title: Capturing and supporting contexts for scientific data sharing via the biological sciences collaboratory

abstract: Scientific collaboration is largely focused on the sharing and joint analysis of scientific data and results. Today, a movement is afoot within the scientific computing community to shift "collaboratory" development from traditional tool-centric approaches to more data-centric ones. Yet, to effectively support data sharing means more than providing a common repository for storing and retrieving shared data sets. In order to reasonably comprehend and apply another researcher's data set, the scientist must grasp the various contexts of the data as it relates to the overall data space, applications, experiments, projects, and the scientific community. Under development at the Pacific Northwest National Laboratory, the Biological Sciences Collaboratory (BSC) enables the sharing of biological data and analyses through diverse capabilities such as metadata capture, electronic laboratory notebooks, data organization views, data provenance tracking, analysis notes, task management, and scientific workflow management. Overall, BSC strives to identify and capture the various social and scientific contexts in which data sharing collaborations in biology take place and to provide collaboration tools and capabilities that can effectively support and facilitate these important data sharing contexts.


title: Scene Reconstruction and Visualization from Internet Photo Collections

abstract: The Internet has become an unprecedented source of visual information about our world, with millions of people uploading photos and videos to media-sharing sites at staggering rates. Virtually all of the world's famous landmarks and cities (and many not-so-famous ones) have been photographed hundreds of thousands or millions of times, and billions of these photos can be found on photo-sharing websites. This richness and variety make such Internet photo collections extremely attractive as a source of data for applications ranging from mapping and visualization to social science. However, a prerequisite to many such applications is recovering structure — often in the form of 3D geometry — from these massive, unorganized collections of imagery. This ever-growing collection of visual data opens up fundamental new questions in computer vision and computer graphics, where traditional techniques designed for small, controlled sets of images cannot be readily applied. This article surveys recent work on applying geometric computer vision to large, unstructured photo collections, as well as applications enabled by these new techniques in scene visualization, location recognition, image editing, and other areas of computer vision and graphics.


title: Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience

abstract: The multidimensional character and inherent conflict with categorisation of interdisciplinarity makes its mapping and evaluation a challenging task. We propose a conceptual framework that aims to capture interdisciplinarity in the wider sense of knowledge integration, by exploring the concepts of diversity and coherence. Disciplinary diversity indicators are developed to describe the heterogeneity of a bibliometric set viewed from predefined categories, i.e. using a top-down approach that locates the set on the global map of science. Network coherence indicators are constructed to measure the intensity of similarity relations within a bibliometric set, i.e. using a bottom-up approach, which reveals the structural consistency of the publications network. We carry out case studies on individual articles in bionanoscience to illustrate how these two perspectives identify different aspects of interdisciplinarity: disciplinary diversity indicates the large-scale breadth of the knowledge base of a publication; network coherence reflects the novelty of its knowledge integration. We suggest that the combination of these two approaches may be useful for comparative studies of emergent scientific and technological fields, where new and controversial categorisations are accompanied by equally contested claims of novelty and interdisciplinarity.


title: A sparse signal reconstruction perspective for source localization with sensor arrays

abstract: We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing penalties based on the /spl lscr//sub 1/-norm. A number of recent theoretical results on sparsifying properties of /spl lscr//sub 1/ penalties justify this choice. Explicitly enforcing the sparsity of the representation is motivated by a desire to obtain a sharp estimate of the spatial spectrum that exhibits super-resolution. We propose to use the singular value decomposition (SVD) of the data matrix to summarize multiple time or frequency samples. Our formulation leads to an optimization problem, which we solve efficiently in a second-order cone (SOC) programming framework by an interior point implementation. We propose a grid refinement method to mitigate the effects of limiting estimates to a grid of spatial locations and introduce an automatic selection criterion for the regularization parameter involved in our approach. We demonstrate the effectiveness of the method on simulated data by plots of spatial spectra and by comparing the estimator variance to the Crame/spl acute/r-Rao bound (CRB). We observe that our approach has a number of advantages over other source localization techniques, including increased resolution, improved robustness to noise, limitations in data quantity, and correlation of the sources, as well as not requiring an accurate initialization.


title: miniAdapton: A Minimal Implementation of Incremental Computation in Scheme

abstract: We describe a complete Scheme implementation of miniAdapton, which implements the core functionality of the Adapton system for incremental computation (also known as self-adjusting computation). Like Adapton, miniAdapton allows programmers to safely combine mutation and memoization. miniAdapton is built on top of an even simpler system, microAdapton. Both miniAdapton and microAdapton are designed to be easy to understand, extend, and port to host languages other than Scheme. We also present adapton variables, a new interface in Adapton for variables intended to represent expressions.


title: In Search of the Bullwhip Effect

abstract: The bullwhip effect is the phenomenon of increasing demand variability in the supply chain from downstream echelons (retail) to upstream echelons (manufacturing). The objective of this study is to document the strength of the bullwhip effect in industry-level U.S. data. In particular, we say an industry exhibits the bullwhip effect if the variance of the inflow of material to the industry (what macroeconomists often refer to as the variance of an industry's “production”) is greater than the variance of the industry's sales. We find that wholesale industries exhibit a bullwhip effect, but retail industries generally do not exhibit the effect, nor do most manufacturing industries. Furthermore, we observe that manufacturing industries do not have substantially greater demand volatility than retail industries. Based on theoretical explanations for observing or not observing demand amplification, we are able to explain a substantial portion of the heterogeneity in the degree to which industries exhibit the bullwhip effect. In particular, the less seasonal an industry's demand, the more likely the industry amplifies volatility---highly seasonal industries tend to smooth demand volatility whereas nonseasonal industries tend to amplify.


title: Dynamic mobile cloudlet clustering for fog computing

abstract: Although mobile devices have obtained more and more capabilities such as CPU power, memory, and battery, they still cannot satisfy the requirement of high resource consuming applications. Fog Computing is one of the solutions for offloading the task of a mobile. However the capability of fog server is still limited due to the high deployment cost. In this paper, we propose a dynamic mobile cloudlet cluster policy (DMCCP) to use cloudlets as a supplement for the fog server for offloading. The main idea is that by monitoring each mobile device resource amount, the DMCCP system clusters the optimal cloudlet to meet the requests of different tasks from the local mobile device. In simulation results, we demonstrated that our proposed policy can cluster the approximated optimal cloudlets for different resource demands of a task in a real-time processing time compared to the optimal method.


title: A maximum entropy approach to species distribution modeling

abstract: We study the problem of modeling species geographic distributions, a critical problem in conservation biology. We propose the use of maximum-entropy techniques for this problem, specifically, sequential-update algorithms that can handle a very large number of features. We describe experiments comparing maxent with a standard distribution-modeling tool, called GARP, on a dataset containing observation data for North American breeding birds. We also study how well maxent performs as a function of the number of training examples and training time, analyze the use of regularization to avoid overfitting when the number of examples is small, and explore the interpretability of models constructed using maxent.


title: The desire for sexual variety as a key to understanding basic human mating strategies

abstract: authors have proposed competing evolutionary theories of human mating. Some argue that both sexes are designed to pursue a singular long-term mating strategy. Others contend that both sexes are designed to function as essentially multiple maters. Sexual Strategies Theory (SST; D.M. Buss & D.P. Schmitt, 1993), in contrast, proposes that men and women have evolved short-term and long-term mating strategies that are pursued differently by each sex depending on theoretically derived dimensions of context. According to SST, the sexes tend to differ in the nature and prominence of the short-term component of human mating-particularly the short-term desire for sexual variety. The current research was designed to test competing empirical predictions from these contrasting theories by focusing on sex differences in the desire for sexual variety. Study 1 (N = 1,049), consisting of five separate samples, found large and consistent sex differences in the desire for short-term sexual variety, even after employing statistical methods to control for skewed distributions and statistical outliers. Study 2 (N = 192) confirmed the results of Study 1 using an older, more mature sample. Study 3 (N = SO) again replicated these sex differences using an observer-based method of inquiry. Study 4 (N = 167) found evidence that short-term mating was unrelated generally to psychological dysfunction and may be related to mentally healthy personality characteristics in men. Discussion focuses on the viability of pluralistic compared with monomorphic evolutionary theories of human mating strategies.


title: Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons

abstract: In this paper we consider the collaborative ranking setting: a pool of users each provides a small number of pairwise preferences between d possible items; from these we need to predict each users preferences for items they have not yet seen. We do so by fitting a rank r score matrix to the pairwise data, and provide two main contributions: ::: ::: (a) we show that an algorithm based on convex optimization provides good generalization guarantees once each user provides as few as O(r log2 d) pairwise comparisons - essentially matching the sample complexity required in the related matrix completion setting (which uses actual numerical as opposed to pairwise information), and ::: ::: (b) we develop a large-scale non-convex implementation, which we call AltSVM, that trains a factored form of the matrix via alternating minimization (which we show reduces to alternating SVM problems), and scales and parallelizes very well to large problem settings. It also outperforms common baselines on many moderately large popular collaborative filtering datasets in both NDCG and in other measures of ranking performance.


title: AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge

abstract: Mood disorders are inherently related to emotion. In particular, the behaviour of people suffering from mood disorders such as unipolar depression shows a strong temporal correlation with the affective dimensions valence, arousal and dominance. In addition to structured self-report questionnaires, psychologists and psychiatrists use in their evaluation of a patient's level of depression the observation of facial expressions and vocal cues. It is in this context that we present the fourth Audio-Visual Emotion recognition Challenge (AVEC 2014). This edition of the challenge uses a subset of the tasks used in a previous challenge, allowing for more focussed studies. In addition, labels for a third dimension (Dominance) have been added and the number of annotators per clip has been increased to a minimum of three, with most clips annotated by 5. The challenge has two goals logically organised as sub-challenges: the first is to predict the continuous values of the affective dimensions valence, arousal and dominance at each moment in time. The second is to predict the value of a single self-reported severity of depression indicator for each recording in the dataset. This paper presents the challenge guidelines, the common data used, and the performance of the baseline system on the two tasks.


title: Machine Learning and Images for Malware Detection and Classification

abstract: Detecting malicious code with exact match on collected datasets is becoming a large-scale identification problem due to the existence of new malware variants. Being able to promptly and accurately identify new attacks enables security experts to respond effectively. My proposal is to develop an automated framework for identification of unknown vulnerabilities by leveraging current neural network techniques. This has a significant and immediate value for the security field, as current anti-virus software is typically able to recognize the malware type only after its infection, and preventive measures are limited. Artificial Intelligence plays a major role in automatic malware classification: numerous machine-learning methods, both supervised and unsupervised, have been researched to try classifying malware into families based on features acquired by static and dynamic analysis. The value of automated identification is clear, as feature engineering is both a time-consuming and time-sensitive task, with new malware studied while being observed in the wild.


title: IoT security (IoTSec) considerations, requirements, and architectures

abstract: This article looks at some of the challenges related to the deployment of the Internet Of Things (IoT), specifically to ascertain that security becomes an integral part of the technology rather than a bolted-on wrapper of limited efficacy. IoT security (IoTSec) is needed at all ‘layers’ of the IoT environment and may be specific to the IoT ‘layer’ in question.


title: Differential Privacy in Telco Big Data Platform

abstract: Differential privacy (DP) has been widely explored in academia recently but less so in industry possibly due to its strong privacy guarantee. This paper makes the first attempt to implement three basic DP architectures in the deployed telecommunication (telco) big data platform for data mining applications. We find that all DP architectures have less than 5% loss of prediction accuracy when the weak privacy guarantee is adopted (e.g., privacy budget parameter e ≥ 3). However, when the strong privacy guarantee is assumed (e.g., privacy budget parameter e ≤ 0:1), all DP architectures lead to 15% ~ 30% accuracy loss, which implies that real-word industrial data mining systems cannot work well under such a strong privacy guarantee recommended by previous research works. Among the three basic DP architectures, the Hybridized DM (Data Mining) and DB (Database) architecture performs the best because of its complicated privacy protection design for the specific data mining algorithm. Through extensive experiments on big data, we also observe that the accuracy loss increases by increasing the variety of features, but decreases by increasing the volume of training data. Therefore, to make DP practically usable in large-scale industrial systems, our observations suggest that we may explore three possible research directions in future: (1) Relaxing the privacy guarantee (e.g., increasing privacy budget e) and studying its effectiveness on specific industrial applications; (2) Designing specific privacy scheme for specific data mining algorithms; and (3) Using large volume of data but with low variety for training the classification models.


title: Cooperative relative positioning of mobile users by fusing IMU inertial and UWB ranging information

abstract: Relative positioning between multiple mobile users is essential for many applications, such as search and rescue in disaster areas or human social interaction. Inertial-measurement unit (IMU) is promising to determine the change of position over short periods of time, but it is very sensitive to error accumulation over long term run. By equipping the mobile users with ranging unit, e.g. ultra-wideband (UWB), it is possible to achieve accurate relative positioning by trilateration-based approaches. As compared to vision or laser-based sensors, the UWB does not need to be with in line-of-sight and provides accurate distance estimation. However, UWB does not provide any bearing information and the communication range is limited, thus UWB alone cannot determine the user location without any ambiguity. In this paper, we propose an approach to combine IMU inertial and UWB ranging measurement for relative positioning between multiple mobile users without the knowledge of the infrastructure. We incorporate the UWB and the IMU measurement into a probabilistic-based framework, which allows to cooperatively position a group of mobile users and recover from positioning failures. We have conducted extensive experiments to demonstrate the benefits of incorporating IMU inertial and UWB ranging measurements.


title: A knowledge-based table recognition method for Chinese bank statement images

abstract: Automatic processing of large volume scanned Chinese bank statements is a urgent demand recently. Conventional methods can not well handle the following challenges of this problem: various layout styles, noises, and especially requirement of fast speed for large Chinese character set. This paper proposes a knowledge based table recognition method to meet fast speed requirement with good accuracy. Two kinds of knowledge are utilized to accelerate the identification of digit columns and the cell recognition: i) geometric knowledge about column alignment and quasi equal digit width, and ii) semantic knowledge about prior format based on the results from an optical character recognition (OCR) engine of digits. Experimental results on a real dataset show the effectiveness of our method.


title: An Effective Via-Based Frequency Adjustment and Minimization Methodology for Single-Layered Frequency-Selective Surfaces

abstract: This paper proposes a new methodology for minimizing the size of the periodic element and tuning the resonant frequency of a single-layered frequency-selective surface (FSS). Vertical vias are introduced to the FSS design in the methodology. This via-based methodology can be applied to nearly all of the single-layered FSSs presented in the literature, which exhibit much more convenience in practice than the multilayered FSSs. Through the proposed methodology, users can easily vary the resonant frequency of the considered via-inserted FSS (VI-FSS), adding only some additional vias and without having to modify the original element pattern. In addition, the proposed methodology can be applied to minimize the element size or lower its resonant frequency. To illustrate the methodology, the well-known Jerusalem Cross (JC) FSS was first considered. A high-order equivalent circuit was suggested to model the physical mechanism of the VI-FSS with the applied methodology. A set of closed-form equations was also provided to calculate efficiently the circuit inductances and capacitances, especially those caused by the added vias. The VI-FSS prototype was fabricated and examined in a fully anechoic chamber. Favorable agreement among the proposed circuit, full-wave simulation, and measurement was achieved. For demonstrating the capability of the via-based methodology further, it was applied to two advanced applications: 1) two miniaturized-element FSSs and 2) an FSS absorber. Thus, a significant performance improvement in these applications reconfirmed the capability of the proposed methodology.


title: Applying Quaternion Fourier Transforms for Enhancing Color Images

abstract: The Fourier transforms play a critical role in a broad range of image processing applications, including enhancement, analysis, restoration, and compression. Until recently, it was common to use the conventional methods to deal with colored images. These methods are based on RGB decomposition of the colored image by separating it into three separate scalar images and computing the Fourier transforms of these images separately. The computing of the Hypercomplex 2D Fourier transform of a color image as a whole unit has only recently been realized. This paper is concerned with frequency domain noise reduction of color images using quaternion Fourier transforms. The approach is based on obtaining quaternion Fourier transform of the color image and applying the Gaussian filter to it in the frequency domain. The filtered image is then obtained by calculating the inverse quaternion Fourier transforms.


title: The efficacy of calorie labelling formats on pre-packaged foods: An experimental study among adolescents and young adults in Canada

abstract: OBJECTIVES: Several countries have proposed changes to calorie labelling on nutrition facts tables (NFTs) on pre-packaged foods. As most research to date has examined general use of NFTs, there is a lack of evidence to guide specific design changes to calorie information on labels. This study examined the efficacy of various calorie labelling formats on recall, comprehension, and consumer preferences for calorie information. METHODS: Experiments were conducted as part of an online survey with a national sample of 2,008 Canadians aged 16–24. In Task 1, participants were shown one of six labelling formats (e.g., %DV , Traffic Light ) with calories in either small or large font, and asked to recall the amount of calories. Task 2 examined comprehension of calories in the context of recommended daily intake (RDI), using the same NFT as in Task 1. Task 3 identified participants’ preferences for labelling formats. RESULTS: NFTs with calories in large font enhanced calorie recall ( p < 0.001). When small font was displayed, the Traffic Light format performed best at improving recall ( p < 0.01). With large font, the highest recall was in the Current , RDI and Traffic Light formats (all p < 0.05). Comprehension of servings per RDI was highest in the Infographic format, with no difference by font size ( p < 0.001). Respondents preferred the large font calorie condition and the Infographic format ( p < 0.001). CONCLUSIONS: Enhancements in visibility and design can improve the efficacy of calorie labelling on pre-packaged foods. The findings have direct implication for proposed changes to calorie labelling on NFTs in Canada and the United States.


title: Robust visual tracking using ℓ1 minimization

abstract: In this paper we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, corruption and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target at a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an l 1 -regularized least squares problem. Then the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. Two additional components further improve the robustness of our approach: 1) the nonnegativity constraints that help filter out clutter that is similar to tracked targets in reversed intensity patterns, and 2) a dynamic template update scheme that keeps track of the most representative templates throughout the tracking procedure. We test the proposed approach on five challenging sequences involving heavy occlusions, drastic illumination changes, and large pose variations. The proposed approach shows excellent performance in comparison with three previously proposed trackers.


title: Identity inference of genomic data using long-range familial searches

abstract: Consumer genomics databases have reached the scale of millions of individuals. Recently, law enforcement authorities have exploited some of these databases to identify suspects via distant familial relatives. Using genomic data of 1.28 million individuals tested with consumer genomics, we investigated the power of this technique. We project that about 60% of the searches for individuals of European descent will result in a third-cousin or closer match, which theoretically allows their identification using demographic identifiers. Moreover, the technique could implicate nearly any U.S. individual of European descent in the near future. We demonstrate that the technique can also identify research participants of a public sequencing project. On the basis of these results, we propose a potential mitigation strategy and policy implications for human subject research.


title: A Dual Prediction Network for Image Captioning

abstract: General captioning practice involves a single forward prediction, with the aim of predicting the word in the next timestep given the word in the current timestep. In this paper, we present a novel captioning framework, namely Dual Prediction Network (DPN), which is end-to-end trainable and addresses the captioning problem with dual predictions. Specifically, the dual predictions consist of a forward prediction to generate the next word from the current input word, as well as a backward prediction to reconstruct the input word using the predicted word. DPN has two appealing properties: 1) By introducing an extra supervision signal on the prediction, DPN can better capture the interplay between the input and the target; 2) Utilizing the reconstructed input, DPN can make another new prediction. During the test phase, we average both predictions to formulate the final target sentence. Experimental results on the MS COCO dataset demonstrate that, benefiting from the reconstruction step, both generated predictions in DPN outperform the predictions of methods based on the general captioning practice (single forward prediction), and averaging them can bring a further accuracy boost. Overall, DPN achieves competitive results with state-of-the-art approaches, across multiple evaluation metrics.


title: Scenes-Objects-Actions: A Multi-task, Multi-label Video Dataset

abstract: This paper introduces a large-scale, multi-label and multi-task video dataset named Scenes-Objects-Actions (SOA). Most prior video datasets are based on a predefined taxonomy, which is used to define the keyword queries issued to search engines. The videos retrieved by the search engines are then verified for correctness by human annotators. Datasets collected in this manner tend to generate high classification accuracy as search engines typically rank “easy” videos first. The SOA dataset adopts a different approach. We rely on uniform sampling to get a better representation of videos on the Web. Trained annotators are asked to provide free-form text labels describing each video in three different aspects: scene, object and action. These raw labels are then merged, split and renamed to generate a taxonomy for SOA. All the annotations are verified again based on the taxonomy. The final dataset includes 562K videos with 3.64M annotations spanning 49 categories for scenes, 356 for objects, 148 for actions, and naturally captures the long tail distribution of visual concepts in the real world. We show that datasets collected in this way are quite challenging by evaluating existing popular video models on SOA. We provide in-depth analysis about the performance of different models on SOA, and highlight potential new directions in video classification. We compare SOA with existing datasets and discuss various factors that impact the performance of transfer learning. A key-feature of SOA is that it enables the empirical study of correlation among scene, object and action recognition in video. We present results of this study and further analyze the potential of using the information learned from one task to improve the others. We also demonstrate different ways of scaling up SOA to learn better features. We believe that the challenges presented by SOA offer the opportunity for further advancement in video analysis as we progress from single-label classification towards a more comprehensive understanding of video data.


title: Fusion of catalytically inactive Cas9 to FokI nuclease improves the specificity of genome modification

abstract: A fusion of the FokI nuclease and a catalytically inactive Cas9 is a highly specific genome editing tool.


title: A simplified design approach for radial power combiners

abstract: It is known that a radial power combiner is very effective in combining a large number of power amplifiers, where high efficiency (greater than 90%) over a relatively wide band can be achieved. However, its current use is limited due to its design complexity. In this paper, we develop a step-by-step design procedure, including both the initial approximate design formulas and suitable models for final accurate design optimization purposes. Based on three-dimensional electromagnetic modeling, predicted results were in excellent agreement with those measured. Practical issues related to the radial-combiner efficiency, its graceful degradation, and the effects of higher order package resonances are discussed here in detail


title: A fuzzy TOPSIS model via chi-square test for information source selection

abstract: The Information Source (IS) selection involves various aspects with different requirements under indeterminate conditions. It is such a complicated process pertaining to seeking for the most appropriate solution that how to resolve the constraint resources needs to be congruously considered. This paper proposes a Multi-Criteria Group Decision Making (MCGDM) model, which uniforms the quantitative and qualitative factual value of different attributes with trapezoidal fuzzy numbers. Analytic Hierarchy Process (AHP) and Entropy Weights (EW) are integrated to alleviate the conflicts by experts' intuitions and provide the accurate weight vector in this model. Besides, the Euclidean Distance (ED) is substituted by the Value of Chi-Square Test (VCST) to refine the Relative Closeness (RC), which theoretically excluded the potential bias arising from relative importance of the two types of distances, in a revised Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The optimal recommendation compromises in a social decision making way. Finally, the software named ''Evaluator'', which is based on the presented model, is illustrated to show how it can be practically used for IS selection with comparative analysis.


title: Sliding Mode Control for Coupled-Tank Liquid Level Control System

abstract: This paper presents the mathematical modeling of coupled-tank system and designing of sliding mode control (SMC) for liquid level control in the system. The non-linear single-input single-output (SISO) mathematical model of plant is developed initially. A simulation is carried out using MATLAB/SIMULINK to control the non-linear model of coupled-tank system for a number of different conditions. The commanded inputs such as that of step, random, square, saw tooth and sinusoidal input signals are fed to the system to test the tracking performance of SMC. Some realistic situations are also included in the plant to examine the robustness of controller. The controller showed robustness for disturbance in the plant and produced an appropriate control signals for the sake of controlling the liquid level in the coupled tank system. The tracking performance of SMC is also compared with conventional PID controller in terms of performance index ITAE. The SMC showed excellent tracking results than PID controller for time varying command signals.


title: Ideology, motivated reasoning, and cognitive reflection

abstract: Decision scientists have identified various plausible sources of ideological polarization over climate change, gun violence, national security, and like issues that turn on empirical evidence. This paper describes a study of three of them: the predominance of heuristic-driven information processing by members of the public; ideologically motivated reasoning; and the cognitive-style correlates of political conservativism. The study generated both observational and experimental data inconsistent with the hypothesis that political conservatism is distinctively associated with either unreflective thinking or motivated reasoning. Conservatives did no better or worse than liberals on the Cognitive Reflection Test (Frederick, 2005), an objective measure of information-processing dispositions associated with cognitive biases. In addition, the study found that ideologically motivated reasoning is not a consequence of over-reliance on heuristic or intuitive forms of reasoning generally. On the contrary, subjects who scored highest in cognitive reflection were the most likely to display ideologically motivated cognition. These findings corroborated an alternative hypothesis, which identifies ideologically motivated cognition as a form of information processing that promotes individuals' interests in forming and maintaining beliefs that signify their loyalty to important affinity groups. The paper discusses the practical significance of these findings, including the need to develop science communication strategies that shield policy-relevant facts from the influences that turn them into divisive symbols of political identity.


BiPOCL: A Discourse-Driven Story Planner for Procedural Narrative Generation (Extended Abstract).


title: Development and validation of the competence in evidence based practice questionnaire (EBP-COQ) among nursing students

abstract: BackgroundNursing educators need rigorously developed instruments to assess competency in evidence based practice (EBP) at undergraduate level. This concept is defined as the capability to choose and use an integrated combination of knowledge, skills and attitudes with the intention to develop a task in a certain context. Also, we understand that EBP is gaining knowledge and skills, as well as increasing positive attitudes toward EBP that will promote a change in behaviour to implement EBP in practice. This study aims to develop a psychometric test of the Evidence Based Practice Evaluation Competence Questionnaire (EBP-COQ) among undergraduate nursing students.MethodsThe questionnaire was developed by item generation through a review of scientific literature and focus groups. The instrument was validated in terms of content validity through an expert review. The EBP-COQ was administered to a cohort of nursing students (n =100) to evaluate test reliability and select the best items. Psychometric properties of the final instrument were assessed in a sample of 261 nursing students.ResultsThe EBP-COQ consisted of 25 items. A factorial analysis grouped the items into the three categories that define competence relating to EBP: attitude, knowledge and skills. Cronbach’s alpha was 0.888 for the entire questionnaire. The factor solution explained 55.55% of the variance.ConclusionsEBP-COQ appears to measure with adequate reliability the attributes of undergraduate nursing students’ competence in EBP. The instrument is quick to disseminate and easy to score, making it a suitable instrument for nursing educators to evaluate students’ self-perceived competence in EBP.


title: Crossbar RRAM Arrays: Selector Device Requirements During Write Operation

abstract: A comprehensive analysis of write operations (SET and RESET) in a resistance-change memory (resistive random access memory) crossbar array is carried out. Three types of resistive switching memory cells-nonlinear, rectifying-SET, and rectifying-RESET-are compared with each other in terms of voltage delivery, current delivery, and power consumption. Two different write schemes, V/2 and V/3, were considered, and the V/2 write scheme is preferred due to much lower power consumption. A simple numerical method was developed that simulates entire current flows and node voltages within a crossbar array and provides a quantitative tool for the accurate analysis of crossbar arrays and guidelines for developing reliable write operation.


title: Novel nanoemulsion for minimizing variations in bioavailability of ezetimibe

abstract: The objectives of the present study were to develop an optimal nanoemulsion of ezetimibe and evaluate its stability, lipid lowering and pharmacokinetic potential. Solubility of ezetimibe was determined in various vehicles. Pseudoternary phase diagrams were constructed to determine the existence of nanoemulsion region. Formulations were selected from the oil/water nanoemulsion region and subjected to various thermodynamic stability and dispersibility tests. Release rate of optimized formulations was determined using in vitro dissolution test. The formulation used for evaluation contained Capryol 90 (10% v/v), Tween 80 (15% v/v), Transcutol® P (30% v/v), double distilled water (45% v/v). The release of drug from the nanoemulsion was highly significant (P <0.001) when compared to the drug suspension. The value of total cholesterol in the group administered with the formulation TF1 was highly significant (P <0.001) with respect to the group administered with the suspension of the drug. The plasma concentratio...


title: Analyzing User Activities, Demographics, Social Network Structure and User-Generated Content on Instagram

abstract: Instagram is a relatively new form of communication where users can instantly share their current status by taking pictures and tweaking them using filters. It has seen a rapid growth in the number of users as well as uploads since it was launched in October 2010. Inspite of the fact that it is the most popular photo sharing application, it has attracted relatively less attention from the web and social media research community. In this paper, we present a large-scale quantitative analysis on millions of users and pictures we crawled over 1 month from Instagram. Our analysis reveals several insights on Instagram which were never studied before: 1) its social network properties are quite different from other popular social media like Twitter and Flickr, 2) people typically post once a week, and 3) people like to share their locations with friends. To the best of our knowledge, this is the first in-depth analysis of user activities, demographics, social network structure and user-generated content on Instagram.


title: Text Document Topical Recursive Clustering and Automatic Labeling of a Hierarchy of Document Clusters

abstract: The overwhelming amount of textual documents available nowadays highlights the need for information organization and discovery. Effectively organizing documents into a hierarchy of topics and subtopics makes it easier for users to browse the documents. This paper borrows community mining from social network analysis to generate a hierarchy of topically coherent document clusters. It focuses on giving the document clusters descriptive labels. We propose to use betweenness centrality measure in networks of co-occurring terms to label the clusters. We also incorporate keyphrase extraction and automatic titling in cluster labeling. The results show that the cluster labeling method utilizing KEA to extract keyphrases from the documents generates the best labels overall comparing to other methods and baselines.


title: Autonomous NAT Traversal

abstract: Traditional NAT traversal methods require the help of a third party for signalling. This paper investigates a new autonomous method for establishing connections to peers behind NAT. The proposed method for autonomous NAT traversal uses fake ICMP messages to initially contact the NATed peer. This paper presents how the method is supposed to work in theory, discusses some possible variations, introduces various concrete implementations of the proposed approach and evaluates empirical results of a measurement study designed to evaluate the efficacy of the idea in practice.


title: Shielding Technique for Planar Matrix Transformers to Suppress Common-Mode EMI Noise and Improve Efficiency

abstract: Isolated high output current dc/dc converters are critical for power supplies in information technology industries. LLC converters with a matrix transformer are suitable for these applications due to their high efficiency and high power density. By pushing switching frequency up to MHz with GaN devices, planar matrix transformers with printed circuit board (PCB) windings are advantageous over conventional transformer designs, but the large interwinding capacitance of the PCB windings provides a critical coupling path for common-mode (CM) noise. In order to analyze the CM noise, the interwinding capacitance of the PCB windings is analyzed, and a related CM noise model is provided in this paper. To suppress the CM noise, shielding layers can automatically be inserted into the PCB windings between the primary and secondary windings in the fabrication process. This paper proposes a novel shielding technique, in which half of the shielding serve as the primary winding. The proposed shielding technique not only suppresses CM noise but improves the efficiency as well. The proposed shielding is verified by experiments on 1-MHz 400 V/12 V 800-W LLC converters with a peak efficiency of 97.7% and a power density of 900 W/in3. The proposed shielding reduces CM noise by 30 dB and improves full-load efficiency by 0.2%.


title: Educational Data Mining / Learning Analytics: Methods, Tasks and Current Trends

abstract: The 1 st international conference on "Educational Data Mining" (EDM) took place in Montreal in 2008 while the 1 st international conference on "Learning Analytics and Knowledge" (LAK) took place in Banff in 2011. Since then the fields have grown and established themselves with an annual international conference, a journal and an association each, and gradually increase their overlapping. This paper begins with some considerations on big data in education. Then the principal analysis methods used with educational data are reviewed and are illustrated with some of the tasks they solve. Current emerging trends are presented. Analysis of educational data on a routine basis to understand learning and teaching better and to improve them is not a reality yet. The paper concludes with challenges on the way.


title: MatrixWave: Visual Comparison of Event Sequence Data

abstract: Event sequence data analysis is common in many domains, including web and software development, transportation, and medical care. Few have investigated visualization techniques for comparative analysis of multiple event sequence datasets. Grounded in the real-world characteristics of web clickstream data, we explore visualization techniques for comparison of two clickstream datasets collected on different days or from users with different demographics. Through iterative design with web analysts, we designed MatrixWave, a matrix-based representation that allows analysts to get an overview of differences in traffic patterns and interactively explore paths through the website. We use color to encode differences and size to offer context over traffic volume. User feedback on MatrixWave is positive. Our study participants made fewer errors with MatrixWave and preferred it over the more familiar Sankey diagram.


title: Correctly rounded floating-point division for DSP-enabled FPGAs

abstract: Floating-point division is a very costly operation in FPGA designs. High-frequency implementations of the classic digit-recurrence algorithms for division have long latencies (of the order of the number fraction bits) and consume large amounts of logic. Additionally, these implementations require important routing resources, making timing closure difficult in complete designs. In this paper we present two multiplier-based architectures for division which make efficient use of the DSP resources in recent Altera FPGAs. By balancing resource usage between logic, memory and DSP blocks, the presented architectures maintain high frequencies is full designs. Additionally, compared to classical algorithms, the proposed architectures have significantly lower latencies. The architectures target faithfully rounded results, similar to most elementary functions implementations for FPGAs but can also be transformed into correctly rounded architectures with a small overhead. The presented architectures are built using the Altera DSP Builder Advanced framework and will be part of the default blockset.


title: Circuit-wide Transcriptional Profiling Reveals Brain Region-Specific Gene Networks Regulating Depression Susceptibility

abstract: Depression is a complex, heterogeneous disorder and a leading contributor to the global burden of disease. Most previous research has focused on individual brain regions and genes contributing to depression. However, emerging evidence in humans and animal models suggests that dysregulated circuit function and gene expression across multiple brain regions drive depressive phenotypes. Here, we performed RNA sequencing on four brain regions from control animals and those susceptible or resilient to chronic social defeat stress at multiple time points. We employed an integrative network biology approach to identify transcriptional networks and key driver genes that regulate susceptibility to depressive-like symptoms. Further, we validated in vivo several key drivers and their associated transcriptional networks that regulate depression susceptibility and confirmed their functional significance at the levels of gene transcription, synaptic regulation, and behavior. Our study reveals novel transcriptional networks that control stress susceptibility and offers fundamentally new leads for antidepressant drug discovery.


title: Occlusion and Abandoned Object Detection for Surveillance Applications

abstract: Object detection is an important step in any video analysis. Difficulties of the object detection are finding hidden objects and finding unrecognized objects. Although many algorithms have been developed to avoid them as outliers, occlusion boundaries could potentially provide useful information about the scene’s structure and composition. A novel framework for blob based occluded object detection is proposed. A technique that can be used to detect occlusion is presented. It detects and tracks the occluded objects in video sequences captured by a fixed camera in crowded environment with occlusions. Initially the background subtraction is modeled by a Mixture of Gaussians technique (MOG). Pedestrians are detected using the pedestrian detector by computing the Histogram of Oriented Gradients descriptors (HOG), using a linear Support Vector Machine (SVM) as the classifier. In this work, a recognition and tracking system is built to detect the abandoned objects in the public transportation area such as train stations, airports etc. Several experiments were conducted to demonstrate the effectiveness of the proposed approach. The results show the robustness and effectiveness of the proposed method. Keyword: Occlusion, Histograms of Oriented Gradients descriptors, Support Vector Machine, mixture of Gaussians techniques, Blob, abandoned object.


title: Back-lobe reduction of printed folded dipole reflector antenna for Wi-Fi applications

abstract: In this work we investigate the back-lobe reduction of a sectoral antenna. The antenna is fed by a coaxial cable and presents a good impedance matching in the 2.4–2.48 GHz WI-FI band. It has 9 dBi of gain in the main lobe direction and most importantly, presents a very high front to back ratio (over 40 dB). The antenna is composed of a printed folded dipole coupled with a metallic plate placed behind the driven element at a distance of tenth the wavelength. The system is low cost and easy to manufacture.


title: Novel Switched Reluctance Machine Configuration With Higher Number of Rotor Poles Than Stator Poles: Concept to Implementation

abstract: There is a great demand for efficient, quiet, reliable, and cost-effective motor drives for propulsion systems in hybrid and plug-in hybrid electric vehicles. Owing to a rigid structure and the absence of magnetic source on the rotor, a switched reluctance machine (SRM) is inherently robust and cost effective. In spite of these advantages, several challenges in the control of this machine remain an issue, including high levels of torque ripple, acoustic noise, and a relatively low torque density. This paper presents a new family of SRMs which have higher number of rotor poles than stator poles. Using a newly defined pole design formula, several novel combinations of the stator-rotor poles have been proposed. From the simulation and experimental analysis of a prototype 6/10 configuration, it has been observed that this machine produces higher torque per unit volume and comparable torque ripple when compared to a conventional 6/4 SRM with similar number of phases and constraints in volume. The results presented in this paper make this family of machines a strong contender for survivable high-performance applications for automotive propulsion systems. The simulation and experimental results for the prototype 6/10 configuration have been presented and compared to a conventional 6/4 design for verification.


Security Requirements Engineering (SRE) Framework for Cyber-Physical Systems (CPS): SRE for CPS.


title: A unified attribute-based access control model covering DAC, MAC and RBAC

abstract: Recently, there has been considerable interest in attribute based access control (ABAC) to overcome the limitations of the dominant access control models (i.e, discretionary-DAC, mandatory-MAC and role based-RBAC) while unifying their advantages. Although some proposals for ABAC have been published, and even implemented and standardized, there is no consensus on precisely what is meant by ABAC or the required features of ABAC. There is no widely accepted ABAC model as there are for DAC, MAC and RBAC. This paper takes a step towards this end by constructing an ABAC model that has "just sufficient" features to be "easily and naturally" configured to do DAC, MAC and RBAC. For this purpose we understand DAC to mean owner-controlled access control lists, MAC to mean lattice-based access control with tranquility and RBAC to mean flat and hierarchical RBAC. Our central contribution is to take a first cut at establishing formal connections between the three successful classical models and desired ABAC models.


title: Reversing Demosaicking and Compression in Color Filter Array Images

abstract: An alternative processing chain of digital still camera (DSC) has been proposed by moving the compression process before demosaicking to avoid additional data redundancy introduced by the demosaicking process. Recent empirical studies have shown that the alternative processing chain can actually out-perform the conventional one in terms of image quality at low compression ratios. To provide theoretically sound basis for such conclusion, we propose analytical models for the reconstruction errors of the two processing chains. The models developed confirm the results of existing empirical studies and also allows performance predictions for more advanced compression and demosaicking methods, thus providing important cues for development in this area.


title: Low-complexity Bayer-pattern video compression using distributed video coding

abstract: Most consumer digital color cameras capture video using a single chip. Single chip cameras do not capture ::: RGB triples for every pixel, but a subsampled version with only one color component per pixel (e.g. Bayer ::: pattern). Conventionally, a full resolution video is constructed from the Bayer pattern by demosaicing before ::: being converted to YUV domain for compression. In order to lower the encoding complexity, we propose in ::: this work a novel color space conversion in the pre-processing step. Compared to the conventional method, ::: the proposed scheme reduces the encoding complexity almost by half. Moreover, it improves the reconstructed ::: video quality by up to 1.5 dB in CPSNR, when H.264/AVC is used for compression. To further lower the ::: encoding complexity, we additionally use our Wyner-Ziv video coder for compression. Again, we observe in our ::: experiments a similar gain of the proposed scheme over the conventional one.


title: Load Shifting in the Smart Grid: To Participate or Not?

abstract: Demand-side management (DSM) has emerged as an important smart grid feature that allows utility companies to maintain desirable grid loads. However, the success of DSM is contingent on active customer participation. Indeed, most existing DSM studies are based on game-theoretic models that assume customers will act rationally and will voluntarily participate in DSM. In contrast, in this paper, the impact of customers’ subjective behavior on each other’s DSM decisions is explicitly accounted for. In particular, a noncooperative game is formulated between grid customers in which each customer can decide on whether to participate in DSM or not. In this game, customers seek to minimize a cost function that reflects their total payment for electricity. Unlike classical game-theoretic DSM studies, which assume that customers are rational in their decision-making, a novel approach is proposed based on the framework of prospect theory (PT) to explicitly incorporate the impact of customer behavior on DSM decisions. To solve the proposed game under both conventional game theory and PT, a new algorithm based on fictitious play is proposed using which the game will reach an ${\epsilon }$ -mixed Nash equilibrium. Simulation results are provided to assess the impact of customer behavior on DSM. In particular, the overall participation level and grid load can depend significantly on the rationality level of the players and their risk aversion tendencies.


Resampling-Based Multiple Testing for Microarray Data Analysis


title: Uncovering individual and collective human dynamics from mobile phone records

abstract: Novel aspects of human dynamics and social interactions are investigated by means of mobile phone data. Using extensive phone records resolved in both time and space, we study the mean collective behavior at large scales and focus on the occurrence of anomalous events. We discuss how these spatiotemporal anomalies can be described using standard percolation theory tools. We also investigate patterns of calling activity at the individual level and show that the interevent time of consecutive calls is heavy-tailed. This finding, which has implications for dynamics of spreading phenomena in social networks, agrees with results previously reported on other human activities.


title: Image-Based Atmospheric Corrections - Revisited and Improved

abstract: A major benefit of multitemporal, remotely sensed images is their applicability to change detection over time. Because of concerns about global and environmental change, these data are becoming increasingly more important. However, to maximize the usefulness of the data from a multitemporal point of view, an easy-to-use, cost-effective, and accurate radiometric calibration and correction procedure is needed. The atmosphere effects the radiance received at the satellite by scattering, absorbing, and refracting light; corrections for these effects, as well as for sensor gains and offsets, solar irradiance, and solar zenith angles, must be included in radiometric correction procedures that are used to convert satellite-recorded digital counts to ground reflectances. To generate acceptable radiometric correction results, a model is required that typically uses in-situ atmospheric measurements and radiative transfer code (RTC) to correct for atmospheric effects. The main disadvantage of this type of correction procedure is that it requires in-situ field measurements during each satellite overflight. This is unacceptable for many applications and is often impossible, as when using historical data or when working in very remote locations. The optimum radiometric correction procedure is one based solely on the digital image and requiring no in-situ field measurements during the satellite overflight. The darkobject subtraction (DOS) method, a strictly image-based technique, is an attempt to achieve this ideal procedure. However, the accuracy is not acceptable for many applications, mostly because it corrects only for the additive scattering effect and not for the multiplicative transmittance effect. This paper presents an entirely image-based procedure that expands on the ~10s model by including a simple multiplicative correction for the effect of atmospheric transmittance. Two straightforward methods to derive the multiplicative transmittance-correction coefficient are presented. The COSITZ) or COST method uses the cosine of the solar zenith angle, which, to a first order, is a good approximation of the atmospheric transmittance for the dates und sites used in this study. The default TAUS method uses the average of the transmittance values computed by using in-situ atmospheric field measurements made during seven different satellite overflights. Published and unpublished data made available for this study by Moran et al. (1992) are used, and my model results are compared with their results. The corrections generated by the entirely image-based COST model are as accurate as those generated by the models that used in-situ atmospheric field measurements and RTC software.


title: Machine Learning Methods for Attack Detection in the Smart Grid

abstract: Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.


title: Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics

abstract: Effectively measuring the usability of any product requires choosing the right metric, applying it, and effectively using the information it reveals. Measuring the User Experience provides the first single source of practical information to enable usability professionals and product developers to do just that. Authors Tullis and Albert organize dozens of metrics into six categories: performance, issues-based, self-reported, web navigation, derived, and behavioral/physiological. They explore each metric, considering best methods for collecting, analyzing, and presenting the data. They provide step-by-step guidance for measuring the usability of any type of product using any type of technology. . Presents criteria for selecting the most appropriate metric for every case. Takes a product and technology neutral approach . Presents in-depth case studies to show how organizations have successfully used the metrics and the information they revealed


Complex Word Identification: Convolutional Neural Network vs. Feature Engineering


title: Y-Net: A deep Convolutional Neural Network for Polyp Detection

abstract: Colorectal polyps are important precursors to colon cancer, the third most common cause of cancer mortality for both men and women. It is a disease where early detection is of crucial importance. Colonoscopy is commonly used for early detection of cancer and precancerous pathology. It is a demanding procedure requiring significant amount of time from specialized physicians and nurses, in addition to a significant miss-rates of polyps by specialists. Automated polyp detection in colonoscopy videos has been demonstrated to be a promising way to handle this problem. {However, polyps detection is a challenging problem due to the availability of limited amount of training data and large appearance variations of polyps. To handle this problem, we propose a novel deep learning method Y-Net that consists of two encoder networks with a decoder network. Our proposed Y-Net method} relies on efficient use of pre-trained and un-trained models with novel sum-skip-concatenation operations. Each of the encoders are trained with encoder specific learning rate along the decoder. Compared with the previous methods employing hand-crafted features or 2-D/3-D convolutional neural network, our approach outperforms state-of-the-art methods for polyp detection with 7.3% F1-score and 13% recall improvement.


Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning


title: Deep Feature Flow for Video Recognition

abstract: Deep convolutional neutral networks have achieved great success on image recognition tasks. Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to videos as per-frame evaluation is too slow and unaffordable. We present deep feature flow, a fast and accurate framework for video recognition. It runs the expensive convolutional sub-network only on sparse key frames and propagates their deep feature maps to other frames via a flow field. It achieves significant speedup as flow computation is relatively fast. The end-to-end training of the whole architecture significantly boosts the recognition accuracy. Deep feature flow is flexible and general. It is validated on two recent large scale video datasets. It makes a large step towards practical video recognition. Code would be released.


title: The LDBC Social Network Benchmark: Interactive Workload

abstract: The Linked Data Benchmark Council (LDBC) is now two years underway and has gathered strong industrial participation for its mission to establish benchmarks, and benchmarking practices for evaluating graph data management systems. The LDBC introduced a new choke-point driven methodology for developing benchmark workloads, which combines user input with input from expert systems architects, which we outline. This paper describes the LDBC Social Network Benchmark (SNB), and presents database benchmarking innovation in terms of graph query functionality tested, correlated graph generation techniques, as well as a scalable benchmark driver on a workload with complex graph dependencies. SNB has three query workloads under development: Interactive, Business Intelligence, and Graph Algorithms. We describe the SNB Interactive Workload in detail and illustrate the workload with some early results, as well as the goals for the two other workloads.


title: Feature Forest Models for Probabilistic HPSG Parsing

abstract: Probabilistic modeling of lexicalized grammars is difficult because these grammars exploit complicated data structures, such as typed feature structures. This prevents us from applying common methods of probabilistic modeling in which a complete structure is divided into sub-structures under the assumption of statistical independence among sub-structures. For example, part-of-speech tagging of a sentence is decomposed into tagging of each word, and CFG parsing is split into applications of CFG rules. These methods have relied on the structure of the target problem, namely lattices or trees, and cannot be applied to graph structures including typed feature structures. ::: ::: This article proposes the feature forest model as a solution to the problem of probabilistic modeling of complex data structures including typed feature structures. The feature forest model provides a method for probabilistic modeling without the independence assumption when probabilistic events are represented with feature forests. Feature forests are generic data structures that represent ambiguous trees in a packed forest structure. Feature forest models are maximum entropy models defined over feature forests. A dynamic programming algorithm is proposed for maximum entropy estimation without unpacking feature forests. Thus probabilistic modeling of any data structures is possible when they are represented by feature forests. ::: ::: This article also describes methods for representing HPSG syntactic structures and predicate-argument structures with feature forests. Hence, we describe a complete strategy for developing probabilistic models for HPSG parsing. The effectiveness of the proposed methods is empirically evaluated through parsing experiments on the Penn Treebank, and the promise of applicability to parsing of real-world sentences is discussed.


title: RGB-D Scene Labeling with Long Short-Term Memorized Fusion Model

abstract: Semantic labeling of RGB-D scenes is crucial to many intelligent applications including perceptual robotics. It generates pixelwise and fine-grained label maps from simultaneously sensed photometric (RGB) and depth channels. This paper addresses this problem by i) developing a novel Long Short-Term Memorized Fusion (LSTM-F) Model that captures and fuses contextual information from multiple channels of photometric and depth data, and ii) incorporating this model into deep convolutional neural networks (CNNs) for end-to-end training. Specifically, global contexts in photometric and depth channels are, respectively, captured by stacking several convolutional layers and a long short-term memory layer; the memory layer encodes both short-range and long-range spatial dependencies in an image along the vertical direction. Another long short-term memorized fusion layer is set up to integrate the contexts along the vertical direction from different channels, and perform bi-directional propagation of the fused vertical contexts along the horizontal direction to obtain true 2D global contexts. At last, the fused contextual representation is concatenated with the convolutional features extracted from the photometric channels in order to improve the accuracy of fine-scale semantic labeling. Our proposed model has set a new state of the art, i.e., 48.1% average class accuracy over 37 categories 11.8% improvement), on the large-scale SUNRGBD dataset.1


title: Electric Vehicle Charging Station Using a Neutral Point Clamped Converter With Bipolar DC Bus

abstract: This paper proposes a novel architecture for plug-in electric vehicles (PEVs) dc charging station at the megawatt level, through the use of a grid-tied neutral point clamped (NPC) converter. The proposed bipolar dc structure reduces the step-down effort on the dc–dc fast chargers. In addition, this paper proposes a balancing mechanism that allows handling any difference on the dc loads while keeping the midpoint voltage accurately regulated. By formally defining the unbalance operation limit, the proposed control scheme is able to provide complementary balancing capabilities by the use of an additional NPC leg acting as a bidirectional dc–dc stage, simulating the minimal load condition and allowing the modulator to keep the control on the dc voltages under any load scenario. The proposed solution enables fast charging for PEVs concentrating several charging units into a central grid-tied converter. In this paper, simulation and experimental results are presented to validate the proposed charging station architecture.


title: Adaptive Personalized Travel Information Systems: A Bayesian Method to Learn Users' Personal Preferences in Multimodal Transport Networks

abstract: Providing personalized advice is an important objective in the development of advanced traveler information systems. In this paper, a Bayesian method to incorporate learning of users' personal travel preferences in a multimodal routing system is proposed. The system learns preference parameters incrementally based on travel choices a user makes. Existing Bayesian inference methods require too much computation time for the learning problem that we are dealing with here. Therefore, an approximation method is developed, which is based on sequential processing of preference parameters and systematic sampling of the parameter space. The data of repetitive travel choices of a representative sample of individuals are used to test the system. The results indicate that the system rapidly adapts to a user and learns his or her preferences effectively. The efficiency of the algorithm allows the system to handle realistically sized learning problems with short response times even when many users are to be simultaneously processed. It is therefore concluded that the approach is feasible; problems for future research are identified.


title: Top-K Nearest Keyword Search on Large Graphs

abstract: It is quite common for networks emerging nowadays to have labels or textual contents on the nodes. On such networks, we study the problem of top-k nearest keyword (k-NK) search. In a network G modeled as an undirected graph, each node is attached with zero or more keywords, and each edge is assigned with a weight measuring its length. Given a query node q in G and a keyword λ, a k-NK query seeks k nodes which contain λ and are nearest to q. k-NK is not only useful as a stand-alone query but also as a building block for tackling complex graph pattern matching problems. ::: ::: The key to an accurate k-NK result is a precise shortest distance estimation in a graph. Based on the latest distance oracle technique, we build a shortest path tree for a distance oracle and use the tree distance as a more accurate estimation. With such representation, the original k-NK query on a graph can be reduced to answering the query on a set of trees and then assembling the results obtained from the trees. We propose two efficient algorithms to report the exact k-NK result on a tree. One is query time optimized for a scenario when a small number of result nodes are of interest to users. The other handles k-NK queries for an arbitrarily large k efficiently. In obtaining a k-NK result on a graph from that on trees, a global storage technique is proposed to further reduce the index size and the query time. Extensive experimental results conform with our theoretical findings, and demonstrate the effectiveness and efficiency of our k-NK algorithms on large real graphs.


title: Choosing work group members: Balancing similarity, competence, and familiarity

abstract: Abstract This study explores one of the contributors to group composition—the basis on which people choose others with whom they want to work. We use a combined model to explore individual attributes, relational attributes, and previous structural ties as determinants of work partner choice. Four years of data from participants in 33 small project groups were collected, some of which reflects individual participant characteristics and some of which is social network data measuring the previous relationship between two participants. Our results suggest that when selecting future group members people are biased toward others of the same race, others who have a reputation for being competent and hard working, and others with whom they have developed strong working relationships in the past. These results suggest that people strive for predictability when choosing future work group members.


title: Sidelobe suppression in ultrasound imaging using dual apodization with cross-correlation

abstract: This paper introduces a novel sidelobe and clutter suppression method in ultrasound imaging called dual apodization with cross-correlation or DAX. DAX dramatically improves the contrast-to-noise ratio (CNR) allowing for easier visualization of anechoic cysts and blood vessels. This technique uses dual apodization or weighting strategies that are effective in removing or minimizing clutter and efficient in terms of computational load and hardware/software needs. This dual apodization allows us to determine the amount of mainlobe versus clutter contribution in a signal by cross-correlating RF data acquired from 2 apodization functions. Simulation results using a 128 element 5 MHz linear array show an improvement in CNR of 139% compared with standard beamformed data with uniform apodization in a 3 mm diameter anechoic cylindrical cyst. Experimental CNR using a tissue-mimicking phantom with the same sized cyst shows an improvement of 123% in a DAX processed image.


One step back for a leap forward: toward operational measurements of elements at risk


title: FixMiner: Mining Relevant Fix Patterns for Automated Program Repair

abstract: Patching is a common activity in software development. It is generally performed on a source code base to address bugs or add new functionalities. In this context, given the recurrence of bugs across projects, the associated similar patches can be leveraged to extract generic fix actions. While the literature includes various approaches leveraging similarity among patches to guide program repair, these approaches often do not yield fix patterns that are tractable and reusable as actionable input to APR systems. In this paper, we propose a systematic and automated approach to mining relevant and actionable fix patterns based on an iterative clustering strategy applied to atomic changes within patches. The goal of FixMiner is thus to infer separate and reusable fix patterns that can be leveraged in other patch generation systems. Our technique, FixMiner, leverages Rich Edit Script which is a specialized tree structure of the edit scripts that captures the AST-level context of the code changes. FixMiner uses different tree representations of Rich Edit Scripts for each round of clustering to identify similar changes. These are abstract syntax trees, edit actions trees, and code context trees. We have evaluated FixMiner on thousands of software patches collected from open source projects. Preliminary results show that we are able to mine accurate patterns, efficiently exploiting change information in Rich Edit Scripts. We further integrated the mined patterns to an automated program repair prototype, PARFixMiner, with which we are able to correctly fix 26 bugs of the Defects4J benchmark. Beyond this quantitative performance, we show that the mined fix patterns are sufficiently relevant to produce patches with a high probability of correctness: 81% of PARFixMiner's generated plausible patches are correct.


title: Design of permanent magnet assisted synchronous reluctance motor with external rotor architecture

abstract: This paper proposes an external rotor Permanent Magnet assisted Synchronous Reluctance Motor (PMa-SynRM). Due to the high material cost of PM machine, there was a continuous effort to reduce the amount of permanent magnets (PM) in manufacturing an electric machine. PMa-SynRM has reduced great amount of PM usage by hybridizing the architectures between Synchronous Reluctance Motor and Permanent Magnet Motor. To further reduce the PM usage and to further increase of torque density, design of PMa-SynRM has been investigated through external rotor architecture. With best of author's knowledge, external rotor has not been properly researched even with great advantage and design flexibility with PMa-SynRM architecture. Analytical comparison between two different types of motor (internal and external) has been investigated to prove the superior design flexibility and performance of proposed method. Performance characteristics such as average torque developed and variation of torque with respect to variation in speed have been analyzed in finite element atmosphere (FEA) to validate the proposed design. A lower torque ripple model with distributed winding structure has been modeled and its FEA simulation results for developed torque have been compared with the concentrated type external rotor PMa-SynRM model.


title: Ridge Preservation with Modified “Socket-Shield” Technique: A Methodological Case Series

abstract: After tooth extraction, the alveolar bone undergoes a remodeling process, which leads to horizontal and vertical bone loss. These resorption processes complicate dental rehabilitation, particularly in connection with implants. Various methods of guided bone regeneration (GBR) have been described to retain the original dimension of the bone after extraction. Most procedures use filler materials and membranes to support the buccal plate and soft tissue, to stabilize the coagulum and to prevent epithelial ingrowth. It has also been suggested that resorption of the buccal bundle bone can be avoided by leaving a buccal root segment (socket shield technique) in place, because the biological integrity of the buccal periodontium (bundle bone) remains untouched. This method has also been described in connection with immediate implant placement. The present case report describes three consecutive cases in which a modified method was applied as part of a delayed implantation. The latter was carried out after six months, and during re-entry the new bone formation in the alveolar bone and the residual ridge was clinically evaluated as proof of principle. It was demonstrated that the bone was clinically preserved with this method. Possibilities and limitations are discussed and directions for future research are disclosed.


title: Pathway selection mechanism of a screw drive in-pipe robot in T-branches

abstract: Pipelines are important infrastructures in today's society. To avoid leakages in these pipelines, efficient robotic pipe inspections are required, and to this date, various types of in-pipe robots have been developed. Some of them can select their path at a T-branch at the expense of additional actuators. However, fewer actuators are better in terms of size reduction, energy conservation, production cost, and maintenance. To reduce the number of actuators, a screw drive mechanism with only one actuator has been developed for propelling an in-pipe robot through straight pipes and elbow pipes. Based on this screw drive mechanism, in this paper, we develop a novel robot that uses only two motors and can select pathways. The robot has three locomotion modes: screw driving, steering, and rolling modes. These modes enable the robot to navigate not only through straight pipes but also elbow pipes and T-branches. We performed experiments to verify the validity of the proposed mechanism.


title: Manifold Regularized Multitask Learning for Semi-Supervised Multilabel Image Classification

abstract: It is a significant challenge to classify images with multiple labels by using only a small number of labeled samples. One option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution. However, such an approach does not perform well in practice when images from multiple concepts are represented by high-dimensional visual features. Thus, manifold regularization is insufficient to control the model complexity. In this paper, we propose a manifold regularized multitask learning (MRMTL) algorithm. MRMTL learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks. It effectively controls the model complexity because different tasks limit one another's search volume, and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold. We conduct extensive experiments, on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes, by comparing MRMTL with popular image classification algorithms. The results suggest that MRMTL is effective for image classification.


title: Natural products in crop protection

abstract: Abstract The tremendous increase in crop yields associated with the ‘green’ revolution has been possible in part by the discovery and utilization of chemicals for pest control. However, concerns over the potential impact of pesticides on human health and the environment has led to the introduction of new pesticide registration procedures, such as the Food Quality Protection Act in the United States. These new regulations have reduced the number of synthetic pesticides available in agriculture. Therefore, the current paradigm of relying almost exclusively on chemicals for pest control may need to be reconsidered. New pesticides, including natural product-based pesticides are being discovered and developed to replace the compounds lost due to the new registration requirements. This review covers the historical use of natural products in agricultural practices, the impact of natural products on the development of new pesticides, and the future prospects for natural products-based pest management.


Modeling , Simulation and Fabrication of a Balancing Robot


title: An Improved Bound for Minimizing the Total Weighted Completion Time of Coflows in Datacenters

abstract: In data-parallel computing frameworks, intermediate parallel data is often produced at various stages which needs to be transferred among servers in the datacenter network (e.g. the shuffle phase in MapReduce). A stage often cannot start or be completed unless all the required data pieces from the preceding stage are received. \emph{Coflow} is a recently proposed networking abstraction to capture such communication patterns. We consider the problem of efficiently scheduling coflows with release dates in a shared datacenter network so as to minimize the total weighted completion time of coflows. ::: Several heuristics have been proposed recently to address this problem, as well as a few polynomial-time approximation algorithms with provable performance guarantees. Our main result in this paper is a polynomial-time deterministic algorithm that improves the prior known results. Specifically, we propose a deterministic algorithm with approximation ratio of $5$, which improves the prior best known ratio of $12$. For the special case when all coflows are released at time zero, our deterministic algorithm obtains approximation ratio of $4$ which improves the prior best known ratio of $8$. The key ingredient of our approach is an improved linear program formulation for sorting the coflows followed by a simple list scheduling policy. Extensive simulation results, using both synthetic and real traffic traces, are presented that verify the performance of our algorithm and show improvement over the prior approaches.


title: SMART-ITEM: IoT-enabled smart living

abstract: The main goal of this proposed project is to harness the emerging IoT technology to empower elderly population to self-manage their own health, stay active, healthy, and independent as long as possible within a smart and secured living environment. An integrated open-sourced IoT ecosystem will be developed. It will encompass the entire data lifecycle which involves the following processes: data acquisition, data transportation; data integration, processing, manipulation and computation; visualisation; data intelligence and exploitation; data sharing; data storage. This innovative cloud-based IoT ecosystem will provide a one-stop shop for integrated smart IoT-enabled services to support older people (greater or equal to 65 years old) who live alone at home (or care homes). Another innovation of this system is the design and implementation of an integrated IoT gateway for wellbeing wearable and home automation system sensors with varying communication protocols. The SMART-ITEM system and services will appropriately address the following (i) smart health and care; (ii) smart quality of life; (iii) SMART-ITEM social community. The development of the system will be based on the User Centred Design methodology so as to ensure active user engagement throughout the entire project lifecycle and necessary standards as well as compliances will be adhered to (e.g. security, trust and privacy) in order to enhance user acceptance.


title: A comprehensive security architecture for SDN

abstract: SDN enables the administrators to configure network resources very quickly and to adjust network-wide traffic flow to meet changing needs dynamically. However, there are some challenges for implementing a full-scale carrier SDN. One of the most important challenges is SDN security, which is beginning to receive attention. With new SDN architecture, some security threats are common to traditional networking, but the profile of these threats (including their likelihood and impact and hence their overall risk level) changes. Moreover, there are some new security challenges such as bypassing predefined mandatory policies by overwriting flow entries and data eavesdropping by inserting fraudulent flow entries. This paper is to design open-flow specific security solutions and propose a comprehensive security architecture to provide security services such as enforcing mandatory network policy correctly and receiving network policy securely for SDN in order to solve these common security issues and new security challenges. It can also help the developers to implement security functions to provide security services when developing the SDN controller.


title: Pharmacological Treatment of Bipolar Disorder with Comorbid Alcohol Use Disorder

abstract: Bipolar disorder (BD) spectrum and alcohol use disorders (AUDs) commonly occur together. Comorbidity between the two conditions predisposes patients to elevated risks of adverse outcomes, including hospitalization and suicide, compared with either condition alone. Despite the consistent relationship observed between BD and AUD, the underlying cause remains incompletely characterized. Few trials conducted have been able to identify promising interventions for patients with these disease states. The antipsychotic quetiapine has been evaluated most commonly as a therapeutic agent for patients with BD and AUD followed by naltrexone and acamprosate. Randomized controlled trials of quetiapine have consistently reported a lack of efficacy for the treatment of patients with BD and AUD. Trials of acamprosate have also been negative but small in size. Results of the sole randomized controlled trial of naltrexone have found large treatment effect sizes, but no statistically significant difference between treatment groups. Other agents including the antipsychotic aripiprazole, mood stabilizing agents including lamotrigine, lithium, and divalproex, and the antiepileptic agent topiramate have also been evaluated for the treatment of BD and AUD with mixed findings. The lone statistically significant treatment effect was observed in a randomized, placebo-controlled trial of divalproex added on to lithium which demonstrated a reduction in alcohol use. This review summarizes the available clinical evidence and current guideline recommendations for the treatment of comorbid BD and AUD, and provides discussion and recommendations based on the current literature.


title: Visual analytics and visualization for android security risk

abstract: Abstract Nowadays, smart devices enable most of the personal tasks such as banking, mailing, and paperwork that people do on their personal computers (PCs). For this reason, personal information on smart devices has become a good target for malware. Especially, Malware targeted at personal information stored on mobile are hard to detect and risks from usage patterns are even more difficult. The security risk tends to be underestimated by device users and causes critical problems especially on personal information leakage and costing money unconsciously. Malware is also often activated unsuspectedly. Therefore, a means for easy recognition of the problems and the smartphone usage is necessary. In this paper, we present a visual analytics system (VA) for Android security risk lifelog using app permissions to recognize the risk. Our system stores the security-related personal information on the smartphone device and utilizes it to analyze the security risk lifelog. For the risk analysis, we define security risk scores based on the app and permission statistics. Then, several linked visualizations are designed to present the risk lifelog. We have collected the security lifelog data from eight Android smartphone users and analyzed their security matters. Our PVA system enables Android smartphone users to observe, mitigate the security risk, and eventually understand how Android security risk affects their lives. Moreover, we present a user study to evaluate the PVA system with user feedback.


CYBER-PHYSICAL MANUFACTURING SYSTEMS – MANUFACTURIN G METROLOGY ASPECTS


title: Optimized 4-coil inductor system arrangement for induction heating appliances

abstract: In this paper, the efficiency of the induction system composed of 4 small coils with a given connection among them for cooking applications is analyzed. The 4-coil configuration has several advantages with respect to the single-coil induction system, for instance, higher flexibility and better heat distribution in the pan. Different configurations can be arranged according to series or parallel connections of the coils and the polarity of the currents. However, the selected configuration connection has a direct impact on the efficiency of the power transference into the pot. Therefore, in this work the efficiency of the different 4-coil configurations is analyzed. The analysis is carried out in terms of equivalent impedances. The experimental test based on impedance measurements is performed to verify the preceding analysis.


title: AEGLE's Cloud Infrastructure for Resource Monitoring and Containerized Accelerated Analytics

abstract: This paper presents the cloud infrastructure of the AEGLE project, that targets to integrate cloud technologies together with heterogeneous reconfigurable computing in large scale healthcare systems for Big Bio-Data analytics. AEGLEs engineering concept brings together the hot big-data engines with emerging acceleration technologies, putting the basis for personalized and integrated health-care services, while also promoting related research activities. We introduce the design of AEGLE’s accelerated infrastructure along with the corresponding software and hardware acceleration stacks to support various big data analytics workloads showing that through effective resource containerization AEGLE’s cloud infrastructure is able to support high heterogeneity regarding to storage types, execution engines, utilized tools and execution platforms. Special care is given to the integration of high performance accelerators within the overall software stack of AEGLE’s infrastructure, which enable efficient execution of analytics, up to 140× according to our preliminary evaluations, over pure software executions.


Effect of foliar application of nano chitosan NPK fertilizer on the chemical composition of wheat grains


title: Deep Learning of Invariant Features via Simulated Fixations in Video

abstract: We apply salient feature detection and tracking in videos to simulate fixations and smooth pursuit in human vision. With tracked sequences as input, a hierarchical network of modules learns invariant features using a temporal slowness constraint. The network encodes invariance which are increasingly complex with hierarchy. Although learned from videos, our features are spatial instead of spatial-temporal, and well suited for extracting features from still images. We applied our features to four datasets (COIL-100, Caltech 101, STL-10, PubFig), and observe a consistent improvement of 4% to 5% in classification accuracy. With this approach, we achieve state-of-the-art recognition accuracy 61% on STL-10 dataset.


title: Sentiment Analysis Using Convolutional Neural Network

abstract: Sentiment analysis of text content is important for many natural language processing tasks. Especially, as the development of the social media, there is a big need in dig meaningful information from the big data on Internet through the sentiment analysis. Inspired by the successes of deep learning, we are interested in handling the sentiment analysis task using deep learning models. In this paper, we propose a framework called Word2vec + Convolutional Neural Network (CNN). Firstly, we use the word2vec proposed by Google to compute vector representations of words, which will be the input for the CNN. The purpose of using word2vec is to gain the vector representation of word and reflect the distance of words. That will lead to initialize the parameters at a good point of CNN, which can efficiently improve the performance of the nets in this problem. Secondly, we design a suitable CNN architecture for the sentiment analysis task. We use 3 pairs of convolutional layers and pooling layers in this architecture. To the best of our knowledge, this is the first time that a 7-layers architecture model is applied using word2vec and CNN to analyze sentences' sentiment. We also use the Parametric Rectified Linear Unit (PReLU), Normalization and Dropout technology to improve the accuracy and generalizability of our model. We test our framework in a public dataset which is the corpus of movie review excerpts that includes fives labels: negative, somewhat negative, neural, somewhat positive and positive. Our network achieves test accuracy of 45.4% in this dataset, which is a better performance than some other neural network model like Recursive Neural Network (RNN) and Matrix-Vector Recursive Neural Network (MV-RNN).


title: LightNN: Filling the Gap between Conventional Deep Neural Networks and Binarized Networks

abstract: Application-specific integrated circuit (ASIC) implementations for Deep Neural Networks (DNNs) have been adopted in many systems because of their higher classification speed. However, although they may be characterized by better accuracy, larger DNNs require significant energy and area, thereby limiting their wide adoption. The energy consumption of DNNs is driven by both memory accesses and computation. Binarized Neural Networks (BNNs), as a trade-off between accuracy and energy consumption, can achieve great energy reduction, and have good accuracy for large DNNs due to its regularization effect. However, BNNs show poor accuracy when a smaller DNN configuration is adopted. In this paper, we propose a new DNN model, LightNN, which replaces the multiplications to one shift or a constrained number of shifts and adds. For a fixed DNN configuration, LightNNs have better accuracy at a slight energy increase than BNNs, yet are more energy efficient with only slightly less accuracy than conventional DNNs. Therefore, LightNNs provide more options for hardware designers to make trade-offs between accuracy and energy. Moreover, for large DNN configurations, LightNNs have a regularization effect, making them better in accuracy than conventional DNNs. These conclusions are verified by experiment using the MNIST and CIFAR-10 datasets for different DNN configurations.


title: Effect of Traffic Volume in Real-Time Disaster Evacuation Guidance Using Opportunistic Communications

abstract: When a major disaster occurs, it is highly possible that mobile communications won't work at all in disaster areas. Although some disaster tolerant applications are available in mobile phones, disaster evacuation guidance still has a problem in gathering and utilizing real-time information regarding the disaster areas under a situation where there is no mobile communication services. Especially, the information, such as positions of impassable roads by building collapse and dangerous areas by fire, is important to realize smooth guidance. To overcome this problem, we propose a real-time disaster evacuation guidance in which evacuees gather the information by themselves and share using opportunistic communications between their mobile phones. Our guidance enables the evacuees to know where the impassable roads and dangerous areas exist beforehand, which can contributes to considerable reduction of evacuation time. We conduct a performance evaluation of the evacuation time by simulating this scenario on some road map graphs. As a result, we find that the more extensive damage a disaster area has, the more prompt evacuation can be achieved with the aid of the opportunistic communications.