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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Qiu, Hong Jin, Heng Wang, Renfang Liu, Xiufeng Gao, Guang Zhejiang Wanli University College of Big Data and Software Engineering Ningbo315200 China Shanghai Ocean University College of Information Shanghai200120 China Technical University of Denmark Department of Technology Management and Economics Denmark Co. Ltd. Ningbo China

ISBN: (纸本)9798350331684

With high spectral resolution, hyperspectral image(HSI) data will result in the Hughes phenomenon, which brings a huge challenge to hyperspectral image classification(HIC). Feature extraction can be applied to address this problem. But several traditional methods often ignore the spatial structure information of HSI data. In this paper, we propose a tensor nuclear norm based matrix regression based projections(TNMRP) for feature extraction of hyperspectral images. Firstly, TNMRP preprocesses the data by a filling method. Then, it automatically builds the graph of block-tensor samples and uses the optimal sparse coding coefficients to obtain the weight matrix. Finally, based on tensor representation, TNMRP calculates the optimal projection matrix. Experiments of classification on Indian Pines and Pavia University databases demonstrate the effectiveness of our proposed method. © 2023 IEEE.

关键词： Matrix algebra

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Spatial and Channel Exchange based on EfficientNet for Detecting Changes of Remote Sensing Images 26

Spatial and Channel Exchange based on EfficientNet for Detec...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Renfang Yang, Zijian Qiu, Hong Liu, Xiufeng Wu, Dun Zhejiang Wanli University College of Big Data and Software Engineering Ningbo315200 China Shanghai Ocean University College of Information Shanghai201306 China Technical University of Denmark Department of Technology Management and Economics Produktionstorvet Denmark Co. Ltd. Ningbo China

ISBN: (纸本)9798350331684

Change detection is an important branch in remote sensing image processing. Deep learning has been widely used in this field. In particular, a wide variety of attention mechanisms have made great achievements. However, some models have become increasingly complex and large, often unfeasible for edge applications. This poses a major obstacle to industrial applications. In this paper, to solve the above challenges, we propose a Lightweight network structure to improve results while taking into account efficiency. Specifically, first, the shallow features are extracted by using the spatial exchange and change exchange of the down-sampling bi-temporal channel of the three-layer EfficientNet backbone network, and then the shallow features are used for low-dimensional skip-connection. After that, a hybrid dual-temporal data module is designed to mix the dual-temporal phase into a single image, then the high-dimensional low-pixel image is restored through the up-sampling. Finally the final change map is generated through the pixel-level classifier. Our method was evaluated on public datasets by evaluation indicators such as OA, IoU, F1, Recall, Precision. © 2023 IEEE.

关键词： Remote sensing

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Spatial Consistency and Feature Diversity Regularization in Transfer Learning for Fine-Grained Visual Categorization

Spatial Consistency and Feature Diversity Regularization in ...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Dai, Zhigang Chen, Junying Mian, Ajmal South China University of Technology Key Laboratory of Big Data and Intelligent Robot Ministry of Education School of Software Engineering Guangzhou China The University of Western Australia Department of Computer Science Perth Australia

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Fine-grained visual categorization is challenged by limited training data by localizing discriminative regions and learning diverse features. We propose an effective regularization method that simultaneously imposes spatial consistency and feature diversity on CNN feature maps from a unified perspective. The former guides different feature map channels to concentrate collaboratively on the discriminative areas while the latter ensures that the feature maps are diverse. The proposed method does not require additional supervision, and leverages the covariance matrix of multi-channel feature maps to regularize the loss at the last convolutional layer where the semantic information is the richest. This allows the influence to be backpropagated to update all convolutional layers. We perform experiments using four network architectures for transfer learning from two source domains to three target domains, and demonstrate that our regularization method improves accuracy in all different settings. The proposed regularization method achieves state-of-the-art performance on CUB-200-2011, Stanford-Cars, and Stanford-Dogs datasets with 89.8%, 94.6%, and 88.5% accuracy, respectively. © 2022 IEEE.

关键词： Covariance matrix

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Effective and Imperceptible Adversarial Textual Attack Via Multi-objectivization

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ACM Transactions on Evolutionary Learning and Optimization 2024年第3期4卷 1-23页

作者： Liu, Shengcai Lu, Ning Hong, Wenjing Qian, Chao Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen College of Engineering South Tower 1088 Xueyuan Avenue Guangdong Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 10F Zhizhen Building Canghai Campus 3688 Nanhai Avenue Guangdong Shenzhen China National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing University Nanjing International College Area Xianlin Campus 163 Xianlin Avenue Jiangsu Nanjing China

The field of adversarial textual attack has significantly grown over the past few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model. However, the imperceptibility of attacks, which is also essential for practical attackers, is often left out by previous studies. In consequence, the crafted AEs tend to have obvious structural and semantic differences from the original human-written text, making them easily perceptible. In this work, we advocate leveraging multi-objectivization to address such an issue. Specifically, we reformulate the problem of crafting AEs as a multi-objective optimization problem, where the attack imperceptibility is considered as an auxiliary objective. Then, we propose a simple yet effective evolutionary algorithm, dubbed HydraText, to solve this problem. HydraText can be effectively applied to both score-based and decision-based attack settings. Exhaustive experiments involving 44,237 instances demonstrate that HydraText consistently achieves competitive attack success rates and better attack imperceptibility than the recently proposed attack approaches. A human evaluation study also shows that the AEs crafted by HydraText are more indistinguishable from human-written text. Finally, these AEs exhibit good transferability and can bring notable robustness improvement to the target model by adversarial training. © 2024 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction

TCSloT: Text Guided 3D Context and Slope Aware Triple Networ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Xinquan Xie, Jinheng Li, Xuechen Li, Xuguang Shen, Linlin Deng, Yongqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China National University of Singapore Show Lab Singapore Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen University General Hospital Department of Stomatology Shenzhen China

ISBN: (纸本)9798350337488

In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. However, such design heavily relies on the manual location of the implant position. When deep neural network has been proposed to assist the dentist in locating the implant position, most of them take a single slice as input, which do not fully explore 3D contextual information and ignores the influence of implant slope. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) to integrate the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes. A Texture Variation Perception (TVP) module is correspondingly design to process the multiple slices and capture the texture variation among slices and a Slope-Aware Loss (SAL) is proposed to dynamically assign adaptive weights for the regression head. Additionally, we design a conditional text guidance (CTG) module to integrate the text condition (i.e., left, middle and right) from the CLIP to assist the implant position prediction. Extensive experiments on a dental implant dataset through five-fold cross-validation, demonstrated that the proposed TCSloT achieves superior performance than existing methods. © 2023 IEEE.

关键词： Dental Implant Multi-module Detection Multi-slice Learning Text Guided Detection Transformer

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A Comprehensive Survey on Communication-Efficient Federated Learning in Mobile Edge Environments

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IEEE Communications Surveys and Tutorials 2025年

作者： Jia, Ninghui Qu, Zhihao Ye, Baoliu Wang, Yanyan Hu, Shihong Guo, Song Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Nanjing211100 China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing210023 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Kowloon Hong Kong

In traditional centralized machine learning, transmitting raw data to a cloud center incurs high communication costs and raises privacy concerns. This is particularly challenging in mobile edge environments, where devices are dynamic and resource-constrained. Federated Learning (FL) addresses these issues by allowing devices to train models locally and upload parameters to a central server without sharing local data. However, limited wireless channel resources and dynamic transmission performance make communication overhead a major bottleneck of FL in mobile edge environments. Concerning this challenge, this survey provides a comprehensive summary of methods to improve communication efficiency in FL, focusing on: 1) minimizing communication complexity to reduce total transmission volume, 2) scheduling resources appropriately to improve training efficiency, 3) utilizing over-the-air computation (OTA) to integrate computation into communication for accommodating the computation/communication characteristics of FL in mobile edge environments. Thus, this work analyzes research from the perspective of convergence and data heterogeneity to reduce communication rounds by optimizing algorithm performance. We hope that this survey could offer insights into communication-efficient FL for future research. © 2025 IEEE.

关键词： Federated learning

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eSROP Attack: Leveraging Signal Handler to Implement Turing-Complete Attack Under CFI Defense 1

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18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Zhang, Tianning Cai, Miao Zhang, Diming Huang, Hao Department of Computer Science and Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer and Information Hohai University Nanjing China College of Computer Engineering Jiangsu University of Science and Technology Nanjing China

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Signal Return Oriented Programming (SROP) is a dangerous code reuse attack method. Recently, defense techniques have been proposed to defeat SROP attacks. In this paper, we leverage the signal nesting mechanism provided by current operating systems and propose a new variant of SROP attack called enhanced SROP (eSROP) attack. eSROP provides the ability of invoking arbitrary system calls, simulating Turing-complete computation, and even bypassing the fine-grained label-based CFI defense, without modifying the return address and instruction register in the signal frame. Because the signal returns to the interrupted instruction, the shadow stack defense can hardly detect our attack. Signal has strong flexibility which can interrupt the normal control flow. We leverage such flexibility to design a new code reuse attack. To evaluate eSROP, we perform two exploits on two real-world programs, namely Proftpd and Wu-ftpd. In our attacks, adversaries can invoke arbitrary system calls and obtain a root shell. Both attacks succeed within 10 min under strict system defense such as data execution prevention, address space layout randomization, and coarse-grained control flow integrity. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Network security

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Integrating Branching and Pruning for Efficient Hyperdimensional Computing

Integrating Branching and Pruning for Efficient Hyperdimensi...

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Jing Liu Zhiqian Guan Di Liu Shengfa Miao Fei Dai School of Software Yunnan University China Department of Computer Science Norwegian University of Science and Technology Norway School of Big Data and Intelligent Engineering Southwest Forestry University China

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

As an emerging brain-inspired computing method, hyperdimensional computing (HDC) has attracted increasing attention. HDC encodes data into a high-dimensional hypervector and makes predictions based on the similarity comparison of the hypervector. However, the computational overhead of an HDC model increases, and the accuracy decreases as the number of predicted classes increases. Branching has been proposed to address this issue in HDC models, but it also introduces a new challenge: increased memory overhead for storing new branching hypervectors. In this paper, we aim to address this issue by integrating branching with the pruning method in HDC models. To this end, we propose a simple yet effective two-level branching structure that can reduce the memory overhead required by branching hypervectors. In addition, we propose a novel variable-position pruning method capable of removing redundant dimensions from hypervectors at various positions. This can best identify the important dimensions of a hypervector and further mitigate the memory issue caused by the branching structure. Experimental results demonstrate that our two-level branching structure can slightly increase the accuracy across various datasets. With only a 0.5 % accuracy loss, our pruning method can remove more than half of the dimensions, thereby improving the HDC model's inference latency and energy consumption.

关键词： Energy consumption Accuracy Computational modeling Memory management Predictive models Brain modeling Encoding

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Research on HIV/AIDS Epidemic Trend Prediction Model Based on ARIMA-LSTM 10

Research on HIV/AIDS Epidemic Trend Prediction Model Based o...

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10th International Conference on Orange technology, ICOT 2022

作者： Guo, Xin Yang, Yichen Tseng, Shih-Pang Yin, Zilong Wang, Cheng School of Information Science and Technology Sanda University Shanghai201209 China School of Software and Big Data Changzhou College of Information Technology Changzhou213164 China Shanghai University of Engineering Science Department of Electronic and Electrical Engineering Shanghai201620 China

ISBN: (纸本)9781665454193

In China, the number of persons who have infected Acquired Immune Deficiency Syndrome (AIDS) is rising quickly every year. Various public health and social issues have been brought on by the Human Immunodeficiency Virus/ Acquired Immune Deficiency Syndrome (HIV/AIDS) epidemic. Understanding the epidemic's development trend is crucial for controlling and preventing HIV/AIDS. Long Short-Term Memory (LSTM) is a nonlinear model, whereas Autoregressive Integrated Moving Average model (ARIMA) is a linear model. Both linear and nonlinear tendencies can be seen in China's HIV/AIDS prevalence rates. In order to forecast the HIV/AIDS pandemic trajectory, this research investigates the combined ARIMA-LSTM model. The increasing number of HIV/AIDS patients was predicted using the ARIMA model, the LSTM model, and the ARIMA-LSTM combination model, and their fitting effects were compared. The findings demonstrate that all four models can be used to predict the rising number of HIV/AIDS patients, however the parallel ARIMA-LSTM combination model has a better fitting prediction effect than the other three models. It works better as a forecasting model for the AIDS pandemic trend. © 2022 IEEE.

关键词： Long short-term memory

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Personalized Audiobook Generation Using Voice Cloning and Text-to-Speech Synthesis

Personalized Audiobook Generation Using Voice Cloning and Te...

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IEEE International Conference on Orange Technologies (ICOT)

作者： Chun-Cheng Wei Long-Chen Hung Si-Yu Gao Yi-Chang Chen Shih-Pang Tseng School of Software and Big Data Changzhou College of Information Technology Changzhou China Department of Electrical Engineering Cheng Shiu University Kaohsiung Taiwan Department of Multimedia Design Tajen University Pingtung Taiwan

ISBN: (数字)9798331523602

ISBN: (纸本)9798331523619

This project leverages advancements in voice cloning and text-to-speech (TTS) to develop a novel audio converter for personalized audiobook creation. Users upload a brief voice sample, allowing the system to learn and replicate unique vocal characteristics, such as tone and pitch, producing customized audio outputs. Integrating voice cloning with natural language processing (NLP) and TTS, the system first analyzes text to identify characters, dialogues, and emotional cues. This enables realistic, expressive narration tailored to each character and context. Unlike traditional TTS, this approach reduces monotony, allowing users to "narrate" stories in their own voice, creating an immersive and individualized listening experience.

关键词： Sentiment analysis Scalability Cloning Production Speech enhancement User experience Real-time systems Text to speech Social implications of technology Usability

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