title: The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations.

text: In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.


Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior


title: USER ACCEPTANCE OF COMPUTER TECHNOLOGY : A COMPARISON OF TWO THEORETICAL MODELS *

text: Computer systems cannot improve organizational performance if they aren't used. Unfortunately, resistance to end-user systems by managers and professionals is a widespread problem. To better predict, explain, and increase user acceptance, we need to better understand why people accept or reject computers. This research addresses the ability to predict peoples' computer acceptance from a measure oftheir intentions, and the ability to explain their intentions in terms oftheir attitudes, subjective norms, perceived usefulness, perceived ease of use, and related variables. In a longitudinal study of 107 users, intentions to use a specific system, measured after a onehour introduction to the system, were correlated 0.35 with system use 14 weeks later. The intentionusage correlation was 0.63 at the end of this time period. Perceived usefulness strongly influenced peoples' intentions, explaining more than half of the variance in intentions at the end of 14 weeks. Perceived ease of use had a small but significant effect on intentions as well, although this effect subsided over time. Attitudes only partially mediated the effects of these beliefs on intentions. Subjective norms had no effect on intentions. These results suggest the possibility of simple but powerful models ofthe determinants of user acceptance, with practical value for evaluating systems and guiding managerial interventions aimed at reducing the problem of underutilized computer technology. (INFORMATION TECHNOLOGY; USER ACCEPTANCE; INTENTION MODELS)


title: Self-perception: An alternative interpretation of cognitive dissonance phenomena.

text: A theory of self-perception is proposed to provide an alternative interpretation for several of the major phenomena embraced by Festinger's theory of cognitive dissonance and to explicate some of the secondary patterns of data that have appeared in dissonance experiments. It is suggested that the attitude statements which comprise the major dependent variables in dissonance experiments may be regarded as interpersonal judgments in which the observer and the observed happen to be the same individual and that it is unnecessary to postulate an aversive motivational drive toward consistency to account for the attitude change phenomena observed. Supporting experiments are presented, and metatheoretical contrasts between the "radical" behavioral approach utilized and the phenomenological approach typified by dissonance theory are discussed.


title: Attitudes, satisfaction and usage: Factors contributing to each in the acceptance of information technology

text: This research tests and develops the Technology Acceptance Model (TAM), introduced by Davis (1986), which attempts to explain end users’ attitudes to computing technologies. It introduces several new variables, including compatibility, user characteristics, system rating and the enduser computing satisfaction (EUCS) construct, a surrogate measure for IT success and acceptance. A questionnaire with over seventy items was completed by a large number of users and LISREL, a technique for modelling a system of structural equations, was used to analyse the responses. The output shows the model as a whole ® ts the data very well and indicates signi® cant relationships between variables in the model. These results con® rm that TAM is a valuable tool for predicting attitudes, satisfaction, and usage from beliefs and external variables. They also show that relative advantage of the system contributed most to attitudes and satisfaction. Compatibility (of the system to the task performed) contributed most to usage and was the most important antecedent of the belief variables, including relative advantage.


title: Cardiorespiratory fitness estimation in free-living using wearable sensors

text:
OBJECTIVE
In this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data.

METHODS
Our methods rely on a computational framework able to contextualize heart rate (HR) in free-living, and use context-specific HR as predictor of CRF without need for laboratory tests. In particular, we propose three estimation steps. Initially, we recognize activity primitives using accelerometer and location data. Using topic models, we group activity primitives and derive activities composites. We subsequently rank activity composites, and analyze the relation between ranked activity composites and CRF across individuals. Finally, HR data in specific activity primitives and composites is used as predictor in a hierarchical Bayesian regression model to estimate CRF level from the participant's habitual behavior in free-living.

RESULTS
We show that by combining activity primitives and activity composites the proposed framework can adapt to the user and context, and outperforms other CRF estimation models, reducing estimation error between 10.3% and 22.6% on a study population of 46 participants.

CONCLUSIONS
Our investigation showed that HR can be contextualized in free-living using activity primitives and activity composites and robust CRF estimation in free-living is feasible.


title: Sindhi Language Processing: A survey

text: In this era of information technology, natural language processing (NLP) has become volatile field because of digital reliance of today's communities. The growth of Internet usage bringing the communities, cultures and languages online. In this regard much of the work has been done of the European and east Asian languages, in the result these languages have reached mature level in terms of computational processing. Despite the great importance of NLP science, still most of the South Asian languages are under developing phase. Sindhi language is one of them, which stands among the most ancient languages in the world. The Sindhi language has a great influence on the large community in Sindh province of Pakistan and some states of India and other countries. But unfortunately, it is at infant level in terms of computational processing, because it has not received such attention of language engineering community, due to its complex morphological structure and scarcity of language resources. Therefore, this study has been carried out in order to summarize the existing work on Sindhi Language Processing (SLP) and to explore future research opportunities, also some potential research problems. This paper will be helpful for the researchers in order to find all the information regarding SLP at one place in a unique way.


title: Multi-View Representation Learning: A Survey from Shallow Methods to Deep Methods

text: Recently, multi-view representation learning has become a rapidly growing direction in machine learning and data mining areas. This paper introduces several principles for multi-view representation learning: correlation, consensus, and complementarity principles. Consequently, we first review the representative methods and theories of multi-view representation learning based on correlation principle, especially on canonical correlation analysis (CCA) and its several extensions. Then from the viewpoint of consensus and complementarity principles we investigate the advancement of multi-view representation learning that ranges from shallow methods including multi-modal topic learning, multi-view sparse coding, and multi-view latent space Markov networks, to deep methods including multi-modal restricted Boltzmann machines, multi-modal autoencoders, and multi-modal recurrent neural networks. Further, we also provide an important perspective from manifold alignment for multi-view representation learning. Overall, this survey aims to provide an insightful overview of theoretical basis and state-of-the-art developments in the field of multi-view representation learning and to help researchers find the most appropriate tools for particular applications.


A survey of the effects of aging on biometric identity verification


title: Gesture recognition for smart home applications using portable radar sensors

text: In this article, we consider the design of a human gesture recognition system based on pattern recognition of signatures from a portable smart radar sensor. Powered by AAA batteries, the smart radar sensor operates in the 2.4 GHz industrial, scientific and medical (ISM) band. We analyzed the feature space using principle components and application-specific time and frequency domain features extracted from radar signals for two different sets of gestures. We illustrate that a nearest neighbor based classifier can achieve greater than 95% accuracy for multi class classification using 10 fold cross validation when features are extracted based on magnitude differences and Doppler shifts as compared to features extracted through orthogonal transformations. The reported results illustrate the potential of intelligent radars integrated with a pattern recognition system for high accuracy smart home and health monitoring purposes.


title: Glomosim: a scalable network simulation environment

text: Large-scale hybrid networks that include wireless, wired, and satellite based communications are becoming common in both military and commercial situations. This paper describes a scalable simulation environment called GloMoSim (for Global Mobile Information System Simulator) that effectively utilizes parallel execution to reduce the simulation time of detailed high-fidelity models of large communication networks. The paper also presents a set of case studies that evaluate the performance of large wireless networks with thousands of nodes and compares the impact of different lower layer protocols on the performance of typical applications.


title: School Leadership and Cyberbullying—A Multilevel Analysis

text: Cyberbullying is a relatively new form of bullying, with both similarities and differences to traditional bullying. While earlier research has examined associations between school-contextual characteristics and traditional bullying, fewer studies have focused on the links to students' involvement in cyberbullying behavior. The aim of the present study is to assess whether school-contextual conditions in terms of teachers' ratings of the school leadership are associated with the occurrence of cyberbullying victimization and perpetration among students. The data are derived from two separate data collections performed in 2016: The Stockholm School Survey conducted among students in the second grade of upper secondary school (ages 17-18 years) in Stockholm municipality, and the Stockholm Teacher Survey which was carried out among teachers in the same schools. The data include information from 6067 students distributed across 58 schools, linked with school-contextual information based on reports from 1251 teachers. Cyberbullying victimization and perpetration are measured by students' self-reports. Teachers' ratings of the school leadership are captured by an index based on 10 items; the mean value of this index was aggregated to the school level. Results from binary logistic multilevel regression models show that high teacher ratings of the school leadership are associated with less cyberbullying victimization and perpetration. We conclude that a strong school leadership potentially prevents cyberbullying behavior among students.


title: Probabilistic graphical models for semi-supervised traffic classification

text: Traffic classification using machine learning continues to be an active research area. The majority of work in this area uses off-the-shelf machine learning tools and treats them as black-box classifiers. This approach turns all the modelling complexity into a feature selection problem. In this paper, we build a problem-specific solution to the traffic classification problem by designing a custom probabilistic graphical model. Graphical models are a modular framework to design classifiers which incorporate domain-specific knowledge. More specifically, our solution introduces semi-supervised learning which means we learn from both labelled and unlabelled traffic flows. We show that our solution performs competitively compared to previous approaches while using less data and simpler features.


title: Risk Management: A Maturity Model Based on ISO 31000

text: Risk Management, according with the ISO Guide 73 is the set of "coordinated activities to direct and control an organization with regard to risk". In a nutshell, Risk Management is the business process used to manage risk in organizations. ISO 31000 defines a framework and process for risk management. However, implementing this standard without a detailed plan can become a burden on organizations. This paper presents a maturity model for the risk management process based on ISO 31000. The purpose of this model is to provide an assessment tool for organizations to use in order to get their current risk management maturity level. The results can then be used to create an improvement plan which will guide organizations to reach their target maturity level. This maturity model allows organizations to assess a risk management process according to the best practices defined in risk management references. The maturity model can also be used as a reference for improving this process since it sets a clear path of how a risk management process should be performed.


title: Recommending citations with translation model

text: Citation Recommendation is useful for an author to find out the papers or books that can support the materials she is writing about. It is a challengeable problem since the vocabulary used in the content of papers and in the citation contexts are usually quite different. To address this problem, we propose to use translation model, which can bridge the gap between two heterogeneous languages. We conduct an experiment and find the translation model can provide much better candidates of citations than the state-of-the-art methods.


title: An Affordable Augmented Reality Based Rehabilitation System for Hand Motions

text: Repetitive practices can facilitate the rehabilitation of the motor functions of the upper extremity for stroke survivors whose motor functions have been impaired. An affordable rehabilitation system for hand motion would be useful to provide intensive training to the patients. Augmented Reality (AR) can provide an intuitive interface to the users with programmable environment and realism feelings. In this research, a low cost viable rehabilitation system for hand motion is developed based on the AR technology. In this application, seamless interaction between the virtual environment and the real hand is supported with markers and a self-designed data-glove. The data-glove is low cost ((


title: A Deep Learning Approach to IoT Authentication

text: At its peak, the Internet-of-Things will largely be composed of low-power devices with wireless radios attached. Yet, secure authentication of these devices amidst adversaries with much higher power and computational capability remains a challenge, even for advanced cryptographic and wireless security protocols. For instance, a high-power software radio could simply replay chunks of signals from a low-power device to emulate it. This paper presents a deep-learning classifier that learns hardware imperfections of low-power radios that are challenging to emulate, even for high- power adversaries. We build an LSTM framework, specifically sensitive to signal imperfections that persist over long durations. Experimental results from a testbed of 30 low-power nodes demonstrate high resilience to advanced software radio adversaries.


title: The Blockchain-Based Digital Content Distribution System

text: The blockchain-based digital content distribution system was developed. Decentralized and pear-to-pear authentication mechanism can be considered as the ideal rights management mechanism. The blockchain has the potential to realize this ideal content distribution system. This is the successful model of the Superdistribution concept which was announced almost 30 years ago. The proposed system was demonstrated and got a lot of feedback for the future practical system.


title: Applying Epidemic Algorithm for Financial Service Based on Blockchain Technology

text: Our global market is emerging transformation strategy that can make the difference between success and failure. Smart contract systems through developments of technological innovations are increasingly seen as alternative technologies to impact transactional processes significantly. Blockchain is a smart contract protocol with trust offering the potential for creating new transaction platforms and thus shows a radical change of the current core value creation in third parties. These results in enormous cost and time savings and the reduced risk for the parties. This study proposed a method to improve the efficiency of distributed consensus in blockchains using epidemic algorithm. The results showed that epidemic protocols can distribute the information similar to blockchain.


title: M4: A Visualization-Oriented Time Series Data Aggregation

text: Visual analysis of high-volume time series data is ubiquitous in many industries, including finance, banking, and discrete manufacturing. Contemporary, RDBMS-based systems for visualization of high-volume time series data have difficulty to cope with the hard latency requirements and high ingestion rates of interactive visualizations. Existing solutions for lowering the volume of time series data disregard the semantics of visualizations and result in visualization errors. In this work, we introduce M4, an aggregation-based time series dimensionality reduction technique that provides errorfree visualizations at high data reduction rates. Focusing on line charts, as the predominant form of time series visualization, we explain in detail the drawbacks of existing data reduction techniques and how our approach outperforms state of the art, by respecting the process of line rasterization. We describe how to incorporate aggregation-based dimensionality reduction at the query level in a visualizationdriven query rewriting system. Our approach is generic and applicable to any visualization system that uses an RDBMS as data source. Using real world data sets from high tech manufacturing, stock markets, and sports analytics domains we demonstrate that our visualization-oriented data aggregation can reduce data volumes by up to two orders of magnitude, while preserving perfect visualizations.


title: Modeling Interestingness with Deep Neural Networks

text: This paper presents a deep semantic similarity model (DSSM), a special type of deep neural networks designed for text analysis, for recommending target documents to be of interest to a user based on a source document that she is reading. We observe, identify, and detect naturally occurring signals of interestingness in click transitions on the Web between source and target documents, which we collect from commercial Web browser logs. The DSSM is trained on millions of Web transitions, and maps source-target document pairs to feature vectors in a latent space in such a way that the distance between source documents and their corresponding interesting targets in that space is minimized. The effectiveness of the DSSM is demonstrated using two interestingness tasks: automatic highlighting and contextual entity search. The results on large-scale, real-world datasets show that the semantics of documents are important for modeling interestingness and that the DSSM leads to significant quality improvement on both tasks, outperforming not only the classic document models that do not use semantics but also state-of-the-art topic models.


title: Developments in Radar Imaging

text: Using range and Doppler information to produce radar images is a technique used in such diverse fields as air-to-ground imaging of objects, terrain, and oceans and ground-to-air imaging of aircraft, space objects, and planets. A review of the range-Doppler technique is presented along with a description of radar imaging forms including details of data acquisition and processing techniques.


title: The Expressive Power of SPARQL

text: This paper studies the expressive power of SPARQL. The main result is that SPARQL and non-recursive safe Datalog with negation have equivalent expressive power, and hence, by classical results, SPARQL is equivalent from an expressive point of view to Relational Algebra. We present explicit generic rules of the transformations in both directions. Among other findings of the paper are the proof that negation can be simulated in SPARQL, that non-safe filters are superfluous, and that current SPARQL W3C semantics can be simplified to a standard


title: Working Memory Deficits can be Overcome : Impacts of Training and Medication on Working Memory in Children with ADHD

text: This study evaluated the impact of two interventions—a training program and stimulant medication—on working memory (WM) function in children with attention deficit hyperactivity disorder (ADHD). Twenty-five children aged between 8 and 11 years participated in training that taxed WM skills to the limit for a minimum of 20 days, and completed other assessments of WM and IQ before and after training, and with and without prescribed drug treatment. While medication significantly improved visuo-spatial memory performance, training led to substantial gains in all components of WM across untrained tasks. Training gains associated with the central executive persisted over a 6month period. IQ scores were unaffected by either intervention. These findings indicate that the WM impairments in children with ADHD can be differentially ameliorated by training and by stimulant medication. Copyright # 2009 John Wiley & Sons, Ltd.


title: Toward Fast and Accurate Neural Discourse Segmentation

text: Discourse segmentation, which segments texts into Elementary Discourse Units, is a fundamental step in discourse analysis. Previous discourse segmenters rely on complicated hand-crafted features and are not practical in actual use. In this paper, we propose an endto-end neural segmenter based on BiLSTMCRF framework. To improve its accuracy, we address the problem of data insufficiency by transferring a word representation model that is trained on a large corpus. We also propose a restricted self-attention mechanism in order to capture useful information within a neighborhood. Experiments on the RST-DT corpus show that our model is significantly faster than previous methods, while achieving new stateof-the-art performance. 1


title: Apollonian Circle Packings: Geometry and Group Theory II. Super-Apollonian Group and Integral Packings

text: Apollonian circle packings arise by repeatedly filling the interstices between four mutually tangent circles with further tangent circles. Such packings can be described in terms of the Descartes configurations they contain, where a Descartes configuration is a set of four mutually tangent circles in the Riemann sphere, having disjoint interiors. Part I showed there exists a discrete group, the Apollonian group, acting on a parameter space of (ordered, oriented) Descartes configurations, such that the Descartes configurations in a packing formed an orbit under the action of this group. It is observed there exist infinitely many types of integral Apollonian packings in which all circles had integer curvatures, with the integral structure being related to the integral nature of the Apollonian group. Here we consider the action of a larger discrete group, the super-Apollonian group, also having an integral structure, whose orbits describe the Descartes quadruples of a geometric object we call a super-packing. The circles in a super-packing never cross each other but are nested to an arbitrary depth. Certain Apollonian packings and super-packings are strongly integral in the sense that the curvatures of all circles are integral and the curvature×centers of all circles are integral. We show that (up to scale) there are exactly 8 different (geometric) strongly integral super-packings, and that


title: Addressing challenges in the clinical applications associated with CRISPR/Cas9 technology and ethical questions to prevent its misuse

text: Xiang Jin Kang, Chiong Isabella Noelle Caparas, Boon Seng Soh, Yong Fan 1 Key Laboratory for Major Obstetric Diseases of Guangdong Province, Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, Center for Reproductive Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China 2 Disease Modeling and Therapeutics Laboratory, A*STAR Institute of Molecular and Cell Biology, 61 Biopolis Drive Proteos, Singapore 138673, Singapore & Correspondence: bssoh@imcb.a-star.edu.sg (B. S. Soh), yongfan011@gzhmu.edu.cn (Y. Fan)


title: Common mode EMI reduction technique for interleaved MHz critical mode PFC converter with coupled inductor

text: Coupled inductor has been widely adopted in VR applications for many years because of its benefits such as reducing current ripple and improving transient performance. In this presentation, coupled inductor concept is applied to interleaved MHz totem-pole CRM PFC converter with GaN devices. The coupled inductor in CRM PFC converter can reduce switching frequency variation, help achieving ZVS and reduce circulating energy. Therefore the coupled inductor can improve the efficiency of PFC converter. In addition, balance technique is applied to help minimize CM noise. This paper will introduce how to achieve balance with coupled inductor. The novel PCB winding inductor design will be provided. The PCB winding coupled inductor will have similar loss with litz wire inductor but much less labor in manufacture.


title: Temporal Relational Reasoning in Videos

text: Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species. In this paper, we introduce an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales. We evaluate TRN-equipped networks on activity recognition tasks using three recent video datasets SomethingSomething, Jester, and Charades which fundamentally depend on temporal relational reasoning. Our results demonstrate that the proposed TRN gives convolutional neural networks a remarkable capacity to discover temporal relations in videos. Through only sparsely sampled video frames, TRN-equipped networks can accurately predict human-object interactions in the Something-Something dataset and identify various human gestures on the Jester dataset with very competitive performance. TRN-equipped networks also outperform two-stream networks and 3D convolution networks in recognizing daily activities in the Charades dataset. Further analyses show that the models learn intuitive and interpretable visual common sense knowledge in videos.


title: Achieving high utilization with software-driven WAN

text: We present SWAN, a system that boosts the utilization of inter-datacenter networks by centrally controlling when and how much traffic each service sends and frequently re-configuring the network's data plane to match current traffic demand. But done simplistically, these re-configurations can also cause severe, transient congestion because different switches may apply updates at different times. We develop a novel technique that leverages a small amount of scratch capacity on links to apply updates in a provably congestion-free manner, without making any assumptions about the order and timing of updates at individual switches. Further, to scale to large networks in the face of limited forwarding table capacity, SWAN greedily selects a small set of entries that can best satisfy current demand. It updates this set without disrupting traffic by leveraging a small amount of scratch capacity in forwarding tables. Experiments using a testbed prototype and data-driven simulations of two production networks show that SWAN carries 60% more traffic than the current practice.


title: B4: experience with a globally-deployed software defined wan

text:
We present the design, implementation, and evaluation of B4, a private WAN connecting Google's data centers across the planet. B4 has a number of unique characteristics: i) massive bandwidth requirements deployed to a modest number of sites, ii) elastic traffic demand that seeks to maximize average bandwidth, and iii) full control over the edge servers and network, which enables rate limiting and demand measurement at the edge.
 These characteristics led to a Software Defined Networking architecture using OpenFlow to control relatively simple switches built from merchant silicon. B4's centralized traffic engineering service drives links to near 100% utilization, while splitting application flows among multiple paths to balance capacity against application priority/demands. We describe experience with three years of B4 production deployment, lessons learned, and areas for future work.


title: BotMiner: Clustering Analysis of Network Traffic for Protocol- and Structure-Independent Botnet Detection

text: Botnets are now the key platform for many Internet attacks, such as spam, distributed denial-of-service (DDoS), identity theft, and phishing. Most of the current botnet detection approaches work only on specific botnet command and control (C&C) protocols (e.g., IRC) and structures (e.g., centralized), and can become ineffective as botnets change their C&C techniques. In this paper, we present a general detection framework that is independent of botnet C&C protocol and structure, and requires noa priori knowledge of botnets (such as captured bot binaries and hence the botnet signatures, and C&C server names/addresses). We start from the definition and essential properties of botnets. We define a botnet as acoordinated groupof malware instances that arecontrolled via C&C communication channels. The essential properties of a botnet are that the bots communicate with some C&C servers/peers, perform malicious activities, and do so in a similar or correlated way. Accordingly, our detection framework clusters similar communication traffic and similar malicious traffic, and performs cross cluster correlation to identify the hosts that share both similar communication patterns and similar malicious activity patterns. These hosts are thus bots in the monitored network. We have implemented our BotMiner prototype system and evaluated it using many real network traces. The results show that it can detect real-world botnets ( IRC-based, HTTP-based, and P2P botnets including Nugache and Storm worm), and has a very low false positive rate.


title: Forwarding metamorphosis: fast programmable match-action processing in hardware for SDN

text: In Software Defined Networking (SDN) the control plane is physically separate from the forwarding plane. Control software programs the forwarding plane (e.g., switches and routers) using an open interface, such as OpenFlow. This paper aims to overcomes two limitations in current switching chips and the OpenFlow protocol: i) current hardware switches are quite rigid, allowing ``Match-Action'' processing on only a fixed set of fields, and ii) the OpenFlow specification only defines a limited repertoire of packet processing actions. We propose the RMT (reconfigurable match tables) model, a new RISC-inspired pipelined architecture for switching chips, and we identify the essential minimal set of action primitives to specify how headers are processed in hardware. RMT allows the forwarding plane to be changed in the field without modifying hardware. As in OpenFlow, the programmer can specify multiple match tables of arbitrary width and depth, subject only to an overall resource limit, with each table configurable for matching on arbitrary fields. However, RMT allows the programmer to modify all header fields much more comprehensively than in OpenFlow. Our paper describes the design of a 64 port by 10 Gb/s switch chip implementing the RMT model. Our concrete design demonstrates, contrary to concerns within the community, that flexible OpenFlow hardware switch implementations are feasible at almost no additional cost or power.


title: Stability and Generalization

text: We define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds based on the empirical error and the leave-one-out error. The methods we use can be applied in the regression framework as well as in the classification one when the classifier is obtained by thresholding a real-valued function. We study the stability properties of large classes of learning algorithms such as regularization based algorithms. In particular we focus on Hilbert space regularization and Kullback-Leibler regularization. We demonstrate how to apply the results to SVM for regression and classification.


title: Zero-voltage switching flyback-boost converter with voltage-doubler rectifier for high step-up applications

text: A zero-voltage switching (ZVS) flyback-boost (FB) converter with a voltage-doubler rectifier (VDR) has been proposed. By combining the common part between a flyback converter and a boost converter as a parallel-input/series-output (PISO) configuration, this proposed circuit can increase a step-up ratio and clamp the surge voltage of switches. The secondary VDR provides a further extended step-up ratio as well as its voltage stress to be clamped. An auxiliary switch instead of a boost diode enables all switches to be turned on under ZVS conditions. The zero-current turn-off of the secondary VDR alleviates its reverse-recovery losses. The operation principles, the theoretical analysis, and the design consideration are investigated. The experimental results from a 250W and 42V-to-400V prototype are shown to verify the proposed scheme.


title: Human Gait Recognition Using Patch Distribution Feature and Locality-Constrained Group Sparse Representation

text: In this paper, we propose a new patch distribution feature (PDF) (i.e., referred to as Gabor-PDF) for human gait recognition. We represent each gait energy image (GEI) as a set of local augmented Gabor features, which concatenate the Gabor features extracted from different scales and different orientations together with the X-Y coordinates. We learn a global Gaussian mixture model (GMM) (i.e., referred to as the universal background model) with the local augmented Gabor features from all the gallery GEIs; then, each gallery or probe GEI is further expressed as the normalized parameters of an image-specific GMM adapted from the global GMM. Observing that one video is naturally represented as a group of GEIs, we also propose a new classification method called locality-constrained group sparse representation (LGSR) to classify each probe video by minimizing the weighted l1, 2 mixed-norm-regularized reconstruction error with respect to the gallery videos. In contrast to the standard group sparse representation method that is a special case of LGSR, the group sparsity and local smooth sparsity constraints are both enforced in LGSR. Our comprehensive experiments on the benchmark USF HumanID database demonstrate the effectiveness of the newly proposed feature Gabor-PDF and the new classification method LGSR for human gait recognition. Moreover, LGSR using the new feature Gabor-PDF achieves the best average Rank-1 and Rank-5 recognition rates on this database among all gait recognition algorithms proposed to date.


title: A series resonant converter with constant on-time control for capacitor charging applications

text: A capacitor charging power supply (CCPS) has been assembled. A full-bridge series resonant topology using MOSFETs as switching elements makes up the bulk of the high-power section of the CCPS. A high-voltage transformer couples the output stage to the resonant inverter, and the secondary current of this transformer is rectified to provide the charging current. The CCPS uses constant on-time control with zero current switching. The control scheme is implemented with Unitrode's UC3860 resonant mode power supply controller. The control circuitry also protects the CCPS against both overvoltage and overcurrent conditions. The CCPS has been tested in the laboratory, where it successfully charged capacitors ranging from 90 nF to 1 mu F to 2000 V.<<ETX>>


title: A vehicular rooftop, shark-fin, multiband antenna for the GPS/LTE/cellular/DSRC systems

text: A shark-fin rooftop multiband antenna for the automotive industry is proposed. The antenna system receives Right Hand Circularly Polarized (RHCP) satellite signals for the Global Positioning System's (GPS) L1-Band and Vertically Linearly Polarized (VLP) signals for the Long Term Evolution (LTE), the cellular frequencies, the Universal Mobile Telecommunications Systems (UMTS) and the Dedicated Short Range Communications (DSRC) system. A ceramic patch antenna element was used for the GPS signals and a printed monopole utilizing printed ground sleeve technique for the cellular and LTE signals. A study of four potential DSRC antenna elements to determine the appropriate antenna element location within the antenna cover to obtain optimum performance is also presented and discussed in detail. The antenna module was measured on a 1-meter diameter ground plane and on a vehicle roof model. The design and measurements results are presented and discussed below.


title: Service specific anomaly detection for network intrusion detection

text: The constant increase of attacks against networks and their resources (as recently shown by the CodeRed worm) causes a necessity to protect these valuable assets. Firewalls are now a common installation to repel intrusion attempts in the first place. Intrusion detection systems (IDS), which try to detect malicious activities instead of preventing them, offer additional protection when the first defense perimeter has been penetrated. ID systems attempt to pin down attacks by comparing collected data to predefined signatures known to be malicious (signature based) or to a model of legal behavior (anomaly based).Anomaly based systems have the advantage of being able to detect previously unknown attacks but they suffer from the difficulty to build a solid model of acceptable behavior and the high number of alarms caused by unusual but authorized activities. We present an approach that utilizes application specific knowledge of the network services that should be protected. This information helps to extend current, simple network traffic models to form an application model that allows to detect malicious content hidden in single network packets. We describe the features of our proposed model and present experimental data that underlines the efficiency of our systems.


title: Wideband and Unidirectional Cavity-Backed Folded Triangular Bowtie Antenna

text: In this paper, a cavity-backed folded triangular bowtie antenna (FTBA) is proposed and investigated. Comparisons show that it has much smaller fluctuation of input impedance and much larger bandwidth (BW) for stable radiation patterns than cavity-backed ordinary TBA. The proposed antenna is fed by a conventional balun composed of a transition from a microstrip line to a parallel stripline. It achieves an impedance BW of 92.2% for |S11| les -10 dB, stable gain of around 9.5 dBi and unidirectional radiation patterns over the whole operating band. A close examination of the antenna aperture efficiency and the electric field distribution in the aperture provides an explanation for the stable gain across such large frequency band. Comparisons are made against several conventional antennas, including short backfire and microstrip patch antennas, to illustrate the salient features of the proposed antenna. A design guideline is also given for practical applications.


title: A new and rapid colorimetric determination of acetylcholinesterase activity.

text: A photometric method for determining acetylcholinesterase activity of tissue extracts, homogenates, cell suspensions, etc., has been described. The enzyme activity is measured by following the increase of yellow color produced from thiocholine when it reacts with dithiobisnitrobenzoate ion. It is based on coupling of these reactions: (enzyme) acetylthiocholine -----+ thiocholine + acetate thiocholine + dithiobisnitrobenzoate -+ yellow color The latter reaction is rapid and the assay is sensitive (i.e. a 10 pl sample of blood is adequate). The use of a recorder has been most helpful, but is not essential. The method has been used to study the enzyme in human erythrocytes and homogenates of rat brain, kidney, lungs, liver and muscle tissue. Kinetic constants determined by this system for erythrocyte cholinesterase are presented. The data obtained with acetylthiocholine as substrate are similar to those with acetylcholine. INTRODUCTION A FEW years ago Bonting and Featherstonel introduced a modification of the Hestrin hydroxamic acid method2 suitable for the determination of cholinesterase levels in small quantities of cells cultured in vitro. This modification was used successfully in several studies involving the control of enzyme levels in cells by manipulating the levels of substrates or closely related compounds present in the medium.3 Several interesting areas of research were indicated by these studies. However, this modified method of enzyme assay, although scaled down to the micro-level, had several disadvantages. Among these was the fact that only a terminal figure could be obtained from the material in one tube of cultured cells. Thus, the time course of the reaction could not be followed without resorting to separate experimental tubes for each time interval desired. The method also had the disadvantage that the color measured *was developed from the remainder of an added substrate-a procedure in which the possibility of error is relatively great when the level of enzyme activity is small. Consideration of the relative merits of various methods which might be useful in studying the time-course of acetylcholinesterase activity in very small tissue samples led us to combine a method reported by Koelle4 with a sulfhydryl reagent studied by Ellman.5 This new method, which is presented here, is extremely sensitive and is applicable to either small amounts of tissue or to low concentrations of enzyme. It makes detailed kinetic studies of acetylcholinesterase activity possible. The progress of the hydrolysis is followed by the measurement of a product of the reaction. 88 A new and rapid calorimetric determination of acetylcholinesterase activity 89 Acetylthiocholine is used as the substrate. This analog of the natural substrate has been used most extensively by Koelle4 for histochemical localization. Other workerss, ’ have used the sulfur analog in the enzyme assay. Their work, in addition to data we shall present, suggests that this compound is a satisfactory substitute for the natural substrate, and differs much less than some of the synthetic substrates frequently used as in assays of phosphatases, trypsin, chymotrypsin, pepsin, etc. The principle of the method is the measurement of the rate of production of thiocholine as acetylthiocholine is hydrolyzed. This is accomplished by the continuous reaction of the thiol with 5 : Sdithiobis-2-nitrobenzoate ion (I)5 (enzyme) H,O + (CH,),&CH,CH,SCOCH, h (CH&CH2CH2S+ + CH,COO+ 2H+ (CH&H$H$+ RSSR + (CH&&CH&H,SSR + RS(I) (II) to produce the yellow anion of 5-thio-2-nitro-benzoic acid (II). The rate of color production is measured at 412 rnp in a photometer. The reaction with the thiol has been shown to be sufficiently rapid so as not to be rate limiting in the measurement of the enzyme, and in the concentrations used does not inhibit the enzymic hydrolysis. By recording the output of the photometer continuously, records of the complete assay can be obtained (Fig. 1). We considered it desirable to establish that this method yields results comparable with other procedures. For this reason, the effects of inhibitors were examined; the kinetic constants were calculated and compared with those obtained by other methods. In addition, we were able to compare assays on blood samples by the ferrichydroxamate methods and the present one. METHODS The reaction rates were recorded with a Beckman DU spectrophotometer equipped with a Beckman adapter and a Minneapolis-Honeywell recorder. The general method used was to place buffer in the photocell and add concentrated solutions of reagents by means of micropipettes. The mixture was stirred by continued blowing through the pipettes while moving them around the bottom of the photometer cells. In this way, reagents were added, mixed, and the cover of the cell compartment replaced within 10-15 sec. Our photometer required 3040 set to become stabilized to new light conditions. Thus, there was about 1 min when the readings were due to a combination of factors (e.g. bubbles rising through the solutions, sulfhydryl material in the “enzyme”, etc.) which were unrelated to the desired measurements. Subsequent readings were strictly dependent on the absorption of the solution under consideration and even rapid changes were followed by the recorder faithfully, as evidenced by the reproducibility of time-transmission curves (Fig. 1). 90 GEORGE L. ELLMAN et. al. Solutions Buffer. Phosphate, 0.1 M, pH 8.0. Substrate. Acetylthiocholine iodide, * O-075 M (21.67 mg/ml). This solution was used successfully for lo-15 days if kept refrigerated. Reagent. Dithiobisnitrobenzoic acid (DTNB) 0.01 M of the 5 : 5-dithiobis-2nitrobenzoic acid7 prepared as described previously,5 39.6 mg were dissolved in 10 ml pH 7.0 phosphate buffer (O-1 M) and 15 mg of sodium bicarbonate were added. The reagent was made up in buffer of pH 7 in which it was more stable than in that of pH 8. Enzyme. Bovine erythrocyte cholinesterase (Nutritional Biochem. Corp., 20,000 units) was dissolved in 20 ml of 1% gelatin. This solution was diluted 1 : 200 with water for use, yielding a solution of 5 units/ml. General method A typical run used : 3.0 ml pH 8.0 buffer, 20.0 ~1 substrate, 100.0 ~1 DTNB (reagent), 50.0 ~1 enzyme. The results of several runs are shown in Fig. 1. The blank for such a run consists of buffer, substrate, and DTNB solutions. The absorbances: were read from the strip charts and plotted on a rectangular graph paper, the best line drawn through the points and the slope measured. In a run such as that described above, the linear portion of the curve describing the hydrolysis was observed during the first 15-20 min of the reaction; the slope is the rate in absorbance units/min. At this pH level, there is an appreciable non-enzymic hydrolysis of the substrate, and for long runs it was necessary to correct for this. The rate of non-enzymic hydrolysis of acetylthiocholine at 25” was 0.0016 absorbance units per min. The procedures have been extended to micro-size. A run comparable to those in Fig. 1 was made in a micro-cell (total solution volume was O-317 ml). The rate was 0*102/min, the same as that determined in the larger cuvettes. Since the extinction coefficient of the yellow anion (II) is known5 the rates can be converted to absolute units, viz. : A absorbance/min rate (moles/l. per min) = 1.36 x lo4 In dealing with cell extracts or suspensions, a blank consisting of extract or suspension, DTNB, and buffer may be required to correct for release of thiol material from the cells and the absorbance of the other materials in the suspension. Method for blood A fairly stable suspension was formed from whole blood or washed human erythrocytes. Since the acetylcholinesterase is on the cell membrane, hemolysis was not necessary. The assay of blood was carried out as follows: (1) A suspension of the blood cells4 in phosphate buffer (pH 8.0, 0.1 M) was prepared. The most practical dilution was 1 : 600 (e.g. 10~1 blood into 6 ml buffer). (2) Exactly 3.0 ml of the suspension were pipetted into a cuvette. * California Corporation for Biochemical Research, Los Angeles, California. t This is now available from the Aldrich Chemical Co.. 2369 No. 29th. Milwaukee 10, Wisconsin. $ Strip charts printed in absorbance units are available from Minneapolis-Honeywell Corporation, Chart 5871. 4 Red cell counts were performed by the clinical laboratory. FI G . 1. P ho to gr ap h of s tr ip ch ar t re co rd of t w o id en tic al as sa ys . A t th e ar ro w , ph ys os tig m in e sa lic yl at e (f in al c on ce nt ra tio n, 3 x IO -’ M ) w as a dd ed to a t hi rd re pl ic at e.


title: Entity-Relationship Extraction from Wikipedia Unstructured Text

text: Wikipedia has been the primary source of information for many automatically-generated Semantic Web data sources. However, they suffer from incompleteness since they largely do not cover information contained in the unstructured texts of Wikipedia. Our goal is to extract structured entity-relationships in RDF from such unstructured texts, ultimately using them to enrich existing data sources. Our extraction technique is aimed to be topic-independent, leveraging grammatical dependency of sentences and semantic refinement. Preliminary evaluations of the proposed approach have shown some promising results.


title: Future Internet of Things: open issues and challenges

text: Internet of Things (IoT) and its relevant technologies have been attracting the attention of researchers from academia, industry, and government in recent years. However, since the requirements of the IoT are quite different from what the Internet today can offer, several innovative techniques have been gradually developed and incorporated into IoT, which is referred to as the Future Internet of Things (FIoT). Among them, how to extract ‘‘data’’ and transfer them into ‘‘knowledge’’ from sensing layer to application layer has become a vital issue. This paper begins with an overview of IoT and FIoT, followed by discussions on how to apply data mining and computational intelligence to FIoT. An intelligent data management framework inspired by swarm optimization will then given. Finally, open issues and future trends of this field


title: Reading and Writing as a Creative Cycle: the Need for a Computational Model

text: The field of computational narratology has produced many efforts aimed at generating narrative by computational means. In recent times, a number of such efforts have considered the task of modelling how a reader might consume the story. Whereas all these approaches are clearly different aspects of the task of generating narrative, so far the efforts to model them have occurred as separate and disjoint initiatives. There is an enormous potential for improvement if a way was found to combine results from these initiatives with one another. The present position paper provides a breakdown of the activity of creating stories into five stages that are conceptually different from a computational point of view and represent important aspects of the overall process as observed either in humans or in existing systems. These stages include a feedback loop that builds interpretations of an ongoing composition and provides feedback based on these to inform the composition process. This model provides a theoretical framework that can be employed first to understand how the various aspects of the task of generating narrative relate to one another, second to identify which of these aspects are being addressed by the different existing research efforts, and finally to point the way towards possible integrations of these aspects within progressively more complex systems.


title: Kinematics, Dynamics and Power Consumption Analyses for Turning Motion of a Six-Legged Robot

text: This paper deals with kinematics, dynamics and power consumption analyses of a six-legged robot generating turning motions to follow a circular path. Direct and inverse kinematics analysis has been carried out for each leg in order to develop an overall kinematics model of the six-legged robot. It aims to estimate energyoptimal feet forces and joint torques of the sixlegged robot, which are necessary to have for its real-time control. To determine the optimum feet forces, two approaches are developed, such as minimization of norm of feet forces and minimization of norm of joint torques using a least square method, and their performances are compared. The developed kinematics and dynamics models are tested through computer simulations for generating turning motion of a statically stable sixlegged robot over flat terrain with four different duty factors. The maximum values of feet forces and joint torques decrease with the increase of duty factor. A power consumption model has S. S. Roy Department of Mechanical Engineering, National Institute of Technology, Durgapur, India e-mail: ssroy99@yahoo.com D. K. Pratihar (B) Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, India e-mail: dkpra@mech.iitkgp.ernet.in been derived for the statically stable wave gaits to minimize the power requirement for both optimal foot force distributions and optimal foot-hold selection. The variations of average power consumption with the height of the trunk body and radial offset have been analyzed in order to find out energy-optimal foothold. A parametric study on energy consumption has been carried out by varying angular velocity of the robot to minimize the total energy consumption during locomotion. It has been found that the energy consumption decreases with the increase of angular velocity for a particular traveled distance.


title: An Autonomous Reliability-Aware Negotiation Strategy for Cloud Computing Environments

text: Cloud computing paradigm allows subscription-based access to computing and storages services over the Internet. Since with advances of Cloud technology, operations such as discovery, scaling, and monitoring are accomplished automatically, negotiation between Cloud service requesters and providers can be a bottleneck if it is carried out by humans. Therefore, our objective is to offer a state-of-the-art solution to automate the negotiation process in Cloud environments. In previous works in the SLA negotiation area, requesters trust whatever QoS criteria values providers offer in the process of negotiation. However, the proposed negotiation strategy for requesters in this work is capable of assessing reliability of offers received from Cloud providers. In addition, our proposed negotiation strategy for Cloud providers considers utilization of resources when it generates new offers during negotiation and concedes more on the price of less utilized resources. The experimental results show that our strategy helps Cloud providers to increase their profits when they are participating in parallel negotiation with multiple requesters.


title: Medication intake adherence with real time activity recognition on IoT

text: Usefulness of health care services is seriously effected by medication adherence. Medication intake is one of the cases where adherence may be difficult for the patients, who are willing to undertake the prescribed therapy. Internet of Things (IoT) infrastructure for activity monitoring is a strong candidate solution to maintain adherence of forgetful patients. In this study, we propose an IoT framework where medication intake is ensured with real time continuous activity recognition. We present our use case with the focus of application and network layers. We utilize an activity classification scheme, which considers inter-activity detection consistency based on non-predefined feature extraction as the application layer. The network layer includes a gateway structure ensuring end-to-end reliability in the connection between a wireless sensor network (WSN) and Internet. Results obtained in simulation environment suggest that the selected application and network layers introduce a feasible solution for the medication intake use case.


title: An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives

text: Assessment of air quality has been traditionally conducted by ground based monitoring, and more recently by manned aircrafts and satellites. However, performing fast, comprehensive data collection near pollution sources is not always feasible due to the complexity of sites, moving sources or physical barriers. Small Unmanned Aerial Vehicles (UAVs) equipped with different sensors have been introduced for in-situ air quality monitoring, as they can offer new approaches and research opportunities in air pollution and emission monitoring, as well as for studying atmospheric trends, such as climate change, while ensuring urban and industrial air safety. The aims of this review were to: (1) compile information on the use of UAVs for air quality studies; and (2) assess their benefits and range of applications. An extensive literature review was conducted using three bibliographic databases (Scopus, Web of Knowledge, Google Scholar) and a total of 60 papers was found. This relatively small number of papers implies that the field is still in its early stages of development. We concluded that, while the potential of UAVs for air quality research has been established, several challenges still need to be addressed, including: the flight endurance, payload capacity, sensor dimensions/accuracy, and sensitivity. However, the challenges are not simply technological, in fact, policy and regulations, which differ between countries, represent the greatest challenge to facilitating the wider use of UAVs in atmospheric research.


title: Predicting India Volatility Index: An Application of Artificial Neural Network

text: Forecasting has always been an area of interest for the researchers in various realms of finance especially in the stock market e. g. stock index, return on a stock, etc. Stock market volatility is one such area. Since the inception of implied volatility index (VIX) by the Chicago Board of Options Exchange (CBOE) in 1993, VIX index has generated a lot of interest. This study examines the predicting ability of several technical indicators related to VIX index to forecast the next trading day's volatility. There is a wide set of methods available for forecasting in finance. In this study, Artificial neural network (ANN) modeling technique has been employed to forecast the upwards or downwards movement in next trading day's volatility using India VIX (a volatility index based on the NIFTY Index Option prices) based indicators. The results of the study reveal that ANN models can be real handy in forecasting the downwards movement in VIX. The knowledge about a more probable downwards movement in volatility might be significant value add for the investors and help them in making decisions related to trading.


title: A Novel Transformer Structure for High power, High Frequency converter

text: Power transformer structure is a key factor for the high power, high frequency converter performance which includes efficiency, thermal performance and power density. This paper proposes a novel transformer structure for the kilo-watt level, high frequency converter which is reinforce insulation needed for the secondary side to primary side. The transformer has spiral-wound primary layers using TIW (triple insulation wire) and PCB-winding secondary layers. All the windings are arranged by full interleaving structure to minimize the leakage inductance and eddy current loss. Further more, the secondary rectifiers and filter capacitors are mounted in PCB-winding secondary layers to further minimize the termination effect. A 1.2 KW (O/P: 12 V/100 A, I/P: 400 V) Mega Hz LLC converter prototype employed the proposed transformer structure is constructed, and over 96% efficiency achieved.


Private local automation clouds built by CPS: Potential and challenges for distributed reasoning


title: Overview: The Design, Adoption, and Analysis of a Visual Document Mining Tool for Investigative Journalists

text: For an investigative journalist, a large collection of documents obtained from a Freedom of Information Act request or a leak is both a blessing and a curse: such material may contain multiple newsworthy stories, but it can be difficult and time consuming to find relevant documents. Standard text search is useful, but even if the search target is known it may not be possible to formulate an effective query. In addition, summarization is an important non-search task. We present Overview, an application for the systematic analysis of large document collections based on document clustering, visualization, and tagging. This work contributes to the small set of design studies which evaluate a visualization system “in the wild”, and we report on six case studies where Overview was voluntarily used by self-initiated journalists to produce published stories. We find that the frequently-used language of “exploring” a document collection is both too vague and too narrow to capture how journalists actually used our application. Our iterative process, including multiple rounds of deployment and observations of real world usage, led to a much more specific characterization of tasks. We analyze and justify the visual encoding and interaction techniques used in Overview's design with respect to our final task abstractions, and propose generalizable lessons for visualization design methodology.


title: Securing Bitcoin wallets via a new DSA / ECDSA threshold signature scheme

text: The Bitcoin ecosystem has suffered frequent thefts and losses affecting both businesses and individuals. Due to the irreversibility, automation, and pseudonymity of transactions, Bitcoin currently lacks support for the sophisticated internal control systems deployed by modern businesses to deter fraud. To address this problem, we present the first threshold signature scheme compatible with Bitcoin’s ECDSA signatures and show how distributed Bitcoin wallets can be built using this primitive. For businesses, we show how our distributed wallets can be used to systematically eliminate single points of failure at every stage of the flow of bitcoins through the system. For individuals, we design, implement, and evaluate a two-factor secure Bitcoin wallet.


title: Joint Learning Templates and Slots for Event Schema Induction

text: Automatic event schema induction (AESI) means to extract meta-event from raw text, in other words, to find out what types (templates) of event may exist in the raw text and what roles (slots) may exist in each event type. In this paper, we propose a joint entity-driven model to learn templates and slots simultaneously based on the constraints of templates and slots in the same sentence. In addition, the entities’ semantic information is also considered for the inner connectivity of the entities. We borrow the normalized cut criteria in image segmentation to divide the entities into more accurate template clusters and slot clusters. The experiment shows that our model gains a relatively higher result than previous work.


title: Information & Environment: IoT-Powered Recommender Systems

text: Internet of Things (IoT) infrastructure within the physical library environment is the basis for an integrative, hybrid approach to digital resource recommenders. The IoT infrastructure provides mobile, dynamic wayfinding support for items in the collection, which includes features for location-based recommendations. The evaluation and analysis herein clarified the nature of users’ requests for recommendations based on their location, and describes subject areas of the library for which users request recommendations. The results indicated that users of IoTbased recommendation are interested in a broad distribution of subjects, with a short-head distribution from this collection in American and English Literature. A long-tail finding showed a diversity of topics that are recommended to users in the library book stacks with IoT-powered recommendations.


title: Giving too much social support: social overload on social networking sites

text: Received: 20 July 2012 Revised: 18 February 2013 2nd Revision: 28 June 2013 3rd Revision: 20 September 2013 4th Revision: 7 November 2013 Accepted: 1 February 2014 Abstract As the number of messages and social relationships embedded in social networking sites (SNS) increases, the amount of social information demanding a reaction from individuals increases as well. We observe that, as a consequence, SNS users feel they are giving too much social support to other SNS users. Drawing on social support theory (SST), we call this negative association with SNS usage ‘social overload’ and develop a latent variable to measure it. We then identify the theoretical antecedents and consequences of social overload and evaluate the social overload model empirically using interviews with 12 and a survey of 571 Facebook users. The results show that extent of usage, number of friends, subjective social support norms, and type of relationship (online-only vs offline friends) are factors that directly contribute to social overload while age has only an indirect effect. The psychological and behavioral consequences of social overload include feelings of SNS exhaustion by users, low levels of user satisfaction, and a high intention to reduce or even stop using SNS. The resulting theoretical implications for SST and SNS acceptance research are discussed and practical implications for organizations, SNS providers, and SNS users are drawn. European Journal of Information Systems advance online publication, 4 March 2014; doi:10.1057/ejis.2014.3; corrected online 11 March 2014


title: A taxonomy of sequential pattern mining algorithms

text: Owing to important applications such as mining web page traversal sequences, many algorithms have been introduced in the area of sequential pattern mining over the last decade, most of which have also been modified to support concise representations like closed, maximal, incremental or hierarchical sequences. This article presents a taxonomy of sequential pattern-mining techniques in the literature with web usage mining as an application. This article investigates these algorithms by introducing a taxonomy for classifying sequential pattern-mining algorithms based on important key features supported by the techniques. This classification aims at enhancing understanding of sequential pattern-mining problems, current status of provided solutions, and direction of research in this area. This article also attempts to provide a comparative performance analysis of many of the key techniques and discusses theoretical aspects of the categories in the taxonomy.


title: Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model

text: Derived from the theory of reasoned action, the technology acceptance model (TAM) focuses on two specific salient beliefs— ease of use and usefulness. It has been applied in the study of user adoption of different technologies, and has emerged as a reliable and robust model. However, this has not discouraged researchers from incorporating additional constructs to the original model in their quest for increased predictive power. Here, an attempt is made in the context of explaining consumer intention to use on-line shopping. Besides ease of use and usefulness, compatibility, privacy, security, normative beliefs, and selfefficacy are included in an augmented TAM. A test of this model, with data collected from 281 consumers, show support for seven of nine research hypotheses. Specifically, compatibility, usefulness, ease of use, and security were found to be significant predictors of attitude towards on-line shopping, but privacy was not. Further, intention to use on-line shopping was strongly influenced by attitude toward on-line shopping, normative beliefs, and self-efficacy. # 2003 Elsevier B.V. All rights reserved.


title: A new approach for sparse matrix vector product on NVIDIA GPUs

text: The sparse matrix vector product (SpMV) is a key operation in engineering and scientific computing and, hence, it has been subjected to intense research for a long time. The irregular computations involved in SpMV make its optimization challenging. Therefore, enormous effort has been devoted to devise data formats to store the sparse matrix with the ultimate aim of maximizing the performance. Graphics Processing Units (GPUs) have recently emerged as platforms that yield outstanding acceleration factors. SpMV implementations for NVIDIA GPUs have already appeared on the scene. This work proposes and evaluates a new implementation of SpMV for NVIDIA GPUs based on a new format, ELLPACK-R, that allows storage of the sparse matrix in a regular manner. A comparative evaluation against a variety of storage formats previously proposed has been carried out based on a representative set of test matrices. The results show that, although the performance strongly depends on the specific pattern of the matrix, the implementation based on ELLPACK-R achieves higher overall performance. Moreover, a comparison with standard state-of-the-art superscalar processors reveals that significant speedup factors are achieved with GPUs. Copyright 2010 John Wiley & Sons, Ltd.


title: Fast scan algorithms on graphics processors

text: Scan and segmented scan are important data-parallel primitives for a wide range of applications. We present fast, work-efficient algorithms for these primitives on graphics processing units (GPUs). We use novel data representations that map well to the GPU architecture. Our algorithms exploit shared memory to improve memory performance. We further improve the performance of our algorithms by eliminating shared-memory bank conflicts and reducing the overheads in prior shared-memory GPU algorithms. Furthermore, our algorithms are designed to work well on general data sets, including segmented arrays with arbitrary segment lengths. We also present optimizations to improve the performance of segmented scans based on the segment lengths. We implemented our algorithms on a PC with an NVIDIA GeForce 8800 GPU and compared our results with prior GPU-based algorithms. Our results indicate up to 10x higher performance over prior algorithms on input sequences with millions of elements.


title: NVIDIA Tesla: A Unified Graphics and Computing Architecture

text: To enable flexible, programmable graphics and high-performance computing, NVIDIA has developed the Tesla scalable unified graphics and parallel computing architecture. Its scalable parallel array of processors is massively multithreaded and programmable in C or via graphics APIs.


title: Iterative methods for sparse linear systems

text: Sometimes we need to solve the linear equation Ax = b for a very big and very sparse A. For example, the Poisson equation where only 5 entries of each row of the matrix A are non-zero. Standard methods such as inverting the matrix A (numerically unstable) or Guass elimination do not take advantage of the sparsity of A. In this lesson we will discuss two iterative methods suitable for sparse linear systems: Jacobi and Gauss-Seidel.


title: Ranking , Boosting , and Model Adaptation

text: We present a new ranking algorithm that combines the strengt hs of two previous methods: boosted tree classification, and LambdaR ank, which has been shown to be empirically optimal for a widely used inform ation retrieval measure. The algorithm is based on boosted regression trees , although the ideas apply to any weak learners, and it is significantly fast er in both train and test phases than the state of the art, for comparable accu racy. We also show how to find the optimal linear combination for any two ran kers, and we use this method to solve the line search problem exactly du ring boosting. In addition, we show that starting with a previously tra ined model, and boosting using its residuals, furnishes an effective techn ique for model adaptation, and we give results for a particularly pressing prob lem in Web Search training rankers for markets for which only small amounts o f labeled data are available, given a ranker trained on much more data from a larger market.


title: Heart rate variability biofeedback increases baroreflex gain and peak expiratory flow.

text:
OBJECTIVE
We evaluated heart rate variability biofeedback as a method for increasing vagal baroreflex gain and improving pulmonary function among 54 healthy adults.

METHODS
We compared 10 sessions of biofeedback training with an uninstructed control. Cognitive and physiological effects were measured in four of the sessions.

RESULTS
We found acute increases in low-frequency and total spectrum heart rate variability, and in vagal baroreflex gain, correlated with slow breathing during biofeedback periods. Increased baseline baroreflex gain also occurred across sessions in the biofeedback group, independent of respiratory changes, and peak expiratory flow increased in this group, independently of cardiovascular changes. Biofeedback was accompanied by fewer adverse relaxation side effects than the control condition.

CONCLUSIONS
Heart rate variability biofeedback had strong long-term influences on resting baroreflex gain and pulmonary function. It should be examined as a method for treating cardiovascular and pulmonary diseases. Also, this study demonstrates neuroplasticity of the baroreflex.


title: Moderating variables of music training-induced neuroplasticity: a review and discussion

text: A large body of literature now exists to substantiate the long-held idea that musicians' brains differ structurally and functionally from non-musicians' brains. These differences include changes in volume, morphology, density, connectivity, and function across many regions of the brain. In addition to the extensive literature that investigates these differences cross-sectionally by comparing musicians and non-musicians, longitudinal studies have demonstrated the causal influence of music training on the brain across the lifespan. However, there is a large degree of inconsistency in the findings, with discordance between studies, laboratories, and techniques. A review of this literature highlights a number of variables that appear to moderate the relationship between music training and brain structure and function. These include age at commencement of training, sex, absolute pitch (AP), type of training, and instrument of training. These moderating variables may account for previously unexplained discrepancies in the existing literature, and we propose that future studies carefully consider research designs and methodologies that control for these variables.


title: KPB-SIFT: a compact local feature descriptor

text: Invariant feature descriptors such as SIFT and GLOH have been demonstrated to be very robust for image matching and object recognition. However, such descriptors are typically of high dimensionality, e.g. 128-dimension in the case of SIFT. This limits the performance of feature matching techniques in terms of speed and scalability. A new compact feature descriptor, called Kernel Projection Based SIFT (KPB-SIFT), is presented in this paper. Like SIFT, our descriptor encodes the salient aspects of image information in the feature point's neighborhood. However, instead of using SIFT's smoothed weighted histograms, we apply kernel projection techniques to orientation gradient patches. The produced KPB-SIFT descriptor is more compact as compared to the state-of-the-art, does not require pre-training step needed by PCA based descriptors, and shows superior advantages in terms of distinctiveness, invariance to scale, and tolerance of geometric distortions. We extensively evaluated the effectiveness of KPB-SIFT with datasets acquired under varying circumstances.


title: NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study

text: This paper introduces a novel large dataset for example-based single image super-resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. The challenge is the first challenge of its kind, with 6 competitions, hundreds of participants and tens of proposed solutions. Our newly collected DIVerse 2K resolution image dataset (DIV2K) was employed by the challenge. In our study we compare the solutions from the challenge to a set of representative methods from the literature and evaluate them using diverse measures on our proposed DIV2K dataset. Moreover, we conduct a number of experiments and draw conclusions on several topics of interest. We conclude that the NTIRE 2017 challenge pushes the state-of-the-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on our newly proposed DIV2K.


title: A multiagent reinforcement learning algorithm using extended optimal response

text: Stochastic games provides a theoretical framework to multiagent reinforcement learning. Based on the framework, a multiagent reinforcement learning algorithm for zero-sum stochastic games was proposed by Littman and it was extended to general-sum games by Hu and Wellman. Given a stochastic game, if all agents learn with their algorithm, we can expect that the policies of the agents converge to a Nash equilibrium. However, agents with their algorithm always try to converge to a Nash equilibrium independent of the policies used by the other agents. In addition, in case there are multiple Nash equilibria, agents must agree on the equilibrium where they want to reach. Thus, their algorithm lacks adaptability in a sense. In this paper, we propose a multiagent reinforcement learning algorithm. The algorithm uses the extended optimal response which we introduce in this paper. It will converge to a Nash equilibrium when other agents are adaptable, otherwise it will make an optimal response. We also provide some empirical results in three simple stochastic games, which show that the algorithm can realize what we intend.


title: Applying MIPVU Metaphor Identification Procedure on Czech

text: This paper represents the current state of the research project aimed at modifying the MIPVU protocol for metaphor annotation for usage on Czech-language texts. Three annotators were trained to use metaphor identification procedure MIPVU and annotated 2 short text excerpts of about 600 tokens length, then the reliability of annotation was measured using Fleiss’ kappa. The resultant interannotator agreement of 0.70 was below kappa values reported by annotators of VU Amsterdam Metaphor Corpus (Steen et al., 2010) and very similar to the agreement that researchers (Badryzlova et al., 2013) got in their first reliability test with unmodified MIPVU procedure applied on Russian texts. Some modifications of the annotation procedure are proposed in order for it to be more suitable for Czech language. The modifications are based on the observations made by annotators in error analysis and by authors of similar projects aimed to transfer MIPVU procedure to Slavic/inflected languages. The functionality of the annotation procedure refinements now have to be tested in the second reliability test.


title: A Novel Phase-Shift Control of Semibridgeless Active Rectifier for Wireless Power Transfer

text: A novel phase-shift control of a semibridgeless active rectifier (S-BAR) is investigated in order to utilize the S-BAR in wireless energy transfer applications. The standard receiver-side rectifier topology is developed by replacing rectifier lower diodes with synchronous switches controlled by a phase-shifted PWM signal. Theoretical and simulation results show that with the proposed control technique, the output quantities can be regulated without communication between the receiver and transmitter. To confirm the performance of the proposed converter and control, experimental results are provided using 8-, 15-, and 23-cm air gap coreless transformer which has dimension of 76 cm × 76 cm, with 120-V input and the output power range of 0 to 1kW with a maximum efficiency of 94.4%.


title: Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks

text: We examine how firms can create word-of-mouth peer influence and social contagion by designing viral features into their products and marketing campaigns. Word-of-mouth (WOM) is generally considered to be more effective at promoting product contagion when it is personalized and active. Unfortunately, the relative effectiveness of different viral features has not been quantified, nor has their effectiveness been definitively established, largely because of difficulties surrounding econometric identification of endogenous peer effects. We therefore designed a randomized field experiment on a popular social networking website to test the effectiveness of a range of viral messaging capabilities in creating peer influence and social contagion among the 1.4 million friends of 9,687 experimental users. Overall, we find that viral product design features can indeed generate econometrically identifiable peer influence and social contagion effects. More surprisingly, we find that passive-broadcast viral messaging generates a 246% increase in local peer influence and social contagion effects, while adding active-personalized viral messaging only generates an additional 98% increase in contagion. Although active-personalized messaging is more effective in encouraging adoption per message and is correlated with more user engagement and sustained product use, passivebroadcast messaging is used more often enough to eclipse those benefits, generating more total peer adoption in the network. In addition to estimating the effects of viral product design on social contagion and product diffusion, our work also provides a model for how randomized trials can be used to identify peer influence effects in networks.


title: Multi-factor Authentication as a Service

text: An architecture for providing multi-factor authentication as a service is proposed, resting on the principle of a loose coupling and separation of duties between network entities and end user devices. The multi-factor authentication architecture leverages Identity Federation and Single-Sign-On technologies, such as the OpenID framework, in order to provide for a modular integration of various factors of authentication. The architecture is robust and scalable enabling service providers to define risk-based authentication policies by way of assurance level requirements, which map to concrete authentication factor capabilities on user devices.


title: Trajectory planning for car-like vehicles: A modular approach

text: We consider the problem of trajectory planning with geometric constraints for a car-like vehicle. The vehicle is described by its dynamic model, considering such effects as lateral slipping and aerodynamic drag. We propose a modular solution, where three different problems are identified and solved by specific modules. The execution of the three modules can be orchestrated in different ways in order to produce efficient solutions to a variety of trajectory planning problems (e.g., obstacle avoidance, or overtake). As a specific example, we show how to generate the optimal lap on a specified racing track. The numeric examples provided in the paper are good witnesses of the effectiveness of our strategy.


title: Annotating Opinions in the World Press

text: In this paper we present a detailed scheme for annotating expressions of opinions, beliefs, emotions, sentiment and speculation (private states) in the news and other discourse. We explore inter-annotator agreement for individual private state expressions, and show that these low-level annotations are useful for producing higher-level subjective sentence annotations.


title: Behavioral and Neural Adaptation in Approach Behavior

text: People often make approachability decisions based on perceived facial trustworthiness. However, it remains unclear how people learn trustworthiness from a population of faces and whether this learning influences their approachability decisions. Here we investigated the neural underpinning of approach behavior and tested two important hypotheses: whether the amygdala adapts to different trustworthiness ranges and whether the amygdala is modulated by task instructions and evaluative goals. We showed that participants adapted to the stimulus range of perceived trustworthiness when making approach decisions and that these decisions were further modulated by the social context. The right amygdala showed both linear response and quadratic response to trustworthiness level, as observed in prior studies. Notably, the amygdala's response to trustworthiness was not modulated by stimulus range or social context, a possible neural dynamic adaptation. Together, our data have revealed a robust behavioral adaptation to different trustworthiness ranges as well as a neural substrate underlying approach behavior based on perceived facial trustworthiness.


title: Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal

text: We introduce a deep network architecture called DerainNet for removing rain streaks from an image. Based on the deep convolutional neural network (CNN), we directly learn the mapping relationship between rainy and clean image detail layers from data. Because we do not possess the ground truth corresponding to real-world rainy images, we synthesize images with rain for training. In contrast to other common strategies that increase depth or breadth of the network, we use image processing domain knowledge to modify the objective function and improve deraining with a modestly sized CNN. Specifically, we train our DerainNet on the detail (high-pass) layer rather than in the image domain. Though DerainNet is trained on synthetic data, we find that the learned network translates very effectively to real-world images for testing. Moreover, we augment the CNN framework with image enhancement to improve the visual results. Compared with the state-of-the-art single image de-raining methods, our method has improved rain removal and much faster computation time after network training.


title: Manga109 dataset and creation of metadata

text: We have created Manga109, a dataset of a variety of 109 Japanese comic books publicly available for use for academic purposes. This dataset provides numerous comic images but lacks the annotations of elements in the comics that are necessary for use by machine learning algorithms or evaluation of methods. In this paper, we present our ongoing project to build metadata for Manga109. We first define the metadata in terms of frames, texts and characters. We then present our web-based software for efficiently creating the ground truth for these images. In addition, we provide a guideline for the annotation with the intent of improving the quality of the metadata.


title: Efficient 3D LIDAR based loop closing using deep neural network

text: Loop closure detection in 3D LIDAR data is an essential but challenging problem in SLAM system. It is important to reduce global inconsistency or re-localize the robot that loses the localization, while is difficult for the lack of prior information. We present a semi-handcrafted representation learning method for LIDAR point cloud using siamese convolution neural network, which states the loop closure detection to a similarity modeling problem. With the learned representation, the similarity between two LIDAR scans is transformed as the Euclidean distance between the representations respectively. Based on it, we furthermore establish kd-tree to accelerate the searching of similar scans. To demonstrate the performance and effectiveness of the proposed method, the KITTI dataset is employed for comparison with other LIDAR loop closure detection methods. The result shows that our method can achieve both higher accuracy and efficiency.


title: Recursive Neural Networks for Learning Logical Semantics

text: Supervised recursive neural network models (RNNs) for sent ence meaning have been successful in an array of sophisticated language tasks , but it remains an open question whether they can learn compositional semantic gra mm rs that support logical deduction. We address this question directly by for the first time evaluating whether each of two classes of neural model — plain RNNs and re cursive neural tensor networks (RNTNs) — can correctly learn relationship such as entailment and contradiction between pairs of sentences, where we have generated controlled data sets of sentences from a logical grammar. Our first exper iment evaluates whether these models can learn the basic algebra of logical r elations involved. Our second and third experiments extend this evaluation to c omplex recursive structures and sentences involving quantification. We find t hat he plain RNN achieves only mixed results on all three experiments, where as the stronger RNTN model generalizes well in every setting and appears capable of learning suitable representations for natural language logical inference.


title: Analysis of dynamically scheduled tile algorithms for dense linear algebra on multicore architectures

text: The objective of this paper is to analyze the dynamic scheduling of dense linear algebra algorithms on shared-memory, multicore architectures. Current numerical libraries, e.g., LAPACK, show clear limitations on such emerging systems mainly due to their coarse granularity tasks. Thus, many numerical algorithms need to be redesigned to better fit the architectural design of the multicore platform. The PLASMA library (Parallel Linear Algebra for Scalable Multi-core Architectures) developed at the University of Tennessee tackles this challenge by using tile algorithms to achieve a finer task granularity. These tile algorithms can then be represented by Directed Acyclic Graphs (DAGs), where nodes are the tasks and edges are the dependencies between the tasks. The paramount key to achieve high performance is to implement a runtime environment to efficiently schedule the DAG across the multicore platform. This paper studies the impact on the overall performance of some parameters, both at the level of the scheduler, e.g., window size and locality, and the algorithms, e.g., Left Looking (LL) and Right Looking (RL) variants. The conclusion of this study claims that some commonly accepted rules for dense linear algebra algorithms may need to be revisited.


title: Underwater electric field measurement and analysis

text: The University of Idaho (UI) is developing electromagnetic field sensor systems that are attachable to autonomous underwater vehicles with the intent of taking survey measurements in underwater ocean environments. This paper presents the testing of these sensors and compares measurements to predictions. Testing was conducted off the coast of Florida with a moving artificial electric field source and an electric field sensor equipped AUV. At the closest pass, the peak value of the AUV-acquired electric field was consistent with the predicted field of the artificial source when the accuracy of AUV position was known to within ∼1 m.


title: 259 Second ring-down time and 4.45 million quality factor in 5.5 kHz fused silica birdbath shell resonator

text: The fused silica birdbath (BB) resonator is an axisymmetric 3D shell resonator that could be used in high-performance MEMS gyroscopes. We report a record quality factor (0 for a 5-mm-radius resonator, which is expected to reduce gyroscope bias drift. We present measurement results for two sizes of resonators with long vibratory decay time constants (τ), high Qs, and low damping asymmetry (Δτ−1) between their n = 2 wine-glass (WG) modes. We find a reduction in damping for larger resonators and a correlation between higher Q and lower as well as evidence of a lower bound on Q for resonators with low damping asymmetry.


title: Feeling Love and Doing More for Distant Others : Specific Positive Emotions Differentially Affect Prosocial Consumption

text: Marketers often employ a variety of positive emotions to encourage consumption or promote a particular behavior (e.g., buying, donating, recycling) to benefit an organization or cause. The authors show that specific positive emotions do not universally increase prosocial behavior but, rather, encourage different types of prosocial behavior. Four studies show that whereas positive emotions (i.e., love, hope, pride, and compassion) all induce prosocial behavior toward close entities (relative to a neutral emotional state), only love induces prosocial behavior toward distant others and international organizations. Love’s effect is driven by a distinct form of broadening, characterized by extending feelings of social connection and the boundary of caring to be more inclusive of others regardless of relatedness. Love—as a trait and a momentary emotion— is unique among positive emotions in fostering connectedness that other positive emotions (hope and pride) do not and broadening behavior in a way that other connected emotions (compassion) do not. This research contributes to the broaden-and-build theory of positive emotion by demonstrating a distinct type of broadening for love and adds an important qualification to the general finding that positive emotions uniformly encourage prosocial behavior.


title: On Compiling CNF into Decision-DNNF

text: Decision-DNNF is a strict subset of decomposable negation normal form (DNNF) that plays a key role in analyzing the complexity of model counters (the searches performed by these counters have their traces in Decision-DNNF). This paper presents a number of results on Decision-DNNF. First, we introduce a new notion of CNF width and provide an algorithm that compiles CNFs into Decision-DNNFs in time and space that are exponential only in this width. The new width strictly dominates the treewidth of the CNF primal graph: it is no greater and can be bounded when the treewidth of the primal graph is unbounded. This new result leads to a tighter bound on the complexity of model counting. Second, we show that the output of the algorithm can be converted in linear time to a sentential decision diagram (SDD), which leads to a tighter bound on the complexity of compiling CNFs into SDDs.


A Redundancy Dual-backup Method of the Battery Pack for the Two-wheeled Self-balanced Vehicle


title: Automatic mood detection of indian music using mfccs and k-means algorithm

text: This paper proposes a method of identifying the mood underlying a piece of music by extracting suitable and robust features from music clip. To recognize the mood, K-means clustering and global thresholding was used. Three features were amalgamated to decide the mood tag of the musical piece. Mel frequency cepstral coefficients, frame energy and peak difference are the features of interest. These features were used for clustering and further achieving silhouette plot which formed the basis of deciding the limits of threshold for classification. Experiments were performed on a database of audio clips of various categories. The accuracy of the mood extracted is around 90% indicating that the proposed technique provides encouraging results.


title: Synthetic aperture radar interferometry

text: Synthetic aperture radar interferometry is an imaging technique for measuring the topography of a surface, its changes over time, and other changes in the detailed characteristic of the surface. By exploiting the phase of the coherent radar signal, interferometry has transformed radar remote sensing from a largely interpretive science to a quantitative tool, with applications in cartography, geodesy, land cover characterization, and natural hazards. This paper reviews the techniques of interferometry, systems and limitations, and applications in a rapidly growing area of science and engineering.


title: The evolution of synthetic aperture radar systems and their progression to the EOS SAR

text: The spaceborne imaging radar program of the National Aeronautics and Space Administration (NASA) has evolved primarily through Seasat and the Shuttle Imaging Radar (SIR) to a multifrequency, multipolarization system capable of monitoring the Earth with a variety of imaging geometries, resolutions, and swaths. In particular, the ability to digitally process and calibrate the data has been a key factor in developing the algorithms which will operationally provide geophysical and biophysical information about the surface of the Earth using synthetic aperture radar (SAR). This paper describes the evolution of the spaceborne imaging radar starting with the Seasat SAR, through the SIR-A, SIR-B, and SIR-CI X-SAR missions, to the EOS SAR which is scheduled for launch as part of the Earth Observing System (EOS) at the end of the 1990s. A summary of the planned international missions, which may produce a permanent active microwave capability in space starting as early as 1991, is also presented, along with a description of the airborne systems which will be essential to the algorithm development and long-term calibration of the spaceborne data. Finally, a brief summary of the planetary missions utilizing SAR and a comparison of their imaging capabilities with those available on Earth is presented.