Measuring and Modeling Group Dynamics in Open-Source Software Development: A Tensor Decomposition Approach

Many open-source software projects depend on a few core developers, who take over both the bulk of coordination and programming tasks. They are supported by peripheral developers, who contribute either via discussions or programming tasks, often for a limited time. It is unclear what role these peripheral developers play in the programming and communication efforts, as well as the temporary task-related sub-groups in the projects. We mine code-repository data and mailing-list discussions to model the relationships and contributions of developers in a social network and devise a method to analyze the temporal collaboration structures in communication and programming, learning about the strength and stability of social sub-groups in open-source software projects. Our method uses multi-modal social networks on a series of time windows. Previous work has reduced the network structure representing developer collaboration to networks with only one type of interaction, which impedes the simultaneous analysis of more than one type of interaction. We use both communication and version-control data of open-source software projects and model different types of interaction over time. To demonstrate the practicability of our measurement and analysis method, we investigate 10 substantial and popular open-source software projects and show that, if sub-groups evolve, modeling these sub-groups helps predict the future evolution of interaction levels of programmers and groups of developers. Our method allows maintainers and other stakeholders of open-source software projects to assess instabilities and organizational changes in developer interaction and can be applied to different use cases in organizational analysis, such as understanding the dynamics of a specific incident or discussion.

Traffic Simulation with Open-Source and Commercial Traffic Microsimulators: A Case Study

The analysis of traffic problems in large urban centers often requires the use of computational tools, which give the possibility to make a more detailed analysis of the issue, suggest solutions, predict behaviors and, above all, support efficient decision-making. Transport microsimulation software programs are a handy set of tools for this type of analysis. This research paper shows a case study where functions and limitations of Aimsun version 8.2.0, a commercial-like European software and Sumo version 1.3.1, a European open-source software, are presented. The input and output data are similar in both software and the interpretation of results is quite intuitive for both, as well. However, Aimsun's graphical interface interprets results more user-friendly, because Sumo is an open-access software presented as an effective alternative tool for transport modeling.

Review on Different Software Tools for Deep Learning

Deep Learning Applications are being applied in various domains in recent years. Training a deep learning model is a very time consuming task. But, many open source frameworks are available to simplify this task. In this review paper we have discussed the features of some popular open source software tools available for deep learning along with their advantages and disadvantages. Software tools discussed in this paper are Tensorflow, Keras, Pytorch, Microsoft Cognitive Toolkit (CNTK). Keywords: Deep Learning, Frameworks, Open Source, Tensorflow, Pytorch, Keras, CNTK

IMPLEMENTASI R-SHINY UNTUK ANALISIS BIPLOT KOMPONEN UTAMA (Studi Kasus: Penggunaan Alat Kontrasepsi pada Peserta Aktif KB di Provinsi Jawa Tengah Tahun 2019)

The population problem is a fairly complex and complicated problem. Therefore, Indonesia seeks to control the birth rate with the Family Planning program. The implementation of this program can be evaluated through statistical data. The statistical analysis used is biplot principal component analysis to see the relationship between districts/cities in choosing the contraceptive device/method used, the variance of each contraceptive device/method, the correlation between contraceptive devices/methods, and the superiority value of the contraceptive device/method in the population. each district/city. The problem with performing the analysis is the limitations of easy-to-use open source software. As with R, users must understand writing code to perform data analysis. Therefore, to perform a biplot analysis of the principal components, an RShiny application has been created using RStudio. The R-Shiny that has been made has many advantages, including complete results which include data display, data transformation, SVD matrix, to graphs along with plot graph interpretation. The results of the principal component biplot analysis using R-Shiny with α =1 have the advantage of a good principal component biplot, which is 95.63%. This shows that the biplot interpretation of the main components produced can be explained well the relationship between the district/city and the contraceptive methods/devices used.

P1OVD: Patch-Based 1-Day Out-of-Bounds Vulnerabilities Detection Tool for Downstream Binaries

In the past decades, due to the popularity of cloning open-source software, 1-day vulnerabilities are prevalent among cyber-physical devices. Detection tools for 1-day vulnerabilities effectively protect users who fail to adopt 1-day vulnerability patches in time. However, manufacturers can non-standardly build the binaries from customized source codes to multiple architectures. The code variants in the downstream binaries decrease the accuracy of 1-day vulnerability detections, especially when signatures of out-of-bounds vulnerabilities contain incomplete information of vulnerabilities and patches. Motivated by the above observations, in this paper, we propose P1OVD, an effective patch-based 1-day out-of-bounds vulnerability detection tool for downstream binaries. P1OVD first generates signatures containing patch information and vulnerability root cause information. Then, P1OVD uses an accurate and robust matching algorithm to scan target binaries. We have evaluated P1OVD on 104 different versions of 30 out-of-bounds vulnerable functions and 620 target binaries in six different compilation environments. The results show that P1OVD achieved an accuracy of 83.06%. Compared to the widely used patch-level vulnerability detection tool ReDeBug, P1OVD ignores 4.07 unnecessary lines on average. The experiments on the x86_64 platform and the O0 optimization show that P1OVD increases the accuracy of the state-of-the-art tool, BinXray, by 8.74%. Besides, it can analyze a single binary in 4 s after a 20-s offline signature extraction on average.

Optical Force on a Metal Nanorod Exerted by a Photonic Jet

In this article, we study the optical force exerted on nanorods. In recent years, the capture of micro-nanoparticles has been a frontier topic in optics. A Photonic Jet (PJ) is an emerging subwavelength beam with excellent application prospects. This paper studies the optical force exerted by photonic jets generated by a plane wave illuminating a Generalized Luneburg Lens (GLLs) on nanorods. In the framework of the dipole approximation, the optical force on the nanorods is studied. The electric field of the photonic jet is calculated by the open-source software package DDSCAT developed based on the Discrete Dipole Approximation (DDA). In this paper, the effects of the nanorods’ orientation and dielectric constant on the transverse force Fx and longitudinal force Fy are analyzed. Numerical results show that the maximum value of the positive force and the negative force are equal and appear alternately at the position of the photonic jet. Therefore, to capture anisotropic nanoscale-geometries (nanorods), it is necessary to adjust the position of GLLs continuously. It is worth emphasizing that manipulations with nanorods will make it possible to create new materials at the nanoscale.

The Gap-Closing Estimand: A Causal Approach to Study Interventions That Close Disparities Across Social Categories

Disparities across race, gender, and class are important targets of descriptive research. But rather than only describe disparities, research would ideally inform interventions to close those gaps. The gap-closing estimand quantifies how much a gap (e.g., incomes by race) would close if we intervened to equalize a treatment (e.g., access to college). Drawing on causal decomposition analyses, this type of research question yields several benefits. First, gap-closing estimands place categories like race in a causal framework without making them play the role of the treatment (which is philosophically fraught for non-manipulable variables). Second, gap-closing estimands empower researchers to study disparities using new statistical and machine learning estimators designed for causal effects. Third, gap-closing estimands can directly inform policy: if we sampled from the population and actually changed treatment assignments, how much could we close gaps in outcomes? I provide open-source software (the R package gapclosing) to support these methods.

US government targets open-source software flaws

Headline US: Government targets open-source software flaws