title: Substrate integrated waveguide (SIW) power amplifier using CBCPW-to-SIW transition for matching network

abstract: In this paper, for the first time a novel substrate integrated waveguide (SIW)-based 10W power amplifier (PA), designed with conductor-backed coplanar waveguide (CBCPW)-to-SIW transition matching network (MN), is presented. Transition between CBCPW and SIW is employed for both input and output MN designs, the proposed SIW PA can be easily connected with any microstrip or SIW-based circuits. Asymmetrical and symmetrical types of CBCPW-to-SIW transition MN are proposed. Output SIW-based MN is designed with asymmetrical structure by using one inductive metalized post and input SIW-based MN is designed with symmetrical structure by using two identical inductive metalized posts. One SIW-based 10W PA using GaN HEMT at 3.62 GHz is designed, fabricated, and measured. Measured results show that the maximum power added efficiency (PAE) is 54.24 % with 39.74 dBm output power and the maximum gain is 13.31 dB. At the design frequency of 3.6 GHz, the size of proposed SIW-based PA is comparable with other microstrip-based PAs.


title: The Wisconsin Card Sorting Test: Theoretical Analysis and Modeling in a Neuronal Network

abstract: Neuropsychologists commonly use the Wisconsin Card Sorting Test as a test of the integrity of frontal lobe functions. However, an account of its range of validity and of the neuronal mechanisms involved is lacking. We analyze the test at 3 different levels. First, the different versions of the test are described, and the results obtained with normal subjects and brain-lesioned patients are reviewed. Second, a computational analysis is used to reveal what algorithms may pass the test, and to predict their respective performances. At this stage, 3 cognitive components are isolated that may critically contribute to performance: the ability to change the current rule when negative reward occurs, the capacity to memorize previously tested rules in order to avoid testing them twice, and the possibility of rejecting some rules a priori by reasoning. Third, a model neuronal network embodying these 3 components is described. The coding units are clusters of neurons organized in layers, or assemblies. A sensorimotor loop enables the network to sort the input cards according to several criteria (color, form, etc.). A higher-level assembly of rule-coding clusters codes for the currently tested rule, which shifts when negative reward is received. Internal testing of the possible rules, analogous to a reasoning process, also occurs, by means of an endogenous auto-evaluation loop. When lesioned, the model reproduces the behavior of frontal lobe patients. Plausible biological or molecular implementations are presented for several of its components.


title: Group decision making process for supplier selection with VIKOR under fuzzy environment

abstract: During recent years, how to determine suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The VIKOR method was developed to solve multiple criteria decision making (MCDM) problems with conflicting and non-commensurable (different units) criteria, assuming that compromising is acceptable for conflict resolution, the decision maker wants a solution that is the closest to the ideal, and the alternatives are evaluated according to all established criteria. In this paper, linguistic values are used to assess the ratings and weights for these factors. These linguistic ratings can be expressed in trapezoidal or triangular fuzzy numbers. Then, a hierarchy MCDM model based on fuzzy sets theory and VIKOR method is proposed to deal with the supplier selection problems in the supply chain system. A numerical example is proposed to illustrate an application of the proposed model.


title: Occurrence and distribution of microplastics in marine sediments along the Belgian coast

abstract: Abstract Plastic debris is known to undergo fragmentation at sea, which leads to the formation of microscopic particles of plastic; the so called ‘microplastics’. Due to their buoyant and persistent properties, these microplastics have the potential to become widely dispersed in the marine environment through hydrodynamic processes and ocean currents. In this study, the occurrence and distribution of microplastics was investigated in Belgian marine sediments from different locations (coastal harbours, beaches and sublittoral areas). Particles were found in large numbers in all samples, showing the wide distribution of microplastics in Belgian coastal waters. The highest concentrations were found in the harbours where total microplastic concentrations of up to 390 particles kg−1 dry sediment were observed, which is 15–50 times higher than reported maximum concentrations of other, similar study areas. The depth profile of sediment cores suggested that microplastic concentrations on the beaches reflect the global plastic production increase.


title: Model Multiple Heterogeneity via Hierarchical Multi-Latent Space Learning

abstract: In many real world applications such as satellite image analysis, gene function prediction, and insider threat detection, the data collected from heterogeneous sources often exhibit multiple types of heterogeneity, such as task heterogeneity, view heterogeneity, and label heterogeneity. To address this problem, we propose a Hierarchical Multi-Latent Space (HiMLS) learning approach to jointly model the triple types of heterogeneity. The basic idea is to learn a hierarchical multi-latent space by which we can simultaneously leverage the task relatedness, view consistency and the label correlations to improve the learning performance. We first propose a multi-latent space framework to model the complex heterogeneity, which is used as a building block to stack up a multi-layer structure so as to learn the hierarchical multi-latent space. In such a way, we can gradually learn the more abstract concepts in the higher level. Then, a deep learning algorithm is proposed to solve the optimization problem. The experimental results on various data sets show the effectiveness of the proposed approach.


title: Topic modeling: beyond bag-of-words

abstract: Some models of textual corpora employ text generation methods involving n-gram statistics, while others use latent topic variables inferred using the "bag-of-words" assumption, in which word order is ignored. Previously, these methods have not been combined. In this work, I explore a hierarchical generative probabilistic model that incorporates both n-gram statistics and latent topic variables by extending a unigram topic model to include properties of a hierarchical Dirichlet bigram language model. The model hyperparameters are inferred using a Gibbs EM algorithm. On two data sets, each of 150 documents, the new model exhibits better predictive accuracy than either a hierarchical Dirichlet bigram language model or a unigram topic model. Additionally, the inferred topics are less dominated by function words than are topics discovered using unigram statistics, potentially making them more meaningful.


title: Approximation algorithms for orienteering and discounted-reward TSP

abstract: In this paper, we give the first constant-factor approximation algorithm for the rooted orienteering problem, as well as a new problem that we call the Discounted-Reward TSP, motivated by robot navigation. In both problems, we are given a graph with lengths on edges and prizes (rewards) on nodes, and a start node s. In the orienteering problem, the goal is to find a path that maximizes the reward collected, subject to a hard limit on the total length of the path. In the Discounted-Reward TSP, instead of a length limit we are given a discount factor /spl gamma/, and the goal is to maximize total discounted reward collected, where reward for a node reached at time t is discounted by /spl gamma//sup t/. This is similar to the objective considered in Markov decision processes (MDPs) except we only receive a reward the first time a node is visited. We also consider tree and multiple-path variants of these problems and provide approximations for those as well. Although the unrooted orienteering problem, where there is no fixed start node s, has been known to be approximable using algorithms for related problems such as k-TSP (in which the amount of reward to be collected is fixed and the total length is approximately minimized), ours is the first to approximate the rooted question, solving an open problem based on B. Awerbuch et al. (1999) and E.M. Arkin (1998).


title: A visualization of music

abstract: Currently, the most popular method of visualizing music is music notation. Through music notation, an experienced musician can gain an impression of how a particular piece of music sounds simply by looking at the notes on paper. However, most listeners are unfamiliar or uncomfortable with the complex nature of music notation. The goal of this project is to present an alternate method for visualizing music that makes use of color and 3D space. This paper describes one method of visualizing music in 3D space. The implementation of this method shows that music visualization is an effective technique, although it is certainly not the only possible method for accomplishing the task. Throughout the course of this project, several variations and alternative approaches were discussed. The final version of this project reflects the decisions that were made in order to present the best possible representation of music data.


title: Characterizing the software process: a maturity framework

abstract: A description is given of a software-process maturity framework that has been developed to provide the US Department of Defense with a means to characterize the capabilities of software-development organizations. This software-development process-maturity model reasonably represents the actual ways in which software-development organizations improve. It provides a framework for assessing these organizations and identifying the priority areas for immediate improvement. It also helps identify those places where advanced technology can be most valuable in improving the software-development process. The framework can be used by any software organization to assess its own capabilities and identify the most important areas for improvement. >


title: Glenohumeral joint injection: a comparative study of ultrasound and fluoroscopically guided techniques before MR arthrography

abstract: To assess the variability in accuracy of contrast media introduction, leakage, required time and patient discomfort in four different centres, each using a different image-guided glenohumeral injection technique. Each centre included 25 consecutive patients. The ultrasound-guided anterior (USa) and posterior approach (USp), fluoroscopic-guided anterior (FLa) and posterior (FLp) approach were used. Number of injection attempts, effect of contrast leakage on diagnostic quality, and total room, radiologist and procedure times were measured. Pain was documented with a visual analogue scale (VAS) pain score. Access to the joint was achieved in all patients. A successful first attempt significantly occurred more often with US (94%) than with fluoroscopic guidance (72%). Leakage of contrast medium did not cause interpretative difficulties. With US guidance mean room, procedure and radiologist times were significantly shorter (p < 0.001). The USa approach was rated with the lowest pre- and post-injection VAS scores. The four image-guided injection techniques are successful in injection of contrast material into the glenohumeral joint. US-guided injections and especially the anterior approach are significantly less time consuming, more successful on the first attempt, cause less patient discomfort and obviate the need for radiation and iodine contrast.


title: Scalable Coating and Properties of Transparent, Flexible, Silver Nanowire

abstract: We report a comprehensive study of transparent and conductive silver nanowire (Ag NW) electrodes,includingascalablefabricationprocess,morphologies,andoptical,mechanicaladhesion,andflexibility properties, and various routes to improve the performance. We utilized a synthesis specifically designed for long andthinwiresforimprovedperformanceintermsofsheetresistanceandopticaltransmittance.Twenty/sqand 80% specular transmittance, and 8 ohms/sq and 80% diffusive transmittance in the visible range are achieved, whichfallinthesamerangeasthebestindiumtinoxide(ITO)samplesonplasticsubstratesforflexibleelectronics andsolarcells.TheAgNWelectrodesshowopticaltransparenciessuperiortoITOfornear-infraredwavelengths(2- foldhighertransmission).Owingtolightscatteringeffects,theAgNWnetworkhasthelargestdifferencebetween diffusive transmittance and specular transmittance when compared with ITO and carbon nanotube electrodes, a propertywhichcouldgreatlyenhancesolarcellperformance.AmechanicalstudyshowsthatAgNWelectrodeson flexiblesubstratesshowexcellentrobustnesswhensubjectedtobending.Wealsostudytheelectricalconductance ofAgnanowiresandtheirjunctionsandreportafacileelectrochemicalmethodforaAucoatingtoreducethewire- to-wire junction resistance for better overallfilm conductance. Simple mechanical pressing was also found to increasetheNWfilmconductanceduetothereductionofjunctionresistance.Theoverallpropertiesoftransparent Ag NW electrodes meet the requirements of transparent electrodes for many applications and could be an immediate ITO replacement forflexible electronics and solar cells.


Segmentation of Brain Tumor from Brain MRI Images Reintroducing K – Means with advanced Dual Localization Method


title: 2003), “Helping a Victim or Helping the Victim: Altruism and Identifiability

abstract: Although it has been claimed that people care more about identifiable than statistical victims, demonstrating this “identifiable victim effect” has proven difficult because identification usually provides information about a victim, and people may respond to the information rather than to identification per se. We show that a very weak form of identifiability—determining the victim without providing any personalizing information—increases caring. In the first, laboratory study, subjects were more willing to compensate others who lost money when the losers had already been determined than when they were about to be. In the second, field study, people contributed more to a charity when their contributions would benefit a family that had already been selected from a list than when told that the family would be selected from the same list.


title: Contextual Outlier Interpretation

abstract: Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority. While many statistical learning and data mining techniques have been used for developing more effective outlier detection algorithms, the interpretation of detected outliers does not receive much attention. Interpretation is becoming increasingly important to help people trust and evaluate the developed models through providing intrinsic reasons why the certain outliers are chosen. It is difficult, if not impossible, to simply apply feature selection for explaining outliers due to the distinct characteristics of various detection models, complicated structures of data in certain applications, and imbalanced distribution of outliers and normal instances. In addition, the role of contrastive contexts where outliers locate, as well as the relation between outliers and contexts, are usually overlooked in interpretation. To tackle the issues above, in this paper, we propose a novel Contextual Outlier INterpretation (COIN) method to explain the abnormality of existing outliers spotted by detectors. The interpretability for an outlier is achieved from three aspects: outlierness score, attributes that contribute to the abnormality, and contextual description of its neighborhoods. Experimental results on various types of datasets demonstrate the flexibility and effectiveness of the proposed framework compared with existing interpretation approaches.


title: Seven-point checklist of dermoscopy revisited

abstract: BACKGROUND ::: Most dermoscopic algorithms to diagnose melanoma were established more than 10 years ago and have been tested primarily on clear-cut melanomas and excised melanocytic naevi. ::: ::: ::: OBJECTIVES ::: To assess the diagnostic performance of pattern analysis and seven-point checklist on lesions that reflect the current clinical setting, compared with a revised seven-point checklist with a lower threshold for excision. ::: ::: ::: METHODS ::: Eight experienced dermatologists viewed dermoscopic images of 100 excised melanomas, 100 excised naevi and 100 monitored naevi. Each lesion was evaluated by pattern analysis and scored as naevus, melanoma or lesion to be excised. Images were then evaluated using the seven-point criteria, with both standard and revised thresholds for excision. ::: ::: ::: RESULTS ::: Pooled data using the pattern analysis algorithm showed that 82% of melanomas and 87·5% of monitored naevi were correctly scored as lesion to be excised and benign naevus, respectively. Using the standard and revised thresholds for the seven-point checklist, excision was recommended for 77·9% and 87·8% of the lesions in the melanoma set, respectively. The standard threshold produced 'no excision' recommendations for 85·6% of the monitored naevi, compared with 74·5% using the revised threshold. Pattern analysis, standard seven-point and revised seven-point algorithms resulted in recommendations of 'excision' for 63·6%, 60·3% and 72·0% of the excised naevi, respectively. ::: ::: ::: CONCLUSIONS ::: The diagnostic approach to naevi and melanoma should be adapted to the current clinical setting, in which patients may present with early-stage melanomas and multiple atypical naevi. To increase sensitivity, a revised seven-point checklist with a lower threshold for excision should be used.


title: High School Risk Factors Associated With Alcohol Trajectories and College Alcohol Use

abstract: Students often drink alcohol at their highest levels in college. It is suggested that students with the most severe drinking problems begin drinking by or in high school, but studies on high school drinking tend to focus on students who have academic problems and are not college-bound. The purpose of this study was to examine high school drinking behaviors that were risk indicators for problematic student drinking in college, and to use these to identify high school-to-college drinking trajectories. Using an electronic survey, data were obtained on 430 first- and second-year male and female college students mandated to a harm-reduction program due to violation of college alcohol-use policies. Data on alcohol use were obtained through a modified version of the Daily Drinking Questionnaire in which students were asked to describe their drinking during high school and college. Students were also asked to report the frequency with which they drank to intoxication and whether or not they had experienced blacko...


title: Trajectory planning for Bertha — A local, continuous method

abstract: In this paper, we present the strategy for trajectory planning that was used on-board the vehicle that completed the 103 km of the Bertha-Benz-Memorial-Route fully autonomously. We suggest a local, continuous method that is derived from a variational formulation. The solution trajectory is the constrained extremum of an objective function that is designed to express dynamic feasibility and comfort. Static and dynamic obstacle constraints are incorporated in the form of polygons. The constraints are carefully designed to ensure that the solution converges to a single, global optimum.


title: Cascade Mask Generation Framework for Fast Small Object Detection

abstract: Detecting small objects is a challenging task. Existing CNN-based objection detection pipeline faces such a dilemma: using a high-resolution image as input incurs high computational cost, but using a low-resolution image as input loses the feature representation of small objects and therefore leads to low accuracy. In this work, we propose a cascade mask generation framework to tackle this issue. The proposed framework takes in multi-scale images as input and processes them in ascending order of the scale. Each processing stage outputs object proposals as well as a region-of-interest (RoI) mask for the next stage. With RoI convolution, the masked regions can be excluded from the computation in the next stage. The procedure continues until the largest scale image is processed. Finally, the object proposals generated from multiple scales are classified by a post classifier. Extensive experiments on Tsinghua-Tencent 100K traffic sign benchmark demonstrate that our approach achieves state-of-the-art small object detection performance at a significantly improved speed-accuracy tradeoff compared with previous methods.


title: Blockchain-based sharing services: What blockchain technology can contribute to smart cities

abstract: Background ::: The notion of smart city has grown popular over the past few years. It embraces several dimensions depending on the meaning of the word “smart” and benefits from innovative applications of new kinds of information and communications technology to support communal sharing.


title: The Impact of Customer Trust and Perception of Security Control on the Acceptance of Electronic Commerce

abstract: The number of Internet users has increased dramatically,but many are reluctant to provide sensitive personal information to Web sites because they do not trust e-commerce security.This paper investigates the impact of customer perceptions of security control on e-commerce acceptance. Trust is examined as the mediating factor of the relationship, using Internet banking as the research domain because bank customers are generally concerned about processing sensitive information like financial information. A Web survey of Internet banking users collected 502 cases. Statistical analyses, using structural equation modeling, indicated that perceptions of nonrepudiation, privacy protection, and data integrity have a significant impact on trust in e-commerce. Trust also has a significant impact on e-commerce acceptance. Implications and further research directions are presented.


title: A definition of peer-to-peer networking for the classification of peer-to-peer architectures and applications

abstract: This paper offers a definition for peer-to-peer networking and makes the differences to common so called client server-architectures clear. With this definition we are able to classifiy currently existing networking concepts in the Internet either as "pure" peer-to-peer, or "hybrid" peer-to-peer or client server architecture.


title: Recombinant Hepatitis B Vaccination in Renal Failure Patients

abstract: Hepatitis B (HB) in haemodialysis patients results in morbidity and mortality, through chronicity, which leads to cirrhosis and liver carcinoma, even after renal transplantation. Hepatitis B vaccination is protective against HB virus infection. Suppressed immunity in renal failure leads to low HB vaccination success rates. Uremia, inadequate dialysis, use of low biocompatibility dialysis material, hyperparathyroidism, anemia, iron overload and malnutrition are all factors contributing to depressed immunity. Renal failure, associated with chronic inflammation, leads to impaired monokine production which results in decreased immunity. This impairment could result from defective HLA-DR B7-2 expression on monocytes. Hepatitis B vaccination non-responders express increased levels of HLA class II alleles (T-cell immune response modulators) DRB1 01 (DR1) and DRB1 15 (DR15). Various methods have been used to enhance the immune response to HB vaccination such as recombinant adjuvants, thymopentine, IL-2, levamisole and GM-CSF: they have produced variable results. Better dialysis biocompatibility and adequacy have also been conducted to overcome this low immune response. Response to conventional intramuscular HB vaccination is considered an index of adequate dialysis and low inflammatory state, both associated with better cardiovascular outcome and survival. HB vaccination reinforcement techniques evolved from an initial intramuscular double/multiple-dosing regimen to more frequent intradermal smaller dose injection. This newer regimen achieves a higher and almost complete seroconversion rate, although frequent boosters shots are necessary to maintain protective levels. Experience with pre-S1/S2, third generation, vaccines is limited and they have not been proven to be more effective than intradermally administered S antigens. Recombinant HB vaccines, intradermally administered, have been shown to elicit an immune response in all renal failure patients. Additionally the use of recombinant erythropoietin treatment to correct anemia contributes to this success.


title: REVIEW - A reference data set for retinal vessel profiles

abstract: This paper describes REVIEW, a new retinal vessel reference dataset. This dataset includes 16 images with 193 vessel segments, demonstrating a variety of pathologies and vessel types. The vessel edges are marked by three observers using a special drawing tool. The paper also describes the algorithm used to process these segments to produce vessel profiles, against which vessel width measurement algorithms can be assessed. Recommendations are given for use of the dataset in performance assessment. REVIEW can be downloaded from http://ReviewDB.lincoln.ac.uk.


title: Emissions analysis from various industrial wastes to be used as raw material for ceramics

abstract: Waste from industrial processes is accumulated in different industries, in most cases without the necessary security control. This fact has led to the development of several methods to reuse or recycle this waste, and the mixture with clay for the production of ceramic material appears as a good alternative. In this case, the high temperature conditions used to obtain sintered products, lead to the combustion of all combustible materials present in the mixtures. The extreme cases are given by the waste called “pore formers”, such as sawdust, discarded polystyrene from panels and packaging industries, and ground rubber. These materials completely burn during the heat treatments. The aim of this study is to analyze the emissions produced during heat treatment of diverse residues, as would happen during the firing of the bricks. Waste such as foundry sand, sludge from steel plants, sawdust, polystyrene, demolition residues, electric arc furnace dust, among others, are studied. Electrochemical sensor technology is used to detect pollutants as CO2, CO, SO2, HC, NOx and laser technology for PM10. In all cases, the detected levels for these pollutants in laboratory essays are within the range thresholds for air quality in Argentina, but for polystyrene they are relatively high when compared with average levels of emissions in the lab, without ongoing heat treatments. However, some results indicate the need for analysis in the industrial environment in case of their application. From the obtained results it is concluded that it is determinant to analyze the environmental aptitude of these processes before being taken to industrial scales.


title: Clinical Psychology Review 27 (2007) 572–581 Quality of life in the anxiety disorders: A meta-analytic review

abstract: There has been significant interest in the impact of anxiety disorders on quality of life. In this meta-analytic review, we empirically evaluate differences in quality of life between patients with anxiety disorders and nonclinical controls. Thirty-two patient samples from 23 separate studies (N=2892) were included in the analysis. The results yielded a large effect size indicating poorer quality of life among anxiety disorder patients vs. controls and this effect was observed across all anxiety disorders. Compared to control samples, no anxiety disorder diagnosis was associated with significantly poorer overall quality of life than was any other anxiety disorder diagnosis. Examination of specific domains of QOL suggests that impairments may be particularly prominent among patients with post-traumatic stress disorder. QOL domains of mental health and social functioning were associated with the highest levels of impairment among anxiety disorder patients. These findings are discussed in the context of future research on the assessment of quality of life in the anxiety disorders. © 2007 Elsevier Ltd. All rights reserved.


title: External Patch Prior Guided Internal Clustering for Image Denoising

abstract: Natural image modeling plays a key role in many vision problems such as image denoising. Image priors are widely used to regularize the denoising process, which is an illposed inverse problem. One category of denoising methods exploit the priors (e.g., TV, sparsity) learned from external clean images to reconstruct the given noisy image, while another category of methods exploit the internal prior (e.g., self-similarity) to reconstruct the latent image. Though the internal prior based methods have achieved impressive denoising results, the improvement of visual quality will become very difficult with the increase of noise level. In this paper, we propose to exploit image external patch prior and internal self-similarity prior jointly, and develop an external patch prior guided internal clustering algorithm for image denoising. It is known that natural image patches form multiple subspaces. By utilizing Gaussian mixture models (GMMs) learning, image similar patches can be clustered and the subspaces can be learned. The learned GMMs from clean images are then used to guide the clustering of noisypatches of the input noisy images, followed by a low-rank approximation process to estimate the latent subspace for image recovery. Numerical experiments show that the proposed method outperforms many state-of-the-art denoising algorithms such as BM3D and WNNM.


title: A Novel Representation and Feature Matching Algorithm for Automatic Pairwise Registration of Range Images

abstract: Automatic registration of range images is a fundamental problem in 3D modeling of free-from objects. Various feature matching algorithms have been proposed for this purpose. However, these algorithms suffer from various limitations mainly related to their applicability, efficiency, robustness to resolution, and the discriminating capability of the used feature representation. We present a novel feature matching algorithm for automatic pairwise registration of range images which overcomes these limitations. Our algorithm uses a novel tensor representation which represents semi-local 3D surface patches of a range image by third order tensors. Multiple tensors are used to represent each range image. Tensors of two range images are matched to identify correspondences between them. Correspondences are verified and then used for pairwise registration of the range images. Experimental results show that our algorithm is accurate and efficient. Moreover, it is robust to the resolution of the range images, the number of tensors per view, the required amount of overlap, and noise. Comparisons with the spin image representation revealed that our representation has more discriminating capabilities and performs better at a low resolution of the range images.


title: “We Need a Popular Discipline”: Contemporary Politics and the Crisis of the Negative

abstract: FILIPPO DEL LUCCHESE and JASON SMITH: We would like to begin by asking you to clarify the relation between philosophy and politics. What do you mean when you speak, for example, of a militant philosophy? ALAIN BADIOU: Since its beginnings, philosophy’s relationship to the political has been fundamental. It’s not something invented by modernity. Plato’s central work is called The Republic, and it is entirely devoted to questions of the city or polis. This link has remained fundamental throughout the history of philosophy. But I think there are two basic ways of structuring this relationship. The first way assigns philosophy the responsibility for finding a foundation for the political. Philosophy is called upon to reconstruct the political on the basis of this foundation. This current argues that it is possible to locate, for every politics, an ethical norm and that philosophy should first have the task of reconstructing or naming this norm and then of judging the relation between this norm and the multiplicity of political practices. In this sense, then, what opens the relation between philosophy and politics is the idea of a foundation as well as an ethical conception of the political. But there is a second orientation that is completely different. This current maintains that in a certain sense politics is primary and that the political exists without, before, and differently from philosophy. The political would be what I call a condition of philosophy. In this case, the relation between philosophy and politics would be, in a certain sense, retroactive. That is, it would be a relation in


title: Automated Conjecturing VII: The Graph Brain Project&Big Mathematics

abstract: The Graph Brain Project is an experiment in how the use of automated mathematical discovery software, databases, large collaboration, and systematic investigation provide a model for how mathematical research might proceed in the future. Our Project began with the development of a program that can be used to generate invariant-relation and property-relation conjectures in many areas of mathematics. This program can produce conjectures which are not implied by existing (published) theorems. Here we propose a new approach to push forward existing mathematical research goals---using automated mathematical discovery software. We suggest how to initiate and harness large-scale collaborative mathematics. We envision mathematical research labs similar to what exist in other sciences, new avenues for funding, new opportunities for training students, and a more efficient and effective use of published mathematical research. And our experiment in graph theory can be imitated in many other areas of mathematical research. Big Mathematics is the idea of large, systematic, collaborative research on problems of existing mathematical interest. What is possible when we put our skills, tools, and results together systematically?


title: Estimating Motion and size of moving non-line-of-sight objects in cluttered environments

abstract: We present a technique for motion and size estimation of non-line-of-sight (NLOS) moving objects in cluttered environments using a time of flight camera and multipath analysis. We exploit relative times of arrival after reflection from a grid of points on a diffuse surface and create a virtual phased-array. By subtracting space-time impulse responses for successive frames, we separate responses of NLOS moving objects from those resulting from the cluttered environment. After reconstructing the line-of-sight scene geometry, we analyze the space of wavefronts using the phased array and solve a constrained least squares problem to recover the NLOS target location. Importantly, we can recover target's motion vector even in presence of uncalibrated time and pose bias common in time of flight systems. In addition, we compute the upper bound on the size of the target by backprojecting the extremas of the time profiles. Ability to track targets inside rooms despite opaque occluders and multipath responses has numerous applications in search and rescue, medicine and defense. We show centimeter accurate results by making appropriate modifications to a time of flight system.


title: Robust multivariate autoregression for anomaly detection in dynamic product ratings

abstract: User provided rating data about products and services is one key feature of websites such as Amazon, TripAdvisor, or Yelp. Since these ratings are rather static but might change over time, a temporal analysis of rating distributions provides deeper insights into the evolution of a products' quality. Given a time-series of rating distributions, in this work, we answer the following questions: (1) How to detect the base behavior of users regarding a product's evaluation over time? (2) How to detect points in time where the rating distribution differs from this base behavior, e.g., due to attacks or spontaneous changes in the product's quality? To achieve these goals, we model the base behavior of users regarding a product as a latent multivariate autoregressive process. This latent behavior is mixed with a sparse anomaly signal finally leading to the observed data. We propose an efficient algorithm solving our objective and we present interesting findings on various real world datasets.


title: Co-localization in Real-World Images

abstract: In this paper, we tackle the problem of co-localization in real-world images. Co-localization is the problem of simultaneously localizing (with bounding boxes) objects of the same class across a set of distinct images. Although similar problems such as co-segmentation and weakly supervised localization have been previously studied, we focus on being able to perform co-localization in real-world settings, which are typically characterized by large amounts of intra-class variation, inter-class diversity, and annotation noise. To address these issues, we present a joint image-box formulation for solving the co-localization problem, and show how it can be relaxed to a convex quadratic program which can be efficiently solved. We perform an extensive evaluation of our method compared to previous state-of-the-art approaches on the challenging PASCAL VOC 2007 and Object Discovery datasets. In addition, we also present a large-scale study of co-localization on ImageNet, involving ground-truth annotations for 3, 624 classes and approximately 1 million images.


title: Do knowledge management infrastructures support organizational knowledge management processes? A case study on Indonesian telecommunication company

abstract: Knowledge is a strategic asset for an organization. Subsequently, effective knowledge management (KM) can improve organization performance. Therefore in implementing KM, the organization must know the role of KM infrastructures in supporting organizational KM processes. This research is aimed to assess the role of KM infrastructures that being implemented by the organization in supporting organizational KM processes. The research model used in this research is a modification between the research model from Allameh, Zare, and Davoodi in year 2011 and the model of KM infrastructures and KM processes from Becerra-Fernandez and Sabherwal in year 2010. This model is used as it covers all KM infrastructures and KM processes that have been researched before. Partial least squares approach is used to analyze data from 58 respondents of a telecommunication company in Indonesia. The data was collected by distributing online and hardcopy questionnaires to the employees of the company. The finding from this study is that more than 80% KM processes are influenced by KM infrastructures so it can be concluded that KM infrastructures provide a significant influence on organizational KM processes.


title: In silico drug combination discovery for personalized cancer therapy

abstract: Drug combination therapy, which is considered as an alternative to single drug therapy, can potentially reduce resistance and toxicity, and have synergistic efficacy. As drug combination therapies are widely used in the clinic for hypertension, asthma, and AIDS, they have also been proposed for the treatment of cancer. However, it is difficult to select and experimentally evaluate effective combinations because not only is the number of cancer drug combinations extremely large but also the effectiveness of drug combinations varies depending on the genetic variation of cancer patients. A computational approach that prioritizes the best drug combinations considering the genetic information of a cancer patient is necessary to reduce the search space. We propose an in-silico method for personalized drug combination therapy discovery. We predict the synergy between two drugs and a cell line using genomic information, targets of drugs, and pharmacological information. We calculate and predict the synergy scores of 583 drug combinations for 31 cancer cell lines. For feature dimension reduction, we select the mutations or expression levels of the genes in cancer-related pathways. We also used various machine learning models. Extremely Randomized Trees (ERT), a tree-based ensemble model, achieved the best performance in the synergy score prediction regression task. The correlation coefficient between the synergy scores predicted by ERT and the actual observations is 0.738. To compare with an existing drug combination synergy classification model, we reformulate the problem as a binary classification problem by thresholding the synergy scores. ERT achieved an F1 score of 0.954 when synergy scores of 20 and -20 were used as the threshold, which is 8.7% higher than that obtained by the state-of-the-art baseline model. Moreover, the model correctly predicts the most synergistic combination, from approximately 100 candidate drug combinations, as the top choice for 15 out of the 31 cell lines. For 28 out of the 31 cell lines, the model predicts the most synergistic combination in the top 10 of approximately 100 candidate drug combinations. Finally, we analyze the results, generate synergistic rules using the features, and validate the rules through the literature survey. Using various types of genomic information of cancer cell lines, targets of drugs, and pharmacological information, a drug combination synergy prediction pipeline is proposed. The pipeline regresses the synergy level between two drugs and a cell line as well as classifies if there exists synergy or antagonism between them. Discovering new drug combinations by our pipeline may improve personalized cancer therapy.


Chapter 3 – Social Capital: Its Origins and Applications in Modern Sociology*


title: B.: True-Randomness and Pseudo-Randomness in Ring Oscillator-Based True Random Number Generators

abstract: The paper deals with true random number generators employing oscillator ::: rings, namely, with the one proposed by Sunar et al. ::: in 2007 and enhanced by Wold and Tan in 2009. Our mathematical analysis ::: shows that both architectures behave identically when ::: composed of the same number of rings and ideal logic components. ::: However, the reduction of the number of rings, as proposed ::: by Wold and Tan, would inevitably cause the loss of entropy. ::: Unfortunately, this entropy insufficiency is masked ::: by the pseudo-randomness caused by XOR-ing clock signals having different ::: frequencies. Our simulation model shows that the ::: generator, using more than 18 ideal jitter-free rings having slightly ::: different frequencies and producing only pseudo-randomness, ::: will let the statistical tests pass. We conclude that a smaller ::: number of rings reduce the security if the entropy reduction is ::: not taken into account in post-processing. Moreover, the designer cannot ::: avoid that some of rings will have the same frequency, ::: which will cause another loss of entropy. In order to confirm this, we ::: show how the attacker can reach a state where over ::: 25% of the rings are locked and thus completely dependent. This effect ::: can have disastrous consequences on the system security.


The Ontology Extraction Maintenance Framework Text-To-Onto


title: The Secrets of Enduring Love: How to Make Relationships Last

abstract: The Secrets of Enduring Love is the only relationship self-help book to be based on an extensive survey and interview study with people in long-term relationships. The book focuses on what we know about what they actually do to sustain their relationships. It’s illustrated with quotes and examples from the research throughout, as well as including activities from it to help readers to reflect on their own relationships and to try things out. ::: ::: One of the key messages from the research is that different things work for different people, and at different times in the relationship. For this reason the book focuses on the diversity of practices that readers might bring into their own relationships; assisting them to recognise the small things which they may be already doing but which ordinarily go by unnoticed, and offering them a helping hand to find out what works best for them.


title: Kinematic Design to Improve Ergonomics in Human Machine Interaction

abstract: This paper introduces a novel kinematic design paradigm for ergonomic human machine interaction. Goals for optimal design are formulated generically and applied to the mechanical design of an upper-arm exoskeleton. A nine degree-of-freedom (DOF) model of the human arm kinematics is presented and used to develop, test, and optimize the kinematic structure of an human arm interfacing exoskeleton. The resulting device can interact with an unprecedented portion of the natural limb workspace, including motions in the shoulder-girdle, shoulder, elbow, and the wrist. The exoskeleton does not require alignment to the human joint axes, yet is able to actuate each DOF of our redundant limb unambiguously and without reaching into singularities. The device is comfortable to wear and does not create residual forces if misalignments exist. Implemented in a rehabilitation robot, the design features of the exoskeleton could enable longer lasting training sessions, training of fully natural tasks such as activities of daily living and shorter dress-on and dress-off times. Results from inter-subject experiments with a prototype are presented, that verify usability over the entire workspace of the human arm, including shoulder and shoulder girdle


title: Considering Different Network Topologies in Optimal Overcurrent Relay Coordination Using a Hybrid GA

abstract: The directional overcurrent relays (DOCRs) coordination problem is usually studied based on a fixed network topology in an interconnected power system, and is formulated as an optimization problem. In practice, the system may be operated in different topologies due to outage of the transmission lines, transformers, and generating units. There are some situations for which the changes in the network topology of a system could cause the protective system to operate without selectivity. The aim of this paper is to study DOCRs coordination considering the effects of the different network topologies in the optimization problem. Corresponding to each network topology, a large number of coordination constraints should be taken into account in the problem formulation. In this situation, in addition to nonlinearity and nonconvexity, the optimization problem experiences many coordination constraints. The genetic algorithm (GA) is selected as a powerful tool in solving this complex and nonconvex optimization problem. In this paper, in order to improve the convergence of the GA, a new hybrid method is introduced. The results show a robust and optimal solution can be efficiently obtained by implementing the proposed hybrid GA method.


The Influence of Marketing Mix and Customer Satisfaction on Customer Loyalty among Hijab Consumers


title: PlayLOGO 3D: A 3D Interactive Video Game for Early Programming Education: Let LOGO Be a Game

abstract: 'PlayLOGO 3D' is a LOGO-like environment to implement Game Based Learning activities especially designed for children aged 6-13 years who learn the very basic instances related to programming. Its educational effectiveness is expected to be shown after school students get involved in formal programming lessons using LOGO or other languages. It is emphasized that students need an introductory level for programming based on their intuitive knowledge before getting involved with algorithmic concepts using formal programming tools. This paper outlines an approach for: A) Using a subset of LOGO language in a 3D environment, B) Propose an alternative method for pre-programming education and C) Design a Serious Games application to address all of the above. Usability evaluation results are discussed in later sections. While the enduser evaluation is still in progress, the Expert Review Method was used for initial evaluation based on a set of heuristics for game usability, game play and educational effectiveness.


title: Fixed budget quantized kernel least-mean-square algorithm

abstract: This paper presents a quantized kernel least mean square algorithm with a fixed memory budget, named QKLMS-FB. In order to deal with the growing support inherent in online kernel methods, the proposed algorithm utilizes a pruning criterion, called significance measure, based on a weighted contribution of the existing data centers. The basic idea of the proposed methodology is to discard the center with the smallest influence on the whole system, when a new sample is included in the dictionary. The significance measure can be updated recursively at each step which is suitable for online operation. Furthermore, the proposed methodology does not need any a priori knowledge about the data and its computational complexity is linear with the center number. Experiments show that the proposed algorithm successfully prunes the least ''significant'' centers and preserves the important ones, resulting in a compact KLMS model with little loss in accuracy.


title: An application of a game development framework in higher education

abstract: This paper describes how a game development framework was used as a learning aid in a software engineering. Games can be used within higher education in various ways to promote student participation, enable variation in how lectures are taught, and improve student interest. In this paper, we describe a case study at the Norwegian University of Science and Technology (NTNU) where a game development framework was applied to make students learn software architecture by developing a computer game. We provide a model for how game development frameworks can be integrated with a software engineering or computer science course. We describe important requirements to consider when choosing a game development framework for a course and an evaluation of four frameworks based on these requirements. Further, we describe some extensions we made to the existing game development framework to let the students focus more on software architectural issues than the technical implementation issues. Finally, we describe a case study of how a game development framework was integrated in a software architecture course and the experiences from doing so.


title: Indexing multi-dimensional data in a cloud system

abstract: Providing scalable database services is an essential requirement for extending many existing applications of the Cloud platform. Due to the diversity of applications, database services on the Cloud must support large-scale data analytical jobs and high concurrent OLTP queries. Most existing work focuses on some specific type of applications. To provide an integrated framework, we are designing a new system, epiC, as our solution to next-generation database systems. In epiC, indexes play an important role in improving overall performance. Different types of indexes are built to provide efficient query processing for different applications. In this paper, we propose RT-CAN, a multi-dimensional indexing scheme in epiC. RT-CAN integrates CAN [23] based routing protocol and the R-tree based indexing scheme to support efficient multi-dimensional query processing in a Cloud system. RT-CAN organizes storage and compute nodes into an overlay structure based on an extended CAN protocol. In our proposal, we make a simple assumption that each compute node uses an R-tree like indexing structure to index the data that are locally stored. We propose a query-conscious cost model that selects beneficial local R-tree nodes for publishing. By keeping the number of persistently connected nodes small and maintaining a global multi-dimensional search index, we can locate the compute nodes that may contain the answer with a few hops, making the scheme scalable in terms of data volume and number of compute nodes. Experiments on Amazon's EC2 show that our proposed routing protocol and indexing scheme are robust, efficient and scalable.


Apply blockchain technology to electric vehicle battery refueling


title: Parameterized models for a RF chip-to-substrate interconnect

abstract: Skyrocketing growth in the cellular personal communications services (PCS) sector has fueled the needs for higher density, more functionality, and greater performance on both handset and base stations. Third generation wireless standards, which require hardware upgrades, loom on the horizon. RF component suppliers are scrambling to find solutions at the IC, package, and PCB levels to meet these challenges. RF module packaging is considered as one of the low cost solutions for the future wireless products. One of the critical design needs for RF interconnects is to understand the electrical performance of wire bond (the RF interconnect of choice) at and above frequency of interest, and to determine the performance limit for the wire bond chip-to-substrate interconnect. The availability of design kit or library would result in a substantial reduction in design cycle times. Using wire bond as example, this paper illustrates the developmental stages that turn electromagnetic characteristics of a physical structure into a design library. Fullwave simulation using Ansoft HFSS and compact models extraction using optimization tool for wire bond are shown, followed by in-depth discussions of wire bond parameterized models. Validation of parameterized model by measurement is presented.


Disease Named Entity Recognition Using Conditional Random Fields


title: Midterm Results of Surgical Intramyocardial Implantation of Autologous Angiogenic Cell Precursors for Cardiomyopathy

abstract: OBJECTIVES: ::: To report midterm results and assess clinical markers which can predict intramyocardial angiogenic cell precursors (ACPs) injection for cardiomyopathy outcomes. ::: METHODS: ::: Between May 2005 and April 2010, 143 consecutive cardiomyopathy patients underwent intramyocardial ACPs injection. Sixty patients were dilated cardiomyopathy (DCM) and 83 were ischemic cardiomyopathy (ICM). Mean age was 59.6 ± 12.1 years. ACPs were isolated from patient’s own blood and cultured. Number of cells prior to injection was 47.3 ± 36.8 million cells. ACPs were injected into non-viable myocardium and hypokinetic segments. Combined coronary artery surgery and cell injection were performed in 34.9% of ICM. Kaplan-Meier was used to estimate survival time. Cox proportional hazard model was applied by fitting data which included age, gender, diagnosis, diabetes, hypertension, hypercholesterolemia, pulmonary hypertension, renal failure, NYHA class, serum creatinine, preop LVEF, type of operations and number of ACPs into the model to identify predictors of death. ::: RESULTS: ::: There was no new ventricular arrhythmia. Thirty-day mortality rate was 3.3% (2/60) for DCM and 8.4% (7/83) for ICM. Overall death rate was 14.5/1000/month (95%CI: 10.6-19.5). Overall survival probability at 12, 24, 36 and 48 months was 79.9% (95%CI: 72.1-85.8), 67.9% (95%CI: 58.5-75.6), 62.9% (95%CI: 52.4-71.7), 55.4% (95%CI: 43.0-66.2), respectively. LVEF improved by 3.6±12.3% (P=0.04) for DCM and 7.6±10.1% (P<0.001) for ICM. ICM patients with combined coronary artery surgery and cell injection showed more LVEF improvement. (11.8±11.6% vs 4.9±8.0%, P=0.007). Cox regression analyses suggested only preop LVEF (hazard ratio 0.91, 95% CI 0.87-0.95, P<0.001) was associated with decreased survival. ::: CONCLUSIONS: ::: Intramyocardial ACPs injection improved LVEF in both DCM and ICM. Preop LVEF was a significant survival predictor


title: Crystal: software-defined storage for multi-tenant object stores

abstract: Object stores are becoming pervasive due to their scalability and simplicity. Their broad adoption, however, contrasts with their rigidity for handling heterogeneous workloads and applications with evolving requirements, which prevents the adaptation of the system to such varied needs. In this work, we present Crystal, the first Software-Defined Storage (SDS) architecture whose core objective is to efficiently support multi-tenancy in object stores. Crystal adds a filtering abstraction at the data plane and exposes it to the control plane to enable high-level policies at the tenant, container and object granularities. Crystal translates these policies into a set of distributed controllers that can orchestrate filters at the data plane based on real-time workload information. We demonstrate Crystal through two use cases on top of OpenStack Swift: One that proves its storage automation capabilities, and another that differentiates IO bandwidth in a multi-tenant scenario. We show that Crystal is an extensible platform to deploy new SDS services for object stores with small overhead.


title: NESTA, The NICTA Energy System Test Case Archive

abstract: In recent years the power systems research community has seen an explosion of work applying operations research techniques to challenging power network optimization problems. Regardless of the application under consideration, all of these works rely on power system test cases for evaluation and validation. However, many of the well established power system test cases were developed as far back as the 1960s with the aim of testing AC power flow algorithms. It is unclear if these power flow test cases are suitable for power system optimization studies. This report surveys all of the publicly available AC transmission system test cases, to the best of our knowledge, and assess their suitability for optimization tasks. It finds that many of the traditional test cases are missing key network operation constraints, such as line thermal limits and generator capability curves. To incorporate these missing constraints, data driven models are developed from a variety of publicly available data sources. The resulting extended test cases form a compressive archive, NESTA, for the evaluation and validation of power system optimization algorithms.


title: Cognitive support by language visualization: A case study with hindi language

abstract: The main objective of this research is to give a cognitive support to a person for understanding Hindi language text using automatic text visualization (ATV). ATV is especially useful for the persons with learning disabilities (LD). This paper focuses on the background and complexity of the problem. The impact on comprehension through visualized text over reading the text is discussed. The architecture of Preksha — a Hindi text visulizer — is given and several illustrative examples of scenes generated using it are given.


title: Survey on Sentiment Analysis using Twitter Dataset

abstract: Social networking sites like twitter have millions of people share their thoughts day by day as tweets. As tweet is characteristic short and basic way of expression. So in this review paper we focused on sentiment analysis of Twitter data. The Sentiment Analysis sees as area of text data mining and NLP. The research of sentiment analysis of Twitter data can be performed in different aspects. This paper shows sentiment analysis types and techniques used to perform extraction of sentiment from tweets. In this survey paper, we have taken comparative study of different techniques and approaches of sentiment analysis having twitter as a data.


title: 2018 Low-Power Image Recognition Challenge

abstract: The Low-Power Image Recognition Challenge (LPIRC, this https URL) is an annual competition started in 2015. The competition identifies the best technologies that can classify and detect objects in images efficiently (short execution time and low energy consumption) and accurately (high precision). Over the four years, the winners' scores have improved more than 24 times. As computer vision is widely used in many battery-powered systems (such as drones and mobile phones), the need for low-power computer vision will become increasingly important. This paper summarizes LPIRC 2018 by describing the three different tracks and the winners' solutions.


title: Effects of time-to-trigger parameter on handover performance in SON-based LTE systems

abstract: In this paper, we evaluate handover (HO) performance when we apply various time-to-trigger (TTT) methods in self-organizing network (SON)-based long term evolution (LTE) systems. Although TTT can mitigate the wasteful ping-pong HO effect, it also can cause undesirable radio link failure (RLF) due to delayed HO. The optimal HO timings that produce the lowest ping-pong rate within allowable RLF rate vary depending on user equipment (UE) speeds and neighboring cell configurations. To achieve efficient HO timings, we propose and investigate two methods: “adaptive” and “grouping.” In the “adaptive method,” we select the adaptive TTT value for each UE speed based on RLF rate of 2%. The “grouping method” classifies UE speeds into three ranges and assigns the proper TTT value to each range. To apply the LTE specification more effectively, we suggest the criteria of grouping, and propose the proper TTT value for each range. We consider HO in two neighboring cell configurations: from a macro cell to either a macro cell or a pico cell. Simulation results show that the HO performance of the “adaptive method” is greatly improved compared to that of applying fixed TTT values. The results also show that the performance of the “grouping method”, using proposed criteria and proper TTT values, is comparable to that of the “adaptive method.”


title: New MRI Grading System for the Cervical Canal Stenosis

abstract: OBJECTIVE. The purpose of this study was to propose a new MRI grading system for cervical canal stenosis and to evaluate the reproducibility of the system. MATERIALS AND METHODS. Cervical canal stenosis was classified according to the T2-weighted sagittal images into the following grades: grade 0, absence of canal stenosis; grade 1, subarachnoid space obliteration exceeding 50%; grade 2, spinal cord deformity; and grade 3, spinal cord signal change. The MRI scans of 82 patients (37 men and 45 women; mean age, 65.2 years; range, 60–86 years) were independently analyzed by six radiologists. Interobserver and intraobserver agreements were analyzed using intraclass correlation coefficient (ICC), along with the percentage agreement and kappa statistics. RESULTS. The ICC for interobserver agreement was 0.716–0.802, indicating good-to-excellent agreement. For the distinction among the four grades, the percentage of agreement was 63–64% (κ = 0.60–0.62). The percentage of agreement for the presence of cervical can...


title: A Current Control Scheme of Brushless DC Motors Driven by Four-Switch Three-Phase Inverters

abstract: Based on the brushless dc motor driven by a four-switch three-phase inverter (FSTPI), a current control scheme is proposed to reduce the current ripple of both the normal conduction region and the commutation region. Assuming c-phase winding is connected to the middle point of a dc-link capacitance, in the normal conduction region when a-phase and b-phase windings conduct, the current of c-phase may not be zero because of c-phase back electromotive force. The proposed strategy adds two regulating vectors into each control cycle based on the traditional PWM scheme, and controls the c-phase current to be zero by controlling the working time of the regulating vectors in each control cycle. In the commutation region, the noncommutated phase or the outgoing phase switch is modulated by comparing the change rate of the incoming and outgoing phase currents to maintain the noncommutated phase current constant. Compared with the traditional current control strategy, good control effect is kept in both the normal conduction region and the commutation region by accurately controlling the working time of the voltage vectors. The proposed strategy does not need to adjust the parameters of the controller, and it is simple and easy to implement. The experimental results prove the correctness and effectiveness of the control strategy.


title: G2DeNet: Global Gaussian Distribution Embedding Network and Its Application to Visual Recognition

abstract: Recently, plugging trainable structural layers into deep convolutional neural networks (CNNs) as image representations has made promising progress. However, there has been little work on inserting parametric probability distributions, which can effectively model feature statistics, into deep CNNs in an end-to-end manner. This paper proposes a Global Gaussian Distribution embedding Network (G2DeNet) to take a step towards addressing this problem. The core of G2DeNet is a novel trainable layer of a global Gaussian as an image representation plugged into deep CNNs for end-to-end learning. The challenge is that the proposed layer involves Gaussian distributions whose space is not a linear space, which makes its forward and backward propagations be non-intuitive and non-trivial. To tackle this issue, we employ a Gaussian embedding strategy which respects the structures of both Riemannian manifold and smooth group of Gaussians. Based on this strategy, we construct the proposed global Gaussian embedding layer and decompose it into two sub-layers: the matrix partition sub-layer decoupling the mean vector and covariance matrix entangled in the embedding matrix, and the square-rooted, symmetric positive definite matrix sub-layer. In this way, we can derive the partial derivatives associated with the proposed structural layer and thus allow backpropagation of gradients. Experimental results on large scale region classification and fine-grained recognition tasks show that G2DeNet is superior to its counterparts, capable of achieving state-of-the-art performance.


title: Gamma and neutron attenuation behaviours of boron carbide–silicon carbide composites

abstract: Abstract In this study, the gamma and neutron attenuation behaviors of pure boron carbide and boron carbide–silicon carbide composites which include three different silicon carbide ratios (20%, 30%, and 40%) by volume were investigated against Cs-137, Co-60 gamma radioisotope sources and Pu–Be neutron source. Transmission technique was used in the experiments to investigate the gamma and neutron attenuation properties of the materials. Linear and mass attenuation coefficients of the samples were determined for 0.662 (Cs-137) and 1.25 MeV (Co-60) energetic gamma rays. In addition the total macroscopic cross-sections (∑ T ) were calculated for the materials against Pu–Be neutron source. Theoretical mass attenuation coefficients were calculated from XCOM computer code. The experimental and theoretical mass attenuation coefficients were compared and evaluated with each other. In addition half value thickness (HVT) calculations were carried out by using linear attenuation coefficients and total macroscopic cross-sections. The results showed that increasing silicon carbide ratio decreases HVTs against Cs-137 and Co-60 gamma radioisotope sources whereas increases HVTs against Pu–Be neutron source. The mass attenuation coefficients were compatible with the theoretical (XCOM) values. Increasing silicon carbide ratio in boron carbide–silicon carbide composites causes higher gamma attenuation and lower neutron attenuation values.


Cross-transmission of Escherichia coli producing OXA-181 in hospitalized patients and failure of carbapenemase detection by commercial and in-house PCR assays


title: SFCPerf: An automatic performance evaluation framework for service function chaining

abstract: Network Function Virtualization allows the provi-sioning and composition of on-demand network function chains tailored to an application or a service. Repeatable compliance tests and performance comparison of network functions and the whole function chains are required for virtual network function manufacturers and telecommunication operators. In this paper, we propose and develop SFCPerf, a framework for an automatic performance evaluation of service function chaining. SFCPerf describes all experimental configuration as a single workflow and provides automatic: (i) environment creation and setup; (ii) network configuration; (iii) experimental data measurement; (iv) experiment execution and control; and (v) data preliminary analysis. Our framework provides repeatability for experimenting different network functions and virtualization infrastructures. To demonstrate SFCPerf functionality, we design and implement a prototype of a service function chain that complies with the Network Service Header (NSH). We show the results of an SFCPerf experiment that evaluates the performance of our prototype, composed of an intrusion detection system (IDS) and a firewall, running on top of the open platform for network function virtualization (OPNFV).


title: Existence Detection of Objects in Images for Robot Vision Using Saliency Histogram Features

abstract: In robotics and computer vision, saliency maps are frequently used to identify regions that contain potential objects of interest and to restrict object detection to those regions only. However, common saliency approaches do not provide information as to whether there really is an interesting object triggering saliency and therefore tend to highlight needless background as potential regions of interest. This paper addresses the problem by exploiting histogram features extracted from saliency maps to predict the existence of interesting objects in images and to quickly prune uninteresting images. To validate our approach, we constructed a database that consists of 1000 background and object images captured in the working environment of our robot. Experimental results demonstrate that our approach achieves good detection performance and outperforms an existing existence detection approach [1].


title: An analysis of weight loss articles and advertisements in mainstream women’s health and fitness magazines

abstract: Background: Magazines are a commonly used source for health and fitness information. Little is known about the nature and extent of weight loss strategies and products presented in mainstream women’s health and fitness magazines. ::: Methods: This preliminary cross-sectional study evaluated the prevalence of articles and advertisements featuring weight loss content and products in mainstream US-based health and fitness magazines, as well as assessed weight loss themes presented. Thirty-one US health and fitness-focused magazine issues were coded. Prevalence of, product type, and themes related to weight loss in articles and advertisements were assessed. ::: Results: Among the 31 issues of the five US-based women’s magazines examined, we identified 39 articles (4.8% [95% CI = 3.3% to 5.5%] of 819 articles) related to weight loss with 14 identified weight loss topics. The most prevalent article topics covered were exercising/workouts(32.0% [95% CI = 28.8% to 33.6%]) followed by dieting (18.6% [95% CI = 15.9% to 19.9%]).The most common product advertised was weight loss pills (46.0% [95% CI = 42.6% to 47.7%]).Fat burners were also frequently advertised (14.9% [95% CI = 12.5% to 16.1%]) followed by hunger reduction strategies (10.3% [95% CI = 8.2% to 11.3%]) and fat blockers (6.9% [95% CI= 5.2% to 7.8%]). ::: Conclusion: Articles presented information about exercise and dieting whereas advertisements supported potentially harmful health beliefs and behaviors. As a well-utilized American media format, health and fitness-focused magazines have an opportunity to communicate frequent,accurate messaging about healthy weight reduction and limit advertisements that may include misleading claims.


title: VisDock: A Toolkit for Cross-Cutting Interactions in Visualization

abstract: Standard user applications provide a range of cross-cutting interaction techniques that are common to virtually all such tools: selection, filtering, navigation, layer management, and cut-and-paste. We present VisDock , a JavaScript mixin library that provides a core set of these cross-cutting interaction techniques for visualization, including selection (lasso, paths, shape selection, etc), layer management (visibility, transparency, set operations, etc), navigation (pan, zoom, overview, magnifying lenses, etc), and annotation (point-based, region-based, data-space based, etc). To showcase the utility of the library, we have released it as Open Source and integrated it with a large number of existing web-based visualizations. Furthermore, we have evaluated VisDock using qualitative studies with both developers utilizing the toolkit to build new web-based visualizations, as well as with end-users utilizing it to explore movie ratings data. Results from these studies highlight the usability and effectiveness of the toolkit from both developer and end-user perspectives.


title: An ontology-based approach to Chinese semantic advertising

abstract: In the web advertising domain, contextual advertising and sponsored search are two of the main advertising channels used to display related advertisements on web pages. A major challenge for contextual advertising is to match advertisements and web pages based on their semantics. When a web page and its semantically related advertisements contain many different words, the performance of the traditional methods can be very poor. In particular, there are few studies presented for Chinese contextual advertising that are based on semantics. To address these issues, we propose an ontology-based approach to Chinese semantic advertising. We utilize an ontology called the Taobao Ontology and populate it by automatically adding related phrases as instances. The ontology is used to match web pages and advertisements on a conceptual level. Based on the Taobao Ontology, the proposed method exploits seven distance functions to measure the similarities between concepts and web pages or advertisements. Then, the similarities between web pages and advertisements are calculated by considering the ontology-based similarities as well as term-based similarities. The empirical experiments indicate that our method is able to match Chinese web pages and advertisements with a relatively high accuracy. Among the seven distance functions, Cosine distance and Tanimoto distance show the best performance in terms of precision, recall, and F-measure. In addition, our method outperforms two contextual advertising methods, i.e., the impedance coupling method and the SVM-based method.


title: Infants rapidly learn word-referent mappings via cross-situational statistics

abstract: First word learning should be difficult because any pairing of a word and scene presents the learner with an infinite number of possible referents. Accordingly, theorists of children’s rapid word learning have sought constraints on word-referent mappings. These constraints are thought to work by enabling learners to resolve the ambiguity inherent in any labeled scene to determine the speaker’s intended referent at that moment. The present study shows that 12- and 14-month old infants can resolve the uncertainty problem in another way, not by unambiguously deciding the referent in a single word-scene pairing, but by rapidly evaluating the statistical evidence across many individually ambiguous words and scenes.


title: Historical Bibliography of Quantum Computing

abstract: This bibliographic essay reviews seminal papers in quantum computing. Although quantum computing is a young science, its researchers have already published thousands of noteworthy articles, far too many to list here. Therefore, this appendix is not a comprehensive chronicle of the emergence and evolution of the field but rather a guided tour of some of the papers that spurred, formalized, and furthered its study. Quantum computing draws on advanced ideas from computer science, physics, and mathematics, and most major papers were written by researchers conversant in all three fields. Nevertheless, all the articles described in this appendix can be appreciated by computer scientists.


title: Embodied hands: modeling and capturing hands and bodies together

abstract: Humans move their hands and bodies together to communicate and solve tasks. Capturing and replicating such coordinated activity is critical for virtual characters that behave realistically. Surprisingly, most methods treat the 3D modeling and tracking of bodies and hands separately. Here we formulate a model of hands and bodies interacting together and fit it to full-body 4D sequences. When scanning or capturing the full body in 3D, hands are small and often partially occluded, making their shape and pose hard to recover. To cope with low-resolution, occlusion, and noise, we develop a new model called MANO (hand Model with Articulated and Non-rigid defOrmations). MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses. The model is realistic, low-dimensional, captures non-rigid shape changes with pose, is compatible with standard graphics packages, and can fit any human hand. MANO provides a compact mapping from hand poses to pose blend shape corrections and a linear manifold of pose synergies. We attach MANO to a standard parameterized 3D body shape model (SMPL), resulting in a fully articulated body and hand model (SMPL+H). We illustrate SMPL+H by fitting complex, natural, activities of subjects captured with a 4D scanner. The fitting is fully automatic and results in full body models that move naturally with detailed hand motions and a realism not seen before in full body performance capture. The models and data are freely available for research purposes at http://mano.is.tue.mpg.de.


title: Adaptive Engagement of Cognitive Control in Context‐Dependent Decision Making

abstract: Many decisions require a context-dependent mapping from sensory evidence to action. The capacity for flexible information processing of this sort is thought to depend on a cognitive control system in frontoparietal cortex, but the costs and limitations of control entail that its engagement should be minimized. Here, we show that humans reduce demands on control by exploiting statistical structure in their environment. Using a context-dependent perceptual discrimination task and model-based analyses of behavioral and neuroimaging data, we found that predictions about task context facilitated decision making and that a quantitative measure of context prediction error accounted for graded engagement of the frontoparietal control network. Within this network, multivariate analyses further showed that context prediction error enhanced the representation of task context. These results indicate that decision making is adaptively tuned by experience to minimize costs while maintaining flexibility.


title: Children exposed to warfare: A longitudinal study

abstract: Following the 1991 Gulf War a group of 94 children in Iraq were interviewed at 6 months, 1 year, and 2 years after the war. The group was exposed to the bombing of a shelter where more than 750 were killed. Selected items from different inventories, including the Impact of Event Scale (IES) assessed children's reactions. Results reveal that children continue to experience sadness and remain afraid of losing their family. Although there was no significant decline in intrusive and avoidance reactions as measured by the IES from 6 months to 1 year following the war, reactions were reduced 2 years after the war. However, the scores were still high, indicating that symptoms persist, with somewhat diminished intensity over time.


title: VLSI design for 3D-Ultrasonic source localization using General Cross Correlation and Triangulation algorithm

abstract: Localization system is part of the study in navigation. One of the most navigation systems which are relatively robust in many propagation media and accommodates inaudible sound is Ultrasonic wave. But ultrasonic wave works in the high-frequency domain so that there will need special treatment for implementation in the navigation system. In this paper, there will be explained VLSI system modeling that accommodates ultrasonic sound localization. Under this research, ultrasonic localizer system is implemented in two basic subsystems, the first is signal conditioning using heterodyne and low pass filter in 34 KHz heterodyne frequency and 44 KHz middle frequency. Then, after the signal has been digitized, it will be processed in Digital signal processor under General Cross Correlation (GCC) and triangulation algorithm to get the final result that represents 3D Cartesian coordinate. The result of this research is 3D ultrasonic localizer has been brought to be successfully implemented in model simulation using Modelsim® and Matlab®. The system has been tested and compared with theoretical computation. For the future implementation, the signal conditioning system is potentially implemented in analog IC Design.


title: The value of self-service: long-term effects of technology-based self-service usage on customer retention

abstract: Advancements in information technology have changed the way customers experience a service encounter and their relationship with service providers. Especially technology-based self-service channels have found their way into the 21st century service economy. While research embraces these channels for their cost-efficiency, it has not examined whether a shift from personal to self-service affects customer--firm relationships. Drawing from the service-dominant logic and its central concept of value-in-context, we discuss customers' value creation in self-service and personal service channels and examine the long-term impact of these channels on customer retention. Using longitudinal customer data, we investigate how the ratio of self-service versus personal service use influences customer defection over time. Our findings suggest that the ratio of self-service to personal service used affects customer defection in a U-shaped manner, with intermediate levels of both self-service and personal service use being associated with the lowest likelihood of defection. We also find that this effect mitigates over time. We conclude that firms should not shift customers toward self-service channels completely, especially not at the beginning of a relationship. Our study underlines the importance of understanding when and how self-service technologies create valuable customer experiences and stresses the notion of actively managing customers' cocreation of value.


title: On Deep Multi-View Representation Learning: Objectives and Optimization

abstract: We consider learning representations (features) in the setting in which we have access to multiple unlabeled views of the data for learning while only one view is available for downstream tasks. Previous work on this problem has proposed several techniques based on deep neural networks, typically involving either autoencoder-like networks with a reconstruction objective or paired feedforward networks with a batch-style correlation-based objective. We analyze several techniques based on prior work, as well as new variants, and compare them empirically on image, speech, and text tasks. We find an advantage for correlation-based representation learning, while the best results on most tasks are obtained with our new variant, deep canonically correlated autoencoders (DCCAE). We also explore a stochastic optimization procedure for minibatch correlation-based objectives and discuss the time/performance trade-offs for kernel-based and neural network-based implementations.


title: Automated data pre-processing via meta-learning

abstract: A data mining algorithm may perform differently on datasets with different characteristics, e.g., it might perform better on a dataset with continuous attributes rather than with categorical attributes, or the other way around. As a matter of fact, a dataset usually needs to be pre-processed. Taking into account all the possible pre-processing operators, there exists a staggeringly large number of alternatives and non-experienced users become overwhelmed. We show that this problem can be addressed by an automated approach, leveraging ideas from meta-learning. Specifically, we consider a wide range of data pre-processing techniques and a set of data mining algorithms. For each data mining algorithm and selected dataset, we are able to predict the transformations that improve the result of the algorithm on the respective dataset. Our approach will help non-expert users to more effectively identify the transformations appropriate to their applications, and hence to achieve improved results.


title: Arabic Dialect Identification Using a Parallel Multidialectal Corpus

abstract: We present a study on sentence-level Arabic Dialect Identification using the newly developed Multidialectal Parallel Corpus of Arabic (MPCA) – the first experiments on such data. Using a set of surface features based on characters and words, we conduct three experiments with a linear Support Vector Machine classifier and a meta-classifier using stacked generalization – a method not previously applied for this task. We first conduct a 6-way multi-dialect classification task in the first experiment, achieving \(74\,\%\) accuracy against a random baseline of \(16.7\,\%\) and demonstrating that meta-classifiers can large performance increases over single classifiers. The second experiment investigates pairwise binary dialect classification within the corpus, yielding results as high as \(94\,\%\), but also highlighting poorer results between closely related dialects such as Palestinian and Jordanian (\(76\,\%\)). Our final experiment conducts cross-corpus evaluation on the widely used Arabic Online Commentary (AOC) dataset and demonstrates that despite differing greatly in size and content, models trained with the MPCA generalize to the AOC, and vice versa. Using only 2, 000 sentences from the MPCA, we classify over 26 k sentences from the radically different AOC dataset with \(74\,\%\) accuracy. We also use this data to classify a new dataset of MSA and Egyptian Arabic tweets with \(97\,\%\) accuracy. We find that character n-g are a very informative feature for this task, in both within- and cross-corpus settings. Contrary to previous results, they outperform word n-grams in several experiments here. Several directions for future work are outlined.


title: Children’s Witnessing of Adult Domestic Violence

abstract: Social service professionals are more frequently identifying children who witness adult domestic violence as victims of that abuse. This article expands common definitions of how children witness adult domestic violence. A total of 31 research articles that met established quality criteria were included in this review. A variety of behavioral, emotional, and cognitive-functioning problems among children were found to be associated with exposure to domestic violence. Factors that appear to moderate the impact of witnessing violence—such as whether the child was also abused, child gender and age, and the time since last exposure to violence—were identified. Concerns about research methodology used in this area of research and the application of this knowledge also are raised.


title: Curation, curation, curation

abstract: The media curation craze has spawned a multitude of new sites that help users to collect and share web content. Some market themselves as spaces to explore a common interest through different types of related media. Others are promoted as a means for creating and sharing stories, or producing personalized newspapers. Still others target the education market, claiming that curation can be a powerful learning tool for web-based content. But who really benefits from the curation task: the content curator or the content consumer? This paper will argue that for curation to fully support learning, on either side, then the curation site has to allow the content curator to research and tell stories through their selected content and for the consumer to rewrite the story for themselves. This brings the curation task inline with museum practice, where museum professionals tell stories through careful selection, organization and presentation of objects in an exhibition, backed up by research. This paper introduces the notion of 'recuration' to describe a process in which shared content can be used as part of learning.


title: A Social Learning Management System Supporting Feedback for Incorrect Answers based on Social Network Services

abstract: Introduction With the development of the Internet, a considerable amount of research has been carried out on Web-based e-learning, which enables learners to study anytime and anywhere without traditional limitations of place and time (Horton, 2000). In addition, a variety of learning management systems (LMSs) has been developed for the systematic management of courses, grades, progress, and attendance of the students. Representative examples of LMSs include Moodle (Cole & Foster, 2007; Kumar, Gankotiya, & Dutta, 2011), Desire2Learn (D2L) (Wang, & Shao, 2008), and Blackboard (Martin, 2008; Pishva, Nishantha, & Dang, 2010). LMSs support and manage learning content allowing various file formats and media. One of the various features of LMSs is the ability to conduct assessment by providing learners with a quiz (or tests) to evaluate their ability, analyze their correct or incorrect answers, and provide feedback on their incorrect answers. The quiz helps learners recognize the information they did not understand during the learning period. An educator can evaluate a learner's grade by using learners' quiz results. In particular, feedback, including an explanation of incorrect answers, helps learners improve their understanding of the learning contents and relearn why their answers were incorrect (Thoms, 2011). However, the quiz and feedback of existing LMSs has the following limitations: a lack of analysis of whether the learners understand the content; static explanations despite the different learning abilities of the respective learners; and restricted interaction and collaboration feedback among learners. First, existing LMSs lack the ability to analyze whether learners understand the precise meaning of a question. Such systems simply measure the level of understanding and insight of learners by depending on the outcome of analyzing their correct or incorrect quiz answers. Some correct answers may have been given by chance, but the learners are not provided with an explanation and feedback for such answers. To improve the learner's understanding of the subject material and evaluate their level of understanding and insight, an LMS system needs to be able to analyze whether the learner correctly understands the questions and answers of a quiz. In addition, existing LMSs only provide a previously defined static explanation of an incorrect answer without analyzing the various reasons why the learners may have answered a question incorrectly. Analyzing the various causes for incorrect answers and providing a corresponding explanation require considerable time and cost. To solve incorrect answers, the learners study and acquire additional information from the internet or the course lectures, or ask the educator or someone despite providing basic static explanation. This additional inconvenient process is frequently repeated. As a result, refinement and further development of LMSs is required for efficiently supporting and managing various analyses based upon the causes of incorrect answers and the different levels of learner achievement. Finally, as a general course-based approach to e- learning, such systems have restricted interaction and collaboration feedback among learners (Dalsgaard, 2007). When a learner has a question related to a course lecture, the learner may ask the educator during the course. The educator can provide feedback to the learners through different questions and lead the students through the course. On the other hand, the relationship among learners is temporary and restricted to the duration of the course. After the end of the course period, the learners may not have the opportunity to ask questions related to their learning. As a result, these limitations make an LMS inappropriate for supporting collaborative learning. Various researches in education investigating interaction, participation, and sharing among learners have been recently conducted with the evolution of Web 2. …


title: Desorption Electrospray Ionization (DESI) Analysis of Tryptic Digests/Peptides

abstract: The analytical utility of desorption electrospray ionization (DESI) is such that it can be applied to qualitative proteomics research in the same way as matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI) methods, although little work has yet been reported in this regard. Because DESI is a surface analysis technique and easily automated, it can be implemented for highthroughput applications, which include the analysis of chromatographic fractions of digested proteins. The analysis of tryptic peptides follows the same protocols as in typical MALDI or ESI methods, except that the mixture is spotted directly onto an insulating surface, allowed to dry, and analyzed directly without adding matrix compounds (as in the case of MALDI methods). The spectral characteristics are similar to those of ESI in that both singly and multiply charged analyte ions are detected. Spectra are highly similar to electrospray spectra of tryptic digests with regard to the overwhelming presence of multiply charged ions of peptides. DESI-mass spectrometry (DESI-MS) is an emerging technique with great promise, but its application range is still being investigated. Therefore, the protocol for DESI-MS analysis of tryptic digests/peptides presented here provides general procedures used for the applications that have been investigated so far. Optimal ion source parameters and surface types may vary, depending on the application.


title: Kalman filter algorithms for a multi-sensor system

abstract: The purpose of this paper is to examine several Kalman filter algorithms that can be used for state estimation with a multiple sensor system. In a synchronous data collection system, the statistically independent data blocks can be processed in parallel or sequentially, or similar data can be compressed before processing; in the linear case these three filter types are optimum and their results are identical. When measurements from each sensor are statistically independent, the data compression method is shown to be computationally most efficient, followed by the sequential processing; the parallel processing is least efficient.


title: Atlanta world: an expectation maximization framework for simultaneous low-level edge grouping and camera calibration in complex man-made environments

abstract: Edges in man-made environments, grouped according to vanishing point directions, provide single-view constraints that have been exploited before as a precursor to both scene understanding and camera calibration. A Bayesian approach to edge grouping was proposed in the "Manhattan World" paper by Coughlan and Yuille, where they assume the existence of three mutually orthogonal vanishing directions in the scene. We extend the thread of work spawned by Coughlan and Yuille in several significant ways. We propose to use the expectation maximization (EM) algorithm to perform the search over all continuous parameters that influence the location of the vanishing points in a scene. Because EM behaves well in high-dimensional spaces, our method can optimize over many more parameters than the exhaustive and stochastic algorithms used previously for this task. Among other things, this lets us optimize over multiple groups of orthogonal vanishing directions, each of which induces one additional degree of freedom. EM is also well suited to recursive estimation of the kind needed for image sequences and/or in mobile robotics. We present experimental results on images of "Atlanta worlds", complex urban scenes with multiple orthogonal edge-groups, that validate our approach. We also show results for continuous relative orientation estimation on a mobile robot.


title: Psychosocial risk factors associated with cyberbullying among adolescents: a population-based study.

abstract: Context To our knowledge, no population study examining psychosocial and psychiatric risk factors associated with cyberbullying among adolescents exists. Objective To study cross-sectional associations between cyberbullying and psychiatric and psychosomatic problems among adolescents. Design Population-based cross-sectional study. Setting Finland. Participants The sample consists of 2215 Finnish adolescents aged 13 to 16 years with complete information about cyberbullying and cybervictimization. Main Outcome Measures Self-reports of cyberbullying and cybervictimization during the past 6 months. Results In the total sample, 4.8% were cybervictims only, 7.4% were cyberbullies only, and 5.4% were cyberbully-victims. Cybervictim-only status was associated with living in a family with other than 2 biological parents, perceived difficulties, emotional and peer problems, headache, recurrent abdominal pain, sleeping difficulties, and not feeling safe at school. Cyberbully-only status was associated with perceived difficulties, hyperactivity, conduct problems, low prosocial behavior, frequent smoking and drunkenness, headache, and not feeling safe at school. Cyberbully-victim status was associated with all of these risk factors. Among cybervictims, being cyberbullied by a same-sex or opposite-sex adult, by an unknown person, and by a group of people were associated with fear for safety, indicating possible trauma. Conclusions Both cyberbullying and cybervictimization are associated with psychiatric and psychosomatic problems. The most troubled are those who are both cyberbullies and cybervictims. This indicates the need for new strategies for cyberbullying prevention and intervention.


Ten myths of multimodal interaction