title: On Your Feet!: Enhancing Self-Motion Perception in Leaning-Based Interfaces through Multisensory Stimuli

abstract: When navigating larger virtual environments and computer games, natural walking is often unfeasible. Here, we investigate how alternatives such as joystick- or leaning-based locomotion interfaces ("human joystick") can be enhanced by adding walking-related cues following a sensory substitution approach. Using a custom-designed foot haptics system and evaluating it in a multi-part study, we show that adding walking related auditory cues (footstep sounds), visual cues (simulating bobbing head-motions from walking), and vibrotactile cues (via vibrotactile transducers and bass-shakers under participants' feet) could all enhance participants' sensation of self-motion (vection) and involement/presence. These benefits occurred similarly for seated joystick and standing leaning locomotion. Footstep sounds and vibrotactile cues also enhanced participants' self-reported ability to judge self-motion velocities and distances traveled. Compared to seated joystick control, standing leaning enhanced self-motion sensations. Combining standing leaning with a minimal walking-in-place procedure showed no benefits and reduced usability, though. Together, results highlight the potential of incorporating walking-related auditory, visual, and vibrotactile cues for improving user experience and self-motion perception in applications such as virtual reality, gaming, and tele-presence.


title: A 0.2–2 Gb/s 6x OSR Receiver Using a Digitally Self-Adaptive Equalizer

abstract: This paper presents a very robust 6x OSR receiver for 0.2-2 Gb/s binary NRZ signals, introducing an adaptive equalizer that is auto-calibrating on sample data statistics for reliable data recovery in presence of excessive intersymbol interference, noise and crosstalk. The proposed time domain analysis of the data eye obtained with the oversampling architecture is used to tune the equalizer transfer characteristic. The auto-calibration scheme is fully implemented in the digital domain, resulting in a hardware and power efficient architecture with low process-voltage-temperature (PVT) sensitivity. This robust and highly digitized receiver is demonstrated in 0.18 CMOS technology and is able to equalize variable cable losses up to 22 dB @ 1 GHz. The self-adaptive equalizer solution occupies only 0.05 and consumes 9 mW from a 1.8 V supply and can handle up to 20 m 100 Omega STP cable @ 2 Gb/s. The entire receiver consumes 110 mW operating at 2 Gb/s with bit error rates of better than < 10-12.


title: Interpolation of skipped image frames for image sequence coding

abstract: This work presents several algorithms for temporal interpolation which permit frame skipping with less degradation in quality of the interpolated frames. In the developed algorithms there is a trade-off between this quality and the amount of additional side-information used. Smoothing of the motion-vector field, based on a selective median filter is intended to prevent blocking effects created by sub-optimal block matching or stray motion. The performance of the proposed algorithms is compared in image-sequence coding simulations. >


title: An Introduction to Adversarial Machine Learning

abstract: Machine learning based system are increasingly being used for sensitive tasks such as security surveillance, guiding autonomous vehicle, taking investment decisions, detecting and blocking network intrusion and malware etc. However, recent research has shown that machine learning models are venerable to attacks by adversaries at all phases of machine learning (e.g., training data collection, training, operation). All model classes of machine learning systems can be misled by providing carefully crafted inputs making them wrongly classify inputs. Maliciously created input samples can affect the learning process of a ML system by either slowing the learning process, or affecting the performance of the learned model or causing the system make error only in attacker’s planned scenario. Because of these developments, understanding security of machine learning algorithms and systems is emerging as an important research area among computer security and machine learning researchers and practitioners. We present a survey of this emerging area named Adversarial machine learning.


title: Noise Analysis of Regenerative Comparators for Reconfigurable ADC Architectures

abstract: The need for highly integrable and programmable analog-to-digital converters (ADCs) is pushing towards the use of dynamic regenerative comparators to maximize speed, power efficiency and reconfigurability. Comparator thermal noise is, however, a limiting factor for the achievable resolution of several ADC architectures with scaled supply voltages. While mismatch in these comparators can be compensated for by calibration, noise can irreparably hinder performance and is less straightforward to be accounted for at design time. This paper presents a method to estimate the input referred noise in fully dynamic regenerative comparators leveraging a reference architecture. A time-domain analysis is proposed that accounts for the time varying nature of the circuit exploiting some basic results from the solution of stochastic differential equations. The resulting symbolic expressions allow focusing designers' attention on the most influential noise contributors. Analysis results are validated by comparison with electrical simulations and measurement results from two ADC prototypes based on the reference comparator architecture, implemented in 0.18-mum and 90-nm CMOS technologies.


The Director and the Playwright: Control over the Means of Production


title: Detection of glaucoma using Neuroretinal Rim information

abstract: Glaucoma is one of the most common causes of blindness in the world. The vision lost due to glaucoma cannot be regained. Early detection of glaucoma is thus very important. The Optic Disk(OD), Optic Cup(OC) and Neuroretinal Rim(NRR) are among the important features of a retinal image that can be used in the detection of glaucoma. In this paper, a computer-assisted method for the detection of glaucoma based on the ISNT rule is presented. The OD and OC are segmented using watershed transformation. The NRR area in the ISNT quadrants is obtained from the segmented OD and OC. The method is applied on the publicly available databases HRF, Messidor, DRIONS-DB, RIM-ONE and a local hospital database consisting of both normal and glaucomatous images. The proposed method is simple, computationally efficient and achieves a sensitivity of 91.82% and an overall accuracy of 94.14%.


title: An investigation of mobile learning readiness in higher education based on the theory of planned behavior

abstract: This study investigated the current state of college students' perceptions toward mobile learning in higher education. Mobile learning is a new form of learning utilizing the unique capabilities of mobile devices. Although mobile devices are ubiquitous on college campuses, student readiness for mobile learning has yet to be fully explored in the United States. The paper describes a conceptual model, based on the theory of planned behavior (TPB), which explains how college students' beliefs influence their intention to adopt mobile devices in their coursework. Structural equation modeling was used to analyze self-report data from 177 college students. The findings showed that the TPB explained college students' acceptance of m-learning reasonably well. More specifically, attitude, subjective norm, and behavioral control positively influenced their intention to adopt mobile learning. The results provide valuable implications for ways to increase college students' acceptance of mobile learning.


title: Contextual Gamification of Social Interaction – Towards Increasing Motivation in Social E-learning

abstract: In current e-learning studies, one of the main challenges is to keep learners motivated in performing desirable learning behaviours and achieving learning goals. Towards tackling this challenge, social e-learning contributes favourably, but it requires solutions that can reduce side effects, such as abusing social interaction tools for ‘chitchat’, and further enhance learner motivation. In this paper, we propose a set of contextual gamification strategies, which apply flow and self-determination theory for increasing intrinsic motivation in social e-learning environments. This paper also presents a social e-learning environment that applies these strategies, followed by a user case study, which indicates increased learners’ perceived intrinsic motivation.


title: Latent social structure in open source projects

abstract: Commercial software project managers design project organizational structure carefully, mindful of available skills, division of labour, geographical boundaries, etc. These organizational "cathedrals" are to be contrasted with the "bazaar-like" nature of Open Source Software (OSS) Projects, which have no pre-designed organizational structure. Any structure that exists is dynamic, self-organizing, latent, and usually not explicitly stated. Still, in large, complex, successful, OSS projects, we do expect that subcommunities will form spontaneously within the developer teams. Studying these subcommunities, and their behavior can shed light on how successful OSS projects self-organize. This phenomenon could well hold important lessons for how commercial software teams might be organized. Building on known well-established techniques for detecting community structure in complex networks, we extract and study latent subcommunities from the email social network of several projects: Apache HTTPD, Python, PostgresSQL, Perl, and Apache ANT. We then validate them with software development activity history. Our results show that subcommunities do indeed spontaneously arise within these projects as the projects evolve. These subcommunities manifest most strongly in technical discussions, and are significantly connected with collaboration behaviour.


title: Beyond eye gaze: What else can eyetracking reveal about cognition and cognitive development?

abstract: Abstract This review provides an introduction to two eyetracking measures that can be used to study cognitive development and plasticity: pupil dilation and spontaneous blink rate. We begin by outlining the rich history of gaze analysis, which can reveal the current focus of attention as well as cognitive strategies. We then turn to the two lesser-utilized ocular measures. Pupil dilation is modulated by the brain’s locus coeruleus-norepinephrine system, which controls physiological arousal and attention, and has been used as a measure of subjective task difficulty, mental effort, and neural gain. Spontaneous eyeblink rate correlates with levels of dopamine in the central nervous system, and can reveal processes underlying learning and goal-directed behavior. Taken together, gaze, pupil dilation, and blink rate are three non-invasive and complementary measures of cognition with high temporal resolution and well-understood neural foundations. Here we review the neural foundations of pupil dilation and blink rate, provide examples of their usage, describe analytic methods and methodological considerations, and discuss their potential for research on learning, cognitive development, and plasticity.


title: Measurement of Reading Characteristics of Multiplexed Image in QR Code

abstract: With the growth of the Internet in recent years, two-dimensional codes, such as a QR code has been widely used in the world. However, as this QR code is just a collective symbol of black and white dots, it does not have a good visual display. To improve this problem, an image or picture is superimposed to this two-dimensional code. This code can still be read without error, but this image is considered to be noisy, which reduces the reading ability. In order to avoid the reduction of the ability for reading the two-dimensional arrangement of code data, the location characteristics of the superimposed image should be studied. In this paper, taking into account the parameters of the error correction, the reading characteristics for different positions of the superimposed image are measured and the best position for it has been studied.


title: Demo: OpenVLC1.0 Platform for Research in Visible Light Communication Networks

abstract: Built around a cost-effective embedded Linux platform, OpenVLC is an open source project (www.openvlc.org) for research in Visible Light Communication (VLC) Networks. In this work, we introduce and demonstrate the OpenVLC1.0 platform, a flexible, software-defined, and low-cost research platform. OpenVLC1.0 consists of a simple electronic design, and a new driver of the Linux operating system that implements the MAC, part of the PHY layers and it offers an interface to Internet protocols. The electronics of OpenVLC implement a flexible optical front-end consisting of commodity low- and high-power Light Emitting Diodes (LEDs), photodiodes (PDs), and ancillary electronic circuitry. In order to quickly start playing with VLC Networks, we have designed and developed a printed circuit board (OpenVLC1.0 cape). The cape can be plugged into the main embedded Beaglebone board. Researchers can then swiftly build PHY and MAC protocols using the software implementation (OpenVLC1.0 driver), and prototype innovative solutions in realistic network setups. In this demo, we show that OpenVLC1.0 can switch between different MAC protocols, it can choose different optical channel for data transmission and reception, and it can be employed jointly with standard TCP/IP diagnostic tools.


title: A Big-Bang Big-Crunch Type-2 Fuzzy Logic System for Generating Interpretable Models in Workforce Optimization

abstract: Efficient utilization of resources (engineers) is critical to optimal service delivery in service-based industries, such as water, electricity or telecom companies. One of the ways in which efficiency can be improved is by optimizing the geographic area in which the engineers operate. This process is known as Work Area (WA) optimization and it is a sub-domain of workforce optimization.In previous attempts to tackle the work area optimization problem, various machine learning algorithms have been proposed to optimize the work areas. However, they don’t provide much insight into when and which WAs must be optimized. This is an important question, as optimizing a WA which is already optimum can lead to a loss in efficiency as the engineers can take time to adjust to the new WA.This paper presents a Type-2 Fuzzy Logic System (FLS) which has been optimized by the Big-Bang Big-Crunch approach to allow maximizing the model interpretability and allow good prediction for the future performance of WAs. We can then use these predictions to determine which WAs must be optimized.We compare the proposed type-2 FLS model against a type-1 counterpart, a stacked autoencoder deep neural network and single hidden layer neural network.The results show that the proposed type-2 FLS provides a highly interpretable model which predicts the future performance of WAs with a reasonable error rate. In addition, it also provides the necessary insight into which parameters of the WA determine the future performance. This allows answering the question of when and which WAs must be optimized.


title: Enhanced Rotational Invariant Convolutional Neural Network for Supernovae Detection

abstract: In this paper, we propose an enhanced CNN model for detecting supernovae (SNe). This is done by applying a new method for obtaining rotational invariance that exploits cyclic symmetry. In addition, we use a visualization approach, the layer-wise relevance propagation (LRP) method, which allows finding the relevant pixels in each image that contribute to discriminate between SN candidates and artifacts. We introduce a measure to assess quantitatively the effect of the rotational invariant methods on the LRP relevance heatmaps. This allows comparing the proposed method, CAP, with the original Deep-HiTS model. The results show that the enhanced method presents an augmented capacity for achieving rotational invariance with respect to the original model. An ensemble of CAP models obtained the best results so far on the HiTS dataset, reaching an average accuracy of 99.53%. The improvement over Deep-HiTS is significant both statistically and in practice.


title: Training Spiking ConvNets by STDP and Gradient Descent

abstract: This paper proposes a new method for training multi-layer spiking convolutional neural networks (CNNs). Training a multi-layer spiking network poses difficulties because the output spikes do not have derivatives and the commonly use backpropagation method for non-spiking networks is not easily applied. Our method uses a novel version of layered spike-timing- dependent plasticity (STDP) that incorporates supervised and unsupervised components. Our method starts with conventional learning methods and converts them to spatio-temporally local rules suited for spiking neural networks (SNNs). The training process uses two components for unsupervised feature extraction and supervised classification. The first component is a new STDP rule for spike-based representation learning which trains convolutional filters. The second introduces a new STDP-based supervised learning rule for spike pattern classification via an approximation to gradient descent. Stacking these components implements a novel spiking CNN of integrate-and-fire (IF) neurons with performances comparable with the state-of-the-art deep SNNs. The experimental results show the success of the proposed model for the MNIST handwritten digit classification. Our network architecture is the only high performance, spiking CNN which provides bio-inspired STDP rules in a hierarchy of feature extraction and classification in an entirely spike-based framework.


title: Emerging Role of Data Analytics in Combating Financial Frauds: the Era of Big Data in India

abstract: India is at a cusp of revival as the world's fastest growing big economy. Over the last decade, India has witnessed a multifold increase in government initiatives to digitally connect India from within and with outside world in order to promote economic growth and wellbeing in the country. This advancement in ICT has created an explosive growth in digital data with almost every industry and business area digitizing their data ushering in the dawn of an era of big data in India. Financial frauds have emerged as an undesirable and inescapable byproduct of this economic growth with numerous chit fund and MLM companies coming under the scanner of government agencies such as SFIO, SEBI and ED for fraudulent activities. To combat the growing menace of fraud with clinching evidences present in the form of digital data, it has become imperative to develop tools and technologies to understand the massive amounts of digital data being collected by government agencies. The paper focuses on various tools and technologies which are currently being used for fraud analysis. Further, it presents a fund flow algorithm that could be used to detect dubious fund transactions using bank transaction statements by analyzing the fund flow between various entities. It takes as input the bank transaction data of various parties of interest and generates as output a directed graph with nodes depicting the source and destination of fund transfer and directed arrows showing the direction of fund flow. Further, this paper focuses on the role of data analytics in understanding the massive volumes of digital data being collected by government enforcement agencies in combating financial frauds in India. The paper discusses the concerns and challenges based on complexities in data capturing, storage, analysis and visualization along with possible future directions.


title: Targeting RAS signalling pathways in cancer therapy

abstract: The RAS proteins control signalling pathways that are key regulators of several aspects of normal cell growth and malignant transformation. They are aberrant in most human tumours due to activating mutations in the RAS genes themselves or to alterations in upstream or downstream signalling components. Rational therapies that target the RAS pathways might inhibit tumour growth, survival and spread. Several of these new therapeutic agents are showing promise in the clinic and many more are being developed.


title: Round-Optimal Password-Based Group Key Exchange Protocols in the Standard Model

abstract: Password-based group key exchange protocols allow group users who share only a short, low entropy password to agree on a cryptographically strong session key. One fundamental complexity measure of such protocols is its round complexity. In this paper, we present the first one-round password-based group key exchange protocol in the common random string model. Furthermore, we propose a completely new approach to remove the need for the common random string and then construct a two-round password-based group key exchange protocol that does not require any setup assumption. This is - to the best of our knowledge - the first password-based group key exchange protocol without trusted setup. Using indistinguishability obfuscation as main tool, both protocols are provably secure in the standard model.


title: Large-Scale Vehicle Detection, Indexing, and Search in Urban Surveillance Videos

abstract: We present a novel approach for visual detection and attribute-based search of vehicles in crowded surveillance scenes. Large-scale processing is addressed along two dimensions: 1) large-scale indexing, where hundreds of billions of events need to be archived per month to enable effective search and 2) learning vehicle detectors with large-scale feature selection, using a feature pool containing millions of feature descriptors. Our method for vehicle detection also explicitly models occlusions and multiple vehicle types (e.g., buses, trucks, SUVs, cars), while requiring very few manual labeling. It runs quite efficiently at an average of 66 Hz on a conventional laptop computer. Once a vehicle is detected and tracked over the video, fine-grained attributes are extracted and ingested into a database to allow future search queries such as “Show me all blue trucks larger than 7 ft. length traveling at high speed northbound last Saturday, from 2 pm to 5 pm”. We perform a comprehensive quantitative analysis to validate our approach, showing its usefulness in realistic urban surveillance settings.


title: Interventions to improve sensory and motor outcomes for young children with central hypotonia: A systematic review

abstract: OBJECTIVE ::: To evaluate evidence supporting physical and occupational therapy interventions used to improve sensory and motor outcomes for children 0-6 years with central hypotonia. ::: ::: ::: METHODS ::: Four electronic databases were searched from 1996 to March 2017. Level of evidence and study conduct was evaluated using American Academy of Cerebral Palsy and Developmental Medicine criteria. Traffic lighting classification identified interventions that were green (proven effective), yellow (possibly effective) or red (proven ineffective or contraindicated). ::: ::: ::: RESULTS ::: Thirty-seven articles were included. Nine studies measured orthotic interventions while four distinct studies published over nine articles measured treadmill interventions. Remaining studies measured impact of compression garments, massage, motor and sensori-motor interventions, positioning and mobility interventions. ::: ::: ::: CONCLUSIONS ::: Green light evidence supports treadmill training (to promote ambulation and gait characteristics) and massage (to positively affect muscle tone, motor development and use of vision) for infants with Down syndrome. These interventions are considered Yellow (possibly effective) for other populations. Green light evidence supports impact of orthoses on foot alignment for ambulatory children with hypotonia, while impact on gait characteristics is Yellow light and motor development may be negatively impacted (Red light) in pre-ambulatory children. All other interventions rated Yellow (possibly effective) and therapists should monitor using sensitive outcome measures.


Moderate alkali-thermophilic ethanologenesis by locally isolated Bacillus licheniformis from Pakistan employing sugarcane bagasse: a comparative aspect of aseptic and non-aseptic fermentations


title: On becoming ready to pursue a goal you don't know you have: Effects of nonconscious goals on evaluative readiness.

abstract: Findings showed that the nonconscious activation of a goal in memory led to increased positive implicit attitudes toward stimuli that could facilitate the goal. This evaluative readiness to pursue the nonconscious goal emerged even when participants were consciously unaware of the goal-relevant stimuli. The effect emerged the most strongly for those with some skill at the goal and for those for whom the goal was most currently important. The effect of implicit goal activation on implicit attitudes emerged in both an immediate condition as well as a delay condition, suggesting that a goal rather than a nonmotivational construct was activated. Participants’ implicit attitudes toward a nonconscious goal also predicted their goal-relevant behavior. These findings suggest that people can become evaluatively ready to pursue a goal whenever it has been activated—a readiness that apparently does not require conscious awareness or deliberation about either the goal or the goal-relevant stimuli. Theoretical implications of this type of implicit goal readiness are discussed.


title: Feature Selection Based on SVM for Credit Scoring

abstract: As the credit industry has been growing rapidly, huge number of consumers’ credit data are collected by the credit department of the bank and credit scoring has become a very important issue. Usually, a large amount of redundant information and features are involved in the credit dataset, which leads to lower accuracy and higher complexity of the credit scoring model, so, effective feature selection methods are necessary for credit dataset with huge number of features. This paper aims at comparing seven well-known feature selection methods for credit scoring. Which are t-test, principle component analysis (PCA), factor analysis (FA), stepwise regression, Rough Set (RS), Classification and regression tree (CART) and Multivariate adaptive regression splines (MARS). Support vector machine (SVM) is used as the classification model. Two credit scoring databases are used in order to provide a reliable conclusion. Regarding the experimental results, the CART and MARS methods outperform the other methods by the overall accuracy and type I error and type II error.


title: Heart Disease Prediction Using Classification with Different Decision Tree Techniques

abstract: Data mining is one of the essential areas of research that is more popular in health organization. Data mining plays an effective role for uncovering new trends in healthcare organization which is helpful for all the parties associated with this field. Heart disease is the leading cause of death in the world over the past 10 years. Heart disease is a term that assigns to a large number of medical conditions related to heart. These medical conditions describe the irregular health condition that directly affects the heart and all its parts. The healthcare industry gathers enormous amount of heart disease data which are not "mined" to discover hidden information for effective decision making. Data mining techniques are useful for analyzing the data from many different dimensions and for identifying relationships. This paper explores the utility of various decision tree algorithms in classify and predict the disease.


title: Learning Bayesian Networks

abstract: Publisher Summary This chapter discusses a knowledge representation, called a Bayesian network, that allows one to learn uncertain relationships in a domain by combining expert domain knowledge and statistical data. A Bayesian network is a graphical representation of uncertain knowledge that most people find easy to construct directly from domain knowledge. In addition, the representation has formal probabilistic semantics, making it suitable for statistical manipulation. Over the past decade, the Bayesian network has become a popular representation for encoding uncertain expert knowledge in expert systems. More recently, researchers have developed methods for learning Bayesian networks from a combination of expert knowledge and data. The techniques that have been developed are new and still evolving, but they have been shown to be remarkably effective in some domains. Learning using Bayesian networks is similar to that using neural networks. The process employing Bayesian networks, however, has two important advantages: (1) one can easily encode expert knowledge in a Bayesian network, and use this knowledge to increase the efficiency and accuracy of learning; and (2) the nodes and arcs in learned Bayesian networks often correspond to recognizable distinctions and causal relationships.


title: Gradient based fingerprint orientation field estimation

abstract: Accurate estimation of ridge orientation is a critical step in image preprocessing methods used in automatic fingerprint identification systems (AFIS). Fingerprint orientation plays important roles in fingerprint enhancement, classification and recognition. The most popular is gradient-based method. This paper reviews the algorithm parameters, determining the compromise between accuracy in high-curvature areas and robustness against noise.


title: Performance Study of IEEE 802.15.4/4g Waveforms over the Mobile Underground Mine Radio-Channel

abstract: This study evaluates the performance of an IEEE 802.15.4/4g low-rate wireless personal area network (LR-WPAN) physical layers to support Wireless Sensor Networks (WSN) applications deployed in underground mines. Two physical layer models were constructed based on the IEEE 802.15.4 and IEEE 802.15.4g specifications. An appropriate channel model for the underground mine environment was selected and presented for both line of sight and non-line of sight; it was also enhanced to support mobility. Extensive computer simulations were conducted for different scenarios to investigate both LR-WPAN standards' performances in terms of bit error rate and throughput.


title: mmWave communications for 5G: implementation challenges and advances

abstract: The requirement of the fifth generation (5G) wireless communication for high throughput motivates the wireless industry to use the mmWave (millimeter wave) communications for its wide bandwidth advantage. To compensate the heavy path loss and increase the communications capacity, phased array beamforming and massive multiple-input multiple-output (MIMO) techniques are employed at both the user equipment (UE) and base stations (BS). Considering the commercial requirements, 5G mmWave large array systems should be implemented in an energy- and cost-efficient way with a small form factor. To address above issues and realize a reliable communications link, taking into account the particular characteristics of 5G mmWave systems, this paper firstly examines the design challenges and trade-offs in system implementations, then some of the design strategies are summarized. At last, recent advance in RF front-end circuits and receiver sub-systems is then highlighted.


title: Broadband Electrically Small Circularly Polarized Directive Antenna

abstract: A broadband electrically small antenna with directive circularly polarized radiation is presented. It is composed of a compact single-feed crossed dipole driver, which is backed by a near-field resonant parasitic (NFRP) reflector to achieve the directive radiation pattern. The NFRP reflector is designed to generate extra resonances and minimum axial ratio (AR) points in the antenna system. These features are combined with those resulting from the driver to broaden the operational bandwidth. The proposed antenna was fabricated and measured. The antenna prototype, with a low profile (0.066 $\lambda _{\mathrm {o}}$ at 1.45 GHz) and an electrically small size ( ka = 0.71 at 1.45 GHz), has a measured $\vert \text{S}_{11}\vert dB bandwidth of 25.66% (1.362–1.763 GHz) and a 3-dB AR bandwidth of 10.56% (1.390–1.545 GHz). Additionally, the measurements resulted in a broadside gain of 2.31 ± 0.4 dBic and an average radiation efficiency of 80% within the operational bandwidth.


title: Linking DNA methylation and histone modification: patterns and paradigms

abstract: Both DNA methylation and histone modification are involved in establishing patterns of gene repression during development. Certain forms of histone methylation cause local formation of heterochromatin, which is readily reversible, whereas DNA methylation leads to stable long-term repression. It has recently become apparent that DNA methylation and histone modification pathways can be dependent on one another, and that this crosstalk can be mediated by biochemical interactions between SET domain histone methyltransferases and DNA methyltransferases. Relationships between DNA methylation and histone modification have implications for understanding normal development as well as somatic cell reprogramming and tumorigenesis.


Multi-grained Attention Network for Aspect-Level Sentiment Classification


title: Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving

abstract: One of the fundamental challenges in the design of perception systems for autonomous vehicles is validating the performance of each algorithm under a comprehensive variety of operating conditions. In the case of vision-based semantic segmentation, there are known issues when encountering new scenarios that are sufficiently different to the training data. In addition, even small variations in environmental conditions, such as illumination and precipitation, can affect the classification performance of the segmentation model. Given the reliance on visual information, these effects often translate into poor semantic pixel classification which can potentially lead to catastrophic consequences when driving autonomously. This paper presents a novel method for analyzing the robustness of semantic segmentation models and provides a number of metrics to evaluate the classification performance over a variety of environmental conditions. The process incorporates an additional sensor (lidar) to automate the process and improve the system integrity, eliminating the need for labor-intensive hand labeling of validation data. The experimental results are presented based on multiple datasets collected at different times of the year with different environmental conditions. We extract the "Road" class using the lidar to demonstrate the concepts, but this could be extended to other classes with different feature detection algorithms. These results show that the semantic segmentation performance varies depending on the weather, camera parameters, and existence of shadows. The results also demonstrate how the metrics can be used to compare and validate the performance after making improvements to a model, and compare the performance of different networks.


title: Energy-aware wireless microsensor networks

abstract: This article describes architectural and algorithmic approaches that designers can use to enhance the energy awareness of wireless sensor networks. The article starts off with an analysis of the power consumption characteristics of typical sensor node architectures and identifies the various factors that affect system lifetime. We then present a suite of techniques that perform aggressive energy optimization while targeting all stages of sensor network design, from individual nodes to the entire network. Maximizing network lifetime requires the use of a well-structured design methodology, which enables energy-aware design and operation of all aspects of the sensor network, from the underlying hardware platform to the application software and network protocols. Adopting such a holistic approach ensures that energy awareness is incorporated not only into individual sensor nodes but also into groups of communicating nodes and the entire sensor network. By following an energy-aware design methodology based on techniques such as in this article, designers can enhance network lifetime by orders of magnitude.


title: Quality of Service Routing for Supporting Multimedia Applications

abstract: Several new architectures have been developed for supporting multimedia applications such as digital video and audio. However, quality-of-service (QoS) routing is an important element that is still missing from these architectures. In this paper, we consider a number of issues in QoS routing. We first examine the basic problem of QoS routing, namely, finding a path that satisfies multiple constraints, and its implications on routing metric selection, and then present three path computation algorithms for source routing and for hop-by-hop routing.


title: Detection of novel network attacks using data mining

abstract: This paper introduces the Minnesota Intrusion Detection System (MINDS), which uses a suite of data mining techniques to automatically detect attacks against computer networks and systems. While the long-term objective of MINDS is to address all aspects of intrusion detection, in this paper we present two specific contributions. First, we present MINDS anomaly detection module that assigns a score to each connection that reflects how anomalous the connection is compared to the normal network traffic. Experimental results on live network traffic at the University of Minnesota show that our anomaly detection techniques have been successful in automatically detecting several novel intrusions that could not be identified using state-of-the-art signature-based tools such as SNORT. Many of these have been reported on the CERT/CC list of recent advisories and incident notes. We also present the results of comparing the MINDS anomaly detection module to SPADE (Statistical Packet Anomaly Detection Engine), which is designed to detect stealthy scans.


title: Open Issues in Evolutionary Robotics

abstract: One of the long-term goals in evolutionary robotics is to be able to automatically synthesize controllers for real autonomous robots based only on a task specification. While a number of studies have shown the applicability of evolutionary robotics techniques for the synthesis of behavioral control, researchers have consistently been faced with a number of issues preventing the widespread adoption of evolutionary robotics for engineering purposes. In this article, we review and discuss the open issues in evolutionary robotics. First, we analyze the benefits and challenges of simulation-based evolution and subsequent deployment of controllers versus evolution on real robotic hardware. Second, we discuss specific evolutionary computation issues that have plagued evolutionary robotics: (1) the bootstrap problem, (2) deception, and (3) the role of genomic encoding and genotype-phenotype mapping in the evolution of controllers for complex tasks. Finally, we address the absence of standard research practices in the field. We also discuss promising avenues of research. Our underlying motivation is the reduction of the current gap between evolutionary robotics and mainstream robotics, and the establishment of evolutionary robotics as a canonical approach for the engineering of autonomous robots.


title: Adding wildcards to the Java programming language

abstract: This paper describes wildcards, a new language construct designed to increase the flexibility of object-oriented type systems with parameterized classes. Based on the notion of use-site variance, wildcards provide a type safe abstraction over different instantiations of parameterized classes, by using '?' to denote unspecified type arguments. Thus they essentially unify the distinct families of classes often introduced by parametric polymorphism. Wildcards are implemented as part of the upcoming addition of generics to the Java™ programming language, and will thus be deployed world-wide as part of the reference implementation of the Java compiler javac available from Sun Microsystems, Inc. By providing a richer type system, wildcards allow for an improved type inference scheme for polymorphic method calls. Moreover, by means of a novel notion of wildcard capture, polymorphic methods can be used to give symbolic names to unspecified types, in a manner similar to the "open" construct known from existential types. Wildcards show up in numerous places in the Java Platform APIs of the upcoming release, and some of the examples in this paper are taken from these APIs.


title: 100-epoch ImageNet Training with AlexNet in 24 Minutes

abstract: Finishing 90-epoch ImageNet-1k training with ResNet-50 on a NVIDIA M40 GPU takes 14 days. This training requires 10^18 single precision operations in total. On the other hand, the world's current fastest supercomputer can finish 2 * 10^17 single precision operations per second (Dongarra et al 2017, https://www.top500.org/lists/2017/06/). If we can make full use of the supercomputer for DNN training, we should be able to finish the 90-epoch ResNet-50 training in five seconds. However, the current bottleneck for fast DNN training is in the algorithm level. Specifically, the current batch size (e.g. 512) is too small to make efficient use of many processors For large-scale DNN training, we focus on using large-batch data-parallelism synchronous SGD without losing accuracy in the fixed epochs. The LARS algorithm (You, Gitman, Ginsburg, 2017, arXiv:1708.03888) enables us to scale the batch size to extremely large case (e.g. 32K). We finish the 100-epoch ImageNet training with AlexNet in 24 minutes. Same as Facebook's result (Goyal et al 2017, arXiv:1706.02677), we finish the 90-epoch ImageNet training with ResNet-50 in one hour. Furthermore, when we increase the batch size to above 10K, our accuracy is much higher than Facebook's on corresponding batch sizes.


title: Human Biomechanical Model Based Optimal Design of Assistive Shoulder Exoskeleton

abstract: Robotic exoskeletons are being developed to assist humans in tasks such as robotic rehabilitation, assistive living, industrial and other service applications. Exoskeletons for the upper limb are required to encompass the shoulder whilst achieving a range of motion so as to not impede the wearer, avoid collisions with the wearer, and avoid kinematic singularities during operation. However this is particularly challenging due to the large range of motion of the human shoulder. In this paper a biomechanical model based optimisation is applied to the design of a shoulder exoskeleton with the objective of maximising shoulder range of motion. A biomechanical model defines the healthy range of motion of the human shoulder. A genetic algorithm maximises the range of motion of the exoskeleton towards that of the human, whilst taking into account collisions and kinematic singularities. It is shown how the optimisation can increase the exoskeleton range of motion towards that equivalent of the human, or towards a subset of human range of motion relevant to specific applications.


title: High-quality motion deblurring from a single image

abstract: We present a new algorithm for removing motion blur from a single image. Our method computes a deblurred image using a unified probabilistic model of both blur kernel estimation and unblurred image restoration. We present an analysis of the causes of common artifacts found in current deblurring methods, and then introduce several novel terms within this probabilistic model that are inspired by our analysis. These terms include a model of the spatial randomness of noise in the blurred image, as well a new local smoothness prior that reduces ringing artifacts by constraining contrast in the unblurred image wherever the blurred image exhibits low contrast. Finally, we describe an effficient optimization scheme that alternates between blur kernel estimation and unblurred image restoration until convergence. As a result of these steps, we are able to produce high quality deblurred results in low computation time. We are even able to produce results of comparable quality to techniques that require additional input images beyond a single blurry photograph, and to methods that require additional hardware.


title: Memory partitioning in Memcached: An experimental performance analysis

abstract: Memcached is a popular component of modern Web architectures, which allows fast response times - a fundamental performance index for measuring the Quality of Experience of end-users - for serving popular objects. In this work, we study how memory partitioning in Memcached works and how it affects system performance in terms of hit rate. Memcached divides the memory into different classes proportionally to the percentage of requests for objects of different sizes. Once all the available memory has been allocated, reallocation is not possible or limited, a problem called “calcification”. Calcification constitutes a symptom indicating that current memory partitioning mechanisms require a more careful design. Using an experimental approach, we show the negative impact of calcification on an important performance metric, the hit rate. We then proceed to design and implement a new memory partitioning scheme, called PSA, which replaces that of vanilla Memcached. With PSA, Memcached achieves a higher hit rate than what is obtained with the default memory partitioning mechanism, even in the absence of calcification. Moreover, we show that PSA is capable of “adapting” to the dynamics of clients' requests and object size distributions, thus defeating the calcification problem.


title: Passive Scanning in Modbus Networks

abstract: This paper describes the design and implementation of a passive scanner for Modbus networks. The tool integrates packet parsing and passive scanning functionality to interpret Modbus transactions and provide accurate network representations. In particular, the scanner monitors Modbus messages to maintain and update state table entries associated with field devices. Entries in the state tables record important information including function codes, transaction state, memory access and memory contents. The performance and reporting capabilities of the passive scanner make it an attractive network troubleshooting and security tool for process control environments.


title: Optimizing Spatial Filters by Minimizing Within-Class Dissimilarities in Electroencephalogram-Based Brain–Computer Interface

abstract: A major challenge in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is the inherent nonstationarities in the EEG data. Variations of the signal properties from intra and inter sessions often lead to deteriorated BCI performances, as features extracted by methods such as common spatial patterns (CSP) are not invariant against the changes. To extract features that are robust and invariant, this paper proposes a novel spatial filtering algorithm called Kullback-Leibler (KL) CSP. The CSP algorithm only considers the discrimination between the means of the classes, but does not consider within-class scatters information. In contrast, the proposed KLCSP algorithm simultaneously maximizes the discrimination between the class means, and minimizes the within-class dissimilarities measured by a loss function based on the KL divergence. The performance of the proposed KLCSP algorithm is compared against two existing algorithms, CSP and stationary CSP (sCSP), using the publicly available BCI competition III dataset IVa and a large dataset from stroke patients performing neuro-rehabilitation. The results show that the proposed KLCSP algorithm significantly outperforms both the CSP and the sCSP algorithms, in terms of classification accuracy, by reducing within-class variations. This results in more compact and separable features.


title: An intelligent clothes search system based on fashion styles

abstract: This work presents an intelligent clothes search system based on domain knowledge, targeted at creating a virtual assistant to search clothes matched to fashion and userpsila expectation using all what have already been in real closet. All what garment essentials and fashion knowledge are from visual images. Users can simply submit the desired image keywords, such as elegant, sporty, casual, and so on, and occasion type, such as formal meeting, outdoor dating, and so on, to the system. And then the fashion style recognition module is activated to search the desired clothes within the personal garment database. Category learning with supervised neural networking is applied to cluster garments into different impression groups. The input stimuli of the neural network are three sensations, warmness, loudness, and softness, which are transformed from the physical garment essentials like major color tone, print type, and fabric material. The system aims to provide such an intelligent user-centric services system functions as a personal fashion advisor.


title: Structured methodology for supplier selection and evaluation in a supply chain

abstract: Supply chain management (SCM) is one of the most important competitive strategies used by modern enterprises. The main aim of supply chain management is to integrate various suppliers to satisfy market demand. Meanwhile, supplier selection and evaluation plays an important role in establishing an effective supply chain. Traditional supplier selection and evaluation methods focus on the requirements of single enterprises, and fail to consider the entire supply chain. Therefore, this study proposes a structured methodology for supplier selection and evaluation based on the supply chain integration architecture. In developing the methodology for supplier selection and evaluation in a supply chain, enterprise competitive strategy is first identified using strengths weaknesses opportunities threats (SWOT) analysis. Based on the competitive strategy, the criteria and indicators of supplier selection are chosen to establish the supplier selection framework. Subsequently, potential suppliers are screened through data envelopment analysis (DEA). Technique for order preference by similarity to ideal solution (TOPSIS), a multi-attribute decision-making (MADA) method is adapted to rank potential suppliers. Finally, the Taiwanese textile industry is used to illustrate the application and feasibility of the proposed methodology. This study facilitates the improvement of collaborator relationships and the management of potential suppliers to help increase product development capability and quality, reduce product lifecycle time and cost, and thus increase product marketability.


title: Automating multi-throw multilateral surgical suturing with a mechanical needle guide and sequential convex optimization

abstract: For supervised automation of multi-throw suturing in Robot-Assisted Minimally Invasive Surgery, we present a novel mechanical needle guide and a framework for optimizing needle size, trajectory, and control parameters using sequential convex programming. The Suture Needle Angular Positioner (SNAP) results in a 3x error reduction in the needle pose estimate in comparison with the standard actuator. We evaluate the algorithm and SNAP on a da Vinci Research Kit using tissue phantoms and compare completion time with that of humans from the JIGSAWS dataset [5]. Initial results suggest that the dVRK can perform suturing at 30% of human speed while completing 86% suture throws attempted. Videos and data are available at: berkeleyautomation.github.io/amts.


title: A practical evaluation of popular volume rendering algorithms

abstract: This paper evaluates and compares four volume rendering algorithms that have become rather popular for rendering datasets described on uniform rectilinear grids: raycasting, splatting, shear-warp, and hardware-assisted 3D texture-mapping. In order to assess both the strengths and the weaknesses of these algorithms in a wide variety of scenarios, a set of real-life benchmark datasets with different characteristics was carefully selected. In the rendering, all algorithm-independent image synthesis parameters, such as viewing matrix, transfer functions, and optical model, were kept constant to enable a fair comparison of the rendering results. Both image quality and computational complexity were evaluated and compared, with the aim of providing both researchers and practitioners with guidelines on which algorithm is most suited in which scenario. Our analysis also indicates the current weakness in each algorithm's pipeline, and possible solutions to these as well as pointers for future research are offered.


title: Development of lightweight sprawling-type quadruped robot TITAN-XIII and its dynamic walking

abstract: In this paper, we discuss the development of the sprawling-type quadruped robot named “TITAN-XIII” and its dynamic walking algorithm. We develop an experimental quadruped robot especially designed for dynamic walking. Unlike dog-like robots, the prototype robot looks like a four-legged spider. As an experimental robot, we focus on the three basic concepts: lightweight, wide range of motion and ease of maintenance. To achieve these goals, we introduce a wire-driven mechanism using a synthetic fiber to transmit power to each axis making use of this wire-driven mechanism, we can locate the motors at the base of the leg, reducing, consequently, its inertia. Additionally, each part of the robot is unitized, and can be easily disassembled. As a dynamic walking algorithm, we proposed what we call “longitudinal acceleration trajectory”. This trajectory was applied to intermittent trot gait. The algorithm was tested with the developed robot, and its performance was confirmed through experiments.


title: Mixed reality: future dreams seen at the border between real and virtual worlds

abstract: Mixed reality (MR) is a kind of virtual reality (VR) but a broader concept than augmented reality (AR), which augments the real world with synthetic electronic data. On the opposite side, there is a term, augmented virtuality (AV), which enhances or augments the virtual environment (VE) with data from the real world. Mixed reality covers a continuum from AR to AV. This concept embraces the definition of MR stated by Milgram and Kishino (1994). We describe some technical achievements we made in the Mixed Reality Project in Japan.


title: The temporal connection between smiles and blinks

abstract: In this paper, we present evidence for a temporal relationship between eye blinks and smile dynamics (smile onset and offset). Smiles and blinks occur with high frequency during social interaction, yet little is known about their temporal integration. To explore the temporal relationship between them, we used an Active Appearance Models algorithm to detect eye blinks in video sequences that contained manually FACS-coded spontaneous smiles (AU 12). We then computed the temporal distance between blinks and smile onsets and offsets. Our data shows that eye blinks are correlated with the end of the smile and occur close to the offset, but before the lip corners stop moving downwards. Furthermore, a marginally significant effect suggests that eye blinks are suppressed (less frequent) before smile onset. For computer-generated characters, this model of the timing of blinks relative to smiles may be useful in creating compelling facial animations.


title: EVE: explainable vector based embedding technique using Wikipedia

abstract: We present an unsupervised explainable vector embedding technique, called EVE, which is built upon the structure of Wikipedia. The proposed model defines the dimensions of a semantic vector representing a concept using human-readable labels, thereby it is readily interpretable. Specifically, each vector is constructed using the Wikipedia category graph structure together with the Wikipedia article link structure. To test the effectiveness of the proposed model, we consider its usefulness in three fundamental tasks: 1) intruder detection—to evaluate its ability to identify a non-coherent vector from a list of coherent vectors, 2) ability to cluster—to evaluate its tendency to group related vectors together while keeping unrelated vectors in separate clusters, and 3) sorting relevant items first—to evaluate its ability to rank vectors (items) relevant to the query in the top order of the result. For each task, we also propose a strategy to generate a task-specific human-interpretable explanation from the model. These demonstrate the overall effectiveness of the explainable embeddings generated by EVE. Finally, we compare EVE with the Word2Vec, FastText, and GloVe embedding techniques across the three tasks, and report improvements over the state-of-the-art.


title: Electrical battery model for use in dynamic electric vehicle simulations

abstract: Simulation of electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles over driving schedules within a full dynamic hybrid and electric vehicle simulator requires battery models capable of predicting state-of-charge, I-V characteristics, and dynamic behavior of different battery types. A battery model capable of reproducing lithium-ion, nickel-metal hydride, and lead- acid I-V characteristics (with minimal model alterations) is proposed. A battery-testing apparatus was designed to measure the proposed parameters of the battery model for all three battery types and simulate driving schedules with a programmed source and load configuration. A multiple time-constant battery model was used for modeling lithium- ion batteries; verification of time constants in the seconds to minutes and hour ranges has been shown in numerous research articles and a time constant in the millisecond range is verified here with experiments. Lack of significant time constants in the millisecond range is validated through direct testing. A modeled capacity-rate effect within the state-of-charge determination portion of the proposed model is verified experimentally to ensure accurate prediction of battery state of charge after lengthy driving schedules. The battery model was programmed into a Matlab/Simulink environment and used as a power source for plug-in hybrid electric vehicle simulations. Results from simulations of lithium-ion battery packs show that the proposed battery model behaves well with the other subcomponents of the vehicle simulator; accuracy of the model and prediction of battery internal losses depends on the extent of tests performed on the battery used for the simulation. Extraction of model parameters and their dependence on temperature and cycle number is ongoing, as well as validation of the Simulink model with hardware- in-the-loop "driving schedule" cycling of real batteries.


title: Comparative evaluation of machines for electric and hybrid vehicles based on dynamic operation and loss minimization

abstract: This paper proposes a method to evaluate induction machines for electric vehicles (EVs) and hybrid electric vehicles (HEVs). Some performance aspects of induction machines are also compared to permanent magnet synchronous machines (PMSMs). An overview of static efficiency maps is presented, but efficiency maps miss dynamic effects and under-predict induction machine efficiencies. The proposed evaluation method is based on dynamic efficiency under loss minimization and overall energy consumption over standard driving cycles that are provided by the U.S. Environmental Protection Agency. Over each of these cycles, the dynamic efficiency and drive-cycle energy are determined based on experimental motor data in combination with a dynamic HEV simulator. Results show that efficiency in the fast-changing dynamic environment of a vehicle can be higher than inferred from static efficiency maps. Overall machine efficiency is compared for rated flux, and for dynamic loss-minimizing flux control. The energy efficiency given optimum flux is typically five points higher than for rated flux. This result is comparable to published PMSM results. A PMSM is also used for comparisons, and results show that both machines can perform well in HEV and EV applications.


title: Deep Reinforcement Learning from Policy-Dependent Human Feedback

abstract: To widen their accessibility and increase their utility, intelligent agents must be able to learn complex behaviors as specified by (non-expert) human users. Moreover, they will need to learn these behaviors within a reasonable amount of time while efficiently leveraging the sparse feedback a human trainer is capable of providing. Recent work has shown that human feedback can be characterized as a critique of an agent's current behavior rather than as an alternative reward signal to be maximized, culminating in the COnvergent Actor-Critic by Humans (COACH) algorithm for making direct policy updates based on human feedback. Our work builds on COACH, moving to a setting where the agent's policy is represented by a deep neural network. We employ a series of modifications on top of the original COACH algorithm that are critical for successfully learning behaviors from high-dimensional observations, while also satisfying the constraint of obtaining reduced sample complexity. We demonstrate the effectiveness of our Deep COACH algorithm in the rich 3D world of Minecraft with an agent that learns to complete tasks by mapping from raw pixels to actions using only real-time human feedback in 10-15 minutes of interaction.


Role of Employee Career as Mediation Relationship between Competency and Performance Employee


A Deep Learning Approach to Contract Element Extraction.


title: Personalisation in MOOCs: a critical literature review

abstract: The advent and rise of Massive Open Online Courses (MOOCs) have brought many issues to the area of educational technology. Researchers in the field have been addressing these issues such as pedagogical quality of MOOCs, high attrition rates, and sustainability of MOOCs. However, MOOCs personalisation has not been subject of the wide discussions around MOOCs. This paper presents a critical literature survey and analysis of the available literature on personalisation in MOOCs to identify the needs, the current states and efforts to personalise learning in MOOCs. The findings illustrate that there is a growing attention to personalisation to improve learners’ individual learning experiences in MOOCs. In order to implement personalised services, personalised learning path, personalised assessment and feedback, personalised forum thread and recommendation service for related learning materials or learning tasks are commonly applied.


Good Automatic Authentication Question Generation


title: Automatic calibration for inertial measurement unit

abstract: The nine degrees-of-freedom (DOF) inertial measurement units (IMU) are generally composed of three kinds of sensor: accelerometer, gyroscope and magnetometer. The calibration of these sensor suites not only requires turn-table or purpose-built fixture, but also entails a complex and laborious procedure in data sampling. In this paper, we propose a method to calibrate a 9-DOF IMU by using a set of casually sampled raw sensor measurement. Our sampling procedure allows the sensor suite to move by hand and only requires about six minutes of fast and slow arbitrary rotations with intermittent pauses. It requires neither the specially-designed fixture and equipment, nor the strict sequences of sampling steps. At the core of our method are the techniques of data filtering and a hierarchical scheme for calibration. All the raw sensor measurements are preprocessed by a series of band-pass filters before use. And our calibration scheme makes use of the gravity and the ambient magnetic field as references, and hierarchically calibrates the sensor model parameters towards the minimization of the mis-alignment, scaling and bias errors. Moreover, the calibration steps are formulated as a series of function optimization problems and are solved by an evolutionary algorithm. Finally, the performance of our method is experimentally evaluated. The results show that our method can effectively calibrate the sensor model parameters from one set of raw sensor measurement, and yield consistent calibration results.


title: Antibiotics as selectors and accelerators of diversity in the mechanisms of resistance: from the resistome to genetic plasticity in the β-lactamases world

abstract: Antibiotics and antibiotic resistance determinants, natural molecules closely related to bacterial physiology and consistent with an ancient origin, are not only present in antibiotic-producing bacteria. Throughput sequencing technologies have revealed an unexpected reservoir of antibiotic resistance in the environment. These data suggest that co-evolution between antibiotic and antibiotic resistance genes has occurred since the beginning of time. This evolutionary race has probably been slow because of highly regulated processes and low antibiotic concentrations. Therefore to understand this global problem, a new variable must be introduced, that the antibiotic resistance is a natural event, inherent to life. However, the industrial production of natural and synthetic antibiotics has dramatically accelerated this race, selecting some of the many resistance genes present in nature and contributing to their diversification. One of the best models available to understand the biological impact of selection and diversification are -lactamases. They constitute the most widespread mechanism of resistance, at least among pathogenic bacteria, with more than 1000 enzymes identified in the literature. In the last years, there has been growing concern about the description, spread and diversification of -lactamases with carbapenemase activity and AmpC-type in plasmids. Phylogenies of these enzymes help the understanding of the evolutionary forces driving their selection. Moreover, understanding the adaptive potential of -lactamases contribute to exploration the evolutionary antagonists trajectories through the design of more efficient synthetic molecules. In this review, we attempt to analyse the antibiotic resistance problem from new perspectives. From intrinsic and environmental resistomes to the adaptive potential of resistance genes and the driving forces involved in their diversification, in order to provide a global perspective of the resistance problem.


title: The SPARK Agent Framework

abstract: There is a need for agent systems that can scale to real-world applications, yet retain the clean semantic underpinning of more formal agent frameworks. We describe the SRI Procedural Agent Realization Kit (SPARK), a new BDI agent framework that combines these two qualities. In contrast to most practical agent frameworks, SPARK has a clear, well-defined formal semantics that is intended to support reasoning techniques such as procedure validation, automated synthesis, and procedure repair. SPARK also provides a variety of capabilities such as introspection and meta-level reasoning to enable more sophisticated methods for agent control, and advisability techniques that support user directability. On the practical side, SPARK has several design constructs that support the development of large-scale agent applications. SPARK is currently being used as the agent infrastructure for a personal assistant system for a manager in an office environment.


title: BROADBAND COPLANAR WAVEGUIDE-FED WIDE-SLOT ANTENNA

abstract: A large bandwidth wide-slot antenna, fed by coplanar waveguide (CPW), is proposed. Experimental investigations and detailed simulations are conducted to understand its behavior and to optimize for broadband operation. The impedance bandwidth, determined by 10-dB return loss of the proposed slot antenna using both measurement and simulation, is about 131% (2.8 to 14GHz). In addition to be small in size, the antenna has low cross polarization, relatively high gain, and it exhibits stable far-fleld radiation characteristics in the entire operating bandwidth. The design with very wide operating bandwidth and improved radiation pattern is obtained by properly choosing the suitable slot shape, selecting similar feed patch shape, and tuning their dimensions. Numerical sensitivity analysis has been used to understand the efiects of changes of various antenna dimensions and to optimize the performance of the designed antenna. Based on our computer simulations it is shown that the antenna dimensions parameters have uncorrelated efiects on the upper edge of the bandwidth. Simulation results show that the impedance matching of this kind of antenna is sensitive to the feed-slot combination and feed gap width. The simulated and measured results for return loss, far-fleld E-plane and H-plane radiation patterns, and gain of the designed antenna are presented and discussed.


title: Explosives under pressure—the crystal structure of γ-RDX as determined by high-pressure X-ray and neutron diffraction

abstract: Using a combination of X-ray single crystal and neutron powder diffraction, the crystal structure of the high-pressure gamma-form of RDX has been determined at 5.2 GPa and shows that the RDX molecules adopt different conformations compared to the conformation found in the ambient-pressure alpha-form.


title: A bidirectional battery charger for electric vehicles with V2G and V2H capability and active and reactive power control

abstract: A bidirectional battery charger using a three-phase voltage source inverter (VSI) is presented to be employed in plug-in electric vehicle applications enabling Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) functionality with simultaneous active and reactive power control. The charger is controlled by a grid supervising unit with voltage droop control, providing it with P (active) and Q (reactive) power requests, and is responsible of reporting back its available power reserves (apparent power). In V2G mode the grid voltage is observed and a current controller on the d-q coordinate system together with the three phase inverter can transfer energy in both directions. Also reactive power compensation, inductive or capacitive, is possible. Asymmetries on the grid load can also be compensated by slight modifications of the control regime. Further, in V2H mode, the charger system can be seen as the master grid-forming generator responsible to provide a stable voltage at the network frequency. A proper synchronization strategy is presented as well, to guarantee smooth reconnection with the power grid. The proposed architecture is modeled in Simulink and aims the realization in existing electric vehicle and smart grid prototypes of the Siemens AG.


title: Comparative evaluation of 6kV Si and SiC power devices for medium voltage power electronics applications

abstract: In order to better assist researchers to select the appropriate power device for medium voltage power electronics applications, this paper presents a comparative evaluation on three typical 6kV level Si and SiC power devices, including 6.5kV/25A Si IGBT from ABB, 6.5kV/15A normally off SiC JFET from USCi and a FREEDM System Center developed 6kV/26A SiC series-connected JFET. The 6.5kV Si IGBT and 6.5kV SiC JFET are packaged in the same module to minimize the effect of different parasitic inductance on the comparison. The 6kV SiC series-connected JFET is developed based on one 1.2kV SiC MOSFET from Cree and four 1.2kV SiC JFETs from Infineon, in this paper, named FREEDM Super-Cascode. A short introduction on the three selected devices are first given, then their forward conduction and switching performances are compared. Also, some additional features are discussed and compared, including the device size, cost, gate driver circuit complexity.


title: Egos Inflating Over Time: A Cross‐Temporal Meta‐Analysis of the Narcissistic Personality Inventory

abstract: ABSTRACT A cross-temporal meta-analysis found that narcissism levels have risen over the generations in 85 samples of American college students who completed the 40-item forced-choice Narcissistic Personality Inventory (NPI) between 1979 and 2006 (total n=16,475). Mean narcissism scores were significantly correlated with year of data collection when weighted by sample size (β=.53, p<.001). Since 1982, NPI scores have increased 0.33 standard deviation. Thus, almost two-thirds of recent college students are above the mean 1979–1985 narcissism score, a 30% increase. The results complement previous studies finding increases in other individualistic traits such as assertiveness, agency, self-esteem, and extraversion.


title: Research on data augmentation for image classification based on convolution neural networks

abstract: The performance of deep convolution neural networks will be further enhanced with the expansion of the training data set. For the image classification tasks, it is necessary to expand the insufficient training image samples through various data augmentation methods. This paper explores the impact of various data augmentation methods on image classification tasks with deep convolution Neural network, in which Alexnet is employed as the pre-training network model and a subset of CIFAR10 and ImageNet (10 categories) are selected as the original data set. The data augmentation methods used in this paper include: GAN/WGAN, Flipping, Cropping, Shifting, PCA jittering, Color jittering, Noise, Rotation, and some combinations. Experimental results show that, under the same condition of multiple increasing, the performance evaluation on small-scale data sets is more obvious, the four individual methods (Cropping, Flipping, WGAN, Rotation) perform generally better than others, and some appropriate combination methods are slightly more effective than the individuals.


title: Transformerless Photovoltaic Inverters Connected to the Grid

abstract: Renewable energy sources are getting more and more widespread, mainly due to the fact that they generate energy by keeping the environment clean. Most of these systems have an isolation transformer included, which if excluded from the system would increase the efficiency and decrease the size of PV installations, furthermore it would lead to a lower cost for the whole investment. But there are some safety issues regarding the missing galvanic isolation. This paper is aiming to analyze and compare the most common single-stage transformerless PV inverter topologies for single-phase and three-phase with respect to the leakage current generation. The best results, both for single-phase and three-phase systems, are obtained when the middle point of the input capacitors is connected to the neutral point, thereby minimizing the voltage fluctuations present at the terminals of the PV panel.


title: Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing

abstract: This paper studies the problem of learning image semantic segmentation networks only using image-level labels as supervision, which is important since it can significantly reduce human annotation efforts. Recent state-of-the-art methods on this problem first infer the sparse and discriminative regions for each object class using a deep classification network, then train semantic a segmentation network using the discriminative regions as supervision. Inspired by the traditional image segmentation methods of seeded region growing, we propose to train a semantic segmentation network starting from the discriminative regions and progressively increase the pixel-level supervision using by seeded region growing. The seeded region growing module is integrated in a deep segmentation network and can benefit from deep features. Different from conventional deep networks which have fixed/static labels, the proposed weakly-supervised network generates new labels using the contextual information within an image. The proposed method significantly outperforms the weakly-supervised semantic segmentation methods using static labels, and obtains the state-of-the-art performance, which are 63.2% mIoU score on the PASCAL VOC 2012 test set and 26.0% mIoU score on the COCO dataset.


title: Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution

abstract: Most existing approaches for zero pronoun resolution are heavily relying on annotated data, which is often released by shared task organizers. Therefore, the lack of annotated data becomes a major obstacle in the progress of zero pronoun resolution task. Also, it is expensive to spend manpower on labeling the data for better performance. To alleviate the problem above, in this paper, we propose a simple but novel approach to automatically generate large-scale pseudo training data for zero pronoun resolution. Furthermore, we successfully transfer the cloze-style reading comprehension neural network model into zero pronoun resolution task and propose a two-step training mechanism to overcome the gap between the pseudo training data and the real one. Experimental results show that the proposed approach significantly outperforms the state-of-the-art systems with an absolute improvements of 3.1% F-score on OntoNotes 5.0 data.


title: Efficient name disambiguation for largescale databases

abstract: Name disambiguation can occur when one is seeking a list of publications of an author who has used different name variations and when there are multiple other authors with the same name. We present an efficient integrative framework for solving the name disambiguation problem: a blocking method retrieves candidate classes of authors with similar names and a clustering method, DBSCAN, clusters papers by author. The distance metric between papers used in DBSCAN is calculated by an online active selection support vector machine algorithm (LASVM), yielding a simpler model, lower test errors and faster prediction time than a standard SVM. We prove that by recasting transitivity as density reachability in DBSCAN, transitivity is guaranteed for core points. For evaluation, we manually annotated 3,355 papers yielding 490 authors and achieved 90.6% pairwise-F1. For scalability, authors in the entire CiteSeer dataset, over 700,000 papers, were readily disambiguated.


title: A miniaturized half-mode siw based semi-circular antenna with arc-shaped slot

abstract: Asemi-circular antenna with the structure resembling that of a half-mode substrate integrated waveguide (HMSIW) is chosen and fed with co-axial feed to operate at 5.22 GHz within the IEEE 802.11a WLAN U-NII Low band. An arc-shaped slot is inserted in the middle of the parent antenna at an angle of θ = 80°, 90° and 100° in order to make the antenna compact. The miniaturized antenna so designed is found to operate at a center frequencyof 4.79 GHz for θ=80°, 4.61 GHz for θ=90° and 4.47 GHz for θ=100°. The shifts in the center frequencies observed are 430 MHz, 610 MHz and 750 MHz respectively from the center frequency of 5.22 GHz of the parent antenna. The various antenna parameters are simulated and studied extensively with the help of HFSS software.


title: An Attention-based Collaboration Framework for Multi-View Network Representation Learning

abstract: Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.


title: Study of insect succession and rate of decomposition on a partially burned pig carcass in an oil palm plantation in Malaysia

abstract: Insects found associated with corpse can be used as one of the indicators in estimating postmortem interval (PMI). The objective of this study was to compare the stages of decomposition and faunal succession between a partially burnt pig (Sus scrofa Linnaeus) and natural pig (as control). The burning simulated a real crime whereby the victim was burnt by murderer. Two young pigs weighed approximately 10 kg were used in this study. Both pigs died from pneumonia and immediately placed in an oil palm plantation near a pig farm in Tanjung Sepat, Selangor, Malaysia. One pig was partially burnt by 1-liter petrol while the other served as control. Both carcasses were visited twice per day for the first week and once thereafter. Adult flies and larvae on the carcasses were collected and later processed in a forensic entomology laboratory. Results showed that there was no significant difference between the rate of decomposition and sequence of faunal succession on both pig carcasses. Both carcasses were completely decomposed to remain stage after nine days. The species of flies visiting the pig carcasses consisted of blow flies (Chrysomya megacephala, Chrysomya rufifacies, Hemipyrellia ligurriens), flesh fly (Sarcophagidae.), muscid fly (Ophyra spinigera), soldier fly (Hermetia illucens), coffin fly (Phoridae) and scavenger fly (Sepsidae). The only difference noted was in the number of adult flies, whereby more flies were seen in the control carcass. Faunal succession on both pig carcasses was in the following sequence: Calliphoridae, Sarcophagidae, Muscidae, Phoridae and lastly Stratiomyidae. However, there was overlap in the appearance of members of these families. Blowflies continued to oviposit on both carcasses. Hence postmortem interval (PMI) can still be estimated from the partially burnt pig carcass.


title: Weakly Supervised Semantic Segmentation Using Web-Crawled Videos

abstract: We propose a novel algorithm for weakly supervised semantic segmentation based on image-level class labels only. In weakly supervised setting, it is commonly observed that trained model overly focuses on discriminative parts rather than the entire object area. Our goal is to overcome this limitation with no additional human intervention by retrieving videos relevant to target class labels from web repository, and generating segmentation labels from the retrieved videos to simulate strong supervision for semantic segmentation. During this process, we take advantage of image classification with discriminative localization technique to reject false alarms in retrieved videos and identify relevant spatio-temporal volumes within retrieved videos. Although the entire procedure does not require any additional supervision, the segmentation annotations obtained from videos are sufficiently strong to learn a model for semantic segmentation. The proposed algorithm substantially outperforms existing methods based on the same level of supervision and is even as competitive as the approaches relying on extra annotations.


title: A survey on data breach challenges in cloud computing security: Issues and threats

abstract: Cloud computing is an innovation methodology for giving pay per utilize access to a gathering of shared resources for specific systems, stockpiling, servers, administration and applications, without physically getting them. So it spares overseeing expense and time for organizations. Numerous businesses, for example, keeping money, social insurance and instruction are moving towards the cloud because of the productivity of administrations gave by the compensation per-use design in view of the assets, for example, preparing influence utilized, exchanges completed, storage capacity devoured, information exchanged, or storage room possessed and so on. Cloud computing is a totally web based innovation where customer information is put away and kept in the server farm of a cloud supplier like Google, Amazon, Salesforce.com and Microsoft and so on. Constrained control over the information may acquire different security issues and threats which incorporate data breach, unreliable connectivity, sharing of resources, data accessibility and inside attacks. A breach of security may lead to the accidental or unlawful destruction, loss, alteration, unauthorized disclosure of, or access to, personal data transmitted, stored or otherwise processed. There are different research challenges likewise there for embracing data breaches on cloud computing. This paper examines about cloud computing, different cloud models and primary security threats and data breach issues that are right now exploring in the cloud computing framework. This paper investigates the noteworthy research and difficulties that presents data breach in cloud computing and provides best practices to service providers and additionally endeavors plan to influence cloud servers to enhance their main concern in this serious economic scenario.


title: TDOA-TWR based positioning algorithm for UWB localization system

abstract: Ultra-wideband positioning systems intended for indoor applications often work in non-line of sight conditions, which result in insufficient precision and accuracy of derived localizations. One of the possible solutions is the implementation of cooperative positioning techniques. The following paper describes a cooperative ultra-wideband positioning system which calculates tag position from TDOA and distance between tags measurements. In the paper positioning system architecture is described and an exemplary transmission scheme for cooperative systems is presented. Considered localization system utilizes an Extended Kalman Filter based algorithm. The algorithm was investigated with simulations and experiments. Conducted experiment consisted in fusing results gathered from typical TDOA positioning system infrastructure and ranging results obtained with ultra-wideband radio modules. The research has shown that the use presented cooperative algorithm increases positioning precision.


title: Dual and multi-stimuli responsive polymeric nanoparticles for programmed site-specific drug delivery

abstract: In the past decades, polymeric nanoparticles have emerged as a most promising and viable technology platform for targeted and controlled drug delivery. As vehicles, ideal nanoparticles are obliged to possess high drug loading levels, deliver drug to the specific pathological site and/or target cells without drug leakage on the way, while rapidly unload drug at the site of action. To this end, various "intelligent" polymeric nanoparticles that release drugs in response to an internal or external stimulus such as pH, redox, temperature, magnetic and light have been actively pursued. These stimuli-responsive nanoparticles have demonstrated, though to varying degrees, improved in vitro and/or in vivo drug release profiles. In an effort to further improve drug release performances, novel dual and multi-stimuli responsive polymeric nanoparticles that respond to a combination of two or more signals such as pH/temperature, pH/redox, pH/magnetic field, temperature/reduction, double pH, pH and diols, temperature/magnetic field, temperature/enzyme, temperature/pH/redox, temperature/pH/magnetic, pH/redox/magnetic, temperature/redox/guest molecules, and temperature/pH/guest molecules have recently been developed. Notably, these combined responses take place either simultaneously at the pathological site or in a sequential manner from nanopartide preparation, nanopartide transporting pathways, to cellular compartments. These dual and multi-stimuli responsive polymeric nanoparticles have shown unprecedented control over drug delivery and release leading to superior in vitro and/or in vivo anti-cancer efficacy. With programmed site-specific drug delivery feature, dual and multi-stimuli responsive nanoparticulate drug formulations have tremendous potential for targeted cancer therapy. In this review paper, we highlight the recent exciting developments in dual and multi-stimuli responsive polymeric nanoparticles for precision drug delivery applications, with a particular focus on their design, drug release performance, and therapeutic benefits. (C) 2013 Elsevier Ltd. All rights reserved.


title: A Novel Grain-Oriented Lamination Rotor Core Assembly for a Synchronous Reluctance Traction Motor With a Reduced Torque Ripple Algorithm

abstract: High torque density and low torque ripple are crucial for traction applications, which allow electrified powertrains to perform properly during start-up, acceleration, and cruising. High-quality anisotropic magnetic materials such as cold-rolled grain-oriented electrical steels can be used for achieving higher efficiency, torque density, and compactness in synchronous reluctance motors equipped with transverse laminated rotors. However, the rotor cylindrical geometry makes utilization of these materials with pole numbers higher than two more difficult. From a reduced torque ripple viewpoint, particular attention to the rotor slot pitch angle design can lead to improvements. This paper presents an innovative rotor lamination design and assembly using cold-rolled grain-oriented electrical steel to achieve higher torque density along with an algorithm for rotor slot pitch angle design for reduced torque ripple. The design methods and prototyping process are discussed, finite-element analyses and experimental examinations are carried out, and the results are compared to verify and validate the proposed methods.


title: Low-Rank Common Subspace for Multi-view Learning

abstract: Multi-view data is very popular in real-world applications, as different view-points and various types of sensors help to better represent data when fused across views or modalities. Samples from different views of the same class are less similar than those with the same view but different class. We consider a more general case that prior view information of testing data is inaccessible in multi-view learning. Traditional multi-view learning algorithms were designed to obtain multiple view-specific linear projections and would fail without this prior information available. That was because they assumed the probe and gallery views were known in advance, so the correct view-specific projections were to be applied in order to better learn low-dimensional features. To address this, we propose a Low-Rank Common Subspace (LRCS) for multi-view data analysis, which seeks a common low-rank linear projection to mitigate the semantic gap among different views. The low-rank common projection is able to capture compatible intrinsic information across different views and also well-align the within-class samples from different views. Furthermore, with a low-rank constraint on the view-specific projected data and that transformed by the common subspace, the within-class samples from multiple views would concentrate together. Different from the traditional supervised multi-view algorithms, our LRCS works in a weakly supervised way, where only the view information gets observed. Such a common projection can make our model more flexible when dealing with the problem of lacking prior view information of testing data. Two scenarios of experiments, robust subspace learning and transfer learning, are conducted to evaluate our algorithm. Experimental results on several multi-view datasets reveal that our proposed method outperforms state-of-the-art, even when compared with some supervised learning methods.


title: Attention please!: learning analytics for visualization and recommendation

abstract: This paper will present the general goal of and inspiration for our work on learning analytics, that relies on attention metadata for visualization and recommendation. Through information visualization techniques, we can provide a dashboard for learners and teachers, so that they no longer need to "drive blind". Moreover, recommendation can help to deal with the "paradox of choice" and turn abundance from a problem into an asset for learning.


title: Practical Secure Aggregation for Privacy-Preserving Machine Learning

abstract: We design a novel, communication-efficient, failure-robust protocol for secure aggregation of high-dimensional data. Our protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner (i.e. without learning each user's individual contribution), and can be used, for example, in a federated learning setting, to aggregate user-provided model updates for a deep neural network. We prove the security of our protocol in the honest-but-curious and active adversary settings, and show that security is maintained even if an arbitrarily chosen subset of users drop out at any time. We evaluate the efficiency of our protocol and show, by complexity analysis and a concrete implementation, that its runtime and communication overhead remain low even on large data sets and client pools. For 16-bit input values, our protocol offers $1.73 x communication expansion for 210 users and 220-dimensional vectors, and 1.98 x expansion for 214 users and 224-dimensional vectors over sending data in the clear.


title: Mobile Phone Sensing Systems: A Survey

abstract: Mobile phone sensing is an emerging area of interest for researchers as smart phones are becoming the core communication device in people's everyday lives. Sensor enabled mobile phones or smart phones are hovering to be at the center of a next revolution in social networks, green applications, global environmental monitoring, personal and community healthcare, sensor augmented gaming, virtual reality and smart transportation systems. More and more organizations and people are discovering how mobile phones can be used for social impact, including how to use mobile technology for environmental protection, sensing, and to leverage just-in-time information to make our movements and actions more environmentally friendly. In this paper we have described comprehensively all those systems which are using smart phones and mobile phone sensors for humans good will and better human phone interaction.


title: Contextualized Word Representations for Reading Comprehension

abstract: Reading a document and extracting an answer to a question about its content has attracted substantial attention recently. While most work has focused on the interaction between the question and the document, in this work we evaluate the importance of context when the question and document are processed independently. We take a standard neural architecture for this task, and show that by providing rich contextualized word representations from a large pre-trained language model as well as allowing the model to choose between context-dependent and context-independent word representations, we can obtain dramatic improvements and reach performance comparable to state-of-the-art on the competitive SQuAD dataset.


title: A multi-step outlier-based anomaly detection approach to network-wide traffic

abstract: Abstract Outlier detection is of considerable interest in fields such as physical sciences, medical diagnosis, surveillance detection, fraud detection and network anomaly detection. The data mining and network management research communities are interested in improving existing score-based network traffic anomaly detection techniques because of ample scopes to increase performance. In this paper, we present a multi-step outlier-based approach for detection of anomalies in network-wide traffic. We identify a subset of relevant traffic features and use it during clustering and anomaly detection. To support outlier-based network anomaly identification, we use the following modules: a mutual information and generalized entropy based feature selection technique to select a relevant non-redundant subset of features, a tree-based clustering technique to generate a set of reference points and an outlier score function to rank incoming network traffic to identify anomalies. We also design a fast distributed feature extraction and data preparation framework to extract features from raw network-wide traffic. We evaluate our approach in terms of detection rate, false positive rate, precision, recall and F -measure using several high dimensional synthetic and real-world datasets and find the performance superior in comparison to competing algorithms.


title: A single-layer SIW slot array antenna with TE20 mode

abstract: The idea of using higher order modes is demonstrated by an SIW slot array antenna with a TE 20 mode to realize boresight radiation. In order to excite the TE 20 mode, an SIW hybrid coupler and a compact SIW 90° phase shifter are designed to offer the two out-of-phase outputs with identical amplitude. Besides, a CPW-SIW transition is adopted for measurement. The SIW slot array antenna with the TE 20 mode is designed at X-band in a single-layer PCB. The measured bandwidth is 5.5% (9.72–10.27 GHz). The measured total efficiency is 80% at 10 GHz. Compared to a conventional SIW slot array just with a power divider, using the TE 20 mode provides the merits of not only reducing the number of vias but also relaxing fabrication limit and tolerance which could be the fabrication bottleneck at high frequencies.


title: Social Media Use for News and Individuals' Social Capital, Civic Engagement and Political Participation

abstract: Recently, scholars tested how digital media use for informational purposes similarly contributes to foster democratic processes and the creation of social capital. Nevertheless, in the context of today's socially-networked-society and the rise of social media applications (i.e., Facebook) new perspectives need to be considered. Based on U.S. national data, results show that after controlling for demographic variables, traditional media use offline and online, political constructs (knowledge and efficacy), and frequency and size of political discussion networks, seeking information via social network sites is a positive and significant predictor of people's social capital and civic and political participatory behaviors, online and offline. © 2012 Wiley Periodicals, Inc.