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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Kong, Fanxuan Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin150080 China

ISBN: (纸本)9798350368741

This paper addresses the limitations of the Contrastive Language-Image Pre-training (CLIP) model's image encoder and proposes a segmentation model WSSS-ECFE with enhanced CLIP feature extraction, aiming to improve the performance of the Weakly Supervised Semantic Segmentation (WSSS) task. WSSS-ECFE employs the Enhanced Bottleneck module proposed in this paper and adds dynamic residual connection to improve the model's processing effect on complex scenes. In terms of implementation, the Enhanced Bottleneck module employs the Swish activation function and the Depthwise Separable Convolution to enhance the feature extraction and segmentation capability of the model, and uses multiple attention mechanisms to further optimize the feature representation and segmentation accuracy. The WSSS task on the public datasets PASCAL VOC 2012 and MS COCO 2014 achieves 82.6% and 56.3% mean intersection over union (mIoU), achieving state-of-the-art performance in models with low resource requirements. © 2025 IEEE.

关键词： Attention Mechanism Bottleneck Feature Extraction Residual Connection Weakly Supervised Semantic Segmentation (WSSS)

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Offline Imitation Learning Using Reward-free Exploratory Data 5

Offline Imitation Learning Using Reward-free Exploratory Dat...

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5th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2022

作者： Wang, Hao Feng, Dawei Ding, Bo Li, Wei College of Computer National University of Defense Technology Changsha Hunan China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Independent Researcher GuangZhou China

ISBN: (纸本)9781450398343

Offline imitative learning(OIL) is often used to solve complex continuous decision-making tasks. For these tasks such as robot control, automatic driving and etc., it is either difficult to design an effective reward for learning or very expensive and time-consuming for agents to collect data interactively with the environment. However, the data used in previous OIL methods are all gathered by reinforcement learning algorithms guided by task-specific rewards, which is not a true reward-free premise and still suffers from the problem of designing an effective reward function in real tasks. To this end, we propose the reward-free exploratory data driven offline imitation learning (ExDOIL) framework. ExDOIL first trains an unsupervised reinforcement learning agent by interacting with the environment, and collects enough unsupervised exploration data during training;Then, a task independent yet simple and efficient reward function is used to relabel the collected data;Finally, an agent is trained to imitate the expert to complete the task through a conventional RL algorithm such as TD3. Extensive experiments on continuous control tasks demonstrate that the proposed framework can achieve better imitation performance(28% higher episode returns on average) comparing with previous SOTA method(ORIL) without any task-specific rewards. © 2022 ACM.

关键词： Reinforcement learning

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Efficient Large Models Fine-tuning on Commodity Servers via Memory-balanced Pipeline parallelism

Efficient Large Models Fine-tuning on Commodity Servers via ...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Yujie Liu Zhiquan Lai Weijie Liu Wei Wang Dongsheng Li National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China

Large models have achieved impressive performance in many downstream tasks. Using pipeline parallelism to fine-tune large models on commodity GPU servers is an important way to make the excellent performance of large models available to the general public. Previous solutions fail to achieve an efficient memory-balanced pipeline parallelism. In this poster, we introduce a memory load-balanced pipeline parallel solution. This solution balances memory consumption across stages on commodity GPU servers via NVLink bridges. It establishes a new pathway to offload data from GPU to CPU by using the PCIe link of adjacent GPUs connected by the NVLink bridge. Furthermore, our method orchestrates offload operations to minimize the offload latency during large model fine-tuning. Experiments demonstrate that our solution can balance the memory footprint among pipeline stages without sacrificing training performance.

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Prediction of the Cyanobacteria Coverage in Time-series Images based on Convolutional Neural Network 21

Prediction of the Cyanobacteria Coverage in Time-series Imag...

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4th International Conference on Control and computer Vision, ICCCV 2021

作者： Ye, Xiangyu Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Processing Computer College National University of Defense Technology China

ISBN: (纸本)9781450390477

In recent years, the problem of lake eutrophication has become increasingly severe. The monitoring and control of cyanobacteria in lakes are of great significance. The information obtained by existing monitoring methods is relatively lagging, and it is impossible to monitor the sudden outbreak of cyanobacteria in time. Getting cyanobacteria information directly through camera images is a breakthrough. In this paper, after analyzing the characteristics of time series cyanobacteria images, we propose a block prediction scheme based on the CNN model. Experiments show that this method can quickly calculate the coverage of cyanobacteria in the monitoring image in a short time. It can also effectively distinguish cyanobacteria-rich water areas, which significantly facilitates water quality monitoring and cyanobacteria management. We can draw a chart of the changes in the coverage of cyanobacteria by analyzing multi-day time-series images. The chart helps us conduct a short-term water quality analysis to better deal with the outbreak of cyanobacteria. © 2021 ACM.

关键词： Lakes

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Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning

arXiv

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arXiv 2023年

作者： Xu, Hongzuo Wang, Yijie Wei, Juhui Jian, Songlei Li, Yizhou Liu, Ning National Key Laboratory of Parallel and Distributed Computing China College of Computer National University of Defense Technology China College of Science National University of Defense Technology China

Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals. Different from current reconstruction-guided generative models and transformation-based contrastive models, we devise novel data-driven supervision for tabular data by introducing a characteristic – scale – as data labels. By representing varied sub-vectors of data instances, we define scale as the relationship between the dimensionality of original sub-vectors and that of representations. Scales serve as labels attached to transformed representations, thus offering ample labeled data for neural network training. This paper further proposes a scale learning-based anomaly detection method. Supervised by the learning objective of scale distribution alignment, our approach learns the ranking of representations converted from varied subspaces of each data instance. Through this proxy task, our approach models inherent regularities and patterns within data, which well describes data "normality". Abnormal degrees of testing instances are obtained by measuring whether they fit these learned patterns. Extensive experiments show that our approach leads to significant improvement over state-of-the-art generative/contrastive anomaly detection methods. © 2023, CC BY.

关键词： Anomaly detection

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A data-assisted physics-informed neural network(DA-PINN)for fretting fatigue lifetime prediction

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International Journal of Mechanical System Dynamics 2024年第3期4卷 361-373页

作者： Can Wang Qiqi Xiao Zhikun Zhou Yongyu Yang Gregor Kosec Lihua Wang Magd Abdel Wahab Soete Laboratory Department of Electrical EnergyMetalsMechanical Constructions and SystemsFaculty of Engineering and ArchitectureGhent UniversityGentBelgium Department of Engineering Mechanics School of Aerospace EngineeringHuazhong University of Science and TechnologyWuhanChina Key Laboratory for Mechanics Anhui Polytechnic UniversityWuhuChina Parallel and Distributed Systems Laboratory Jožef Stefan InstituteLjubljanaSlovenia School of Aerospace Engineering and Applied Mechanics Tongji UniversityShanghaiChina College of Engineering Yuan Ze UniversityTaiwanChina

In this study,we present for the first time the application of physics-informed neural network(PINN)to fretting fatigue *** PINN has recently been applied to pure fatigue lifetime prediction,it has not yet been explored in the case of fretting *** propose a data-assisted PINN(DA-PINN)for predicting fretting fatigue crack initiation *** traditional PINN that solves partial differential equations for specific problems,DA-PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction *** DA-PINN method,employed in this study,consists of two main ***,damage parameters are obtained from the finite element method by using critical plane method,which generates a data set used to train an artificial neural network(ANN)for predicting damage parameters in other ***,the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA-PINN models,which predict fretting fatigue *** results demonstrate that DA-PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is ***,the choice of loss-function methods in DA-PINN models plays a crucial role in determining its performance.

关键词： crack initiation data-assisted fretting fatigue finite element method physics-informed neural network

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A Bidirectional Representation Recommendation Based On Relevance Information Enhancement To Solve User Interest Drift

A Bidirectional Representation Recommendation Based On Relev...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Wang, Nan Li, Kun Jiang, Xuefeng Li, Jinbao College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing Heilongjiang University Harbin China Qilu University of Technology Shandong Artificial Intelligence Institute School of Mathematics and Statistics Jinan China

ISBN: (纸本)9798350304855

The phenomenon that user interests change dynamically over time is called user interest drift, which makes it difficult for recommendation systems to obtain users' real interests. In order to better provide users with personalized services, it is very important to study recommendation methods that can be applied to user interest drift. However, the new interests of users are often very different from the interests that users have interacted with historically. It makes the correlation between the original user's historical interaction and the user's new interest very weak. Meanwhile, the information contained in user's sessions is not enough to support the inference of the new interest, resulting in a serious decrease in model performance. To alleviate the above problem, we propose a bidirectional representation recommendation based on relevance information enhancement to solve user interest drift (RA-Bert). The model is based on Bert and uses bidirectional self-attention mechanism to represent user sessions. In this model, we design a relevance information enhancement module to adaptively capture the information associated with the user's new interest from neighbors' sessions. Then, we integrate the relevance information into user's history sessions, which can effectively enhance the correlation between user's history sessions and the new interest. Further, in order to effectively supplement the missing relevance information in users' sessions, we design a new attention unit, which explicitly models the difference information between users and neighbors. Extensive experiments have demonstrated the effectiveness of our approach. Our code is publicly available at the link: https://***/LucasZen/paper_code. © 2023 IEEE.

关键词： Recommender systems

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Cross-level Relational Graph Contrastive Learning for Bundle Recommendation

Cross-level Relational Graph Contrastive Learning for Bundle...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Wang, Nan Sun, Jiabao Li, Jinbao College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang University Harbin China Qilu University of Technology Shandong Artificial Intelligence Institute School of Mathematics and Statistics Jinan China

ISBN: (纸本)9798350304855

Different from the traditional recommendation system which regards a single item as an atomic unit, the purpose of bundle recommendation is to recommend a set of items to users. In recent years, some work often uses the model based on GNN to solve the bundle recommendation problem. However, compared with the traditional recommendation models, the GNN-based bundle recommendation has a more serious data sparse problem, which significantly reduces the performance of the model. Meanwhile, due to the lack of modeling ability of complex interactive information and dependent information, it is still far from enough to learn the representation of users and bundles. In view of the advantages of contrast learning in mining its own supervisory signals, we propose a model named Cross-level relational graph Contrastive Learning for Bundle Recommendation (CL2BRec). Different from previous contrastive learning methods, our method learns representations of users and bundles from multiple relational graphs of different levels through a crossview contrastive learning paradigm. Specifically, we regard the user-item/bundle graph as a local interaction relation view, the bundle-item graph as a local dependency relation view, and the user-bundle-item tripartite graph as a global structure view to build a cross-level bundle recommendation model. Extensive experiments conducted on two benchmark datasets demonstrate that CL2BRec outperforms the state-of-the-art methods by over 12.56% and 8.6%, respectively. © 2023 IEEE.

关键词： Recommender systems

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Enhancing Code Representation Learning for Code Search with Abstract Code Semantics

Enhancing Code Representation Learning for Code Search with ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shaojie Zhang Yiwei Ding Enrui Hu Yue Yu Yu Zhang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Peng Cheng Laboratory Shenzhen China Distributed and Parallel Software Lab Huawei Shenzhen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Code representation learning is an important way to encode the semantics of source code through pre-training. The learned representation supports a variety of downstream tasks, such as natural language code search and code defect detection. Inspired by pre-trained models for natural language representation learning, existing approaches often treat the source code or its structural information (e.g., Abstract Syntax Tree or AST) as a plain token sequence. Unlike natural language, programming language has its unique code unit information (e.g., identifiers and expressions) and logic information (e.g., the functionality of a code snippet). To further explore those properties, we propose Abstract Code Embedding (AbCE), a self-supervised learning method that considers the abstract semantics of code logic. Instead of scattered tokens, AbCE treats an entire node or a subtree in an AST as a basic code unit during pre-training, which preserves the entirety of a coding unit. Moreover, AbCE learns the abstract semantics of AST nodes via a self-distillation way. Experimental results show that it achieves significant improvements over state-of-the-art baselines on code search tasks and comparable performance on code clone detection and defect detection tasks even without using contrastive learning or curriculum learning.

关键词： Representation learning Codes Source coding Semantics Natural languages Cloning Syntactics

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High-performance Network Traffic Classification Based on Graph Neural Network

High-performance Network Traffic Classification Based on Gra...

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IEEE Information technology, Networking, Electronic and Automation Control Conference

作者： Bo Pang Yongquan Fu Siyuan Ren Yan Jia College of Computer Science and Technology Harbin Institute of Technology Shenzhen China National Key Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changesha China Peng Cheng Laboratory Shen Zhen China

Network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to automatically extract features from packet streams. Unfortunately, current approaches fail to effectively combine the structural information of traffic packets with the content features of the packets, resulting in limited classification accuracy. In this paper, we propose a graph neural network model for network traffic classification, which can well perceive the interaction feature of packets in traffic. Firstly, we design a graph structure for packets’ flows to hold the interaction information between packets, which embeds both packet contents and sequence relationships into a unified graph. Secondly, we propose a graph neural network framework for graph classification to automatically learn the structural features of the packets’ flows together with the packets’ features. Extensive evaluation results on real-world traffic data show that the proposed model improves the prediction accuracy of improves the prediction accuracy by 2% to 37% for malicious traffic classification.

关键词： Deep learning Automation Telecommunication traffic Predictive models Network security Feature extraction Graph neural networks

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