A pull request(PR) is an event in Git where a contributor asks project maintainers to review code he/she wants to merge into a project. The PR mechanism greatly improves the efficiency of distributed software developm...
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A pull request(PR) is an event in Git where a contributor asks project maintainers to review code he/she wants to merge into a project. The PR mechanism greatly improves the efficiency of distributed software development in the opensource community. Nevertheless, the massive number of PRs in an open-source software(OSS) project increases the workload of developers. To reduce the burden on developers, many previous studies have investigated factors that affect the chance of PRs getting accepted and built prediction models based on these factors. However, most prediction models are built on the data after PRs are submitted for a while(e.g., comments on PRs), making them not useful in practice. Because integrators still need to spend a large amount of effort on inspecting PRs. In this study, we propose an approach named E-PRedictor(earlier PR predictor) to predict whether a PR will be merged when it is created. E-PRedictor combines three dimensions of manual statistic features(i.e., contributor profile, specific pull request, and project profile) and deep semantic features generated by BERT models based on the description and code changes of PRs. To evaluate the performance of E-PRedictor, we collect475192 PRs from 49 popular open-source projects on GitHub. The experiment results show that our proposed approach can effectively predict whether a PR will be merged or not. E-PRedictor outperforms the baseline models(e.g., Random Forest and VDCNN) built on manual features significantly. In terms of F1@Merge, F1@Reject, and AUC(area under the receiver operating characteristic curve), the performance of E-PRedictor is 90.1%, 60.5%, and 85.4%, respectively.
1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the c...
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1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the complex interactions between multiple individuals.
作者：
Li, ZhilinMa, XutongHu, MengzeYan, Jun
State Key Lab. of Computer Science Ins. of Software CAS University of Chinese Academy of Sciences Beijing China
State Key Lab. of Computer Science Ins. of Software CAS Beijing China
State Key Lab. of Computer Science Ins. of Software CAS Tech. Center of Software Eng. Ins. of Software CAS University of Chinese Academy of Sciences Beijing China
Sequence Containers (SC) in the C++ Standard Template Library (STL), such as the vector, are widely used in large-scale projects for their maintainability and flexibility. However, accessing the elements in an SC is b...
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ISBN:
(数字)9798400712487
ISBN:
(纸本)9798400712487
Sequence Containers (SC) in the C++ Standard Template Library (STL), such as the vector, are widely used in large-scale projects for their maintainability and flexibility. However, accessing the elements in an SC is bug-prone, as such operations will not check their boundaries during compilation or execution, which can lead to memory errors, such as buffer overflow problems. And these bugs are difficult to detect with availab.e static analyzers, since the size of SCs and the target of iterators cannot be precisely tracked without accurate analysis of the behavior of SCs and *** address this problem, we propose a combined model of SC sizes and iterator targets by tracking them simultaneously through a set of meta-operations extracted from corresponding method calls, and report improper operation usages according to three bug patterns. We implement the approach as a static analyzer, Scasa, on the top of the Clang Static Analyzer (CSA) framework, and evaluate its effectiveness and efficiency against CSA and other state-of-the-art static analyzers on a benchmark composed of 2,230 manually created code snippets and eight popular open-source C++ projects with a lot of SC usages. The experimental results reveal that Scasa effectively identifies nearly all inherent bugs within the manual code snippets and generates 125 reports for these projects (with a time loss of 5 - 85%) where 72 of them are marked as correct with a manual revision. And to further confirm these correct reports, we also select some important ones for developers. These results show that accessing elements of SCs is bug-prone, and cooperatively tracking SC sizes and iterator targets can accurately detect these bugs with acceptable overhead. Copyright held by the owner/author(s).
Interactive mobile augmented reality (MAR) applications such as Connected Lens are becoming popular, which often rely on deep neural network (NN)-based video analytics techniques to understand the real world. However,...
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Deploying deep convolutional neural network (CNN) to perform video analytics at edge poses a substantial system challenge, as running CNN inference incurs a prohibitive cost in computational resources. Model partition...
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1 *** superior performance of deep models in classification tasks relies heavily on large-scale supervision data with rich features[1].Recent research has shown that improving the feature diversity while expanding the...
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1 *** superior performance of deep models in classification tasks relies heavily on large-scale supervision data with rich features[1].Recent research has shown that improving the feature diversity while expanding the data scale can improve the classification performance[2,3].Time series augmentation possessing the dual strategy is essential in successfully applying deep models in time series classification.
Sharding is a promising technique to tackle the critical weakness of scalab.lity in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions cal...
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Sharding is a promising technique to tackle the critical weakness of scalab.lity in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalab.lity,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalab.lity brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.
Wearing a helmet is one of the effective measures to protect workers' safety. To address the challenges of severe occlusion, multi-scale, and small target issues in helmet detection, this paper proposes a helmet d...
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Wearing a helmet is one of the effective measures to protect workers' safety. To address the challenges of severe occlusion, multi-scale, and small target issues in helmet detection, this paper proposes a helmet detection algorithm based on deformable attention transformers. The main contributions of this paper are as follows. A compact end-to-end network architecture for safety helmet detection based on transformers is proposed. It cancels the computationally intensive transformer encoder module in the existing detection transformer(DETR) and uses the transformer decoder module directly on the output of feature extraction for query decoding, which effectively improves the efficiency of helmet detection. A novel feature extraction network named Swin transformer with deformable attention module(DSwin transformer) is proposed. By sparse cross-window attention, it enhances the contextual awareness of multi-scale features extracted by Swin transformer, and keeps high computational efficiency simultaneously. The proposed method generates the query reference points and query embeddings based on the joint prediction probabilities, and selects an appropriate number of decoding feature maps and sparse sampling points for query decoding, which further enhance the inference capability and processing speed. On the benchmark safety-helmet-wearing-dataset(SHWD), the proposed method achieves the average detection accuracy mAP@0.5 of 95.4% with 133.35G floating-point operations per second(FLOPs) and 20 frames per second(FPS), the state-of-the-art method for safety helmet detection.
Huazhong University of Science and Technology is building a cyclotron-based Proton Therapy Facility (HUST-PTF). The facility mainly consists of a 240 MeV superconducting cyclotron, a beam transport line, a fixed treat...
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Specially designed backlight systems can cast information from display screen to designated zone. Here we introduce an ultra-thin multi-directional backlight system. The main components of the system include microlens...
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