All about me: Disclosure in online social networking profiles: The case of FACEBOOK

The present research examined disclosure in online social networking profiles (i.e., FACEBOOK™). Three studies were conducted. First, a scoring tool was developed in order to comprehensively assess the content of the personal profiles. Second, grouping categories (default/standard information, sensitive personal information, and potentially stigmatizing information) were developed to examine information pertinent to identity threat, personal and group threat. Third, a grouping strategy was developed to include all information present in FACEBOOK™, but to organize it in a meaningful way as a function of the content that was presented. Overall, approximately 25% of all possible information that could potentially be disclosed by users was disclosed. Presenting personal information such as gender and age was related to disclosure of other sensitive and highly personal information. Age and relationship status were important factors in determining disclosure. As age increased, the amount of personal information in profiles decreased. Those seeking a relationship were at greatest risk of threat, and disclosed the greatest amount of highly sensitive and potentially stigmatizing information. These implications of these findings with respect to social and legal threats, and potential means for identifying users placing themselves at greatest risk, are discussed.     

A study of maintenance policies for second-hand products

This paper proposes two maintenance schemes for reused products. In order to reduce the purchasing cost or conform to the concept of environmental protection, reuse is considered as one of the most efficient strategies. However, the initial quality of reused product is often inferior to the new one and then product failures will occur frequently during usage period. Therefore, two periodical preventive maintenance (PM) policies in this paper are developed to decrease the high failure rate of the second-hand products. When a second-hand product with Weibull life time distribution of known age is intended to be used for a pre-specified period of time, the optimal number of PM actions and the corresponding maintenance degree are derived such that the expected total maintenance cost is minimized. The impact of providing preventive maintenance is illustrated through numerical examples.     

A low-order model of biological neural networks

A biologically plausible low-order model (LOM) of biological neural networks is proposed. LOM is a recurrent hierarchical network of models of dendritic nodes and trees; spiking and nonspiking neurons; unsupervised, supervised covariance and accumulative learning mechanisms; feedback connections; and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect, and recognize multiple and hierarchical corrupted, distorted, and occluded temporal and spatial patterns. Four models of dendritic nodes are given that are all described as a hyperbolic polynomial that acts like an exclusive-OR logic gate when the model dendritic nodes input two binary digits. A model dendritic encoder that is a network of model dendritic nodes encodes its inputs such that the resultant codes have an orthogonality property. Such codes are stored in synapses by unsupervised covariance learning, supervised covariance learning, or unsupervised accumulative learning, depending on the type of postsynaptic neuron. A masking matrix for a dendritic tree, whose upper part comprises model dendritic encoders, enables maximal generalization on corrupted, distorted, and occluded data. It is a mathematical organization and idealization of dendritic trees with overlapped and nested input vectors. A model nonspiking neuron transmits inhibitory graded signals to modulate its neighboring model spiking neurons. Model spiking neurons evaluate the subjective probability distribution (SPD) of the labels of the inputs to model dendritic encoders and generate spike trains with such SPDs as firing rates. Feedback connections from the same or higher layers with different numbers of unit-delay devices reflect different signal traveling times, enabling LOM to fully utilize temporally and spatially associated information. Biological plausibility of the component models is discussed. Numerical examples are given to demonstrate how LOM operates in retrieving, generalizing, and unsupervised and supervised learning.     

The relationship between online game playing motivation and selection of online game characters – the case of Taiwan

The alarming growth rate in the online game industry calls for a need of understanding the determinants of online gamers’ behaviour. Investigating how online gamers choose different online characters assists online game vendors to design characters that match the needs of different online gamers. Recently, numerous online game designers have introduced celebrity figures as characters for certain online games. The popularity of those celebrities has resulted in greater awareness of these games, and hopefully further increases the playability of online games. While different online gamers are motivated by different desires for playing, it is insufficiently researched whether these online celebrity characters have actually altered players’ choice in giving preference to these games. Thus, this study conducts an experimental design to study the reasons behind players’ choice of characters by studying how online games perceive the interpersonal attractiveness of celebrity online characters, and how online gamers with different playing motivations display different behaviours in their choice of online game characters. Results from the survey filled out by participants after the experiments show that the attractiveness of celebrity online characters is significantly higher than that of non-celebrity characters. Further, online game players with a social playing motivation tend to choose celebrities as their online game characters.     

Why and how developers fork what from whom in GitHub

Forking is the creation of a new software repository by copying another repository. Though forking is controversial in traditional open source software (OSS) community, it is encouraged and is a built-in feature in GitHub. Developers freely fork repositories, use codes as their own and make changes. A deep understanding of repository forking can provide important insights for OSS community and GitHub. In this paper, we explore why and how developers fork what from whom in GitHub. We collect a dataset containing 236,344 developers and 1,841,324 forks. We make surveys, and analyze programming languages and owners of forked repositories. Our main observations are: (1) Developers fork repositories to submit pull requests, fix bugs, add new features and keep copies etc. Developers find repositories to fork from various sources: search engines, external sites (e.g., Twitter, Reddit), social relationships, etc. More than 42 % of developers that we have surveyed agree that an automated recommendation tool is useful to help them pick repositories to fork, while more than 44.4 % of developers do not value a recommendation tool. Developers care about repository owners when they fork repositories. (2) A repository written in a developer’s preferred programming language is more likely to be forked. (3) Developers mostly fork repositories from creators. In comparison with unattractive repository owners, attractive repository owners have higher percentage of organizations, more followers and earlier registration in GitHub. Our results show that forking is mainly used for making contributions of original repositories, and it is beneficial for OSS community. Moreover, our results show the value of recommendation and provide important insights for GitHub to recommend repositories.     

Using ontologies to perform threat analysis and develop defensive strategies for mobile security

Existing studies on the detection of mobile malware have focused mainly on static analyses performed to examine the code-structure signature of viruses, rather than the dynamic behavioral aspects. By contrast, the unidentified behavior of new mobile viruses using the self-modification, polymorphic, and mutation techniques for variants have largely been ignored. The problem of precision regarding malware variant detection has become one of the key concerns in mobile security. Accordingly, the present study proposed a threat risk analysis model for mobile viruses, using a heuristic approach incorporating both malware behavior analysis and code analysis to generate a virus behavior ontology associated with the Protégé platform. The proposed model can not only explicitly identify an attack profile in accordance with structural signature of mobile viruses, but also overcome the uncertainty regarding the probability of an attack being successful. This model is able to achieve this by extending frequent episode rules to investigate the attack profile of a given malware, using specific event sequences associated with the sandbox technique for mobile applications (apps) and hosts. For probabilistic analysis, defense evaluation metrics for each node were used to simulate the results of an attack. The simulations focused specifically on the attack profile of a botnet to assess the threat risk. The validity of the proposed approach was demonstrated numerically by using two malware cyber-attack examples. Overall, the results presented in this paper prove that the proposed scheme offers an effective countermeasure, evaluated using a set of security metrics, for mitigating network threats by considering the interaction between the attack profiles and defense needs.     

Prediction of noisy chaotic time series using an optimal radialbasis function neural network

This paper considers the problem of optimum prediction of noisy chaotic time series using a basis function neural network, in particular, the radial basis function (RBF) network. In the noiseless environment, predicting a chaotic time series is equivalent to approximating a nonlinear function. The optimal generalization is achieved when the number of hidden units of a RBF predictor approaches infinity. When noise exists, it is shown that an optimal RBF predictor should use a finite number of hidden units. To determine the structure of an optimal RBF predictor, we propose a new technique called the cross-validated subspace method to estimate the optimum number of hidden units. While the subspace technique is used to identify a suitable number of hidden units by detecting the dimension of the subspace spanned by the signal eigenvectors, the cross validation method is applied to prevent the problem of overfitting. The effectiveness of this new method is evaluated using simulated noisy chaotic time series as well as real-life oceanic radar signals. Results show that the proposed method can find the correct number of hidden units of an RBF network for an optimal prediction.     

Moving cast shadow detection by exploiting multiple cues

Background subtraction or temporal differencing is commonly applied on an image sequence for foreground/background segmentation. However, cast shadows of moving foreground objects in a scene often result in detection errors for many vision-based applications. To address this problem, the authors propose an algorithm exploiting the information of colour, shading, texture, neighbourhood and temporal consistency to detect shadows efficiently and adaptively. The experimental results show that the proposed method can detect the penumbra as well as the umbra in different kinds of scenarios under various illumination conditions.