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3rd International Conference on Electronics and Information technology, EIT 2024

作者： Zhong, Caiting Wei, Le Chengdu University of Information Technology School of Software Engineering Chengdu China Automatic Software Generation & Intelligence Service Key Laboratory of Sichuan Province Sichuan Manufacturing Information Industry Collaborative Innovation Center Chengdu China

ISBN: (纸本)9798350369151

Aiming at the shortcomings of existing code summary generation methods in dealing with the semantic complexity, structural diversity of code and the fusion of search summary and generation summary, a code summary generation method combining contrast learning and heterogeneous graph is proposed. Firstly, a dense vector retrieval framework based on double encoders is adopted to enhance the retrieval accuracy and semantic diversity of large-scale code data. Secondly, the abstract syntax tree is reconstructed into a heterogeneous graph. By introducing various node and edge types, the model's ability to capture complex semantic relationships between different node and edge types is deepened. Finally, the copy gate mechanism is introduced to realize the dynamic fusion of retrieval abstracts and generated model output in semantic level, which guarantees the semantic accuracy and relevance of generated abstracts. Experimental results confirm the effectiveness of this approach in improving the precision and relevance of generated code summaries. © 2024 IEEE.

关键词： Contrastive Learning

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YFLM: An Improved Levenberg-Marquardt Algorithm for Global Bundle Adjustment 41st

YFLM: An Improved Levenberg-Marquardt Algorithm for Global ...

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41st Computer Graphics International Conference, CGI 2024

作者： Peng, Jiaxin Li, Tao Jiang, Qin Liu, Jie Wang, Ruibo Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Hunan Changsha410073 China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9783031820205

The conventional Levenberg-Marquardt (LM) algorithm is a state-of-the-art trust-region optimization method for solving bundle adjustment problems in the Structure-from-Motion community, which not only takes advantage of the fast convergence of the Gauss-Newton method, but also the stability of the gradient descent method when approaching optimal solutions. However, the damping ratio of LM is simply provided by trial-and-error, which causes slow convergence rate for large-scale problems. This paper proposes the Yamashita-Fukushima LM (YFLM) algorithm to reduce the time complexity for global bundle adjustment, where the damping factor is determined by Yamashita and Fukushima’s method. YFLM dynamically calculates a more reasonable and optimal damping ratio according to the newest reprojection error. The experimental results show that the YFLM algorithm outperforms the conventional LM algorithm for most public bundle adjustment datasets. Besides this, the convergence of the YFLM algorithm is also evaluated with different σ∈(0,2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damping

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A weakly supervised back-labeling-based method for named entity recognition in automotive manufacturing: MBIC-NER 3

A weakly supervised back-labeling-based method for named ent...

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3rd International Conference on Electronics and Information technology, EIT 2024

作者： Yang, Heyu Wei, Le Chengdu University of Information Technology School of Software Engineering Chengdu China Automatic Software Generation & Intelligence Service Key Laboratory of Sichuan Province Sichuan Manufacturing Information Industry Collaborative Innovation Center Chengdu China

ISBN: (纸本)9798350369151

Aiming at the problems of lack of corpus, lack of entity annotation dataset and fragmented data in named entity recognition in the automobile manufacturing field, a new named entity recognition method is proposed: MBIC-NER. The method firstly constructs an automobile manufacturing annotation dataset by using the idea of weakly supervised back annotation, and generates word vector sequences of the input sentences by using MacBERT pre-training model in order to better understand the context and semantic associations, and improves the accuracy and effect of text processing in the field of automobile manufacturing;Secondly, BiLSTM model and IDCNN model are constructed to perform feature extraction on the input word vector sequences to form a multilevel feature representation;Finally, the character sequences are continued to be decoded through the CRF, and the corresponding label of each character is obtained. The result show that the F1-score of MBIC-NER for named entity recognition in the self-constructed automobile manufacturing domain dataset reaches 87.03%, which is better than the other named entity recognition methods in comparison. © 2024 IEEE.

关键词： Semantics

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Energy-Efficient Task Offloading in UAV-Enabled MEC via Multi-agent Reinforcement Learning 18th

Energy-Efficient Task Offloading in UAV-Enabled MEC via Mu...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Gao, Jiakun Zhang, Jie Xu, Xiaolong Qi, Lianyong Yuan, Yuan Li, Zheng Dou, Wanchun School of Software Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China QingDao266580 China School of Computer Science and Engineering Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Zhongguancun Laboratory Beihang University Beijing100191 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819998951

Nowadays, artificial intelligence-based tasks are imposing increasing demands on computation resources and energy consumption. Unmanned aerial vehicles (UAVs) are widely utilized in mobile edge computing (MEC) due to maneuverability and integration of MEC servers, providing computation assistance to ground terminals (GTs). The task offloading process from GTs to UAVs in UAV-enabled MEC faces challenges such as workload imbalance among UAVs due to uneven GT distribution and conflicts arising from the increasing number of GTs and limited communication resources. Additionally, the dynamic nature of communication networks and workload needs to be considered. To address these challenges, this paper proposes a Multi-Agent Deep Deterministic Policy Gradient based distributed offloading method, named DMARL, treating each GT as an independent decision-maker responsible for determining task offloading strategies and transmission power. Furthermore, a UAV-enabled MEC with Non-Orthogonal Multiple Access architecture is introduced, incorporating task computation and transmission queue models. In addition, a differential reward function that considers both system-level rewards and individual rewards for each GT is designed. Simulation experiments conducted in three different scenarios demonstrate that the proposed method exhibits superior performance in balancing latency and energy consumption. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Reinforcement learning

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Think Before You Act: A Two-Stage Framework for Mitigating Gender Bias Towards Vision-Language Tasks

Think Before You Act: A Two-Stage Framework for Mitigating G...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Zhang, Yunqi Li, Songda Deng, Chunyuan Wang, Luyi Zhao, Hui Software Engineering Institute East China Normal University China Georgia Institute of Technology United States Shanghai Key Laboratory of Trustworthy Computing Shanghai China

ISBN: (纸本)9798891761148

Gender bias in vision-language models (VLMs) can reinforce harmful stereotypes and discrimination. In this paper, we focus on mitigating gender bias towards vision-language tasks. We identify object hallucination as the essence of gender bias in VLMs. Existing VLMs tend to focus on salient or familiar attributes in images but ignore contextualized nuances. Moreover, most VLMs rely on the co-occurrence between specific objects and gender attributes to infer the ignored features, ultimately resulting in gender bias. We propose GAMA, a task-agnostic generation framework to mitigate gender bias. GAMA consists of two stages: narrative generation and answer inference. During narrative generation, GAMA yields all-sided but gender-obfuscated narratives, which prevents premature concentration on localized image features, especially gender attributes. During answer inference, GAMA integrates the image, generated narrative, and a task-specific question prompt to infer answers for different vision-language tasks. This approach allows the model to rethink gender attributes and answers. We conduct extensive experiments on GAMA, demonstrating its debiasing and generalization ability. © 2024 Association for Computational Linguistics.

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Ship Pipe Layout Optimization Based on Improved Particle Swarm Optimization

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Journal of Shanghai Jiaotong university(Science) 2024年第5期29卷 737-746页

作者： LIN Yan BIAN Xuanyi DONG Zongran School of Naval Architecture and Ocean Engineering Dalian University of TechnologyDalian116024LiaoningChina State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of TechnologyDalian116024LiaoningChina School of Software Dalian University of Foreign LanguagesDalian116044LiaoningChina

Ship pipe layout optimization is one of the difficulties and hot spots in ship intelligent production design.A high-dimensional vector coding is proposed based on the research of related pipe coding and ship pipe route features in this *** advantages of this coding method are concise structure,strong compatibility,and independence from the gridding *** on the proposed coding,the particle swarm optimization algorithm is implemented,and the algorithm is improved by the pre-selected path strategy and the branch-pipe processing ***,two simulation results reveal that the proposed coding and algorithm have feasibility and engineering practicability.

关键词： ship pipe layout optimization coding technology particle swarm optimization automation design

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Time series classification based on Unet with 2D transformation 12

Time series classification based on Unet with 2D transformat...

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12th IEEE International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2024

作者： Du, Mingsen Wang, Yunxia Wei, Yanxuan Wu, Xiaona Ji, Cun Shandong Normal University School of Information Science and Engineering Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9798331510633

Time series classification has gained significant attention in the data mining field in recent years. Despite the proposal of numerous methods over the past decades, most of these approaches focus on feature extraction in one-dimensional space, which often results in lower classification accuracy. To address this issue, this paper proposes a Time Series Classification method based on U-Net with 2D transformation (TSC-Unet). This method first transforms univariate time series into 2D space and augments the data, effectively enhancing the diversity of the dataset. Subsequently, features are extracted from the 2D space using a fully convolutional network, fully exploiting multi-scale feature information. U-Netwith skip connections, effectively retains and transmits high-resolution features, significantly reducing information loss and improving segmentation performance. Additionally, the ability to learn multi-scale features enables the model to capture details at different levels, thereby enhancing its understanding of complex data. Experimental results demonstrate that TSC-Unet achieves high classification accuracy on the UCR datasets, validating the effectiveness and superiority of the proposed method. © 2024 IEEE.

关键词： Time series

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Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 1-14页

作者： Tan, Mingkui Chen, Guohao Wu, Jiaxiang Zhang, Yifan Chen, Yaofo Zhao, Peilin Niu, Shuaicheng South China University of Technology School of Software Engineering China Pazhou Laboratory Guangzhou China XVERSE China National University of Singapore School of Computing Singapore Tencent AI Lab China Nanyang Technological University College of Computing and Data Science Singapore

Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particularly important when the test environment changes frequently. Although some recent attempts have been made to handle this task, we still face two key challenges: 1) prior methods have to perform backpropagation for each test sample, resulting in unbearable optimization costs to many applications;2) while existing TTA solutions can significantly improve the test performance on out-of-distribution data, they often suffer from severe performance degradation on in-distribution data after TTA (known as catastrophic forgetting). To this end, we have proposed an Efficient Anti-Forgetting Test-Time Adaptation (EATA) method which develops an active sample selection criterion to identify reliable and non-redundant samples for test-time entropy minimization. To alleviate forgetting, EATA introduces a Fisher regularizer estimated from test samples to constrain important model parameters from drastic changes. However, in EATA, the adopted entropy loss consistently assigns higher confidence to predictions even when the samples are underlying uncertain, leading to overconfident predictions that underestimate the data uncertainty. To tackle this, we further propose EATA with Calibration (EATA-C) to separately exploit the reducible model uncertainty and the inherent data uncertainty for calibrated TTA. Specifically, we compare the divergence between predictions from the full network and its sub-networks to measure the reducible model uncertainty, on which we propose a test-time uncertainty reduction strategy with divergence minimization loss to encourage consistent predictions instead of overconfident ones. To further re-calibrate predicting confidence on different samples, we utilize the disagreement among predicted labels as an indicator of the data uncertainty. Based on this, we devise a min-max entropy

关键词： Semantic Segmentation

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Handover Scheme in LEO Satellite Networks Based on QoE for Streaming Services 24

Handover Scheme in LEO Satellite Networks Based on QoE for S...

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24th IEEE International Conference on Communication technology, ICCT 2024

作者： Feng, Huazhi Zhu, Lidong Zhou, Jianhong University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu China Xihua University School of Computer and Software Engineering Chengdu China

ISBN: (纸本)9798350363760

The development of satellite communications has received considerable attention in recent years. Early satellite communications were dominated by voice and low-speed data services, but now they must support high-speed multimedia services. Low Earth orbit (LEO) satellites are an important part of the future realization of high-bandwidth and low-latency multimedia services. However, LEO satellites face many challenges in accessing and handover due to their fast-moving speed. For example, few studies have been conducted on the performance of multimedia services (including streaming services) in satellite communication environments at the Quality of Experience (QoE) level, and there are no relevant simulation systems to evaluate and examine them. Therefore, this paper builds a simulation system for streaming media services in LEO satellite communication scenarios in order to simulate the initial buffering, rebuffering, and idle state of the users during service. Then, an access and handover scheme for the QoE level of streaming media service is proposed. Finally, our proposed scheme is evaluated based on this simulation system. From the simulation results, the simulation system proposed in this paper can successfully realize the various functions of users in on- demand streaming media services. And the streaming QoE-based access and handover scheme can perform well in satellite, a resource-constrained scenario. © 2024 IEEE.

关键词： Satellite communication systems

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MFN: Explainable DNA triple helixes Stabilized Design based on mCGR and flow network

MFN: Explainable DNA triple helixes Stabilized Design based ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wen, Xiaoru Sun, Lijun Xie, Lei Zheng, Yanfen Cao, Ben Wang, Bin Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350386226

DNA triple helix structure, as a highly specific gene targeting tool, enable gene regulation by precisely identifying and binding to target DNA sequences. However, the limits of design quality and efficiency affect their wide application in gene therapy. Therefore, in this paper, we propose an antiparallel DNA triple helixes design method-MFN based on matrix chaotic game representation (mCGR) and flow network. Leveraging the structural characteristics of DNA sequences, this method employs the mCGR algorithm to construct an initial matrix, generating a set of DNA sequences that conform to foundational constraints. Then, these sequences are mapped as stream network nodes to screen crosstalk structures by path search, and the whole process is observable and interpretable. Experimental results show that MFN significantly improves the design efficiency of triple helix structure and reduces crosstalk phenomenon. Wet experiments further verify the effectiveness of the method. In summary, MFN achieves an efficient and high-quality design of DNA triple helixes and provides a new idea for targeted gene therapy. © 2024 IEEE.

关键词： Crosstalk

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