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.     

Electroclinical manifestations elicited by intracerebral electric stimulation "shocks" in temporal lobe epilepsy

In patients with severe drug-resistant partial epilepsy, undergoing Stereo-EEG investigations, spatial definition of the "epileptogenic area" is mainly based on spontaneous seizures recordings, but also on seizures induced by intracerebral electrical stimulation (ES). Only "trains" ES (TES, 50 pps) are currently used with this aim; "shocks" ES (SES, 1 pps) are principally applied to localize motor pathways. We have shown, during a prospective study concerning 10 temporal lobe epileptic patients, that SES could frequently induce seizures, especially when stimulation is applied in the anterior part of the Ammon's horn. Even if its efficacy seems lower than by TES, this kind of stimulation, in the majority of the cases, does reproduce isolated ictal subjective symptomatology, allowing the visualization of the progressive organisation of ictal electrical discharges, and avoids "unexpected" ("false positive"?) clinical responses.     

Optical activity and conformation of cobra neurotoxin

Cobra neurotoxin from Formosan cobra (Naja naja atra) venom is a compact globular protein having an intrinsic viscosity of 4.5 mL/g. The protein is stable in 7.5 M urea but can be denatured in 4.1 M guanidine hydrochloride or at elevated temperature (above 70 degrees C). Its conformation remains virtually the same in solvents of lower polarity than water such as 1,2-ethanediol or a mixed solvent of 1-propanol-1,2-ethanediol-water (5:1:1 by volume). The circular dichroism spectrum is "atypical" in water in that the peptide chromophores show a small negative circular dichroic (CD) band at 215 nm, a large positive one at 199 nm, and another large negative one below 190 nm. The CD pattern resembles to some extent that of a beta form but differs in both positions and magnitudes from the latter. It agrees qualitatively with the theoretical calculations of the reverse beta bends, suggesting that cobra toxin contains a considerable amount of beta turns and possibly a mixture of beta form and beta turns.     

Learning extended FSA from software: An empirical assessment

A number of techniques that infer finite state automata from execution traces have been used to support test and analysis activities. Some of these techniques can produce automata that integrate information about the data-flow, that is, they also represent how data values affect the operations executed by programs.The integration of information about operation sequences and data values into a unique model is indeed conceptually useful to accurately represent the behavior of a program. However, it is still unclear whether handling heterogeneous types of information, such as operation sequences and data values, necessarily produces higher quality models or not.In this paper, we present an empirical comparative study between techniques that infer simple automata and techniques that infer automata extended with information about data-flow. We investigate the effectiveness of these techniques when applied to traces with different levels of sparseness, produced by different software systems. To the best of our knowledge this is the first work that quantifies both the effect of adding data-flow information within automata and the effectiveness of the techniques when varying sparseness of traces.