A data-driven game theoretic strategy for developers in software crowdsourcing: a case study

Crowdsourcing has the advantages of being cost-effective and saving time, which is a typical embodiment of collective wisdom and community workers’ collaborative development. However, this development paradigm of software crowdsourcing has not been used widely. A very important reason is that requesters have limited knowledge about crowd workers’ professional skills and qualities. Another reason is that the crowd workers in the competition cannot get the appropriate reward, which affects their motivation. To solve this problem, this paper proposes a method of maximizing reward based on the crowdsourcing ability of workers, they can choose tasks according to their own abilities to obtain appropriate bonuses. Our method includes two steps: Firstly, it puts forward a method to evaluate the crowd workers’ ability, then it analyzes the intensity of competition for tasks at Topcoder.com—an open community crowdsourcing platform—on the basis of the workers’ crowdsourcing ability; secondly, it follows dynamic programming ideas and builds game models under complete information in different cases, offering a strategy of reward maximization for workers by solving a mixed-strategy Nash equilibrium. This paper employs crowdsourcing data from Topcoder.com to carry out experiments. The experimental results show that the distribution of workers’ crowdsourcing ability is uneven, and to some extent it can show the activity degree of crowdsourcing tasks. Meanwhile, according to the strategy of reward maximization, a crowd worker can get the theoretically maximum reward

Photocatalytic Degradation of Toluene by Nano-TIO2 in a Fluidized Bed

The mixed gas of toluene and air was used as treated gas, self-made fluidized bed and ultraviolet lamp and TiO2/silica gel prepared in laboratory used as photo reactor and light source and photo-catalyst respectively to degrade toluene in the treated gas. The effects of operating parameters including initial concentration, operating gas velocity, ultraviolet light intensity and bed layer height on degradation rate of toluene have been probed into in this research. The results showed that degradation rate of toluene was independent of initial concentration (20～55mg/m3) but decreased with higher initial concentration; the degradation rate of toluene increased with gas velocity (below 3.98cm·s-1) but decreased in case of higher gas velocity(above 3.98cm•s-1). The results also showed that the degradation rate of toluene increased with higher light intensity or bed layer height

Topic-based integrator matching for pull request

Pull Request (PR) is the main method for code contributions from the external contributors in GitHub. PR review is an essential part of open source software developments to maintain the quality of software. Matching a new PR for an appropriate integrator will make the PR reviewing more effective. However, PR and integrator matching are now organized manually in GitHub. To make this process more efficient, we propose a Topic-based Integrator Matching Algorithm (TIMA) to predict highly relevant collaborators(the core developers) as the integrator to incoming PRs . TIMA takes full advantage of the textual semantics of PRs. To define the relationships between topics and collaborators, TIMA builds a relation matrix about topic and collaborators. According to the relevance between topics and collaborators, TIMA matches the suitable collaborators as the PR integrator

An oil painters recognition method based on cluster multiple kernel learning algorithm

A lot of image processing research works focus on natural images, such as in classification, clustering, and the research on the recognition of artworks (such as oil paintings), from feature extraction to classifier design, is relatively few. This paper focuses on oil painter recognition and tries to find the mobile application to recognize the painter. This paper proposes a cluster multiple kernel learning algorithm, which extracts oil painting features from three aspects: color, texture, and spatial layout, and generates multiple candidate kernels with different kernel functions. With the results of clustering numerous candidate kernels, we selected the sub-kernels with better classification performance, and use the traditional multiple kernel learning algorithm to carry out the multi-feature fusion classification. The algorithm achieves a better result on the Painting91 than using traditional multiple kernel learning directly

Steady State of Pedestrian Flow in Bottleneck Experiments

Experiments with pedestrians could depend strongly on initial conditions. Comparisons of the results of such experiments require to distinguish carefully between transient state and steady state. In this work, a feasible algorithm - Cumulative Sum Control Chart - is proposed and improved to automatically detect steady states from density and speed time series of bottleneck experiments. The threshold of the detection parameter in the algorithm is calibrated using an autoregressive model. Comparing the detected steady states with previous manually selected ones, the modified algorithm gives more reproducible results. For the applications, three groups of bottleneck experiments are analysed and the steady states are detected. The study about pedestrian flow shows that the difference between the flows in all states and in steady state mainly depends on the ratio of pedestrian number to bottleneck width. When the ratio is higher than a critical value (approximately 115 persons/m), the flow in all states is almost identical with the flow in steady state. Thus we have more possibilities to compare the flows from different experiments, especially when the detection of steady states is difficult.Comment: 19 pages, 7 figure

Empirical research on the evaluation model and method of sustainability of the open source ecosystem

The development of open source brings new thinking and production modes to software engineering and computer science, and establishes a software development method and ecological environment in which groups participate. Regardless of investors, developers, participants, and managers, they are most concerned about whether the Open Source Ecosystem can be sustainable to ensure that the ecosystem they choose will serve users for a long time. Moreover, the most important quality of the software ecosystem is sustainability, and it is also a research area in Symmetry. Therefore, it is significant to assess the sustainability of the Open Source Ecosystem. However, the current measurement of the sustainability of the Open Source Ecosystem lacks universal measurement indicators, as well as a method and a model. Therefore, this paper constructs an Evaluation Indicators System, which consists of three levels: The target level, the guideline level and the evaluation level, and takes openness, stability, activity, and extensibility as measurement indicators. On this basis, a weight calculation method, based on information contribution values and a Sustainability Assessment Model, is proposed. The models and methods are used to analyze the factors affecting the sustainability of Stack Overflow (SO) ecosystem. Through the analysis, we find that every indicator in the SO ecosystem is partaking in different development trends. The development trend of a single indicator does not represent the sustainable development trend of the whole ecosystem. It is necessary to consider all of the indicators to judge that ecosystem’s sustainability. The research on the sustainability of the Open Source Ecosystem is helpful for judging software health, measuring development efficiency and adjusting organizational structure. It also provides a reference for researchers who study the sustainability of software engineering