Query: Trends, Tips, Tolls: A Longitudinal Study of Bitcoin Transaction Fees

Relevant passages:
Two Bitcoins at the Price of One? Double-Spending Attacks on Fast Payments in Bitcoin <=SEP=> Bitcoin is a decentralized payment system that is based on Proof-of-Work. Bitcoin is currently gaining popularity as a digital currency; several businesses are starting to accept Bitcoin transactions. An example case of the growing use of Bitcoin was recently reported in the media; here, Bitcoins were used as a form of fast payment in a local fast-food restaurant. In this paper, we analyze the security of using Bitcoin for fast payments, where the time between the exchange of currency and goods is short (i.e., in the order of few seconds). We focus on doublespending attacks on fast payments and demonstrate that these attacks can be mounted at low cost on currently deployed versions of Bitcoin. We further show that the measures recommended by Bitcoin developers for the use of Bitcoin in fast transactions are not always effective in resisting double-spending; we show that if those recommendations are integrated in future Bitcoin implementations, double-spending attacks on Bitcoin will still be possible. Finally, we leverage on our findings and propose a lightweight countermeasure that enables the detection of doublespending attacks in fast transactions.

Empirical Analysis of Denial-of-Service Attacks in the Bitcoin Ecosystem <=SEP=> We present an empirical investigation into the prevalence and impact of distributed denial-of-service (DDoS) attacks on operators in the Bitcoin economy. To that end, we gather and analyze posts mentioning “DDoS” on the popular Bitcoin forum bitcointalk.org. Starting from around 3 000 different posts made between May 2011 and October 2013, we document 142 unique DDoS attacks on 40 Bitcoin services. We find that 7% of all known operators have been attacked, but that currency exchanges, mining pools, gambling operators, eWallets, and financial services are much more likely to be attacked than other services. Not coincidentally, we find currency exchanges and mining pools are much more likely to have DDoS protection such as CloudFlare, Incapsula, or Amazon Cloud. We show that those services that have been attacked are more than three times as likely to buy anti-DDoS services than operators who have not been attacked. We find that big mining pools (those with historical hashrate shares of at least 5%) are much more likely to be DDoSed than small pools. We investigate Mt. Gox as a case study for DDoS attacks on currency exchanges and find a disproportionate amount of DDoS reports made during the large spike in trading volume and exchange rates in spring 2013. We conclude by outlining future opportunities for researching DDoS attacks on Bitcoin.

Beware the Middleman: Empirical Analysis of Bitcoin-Exchange Risk <=SEP=> Bitcoin has enjoyed wider adoption than any previous crypto-currency; yet its success has also attracted the attention of fraudsters who have taken advantage of operational insecurity and transaction irreversibility. We study the risk investors face from Bitcoin exchanges, which convert between Bitcoins and hard currency. We examine the track record of 40 Bitcoin exchanges established over the past three years, and find that 18 have since closed, with customer account balances often wiped out. Fraudsters are sometimes to blame, but not always. Using a proportional hazards model, we find that an exchange’s transaction volume indicates whether or not it is likely to close. Less popular exchanges are more likely to be shut than popular ones. We also present a logistic regression showing that popular exchanges are more likely to suffer a security breach.

Game-Theoretic Analysis of DDoS Attacks Against Bitcoin Mining Pools <=SEP=> One of the unique features of the digital currency Bitcoin is that new cash is introduced by so-called miners carrying out resourceintensive proof-of-work operations. To increase their chances of obtaining freshly minted bitcoins, miners typically join pools to collaborate on the computations. However, intense competition among mining pools has recently manifested in two ways. Miners may invest in additional computing resources to increase the likelihood of winning the next mining race. But, at times, a more sinister tactic is also employed: a mining pool may trigger a costly distributed denial-of-service (DDoS) attack to lower the expected success outlook of a competing mining pool. We explore the trade-off between these strategies with a series of game-theoretical models of competition between two pools of varying sizes. We consider differences in costs of investment and attack, as well as uncertainty over whether a DDoS attack will succeed. By characterizing the game’s equilibria, we can draw a number of conclusions. In particular, we find that pools have a greater incentive to attack large pools than small ones. We also observe that larger mining pools have a greater incentive to attack than smaller ones.

Towards Risk Scoring of Bitcoin Transactions <=SEP=> If Bitcoin becomes the prevalent payment system on the Internet, crime fighters will join forces with regulators and enforce blacklisting of transaction prefixes at the parties who offer real products and services in exchange for bitcoin. Blacklisted bitcoins will be hard to spend and therefore less liquid and less valuable. This requires every recipient of Bitcoin payments not only to check all incoming transactions for possible blacklistings, but also to assess the risk of a transaction being blacklisted in the future. We elaborate this scenario, specify a risk model, devise a prediction approach using public knowledge, and present preliminary results using data from selected known thefts. We discuss the implications on markets where bitcoins are traded and critically revisit Bitcoin’s ability to serve as a unit of account.

Mastering Digital Transformation: the Path of a Financial Services Provider towards a Digital Transformation Strategy <=SEP=> To master the challenges of a digital transformation and to systematically address IT’s multifaceted transformative impacts on an organization’s inner and outer environments, top management is increasingly formulating and implementing a digital transformation strategy (DTS). To date, there have been few details of DTS formation concerning its underlying processes and activities. In this study, an interpretive case study approach is employed and DTS formation is investigated from a process/activity perspective. By using an activity-based process model that builds on IS strategizing, an in-depth case study at a large financial services provider was conducted. The results show that this DTS was predominantly shaped by a diversity of emergent strategizing activities through a bottom-up process and prior to the introduction of a holistic approach by top management. Top management then sought to formalize emergent strategy contents by formulating and implementing a DTS that comprised a shared target picture, distinct digital transformation governance, and measures to increase the share of deliberate strategy contents. Besides providing practical implications for DTS formulation and implementation, this study contributes to the literature on digital transformation, IS strategy, and IS strategizing.

A comprehensive physical model for light reflection <=SEP=> A new general reflectance model for computer graphics is presented. The model is based on physical optics and describes specular, directional diffuse, and uniform diffuse reflection by a surface. The reflected light pattern depends on wavelength, incidence angle, two surface roughness parameters, and surface refractive index. The formulation is self consistent in terms of polarization, surface roughness, masking/shadowing, and energy. The model applies to a wide range of materials and surface finishes and provides a smooth transition from diffuse-like to specular reflection as the wavelength and incidence angle are increased or the surface roughness is decreased. The model is analytic and suitable for Computer Graphics applications. Predicted reflectance distributions compare favorably with experiment. The model is applied to metallic, nonmetallic, and plastic materials, with smooth and rough surfaces.

Labor Costs and Foreign Direct Investment : A Panel VAR Approach <=SEP=> This paper examines the endogenous interaction between labor costs and Foreign Direct Investment (FDI) in the OECD countries via the Panel VAR approach under system GMM estimates for the period 1995–2009. The available data allows identifying the relevance of the components of labor costs, and allows a detailed analysis across different sectors. Empirical findings have revealed that sectoral composition of FDI and the decomposition of labor costs play a significant role in investigating the dynamic association between labor costs and FDI. Further, results suggest that labor market policies should focus on productivity-enhancing tools in addition to price hindering tools.

Hairstyle recommendation system for women <=SEP=> A perfect hairstyle enhanced anyone's self-confidence, especially women. However, in order to choose a good hairstyle, one was limited to rely on knowledge of a beauty expert. This paper presented a hairstyle recommendation system for women based on hairstyle experts' knowledge and a face shape classification scheme that the authors devised in a previous study. The system showed a user's face with a recommended or not recommended hairstyle on a monitor.

Harmonic Resonance Evaluation for Hub Traction Substation Consisting of Multiple High-Speed Railways <=SEP=> In many hub traction power-supply systems (TPSSs), the 27.5 kV busbars in the traction substation (TSS) have supplied several railway lines in a local area for reducing the number of TSSs. However, the multiple railway lines connected to the busbar may affect the resonance behaviors of the entire system. It is therefore necessary to investigate the resonance behaviors under such hub TPSS conditions since the harmonic current collection of all railway lines will aggravate the resonance problem. Thus, resonance-mode assessment and its sensitivity method is adopted for investigating the resonance behaviors of hub TPSS affected by different numbers of connected lines, lengths of supply lines, system capacities, etc. The numerical results of modal sensitivity indices are compared to investigate and quantify the harmonic resonance issue caused by multiple electrical railway lines. In addition, the resonance frequency-shift technique and modal impedance sensitivity index-based method are also employed to mitigate the critical harmonic resonances. The results illustrate that it is effective to analyze resonance issues by adopting the harmonic model and modal methods in this paper. The two types of resonance behaviors (i.e., primary resonance and resonance band) caused from the hub TPSS have been fully investigated, and the resulting harmonic distortion of hub TPSS is suppressed.

Learning styles and performance in the introductory programming sequence <=SEP=> This paper reports on the implication of different preferred learning styles on students' performance in the introductory programming sequence and on work in progress on how to accommodate these different styles.Students were given a learning styles preference test and then their preferred learning styles were compared to their performance on the exam and the practical programming part of the introductory programming module. There were significant differences in performance between groups of students.This result could lead one to two possible conclusions. One might be that some students' learning styles are more suited to learning programming than others.An alternative explanation is that our current methods of teaching advantage students with certain learning preference styles. We are at present in the process of testing this second assumption by providing students with a wider range of learning materials. We will then see if student performance is improved by using our current results as a baseline for comparison

Generalized State Coherence Transform for multidimensional localization of multiple sources <=SEP=> In our recent work an effective method for multiple source localization has been proposed under the name of cumulative State Coherence Transform (cSCT). Exploiting the physical meaning of the frequency-domain blind source separation and the sparse time-frequency dominance of the acoustic sources, multiple reliable TDOAs can be estimated with only two microphones, regardless of the permutation problem and of the microphone spacing. In this paper we present a multidimensional generalization of the cSCT which allows one to localize several sources in the P-dimensional space. An important approximation is made in order to perform a disjoint TDOA estimation over each dimension which reduces the localization problem to linear complexity. Furthermore the approach is invariant to the array geometry and to the assumed acoustic propagation model. Experimental results on simulated data show a precise 2-D localization of 7 sources by only using an array of three elements.

A class D output stage with zero dead time <=SEP=> An integrated class-D output stage has been realized with zero dead time, thereby removing one of the dominant sources of distortion in class-D amplifiers. Dead time is eliminated through proper dimensioning of the power transistor drivers and accurate matching of switch timing. Open-loop distortion of this output stage stays below 0.1% up to 35 W.

Communication-Efficient Distributed Statistical Inference <=SEP=> We present a Communication-efficient Surrogate Likelihood (CSL) framework for solving distributed statistical inference problems. CSL provides a communication-efficient surrogate to the global likelihood that can be used for low-dimensional estimation, high-dimensional regularized estimation and Bayesian inference. For low-dimensional estimation, CSL provably improves upon naive averaging schemes and facilitates the construction of confidence intervals. For high-dimensional regularized estimation, CSL leads to a minimax-optimal estimator with controlled communication cost. For Bayesian inference, CSL can be used to form a communication-efficient quasi-posterior distribution that converges to the true posterior. This quasi-posterior procedure significantly improves the computational efficiency of MCMC algorithms even in a non-distributed setting. We present both theoretical analysis and experiments to explore the properties of the CSL approximation.

A Survey Paper : Areas , Techniques and Challenges of Opinion Mining <=SEP=> Opinion Mining is a promising discipline, defined as an intersection of information retrieval and computational linguistic techniques to deal with the opinions expressed in a document. The field aims at solving the problems related to opinions about products, Politian in newsgroup posts, review sites, comments on Facebook posts and twitter etc. This paper is about to covers the techniques, applications, long and short future research areas, Research gaps and future challenges in opinion mining. Further, an attempt has been made to discuss in detail the use of supervised, unsupervised, machine learning and case based reasoning techniques in opinion mining to perform computational treatment of sentiments.

Social Interactions in Online Gaming <=SEP=> This paper briefly overviews five studies examining massively multiplayer online role-playing games (MMORPGs). The first study surveyed 540 gamers and showed that the social aspects of the game were the most important factor for many gamers. The second study explored the social interactions of 912 MMORPG players and showed they created strong friendships and emotional relationships. A third study examined the effect of online socializing in the lives of 119 online gamers. Significantly more male gamers than female gamers said that they found it easier to converse online than offline, and 57% of gamers had engaged in gender swapping. A fourth study surveyed 7,069 gamers and found that 12% of gamers fulfilled at least three diagnostic criteria of addiction. Finally, an interview study of 71 gamers explored attitudes, experiences, and feelings about online gaming. They provided detailed descriptions of personal problems that had arisen due to playing MMORPGs. an individualistic character (Griffiths, Davies, & Chappell, 2004). This is the only setting where millions of users voluntarily immerse themselves in a graphical virtual environment and interact with each other through avatars on a daily basis (Yee, 2007). Research suggests that the game play within these virtual worlds is enhanced because players use them as traditional games as well as arenas in which to explore DOI: 10.4018/ijgbl.2011100103 International Journal of Game-Based Learning, 1(4), 20-36, October-December 2011 21 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. new relationships, new places and themselves (Krotoski, 2004). Despite the massive amounts of money spent on online gaming, very little research has been carried out regarding the positive social aspects of these games. Much of the debate over the last 30 years has focused on the dangers of computer gaming in the adolescent population, including increased aggression and addiction. Research has also been carried out examining the potentially harmful effects playing computer games may have on social development, self-esteem, social inadequacy, and social anxiety. MMORPGs are very (virtually) socially interactive but little social interaction in the real world is needed when playing them as only one person can play them at any one time from a single computer, unlike some popular two-player console games such as Mortal Kombat. Yee (2001, 2006, 2007) has carried out research into MMORPGs and notes that they allow new forms of social identity and social interaction. Yee’s research has shown that MMORPGs appeal to adults and teenagers from a wide range of backgrounds, and they spend on average more than half a working week in these environments. In a study by Utz (2000), it was found that 77% of respondents reported that they had some sort of relation with other Multi-User Dungeon (MUD) Gamers. It has also been suggested that college students can develop compulsions to play MMORPGs leading to social isolation, poor academic performance, and sleep deprivation. In 2004, a survey of over 54,000 American students found 11% of females and 20% of males said their recreational computer use had significantly hindered their performance at college and University (American College Health Association, 2005). Players can become fixated on their virtual characters, striving to obtain the best armour, experience and reputation in the game, ignoring the fact that their grades are dropping and their friends have drifted apart. It is clear to see that computer games appear to play a role in the socialisation of heavy game players particularly for those who play MMORPGs. Krotoski (2004) maintains that MMORPGs encourage group interaction and involvement, flexibility and mastery, resulting in significant friendships and personal empowerment. It is important to realise that gaming has shown elements of being a compulsive behaviour, with players feeling addicted, experiencing a strong impulse to play the games and finding it hard to resist the games (Griffiths & Davies, 2005). Positive social interaction is paramount in MMORPGs because they require a large number of players to cooperate together and work as a team at the same time. MMORPGs also have multiple tasks that require different characters with different skills in order to complete a challenge or quest. This teaches gamers to be dependent on one another that reinforce their relationships, providing a good understanding of teamwork. The purpose of the research here is to examine the social interactions that occur both within and outside of MMORPGs. The development of virtual friendships can be very enjoyable for gamers, and anecdotal evidence has suggested they sometimes develop into serious real-life friendships and relationships. Not only do MMORPGs facilitate formation of relationships, they are also windows into and catalysts in existing relationships. To date, there has been relatively little research into massively multiplayer online roleplaying games (MMORPGs). This paper briefly overviews five studies by the authors (Griffiths, Davies & Chappell, 2004; Cole & Griffiths, 2007; Hussain & Griffiths, 200, 2009; Grüsser, Thalemann, & Griffiths, 2007). In all studies outlined below, participants were informed that participation was entirely voluntary and that the research was conducted according to the British Psychological Society’s Ethical Code of Conduct for Psychologists. In the case of the surveys, if participants no longer wished to take part they simply had to close the Internet browser. All players were guaranteed anonymity and confidentiality. 15 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/social-interactions-onlinegaming/60132?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Library Science, Information Studies, and Education, InfoSci-Select, InfoSci-Select, InfoSciCommunications, Online Engagement, and Media eJournal Collection, InfoSci-Educational Leadership, Administration, and Technologies eJournal Collection, InfoSci-Select, InfoSci-Select, InfoSci-Journal Disciplines Communications and Social Science. Recommend this product to your

THE AUDIT RISK MODEL UNDER THE RISK OF FRAUD <=SEP=> In this article, we derive an audit risk formula for a simple situation. This formula closely resembles the SAS 47 model when we assume that no material misstatement due to fraud exists. A simple case illustrates how the risk of material misstatement due to management fraud impacts audit risk and how performing special audit procedures to detect such irregularities can decrease overall audit risk. While SAS 53 requires auditors to assess the risk of errors and irregularities and plan the audit to provide reasonable assurance of detecting errors and irregularities, the audit risk model of SAS 47 does not directly address the risk of management fraud. This article develops an audit risk model using the belief-function framework that considers the risks faced by auditors due to random errors, defalcations (employee fraud) and management fraud. We consider two cases. In the first, we consider only affirmative items of evidence and derive an analytical formula for the audit risk. In the second, we consider mixed items of evidence (both affirmative and negative), which models the situation more frequently faced by auditors. We demonstrate that a serious underestimation of audit risk can occur if the audit risk model is used without specifically considering the risks associated with management fraud.

The Effects of Hyperparameters on SGD Training of Neural Networks <=SEP=> The performance of neural network classifiers is determined by a number of hyperparameters, including learning rate, batch size, and depth. A number of attempts have been made to explore these parameters in the literature, and at times, to develop methods for optimizing them. However, exploration of parameter spaces has often been limited. In this note, I report the results of large scale experiments exploring these different parameters and their interactions. 1 Datasets and Libraries All experiments reported here were carried out using the Torch library [1] and CUDA (some of the experiments have been reproduced on a smaller scale with other libraries). The dataset for all the experiments is MNIST [3, 2]. Characters were deskewed prior to all experiments. Deskewing significantly reduces error rates in nearest neighbor classifiers. Skew corresponds to a simple one-parameter family of linear transformations in feature space and causes decision regions to become highly anisotropic. Without deskewing, differences in performance between different architectures might primarily reduce to their ability to “learn deskewing”. With deskewing, MNIST character classification become more of an instance of a typical classification problem. Prior results on classifying deskewed MNIST data both with neural networks and with other methods are shown in the table below. 2 Logistic vs Softmax Outputs Multi-Layer Perceptrons (MLPs) used for classification usually attempt to approximate posterior probabilities and use those as their discriminant function. Two common approaches to this are the use of least square regression with logistic output units trained with a least square error measure (“logistic outputs”) 1 ar X iv :1 50 8. 02 78 8v 1 [ cs .N E ] 1 2 A ug 2 01 5 Method Test Error Preprocessing Reference Reduced Set SVM deg 5 polynomial 1 deskewing LeCun et al. 1998 SVM deg 4 polynomial 1.1 deskewing LeCun et al. 1998 K-nearest-neighbors, L3 1.22 deskewing, noise removal, blurring, 2 pixel shift Kenneth Wilder, U. Chicago K-nearest-neighbors, L3 1.33 deskewing, noise removal, blurring, 1 pixel shift Kenneth Wilder, U. Chicago 2-layer NN, 300 HU 1.6 deskewing LeCun et al. 1998 Table 1: Other previously reported results on the MNIST database. and a softmax output layer (“softmax outputs”). In the limit of infinite amounts of training data, both approaches converge to true posterior probability estimates. Softmax output layers have the property that they are guaranteed to produce a normalized posterior probability distribution across all classes, while least square regression with logistic output units generates independent probability estimates for each class membership without any guarantees that these probabilities sum up to one. Softmax is often preferred, although there is no obvious theoretical reason why it should yield better discriminant functions or lower classification error for finite training sets. In OCR and speech recognition, some practitioners have observed that logistic outputs yield better posterior probability estimates and better results when combined with probabilistic language models. In addition, when the sum of the posterior probability estimates derived from logistic outputs differs significantly from unity, that is a strong indication that the input lies outside the training set and should be rejected. Figure 1 shows a scatterplot of test vs training error for a large number of MLPs with one hidden layer at different learning rates, different number of hidden units, and different batch sizes. Such scatterplots show what error rates are achievable by the different architectures, hyperparameter choices, initializations, and order of sample presentations. The lowest points in the vertical direction indicate the lowest test set error achievable by the architecture in this set of experiments. The scatterplot shows that logistic outputs achieve test set error rates of about 1.0% vs 1.1% for softmax outputs. At the same time, logistic outputs never achieve zero percent training set error, while softmax outputs frequently do. In order to ascertain that the difference in test set error between the two

Statistical Edge Detection: Learning and Evaluating Edge Cues <=SEP=> We formulate edge detection as statistical inference. This statistical edge detection is data driven, unlike standard methods for edge detection which are model based. For any set of edge detection filters (implementing local edge cues) we use pre-segmented images to learn the probability distributions of filter responses conditioned on whether they are evaluated on or off an edge. Edge detection is formulated as a discrimination task specified by a likelihood ratio test on the filter responses. This approach emphasizes the necessity of modeling the image background (the off-edges). We represent the conditional probability distributions non-parametrically and learn them on two different datasets of 100 (Sowerby) and 50 (South Florida) images. Multiple edges cues, including chrominance and multiple-scale, are combined by using their joint distributions. Hence this cue combination is optimal in the statistical sense. We evaluate the effectiveness of different visual cues using the Chernoff information and Receiver Operator Characteristic (ROC) curves. This shows that our approach gives quantitatively better results than the Canny edge detector when the image background contains

A Three-Winding Coupled-Inductor DC–DC Converter Topology With High Voltage Gain and Reduced Switch Stress <=SEP=> This paper presents a dc–dc boost converter topology for low input and high output voltage applications, such as photovoltaic systems, fuel cell systems, high-intensity discharge lamp, and electric vehicles. The suggested configuration consists of a three-winding coupled-inductor, a single switch and two hybrid voltage multiplier cells. Furthermore, two independent hybrid voltage multiplier cells are in parallel when the single switch S is turned on, and they are in series when the switch S is turned off. So, the advantages of the proposed converter structure are summarized as follows: 1) A coupled inductor with three windings is introduced in the presented converter structure. The two secondary windings of the coupled inductor are, respectively, used to form a hybrid multiplier cell on the one hand, on the other hand, it increases the control freedom of the voltage gain, enhances the utility rate of magnetic core and power density, and reduces the stress of power components to provide a stable constant dc output voltage. 2) The two hybrid multiplier cells can absorb synchronously the energy of stray inductance, which not only reduces the current stress of corresponding diodes, but also greatly alleviates the spike voltage of the main switch, which improves the efficiency. 3) The two hybrid multiplier cells are connected in series to supply power energy for the load, so the voltage gain is extended greatly due to this particular structure. Thus, low-voltage low-conduction-loss devices can be selected and the reverse-recovery currents within the diodes are inhibited. The operating principles and the steady state analyses of the proposed converter are discussed in detail. Finally, a test prototype has been implemented in the laboratory, and the simulated and experimental results show satisfactory agreement with the theoretical analysis.

Twitter Sentiment Analysis: A Bootstrap Ensemble Framework <=SEP=> Twitter sentiment analysis has become widely popular. However, stable Twitter sentiment classification performance remains elusive due to several issues: heavy class imbalance in a multi-class problem, representational richness issues for sentiment cues, and the use of diverse colloquial linguistic patterns. These issues are problematic since many forms of social media analytics rely on accurate underlying Twitter sentiments. Accordingly, a text analytics framework is proposed for Twitter sentiment analysis. The framework uses an elaborate bootstrapping ensemble to quell class imbalance, sparsity, and representational richness issues. Experiment results reveal that the proposed approach is more accurate and balanced in its predictions across sentiment classes, as compared to various comparison tools and algorithms. Consequently, the bootstrapping ensemble framework is able to build sentiment time series that are better able to reflect events eliciting strong positive and negative sentiments from users. Considering the importance of Twitter as one of the premiere social media platforms, the results have important implications for social media analytics and social intelligence.

DeFlaker: Automatically Detecting Flaky Tests <=SEP=> Developers often run tests to check that their latest changes to a code repository did not break any previously working functionality. Ideally, any new test failures would indicate regressions caused by the latest changes. However, some test failures may not be due to the latest changes but due to non-determinism in the tests, popularly called flaky tests. The typical way to detect flaky tests is to rerun failing tests repeatedly. Unfortunately, rerunning failing tests can be costly and can slow down the development cycle.
 We present the first extensive evaluation of rerunning failing tests and propose a new technique, called DeFlaker, that detects if a test failure is due to a flaky test without rerunning and with very low runtime overhead. DeFlaker monitors the coverage of latest code changes and marks as flaky any newly failing test that did not execute any of the changes. We deployed DeFlaker live, in the build process of 96 Java projects on TravisCI, and found 87 previously unknown flaky tests in 10 of these projects. We also ran experiments on project histories, where DeFlaker detected 1, 874 flaky tests from 4, 846 failures, with a low false alarm rate (1.5%). DeFlaker had a higher recall (95.5% vs. 23%) of confirmed flaky tests than Maven's default flaky test detector.

Understanding the influence and service type of trusted third party on consumers' online trust: evidence from Australian B2C marketplace <=SEP=> In this study, the trusted third party (TTP) in Australia's B2C marketplace is studied and the factors influencing consumers' trust behaviour are examined from the perspective of consumers' online trust. Based on the literature review and combined with the development status and background of Australia's e-commerce, underpinned by the Theory of Planned Behaviour (TPB) and a conceptual trust model, this paper expatiates the specific factors and influence mechanism of TTP on consumers' trust behaviour. Also this paper explains two different functions of TTP to solve the online trust problem faced by consumers. Meanwhile, this paper summarizes five different types of services provided by TTPs during the establishment of the trust relationship. Finally, the present study selects 100 B2C enterprises by the simple random sampling method and makes a detailed analysis of their TTPs, to verify the services and functions of the proposed TTP in the trust model. This study is of some significance for comprehending the influence mechanism, functions and services of TTPs on consumers' trust behaviour in the realistic Australian B2C environment.

Exploration and Mapping with Autonomous Robot Teams Results from the MAGIC 2010 Competition <=SEP=> The potential impact of autonomous robotics is magnified when those robots are deployed in teams: a team of cooperating robots can greatly increase the effectiveness of a human working alone, making short work of search-and-rescue and reconnaissance tasks. To achieve this potential, however, a number of challenging problems ranging from multi-robot planning, state estimation, object detection, and human-robot interfaces must first be solved. The MAGIC 2010 competition, like the DARPA grand challenges that preceded it, presented a formidable robotics problem designed to foster fundamental advances in these difficult areas. MAGIC asked teams of robots to collaboratively explore and map a 500× 500m area, detect and track benign and dangerous objects, and collaborate with human commanders while respecting their cognitive limits. This paper describes our winning entry in the MAGIC contest, where we fielded a team of 14 autonomous robots supervised by two human operators. While the challenges in MAGIC were diverse, we believe that cooperative multi-robot state estimation is ultimately the critical factor in building a successful system. In this paper, we describe our system and some of the technological advances that we believe were responsible for our success. We also contrast our approach to those of other teams. a)

A segmented claw-pole motor for traction applications considering recycling aspects <=SEP=> With the expansion of the fleet of electric and hybrid electric vehicles worldwide, it is of interest to consider recycling aspects of the parts that are introduced in these new vehicles. This paper focuses on the design of an electrical machine, a claw-pole motor, considering recycling of its components. The suggested design has segmented core parts of soft magnetic composites which yield a design very suitable for recycling as the core material is brittle and it thus simplifies the access of the copper winding. The windings are pre-formed ring-coils with a high fill-factor and with no end-windings. Through the use of direct water cooling, employing space between the claws, the current density can be high, and the machine can be compact. The results of finite element simulations of the electromagnetic properties (previously validated against measurements) show that a high performance recyclable motor with ratings above 10 kW can be made. In comparison with a commercial motor, the claw-pole motor has similar performance, a higher core and magnet weight and a lower copper weight. It also has an expected advantage regarding manufacturing cost and cost of recycling and it has lower copper loss.

Force Jacket: Pneumatically-Actuated Jacket for Embodied Haptic Experiences <=SEP=> Immersive experiences seek to engage the full sensory system in ways that words, pictures, or touch alone cannot. With respect to the haptic system, however, physical feedback has been provided primarily with handheld tactile experiences or vibration-based designs, largely ignoring both pressure receptors and the full upper-body area as conduits for expressing meaning that is consistent with sight and sound. We extend the potential for immersion along these dimensions with the Force Jacket, a novel array of pneumatically-actuated airbags and force sensors that provide precisely directed force and high frequency vibrations to the upper body. We describe the pneumatic hardware and force control algorithms, user studies to verify perception of airbag location and pressure magnitude, and subsequent studies to define full-torso, pressure and vibration-based feel effects such as punch, hug, and snake moving across the body. We also discuss the use of those effects in prototype virtual reality applications.

Adoption of business continuity planning processes in IT service management <=SEP=> For any fault of the same severity level, traditional fault discovery and notification tools provide equal weighting from business points of view. To improve the fault correlation from business perspectives, we proposed a framework to automate network and system alerts with respect to its business service impact for proactive notification to IT operations management. This paper outlines the value of business continuity planning (BCP) during the course of service impact analysis, placing particular emphasis on the business perspective in the processes of IT service management. The framework explicitly employs BCP relevant processes in order to identify the relationships between business services and IT resources A practical case in IT operations to illustrate the concept was then conducted.

ADMIRAL: A Data Mining Based Financial Trading System <=SEP=> This paper presents a novel framework for predicting stock trends and making financial trading decisions based on a combination of data and text mining techniques. The prediction models of the proposed system are based on the textual content of time-stamped Web documents in addition to traditional numerical time series data, which is also available from the Web. The financial trading system based on the model predictions (ADMIRAL) is using three different trading strategies. In this paper, the ADMIRAL system is simulated and evaluated on real-world series of news stories and stocks data using the C4.5 decision tree induction algorithm. The main performance measures are the predictive accuracy of the induced models and, more importantly, the profitability of each trading strategy using these predictions

Linear cross-modal hashing for efficient multimedia search <=SEP=> Most existing cross-modal hashing methods suffer from the scalability issue in the training phase. In this paper, we propose a novel cross-modal hashing approach with a linear time complexity to the training data size, to enable scalable indexing for multimedia search across multiple modals. Taking both the intra-similarity in each modal and the inter-similarity across different modals into consideration, the proposed approach aims at effectively learning hash functions from large-scale training datasets. More specifically, for each modal, we first partition the training data into $k$ clusters and then represent each training data point with its distances to $k$ centroids of the clusters. Interestingly, such a k-dimensional data representation can reduce the time complexity of the training phase from traditional O(n2) or higher to O(n), where $n$ is the training data size, leading to practical learning on large-scale datasets. We further prove that this new representation preserves the intra-similarity in each modal. To preserve the inter-similarity among data points across different modals, we transform the derived data representations into a common binary subspace in which binary codes from all the modals are "consistent" and comparable. nThe transformation simultaneously outputs the hash functions for all modals, which are used to convert unseen data into binary codes. Given a query of one modal, it is first mapped into the binary codes using the modal's hash functions, followed by matching the database binary codes of any other modals. Experimental results on two benchmark datasets confirm the scalability and the effectiveness of the proposed approach in comparison with the state of the art.

Armadillo: An Open Source C++ Linear Algebra Library for Fast Prototyping and Computationally Intensive Experiments <=SEP=> In this report we provide an overview of the open source Armadillo C++ linear algebra library (matrix maths). The library aims to have a good balance between speed and ease of use, and is useful if C++ is the language of choice (due to speed and/or integration capabilities), rather than another language like Matlab or Octave. In particular, Armadillo can be used for fast prototyping and computationally intensive experiments, while at the same time allowing for relatively painless transition of research code into production environments. It is distributed under a license that is applicable in both open source and proprietary software development contexts. The library supports integer, floating point and complex numbers, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK, or one its high-performance drop-in replacements, such as MKL from Intel or ACML from AMD. A delayed evaluation approach is employed (during compile time) to combine several operations into one and reduce (or eliminate) the need for temporaries. This is accomplished through C++ template meta-programming. Performance comparisons suggest that the library is considerably faster than Matlab and Octave, as well as previous C++ libraries such as IT++ and Newmat. This report reflects a subset of the functionality present in Armadillo v2.4. Armadillo can be downloaded from: • http://arma.sourceforge.net If you use Armadillo in your research and/or software, we would appreciate a citation to this document. Please cite as: • Conrad Sanderson. Armadillo: An Open Source C++ Linear Algebra Library for Fast Prototyping and Computationally Intensive Experiments. Technical Report, NICTA, 2010. Armadillo: An Open Source C++ Linear Algebra Library NICTA Technical Report