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28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Deng, Yuxuan Li, Xiuhua Sun, Chuan Hao, Jinlong Wang, Xiaofei Leung, Victor C. M. School of Big Data and Software Engineering Chongqing University Chongqing China Chongqing Changan Automobile Software Technology Co. Ltd Chongqing China TKLAN College of Intelligence and Computing Tianjin University Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350300482

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-Tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-Term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS. © 2023 IEEE.

关键词： Reinforcement learning

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Fine-Grained Discrepancy Contrastive Learning for Robust Fake News Detection

Fine-Grained Discrepancy Contrastive Learning for Robust Fak...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Junwei Yin Min Gao Kai Shu Jia Wang Yinqiu Huang Wei Zhou School of Big Data and Software Engineering Chongqing University Chongqing China Illinois Institute of Technology Chicago IL USA

In recent years, fake news on social media has become a significant threat to societal security, elevating fake news detection to a research priority. Among various strategies, fact-checking detection methods stand out for their accuracy, leveraging evidence from dedicated fact databases. However, these methods often retrieve raw truth, including vast amounts of irrelevant data, based on semantic similarity. This approach results in information redundancy and risks missing the nuanced differences between fake news and the truth. As a result, subtle changes in fake news can greatly increase the risk of misclassification, compromising the methods’ robustness. To this end, we propose a robust fake news detection framework with Fine-grained Discrepancy Contrastive Learning (FinDCL). By simulating subtle discrepancies between fake news and event-related truth, our method enhances the capture and identification of nuanced falsehoods. Specifically, we construct an adversarial dataset to pre-train a fine-grained discrepancy calculation module with contrastive learning. Moreover, the truth extraction module is devised to alleviate information redundancy by extracting event-related truth. At last, FinDCL jointly utilizes the aforementioned modules to detect fake news in event truth-known and truth-unknown scenarios. Extensive experiments on six real-world datasets demonstrate the effectiveness of FinDCL.

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AW4C: A Commit-Aware C dataset for Actionable Warning Identification

AW4C: A Commit-Aware C Dataset for Actionable Warning Identi...

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International Mining software Repositories, MSR

作者： Zhipeng Liu Meng Yan Zhipeng Gao Dong Li Xiaohong Zhang Dan Yang School of Big Data and Software Engineering Chongqing University China Shanghai Institute for Advanced Study Zhejiang University China

ISBN: (数字)9798400705878

ISBN: (纸本)9798350363982

Excessive non-actionable warnings generated by static program analysis tools can hinder developers from utilizing these tools effectively. Leveraging learning-based approaches for actionable warning identification has demonstrated promise in boosting developer productivity, minimizing the risk of bugs, and reducing code smells. However, the small sizes of existing datasets have limited the model choices for machine learning researchers, and the lack of aligned fix commits limits the scope of the dataset for research. In this paper, we present AW4C, an actionable warning C dataset that contains 38,134 actionable warnings mined from more than 500 repositories on GitHub. These warnings are generated via Cppcheck, and most importantly, each warning is precisely mapped to the commit where the corrective action occurred. To the best of our knowledge, this is the largest publicly available actionable warning dataset for C programming language to date. The dataset is suited for use in machine/deep learning models and can support a wide range of tasks, such as actionable warning identification and vulnerability detection. Furthermore, we have released our dataset 1 and a general framework for collecting actionable warnings on GitHub 2 to facilitate other researchers to replicate our work and validate their innovative *** Concepts• software and its engineering → software maintenance tools; software creation and management; software testing and debugging;• Mathematics of computing → data mining.

关键词： software testing Productivity software maintenance Computer languages Codes Computer bugs Mathematics

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Pretraining binarization encoders for recommendation acceleration 3

Pretraining binarization encoders for recommendation acceler...

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3rd International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2022

作者： Wei, Feng Chen, Shuyu Yin, Yuan Hu, Hao School of Big Data & Software Engineering Chongqing University Chongqing400044 China China Telecom Corporation Limited Chongqing Branch Chongqing401121 China

ISBN: (纸本)9781510657281

Recommendations based on similarity search are widely used. However, when the number of users and items is large, it may become an efficiency bottleneck. In this paper, we propose a high-precision, modular, and efficient recommendation component: the binarization encoder. We use Siamese neural networks to jointly learn binary codes of users and items. The Siamese networks are composed of two autoencoders, whose encoder and decoder are sharing weights further. Their latent factors are discretized as efficient representations of users and items. In order to improve the learning ability of this model, a comprehensive objective function is proposed, which considers three objectives: improving the reconstruction quality of input vectors, reducing the information redundancy between dimensions, and finetuning the predictions with observed ratings. We use collaborative filtering based on matrix factorization on real-world datasets as basic systems and evaluate the proposed method. The experimental results prove the effectiveness of our method. © 2022 SPIE.

关键词： Efficiency

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SparseCoder: Identifier-Aware Sparse Transformer for File-Level Code Summarization

arXiv

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arXiv 2024年

作者： Wang, Yanlin Huang, Yanxian Guo, Daya Zhang, Hongyu Zheng, Zibin School of Software Engineering Sun Yat-sen University China School of Computer Science and Technology Sun Yat-sen University China School of Big Data and Software Engineering Chongqing University China

Code summarization aims to generate natural language descriptions of source code, facilitating programmers to understand and maintain it rapidly. While previous code summarization efforts have predominantly focused on method-level, this paper studies file-level code summarization, which can assist programmers in understanding and maintaining large source code projects. Unlike method-level code summarization, file-level code summarization typically involves long source code within a single file, which makes it challenging for Transformer-based models to understand the code semantics for the maximum input length of these models is difficult to set to a large number that can handle long code input well, due to the quadratic scaling of computational complexity with the input sequence length. To address this challenge, we propose SparseCoder, an identifier-aware sparse transformer for effectively handling long code sequences. Specifically, the SparseCoder employs a sliding window mechanism for self-attention to model short-term dependencies and leverages the structure message of code to capture long-term dependencies among source code identifiers by introducing two types of sparse attention patterns named global and identifier attention. To evaluate the performance of SparseCoder, we construct a new dataset FILE-CS for file-level code summarization in Python. Experimental results show that our SparseCoder model achieves state-of-the-art performance compared with other pretrained models, including full self-attention and sparse models. Additionally, our model has low memory overhead and achieves comparable performance with models using full self-attention mechanism. Furthermore, we verify the generality of SparseCoder on other code understanding tasks, i.e., code clone detection and code search, and results show that our model outperforms baseline models in both tasks, demonstrating that our model can generate better code representations for various downstream tasks. Our

关键词： Semantics

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FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph Neural Network 27

FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph N...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Du, Boyu Zhou, Jingya Liu, Ling She, Xiaolong School of Computer Science and Technology Soochow University China Engineering Lab.of Big Data and Intelligence of Jiangsu Province Soochow University China State Key Lab.for Novel Software Technology Nanjing University China School of Computer Science Georgia Institute of Technology United States

ISBN: (纸本)9781643685489

Graph Neural Networks (GNNs) have become an important graph feature learning paradigm that is extensively applied to graph inference ***, GNNs still have limitations in some aspects such as representation capability, interpretability, *** this end, this paper introduces a novel hybrid Fuzzy-Logic Graph Neural Network (FL-GNN) that synergistically combines Fuzzy Neural Network (FNN) and GNN to effectively capture and aggregate local information flows within the ***-GNN exhibits three distinctive ***, we incorporate a specialized structure fuzzy rule to enhance FL-GNN's graph inference capability, surpassing representative GNN ***, we augment the interpretability of FL-GNN by integrating analytical exploration methods from two perspectives: Fuzzy Inference System and Message Passing Algorithm (MPA).Lastly, we refine the structure of FL-GNN based on MPA to optimize its calculation complexity and consequently improve learning *** results show that FL-GNN outperforms existing representative graph neural networks for graph inference tasks. © 2024 The Authors.

关键词： Graph neural networks

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SparseCoder: Identifier-Aware Sparse Transformer for File- Level Code Summarization

SparseCoder: Identifier-Aware Sparse Transformer for File- L...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Yanlin Wang Yanxian Huang Daya Guo Hongyu Zhang Zibin Zheng School of Software Engineering Sun Yat-sen University China School of Computer Science and Technology Sun Yat-sen University China School of Big Data and Software Engineering Chongqing University China

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

Code summarization aims to generate natural language descriptions of source code, facilitating programmers to understand and maintain it rapidly. While previous code summarization efforts have predominantly focused on method-level, this paper studies file-level code summarization, which can assist programmers in understanding and maintaining large source code projects. Unlike method-level code summarization, file-level code summarization typically involves long source code within a single file, which makes it challenging for Transformer-based models to understand the code semantics for the maximum input length of these models is difficult to set to a large number that can handle long code input well, due to the quadratic scaling of computational complexity with the input sequence length. To address this challenge, we propose SparseCoder, an identifier-aware sparse transformer for effectively handling long code sequences. Specifically, the SparseCoder employs a sliding window mechanism for self-attention to model short-term dependencies and leverages the structure message of code to capture long-term dependencies among source code identifiers by introducing two types of sparse attention patterns named global and identifier attention. To evaluate the performance of SparseCoder, we construct a new dataset FILE-CS for file-level code summarization in Python. Experimental results show that our SparseCoder model achieves state-of-the-art performance compared with other pre-trained models, including full self-attention and sparse models. Additionally, our model has low memory overhead and achieves comparable performance with models using full self-attention mechanism. Furthermore, we verify the generality of SparseCoder on other code understanding tasks, i.e., code clone detection and code search, and results show that our model outperforms baseline models in both tasks, demonstrating that our model can generate better code representations for various downstream tasks. Our

关键词： Codes Source coding Computational modeling Semantics Natural languages Cloning Transformers

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Predicting Relations in SG-CIM Model Based on Graph Structure and Semantic Information 8

Predicting Relations in SG-CIM Model Based on Graph Structur...

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8th IEEE International Conference on big data Security on Cloud, 8th IEEE International Conference on High Performance and Smart Computing, and 8th IEEE International Conference on Intelligent data and Security, bigdataSecurity/HPSC/IDS 2022

作者： Zhang, Pengyu Li, Yufei Ge, Xinliang Hu, Wenhui Sun, Lizhuang Liu, Xueyang State Grid Corporation of China Big Data Center Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China Peking University School of Software & Microelectronics Beijing China

ISBN: (纸本)9781665480697

State Grid Enterprise Common data Model (SG-CIM) is a semantically unified data model for smart grid business applications. The model is divided into entity and relation files. Since the relations in SG-CIM model are manually labeled, which consumes a lot of labor costs and is also prone to omission and mislabeling. In order to solve these problems, this paper introduces a method for predicting complex relations in SG-CIM model based on graph structure and semantic information. The relations of graph structure and semantic information are extracted and used by BERT and ERNIE models to predict the relation types between head and tail entities. In order to improve the efficiency of relation prediction, the five relation types are classified into the parallel type and the inheritance type based on the structural relation between entities, and the pairs of entities are separately mined using frequent term mining and K-nearest neighbor methods. Using these methods for predicting complex relations in SG-CIM models can effectively save labor costs. © 2022 IEEE.

关键词： Forecasting

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Research on Malicious software Detection Based on CNN-LSTM Hybrid Model 5

Research on Malicious Software Detection Based on CNN-LSTM H...

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5th International Conference on Machine Learning, big data and Business Intelligence, MLBDBI 2023

作者： Qi, Xiaoxia Johar, Md Gapar Md Khatibi, Ali Tham, Jacquline Zhang, Rui Mao, Sijie Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei China Management and Science University Software Engineering and Digital Innovation Centre Shah Alam Malaysia Management and Science University School of Graduate Studies Shah Alam Malaysia

ISBN: (纸本)9798350359930

The proliferation of malicious applications has increased rapidly, requiring a more effective approach than traditional methods for auditing and matching code. The behavior of malicious software provides valuable information, with the API call sequence, which represents the order in which operations are generated by both malicious and benign software in a sandbox environment, has become a crucial data source. In this paper, we propose a hybrid model that combines convolutional neural network (CNN) and long short-term memory network (LSTM) to detect and classify malware. We train and classify the model using a large dataset of API sequences called by both malicious and non-malicious software. Compared to traditional machine learning methods, our model eliminates the need for manual extraction of specific instruction calls from assembly code. Furthermore, compared to other deep learning models, hybrid model with LSTM shows significantly improved detection performance. Specifically, the CNN-LSTM model achieves a 24% and 22% increase in accuracy and at least a 16% and 14% higher F1 score compared to other models. These results demonstrate the effectiveness of our proposed model in enhancing malware detection capabilities. © 2023 IEEE.

关键词： Long short-term memory

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Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal for Multi-Sensor Wearable Human Activity Recognition

arXiv

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arXiv 2025年

作者： Xie, Haoyu Li, Haoxuan Zheng, Chunyuan Yuan, Haonan Liao, Guorui Liao, Jun Liu, Li School of Big Data and Software Engineering Chongqing University China Center for Data Science Peking University China School of Mathematical Sciences Peking University China School of Computer Science and Engineering Beihang University China

Wearable Human Activity Recognition (WHAR) is a prominent research area within ubiquitous computing. Multi-sensor synchronous measurement has proven to be more effective for WHAR than using a single sensor. However, existing WHAR methods use shared convolutional kernels for indiscriminate temporal feature extraction across each sensor variable, which fails to effectively capture spatio-temporal relationships of intra-sensor and inter-sensor variables. We propose the DecomposeWHAR model consisting of a decomposition phase and a fusion phase to better model the relationships between modality variables. The decomposition creates high-dimensional representations of each intra-sensor variable through the improved Depth Separable Convolution to capture local temporal features while preserving their unique characteristics. The fusion phase begins by capturing relationships between intra-sensor variables and fusing their features at both the channel and variable levels. Long-range temporal dependencies are modeled using the State Space Model (SSM), and later cross-sensor interactions are dynamically captured through a self-attention mechanism, highlighting inter-sensor spatial correlations. Our model demonstrates superior performance on three widely used WHAR datasets, significantly outperforming state-of-the-art models while maintaining acceptable computational efficiency. © 2025, CC BY.

关键词： Ambient intelligence

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