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Chinese Journal of Electronics 2021年第1期30卷 55-63页

作者： BO Lili ZHU Xuanrui SUN Xiaobing NI Zhen LI Bin School of Information Engineering Yangzhou University Jiangsu Engineering Research Center of Knowledge Management and Intelligent Service State Key Laboratory for Novel Software Technology Nanjing University

Fine-grained change operations can help software developers fix software bugs more accurately and efficiently. However, the current fine-grained change operations are only used in specific fixing process, such as fixing of If statement. In this paper, we conducted an empirical study to explore the fine-grained change operations for bug fixing. Based on the Mozilla bug data,we examined whether similar bugs are fixed with similar change operations. The results show that: First, for bug reports with similar descriptions or bug-fix commits with similar descriptions, their corresponding fine-grained change operations are not related; Second, in the case where the descriptions of both bug reports and bug-fix commits are similar, the fine-grained change operations in patch code are not related; Third, by classifying bug reports, we find that the change operations in the same bug report category are similar; Finally, by analyzing the fine-grained change operations for each bug, we present some combined patterns that are often used together.

关键词： bug reports bug fixing If statement Mozilla bug data bug-fix commits similar descriptions program debugging fine-grained change operations

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Realtime prediction of hard rock TBM advance rate using temporal convolutional network(TCN)with tunnel construction big data

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Frontiers of Structural and Civil engineering 2022年第4期16卷 401-413页

作者： Zaobao LIU Yongchen WANG Long LI Xingli FANG Junze WANG Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Institute of Deep Engineering and Intelligent TechnologyNortheastern UniversityShenyang 110819China State Key Laboratory of Hydraulics and Mountain River Engineering Sichuan UniversityChengdu 610065China Software College Northeastern UniversityShenyang 110819China

Real-time dynamic adjustment of the tunnel bore machine(TBM)advance rate according to the rockmachine interaction parameters is of great significance to the adaptability of TBM and its efficiency in *** paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network(TCN),based on TBM construction big *** prediction model was built using an experimental database,containing 235 data sets,established from the construction data from the Jilin Water-Diversion Tunnel Project in *** TBM operating parameters,including total thrust,cutterhead rotation,cutterhead torque and penetration rate,are selected as the input parameters of the *** TCN model is found outperforming the recurrent neural network(RNN)and long short-term memory(LSTM)model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter *** penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next *** the contrary,the influence of the cutterhead rotation and total thrust is *** work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.

关键词： hard rock tunnel tunnel bore machine advance rate prediction temporal convolutional networks soft computing construction big data

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A Text Structure-based Extractive And Abstractive Summarization Method 7

A Text Structure-based Extractive And Abstractive Summarizat...

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7th International Conference on intelligent Computing and Signal Processing, ICSP 2022

作者： Yan, Jing Zhou, Shihua Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian China

ISBN: (纸本)9781665478571

Extraction summarization and abstraction summarization have advantages and disadvantages, so how to better combine these two ways has become a difficult problem. To address this challenge, this paper proposed a new fusion method. The major novelty lies in the design of the new method. Wherein our approach, first, use the text structure information and the idea of the K-means algorithm to divide the text into regions, and then the main part and the non-main parts are determined according to the distribution of the subject words. Next, apply information extraction on the main part and text generation on the non-main parts. Finally, the two parts of the summarization are merged according to the sequence of the text. Experimental results show that the quality of the summarization is better than that of extraction summarization and abstraction summarization. In addition, to make the method more targeted in Chinese text processing, the Cw2vec model based on Chinese stroke information is used in the encoding process, and the experiment proves that the quality of summarization can be further improved. © 2022 IEEE.

关键词： Text processing

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Detection of Deepfakes: Protecting Images and Vedios Against Deepfake 21

Detection of Deepfakes: Protecting Images and Vedios Against...

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21st International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Tian, Ying Zhou, Wang Haq, Amin Ul Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province LuZhou China School of Computer and Software Engineering Xihua University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331519254

In recent years, with the rapid development of deep learning and computer vision technology, the forgery technology of images and videos has become increasingly mature, posing new challenges to information security and social stability. Behind the re-evolution of deepfake lies the rampant proliferation of fake content, which is used for election tampering, identity fraud, fraud, spreading fake news, and so on. To address these challenges, researchers are constantly exploring and developing image-based deepfake detection techniques, which aim to effectively identify and prevent deepfake content in images and videos. This article will introduce the current development status of deepfake detection technology, present its principles and methods in detail, and look ahead to its future development directions. © 2024 IEEE.

关键词： Convolutional neural networks

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Byzantine-Robust Federated Learning on Non-IID Data via Inversing Artificial Gradients

Byzantine-Robust Federated Learning on Non-IID Data via Inve...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Minyang Tang, Xiangyun Zhang, Tao Liu, Guangyuan Weng, Yu Minzu University of China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance China Beijing Jiaotong University School of Software Engineering China Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798350351255

Federated Learning (FL) is a distributed machine learning framework that enhances privacy by enabling multiple participants to train a global model without sharing their raw data. However, FL still faces the threat posed by data and Byzantine attacks (e.g., data poisoning and model poisoning attacks) from malicious clients aimed to decrease the FL global training accuracy. Previous studies have proposed solutions to improve the robustness of FL systems. However, these robust approaches have not effectively defended against Byzantine attacks from malicious clients in non-Independent and Identically Distributed (non-IID) environments, decreasing overall training accuracy. In this work to address this issues, we propose a new defence framework that aims to enhance model accuracy against Byzantine attacks from malicious clients in non-IID environments. In our approach, the central server performs the Inverse Deep Learning Gradient attack using the gradient data submitted by users in each round, obtaining an inversed artificial gradient. We then assess the squared L2 norm difference between this inversed gradient and the actual gradients to detect malicious clients. Simultaneously, we assign weights to each client involved in the aggregation based on differences in the squared L2 norm, aiming to minimize the impact of malicious actors on the overall model. We have experimentally evaluated our method and demonstrated its ability to maintain training accuracy under attack in non-IID environments using standard datasets. © 2024 IEEE.

关键词： Federated learning

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CMIX: Causal Value Decomposition for Cooperative Multi-Agent Reinforcement Learning

CMIX: Causal Value Decomposition for Cooperative Multi-Agent...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yao, Dunqi Sun, Chuxiong Li, Kai Zhou, Kaijie Li, Hanyu Wang, Rui Liu, Lixiang University of Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Intelligent Game Beijing China Research Institute of Intelligent Decision Engineering China Aerospace Science and Industry Corporation Limited Beijing China

ISBN: (纸本)9781665410205

Value decomposition plays a pivotal role in ensuring effective credit assignment within Multi-Agent Reinforcement Learning (MARL), particularly in cooperative multi-agent tasks where agents are limited to accessing team rewards only. However, existing methods treat the mixing network as a black box, implicitly assuming that neural networks can autonomously extract important information and achieve rational credit assignment during policy learning. This approach not only lacks interpretability but may also prove inefficient in complex scenarios. To enhance the interpretability and rationality of value decomposition, we propose an innovative approach called 'Causal Value Decomposition'(CMIX). CMIX employs causal inference-based models, introducing a set of metrics beyond environmental rewards to enhance robustness and model interpretability. Specifically, CMIX establishes intricate relational structures among agents in complex environments and leverages causal relationships between agents and their surroundings to address the credit assignment challenge in MARL. By employing do-calculus, CMIX accurately measures the impact of each agent's actions on environmental states, precisely determining their contribution to the collective reward. This approach not only enhances the interpretability of existing black-box models but also improves the accuracy of credit assignment in multi-agent systems. Moreover, CMIX exhibits high scalability and complements existing value decomposition techniques. Its effectiveness and scalability have been rigorously tested across various settings, including MPE, LBF, and SMAC environments. © 2024 IEEE.

关键词： Reinforcement learning

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Anatomical Prior Guided Spatial Contrastive Learning for Few-Shot Medical Image Segmentation 24

Anatomical Prior Guided Spatial Contrastive Learning for Few...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Huang, Wendong Hu, Jinwu Bi, Xiuli Xiao, Bin Chongqing Key Laboratory of Image Cognition Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Post and Telecommunications Chongqing China Pazhou Lab School of Software Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798400706868

Few-shot semantic segmentation has considerable potential for low-data scenarios, especially for medical images that require expert-level dense annotations. Existing few-shot medical image segmentation methods strive to deal with the task by means of prototype learning. However, this scheme relies on support prototypes to guide the segmentation of query images, ignoring the rich anatomical prior knowledge in medical images, which hinders effective feature enhancement for medical images. In this paper, we propose an anatomical prior guided spatial contrastive learning, called APSCL, which exploits anatomical prior knowledge derived from medical images to construct contrastive learning from a spatial perspective for few-shot medical image segmentation. The new framework forces the model to learn the features in line with the embedded anatomical representations. Besides, to fully exploit the guidance information of the support samples, we design a mutual guidance decoder to predict the label of each pixel in the query image. Furthermore, our APSCL can be trained end-to-end in the form of episodic training. Comprehensive experiments on three challenging medical image datasets, i.e., CHAOS-T2, MS-CMRSeg, and Synapse, prove that our method significantly surpasses state-of-the-art few-shot medical segmentation methods, with a mean improvement of 3.61%, 2.30%, and 6.38% on the Dice score, respectively. © 2024 ACM.

关键词： Contrastive Learning

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Optimizing Recommender System Using Federated Clustering 2

Optimizing Recommender System Using Federated Clustering

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2nd International Conference on Applied Statistics, Computational Mathematics, and software engineering, ASCMSE 2023

作者： Tsiamchyk, Ilya Wang, Guanghui Zuo, Fang Huang, Xiaolin School of Software Henan University Kaifeng China Henan International Joint Laboratory of Intelligent Network Theory and Key Technology Henan University Kaifeng China Subject Innovation and Intelligence Introduction Base of Henan Higher Educational Institution Software Engineering Intelligent Information Processing Innovation and Intelligence Introduction Base Henan University China

ISBN: (数字)9781510668195

ISBN: (纸本)9781510668188

Currently, recommender systems are widely applied in various fields. However, due to the limited needs and special circumstances of users to be recommended, it is difficult for a recommender system to cover all users' interest lists at the same time. In this work, we present a kind of optimized federated clustering scheme (OP-Fed-Clustering) for users' private tendency data. The scheme starts by coding the initial data objects to protect privacy and then optimizes the assignment of data points based on object similarity. We also validates the algorithm's effectiveness on the FoodRecipe dataset and compares the algorithm to initial K-FED. Our tentative data show that the effectiveness of the proposed OP-Fed-Clustering algorithm, demonstrating universally superior performance while preserving user data confidentiality. © 2023 SPIE.

关键词： Recommender systems

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A Spatio-Temporal Transformer for Enhancing the Coherence on 3D Human Motion Prediction 18th

A Spatio-Temporal Transformer for Enhancing the Coherence ...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Xie, Miner Wang, Lei Liu, Feng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9789819610709

Human motion prediction is of great importance for artificial intelligence systems, particularly in fields like autonomous driving and human-computer interaction. Existing methods have achieved good results in simple motions with small changes but still struggle with coherence in predicting complex motions with large changes in a short time. Regarding the issue in current methods, this paper proposes a novel network composed of three modules: spatial encoders that captures correlations between joints, temporal encoders that models the movement patterns of joints across frames, and a multi-frame temporal encoder that learns high-level temporal correlations to enhance prediction coherence. In addition, we propose to use Weighted Mean Per-Joint Position Error (WMPJPE) as the loss function by considering that different joints of the human skeleton have different effects on the entire action. We evaluate our method on the Human3.6M, AMASS, and 3DPW datasets. The quantitative results indicate that our method notably outperforms other methods in the prediction range of 160ms to 560ms. The qualitative results show that our method can improve the coherence of prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Heterogeneous-Training: A Semi-supervised Text Classification Method 23

Heterogeneous-Training: A Semi-supervised Text Classificatio...

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7th International Conference on Big Data and Internet of Things, BDIOT 2023

作者： Shen, Yuhao Li, Bo Xu, Xinlan Luo, Bing Zhang, Chao Hao, Fei School of Computer and Software Engineering Xihua University Chengdu Sichuan610039 China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou Sichuan646000 China

ISBN: (纸本)9798400708015

With the advent of the information age, there are more and more text data on the Internet. As the most widely distributed information carrier with the largest amount of data, it is particularly important to use text classification technology to organize and manage massive data scientifically. In this paper, a semi-supervised ensemble learning algorithm Heterogeneous-Training is proposed and applied to the field of text classification. Based on the Tri-Training algorithm, the Heterogeneous-Training algorithm improves the traditional Tri-Training algorithm by using different classifiers, dynamically updating the probability threshold and adaptively editing data. A large number of experiments show that our method always outperforms Tri-Training algorithm in text classification on benchmark text data sets. © 2023 ACM.

关键词： Learning algorithms

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