title: Pain in Ehlers-Danlos Syndrome Is Common, Severe, and Associated with Functional Impairment

abstract: Abstract Context The Ehlers-Danlos Syndrome (EDS) is a clinically and genetically heterogeneous group of heritable connective tissue disorders characterized by joint hypermobility, skin hyperextensibility, and tissue fragility. Musculoskeletal pain is mentioned in the diagnostic criteria and described as early in onset, chronic, and debilitating. However, systematic research on pain in EDS is scarce. Objectives We investigated prevalence and impact of pain and associated features in a large group of EDS patients. Methods We performed a study among members of the Dutch EDS patient organization ( n =273) and included the McGill Pain Questionnaire to investigate various aspects of pain, the Sickness Impact Profile to study functional impairment, the Symptom Checklist subscale sleep to evaluate sleep disturbances, and the Checklist Individual Strength subscale fatigue to determine fatigue severity. Results The results of this study show that 1) chronic pain in EDS is highly prevalent and associated with regular use of analgesics; 2) pain is more prevalent and more severe in the hypermobility type than in the classic type; 3) pain severity is correlated with hypermobility, dislocations, and previous surgery; 4) pain is correlated with low nocturnal sleep quality; and 5) pain contributes to functional impairment in daily life, independent of the level of fatigue. Conclusion From this large cohort of EDS patients, we conclude that pain is common and severe in EDS. Pain is related to hypermobility, dislocations, and previous surgery and associated with moderate to severe impairment in daily functioning. Therefore, treatment of pain should be a prominent aspect of symptomatic management of EDS.


title: Refining the relationship between personality and subjective well-being.

abstract: Understanding subjective well-being (SWB) has historically been a core human endeavor and presently spans fields from management to mental health. Previous meta-analyses have indicated that personality traits are one of the best predictors. Still, these past results indicate only a moderate relationship, weaker than suggested by several lines of reasoning. This may be because of commensurability, where researchers have grouped together substantively disparate measures in their analyses. In this article, the authors review and address this problem directly, focusing on individual measures of personality (e.g., the Neuroticism-Extroversion-Openness Personality Inventory; P. T. Costa & R. R. McCrae, 1992) and categories of SWB (e.g., life satisfaction). In addition, the authors take a multivariate approach, assessing how much variance personality traits account for individually as well as together. Results indicate that different personality and SWB scales can be substantively different and that the relationship between the two is typically much larger (e.g., 4 times) than previous meta-analyses have indicated. Total SWB variance accounted for by personality can reach as high as 39% or 63% disattenuated. These results also speak to meta-analyses in general and the need to account for scale differences once a sufficient research base has been generated.


title: P2-12: Thermal analysis of slow wave structure for a space helix TWT

abstract: In this paper thermal analysis of the slow-wave structure for Ku-band helix space TWT has been carried out using ANSYS software v.10.1. To evaluate the thermal aspects of the helix slow-wave structure with three APBN support rods and barrel assembly, the related studies of the slow-wave structure assembly with six turn, ten turn and sixteen turn of helix for the Ku-band 140 W space TWT during extreme case of operation, i.e. at saturation and with perfectly rigid base-plate at 80 °C have been carried out. The CPU time on Pentium-4, window XP system with 512 MB RAM was 45 minutes for six turn, one hour for ten turn of helix.


ON LINEARIZATION OF NONLINEAR DYNAMIC SYSTEMS DESCRIBED BY STATE-DEPENDENT-PARAMETER ( SDP ) DISCRETE-TIME MODEL


title: Image processing based vehicle detection and tracking method

abstract: Vehicle detection and tracking plays an effective and significant role in the area of traffic surveillance system where efficient traffic management and safety is the main concern. In this paper, we discuss and address the issue of detecting vehicle / traffic data from video frames. Although various researches have been done in this area and many methods have been implemented, still this area has room for improvements. With a view to do improvements, it is proposed to develop an unique algorithm for vehicle data recognition and tracking using Gaussian mixture model and blob detection methods. First, we differentiate the foreground from background in frames by learning the background. Here, foreground detector detects the object and a binary computation is done to define rectangular regions around every detected object. To detect the moving object correctly and to remove the noise some morphological operations have been applied. Then the final counting is done by tracking the detected objects and their regions. The results are encouraging and we got more than 91% of average accuracy in detection and tracking using the Gaussian Mixture Model and Blob Detection methods.


title: Visualization Support for Comparing Energy Consumption Data

abstract: Providing effective feedback can empower users to change their behaviour and take the necessary actions to reduce their energy consumption. The types of feedback that allow comparison of energy usage seem to be particularly valuable. This paper introduces the time-stack visualization, which has been designed to support comparisons of individual and collective energy usage data. It also describes a user study conducted to compare the effectiveness of time-stack against a similar visualization called time-pie. The results show that although the two visualizations are generally comparable in their effectiveness, users rate time-stack more favourably.


title: Requirements Engineering in the Year 00: A Research Perspective

abstract: Requirements engineering (RE) is concerned with the identification of the goals to be achieved by the envisioned system, the operationalization of such goals into services and constraints, and the assignment of responsibilities for the resulting requirements to agents such as humans, devices, and software. The processes involved in RE include domain analysis, elicitation, specification, assessment, negotiation, documentation, and evolution. Getting high-quality requirements is difficult and critical. Recent surveys have confirmed the growing recognition of RE as an area of utmost importance in software engineering research and practice. The paper presents a brief history of the main concepts and techniques developed to date to support the RE task, with a special focus on modeling as a common denominator to all RE processes. The initial description of a complex safety-critical system is used to illustrate a number of current research trends in RE-specific areas such as goal-oriented requirements elaboration, conflict management, and the handling of abnormal agent behaviors. Opportunities for goal-based architecture derivation are also discussed together with research directions to let the field move towards more disciplined habits.


title: Header space analysis: Static checking for networks

abstract: Today's networks typically carry or deploy dozens of protocols and mechanisms simultaneously such as MPLS, NAT, ACLs and route redistribution. Even when individual protocols function correctly, failures can arise from the complex interactions of their aggregate, requiring network administrators to be masters of detail. Our goal is to automatically find an important class of failures, regardless of the protocols running, for both operational and experimental networks. ::: ::: To this end we developed a general and protocol-agnostic framework, called Header Space Analysis (HSA). Our formalism allows us to statically check network specifications and configurations to identify an important class of failures such as Reachability Failures, Forwarding Loops and Traffic Isolation and Leakage problems. In HSA, protocol header fields are not first class entities; instead we look at the entire packet header as a concatenation of bits without any associated meaning. Each packet is a point in the {0,1}L space where L is the maximum length of a packet header, and networking boxes transform packets from one point in the space to another point or set of points (multicast). ::: ::: We created a library of tools, called Hassel, to implement our framework, and used it to analyze a variety of networks and protocols. Hassel was used to analyze the Stanford University backbone network, and found all the forwarding loops in less than 10 minutes, and verified reachability constraints between two subnets in 13 seconds. It also found a large and complex loop in an experimental loose source routing protocol in 4 minutes.


title: A decision tree based approach for microgrid islanding detection

abstract: This paper proposes a passive islanding detection technique for microgrid. The proposed technique relies on capturing the underlying signatures of a wide variety of system events on critical system parameters through the utilization of pattern recognition tools for islanding detection in a microgrid. The proposed technique is tested on a microgrid model implemented on IEEE 13-node distribution feeder system under a wide variety of system operating states. Results from test case study have been analyzed to evaluate the effectiveness of the proposed method. Case study results indicate that the proposed method can detect islanding events with high accuracy and reliability.


title: A fuzzy commitment scheme

abstract: We combine well-known techniques from the areas of error-correcting codes and cryptography to achieve a new type of cryptographic primitive that we refer to as a fuzzy commitment scheme. Like a conventional cryptographic commitment scheme, our fuzzy commitment scheme is both concealing and binding : it is infeasible for an attacker to learn the committed value, and also for the committer to decommit a value in more than one way. In a conventional scheme, a commitment must be opened using a unique witness , which acts, essentially, as a decryption key. By contrast, our scheme is fuzzy in the sense that it accepts a witness that is close to the original encrypting witness in a suitable metric, but not necessarily identical. This characteristic of our fuzzy commitment scheme makes it useful for applications such as biometric authentication systems, in which data is subject to random noise. Because the scheme is tolerant of error, it is capable of protecting biometric data just as conventional cryptographic techniques, like hash functions, are used to protect alphanumeric passwords. This addresses a major outstanding problem in the theory of biometric authentication. We prove the security characteristics of our fuzzy commitment scheme relative to the properties of an underlying cryptographic hash function.


title: Hierarchical decomposition of laparoscopic surgery: a human factors approach to investigating the operating room environment

abstract: Hierarchical decomposition of complex behaviour and systems is a valuable research methodology from human factors and information-processing psychology that can be applied to laparoscopic surgery. This article describes results of research on surgeons performing several different laparoscopic procedures, conducted in Vancouver, Canada 1995–98. Through top-down analyses of surgical procedures and bottom-up analyses of tool motions, results included detailed decomposition of the procedures through surgical steps, sub-steps, tasks, sub-tasks and tool motions. Analyses at all levels provided valuable information. In addition to specific surgeon- and technology-related observations, such as the effect of dividing the short gastrics on performance of Nissen fundoplication, gaze patterns of surgeons and factors related to patient safety were analysed. The hierarchical decomposition approach can be extended to other aspects of the complex system that consists of the surgeon and operating room team, the technologies and the operating room environment. Other frameworks for assessment are also considered.


title: TrustJS: Trusted Client-side Execution of JavaScript

abstract: Client-side JavaScript has become ubiquitous in web applications to improve user experience and reduce server load. However, since clients are untrusted, servers cannot rely on the confidentiality or integrity of client-side JavaScript code and the data that it operates on. For example, client-side input validation must be repeated at server side, and confidential business logic cannot be offloaded. In this paper, we present TrustJS, a framework that enables trustworthy execution of security-sensitive JavaScript inside commodity browsers. TrustJS leverages trusted hardware support provided by Intel SGX to protect the client-side execution of JavaScript, enabling a flexible partitioning of web application code. We present the design of TrustJS and provide initial evaluation results, showing that trustworthy JavaScript offloading can further improve user experience and conserve more server resources.


title: Improving Siamese Networks for One Shot Learning using Kernel Based Activation functions

abstract: The lack of a large amount of training data has always been the constraining factor in solving a lot of problems in machine learning, making One Shot Learning one of the most intriguing ideas in machine learning. It aims to learn information about object categories from one, or only a few training examples. This process of learning in deep learning is usually accomplished by proper objective function, i.e; loss function and embeddings extraction i.e; architecture. In this paper, we discussed about metrics based deep learning architectures for one shot learning such as Siamese neural networks and present a method to improve on their accuracy using Kafnets (kernel-based non-parametric activation functions for neural networks) by learning proper embeddings with relatively less number of epochs. Using kernel activation functions, we are able to achieve strong results which exceed those of ReLU based deep learning models in terms of embeddings structure, loss convergence, and accuracy.


title: An event-condition-action language for XML

abstract: XML repositories are now a widespread means for storing and exchanging information on the Web. As these repositories become increasingly used in dynamic applications such as e-commerce, there is a rapidly growing need for a mechanism to incorporate reactive functionality in an XML setting. Event-condition-action (ECA) rules are a technology from active databases and are a natural method for supporting suchfunctionality. ECA rules can be used for activities such as automatically enforcing document constraints, maintaining repository statistics, and facilitating publish/subscribe applications. An important question associated with the use of a ECA rules is how to statically predict their run-time behaviour. In this paper, we define a language for ECA rules on XML repositories. We then investigate methods for analysing the behaviour of a set of ECA rules, a task which has added complexity in this XML setting compared with conventional active databases.


title: A constant-time SLAM back-end in the continuum between global mapping and submapping: application to visual stereo SLAM

abstract: This work addresses the development and application of a novel approach, called sparser relative bundle adjustment SRBA, which exploits the inherent flexibility of the relative bundle adjustment RBA framework to devise a continuum of strategies, ranging from RBA with linear graphs to classic bundle adjustment BA in global coordinates, where submapping with local maps emerges as a natural intermediate solution. This method leads to graphs that can be optimized in bounded time even at loop closures, regardless of the loop length. Furthermore, it is shown that the pattern in which relative coordinate variables are defined among keyframes has a significant impact on the graph optimization problem. By using the proposed scheme, optimization can be done more efficiently than in standard RBA, allowing the optimization of larger local maps for any given maximum computational cost. The main algorithms involved in the graph management, along with their complexity analyses, are presented to prove their bounded-time nature. One key advance of the present work is the demonstration that, under mild assumptions, the spanning trees for every single keyframe in the map can be incrementally built by a constant-time algorithm, even for arbitrary graph topologies. We validate our proposal within the scope of visual stereo simultaneous localization and mapping SLAM by developing a complete system that includes a front-end that seamlessly integrates several state-of-the-art computer vision techniques such as ORB features and bag-of-words, along with a decision scheme for keyframe insertion and a SRBA-based back-end that operates as graph optimizer. Finally, a set of experiments in both indoor and outdoor conditions is presented to test the capabilities of this approach. Open-source implementations of the SRBA back-end and the stereo front-end have been released online.


title: The Difficulty of Defining Mindfulness: Current Thought and Critical Issues

abstract: In the last decade, a surge of interest has been directed towards the empirical investigation of the concept and applications of mindfulness. If one considers the increasing evidence about the clinical benefits and the psychological and neurobiological correlates of current mindfulness based interventions (MBIs), it is surprising that significantly lower effort has been directed towards the achievement of a consensus about an unequivocal operationalization of mindfulness within modern Western psychology. Accordingly, the present review aims to summarize traditional and current perspectives about mindfulness, to discuss the extent to which modern definitions of mindfulness differ from more traditional definitions and, more specifically, the limitations of current questionnaires that are thought to measure mindfulness levels, and to provide suggestions for future research on this topic. In sum, according to authors well versed in the original Buddhist literature, from which several MBIs are overtly or implicitly derived, modern attempts to operationalize mindfulness have consistently failed to provide an unequivocal definition of mindfulness, which takes into account the complexity of the original definitions of mindfulness. Although the concept of mindfulness remains elusive and difficult to capture by means of modern self-report questionnaires, however, several alternatives exist that could shed light on closely related constructs, which could deepen our understanding of mindfulness and that could lead to the development of new, not yet considered, categories of psychological effects associated with mindfulness training.


title: Deontological and utilitarian inclinations in moral decision making: A process dissociation approach.

abstract: Dual-process theories of moral judgment suggest that responses to moral dilemmas are guided by two moral principles: the principle of deontology states that the morality of an action depends on the intrinsic nature of the action (e.g., harming others is wrong regardless of its consequences); the principle of utilitarianism implies that the morality of an action is determined by its consequences (e.g., harming others is acceptable if it increases the well-being of a greater number of people). Despite the proposed independence of the moral inclinations reflecting these principles, previous work has relied on operationalizations in which stronger inclinations of one kind imply weaker inclinations of the other kind. The current research applied Jacoby's (1991) process dissociation procedure to independently quantify the strength of deontological and utilitarian inclinations within individuals. Study 1 confirmed the usefulness of process dissociation for capturing individual differences in deontological and utilitarian inclinations, revealing positive correlations of both inclinations to moral identity. Moreover, deontological inclinations were uniquely related to empathic concern, perspective-taking, and religiosity, whereas utilitarian inclinations were uniquely related to need for cognition. Study 2 demonstrated that cognitive load selectively reduced utilitarian inclinations, with deontological inclinations being unaffected. In Study 3, a manipulation designed to enhance empathy increased deontological inclinations, with utilitarian inclinations being unaffected. These findings provide evidence for the independent contributions of deontological and utilitarian inclinations to moral judgments, resolving many theoretical ambiguities implied by previous research. (PsycINFO Database Record (c) 2013 APA, all rights reserved)


title: A Multiple Blockchains Architecture on Inter-Blockchain Communication

abstract: Blockchain is a new technology for data sharing between untrusted peers. However, it does not work well with massive transactions. Besides, there are high barriers between heterogeneous blockchain systems. In this paper, we proposed an innovative component-based framework for exchanging information across arbitrary blockchain system called interactive multiple blockchain architecture. In our architecture, a dynamic network of multi-chain is created for inter-blockchain communication. We propose the inter-blockchain connection model for routing management and messages transferring. Additionally, our proposed protocols provide transactions with atomicity and consistency in crossing-chain scene. In the end, our experiment results based on a network of private multiple blockchain systems show that the throughput is increased by a number of chains parallel running.


title: A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval

abstract: The recent advances brought by deep learning allowed to improve the performance in image retrieval tasks. Through the many convolutional layers, available in a Convolutional Neural Network (CNN), it is possible to obtain a hierarchy of features from the evaluated image. At every step, the patches extracted are smaller than the previous levels and more representative. Following this idea, this paper introduces a new detector applied on the feature maps extracted from pre-trained CNN. Specifically, this approach lets to increase the number of features in order to increase the performance of the aggregation algorithms like the most famous and used VLAD embedding. The proposed approach is tested on different public datasets: Holidays, Oxford5k, Paris6k and UKB.


title: Filter methods for feature selection: a comparative study

abstract: Adequate selection of features may improve accuracy and efficiency of classifier methods. There are two main approaches for feature selection: wrapper methods, in which the features are selected using the classifier, and filter methods, in which the selection of features is independent of the classifier used. Although the wrapper approach may obtain better performances, it requires greater computational resources. For this reason, lately a new paradigm, hybrid approach, that combines both filter and wrapper methods has emerged. One of its problems is to select the filter method that gives the best relevance index for each case, and this is not an easy to solve question. Different approaches to relevance evaluation lead to a large number of indices for ranking and selection. In this paper, several filter methods are applied over artificial data sets with different number of relevant features, level of noise in the output, interaction between features and increasing number of samples. The results obtained for the four filters studied (ReliefF, Correlation-based Feature Selection, Fast Correlated Based Filter and INTERACT) are compared and discussed. The final aim of this study is to select a filter to construct a hybrid method for feature selection.


title: The CPRO Cannabis germplasm collection

abstract: A collection of more than a 150 Cannabis accessions is being established as a source for evaluation and breeding experiments. Origin of the accessions and maintenance of the collection are described.


title: New onset refractory status epilepticus (NORSE)

abstract: Abstract Purpose To summarize the clinical features, suggested work-up, treatment and prognosis of new-onset refractory status epilepticus (NORSE), a condition recently defined as the occurrence of refractory status epilepticus (RSE) in a patient without active epilepsy, and without a clear acute or active structural, toxic or metabolic cause; and of the related syndrome of febrile infection-related epilepsy syndrome (FIRES), also recently defined as a subgroup of NORSE preceded by a febrile illness between 2 weeks and 24 h prior to the onset of RSE. Method Narrative review of the medical literature about NORSE and FIRES. Results NORSE and FIRES mainly affect school-age children and young adults. A prodromal phase with flu-like symptoms precedes the SE onset in two third of NORSE cases, and by definition in all FIRES. Status epilepticus usually starts with repeated focal seizures with secondary bilateralization. Most cases evolve to super RSE (SRSE) and have unfavorable outcome, with short-term mortality of 12–27%, long-term disability and epilepsy. No specific imaging or laboratory abnormalities have been identified so far that allows an early diagnosis and half of adult cases remain of unknown etiology. A standardized diagnostic algorithm is provided and. Autoimmune encephalitis is the most frequent identified cause. In the absence of specific diagnosis, immunotherapy could be tried in addition to antiepileptic treatment. Conclusions This review presents the rare but devastating syndrome of NORSE, including the subcategory of FIRES. Early recognition with complete work-up is primordial to identify the underlying cause and promptly start appropriate treatment.


title: Graph Cuts Stereo Matching Based on Patch-Match and Ground Control Points Constraint

abstract: Stereo matching methods based on Patch-Match obtain good results on complex texture regions but show poor ability on low texture regions. In this paper, a new method that integrates Patch-Match and graph cuts GC is proposed in order to achieve good results in both complex and low texture regions. A label is randomly assigned for each pixel and the label is optimized through propagation process. All these labels constitute a label space for each iteration in GC. Also, a Ground Control Points GCPs constraint term is added to the GC to overcome the disadvantages of Patch-Match stereo in low texture regions. The proposed method has the advantage of the spatial propagation of Patch-Match and the global property of GC. The results of experiments are tested on the Middlebury evaluation system and outperform all the other PatchMatch based methods.


title: Drawing and Recognizing Chinese Characters with Recurrent Neural Network

abstract: Recent deep learning based approaches have achieved great success on handwriting recognition. Chinese characters are among the most widely adopted writing systems in the world. Previous research has mainly focused on recognizing handwritten Chinese characters. However, recognition is only one aspect for understanding a language, another challenging and interesting task is to teach a machine to automatically write (pictographic) Chinese characters. In this paper, we propose a framework by using the recurrent neural network (RNN) as both a discriminative model for recognizing Chinese characters and a generative model for drawing (generating) Chinese characters. To recognize Chinese characters, previous methods usually adopt the convolutional neural network (CNN) models which require transforming the online handwriting trajectory into image-like representations. Instead, our RNN based approach is an end-to-end system which directly deals with the sequential structure and does not require any domain-specific knowledge. With the RNN system (combining an LSTM and GRU), state-of-the-art performance can be achieved on the ICDAR-2013 competition database. Furthermore, under the RNN framework, a conditional generative model with character embedding is proposed for automatically drawing recognizable Chinese characters. The generated characters (in vector format) are human-readable and also can be recognized by the discriminative RNN model with high accuracy. Experimental results verify the effectiveness of using RNNs as both generative and discriminative models for the tasks of drawing and recognizing Chinese characters.


title: Systematic Review of Software Behavioral Model Consistency Checking

abstract: In software development, models are often used to represent multiple views of the same system. Such models need to be properly related to each other in order to provide a consistent description of the developed system. Models may contain contradictory system specifications, for instance, when they evolve independently. Therefore, it is very crucial to ensure that models conform to each other. In this context, we focus on consistency checking of behavior models. Several techniques and approaches have been proposed in the existing literature to support behavioral model consistency checking. This article presents a Systematic Literature Review (SLR) that was carried out to obtain an overview of the various consistency concepts, problems, and solutions proposed regarding behavior models. In our study, the identification and selection of the primary studies was based on a well-planned search strategy. The search process identified a total of 1770 studies, out of which 96 have been thoroughly analyzed according to our predefined SLR protocol. The SLR aims to highlight the state-of-the-art of software behavior model consistency checking and identify potential gaps for future research. Based on research topics in selected studies, we have identified seven main categories: targeted software models, types of consistency checking, consistency checking techniques, inconsistency handling, type of study and evaluation, automation support, and practical impact. The findings of the systematic review also reveal suggestions for future research, such as improving the quality of study design and conducting evaluations, and application of research outcomes in industrial settings. For this purpose, appropriate strategy for inconsistency handling, better tool support for consistency checking and/or development tool integration should be considered in future studies.


title: Powering the Future of Industry: High-Power Adjustable Speed Drive Topologies

abstract: The HP-ASD technology has experienced a huge development in the last decade. This can be appreciated by the large number of recently introduced drive configurations on the market. In addition, many industrial applications are reaching MV operation and megawatt range or have experienced changes in requirements on efficiency, performance, and power quality, making the use of HP-ASDs more attractive. It can be concluded that, HP-ASDs is an enabling technology ready to continue powering the future of industry for the decades to come.


title: SORT: Second-Order Response Transform for Visual Recognition

abstract: In this paper, we reveal the importance and benefits of introducing second-order operations into deep neural networks. We propose a novel approach named Second-Order Response Transform (SORT), which appends element-wise product transform to the linear sum of a two-branch network module. A direct advantage of SORT is to facilitate cross-branch response propagation, so that each branch can update its weights based on the current status of the other branch. Moreover, SORT augments the family of transform operations and increases the nonlinearity of the network, making it possible to learn flexible functions to fit the complicated distribution of feature space. SORT can be applied to a wide range of network architectures, including a branched variant of a chain-styled network and a residual network, with very light-weighted modifications. We observe consistent accuracy gain on both small (CIFAR10, CIFAR100 and SVHN) and big (ILSVRC2012) datasets. In addition, SORT is very efficient, as the extra computation overhead is less than 5%.


title: Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate

abstract: When a message, such as a piece of news, spreads in social networks, how can we classify it into categories of interests, such as genuine or fake news? Classification of social media content is a fundamental task for social media mining, and most existing methods regard it as a text categorization problem and mainly focus on using content features, such as words and hashtags. However, for many emerging applications like fake news and rumor detection, it is very challenging, if not impossible, to identify useful features from content. For example, intentional spreaders of fake news may manipulate the content to make it look like real news. To address this problem, this paper concentrates on modeling the propagation of messages in a social network. Specifically, we propose a novel approach, TraceMiner, to (1) infer embeddings of social media users with social network structures; and (2) utilize an LSTM-RNN to represent and classify propagation pathways of a message. Since content information is sparse and noisy on social media, adopting TraceMiner allows to provide a high degree of classification accuracy even in the absence of content information. Experimental results on real-world datasets show the superiority over state-of-the-art approaches on the task of fake news detection and news categorization.


title: NEAT Particles: Design, Representation, and Animation of Particle System Effects

abstract: Particle systems are a representation, computation, and rendering method for special effects such as fire, smoke, explosions, electricity, water, magic, and many other phenomena. This paper presents NEAT particles, a new design, representation, and animation method for particle systems tailored to real-time effects in video games and simulations. In NEAT particles, the neuroevolution of augmenting topologies (NEAT) method evolves artificial neural networks (ANN) that control the appearance and motion of particles. NEAT particles affords three primary advantages over traditional particle effect development methods. First, it decouples the creation of new particle effects from mathematics and programming, enabling users with little knowledge of either to produce complex effects. Second, it allows content designers to evolve a broader range of effects than typical development tools through a form of interactive evolutionary computation (IEC). And finally, it acts as a concept generator, allowing users to interactively explore the space of possible effects. In the future such a system may allow content to be evolved in the game itself, as it is played


title: FPGA implementation of K-means algorithm for bioinformatics application: An accelerated approach to clustering Microarray data

abstract: The Microarray is a technique used by biologists to perform many genome experiments simultaneously, which produces very large datasets. Analysis of these datasets is a challenge for scientists especially as the number of genome databases is increasing rapidly every year. K-means clustering is an unsupervised data mining technique used widely by bioinformaticians to analyze Microarray data. However, K-means can take between a few seconds to several days to process Microarray data depending on the size of these datasets. This puts a limit on the complexity of biological problems which can be asked by bioinfomaticians, and hence may result in an incomplete solution to the problem. In order to overcome such problems, we propose a highly parallel hardware design to accelerate the K-means clustering of Microarray data by implementing the K-means algorithm in Field Programmable Gate Arrays (FPGA). Our implementation is particularly suitable for server solution as it allows for processing many different datasets simultaneously. We have designed, and implemented five k-mean cores on Xilinx Virtex4 XC4VLX25 FPGA, and tested them on a sample of real Yeast Microarray data. Our design achieved about 51.7× speed-up when compared to a software model while being 206.8× more energy efficient.


title: Optimizing Energy Consumption with Task Consolidation in Clouds

abstract: Task consolidation is a way to maximize utilization of cloud computing resources. Maximizing resource utilization provides various benefits such as the rationalization of maintenance, IT service customization, QoS and reliable services, etc. However, maximizing resource utilization does not mean efficient energy use. Much of the literature shows that energy consumption and resource utilization in clouds are highly coupled. Consequently, some of the literature aims to decrease resource utilization in order to save energy, while others try to reach a balance between resource utilization and energy consumption. In this paper, we present an energy-aware task consolidation (ETC) technique that minimizes energy consumption. ETC achieves this by restricting CPU use below a specified peak threshold. ETC does this by consolidating tasks amongst virtual clusters. In addition, the energy cost model considers network latency when a task migrates to another virtual cluster. To evaluate the performance of ETC we compare it against MaxUtil. MaxUtil is a recently developed greedy algorithm that aims to maximize cloud computing resources. The simulation results show that ETC can significantly reduce power consumption in a cloud system, with 17% improvement over MaxUtil.


title: Buffer Overflow Vulnerability Prediction from x86 Executables Using Static Analysis and Machine Learning

abstract: Mining static code attributes for predicting software vulnerabilities has received some attention recently. There are a number of approaches for detecting vulnerabilities from source code, but commercial off the shelf components are, in general, distributed in binary form. Before using such third-party components it is imperative to check for presence of vulnerabilities. We investigate the use of static analysis and machine learning for predicting buffer overflow vulnerabilities from binaries in this study. To mitigate buffer overflows, developers typically perform size checks and input validation. We propose static code attributes characterizing buffer usage and defense mechanisms implemented in the code for preventing buffer overflows. The proposed approach starts by identifying potential vulnerable statement constructs during binary program analysis and extracts static code attributes for each of them as per proposed characterization scheme to capture buffer usage patterns and defensive mechanisms employed in the code. Data mining methods are then used on these collected code attributes for predicting buffer overflows. Our experimental evaluation on standard buffer overflow benchmark binaries shows that the proposed static code attributes are effective in predicting buffer overflow vulnerabilities.


title: Agile manufacturing: A framework for research and development

abstract: Agile manufacturing (AM) is a new concept in manufacturing intended to improve the competitiveness of "rms. Manufacturing processes based on AM are characterized by customer}supplier integrated process for product design, manufacturing, marketing, and support services. This needs decision-making at functional knowledge levels, stable unit costs, #exible manufacturing, easy access to integrated data, and modular production facilities. Agile manufacturing requires enriching of the customer, co-operating with competitors, organizing to manage change, uncertainty and complexity, and leveraging people and information. In the recent years, a number of research papers have been published in the area of AM. However, a framework for the development of AM has not received due attention from both researchers and practitioners. Realizing the importance of agile manufacturing in the 21st century manufacturing competitiveness, an attempt has been made in this paper to review the literature available on AM with the objective to: (i) identify key strategies and techniques of AM, (ii) suggest some future research directions and (iii) develop a framework for the development of agile manufacturing systems (AMSs) along four key dimensions which include strategies, technologies, systems and people. ( 1999 Elsevier Science B.V. All rights reserved.


Systematic Exploration of Computational Music Structure Research


title: Communication applications of virtual reality

abstract: Virtual reality's (VR) final destination may well be as a multipurpose communication medium -a combination of the television and telephone wrapped delicately around the senses. Even NASA scientists like Steven Ellis (1991a)admitthat"Virtualenvironments. ..arecommunicationmedia"(p. 321). Introductory VR books often describe virtual reality as the next logical step in the history of communication media (e.g., Hamit, 1993; Rheingold, 1991). A Delphi panel survey predicts that communication applications of virtual reality will amount to more than 600/0 of the marketplace when the technology matures (Miller, Walker, & Rupnow, 1992). But what are virtual reality's communication applications? One could argue that all VR applications are communication applications. In some ways this is valid; after all, all applications involve man-machine communication and human-to-human communication. But, maybe we should ask a more confined question: What are VR's applications in the traditional domains of entertainment, news and information, and telecommunication? What shape might they take? What design challenges do they present? As the opening chapters noted, the mid-1990s are full of turbulent change in the communication and computer industries. Virtual reality applications are being formed in this bubbling cauldron of activity. In this chapter we use the best available evidence and suggest the outlines of some key VR communication applications.


title: UCB EXPLORATION VIA Q-ENSEMBLES

abstract: We show how an ensemble of $Q^*$-functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit setting, and adapt them to the $Q$-learning setting. We propose an exploration strategy based on upper-confidence bounds (UCB). Our experiments show significant gains on the Atari benchmark.


title: The use of priorities on token-ring networks for multimedia traffic

abstract: The authors consider the transport of multimedia traffic on a token-ring LAN. In order to guarantee acceptable QoS for multimedia traffic, resource management is required to insure that sufficient capacity from all shared resources is allocated to the priority traffic. Once a multimedia session is accepted and resources have been allocated, the next task is to insure that the high priority multimedia traffic actually obtains the resources which have been allocated to it. >


title: SIFT based automatic number plate recognition

abstract: The aim of this paper is on presenting a new and simple, but fast and efficient technique for automatic number plate recognition (ANPR) using SIFT (Scale Invariant Feature Transform) features. The proposed system is used to automatically locate and recognize, as a special case, the Jordanian license plates. In the core of our system, SIFT-based template matching technique is used to locate special marks in the license plate. Upon successful detection of those marks, the license plate is segmented out from the original image and OCR (Optical Character Recognition) is used to recognize the characters or numbers from the plate. Due to the various invariance virtues of SIFT, our method can adaptively deal with various changes in the license plates, such as rotation, scaling, and illumination. Experimental results using real datasets are presented, which show that our system has a good performance.


title: Community Preferences for the Allocation &Donation of Organs - The PAraDOx Study

abstract: BACKGROUND ::: Transplantation is the treatment of choice for people with severe organ failure. However, demand substantially exceeds supply of suitable organs; consequently many people wait months, or years to receive an organ. Reasons for the chronic shortage of deceased organ donations are unclear; there appears to be no lack of 'in principle' public support for organ donation. ::: ::: ::: METHODS/DESIGN ::: The PAraDOx Study examines community preferences for organ donation policy in Australia. The aims are to 1) determine which factors influence decisions by individuals to offer their organs for donation and 2) determine the criteria by which the community deems the allocation of donor organs to be fair and equitable. Qualitative and quantitative methods will be used to assess community preferences for organ donation and allocation.Focus group participants from the general community, aged between 18-80, will be purposively sampled to ensure a variety of cultural backgrounds and views on organ donation. Each focus group will include a ranking exercise using a modified nominal group technique. Focus groups of organ recipients, their families, and individuals on a transplant waiting list will also be conducted.Using the qualitative work, a discrete choice study will be designed to quantitatively assess community preferences. Discrete choice methods are based on the premise that goods and services can be described in terms of a number of separate attributes. Respondents are presented with a series of choices where levels of attributes are varied, and a mathematical function is estimated to describe numerically the value respondents attach to different options. Two community surveys will be conducted in approximately 1000 respondents each to assess community preferences for organ donation and allocation. A mixed logit model will be used; model results will be expressed as parameter estimates (β) and the odds of choosing one option over an alternative. Trade-offs between attributes will also be calculated. ::: ::: ::: DISCUSSION ::: By providing a better understanding of current community preferences in relation to organ donation and allocation, the PAraDOx study will highlight options for firstly, increasing the rate of organ donation and secondly, allow for more transparent and equitable policies in relation to organ allocation.


title: Models of cooperative teaching and learning

abstract: While most supervised machine learning models assume that training examples are sampled at random or adversarially, this article is concerned with models of learning from a cooperative teacher that selects "helpful" training examples. The number of training examples a learner needs for identifying a concept in a given class C of possible target concepts (sample complexity of C) is lower in models assuming such teachers, that is, "helpful" examples can speed up the learning process. The problem of how a teacher and a learner can cooperate in order to reduce the sample complexity, yet without using "coding tricks", has been widely addressed. Nevertheless, the resulting teaching and learning protocols do not seem to make the teacher select intuitively "helpful" examples. The two models introduced in this paper are built on what we call subset teaching sets and recursive teaching sets. They extend previous models of teaching by letting both the teacher and the learner exploit knowing that the partner is cooperative. For this purpose, we introduce a new notion of "coding trick"/"collusion". We show how both resulting sample complexity measures (the subset teaching dimension and the recursive teaching dimension) can be arbitrarily lower than the classic teaching dimension and known variants thereof, without using coding tricks. For instance, monomials can be taught with only two examples independent of the number of variables. The subset teaching dimension turns out to be nonmonotonic with respect to subclasses of concept classes. We discuss why this nonmonotonicity might be inherent in many interesting cooperative teaching and learning scenarios.


title: Blog or block : Detecting blog bots through behavioral biometrics

abstract: Blog bots are automated scripts or programs that post comments to blog sites, often including spam or other malicious links. An effective defense against the automatic form filling and posting from blog bots is to detect and validate the human presence. Conventional detection methods usually require direct participation of human users, such as recognizing a CAPTCHA image, which can be burdensome for users. In this paper, we present a new detection approach by using behavioral biometrics, primarily mouse and keystroke dynamics, to distinguish between human and bot. Based on passive monitoring, the proposed approach does not require any direct user participation. We collect real user input data from a very active online community and blog site, and use this data to characterize behavioral differences between human and bot. The most useful features for classification provide the basis for a detection system consisting of two main components: a webpage-embedded logger and a server-side classifier. The webpage-embedded logger records mouse movement and keystroke data while a user is filling out a form, and provides this data in batches to a server-side detector, which classifies the poster as human or bot. Our experimental results demonstrate an overall detection accuracy greater than 99%, with negligible overhead.


title: Building an Efficient Put-Intensive Key-Value Store with Skip-Tree

abstract: Multi-component based Log-Structured Merge-tree (LSM-tree) has been becoming one of the mainstream indexes. LSM-tree adopts component-by-component KV item flowing down mechanism to push each KV item from one smaller component to the adjacent larger component during compaction procedures until the KV items reach the largest component. This process incurs significant write amplification and limits the write throughput. In this paper, we propose one multi-component Skip-tree to aggressively push the KV items to the non-adjacent larger components via skipping some components and then make the KV items’ top-down move more efficient. We develop adaptive and reliable KV item movements among components. By reducing the number of steps during the flowing process from memory-resident component to the disk-resident largest component, Skip-tree can effectively reduce the write amplification and thus improve the system throughput. We design and implement one high performance key-value store, named SkipStore, based on Skip-tree. The experiments demonstrate that SkipStore outperforms the state-of-the-art open-sourced system RocksDB in Facebook by 66.5 percent under HDD and 61 percent under SSD.


title: Augmented Human: Augmented Reality and Beyond

abstract: Will Augmented Reality (AR) allow us to access digital information, experience others' stories, and thus explore alternate realities? AR has recently attracted attention again due to the rapid advances in related multimedia technologies as well as various glass-type AR display devices. However, in order to widely adopt AR in alternated realities, it is necessary to improve various core technologies and integrate them into an AR platform. Especially, there are several remaining technical challenges such as 1) real-time recognition and tracking of multiple objects while generating an environment map, 2) organic user interface with awareness of user's implicit needs, intentions or emotion as well as explicit requests, 3) immersive multimodal content augmentation and just-in-time information visualization, 4) multimodal interaction and collaboration during augmented telecommunication, etc. In addition, in order to encourage user engagement and enable an AR ecosystem, AR standards should be established that support creating AR content, capturing user experiences, and sharing the captured experiences.


title: Semantic Image Inpainting with Progressive Generative Networks

abstract: Recently, image inpainting task has revived with the help of deep learning techniques. Deep neural networks, especially the generative adversarial networks~(GANs) make it possible to recover the missing details in images. Due to the lack of sufficient context information, most existing methods fail to get satisfactory inpainting results. This work investigates a more challenging problem, e.g., the newly-emerging semantic image inpainting - a task to fill in large holes in natural images. In this paper, we propose an end-to-end framework named progressive generative networks~(PGN), which regards the semantic image inpainting task as a curriculum learning problem. Specifically, we divide the hole filling process into several different phases and each phase aims to finish a course of the entire curriculum. After that, an LSTM framework is used to string all the phases together. By introducing this learning strategy, our approach is able to progressively shrink the large corrupted regions in natural images and yields promising inpainting results. Moreover, the proposed approach is quite fast to evaluate as the entire hole filling is performed in a single forward pass. Extensive experiments on Paris Street View and ImageNet dataset clearly demonstrate the superiority of our approach. Code for our models is available at https://github.com/crashmoon/Progressive-Generative-Networks.


title: Delayed high school start times later than 8:30 am and impact on graduation rates and attendance rates

abstract: OBJECTIVES ::: The first purpose of this study was to investigate changes in high school graduation rates with a delayed school start time of later than 8:30am. The second aim of the study was to analyze the association between a delayed high school start time later than 8:30am and attendance rates. ::: ::: ::: DESIGN ::: In the current study, a pre-post design using a repeated-measures analysis of variance was used to examine changes in attendance and graduation rates 2 years after a delayed start was implemented. ::: ::: ::: SETTING ::: Public high schools from 8 school districts (n=29 high schools) located throughout 7 different states. Schools were identified using previous research from the Children's National Medical Center's Division of Sleep Medicine Research Team. ::: ::: ::: PARTICIPANTS AND MEASUREMENTS ::: A total membership of more than 30,000 high school students enrolled in the 29 schools identified by the Children's National Medical Center's Research Team. A pre-post design was used for a within-subject design, controlling for any school-to-school difference in the calculation of the response variable. This is the recommended technique for a study that may include data with potential measurement error. ::: ::: ::: RESULTS ::: Findings from this study linked a start time of later than 8:30am to improved attendance rates and graduation rates. ::: ::: ::: CONCLUSIONS ::: Attendance rates and graduation rates significantly improved in schools with delayed start times of 8:30am or later. School officials need to take special notice that this investigation also raises questions about whether later start times are a mechanism for closing the achievement gap due to improved graduation rates.


title: Factors Determining Colorectal Cancer: The Role of the Intestinal Microbiota

abstract: The gastrointestinal tract, in particular the colon, holds a complex community of microorganisms, which are essential for maintaining homeostasis. However, in recent years, many studies have implicated microbiota in the development of colorectal cancer (CRC), with this disease considered a major cause of death in the western world. The mechanisms underlying bacterial contribution in its development are complex and are not yet fully understood. However, there is increasing evidence showing a connection between intestinal microbiota and CRC. Intestinal microorganisms cause the onset and progression of CRC using different mechanisms, such as the induction of a chronic inflammation state, the biosynthesis of genotoxins that interfere with cell cycle regulation, the production of toxic metabolites or heterocyclic amine activation of pro-diet carcinogenic compounds. Despite these advances additional studies in humans and animal models will further decipher the relationship between microbiota and CRC, and aid in developing alternate therapies based on microbiota manipulation.


title: Environmental influences on eating and physical activity.

abstract: Obesity has increased dramatically over the past two decades and currently about 50% of US adults and 25% of US children are overweight. The current epidemic of obesity is caused largely by an environment that promotes excessive food intake and discourages physical activity. This chapter reviews what is known about environmental influences on physical activity and eating behaviors. Recent trends in food supply, eating out, physical activity, and inactivity are reviewed, as are the effects of advertising, promotion, and pricing on eating and physical activity. Public health interventions, opportunities, and potential strategies to combat the obesity epidemic by promoting an environment that supports healthy eating and physical activity are discussed.


title: Spoof surface plasmon polaritons (SSPP) based multi-band bandpass filter

abstract: This paper reports the spoof surface plasmons polaritons (SSPPs) based multi-band bandpass filter. An efficient back to back transition from Quasi TEM mode of microstrip line to SSPP mode has been designed by etching a gradient corrugated structure on the metal strip; while keeping ground plane unaltered. SSPP wave is found to be highly confined within the teeth part of corrugation. Complementary split ring resonator has been etched in the ground plane to obtained multiband bandpass filter response. Excellent conversion from QTEM mode to SSPP mode has been observed.


title: Software Cost Estimation Framework for Service-Oriented Architecture Systems using Divide-and-Conquer Approach

abstract: Due to the complexity of Service-Oriented Architecture (SOA), cost and effort estimation for SOA-based software development is more difficult than that for traditional software development. Unfortunately, there is a lack of published work about cost and effort estimation for SOA-based software. Existing cost estimation approaches are inadequate to address the complex service-oriented systems. This paper proposes a novel framework based on Divide-and-Conquer (D&C) for cost estimation for building SOA-based software. By dealing with separately development parts, the D&C framework can help organizations simplify and regulate SOA implementation cost estimation. Furthermore, both cost estimation modeling and software sizing work can be satisfied respectively by switching the corresponding metrics within this framework. Given the requirement of developing these metrics, this framework also defines the future research in four different directions according to the separate cost estimation sub-problems.


title: Dynamic lightweight text compression

abstract: We address the problem of adaptive compression of natural language text, considering the case where the receiver is much less powerful than the sender, as in mobile applications. Our techniques achieve compression ratios around 32p and require very little effort from the receiver. Furthermore, the receiver is not only lighter, but it can also search the compressed text with less work than that necessary to decompress it. This is a novelty in two senses: it breaks the usual compressor/decompressor symmetry typical of adaptive schemes, and it contradicts the long-standing assumption that only semistatic codes could be searched more efficiently than the uncompressed text. Our novel compression methods are preferable in several aspects over the existing adaptive and semistatic compressors for natural language texts.


title: Individualism, Collectivism, and Opportunism: A Cultural Perspective on Transaction Cost Economics

abstract: Researchers criticize the transaction cost economics (TCE) paradigm for over-generalizing the assumption of opportunism as human nature. We suggest that opportunistic propensity is affected by cultural prior conditioning of individualism-collectivism (I-C). Specifically, we propose that individualists have a higher opportunistic propensity in intra-group transactions, and collectivists in inter-group transactions. Our cultural specification of opportunism helps TCE to more effectively accommodate some criticisms and more realistically deal with problems of economic organization in today’s global economy.


title: An Informatics Approach to Demand Response Optimization in Smart Grids

abstract: Power utilities are increasingly rolling out “smart” grids with the ability to track consumer power usage in near real-time using smart meters that enable bidirectional communication. However, the true value of smart grids is unlocked only when the veritable explosion of data that will become available is ingested, processed, analyzed and translated into meaningful decisions. These include the ability to forecast electricity demand, respond to peak load events, and improve sustainable use of energy by consumers, and are made possible by energy informatics. Information and software system techniques for a smarter power grid include pattern mining and machine learning over complex events and integrated semantic information, distributed stream processing for low latency response, Cloud platforms for scalable operations and privacy policies to mitigate information leakage in an information rich environment. Such an informatics approach is being used in the DoE sponsored Los Angeles Smart Grid Demonstration Project, and the resulting software architecture will lead to an agile and adaptive Los Angeles Smart Grid.


title: Spatio-Temporal Data Mining: A Survey of Problems and Methods

abstract: Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains, including climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differ from relational data for which computational approaches are developed in the data-mining community for multiple decades in that both spatial and temporal attributes are available in addition to the actual measurements/attributes. The presence of these attributes introduces additional challenges that needs to be dealt with. Approaches for mining spatio-temporal data have been studied for over a decade in the data-mining community. In this article, we present a broad survey of this relatively young field of spatio-temporal data mining. We discuss different types of spatio-temporal data and the relevant data-mining questions that arise in the context of analyzing each of these datasets. Based on the nature of the data-mining problem studied, we classify literature on spatio-temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining. We discuss the various forms of spatio-temporal data-mining problems in each of these categories.


title: Personal photo album summarization

abstract: Photo album summarization is the process of selecting a subset of photos from a larger collection which best preserves the information in the entire set and is semantically coherent. In this paper we propose a system which uses heterogeneous information sources associated with digital photos and generates a summary. Our algorithm adapts itself based on the type of event it is summarizing (Yearbook, Week or Single Day Event) We model the summarization problem as a retrieval problem based on different types of queries. We propose some evaluation metrics for the summary. We use an intuitive web based interface to present the results so that users can further explore the summary in an interactive way. This system is our submission to the CeWe Challenge for the Next Generation of Tangible Multimedia Products.


title: Reliability in MEMS Packaging

abstract: Cost effective packaging and robust reliability are two critical factors for successful commercialization of MEMS and microsystems. While packaging contributes to the effective production cost of MEMS devices, reliability addresses consumer's confidence in and expectation on sustainable performance of the products. There are a number of factors that contribute to the reliability of MEMS; packaging, in particular, in bonding and sealing, material characterization relating to operating and environmental conditions, credible design considerations, the techniques for mitigating intrinsic stresses/strains induced by fabrications and testing for reliability are a few of these factors. This paper will offer an overview of these factors with proposed resolutions to issues relating to the reliability of these products.


title: DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS

abstract: Abstract. This papers presents a multi-scale method that computes robust geometric features on lidar point clouds in order to retrieve the optimal neighborhood size for each point. Three dimensionality features are calculated on spherical neighborhoods at various radius sizes. Based on combinations of the eigenvalues of the local structure tensor, they describe the shape of the neighborhood, indicating whether the local geometry is more linear (1D), planar (2D) or volumetric (3D). Two radius-selection criteria have been tested and compared for ﬁnding automatically the optimal neighborhood radius for each point. Besides, such procedure allows a dimensionality labelling, giving signiﬁcant hints for classiﬁcation and segmentation purposes. The method is successfully applied to 3D point clouds from airborne, terrestrial, and mobile mapping systems since no a priori knowledge on the distribution of the 3D points is required. Extracted dimensionality features and labellings are then favorably compared to those computed from constant size neighborhoods.


title: Learning from Noisy Labels with Distillation

abstract: The ability of learning from noisy labels is very useful in many visual recognition tasks, as a vast amount of data with noisy labels are relatively easy to obtain. Traditionally, label noise has been treated as statistical outliers, and techniques such as importance re-weighting and bootstrapping have been proposed to alleviate the problem. According to our observation, the real-world noisy labels exhibit multimode characteristics as the true labels, rather than behaving like independent random outliers. In this work, we propose a unified distillation framework to use “side” information, including a small clean dataset and label relations in knowledge graph, to “hedge the risk” of learning from noisy labels. Unlike the traditional approaches evaluated based on simulated label noises, we propose a suite of new benchmark datasets, in Sports, Species and Artifacts domains, to evaluate the task of learning from noisy labels in the practical setting. The empirical study demonstrates the effectiveness of our proposed method in all the domains.


title: An Optical Fiber Fabry–Pérot Pressure Sensor Using an SU-8 Structure and Angle Polished Fiber

abstract: In this letter, a novel pressure sensor using an SU-8 structure and angle polished fiber is proposed. There are two different Fabry–Perot cavities in the design, and the cavity lengths can be demodulated at the same time to realize temperature compensation. SU-8 photoresist is suited for forming a high-aspect-ratio structure and the thickness can be controlled precisely. The fabrication process is described and the lithography technology is employed. The characteristics of both pressure and temperature are tested. Experimental results show that the sensor has a good linearity over the pressure range of 0–0.1 MPa. The R-square is 0.998. The sensitivity (change in cavity/loaded pressure) is 60.9 $\mu \text{m}$ /MPa.


title: An empirical study of privacy-violating information flows in JavaScript web applications

abstract: The dynamic nature of JavaScript web applications has given rise to the possibility of privacy violating information flows. We present an empirical study of the prevalence of such flows on a large number of popular websites. We have (1) designed an expressive, fine-grained information flow policy language that allows us to specify and detect different kinds of privacy-violating flows in JavaScript code,(2) implemented a new rewriting-based JavaScript information flow engine within the Chrome browser, and (3) used the enhanced browser to conduct a large-scale empirical study over the Alexa global top 50,000 websites of four privacy-violating flows: cookie stealing, location hijacking, history sniffing, and behavior tracking. Our survey shows that several popular sites, including Alexa global top-100 sites, use privacy-violating flows to exfiltrate information about users' browsing behavior. Our findings show that steps must be taken to mitigate the privacy threat from covert flows in browsers.


Provable Models for Robust Low-Rank Tensor Completion


title: Analysis of a ferrofluid core LVDT displacement sensor

abstract: In this work, we present the analysis of a Linear Variable Differential Transformer (LVDT), which movable core is an encapsulated ferrofluid. A ferrofluid is a ferromagnetic nanoparticles composed fluid, which physical properties, including geometry, vary when it is under the influence magnetic field. Our proposed analysis is on the performance of a ferrofluid core LVDT, based in the fact that as the ferrofluid core moves between the magnetic fields generated in the coils of the LVDT, the core geometry varies. A sensibility and linearity comparison between a conventional LVDT and the ferrofluid core LVDT is presented, along with three different quantities of encapsulated ferrofluid inside the ferrofluid cores. The results of this work will be further used in order to obtain industrial grade sensors and actuators.


title: Robust moment estimation and improved clustering via sum of squares

abstract: We develop efficient algorithms for estimating low-degree moments of unknown distributions in the presence of adversarial outliers and design a new family of convex relaxations for k-means clustering based on sum-of-squares method. As an immediate corollary, for any γ > 0, we obtain an efficient algorithm for learning the means of a mixture of k arbitrary distributions in d in time dO(1/γ) so long as the means have separation Ω(kγ). This in particular yields an algorithm for learning Gaussian mixtures with separation Ω(kγ), thus partially resolving an open problem of Regev and Vijayaraghavan regev2017learning. The guarantees of our robust estimation algorithms improve in many cases significantly over the best previous ones, obtained in the recent works. We also show that the guarantees of our algorithms match information-theoretic lower-bounds for the class of distributions we consider. These improved guarantees allow us to give improved algorithms for independent component analysis and learning mixtures of Gaussians in the presence of outliers. We also show a sharp upper bound on the sum-of-squares norms for moment tensors of any distribution that satisfies the Poincare inequality. The Poincare inequality is a central inequality in probability theory, and a large class of distributions satisfy it including Gaussians, product distributions, strongly log-concave distributions, and any sum or uniformly continuous transformation of such distributions. As a consequence, this yields that all of the above algorithmic improvements hold for distributions satisfying the Poincare inequality.


title: Evaluation of the Masked Logic Style MDPL on a Prototype Chip

abstract: MDPL has been proposed as a masked logic style that counteracts DPA attacks. Recently, it has been shown that the so-called "early propagation effect" might reduce the security of this logic style significantly. In the light of these findings, a 0.13 μmprototype chip that includes the implementation of an 8051-compatible microcontroller in MDPL has been analyzed. Attacks on the measured power traces of this implementation show a severe DPA leakage. In this paper, the results of a detailed analysis of the reasons for this leakage are presented. Furthermore, a proposal is made on how to improve MDPL with respect to the identified problems.


title: SCADASim—A Framework for Building SCADA Simulations

abstract: Supervisory Control and Data Acquisition (SCADA) systems control and monitor industrial and critical infrastructure functions, such as electricity, gas, water, waste, railway, and traffic. Recent attacks on SCADA systems highlight the need of stronger SCADA security. It is important to analyze the security risks and develop appropriate security solutions to protect such systems. However, a key problem is the lack of proper modeling tools to evaluate the security of SCADA systems. As widely accepted in academic and industrial communities, it is impractical to conduct security experiments on live systems. A modeling simulation tool would enable the simulation of SCADA systems with the benefit of testing different attack and security solutions. This paper proposes a simulation tool for building SCADA simulations that supports the integration of external devices and applications. A key benefit of this tool is the ability to test the effect of attacks on real devices and applications, even though using a simulated environment. The paper further describes two case studies that demonstrate how the tool can be efficiently used to create SCADA simulations and perform malicious attacks.


title: The neurobiological basis of seeing words

abstract: This review summarizes recent ideas about the cortical circuits for seeing words, an important part of the brain system for reading. Historically, the link between the visual cortex and reading has been contentious. One influential position is that the visual cortex plays a minimal role, limited to identifying contours, and that information about these contours is delivered to cortical regions specialized for reading and language. An alternative position is that specializations for seeing words develop within the visual cortex itself. Modern neuroimaging measurements—including both functional magnetic resonance imaging (fMRI) and diffusion weighted imaging with tractography (DTI) data—support the position that circuitry for seeing the statistical regularities of word forms develops within the ventral occipitotemporal cortex, which also contains important circuitry for seeing faces, colors, and forms. This review explains new findings about the visual pathways, including visual field maps, as well as new findings about how we see words. The measurements from the two fields are in close cortical proximity, and there are good opportunities for coordinating theoretical ideas about function in the ventral occipitotemporal cortex.


title: Mamdani fuzzy inference system for breast cancer risk detection

abstract: Diagnosing various diseases in medical field is very difficult even for medical expert. For solving this problem data mining was introduced. It discovers knowledge from the database. There are many subfields in data mining. One of the subfield is fuzzy logic. It is applied in many fields such as control theory, Artificial Intelligence (AI) and also in the field of medicine. This paper focuses on detecting the risk of breast cancer by using fuzzy logic. The dataset used in this work is retrieved from UCI machine learning repository. The aim of this proposed work is to detect the breast cancer by reducing the variables, so that it reduces the time taken for diagnosing the disease. The features were extracted by using one of the feature selection method called Linear Discriminant Analysis (LDA) and training is done by using one of the fuzzy inference method called Mamdani Fuzzy inference model. The results were evaluated by using the above model. It gave the result of 93%.


title: Letor: Benchmark dataset for research on learning to rank for information retrieval

abstract: This paper is concerned with learning to rank for information retrieval (IR). Ranking is the central problem for information retrieval, and employing machine learning techniques to learn the ranking function is viewed as a promising approach to IR. Unfortunately, there was no benchmark dataset that could be used in comparison of existing learning algorithms and in evaluation of newly proposed algorithms, which stood in the way of the related research. To deal with the problem, we have constructed a benchmark dataset referred to as LETOR and distributed it to the research communities. Specifically we have derived the LETOR data from the existing data sets widely used in IR, namely, OHSUMED and TREC data. The two collections contain queries, the contents of the retrieved documents, and human judgments on the relevance of the documents with respect to the queries. We have extracted features from the datasets, including both conventional features, such as term frequency, inverse document frequency, BM25, and language models for IR, and features proposed recently at SIGIR, such as HostRank, feature propagation, and topical PageRank. We have then packaged LETOR with the extracted features, queries, and relevance judgments. We have also provided the results of several state-ofthe-arts learning to rank algorithms on the data. This paper describes in details about LETOR.


Intensional transitive verbs and abstract clausal complementation


title: Applications of Next-Generation Sequencing Technologies to Diagnostic Virology

abstract: Novel DNA sequencing techniques, referred to as “next-generation” sequencing (NGS), provide high speed and throughput that can produce an enormous volume of sequences with many possible applications in research and diagnostic settings. In this article, we provide an overview of the many applications of NGS in diagnostic virology. NGS techniques have been used for high-throughput whole viral genome sequencing, such as sequencing of new influenza viruses, for detection of viral genome variability and evolution within the host, such as investigation of human immunodeficiency virus and human hepatitis C virus quasispecies, and monitoring of low-abundance antiviral drug-resistance mutations. NGS techniques have been applied to metagenomics-based strategies for the detection of unexpected disease-associated viruses and for the discovery of novel human viruses, including cancer-related viruses. Finally, the human virome in healthy and disease conditions has been described by NGS-based metagenomics.


title: Semisupervised Autoencoders for Speech Emotion Recognition

abstract: Despite the widespread use of supervised learning methods for speech emotion recognition, they are severely restricted due to the lack of sufficient amount of labelled speech data for the training. Considering the wide availability of unlabelled speech data, therefore, this paper proposes semisupervised autoencoders to improve speech emotion recognition. The aim is to reap the benefit from the combination of the labelled data and unlabelled data. The proposed model extends a popular unsupervised autoencoder by carefully adjoining a supervised learning objective. We extensively evaluate the proposed model on the INTERSPEECH 2009 Emotion Challenge database and other four public databases in different scenarios. Experimental results demonstrate that the proposed model achieves state-of-the-art performance with a very small number of labelled data on the challenge task and other tasks, and significantly outperforms other alternative methods.


title: Models and tools support to the Competitive Intelligence process

abstract: Today, Competitive intelligence (CI) became a multi-disciplinary field of studies having for main object the definition of the models and tools for the acquisition of the useful information starting from the environment of the enterprise whose objective is the support of strategic decision making. Our objective in this article is to present the models and the tools support to the CI process to propose a standard framework of a CI tool. In order to achieve this goal, we carried out a state of the art starting from the articles of research on CI field: Process, models and tools. Then, we analyzed these models and tools which supports the CI process in order to propose a classification of the latter. Moreover, we worked out a standard framework of a CI tool.


title: Scalable Approximate Query Tracking over Highly Distributed Data Streams

abstract: The recently-proposed Geometric Monitoring (GM) method has provided a general tool for the distributed monitoring of arbitrary non-linear queries over streaming data observed by a collection of remote sites, with numerous practical applications. Unfortunately, GM-based techniques can suffer from serious scalability issues with increasing numbers of remote sites. In this paper, we propose novel techniques that effectively tackle the aforementioned scalability problems by exploiting a carefully designed sample of the remote sites for efficient approximate query tracking. Our novel sampling-based scheme utilizes a sample of cardinality proportional to √N (compared to N for the original GM), where $N$ is the number of sites in the network, to perform the monitoring process. Our experimental evaluation over a variety of real-life data streams demonstrates that our sampling-based techniques can significantly reduce the communication cost during distributed monitoring with controllable, predefined accuracy guarantees.


title: Inner Rectangular Drawings of Plane Graphs (Extended Abstract)

abstract: A drawing of a plane graph is called an inner rectangular drawing if every edge is drawn as a horizontal or vertical line segment so that every inner face is a rectangle. In this paper we show that a plane graph G has an inner rectangular drawing D if and only if a new bipartite graph constructed from G has a perfect matching. We also show that D can be found in time O(n 1.5 / log n )i fG has n vertices and a sketch of the outer face is prescribed, that is, all the convex outer vertices and concave ones are prescribed.


title: Mitotic activity patterns and cytoskeletal changes throughout the progression of diapause developmental program in Daphnia

abstract: BackgroundDiapause is a form of dormancy that is genetically predetermined to allow animals to overcome harsh environmental conditions. It is induced by predictive environmental cues bringing cellular activity levels into a state of suspended animation. Entering diapause requires organismal, molecular and cellular adaptation to severely reduced energy flows. Cells must therefore have evolved strategies that prepare them for periods with limited metabolic resources. However, changes that occur on the (sub-)cellular level have not been thoroughly described.ResultsWe investigated mitotic activity and we monitored cytoskeletal network changes in successive stages of diapausing and non-diapausing Daphnia magna embryos using (immuno-)fluorescent labeling. We find that embryos destined to diapause show a delayed and 2.5x slower mitotic activity in comparison to continuously developing embryos. Development is halted when D. magna embryos reach ~ 3500 cells, whereupon mitotic activity is absent and cytoskeletal components are severely reduced, rendering diapause cells compact and condensed.ConclusionIn the initiation phase of diapause, the slower cell division rate points to prolonged interphase duration, preparing the cells for diapause maintenance. During diapause, cytoskeletal depletion and cellular condensation may be a means to save energy resources. Our data provide insights into the sub-cellular change of diapause in Daphnia.


Named Entity Recognition and Compositional Morphology for Word Representations in Chinese


Investigating the feasibility of HIV self-testing in Zambia


title: Pain, aggression, fantasy and concepts of sadomasochism

abstract: Traumatized infants and children may exhibit syndromes of aggressive, pain-seeking, and self-destructive behavior resembling the so-called sadomasochism seen in adults. Three hypotheses are offered to account for the repetition of sadomasochistic phenomena in childhood and later character disorders: 1) pain and painful affects are sources of aggression; 2) the need to control aggression plays an important role in the development of psychic structure; 3) child abuse and trauma impair the ability to use fantasy for the mastery of impulses. Difficulty in expression and control of aggression are central issues in character disorders. Language: en


title: Using Word Embeddings for Improving Statistical Machine Translation of Phrasal Verbs

abstract: We examine the employment of word embeddings for machine translation (MT) of phrasal verbs (PVs), a linguistic phenomenon with challenging semantics. Using word embeddings, we augment the translation model with two features: one modelling distributional semantic properties of the source and target phrase and another modelling the degree of compositionality of PVs. We also obtain paraphrases to increase the amount of relevant training data. Our method leads to improved translation quality for PVs in a case study with English to Bulgarian MT system.


Ultrasonographic Evaluation of Fetal Face by 3D/4D Sonography


Which type of motivation is capable of driving achievement behaviors such as exercise in different personalities


Travelling Profile Solutions For Nonlinear Degenerate Parabolic Equation And Contour Enhancement In Image Processing


title: Generalization Properties and Implicit Regularization for Multiple Passes SGM

abstract: We study the generalization properties of stochastic gradient methods for learning with convex loss functions and linearly parameterized functions. We show that, in the absence of penalizations or constraints, the stability and approximation properties of the algorithm can be controlled by tuning either the step-size or the number of passes over the data. In this view, these parameters can be seen to control a form of implicit regularization. Numerical results complement the theoretical findings.


Wireless sensors in agriculture and food industry — Recent development and future perspective


title: Designing high-quality interactive multimedia learning modules

abstract: Abstract Modern research has broadened scientific knowledge and revealed the interdisciplinary nature of the sciences. For today's students, this advance translates to learning a more diverse range of concepts, usually in less time, and without supporting resources. Students can benefit from technology-enhanced learning supplements that unify concepts and are delivered on-demand over the Internet. Such supplements, like imaging informatics databases, serve as innovative references for biomedical information, but could improve their interaction interfaces to support learning. With information from these digital datasets, multimedia learning tools can be designed to transform learning into an active process where students can visualize relationships over time, interact with dynamic content, and immediately test their knowledge. This approach bridges knowledge gaps, fosters conceptual understanding, and builds problem-solving and critical thinking skills—all essential components to informatics training for science and medicine. Additional benefits include cost-free access and ease of dissemination over the Internet or CD-ROM. However, current methods for the design of multimedia learning modules are not standardized and lack strong instructional design. Pressure from administrators at the top and students from the bottom are pushing faculty to use modern technology to address the learning needs and expectations of contemporary students. Yet, faculty lack adequate support and training to adopt this new approach. So how can faculty learn to create educational multimedia materials for their students? This paper provides guidelines on best practices in educational multimedia design, derived from the Virtual Labs Project at Stanford University. The development of a multimedia module consists of five phases: (1) understand the learning problem and the users needs; (2) design the content to harness the enabling technologies; (3) build multimedia materials with web style standards and human factors principles; (4) user testing; (5) Evaluate and improve design.


Improvement of Random Forest Classifier through Localization of Persian Handwritten OCR


title: Meta-analysis of coefficient alpha.

abstract: The meta-analysis of coefficient alpha across many studies is becoming more common in psychology by a methodology labeled reliability generalization. Existing reliability generalization studies have not used the sampling distribution of coefficient alpha for precision weighting and other common meta-analytic procedures. A framework is provided for a statistically grounded meta-analysis of coefficient alpha using its sampling distribution. Two empirical examples are offered to illustrate these methods, and limitations of reliability generalization are described.


title: An Efficient Algorithm for Disease Prediction with Multi Dimensional Data

abstract: The main objective of this study is to create a fast, easy and an efficient algorithm for disease prediction , with less error rate and can apply with even large data sets and show reasonable patterns with dependent variables. For disease identification and prediction in data mining a new hybrid algorithm was constructed. The Disease Identification and Prediction(DIP) algorithm, which is the combination of decision tree and association rule is used to predict the chances of some of the disease hits in some particular areas. It also shows the relationship between different parameters for the prediction. To implement this algorithm using vb.net software was also developed.


title: Finding the Best-Fit Bounding-Boxes

abstract: The bounding-box of a geometric shape in 2D is the rectangle with the smallest area in a given orientation (usually upright) that complete contains the shape. The best-fit bounding-box is the smallest bounding-box among all the possible orientations for the same shape. In the context of document image analysis, the shapes can be characters (individual components) or paragraphs (component groups). This paper presents a search algorithm for the best-fit bounding-boxes of the textual component groups, whose shape are customarily rectangular in almost all languages. One of the applications of the best-fit bounding-boxes is the skew estimation from the text blocks in document images. This approach is capable of multi-skew estimation and location, as well as being able to process documents with sparse text regions. The University of Washington English Document Image Database (UW-I) is used to verify the skew estimation method directly and the proposed best-fit bounding-boxes algorithm indirectly.


title: Evaluating evaluation methods for generation in the presence of variation

abstract: Recent years have seen increasing interest in automatic metrics for the evaluation of generation systems. When a system can generate syntactic variation, automatic evaluation becomes more difficult. In this paper, we compare the performance of several automatic evaluation metrics using a corpus of automatically generated paraphrases. We show that these evaluation metrics can at least partially measure adequacy (similarity in meaning), but are not good measures of fluency (syntactic correctness). We make several proposals for improving the evaluation of generation systems that produce variation.


The object-oriented hypermedia design model


Mobile Cooperative Computing: Energy-Efficient Peer-to-Peer Computation Offloading.


title: Modeling, analysis and parameters design of rotor current control in DFIG-based wind turbines for dynamic performance optimizing

abstract: As doubly fed induction generator (DFIG) based wind turbines (WTs) have already become a kind of significant generators in modern power systems, a reliable parameters design method for an excellent dynamic performance in rotor current control is a basic requirement for outer control loops design and system analysis. Due to the amplitude characteristic is neglected in conventional frequency domain design method, rotor side converter (RSC) is designed as a voltage-source converter (VSC) with 0.707 damping ratio in many studies and projects. However, closed-loop rotor current control system is peculiar and complicated for its multi-input multi-output (MIMO) and high-order properties. In this paper, based on proposed mathematical model, dynamic performance of rotor current control system is analyzed systematically through obtained analytical expressions in time domain. Finally, in order to achieve an optimized performance on reference tracking and disturbance rejection, parameters design procedures are proposed and verified with a 10-kW DFIG prototype.


title: RFM Variables Revisited Using Quantile Regression

abstract: We revisit well-known variables for database marketing/CRM and relationship marketing using a new methodology: Binary Bayesian Quantile regression. This method allows for a more thorough investigation of the relationship between the response variable and the covariates. The main conclusion is that taking intentions as a proxy for real churn behavior yields biased results because the effects are differentially inflated across quantiles. Moreover, effects are shown to differ relative to the median effect resulting in even more inaccurate information for marketing decision making. This study clearly demonstrates that marketing practitioners can benefit from using quantile regression and shows the importance of obtaining and analyzing real observed behavior.


title: Decentralized Control of Satellite Clusters Under Limited Communication

abstract: Two decentralized control algorithms, each employing a unique information exchange scheme, are considered. One of these algorithms employs a communication paradigm in which control and local state estimate information is exchanged between members of a formation after any one of the members executes a control. The other algorithm requires only that control information be exchanged between members; this is thought to be the least information exchange that can sustain an affine control law. The performance of these algorithms when applied to a satellite formation control problem is evaluated. To this end, the algorithms are applied in the context of a nonlinear satellite cluster simulation, where Earth oblateness effects and measurement uncertainties are taken into account. The cluster is required to achieve a specified formation at a defined point on an elliptic reference orbit. Simulation results are obtained for several different orbits and compared to a baseline case involving a centralized controller design.


title: SemSOS: Semantic sensor Observation Service

abstract: Sensor Observation Service (SOS) is a Web service specification defined by the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) group in order to standardize the way sensors and sensor data are discovered and accessed on the Web. This standard goes a long way in providing interoperability between repositories of heterogeneous sensor data and applications that use this data. Many of these applications, however, are ill equipped at handling raw sensor data as provided by SOS and require actionable knowledge of the environment in order to be practically useful. There are two approaches to deal with this obstacle, make the applications smarter or make the data smarter. We propose the latter option and accomplish this by leveraging semantic technologies in order to provide and apply more meaningful representation of sensor data. More specifically, we are modeling the domain of sensors and sensor observations in a suite of ontologies, adding semantic annotations to the sensor data, using the ontology models to reason over sensor observations, and extending an open source SOS implementation with our semantic knowledge base. This semantically enabled SOS, or SemSOS, provides the ability to query high-level knowledge of the environment as well as low-level raw sensor data.


title: Fan Shape Model for object detection

abstract: We propose a novel shape model for object detection called Fan Shape Model (FSM). We model contour sample points as rays of final length emanating for a reference point. As in folding fan, its slats, which we call rays, are very flexible. This flexibility allows FSM to tolerate large shape variance. However, the order and the adjacency relation of the slats stay invariant during fan deformation, since the slats are connected with a thin fabric. In analogy, we enforce the order and adjacency relation of the rays to stay invariant during the deformation. Therefore, FSM preserves discriminative power while allowing for a substantial shape deformation. FSM allows also for precise scale estimation during object detection. Thus, there is not need to scale the shape model or image in order to perform object detection. Another advantage of FSM is the fact that it can be applied directly to edge images, since it does not require any linking of edge pixels to edge fragments (contours).


title: Collaborative Learning, Social Presence and Student Satisfaction in a Blended Learning Environment

abstract: The paper describes the research design and main findings of a study whose objective was to examine the relationships between perceived collaborative learning, social presence and student satisfaction in blended courses at one campus belonging to a US community college system. Based on a Social Cognitive Framework for Blended Learning, a quantitative correlational design was employed and data was collected using a Collaborative Learning, Social Presence, and Satisfaction Questionnaire. The study found that there is a relationship between the constructs of perceived collaboration, perceived social presence, and self-reported student satisfaction in blended learning courses.