scholarly journals BurstBiRank: Co-Ranking Developers and Projects in GitHub with Complex Network Structures and Bursty Interactions

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Dengcheng Yan ◽  
Zhen Shao ◽  
Yiwen Zhang ◽  
Bin Qi
Keyword(s):  
Large Scale ◽  
Driving Forces ◽  
Research Direction ◽  
Technical System ◽  
Bipartite Network ◽  
Ranking Methods ◽  
Critical Nodes ◽  
Ranking Score ◽  
Complex Technical System ◽  
Collaborative Coding

With the wide adoption of social collaborative coding, more and more developers participate and collaborate on platforms such as GitHub through rich social and technical relationships, forming a large-scale complex technical system. Like the functionalities of critical nodes in other complex systems, influential developers and projects usually play an important role in driving this technical system to more optimized states with higher efficiency for software development, which makes it a meaningful research direction on identifying influential developers and projects in social collaborative coding platforms. However, traditional ranking methods seldom take into account the continuous interactions and the driving forces of human dynamics. In this paper, we combine the bursty interactions and the bipartite network structure between developers and projects and propose the BurstBiRank model. Firstly, the burstiness between each pair of developers and projects is calculated. Secondly, a weighted developer-project bipartite network is constructed using the burstiness as weight. Finally, an iterative score diffusion process is applied to this bipartite network and a final ranking score is obtained at the stationary state. The real-world case study on GitHub demonstrates the effectiveness of our proposed BurstBiRank and the outperformance of traditional ranking methods.

scholarly journals M-BiRank: Co-ranking developers and projects using multiple developer-project interactions in open source software community

2020 ◽  
Author(s):  
Dengcheng Yan ◽  
Bin Qi ◽  
Yiwen Zhang ◽  
Zhen Shao
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Real World ◽  
Single Layer ◽  
Bipartite Network ◽  
Degree Centrality ◽  
Ranking Methods ◽  
Ranking Model ◽  
Multiple Interactions ◽  
Collaborative Coding

Abstract Social collaborative coding is a popular trend in software development and such platforms as GitHub provides rich social and technical functionalities for developers to collaborate on open source projects through multiple interactions. Developers often follow popular developers and projects for learning, technical selection and collaboration. Thus identifying popular developers and projects is very meaningful. In this paper, we propose a multiplex bipartite network ranking model, M-BiRank, to co-rank developers and projects using multiple developer-project interactions. Firstly, multiple developer-project interactions such as commit, issue and watch is extracted and a multiplex developer-project bipartite network is constructed. Secondly, a random layer is selected from this multiplex bipartite network and initial ranking scores are calculated for developers and projects using BiRank. Finally, initial ranking scores diffuse to other layers and mutual reinforcement is taken into consideration to iteratively calculate ranking scores of developers and projects in different layers. Experiments on real world GitHub dataset show that M-BiRank outperforms degree centrality, traditional single layer ranking methods as well as multiplex ranking method.

Salt weathering and geoheritage - Sulfate efflorescences in historical underground quarries of tuff

2020 ◽  
Author(s):  
Luigi Germinario ◽  
Chiaki T. Oguchi
Keyword(s):  
Relative Humidity ◽  
Large Scale ◽  
Driving Forces ◽  
Vernacular Architecture ◽  
Research Direction ◽  
Salt Crystallization ◽  
Spatial And Temporal Variability ◽  
Salt Weathering ◽  
History Museum ◽  
Tourist Attractions

<p>One of the most popular and intensively extracted building stones in Japan is a Miocene dacite-rhyolite ignimbrite known as Oya-ishi, quarried nearby the city of Utsunomiya (Tochigi). Traces of its historical usage in the last 1,500 years survive in vernacular architecture, construction, rock-cut and relief sculpture, but large-scale exploitation commenced only in the Edo period (i.e., from the 17<sup>th</sup> century), an epoch of economic growth and flourishing arts and culture. Among the over 200 underground quarries in the region, few are still active, the others abandoned or converted into geoheritage and tourist attractions (e.g., History Museum, Heiwa Kannon monument, Keikan Park). Salt weathering is one of the decay aspects of Oya stone jeopardizing the preservation of those sites of historical and geological interest and, indirectly, visitor safety. The efflorescences on the tuff quarry walls turn out to be composed of sulfates, namely gypsum, mirabilite, and thenardite, their crystallization being controlled by the relevant microenvironmental conditions. In the extremely humid underground spaces, the phases having a very high deliquescence relative humidity are stable: gypsum is essentially ubiquitous, even in the deepest quarry levels, the most environmentally isolated; mirabilite needs a slightly dryer environment, so is observable in the middle levels or semi-underground quarries; thenardite requires further dryer conditions, and is mainly detected in the open air. The mechanisms of formation of these efflorescences are still under investigation: the classic minero-petrographic and geochemical characterization of the rock and its weathering phases is being supported by a microclimatic monitoring in different sites and seasons, and the chemical analysis of rainwater and groundwater. The research direction is aimed at the identification of the environmental and lithological constraints on the salt weathering of Oya tuff, that is: the spatial and temporal variability of relative humidity, and its influence on the cycles of salt crystallization/dissolution and the resulting mechanical stresses; the chemical driving forces, related to the rock mineralogy (zeolites, feldspar alteration, etc.) and water quality.</p>

scholarly journals M-BiRank: co-ranking developers and projects using multiple developer-project interactions in open source software community

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Dengcheng Yan ◽  
Bin Qi ◽  
Yiwen Zhang ◽  
Zhen Shao
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Real World ◽  
Single Layer ◽  
Bipartite Network ◽  
Degree Centrality ◽  
Ranking Methods ◽  
Ranking Model ◽  
Multiple Interactions ◽  
Collaborative Coding

Abstract Social collaborative coding is a popular trend in software development, and such platforms as GitHub provide rich social and technical functionalities for developers to collaborate on open source projects through multiple interactions. Developers often follow popular developers and projects for learning, technical selection, and collaboration. Thus, identifying popular developers and projects is very meaningful. In this paper, we propose a multiplex bipartite network ranking model, M-BiRank, to co-rank developers and projects using multiple developer-project interactions. Firstly, multiple developer-project interactions such as commit, issue, and watch are extracted and a multiplex developer-project bipartite network is constructed. Secondly, a random layer is selected from this multiplex bipartite network and initial ranking scores are calculated for developers and projects using BiRank. Finally, initial ranking scores diffuse to other layers and mutual reinforcement is taken into consideration to iteratively calculate ranking scores of developers and projects in different layers. Experiments on real-world GitHub dataset show that M-BiRank outperforms degree centrality, traditional single layer ranking methods, and multiplex ranking method.

scholarly journals M-BiRank: Co-ranking developers and projects using multiple developer-project interactions in open source software community

2020 ◽  
Author(s):  
Dengcheng Yan ◽  
Bin Qi ◽  
Yiwen Zhang ◽  
Zhen Shao
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Real World ◽  
Single Layer ◽  
Bipartite Network ◽  
Degree Centrality ◽  
Ranking Methods ◽  
Ranking Model ◽  
Multiple Interactions ◽  
Collaborative Coding

Abstract Social collaborative coding is a popular trend in software development and such platforms as GitHub provides rich social and technical functionalities for developers to collaborate on open source projects through multiple interactions. Developers often follow popular developers and projects for learning, technical selection and collaboration. Thus identifying popular developers and projects is very meaningful. In this paper, we propose a multiplex bipartite network ranking model, M-BiRank, to co-rank developers and projects using multiple developer-project interactions. Firstly, multiple developer-project interactions such as commit, issue and watch is extracted and a multiplex developer-project bipartite network is constructed. Secondly, a random layer is selected from this multiplex bipartite network and initial ranking scores are calculated for developers and projects using BiRank. Finally, initial ranking scores diffuse to other layers and mutual reinforcement is taken into consideration to iteratively calculate ranking scores of developers and projects in different layers. Experiments on real world GitHub dataset show that M-BiRank outperforms degree centrality, traditional single layer ranking methods as well as multiplex ranking method.

scholarly journals Land Use Transition and Driving Forces in Chinese Loess Plateau: A Case Study from Pu County, Shanxi Province

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 67
Author(s):  
Han Huang ◽  
Yang Zhou ◽  
Mingjie Qian ◽  
Zhaoqi Zeng
Keyword(s):  
Land Use ◽  
Food Security ◽  
Loess Plateau ◽  
Large Scale ◽  
Driving Forces ◽  
Chinese Loess Plateau ◽  
Shanxi Province ◽  
Forest Land ◽  
Land Use Transition ◽  
Macro Scale

Land use transition is essentially one of the manifestations of land use/cover change (LUCC). Although a large number of studies have focused on land use transitions on the macro scale, there are few studies on the micro scale. Based on the data of two high-resolution land use surveys, this study used a land use transfer matrix and GeoDetector model to explore the spatial-temporal patterns and driving forces of land use transitions at the village level in Pu County over a ten-year period. Results show that Pu County has experienced a drastic process of land use transition. More than 80% of cropland and grassland have been converted to forest land, and over 90% of the expansion of built-up land came from the occupation of forest land, cropland, and grassland. The driving forces of land use transition and its magnitude depended on the type of land use. The implementation of the policy of returning farmland to forest, or grain-for-green (GFG) was the main driving force for the large-scale conversion of cultivated land to forest land in Pu County. In the context of policy of returning farmland to forests, the hilly and gully regions of China’s Loess Plateau must balance between protecting the ecology and ensuring food security. Promoting the comprehensive consolidation of gully land and developing modern agriculture may be an important way to achieve a win-win goal of ecological protection and food security.

DIGITAL DESIGN OPTIMIZATION OF A COMPLEX TECHNOLOGICAL SYSTEM

2021 ◽  
Vol 3 ◽  
pp. 85-90
Author(s):  
A.R. ABLAEV ◽  
E.V. KHROMOV ◽  
R.R. ABLAEV ◽  
A.P. POLYAKOV
Keyword(s):  
Design Optimization ◽  
Level Structure ◽  
Phenomenological Approach ◽  
Digital Design ◽  
Technical System ◽  
Software Support ◽  
Complex Technical System ◽  
Complex Optimization ◽  
Complex Technical Systems ◽  
Technical Systems

The article investigates the issue of optimization of a complex technical system at the stage of its design using a heuristic–phenomenological approach. The analysis of the principles of complex optimization of complex technical systems is carried out. A four–level structure for the synthesis of methodological, informational and software support for complex optimization of complex technical systems is proposed, which will allow controlling the programmable parameters of complex technical systems at each stage of their design.

scholarly journals Bottlenecks, Modules, and Dynamic Architectural Capabilities

2018 ◽  
Author(s):  
Carliss Y. Baldwin
Keyword(s):  
Property Rights ◽  
Value Creation ◽  
Technical System ◽  
Maximum Effect ◽  
Organizational Boundaries ◽  
Value Capture ◽  
Technical Problems ◽  
Technical Architecture ◽  
Complex Technical System ◽  
Technical Systems

How do firms create and capture value in large technical systems? In this paper, I argue that the points of both value creation and value capture are the system’s bottlenecks. Bottlenecks arise first as important technical problems to be solved. Once the problem is solved, Then the solution in combination with organizational boundaries and property rights can be used to capture a stream of rents. The tools a firm can use to manage bottlenecks are, first, an understanding first of the technical architecture of the system; and, second, an understanding of the industry architecture in which the technical system is embedded. Although these tools involve disparate bodies of knowledge, they must be used in tandem to achieve maximum effect. Dynamic architectural capabilities provide managers with the ability to see a complex technical system in an abstract way and change the system’s structure to manage bottlenecks and modules in conjunction with the firm’s organizational boundaries and property rights.

scholarly journals FEATURES OF DIAGNOSTIC ARTIFICIAL NEURAL NETWORKS FOR HYBRID EXPERT SYSTEMS

2019 ◽  
Vol 26 ◽  
pp. 36-46
Author(s):  
S. KONOVALOV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Expert Systems ◽  
Technical System ◽  
Bulletin Board ◽  
Confidence Factor ◽  
Complex Technical System ◽  
Artificial Neural

In the proposed article, various methods of constructing an artificial neural network as one of the components of a hybrid expert system for diagnosis were investigated. A review of foreign literature in recent years was conducted, where hybrid expert systems were considered as an integral part of complex technical systems in the field of security. The advantages and disadvantages of artificial neural networks are listed, and the main problems in creating hybrid expert systems for diagnostics are indicated, proving the relevance of further development of artificial neural networks for hybrid expert systems. The approaches to the analysis of natural language sentences, which are used for the work of hybrid expert systems with artificial neural networks, are considered. A bulletin board is shown, its structure and principle of operation are described. The structure of the bulletin board is divided into levels and sublevels. At sublevels, a confidence factor is applied. The dependence of the values of the confidence factor on the fulfillment of a particular condition is shown. The links between the levels and sublevels of the bulletin board are also described. As an artificial neural network architecture, the «key-threshold» model is used, the rule of neuron operation is shown. In addition, an artificial neural network has the property of training, based on the application of the penalty property, which is able to calculate depending on the accident situation. The behavior of a complex technical system, as well as its faulty states, are modeled using a model that describes the structure and behavior of a given system. To optimize the data of a complex technical system, an evolutionary algorithm is used to minimize the objective function. Solutions to the optimization problem consist of Pareto solution vectors. Optimization and training tasks are solved by using the Hopfield network. In general, a hybrid expert system is described using semantic networks, which consist of vertices and edges. The reference model of a complex technical system is stored in the knowledge base and updated during the acquisition of new knowledge. In an emergency, or about its premise, with the help of neural networks, a search is made for the cause and the control action necessary to eliminate the accident. The considered approaches, interacting with each other, can improve the operation of diagnostic artificial neural networks in the case of emergency management, showing more accurate data in a short time. In addition, the use of such a network for analyzing the state of health, as well as forecasting based on diagnostic data using the example of a complex technical system, is presented.

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