title: Three-Year Follow-up and Clinical Implications of a Mindfulness Meditation-Based Stress Reduction Intervention in the Treatment of Anxiety Disorders

abstract: A previous study of 22 medical patients with DSM-III-R-defined anxiety disorders showed clinically and statistically significant improvements in subjective and objective symptoms of anxiety and panic following an 8-week outpatient physician-referred group stress reduction intervention based on mindfulness meditation. Twenty subjects demonstrated significant reductions in Hamilton and Beck Anxiety and Depression scores post-intervention and at 3-month follow-up. In this study, 3-year follow-up data were obtained and analyzed on 18 of the original 22 subjects to probe long-term effects. Repeated measures analysis showed maintenance of the gains obtained in the original study on the Hamilton {F(2,32) = 13.22; p < 0.001} and Beck {F(2,32) = 9.83; p< 0.001} anxiety scales as well as on their respective depression scalgs, on the Hamilton panic score, in the number and severity of panic attacks, and on the Mobility Index—Accompanied and the Fear Survey. A 3-year follow-up comparison of this cohort with a larger ...


title: Contact manipulation and geometric reasoning

abstract: In assembly tasks, part-mating involves contact between parts with size tolerances and for some assembly strategies, jamming results. A way to avoid such problems is to explicitly use the contact during motion with an active compliant procedure. This paper deals with contact manipulation.


title: Training RNN simulated vehicle controllers using the SVD and evolutionary algorithms

abstract: We describe an approach to creating a controller for The Open Car Racing Simulator (TORCS), based on The Simulated Car Racing Championship (SCRC) client, using unsupervised evolutionary learning for recurrent neural networks. Our method of training the recurrent neural network controllers relies on combining the components of the singular value decomposition of two different neural network connection matrices.


title: Making papercraft toys from meshes using strip-based approximate unfolding

abstract: We propose a new method for producing unfolded papercraft patterns of rounded toy animal figures from triangulated meshes by means of strip-based approximation. Although in principle a triangulated model can be unfolded simply by retaining as much as possible of its connectivity while checking for intersecting triangles in the unfolded plane, creating a pattern with tens of thousands of triangles is unrealistic. Our approach is to approximate the mesh model by a set of continuous triangle strips with no internal vertices. Initially, we subdivide our mesh into parts corresponding to the features of the model. We segment each part into zonal regions, grouping triangles which are similar topological distances from the part boundary. We generate triangle strips by simplifying the mesh while retaining the borders of the zonal regions and additional cut-lines. The pattern is then created simply by unfolding the set of strips. The distinguishing feature of our method is that we approximate a mesh model by a set of continuous strips, not by other ruled surfaces such as parts of cones or cylinders. Thus, the approximated unfolded pattern can be generated using only mesh operations and a simple unfolding algorithm. Furthermore, a set of strips can be crafted just by bending the paper (without breaking edges) and can represent smooth features of the original mesh models.


title: Turkish Paraphrase Corpus

abstract: Paraphrases are alternative syntactic forms in the same language expressing the same semantic content. Speakers of all languages are inherently familiar with paraphrases at different levels of granularity (lexical, phrasal, and sentential). For quite some time, the concept of paraphrasing is getting a growing attention by the research community and its potential use in several natural language processing applications (such as text summarization and machine translation) is being investigated. In this paper, we present, what is to our best knowledge, the first Turkish paraphrase corpus. The corpus is gleaned from four different sources and currently contains 1270 paraphrase pairs. All paraphrase pairs are carefully annotated by native Turkish speakers with the identified semantic correspondences between paraphrases. The work for expanding the corpus is still under way.


title: Functional MRI activation in children with and without dyslexia during pseudoword aural repeat and visual decode : Before and after treatment

abstract: Children without dyslexia (n = 10) received nonphonological treatment, and those with dyslexia received phonological (n = 11) or nonphonological (n = 9) treatment. Before and after treatment they performed aural repeat, visual decode, and aural match pseudoword tasks during functional MRI scanning that separated stimulus input from response production. Group map analysis indicated that children with dyslexia overactivated compared with good readers during the aural-repeat/aural-match contrast in bilateral frontal (Brodmann's area [BA] 3, 4, 5, 6, 9), left parietal (BA 2, 3), left temporal (BA 38), and right temporal (BA 20, 21, 37) regions (stimulus input) and underactivated in right frontal (BA 24, 32) and right insula (BA 48) regions (response production); they underactivated in BA 19/V5 during the visual-decode/aural-match contrast (response production). Individual brain analysis for children with dyslexia revealed that during the aural-repeat/aural-match contrast (stimulus input), phonological treatment decreased and normalized activation in left supramarginal gyrus and postcentral gyrus. Nonphonological treatment increased and normalized activation during the visual-decode/aural-match contrast (response production) in BA19/V5 and changed activation in the same direction as good readers during aural-repeat/aural-match contrast (stimulus input) in left postcentral gyrus. The significance of the findings for competing theories of dyslexia is discussed.


title: Pathophysiological Significance of Blood Rheology

abstract: Tissue perfusion is determined by both blood vessel geometry and the rheological properties of blood. Blood is a non-Newtonian fluid, its viscosity being dependent on flow conditions. Blood and plasma viscosities, as well as the rheological properties of blood cells (e.g., deformability and aggregation of red blood cells), are influenced by disease processes and extreme physiological conditions. These rheological parameters may in turn affect the blood flow in vessels, and hence tissue perfusion. Unfortunately it is not always possible to determine if a change in rheological parameters is the cause or the result of a disease process. The hemorheology-tissue perfusion relationship is further complicated by the distinct in vivo behavior of blood. Besides the special hemodynamic mechanisms affecting the composition of blood in various regions of the vascular system, autoregulation based on vascular control mechanisms further complicates this relationship. Hemorheological parameters may be especially important for adequate tissue perfusion if the vascular system is geometrically challenged.


title: Control-Flow Integrity: Precision, Security, and Performance

abstract: Memory corruption errors in C/C++ programs remain the most common source of security vulnerabilities in today’s systems. Control-flow hijacking attacks exploit memory corruption vulnerabilities to divert program execution away from the intended control flow. Researchers have spent more than a decade studying and refining defenses based on Control-Flow Integrity (CFI); this technique is now integrated into several production compilers. However, so far, no study has systematically compared the various proposed CFI mechanisms nor is there any protocol on how to compare such mechanisms. We compare a broad range of CFI mechanisms using a unified nomenclature based on (i) a qualitative discussion of the conceptual security guarantees, (ii) a quantitative security evaluation, and (iii) an empirical evaluation of their performance in the same test environment. For each mechanism, we evaluate (i) protected types of control-flow transfers and (ii) precision of the protection for forward and backward edges. For open-source, compiler-based implementations, we also evaluate (iii) generated equivalence classes and target sets and (iv) runtime performance.


Graph Theoretic Methods in Multiagent Networks


Kernel methods for Multi-labelled classification and Categorical regression problems.


title: A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior

abstract: This paper introduces a method to detect road anomalies by analyzing driver behaviours. The analysis is based on the data and the features extracted from smartphone inertial sensors to calculate the angle of swerving and also based on distinctive states of a driver behaviour event. A novel approach is introduced to deal with the gyroscope drift, reducing the average angle estimation error for curves up to 2° and the overall average angle error up to 5°. Using a simple machine learning approach and a clustering algorithm, the method can detect 70% of the swerves and 95% of the turns on the road.


title: A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues

abstract: Discourse structures are beneficial for various NLP tasks such as dialogue understanding, question answering, sentiment analysis, and so on. This paper presents a deep sequential model for parsing discourse dependency structures of multi-party dialogues. The proposed model aims to construct a discourse dependency tree by predicting dependency relations and constructing the discourse structure jointly and alternately. It makes a sequential scan of the Elementary DiscourseUnits(EDUs)1 in a dialogue. For each EDU, the model decides to which previous EDU the current one should link and what the corresponding relation type is. The predicted link and relation type are then used to build the discourse structure incrementally with a structured encoder. During link prediction and relation classification, the model utilizes not only local information that represents the concerned EDUs, but also global information that encodes the EDU sequence and the discourse structure that is already built at the current step. Experiments show that the proposed model outperforms all the state-of-the-art baselines.


title: Tsallis entropy and the long-range correlation in image thresholding

abstract: Thresholding methods based on entropy have been proposed and developed over the years. In this paper, an improved Tsallis entropy based thresholding method is proposed for segmenting the images which presenting local long-range correlation rather than global long-range correlation. The advantage of the proposed method is to distinguish the pixels' local long-range correlation by the nonextensive parameter q. And the experimental results of various infrared images as well as nondestructive test ones show the effectiveness of the proposed method.


title: Standardizing generic cross-domain applications in Internet of Things

abstract: Domain-specific Internet of Things (IoT) applications are becoming more and more popular. Each of these applications uses their own technologies and terms to describe sensors and their measurements. This is a difficult task to help users build generic IoT applications to combine several domains. To explicitly describe sensor measurements in uniform way, we propose to enrich them with semantic web technologies. Domain knowledge is already defined in more than 200 ontology and sensor-based projects that we could reuse to build cross-domain IoT applications. There is a huge gap to reason on sensor measurements without a common nomenclature and best practices to ease the automation of generic IoT applications. We present our Machine-to-Machine Measurement (M3) framework and share lessons learned to improve existing standards such as oneM2M, ETSI M2M, W3C Web of Things and W3C Semantic Sensor Network.


Algorithms for Quantum Computation: Discrete Log and Factoring (Extended Abstract)


title: Comparing Clustering with Pairwise and Relative Constraints: A Unified Framework

abstract: Clustering can be improved with the help of side information about the similarity relationships among instances. Such information has been commonly represented by two types of constraints: pairwise constraints and relative constraints, regarding similarities about instance pairs and triplets, respectively. Prior work has mostly considered these two types of constraints separately and developed individual algorithms to learn from each type. In practice, however, it is critical to understand/compare the usefulness of the two types of constraints as well as the cost of acquiring them, which has not been studied before. This paper provides an extensive comparison of clustering with these two types of constraints. Specifically, we compare their impacts both on human users that provide such constraints and on the learning system that incorporates such constraints into clustering. In addition, to ensure that the comparison of clustering is performed on equal ground (without the potential bias introduced by different learning algorithms), we propose a probabilistic semi-supervised clustering framework that can learn from either type of constraints. Our experiments demonstrate that the proposed semi-supervised clustering framework is highly effective at utilizing both types of constraints to aid clustering. Our user study provides valuable insights regarding the impact of the constraints on human users, and our experiments on clustering with the human-labeled constraints reveal that relative constraint is often more efficient at improving clustering.


DenseNet : Implementing Efficient ConvNet Descriptor Pyramids Technical Report


title: Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN

abstract: Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha–beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.


Assess the Knowledge Regarding Pre-eclampsia and Its Self-care Measures among Antenatal Women Attending Antenatal Outpatient Department of KLES Dr Prabhakar Kore Hospital, Belgaum


title: Technical performance and match-to-match variation in elite football teams.

abstract: ABSTRACTRecent research suggests that match-to-match variation adds important information to performance descriptors in team sports, as it helps measure how players fine-tune their tactical behaviours and technical actions to the extreme dynamical environments. The current study aims to identify the differences in technical performance of players from strong and weak teams and to explore match-to-match variation of players’ technical match performance. Performance data of all the 380 matches of season 2012–2013 in the Spanish First Division Professional Football League were analysed. Twenty-one performance-related match actions and events were chosen as variables in the analyses. Players’ technical performance profiles were established by unifying count values of each action or event of each player per match into the same scale. Means of these count values of players from Top3 and Bottom3 teams were compared and plotted into radar charts. Coefficient of variation of each match action or event within a pla...


Multiple-Attribute Text Rewriting


A Morphological Analyzer for Gulf Arabic Verbs


title: Cloud based mental state monitoring system for suicide risk reconnaissance using wearable bio-sensors

abstract: Development of wireless body- and bio-sensor network opens a new horizon of healthcare especially to measure the vital physical signs of personnel for disease diagnosis and patient monitoring. And the cloud computing technology has enabled patient monitoring service dynamically scalable and ubiquitously accessible. The success of suicide risk reconnaissance is depends on effective prediction of mental states. This paper proposes a suicide risk scouting prototype by predicting mental states in cloud environment. In this system, patients' real-time vital diseases symptoms are collected through wireless body area network (WBAN) and then analyzed the collected data in healthcare cloud platform with patient's historical repository of diseases, habits, rehabilitations and genetics. Here, the mental statuses of patients have been modeled as the discrete set of states of hidden Markov model (HMM), where WBANs annotations and stored facts of patients in cloud are considered as the observations of HMM. Subsequently, the Viterbi, a machine learning algorithm has been applied to generate the most probable mental state sequence to monitor suicide risk of mentally disordered patients. Finally, the proposed system is validated by deploying this model on mental patients dataset.


title: Mild Uncertainty Promotes Checking Behavior in Subclinical Obsessive-Compulsive Disorder

abstract: Patients with obsessive-compulsive disorder respond to clinical uncertainty with perseverative checking, which, ironically, enhances uncertainty. However, patients also display general subclinical uncertainty, which may tempt vulnerable individuals to seek reassurance by perseveration in response to mild uncertainty that is superimposed on general uncertainty. An experimental eye-tracking paradigm was developed to investigate whether mild uncertainty indeed induces checking behavior in people with high obsessive-compulsive tendencies (OC+, n = 34), compared to people with low obsessive-compulsive tendencies (OC–, n = 31). Participants were presented 50 visual search displays, and they indicated whether a target was “present” or “absent.” Decisions about target presence induced little uncertainty, but decisions about its absence were more ambiguous, because participants relied on not having overlooked the target. Results revealed no differences on target-present trials. However, in target-absent trials, OC...


title: Demand based hierarchical QoS using storage resource pools

abstract: The high degree of storage consolidation in modern virtualized datacenters requires flexible and efficient ways to allocate IO resources among virtual machines (VMs). Existing IO resource management techniques have two main deficiencies: (1) they are restricted in their ability to allocate resources across multiple hosts sharing a storage device, and (2) they do not permit the administrator to set allocations for a group of VMs that are providing a single service or belong to the same application. ::: ::: In this paper we present the design and implementation of a novel software system called Storage Resource Pools (SRP). SRP supports the logical grouping of related VMs into hierarchical pools. SRP allows reservations, limits and proportional shares, at both the VM and pool levels. Spare resources are allocated to VMs in the same pool in preference to other VMs. The VMs may be distributed across multiple physical hosts without consideration of their logical groupings. We have implemented a prototype of storage resource pools in the VMware ESX hypervisor. Our results demonstrate that SRP provides hierarchical performance isolation and sharing among groups of VMs running across multiple hosts, while maintaining high utilization of the storage device.


title: Design and Development of a Comprehensive Omni directional Soccer Player Robot

abstract: Omni directional mobile robots have been popularly employed in several applications especially in soccer player robots considered in Robocup competitions. However, omni directional navigation system, omni-vision system and omni-kick mechanism in such soccer player robots have not ever been combined. This situation brings the idea of a robot with no head direction into existence, i.e. a comprehensive omni directional soccer player robot. Such a robot can respond more quickly and it would be capable for more sophisticated behaviors such as ball passing or goal keeping. This paper has tried to focus on description of areas such as omni directional mechanisms, mechanical structure, control, optimized odometry system, omni-vision sensor for self localization and other subjects related to soccer player robot's software.


title: Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

abstract: Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, (1) How much data from the new domain is required for a decent adaptation of the original network?; and, (2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset. The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.


title: Mining and summarizing customer reviews

abstract: Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.


title: High-performance complex event processing over streams

abstract: In this paper, we present the design, implementation, and evaluation of a system that executes complex event queries over real-time streams of RFID readings encoded as events. These complex event queries filter and correlate events to match specific patterns, and transform the relevant events into new composite events for the use of external monitoring applications. Stream-based execution of these queries enables time-critical actions to be taken in environments such as supply chain management, surveillance and facility management, healthcare, etc. We first propose a complex event language that significantly extends existing event languages to meet the needs of a range of RFID-enabled monitoring applications. We then describe a query plan-based approach to efficiently implementing this language. Our approach uses native operators to efficiently handle query-defined sequences, which are a key component of complex event processing, and pipeline such sequences to subsequent operators that are built by leveraging relational techniques. We also develop a large suite of optimization techniques to address challenges such as large sliding windows and intermediate result sizes. We demonstrate the effectiveness of our approach through a detailed performance analysis of our prototype implementation under a range of data and query workloads as well as through a comparison to a state-of-the-art stream processor.


Digital Steganography


title: Introducing MASC: A Movie for the Assessment of Social Cognition

abstract: In the present study we introduce a sensitive video-based test for the evaluation of subtle mindreading difficulties: the Movie for the Assessment of Social Cognition (MASC). This new mindreading tool involves watching a short film and answering questions referring to the actors’ mental states. A group of adults with Asperger syndrome (n = 19) and well-matched control subjects (n = 20) were administered the MASC and three other mindreading tools as part of a broader neuropsychological testing session. Compared to control subjects, Asperger individuals exhibited marked and selective difficulties in social cognition. A Receiver Operating Characteristic (ROC) analysis for the mindreading tests identified the MASC as discriminating the diagnostic groups most accurately. Issues pertaining to the multidimensionality of the social cognition construct are discussed.


title: Scalability, Elasticity, and Efficiency in Cloud Computing: a Systematic Literature Review of Definitions and Metrics

abstract: Context: In cloud computing, there is a multitude of definitions and metrics for scalability, elasticity, and efficiency. However, stakeholders have little guidance for choosing fitting definitions and metrics for these quality properties, thus leading to potential misunderstandings. For example, cloud consumers and providers cannot negotiate reliable and quantitative service level objectives directly understood by each stakeholder. Objectives: Therefore, we examine existing definitions and metrics for these quality properties from the viewpoint of cloud consumers, cloud providers, and software architects with regard to commonly used concepts. Methods: We execute a systematic literature review (SLR), reproducibly collecting common concepts in definitions and metrics for scalability, elasticity, and efficiency. As quality selection criteria, we assess whether existing literature differentiates the three properties, exemplifies metrics, and considers typical cloud characteristics and cloud roles. Results: Our SLR yields 418 initial results from which we select 20 for in-depth evaluation based on our quality selection criteria. In our evaluation, we recommend concepts, definitions, and metrics for each property. Conclusions: Software architects can use our recommendations to analyze the quality of cloud computing applications. Cloud providers and cloud consumers can specify service level objectives based on our metric suggestions.


title: Neural Style Transfer: A Review

abstract: The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively. The review concludes with a discussion of various applications of NST and open problems for future research. A list of papers discussed in this review, corresponding codes, pre-trained models and more comparison results are publicly available at this https URL.


title: A deep residual neural network for low altitude remote sensing image classification

abstract: In the recent years the applications of deep neural networks are increasing rapidly. There are two important factors determining the efficiency of training a computer vision system using deep neural networks. The first factor is the difficulty of training a very deep neural network with large number of parameters. The second factor is the efficiency of the trained network for decreasing the computational cost. In this paper an efficient deep neural network which uses the grid size reduction, factorization and hyper parameter tuning is proposed. In order to deal with large number of layers the residual units are used. A series of experimental simulations are performed on the application of the proposed deep neural network for classification of aerial images. The experimental results show that the proposed architecture has acceptable accuracy for aerial scene classification.


title: Analyzing The Structure Of Argumentative Discourse

abstract: Consider a discourse situation where the speaker tries to convince the hearer of a particular point of view. The first task for the hearer is to understand what it is the speaker wants him to believe--to analyze the structure of the argument being presented, before judging credibility and eventually responding.This paper describes a model for the analysis of arguments that includes:• a theory of expected coherent structure which is used to limit analysis to the reconstruction of particular transmission forms;• a theory of linguistic clues which assigns a functional interpretation to special words and phrases used by the speaker to indicate the structure of the argument;• a theory of evidence relationships which includes the demand for pragmatic analysis to accommodate beliefs not currently held.The implications of this particular design for dialogue analysis in general are thus:• structure is an important feature to extract in a representation to control the processing;• linguistic constructions can be assigned useful interpretations;• pragmatic analysis is crucial in cases where the participants differ in beliefs.


title: CONCEPTUALIZING BUSINESS INTELLIGENCE ARCHITECTURE

abstract: Given today’s business environment, at times a corporate executive may feel overwhelmed by the amount of information available and the coverage of what is actually known as Business Intelligence (BI) architecture. There are literally hundreds of product offerings to choose from. For example, a simple Google search for “business intelligence software” yields more than 24 million responses including more than 50 different vendors. Shariat and Nwakanma (2006) strongly recommend that customers view the future as involving collaboration and cooperation between all parties within the business as well as the external vendors in order to properly manage enterprise information technology systems. This manuscript defines business intelligence, explores the basic difficulty in managing a firm’s business intelligence architecture, and offers a comprehensive BI framework to aid an executive with appreciating the complete enterprise view. This is in total contrast to today’s vendor “BI dashboard” view that appears to be easy to design, quick to implement, and simple to


title: Robust test automation using contextual clues

abstract: Despite the seemingly obvious advantage of test automation, significant skepticism exists in the industry regarding its cost-benefit tradeoffs. Test scripts for web applications are fragile: even small changes in the page layout can break a number of tests, requiring the expense of re-automating them. Moreover, a test script created for one browser cannot be relied upon to run on a different web browser: it requires duplicate effort to create and maintain versions of tests for a variety of browsers. Because of these hidden costs, organizations often fall back to manual testing. We present a fresh solution to the problem of test-script fragility. Often, the root cause of test-script fragility is that, to identify UI elements on a page, tools typically record some metadata that depends on the internal representation of the page in a browser. Our technique eliminates metadata almost entirely. Instead, it identifies UI elements relative to other prominent elements on the page. The core of our technique automatically identifies a series of contextual clues that unambiguously identify a UI element, without recording anything about the internal representation. Empirical evidence shows that our technique is highly accurate in computing contextual clues, and outperforms existing techniques in its resilience to UI changes as well as browser changes.


title: Image-Based Road Type Classification

abstract: The ability to automatically determine the road type from sensor data is of great significance for automatic annotation of routes and autonomous navigation of robots and vehicles. In this paper, we present a novel algorithm for content-based road type classification from images. The proposed method learns discriminative features from training data in an unsupervised manner, thus not requiring domain-specific feature engineering. This is an advantage over related road surface classification algorithms which are only able to make a distinction between pre-specified uniform terrains. In order to evaluate the proposed approach, we have constructed a challenging road image dataset of 20,000 samples from real-world road images in the paved and unpaved road classes. Experimental results on this dataset show that the proposed algorithm can achieve state-of-the-art performance in road type classification.


title: SafeRoute: Learning to Navigate Streets Safely in an Urban Environment

abstract: Recent studies show that 85% of women have changed their traveled route to avoid harassment and assault. Despite this, current mapping tools do not empower users with information to take charge of their personal safety. We propose SafeRoute, a novel solution to the problem of navigating cities and avoiding street harassment and crime. Unlike other street navigation applications, SafeRoute introduces a new type of path generation via deep reinforcement learning. This enables us to successfully optimize for multi-criteria path-finding and incorporate representation learning within our framework. Our agent learns to pick favorable streets to create a safe and short path with a reward function that incorporates safety and efficiency. Given access to recent crime reports in many urban cities, we train our model for experiments in Boston, New York, and San Francisco. We test our model on areas of these cities, specifically the populated downtown regions where tourists and those unfamiliar with the streets walk. We evaluate SafeRoute and successfully improve over state-of-the-art methods by up to 17% in local average distance from crimes while decreasing path length by up to 7%.


title: Exploiting Social Tie Structure for Cooperative Wireless Networking: A Social Group Utility Maximization Framework

abstract: We develop a social group utility maximization (SGUM) framework for cooperative wireless networking that takes into account both social relationships and physical coupling among users. Specifically, instead of maximizing its individual utility or the overall network utility, each user aims to maximize its social group utility that hinges heavily on its social tie structure with other users. We show that this framework provides rich modeling flexibility and spans the continuum between non-cooperative game and network utility maximization (NUM)—two traditionally disjoint paradigms for network optimization. Based on this framework, we study three important applications of SGUM, in database assisted spectrum access, power control, and random access control, respectively. For the case of database assisted spectrum access, we show that the SGUM game is a potential game and always admits a socially-aware Nash equilibrium (SNE). We also develop a distributed spectrum access algorithm that can converge to the SNE and also quantify the trade-off between the performance and convergence time of the algorithm. For the cases of power control and random access control, we show that there exists a unique SNE and the network performance improves as the strength of social ties increase. Numerical results corroborate that the SGUM solutions can achieve superior performance using real social data trace. Furthermore, we show that the SGUM framework can be generalized to take into account both positive and negative social ties among users, which can be a useful tool for studying network security problems.


title: A survey of design issues in spatial input

abstract: We present a survey of design issues for developing effective free-space three-dimensional (3D) user interfaces. Our survey is based upon previous work in 3D interaction, our experience in developing free-space interfaces, and our informal observations of test users. We illustrate our design issues using examples drawn from instances of 3D interfaces. For example, our first issue suggests that users have difficulty understanding three-dimensional space. We offer a set of strategies which may help users to better perceive a 3D virtual environment, including the use of spatial references, relative gesture, two-handed interaction, multisensory feedback, physical constraints, and head tracking. We describe interfaces which employ these strategies. Our major contribution is the synthesis of many scattered results, observations, and examples into a common framework. This framework should serve as a guide to researchers or systems builders who may not be familiar with design issues in spatial input. Where appropriate, we also try to identify areas in free-space 3D interaction which we see as likely candidates for additional research. An extended and annotated version of the references list for this paper is available on-line through mosaic at address http://uvacs.cs.virginia.edu/~kph2q/ .


title: Pose estimation of texture-less cylindrical objects in bin picking using sensor fusion

abstract: We propose an approach for emptying of bin using a combination of Image and Range sensor. Offering a complete solution: calibration, segmentation and pose estimation, along with approachability analysis for the estimated pose. The work is novel in the sense that the objects to be picked are featureless and uniformly black in colour, hence existing approaches are not directly applicable. A key point involves optimal utilization of range data acquired from the laser scanner for 3-D segmentation using localized geometric information. This information guides segmentation of the image for better object pose estimation, used for pick-and-drop. We analytically assure the approachability of the object to avoid collision of the manipulator with the bin. Disturbance of objects caused during pick up has been modelled, which allows pickup of multiple pellets based on information from a single range scan. This eliminates the necessity of repeated scanning and data conditioning. The proposed method offers high object detection rate and pose estimation accuracy. The innovative techniques aimed at reducing the average pickup time makes it suitable for robust industrial operation.


title: Design of Spiral and Multiple Split-Ring Resonators for the Realization of Miniaturized Metamaterial Samples

abstract: We present the design of miniaturized resonant inclusions to be employed in the practical realization of metamaterial samples with anomalous values of the real part of the permeability. Such inclusions, in fact, can be employed in the design of both mu-negative (MNG) materials and artificial magnetodielectrics (with negative and high-positive values of the real part of the permeability, respectively). The inclusions here considered are the multiple split-ring resonators (MSRRs), that represent a straightforward extension of the commonly used split-ring resonators (SRRs), and the spiral resonators (SRs), that enable a greater miniaturization rate. Some physical insights on the resonance mechanism and on the inherent saturation of the resonant frequency when increasing the number of the rings of the MSRRs and the number of the turns of the SRs are given in the paper. New and accurate analytical design formulas, based on a quasi-static model, for both MSRRs and SRs are derived and tested through a proper comparison with the existing formulas and full-wave numerical results. Both MSRRs and SRs are shown to be useful to reduce the electrical dimensions of the resonant inclusions when synthesizing artificial metamaterials.


title: A Triaxial Accelerometer Calibration Method Using a Mathematical Model

abstract: This paper presents a new triaxial accelerometer calibration method using a mathematical model of six calibration parameters: three gain factors and three biases. The fundamental principle of the proposed calibration method is that the sum of the triaxial accelerometer outputs is equal to the gravity vector when the accelerometer is stationary. The proposed method requires the triaxial accelerometer to be placed in six different tilt angles to estimate the six calibration parameters. Since the mathematical model of the calibration parameters is nonlinear, an iterative method is used. The results are verified via simulations by comparing the estimated gain factors and biases with the true gain factors and biases. The simulation results confirm that the proposed method is applicable in extreme cases where the gain factor is 1000 V/(m/s2) and the bias is ±100 V, as well as the cases where the gain factor is 0.001 V/(m/s2) and the bias is 0 V. The proposed calibration method is also experimentally tested with two different triaxial accelerometers, and the results are validated using a mechanical inclinometer. The experimental results show that the proposed method can accurately estimate gain factors and biases even when the initial guesses are not close to the true values. In addition, the proposed method has a low computational cost because the calculation is simple, and the iterative method usually converges within three iteration steps. The error sources of the experiments are discussed in this paper.


title: From tools to theories: A heuristic of discovery in cognitive psychology

abstract: The study of scientific discovery--where do new ideas come from? has long been denigrated by philosophers as irrelevant to analyzing the growth of scientific knowledge. In particular, little is known about how cognitive theories are discovered, and neither the classical accounts of discovery as either probabilistic induction (e.g., Reichenbach, 1938) or lucky guesses (e.g., Popper, 1959), nor the stock anecdotes about sudden "eureka" moments deepen the insight into discovery. A heuristics approach is taken in this review, where heuristics are understood as strategies of discovery less general than a supposed unique logic of discovery but more general than lucky guesses. This article deals with how scientists' tools shape theories of mind, in particular with how methods of statistical inference have turned into metaphors of mind. The tools-to-theories heuristic explains the emergence of a broad range of cognitive theories, from the cognitive revolution of the 1960s up to the present, and it can be used to detect both limitations and new lines of development in current cognitive theories that investigate the mind as an "intuitive statistician."


title: A fast framework construction and visualization method for particle-based fluid

abstract: Fast and vivid fluid simulation and visualization is a challenge topic of study in recent years. Particle-based simulation method has been widely used in the art animation modeling and multimedia field. However, the requirements of huge numerical calculation and high quality of visualization usually result in a poor computing efficiency. In this work, in order to improve those issues, we present a fast framework for 3D fluid fast constructing and visualization which parallelizes the fluid algorithm based on the GPU computing framework and designs a direct surface visualization method for particle-based fluid data such as WCSPH, IISPH, and PCISPH. Considering on conventional polygonization or adaptive mesh methods may incur high computing costs and detail losses, an improved particle-based method is provided for real-time fluid surface rendering with the screen-space technology and the utilities of the modern graphics hardware to achieve the high performance rendering; meanwhile, it effectively protects fluid details. Furthermore, to realize the fast construction of scenes, an optimized design of parallel framework and interface is also discussed in our paper. Our method is convenient to enforce, and the results demonstrate a significant improvement in the performance and efficiency by being compared with several examples.


title: Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective

abstract: Machine learning sits at the core of many essential products and services at Facebook. This paper describes the hardware and software infrastructure that supports machine learning at global scale. Facebook's machine learning workloads are extremely diverse: services require many different types of models in practice. This diversity has implications at all layers in the system stack. In addition, a sizable fraction of all data stored at Facebook flows through machine learning pipelines, presenting significant challenges in delivering data to high-performance distributed training flows. Computational requirements are also intense, leveraging both GPU and CPU platforms for training and abundant CPU capacity for real-time inference. Addressing these and other emerging challenges continues to require diverse efforts that span machine learning algorithms, software, and hardware design.


title: RF-scanner: Shelf scanning with robot-assisted RFID systems

abstract: Shelf scanning is one of the most important processes for inventory management in a library. It helps the librarians and library users discover the miss-shelved books and pinpoint where they are, improving the quality of service. By traditional means, however, manually checking each bookshelf suffers from extremely intensive labor and long scanning delay. Although some existing RFID-enabled approaches have been proposed, they suffer from either high-cost infrastructure or complicated system deployment, forming a great barrier to commercial adoption. In light of this, we in this paper propose a smart system called RF-Scanner that can perform the shelf scanning automatically by combining the robot technology and the RFID technology. The former is used for replacing the librarians and liberating them from intensively manual labor. The later is installed on the robot and moves with the robot to scan the on-the-shelf books. We formulate two important issues concerned by librarians, the book localization and the lying-down book detection, and give the sophisticated solutions to them. Besides, we implement RF-Scanner and put it into practical use in our school library. Long-term experiments and studies show that RF-Scanner provides fine-grained book localization with a mean error of just 1.3 cm and accurate detection accuracy of lying-down books with a mean error of 6%.


title: Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations

abstract: Deep learning (DL) aims at learning the meaningful representations . A meaningful representation gives rise to significant performance improvement of associated machine learning (ML) tasks by replacing the raw data as the input. However, optimal architecture design and model parameter estimation in DL algorithms are widely considered to be intractable. Evolutionary algorithms are much preferable for complex and nonconvex problems due to its inherent characteristics of gradient-free and insensitivity to the local optimal. In this paper, we propose a computationally economical algorithm for evolving unsupervised deep neural networks to efficiently learn meaningful representations , which is very suitable in the current big data era where sufficient labeled data for training is often expensive to acquire. In the proposed algorithm, finding an appropriate architecture and the initialized parameter values for an ML task at hand is modeled by one computational efficient gene encoding approach, which is employed to effectively model the task with a large number of parameters. In addition, a local search strategy is incorporated to facilitate the exploitation search for further improving the performance. Furthermore, a small proportion labeled data is utilized during evolution search to guarantee the learned representations to be meaningful. The performance of the proposed algorithm has been thoroughly investigated over classification tasks. Specifically, error classification rate on MNIST with 1.15% is reached by the proposed algorithm consistently, which is considered a very promising result against state-of-the-art unsupervised DL algorithms.


title: Syntax-Directed Variational Autoencoder for Structured Data

abstract: Deep generative models have been enjoying success in modeling continuous data. However it remains challenging to capture the representations for discrete structures with formal grammars and semantics, e.g., computer programs and molecular structures. How to generate both syntactically and semantically correct data still remains largely an open problem. Inspired by the theory of compiler where the syntax and semantics check is done via syntax-directed translation (SDT), we propose a novel syntax-directed variational autoencoder (SD-VAE) by introducing stochastic lazy attributes. This approach converts the offline SDT check into on-the-fly generated guidance for constraining the decoder. Comparing to the state-of-the-art methods, our approach enforces constraints on the output space so that the output will be not only syntactically valid, but also semantically reasonable. We evaluate the proposed model with applications in programming language and molecules, including reconstruction and program/molecule optimization. The results demonstrate the effectiveness in incorporating syntactic and semantic constraints in discrete generative models, which is significantly better than current state-of-the-art approaches.


title: Scalable Private Learning with PATE

abstract: The rapid adoption of machine learning has increased concerns about the privacy implications of machine learning models trained on sensitive data, such as medical records or other personal information. To address those concerns, one promising approach is Private Aggregation of Teacher Ensembles, or PATE, which transfers to a "student" model the knowledge of an ensemble of "teacher" models, with intuitive privacy provided by training teachers on disjoint data and strong privacy guaranteed by noisy aggregation of teachers' answers. However, PATE has so far been evaluated only on simple classification tasks like MNIST, leaving unclear its utility when applied to larger-scale learning tasks and real-world datasets. ::: In this work, we show how PATE can scale to learning tasks with large numbers of output classes and uncurated, imbalanced training data with errors. For this, we introduce new noisy aggregation mechanisms for teacher ensembles that are more selective and add less noise, and prove their tighter differential-privacy guarantees. Our new mechanisms build on two insights: the chance of teacher consensus is increased by using more concentrated noise and, lacking consensus, no answer need be given to a student. The consensus answers used are more likely to be correct, offer better intuitive privacy, and incur lower-differential privacy cost. Our evaluation shows our mechanisms improve on the original PATE on all measures, and scale to larger tasks with both high utility and very strong privacy ($\varepsilon$ < 1.0).


title: Acceleration of Stochastic Approximation by Averaging

abstract: A new recursive algorithm of stochastic approximation type with the averaging of trajectories is investigated. Convergence with probability one is proved for a variety of classical optimization and identification problems. It is also demonstrated for these problems that the proposed algorithm achieves the highest possible rate of convergence.


title: The rise of the machines: Artificial intelligence meets scholarly content

abstract: Key points ::: New forms of human/machine dialogue are emerging as robots understand vast amounts of content rather than simply indexing content as strings of characters. ::: Recognizing strings of characters as entities (e.g. = names = authors) allows for meaningful associations between entities and reasoning over these relationships. ::: Web-scale adoption of the Semantic Web approach has been slow because it is too complex to implement and does not scale. ::: User intent, discovered through conversational models of human–computer interaction, allows for a deeper understanding of exactly what researchers are looking for. ::: Personal agents hold the promise of finding information that we will find useful before we have started to look for it. ::: Publishers can use Academic Knowledge APIs to interpret academic user queries and find rich information from the Microsoft Academic Graph.


title: The loss surface of deep and wide neural networks

abstract: While the optimization problem behind deep neural networks is highly non-convex, it is frequently observed in practice that training deep networks seems possible without getting stuck in suboptimal points. It has been argued that this is the case as all local minima are close to being globally optimal. We show that this is (almost) true, in fact almost all local minima are globally optimal, for a fully connected network with squared loss and analytic activation function given that the number of hidden units of one layer of the network is larger than the number of training points and the network structure from this layer on is pyramidal.


title: Radical-level Ideograph Encoder for RNN-based Sentiment Analysis of Chinese and Japanese

abstract: The character vocabulary can be very large in non-alphabetic languages such as Chinese and Japanese, which makes neural network models huge to process such languages. We explored a model for sentiment classification that takes the embeddings of the radicals of the Chinese characters, i.e, hanzi of Chinese and kanji of Japanese. Our model is composed of a CNN word feature encoder and a bi-directional RNN document feature encoder. The results achieved are on par with the character embedding-based models, and close to the state-of-the-art word embedding-based models, with 90% smaller vocabulary, and at least 13% and 80% fewer parameters than the character embedding-based models and word embedding-based models respectively. The results suggest that the radical embedding-based approach is cost-effective for machine learning on Chinese and Japanese.


title: Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings

abstract: Spectral clustering is one of the most prominent clustering approaches. However, it is highly sensitive to noisy input data. In this work, we propose a robust spectral clustering technique able to handle such scenarios. To achieve this goal, we propose a sparse and latent decomposition of the similarity graph used in spectral clustering. In our model, we jointly learn the spectral embedding as well as the corrupted data - thus, enhancing the clustering performance overall. We propose algorithmic solutions to all three established variants of spectral clustering, each showing linear complexity in the number of edges. Our experimental analysis confirms the significant potential of our approach for robust spectral clustering. Supplementary material is available at www.kdd.in.tum.de/RSC.


title: Decision strategies and susceptibility to phishing

abstract: Phishing emails are semantic attacks that con people into divulging sensitive information using techniques to make the user believe that information is being requested by a legitimate source. In order to develop tools that will be effective in combating these schemes, we first must know how and why people fall for them. This study reports preliminary analysis of interviews with 20 non-expert computer users to reveal their strategies and understand their decisions when encountering possibly suspicious emails. One of the reasons that people may be vulnerable to phishing schemes is that awareness of the risks is not linked to perceived vulnerability or to useful strategies in identifying phishing emails. Rather, our data suggest that people can manage the risks that they are most familiar with, but don't appear to extrapolate to be wary of unfamiliar risks. We explore several strategies that people use, with varying degrees of success, in evaluating emails and in making sense of warnings offered by browsers attempting to help users navigate the web.


title: Fast compact prime number sieves (among others)

abstract: Abstract A parameterized family of algorithms is presented for the problem of finding all prime numbers up to a limit N . Many previous algorithms for this problem are shown to correspond to members of this family. A particular choice of parameters leads to an algorithm that requires only Θ ( N ) additions and that runs in o( N ) bits. The small storage requirement is made possible by a provably compact method of storing all the primes up to some limit.


DIRECT DETECTION IN OPTICAL COMMUNICATION USING INTENSITY MODULATION


title: Bone-Conduction-Based Brain Computer Interface Paradigm -- EEG Signal Processing, Feature Extraction and Classification

abstract: The paper presents a novel bone-conduction based brain-computer interface paradigm. Four sub-threshold acoustic frequency stimulus patterns are presented to the subjects in an oddball paradigm allowing for "aha-responses" generation to the attended targets. This allows for successful implementation of the bone-conduction based brain-computer interface (BCI) paradigm. The concept is confirmed with seven subjects in online bone-conducted auditory Morse-code patterns spelling BCI paradigm. We report also brain electrophysiological signal processing and classification steps taken to achieve the successful BCI paradigm. We also present a finding of the response latency variability in a function of stimulus difficulty.


title: Self-Evolving Trading Strategy Integrating Internet of Things and Big Data

abstract: In the era of Internet of Things (IoT) and big data, data has increased dramatically. Computers have been used in various fields. Algorithmic trading is beginning to develop rapidly in the trading market, more and more algorithms begin to be used in the transaction market. As a form of machine learning, neural network can fully reveal the complex trading market. Based on the characteristics of commodity futures market, this paper chooses back propagation neural network to establish price forecasting model. And then, according to the rules of futures market, a self-evolving commodity futures trading strategy is proposed. We also use the data of the Shanghai Futures Exchange and the Dalian Futures Exchange to back-testing the strategy. Finally, we compare the proposed strategies and traditional strategies, and illustrate the evolution of our strategy. Experiments show that our strategies are superior to other compared strategies in the proposed evaluation indicator. Our strategy has a good performance both in yield and risk. It also proves the feasibility of the model and the strategy. The research of this paper is significant to the research of the futures market, and it also provides a new idea for the application of machine learning in algorithmic trading.


title: A comparative study: MongoDB vs. MySQL

abstract: In this paper we will try to present a comparative study of non-relational databases and relational databases. We mainly focus our presentation on one implementation of the NoSQL database technology, namely MongoDB, and make a comparison with another implementation of relational databases, namely MySQL, and thus justifying why MongoDB is more efficient than MySQL. We will also present the advantages of using a non-relational database compared to a relational database, integrated in a forum in the field of personal and professional development. The NoSQL database used to develop the forum is MongoDB, and was chosen from a variety of non-relational databases, thanks to some aspects that we will highlight in this article. The database integration in the framework will also be presented.


title: Blockchain-Oriented Software Engineering: Challenges and New Directions

abstract: The Blockchain technology is reshaping finance, economy, money to the extent that its disruptive power is compared to that of the Internet and the Web in their early days. As a result, all the software development revolving around the Blockchain technology is growing at a staggering rate. In this paper, we acknowledge the need for software engineers to devise specialized tools and techniques for blockchain-oriented software development. From current challenges concerning the definition of new professional roles, demanding testing activities and novel tools for software architecture, we take a step forward by proposing new directions on the basis of a curate corpus of blockchain-oriented software repositories, detected by exploiting the information enclosed in the 2016 Moody's Blockchain Report and teh market capitalization of cryptocurrencies. Ensuring effective testing activities, enhancing collaboration in large teams, and facilitating the development of smart contracts all appear as key factors in the future of blockchain-oriented software development.


title: Handling big data: research challenges and future directions

abstract: Today, an enormous amount of data is being continuously generated in all walks of life by all kinds of devices and systems every day. A significant portion of such data is being captured, stored, aggregated and analyzed in a systematic way without losing its "4V" (i.e., volume, velocity, variety, and veracity) characteristics. We review major drivers of big data today as well the recent trends and established platforms that offer valuable perspectives on the information stored in large and heterogeneous data sets. Then, we present a classification of some of the most important challenges when handling big data. Based on this classification, we recommend solutions that could address the identified challenges, and in addition we highlight cross-disciplinary research directions that need further investigation in the future.


title: Genetic Evidence for the Convergent Evolution of Light Skin in Europeans and East Asians

abstract: Human skin pigmentation shows a strong positive correlation with ultraviolet radiation intensity, suggesting that variation in skin color is, at least partially, due to adaptation via natural selection. We investigated the evolution of pigmentation variation by testing for the presence of positive directional selection in 6 pigmentation genes using an empirical F ST approach, through an examination of global diversity patterns of these genes in the Centre d'Etude du Polymorphisme Humain (CEPH)-Diversity Panel, and by exploring signatures of selection in data from the International HapMap project. Additionally, we demonstrated a role for MATP in determining normal skin pigmentation variation using admixture mapping methods. Taken together (with the results of previous admixture mapping studies), these results point to the importance of several genes in shaping the pigmentation phenotype and a complex evolutionary history involving strong selection. Polymorphisms in 2 genes, ASIP and OCA2, may play a shared role in shaping light and dark pigmentation across the globe, whereas SLC24A5, MATP, and TYR have a predominant role in the evolution of light skin in Europeans but not in East Asians. These findings support a case for the recent convergent evolution of a lighter pigmentation phenotype in Europeans and East Asians.


title: Social Work in an Enterprise Society

abstract: The enterprise of social work the role of social work in dealing with societal stress - capabilities and aspirations political economy and social work social work values and the social fund social work education - home thoughts from abroad social work and social policy in the 20th century - retrospect and prospect the quest for community - from the settlement movement to the Griffiths report social welfare in an enterprise society - new models and old expectations.


title: Extracting Gait Velocity and Stride Length from Surrounding Radio Signals

abstract: Gait velocity and stride length are critical health indicators for older adults. A decade of medical research shows that they provide a predictor of future falls, hospitalization, and functional decline among seniors. However, currently these metrics are measured only occasionally during medical visits. Such infrequent measurements hamper the opportunity to detect changes and intervene early in the impairment process. In this paper, we develop a sensor that uses radio signals to continuously measure gait velocity and stride length at home. Our sensor hangs on a wall like a picture frame. It does not require the monitored person to wear or carry a device on her body. Our approach builds on recent advances in wireless systems which have shown that one can locate people based on how their bodies impact the surrounding radio signals. We demonstrate the accuracy of our method by comparing it to the gold standard in clinical tests, and the VICON motion tracking system. Our experience from deploying the sensor in 14 homes indicates comfort with the technology and a high acceptance rate.


title: New graduate nurses' experiences in their first year of practice

abstract: Summary Aims This study aimed to explore new graduates' experiences of entering the nursing workforce in NSW, Australia, and to identify factors that impact on their transition to the workforce, satisfaction and likelihood of retention. Background The nature of new graduates' experiences in their first year of employment has been shown to have a significant impact on their future career directions. It is well reported that often these experiences are stressful and unsatisfying. Methods A mixed method cross sectional design was used combining quantitative and qualitative approaches. Data was gathered by online survey and focus groups. Results A total of 282 new graduates, aged 21 to 54, responded to the online survey (response rate 24%). Overall, respondents were satisfied with their recruitment process (mean 3.54) and support for professional development (mean 3.37) but job satisfaction was rated lower (mean 2.91). Qualitative findings from focus groups and survey comments revealed a number of key factors impacting on the experience of transition for new graduates. These are; the nature of the workplace environment, the level and nature of support available to new graduates, together with their propensity to learn and adapt to workplace cultures and to accommodate their own expectations and the expectations of others, and to a lesser degree, the amount of prior experience. Conclusion There is an urgent need to develop and test a range of evidence based approaches that will both empower nurses and embed systematic approaches that enable equitable and contextually relevant stewardship of new graduate nurses into the future.


title: Analytical determination of optimal split ratio for overlapping and non-overlapping winding external rotor PM brushless machines

abstract: The split ratio between the rotor and stator outer diameters is an important design parameter. Considerable research has been carried out for the analytical determination of the optimal split ratio in both internal rotor and tubular permanent magnet brushless machines over last decade. In this paper, the optimal split ratio for both non-overlapping and overlapping winding external rotor permanent magnet brushless machines have been derived analytically, with due account for the influence of both fixed and variable rotor back-iron thickness, curvature effect, pole number, slot number, winding factor, tooth-tip, and end-winding length. Moreover, the variation of optimal split ratio with slot and pole combinations, tooth-tips, and maximum flux density in the stator/rotor lamination is also investigated. The theoretical analyses are verified by finite element analyses.


title: PID Controller Design for a Flexible-Link Manipulator

abstract: This paper investigates the application of the H ∞ proportional-integral-derivative (PID) control synthesis method to tip position control of a flexible-link manipulator. To achieve high performance of PID control, this particular control design problem is cast into the H ∞ framework. Based on the recently proposed H ∞ PID control synthesis method, a set of admissible controllers is then obtained to be robust against uncertainty introduced by neglecting the higher-order modes of the link and to achieve the desired time-response specifications. The most important feature of the H ∞ PID control synthesis method is the ability to provide the knowledge of the entire admissible PID controller gain space which can facilitate controller fine tuning. Finally, experimental results are given to demonstrate the effectiveness of H ∞ PID control.


Blockchain for Healthcare: The Next Generation of Medical Records?


title: Software measurement: a necessary scientific basis

abstract: Software measurement, like measurement in any other discipline, must adhere to the science of measurement if it is to gain widespread acceptance and validity. The observation of some very simple, but fundamental, principles of measurement can have an extremely beneficial effect on the subject. Measurement theory is used to highlight both weaknesses and strengths of software metrics work, including work on metrics validation. We identify a problem with the well-known Weyuker properties (E.J. Weyuker, 1988), but also show that a criticism of these properties by J.C. Cherniavsky and C.H. Smith (1991) is invalid. We show that the search for general software complexity measures is doomed to failure. However, the theory does help us to define and validate measures of specific complexity attributes. Above all, we are able to view software measurement in a very wide perspective, rationalising and relating its many diverse activities. >


title: ORIGINAL ARTICLE EVALUATION OF DRUG RELEASE KINETICS FROM IBUPROFEN MATRIX TABLETS USING HPMC

abstract: The aim of this study is to develop a once-daily sustained release matrix tablet of ibuprofen using hydroxypropyl methylcellulose (HPMC) as release controlling factor and to evaluate drug release parameters as per various release kinetic models. In order to achieve required sustained release profile tablets were directly compressed using Avicel pH 101 and Magnesium stearate. The formulated tablets were also characterized by physical and chemical parameters and results were found in acceptable limits. Different dissolution models were applied to drug release data in order to evaluate release mechanisms and kinetics. Criteria for selecting the most appropriate model was based on linearity (coefficient of correlation). The drug release data fit well to the Higuchi expression. Drug release mechanism was found as a complex mixture of diffusion, swelling and erosion.


title: Stress, burnout and depression: A systematic review on DNA methylation mechanisms

abstract: Despite that burnout presents a serious burden for modern society, there are no diagnostic criteria. Additional difficulty is the differential diagnosis with depression. Consequently, there is a need to dispose of a burnout biomarker. Epigenetic studies suggest that DNA methylation is a possible mediator linking individual response to stress and psychopathology and could be considered as a potential biomarker of stress-related mental disorders. Thus, the aim of this review is to provide an overview of DNA methylation mechanisms in stress, burnout and depression. In addition to state-of-the-art overview, the goal of this review is to provide a scientific base for burnout biomarker research. We performed a systematic literature search and identified 25 pertinent articles. Among these, 15 focused on depression, 7 on chronic stress and only 3 on work stress/burnout. Three epigenome-wide studies were identified and the majority of studies used the candidate-gene approach, assessing 12 different genes. The glucocorticoid receptor gene (NR3C1) displayed different methylation patterns in chronic stress and depression. The serotonin transporter gene (SLC6A4) methylation was similarly affected in stress, depression and burnout. Work-related stress and depressive symptoms were associated with different methylation patterns of the brain derived neurotrophic factor gene (BDNF) in the same human sample. The tyrosine hydroxylase (TH) methylation was correlated with work stress in a single study. Additional, thoroughly designed longitudinal studies are necessary for revealing the cause-effect relationship of work stress, epigenetics and burnout, including its overlap with depression.


title: Towards Understanding the Dynamics of Generative Adversarial Networks

abstract: Generative Adversarial Networks (GANs) have recently been proposed as a promising avenue towards learning generative models with deep neural networks. While GANs have demonstrated state-of-the-art performance on multiple vision tasks, their learning dynamics are not yet well understood, both in theory and in practice. To address this issue, we take a first step towards a rigorous study of GAN dynamics. We propose a simple model that exhibits several of the common problematic convergence behaviors (e.g., vanishing gradient, mode collapse, diverging or oscillatory behavior) and still allows us to establish the first convergence bounds for parametric GAN dynamics. We find an interesting dichotomy: a GAN with an optimal discriminator provably converges, while a first order approximation of the discriminator leads to unstable GAN dynamics and mode collapse. Our model and analysis point to a specific challenge in practical GAN training that we call discriminator collapse.


title: Learning Combinatorial Optimization Algorithms over Graphs

abstract: The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error. Can we automate this challenging, tedious process, and learn the algorithms instead? In many real-world applications, it is typically the case that the same optimization problem is solved again and again on a regular basis, maintaining the same problem structure but differing in the data. This provides an opportunity for learning heuristic algorithms that exploit the structure of such recurring problems. In this paper, we propose a unique combination of reinforcement learning and graph embedding to address this challenge. The learned greedy policy behaves like a meta-algorithm that incrementally constructs a solution, and the action is determined by the output of a graph embedding network capturing the current state of the solution. We show that our framework can be applied to a diverse range of optimization problems over graphs, and learns effective algorithms for the Minimum Vertex Cover, Maximum Cut and Traveling Salesman problems.


title: Bounding Gait in a Hybrid Wheeled-Leg Robot

abstract: This paper discusses the first implementation of a dynamically stable bounding gait on a hybrid wheeled-leg robot. Design of the robot is reviewed and the controllers which allow this mode of mobility to occur are discussed. Experimental results demonstrating the key dynamic characteristics of the gait, including footfall patterns, are given. The hypothesis that varying leg takeoff angles can lead to regulation of forward speed of the bounding gait is presented and verified. In addition, comparisons are made between the bounding gait which uses active wheel control and bounding which uses passive mechanical blocking of the wheels.


title: Data Augmentation via Levy Processes

abstract: If a document is about travel, we may expect that short snippets of the document should also be about travel. We introduce a general framework for incorporating these types of invariances into a discriminative classifier. The framework imagines data as being drawn from a slice of a Levy process. If we slice the Levy process at an earlier point in time, we obtain additional pseudo-examples, which can be used to train the classifier. We show that this scheme has two desirable properties: it preserves the Bayes decision boundary, and it is equivalent to fitting a generative model in the limit where we rewind time back to 0. Our construction captures popular schemes such as Gaussian feature noising and dropout training, as well as admitting new generalizations.


title: The Routledge critical dictionary of feminism and postfeminism

abstract: Approachable for general readers as well as for students in women's studies-related courses at all levels, this guide follows the critical dictionary format in combining over a dozen in-depth background chapters with more than 400 A-Z dictionary entries. The background chapters are written by major voices in the field of feminist studies, and include thorough coverage of the history of feminism, as well as extensive discussions of topics such as post-feminism, men in feminism, feminism and new technologies and feminism and philosophy. The dictionary entries cover the major individuals and issues essential to an understanding both of feminism's roots and of the trends that are shaping its future. Readers will find entries on people such as Aphra Behn, Simone de Beauvoir, Princess Diana, Courtney Love and Robert Bly, and on subjects such as Afro-American feminism, cosmetic surgery, the "new man", prostitution, reproductive technologies and "slasher" films.


title: A Probabilistic Classification System for Predicting the Cellular Localization Sites of Proteins

abstract: We have defined a simple model of classification which combines human provided expert knowledge with probabilistic reasoning. We have developed software to implement this model and have applied it to the problem of classifying proteins into their various cellular localization sites based on their amino acid sequences. Since our system requires no hand tuning to learn training data, we can now evaluate the prediction accuracy of protein localization sites by a more objective cross-validation method than earlier studies using production rule type expert systems. 336 E.coli proteins were classified into 8 classes with an accuracy of 81~ while 1484 yeast proteins were classified into 10 classes with an accuracy of 55~0. Additionally we report empirical results using three different strategies for handling continuously valued variables in our probabilistic reasoning system.


title: Improved efficient ellipse hough transform for fetal head measurement

abstract: In this paper we proposed a technique to gain performance improvement on ellipse detection using only one dimensional parameter space Hough Transform. Moreover, this paper also shows how ellipse detection is achieved by using only one dimensional accumulator array efficiently. The result of this research shows that this technique can improve the speed of previous research and can detect ellipse without depending on ellipse's major axis existence.


title: Values as lived experience: evolving value sensitive design in support of value discovery

abstract: The Value Sensitive Design (VSD) methodology provides a comprehensive framework for advancing a value-centered research and design agenda. Although VSD provides helpful ways of thinking about and designing value-centered computational systems, we argue that the specific mechanics of VSD create thorny tensions with respect to value sensitivity. In particular, we examine limitations due to value classifications, inadequate guidance on empirical tools for design, and the ways in which the design process is ordered. In this paper, we propose ways of maturing the VSD methodology to overcome these limitations and present three empirical case studies that illustrate a family of methods to effectively engage local expressions of values. The findings from our case studies provide evidence of how we can mature the VSD methodology to mitigate the pitfalls of classification and engender a commitment to reflect on and respond to local contexts of design.


Magnetic Storms and Solar Activity, 1874 to 1927


title: Adaptive neuro-fuzzy inference system ( ANFIS ) islanding detection based on wind turbine simulator

abstract: This paper presents a passive islanding detection method based on means of a neuro-fuzzy approach for wind turbines. Several methods based on passive and active detection scheme have been proposed. While passive schemes have a large Non detection zone (NDZ), concern has been raised on active method due to its degrading power quality effect. Reliably detecting this condition is regarded by many as an ongoing challenge as existing methods are not entirely satisfactory. The proposed method is based on voltage measurements and processing of the hybrid intelligent system called the Adaptive neuro-fuzzy inference system (ANFIS) for islanding detection. This new method based on passive methods will help to reduce the NDZ without any perturbation that deteriorates the output power quality opposite active methods. This method detects the islanding conditions with the analysis of these signals. The studies reported in this paper are based on an experimental system (wind turbine simulator). The results showed that, the ANFIS-based algorithm detects islanding situation accurate than other islanding detection algorithms. Moreover, for those regions which are in need of a better visualization, the proposed approach would serve as an efficient aid such that the mains power disconnection can be better distinguished. ::: ::: ::: ::: Key words: Distributed generation, islanding detection, non detection zone, adaptive neuro fuzzy inference system, fuzzy subtractive clustering.


title: Community food security: background and future directions.

abstract: Nutrition educators research, teach, and conduct outreach within the field of community food security (CFS), yet no clear consensus exists concerning what the field encompasses. Nutrition education needs to be integrated into the CFS movement for the fundamental reason that optimal health, well-being, and sustainability are at the core of both nutrition education and CFS. Establishing commonalities at the intersection of academic research, public policy development, and distinctive nongovernmental organizations expands opportunities for professional participation. Entry points for nutrition educators' participation are provided, including efforts dedicated to education, research, policy, programs and projects, and human rights.


title: Object based Information Extraction from High Resolution Satellite Imagery using eCognition

abstract: High resolution images offer rich contextual information, including spatial, spectral and contextual information. In order to extract the information from these high resolution images, we need to utilize the spatial and contextual information of an object and its surroundings. If pixel based approaches are applied to extract information from such remotely sensed data, only spectral information is used. Thereby, in Pixel based approaches, information extraction is based exclusively on the gray level thresholding methods. To extract the certain features only from high resolution satellite imagery, this situation becomes worse. To overcome this situation an object-oriented approach is implemented. This paper demonstrated the concept of objectoriented information extraction from high resolution satellite imagery using eCognition software, allows the classification of remotely-sensed data based on different object features, such as spatial, spectral, and contextual information. Thus with the object based approach, information is extracted on the basis of meaningful image objects rather than individual gray values of pixels. The test area has different discrimination, assigned to different classes by this approach: Agriculture, built-ups, forest and water. The Multiresolution segmentation and the nearest neighbour (NN) classification approaches are used and overall accuracy is assessed. The overall accuracy reaches to 97.30% thus making it an efficient and practical approach for information extraction.


title: Infection control practices of laryngoscope blades: a review of the literature.

abstract: www.aana.com/aanajournalonline Current procedures for cleaning anesthesia airway equipment as assessed by the presence of visible and occult blood on laryngoscope blades and handles as labeled “ready for patient use”has been reported to be ineffective. Human immunodeficiency virus (HIV) and the hepatitis B virus (HBV) are 2 commonly seen pathogens that frequently are found in the healthcare setting. It has been shown that HBV can survive on a dry surface for at least 7 days and both HIV and HBV are transmitted via blood. The potential for crosscontamination from airway equipment to patient has been shown in several studies. To prevent further potential infections, it should be ascertained why anesthesia providers are not all using disposable laryngoscope blades. The purpose of this literature review is to determine the use and infection control practices of disposable laryngoscope blades. Their frequency of use, their evaluation of ease of use, and any complications encountered when using the disposable blade are reviewed, as well as the perceptions of anesthesia providers regarding disposable laryngoscope blades.


title: Recent technologies of leaf disease detection using image processing approach — A review

abstract: The economic growth of the country largely hinge on the crop production quality and quantity. The production rate can be improved by disease detection in well-time. Many image processing based approaches have been developed for leaf disease detection in last few years. This paper presents, a survey on different technologies of leaf disease detection using image processing approach and classified them based on the type of analysis tool and applications. Almost, prevailing technologies used in leaf disease detection system are critically reviewed and discussed in brief; comparison of available approaches are examined and presented. The key issues and challenges in leaf diseases detection are highlighted. A vast collection of papers, books and standards are listed in the reference list, which is useful to the researchers, farmers and policy-makers in the area of agriculture.


title: Digital Forensic Investigation of Cloud Storage Services

abstract: Abstract The demand for cloud computing is increasing because of the popularity of digital devices and the wide use of the Internet. Among cloud computing services, most consumers use cloud storage services that provide mass storage. This is because these services give them various additional functions as well as storage. It is easy to access cloud storage services using smartphones. With increasing utilization, it is possible for malicious users to abuse cloud storage services. Therefore, a study on digital forensic investigation of cloud storage services is necessary. This paper proposes new procedure for investigating and analyzing the artifacts of all accessible devices, such as Windows system, Mac system, iPhone, and Android smartphone.


title: Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services

abstract: Presently, a very large number of public and private data sets are available from local governments. In most cases, they are not semantically interoperable and a huge human effort would be needed to create integrated ontologies and knowledge base for smart city. Smart City ontology is not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation, the management of the complexity, to allow the data reasoning. In this paper, a system for data ingestion and reconciliation of smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows managing a big data volume of data coming from a variety of sources considering both static and dynamic data. These data are mapped to a smart-city ontology, called KM4City (Knowledge Model for City), and stored into an RDF-Store where they are available for applications via SPARQL queries to provide new services to the users via specific applications of public administration and enterprises. The paper presents the process adopted to produce the ontology and the big data architecture for the knowledge base feeding on the basis of open and private data, and the mechanisms adopted for the data verification, reconciliation and validation. Some examples about the possible usage of the coherent big data knowledge base produced are also offered and are accessible from the RDF-store and related services. The article also presented the work performed about reconciliation algorithms and their comparative assessment and selection.


title: A fully on-line adaptive BCI

abstract: A viable fully on-line adaptive brain computer interface (BCI) is introduced. On-line experiments with nine naive and able-bodied subjects were carried out using a continuously adaptive BCI system. The data were analyzed and the viability of the system was studied. The BCI was based on motor imagery, the feature extraction was performed with an adaptive autoregressive model and the classifier used was an adaptive quadratic discriminant analysis. The classifier was on-line updated by an adaptive estimation of the information matrix (ADIM). The system was also able to provide continuous feedback to the subject. The success of the feedback was studied analyzing the error rate and mutual information of each session and this analysis showed a clear improvement of the subject's control of the BCI from session to session.


title: Production-level facial performance capture using deep convolutional neural networks

abstract: We present a real-time deep learning framework for video-based facial performance capture---the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5--10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular video sequence of that subject. Since this 3D facial performance capture is fully automated, our system can drastically reduce the amount of labor involved in the development of modern narrative-driven video games or films involving realistic digital doubles of actors and potentially hours of animated dialogue per character. We compare our results with several state-of-the-art monocular real-time facial capture techniques and demonstrate compelling animation inference in challenging areas such as eyes and lips.


title: Stack Structure Dependence of Magnetic Properties of PtMn/[Co/Ni] Films for Spin-Orbit Torque Switching Device

abstract: We investigate the stack structure dependence of magnetic properties on thin films that consist of an antiferromagnetic PtMn and a ferromagnetic Co/Ni multilayer for field-free spin-orbit torque-induced magnetization switching devices. Magnetic parameters, such as the spontaneous magnetization, effective and interfacial magnetic anisotropies, and exchange bias field are quantified as a function of stack structure. Engineering of the stack allows the improvement of current-induced magnetization switching characteristics compared with a previous work, which is confirmed using patterned Hall cross devices.


title: Improvement of the Phase Shifter in 90 $^{\circ}$ Power Splitter for UWB Applications

abstract: This letter presents an improvement of the T-type high-pass network inside the phase shifter of a broadband 90 $^{\circ}$ or quadrature power splitter (QPS). This broadband QPS consists of a broadband Wilkinson power divider and a broadband 90 $^{\circ}$ phase shifter. By the proposed design, the bottom layers of MIM capacitors are utilized as a coupled line to improve the bandwidth of phase error and return loss. The measured results of this QPS show the phase error of less than $\pm 5^{\circ}$ and the imbalance at output ports of smaller than 0.3 dB from 3.1 to 11.1 GHz. Return losses of this QPS are better than 11.7 dB, and the isolation between two output ports is better than 15 dB within the same frequency range.


title: eQTL Mapping Using RNA-seq Data

abstract: As RNA-seq is replacing gene expression microarrays to assess genome-wide transcription abundance, gene expression Quantitative Trait Locus (eQTL) studies using RNA-seq have emerged. RNA-seq delivers two novel features that are important for eQTL studies. First, it provides information on allele-specific expression (ASE), which is not available from gene expression microarrays. Second, it generates unprecedentedly rich data to study RNA-isoform expression. In this paper, we review current methods for eQTL mapping using ASE and discuss some future directions. We also review existing works that use RNA-seq data to study RNA-isoform expression and we discuss the gaps between these works and isoform-specific eQTL mapping.


title: Dual polarized active frequency selective surface for high power applications at X-band

abstract: An active frequency selective surface (AFSS) with switchable transmissive/reflective properties at X-band frequencies is introduced. This surface achieves a unique combination of maintaining a high dynamic range between active states, demonstrating an insensitivity to incoming orthogonal linear polarizations, and operating across a 10% bandwidth at X-band frequencies. This is achieved through a novel implementation of PIN diodes as short-circuit boundary conditions across a “window frame” aperture pattern that produces either constructive or destructive resonance depending on the biasing state of the diodes. The DC biasing structure maintains element symmetry while mitigating inter-element coupling, allowing adjacent FSS cells to be connected in series without distorting their resonance or limiting the polarization response of the surface. The AFSS has a measured reflective-state isolation of greater than 17 dB and transmissive-state loss of less than 1 dB across an operating band of 9 to 10 GHz. We believe this combination of performance and versatility at X-band to be unique in the literature, and is implemented here in such a way as to make this AFSS suitable for future high power applications.


title: Pulse induction parking sensor

abstract: This paper presents a pulse induction parking sensor, which measures the induced voltage generated by the presence of a vehicle. The parking information can be then shared into a network to inform the drivers about the on-street parking availability. According to our preliminary tests, this parking sensor is 100% reliable thus detecting all the car departure/arrival events. A parking sensor prototype was implemented and experimental results obtained during field tests are reported.


Imaging in Idiopathic Retroperitoneal Fibrosis : What are the Options for Diagnostic , Disease Assessment and Follow-Up ?