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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Liu, Chao Bao, Xuanlin Zhang, Hongyu Zhang, Neng Hu, Haibo Zhang, Xiaohong Yan, Meng School of Big Data and Software Engineering Chongqing University China School of Software Engineering Sun Yat-sen University China

ISBN: (纸本)9798350330663

Automated code generation is a powerful technique for software development, which can significantly reduce developers' effort and time for writing code. Recently, OpenAI's large language model ChatGPT has emerged as a powerful tool for generating human-like responses to a wide range of textual inputs (i.e., prompts), including those related to code generation. However, the effectiveness of ChatGPT in code generation is still not well understood. The code generation performance could also be heavily influenced by the choice of prompts, which should be further explored. In this paper, we report an empirical study on ChatGPT's capabilities for two types of code generation tasks, namely text-to-code and code-to-code generation. We investigate different types of prompts by leveraging the chain-of-thought strategy with multi-step optimizations. Our empirical results show that by carefully designing prompts to guide ChatGPT, the code generation performance can be improved substantially. We also analyze the factors that influence the prompt design and provide insights that could guide future research. © 2024 IEEE.

关键词： software design

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ConvNeXt-Mask2Former: A Semantic Segmentation Model for Land Classification in Remote Sensing Images 5

ConvNeXt-Mask2Former: A Semantic Segmentation Model for Land...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Zheng, Jing Zhejiang Wanli University Zjwl Big Data and Software Engineering Department Ningbo China

ISBN: (纸本)9798350373820

With the rapid development of remote sensing technology and deep learning, significant progress has been made in land classification of remote sensing images using semantic segmentation models. However, it still faces some deficiencies and challenges in terms of multi-scale features of images, the computational burden of high resolution images, and long-range dependencies. Therefore, in this paper, I design a semantic segmentation network based on encoder and decoder (Encoder-Decoder) architecture, using ConvNeXt as the encoder, i.e., the backbone network, and Mask2Former as the decoder, with the addition of an auxiliary segmentation module (ASM), and at the same time, an image enhancement pipeline is used to assist the training in the image preprocessing stage. Mask2Former is an architecture with good flexibility and generalization ability, which can be easily used in different visual tasks. At the same time, as a convolutional network without the addition of an attention mechanism, ConvNeXt has excellent feature extraction capabilities. I evaluate my model on the widely used high resolution image datasets Potsdam and Vaihinge and compare the results with the current mainstream semantic segmentation models, and the experimental results show that my model can be optimized for the accurate segmentation of land categories. Specifically, my model has an optimal mPA of 9 3. 7 9% and mIoU of 86.80% on the Potsdam dataset, and an optimal mPA of 82.32% and mIoU of 9 0. 0 5% on the Vaihinge dataset, which is both significantly higher than the existing models, and provide important references for land classification tasks and land use. © 2024 IEEE.

关键词： Classification (of information)

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Next POI Recommendation Based on Spatial and Temporal Disentanglement Representation

Next POI Recommendation Based on Spatial and Temporal Disent...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Tao, Hongjin Zeng, Jun Wang, Ziwei Gao, Min Wen, Junhao Chongqing University School of Big Data and Software Engineering China

ISBN: (纸本)9798350304855

The next Point-of-interest (POI) recommendation task is to predict the next POI that users may be interested in. POI check-in sequence implicitly reflects the user's location transition patterns, and the sequence modeling relies on the engineering of multiple independent features. Even though traditional POI recommendation models can fulfill the predicting task via entangled features, those black box models fail to mine the intrinsic check-in intention. The disentanglement representation learning method enables models to disentangle targeting intentions and provide interpretability for recommended results. However, existing disentanglement representation studies focus on disentangling user preference but neglect the entangled location characteristic. Besides, unrecorded check-ins result in inconsecutive transition sequences, which may influence their disentanglement qualities. Therefore, we proposed CrossDR, a Cross-sequence Location Disentanglement Representation method for the next POI recommendation, to explore how spatial and temporal factors influence check-in behaviors by a global view of location transitions. We apply disentanglement learning along with the time-masked data augmentation method and frequency-domain learning technique to further alleviate the short trajectory cold start problem caused by consecutive sequence generation. Experiments on two real-world datasets show our model has competitive capability compared to strong baselines. © 2023 IEEE.

关键词： Location

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BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation

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Frontiers of Computer Science 2023年第5期17卷 103-118页

作者： Jinwei LUO Mingkai HE Weike PAN Zhong MING College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen 518060China

Session-based recommendation(SBR)and multibehavior recommendation(MBR)are both important problems and have attracted the attention of many researchers and *** from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics,heterogeneous SBR(HSBR)that exploits different types of behavioral information(e.g.,examinations like clicks or browses,purchases,adds-to-carts and adds-to-favorites)in sequences is more consistent with real-world recommendation scenarios,but it is rarely *** efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of ***,all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of ***,all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of *** limitation hinders the development of HSBR and results in unsatisfactory *** a response,we propose a novel behavior-aware graph neural network(BGNN)for *** BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a ***,our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified *** then conduct extensive empirical studies on three real-world datasets,and find that our BGNN outperforms the best baseline by 21.87%,18.49%,and 37.16%on average correspondingly.A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our *** exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can e

关键词： session-based recommendation graph neural network heterogeneous behaviors

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A Helmet Detection Algorithm Based on Transformers with Deformable Attention Module

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Chinese Journal of Electronics 2025年第1期34卷 229-241页

作者： Songle Chen Hongbo Sun Yuxin Wu Lei Shang Xiukai Ruan Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Nanjing University of Posts and Telecommunications State Key Lab for Novel Software Technology Nanjing University Institute of Intelligent Locks Wenzhou University

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.

关键词： Head Accuracy Network architecture Benchmark testing Transformers Feature extraction Safety Decoding Computational efficiency Detection algorithms

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A novel thermally-stable single-phase phosphor Ca_(2)InTaO_(6):Dy^(3+)for NUV-excited white LEDs

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Journal of Rare Earths 2024年第10期42卷 1855-1864页

作者： Jian Hu Ruirui Cui Jun Zhang Peng Linghu Youzhen Shi Xiang Guo Key Laboratory of Micro-Nano-Electronics and Software Technology of Guizhou Province College of Big Data and Information EngineeringGuizhou UniversityGuiyang550025China College of Computer and Information Engineering Guizhou University of CommerceGuiyang550014China

The work focuses on the utilization of the conventional solid-state sintering procedure to synthesize white phosphors Ca_(2)InTaO_(6):xDy^(3+)(0.02≤x≤0.12).Utilizing X-ray diffraction,the phase structure of samples was examined,and the crystal structure was refined using the Rietveld method.A scanning electron microscope was used to analyze the microstructure of ***-principles calculations confirm that the indirect bandgap of Ca_(2)InTaO_(6)is 3.786 eV,The luminous properties and energy transfer mechanism of Ca_(2)InTaO_(6):xDy^(3+)were studied using photoluminescence ***^(4)F_(9/2)→^(6)H_(13/2)transition of Dy^(3+)ions is responsible for the greatest emission peak,which was measured at 575 *** to research,the lifespan falls as the concentration of Dy^(3+)doping amount rises because of frequent interaction and ene rgy transfer between Dy^(3+)*** correlated color temperature of the WLEDs packaged with Ca_(2)InTaO_(6):0.08Dy^(3+)is 4677 K and CIE 1931 chromaticity coordinates are(0.3578,0.3831).Meantime,the phosphor also shows outstanding te mperature stability property,which maintains 83.8%of its initial emission intensity at 450 K(activation energy of 0.1467 eV).The W-LEDs retain their performance for 100 min when powered at 3.4 V voltage and 600 mA current,demonstrating the packed W-LEDs'sustaine d operation at high temperatures.

关键词： Ca_(2)InTaO_(6):Dy^(3+)phosphors Thermal stability Photoluminescence W-LEDs Thermal imaging Rare earths

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Just-In-Time Method Name Updating With Heuristics and Neural Model 23

Just-In-Time Method Name Updating With Heuristics and Neural...

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23rd IEEE International Conference on software Quality, Reliability, and Security, QRS 2023

作者： Guo, Zhenting Yan, Meng Li, Hongyan Chen, Zhezhe Sun, Weifeng Chongqing University School of Big Data and Software Engineering Chongqing China

ISBN: (纸本)9798350319583

Ensuring the quality and conciseness of method names is pivotal for the readability and maintainability of source code. However, for developers, it often presents challenges, particularly during the course of code evolution. Throughout this process, developers sometimes may neglect to update the method name, resulting in inconsistency which could potentially mislead developers and introduce future bugs. In this paper, we propose the task of 'Just-In-Time (JIT) Method Name Updating' which automatically performs method name updates to avoid inconsistent names and fix them before being introduced into code bases. Specifically, we propose an approach that combines heuristic rules and a neural model. The heuristic rule-based component mainly focuses on the single-token changes for our empirical findings that the proportion of single-token modifications is extensive, and often corresponds to code-indicative updates. The neural model-based component is a customized Seq2seq model considering code changes and the new method body's token type. To evaluate our approach, we conduct extensive experiments on the collected dataset with over 108K method name-body co-change samples from popular Java projects. The results show that our method outperforms the three baselines on all metrics. In particular, our approach achieves a significant improvement in Accuracy and improves method generation baseline by 23.5%. © 2023 IEEE.

关键词： Heuristic methods

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A sharding blockchain-based UAV system for search and rescue missions

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Frontiers of Computer Science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalability 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 scalability,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 scalability 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.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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A New Complex Deng Entropy with Its Application in Pattern Classification

A New Complex Deng Entropy with Its Application in Pattern C...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Liu, Shuoshuo Zhao, Yangyang Xiao, Fuyuan The School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9798350316308

Complex evidence theory is an effective method for modeling and reasoning uncertain information. But how to handle and measure the uncertainty of complex basic belief assignments is still an open issue. The meaning of this paper is to propose a new complex Deng entropy in complex evidence theory including the information on phase angle and measure of discord and non-specificity. It effectively expresses the uncertainty of complex basic belief assignments and considers more phase information, that is, the physical meaning of the phase angle. When the phase is zero, the number degenerates into a real number, and the new complex Deng entropy degenerates into Deng entropy. And Deng entropy degenerates to Shannon entropy when the space is assumed to be a single subset. We give some numerical examples and discuss them to study the compatibility and effectiveness of the new complex Deng entropy. At last, we apply the proposed new complex Deng entropy to pattern classification. The results further verify that the new complex Deng entropy has better recognition accuracy than the complex-valued Deng entropy on some data sets. © 2023 IEEE.

关键词： Pattern recognition

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Improved Pre-copy Container Live Migration Optimization

Improved Pre-copy Container Live Migration Optimization

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7th International Conference on Electronics Technology, ICET 2024

作者： Zhang, Liangbin Du, Hongnan Wang, Yuanming Liu, Xinwei Zhejiang wanli University Ningbo College of Big Data and Software Engineering China

ISBN: (纸本)9798350363951

Container live migration completely migrates the running container from the current host node to another host node to continue running in the shortest downtime, which is a key technology for the management and maintenance of container cloud platform in load balancing, fault tolerance and high availability. This paper proposes a pre-copy improved container live migration optimization scheme. The combination condition of pre-copy termination is set to alleviate the nonconvergence problem of the iterative process. Page server and ramfs file system are used to reduce the number of I/O reads and writes of the dump disk, and the target host node is pre-merged in advance to speed up the iterative recovery. Experimental results show that for memory-intensive container live migration scenarios, the proposed scheme has a better improvement in downtime and total migration time. © 2024 IEEE.

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