What are users’ intentions towards real money trading in massively multiplayer online games?

This study investigates user behaviour in massively multiplayer online games from the perspective of their intentions to engage in real money trading. Players who engage in real money trading purchase resources instead of spending time to acquire them in the game. This behaviour influences not just their own gaming experience, but those of other players as well as the operator??s revenues. We present an online survey which targets the players of World of Warcraft. Players?? relationships with real money trading are investigated using insights from behavioural economics. We propose a model which includes a set of behavioural determinants grounded in empirical research on online games. The study??s findings indicate that a player??s social status and the disinhibiting effects of online play are positive influences on players?? intentions to engage in real money trading, while perceived fairness, anticipated regret and uncertainty about the seller??s behaviour are negative influences. Interestingly, neither the perceived enjoyment nor the potential punishments influence intentions.     

A comparison of distribution and composition of intramuscular fat in Duroc Jersey and Hampshire pigs at 100 kg liveweight

The intramuscular fat content, its fatty acid composition and that from the triglyceride fraction, were determined in several muscles from the right sides of four Duroc Jersey and four Hampshire castrated male pigs slaughtered at 100 kg liveweight. The Hampshires had heavier muscles than the Duroc Jersey pigs, but did not show significant differences in the percentages of intramuscular fat. The concentrations of linoleic acid were, in general, higher in the Hampshire than in the Duroc Jersey pigs and in many muscles the differences were highly significant (p < 0·05) The concentrations of linoleic acid (18:2) in the triglyceride fraction did not differ among muscles within breeds but were higher in Hampshire than in Duroc Jersey pigs.     

Mathematical modeling of the freezing process of concrete and aggregates

A two dimensional finite element computer model for predicting frost penetration in saturated porous materials is presented. Based on available second order parabolic differential equations for predicting temperature and moisture content, and equations relating freezing temperature and water potential, the finite element model predicts temperature, water and ice content, frost penetration, and generated pore water pressures as a function of freezing time and given boundary conditions, Comparison of models results with available experimental data for mature cement pastes rendered reasonable agreement between the model and data.     

Applying a formal analysis technique to the CCITT X.509 strong two-way authentication protocol

In the quest for open systems, standardization of security mechanisms, framework, and protocols are becoming increasingly important. This puts high demands on the correctness of the standards. In this paper we use a formal logic-based approach to protocol analysis introduced by Burrows et al. [1]. We extend this logic to deal with protocols using public key cryptography, and with the notion of duration to capture some time-related aspects. The extended logic is used to analyse an important CCITT standard, the X.509 Authentication Framework. We conclude that protocol analysis can benefit from the use of the notation and that it highlights important aspects of the protocol analysed. Some aspects of the formalism need further study.This research was sponsored by the Royal Norwegian Council for Scientific and Industrial Research under Grant IT 0333.22222, and was performed while K. Gaarder was at Alcatel STK Research Centre.     

Feature selection via sensitivity analysis of SVM probabilistic outputs

Feature selection is an important aspect of solving data-mining and machine-learning problems. This paper proposes a feature-selection method for the Support Vector Machine (SVM) learning. Like most feature-selection methods, the proposed method ranks all features in decreasing order of importance so that more relevant features can be identified. It uses a novel criterion based on the probabilistic outputs of SVM. This criterion, termed Feature-based Sensitivity of Posterior Probabilities (FSPP), evaluates the importance of a specific feature by computing the aggregate value, over the feature space, of the absolute difference of the probabilistic outputs of SVM with and without the feature. The exact form of this criterion is not easily computable and approximation is needed. Four approximations, FSPP1-FSPP4, are proposed for this purpose. The first two approximations evaluate the criterion by randomly permuting the values of the feature among samples of the training data. They differ in their choices of the mapping function from standard SVM output to its probabilistic output: FSPP1 uses a simple threshold function while FSPP2 uses a sigmoid function. The second two directly approximate the criterion but differ in the smoothness assumptions of criterion with respect to the features. The performance of these approximations, used in an overall feature-selection scheme, is then evaluated on various artificial problems and real-world problems, including datasets from the recent Neural Information Processing Systems (NIPS) feature selection competition. FSPP1-3 show good performance consistently with FSPP2 being the best overall by a slight margin. The performance of FSPP2 is competitive with some of the best performing feature-selection methods in the literature on the datasets that we have tested. Its associated computations are modest and hence it is suitable as a feature-selection method for SVM applications. Editor: Risto Miikkulainen.     

A case study comparing defect profiles of a reused framework and of applications reusing it

The benefits of software reuse have been studied for many years. Several previous studies have observed that reused software has a lower defect density than newly built software. However, few studies have investigated empirically the reasons for this phenomenon. To date, we have only the common sense observation that as software is reused over time, the fixed defects will accumulate and will result in high-quality software. This paper reports on an industrial case study in a large Norwegian Oil and Gas company, involving a reused Java class framework and two applications that use that framework. We analyzed all trouble reports from the use of the framework and the applications according to the Orthogonal Defect Classification (ODC), followed by a qualitative Root Cause Analysis (RCA). The results reveal that the framework has a much lower defect density in total than one application and a slightly higher defect density than the other. In addition, the defect densities of the most severe defects of the reused framework are similar to those of the applications that are reusing it. The results of the ODC and RCA analyses reveal that systematic reuse (i.e. clearly defined and stable requirements, better design, hesitance to change, and solid testing) lead to lower defect densities of the functional-type defects in the reused framework than in applications that are reusing it. However, the different “nature” of the framework and the applications (e.g. interaction with other software, number and complexity of business logic, and functionality of the software) may confound the causal relationship between systematic reuse and the lower defect density of the reused software. Using the results of the study as a basis, we present an improved overall cause–effect model between systematic reuse and lower defect density that will facilitate further studies and implementations of software reuse.

Robust artificial life via artificial programmed death

We propose a novel approach to self-regenerating continuously-operating systems. Such systems provide best-case solutions in security surveillance or decision making centers. We introduce HADES, a self-regenerating system whose agents acknowledge their “citizenship” or faithfulness to the good of the system and are able to monitor their environment. When agents of HADES find irregularity in themselves they first try to repair, and will self-kill if repair fails. When an agent senses that there are persistent malfunctioning agents in its environment, it sends messages to entice them to self-kill. The neighbors then proceed to generate new healthy agents to replace the killed agent. We experiment with HADES on various impairments including the most difficult one of excessive regeneration of irregular aggressive agents. These agents may use all of the system's resources and thus take over the system, reminiscent of biologically grown tumors. We study how irregular growth may occur and then develop protocols of killing these agents to optimize the system's longevity. While some of the inspiration is from the immune system and tumor therapy, we contribute to the field of AI by introducing protocols for system robustness via the notion of active citizenship and the fundamental property of programmed death.     

Resolving force indeterminacy in contact dynamics using compatibility conditions

Contact dynamics (CD) is a powerful method to solve the dynamics of large systems of colliding rigid bodies. CD can be computationally more efficient than classical penalty-based discrete element methods (DEM) for simulating contact between stiff materials such as rock, glass, or engineering metals. However, by idealizing bodies as perfectly rigid, contact forces computed by CD can be non-unique due to indeterminacy in the contact network, which is a common occurence in dense granular flows. We propose a frictionless CD method that is designed to identify only the unique set of contact forces that would be predicted by a soft particle method, such as DEM, in the limit of large stiffness. The method involves applying an elastic compatibility condition to the contact forces, which maintains no-penetration constraints but filters out force distributions that could not have arisen from stiff elastic contacts. The method can be used as a post-processing step that could be integrated into existing CD codes with minimal effort. We demonstrate its efficacy in a variety of indeterminate problems, including some involving multiple materials, non-spherical shapes, and nonlinear contact constitutive laws.     

Inactivating microorganisms using a pulsed electric fieldcontinuous treatment system

High-voltage pulsed electric fields (PEFs) can be used to inactivate microorganisms in liquids. Applying PEF technology to food pasteurization is a promising nonthermal method, which may radically change food preservation processes and provide consumers with microbiologically safe, minimally processed, fresh-like products. A continuous-flow system in a laboratory-size prototype was constructed for the nonthermal pasteurization of liquid foods with PEF technology. Major components in the prototype include a high-voltage repetitive pulse generator, a coaxial liquid food treatment chamber, a fiber-optic temperature sensing instrument and a data acquisition system. Microbial inactivation tests were conducted in the continuous PEF treatment system. Repetitive high-voltage pulses with an exponential decaying waveshape were applied to the liquid food which was pumped through the treatment chamber. Test microorganisms selected for inactivation were Escherichia coli, Staphylococcus aureus and Saccharomyces cerevisiae. Over 6-order-of-magnitude reductions in the viability of selected microorganisms were achieved while the food temperature was maintained below 40°C     

Cluster analysis and disease mapping--why, when, and how? A step by step guide

Growing public awareness of environmental hazards has led to an increased demand for public health authorities to investigate geographical clustering of diseases. Although such cluster analysis is nearly always ineffective in identifying causes of disease, it often has to be used to address public concern about environmental hazards. Interpreting the resulting data is not straightforward, however, and this paper presents a guide for the non-specialist. The pitfalls include the fact that cluster analyses are usually done post hoc, and not as a result of a prior hypothesis. This is particularly true for investigations prompted by reported clusters, which have the inherent danger of overestimating the disease rate through "boundary shrinkage" of the population from which the cases are assumed to have arisen. In disease surveillance the problem of making multiple comparisons can be overcome by testing for clustering and autocorrelation. When rates of disease are illustrated in disease maps undue focus on areas where random fluctuation is greatest can be minimised by smoothing techniques. Despite the fact that cluster analyses rarely prove fruitful in identifying causation, they may-like single case reports-have the potential to generate new knowledge.