title: Hybrid Pulling/Pushing for I/O-Efficient Distributed and Iterative Graph Computing

abstract: Billion-node graphs are rapidly growing in size in many applications such as online social networks. Most graph algorithms generate a large number of messages during iterative computations. Vertex-centric distributed systems usually store graph data and message data on disk to improve scalability. Currently, these distributed systems with disk-resident data take a push-based approach to handle messages. This works well if few messages reside on disk. Otherwise, it is I/O-inefficient due to expensive random writes. By contrast, the existing memory-resident pull-based approach individually pulls messages for each vertex on demand. Although it can be used to avoid disk operations regarding messages, expensive I/O costs are incurred by random and frequent access to vertices. This paper proposes a hybrid solution to support switching between push and pull adaptively, to obtain optimal performance for distributed systems with disk-resident data in different scenarios. We first employ a new block-centric technique (b-pull) to improve the I/O-performance of pulling messages, although the iterative computation is vertex-centric. I/O costs of data accesses are shifted from the receiver side where messages are written/read by push to the sender side where graph data are read by b-pull. Graph data are organized by clustering vertices and edges to achieve high I/O-efficiency in b-pull. Second, we design a seamless switching mechanism and a prominent performance prediction method to guarantee efficiency when switching between push and b-pull. We conduct extensive performance studies to confirm the effectiveness of our proposals over existing up-to-date solutions using a broad spectrum of real-world graphs.


title: Physiological-emotional reactivity to nightmare-related imagery in trauma-exposed persons with chronic nightmares.

abstract: Script-driven imagery was used to assess nightmare imagery-evoked physiological–emotional reactivity (heart rate, skin conductance, facial electromyogram, subjective ratings) in trauma-exposed persons suffering from chronic nightmares. Goals were to determine the efficacy of nightmare imagery to evoke physiological–emotional reactivity, correlates (mental health, nightmare characteristics) of reactivity, and consequences (sleep and health problems) of reactivity. Nightmare imagery resulted in significant reactivity relative to control imagery. No mental health variable (posttraumatic stress disorder status, depressive symptoms, dissociation) or nightmare characteristic (months experienced, frequency, similarity to trauma) was associated with reactivity level. However, nightmare imagery-evoked autonomic responses were associated with greater sleep disturbance and reported health symptoms, even when nightmare frequency was controlled. These results suggest nightmare-related autonomic reactions may contribut...


title: Blocks and Fuel: Frameworks for deep learning

abstract: We introduce two Python frameworks to train neural networks on large datasets: Blocks and Fuel. Blocks is based on Theano, a linear algebra compiler with CUDA-support (Bastien et al., 2012; Bergstra et al., 2010). It facilitates the training of complex neural network models by providing parametrized Theano operations, attaching metadata to Theano’s symbolic computational graph, and providing an extensive set of utilities to assist training the networks, e.g. training algorithms, logging, monitoring, visualization, and serialization. Fuel provides a standard format for machine learning datasets. It allows the user to easily iterate over large datasets, performing many types of pre-processing on the fly.


title: Emotion work and psychological well-being: A review of the literature and some conceptual considerations

abstract: Abstract In this article, the state of the art of research on emotion work (emotional labor) is summarized with an emphasis on its effects on well-being. It starts with a definition of what emotional labor or emotion work is. Aspects of emotion work, such as automatic emotion regulation, surface acting, and deep acting, are discussed from an action theory point of view. Empirical studies so far show that emotion work has both positive and negative effects on health. Negative effects were found for emotional dissonance. Concepts related to the frequency of emotion expression and the requirement to be sensitive to the emotions of others had both positive and negative effects. Control and social support moderate relations between emotion work variables and burnout and job satisfaction. Moreover, there is empirical evidence that the cooccurrence of emotion work and organizational problems leads to high levels of burnout.


title: Open-Source Implementation of Monitoring and Controlling Services for EMS/SCADA Systems by Means of Web Services— IEC 61850 and IEC 61970 Standards

abstract: This paper presents an implementation of monitoring and controlling services for energy-management system/supervisory-control-and-data-acquisition systems, based on IEC 61970 and IEC 61850 international standards. The proposed solution is based on Web services technology that enables the typical advantages of an open-source framework, such as applications decoupling, applications integration, reduction of upgrading costs, reuse of existing infrastructures, and ease of development. Standardized interfaces are provided by the generic interface definition interfaces of IEC 61970. The Web server that has been developed has been tested in a simulation environment where the physical devices of a generic power system were simulated with databases whose data models were provided by the common information model of IEC 61970.


Eating Situations, Food Appropriateness, and Consumption


title: A Conceptual Framework for E-Learning in Developing Countries: A Critical Review of Research Challenges

abstract: This paper presents a critical review of research on challenges for e-learning with a particular focus on developing countries. A comprehensive literature review including 60 papers on e-learning c ...


title: Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms

abstract: The purpose of this project was to determine whether Contrast Limited Adaptive Histogram Equalization (CLAHE) improves detection of simulated spiculations in dense mammograms. Lines simulating the appearance of spiculations, a common marker of malignancy when visualized with masses, were embedded in dense mammograms digitized at 50 micron pixels, 12 bits deep. Film images with no CLAHE applied were compared to film images with nine different combinations of clip levels and region sizes applied. A simulated spiculation was embedded in a background of dense breast tissue, with the orientation of the spiculation varied. The key variables involved in each trial included the orientation of the spiculation, contrast level of the spiculation and the CLAHE settings applied to the image. Combining the 10 CLAHE conditions, 4 contrast levels and 4 orientations gave 160 combinations. The trials were constructed by pairing 160 combinations of key variables with 40 backgrounds. Twenty student observers were asked to detect the orientation of the spiculation in the image. There was a statistically significant improvement in detection performance for spiculations with CLAHE over unenhanced images when the region size was set at 32 with a clip level of 2, and when the region size was set at 32 with a clip level of 4. The selected CLAHE settings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved.


title: Causal Discovery in the Presence of Measurement Error: Identifiability Conditions

abstract: Measurement error in the observed values of the variables can greatly change the output of various causal discovery methods. This problem has received much attention in multiple fields, but it is not clear to what extent the causal model for the measurement-error-free variables can be identified in the presence of measurement error with unknown variance. In this paper, we study precise sufficient identifiability conditions for the measurement-error-free causal model and show what information of the causal model can be recovered from observed data. In particular, we present two different sets of identifiability conditions, based on the second-order statistics and higher-order statistics of the data, respectively. The former was inspired by the relationship between the generating model of the measurement-error-contaminated data and the factor analysis model, and the latter makes use of the identifiability result of the over-complete independent component analysis problem.


Building a Semantic Ontology for Internet of Things (IoT) Systems


title: Sematch: Semantic Entity Search from Knowledge Graph

abstract: As an increasing amount of the knowledge graph is published as Linked Open Data, semantic entity search is required to develop new applications. However, the use of structured query languages such as SPARQL is challenging for non-skilled users who need to master the query language as well as acquiring knowledge of the underlying ontology of Linked Data knowledge bases. In this article, we propose the Sematch framework for entity search in the knowledge graph that combines natural language query processing, entity linking, entity type linking and semantic similarity based query expansion. The system has been validated in a dataset and a prototype has been developed that translates natural language queries into SPARQL.


title: Global landscape of protein complexes in the yeast Saccharomyces cerevisiae

abstract: Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein-protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein-protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.


Device-To-Device (D2D) Communication Under LTE-Advanced Networks


title: Millimetre-wave T-shaped antenna with defected ground structures for 5G wireless networks

abstract: This paper presents a T-shaped antenna at millimetre-wave (MMW) frequency ranges to offer a number of advantages including simple structure, high operating bandwidth, and high gain. Defected ground structures (DGS) have been symmetrically added in ground in order to produce multiple resonating bands, accompanied by partial ground plane to achieve continuous operating bandwidth. The antenna consists of T-shaped radiating patch with a coplanar waveguide (CPW) feed. The bottom part has a partial ground plane loaded with five symmetrical split-ring slots. Measured results of antenna prototype show a wide bandwidth of 25.1–37.5 GHz. Moreover, simulation evaluation of peak gain of the antenna is 9.86 dBi at 36.8 GHz, and efficiency is higher than 80% in complete range of operation. The proposed antenna is considered as a potential candidate for the 5G wireless networks and applications.


title: Age-associated B cells: are they the key to understanding why autoimmune diseases are more prevalent in women?

abstract: “A significant body of research suggests roles for sex hormones as well as genetic factors in the development and/or progression of autoimmunity.”


title: 3d model retrieval with spherical harmonics and moments

abstract: We consider 3D object retrieval in which a polygonal mesh serves as a query and similar objects are retrieved from a collection of 3D objects. Algorithms proceed first by a normalization step in which models are transformed into canonical coordinates. Second, feature vectors are extracted and compared with those derived from normalized models in the search space. In the feature vector space nearest neighbors are computed and ranked. Retrieved objects are displayed for inspection, selection, and processing. Our feature vectors are based on rays cast from the center of mass of the object. For each ray the object extent in the ray direction yields a sample of a function on the sphere. We compared two kinds of representations of this function, namely spherical harmonics and moments. Our empirical comparison using precision-recall diagrams for retrieval results in a data base of 3D models showed that the method using spherical harmonics performed better.


title: Interpretable Visual Question Answering by Reasoning on Dependency Trees

abstract: Collaborative reasoning for understanding each image-question pair is very critical but underexplored for an interpretable visual question answering system. Although very recent works also attempted to use explicit compositional processes to assemble multiple subtasks embedded in the questions, their models heavily rely on annotations or handcrafted rules to obtain valid reasoning processes, leading to either heavy workloads or poor performance on composition reasoning. In this paper, to better align image and language domains in diverse and unrestricted cases, we propose a novel neural network model that performs global reasoning on a dependency tree parsed from the question, and we thus phrase our model as parse-tree-guided reasoning network (PTGRN). This network consists of three collaborative modules: i) an attention module to exploit the local visual evidence for each word parsed from the question, ii) a gated residual composition module to compose the previously mined evidence, and iii) a parse-tree-guided propagation module to pass the mined evidence along the parse tree. Our PTGRN is thus capable of building an interpretable VQA system that gradually derives the image cues following a question-driven parse-tree reasoning route. Experiments on relational datasets demonstrate the superiority of our PTGRN over current state-of-the-art VQA methods.


Benchmark Machine Learning Approaches with Classical Time Series Approaches on the Blood Glucose Level Prediction Challenge


title: Visual Analytics for Mobile Eye Tracking

abstract: The analysis of eye tracking data often requires the annotation of areas of interest (AOIs) to derive semantic interpretations of human viewing behavior during experiments. This annotation is typically the most time-consuming step of the analysis process. Especially for data from wearable eye tracking glasses, every independently recorded video has to be annotated individually and corresponding AOIs between videos have to be identified. We provide a novel visual analytics approach to ease this annotation process by image-based, automatic clustering of eye tracking data integrated in an interactive labeling and analysis system. The annotation and analysis are tightly coupled by multiple linked views that allow for a direct interpretation of the labeled data in the context of the recorded video stimuli. The components of our analytics environment were developed with a user-centered design approach in close cooperation with an eye tracking expert. We demonstrate our approach with eye tracking data from a real experiment and compare it to an analysis of the data by manual annotation of dynamic AOIs. Furthermore, we conducted an expert user study with 6 external eye tracking researchers to collect feedback and identify analysis strategies they used while working with our application.


title: Context-Aware Active Authentication Using Smartphone Accelerometer Measurements

abstract: While body movement patterns recorded by a smartphone accelerometer are now well understood to be discriminative enough to separate users, little work has been done to address the question of if or how the position in which the phone is held affects user authentication. In this work, we show through a combination of supervised learning methods and statistical tests, that there are certain users for whom exploitation of information of how a phone is held drastically improves classification performance. We propose a two-stage authentication framework that identifies the location of the phone before performing authentication, and show its benefits based on a dataset of 30 users. Our work represents a first step towards bridging the gap between accelerometer-based authentication systems analyzed from the context of a laboratory environment and a real accelerometer-based authentication system in the wild where phone positioning cannot be assumed.


title: LVDS receiver with 7mW consumption at 1.5 Gbps

abstract: This paper presents an LVDS receiver compatible with ANSI and IEEE standards at 1.5 Gbps. The proposed receiver aims to be compatible with the standard over PVT corners and to have optimized power consumption using 150nm technology with two voltage supplies 3.3 and 1.8V. The receiver design methodology is explained and where is the critical specifications of the standard to be met for other different designs. The presented design consumes 7mW at 1.5Gbps.


title: Deep multi-resolution color constancy

abstract: In this paper, a computational color constancy method is proposed via estimating the illuminant chromaticity in a scene by pooling from many local estimates. To this end, first, for each image in a dataset, we form an image pyramid consisting of several scales of the original image. Next, local patches of certain size are extracted from each scale in this image pyramid. Then, a convolutional neural network is trained to estimate the illuminant chromaticity per-patch. Finally, two more consecutive trainings are conducted, where the estimation is made per-image via taking the mean (1st training) and median (2nd training) of local estimates. The proposed method is shown to outperform the state-of-the-art in a widely used color constancy dataset.


title: Weakly supervised food image segmentation using class activation maps

abstract: Food image segmentation plays a crucial role in image-based dietary assessment and management. Successful methods for object segmentation generally rely on a large amount of labeled data on the pixel level. However, such training data are not yet available for food images and expensive to obtain. In this paper, we describe a weakly supervised convolutional neural network (CNN) which only requires image level annotation. We propose a graph based segmentation method which uses the class activation maps trained on food datasets as a top-down saliency model. We evaluate the proposed method for both classification and segmentation tasks. We achieve competitive classification accuracy compared to the previously reported results.


title: Natural Language Inference over Interaction Space: ICLR 2018 Reproducibility Report

abstract: We have tried to reproduce the results of the paper "Natural Language Inference over Interaction Space" submitted to ICLR 2018 conference as part of the ICLR 2018 Reproducibility Challenge. Initially, we were not aware that the code was available, so we started to implement the network from scratch. We have evaluated our version of the model on Stanford NLI dataset and reached 86.38% accuracy on the test set, while the paper claims 88.0% accuracy. The main difference, as we understand it, comes from the optimizers and the way model selection is performed.


title: Testing the Reliability of Frontal Sinuses in Positive Identification

abstract: The use of frontal sinus radiographs in positive identification has become an increasingly applied and accepted technique among forensic anthropologists, radiologists, and pathologists. From an evidentiary standpoint, however, it is important to know whether frontal sinus radiographs are areliable method for confirming or rejecting an identification, and standardized methods should be applied when making comparisons. The purpose of the following study is to develop an objective, standardized comparison method, and investigate the reliability of that method. Elliptic Fourier analysis (EFA) was used to assess the variation in 808 outlines of frontal sinuses by calculating likelihood ratios and posterior probabilities from EFA coefficients. Results show that using EFA coefficient comparison to estimate the probability of a correct identification is a reliable technique, and EFA comparison of frontal sinus outlines is recommended when it may be necessary to provide quantitative substantiation for a forensic identification based on these structures.


Survey on Visualizing Dynamic, Weighted, and Directed Graphs in the Context of Data-Driven Journalism


title: Driver design for 3kW 13.56 MHz multiphase resonant inverter

abstract: In half-bridge inverter topology, it is well known that the driver circuit design at high switching frequency is always the challenge due to the ground floating of the high-side driver circuit. At very high switching frequency such as 13.56 MHz, the common mode noise becomes a critical issue because it strongly effects to the stability and the efficiency of the inverter especially in case of the high power condition. Furthermore, in the case of multiphase resonant inverter topology, the synchronous of the driver pulse among phases to obtain the same class DE operation mode in every phase is also another challenge. In this paper, the driver circuit design for 3kW 13.56 MHz multiphase resonant inverter with the efficiency of 96.1% is presented in detail.


title: The metaDESK: models and prototypes for tangible user interfaces

abstract: The metaDESK is a user interface platform demonstrating new interaction techniques we call "tangible user inter- faces." We explore the physical instantiation of interface elements from the graphical user interface paradigm, giving physical form to windows, icons, handles, menus, and controls. The design and implementation of the metaDESK display, sensor, and software architectures is discussed. A prototype application driving an interaction with geographi- cal space, Tangible Geospace, is presented to demonstrate these concepts.


title: A competition on generalized software-based face presentation attack detection in mobile scenarios

abstract: In recent years, software-based face presentation attack detection (PAD) methods have seen a great progress. However, most existing schemes are not able to generalize well in more realistic conditions. The objective of this competition is to evaluate and compare the generalization performances of mobile face PAD techniques under some real-world variations, including unseen input sensors, presentation attack instruments (PAI) and illumination conditions, on a larger scale OULU-NPU dataset using its standard evaluation protocols and metrics. Thirteen teams from academic and industrial institutions across the world participated in this competition. This time typical liveness detection based on physiological signs of life was totally discarded. Instead, every submitted system relies practically on some sort of feature representation extracted from the face and/or background regions using hand-crafted, learned or hybrid descriptors. Interesting results and findings are presented and discussed in this paper.


title: On Density-Based Data Streams Clustering Algorithms: A Survey

abstract: Clustering data streams has drawn lots of attention in the last few years due to their ever-growing presence. Data streams put additional challenges on clustering such as limited time and memory and one pass clustering. Furthermore, discovering clusters with arbitrary shapes is very important in data stream applications. Data streams are infinite and evolving over time, and we do not have any knowledge about the number of clusters. In a data stream environment due to various factors, some noise appears occasionally. Density-based method is a remarkable class in clustering data streams, which has the ability to discover arbitrary shape clusters and to detect noise. Furthermore, it does not need the number of clusters in advance. Due to data stream characteristics, the traditional density-based clustering is not applicable. Recently, a lot of density-based clustering algorithms are extended for data streams. The main idea in these algorithms is using density-based methods in the clustering process and at the same time overcoming the constraints, which are put out by data stream’s nature. The purpose of this paper is to shed light on some algorithms in the literature on density-based clustering over data streams. We not only summarize the main density-based clustering algorithms on data streams, discuss their uniqueness and limitations, but also explain how they address the challenges in clustering data streams. Moreover, we investigate the evaluation metrics used in validating cluster quality and measuring algorithms’ performance. It is hoped that this survey will serve as a steppingstone for researchers studying data streams clustering, particularly density-based algorithms.


title: 3D Selective Search for obtaining object candidates

abstract: We propose a new method for obtaining object candidates in 3D space. Our method requires no learning, has no limitation of object properties such as compactness or symmetry, and therefore produces object candidates using a completely general approach. This method is a simple combination of Selective Search, which is a non-learning-based objectness detector working in 2D images, and a supervoxel segmentation method, which works with 3D point clouds. We made a small but non-trivial modification to supervoxel segmentation; it brings better “seeding” for supervoxels, which produces more proper object candidates as a result. Our experiments using a couple of publicly available RGB-D datasets demonstrated that our method outperformed state-of-the-art methods of generating object proposals in 2D images.


title: Occupational Therapy Using a Sensory Integrative Approach: A Case Study of Effectiveness

abstract: OBJECTIVE. This article presents a case report of a child with poor sensory processing and describes the disorder�s impact on the child�s occupational behavior and the changes in occupational performance during 10 months of occupational therapy using a sensory integrative approach (OT-SI). ::: ::: METHOD. Retrospective chart review of assessment data and analysis of parent interview data are reviewed. Progress toward goals and objectives is measured using goal attainment scaling. Themes from parent interview regarding past and present occupational challenges are presented. ::: ::: RESULTS. Notable improvements in occupational performance are noted on goal attainment scales, and these are consistent with improvements in behavior. Parent interview data indicate noteworthy progress in the child�s ability to participate in home, school, and family activities. ::: ::: CONCLUSION. This case report demonstrates a model for OT-SI. The findings support the theoretical underpinnings of sensory integration theory: that improvement in the ability to process and integrate sensory input will influence adaptive behavior and occupational performance. Although these findings cannot be generalized, they provide preliminary evidence supporting the theory and the effectiveness of this approach.


title: McKibben artificial muscle can be in accordance with the Hill skeletal muscle model

abstract: McKibben pneumatic artificial muscle shows a great functional analogy with skeletal muscle. To better characterize this dynamic analogous behavior, we analyze in what conditions McKibben muscle can be applied in accordance with the fundamental Hill muscle model. We show that the choice of an adequate textile sheath can lead to a velocity-tension curve in conformity with the Hill model equation. We propose to relate this phenomenon to specific textile yarn hydrodynamic friction. In terms of control we emphasize the interest of this agreement with the Hill model for giving the artificial muscle a dynamic behaviour more naturally adapted to load variations


title: Upper lateral cartilage fold-in flap: a combined spreader and/or splay graft effect without cartilage grafts.

abstract: Dorsal hump reduction almost always breaks the internal nasal valve and nasal obstruction is likely to occur postoperatively, unless reconstructed. One hundred eighty patients were operated using both open and closed rhinoplasty approaches. Upper lateral cartilages were meticulously separated from their junction with septum. Following bony and septal cartilaginous hump removal, upper lateral cartilages were folded inward. Either transcartilaginous horizontal mattress/simple sutures or perichondrial sutures were used depending of the desired width of the middle vault and the necessity for a splay-graft effect. In 7 patients unilateral, and in 1 patient bilateral, nasal synechia occurred and they were all treated under local anesthesia. All patients but 9 stated significantly improved nasal breathing. There was no inverted-V deformity or middle-vault narrowing observed. This technique is simple and physiologic, might be applicable for almost all primary rhinoplasty patients. Although it is possible with closed rhinoplasty approaches, it is easier with an open approach.


title: Noninvasive Blood Glucose Measurement Based on NIR Spectrums and Double ANN Analysis

abstract: This paper presents a new ::: noninvasive blood glucose monitoring method based on four near infrared ::: spectrums and double artificial neural network analysis. We choose four near ::: infrared wavelengths, 820 nm, 875 nm, 945 nm, 1050 nm, as transmission ::: spectrums, and capture four fingers transmission PPG signals simultaneously. ::: The wavelet transform algorithm is used to remove baseline drift, smooth ::: signals and extract eight eigenvalues of each PPG signal. The eigenvalues are ::: the input parameters of double artificial neural network analysis model. Double ::: artificial neural network regression combines the classification recognition ::: algorithm with prediction algorithm to improve the accuracy of measurement. ::: Experiments show that the root mean square error of the prediction is between ::: 0.97 mg/dL - 6.69 mg/dL, the average of root mean square error is 3.80 mg/dL.


title: Meeting Suffering With Kindness: Effects of a Brief Self-Compassion Intervention for Female College Students

abstract: Objective ::: The present study investigated the effectiveness of a newly developed 3-week self-compassion group intervention for enhancing resilience and well-being among female college students. ::: ::: Method ::: Fifty-two students were randomly assigned to either an intervention designed to teach skills of self-compassion (n = 27) or an active control group intervention in which general time management skills were taught (n = 25). Both interventions comprised 3 group meetings held over 3 weeks. To measure resilience and well-being gains, participants filled out a number of questionnaires before and after the intervention. ::: ::: Results ::: Results showed that the self-compassion intervention led to significantly greater increases in self-compassion, mindfulness, optimism, and self-efficacy, as well as significantly greater decreases in rumination in comparison to the active control intervention. Whereas both interventions increased life satisfaction and connectedness, no differences were found for worry and mood. ::: ::: Conclusion ::: These findings suggest that a brief self-compassion intervention has potential for improving student resilience and well-being.


title: The Effectiveness of Urban Design and Land Use and Transport Policies and Practices to Increase Physical Activity: A Systematic Review

abstract: Background: Although a number of environmental and policy interventions to promote physical activity are being widely used, there is sparse systematic information on the most effective approaches to guide population-wide interventions. Methods: We reviewed studies that addressed the following environmental and policy strategies to promote physical activity: community-scale urban design and land use policies and practices to increase physical activity; street-scale urban design and land use policies to increase physical activity; and transportation and travel policies and practices. These systematic reviews were based on the methods of the independent Task Force on Community Preventive Services. Exposure variables were classified according to the types of infrastructures/policies present in each study. Measures of physical activity behavior were used to assess effectiveness. Results: Two interventions were effective in promoting physical activity (community-scale and street-scale urban design and land use ...


title: SIFT -- A Tool for Wide-Area Information Dissemination

abstract: The dissemination model is becoming increasingly important in wide-area information system. In this model, the user subscribes to an information dissemination service by submitting profiles that describe his interests. He then passively receives new, filtered information. The Stanford Information Filtering Tool (SIFT) is a tool to help provide such service. It supports full-text filtering using well-known information retrieval models. The SIFT filtering engine implements novel indexing techniques, capable of processing large volumes of information against a large number of profiles. It runs on several major Unix platforms and is freely available to the public. In this paper we present SIFT's approach to user interest modeling and user-server communication. We demonstrate the processing capability of SIFT by describing a running server that disseminates USENET News. We present an empirical study of SIFT's performance, examining its main memory requirement and ability to scale with information volume and user population.


title: Compact UWB Filter With Double Notch-Bands Using Multilayer LCP Technology

abstract: A novel ultra wideband (UWB) bandpass filter with double notch-bands is presented in this paper. Multilayer schematic is adopted to achieve compact size. Stepped impedance resonators (SIRs), which can also suppress harmonic response, are designed on top and second layers, respectively, and broadside coupling technique is used to achieve tight couplings for a wide passband. Folded SIRs that can provide desired notch-bands are designed on the third layer and coupled underneath the second layer SIRs. The designed prototype is fabricated using multilayer liquid crystal polymer (LCP) technology. Good agreement between simulated and measured response is observed. The fabricated filter has dual notch-bands with center frequencies of 6.4/8.0 GHz with 3 dB bandwidths of 9.5%/13.4% and high rejection levels up to 26.4 dB and 43.7 dB at 6.4/8.0 GHz are observed, respectively. It also has low-insertion losses and flat group delay in passbands, and excellent stopband rejection level higher than 30.0 dB from 11.4 GHz to 18.0 GHz.


Human Identification Method : SCLERA RECOGNITION


title: Prevalence, biological phenotype and genotype in moderate/mild hemophilia A with discrepancy between one-stage and chromogenic factor VIII activity

abstract: BACKGROUND ::: In most laboratories, the severity of hemophilia A is assessed by the factor VIII activity (FVIII:C) one-stage assay. However, comparisons of these results with those of two-stage assays can reveal discrepancies and suggest misdiagnosis. ::: ::: ::: PATIENTS/METHODS ::: In this monocentric study, we measured FVIII:C with two methods (one-stage chronometric and chromogenic assays) in 307 (173 families) patients with moderate/mild hemophilia A. To compare results, we used a chronometric/chromogenic ratio. Discrepancy was defined as a ratio < 0.5 or > 1.5. We studied their putative involvement at known FVIII functional sites, their interspecies conservation status, and their spatial position within the FVIII structure. ::: ::: ::: RESULTS ::: Thirty-six patients from 17 families exhibited a discrepancy between the two assays: 12 (6.9%) families had a low ratio (< 0.5), and five (2.9%) families had a high ratio (> 1.5). Qualitative deficiency was diagnosed in about 16% of the families. Molecular studies were performed in 15 of these 17 families, resulting in each case in the identification of missense mutations, including three novel mutations. We were further able to propose a pathophysiologic explanation. ::: ::: ::: CONCLUSIONS ::: In this monocentric study, we have demonstrated a discrepancy between FVIII:C assay results in 10% of families with moderate/mild hemophilia A. The prevalence of 'inverse' discrepancy (i.e. low chronometric/chromogenic ratio) is high as compared with previous reports. We suggest that both FVIII:C assays are recommended in patients with moderate/mild hemophilia A for a complete biological phenotype. This could also improve our knowledge of the FVIII structure-function relationships.


An open implementation of the SIFT detector and descriptor


title: Moving object classification using local shape and HOG features in wavelet-transformed space with hierarchical SVM classifiers

abstract: An integrated system that segments and classifies four moving objects.A weight mask is proposed to enhance the distinguishing pixels in a segmented object.A new classification feature vector extracted from a wavelet-transformed space.A hierarchical linear support vector machine classification configuration is proposed. This paper proposes an integrated system for the segmentation and classification of four moving objects, including pedestrians, cars, motorcycles, and bicycles, from their side-views in a video sequence. Based on the use of an adaptive background in the red-green-blue (RGB) color model, each moving object is segmented with its minimum enclosing rectangle (MER) window by using a histogram-based projection approach or a tracking-based approach. Additionally, a shadow removal technique is applied to the segmented objects to improve the classification performance. For the MER windows with different sizes, a window scaling operation followed by an adaptive block-shifting operation is applied to obtain a fixed feature dimension. A weight mask, which is constructed according to the frequency of occurrence of an object in each position within a square window, is proposed to enhance the distinguishing pixels in the rescaled MER window. To extract classification features, a two-level Haar wavelet transform is applied to the rescaled MER window. The local shape features and the modified histogram of oriented gradients (HOG) are extracted from the level-two and level-one sub-bands, respectively, of the wavelet-transformed space. A hierarchical linear support vector machine classification configuration is proposed to classify the four classes of objects. Six video sequences are used to test the classification performance of the proposed method. The computer processing times of the object segmentation, object tracking, and feature extraction and classification approaches are 79ms, 211ms, and 0.01ms, respectively. Comparisons with different well-known classification approaches verify the superiority of the proposed classification method.


title: Malware class recognition using image processing techniques

abstract: Increasing suspicious instructions of various malware through a challenge to the malware analysts to identify and classify samples belongs to the malicious family. They have witnessed the very fast increase in both the number and complexity of malware set of instructions. Malware invest profoundly in technology and capability to reorganize the process of building and mutate existing malware set of instructions to avoid traditional protection. Classify malware variants by applying image processing techniques. The textures play an important role in many image processing applications. In this paper we proposed the Support Vector Machine (SVM) multi-class malware image classification challenge from an image processing perspective. The multi-resolution and wavelets are used to build effective texture feature vector using Gabor Wavelet, GIST and Discrete wavelet Transform and other features. The proposed algorithm experimented on Malimg Dataset of malware total 12,470 samples are used. In that 1610 samples are trained and 1710 samples are tested on 8 malware family which is randomly selected from the dataset. We compare this approach to existing malware classification approaches previously published research work. This is an efficient and more accurate malware detection algorithm using Wavelet Transform with machine learning classifiers techniques to detect malware samples more capably compare to existing work.


title: Analysis of Physiological Responses to a Social Situation in an Immersive Virtual Environment

abstract: An experiment was conducted in a Cave-like environment to explore the relationship between physiological responses and breaks in presence and utterances by virtual characters towards the participants. Twenty people explored a virtual environment (VE) that depicted a virtual bar scenario. The experiment was divided into a training and an experimental phase. During the experimental phase breaks in presence (BIPs) in the form of whiteouts of the VE scenario were induced for 2 s at four equally spaced times during the approximately 5 min in the bar scenario. Additionally, five virtual characters addressed remarks to the subjects. Physiological measures including electrocardiagram (ECG) and galvanic skin response (GSR) were recorded throughout the whole experiment. The heart rate, the heart rate variability, and the event-related heart rate changes were calculated from the acquired ECG data. The frequency response of the GSR signal was calculated with a wavelet analysis. The study shows that the heart rate and heart rate variability parameters vary significantly between the training and experimental phase. GSR parameters and event-related heart rate changes show the occurrence of breaks in presence. Event-related heart rate changes also signified the virtual character utterances. There were also differences in response between participants who report more or less socially anxious.


title: A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN)

abstract: Distributed Denial of Service (DDoS) is one of the most prevalent attacks that an organizational network infrastructure comes across nowadays. We propose a deep learning based multi-vector DDoS detection system in a software-defined network (SDN) environment. SDN provides flexibility to program network devices for different objectives and eliminates the need for third-party vendor-specific hardware. We implement our system as a network application on top of an SDN controller. We use deep learning for feature reduction of a large set of features derived from network traffic headers. We evaluate our system based on different performance metrics by applying it on traffic traces collected from different scenarios. We observe high accuracy with a low false-positive for attack detection in our proposed system.


title: Anomaly Detection in Cyber Physical Systems Using Recurrent Neural Networks

abstract: This paper presents a novel unsupervised approach to detect cyber attacks in Cyber-Physical Systems (CPS). We describe an unsupervised learning approach using a Recurrent Neural network which is a time series predictor as our model. We then use the Cumulative Sum method to identify anomalies in a replicate of a water treatment plant. The proposed method not only detects anomalies in the CPS but also identifies the sensor that was attacked. The experiments were performed on a complex dataset which is collected through a Secure Water Treatment Testbed (SWaT). Through the experiments, we show that the proposed technique is able to detect majority of the attacks designed by our research team with low false positive rates.


title: Broadband multiband phased array antennas for cellular communications

abstract: More and more frequency bands are used in cellular (mobile) communications, driving base station antennas to be broadband and multiband. Architecture, design principles and methods of novel dual-polarized multiband phased arrays (nesting, cloaking, choking) are discussed and illustrated with practical examples of these high-complexity antennas.


title: Fantasy Football Prediction

abstract: The ubiquity of professional sports and specifically the NFL have lead to an increase in popularity for Fantasy Football. Users have many tools at their disposal: statistics, predictions, rankings of experts and even recommendations of peers. There are issues with all of these, though. Especially since many people pay money to play, the prediction tools should be enhanced as they provide unbiased and easy-to-use assistance for users. This paper provides and discusses approaches to predict Fantasy Football scores of Quarterbacks with relatively limited data. In addition to that, it includes several suggestions on how the data could be enhanced to achieve better results. The dataset consists only of game data from the last six NFL seasons. I used two different methods to predict the Fantasy Football scores of NFL players: Support Vector Regression (SVR) and Neural Networks. The results of both are promising given the limited data that was used.


title: Contention-Aware Scheduling on Multicore Systems

abstract: Contention for shared resources on multicore processors remains an unsolved problem in existing systems despite significant research efforts dedicated to this problem in the past. Previous solutions focused primarily on hardware techniques and software page coloring to mitigate this problem. Our goal is to investigate how and to what extent contention for shared resource can be mitigated via thread scheduling. Scheduling is an attractive tool, because it does not require extra hardware and is relatively easy to integrate into the system. Our study is the first to provide a comprehensive analysis of contention-mitigating techniques that use only scheduling. The most difficult part of the problem is to find a classification scheme for threads, which would determine how they affect each other when competing for shared resources. We provide a comprehensive analysis of such classification schemes using a newly proposed methodology that enables to evaluate these schemes separately from the scheduling algorithm itself and to compare them to the optimal. As a result of this analysis we discovered a classification scheme that addresses not only contention for cache space, but contention for other shared resources, such as the memory controller, memory bus and prefetching hardware. To show the applicability of our analysis we design a new scheduling algorithm, which we prototype at user level, and demonstrate that it performs within 2p of the optimal. We also conclude that the highest impact of contention-aware scheduling techniques is not in improving performance of a workload as a whole but in improving quality of service or performance isolation for individual applications and in optimizing system energy consumption.


title: An automotive stacked ceramic patch antenna with an integrated GNSS and SDARS antenna

abstract: This paper propose a stacked ceramic patch antenna that is composed with two ceramic patch antenna for Global Navigation Satellite Systems (GNSS) and Satellite Digital Audio Radio Service (SDARS). The proposed antenna stacks up GNSS antenna on SDARS antenna to reduce the antenna volume without degradation of antenna performances. The proposed antenna covers all of the required gain at the entire azimuth range. The overall volume of proposed antenna is 36×36×9 mm3. The antenna with high performance and minimized structure is a highly marketable product.


title: Subgroup Discovery Algorithms: A Survey and Empirical Evaluation

abstract: Subgroup discovery is a data mining technique that discovers interesting associations among different variables with respect to a property of interest. Existing subgroup discovery methods employ different strategies for searching, pruning and ranking subgroups. It is very crucial to learn which features of a subgroup discovery algorithm should be considered for generating quality subgroups. In this regard, a number of reviews have been conducted on subgroup discovery. Although they provide a broad overview on some popular subgroup discovery methods, they employ few datasets and measures for subgroup evaluation. In the light of the existing measures, the subgroups cannot be appraised from all perspectives. Our work performs an extensive analysis on some popular subgroup discovery methods by using a wide range of datasets and by defining new measures for subgroup evaluation. The analysis result will help with understanding the major subgroup discovery methods, uncovering the gaps for further improvement and selecting the suitable category of algorithms for specific application domains.


title: Understanding mobile handheld device use and adoption

abstract: Without device adoption, there is no mobile commerce.


title: Approximate symbolic model checking of continuous-time Markov chains (Extended Abstract)

abstract: This paper presents a symbolic model checking algorithm for continuous-time Markov chains for an extension of the continuous stochastic logic CSL of Aziz et al [1]. The considered logic contains a time-bounded until-operator and a novel operator to express steady-state probabilities. We show that the model checking problem for this logic reduces to a system of linear equations (for unbounded until and the steady state-operator) and a Volterra integral equation system for time-bounded until. We propose a symbolic approximate method for solving the integrals using MTDDs (multi-terminal decision diagrams), a generalisation of MTBDDs. These new structures are suitable for numerical integration using quadrature formulas based on equally-spaced abscissas. like trapezoidal, Simpson and Romberg integration schemes.


title: Photocatalytic Oxidation of Alkanes with Dioxygen by Visible Light and Copper(II) and Iron(III) Chlorides: Preference Oxidation of Alkanes over Alcohols and Ketones

abstract: Visible light irradiation of alkanes in acetonitrile with CuCl 2 and FeCl 3 catalysts under atmospheric dioxygen gave the corresponding alcohols and ketones effectively; in these reactions, the total selectivity of the products did not decrease so much with increase of alkane conversion. For example, cyclohexanol and cyclohexanone were formed with ca. 70% selectivity at 50% conversion, because overoxidation of the products took place more slowly than cyclohexane oxidation. The relative reactivity values of cycloalkanes increased as their ring-sizes decreased. In the oxidation of hexane, the reactivity ratio of C 1 -/C 2 -/C 3 -H was found to be 1.0/1.4/1.8 with CuCl 2 and 1.0/4.6/6.6 with FeCl 3 , respectively. Toluene and diphenylmethane were more reactive than cyclohexane with FeCl 3 , as expected, whereas the alkane was oxidized faster than the benzylic compounds in the separate reaction with CuCl 2 . Moreover, the alkane oxidation could be comparably performed by sunlight instead of an artificial lamp.


title: Stress intensity factors for a crack in two intersecting uniformly stressed sheets

abstract: Abstract A model is considered for a crack growing simultaneously in the stiffener and the main sheet of a panel in which the stiffener is continuously attached. Stress intensity factors are determined for various ratios in the stiffnesses of the sheet and the stiffener. The panel is modelled with two uniformly stressed mutually perpendicular infinite sheets each containing a crack perpendicular to and bisected by the line of intersection of the sheets. Compatibility of displacements is maintained along the line of intersection of the sheets and the problem is reduced to the solution of a singular integral equation.


title: Visual closed-loop control for pouring liquids

abstract: Pouring a specific amount of liquid is a challenging task. In this paper we develop methods for robots to use visual feedback to perform closed-loop control for pouring liquids. We propose both a model-based and a model-free method utilizing deep learning for estimating the volume of liquid in a container. Our results show that the model-free method is better able to estimate the volume. We combine this with a simple PID controller to pour specific amounts of liquid, and show that the robot is able to achieve an average 38ml deviation from the target amount. To our knowledge, this is the first use of raw visual feedback to pour liquids in robotics.


title: A quasi-orthogonal space-time block code

abstract: It has been shown that a complex orthogonal design which provides full diversity and full transmission rate for a space-time block code is not possible for more than two antennas. Previous attempts have been concentrated in generalizing orthogonal designs which provide space-time block codes with full diversity and a high transmission rate. In this work, we design rate one codes which are quasi-orthogonal and provide partial diversity. The decoder of the proposed codes works with pairs of transmitted symbols instead of single symbols.


title: Live Deduplication Storage of Virtual Machine Images in an Open-Source Cloud

abstract: Deduplication is an approach of avoiding storing data blocks with identical content, and has been shown to effectively reduce the disk space for storing multi-gigabyte virtual machine (VM) images. However, it remains challenging to deploy deduplication in a real system, such as a cloud platform, where VM images are regularly inserted and retrieved. We propose LiveDFS, a live deduplication file system that enables deduplication storage of VM images in an open-source cloud that is deployed under low-cost commodity hardware settings with limited memory footprints. LiveDFS has several distinct features, including spatial locality, prefetching of metadata, and journaling. LiveDFS is POSIX-compliant and is implemented as a Linux kernel-space file system. We deploy our LiveDFS prototype as a storage layer in a cloud platform based on OpenStack, and conduct extensive experiments. Compared to an ordinary file system without deduplication, we show that LiveDFS can save at least 40% of space for storing VM images, while achieving reasonable performance in importing and retrieving VM images. Our work justifies the feasibility of deploying LiveDFS in an open-source cloud.


title: Hyperspectral Band Selection Using Improved Firefly Algorithm

abstract: An improved firefly algorithm (FA)-based band selection method is proposed for hyperspectral dimensionality reduction (DR). In this letter, DR is formulated as an optimization problem that searches a small number of bands from a hyperspectral data set, and a feature subset search algorithm using the FA is developed. To avoid employing an actual classifier within the band searching process to greatly reduce computational cost, criterion functions that can gauge class separability are preferred; specifically, the minimum estimated abundance covariance and Jeffreys-Matusita distances are employed. The proposed band selection technique is compared with an FA-based method that actually employs a classifier, the well-known sequential forward selection, and particle swarm optimization algorithms. Experimental results show that the proposed algorithm outperforms others, providing an effective option for DR.


title: Categorizing facial creases: A review

abstract: Ensuring uniformity in the nomenclature standardization of facial creases is important to enable the scholarly community to follow and debate the advancements in research. This review highlights the prevailing disparity in the nomenclature that refers to the same facial crease by researchers and laypeople, and suggests uniform names for the facial creases based on available literature. The previous and current trends in facial crease classification are also discussed. The nomenclature of the facial creases considered for this review include the following: the nasolabial fold, corner of the mouth lines, upper and lower lip creases around the mouth region, the mandibular folds, the bifid nose, the transverse nasal line, the vertical glabellar line, chin crease, the mental crease, four type of creases around the eyes, forehead creases, and periauricular creases. A figure illustrating the above facial creases is included as reference. It is hoped that the proposed standardization of nomenclature would ensure a more scientific referencing of facial creases enabling more effective scientific interaction among the scholarly community as well as the laypeople interested in the research and application of facial creases.


title: State space reduction for hierarchical reinforcement learning

abstract: This paper provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, to construct a partition space that has a smaller number of states than the original MDP. As a result, learning policies on the partition space should be faster than on the original state space. The technique presented here extends reduction to SMDPs by executing a policy instead of a single action, and grouping all states which have a small difference in transition probabilities and reward function under a given policy. When the reward structure is not known, a two-phase method for state aggregation is introduced and a theorem in this paper shows the solvability of tasks using the two-phase method partitions. These partitions can be further refined when the complete structure of reward is available. Simulations of different state spaces show that the policies in both MDP and this representation achieve similar results and the total learning time in partition space in presented approach is much smaller than the total amount of time spent on learning on the original state space. Introduction Markov decision processes (MDPs) are useful ways to model stochastic environments, as there are well established algorithms to solve these models. Even though these algorithms find an optimal solution for the model, they suffer from the high time complexity when the number of decision points is large(Parr 1998; Dietterich 2000). To address increasingly complex problems a number of approaches have been used to design state space representations in order to increase the efficiency of learning (Dean Thomas; Kaelbling & Nicholson 1995; Dean & Robert 1997). Here particular features are hand-designed based on the task domain and the capabilities of the learning agent. In autonomous systems, however, this is generally a difficult task since it is hard to anticipate which parts of the underlying physical state are important for the given decision making problem. Moreover, in hierarchical learning approaches the required information might change over time as increasingly competent actions become available. The same can be observed in biological systems where information about all muscle Copyright c © 2004, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. fibers is initially instrumental to generate strategies for coordinated movement. However, as such strategies become established and ready to be used, this low-level information does no longer have to be consciously taken into account. The methods presented here build on the -reduction technique developed by Dean et al.(Givan & Thomas 1995) to derive representations in the form of state space partitions that ensure that the utility of a policy learned in the reduced state space is within a fixed bound of the optimal policy. The presented methods here extend the -reduction technique by including policies as actions and thus using it to find approximate SMDP reductions. Furthermore it derives partitions for individual actions and composes them into representations for any given subset of the action space. This is further extended by permitting the definition of two-phase partitioning that is initially reward independent and can later be refined once the reward function is known. In particular the techniques described in the following subsections are to extend -reduction(Thomas Dean & Leach 1997) by introducing the following methods: • Temporal abstraction • Action dependent decomposition • Two-phase decomposition Formalism A Markov decision processes (MDP ) is a 4-tuple (S,A, P,R) where S is the set of states, A is a set of actions available in each state, P is a transition probability function that assigns a value 0 ≤ p ≤ 1 to each state-action pair, and R is the reward function. A transition function is a map P : S × A × S → [0, 1] and usually is denoted by P (s|s, a), which is the probability that executing action a in state s will lead to state s . Similarly, a reward function is a map R : S × A → and R(s, a) denotes the reward gained by executing action a in state s. Any policy defines a value function and the Bellman equation (Bellman 1957; Puterman 1994) creates a connection between the value of each state and the value of other states by: V π(s) = R(s, π(s)) + γ ∑ s ′ P (s′|s, π(s))V π(s′) Previous Work State space reduction methods use the basic concepts of a MDP such as transition probabilities and reward function to represent a large class of states with a single state of the abstract space.The most important issues that show the generated abstraction is a valid approximate MDP are: 1. The difference between the transition function and reward function in both models has to be a small value. 2. For each policy on the original state space there must exist a policy in the abstract model. And if a state s is not reachable from state s in the abstract model, then there should not exist a policy that leads from s to s in the original state space. SMDPs One of the approaches in treating temporal abstraction is to use the theory of semi Markov decision processes (SMDPs). The actions in SMDPs take a variable amount of time and are intended to model temporally extended actions, represented as a sequence of primary actions. Policies: A policy (option) in SMDPs is a triple oi = (Ii, πi, βi)(Boutillier & Hanks 1995), where Ii is an initiation set, πi : S × A −→ [0, 1] is a primary policy and βi : S −→ [0, 1] is a termination condition. When a policy oi is executed, actions are chosen according to πi until the policy terminates stochastically according to βi. The initiation set and termination condition of a policy limit the range over which the policy needs to be defined and determine its termination. Given any set of multi-step actions, we consider the policy over those actions. In this case we need to generalize the definition of value function. The value of a state s under an SMDP policy π is defined as(Boutillier & Goldszmidt 1994):


A soft stretchable bending sensor and data glove applications


title: Parkinsonism: upturned walking stick as an aid to locomotion.

abstract: Three parkinsonian patients are described who independently discovered that their gait was facilitated by inverting a walking stick and using the handle, carried a few inches from the ground, as a visual cue or target to step over and initiate walking. It is suggested that the "inverted" walking stick have wider application in patients with Parkinson's disease as an aid to walking, particularly if they have difficulty with step initiation and maintenance of stride length.


Identification of Alzheimer using Neuro Fuzzy logic


title: NIST Net: a Linux-based network emulation tool

abstract: Testing of network protocols and distributed applications has become increasingly complex, as the diversity of networks and underlying technologies increase, and the adaptive behavior of applications becomes more sophisticated. In this paper, we present NIST Net, a tool to facilitate testing and experimentation with network code through emulation. NIST Net enables experimenters to model and effect arbitrary performance dynamics (packet delay, jitter, bandwidth limitations, congestion, packet loss and duplication) on live IP packets passing through a commodity Linux-based PC router. We describe the emulation capabilities of NIST Net; examine its architecture; and discuss some of the implementation challenges encountered in building such a tool to operate at very high network data rates while imposing minimal processing overhead. Calibration results are provided to quantify the fidelity and performance of NIST Net over a wide range of offered loads (up to 1 Gbps), and a diverse set of emulated performance dynamics.


title: How do we read algorithms? A case study

abstract: The authors report on an experiment that explored the way subjects view an algorithm, written in Pascal, and the graphical representation of its behavior. They designed the experiment to examine the influence of programming experience on how subjects view a short but complex algorithm, and they analyzed the scan patterns of eye movement for subjects at two experience levels. They describe the experimental procedure in detail, and discuss the results. An important finding was that all subjects needed numerous fixations in most areas of the algorithm and spent more time viewing relevant slide areas than did subjects in studies using simple text. This supports the hypothesis that viewing strategies for algorithms differ from those for prose. >


title: Self-organized formation of topologically correct feature maps

abstract: This work contains a theoretical study and computer simulations of a new self-organizing process. The principal discovery is that in a simple network of adaptive physical elements which receives signals from a primary event space, the signal representations are automatically mapped onto a set of output responses in such a way that the responses acquire the same topological order as that of the primary events. In other words, a principle has been discovered which facilitates the automatic formation of topologically correct maps of features of observable events. The basic self-organizing system is a one- or two-dimensional array of processing units resembling a network of threshold-logic units, and characterized by short-range lateral feedback between neighbouring units. Several types of computer simulations are used to demonstrate the ordering process as well as the conditions under which it fails.


title: Heterogeneous vehicular communications: A comprehensive study

abstract: Abstract Vehicular communications have developed rapidly contributing to the success of intelligent transportation systems. In VANET, continuous connectivity is a huge challenge caused by the extremely dynamic network topology and the highly variable number of mobile nodes. Moreover, message dissemination efficiency is a serious issue in traffic effectiveness and road safety. The heterogeneous vehicular network, which integrates cellular networks with DSRC, has been suggested and attracted significant attention recently. VANET-cellular integration offers many potential benefits, for instance, high data rates, low latency, and extended communication range. Due to the heterogeneous wireless access, a seamless handover decision is required to guarantee QoS of communications and to maintain continuous connectivity between the vehicles. On the other hand, VANET heterogeneous wireless networks integration will significantly help autonomous cars to be functional in reality. This paper surveys and reviews some related studies in the literature that deals with VANET heterogeneous wireless networks communications in term of vertical handover, data dissemination and collection, gateway selection and other issues. The comparison between different works is based on parameters like bandwidth, delay, throughput, and packet loss. Finally, we outline open issues that help to identify the future research directions of VANET in the heterogeneous environment.


title: Defining digital medicine

abstract: Digital medicine is poised to transform biomedical research, clinical practice and the commercial sector. Here we introduce a monthly column from R&D/venture creation firm PureTech tracking digital medicine's emergence.


title: Sequential Labeling with online Deep Learning

abstract: Deep learning has attracted great attention recently and yielded the state of the art performance in dimension reduction and classification problems. However, it cannot effectively handle the structured output prediction, e.g. sequential labeling. In this paper, we propose a deep learning structure, which can learn discriminative features for sequential labeling problems. More specifically, we add the inter-relationship between labels in our deep learning structure, in order to incorporate the context information from the sequential data. Thus, our model is more powerful than linear Conditional Random Fields (CRFs) because the objective function learns latent non-linear features so that target labeling can be better predicted. We pretrain the deep structure with stacked restricted Boltzmann machines (RBMs) for feature learning and optimize our objective function with online learning algorithm, a mixture of perceptron training and stochastic gradient descent. We test our model on different challenge tasks, and show that our model outperforms significantly over the completive baselines.


title: A review of factors that influenced online trust in social commerce

abstract: The recent advancements in social media and electronic commerce have produced a new business model called social commerce. This new business model has facilitated online business transactions via social media. Despite the advancement of technology, the issue of online trust has increased in social commerce. The purpose of this paper is to identify the factors that influence online trust in social commerce. Seven models related to online trust in electronic commerce, electronic business, and social commerce were reviewed and analyzed. This has resulted in identifying six factors, which are usefulness, ease of use, security, privacy, website design, and electronic word of mouth; as the most influential factors that influence online trust in social commerce. Discussion and direction of future work are pointed out in this paper.


title: Towards Smart City: Sensing Air Quality in City based on Opportunistic Crowd-sensing

abstract: Cities are expanding and more and more citizens are exposed to air pollutants both indoors and outdoors. This may have adverse effects on citizens' health. In this paper, we present AirSense, an opportunistic crowd-sensing based air quality monitoring system, aimed at collecting and aggregating sensor data to monitor air pollution in the vicinity (building/neighbourhood) and the city. We introduce a light weight, low power and low cost air quality monitoring device (AQMD) and demonstrate how AQMD and smartphones in a crowd collaboratively gather and share data of interest to the cloud. In cloud, collected data are analyzed and an aggregate view is generated from data collected from various sensors and from different users for providing an air pollution heat map of the city. Unlike previous works, both micro and macro level air quality monitoring is possible with Airsense. End user can view his/her pollution footprint for the whole day, the neighborhood (local) air quality and AQImap (air quality index map) of the city on his/her smartphone. The system is implemented and the prototype is also evaluated.


title: A literature review of expert problem solving using analogy

abstract: We consider software project cost estimation from a problem solving perspective. Taking a cognitive psychological approach, we argue that the algorithmic basis for CBR tools is not representative of human problem solving and this mismatch could account for inconsistent results. We describe the fundamentals of problem solving, focusing on experts solving ill-defined problems. This is supplemented by a systematic literature review of empirical studies of expert problem solving of nontrivial problems. We identified twelve studies. These studies suggest that analogical reasoning plays an important role in problem solving, but that CBR tools do not model this in a biologically plausible way. For example, the ability to induce structure and therefore find deeper analogies is widely seen as the hallmark of an expert. However, CBR tools fail to provide support for this type of reasoning for prediction. We conclude this mismatch between experts' cognitive processes and software tools contributes to the erratic performance of analogy-based prediction.


title: Sufficiency model-action clarification for simulation optimization applied to an election system

abstract: Many inputs for simulation optimization models are assumed to come from known distributions. When such distributions are obtained from small sample sizes, the parameters of these distributions may be associated with an "uncertainty set" or ranges. The presence of this uncertainty means that one or more solutions may be optimal depending on which parameters from the set are used. In this paper, we present a graphical methodology that combines bootstrap sampling and cross-evaluation techniques to visualize the data driven support for alternative solutions for problems in which distribution parameters are estimated using small sample sizes. We illustrate the methodology using a voting machine allocation problem.


title: Are Bullies More Productive? Empirical Study of Affectiveness vs. Issue Fixing Time

abstract: Human Affectiveness, i.e., the emotional state of a person, plays a crucial role in many domains where it can make or break a team's ability to produce successful products. Software development is a collaborative activity as well, yet there is little information on how affectiveness impacts software productivity. As a first measure of this impact, this paper analyzes the relation between sentiment, emotions and politeness of developers in more than 560K Jira comments with the time to fix a Jira issue. We found that the happier developers are (expressing emotions such as JOY and LOVE in their comments), the shorter the issue fixing time is likely to be. In contrast, negative emotions such as SADNESS, are linked with longer issue fixing time. Politeness plays a more complex role and we empirically analyze its impact on developers' productivity.


title: A discriminatively trained, multiscale, deformable part model

abstract: This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge in ten out of twenty categories. The system relies heavily on deformable parts. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL challenge. Our system also relies heavily on new methods for discriminative training. We combine a margin-sensitive approach for data mining hard negative examples with a formalism we call latent SVM. A latent SVM, like a hidden CRF, leads to a non-convex training problem. However, a latent SVM is semi-convex and the training problem becomes convex once latent information is specified for the positive examples. We believe that our training methods will eventually make possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose.


title: SCADA security in the light of Cyber-Warfare

abstract: Supervisory Control and Data Acquisition (SCADA) systems are deployed worldwide in many critical infrastructures ranging from power generation, over public transport to industrial manufacturing systems. Whilst contemporary research has identified the need for protecting SCADA systems, these information are disparate and do not provide a coherent view of the threats and the risks resulting from the tendency to integrate these once isolated systems into corporate networks that are prone to cyber attacks. This paper surveys ongoing research and provides a coherent overview of the threats, risks and mitigation strategies in the area of SCADA security.


title: A systematic review identifies valid comorbidity indices derived from administrative health data

abstract: Abstract Objectives To conduct a systematic review of studies reporting on the development or validation of comorbidity indices using administrative health data and compare their ability to predict outcomes related to comorbidity (ie, construct validity). Study Design and Setting We conducted a comprehensive literature search of MEDLINE and EMBASE, until September 2012. After title and abstract screen, relevant articles were selected for review by two independent investigators. Predictive validity and model fit were measured using c-statistic for dichotomous outcomes and R2 for continuous outcomes. Results Our review includes 76 articles. Two categories of comorbidity indices were identified: those identifying comorbidities based on diagnoses, using International Classification of Disease codes from hospitalization or outpatient data, and based on medications, using pharmacy data. The ability of indices studied to predict morbidity-related outcomes ranged from poor (C statistic ≤0.69) to excellent (C statistic >0.80) depending on the specific index, outcome measured, and study population. Diagnosis-based measures, particularly the Elixhauser Index and the Romano adaptation of the Charlson Index, resulted in higher ability to predict mortality outcomes. Medication-based indices, such as the Chronic Disease Score, demonstrated better performance for predicting health care utilization. Conclusion A number of valid comorbidity indices derived from administrative data are available. Selection of an appropriate index should take into account the type of data available, study population, and specific outcome of interest.


title: Social Network Data Analytics

abstract: Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.


title: Anal Sex Role, Circumcision Status, and HIV Infection Among Men Who Have Sex with Men in Chongqing, China

abstract: Men who have sex with men (MSM) in China face a rapidly expanding HIV epidemic. Anal sex role plays a significant role in HIV infection. Research has already begun in China investigating the potential for circumcision-based interventions to slow the rise of HIV among Chinese MSM. Using peer referral recruitment, we sampled 491 men who reported anal sex role preference. We analyzed preferred anal sex role, enacted sex role during recent sexual behavior, and circumcision status and HIV infection among MSM in one Chinese city. Men reported on their anal sex role preference and reported on up to three male sexual partners. Men were asked to report on whether they were “top” or “bottom” with each of the partners. Those that preferred being bottom and versatile were significantly younger than those who preferred being top. Men who preferred bottoming and those that preferred the versatile role were significantly more likely to be HIV-infected than those who preferred to be tops. There was no significant association between circumcision and HIV infection among men who maintained their preferred top role. In terms of anal sex role behavior, prevalence was not statistically different across anal sex roles. Circumcision conferred no additional protection to men who preferred and who engaged the top role during anal sex. HIV interventions will need to address anal sex roles in more sophisticated ways than perhaps originally thought. Simplistic assumptions that anal sex role is a fixed behavior undermines interventions such as circumcision among MSM.


title: Electrical PV Array Reconfiguration Strategy for Energy Extraction Improvement in Grid-Connected PV Systems

abstract: This paper applies a dynamical electrical array reconfiguration (EAR) strategy on the photovoltaic (PV) generator of a grid-connected PV system based on a plant-oriented configuration, in order to improve its energy production when the operating conditions of the solar panels are different. The EAR strategy is carried out by inserting a controllable switching matrix between the PV generator and the central inverter, which allows the electrical reconnection of the available PV modules. As a result, the PV system exhibits a self-capacity for real-time adaptation to the PV generator external operating conditions and improves the energy extraction of the system. Experimental results are provided to validate the proposed approach.


title: Chinese Short Text Classification Based on Domain Knowledge

abstract: People are generating more and more short texts. There is an urgent demand to classify short texts into different domains. Due to the shortness and sparseness of short texts, con-ventional methods based on Vector Space Model (VSM) have limitations. To tackle the data scarcity problem, we propose a new mod- ::: el to directly measure the correlation between a short text instance and a domain instead of representing short texts as vectors of weights. We firstly draw domain knowledge for each user-defined domain using an external corpus ::: of longer documents. Secondly, the correlation is calculated by measuring the proportion of the overlapping part of the instance and the domain knowledge. Finally, if the correlation is greater than a threshold, the instance will be classified into the domain. Experimental results show that the classifier based on the proposed model outperforms the state-of-the-art baselines based on VSM.


title: On modifying model reference adaptive control schemes for performance improvement

abstract: The idea of improving the transient behavior in model reference adaptive control by using the estimation error to modify the underlying controller structure has attracted attention in the literature. In this note, we show that the modifications proposed so far can in fact all be derived as a linear feedback from the tracking error. Consequently, the improved performance obtained with such a scheme is solely due to a nonadaptive high gain feedback. >


title: No-drift™ BAW-SMR: Over-moded reflector for temperature compensation

abstract: As more LTE bands are squeezed into the crowded global RF spectrum interference becomes a major concern. Some cases require profoundly steep transitions from passband to the tightly packed neighboring bands. The most demanding high frequency LTE bands may only be served with high-performance BAW resonators that have been temperature compensated to have essentially zero temperature drift. There is a variety of known challenges associated with the traditional temperature compensation techniques applied to BAW devices. This work will address those challenges and present a novel no-drift BAW-SMR using over-moded acoustic reflector layers. A solution for Band 30 utilizing the over-moded reflector will be presented.


title: Capacity-Constrained Voronoi Diagrams in Continuous Spaces

abstract: A Voronoi diagram of a set of sites partitions a bounded space into regions of different areas. A capacity-constrained Voronoi diagram is a partition in which the area for each Voronoi region is predefined. In this paper, we present two approaches for computing such capacity-constrained Voronoi diagrams in continuous spaces. Our firstapproach is based on ordinary (non-weighted) distance functions and achieves the capacity constraint by a general optimization of the site locations. Our second approach is based on weighted distance functions and optimizes the weights of the sites. Both approaches are iterative methods that start with an initial set of sites and then optimize the area of one Voronoi region at a time. As a consequence, the dimensionality of the individual optimization problem is minimal, which enables a reliable and fast convergence even for large sets of sites.


title: An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering

abstract: A new approach to data stream clustering with the help of an ensemble of adaptive neuro-fuzzy systems is proposed. The proposed ensemble is formed with adaptive neuro-fuzzy self-organizing Kohonen maps in a parallel processing mode. Their learning procedure is carried out with different parameters that define a nature of cluster borders' blurriness. Clusters' quality is estimated in an online mode with the help of a modified partition coefficient which is calculated in a recurrent form. A final result is chosen by the best neuro-fuzzy self-organizing Kohonen map.


title: Current state of imaging in dermatology.

abstract: Abstract Medical imaging has dramatically transformed the practice of medicine, especially the field of dermatology. Imaging is used to facilitate the transfer of information between providers, document cutaneous disease, assess response to therapy, and plays a crucial role in monitoring and diagnosing skin cancer. Advancements in imaging technology and overall improved quality of imaging have augmented the utility of photography. We provide an overview of current imaging technologies used in dermatology with a focus on their role in skin cancer diagnosis. Future technologies include three-dimensional, total-body photography, mobile smartphone applications, and computerassisted diagnostic devices. With these advancements, we are better equipped to capture and monitor skin conditions longitudinally and achieve improved diagnostic accuracy of skin cancer.


title: Word2vec applied to recommendation: hyperparameters matter

abstract: Skip-gram with negative sampling, a popular variant of Word2vec originally designed and tuned to create word embeddings for Natural Language Processing, has been used to create item embeddings with successful applications in recommendation. While these fields do not share the same type of data, neither evaluate on the same tasks, recommendation applications tend to use the same already tuned hyperparameters values, even if optimal hyperparameters values are often known to be data and task dependent. We thus investigate the marginal importance of each hyperparameter in a recommendation setting through large hyperparameter grid searches on various datasets. Results reveal that optimizing neglected hyperparameters, namely negative sampling distribution, number of epochs, subsampling parameter and window-size, significantly improves performance on a recommendation task, and can increase it by an order of magnitude. Importantly, we find that optimal hyper-parameters configurations for Natural Language Processing tasks and Recommendation tasks are noticeably different.


title: Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl

abstract: We present DepCC, the largest-to-date linguistically analyzed corpus in English including 365 million documents, composed of 252 billion tokens and 7.5 billion of named entity occurrences in 14.3 billion sentences from a web-scale crawl of the \textsc{Common Crawl} project. The sentences are processed with a dependency parser and with a named entity tagger and contain provenance information, enabling various applications ranging from training syntax-based word embeddings to open information extraction and question answering. We built an index of all sentences and their linguistic meta-data enabling quick search across the corpus. We demonstrate the utility of this corpus on the verb similarity task by showing that a distributional model trained on our corpus yields better results than models trained on smaller corpora, like Wikipedia. This distributional model outperforms the state of art models of verb similarity trained on smaller corpora on the SimVerb3500 dataset.


title: Practices of anesthesiologists to alleviate anxiety in children and adolescents in Canada

abstract: To the Editor, Preoperative anxiety is common in children awaiting surgery. Both pharmacological (e.g., sedatives) and nonpharmacological (e.g., parental presence during induction of anesthesia [PPIA]) interventions are used to alleviate such anxiety. According to a survey performed in the United States from 1995 to 2002, midazolam was the most frequently employed pharmacologic agent in children and adolescents, and PPIA was frequently used for school-age children. These findings may not apply to the current Canadian situation because of differences in the healthcare systems and evolution of practices since these data were collected. Consequently, we performed a survey to address the issue of methods used to alleviate anxiety in children waiting for surgery. One hundred fifty-five anesthesiologists providing care for children and adolescents in Canada completed a survey conducted from January 2009 to February 2010 designed to assess: (1) the type of premedicant, route of administration, and frequency of use; (2) general opinions and practices regarding premedication, parental presence, and other nonpharmacological interventions used to alleviate preoperative anxiety; and (3) demographics of the participants. Consent was obtained, and the survey was completed online via SurveyMonkey . The study was approved by the University of Regina Research Ethics Board on December 23, 2008. Demographic data were summarized as mean and standard deviations for continuous data and frequencies for categorical data. We compared means between subgroups using independent sample Student’s t tests. Categorical items were analyzed by frequency distribution and Chi square analyses. A small percentage (10%; n = 16) of the anesthesiologists indicated that they did not premedicate anyone across the age ranges of interest. The Table shows premedication use across age groups. Most of those who endorsed premedication indicated that they used it occasionally (1-25% of the time). The most common type of premedication was midazolam, and the most frequent route of administration was oral. The two most valued qualities of premedication were reduction of anxiety and cooperation. The decision whether or not to premedicate was not related to the sex of the anesthesiologist, subspecialty anesthesia training, or the primary type of hospital practice. Nevertheless, anesthesiologists who premedicated children were younger (P = 0.026) and had practiced fewer years (P = 0.009). Only 14.2% (n = 22) of anesthesiologists reported never using PPIA across all age ranges, and the majority of the respondents who endorsed PPIA indicated that they used it occasionally (1-25% of the time). Parental presence was used most frequently in younger age groups (B seven years) (Table). Procedural talk (talking to the child about the procedure) was used more commonly in older age groups. There were no differences with respect to age, K. D. Wright, PhD (&) D. J. Lee, B.A.Hons D. Sharpe, PhD Department of Psychology, University of Regina, Regina, SK, Canada e-mail: kristi.wright@uregina.ca


title: An analysis of LSM caching in NVRAM

abstract: The rise of NVRAM technologies promises to change the way we think about system architectures. In order to fully exploit its advantages, it is required to develop systems specially tailored for NVRAM devices. Not only this imposes great challenges, but developing full system architectures from scratch is undesirable in many scenarios due to prohibitive development costs. Instead, we analyze in this paper the behavior of an existing log-structured persistent key-value store, namely LevelDB, when run on top of an emulated NVRAM device. We investigate initial opportunities for improvement when adapting a system tailored for HDD/SSDs to run on top of an NVRAM environment. Furthermore, we analyze the behavior of the legacy DRAM caching component of LevelDB and whether more suitable caching policies are required.


title: UNDERSTANDING E-PROCUREMENT SYSTEM BENEFITS USING ORGANISATIONAL ADOPTION MOTIVATION LENS: A CASE STUDY

abstract: Adoption of e-procurement systems and the likely benefits that organisations can achieve have been widely discussed. However, what still remains puzzling is the variation in benefits that organisations experience from the adoption of even the same e-procurement system. A few e-commerce scholars applied the factor-based approach to help explain difference in e-procurement benefits, but with limited success. In this paper, we propose an alternative theoretical framework (known as EPAMS) that draws inspirations from the notion of ‘organisational motivation’ for Inter-Organisational System (IOS) adoption context. Our framework relates organisational adoption motivations for eprocurement systems with three types of benefits. We further report the outcomes of analysing eprocurement benefits experience of a large municipality located in a state capital city of Australia. We explain the municipality’s attainment of e-procurement benefits in terms of motivation it had established for adopting the e-procurement system in the first place. The case study data provide preliminary support for our assertion of an association between an organisation’s e-procurement systems adoption motivation and the benefits these systems can offer. This finding has implications for the IS/e-commerce scholars and practitioners alike.


title: A cellular automaton model for freeway traffic

abstract: We introduce a stochastic discrete automaton model to simulate freeway traffic. Monte-Carlo simulations of the model show a transition from laminar traffic flow to start-stop- waves with increasing vehicle density, as is observed in real freeway traffic. For special cases analytical results can be obtained.


title: 5G Millimeter-Wave Mobile Broadband: Performance and Challenges

abstract: Future cellular systems will employ the so-called big three technologies: UDN, massive MIMO, and mmWave (and/or terahertz) communications. The goal is to support the explosive demands for mobile broadband services foreseen for the next decade. In this work, we investigate the joint impact of the three technologies. Using system-level simulations, we evaluate the performance of two-tier cellular networks with μ Wave macrocells and densely deployed mmWave small cells. Our results show that much higher capacity can be realized with UDNs than in macrocell-only setups. The results also reveal that performance does not scale proportionally with increase in the employed mmWave bandwidth. The corresponding increase in noise (due to larger bandwidths) reduces the SINR. Outdoor users experience promising data rates notwithstanding, but the throughputs of indoor users are highly degraded. This is due to the additional wall and indoor losses (on top of the inherently high path loss at mmWave frequencies), which further reduce the SINR of indoor users. Therefore, techniques that significantly enhance the SINR are highly import ant design considerations in unleashing the amazing prospects and potentials of mmWave UDNs.


title: Holographic Embeddings of Knowledge Graphs

abstract: Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HOLE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator, HOLE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. Experimentally, we show that holographic embeddings are able to outperform state-of-the-art methods for link prediction on knowledge graphs and relational learning benchmark datasets.