Recently, many self-supervised pre-training methods have been proposed to improve the performance of deep neural networks (DNNs) for 3D point clouds processing. However, the common mechanism underlying the effectiveness of different pre-training methods remains unclear. In this paper, we use game-theoretic interactions as a unified approach to explore the common mechanism of pre-training methods. Specifically, we decompose the output score of a DNN into the sum of numerous effects of interactions, with each interaction representing a distinct 3D substructure of the input point cloud. Based on the decomposed interactions, we draw the following conclusions. (1) The common mechanism across different pre-training methods is that they enhance the strength of high-order interactions encoded by DNNs, which represent complex and global 3D structures, while reducing the strength of low-order interactions, which represent simple and local 3D structures. (2) Sufficient pre-training and adequate fine-tuning data for downstream tasks further reinforce the mechanism described above. (3) Pre-training methods carry a potential risk of reducing the transferability of features encoded by DNNs. Inspired by the observed common mechanism, we propose a new method to directly enhance the strength of high-order interactions and reduce the strength of low-order interactions encoded by DNNs, improving performance without the need for pre-training on large-scale datasets. Experiments show that our method achieves performance comparable to traditional pre-training methods.

Peer code review has become a must-have in modern software development. However, many code review requests (CRRs) could be a backlog for large-scale and active projects, blocking continuous integration and continuous delivery (CI/CD). Prioritizing CRRs to make the relevant ones to be reviewed first is a critical method for addressing this issue. Early studies have shown that many factors affect the review priority of a CRR, including its properties and relationships with other CRRs. However, the relationships, e.g., modifying the same files and sharing the same authors, are rarely considered when developing CRR prioritizers. In this paper, we propose a Graph-Powered Prioritizer (namely GPP) to make full use of the properties and relationships of CRRs. GPP uses the multi-graph structure to develop an initial representation of a collection of CRRs and uses the graph neural network algorithm to learn the prioritization-adapted representation, and eventually, outputs an ordered list of CRRs based on it. With experimental evaluation, we define relevant CRRs in the context of CI/CD as those that are likely to achieve three objectives, i.e., being merged while undergoing a few iterations in a short duration. We compare GPP against two rule-based and six learning-based prioritizers on 15 open-source software projects with more than 420K CRRs. The experimental results indicate that GPP outperforms the baselines on three basic ranking-aware evaluation metrics, including NDCG (82.94%), MRR (36.52%), and MAP (63.80%); while providing benefits in recommending the most relevant CRRs and balancing multiple objectives. Data&materials: https://figshare.com/s/133f23da558b7b254041

DJ DreamFactory is a web-based integrated platform for interactive broadcasting over the Internet. In DJ DreamFactory, users are able to set up Internet-based broadcasting stations with minimal effort. Audience not only can receive broadcasting programs from multiple stations through this unified platform, but can also "talk" and "write" to the broadcasters as well as to other audience via real-time text and/or audio interactions.

In this paper, we present a web-based aggregated platform, DJ DreamFactory, which enables average users to effortlessly participate in and contribute to interactive media broadcasting over the Internet. The platform overcomes several shortcomings of existing Internet-based broadcasting systems, such as inconvenience in channel surfing and content browsing due to the scattering and isolating of broadcasting stations, difficulties in setting up a broadcasting station, lack of communications between broadcasters and audience, and little support for personalized experience. The proposed platform facilitates users' media access by seamlessly aggregating sporadic broadcasting stations run by individual hosts, and enables a virtual community where grassroots users can contribute to media broadcasting, sharing, organizing and annotating through social networking. In addition, it supports real-time multimodal interaction between audience and hosts, provides customized services for both broadcasters and audience, supports personalized media experiences by mining and managing audience's preferences, and facilitates the organization of unstructured media data collections as well as collective human intelligence on the Web.