Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on ...
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Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for *** paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source *** proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive *** process involves graph construction,feature learning through graph embedding and LSTM,and defect *** evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.
Online advertising stands as a significant revenue source of the Internet. Recently, the trend among advertisers tilting towards the use of auto-bidding tools has heralded the emergence of a new model of bidders opera...
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This paper addresses the security vulnerabilities present in current translation tools for synchronous data flow languages and proposes a trustworthy translation method designed to eliminate implicit type conversions....
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ISBN:
(数字)9798331528829
ISBN:
(纸本)9798331528836
This paper addresses the security vulnerabilities present in current translation tools for synchronous data flow languages and proposes a trustworthy translation method designed to eliminate implicit type conversions. First, the paper identifies and analyzes limitations in existing tools, particularly regarding secure coding standards. An enhanced translation algorithm is then developed using the Coq theorem prover, which modifies the Ctemp abstract syntax tree to eliminate implicit type conversions in the generated code. Subsequently, formal verification is applied to the translation functions to establish the correctness of the translation process. Finally, the implementation of a trusted translation tool for synchronous data flow languages is completed and tested rigorously. Experimental results demonstrate that this approach effectively eliminates implicit types in the generated code, significantly improving code security. This work integrates secure coding standards with synchronous data flow language translation, offering new approaches and methodologies for trusted compilation in the synchronous data flow language domain.
Due to strong learning ability,convolutional neural networks(CNNs)have been developed in image ***,convolutional operations may change original distributions of noise in corrupted images,which may increase training di...
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Due to strong learning ability,convolutional neural networks(CNNs)have been developed in image ***,convolutional operations may change original distributions of noise in corrupted images,which may increase training difficulty in image *** relations of surrounding pixels can effectively resolve this *** by that,we propose a robust deformed denoising CNN(RDDCNN)in this *** proposed RDDCNN contains three blocks:a deformable block(DB),an enhanced block(EB)and a residual block(RB).The DB can extract more representative noise features via a deformable learnable kernel and stacked convolutional architecture,according to relations of surrounding *** EB can facilitate contextual interaction through a dilated convolution and a novel combination of convolutional layers,batch normalisation(BN)and ReLU,which can enhance the learning ability of the proposed *** address long-term dependency problem,the RB is used to enhance the memory ability of shallow layer on deep layers and construct a clean ***,we implement a blind denoising *** results demonstrate that our denoising model outperforms popular denoising methods in terms of qualitative and quantitative *** can be obtained at https://***/hellloxiaotian/RDDCNN.
In this paper, we investigate the competitive content placement problem in Mobile Edge Caching (MEC) systems, where Edge Data Providers (EDPs) cache appropriate contents and trade them with requesters at a suitable pr...
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The rise of smart cities is directly connected to the increasing use of vehicles. The growing vehicle utilization has driven the emergence of Vehicular Ad-hoc Networks (VANETs), facilitating instant information exchan...
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This paper addresses the security vulnerabilities present in current translation tools for synchronous data flow languages and proposes a trustworthy translation method designed to eliminate implicit type conversions....
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This work presents Dual-Stick, a novel controller with two sticks connected at the end that innovates a Dual-Ray interaction paradigm to enrich raycasting input in Virtual Reality (VR). Dual-Stick leverages the inhere...
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The reverse-time migration (RTM) imaging algorithm, which is used for complex underground structure analysis, is known as one of the current mainstream method of high-precision seismic imaging. Nowadays, as the fast d...
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The solar energy harvesting communication system is the basic support layer that is associated with the Internet of Things (IoT). However, the inherent stochasticity of solar energy brings about extensive research on ...
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