title: Learners’ Perspective on Critical Factors to LMS Success in Blended Learning: An Empirical Investigation

abstract: The use of Learning Management System (LMS) in academic institutions is becoming an imperative for many institutions. The success of LMS in academic institutions may be initiated by instructors’ adoption; however, LMS survives in the long run by learners’ continuous adoption and use. Consequently, the objective of this article is to examine the critical factors that influence the success of LMS in blended learning in terms of actual usage, perceived usefulness, perceived ease of use, and user satisfaction from the learners’ perspective. The study also examines how these success measures impact learners’ continuous intention to use LMS in blended learning. These critical factors are related to the major entities of LMS adoption: learner characteristics (computer anxiety, technology experience, self-efficacy, and personal innovativeness), instructor characteristics (attitude, teaching style, control, and responsiveness), LMS characteristics (system quality, information quality, and service quality), classmates characteristics (attitude and interaction), course characteristics (quality and flexibility), and organization characteristics (management support and training). Based on 512 learners, the results showed that all of these factors are critical to one or several success measures, except for learner self-efficacy, instructor online responsiveness, and management support. The results also showed that all success measures are critical to learners’ continuous intention to use LMS in blended learning.


title: Play and Rewind: Optimizing Binary Representations of Videos by Self-Supervised Temporal Hashing

abstract: We focus on hashing videos into short binary codes for efficient Content-based Video Retrieval (CBVR), which is a fundamental technique that supports access to the ever-growing abundance of videos on the Web. Existing video hash functions are built on three isolated stages: frame pooling, relaxed learning, and binarization, which have not adequately explored the temporal order of video frames in a joint binary optimization model, resulting in severe information loss. In this paper, we propose a novel unsupervised video hashing framework called Self-Supervised Temporal Hashing (SSTH) that is able to capture the temporal nature of videos in an end-to-end learning-to-hash fashion. Specifically, the hash function of SSTH is an encoder RNN equipped with the proposed Binary LSTM (BLSTM) that generates binary codes for videos. The hash function is learned in a self-supervised fashion, where a decoder RNN is proposed to reconstruct the original video frames in both forward and reverse orders. For binary code optimization, we develop a backpropagation rule that tackles the non-differentiability of BLSTM. This rule allows efficient deep network training without suffering from the binarization loss. Through extensive CBVR experiments on two real-world consumer video datasets of Youtube and Flickr, we show that SSTH consistently outperforms state-of-the-art video hashing methods, eg., in terms of mAP@20, SSTH using only 128 bits can still outperform others using 256 bits by at least 9% to 15% on both datasets.


title: The P2P war: someone is monitoring your activities!

abstract: In an effort to prosecute P2P users, RIAA and MPAA have reportedly started to create decoy users: they participate in P2P networks in order to identify illegal sharing of content. This has reportedly scared some users who are afraid of being caught. The question we attempt to answer is how prevalent is this phenomenon: how likely is it that a user will run into such a "fake user" and thus run the risk of a lawsuit? The first challenge is identifying these "fake users". We collect this information from a number of free open source software projects which are trying to identify such IP address ranges by forming the so-called blocklists. The second challenge is running a large scale experiment in order to obtain reliable and diverse statistics. Using Planetlab, we conduct active measurements, spanning a period of 90 days, from January to March 2006, spread over 3 continents. Analyzing over a 100 GB of TCP header data, we quantify the probability of a P2P user of being contacted by such entities. We observe that 100% of our nodes run into entities in these lists. In fact, 12 to 17% of all distinct IPs contacted by any node were listed on blocklists. Interestingly, a little caution can have significant effect: the top five most prevalent blocklisted IP ranges contribute to nearly 94% of all blocklisted IPs and avoiding these can reduce the probability of encountering blocklisted IPs to about 1%. In addition, we examine other factors that affect the probability of encountering blocklisted IPs, such as the geographical location of the users. Finally, we find another surprising result: less than 0.5% of all unique blocklisted IPs contacted are owned explicitly by media companies.


title: Burnout and Depression: Two Entities or One?.

abstract: Objectives ::: The purpose of this study was to examine the overlap in burnout and depression. ::: ::: Method ::: The sample comprised 1,386 schoolteachers (mean [M]age = 43; Myears taught = 15; 77% women) from 18 different U.S. states. We assessed burnout, using the Shirom-Melamed Burnout Measure, and depression, using the depression module of the Patient Health Questionnaire. ::: ::: Results ::: Treated dimensionally, burnout and depressive symptoms were strongly correlated (.77; disattenuated correlation, .84). Burnout and depressive symptoms were similarly correlated with each of 3 stress-related factors, stressful life events, job adversity, and workplace support. In categorical analyses, 86% of the teachers identified as burned out met criteria for a provisional diagnosis of depression. Exploratory analyses revealed a link between burnout and anxiety. ::: ::: Conclusions ::: This study provides evidence that past research has underestimated burnout–depression overlap. The state of burnout is likely to be a form of depression. Given the magnitude of burnout–depression overlap, treatments for depression may help workers identified as “burned out.”


title: A novel 5GHz RF power detector

abstract: The principles of a RF power detector using bipolar transistors are analyzed and a new RF power detector for embedded RF IC test is proposed. The new detector uses a voltage divider to provide unequal ac signals for its differential input. It has a more linear transfer function for large-signal detection than previously reported detectors. Also, the detector retains its square-law transfer characteristics for low-level-signal detection. The errors in linear or square-law region operation are within 2%. The crossover region between detector linear and square-law operation is minimized to 30/spl sim/50mV and its detection error is within 8% using either linear or square-law estimation. The detector has 65dB dynamic range and it can work with 5GHz or higher signals as compared to roughly 30dB dynamic range in R.G. Meyer (1995).


Intra-body communication for biomedical sensor networks


title: Case Study Research in Software Engineering

abstract: Based on their own experiences of in-depth case studies of software projects in international corporations, in this bookthe authors present detailed practical guidelines on the preparation, conduct, design and reporting of case studies of software engineering. This is the first software engineering specific book on thecase study research method.


Deep Learning for Aspect Based Sentiment Detection


title: Techniques of Data Mining In Healthcare: A Review

abstract: Data mining is gaining popularity in disparate research fields due to its boundless applications and approaches to mine the data in an appropriate manner. Owing to the changes, the current world acquiring, it is one of the optimal approach for approximating the nearby future consequences. Along with advanced researches in healthcare monstrous of data are available, but the main difficulty is how to cultivate the existing information into a useful practices. To unfold this hurdle the concept of data mining is the best suited. Data mining have a great potential to enable healthcare systems to use data more efficiently and effectively. Hence, it improves care and reduces costs. This paper reviews various Data Mining techniques such as classification, clustering, association, regression in health domain. It also highlights applications, challenges and future work of Data Mining in healthcare.


title: Trail Pheromones: An Integrative View of Their Role in Social Insect Colony Organization

abstract: Trail pheromones do more than simply guide social insect workers from point A to point B. Recent research has revealed additional ways in which they help to regulate colony foraging, often via positive and negative feedback processes that influence the exploitation of the different resources that a colony has knowledge of. Trail pheromones are often complementary or synergistic with other information sources, such as individual memory. Pheromone trails can be composed of two or more pheromones with different functions, and information may be embedded in the trail network geometry. These findings indicate remarkable sophistication in how trail pheromones are used to regulate colony-level behavior, and how trail pheromones are used and deployed at the individual level.


title: Measurement of Safe Driving Distance Based on Stereo Vision

abstract: The measurement of safe driving distance based on stereo vision is proposed. The model of camera imaging is established using traditional camera calibration method firstly. Secondly, the projection matrix is deduced according to camera's internal and external parameter and used to calibrate the camera. The method of camera calibration based on two-dimensional target plane is adopted. Then the distortion parameters are calculated when the nonlinear geometric model of camera imaging is built. Moreover, the camera's internal and external parameters are optimized on the basis of the projection error' least squares criterion so that the un-distortion image can be obtained. The matching is done between the left image and the right image corresponding to angular point. The parallax error and the distance between the target vehicle and the camera can be calculated. The experimental results show that the measurement scheme is an effective one in a security vehicles spacing survey. The proposed system is convenient for driver to control in time and precisely. It is able to increase the security in intelligent transportation vehicles.


title: A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use

abstract: Internet addiction is a rapidly growing field of research, receiving attention from researchers, journalists and policy makers. Despite much empirical data being collected and analyzed clear results and conclusions are surprisingly absent. This paper argues that conceptual issues and methodological shortcomings surrounding internet addiction research have made theoretical development difficult. An alternative model termed compensatory internet use is presented in an attempt to properly theorize the frequent assumption that people go online to escape real life issues or alleviate dysphoric moods and that this sometimes leads to negative outcomes. An empirical approach to studying compensatory internet use is suggested by combining the psychological literature on internet addiction with research on motivations for internet use. The theoretical argument is that by understanding how motivations mediate the relationship between psychosocial well-being and internet addiction, we can draw conclusions about how online activities may compensate for psychosocial problems. This could help explain why some people keep spending so much time online despite experiencing negative outcomes. There is also a methodological argument suggesting that in order to accomplish this, research needs to move away from a focus on direct effects models and consider mediation and interaction effects between psychosocial well-being and motivations in the context of internet addiction. This is key to further exploring the notion of internet use as a coping strategy; a proposition often mentioned but rarely investigated.


title: A Case Study in Ontology Library Construction

abstract: The goal of our work is to facilitate the development of medical knowledge-based systems by providing a library of reusable ontologies. The availability of such a library reduces the amount of knowledge acquisition required to create knowledge bases of new applications, and makes it easier to connect a knowledge-based system to existing data bases. This article presents a case study in constructing such a library. The emphasis is on studying the principles that underly the internal structure of the library as well as on the process of constructing and using the library. We envision that, in the future, application ontologies can be constructed by the selection and refinement of generic ontologies and domain ontologies from such a library.


AMBIENT VIBRATION TESTS OF STRUCTURES−A REVIEW


title: Experimental Methods for Linguists

abstract: Linguists are increasingly using experiments to provide insight into linguistic representations and linguistic processing. But linguists are rarely trained to think experimentally, and designing a carefully controlled study is not trivial. This paper provides a practical introduction to experiments. We examine issues in experimental design and survey several methodologies. The goal is to provide readers with some tools for understanding and evaluating the rapidly growing literature using experimental methods, as well as for beginning to design experiments in their own research. © 2013 The Author. Language and Linguistics Compass © 2013 Blackwell Publishing Ltd.


title: Electronic travel aids for the blind based on sensory substitution

abstract: With the development of advanced human-machine interface, effective information processing algorithms, and more powerful microprocessors, it is possible to enable the blind to achieve additional perception of the environment. In 1970s, the blind-assistant facility based on sensory substitution was introduced[1]. It called as ETA (short for Electronic Travel Aids), bases on the other natural senses of the blind, such as hearing, touch, smell, feeling and etc. Then the research of vision aids has been broadly extended. After introducing the traditional methods for guiding blind, two typical input modes of travel aid are presented in this paper. One is sonar-based ETAs and the other technique is camera-based ETAs. Its principle of each modes is discussed in detail. Besides these, comparisons between the above two techniques have also been indicated in this paper.


title: Matterport3D: Learning from RGB-D Data in Indoor Environments

abstract: Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding algorithms. However, existing datasets still cover only a limited number of views or a restricted scale of spaces. In this paper, we introduce Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided with surface reconstructions, camera poses, and 2D and 3D semantic segmentations. The precise global alignment and comprehensive, diverse panoramic set of views over entire buildings enable a variety of supervised and self-supervised computer vision tasks, including keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and region classification.


title: 2-D periodic leaky-wave Antennas-part II: slot design

abstract: The far-field radiation patterns of a two-dimensional (2-D) periodic slot leaky-wave antenna (LWA) are studied. The antenna consists of a two-dimensional periodic array of slots in a conducting plane that is printed on top of a grounded dielectric slab. The antenna is excited by a simple source such as a dipole inside the slab. Reciprocity along with the spectral-domain method is used to calculate the far-field pattern, and the radiation characteristics of the structure are investigated. A comparison between the present periodic slot LWA and a 2-D periodic patch LWA discussed in Part I is given to show the advantages of the slot antenna for certain applications. The slot LWA can achieve high directivity patterns, and a circularly-polarized version of the antenna can achieve good circular-polarization at broadside.


title: Roofline: an insightful visual performance model for multicore architectures

abstract: The Roofline model offers insight on how to improve the performance of software and hardware.


title: Determining the outline contour of vehicles in 3D-LIDAR-measurements

abstract: This paper presents a novel and robust method to determine the outline contour of vehicles in 3D-LIDAR (Light Detection and Ranging) measurements. To calculate the outline contour, a vehicle is described by its geometrical properties. These properties are used as constraints to fit a surface to unordered, scattered and error-contaminated 3D measurements. The surface can be used to calculate a corresponding 2D outline contour. The algorithm is tested with two different laser scanners. One scanner has 64, the other only 4 layers.


title: OCC applied to a 180W HPF single-stage LED driver based on Zeta converter in CCM

abstract: This paper presents a high power factor single-stage driver powering a high-power-LED. The driver employs a PFC based on a Zeta converter, using the One-Cycled Control (OCC) technique. A design procedure for Zeta converter is presented, which is based on the flyback converter as proposed by (Lamar, et al. [1]). Through simulation results of Zeta converter working as PFC, classical OCC and exponential ramp OCC approaches are compared and discussed. Showing that the ramp OCC approach results in better performance of the PFC, in relationship to conventional OCC. This paper also presents the stages of operation of the Zeta converter in the continuous conduction mode (CCM).


title: Anxiety sensitivity, anxiety frequency and the prediction of fearfulness

abstract: Abstract A distinction is proposed between anxiety (frequency of symptom occurrence) and anxiety sensitivity (beliefs that anxiety experiences have negative implications). In Study 1, a newly-constructed Anxiety Sensitivity Index (ASI) was shown to have sound psychometric properties for each of two samples of college students. The important finding was that people who tend to endorse one negative implication for anxiety also tend to endorse other negative implications. In Study 2, the ASI was found to be especially associated with agoraphobia and generally associated with anxiety disorders. In Study 3, the ASI explained variance on the Fear Survey Schedule—II that was not explained by either the Taylor Manifest Anxiety Scale or a reliable Anxiety Frequency Checklist. In predicting the development of fears, and possibly other anxiety disorders, it may be more important to know what the person thinks will happen as a result of becoming anxious than how often the person actually experiences anxiety. Implications are discussed for competing views of the ‘fear of fear’.


title: Analog-to-Information and the Nyquist Folding Receiver

abstract: Recovering even a small amount of information from a broadband radio frequency (RF) environment using conventional analog-to-digital converter (ADC) technology is computationally complex and presents significant challenges. For sparse or compressible RF environments, an alternate approach to conventional sampling is analog-to-information (A2I) to enable sub-Nyquist rate sampling based on compressive sensing (CS) principles. This paper presents the Nyquist Folding Receiver (NYFR), an efficient A2I architecture that folds the broadband RF input prior to digitization by a narrowband ADC. The folding is achieved by undersampling the RF spectrum with a stream of short pulses that have a phase modulated sampling period. The undersampled signals then fold down into a low pass interpolation filter. The pulse sample time modulation induces a corresponding phase modulation on the received signals that is scaled by an integer modulation index that varies with the Nyquist zone (i.e., fold number), allowing the signals to be separated based on the measured modulation index. Unlike many schemes motivated by CS that randomize the RF prior to digitization, the NYFR substantially preserves signal structure. This enables information recovery with very low computational complexity algorithms in addition to traditional CS reconstruction techniques. The paper includes a comparison of seven other A2I architectures with the NYFR.


title: Case Studies and Theory Development in the Social Sciences

abstract: A text that emphasizes the importance of case studies in social science scholarship and shows how to make case study practices more rigorous.


title: Machine learning applications in genetics and genomics

abstract: Machine learning methods are becoming increasingly important in the analysis of large-scale genomic, epigenomic, proteomic and metabolic data sets. In this Review, the authors consider the applications of supervised, semi-supervised and unsupervised machine learning methods to genetic and genomic studies. They provide general guidelines for the selection and application of algorithms that are best suited to particular study designs.


title: Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5

abstract: Fine particulate matter ($$\hbox {PM}_{2.5}$$PM2.5) has a considerable impact on human health, the environment and climate change. It is estimated that with better predictions, US$9 billion can be saved over a 10-year period in the USA (State of the science fact sheet air quality. http://www.noaa.gov/factsheets/new, 2012). Therefore, it is crucial to keep developing models and systems that can accurately predict the concentration of major air pollutants. In this paper, our target is to predict $$\hbox {PM}_{2.5}$$PM2.5 concentration in Japan using environmental monitoring data obtained from physical sensors with improved accuracy over the currently employed prediction models. To do so, we propose a deep recurrent neural network (DRNN) that is enhanced with a novel pre-training method using auto-encoder especially designed for time series prediction. Additionally, sensors selection is performed within DRNN without harming the accuracy of the predictions by taking advantage of the sparsity found in the network. The numerical experiments show that DRNN with our proposed pre-training method is superior than when using a canonical and a state-of-the-art auto-encoder training method when applied to time series prediction. The experiments confirm that when compared against the $$\hbox {PM}_{2.5}$$PM2.5 prediction system VENUS (National Institute for Environmental Studies. Visual Atmospheric Environment Utility System. http://envgis5.nies.go.jp/osenyosoku/, 2014), our technique improves the accuracy of $$\hbox {PM}_{2.5}$$PM2.5 concentration level predictions that are being reported in Japan.


title: Light Field Super-Resolution Via Graph-Based Regularization

abstract: Light field cameras capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post-capture refocus, to depth estimation and image-based rendering. However, light field cameras suffer by design from strong limitations in their spatial resolution, which should therefore be augmented by computational methods. On the one hand, off-the-shelf single-frame and multi-frame super-resolution algorithms are not ideal for light field data, as they do not consider its particular structure. On the other hand, the few super-resolution algorithms explicitly tailored for light field data exhibit significant limitations, such as the need to estimate an explicit disparity map at each view. In this work we propose a new light field super-resolution algorithm meant to address these limitations. We adopt a multi-frame alike super-resolution approach, where the complementary information in the different light field views is used to augment the spatial resolution of the whole light field. We show that coupling the multi-frame approach with a graph regularizer, that enforces the light field structure via nonlocal self similarities, permits to avoid the costly and challenging disparity estimation step for all the views. Extensive experiments show that the new algorithm compares favorably to the other state-of-the-art methods for light field super-resolution, both in terms of PSNR and visual quality.


title: Development of the SRI / Nightingale Arabic ASR system

abstract: We describe the large vocabulary automatic speech recognition system developed for Modern Standard Arabic by the SRI/Nightingale team, and used for the 2007 GALE evaluation as part of the speech translation system. We show how system performance is affected by different development choices, ranging from text processing and lexicon to decoding system architecture design. Word error rate results are reported on broadcast news and conversational data from the GALE development and evaluation test sets.


title: Synchronous data flow

abstract: Data flow is a natural paradigm for describing DSP applications for concurrent implementation on parallel hardware. Data flow programs for signal processing are directed graphs where each node represents a function and each arc represents a signal path. Synchronous data flow (SDF) is a special case of data flow (either atomic or large grain) in which the number of data samples produced or consumed by each node on each invocation is specified a priori. Nodes can be scheduled statically (at compile time) onto single or parallel programmable processors so the run-time overhead usually associated with data flow evaporates. Multiple sample rates within the same system are easily and naturally handled. Conditions for correctness of SDF graph are explained and scheduling algorithms are described for homogeneous parallel processors sharing memory. A preliminary SDF software system for automatically generating assembly language code for DSP microcomputers is described. Two new efficiency techniques are introduced, static buffering and an extension to SDF to efficiently implement conditionals.


title: Knowledge and attitude about sexually transmitted diseases among youth in Saudi Arabia

abstract: Background: Sexually transmitted diseases (STDs) are one of the most serious diseases in the world. Our aim was to explore the knowledge and attitude about STDs among the youth. ::: Patients and Methods: This cross-sectional survey was conducted from August 1 to August 13, 2016. Male and female youth between 18 and 25 years were invited to participate in the survey using social media open to all Internet users consisting of questions and statements about STDs, and then the data were analyzed ::: Results: We received 5040 responses to the survey; out of these participants 76.6% were females and 23.4% were males with a mean age 21.5 and most of them were single (85.1%). We noticed that most of the respondents selected the Internet (71.7%) as the main source of their knowledge about STDs followed by school, television, and others, respectively. In spite of, 94.08% knew that human immunodeficiency virus/AIDS is an STD, only 43.61% knew that herpes simplex virus is an STD, and only 31.03% knew that Chlamydia is an STD. A high percent (93.1%) agreed that sexual intercourse transmits STDs. Only 59.6% agreed that condom does not provide complete protection from STDs. To our surprise, only 55% considered themselves capable of protecting themselves against STDs. About 95.8% of the participants agreed that STDs should be taught in schools, while 4.2% disagreed. ::: Conclusion: We noticed a lack of participant's knowledge regarding the types, mode of transmission, and the ways of protections from STDs and their desire to find out information about STDs. Hence, awareness programs about STDs should be started that aim at encouraging youth to follow our religion and culture.


title: Fuzzy logic controller for an inverted pendulum system using quantum genetic optimization

abstract: In this paper, we propose a new generalized design methodology of intelligent robust fuzzy control systems based on quantum genetic algorithm (QGA) called quantum fuzzy model that enhance robustness of fuzzy logic controllers. The QGA is adopted because of their capabilities of directed random search for global optimization to find the parameters of the shape and width of membership functions and rule set of the FLC to obtain the optimal fuzzy controller simultaneously. We test the optimal FLC obtained by the quantum computing applied on the control of dynamic balance and motion of cart-pole balancing system. We compare the proposed technique with existing mamdani fuzzy logic controller which is designed through conventional genetic algorithm. Simulation results reveal that QGA performs better than conventional GA in terms of running speed and optimizing capability.


title: A deep learning framework for the automated inspection of complex dual-energy x-ray cargo imagery

abstract: Previously, we investigated the use of Convolutional Neural Networks (CNNs) to detect so-called Small Metallic Threats (SMTs) hidden amongst legitimate goods inside a cargo container. We trained a CNN from scratch on data produced by a Threat Image Projection (TIP) framework that generates images with realistic variation to robustify performance. The system achieved 90% detection of containers that contained a single SMT, while raising 6% false positives on benign containers. The best CNN architecture used the raw high energy image (single-energy) and its logarithm as input channels. Use of the logarithm improved performance, thus echoing studies on human operator performance. However, it is an unexpected result with CNNs. In this work, we (i) investigate methods to exploit material information captured in dual-energy images, and (ii) introduce a new CNN training scheme that generates ‘spot-the-difference’ benign and threat pairs on-the-fly. To the best of our knowledge, this is the first time that CNNs have been applied directly to raw dual-energy X-ray imagery, in any field. To exploit dual-energy, we experiment with adapting several physics-derived approaches to material discrimination from the cargo literature, and introduce three novel variants. We hypothesise that CNNs can implicitly learn about the material characteristics of objects from the raw dual-energy images, and use this to suppress false positives. The best performing method is able to detect 95% of containers containing a single SMT, while raising 0.4% false positives on benign containers. This is a step change improvement in performance over our prior work


1 Slayer of Lactobacillus helveticus MIMLh 5 promotes endocytosis by 1 dendritic cells 2 Running title : MIMLh 5 ’ s Slayer drives endocytosis in DCs 3 4


title: Automated localisation of retinal optic disk using Hough transform

abstract: The retinal fundus photograph is widely used in the diagnosis and treatment of various eye diseases such as diabetic retinopathy and glaucoma. Medical image analysis and processing has great significance in the field of medicine, especially in non-invasive treatment and clinical study. Normally fundus images are manually graded by specially trained clinicians in a time-consuming and resource-intensive process. A computer-aided fundus image analysis could provide an immediate detection and characterisation of retinal features prior to specialist inspection. This paper describes a novel method to automatically localise one such feature: the optic disk. The proposed method consists of two steps: in the first step, a circular region of interest is found by first isolating the brightest area in the image by means of morphological processing, and in the second step, the Hough transform is used to detect the main circular feature (corresponding to the optical disk) within the positive horizontal gradient image within this region of interest. Initial results on a database of fundus images show that the proposed method is effective and favourable in relation to comparable techniques.


title: Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms

abstract: Cloud research to date has lacked data on the characteristics of the production virtual machine (VM) workloads of large cloud providers. A thorough understanding of these characteristics can inform the providers' resource management systems, e.g. VM scheduler, power manager, server health manager. In this paper, we first introduce an extensive characterization of Microsoft Azure's VM workload, including distributions of the VMs' lifetime, deployment size, and resource consumption. We then show that certain VM behaviors are fairly consistent over multiple lifetimes, i.e. history is an accurate predictor of future behavior. Based on this observation, we next introduce Resource Central (RC), a system that collects VM telemetry, learns these behaviors offline, and provides predictions online to various resource managers via a general client-side library. As an example of RC's online use, we modify Azure's VM scheduler to leverage predictions in oversubscribing servers (with oversubscribable VM types), while retaining high VM performance. Using real VM traces, we then show that the prediction-informed schedules increase utilization and prevent physical resource exhaustion. We conclude that providers can exploit their workloads' characteristics and machine learning to improve resource management substantially.


title: Does culture influence what and how we think? Effects of priming individualism and collectivism.

abstract: is inconclusive. Cross-national evidence is insufficient to argue for a causal process, and comparative data cannot specify if effects are due to both individualism and collectivism, only individualism, only collectivism, or other factors (including other aspects of culture). To address these issues, the authors conducted a meta-analysis of the individualism and collectivism priming literature, with follow-up moderator analyses. Effect sizes were moderate for relationality and cognition, small for self-concept and values, robust across priming methods and dependent variables, and consistent in direction and size with cross-national effects. Results lend support to a situated model of culture in which cross-national differences are not static but dynamically consistent due to the chronic and moment-to-moment salience of individualism and collectivism. Examination of the unique effects of individualism and collectivism versus other cultural factors (e.g., honor, power) awaits the availability of research that primes these factors.


title: Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics

abstract: Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. The problem occurs, in the binary case, when the number of instances in one class significantly outnumbers the number of instances in the other class. This situation is a handicap when trying to identify the minority class, as the learning algorithms are not usually adapted to such characteristics. The approaches to deal with the problem of imbalanced datasets fall into two major categories: data sampling and algorithmic modification. Cost-sensitive learning solutions incorporating both the data and algorithm level approaches assume higher misclassification costs with samples in the minority class and seek to minimize high cost errors. Nevertheless, there is not a full exhaustive comparison between those models which can help us to determine the most appropriate one under different scenarios. The main objective of this work is to analyze the performance of data level proposals against algorithm level proposals focusing in cost-sensitive models and versus a hybrid procedure that combines those two approaches. We will show, by means of a statistical comparative analysis, that we cannot highlight an unique approach among the rest. This will lead to a discussion about the data intrinsic characteristics of the imbalanced classification problem which will help to follow new paths that can lead to the improvement of current models mainly focusing on class overlap and dataset shift in imbalanced classification.


title: User-Centered Design and Agile Methods: A Systematic Review

abstract: This paper presents the results of a systematic review of existing literature on the integration of agile software development with user-centered design approaches. It shows that a common process model underlies such approaches and discusses which artifacts are used to support the collaboration between designers and developers.


title: Painting-to-3D model alignment via discriminative visual elements

abstract: This article describes a technique that can reliably align arbitrary 2D depictions of an architectural site, including drawings, paintings, and historical photographs, with a 3D model of the site. This is a tremendously difficult task, as the appearance and scene structure in the 2D depictions can be very different from the appearance and geometry of the 3D model, for example, due to the specific rendering style, drawing error, age, lighting, or change of seasons. In addition, we face a hard search problem: the number of possible alignments of the painting to a large 3D model, such as a partial reconstruction of a city, is huge. To address these issues, we develop a new compact representation of complex 3D scenes. The 3D model of the scene is represented by a small set of discriminative visual elements that are automatically learned from rendered views. Similar to object detection, the set of visual elements, as well as the weights of individual features for each element, are learned in a discriminative fashion. We show that the learned visual elements are reliably matched in 2D depictions of the scene despite large variations in rendering style (e.g., watercolor, sketch, historical photograph) and structural changes (e.g., missing scene parts, large occluders) of the scene. We demonstrate an application of the proposed approach to automatic rephotography to find an approximate viewpoint of historical paintings and photographs with respect to a 3D model of the site. The proposed alignment procedure is validated via a human user study on a new database of paintings and sketches spanning several sites. The results demonstrate that our algorithm produces significantly better alignments than several baseline methods.


title: Improving point-cloud accuracy from a moving platform in field operations

abstract: This paper presents a method for improving the quality of distorted 3D point clouds made from a vehicle equipped with a laser scanner moving over uneven terrain. Existing methods that use 3D point-cloud data (for tasks such as mapping, localisation, and object detection) typically assume that each point cloud is accurate. For autonomous robots moving in rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one point cloud, in which case the data will be distorted. The method proposed in this paper is capable of increasing the accuracy of 3D point clouds, without assuming any specific features of the environment (such as planar walls), without resorting to a “stop-scan-go” approach, and without relying on specialised and expensive hardware. Each new point cloud is matched to the previous using normal-distribution-transform (NDT) registration, after which a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. The proposed method increases the accuracy of both the measured platform trajectory and the point cloud. The method is validated on both real-world and simulated data.


title: Calcium inhibition of efrotomycin production byNocardia lactamdurans

abstract: Efrotomycin is a modified polyketide antibiotic of the elfamycin family that has use in the area of pig husbandry. Optimization of the fermentation medium for production of efrotomycin by Nocardia lactamdurans revealed that the fermentation is sensitive to hard water and certain lots of cottonseed flour used to prepare a complex fermentation medium. A limited metal ion analysis of the hard water indicated that calcium ions are present at concentrations found to be inhibitory by the addition of calcium chloride to medium prepared with distilled water. Similarly, a correlation between lots of cottonseed flour that poorly supported the fermentation and high calcium levels is presented. Further experimentation revealed that by altering the sterilization conditions of the cottonseed flour, the inhibitory effect of poor lots could be prevented.


title: A comparative study of shilling attack detectors for recommender systems

abstract: Uncovering shilling attackers hidden in recommender systems is very crucial to enhance the robustness and trustworthiness of product recommendation. Many shilling attack detection algorithms have been proposed so far, and they exhibit complementary advantage and disadvantage towards various types of attacks. In this paper, we provide a thorough experimental comparison of several well-known detectors, including supervised and unsupervised methods. MovieLens 100K is the most widely-used dataset in the realm of shilling attack detection, and thus it is selected as the benchmark dataset. Meanwhile, seven types of shilling attacks generated by average-filling and random-filling model are tested in our experiments. As a result of our analysis, we show clearly causes and essential characteristics insider attackers that might determine the success or failure of different kinds of detectors.


title: Ensemble-based algorithms to detect disjoint and overlapping communities in networks

abstract: Given a set ${\cal AL}$ of community detection algorithms and a graph $G$ as inputs, we propose two ensemble methods $\mathtt{EnDisCO}$ and $\mathtt{MeDOC}$ that (respectively) identify disjoint and overlapping communities in $G$. $\mathtt{EnDisCO}$ transforms a graph into a latent feature space by leveraging multiple base solutions and discovers disjoint community structure. $\mathtt{MeDOC}$ groups similar base communities into a meta-community and detects both disjoint and overlapping community structures. Experiments are conducted at different scales on both synthetically generated networks as well as on several real-world networks for which the underlying ground-truth community structure is available. Our extensive experiments show that both algorithms outperform state-of-the-art non-ensemble algorithms by a significant margin. Moreover, we compare $\mathtt{EnDisCO}$ and $\mathtt{MeDOC}$ with a recent ensemble method for disjoint community detection and show that our approaches achieve superior performance. To the best of our knowledge, $\mathtt{MeDOC}$ is the first ensemble approach for overlapping community detection.


title: Data Management in the Cloud: Promises, State-of-the-art, and Open Questions

abstract: Cloud Computing has the potential to significantly change the IT world. It promises dramatic reductions in cost and time-to-market. This paper gives an introduction to cloud computing, thereby stating how cloud computing tries to fulfill these promises. Furthermore, this paper studies the state-of-the-art of cloud computing platforms and answers the question of how well the current generation of systems meets these promises. Based on that analysis, this paper states several fundamental questions that need to be addressed when designing a cloud computing platform. The focus of the paper is on database workloads; more specifically, on online transaction processing workloads (OLTP) in public clouds.


Data Mining Model for Predicting Student Enrolment in STEM Courses in Higher Education Institutions


title: Robust ground plane estimation based on particle filters

abstract: This paper describes a Particle Filter based approach for estimating the ground plane from an image sequence. Based on a Bayesian framework, the Particle Filter provides a robust estimation of the plane parameters, since it can handle non-linearities, while allowing a high flexibility for integrating new cues into the system. Furthermore, the different modes of the resulting probability density function are segmented by means of a mean-shift algorithm, resulting in better localization of the estimate with the highest posterior probability. Our method has been tested on both synthetic and real world scenarios and has shown to be robust to missing and unstable measurements. On synthetic data of representative runs the angular error is well within 0.5° with a standard deviation of less than 0.3°.


title: Analytical Performance Modeling for Top-K Query Processing

abstract: Top-K query processing is one of the most important problems in large-scale Information Retrieval systems. Since query processing time varies for different queries, an accurate run-time performance prediction is critical for online query scheduling and load balancing, which could eventually reduce the query waiting time and improve the throughput. Previous studies estimated the query processing time based on the combination of term-level features. Unfortunately, these features were often selected arbitrarily, and the linear combination of these features might not be able to accurately capture the complexity in the query processing. In this paper, we propose a novel analytical performance modeling framework for top-K query processing. Our goal is to provide a systematic way of identifying important features for the efficiency prediction and then develop a general framework for estimating the query processing time. Specifically, we divide the query processing into three stages, identify useful features and discuss how to use them to model the query processing time for each stage. After that, we propose to fit the model using a step-by-step strategy and compute the approximated feature values based on easily obtained statistics. Experimental results on TREC collections show that the developed performance model can predict the query processing time more accurately than the state of the art efficiency predictor, in particular for the dynamic pruning methods.


The Public Acquisition of Commonsense Knowledge


title: The FASEB Journal • Life Sciences Forum Dose translation from animal to human studies revisited

abstract: As new drugs are developed, it is essential to appropriately translate the drug dosage from one animal species to another. A misunderstanding appears to exist regarding the appropriate method for allometric dose translations, especially when starting new animal or clinical studies. The need for education regarding appropriate translation is evident from the media response regarding some recent studies where authors have shown that resveratrol, a compound found in grapes and red wine, improves the health and life span of mice. Immediately after the online publication of these papers, the scientific community and popular press voiced concerns regarding the relevance of the dose of resveratrol used by the authors. The animal dose should not be extrapolated to a human equivalent dose (HED) by a simple conversion based on body weight, as was reported. For the more appropriate conversion of drug doses from animal studies to human studies, we suggest using the body surface area (BSA) normalization method. BSA co...


title: A Formal Model and Analysis of the MQ Telemetry Transport Protocol

abstract: We present a formal model of the MQ Telemetry Transport version 3.1 protocol based on a timed message-passing process algebra. We explain the modeling choices that we made, including pointing out ambiguities in the original protocol specification, and we carry out a static analysis of the formal protocol model, which is based on an approximation of a name-substitution semantics for algebra. The analysis reveals that the protocol behaves correctly as specified against the first two quality of service modes of operation providing at most once and at least once delivery semantics to the subscribers. However, we find that the third and highest quality of service semantics is prone to error and at best ambiguous in certain aspects of its specification. Finally, we suggest an enhancement of this level of QoS for the protocol.


title: UBY - A Large-Scale Unified Lexical-Semantic Resource Based on LMF

abstract: We present Uby, a large-scale lexical-semantic resource combining a wide range of information from expert-constructed and collaboratively constructed resources for English and German. It currently contains nine resources in two languages: English WordNet, Wiktionary, Wikipedia, FrameNet and VerbNet, German Wikipedia, Wiktionary and GermaNet, and multilingual OmegaWiki modeled according to the LMF standard. For FrameNet, VerbNet and all collaboratively constructed resources, this is done for the first time. Our LMF model captures lexical information at a fine-grained level by employing a large number of Data Categories from ISOCat and is designed to be directly extensible by new languages and resources. All resources in Uby can be accessed with an easy to use publicly available API.


NLP-driven citation analysis for scientometrics


title: Identification and classification of ERP critical failure factors in Iranian industries

abstract: Implementation of Enterprise Resource Planning systems (ERPs) is a complex and costly process, which usually results in serious failures. The majority of previous studies and research projects have been conducted in identifying ERP Critical Success Factors (CSFs) rather than Critical Failure Factors (CFFs). On the other hand, most studies have been devoted to developed countries, while in developing countries, many companies have moved towards using such systems. The majority of IT/IS management standards and guidelines have been developed by technologically-leading countries. But developing countries, which mostly confront with especial challenges, have a different condition from the implicit assumptions of leading countries. Iran is one of these countries that many of its ERP implementation projects led to failure. There is no comprehensive study that identifies ERP CFFs in Iranian industries. The main purpose of this study is to identify such factors and classify them to help other industries, consultants and implementers to prevent failures in the implementation of ERP projects. So, at first, with the semi structured interviews and literature reviews, 47 failure factors were identified. After that a questionnaire developed and sent to ERP project team members in Iranian industries that failed in their ERP projects. Robust Exploratory Factor Analysis (EFA) has been used for data analysis, which finally classified critical failure factors in seven groups named as vendor and consultant, human resources, managerial, project management, processes, organizational and technical. The results of this study have provided a very useful reference for scholars and managers to identify the relevant issues of ERP projects failure in developing countries.


title: Images Don't Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank

abstract: Search is at the heart of modern e-commerce. As a result, the task of ranking search results automatically (learning to rank) is a multibillion dollar machine learning problem. Traditional models optimize over a few hand-constructed features based on the item's text. In this paper, we introduce a multimodal learning to rank model that combines these traditional features with visual semantic features transferred from a deep convolutional neural network. In a large scale experiment using data from the online marketplace Etsy, we verify that moving to a multimodal representation significantly improves ranking quality. We show how image features can capture fine-grained style information not available in a text-only representation. In addition, we show concrete examples of how image information can successfully disentangle pairs of highly different items that are ranked similarly by a text-only model.


title: Black hole: A new heuristic optimization approach for data clustering

abstract: Nature has always been a source of inspiration. Over the last few decades, it has stimulated many successful algorithms and computational tools for dealing with complex and optimization problems. This paper proposes a new heuristic algorithm that is inspired by the black hole phenomenon. Similar to other population-based algorithms, the black hole algorithm (BH) starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. At each iteration of the black hole algorithm, the best candidate is selected to be the black hole, which then starts pulling other candidates around it, called stars. If a star gets too close to the black hole, it will be swallowed by the black hole and is gone forever. In such a case, a new star (candidate solution) is randomly generated and placed in the search space and starts a new search. To evaluate the performance of the black hole algorithm, it is applied to solve the clustering problem, which is a NP-hard problem. The experimental results show that the proposed black hole algorithm outperforms other traditional heuristic algorithms for several benchmark datasets.


title: A dynamic pari-mutuel market for hedging, wagering, and information aggregation

abstract: I develop a new mechanism for risk allocation and information speculation called a dynamic pari-mutuel market (DPM). ADPM acts as hybrid between a pari-mutuel market and a continuous double auction (CDA), inheriting some of the advantages of both. Like a pari-mutuel market, a DPM offers infinite buy-in liquidity and zero risk for the market institution; like a CDA, a DPM cancontinuously react to new information, dynamically incorporate information into prices, and allow traders to lock in gains or limit losses by selling prior to event resolution. The trader interface can be designed to mimic the familiar double auction format with bid-ask queues, though with an addition variable called the payoff per share. The DPM price function can be viewed as an automated market maker always offering to sell at some price, and moving the price appropriately according to demand. Since the mechanism is pari-mutuel (i.e., redistributive), it is guaranteed to pay out exactly the amount of money taken in. Iexplore a number of variations on the basic DPM, analyzing the properties of each, and solving in closed form for their respective price functions.


The Topic Browser An Interactive Tool for Browsing Topic Models


title: Efficient Graphlet Counting for Large Networks

abstract: From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks at both the global macro-level as well as the local micro-level. While graphlets have witnessed a tremendous success and impact in a variety of domains, there has yet to be a fast and efficient approach for computing the frequencies of these subgraph patterns. However, existing methods are not scalable to large networks with millions of nodes and edges, which impedes the application of graphlets to new problems that require large-scale network analysis. To address these problems, we propose a fast, efficient, and parallel algorithm for counting graphlets of size k={3,4}-nodes that take only a fraction of the time to compute when compared with the current methods used. The proposed graphlet counting algorithms leverages a number of proven combinatorial arguments for different graphlets. For each edge, we count a few graphlets, and with these counts along with the combinatorial arguments, we obtain the exact counts of others in constant time. On a large collection of 300+ networks from a variety of domains, our graphlet counting strategies are on average 460x faster than current methods. This brings new opportunities to investigate the use of graphlets on much larger networks and newer applications as we show in the experiments. To the best of our knowledge, this paper provides the largest graphlet computations to date as well as the largest systematic investigation on over 300+ networks from a variety of domains.


title: Association of polymorphisms in the cytochrome P450 CYP2C9 with warfarin dose requirement and risk of bleeding complications

abstract: BACKGROUND ::: The cytochrome P450 CYP2C9 is responsible for the metabolism of S-warfarin. Two known allelic variants CYP2C9*2 and CYP2C9*3 differ from the wild type CYP2C9*1 by a single aminoacid substitution in each case. The allelic variants are associated with impaired hydroxylation of S-warfarin in in-vitro expression systems. We have studied the effect of CYP2C9 polymorphism on the in-vivo warfarin dose requirement. ::: ::: ::: METHODS ::: Patients with a daily warfarin dose requirement of 1.5 mg or less (low-dose group, n=36), randomly selected patients with a wide range of dose requirements from an anticoagulant clinic in north-east England (clinic control group, n=52), and 100 healthy controls from the community in the same region were studied. Genotyping for the CYP2C9*2 and CYP2C9*3 alleles was done by PCR analysis. Case notes were reviewed to assess the difficulties encountered during the induction of warfarin therapy and bleeding complications in the low-dose and clinic control groups. ::: ::: ::: FINDINGS ::: The odds ratio for individuals with a low warfarin dose requirement having one or more CYP2C9 variant alleles compared with the normal population was 6.21 (95% CI 2.48-15.6). Patients in the low-dose group were more likely to have difficulties at the time of induction of warfarin therapy (5.97 [2.26-15.82]) and have increased risk of major bleeding complications (rate ratio 3.68 [1.43-9.50]) when compared with randomly selected clinic controls. ::: ::: ::: INTERPRETATION ::: We have shown that there is a strong association between CYP2C9 variant alleles and low warfarin dose requirement. CYP2C9 genotyping may identify a subgroup of patients who have difficulty at induction of warfarin therapy and are potentially at a higher risk of bleeding complications.


title: KbQAS: A Knowledge-based QA System

abstract: We present the first ontology-based Vietnamese QA system KbQAS where a new knowledge acquisition approach for analyzing English and Vietnamese questions is integrated.


Exploring What not to Clean in Urban Data: A Study Using New York City Taxi Trips


title: A secure cookie protocol

abstract: Cookies are the primary means for Web applications to authenticate HTTP requests and to maintain client states. Many Web applications (such as electronic commerce) demand a secure cookie protocol. Such a protocol needs to provide the following four services: authentication, confidentiality, integrity and antireplay. Several secure cookie protocols have been proposed in previous literature; however, none of them are completely satisfactory. In this paper, we propose a secure cookie protocol that is effective, efficient, and easy to deploy. In terms of effectiveness, our protocol provides all of the above four security services. In terms of efficiency, our protocol does not involve any database lookup or public key cryptography. In terms of deployability, our protocol can be easily deployed on an existing Web server, and it does not require any change to the Internet cookie specification. We implemented our secure cookie protocol using PHP, and the experimental results show that our protocol is very efficient.


title: Imitation Learning: A Survey of Learning Methods

abstract: Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years; however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations, without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction, and computer games, to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this article, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications, and highlight current and future research directions.


title: Combining image and text features: a hybrid approach to mobile book spine recognition

abstract: Despite the successful use of local image features for large-scale object recognition, they are not effective in recognizing book spines on bookshelves. This is because some book spines contain only text components that do not yield distinguishing image features. To overcome this issue, we develop a new approach that combines a text-based spine recognition pipeline with an image feature-based spine recognition pipeline. The text within the book spine image is recognized and used as keywords to search a book spine text database. The image features of the book spine image are searched through a book spine image database. The search results of the two approaches are then carefully combined to form the final result. We implement the proposed hybrid book recognition pipeline used in a book inventory management system, and conduct extensive experiments to evaluate its performance. The experimental results show that while text-based or image feature-based systems only achieve a recall of 72%, the proposed hybrid system achieves a recall of ~91%.


title: True upper bounds for Bermudan products via Non-nested Monte Carlo

abstract: We present a generic non-nested Monte Carlo procedure for computing true upper bounds for Bermudan products, given an approximation of the Snell envelope. The pleonastic true stresses that, by construction, the estimator is biased above the Snell envelope. The key idea is a regression estimator for the Doob martingale part of the approximative Snell envelope, which preserves the martingale property. The so constructed martingale can be employed for computing tight dual upper bounds without nested simulation. In general, this martingale can also be used as a control variate for simulation of conditional expectations. In this context, we develop a variance reduced version of the nested primal-dual estimator. Numerical experiments indicate the efficiency of the proposed algorithms.


title: Consensus Tracking of Multi-Agent Systems With Lipschitz-Type Node Dynamics and Switching Topologies

abstract: Distributed consensus tracking is addressed in this paper for multi-agent systems with Lipschitz-type node dynamics. The main contribution of this work is solving the consensus tracking problem without the assumption that the topology among followers is strongly connected and fixed. By using tools from M-matrix theory, a class of consensus tracking protocols based only on the relative states among neighboring agents is designed. By appropriately constructing Lyapunov function, it is proved that consensus tracking in the closed-loop multi-agent systems with a fixed topology having a directed spanning tree can be achieved if the feedback gain matrix and the coupling strength are suitably selected. Furthermore, with the assumption that each possible topology contains a directed spanning tree, it is theoretically shown that consensus tracking under switching directed topologies can be achieved if the control parameters are suitably selected and the dwell time is larger than a positive threshold. The results are then extended to the case where the communication topology contains a directed spanning tree only frequently as the system evolves with time. Finally, some numerical simulations are given to verify the theoretical analysis.


title: DRAMSpec: A High-Level DRAM Timing, Power and Area Exploration Tool

abstract: In systems ranging from mobile devices to servers, DRAM has a big impact on performance and contributes a significant part of the total consumed power. The performance and power of the system depends on the architecture of the DRAM chip, the design of the memory controller and the access patterns received by the memory controller. Thus, evaluating the impact of DRAM design decisions requires a holistic approach that includes an appropriate model of the DRAM bank, a realistic controller and DRAM power model, and a representative workload, which requires a full system simulator running a complete software stack. In this paper, we introduce DRAMSpec, a high-level DRAM bank/chip modeling tool. Our contribution is to move the DRAM modeling abstraction level from the circuit level to the DRAM bank and by the integration in full system simulators we allow system or processor designers (non-DRAM experts) to tune future DRAM architectures for their target applications. We demonstrate the merits of DRAMSpec by exploring the influence of DRAM row-buffer (page) size and the number of banks on performance and power of a server application (memcached). Our new DRAM design offers a 16% DRAM performance improvement and 13% DRAM energy saving compared to standard comodity DDR3 devices. Additionally, we demonstrate how our tool is able to aid in evaluating novel DRAM architectures, such as the Hybrid Memory Cube (HMC), for which no DRAM datasheets are available. Finally, we highlight the DRAM technology scaling for a specific HMC architecture and we quantify the impact on latency and power.


title: Circularly Polarized Helical Antenna for ISM-Band Ingestible Capsule Endoscope Systems

abstract: A multilayer miniaturized circularly polarized (CP) helical antenna is designed and experimentally demonstrated for industrial, scientific, and medical (ISM) (2.4-2.48 GHz) ingestible capsule endoscope systems. The proposed antenna is composed of three open loops at various layers connected by via holes to form an axial-mode helical structure to generate traveling wave radiation. A one-layer muscle phantom model is used for initial design and optimization. The footprint of the proposed antenna is π×(5.5) ::: 2 ::: ×3.81 mm 3 ::: . The simulated and measured impedance bandwidth is over 40% and 26% in the one-layer muscle phantom, respectively. The simulated axial ratio (AR) bandwidth is around 33.3%. The CP purity of the proposed antenna is calculated by comparing the communication link levels for two orthogonal polarizations. Additionally, electrical components are modeled inside the capsule to evaluate the effects on the antenna performance. CST voxel Gustav human body is utilized to study the design in a realistic environment. Finally, an omnidirectional CP exterior antenna is designed and the communication link is evaluated.


title: SybilGuard: defending against sybil attacks via social networks

abstract: Peer-to-peer and other decentralized, distributed systems are known to be particularly vulnerable to sybil attacks. In a sybil attack, a malicious user obtains multiple fake identities and pretends to be multiple, distinct nodes in the system. By controlling a large fraction of the nodes in the system, the malicious user is able to ldquoout voterdquo the honest users in collaborative tasks such as Byzantine failure defenses. This paper presents SybilGuard, a novel protocol for limiting the corruptive influences of sybil attacks. Our protocol is based on the ldquosocial networkrdquo among user identities, where an edge between two identities indicates a human-established trust relationship. Malicious users can create many identities but few trust relationships. Thus, there is a disproportionately small ldquocutrdquo in the graph between the sybil nodes and the honest nodes. SybilGuard exploits this property to bound the number of identities a malicious user can create. We show the effectiveness of SybilGuard both analytically and experimentally.


title: A Supervised Approach to Extractive Summarisation of Scientific Papers

abstract: Automatic summarisation is a popular approach to reduce a document to its main arguments. Recent research in the area has focused on neural approaches to summarisation, which can be very data-hungry. However, few large datasets exist and none for the traditionally popular domain of scientific publications, which opens up challenging research avenues centered on encoding large, complex documents. In this paper, we introduce a new dataset for summarisation of computer science publications by exploiting a large resource of author provided summaries and show straightforward ways of extending it further. We develop models on the dataset making use of both neural sentence encoding and traditionally used summarisation features and show that models which encode sentences as well as their local and global context perform best, significantly outperforming well-established baseline methods.


title: Investigation of Possibilities to Detect Malware Using Existing Tools

abstract: Malware stands for malicious software, which is installed on a computer system without the knowledge of the system owner. It performs malicious actions such as stealing confidential information and allowing remote code execution, and it can cause denial of service. Recently, malware creators started to publish new malware, which can bypass anti-malware software, intrusion detection systems (IDS) and sandbox execution. Due to this evasion, the protection of computer networks and computerized systems against these programs has become one of the biggest challenges in the information security realm. This paper proposes a methodology to learn the well-known malware analysis and detection tools, to implement these tools on well-known malware and benign programs and to compare the obtained results. Further, this research will suggest to users how to analyze and detect existing and unknown malware. In a test case, 100 malware and 100 benign program samples were collected from different sources and analyzed under different versions of Windows machines. The test results indicated that it is almost impossible to detect malware by only using one tool. Using static and dynamic analysis tools together increased accuracy and the detection rate. The test results also showed that dynamic malware analysis tools outperformed static analysis tools.


title: Application neutrality and a paradox of side payments

abstract: The ongoing debate over net neutrality covers a broad set of issues related to the regulation of public networks. In two ways, we extend an idealized usage-priced game-theoretic framework based on a common linear demand-response model. First, we study the impact of "side payments" among a plurality of Internet service (access) providers and content providers. In the non-monopolistic case, our analysis reveals an interesting "paradox" of side payments in that overall revenues are reduced for those that receive them. Second, assuming different application types (e.g., HTTP web traffic, peer-to-peer file sharing, media streaming, interactive VoIP), we extend this model to accommodate differential pricing among them in order to study the issue of application neutrality. Revenues for neutral and non-neutral pricing are compared for the case of two application types.


title: Color image encryption using chaotic maps, triangular scrambling, with DNA sequences

abstract: Applying security to the transmitted image is very important issues, because the transmission channel is open and can be compromised by attackers. To secure this channel from the eavesdropping attack, man in the middle attack, and so on. A new hybrid encryption image mechanism that utilize triangular scrambling, DNA encoding and chaotic map is implemented. The scheme takes a master key with a length of 320 bit, and produces a group of sub-keys with two length (32 and 128 bit) to encrypt the blocks of images, then a new triangular scrambling method is used to increase the security of the image. Many experiments are implemented using several different images. The analysis results for these experiments show that the security obtained on by using the proposed method is very suitable for securing the transmitted images. The current work has been compared with other works and the result of comparison shows that the current work is very strong against attacks.


title: An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends

abstract: Blockchain, the foundation of Bitcoin, has received extensive attentions recently. Blockchain serves as an immutable ledger which allows transactions take place in a decentralized manner. Blockchain-based applications are springing up, covering numerous fields including financial services, reputation system and Internet of Things (IoT), and so on. However, there are still many challenges of blockchain technology such as scalability and security problems waiting to be overcome. This paper presents a comprehensive overview on blockchain technology. We provide an overview of blockchain architechture firstly and compare some typical consensus algorithms used in different blockchains. Furthermore, technical challenges and recent advances are briefly listed. We also lay out possible future trends for blockchain.


title: Vulnerable live patients, powerful dead patients: A textual analysis of doctor-patient relationships in popular Chinese medical dramas

abstract: Using Framing Theory as a theoretical framework, this study examined depictions of patients and doctor-patient communication in Chinese medical dramas. The textual analysis had two major fi...


title: Photo-inspired model-driven 3D object modeling

abstract: We introduce an algorithm for 3D object modeling where the user draws creative inspiration from an object captured in a single photograph. Our method leverages the rich source of photographs for creative 3D modeling. However, with only a photo as a guide, creating a 3D model from scratch is a daunting task. We support the modeling process by utilizing an available set of 3D candidate models. Specifically, the user creates a digital 3D model as a geometric variation from a 3D candidate. Our modeling technique consists of two major steps. The first step is a user-guided image-space object segmentation to reveal the structure of the photographed object. The core step is the second one, in which a 3D candidate is automatically deformed to fit the photographed target under the guidance of silhouette correspondence. The set of candidate models have been pre-analyzed to possess useful high-level structural information, which is heavily utilized in both steps to compensate for the ill-posedness of the analysis and modeling problems based only on content in a single image. Equally important, the structural information is preserved by the geometric variation so that the final product is coherent with its inherited structural information readily usable for subsequent model refinement or processing.


title: Improved 6.5kV FREEMD-Pair based on SiC JFET and Si IGBT

abstract: The newly proposed FREEDM-Pair is an ideal and economical solution to address high cost issue in high power SiC power devices. The FREEDM-Pair, in which a Si IGBT and a SiC JFET are connected in parallel, combines the advantages of SiC JFET's low switching losses and Si IGBT's superior forward conduction characteristics. One issue of the JFET based FREEDM-Pair is the incompatible gate drive voltage for the SiC JFET and Si IGBT which complicates the gate driver design and increases the total cost. Also, the high voltage SiC JBS reverse diode in FREEDM-Pair is indispensable for the reverse current conduction, leading to higher cost and larger package size. To address these issues, an improved FREEDM-Pair is proposed in this paper, in which the SiC JBS diode is eliminated and the normally-off SiC JFET is operated in cascode configuration to unify the gate driver voltage level and speed up the switching of the JFET. The design and operation of improved FREEDM-Pair is elaborated and experimental results verified its advantages. Also, the affordable cost demonstrates that this promising concept is an ideal step to introduce high voltage SiC power devices.


title: Recovery from stress: an experimental examination of focused attention meditation in novices

abstract: Identifying strategies that aid in recovery from stress may benefit cardiovascular health. Ninety-nine undergraduate meditation novices were randomly assigned to meditate, listen to an audio book, or sit quietly after a standardized stressor. During recovery, meditators’ heart rate variability and skin conductance levels returned to baseline, whereas only heart rate variability returned to baseline for the audio book and control groups. Positive and negative affect were no different than baseline following meditation, whereas, both audio book and control groups had lower positive affect and higher negative affect following the intervention. Findings suggest that the sympathetic nervous system is uniquely affected by meditation, and novices may benefit emotionally from meditating after a stressor. Further research is needed to determine meditation’s utility in recovering from stress.


title: TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition

abstract: This work tackles the face recognition task on images captured using thermal camera sensors which can operate in the non-light environment. While it can greatly increase the scope and benefits of the current security surveillance systems, performing such a task using thermal images is a challenging problem compared to face recognition task in the Visible Light Domain (VLD). This is partly due to the much smaller amount number of thermal imagery data collected compared to the VLD data. Unfortunately, direct application of the existing very strong face recognition models trained using VLD data into the thermal imagery data will not produce a satisfactory performance. This is due to the existence of the domain gap between the thermal and VLD images. To this end, we propose a Thermal-to-Visible Generative Adversarial Network (TV-GAN) that is able to transform thermal face images into their corresponding VLD images whilst maintaining identity information which is sufficient enough for the existing VLD face recognition models to perform recognition. Some examples are presented in Figure 1. Unlike the previous methods, our proposed TV-GAN uses an explicit closed-set face recognition loss to regularize the discriminator network training. This information will then be conveyed into the generator network in the forms of gradient loss. In the experiment, we show that by using this additional explicit regularization for the discriminator network, the TV-GAN is able to preserve more identity information when translating a thermal image of a person which is not seen before by the TV-GAN.


title: Then suddenly he went right off the rails : mothers' stories of adolescent cannabis use

abstract: AbstractAdolescent drug use provides challenges for parents, health professionals and the wider community. Of those adolescents who use drugs, cannabis is often the drug of choice. A number of adolescents will develop problems associated with cannabis use, and the burden of managing these problems will fall on the family, particularly the mother. This paper explores adolescent cannabis use from the perspective of mothers of affected young people. Twelve women took part in conversational style audio-taped interviews. Stories were extracted from the resulting narratives and were analysed guided by the work of Anderson and Jack (1991). Detailed findings are grouped under the themes: First awareness: ‘I couldn’t deny it was happening’; Constant vigilance: ‘I watched his every move’; Cannabis and family life: ‘I’d start to shake when he walked into the house’; Disrupted lives: ‘I was terrified he’d go to prison’; Shifting aspirations: ‘just staying alive’; and, Maintaining a loving relationship: ‘always told h...


title: Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi

abstract: Tracking human vital signs of breathing and heart rates during sleep is important as it can help to assess the general physical health of a person and provide useful clues for diagnosing possible diseases. Traditional approaches (e.g., Polysomnography (PSG)) are limited to clinic usage. Recent radio frequency (RF) based approaches require specialized devices or dedicated wireless sensors and are only able to track breathing rate. In this work, we propose to track the vital signs of both breathing rate and heart rate during sleep by using off-the-shelf WiFi without any wearable or dedicated devices. Our system re-uses existing WiFi network and exploits the fine-grained channel information to capture the minute movements caused by breathing and heart beats. Our system thus has the potential to be widely deployed and perform continuous long-term monitoring. The developed algorithm makes use of the channel information in both time and frequency domain to estimate breathing and heart rates, and it works well when either individual or two persons are in bed. Our extensive experiments demonstrate that our system can accurately capture vital signs during sleep under realistic settings, and achieve comparable or even better performance comparing to traditional and existing approaches, which is a strong indication of providing non-invasive, continuous fine-grained vital signs monitoring without any additional cost.


title: Acid-facilitated product release from a Mo(IV) center: relevance to oxygen atom transfer reactivity of molybdenum oxotransferases

abstract: We report that pyridinium ions (HPyr+) accelerate the conversion of [Tp*MoIVOCl(OPMe3)] (1) to [Tp*MoIVOCl(NCCH3)] (2) by 103-fold, affording 2 in near-quantitative yield; Tp* = hydrotris(3,5-dimethyl-1-pyrazolyl)borate. This novel reactivity and the mechanism of this reaction were investigated in detail. The formation of 2 followed pseudo-first-order kinetics, with the observed pseudo-first-order rate constant (k obs) linearly correlated with [HPyr+]. An Eyring plot revealed that this HPyr+-facilitated reaction has a small positive value of ∆S ‡ indicative of a dissociative interchange (Id) mechanism, different from the slower associative interchange (Ia) mechanism in the absence of HPyr+ marked with a negative ∆S ‡. Interestingly, log(k obs) was found to be linearly correlated to the acidity of substituted pyridinium ions. This novel reactivity is further investigated using combined DFT and ab initio coupled cluster methods. Different reaction pathways, including Id, Ia, and possible alternative routes in the absence or presence of HPyr+, were considered, and enthalpy and free energies were calculated for each pathway. Our computational results further underscored that the Id route is energetically favored in the presence of HPyr+, in contrast with the preferred Ia-NNO pathway in the absence of HPyr+. Our computational results also revealed molecular-level details for the HPyr+-facilitated Id route. Specifically, HPyr+ initially becomes hydrogen-bonded to the oxygen atom of the Mo(IV)-OPMe3 moiety, which lowers the activation barrier for the Mo-OPMe3 bond cleavage in a rate-limiting step to dissociate the OPMe3 product. The implications of our results were discussed in the context of molybdoenzymes, particularly the reductive half-reaction of sulfite oxidase.


title: RecoFit: using a wearable sensor to find, recognize, and count repetitive exercises

abstract: Although numerous devices exist to track and share exercise routines based on running and walking, these devices offer limited functionality for strength-training exercises. We introduce RecoFit, a system for automatically tracking repetitive exercises - such as weight training and calisthenics - via an arm-worn inertial sensor. Our goal is to provide real-time and post-workout feedback, with no user-specific training and no intervention during a workout. Toward this end, we address three challenges: (1) segmenting exercise from intermittent non-exercise periods, (2) recognizing which exercise is being performed, and (3) counting repetitions. We present cross-validation results on our training data and results from a study assessing the final system, totaling 114 participants over 146 sessions. We achieve precision and recall greater than 95% in identifying exercise periods, recognition of 99%, 98%, and 96% on circuits of 4, 7, and 13 exercises respectively, and counting that is accurate to ±1 repetition 93% of the time. These results suggest that our approach enables a new category of fitness tracking devices.


title: Longley-Rice and ITU-P.1546 Combined: A New International Terrain-Specific Propagation Model

abstract: A new propagation model and software implementation combines the best of the NTIA-ITS Longley-Rice methodology and it's Irregular Terrain Methodology (ITM) software implementation, a set of deterministic approximation equations derived from the empirical data in ITU-R P.1546 and other ITU Recommendations, and Snell's Law and Beer's Law, to create the first truly point-to-point, terrain-specific international radio reception signal strength prediction model. This implementation utilizes 3-arc-second Satellite Radar Terrain data that is available internationally, making this a propagation model that can be implemented internationally. This new model more fully implements the original Longley-Rice Tech Note 101 methodology that does the ITM. The core software of this model, the equivalent of the ITM core, is the Irregular Terrain with Obstructions Model, the ITWOM. This model is specific and accurate enough to be used for the interference studies for which it was developed, has been in continuous development since 2005, and has been in use for two years by Givens & Bell to produce FM vehicular reception studies for analog and digital (IBOC) reception. Source code for a basic version of the ITWOM, designed for 3-arc-second database use and limited to the minimum range of the ITM and P.1546 recommendations, will be released.


title: Using the WordNet Ontology for Interpreting Medical Records

abstract: As hospitals throughout Europe are striving exploit advantages of IT and network technologies, electronic medical records systems are starting to replace paper based archives. This paper suggests and describes an add-on service to electronic medical record systems that will help regular patients in getting insight to their diagnoses and medical record. The add-on service is based annotating polysemous and foreign terms with WordNet synsets. By exploiting the way that relationships between synsets are structured and described in WordNet, it is shown how patients can get interactive opportunities to generalize and understand their personal records.


title: Formal refinement patterns for goal-driven requirements elaboration

abstract: Requirements engineering is concerned with the identification of high-level goals to be achieved by the system envisioned, the refinement of such goals, the operationalization of goals into services and constraints, and the assignment of responsibilities for the resulting requirements to agents such as humans, devices and programs. Goal refinement and operationalization is a complex process which is not well supported by current requirements engineering technology. Ideally some form of formal support should be provided, but formal methods are difficult and costly to apply at this stage.This paper presents an approach to goal refinement and operationalization which is aimed at providing constructive formal support while hiding the underlying mathematics. The principle is to reuse generic refinement patterns from a library structured according to strengthening/weakening relationships among patterns. The patterns are once for all proved correct and complete. They can be used for guiding the refinement process or for pointing out missing elements in a refinement. The cost inherent to the use of a formal method is thus reduced significantly. Tactics are proposed to the requirements engineer for grounding pattern selection on semantic criteria.The approach is discussed in the context of the multi-paradigm language used in the KAOS method; this language has an external semantic net layer for capturing goals, constraints, agents, objects and actions together with their links, and an inner formal assertion layer that includes a real-time temporal logic for the specification of goals and constraints. Some frequent refinement patterns are high-lighted and illustrated through a variety of examples.The general principle is somewhat similar in spirit to the increasingly popular idea of design patterns, although it is grounded on a formal framework here.


title: Filtering the surface EMG signal: Movement artifact and baseline noise contamination.

abstract: The surface electromyographic (sEMG) signal that originates in the muscle is inevitably contaminated by various noise signals or artifacts that originate at the skin-electrode interface, in the electronics that amplifies the signals, and in external sources. Modern technology is substantially immune to some of these noises, but not to the baseline noise and the movement artifact noise. These noise sources have frequency spectra that contaminate the low-frequency part of the sEMG frequency spectrum. There are many factors which must be taken into consideration when determining the appropriate filter specifications to remove these artifacts; they include the muscle tested and type of contraction, the sensor configuration, and specific noise source. The band-pass determination is always a compromise between (a) reducing noise and artifact contamination, and (b) preserving the desired information from the sEMG signal. This study was designed to investigate the effects of mechanical perturbations and noise that are typically encountered during sEMG recordings in clinical and related applications. The analysis established the relationship between the attenuation rates of the movement artifact and the sEMG signal as a function of the filter band pass. When this relationship is combined with other considerations related to the informational content of the signal, the signal distortion of filters, and the kinds of artifacts evaluated in this study, a Butterworth filter with a corner frequency of 20 Hz and a slope of 12 dB/oct is recommended for general use. The results of this study are relevant to biomechanical and clinical applications where the measurements of body dynamics and kinematics may include artifact sources.


title: Video Game Playing, Attention Problems, and Impulsiveness: Evidence of Bidirectional Causality

abstract: The present study examines video game playing as it relates to attention problems and impulsiveness in a sample of 3,034 children and adolescents from Singapore measured over 3 years. Consistent with previous research, those who spend more time playing video games subsequently have more attention problems, even when earlier attention problems, sex, age, race, and socioeconomic status are statistically controlled. Violent content may have a unique effect on attention problems and impulsiveness, but total time spent with video games appears to be a more consistent predictor. Individuals who are more impulsive or have more attention problems subsequently spend more time playing video games, even when initial video game playing is statistically controlled, suggesting bidirectional causality between video game playing and attention problems/ impulsiveness.


title: Multiband Circularly Polarized Cavity-Backed Crossed Dipole Antenna

abstract: In this paper, novel designs of miniaturized and multiband circularly polarized (CP) cavity-backed crossed dipole antennas are proposed. Single-band and dual-band LC resonators are placed along both the arms of double-sided printed dipoles to realize dual-band and tri-band operations, respectively. Identical pairs of loaded dipoles are placed in crossed configuration to achieve dual-band and tri-band CP operations using vacant printed ring. Cavity-backed reflector is further employed to get enhancement in the 10-dB impedance bandwidth (IBW) and 3-dB axial ratio bandwidth (ARBW) along with unidirectional radiation pattern and wide 3-dB axial ratio beamwidth in the operating bands. Measured results confirm that the proposed dual-band cavity-backed crossed dipole antenna exhibits CP operation bandwidths ( $\text {AR} and ${{\text S}_{{11}}} ) of 7.50% and 26.15%, with center frequencies at 1.87 and 2.83 GHz, respectively. The corresponding CP operating bandwidths for the tri-band cavity-backed crossed dipole antenna are 1.24%, 9.55%, and 14.95% centered at 1.61, 2.20, and 2.81 GHz, respectively. The proposed dual-band and tri-band antennas can be used in DCS/PCS/Global Star/UMTS/WLAN applications.


title: Barrier impact on organizational learning within complex organizations

abstract: Purpose – The purpose of this research is to examine the manner in which employees access, create and share information and knowledge within a complex supply chain with a view to better understanding how to identify and manage barriers which may inhibit such exchanges.Design/methodology/approach – An extensive literature review combined with an in‐depth case study analysis identified a range of potential transfer barriers. These in turn were examined in terms of their consistency of impact by an end‐to‐end process survey conducted within an IBM facility.Findings – Barrier impact cannot be assumed to be uniform across the core processes of the organization. Process performance will be impacted upon in different ways and subject to varying degrees of influence by the transfer barriers. Barrier identification and management must take place at a process rather than at the organizational level.Research limitations/implications – The findings are based, in the main, on an extensive single company study. Althoug...


title: Tossing NoSQL-Databases Out to Public Clouds

abstract: Cloud-Service Providers (CSPs) can now handle heavy workloads by occasionally renting resources from public clouds. The capabilities and respective lease prices of such infrastructure may significantly vary over time. In this environment, two distinct types of SLAs have to work in tandem: a) the SLA furnished by the private cloud to the end user of the application (or database), and b) the SLA offered by the public cloud to the application through its host private cloud. This dual and continuously evolving relationship inherently complicates the computation of the operation of cloud applications. In this paper, we present a cost-aware resource provisioning algorithm for No SQL-databases that aims to meet Quality of Service (QoS) requirements while minimizing the total cost incurred by its deployment on multiple cloud tiers. Our method is based on look-ahead optimization and takes into account the costs incurred by potential database transitions to new configurations in a heterogeneous multi-cloud environment. Experimentation with a prototype shows that our approach reflects the total cost of a cloud application more accurately than the conventional technique of minimizing SLA violations. More importantly, it avoids thrashing of resources.