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arXiv 2025年

作者： Peng, Yubo Xiang, Luping Yang, Kun Wang, Kezhi Debbah, Mérouane State Key Laboratory of Novel Software Technology Nanjing University Nanjing China Suzhou China Department of Computer Science Brunel University London United Kingdom Department of Electrical Engineering and Computer Science The KU 6G Center Khalifa University Abu Dhabi127788 United Arab Emirates

Despite the widespread adoption of vision sensors in edge applications, such as surveillance, the transmission of video data consumes substantial spectrum resources. Semantic communication (SC) offers a solution by extracting and compressing information at the semantic level, preserving the accuracy and relevance of transmitted data while significantly reducing the volume of transmitted information. However, traditional SC methods face inefficiencies due to the repeated transmission of static frames in edge videos, exacerbated by the absence of sensing capabilities, which results in spectrum inefficiency. To address this challenge, we propose a SC with computer vision sensing (SCCVS) framework for edge video transmission. The framework first introduces a compression ratio (CR) adaptive SC (CRSC) model, capable of adjusting CR based on whether the frames are static or dynamic, effectively conserving spectrum resources. Additionally, we implement an object detection and semantic segmentation models-enabled sensing (OSMS) scheme, which intelligently senses the changes in the scene and assesses the significance of each frame through in-context analysis. Hence, The OSMS scheme provides CR prompts to the CRSC model based on real-time sensing results. Moreover, both CRSC and OSMS are designed as lightweight models, ensuring compatibility with resource-constrained sensors commonly used in practical edge applications. Experimental simulations validate the effectiveness of the proposed SCCVS framework, demonstrating its ability to enhance transmission efficiency without sacrificing critical semantic information. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Multi-MedVit: a deep learning approach for the diagnosis of COVID-19 with the CT images

Multi-MedVit: a deep learning approach for the diagnosis of ...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Cai, Yunjie Zheng, Zeqi Nie, Shanling Guo, Yue Zhang, Ruijie Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China University of Southampton School of Electronics and Computer Science United Kingdom

ISBN: (纸本)9781665468190

The grim situation of novel coronavirus pneumonia 2019 (COVID-19) and its terrible spreading speed have already constituted a severe risk to human life, so it is ultimately essential to rapidly and accurately diagnose for COVID-19 pneumonia. Based on this study's 746 lung CT images, we propose Multi-MedVit, a novel auxiliary COVID-19 diagnosis framework based on the multi-input Transformer. We compare Multi-MedVit with state-of-the-art deep learning methods, such as CNN, VGG16, and ResNet50. Multi-MedVit outperformed the other methods on the benchmark dataset and proved that multiscale data input for data augmentation helped enhance model stability. Based on an interpretable analysis of the input and output of Multi-MedVit, we found that with the support of the training set data, the model has been possible to accurately focus on the lesion area for diagnosis of COVID-19 without expert annotations, which can provide initial references containing more potential information to doctors more precisely and fleetly. © 2022 IEEE.

关键词： COVID-19

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Predicted Robustness as QoS for Deep Neural Network Models

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Journal of computer science & technology 2020年第5期35卷 999-1015页

作者： Yue-Huan Wang Ze-Nan Li Jing-Wei Xu Ping Yu Taolue Chen Xiao-Xing Ma State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science University of SurreyGuilfordGU27XHU.K.

The adoption of deep neural network(DNN)model as the integral part of real-world software systems necessitates explicit consideration of their quality-of-service(QoS).It is well-known that DNN models are prone to adversarial attacks,and thus it is vitally important to be aware of how robust a modeFs prediction is for a given input instance.A fragile prediction,even with high confidence,is not trustworthy in light of the possibility of adversarial *** propose that DNN models should produce a robustness value as an additional QoS indicator,along with the confidence value,for each prediction they *** approaches for robustness computation are based on adversarial searching,which are usually too expensive to be excised in real *** this paper,we propose to predict,rather than to compute,the robustness measure for each input ***,our approach inspects the output of the neurons of the target model and trains another DNN model to predict the *** focus on convolutional neural network(CNN)models in the current *** show that our approach is accurate,with only 10%-34%additional errors compared with the offline heavy-weight robustness *** also significantly outperforms some alternative *** further validate the effectiveness of the approach when it is applied to detect adversarial attacks and out-of-distribution *** approach demonstrates a better performance than,or at least is comparable to,the state-of-the-art techniques.

关键词： deep neural network quality of service robustness prediction

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Norm Augmented Graph AutoEncoders for Link Prediction

Norm Augmented Graph AutoEncoders for Link Prediction

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

作者： Yunhui Liu Huaisong Zhang Xinyi Gao Liuye Guo Zhen Tao Tieke He State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Link Prediction (LP) is a crucial problem in graph-structured data. Graph Neural Networks (GNNs) have gained prominence in LP, with Graph AutoEncoders (GAEs) being a notable representation. However, our empirical findings reveal that GAEs’ LP performance suffers heavily from the long-tailed node degree distribution, i.e., low-degree nodes tend to exhibit inferior LP performance compared to high-degree nodes. What causes this degree-related bias, and how can it be mitigated? In this study, we demonstrate that the norm of node embeddings learned by GAEs exhibits variation among nodes with different degrees, underscoring its central significance in influencing the final performance of LP. Specifically, embeddings with larger norms tend to guide the decoder towards predicting higher scores for positive links and lower scores for negative links, thereby contributing to superior performance. This observation motivates us to improve GAEs’ LP performance on low-degree nodes by increasing their embedding norms, which can be implemented simply yet effectively by introducing additional self-loops into the training objective for low-degree nodes. This norm augmentation strategy can be seamlessly integrated into existing GAE methods with light computational cost. Extensive experiments on various datasets and GAE methods show the superior performance of norm-augmented GAEs.

关键词： Training Bridges Heavily-tailed distribution Autoencoders Signal processing Graph neural networks Acoustics Decoding Computational efficiency Speech processing

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An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning Frameworks

SSRN

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SSRN 2024年

作者： Yang, Chen Liang, Peng Ma, Zinan School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen518055 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science Wuhan University Wuhan430072 China Department of Computer Science and Engineering Chalmers University of Technology GothenburgS-412 96 Sweden

Context: Stakeholders constantly make assumptions in the development of deep learning (DL) frameworks. These assumptions are related to various types of software artifacts (e.g., requirements, design decisions, and technical debt) and can turn out to be invalid, leading to system failures. Existing approaches and tools for assumption management usually depend on manual identification of assumptions. However, assumptions are scattered in various sources (e.g., code comments, commits, pull requests, and issues) of DL framework development, and manually identifying assumptions has high costs (e.g., time and resources).Objective: The objective of the study is to evaluate different classification models for the purpose of identification with respect to assumptions from the point of view of developers and users in the context of DL framework projects (i.e., issues, pull requests, and commits) on ***: We constructed a new and largest dataset (i.e., AssuEval) of assumptions collected from the TensorFlow and Keras repositories on GitHub;explored the performance of seven traditional machine learning models (e.g., Support Vector Machine, Classification and Regression Trees), a popular DL model (i.e., ALBERT), and a large language model (i.e., ChatGPT) of identifying assumptions on the ***: The experiment results show that ALBERT achieves the best performance (f1-score: 0.9584) of identifying assumptions on the AssuEval dataset, which is much better than the other models (the 2nd best f1-score is 0.6211, achieved by ChatGPT). Though ChatGPT is the most popular large language model, we do not recommend using it to identify assumptions in DL framework development because of its low performance on the task. Fine-tuning ChatGPT specifically for assumption identification could improve the ***: This study provides researchers with the largest dataset of assumptions for further research (e.g., assumption classification, evaluation, and

关键词： Large datasets

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On the Noise Robustness of In-Context Learning for Text Generation

arXiv

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arXiv 2024年

作者： Gao, Hongfu Zhang, Feipeng Jiang, Wenyu Shu, Jun Zheng, Feng Wei, Hongxin Department of Statistics and Data Science Southern University of Science and Technology China School of Economics and Finance Xi’an Jiaotong University China National Key Laboratory for Novel Software Technology Nanjing University China School of Mathematics and Statistics Xi’an Jiaotong University China Department of Computer Science and Engineering Southern University of Science and Technology China

Large language models (LLMs) have shown impressive performance on downstream tasks by in-context learning (ICL), which heavily relies on the quality of demonstrations selected from a large set of annotated examples. Recent works claim that in-context learning is robust to noisy demonstrations in text classification. In this work, we show that, on text generation tasks, noisy annotations significantly hurt the performance of in-context learning. To circumvent the issue, we propose a simple and effective approach called Local Perplexity Ranking (LPR), which replaces the "noisy" candidates with their nearest neighbors that are more likely to be clean. Our method is motivated by analyzing the perplexity deviation caused by noisy labels and decomposing perplexity into inherent perplexity and matching perplexity. Our key idea behind LPR is thus to decouple the matching perplexity by performing the ranking among the neighbors in semantic space. Our approach can prevent the selected demonstrations from including mismatched input-label pairs while preserving the effectiveness of the original selection methods. Extensive experiments demonstrate the effectiveness of LPR, improving the EM score by up to 18.75 on common benchmarks with noisy annotations. Our code is available at https://***/ml-stat-Sustech/Local-Perplexity-Ranking © 2024, CC0.

关键词： Semantics

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What is Discussed about COVID-19:A Multi-Modal Framework for Analyzing Microblogs from Sina Weibo without Human Labeling

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computers, Materials & Continua 2020年第9期64卷 1453-1471页

作者： Hengyang Lu Yutong Lou Bin Jin Ming Xu School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing UniversityNanjing210023China

Starting from late 2019,the new coronavirus disease(COVID-19)has become a global *** the development of online social media,people prefer to express their opinions and discuss the latest news *** have witnessed the positive influence of online social media,which helped citizens and governments track the development of this pandemic in *** is necessary to apply artificial intelligence(AI)techniques to online social media and automatically discover and track public opinions posted *** this paper,we take Sina Weibo,the most widely used online social media in China,for analysis and *** collect multi-modal microblogs about COVID-19 from 2020/1/1 to 2020/3/31 with a web crawler,including texts and images posted by *** order to effectively discover what is being discussed about COVID-19 without human labeling,we propose a unified multi-modal framework,including an unsupervised short-text topic model to discover and track bursty topics,and a self-supervised model to learn image features so that we can retrieve related images about *** results have shown the effectiveness and superiority of the proposed models,and also have shown the considerable application prospects for analyzing and tracking public opinions about COVID-19.

关键词： COVID-19 public opinion microblog topic model self-supervised learning

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Vision mamba mender 24

Vision mamba mender

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jiacong Hu Anda Cao Zunlei Feng Shengxuming Zhang Yi Wang Lingxiang Jia Mingli Song College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University College of Computer Science and Technology Zhejiang University School of Software Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security School of Software Technology Zhejiang University College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security

ISBN: (纸本)9798331314385

Mamba, a state-space model with selective mechanisms and hardware-aware architecture, has demonstrated outstanding performance in long sequence modeling tasks, particularly garnering widespread exploration and application in the field of computer vision. While existing works have mixed opinions of its application in visual tasks, the exploration of its internal workings and the optimization of its performance remain urgent and worthy research questions given its status as a novel model. Existing optimizations of the Mamba model, especially when applied in the visual domain, have primarily relied on predefined methods such as improving scanning mechanisms or integrating other architectures, often requiring strong priors and extensive trial and error. In contrast to these approaches, this paper proposes the Vision Mamba Mender, a systematic approach for understanding the workings of Mamba, identifying flaws within, and subsequently optimizing model performance. Specifically, we present methods for predictive correlation analysis of Mamba's hidden states from both internal and external perspectives, along with corresponding definitions of correlation scores, aimed at understanding the workings of Mamba in visual recognition tasks and identifying flaws therein. Additionally, tailored repair methods are proposed for identified external and internal state flaws to eliminate them and optimize model performance. Extensive experiments validate the efficacy of the proposed methods on prevalent Mamba architectures, significantly enhancing Mamba's performance. For more information, please visit https://***/.

关键词：

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How to get better embeddings with code pre-trained models? An empirical study

arXiv

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arXiv 2023年

作者： Zhao, Yu Gong, Lina Zhang, Haoxiang Yu, Yaoshen Huang, Zhiqiu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics State Key Lab for Novel Software Technology Nanjing University Key Laboratory of Safety-Critical Software Nanjing University of Aeronautics and Astronautics China Queen’s University KingstonON Canada

Pre-trained language models have demonstrated powerful capabilities in the field of natural language processing (NLP). Recently, code pretrained model (PTM), which draw from the experiences of the NLP field, have also achieved state-of-the-art results in many software engineering (SE) downstream tasks. These code PTMs take into account the differences between programming languages and natural languages during pre-training and make adjustments to pre-training tasks and input data. However, researchers in the SE community still inherit habits from the NLP field when using these code PTMs to generate embeddings for SE downstream classification tasks, such as generating semantic embeddings for code snippets through special tokens and inputting code and text information in the same way as pre-training the PTMs. In this paper, we empirically study five different PTMs (i.e. CodeBERT, CodeT5, PLBART, CodeGPT and CodeGen) with three different architectures (i.e. encoder-only, decoder-only and encoder-decoder) on four SE downstream classification tasks (i.e. code vulnerability detection, code clone detection, just-in-time defect prediction and function docstring mismatch detection) with respect to the two aforementioned aspects. Our experimental results indicate that (1) regardless of the architecture of the code PTMs used, embeddings obtained through special tokens do not sufficiently aggregate the semantic information of the entire code snippet;(2) the quality of code embeddings obtained by combing code data and text data in the same way as pretraining the PTMs is poor and cannot guarantee richer semantic information;(3) using the method that aggregates the vector representations of all code tokens, the decoder-only PTMs can obtain code embeddings with semantics as rich as or even better quality than those obtained from the encoder-only and encoder-decoder PTMs. Based on our findings, we recommend that researchers in the SE community (1) pay attention to the vector represen

关键词： Semantics

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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

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Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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