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IEEE Open Journal of the Communications Society 2023年 4卷 2684-2696页

作者： Wang, Yan Shu, Feng Zhuang, Zhihong Dong, Rongen Zhang, Qi Wu, Di Wang, Xuehui Yang, Liang Wang, Jiangzhou Hainan University School of Information and Communication Engineering Haikou570228 China Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Hainan University Communication Engineering and Collaborative Innovation Center of Information Technology Haikou570228 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China University of Kent School of Engineering CanterburyCT2 7NT United Kingdom

In this paper, the dominant factor affecting the performance of active intelligent reflecting surface (IRS) aided wireless communication networks in Rayleigh fading channel, namely the average signal-to-noise ratio (SNR) γ 0 at IRS, is studied. Making use of the weak law of large numbers, the simple asymptotic expressions for the received SNR at user and the average SNR at IRS are derived as the number N of IRS elements goes to medium-scale and large-scale. When N tends to large-scale, the asymptotic received SNR at user is proved to be a linear increasing function of a product of γ 0 and N. Subsequently, when the base station (BS) transmit power is fixed, there exists an optimal limited reflective power at IRS. At this point, more IRS reflect power will degrade the SNR performance. Additionally, under the total power sum constraint of the BS transmit power and the power reflected by the IRS, an optimal power allocation (PA) strategy is derived and shown to achieve 0.83 bit rate gain over equal PA. Finally, an IRS with finite phase shifters being taken into account, generates phase quantization errors, and further leads to a degradation of receive performance. The corresponding closed-form performance loss expressions for user's asymptotic SNR, achievable rate (AR), and bit error rate (BER) are derived for active IRS. Numerical simulation results show that a 3-bit discrete phase shifter is required to achieve a trivial performance loss for a large-scale active IRS. © 2020 IEEE.

关键词： Wireless networks

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A Novel Data-Annotated Label Collection and Deep-Learning Based Medical Image Segmentation in Reversible Data Hiding Domain

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computer Modeling in engineering & sciences 2025年第5期143卷 1635-1660页

作者： Lord Amoah Jinwei Wang Bernard-Marie Onzo School of Computer Science Nanjing University of Information Science&TechnologyNanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science&Technology Nanjing210044China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou45001China

Medical image segmentation,i.e.,labeling structures of interest in medical images,is crucial for disease diagnosis and treatment in *** reversible data hiding in medical images(RDHMI),segmentation consists of only two regions:the focal and nonfocal *** focal region mainly contains information for diagnosis,while the nonfocal region serves as the monochrome *** current traditional segmentation methods utilized in RDHMI are inaccurate for complex medical images,and manual segmentation is time-consuming,poorly reproducible,and *** state-of-the-art deep learning(DL)models will facilitate key benefits,but the lack of domain-specific labels for existing medical datasets makes it *** address this problem,this study provides labels of existing medical datasets based on a hybrid segmentation approach to facilitate the implementation of DL segmentation models in this ***,an initial segmentation based on a 33 kernel is performed to analyze×identified contour pixels before classifying pixels into focal and nonfocal ***,several human expert raters evaluate and classify the generated labels into accurate and inaccurate *** inaccurate labels undergo manual segmentation by medical practitioners and are scored based on a hierarchical voting scheme before being assigned to the proposed *** ensure reliability and integrity in the proposed dataset,we evaluate the accurate automated labels with manually segmented labels by medical practitioners using five assessment metrics:dice coefficient,Jaccard index,precision,recall,and *** experimental results show labels in the proposed dataset are consistent with the subjective judgment of human experts,with an average accuracy score of 94%and dice coefficient scores between 90%-99%.The study further proposes a ResNet-UNet with concatenated spatial and channel squeeze and excitation(scSE)architecture for semantic segmentation to validate an

关键词： Reversible data hiding medical image segmentation medical image dataset deep learning

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Ethereum Smart Contracts Vulnerabilities Detection Leveraging Fine-Tuning DistilBERT 10

Ethereum Smart Contracts Vulnerabilities Detection Leveragin...

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10th International Conference on Platform Technology and Service, PlatCon 2024

作者： Kim, Jaehyun Le, Thi-Thu-Huong Lee, Sangmyeong Kim, Howon School of Computer Science and Engineering Pusan National University Busan Korea Republic of Blockchain Platform Research Center Pusan National University Busan Korea Republic of

ISBN: (纸本)9798350367874

In recent years, the proliferation of blockchain technology has led to the widespread adoption of smart contracts, particularly on the Ethereum platform. However, these smart contracts are prone to vulnerabilities that can be exploited, leading to significant financial losses and security breaches. This paper explores the application of Distil-BERT, a distilled version of the BERT (Bidirectional Encoder Representations from Transformers) model, to detect vulnerabilities in Ethereum smart contracts. By fine-tuning Distil-BERT on the SB-curate dataset, a curated dataset of vulnerable Solidity smart contracts, we achieve a performance accuracy exceeding 97%. We provide natural language processing (NLP) techniques that can be leveraged to enhance the accuracy and efficiency of these methods. Our experiments show that Distil-BERT can effectively identify common vulnerabilities, such as reentrancy and integer overflows, outperforming traditional static analysis tools. This research contributes to developing more secure smart contract systems and offers insights into integrating advanced machine learning models in blockchain security. © 2024 IEEE.

关键词： Ethereum

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Deep Convolutional Neural Network based Wheat Leaf Disease Detection in Precision Agriculture 5

Deep Convolutional Neural Network based Wheat Leaf Disease D...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Dey, Sreyashi Naidu, Roshan Dey, Om Subrato Balasundaram, A. Shaik, Ayesha Vellore Institute of Technology Chennai School of Computer Science and Engineering Chennai India Vellore Institute of Technology Chennai Center for Cyber Physical Systems Chennai India

ISBN: (纸本)9798331504403

Wheat is one of the fundamental crops that sustains billions of people globally. Unfortunately its production faces major challenges from numerous plant based diseases which can drastically decrease yields as well as the economic returns. There exist a variety of frameworks utilising computer vision and deep learning techniques which can be invaluable for numerous tasks aiding the stakeholders in agriculture and crop management by identifying diseased sections of *** methods to detect the disease are prone to human errors and such limitations underscore the need for automated and reliable disease detection systems. Also AI is likely to predict a future outbreak of any such disease by analysing the historical and geographical data. In this paper, a novel technique have been proposed whose novelty lies in the result that has been achieved using the proposed algorithm which when compared to two other deep learning models that are ResNet50 and VGG16 achieved a higher accuracy. The deep learning approach for wheat leaf disease detection implemented in this paper is performed using a convolutional neural network (CNN) architecture implemented in Python, OpenCV, NumPy, and TensorFlow alongside implementing some data augmentation leveraging the power of deep learning to extract complex features from wheat leaf images enabling accurate classification between healthy and diseased leaves. The aforementioned algorithm has been trained using a default Crop Disease Dataset extracted from a Github repository which has been able to achieve an accuracy of 88.53% with one full cycle of 30 epochs recording its final epoch with 91.04% accuracy and the final K Fold procedure resulting in 91.42% accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Pedestrian Detection in Foggy Weather Through YOLOv8 Based on FEAttention 1

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41st computer Graphics International Conference, CGI 2024

作者： Sun, Meng Zhu, Jianlin Yang, Bo Huang, Jin Zhang, Xiao College of Computer Science South-Central Minzu University Wuhan430074 China Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises South-Central Minzu University Wuhan430074 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan430200 China

ISBN: (数字)9783031820243

ISBN: (纸本)9783031820236

Despite the high accuracy achieved by current pedestrian detection technologies, their performance is significantly hindered in foggy weather conditions due to image color distortion and an increased false alarm. This often leads to false positives, false negative, and other related issues. To address these limitations, we propose an enhanced FDW-YOLOv8 (Feature-Enhancing-Attention Dark Channel Prior Wise-IOU YOLOv8) model based on the YOLOv8 algorithm. This model incorporates FEAttention(Feature-Enhancing-Attention) mechanism into the original YOLOv8 network to improve the discriminability and robustness of extracted features, thus reducing the miss rate(MR). Furthermore, we employ the WiseIOU(WIoU) loss function to expedite model convergence and optimize the accuracy of anchor frames, effectively handling challenges posed by occlusions and scale variations in foggy environments. Prior to model prediction, we utilize the Dark Channel Prior(DCP) defogging algorithm to preprocess input images, effectively extracting pedestrian information while suppressing irrelevant details, thus minimizing the false positive rate (FPR). To evaluate the performance of our model, we create a specialized Foggy-Pedestrian dataset, focusing on pedestrian detection in foggy conditions. Our experiments demonstrate that the proposed FDW-YOLOv8 model significantly outperforms the benchmark model in terms of reducing FPR and MR, exhibiting high generalization and practical applicability for real-world applications in foggy environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Benchmarking

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Inaudible Sound Covert Channel with Anti-Jamming Capability: Attacks vs. Countermeasure

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Journal of Electronic science and Technology 2022年第4期20卷 396-415页

作者： Xiao-Hang Wang Shou-Bin Li Ying-Tao Jiang Amit Kumar Singh Bi-Yun Ma Le-Tian Huang Mei Yang Fen Guo School of Cyber Science and Technology Zhejiang UniversityHangzhou 310012 ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 310012 School of Software Engineering South China University of TechnologyGuangzhou 510006 Department of Electrical and Computer Engineering University of NevadaLas Vegas 89154 School of Computer Science and Electronic Engineering University of EssexColchester CO43SQ School of Electronics and Information Engineering South China University of TechnologyGuangzhou 510640 School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610054

When an inaudible sound covert channel(ISCC)attack is launched inside a computer system,sensitive data are converted to inaudible sound waves and then *** receiver at the other end picks up the sound signal,from which the original sensitive data can be *** a forceful countermeasure against the ISCC attack,strong noise can be used to jam the channel and literally shut down any possible sound data *** this paper,enhanced ISCC is proposed,whose transmission frequency can be dynamically ***,if the transmitter detects that the covert channel is being jammed,the transmitter and receiver both will switch to another available frequency and re-establish their communications,following the proposed communications *** results show that the proposed enhanced ISCC can remain connected even in the presence of a strong jamming noise ***,a detection method based on frequency scanning is proposed to help to combat such an anti-jamming sound *** the proposed countermeasure,the bit error rate(BER)of the data communications over enhanced ISCC soars to more than 48%,essentially shutting down the data transmission,and thus neutralizing the security threat.

关键词： Countermeasure inaudible sound covert channel(ISCC)

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Accelerated learning and co-optimization of elastocaloric effect and stress hysteresis of elastocaloric alloys

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Rare Metals 2024年第12期43卷 6606-6624页

作者： Shi-Yu He Fei Xiao Rui-Hang Hou Shun-Gui Zuo Ying Zhou Xiao-Rong Cai Zhu Li Yan-Ming Wang Aysu Catal-Isik Enrique Galindo-Nava Xue-Jun Jin State Key Lab of Metal Matrix Composite School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai200240China Materials Genome Initiative Center Shanghai Jiao Tong UniversityShanghai200240China Institute of Medical Robotics Shanghai Jiao Tong UniversityShanghai200240China Department of Materials Science and Engineering Graduate School of EngineeringOsaka UniversityOsaka565-0871Japan University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong UniversityShanghai200240China Department of Mechanical Engineering University College LondonTorrington PlaceLondonWC1E 7JEUK

The development of high-performance advanced elastocaloric alloys is crucial for the implementation of elastocaloric ***,we present a machine learning(ML)framework to accelerate the development of novel elastocaloric alloys with large adiabatic temperature change(ΔT_(ad))and low stress hysteresis(Δσ_(hy)).The comprehensive framework comprises database construction,feature selection,model construction,alloy design and validation,and model *** are selected according to the physical attributes they *** that may reflect the compatibility between parent and product phases,lattice distortion and the free energy in the alloy are considered in the *** them,the key features are selected by recursive feature elimination and exhaustive search *** trained models in combination with the Bayesian optimization method are exploited to achieve multi-objective *** to the results,a newly designed elastocaloric alloy shows a large adiabatic temperature change of 15.2 K and low average stress hysteresis of70.3 MPa at room temperature,which is consistent with our *** predictions of our ML model are interpreted by the Shapley additive exPlainations(SHAP)approach,which explicitly quantifies the effects of each feature in our model on the adiabatic temperature change and stress ***,we employ the Sureindependence screening and sparsifying operator(SISSO)method in conjunction with the key features to formulate explicit model.

关键词： Elastocaloric alloys Adiabatic temperature change Stress hysteresis Machine learning Multi-objective optimization

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PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer

PSST: A Benchmark for Evaluation-driven Text Public-Speaking...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Sun, Huashan Wu, Yixiao Ye, Yuhao Yang, Yizhe Li, Yinghao Li, Jiawei Gao, Yang School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

ISBN: (纸本)9798891761681

Language style is necessary for AI systems to understand and generate diverse human language ***, previous text style transfer primarily focused on sentence-level data-driven approaches, limiting exploration of potential problems in large language models (LLMs) and the ability to meet complex application *** overcome these limitations, we introduce a novel task called Public-Speaking Style Transfer (PSST), which aims to simulate humans to transform passage-level, official texts into a public-speaking *** in the analysis of real-world data from a linguistic perspective, we decompose public-speaking style into key sub-styles to pose challenges and quantify the style modeling capability of *** such intricate text style transfer, we further propose a fine-grained evaluation framework to analyze the characteristics and identify the problems of stylized *** experiments suggest that current LLMs struggle to generate public speaking texts that align with human preferences, primarily due to excessive stylization and loss of semantic information. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Generative Image Steganography Based on Latent Space Vector Coding and Diffusion Model 1

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2nd International Conference on Artificial Intelligence Security and Privacy, AIS and P 2024

作者： Liu, Weisong Yang, Jianfeng Huang, Jiewen El-Latif, Ahmed A. Abd Zhou, Zhili School of Artificial Intelligence Guangzhou University Guangzhou China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia

ISBN: (数字)9789819611485

ISBN: (纸本)9789819611478

Image steganography is a technology that embed secret information within a cover image to obtain a stego-image for covert communication. The transmission of undetectable stego-images via social media can facilitate secure and efficient covert communication. Recently, the generative image steganography has achieved promising performance with the rapid development of generative models. However, existing generative image steganography suffers from issues, i.e., the low quality of the generated stego-image and the limited hiding capacity. To address these issues, this paper proposes a generative image steganography scheme based on latent space vector coding and diffusion model. It consists of an optimized diffusion denoising network, the latent space vector coding mapping network, and the information extraction network. In this scheme, the secret information is encoded as a latent space vector, which is then transformed in the input diffusion model to generate the stego-image. Experimental results demonstrate that this scheme achieves superior performance compared with existing generative image steganography methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Vectors

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Gait Phase Detection and Prediction with Machine Learning Models Based on sEMG

Gait Phase Detection and Prediction with Machine Learning Mo...

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Artificial Intelligence, Human-computer Interaction and robotics (AIHCIR), International Conference on

作者： Youping Sun Jiachen Wang Zixian Wang Jiasen Li Yibin Li Rui Song Huanghe Zhang The Center for Robotics School of Control Science and Engineering Shandong University Jinan Shandong China

ISBN: (数字)9798331534035

ISBN: (纸本)9798331534042

In this study, we utilized surface electromyog-raphy (sEMG) to detect and predict five key gait phases: heel strike(HS), maximum stance flexion(MSF), maximum stance extension(MSE), toe-off(TO), and maximum swing flex-ion(MSF). These gait phases provide an in-depth understanding of gait dynamics. Data preprocessing employed a sliding window technique to process the sEMG signals. We demonstrate that artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (KNN) can effectively detect and predict gait phases from sEMG signals. The performance of these three fundamental machine learning models was evaluated using hold-out validation, 10-fold cross-validation(10-Fold), and leave-one-subject-out cross-validation(LOOCV). The 2-phase detection achieved an accu-racy of 0.9879 ± 0.015, while the 5-phase detection yielded 0.9681 ± 0.0040. In prediction, the 2-phase achieved 0.974 ± 0.013, and the 5-phase reached 0.978 ± 0.016. Our results confirm the effectiveness of machine learning models in de-tecting and predicting gait phases using sEMG. The findings suggest that the proposed method has potential for personalized gait rehabilitation and fall prevention, offering a promising direction for clinical applications.

关键词： Support vector machines Phase measurement Prevention and mitigation Machine learning Artificial neural networks Predictive models Nearest neighbor methods Phase detection Surface treatment Robots

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