ABSTRACT Developers utilize issue tracking systems to track ideas, feedback, tasks, and bugs for projects in the open‐source software ecosystem of GitHub. In this context, extensive bug reports and feature requests are raised as issues that need to be resolved. This makes issue resolution prediction become more and more important in project management. To address this problem, this paper constructed a multiple feature set from the perspectives of project, issue, and developer, by combining static and dynamic features of issues. Then, we refine a feature set based on the feature's importance. Furthermore, we proposed a method to explore what features and how these features affect the prediction of issue resolution time. Experiments are conducted on a dataset of 46,735 resolved issues from 18 popular GitHub projects to validate the effectiveness of the refined feature set. The results show that our prediction method outperforms the baseline methods.

In this article, different from previous traditional multi-exposure image fusion (MEF) algorithms that use hand-designed feature extraction approaches or deep learning-based algorithms that utilize convolutional neural networks for information preservation, we propose a novel multi-Exposure image fusion method via Adversarial learning and focal Transformer, named EAT. In our framework, a Focal Transformer is proposed to focus on more remarkable regions and construct long-range multi-exposure relationships, with which the fusion model can simultaneously extract local and global multi-exposure properties and therefore generate promising fusion results. To further improve the fusion performance, we introduce adversarial learning to train the proposed method in an adversarial manner with the guidance of ground truth. By doing so, the fused images exhibit better visual perception and color fidelity. Extensive experiments conducted on publicly available databases provide compelling evidence that EAT surpasses other state-of-the-art approaches on both quantitative and qualitative evaluations. Furthermore, we directly employ our trained model to address another benchmark MEF dataset. The impressive fusion performance serves as evidence of the credible generalization ability of EAT.

3D point cloud has been wildly used in security crucial domains, such as self-driving and 3D face recognition. Backdoor attack is a serious threat that usually destroy Deep Neural Networks (DNN) in the training stage. Though a few 3D backdoor attacks are designed to achieve guaranteed attack efficiency, their deformation will alarm human inspection. To obtain invisible backdoored point cloud, this paper proposes a novel 3D backdoor attack, named IBAPC, which generates backdoor trigger in the graph spectral domain. The effectiveness is grounded by the advantage of graph spectral signal that it can induce both global structure and local points to be responsible for the caused deformation in spatial domain. In detail, a new backdoor implanting function is proposed whose aim is to transform point cloud to graph spectral signal for conducting backdoor trigger. Then, we design a backdoor training procedure which updates the parameter of backdoor implanting function and victim 3D DNN alternately. Finally, the backdoored 3D DNN and its associated backdoor implanting function is obtained by finishing the backdoor training procedure. Experiment results suggest that IBAPC achieves SOTA attack stealthiness from three aspects including objective distance measurement, subjective human evaluation, graph spectral signal residual. At the same time, it obtains competitive attack efficiency. The code is available at https://github.com/f-lk/IBAPC.

Code review is an essential component of software development, playing a vital role in ensuring a comprehensive check of code changes. However, the continuous influx of pull requests and the limited pool of available reviewer candidates pose a significant challenge to the review process, making the task of assigning suitable reviewers to each review request increasingly difficult. To tackle this issue, we present MIRRec, a novel code reviewer recommendation method that leverages a hypergraph with multiplex relationships. MIRRec encodes high-order correlations that go beyond traditional pairwise connections using degree-free hyperedges among pull requests and developers. This way, it can capture high-order implicit connectivity and identify potential reviewers. To validate the effectiveness of MIRRec, we conducted experiments using a dataset comprising 48,374 pull requests from ten popular open-source software projects hosted on GitHub. The experiment results demonstrate that MIRRec, especially without PR-Review Commenters relationship, outperforms existing state-of-the-art code reviewer recommendation methods in terms of ACC and MRR, highlighting its significance in improving the code review process.

Abstract The phenomenon of crossover cooperation and convergence among services has gained increasing attention in the modern service industry. Service boundaries have been expansively stretched into other domains rather than limited to their original domains to achieve value creation, fostering the emergence of crossover services. Consequently, a complex service ecosystem takes shape. However, there is a lack of the convergence‐evolution mechanism of crossover services for the adaptive transformation of service providers' businesses in this context. To address this problem, this paper proposes population‐based and community‐based convergence‐evolution patterns from the ecological perspective. Based on the analysis of these evolution patterns and the driven force of service evolution, we propose an ecology‐oriented evolution analysis method. Furthermore, we devise an automated tool to support the evolution design of crossover service ecosystems. Case studies and evaluation experiments show the feasibility and effectiveness of our proposed method and the corresponding tool.

The fusion of infrared and visible images aims to generate a composite image that can simultaneously contain the thermal radiation information of an infrared image and the plentiful texture details of a visible image to detect targets under various weather conditions with a high spatial resolution of scenes. Previous deep fusion models were generally based on convolutional operations, resulting in a limited ability to represent long-range context information. In this paper, we propose a novel end-to-end model for infrared and visible image fusion via a dual attention Transformer termed DATFuse. To accurately examine the significant areas of the source images, a dual attention residual module (DARM) is designed for important feature extraction. To further model long-range dependencies, a Transformer module (TRM) is devised for global complementary information preservation. Moreover, a loss function that consists of three terms, namely, pixel loss, gradient loss, and structural loss, is designed to train the proposed model in an unsupervised manner. This can avoid manually designing complicated activity-level measurement and fusion strategies in traditional image fusion methods. Extensive experiments on public datasets reveal that our DATFuse outperforms other representative state-of-the-art approaches in both qualitative and quantitative assessments. The proposed model is also extended to address other infrared and visible image fusion tasks without fine-tuning, and the promising results demonstrate that it has good generalization ability. The source code is available athttps://github.com/tthinking/DATFuse.

Infrared and visible image fusion is aims to generate a composite image that can simultaneously describe the salient target in the infrared image and texture details in the visible image of the same scene. Since deep learning (DL) exhibits great feature extraction ability in computer vision tasks, it has also been widely employed in handling infrared and visible image fusion issue. However, the existing DL-based methods generally extract complementary information from source images through convolutional operations, which results in limited preservation of global features. To this end, we propose a novel infrared and visible image fusion method, i.e., the Y-shape dynamic Transformer (YDTR). Specifically, a dynamic Transformer module (DTRM) is designed to acquire not only the local features but also the significant context information. Furthermore, the proposed network is devised in a Y-shape to comprehensively maintain the thermal radiation information from the infrared image and scene details from the visible image. Considering the specific information provided by the source images, we design a loss function that consists of two terms to improve fusion quality: a structural similarity (SSIM) term and a spatial frequency (SF) term. Extensive experiments on mainstream datasets illustrate that the proposed method outperforms both classical and state-of-the-art approaches in both qualitative and quantitative assessments. We further extend the YDTR to address other infrared and RGB-visible images and multi-focus images without fine-tuning, and the satisfactory fusion results demonstrate that the proposed method has good generalization capability.

Owing to the limitations of imaging sensors, it is challenging to obtain a medical image that simultaneously contains functional metabolic information and structural tissue details. Multimodal medical image fusion, an effective way to merge the complementary information in different modalities, has become a significant technique to facilitate clinical diagnosis and surgical navigation. With powerful feature representation ability, deep learning (DL)-based methods have improved such fusion results but still have not achieved satisfactory performance. Specifically, existing DL-based methods generally depend on convolutional operations, which can well extract local patterns but have limited capability in preserving global context information. To compensate for this defect and achieve accurate fusion, we propose a novel unsupervised method to fuse multimodal medical images via a multiscale adaptive Transformer termed MATR. In the proposed method, instead of directly employing vanilla convolution, we introduce an adaptive convolution for adaptively modulating the convolutional kernel based on the global complementary context. To further model long-range dependencies, an adaptive Transformer is employed to enhance the global semantic extraction capability. Our network architecture is designed in a multiscale fashion so that useful multimodal information can be adequately acquired from the perspective of different scales. Moreover, an objective function composed of a structural loss and a region mutual information loss is devised to construct constraints for information preservation at both the structural-level and the feature-level. Extensive experiments on a mainstream database demonstrate that the proposed method outperforms other representative and state-of-the-art methods in terms of both visual quality and quantitative evaluation. We also extend the proposed method to address other biomedical image fusion issues, and the pleasing fusion results illustrate that MATR has good generalization capability. The code of the proposed method is available at https://github.com/tthinking/MATR.