title: Local peak valley co-occurrence patterns: A new feature descriptor for image retrieval

abstract: In this paper, we propose a novel feature extraction and retrieval technique for medical images. Our proposed technique (LPVCoP) extracts similarity between grayscale images by using the relationship between reference pixel and its surrounding neighbor pixels through peak/valley edges which are obtained by taking directional derivatives. LPVCoP uses the co-occurrence among the first-order directional derivatives of reference pixel and second neighborhood adjoining pixels. The working of the proposed technique has been checked by implementing it in MATLAB and verifying the result on Computer Tomography (CT) databases. The retrieval results of the proposed technique are compared with the existing technique of image retrieval in terms of average retrieval rate (ARR) and average retrieval precision (ARP). The retrieval results of the LPVCoP are better as compared to the existing techniquesfor image retrieval.


title: Geometric control of multiple quadrotor UAVs transporting a cable-suspended rigid body

abstract: This paper investigates tracking controls for an arbitrary number of cooperating quadrotor unmanned aerial vehicles with a suspended rigid body payload. Assuming that a rigid body is connected to multiple quadrotors by rigid massless links, control systems for quadrotors are constructed such that the rigid body asymptotically follows a given desired position and attitude trajectory. These are developed in a coordinate-free fashion to avoid singularities and complexities associated with local parameterizations.


title: Approximation Algorithms for Mixed Postman Problem

abstract: The mixed postman problem, a generalization of the Chinese postman problem, is that of finding a shortest tour that traverses each edge of a given mixed graph (a graph containing both undirected and directed edges) at least once. The problem is solvable in polynomial time either if the graph is undirected or if the graph is directed, but NP-hard in mixed graphs. An approximation algorithm with a performance ratio of 3/2 for the postman problem on mixed graphs is presented.


title: Bridging integrator 1 (Bin1) deficiency in zebrafish results in centronuclear myopathy

abstract: Autosomal recessive centronuclear myopathy (CNM2), caused by mutations in bridging integrator 1 (BIN1), is a mildly progressive neuromuscular disorder characterized by abnormally centralized myonuclei and muscle weakness. BIN1 is important for membrane sensing and remodeling in vitro in different cell types. However, to fully understand the biological roles of BIN1 in vivo and to answer critical questions concerning the muscle-specific function of BIN1 in vertebrates, robust small animal models are required. In this study, we create and characterize a novel zebrafish model of CNM2 using antisense morpholinos. Immunofluorescence and histopathological analyses of Bin1-deficient zebrafish skeletal muscle reveal structural defects commonly reported in human CNM2 biopsies. Live imaging of zebrafish embryos shows defective calcium release in bin1 morphants, linking the presence of abnormal triads to impairments in intracellular signaling. RNA-mediated rescue assays demonstrate that knockdown of zebrafish bin1 can reliably examine the pathogenicity of novel BIN1 mutations in vivo. Finally, our results strongly suggest that the phosphoinositide-binding domain of BIN1, present only in skeletal muscle isoforms, may be more critical for muscle maturation and maintenance than for early muscle development. Overall, our data support that BIN1 plays an important role in membrane tubulation and may promote skeletal muscle weakness in CNM2 by disrupting machinery necessary for excitation–contraction coupling in vertebrate organisms. The reproducible phenotype of Bin1-deficient zebrafish, together with the generalized advantages of the teleost system, makes this model readily adaptable to high-throughput screening strategies and may be used to identify therapies for CNM2 and related neuromuscular diseases.


Patient assaults on residents.


title: Automated Detection and Extraction of Brain Tumor from MRI Images

abstract: segmentation algorithms and techniques find its applications in a wide number of domains. Segmentation of brain tumor and overall internal structure of the brain is one of the main applications in the field of medical imaging. Magnetic resonance imaging (MRI) technique is one of the many imaging modalities that are available to scan and capture the internal soft tissue structures of the body. In this paper, proposed technique has been given to extract the tumor portion, successfully demarcate the tumor boundary, locate the tumor with a bounding circle and to diagnose whether the tumor is present or absent. A fuzzy clustering-based technique is proposed which helps to study & analyze the intricate structure of the brain, hence can be used as a visual analysis and a study tool.


title: Vibrotactile masking experiments reveal accelerated somatosensory processing in congenitally blind Braille readers

abstract: Braille reading is a demanding task that requires the identification of rapidly varying tactile patterns. During proficient reading, neighboring characters impact the fingertip at ∼100 ms intervals, and adjacent raised dots within a character at 50 ms intervals. Because the brain requires time to interpret afferent sensorineural activity, among other reasons, tactile stimuli separated by such short temporal intervals pose a challenge to perception. How, then, do proficient Braille readers successfully interpret inputs arising from their fingertips at such rapid rates? We hypothesized that somatosensory perceptual consolidation occurs more rapidly in proficient Braille readers. If so, Braille readers should outperform sighted participants on masking tasks, which demand rapid perceptual processing, but would not necessarily outperform the sighted on tests of simple vibrotactile sensitivity. To investigate, we conducted two-interval forced-choice vibrotactile detection, amplitude discrimination, and masking tasks on the index fingertips of 89 sighted and 57 profoundly blind humans. Sighted and blind participants had similar unmasked detection (25 ms target tap) and amplitude discrimination (compared with 100 μm reference tap) thresholds, but congenitally blind Braille readers, the fastest readers among the blind participants, exhibited significantly less masking than the sighted (masker, 50 Hz, 50 μm; target–masker delays, ±50 and ±100 ms). Indeed, Braille reading speed correlated significantly and specifically with masking task performance, and in particular with the backward masking decay time constant. We conclude that vibrotactile sensitivity is unchanged but that perceptual processing is accelerated in congenitally blind Braille readers.


title: Detecting Topic Drift with Compound Topic Models.

abstract: The Latent Dirichlet Allocation topic model of Blei, Ng, & Jordan (2003) is well-established as an effective approach to recovering meaningful topics of conversation from a set of documents. However, a useful analysis of user-generated content is concerned not only with the recovery of topics from a static data set, but with the evolution of topics over time. We employ a compound topic model (CTM) to track topics across two distinct data sets (i.e. past and present) and to visualize trends in topics over time; we evaluate several metrics for detecting a change in the distribution of topics within a time-window; and we illustrate how our approach discovers emerging conversation topics related to current events in real data sets.


title: Learning to Distinguish Hypernyms and Co-Hyponyms

abstract: This work is concerned with distinguishing different semantic relations which exist between distributionally similar words. We compare a novel approach based on training a linear Support Vector Machine on pairs of feature vectors with state-of-the-art methods based on distributional similarity. We show that the new supervised approach does better even when there is minimal information about the target words in the training data, giving a 15% reduction in error rate over unsupervised approaches.


title: Virtual makeover: Selfie-taking and social media use increase selfie-editing frequency through social comparison

abstract: Studies have explored the predictors of selfie-posting, but rarely investigated selfie-editing, a virtual makeover for online self-presentation. This study, based on social comparison theory, examined a psychological pathway from individual characteristics to selfie-editing behavior through social comparison. It was hypothesized that selfie-taking, public self-consciousness, social media use, and satisfaction with facial appearance would indirectly influence selfie-editing through social comparison of appearance (with friends or social media influencers/celebrities). A two-wave longitudinal online survey was conducted in South Korea among female smartphone users aged 20 to 39 (N=1064at Wave 1 and 782at Wave 2). The results revealed that frequent selfie-taking, higher levels of public self-consciousness, and more use of social media at Wave 1 were associated with social comparison with friends at Wave 1, which increased selfie-editing behavior at Wave 2. However, those three independent variables did not have indirect effects on selfie-editing at Wave 2 through social comparison with influencers/celebrities. Also, satisfaction with facial appearance had neither direct nor indirect effect on selfie-editing at Wave 2. The findings suggest that individuals engage in social comparison and resulting selfie-editing not because of their dissatisfaction with appearance, but because of the desire for more ideal online self-presentation. Selfie-taking is positively associated with social comparison of appearance.Social media use is positively associated with social comparison of appearance.Public self-consciousness positively relates to social comparison of appearance.The three variables indirectly increase selfie-editing over time through comparison.Selfie-editing is not associated with one's satisfaction with facial appearance.


title: GA design of a thin-wire bow-tie antenna for GPR applications

abstract: A microgenetic algorithm has been applied to design a new ultrawideband thin-wire bow-tie antenna for ground-penetrating radar applications. The broadband performance of the antenna is achieved by resistive loading and by optimizing the number of wires and the angular distances between those wires. The radiation characteristics of the optimized antenna are discussed, and its performance is compared to that of a resistively loaded Wu-King dipole.


Phase Noise repression in Fractional-N PLLs using Glitch-Free Phase Switching Multi-Modulus Frequency Divider


title: CSTBT (III) as the next generation IGBT

abstract: Recently, the performance of Si power devices gradually approaches the physical limit, and the latest SiC device seemingly has the ability to substitute the Si insulated gate bipolar transistor (IGBT) in 1200 V class. In this paper, we demonstrate the feasibility of further improving the Si IGBT based on the new concept of CSTBTtrade. In point of view of low turn-off loss and high uniformity in device characteristics, we employ the techniques of fine-pattern and retro grade doping in the design of new device structures, resulting in significant reduction on the turn-off loss and the VGE(th) distribution, respectively.


title: Development of a Human Airbag System for Fall Protection Using MEMS Motion Sensing Technology

abstract: This paper describes the development of a human airbag system which is designed to reduce the impact force from falls. A micro inertial measurement unit (muIMU), based on MEMS accelerometers and gyro sensors is developed as the motion sensing part of the system. A recognition algorithm is used for real-time fall determination. With the algorithm, a microcontroller integrated with the muIMU can discriminate falling-down motion from normal human motions and trigger an airbag system when a fall occurs. Our airbag system is designed to have fast response with moderate input pressure, i.e., the experimental response time is less than 0.3 second under 0.4 MPa. In addition, we present our progress on using support vector machine (SVM) training together with the muIMU to better distinguish falling and normal motions. Experimental results show that selected eigenvector sets generated from 200 experimental data sets can be accurately separated into falling and other motions


title: Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment

abstract: We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers for feature extraction, and two fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that: 1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and 2) it allows for joint learning of local quality and local weights, i.e., relative importance of local quality to the global quality estimate, in an unified framework. Our approach is purely data-driven and does not rely on hand-crafted features or other types of prior domain knowledge about the human visual system or image statistics. We evaluate the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the LIVE In the wild image quality challenge database and show superior performance to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation shows a high ability to generalize between different databases, indicating a high robustness of the learned features.


title: Blind image deconvolution: theory and applications

abstract: BLIND IMAGE DECONVOLUTION: PROBLEM FORMULATION AND EXISTING APPROACHES Tom E. Bishop, S. Derin Babacan, Bruno Amizic, Aggelos K. Katsaggelos, Tony Chan, and Rafael Molina Introduction Mathematical Problem Formulation Classification of Blind Image Deconvolution Methodologies Bayesian Framework for Blind Image Deconvolution Bayesian Modeling of Blind Image Deconvolution Bayesian Inference Methods in Blind Image Deconvolution Non-Bayesian Blind Image Deconvolution Models Conclusions References BLIND IMAGE DECONVOLUTION USING BUSSGANG TECHNIQUES: APPLICATIONS TO IMAGE DEBLURRING AND TEXTURE SYNTHESIS Patrizio Campisi, Alessandro Neri, Stefania Colonnese, Gianpiero Panci, and Gaetano Scarano Introduction Bussgang Processes Single-Channel Bussgang Deconvolution Multichannel Bussgang deconvolution Conclusions References BLIND MULTIFRAME IMAGE DECONVOLUTION USING ANISOTROPIC SPATIALLY ADAPTIVE FILTERING FOR DENOISING AND REGULARIZATION Vladimir Katkovnik, Karen Egiazarian, and Jaakko Astola Introduction Observation Model and Preliminaries Frequency Domain Equations Projection Gradient Optimization Anisotropic LPA-ICI Spatially Adaptive Filtering Blind Deconvolution Algorithm Identifiability and Convergence Simulations Conclusions Acknowledgments References BAYESIAN METHODS BASED ON VARIATIONAL APPROXIMATIONS FOR BLIND IMAGE DECONVOLUTION Aristidis Likas and Nikolas P. Galatsanos Introduction Background on Variational Methods Variational Blind Deconvolution Numerical Experiments Conclusions and Future Work APPENDIX A: Computation of the Variational Bound F(q,?) APPENDIX B: Maximization of F(q,?) References DECONVOLUTION OF MEDICAL IMAGES FROM MICROSCOPIC TO WHOLE BODY IMAGES Oleg V. Michailovich and Dan R. Adam Introduction Nonblind Deconvolution Blind Deconvolution in Ultrasound Imaging Blind Deconvolution in SPECT Blind Deconvolution in Confocal Microscopy Summary References BAYESIAN ESTIMATION OF BLUR AND NOISE IN REMOTE SENSING IMAGING Andre Jalobeanu, Josiane Zerubia, and Laure Blanc-Feraud Introduction The Forward Model Bayesian Estimation: Invert the Forward Model Possible Improvements and Further Development Results Conclusions Acknowledgments References DECONVOLUTION AND BLIND DECONVOLUTION IN ASTRONOMY Eric Pantin, Jean-luc Starck, and Fionn Murtagh Introduction The Deconvolution Problem Linear Regularized Methods CLEAN Bayesian Methodology Iterative Regularized Methods Wavelet-Based Deconvolution Deconvolution and Resolution Myopic and Blind Deconvolution Conclusions and Chapter Summary Acknowledgments References MULTIFRAME BLIND DECONVOLUTION COUPLED WITH FRAME REGISTRATION AND RESOLUTION ENHANCEMENT Filip Sroubek, Jan Flusser, and Gabriel Cristobal Introduction Mathematical Model Polyphase Formulation Reconstruction of Volatile Blurs Blind Superresolution Experiments Conclusions Acknowledgments References BLIND RECONSTRUCTION OF MULTIFRAME IMAGERY BASED ON FUSION AND CLASSIFICATION Dimitrios Hatzinakos, Alexia Giannoula, and Jianxin Han Introduction System Overview Recursive Inverse Filtering with Finite Normal-Density Mixtures (RIF-FNM) Optimal Filter Adaptation Effects of Noise The Fusion and Classification Recursive Inverse Filtering Algorithm (FAC-RIF) Experimental Results Final Remarks References BLIND DECONVOLUTION AND STRUCTURED MATRIX COMPUTATIONS WITH APPLICATIONS TO ARRAY IMAGING Michael K. Ng and Robert J. Plemmons Introduction One-Dimensional Deconvolution Formulation Regularized and Constrained TLS Formulation Numerical Algorithms Two-Dimensional Deconvolution Problems Numerical Examples Application: High-Resolution Image Reconstruction Concluding Remarks and Current Work Acknowledgments References INDEX


title: Holistic Person Processing: Faces With Bodies Tell the Whole Story

abstract: Faces and bodies are typically encountered simultaneously, yet little research has explored the visual processing of the full person. Specifically, it is unknown whether the face and body are perceived as distinct components or as an integrated, gestalt-like unit. To examine this question, we investigated whether emotional face–body composites are processed in a holistic-like manner by using a variant of the composite face task, a measure of holistic processing. Participants judged facial expressions combined with emotionally congruent or incongruent bodies that have been shown to influence the recognition of emotion from the face. Critically, the faces were either aligned with the body in a natural position or misaligned in a manner that breaks the ecological person form. Converging data from 3 experiments confirm that breaking the person form reduces the facilitating influence of congruent body context as well as the impeding influence of incongruent body context on the recognition of emotion from the face. These results show that faces and bodies are processed as a single unit and support the notion of a composite person effect analogous to the classic effect described for faces.


title: Super-Scalar RAM-CPU Cache Compression

abstract: textabstractHigh-performance data-intensive query processing tasks like OLAP, data mining or scientific data analysis can be severely I/O bound, even when high-end RAID storage systems are used. Compression can alleviate this bottleneck only if encoding and decoding speeds significantly exceed RAID I/O bandwidth. For this purpose, we propose three new versatile compression schemes (PDICT, PFOR, and PFOR-DELTA) that are specifically designed to extract maximum IPC from modern CPUs. We compare these algorithms with compression techniques used in (commercial) database and information retrieval systems. Our experiments on the MonetDB/X100 database system, using both DSM and PAX disk storage, show that these techniques strongly accelerate TPC-H performance to the point that the I/O bottleneck is eliminated


title: Attacks and design of image recognition CAPTCHAs

abstract: We systematically study the design of image recognition CAPTCHAs (IRCs) in this paper. We first review and examine all existing IRCs schemes and evaluate each scheme against the practical requirements in CAPTCHA applications, particularly in large-scale real-life applications such as Gmail and Hotmail. Then we present a security analysis of the representative schemes we have identified. For the schemes that remain unbroken, we present our novel attacks. For the schemes for which known attacks are available, we propose a theoretical explanation why those schemes have failed. Next, we provide a simple but novel framework for guiding the design of robust IRCs. Then we propose an innovative IRC called Cortcha that is scalable to meet the requirements of large-scale applications. It relies on recognizing objects by exploiting the surrounding context, a task that humans can perform well but computers cannot. An infinite number of types of objects can be used to generate challenges, which can effectively disable the learning process in machine learning attacks. Cortcha does not require the images in its image database to be labeled. Image collection and CAPTCHA generation can be fully automated. Our usability studies indicate that, compared with Google's text CAPTCHA, Cortcha allows a slightly higher human accuracy rate but on average takes more time to solve a challenge.


Parental influences on children's social and overt aggression in China: Effects of parenting behavior, marital conflict, and parent -child attachment


title: Advances in Artificial Neural Networks – Methodological Development and Application

abstract: Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological engineering.


title: Enhancing the Usability and Utilization of Accelerated Architectures via Docker

abstract: Accelerated architectures such as GPUs (Graphics Processing Units) and MICs (Many Integrated Cores) have been proven to increase the performance of many algorithms compared to their CPU counterparts and are widely available in local, campus-wide and national infrastructures, however, their utilization is not following the same pace as their deployment. Reasons for the underutilization lay partly on the software side with proprietary and complex interfaces for development and usage. A common API providing an extra layer to abstract the differences and specific characteristics of those architectures would deliver a far more portable interface for application developers. This cloud challenge proposal presents such an API that addresses these issues using a container-based approach. The resulting environment provides Docker-based containers for deploying accelerator libraries, such as CUDA Toolkit, OpenCL and OpenACC, onto a wide variety of different platforms and operating systems. By leveraging the container approach, we can overlay accelerator libraries onto the host without needing to be concerned about the intricacies of underlying operating system of the host. Docker therefore provides the advantage of being easily applicable on diverse architectures, virtualizing the necessary environment and including libraries as well as applications in a standardized way. The novelty of our approach is the extra layer for utilization and device discovery in this layer improving the usability and uniform development of accelerated methods with direct access to resources.


title: High-Dimensional Continuous Control Using Generalized Advantage Estimation

abstract: Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear function approximators such as neural networks. The two main challenges are the large number of samples typically required, and the difficulty of obtaining stable and steady improvement despite the nonstationarity of the incoming data. We address the first challenge by using value functions to substantially reduce the variance of policy gradient estimates at the cost of some bias, with an exponentially-weighted estimator of the advantage function that is analogous to TD(lambda). We address the second challenge by using trust region optimization procedure for both the policy and the value function, which are represented by neural networks. ::: Our approach yields strong empirical results on highly challenging 3D locomotion tasks, learning running gaits for bipedal and quadrupedal simulated robots, and learning a policy for getting the biped to stand up from starting out lying on the ground. In contrast to a body of prior work that uses hand-crafted policy representations, our neural network policies map directly from raw kinematics to joint torques. Our algorithm is fully model-free, and the amount of simulated experience required for the learning tasks on 3D bipeds corresponds to 1-2 weeks of real time.


title: Bayesian Nonparametric Adaptive Control Using Gaussian Processes

abstract: Most current model reference adaptive control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element is radial basis function networks (RBFNs), with RBF centers preallocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become noneffective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semiglobal in nature. This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. The GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed-loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.


title: In-vehicle hand activity recognition using integration of regions

abstract: In this paper, we focus on the analysis of naturalistic driver behavior using hand activity. To that end, a dataset of color and depth images under varying operating modes and illumination settings was collected. The proposed framework provides a robust solution for localizing the hands by partitioning visible and depth images into disjoint sub-regions which may be of interest for studying the state of the driver: wheel, lap, hand rest, gear, and infotainment region. Different feature extraction methods are proposed and thoroughly studied in terms of speed and performance for each of the five regions. A model for hand presence is learned for each region separately, and these are integrated using a second-stage classifier. As the appearance of hands varies among regions and the hands can only be found in a subset of the regions chosen, the technique leverages information and confidence from multiple regions to produce hand activity classification.


title: Discourse and the study of organization: Toward a structurational perspective

abstract: Existing approaches to organizational discourse, which we label as 'managerialist', 'interpretive' and 'critical', either privilege agency at the expense of structure or the other way around. This tension reflects that between approaches to discourse in the social sciences more generally but is sharper in the organizational context, where discourse is typically temporally and contextually specific and imbued with attributions of instrumental intent. As the basis for a more sophisticated understanding of organizational discourse, we draw on the work of Giddens to develop a structurational conceptualization in which discourse is viewed as a duality of communicative actions and structural properties, recursively linked through the modality of actors' interpretive schemes. We conclude by exploring some of the theoretical implications of this conceptualization and its consequences for the methodology of organizational discourse analysis.abs>


title: Aemoo: Linked Data exploration based on Knowledge Patterns

abstract: This paper presents a novel approach to Linked Data exploration that uses Encyclopedic Knowledge Patterns (EKPs) as relevance criteria for selecting, organising, and visualising knowledge. EKP are discovered by mining the linking structure of Wikipedia and evaluated by means of a user-based study, which shows that they are cognitively sound as models for building entity summarisations. We implemented a tool named Aemoo that supports EKP-driven knowledge exploration and integrates data coming from heterogeneous resources, namely static and dynamic knowledge as well as text and Linked Data. Aemoo is evaluated by means of controlled, task-driven user experiments in order to assess its usability, and ability to provide relevant and serendipitous information as compared to two existing tools: Google and RelFinder.


title: Facebook wall posts: a model of user behaviors

abstract: How do people interact with their Facebook wall? At a high level, this question captures the essence of our work. While most prior efforts focus on Twitter, the much fewer Facebook studies focus on the friendship graph or are limited by the amount of users or the duration of the study. In this work, we model Facebook user behavior: we analyze the wall activities of users focusing on identifying common patterns and surprising phenomena. We conduct an extensive study of roughly 7k users over 3 years during 4-month intervals each year. We propose PowerWall, a lesser known heavy-tailed distribution to fit our data. Our key results can be summarized in the following points. First, we find that many wall activities, including number of posts, number of likes, number of posts of type photo, can be described by the PowerWall distribution. What is more surprising is that most of these distributions have similar slope, with a value close to 1! Second, we show how our patterns and metrics can help us spot surprising behaviors and anomalies. For example, we find a user posting every two days, exactly the same count of posts; another user posting at midnight, with no other activity before or after. Our work provides a solid step toward a systematic and quantitative wall-centric profiling of Facebook user activity.


title: The Neuronal Gene <ce:italic>Arc</ce:italic> Encodes a Repurposed Retrotransposon Gag Protein that Mediates Intercellular RNA Transfer

abstract: Summary The neuronal gene Arc is essential for long-lasting information storage in the mammalian brain, mediates various forms of synaptic plasticity, and has been implicated in neurodevelopmental disorders. However, little is known about Arc's molecular function and evolutionary origins. Here, we show that Arc self-assembles into virus-like capsids that encapsulate RNA. Endogenous Arc protein is released from neurons in extracellular vesicles that mediate the transfer of Arc mRNA into new target cells, where it can undergo activity-dependent translation. Purified Arc capsids are endocytosed and are able to transfer Arc mRNA into the cytoplasm of neurons. These results show that Arc exhibits similar molecular properties to retroviral Gag proteins. Evolutionary analysis indicates that Arc is derived from a vertebrate lineage of Ty3/ gypsy retrotransposons, which are also ancestors to retroviruses. These findings suggest that Gag retroelements have been repurposed during evolution to mediate intercellular communication in the nervous system.


title: Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry

abstract: An industry specific definition of the Industry 4.0 concept for construction has been derived based on a content analysis.A systematic literature review is conducted to explore the state of the art of Industry 4.0 in the construction industry.A practical view has been added by performing a multiple case study analysis.A PESTEL framework has been illustrated to present the implications of Industry 4.0 for the construction industry.Recommendations for further research are provided within a research agenda. In recent years, Industry 4.0 has been introduced as a popular term to describe the trend towards digitisation and automation of the manufacturing environment. Despite its potential benefits in terms of improvements in productivity and quality, this concept has not gained much attention in the construction industry. This development is founded in the fact that the far-reaching implications of the increasingly digitised and automated manufacturing environment are still widely unknown. Against this backdrop, the primary objective of this paper is to explore the state of the art as well as the state of practice of Industry 4.0 relating technologies in the construction industry by pointing out the political, economic, social, technological, environmental and legal implications of its adoption. In this context, we present the results of our triangulation approach, which consists of a comprehensive systematic literature review and case study research, by illustrating a PESTEL framework and a value chain model. Additionally, we provide recommendations for further research within a research agenda.


title: Somatosensory Evoked Responses in Down Syndrome

abstract: Several characteristics of somatosensory evoked responses (SER) were measured in a modified recovery function procedure in 22 bright young adults and 22 mongoloid and ten "idiopathic" retardates. The two retardate groups were of similar IQ to distinguish non-specific SER deviations associated with retardation from those related to the specific cause of mongolism. Findings related to mongolism were: (1) For a given conduction pathway length, latency of the initial SER peak was longer in mongoloids. (2) The usually high correlation between conduction pathway length and initial latency was abnormally low in mongoloids (r = 0.35 vs 0.8 in normals and idiopathics). (3) There were significant sex differences in mongoloids and not in the other groups: women had larger amplitudes. Amplitude recovery was greater than normal in both retardate groups.


title: The numbers in Jeff Bezos's head

abstract: The article discusses the business strategy of Internet company Amazon's Chief Executive Officer (CEO) Jeff Bezos, focusing on Bezos as the top choice on the magazine's list of best-performing chief executive officers (CEOs) for 2014. Topics include how he developed the financial strategy to grow the company, the development of the company's purchase program Amazon Prime, and re-investing in the company to diversify the company's industries. It also includes an excerpt from an interview with Bezos and the "Harvard Business Review."


title: Can your paper evade the editors axe? Towards an AI assisted peer review system

abstract: This work is an exploratory study of how we could progress a step towards an AI assisted peer- review system. The proposed approach is an ambitious attempt to automate the Desk-Rejection phenomenon prevalent in academic peer review. In this investigation we first attempt to decipher the possible reasons of rejection of a scientific manuscript from the editors desk. To seek a solution to those causes, we combine a flair of information extraction techniques, clustering, citation analysis to finally formulate a supervised solution to the identified problems. The projected approach integrates two important aspects of rejection: i) a paper being rejected because of out of scope and ii) a paper rejected due to poor quality. We extract several features to quantify the quality of a paper and the degree of in-scope exploring keyword search, citation analysis, reputations of authors and affiliations, similarity with respect to accepted papers. The features are then fed to standard machine learning based classifiers to develop an automated system. On a decent set of test data our generic approach yields promising results across 3 different journals. The study inherently exhibits the possibility of a redefined interest of the research community on the study of rejected papers and inculcates a drive towards an automated peer review system.


title: Recommender Systems for the Semantic Web

abstract: This paper presents a semantic approach to Recommender Systems (RS), to exploit available contextual information about both the items to be recommended and the recommendation process, in an attempt to overcome some of the shortcomings of traditional RS implementations. An ontology is used as a backbone to the system in the proposed architecture to represent the problem domain, while multiple web services are orchestrated to compose a suitable recommendation model, matching the current recommendation context at run-time. In order to allow for such dynamic behaviour, the proposed system tackles the recommendation problem by applying existing RS techniques on three different levels: the selection of appropriate sets of features, recommendation model and recommendable items.


title: Internet of Things: A survey related to various recent architectures and platforms available

abstract: IP-based Internet is the largest network in the world therefore; there are excessive steps towards connecting Wireless Sensor Networks (WSNs) to the Internet. It is popularly known as to IoT (Internet of Things). IETF has developed a suite of protocols and open standards for accessing applications and services for wireless resource constrained networks such as IoT. Development of application requires standardized architecture and platform for design and analysis of new ideas. This paper provides a brief awareness about recent IoT architectures and platforms. It is also discussed some of the gaps issues of the platforms related to usability of the user. This helps researcher to select a particular platform according to need.


title: Analysis of 360° Video Viewing Behaviours

abstract: In this thesis we study users' viewing motions when watching 360° videos in order to provide information that can be used to optimize future view-dependent streaming protocols. More specifically, w ...


title: Learning classifiers from only positive and unlabeled data

abstract: The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and the other set consists of negative examples. However, it is often the case that the available training data are an incomplete set of positive examples, and a set of unlabeled examples, some of which are positive and some of which are negative. The problem solved in this paper is how to learn a standard binary classifier given a nontraditional training set of this nature. Under the assumption that the labeled examples are selected randomly from the positive examples, we show that a classifier trained on positive and unlabeled examples predicts probabilities that differ by only a constant factor from the true conditional probabilities of being positive. We show how to use this result in two different ways to learn a classifier from a nontraditional training set. We then apply these two new methods to solve a real-world problem: identifying protein records that should be included in an incomplete specialized molecular biology database. Our experiments in this domain show that models trained using the new methods perform better than the current state-of-the-art biased SVM method for learning from positive and unlabeled examples.


title: Determination of mandelic acid enantiomers in urine by gas chromatography and electron-capture or flame ionisation detection.

abstract: A sensitive and stereospecific GC method was developed for the analysis of R- and S-enantiomers of mandelic acid (MA) in urine, using a chiral CP Chirasil-Dex-CB column. The enantiomers of MA were derivatised with isopropanol into their corresponding isopropyl esters and determined either directly with flame ionisation detection (FID) or after subsequent derivatisation of a hydroxy group with pentafluoropropionic anhydride with electron-capture detection (ECD). Both derivatisation steps proceeded with negligible inversion of enantiomers (<1%). The limit of detection of the FID determination was 8 and 5 mg/l for R-MA and S-MA, respectively and of the ECD determination 1 mg/l for both enantiomers. Repeatability (within-day precision) and reproducibility (day-to-day precision) was for both enantiomers below 7.5% for the FID and below 5.8% for the ECD analysis. The method was applied to urine of volunteers exposed to 105 and 420 mg styrene/m3 air. In the urine of the exposed volunteers, the S-enantiomer showed higher excretion compared to that of the R-enantiomer, with marked interindividual differences in excretion of both enantiomers.


title: Noise reduction for non-contact electrocardiogram measurement in daily life

abstract: Electrocardiogram (ECG) measurement without skin-contact is essential for u-healthcare. ECG measurement using capacitive-coupled electrode (CC-electrode) is a well-known method for unconstrained ECG measurement. Although the CC-electrode has the advantage of non-contact measurement, common mode noise is increased, which decreases the signal-to-noise ratio (SNR). In this study, we proposed non-contact ECG measurement system using CC-electrode and driven circuit to reduce noise. The components of driven circuit were similar to those of driven-right-leg circuit and conductive sheet was employed for driven electrode to contact uniformly to the body over clothes. We evaluated the performance of the driven circuit under different conditions, including a contact area to the body and a gain of the driven circuit to find out a relationship between them and the SNR of ECG. As the results, as contact area became larger and gain became higher, SNR increased.


title: Development and validation of Arabic version of the Short-Form McGill Pain Questionnaire

abstract: INTRODUCTION ::: The Short-Form McGill Pain Questionnaire (SF-MPQ) is a widely used tool for qualitative and quantitative pain assessment. Our aim was to translate, culturally adapt, and validate the SF-MPQ in Arabic. ::: ::: ::: METHODS ::: A systematic translation process was used to translate the original English SF-MPQ into Arabic. After the pilot study, we validated our version in patients with chronic pain at two tertiary care centers. We tested the reliability of our version using internal consistency and test-retest reliability. We examined the validity by assessing construct validity, concurrent validity (by investigating the associations between SF-MPQ, Brief Pain Inventory [BPI], and Self-completed Leeds Assessment of Neuropathic Symptoms and Signs [S-LANSS]), and face validity. The questionnaire was administered twice to examine responsiveness. ::: ::: ::: RESULTS ::: A total of 142 participants (68 men and 74 women) were included in this study. Cronbach's α was 0.85 (95% confidence interval: 0.81 - 0.89), and interclass correlation coefficients were 0.71 (0.62-0.79) for the whole scale. SF-MPQ was moderately associated with patients' present pain (r = 0.55, P < 0.001) and the numerical rating scale (r = 0.42, P < 0.001). The total pain score was moderately correlated with pain severity and interference assessed with the BPI (rs = 0.39 to 0.49, all Ps < 0.001). SF-MPQ total pain score was weakly associated with neuropathic pain assessed with S-LANSS (r = 0.26, P < 0.01). Most patients found the SF-MPQ questions to be clear and easy to understand and thought the questionnaire items covered all their problem areas regarding their pain. ::: ::: ::: CONCLUSION ::: Our translated version of SF-MPQ was reliable and valid for use among Arabic-speaking patients. The SF-MPQ is a good qualitative and quantitative assessment tool for pain but is only weakly associated with neuropathic pain.


title: Powered robotic exoskeletons in post-stroke rehabilitation of gait: a scoping review

abstract: Powered robotic exoskeletons are a potential intervention for gait rehabilitation in stroke to enable repetitive walking practice to maximize neural recovery. As this is a relatively new technology for stroke, a scoping review can help guide current research and propose recommendations for advancing the research development. The aim of this scoping review was to map the current literature surrounding the use of robotic exoskeletons for gait rehabilitation in adults post-stroke. Five databases (Pubmed, OVID MEDLINE, CINAHL, Embase, Cochrane Central Register of Clinical Trials) were searched for articles from inception to October 2015. Reference lists of included articles were reviewed to identify additional studies. Articles were included if they utilized a robotic exoskeleton as a gait training intervention for adult stroke survivors and reported walking outcome measures. Of 441 records identified, 11 studies, all published within the last five years, involving 216 participants met the inclusion criteria. The study designs ranged from pre-post clinical studies (n = 7) to controlled trials (n = 4); five of the studies utilized a robotic exoskeleton device unilaterally, while six used a bilateral design. Participants ranged from sub-acute (<7 weeks) to chronic (>6 months) stroke. Training periods ranged from single-session to 8-week interventions. Main walking outcome measures were gait speed, Timed Up and Go, 6-min Walk Test, and the Functional Ambulation Category. Meaningful improvement with exoskeleton-based gait training was more apparent in sub-acute stroke compared to chronic stroke. Two of the four controlled trials showed no greater improvement in any walking outcomes compared to a control group in chronic stroke. In conclusion, clinical trials demonstrate that powered robotic exoskeletons can be used safely as a gait training intervention for stroke. Preliminary findings suggest that exoskeletal gait training is equivalent to traditional therapy for chronic stroke patients, while sub-acute patients may experience added benefit from exoskeletal gait training. Efforts should be invested in designing rigorous, appropriately powered controlled trials before powered exoskeletons can be translated into a clinical tool for gait rehabilitation post-stroke.


title: Multiplexed electrical detection of cancer markers with nanowire sensor arrays

abstract: We describe highly sensitive, label-free, multiplexed electrical detection of cancer markers using silicon-nanowire field-effect devices in which distinct nanowires and surface receptors are incorporated into arrays. Protein markers were routinely detected at femtomolar concentrations with high selectivity, and simultaneous incorporation of control nanowires enabled discrimination against false positives. Nanowire arrays allowed highly selective and sensitive multiplexed detection of prostate specific antigen (PSA), PSA-a1-antichymotrypsin, carcinoembryonic antigen and mucin-1, including detection to at least 0.9 pg/ml in undiluted serum samples. In addition, nucleic acid receptors enabled real-time assays of the binding, activity and small-molecule inhibition of telomerase using unamplified extracts from as few as ten tumor cells. The capability for multiplexed real-time monitoring of protein markers and telomerase activity with high sensitivity and selectivity in clinically relevant samples opens up substantial possibilities for diagnosis and treatment of cancer and other complex diseases.


title: Efficient Hardware Architectures for Deep Convolutional Neural Network

abstract: Convolutional neural network (CNN) is the state-of-the-art deep learning approach employed in various applications. Real-time CNN implementations in resource limited embedded systems are becoming highly desired recently. To ensure the programmable flexibility and shorten the development period, field programmable gate array is appropriate to implement the CNN models. However, the limited bandwidth and on-chip memory storage are the bottlenecks of the CNN acceleration. In this paper, we propose efficient hardware architectures to accelerate deep CNN models. The theoretical derivation of parallel fast finite impulse response algorithm (FFA) is introduced. Based on FFAs, the corresponding fast convolution units (FCUs) are developed for the computation of convolutions in the CNN models. Novel data storage and reuse schemes are proposed, where all intermediate pixels are stored on-chip and the bandwidth requirement is reduced. We choose one of the largest and most accurate networks, VGG16, and implement it on Xilinx Zynq ZC706 and Virtex VC707 boards, respectively. We achieve the top-5 accuracy of 86.25% using an equal distance non-uniform quantization method. It is estimated that the average performances are 316.23 GOP/s under 172-MHz working frequency on Xilinx ZC706 and 1250.21 GOP/s under 170-MHz working frequency on VC707, respectively. In brief, the proposed design outperforms the existing works significantly, in particular, surpassing related designs by more than two times in terms of resource efficiency.


title: Asking ‘Why’ in AI: Explainability of intelligent systems - perspectives and challenges

abstract: Recent rapid progress in machine learning (ML), particularly so‐called ‘deep learning’, has led to a resurgence in interest in explainability of artificial intelligence (AI) systems, reviving an area of research dating back to the 1970s. The aim of this article is to view current issues concerning ML‐based AI systems from the perspective of classical AI, showing that the fundamental problems are far from new, and arguing that elements of that earlier work offer routes to making progress towards explainable AI today.


title: WiWho: WiFi-Based Person Identification in Smart Spaces

abstract: There has been a growing interest in equipping the objects and environment surrounding users with sensing capabilities. Smart indoor spaces such as smart homes and offices can implement the sensing and processing functionality, relieving users from the need of wearing/carrying smart devices. Enabling such smart spaces requires device-free, effortless sensing of user's identity and activities. Device-free sensing using WiFi has shown great potential in such scenarios, however, a fundamental question of person identification has remained unsolved. In this paper, we present WiWho, a framework that can identify a person from a small group of people in a device-free manner using WiFi. We show that Channel State Information (CSI) used in recent WiFi can identify a person's steps and walking gait. The walking gait being distinguishing characteristics for different people, WiWho uses CSI-based gait for person identification. We demonstrate how step and walk analysis can be used to identify a person's walking gait from CSI, and how this information can be used to identify a person. WiWho does not require a person to carry any device and is effortless since it only requires the person to walk for a few steps (e.g. entering a home or an office). We evaluate WiWho using experiments at multiple locations with a total of 20 volunteers, and show that it can identify a person with average accuracy of 92% to 80% from a group of 2 to 6 people respectively. We also show that in most cases walking as few as 2-3 meters is sufficient to recognize a person's gait and identify the person. We discuss the potential and challenges of WiFi-based person identification with respect to smart space applications.


title: A fuzzy approach for modelling visual texture properties

abstract: Abstract In this paper, we address the problem of simulating the human perception of texture properties in images. In particular, we have focused our study on the properties of coarseness, contrast and directionality, that play a fundamental role in the human perception of texture. The objective is not to precisely identify, classify or discriminate between different textures as a whole, but to be able to assess the presence of each texture property in the image. For this purpose, fuzzy sets defined on the domain of different groups of measures are employed in order to model each property by using parametric membership functions. The corresponding parameters are obtained by learning a functional relationship between the computational values given by the measures and the human perception. The performance of each fuzzy set is analyzed and tested with the human assessments, and a ranking of subsets of measures is obtained according to their ability to represent the perception of the property, allowing us to identify the most suitable combination of measures.


title: Do pediatric chief residents recognize details of prepubertal female genital anatomy: a national survey

abstract: Abstract Objective: To evaluate how well a group of recently trained pediatric chief residents could label anatomic structures on two different photographs of female prepubertal genitalia. Additionally, the study sought to explore aspects of pediatric training in sexual abuse and clinical practice issues surrounding the routine genital examination. Method: A 38-item questionnaire was mailed to pediatric chief residents at all of the officially listed pediatric residency-training programs in the continental US. Comparisons were made between this study and the responses to two previous surveys, which asked a more heterogeneous group of physicians to label one of the photographs used in the study. The second photograph was added because of its improved clarity of each anatomic structure when compared to the first photograph used in the previous studies. The study also asked about clinical practice issues surrounding the prepubertal genital examination. Results: An overall response rate of 73% was achieved and analysis was done on 139 respondents. One-half of chief residents thought that their training during residency on sexual abuse was inadequate for practice. Sixty-four percent of chief residents correctly labeled the hymen on the photograph used in the previous studies, which was not significantly different from the 62% and 59% of physicians who correctly labeled the hymen in the previous surveys. In the second photograph, which more clearly displayed the various anatomic structures, 71% correctly labeled the hymen. Conclusion: Pediatric chief residents reported variable amounts of training on issues pertaining to child sexual abuse during residency, think that this time was inadequate, and, while doing slightly better than a more diverse group of previously studied physicians, did not achieve 100% accuracy in identifying basic genital structures correctly on two different photographs.


title: Women, Pets, and Imperialism: The British Pekingese Dog and Nostalgia for Old China

abstract: Pekingese dogs are said to have originated in the Imperial courts of China many centuries ago. This article is a unique study of the growth in popularity of Pekingese dogs in Britain in the early-twentieth century, in terms of class, race, gender and imperialist subjectivities. ::: ::: Existing studies of human relationships with the animal kingdom and of pedigree dog breeding have established the cultural importance of this area (Ritvo 1987; Derry 2003), however, there has been no work on the colonial aspects of fashionable canine breeding, and no attention has been paid to Sino-British contexts. ::: ::: My article showcases new research into the history of Pekingese dog breeding in Britain, using primary evidence from contemporary magazines, from canine literature, and from the records of the Kennel Club. The article identifies the concept of colonial nostalgia as crucial to both the peke’s mythology and the formation of British upper-class subjectivities, and is the first piece of work to explore how women were able to respond to an Orientalist discourse that was centred around nostalgia and colonial warfare in China. The article also considers the role of the Chinese mandarin as a figure of authority for Pekingese dog breeders.


title: The Bunker Cache for spatio-value approximation

abstract: The cost of moving and storing data is still a fundamental concern for computer architects. Inefficient handling of data can be attributed to conventional architectures being oblivious to the nature of the values that these data bits carry. We observe the phenomenon of spatio-value similarity, where data elements that are approximately similar in value exhibit spatial regularity in memory. This is inherent to 1) the data values of real-world applications, and 2) the way we store data structures in memory. We propose the Bunker Cache, a design that maps similar data to the same cache storage location based solely on their memory address, sacrificing some application quality loss for greater efficiency. The Bunker Cache enables performance gains (ranging from 1.08x to 1.19x) via reduced cache misses and energy savings (ranging from 1.18x to 1.39x) via reduced off-chip memory accesses and lower cache storage requirements. The Bunker Cache requires only modest changes to cache indexing hardware, integrating easily into commodity systems.


title: Access Control for Plugins in Cordova-Based Hybrid Applications

abstract: Hybrid application frameworks such as Cordovaallow mobile application (app) developers to create platformindependent apps. The code is written in JavaScript, with special APIs to access device resources in a platform-agnostic way. In this paper, we present a novel app-repackaging attack that repackages hybrid apps with malicious code, this code can exploit Cordova's plugin interface to tamper with device resources. We further demonstrate a defense against this attack through the use of a novel runtime access control mechanism that restricts access based on the mobile user's judgement. Our mechanism is easy to introduce to existing Cordova apps, and allows developers to produce apps that are resistant to app-repackaging attacks.


title: Signature Segmentation from Machine Printed Documents Using Conditional Random Field

abstract: Automatic separation of signatures from a document page involves difficult challenges due to the free-flow nature of handwriting, overlapping/touching of signature parts with printed text, noise, etc. In this paper, we have proposed a novel approach for the segmentation of signatures from machine printed signed documents. The algorithm first locates the signature block in the document using word level feature extraction. Next, the signature strokes that touch or overlap with the printed texts are separated. A stroke level classification is then performed using skeleton analysis to separate the overlapping strokes of printed text from the signature. Gradient based features and Support Vector Machine (SVM) are used in our scheme. Finally, a Conditional Random Field (CRF) model energy minimization concept based on approximated labeling by graph cut is applied to label the strokes as "signature" or "printed text" for accurate segmentation of signatures. Signature segmentation experiment is performed in "tobacco" dataset1 and we have obtained encouraging results.


title: A Ku-band 6-bit digital phase shifter MMIC for phased array antenna systems

abstract: This paper presents a 6-bit digital phase shifter designed at Ku-band for electronic beam steering of satellite based phased array antenna systems. The phase shifter is implemented using space qualified 0.25-μm pHEMT GaAs MMIC process. Each individual bit of the phase shifter is designed using optimum topology to achieve minimal insertion loss, accurate phase shifting and low amplitude variations. The measured performance of all 64 states of the phase shifter demonstrates an insertion loss of 6.1±0.6 dB with RMS phase error < 1.3°, RMS amplitude error < 0.35 dB and return losses better than 15 dB over 12–13 GHz frequency range. The circuit consumes negligible power (<0.5mW) and has dimensions of 6.1×1.5 mm2 to fit in a multi-function single chip beam forming MMIC. The low insertion loss and high accuracy of the phase shifter are key features of the MMIC, making it suitable for high precision beam forming applications.


title: A body and Garment Creation Method for an Internet Based Virtual Fitting Room.

abstract: In this paper we present a new methodology for producing 3D clothes, with realistic behavior, providing users with an apposite feel for the garment’s details. The 3D garments that are produced, using our methodology, originate from 2D CAD patterns of real garments and correspond to the regular sizes that can be found on a shop floor.


title: Gender incongruence of childhood in the ICD-11: controversies, proposal, and rationale

abstract: Summary As part of the development of the eleventh revision of the International Classification of Diseases (ICD-11), WHO appointed a Working Group on Sexual Disorders and Sexual Health to recommend changes necessary in the classification of mental and behavioural disorders in ICD-10 that are related to sexuality and gender identity. This Personal View focuses on the Working Group's proposals to include the diagnosis gender incongruence of childhood in ICD-11 and to move gender incongruence of childhood out of the mental and behavioural disorders chapter of ICD-11. We outline the history of ICD and DSM child gender diagnoses, expert consensus, knowledge gaps, and controversies related to the diagnosis and treatment of extremely gender-variant children. We argue that retaining the gender incongruence of childhood category is justified as a basis to structure clinical care and to ensure access to appropriate services for this vulnerable population, which provides opportunities for education and informed consent, the development of standards and pathways of care to help guide clinicians and family members, and a basis for future research efforts.


title: Improving Iris Recognition Accuracy By Score Based Fusion Method

abstract: Iris recognition technology, used to identify individuals by photographing the iris of their eye, has become popular in security applications because of its ease of use, accuracy, and safety in controlling access to high-security areas. Fusion of multiple algorithms for biometric verification performance improvement has received considerable attention. The proposed method combines the zero-crossing 1 D wavelet Euler number, and genetic algorithm based for feature extraction. The output from these three algorithms is normalized and their score are fused to decide whether the user is genuine or imposter. This new strategies is discussed in this paper, in order to compute a multimodal combined score.


title: Obtaining Calibrated Probabilities from Boosting

abstract: Boosted decision trees typically yield good accuracy, precision, and ROC area. However, because the outputs from boosting are not well calibrated posterior probabilities, boosting yields poor squared error and cross-entropy. We empirically demonstrate why AdaBoost predicts distorted probabilities and examine three calibration methods for correcting this distortion: Platt Scaling, Isotonic Regression, and Logistic Correction. We also experiment with boosting using log-loss instead of the usual exponential loss. Experiments show that Logistic Correction and boosting with log-loss work well when boosting weak models such as decision stumps, but yield poor performance when boosting more complex models such as full decision trees. Platt Scaling and Isotonic Regression, however, significantly improve the probabilities predicted by


title: Food Recommendation using Ontology and Heuristics

abstract: Recommender systems are needed to find food items of one’s interest. This paper reviews recommender systems and recommendation methods, then propose a food personalization framework based on adaptive hypermedia and extend Hermes framework with food recommendation functionality. Moreover, it combines TF-IDF term extraction method with cosine similarity measure. Healthy heuristics and standard food database are incorporated into the knowledgebase. Based on the performed evaluation, we conclude that semantic recommender systems in general outperform traditional recommenders systems with respect to accuracy, precision, and recall, and that the proposed recommender has a better F-measure than existing semantic recommenders.


title: Traffic sign detection and recognition via transfer learning

abstract: Automatic driving has become a extremely hot issue in recent years, and the detection of stop signs is critical for autonomous driving. Different from precious methods in which target features were extracted and then feed to SVM classifier to classify different types of traffic signs, this paper introduces a kind of transfer learning method based on the convolutional neural network(CNN). A deep convolution neural network is trained using a large data sets, and then a valid region convolutional neural network(RCNN) detection can be obtained through a small amount of traffic standard training samples. At the end of this paper, the classic GTSDB data sets and some other data of shenzhen university town are used to show the effectiveness of the transfer learning approach.


title: An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

abstract: Few ideas have enjoyed as large an impact on deep learning as convolution. For any problem involving pixels or spatial representations, common intuition holds that convolutional neural networks may be appropriate. In this paper we show a striking counterexample to this intuition via the seemingly trivial coordinate transform problem, which simply requires learning a mapping between coordinates in (x,y) Cartesian space and one-hot pixel space. Although convolutional networks would seem appropriate for this task, we show that they fail spectacularly. We demonstrate and carefully analyze the failure first on a toy problem, at which point a simple fix becomes obvious. We call this solution CoordConv, which works by giving convolution access to its own input coordinates through the use of extra coordinate channels. Without sacrificing the computational and parametric efficiency of ordinary convolution, CoordConv allows networks to learn either complete translation invariance or varying degrees of translation dependence, as required by the end task. CoordConv solves the coordinate transform problem with perfect generalization and 150 times faster with 10--100 times fewer parameters than convolution. This stark contrast raises the question: to what extent has this inability of convolution persisted insidiously inside other tasks, subtly hampering performance from within? A complete answer to this question will require further investigation, but we show preliminary evidence that swapping convolution for CoordConv can improve models on a diverse set of tasks. Using CoordConv in a GAN produced less mode collapse as the transform between high-level spatial latents and pixels becomes easier to learn. A Faster R-CNN detection model trained on MNIST showed 24% better IOU when using CoordConv, and in the RL domain agents playing Atari games benefit significantly from the use of CoordConv layers.


title: Efficiency Resource Allocation for Device-to-Device Underlay Communication Systems: A Reverse Iterative Combinatorial Auction Based Approach

abstract: Peer-to-peer communication has been recently considered as a popular issue for local area services. An innovative resource allocation scheme is proposed to improve the performance of mobile peer-to-peer, i.e., device-to-device (D2D), communications as an underlay in the downlink (DL) cellular networks. To optimize the system sum rate over the resource sharing of both D2D and cellular modes, we introduce a reverse iterative combinatorial auction as the allocation mechanism. In the auction, all the spectrum resources are considered as a set of resource units, which as bidders compete to obtain business while the packages of the D2D pairs are auctioned off as goods in each auction round. We first formulate the valuation of each resource unit, as a basis of the proposed auction. And then a detailed non-monotonic descending price auction algorithm is explained depending on the utility function that accounts for the channel gain from D2D and the costs for the system. Further, we prove that the proposed auction-based scheme is cheat-proof, and converges in a finite number of iteration rounds. We explain non-monotonicity in the price update process and show lower complexity compared to a traditional combinatorial allocation. The simulation results demonstrate that the algorithm efficiently leads to a good performance on the system sum rate.


title: DeepMind_Commentary

abstract: We agree with Lake and colleagues on their list of key ingredients for building humanlike intelligence, including the idea that model-based reasoning is essential. However, we favor an approach that centers on one additional ingredient: autonomy. In particular, we aim toward agents that can both build and exploit their own internal models, with minimal human hand-engineering. We believe an approach centered on autonomous learning has the greatest chance of success as we scale toward real-world complexity, tackling domains for which ready-made formal models are not available. Here we survey several important examples of the progress that has been made toward building autonomous agents with humanlike abilities, and highlight some outstanding challenges.


title: Zone Controlled Induction Heating (ZCIH) A New Concept in Induction Heating

abstract: This paper proposes a new "zone controlled induction heating" (ZCIH) system. The ZCIH system consists of two or more sets of a high-frequency inverter and a split work coil, which adjusts the coil current amplitude in each zone independently. The ZCIH system has capability of controlling the exothermic distribution on the work piece to avoid the strain caused by a thermal expansion. As a result, the ZCIH system enables a rapid heating performance as well as an temperature uniformity. This paper proposes current phase control making the coil current in phase with each other, to adjust the coil current amplitude even when a mutual inductance exists between the coils. This paper presents operating principle, theoretical analysis, and experimental results obtained from a laboratory setup and a six-zone prototype for a semiconductor processing.


title: Evaluation of Text Summarization in a Cross-lingual Information Retrieval Framework

abstract: We report on research in multi-document summarization and on evaluation of summarization in the framework of cross-lingual information retrieval. This work was carried out during a summer workshop on Language Engineering held at Johns Hopkins University by a team of nine researchers from seven universities. The goals of the research were as follows: (1) to develop a toolkit for evaluation of single-document and multi-document summarizers, (2) to develop a modular multi-document summarizer, called MEAD, that works in both English and Chinese, and (3) to perform a meta-evaluation of four automatic summarizers, including MEAD, using several types of evaluation measures: some currently used by summarization researchers and a couple of novel techniques. Central to the experiments in this workshop was the cross-lingual experimental setup based on a large-scale Chinese and English parallel corpus. An extensive set of human judgments were specifically prepared by the Linguistic Data Consortium for our research. These human judgments include a) which documents are relevant to a certain query and b) which sentences in the relevant documents are most relevant to the query and which therefore constitute a good summary of the cluster. These judgments were used to construct variable-length multiand single document summaries as model summaries. Since one of the novel evaluation metrics that we used, Relevance Correlation, is based on the premise that good summaries preserve query relevance both within a language and across languages, we made use of a cross-lingual Information Retrieval (IR) engine. We evaluated the quality of the automatic summaries using co-selection and content-based evaluation, two established techniques. A relatively new metric, relative utility, was also extensively tested. Part of the new scientific contribution is the measurement of relevance correlation, which we introduced and systematically examined in this workshop. Relevance correlation measures the quality of summaries in comparison to the entire documents as a function of how much document relevance drops if summaries are indexed instead of documents. Our results show that this measure is sensible, in that it correlates with more established evaluation measures. Another contribution is the cross-lingual setup which allows us to automatically translate English queries into Chinese, perform Chinese IR with or without summarization. This allows us to calculate relevance correlation for English and for Chinese in parallel (i.e., for the same queries) and to take direct cross-lingual comparisons of evaluations. Additionally, an alternative way of constructing Chinese model summaries from English ones was implemented which relies on the sentence alignment of English and Chinese documents. The results of our large-scale meta-evaluation are numerous, but some of the highlights are the following: (1) All evaluation measures rank human summaries first, which is an appropriate and expected property of such measures, (2) Both relevance correlation and the content-based measures place leading sentence extracts ahead of the more sophisticated summarizers, (3) Relative utility ranks our system, MEAD, as the best summarizer for shorter summaries, although for longer summaries, lead-based summaries outperform MEAD, (4) Co-selection measurements show overall low agreement amongst humans (above chance), whereas relative utility reports higher numbers on the same data (but does not normalize for chance). The deliverable resources and software include: (1) a turn-key extractive multi-document summarizer, MEAD, which allows users to add their own features based on single sentences or pairs of sentences, (2) a large corpus of summaries produced by several automatic methods, including baseline and random summaries, (3) a collection of manual summaries produced by the Linguistic Data Consortium (LDC), (4) a battery of evaluation routines, (5) a collection of IR queries in English and Chinese and the corresponding relevance judgments from the Hong Kong news collection, (6) SMART relevance outputs for both full documents and summaries, (7) XML tools for processing of documents and summaries. JHU 2001 Summer workshop final report Evaluation of Text Summarization


title: Non-isolated interleaved high step-up converter with reduced voltage multiplier stages and a regenerative turn-off snubber

abstract: This paper proposes a novel non-isolated high step-up converter suitable for miniaturization by high frequency design. High step-up converters with voltage multipliers are promising for high frequency design because many of them can be free from coupled inductors or transformers, which may lower the voltage gain due to the leakage inductance or may cause significant AC conduction loss in the primary winding. However, two issues can still hinder further miniaturization. One is that many multiplier stages may be needed to achieve extremely high voltage gain. The other is that high switching frequency may be restricted by severe turn-off voltage surge caused by high speed switching. The proposed converter addresses the former by applying a recently reported technique to a non-isolated boost converter. Additionally, the converter addresses the latter by proposing a simple passive regenerative turn-off snubber implementable only by two diodes and a capacitor. Experiments verified that the proposed converter achieves high voltage gain with fewer voltage multiplier stages compared to prior non-isolated high step-up converters. Additionally, the snubber successfully reduced the turn-off surge into half approximately without apparent deterioration of the efficiency.


Evaluating Filter Materials for E. Coli Removal from Stormwater


title: An Alternative Definition of Finite Sample Breakdown Point with Applications to Regression Model Estimators

abstract: Abstract We propose an alternative definition of the finite-sample breakdown point. This breakdown point is invariant with respect to reparameterization and compatible with the Donoho-Huber breakdown point in linear regression situations. It also overcomes certain limitations of the definition proposed by Stromberg and Ruppert and can be used in a wide range of estimation problems. We investigate the breakdown properties of some nonlinear regression estimators. These results alert us to the danger of using familiar M estimators with data sets containing outliers and to the advantages of using estimators based on Hampel's proposal, such as S estimators.


title: Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders

abstract: While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses. Unlike past work that has focused on diversifying the output of the decoder at word-level to alleviate this problem, we present a novel framework based on conditional variational autoencoders that captures the discourse-level diversity in the encoder. Our model uses latent variables to learn a distribution over potential conversational intents and generates diverse responses using only greedy decoders. We have further developed a novel variant that is integrated with linguistic prior knowledge for better performance. Finally, the training procedure is improved by introducing a bag-of-word loss. Our proposed models have been validated to generate significantly more diverse responses than baseline approaches and exhibit competence in discourse-level decision-making.


title: PrivacyGuard: Enforcing Private Data Usage with Blockchain and Attested Execution

abstract: In the upcoming evolution of the Internet of Things (IoT), it is anticipated that billions of devices will be connected to the Internet. Many of these devices are capable of collecting information from individual users and their physical surroundings. They are also capable of taking smart actions, which are usually from a backend cloud server in the IoT system. While IoT promises a more connected and smarter world, this pervasive large-scale data collection, storage, sharing, and analysis raise many privacy concerns.


title: Contextual QR Codes

abstract: QR codes (2D barcodes) are used to encode and decode data at a rapid rate. Using camera phones to read two dimensional barcodes for various purposes is currently a popular topic in both research and in practical applications. But until now, the information provided by QR codes was solely static. What you see is exactly what was encoded. This paper proposes the notion of contextual QR codes that merge a public QR code and private information, in order to provide data related to a particular context. For example, a public tag will be decoded and translated into ldquoGood evening John Smithrdquo or ldquoBonjour Jean Dupondrdquo, if the system is able to obtain data such as the name of the user, the language used on the machine (English or French for instance) and the moment when the interaction takes place.


title: A Flexible Table Driven LR ( 1 ) Parser

abstract: The implementation of a flexible table driven LR(1) parser is presented in this paper. The LR(1) parsing algorithm is based on modified bottom up strategy described in [1] and it stays the same for all grammars. A parser with control tables that are initialized within the source text has no flexibility because each new LR(1) parser implementation needs to recompile the source text in order to adjust its data control structures like terminal symbols, non terminal symbols, grammar rules and control table. To avoid this drawback a configuration text edited file is to be created and read as input to adapt the parser before the syntax analysis process starts execution.


title: Detection of Fraudulent Sellers in Online Marketplaces using Support Vector Machine Approach

abstract: The e-commerce share in the global retail spend is showing a steady increase over the years indicating an evident shift of consumer attention from bricks and mortar to clicks in retail sector. In recent years, online marketplaces have become one of the key contributors to this growth. As the business model matures, the number and types of frauds getting reported in the area is also growing on a daily basis. Fraudulent e-commerce buyers and their transactions are being studied in detail and multiple strategies to control and prevent them are discussed. Another area of fraud happening in marketplaces are on the seller side and is called merchant fraud. Goods/services offered and sold at cheap rates, but never shipped is a simple example of this type of fraud. This paper attempts to suggest a framework to detect such fraudulent sellers with the help of machine learning techniques. The model leverages the historic data from the marketplace and detect any possible fraudulent behaviours from sellers and alert to the marketplace.


title: Reduced-Order Model and Stability Analysis of Low-Voltage DC Microgrid

abstract: Depleting fossil fuels, increasing energy demand, and need for high-reliability power supply motivate the use of dc microgrids. This paper analyzes the stability of low-voltage dc microgrid systems. Sources are controlled using a droop-based decentralized controller. Various components of the system have been modeled. A linearized system model is derived using small-signal approximation. The stability of the system is analyzed by identifying the eigenvalues of the system matrix. The sufficiency condition for stable operation of the system is derived. It provides upper bound on droop constants and is useful during planning and designing of dc microgrids. Furthermore, the sensitivity of system poles to variation in cable resistance and inductance is identified. It is proved that the poles move further inside the negative real plane with a decrease in inductance or an increase in resistance. The method proposed in this paper is applicable to any interconnecting structure of sources and loads. The results obtained by analysis are verified by detailed simulation study. Root locus plots are included to confirm the movement of system poles. The viability of the model is confirmed by experimental results from a scaled-down laboratory prototype of a dc microgrid developed for the purpose.


title: Beyond Behavioral Adjustments: How Determinants of Contemporary Caribbean Masculinities Thwart Efforts to Eliminate Domestic Violence

abstract: This study and its ancillary behavioral health program identified and addressed a Caribbean cultural paradigm that is no longer viable in 21st century Caribbean society. A complex web of cultural and social determinants binds Caribbean men in a depressing scenario of limited educational opportunity, inconsistent seasonal employment, and proclivity toward violence against women. Our study chronicles a Caribbean domestic violence initiative, Partnership for Peace (PFP), with qualitative data from 32 Grenadian male domestic violence offenders and nine of their female counterparts. We found that Caribbean-gendered relations are intrinsically linked, and any attempt to foster behavioral adjustments among male perpetrators will not be sustainable without addressing gendered disparities in education and employment.


title: Deletion discussions in Wikipedia: decision factors and outcomes

abstract: Deletion of articles is a common process in Wikipedia, in order to ensure the overall quality of the encyclopedia. Yet, there is a need to better understand the procedures in order to promote the best decisions without unnecessary community work. In this paper, we study deletion in Wikipedia, drawing from factor analysis, and taking an in-depth, content-analysis-based approach. We address three research questions: First, what factors contribute to the decision about whether to delete a given article? Second, when multiple factors are given, what is the relative importance of those factors? Third, what are the outcomes of deletion discussions, both for articles and for the community? We find that multiple factors contribute to the assessment of an article, and we discuss their relative frequency. Further, we show how the assessment timeline focuses attention on improving borderline articles that have the potential to meet Wikipedia's content inclusion policies, and we highlight the role of novice contributors in this improvement process.


title: Attention Drainage Effect: How Background Music Effects Concentration in Taiwanese College Students

abstract: The purpose of this study was to see whether different types of background music affect the performance of a reading comprehension task in Taiwanese college students. There are two major research questions in this study. First, this study tries to find out whether listening to music affect the learner's concentration when they are doing a task such as reading. The second research question is whether light classical music is more distracting or less distracting than hip hop music during a reading comprehension task. An experiment involving 133 participants from a medium-size college in southern Taiwan was conducted where the participants performed a reading comprehension task with either light classical music, hip hop music, or with no music in the background. The result of the study showed that music with a higher intensity is more distracting and has a greater effect on task performance and concentration. The result helped formulate the Attention Drainage Effect theory, which is based on Kahneman's (1973) capacity model of attention.


title: Measuring IT Service Management Capability: Scale Development and Empirical Validation

abstract: This paper conceptualizes IT service management (ITSM) capability, a key competence of today's IT provider organizations, and presents a survey instrument to facilitate the measurement of an ITSM capability for research and practice. Based on the review of four existing ITSM maturity models (CMMI- SVC, COBIT 4.1, SPICE, ITIL v3), we first develop a multi-attributive scale to assess maturity on an ITSM process level. We then use this scale in a survey with 205 ITSM key informants who assessed IT provider organizations along a set of 26 established ITSM processes. Our exploratory factor analysis and measurement model assessment results support the validity of an operationali- zation of ITSM capability as a second-order construct formed by ITSM pro- cesses that span three dimensions: service planning, service transition, and ser- vice operation. The practical utility of our survey instrument and avenues for future research on ITSM capability are outlined.


title: Design Techniques for Exploring Automotive Interaction in the Drive towards Automation

abstract: Automotive interaction design is undergoing a major shift due to the disruptive forces of automation and information technology. This paper reviews current challenges in human vehicle interaction design and argues that these challenges demand that interaction become a primary consideration in designing automotive user experiences. We survey exploratory interaction design techniques for human vehicle interactions, showing examples from our research of each technique in action. The techniques are enactments, contextual inquiry, scale scenarios, Wizard of Oz, field experiments and video and animation prototyping. We reflect upon our experiences with these methods, and discuss as yet unmet needs in interaction design for the road ahead.


title: Analysis of a new topology of flexible PCB winding for slotless BLDC machines

abstract: The slotless BLDC motor is one of the most efficient motor in the fractional horse power range. However, the demand for higher performance, incessantly growing, asks for break-through improvements. A solution to achieve better performances is to use windings printed on a flexible PCB, allowing more elaborated winding shapes and topologies. In this paper, a new winding topology is introduced and compared to three other PCB windings. An analytical magnetic model is presented, and validated by FEM. The comparison shows that the new topology achieves a motor power density 10% higher than the best existing PCB winding. Further, this gain reaches 40% when comparing with the winding shape and topology currently used in the classical windings made of copper wire, suggesting a consequent breakthrough in the performances achievable in the fractional horse power range.


title: Application service placement in stochastic grid environments using learning and ant-based methods

abstract: Achieving acceptable application performance in a grid environment remains a difficult challenge. In particular, this is true for applications composed of services that require certain criteria regarding quality to be fulfilled in order to satisfy users' needs. The problem considered here is the partitioning of application services onto the available execution nodes of a grid environment in such a way that they satisfy certain minimum criteria regarding quality. Fundamentally, this is an NP-hard problem. We propose three algorithms based on the concepts of learning automata and the metaphor of foraging ants. The algorithms naturally follow a decentralised multi-agent method for solving the service partitioning problem. Moreover, they establish a distributed problem-solving mechanism that does not require the use of a central controller. The proposed algorithms have been rigorously tested and evaluated through extensive simulations on randomly generated application services and grid environments. The results indicate that learning is an essential component for achieving scalability and efficiency in nature-inspired systems.


title: The Impact of Weather Condition on Radio-Based Distance Estimation: A Case Study in GSM Networks With Mobile Measurements

abstract: Distance estimation based on the received signal strength (RSS) of wireless radio has been extensively studied; however, the estimation accuracy is susceptible to radio propagation conditions, particularly during periods of precipitation. This study performed experiments on the 1.8-GHz band of Global System for Mobile Communications (GSM) networks at Yuan Ze University, Taoyuan, Taiwan, sensing actual radio power signals using mobile phones and recording transmission conditions during various weather events. The aim was to compare RSS performance under dry and wet weather conditions and study the dynamics of distance estimation from theoretical and empirical perspectives. Traditional rain attenuation models have largely discounted the impact of precipitation on GSM signals due to the relatively low frequency band; however, on-site experimental results demonstrated that dynamic weather significantly degrades accuracy in distance estimation. This study employed a typical rain attenuation model and a radio propagation model to evaluate errors in distance estimation under various rain conditions, i.e., heavy rain, extremely heavy rain, torrential rain, and extremely torrential rain, in accordance with the standards set out by the Central Weather Bureau, Taiwan. This study also investigated the RSS attenuation under various wind speeds. The Cramer–Rao lower bound (CRLB) was used as a benchmark for comparison.


title: Deep Convolutional Neural Networks for Large-scale Speech Tasks

abstract: Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and model spectral correlations which exist in signals. Since speech signals exhibit both of these properties, we hypothesize that CNNs are a more effective model for speech compared to Deep Neural Networks (DNNs). In this paper, we explore applying CNNs to large vocabulary continuous speech recognition (LVCSR) tasks. First, we determine the appropriate architecture to make CNNs effective compared to DNNs for LVCSR tasks. Specifically, we focus on how many convolutional layers are needed, what is an appropriate number of hidden units, what is the best pooling strategy. Second, investigate how to incorporate speaker-adapted features, which cannot directly be modeled by CNNs as they do not obey locality in frequency, into the CNN framework. Third, given the importance of sequence training for speech tasks, we introduce a strategy to use ReLU+dropout during Hessian-free sequence training of CNNs. Experiments on 3 LVCSR tasks indicate that a CNN with the proposed speaker-adapted and ReLU+dropout ideas allow for a 12%-14% relative improvement in WER over a strong DNN system, achieving state-of-the art results in these 3 tasks.


title: Real-time detection and recognition of traffic signs

abstract: Automated recognition of traffic signs is becoming a very interesting area in computer vision with clear possibilities of its application in automotive industry. For example, it would be possible to design a system which could recognize the current speed limit on the road and notify the driver in an appropriate manner. In this paper we deal with methods for automated localization of certain traffic signs, and classification of those signs according to the official designations. We propose two different approaches of determining the current speed limit after the sign was localized. A demo software system was developed to demonstrate the presented methods. Finally, we compare results obtained from the developed software, and discuss the influence of different parameters on recognition performance and quality.


title: Detecting social media mobile botnets using user activity correlation and artificial immune system

abstract: With the rapidly growing development of cellular networks and powerful smartphones, botnets have invaded the mobile domain. Social media, like Twitter, Facebook, and YouTube have created a new communication channel for attackers. Recently, bot masters started to exploit social media for different malicious activity, such as sending spam, recruitment of new bots, and botnet command and control. In this paper we propose a detection technique for social mediabased mobile botnets using Twitter. The proposed method combines the correlation between tweeting and user activity, such as clicks or taps, and an Artificial Immune System detector, to detect tweets caused by bots and differentiate them from tweets generated by user or by user-approved applications. This detector creates a signature of the tweet and compares it with a dynamically updated signature library of bot behavior signatures. The proposed system has been fully implemented on Android platform and tested under several sets of generated tweets. The test results show that the proposed method has a very high accuracy in detecting bot tweets with about 95% detection ratio.


title: World ocean simulation system (WOSS): a simulation tool for underwater networks with realistic propagation modeling

abstract: Network simulators are a fundamental tool for the performance evaluation of protocols and applications in complex scenarios, which would be too expensive or infeasible to realize in practice. With the aim to provide a shared environment for the simulation of underwater networks we have adapted the ns2 network simulator to provide a detailed reproduction of the propagation of sound in water (i.e., by means of ray tracing instead of empirical relations). This has been tied to formerly available simulation frameworks (such as the MIRACLE extensions to ns2) to provide a completely customizable tool, including acoustic propagation, physical layer modeling, and cross-layer specification of networking protocols. In this paper, we describe our tool, and use it for a case study involving the comparison of three MAC protocols for underwater networks over different kinds of physical layers. Our results compare the transmission coordination approach chosen by each protocol, and show when it is better to rely on random access, as opposed to loose or tight coordination.