An exploratory study of open source software based on public project archives

This thesis conducts an exploratory study of Open Source Software (OSS) from various perspectives in order to discover and demonstrate fertile research areas in OSS that can benefit from the public archives of OSS projects. It follows a horizontal research method which combines theoretical model building with empirical data analysis. On the theoretical side, it classifies existing quantitative OSS studies and categorizes the public archives. It defines the concept of an OSS project by delineating its four critical components--community, methodology, products, and services. It specifies the roles in OSS communities, examines the speed, cost, and quality of OSS development, and reveals the impacts of programming languages on software projects. Most importantly, it originates a new approach to OSS adoption research which comprises strategic level study of OSS adoption and an assessment framework for OSS projects. A rich set of propositions are formulated for future study. On the empirical side, it analyzes summary statistics of 48,331 OSS projects and more detailed attributes of 1,907 projects which use Python as one of their programming languages. It depicts the portraits of OSS projects in general, and Python projects in specific. Caveats, for pitfalls and weaknesses of OSS projects discovered from the analysis, such as small development teams and competing projects, are announced with suggestions for improvements. Inspiring original ideas into a burgeoning research domain, this thesis contributes to both comprehension and practice of OSS

Multimodal estimation of distribution algorithms

Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed, which operate at the niche level. Then these two algorithms are equipped with three distinctive techniques: 1) a dynamic cluster sizing strategy; 2) an alternative utilization of Gaussian and Cauchy distributions to generate offspring; and 3) an adaptive local search. The dynamic cluster sizing affords a potential balance between exploration and exploitation and reduces the sensitivity to the cluster size in the niching methods. Taking advantages of Gaussian and Cauchy distributions, we generate the offspring at the niche level through alternatively using these two distributions. Such utilization can also potentially offer a balance between exploration and exploitation. Further, solution accuracy is enhanced through a new local search scheme probabilistically conducted around seeds of niches with probabilities determined self-adaptively according to fitness values of these seeds. Extensive experiments conducted on 20 benchmark multimodal problems confirm that both algorithms can achieve competitive performance compared with several state-of-the-art multimodal algorithms, which is supported by nonparametric tests. Especially, the proposed algorithms are very promising for complex problems with many local optima

Empirical Analysis on the International Competitiveness Of Shannxi Agricultural Product

On the foundation of reviewing the trade status of Shannxi agricultural product, adopting Trade Competitive Index, Revealed Comparative Advantage Index and Competitive Advantage Index, this paper calculates and evaluates the international competitiveness of Shannxi agricultural product from 1997 to 2004. It is found that from TC and CA indexes, Shannxi agriculture product is in an advantage position of international competition, while from the RCA index, it doesn’t have a very strong international advantage. In actual application, according to the trade situation of Shannxi agriculture product, we should consider synthetically the calculation results of these three indexes and develop the competitive advantage on the basis of comparative advantage. The conclusion supplies actual mentalities for promoting the international competitiveness of Shannxi agricultural product. Key words: Empirical analysis, International competitiveness, Shannxi agricultural product Résumé: Sur la base de la rétrospection du statut commercial du produit agricole du Shannxi et en adoptant l’Index compétitif du commerce, l’Index de l’avantage comparatif révélé et l’Index de l’avantage compétitif, cet essai calcule et évalue la compétitivité internaionale du produit agricole du Shannxi de 1997 à 2004. On trouve que, selon le premier et le troisième indexs, le produit agricole du Shannxi occupe une position avantageuse dans la compétition internationale, alors que d’après le deuxième index, il ne possède pas un avantage international très solide. Dans l’application actuelle, conformément à la situation du commerce du produit agricole du Shannxi, on doit considérer synthétiquement les résultats de calculation des trois index et développer l’avantage compétitif sur la base de l’avantage comparatif. La conclusion justifie les mentalités actuelles qui insistent à promouvoir la compétitivité internationale du produit agricole du Shannxi. Mots-Clés: analyse empirique, compétitivité internationale, produit agricole du Shannx

Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment

No-reference point cloud quality assessment (NR-PCQA) aims to automatically evaluate the perceptual quality of distorted point clouds without available reference, which have achieved tremendous improvements due to the utilization of deep neural networks. However, learning-based NR-PCQA methods suffer from the scarcity of labeled data and usually perform suboptimally in terms of generalization. To solve the problem, we propose a novel contrastive pre-training framework tailored for PCQA (CoPA), which enables the pre-trained model to learn quality-aware representations from unlabeled data. To obtain anchors in the representation space, we project point clouds with different distortions into images and randomly mix their local patches to form mixed images with multiple distortions. Utilizing the generated anchors, we constrain the pre-training process via a quality-aware contrastive loss following the philosophy that perceptual quality is closely related to both content and distortion. Furthermore, in the model fine-tuning stage, we propose a semantic-guided multi-view fusion module to effectively integrate the features of projected images from multiple perspectives. Extensive experiments show that our method outperforms the state-of-the-art PCQA methods on popular benchmarks. Further investigations demonstrate that CoPA can also benefit existing learning-based PCQA models