Query: Distributed Privacy-Preserving Collaborative Intrusion Detection Systems for VANETs

Relevant passages:
Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds <=SEP=> Convex empirical risk minimization is a basic tool in machine learning and statistics. We provide new algorithms and matching lower bounds for differentially private convex empirical risk minimization assuming only that each data point's contribution to the loss function is Lipschitz and that the domain of optimization is bounded. We provide a separate set of algorithms and matching lower bounds for the setting in which the loss functions are known to also be strongly convex. Our algorithms run in polynomial time, and in some cases even match the optimal nonprivate running time (as measured by oracle complexity). We give separate algorithms (and lower bounds) for (ε, 0)and (ε, δ)-differential privacy; perhaps surprisingly, the techniques used for designing optimal algorithms in the two cases are completely different. Our lower bounds apply even to very simple, smooth function families, such as linear and quadratic functions. This implies that algorithms from previous work can be used to obtain optimal error rates, under the additional assumption that the contributions of each data point to the loss function is smooth. We show that simple approaches to smoothing arbitrary loss functions (in order to apply previous techniques) do not yield optimal error rates. In particular, optimal algorithms were not previously known for problems such as training support vector machines and the high-dimensional median.

Exposure: A Passive DNS Analysis Service to Detect and Report Malicious Domains <=SEP=> A wide range of malicious activities rely on the domain name service (DNS) to manage their large, distributed networks of infected machines. As a consequence, the monitoring and analysis of DNS queries has recently been proposed as one of the most promising techniques to detect and blacklist domains involved in malicious activities (e.g., phishing, spam, botnets command-and-control, etc.). EXPOSURE is a system we designed to detect such domains in real time, by applying 15 unique features grouped in four categories.
 We conducted a controlled experiment with a large, real-world dataset consisting of billions of DNS requests. The extremely positive results obtained in the tests convinced us to implement our techniques and deploy it as a free, online service. In this article, we present the Exposure system and describe the results and lessons learned from 17 months of its operation. Over this amount of time, the service detected over 100K malicious domains. The statistics about the time of usage, number of queries, and target IP addresses of each domain are also published on a daily basis on the service Web page.

Practical privacy: the SuLQ framework <=SEP=> We consider a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries. In such a database, a query consists of a pair (S, f) where S is a set of rows in the database and f is a function mapping database rows to {0, 1}. The true answer is ΣiεS f(di), and a noisy version is released as the response to the query. Results of Dinur, Dwork, and Nissim show that a strong form of privacy can be maintained using a surprisingly small amount of noise -- much less than the sampling error -- provided the total number of queries is sublinear in the number of database rows. We call this query and (slightly) noisy reply the SuLQ (Sub-Linear Queries) primitive. The assumption of sublinearity becomes reasonable as databases grow increasingly large.We extend this work in two ways. First, we modify the privacy analysis to real-valued functions f and arbitrary row types, as a consequence greatly improving the bounds on noise required for privacy. Second, we examine the computational power of the SuLQ primitive. We show that it is very powerful indeed, in that slightly noisy versions of the following computations can be carried out with very few invocations of the primitive: principal component analysis, k means clustering, the Perceptron Algorithm, the ID3 algorithm, and (apparently!) all algorithms that operate in the in the statistical query learning model [11].

Differentially Private Empirical Risk Minimization <=SEP=> Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance.

Differentially Private Distributed Constrained Optimization <=SEP=> Many resource allocation problems can be formulated as an optimization problem whose constraints contain sensitive information about participating users. This paper concerns a class of resource allocation problems whose objective function depends on the aggregate allocation (i.e., the sum of individual allocations); in particular, we investigate distributed algorithmic solutions that preserve the privacy of participating users. Without privacy considerations, existing distributed algorithms normally consist of a central entity computing and broadcasting certain public coordination signals to participating users. However, the coordination signals often depend on user information, so that an adversary who has access to the coordination signals can potentially decode information on individual users and put user privacy at risk. We present a distributed optimization algorithm that preserves differential privacy, which is a strong notion that guarantees user privacy regardless of any auxiliary information an adversary may have. The algorithm achieves privacy by perturbing the public signals with additive noise, whose magnitude is determined by the sensitivity of the projection operation onto user-specified constraints. By viewing the differentially private algorithm as an implementation of stochastic gradient descent, we are able to derive a bound for the suboptimality of the algorithm. We illustrate the implementation of our algorithm via a case study of electric vehicle charging. Specifically, we derive the sensitivity and present numerical simulations for the algorithm. Through numerical simulations, we are able to investigate various aspects of the algorithm when being used in practice, including the choice of step size, number of iterations, and the trade-off between privacy level and suboptimality.

The role of transparency in recommender systems <=SEP=> Recommender Systems act as a personalized decision guides, aiding users in decisions on matters related to personal taste. Most previous research on Recommender Systems has focused on the statistical accuracy of the algorithms driving the systems, with little emphasis on interface issues and the user's perspective. The goal of this research was to examine the role of transprency (user understanding of why a particular recommendation was made) in Recommender Systems. To explore this issue, we conducted a user study of five music Recommender Systems. Preliminary results indicate that users like and feel more confident about recommendations that they perceive as transparent.

Recognizing Abnormal Heart Sounds Using Deep Learning <=SEP=> The work presented here applies deep learning to the task of automated cardiac auscultation, i.e. recognizing abnormalities in heart sounds. We describe an automated heart sound classification algorithm that combines the use of time-frequency heat map representations with a deep convolutional neural network (CNN). Given the cost-sensitive nature of misclassification, our CNN architecture is trained using a modified loss function that directly optimizes the trade-off between sensitivity and specificity. We evaluated our algorithm at the 2016 PhysioNet Computing in Cardiology challenge where the objective was to accurately classify normal and abnormal heart sounds from single, short, potentially noisy recordings. Our entry to the challenge achieved a final specificity of 0.95, sensitivity of 0.73 and overall score of 0.84. We achieved the greatest specificity score out of all challenge entries and, using just a single CNN, our algorithm differed in overall score by only 0.02 compared to the top place finisher, which used an ensemble approach.

The Tire as an Intelligent Sensor <=SEP=> Active safety systems are based upon the accurate and fast estimation of the value of important dynamical variables such as forces, load transfer, actual tire-road friction (kinetic friction) muk, and maximum tire-road friction available (potential friction) mup. Measuring these parameters directly from tires offers the potential for improving significantly the performance of active safety systems. We present a distributed architecture for a data-acquisition system that is based on a number of complex intelligent sensors inside the tire that form a wireless sensor network with coordination nodes placed on the body of the car. The design of this system has been extremely challenging due to the very limited available energy combined with strict application requirements for data rate, delay, size, weight, and reliability in a highly dynamical environment. Moreover, it required expertise in multiple engineering disciplines, including control-system design, signal processing, integrated-circuit design, communications, real-time software design, antenna design, energy scavenging, and system assembly.

Surgical precision JIT compilers <=SEP=> Just-in-time (JIT) compilation of running programs provides more optimization opportunities than offline compilation. Modern JIT compilers, such as those in virtual machines like Oracle's HotSpot for Java or Google's V8 for JavaScript, rely on dynamic profiling as their key mechanism to guide optimizations. While these JIT compilers offer good average performance, their behavior is a black box and the achieved performance is highly unpredictable.
 In this paper, we propose to turn JIT compilation into a precision tool by adding two essential and generic metaprogramming facilities: First, allow programs to invoke JIT compilation explicitly. This enables controlled specialization of arbitrary code at run-time, in the style of partial evaluation. It also enables the JIT compiler to report warnings and errors to the program when it is unable to compile a code path in the demanded way. Second, allow the JIT compiler to call back into the program to perform compile-time computation. This lets the program itself define the translation strategy for certain constructs on the fly and gives rise to a powerful JIT macro facility that enables "smart" libraries to supply domain-specific compiler optimizations or safety checks.
 We present Lancet, a JIT compiler framework for Java bytecode that enables such a tight, two-way integration with the running program. Lancet itself was derived from a high-level Java bytecode interpreter: staging the interpreter using LMS (Lightweight Modular Staging) produced a simple bytecode compiler. Adding abstract interpretation turned the simple compiler into an optimizing compiler. This fact provides compelling evidence for the scalability of the staged-interpreter approach to compiler construction.
 In the case of Lancet, JIT macros also provide a natural interface to existing LMS-based toolchains such as the Delite parallelism and DSL framework, which can now serve as accelerator macros for arbitrary JVM bytecode.

An LDA-Based Approach to Scientific Paper Recommendation <=SEP=> Recommendation of scientific papers is a task aimed to support researchers in accessing relevant articles from a large pool of unseen articles. When writing a paper, a researcher focuses on the topics related to her/his scientific domain, by using a technical language. The core idea of this paper is to exploit the topics related to the researchers scientific production (authored articles) to formally define her/his profile; in particular we propose to employ topic modeling to formally represent the user profile, and language modeling to formally represent each unseen paper. The recommendation technique we propose relies on the assessment of the closeness of the language used in the researchers papers and the one employed in the unseen papers. The proposed approach exploits a reliable knowledge source for building the user profile, and it alleviates the cold-start problem, typical of collaborative filtering techniques. We also present a preliminary evaluation of our approach on the DBLP.

Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning <=SEP=> In this paper, we focus on developing a novel mechanism to preserve differential privacy in deep neural networks, such that: (1) The privacy budget consumption is totally independent of the number of training steps; (2) It has the ability to adaptively inject noise into features based on the contribution of each to the output; and (3) It could be applied in a variety of different deep neural networks. To achieve this, we figure out a way to perturb affine transformations of neurons, and loss functions used in deep neural networks. In addition, our mechanism intentionally adds "more noise" into features which are "less relevant" to the model output, and vice-versa. Our theoretical analysis further derives the sensitivities and error bounds of our mechanism. Rigorous experiments conducted on MNIST and CIFAR-10 datasets show that our mechanism is highly effective and outperforms existing solutions.

Why do you play World of Warcraft? An in-depth exploration of self-reported motivations to play online and in-game behaviours in the virtual world of Azeroth <=SEP=> Massively multiplayer online role-playing games (MMORPGs) are video games in which players create an avatar that evolves and interacts with other avatars in a persistent virtual world. Motivations to play MMORPGs are heterogeneous (e.g. achievement, socialisation, immersion in virtual worlds). This study investigates in detail the relationships between self-reported motives and actual in-game behaviours. We recruited a sample of 690 World of Warcraft players (the most popular MMORPG) who agreed to have their avatar monitored for 8 months. Participants completed an initial online survey about their motives to play. Their actual in-game behaviours were measured through the game’s official database (the Armory website). Results showed specific associations between motives and in-game behaviours. Moreover, longitudinal analyses revealed that teamworkand competition-oriented motives are the most accurate predictors of fast progression in the game. In addition, although specific associations exist between problematic use and certain motives (e.g. advancement, escapism), longitudinal analyses showed that high involvement in the game is not necessarily associated with a negative impact upon daily living. 2012 Elsevier Ltd. All rights reserved.

Policy ratification <=SEP=> It is not sufficient to merely check the syntax of new policies before they are deployed in a system; policies need to be analyzed for their interactions with each other and with their local environment. That is, policies need to go through a ratification process. We believe policy ratification becomes an essential part of system management as the number of policies in the system increases and as the system administration becomes more decentralized. In this paper, we focus on the basic tasks involved in policy ratification. To a large degree, these basic tasks can be performed independent of policy model and language and require little domain-specific knowledge. We present algorithms from constraint, linear, and logic programming disciplines to help perform ratification tasks. We provide an algorithm to efficiently assign priorities to the policies based on relative policy preferences indicated by policy administrators. Finally, with an example, we show how these algorithms have been integrated with our policy system to provide feedback to a policy administrator regarding potential interactions of policies with each other and with their deployment environment.

Centrality in valued graphs : A measure of betweenness based on network flow <=SEP=> A new measure of centrality, C,, is introduced. It is based on the concept of network flows. While conceptually similar to Freeman’s original measure, Ca, the new measure differs from the original in two important ways. First, C, is defined for both valued and non-valued graphs. This makes C, applicable to a wider variety of network datasets. Second, the computation of C, is not based on geodesic paths as is C, but on all the independent paths between all pairs of points in the network.

Distributed Energy Resources Topology Identification via Graphical Modeling <=SEP=> Distributed energy resources (DERs), such as photovoltaic, wind, and gas generators, are connected to the grid more than ever before, which introduces tremendous changes in the distribution grid. Due to these changes, it is important to understand where these DERs are connected in order to sustainably operate the distribution grid. But the exact distribution system topology is difficult to obtain due to frequent distribution grid reconfigurations and insufficient knowledge about new components. In this paper, we propose a methodology that utilizes new data from sensor-equipped DER devices to obtain the distribution grid topology. Specifically, a graphical model is presented to describe the probabilistic relationship among different voltage measurements. With power flow analysis, a mutual information-based identification algorithm is proposed to deal with tree and partially meshed networks. Simulation results show highly accurate connectivity identification in the IEEE standard distribution test systems and Electric Power Research Institute test systems.

Performance of Reliable Transport Protocol over IEEE 802.11 Wireless LAN: Analysis and Enhancement <=SEP=> IEEE 802.11 Medium Access Control(MAC) is proposed to support asynchronous and time bounded delivery of radio data packets in infrastructure and ad hoc networks. The basis of the IEEE 802.11 WLAN MAC protocol is Distributed Coordination Function(DCF), which is a Carrier Sense Multiple Access with Collision Avoidance(CSMA/CA) with binary slotted exponential back-off scheme. Since IEEE 802.11 MAC has its own characteristics that are different from other wireless MAC protocols, the performance of reliable transport protocol over 802.11 needs further study. This paper proposes a scheme named DCF+, which is compatible with DCF, to enhance the performance of reliable transport protocol over WLAN. To analyze the performance of DCF and DCF+, this paper also introduces an analytical model to compute the saturated throughput of WLAN. Comparing with other models, this model is shown to be able to predict the behaviors of 802.11 more accurately. Moreover, DCF+ is able to improve the performance of TCP over WLAN, which is verified by modeling and elaborate simulation results.

Utilization of Data mining Approaches for Prediction of Life Threatening Diseases Survivability <=SEP=> Data mining now-a-days plays an important role in prediction of diseases in health care industry. The Health care industry utilizes data mining Techniques and finds out the information which is hidden in the data set. Many diagnoses have been done for predicting diseases. Without knowing the knowledge of profound medicine and clinical experience the treatment goes wrong. The time taken to recover from diseases depends on patients&apos; severity. For finding out the disease, number of test needs to be taken by patient. In most cases not all test become more effective. And at last it leads to the death of the patient. Many experiments have been conducted by comparing the performance of predictive data mining for reducing the number of test taken by the patient indirectly. This research paper is to present a survey on predicting the presence of life threatening diseases which causes to death and list out the various classification algorithms that has been used with number of attributes for prediction.

A 45GHz Doherty power amplifier with 23% PAE and 18dBm output power, in 45nm SOI CMOS <=SEP=> A 45GHz Doherty power amplifier is implemented in 45nm SOI CMOS. Two-stack FET amplifiers are used as main and auxiliary amplifiers, allowing a supply voltage of 2.5V and high output power. The use of slow-wave coplanar waveguides (CPW) improves the PAE and gain by approximately 3% and 1dB, and reduces the die area by 20%. This amplifier exhibits more than 18dBm saturated output power, with peak power gain of 7dB. It occupies 0.64mm2 while achieving a peak PAE of 23%; at 6dB back-off the PAE is 17%.

Review of Micro Thermoelectric Generator <=SEP=> Used for thermal energy harvesting, thermoelectric generator (TEG) can convert heat into electricity directly. Structurally, the main part of TEG is the thermopile, which consists of thermocouples connected in series electrically and in parallel thermally. Benefiting from massive progress achieved in a microelectromechanical systems technology, micro TEG (<inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG) with advantages of small volume and high output voltage has obtained attention in recent 20 years. The review gives a comprehensive survey of the development and current status of <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG. First, the principle of operation is introduced and some key parameters used for characterizing the performance of <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG are highlighted. Next, <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEGs are classified from the perspectives of structure, material, and fabrication technology. Then, almost all the relevant works are summarized for the convenience of comparison and reference. Summarized information includes the structure, material property, fabrication technology, output performance, and so on. This will provide readers with an overall evaluation of different studies and guide them in choosing the suitable <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEGs for their applications. In addition, the existing and potential applications of <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG are shown, especially the applications in the Internet of things. Finally, we summarize the challenges encountered in improving the output power of <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG and predicted that more researchers would focus their efforts on the flexible structure <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG, and combination of <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG and other energy harvestings. With the emergence of more low-power devices and the gradual improvement of <italic>ZT</italic> value of the thermoelectric material, <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG is promising for applications in various fields. [2017-0610]

Modern Release Engineering in a Nutshell -- Why Researchers Should Care <=SEP=> The release engineering process is the process that brings high quality code changes from a developer's workspace to the end user, encompassing code change integration, continuous integration, build system specifications, infrastructure-as-code, deployment and release. Recent practices of continuous delivery, which bring new content to the end user in days or hours rather than months or years, have generated a surge of industry-driven interest in the release engineering pipeline. This paper argues that the involvement of researchers is essential, by providing a brief introduction to the six major phases of the release engineering pipeline, a roadmap of future research, and a checklist of three major ways that the release engineering process of a system under study can invalidate the findings of software engineering studies. The main take-home message is that, while release engineering technology has flourished tremendously due to industry, empirical validation of best practices and the impact of the release engineering process on (amongst others) software quality is largely missing and provides major research opportunities.

A Review on Performance Analysis of Cloud Computing Services for Scientific Computing <=SEP=> Cloud computing has emerged as a very important commercial infrastructure that promises to reduce the need for maintaining costly computing facilities by organizations and institutes. Through the use of virtualization and time sharing of resources, clouds serve with a single set of physical resources as a large user base with altogether different needs. Thus, the clouds have the promise to provide to their owners the benefits of an economy of calibration and, at the same time, become a substitute for scientists to clusters, grids, and parallel production conditions. However, the present commercial clouds have been built to support web and small database workloads, which are very different from common scientific computing workloads. Furthermore, the use of virtualization and resource time sharing may introduce significant performance penalties for the demanding scientific computing workloads. In this paper, we analyze the performance of cloud computing services for scientific computing workloads. This paper evaluate the presence in real scientific computing workloads of Many-Task Computing users, that is, of users who employ loosely coupled applications comprising many tasks to achieve their scientific goals. Our effective method demonstrates to yield comparative and even better results than the more complex state-of-the-art techniques but has the advantage to be appropriate for real-time applications.

Cedalion: a language for language oriented programming <=SEP=> Language Oriented Programming (LOP) is a paradigm that puts domain specific programming languages (DSLs) at the center of the software development process. Currently, there are three main approaches to LOP: (1) the use of internal DSLs, implemented as libraries in a given host language; (2) the use of external DSLs, implemented as interpreters or compilers in an external language; and (3) the use of language workbenches, which are integrated development environments (IDEs) for defining and using external DSLs. In this paper, we contribute: (4) a novel language-oriented approach to LOP for defining and using internal DSLs. While language workbenches adapt internal DSL features to overcome some of the limitations of external DSLs, our approach adapts language workbench features to overcome some of the limitations of internal DSLs. We introduce Cedalion, an LOP host language for internal DSLs, featuring static validation and projectional editing. To validate our approach we present a case study in which Cedalion was used by biologists in designing a DNA microarray for molecular Biology research.

Health chair: implicitly sensing heart and respiratory rate <=SEP=> People interact with chairs frequently, making them a potential location to perform implicit health sensing that requires no additional effort by users. We surveyed 550 participants to understand how people sit in chairs and inform the design of a chair that detects heart and respiratory rate from the armrests and backrests of the chair respectively. In a laboratory study with 18 participants, we evaluated a range of common sitting positions to determine when heart rate and respiratory rate detection was possible (32% of the time for heart rate, 52% for respiratory rate) and evaluate the accuracy of the detected rate (83% for heart rate, 73% for respiratory rate). We discuss the challenges of moving this sensing to the wild by evaluating an in-situ study totaling 40 hours with 11 participants. We show that, as an implicit sensor, the chair can collect vital signs data from its occupant through natural interaction with the chair.

The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings <=SEP=> We motivate and describe a new freely available human-human dialogue data set for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; Mills and Healey, submitted) with a novel task, where a Learner needs to learn invented visual attribute words (such as “burchak” for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include selfand other-correction, mid-sentence continuations, interruptions, overlaps, fillers, and hedges. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78% turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rulebased system built previously.

GUSS: Solving Collections of Data Related Models Within GAMS <=SEP=> In many applications, optimization of a collection of problems is required where each problem is structurally the same, but in which some or all of the data defining the instance is updated. Such models are easily specified within modern modeling systems, but have often been slow to solve due to the time needed to regenerate the instance, and the inability to use advance solution information (such as basis factorizations) from previous solves as the collection is processed. We describe a new language extension, GUSS, that gathers data from different sources/symbols to define the collection of models (called scenarios), updates a base model instance with this scenario data and solves the updated model instance and scatters the scenario results to symbols in the GAMS database. We demonstrate the utility of this approach in three applications, namely data envelopment analysis, cross validation and stochastic dual dynamic programming. The language extensions are available for general use in all versions of GAMS starting with release 23.7.

30 + years of research and practice of outsourcing – Exploring the past and anticipating the future <=SEP=> Article history: Received 7 January 2008 Received in revised form 24 June 2008 Accepted 31 July 2008 Available online 5 April 2009 Outsourcing is a phenomenon that as a practice originated in the 1950s, but it was not until the 1980s when the strategy became widely adopted in organizations. Since then, the strategy has evolved from a strictly cost focused approach towards more cooperative nature, in which cost is only one, often secondary, decision-making criterion. In the development of the strategy, three broad and somewhat overlapping, yet distinct phases can be identified: the era of the Big Bang, the era of the Bandwagon, and the era of Barrierless Organizations. This paper illustrates that the evolution of the practice has caused several contradictions among researchers, as well as led to the situation where the theoretical background of the phenomenon has recently become much richer. Through examining existing research, this paper intends to identify the development of outsourcing strategy from a practical as well as a theoretical perspective from its birth up to today. In addition, through providing insights from managers in the information technology industry, this paper aims at providing a glimpse from the future – that is – what may be the future directions and research issues in this complex phenomenon? © 2009 Elsevier Inc. All rights reserved.

Virtualizing I/O Devices on VMware Workstation's Hosted Virtual Machine Monitor <=SEP=> Virtual machines were developed by IBM in the 1960’s to provide concurrent, interactive access to a mainframe computer. Each virtual machine is a replica of the underlying physical machine and users are given the illusion of running directly on the physical machine. Virtual machines also provide benefits like isolation and resource sharing, and the ability to run multiple flavors and configurations of operating systems. VMware Workstation brings such mainframe-class virtual machine technology to PC-based desktop and workstation computers. This paper focuses on VMware Workstation’s approach to virtualizing I/O devices. PCs have a staggering variety of hardware, and are usually pre-installed with an operating system. Instead of replacing the pre-installed OS, VMware Workstation uses it to host a user-level application (VMApp) component, as well as to schedule a privileged virtual machine monitor (VMM) component. The VMM directly provides high-performance CPU virtualization while the VMApp uses the host OS to virtualize I/O devices and shield the VMM from the variety of devices. A crucial question is whether virtualizing devices via such a hosted architecture can meet the performance required of high throughput, low latency devices. To this end, this paper studies the virtualization and performance of an Ethernet adapter on VMware Workstation. Results indicate that with optimizations, VMware Workstation’s hosted virtualization architecture can match native I/O throughput on standard PCs. Although a straightforward hosted implementation is CPU-limited due to virtualization overhead on a 733 MHz Pentium R III system on a 100 Mb/s Ethernet, a series of optimizations targeted at reducing CPU utilization allows the system to match native network throughput. Further optimizations are discussed both within and outside a hosted architecture.

Detecting epistasis in human complex traits <=SEP=> Genome-wide association studies (GWASs) have become the focus of the statistical analysis of complex traits in humans, successfully shedding light on several aspects of genetic architecture and biological aetiology. Single-nucleotide polymorphisms (SNPs) are usually modelled as having additive, cumulative and independent effects on the phenotype. Although evidently a useful approach, it is often argued that this is not a realistic biological model and that epistasis (that is, the statistical interaction between SNPs) should be included. The purpose of this Review is to summarize recent directions in methodology for detecting epistasis and to discuss evidence of the role of epistasis in human complex trait variation. We also discuss the relevance of epistasis in the context of GWASs and potential hazards in the interpretation of statistical interaction terms.

Interactive Entity Resolution in Relational Data: A Visual Analytic Tool and Its Evaluation <=SEP=> Databases often contain uncertain and imprecise references to real-world entities. Entity resolution, the process of reconciling multiple references to underlying real-world entities, is an important data cleaning process required before accurate visualization or analysis of the data is possible. In many cases, in addition to noisy data describing entities, there is data describing the relationships among the entities. This relational data is important during the entity resolution process; it is useful both for the algorithms which determine likely database references to be resolved and for visual analytic tools which support the entity resolution process. In this paper, we introduce a novel user interface, D-Dupe, for interactive entity resolution in relational data. D-Dupe effectively combines relational entity resolution algorithms with a novel network visualization that enables users to make use of an entity's relational context for making resolution decisions. Since resolution decisions often are interdependent, D-Dupe facilitates understanding this complex process through animations which highlight combined inferences and a history mechanism which allows users to inspect chains of resolution decisions. An empirical study with 12 users confirmed the benefits of the relational context visualization on the performance of entity resolution tasks in relational data in terms of time as well as users' confidence and satisfaction.

High-power-transfer-density capacitive wireless power transfer system for electric vehicle charging <=SEP=> This paper introduces a large air-gap capacitive wireless power transfer (WPT) system for electric vehicle charging that achieves a power transfer density exceeding the state-of-the-art by more than a factor of four. This high power transfer density is achieved by operating at a high switching frequency (6.78 MHz), combined with an innovative approach to designing matching networks that enable effective power transfer at this high frequency. In this approach, the matching networks are designed such that the parasitic capacitances present in a vehicle charging environment are absorbed and utilized as part of the wireless power transfer mechanism. A new modeling approach is developed to simplify the complex network of parasitic capacitances into equivalent capacitances that are directly utilized as the matching network capacitors. A systematic procedure to accurately measure these equivalent capacitances is also presented. A prototype capacitive WPT system with 150 cm2 coupling plates, operating at 6.78 MHz and incorporating matching networks designed using the proposed approach, is built and tested. The prototype system transfers 589 W of power across a 12-cm air gap, achieving a power transfer density of 19.6 kW/m2.