized Least Squares Value Iteration (RLSVI), an algorithm that samples its policy from a posterior distribution on optimal policies. With larger state-action spaces, efficient exploration becomes increasingly important, as evidenced by the faster learning in RLSVI.


title: Purposeful by design?: a serious game design assessment framework

abstract: The lack of assessment tools to analyze serious games and insufficient knowledge on their impact on players is a recurring critique in the field of game and media studies, education science and psychology. Although initial empirical studies on serious games usage deliver discussable results, numerous questions remain unacknowledged. In particular, questions regarding the quality of their formal conceptual design in relation to their purpose mostly stay uncharted. In the majority of cases the designers' good intentions justify incoherence and insufficiencies in their design. In addition, serious games are mainly assessed in terms of the quality of their content, not in terms of their intention-based design. This paper argues that analyzing a game's formal conceptual design, its elements, and their relation to each other based on the game's purpose is a constructive first step in assessing serious games. By outlining the background of the Serious Game Design Assessment Framework and exemplifying its use, a constructive structure to examine purpose-based games is introduced. To demonstrate how to assess the formal conceptual design of serious games we applied the SGDA Framework to the online games "Sweatshop" (2011) and "ICED" (2008).


title: Cross-Domain Entity Resolution in Social Media

abstract: The challenge of associating entities across multiple domains is a key problem in social media understanding. Successful cross-domain entity resolution provides integration of information from multiple sites to create a complete picture of user and community activities, characteristics, and trends. In this work, we examine the problem of entity resolution across Twitter and Instagram using general techniques. Our methods fall into three categories: profile, content, and graph based. For the profile-based methods, we consider techniques based on approximate string matching. For content-based methods, we perform author identification. Finally, for graph-based methods, we apply novel cross-domain community detection methods and generate neighborhood-based features. The three categories of methods are applied to a large graph of users in Twitter and Instagram to understand challenges, determine performance, and understand fusion of multiple methods. Final results demonstrate an equal error rate less than 1%.


title: Mental Representations of Attachment Figures Facilitate Recovery Following Upsetting Autobiographical Memory Recall

abstract: A growing literature shows that even the symbolic presence of an attachment figure facilitates the regulation of negative affect triggered by external stressors. Yet, in daily life, pernicious stressors are often internally generated—recalling an upsetting experience reliably increases negative affect, rumination, and susceptibility to physical and psychological health problems. The present research provides the first systematic examination of whether activating the mental representation of an attachment figure enhances the regulation of affect triggered by thinking about upsetting memories. Using 2 different techniques for priming attachment figure representations and 2 types of negative affect measures (explicit and implicit), activating the mental representation of an attachment figure (vs. an acquaintance or stranger) after recalling an upsetting memory enhanced recovery—eliminating the negative effects of the memory recall (Studies 1–3). In contrast, activating the mental representation of an attachment figure before recalling an upsetting memory had no such effect (Studies 1 and 2). Furthermore, activating the mental representation of an attachment figure after thinking about upsetting memories reduced negative thinking in a stream of consciousness task, and the magnitude of the attachment-induced affective recovery effects as assessed with explicit affect measures predicted mental and physical health in daily life (Study 3). Finally, a meta-analysis of the 3 studies (Study 4) showed that the regulatory benefits conferred by the mental representation of an attachment figure were weaker for individuals high on attachment avoidance. The implications of these findings for attachment, emotion regulation, and mental and physical health are discussed.


title: On modeling and simulation of game theory-based defense mechanisms against DoS and DDoS attacks

abstract: As cyber attacks continue to grow in number, scope, and severity, the cyber security problem has become increasingly important and challenging to both academic researchers and industry practitioners. We explore the applicability of game theoretic approaches to the cyber security problem with focus on active bandwidth depletion attacks. We model the interaction between the attacker and the defender as a two-player non-zero-sum game in two attack scenarios: (i) one single attacking node for Denial of Service (DoS) and (ii) multiple attacking nodes for Distributed DoS (DDoS). The defender's challenge is to determine optimal firewall settings to block rogue traffics while allowing legitimate ones. Our analysis considers the worst-case scenario where the attacker also attempts to find the most effective sending rate or botnet size. In either case, we build both static and dynamic game models to compute the Nash equilibrium that represents the best strategy of the defender. We validate the effectiveness of our game theoretic defense mechanisms via extensive simulation-based experiments using NS-3.


title: GEOMETRICAL ASPECTS OF AIRBORNE LASER SCANNING AND TERRESTRIAL LASER SCANNING

abstract: This paper reviews the current state of laser scanning from airborne and terrestrial platforms for geometric reconstruction of object shape and size. The current performance figures of sensor systems are presented in an overview. Next, their calibration and the orientation of the acquired point clouds is discussed. For airborne deployment this is usually one step, whereas in the terrestrial case laboratory calibration and registration of point clouds are (still) two distinct, independent steps. As laser scanning is an active measurement technology, the interaction of the emitted energy with the object surface has influences on the range measurement. This has to be considered in order to explain geometric phenomena in the data. While the problems, e.g. multiple scattering, are understood well, there is currently a lack of remedies. Then, in analogy to the processing chain, segmentation approaches for laser scanning data are reviewed. Segmentation is a task relevant for almost all applications. Likewise, DTM (digital terrain model) reconstruction is relevant for many applications of airborne laser scanning, and is therefore discussed, too. This paper reviews the main processing steps necessary for many applications of laser scanning.


title: Super-resolution of HEVC videos via convex optimization

abstract: Super Resolution (SR) addresses the problem of image and video upscaling. Most of the best performing SR methods do not take into account any compression prior into the degradation model. Consequently, compression artifacts can be undesirably amplified during SR. In the present work, we propose a novel HEVC-dedicated approach for embedding SR results into a domain that closely fits the compressed observation. Our main contribution is the inclusion of HEVC syntax (block size, quantization parameters etc.) into the degradation model. A recent convex optimization approach is used to solve the associated minimization problem. Over a wide range of resolutions and bitrates, we show that our method improves the results obtained with state of the art SR.


title: Actions as Activities and Activities as Petri nets

abstract: This paper proposes ways to view the rich semantics of actions as activities. This is achieved by unfolding the invoked activities and the implicit joins and forks. The unfolding offers an explanation for the semantics of actions by activities and also allows a simple translation from an activities subset to a well-known Petri net class. One example of an activity diagram and the respective Petri net is included.


title: An end-to-end generative adversarial network for crowd counting under complicated scenes

abstract: Crowd counting and analyzing its distribution is a challenging video surveillance application. In this paper, a totally novel, end-to-end way to estimate the crowd number under complicated scenes is proposed. For the purpose, we apply the conditional adversarial networks to translate the input image to its density map. The conditional generative adversarial model is trained with input image and its corresponding density image. The proposed method avoid the design of complex CNN architecture to extract specific property features. Besides, no more data augmentation is needed in our method. Evaluated on the dataset of Shanghaitech which consists of two challenge parts, our methods shows convincing counting results with high quality estimated density images. Moreover, our experiments can been done in an efficient and labor saving way.


title: The stability of error-related brain activity with increasing trials

abstract: The error-related negativity (ERN) and error positivity (Pe) are increasingly being examined as neural correlates of response monitoring. The minimum number of error trials included in grand averages varies across studies; indeed, there has not been a systematic investigation on the number of trials required to obtain a stable ERN and Pe. In the current study, the ERN and Pe were quantified as two random trials were added to participants’ (N 5 53) ERP averages. Adding trials increased the correlation with the grand average ERN and Pe; however, high correlations (rs4.80) were obtained with only 6 trials. Internal reliability of the ERN and Pe reached moderate levels after 6 and 2 trials and the signal-to-noise ratio of the ERN and Pe did not change after 8 and 4 trials, respectively. Combined, these data suggest that the ERN and Pe can be quantified using a minimum of between 6 and 8 error trials.


title: Towards context-sensitive domain of Islamic knowledge ontology extraction

abstract: Ontology is one of the essential topics in the scope of an important area of current computer science and Semantic Web. Ontologies present well defined, straightforward and standardized form of the repositories (vast and reliable knowledge) where it can be interoperable and machine understandable. There are many possible utilization of ontologies from automatic annotation of web resources to domain representation and reasoning task. Ontology is an effective conceptualism used for the semantic web. However there is none of the research try to construct an ontology from Islamic knowledge which consist of Holy Quran, Hadiths and etc. Therefore as a first stage, in this paper we try to propose a simple methodology in order to extract a concept based on Al-Quran. Finally, we discuss about the experiment that have been conducted.


title: The structure of Design Problem Spaces

abstract: It is proposed that there are important generalizations about problem solving in design activity that reach across specific disciplines. A framework for the study of design is presented that (a) characterizes design as a radial category and fleshes out the task environment of the prototypical cases; (b) takes the task environment seriously; (c) shows that this task environment occurs in design tasks, but does not occur in every nondesign task; (d) explicates the impact of this task environment on the design problem space; and (e) demonstrates that, given the structure of the information-processing system, the features noted in the problem spaces of design tasks will not all occur in problem spaces where the task environment is vastly different. This analysis leads to the claim that there are a set of invariant features in the problem spaces of design situations that collectively constitute a design problem space. Protocol studies are reported in which the problem spaces of three design tasks in architecture, mechanical engineering, and instructional design are explored and compared with several protocols from nondesign problem-solving tasks.


title: ZooBP: Belief Propagation for Heterogeneous Networks

abstract: Given a heterogeneous network, with nodes of different types - e.g., products, users and sellers from an online recommendation site like Amazon - and labels for a few nodes ('honest', 'suspicious', etc), can we find a closed formula for Belief Propagation (BP), exact or approximate? Can we say whether it will converge? ::: ::: BP, traditionally an inference algorithm for graphical models, exploits so-called "network effects" to perform graph classification tasks when labels for a subset of nodes are provided; and it has been successful in numerous settings like fraudulent entity detection in online retailers and classification in social networks. However, it does not have a closed-form nor does it provide convergence guarantees in general. We propose ZooBP, a method to perform fast BP on undirected heterogeneous graphs with provable convergence guarantees. ZooBP has the following advantages: (1) Generality: It works on heterogeneous graphs with multiple types of nodes and edges; (2) Closed-form solution: ZooBP gives a closed-form solution as well as convergence guarantees; (3) Scalability: ZooBP is linear on the graph size and is up to 600× faster than BP, running on graphs with 3.3 million edges in a few seconds. (4) Effectiveness: Applied on real data (a Flipkart e-commerce network with users, products and sellers), ZooBP identifies fraudulent users with a near-perfect precision of 92.3 % over the top 300 results.


title: Coalition Formation: From Software Agents to Robots

abstract: A problem that has recently attracted the attention of the research community is the autonomous formation of robot teams to perform complex multi-robot tasks. The corresponding problem for software agents is also known in the multi-agent community as the coalition formation problem. Numerous algorithms for software agent coalition formation have been provided that allow for efficient cooperation in both competitive and cooperative environments. However, despite the plethora of relevant literature on the software agent coalition formation problem, and the existence of similar problems in theoretical computer science, the multi-robot coalition formation problem has not been sufficiently grounded for different tasks and task environments. In this paper, comparisons are drawn to highlight the differences between software agents and robotics, and parallel problems from theoretical computer science are identified. This paper further explores robot coalition formation in different practical robotic environments. A heuristic-based coalition formation algorithm from our previous work was extended to operate in precedence ordered cooperative environments. In order to explore coalition formation in competitive environments, the paper also studies the RACHNA system, a market based coalition formation system. Finally, the paper investigates the notion of task preemption for complex multi-robot tasks in random allocation environments.


title: Virtual Model Control: An Intuitive Approach for Bipedal Locomotion

abstract: Virtual model control is a motion control framework that uses virtual components to create virtual forces generated when the virtual components interact with a robot system. An algorithm derived based on the virtual model control framework is applied to a physical planar bipedal robot. It uses a simple set of virtual components that allows the robot to walk successfully over level terrain. This paper also describes how the algorithm can be augmented for rough terrain walking based on geometric consideration. The resulting algorithm is very simple and does not require the biped to have an extensive sensory system. The robot does not know the slope gradients and transition locations in advance. The ground is detected using foot contact switches. Using the algorithm, we have successfully compelled a simulated seven-link planar biped to walk blindly up and down slopes and over rolling terrain.


title: Automated Optic Disc Detection in Retinal Images of Patients with Diabetic Retinopathy and Risk of

abstract: In this paper, a new automated methodology to detect the optic disc (OD) automatically in retinal images from patients with risk of being affected by Diabetic Retinopathy (DR) and Macular Edema (ME) is presented. The detection procedure comprises two independent methodologies. On one hand, a location methodology obtains a pixel that belongs to the OD using image contrast analysis and structure filtering techniques and, on the other hand, a boundary segmentation methodology estimates a circular approximation of the OD boundary by applying mathematical morphology, edge detection techniques and the Circular Hough Transform. The methodologies were tested on a set of 1200 images composed of 229 retinographies from patients affected by DR with risk of ME, 431 with DR and no risk of ME and 540 images of healthy retinas. The location methodology obtained 98.83% success rate, whereas the OD bound- ary segmentation methodology obtained good circular OD boundary approximation in 94.58% of cases. The average computational time measured over the total set was 1.67 seconds for OD location and 5.78 seconds for OD boundary segmentation.