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Results in engineering 2025年 26卷

作者： V. Selva Kumar K.R. Mohan Raj S. Gopalakrishnan G. Vennila D. Dhinakaran P. Kavitha Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi India Department of Information Technology Velammal Engineering College Chennai India Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Artificial Intelligence and Machine Learning School of Computing Mohan Babu University Tirupati India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering R.M.K. Engineering college Chennai India

The increasing prevalence and sophistication of Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks present significant challenges in ensuring the security and stability of modern networked systems. These attacks, characterized by their ability to disrupt services and compromise resources, require innovative and robust detection mechanisms to safeguard highly interactive environments such as honeypot systems. Traditional detection techniques often fall short in addressing the complexities posed by dynamic traffic patterns, diverse attack types, and real-time processing demands. This study introduces the Adaptive Distributed Honeypot Detection Network (ADHDN), a novel framework that leverages deep learning and probabilistic modeling to address the limitations of existing solutions. ADHDN employs a combination of Deep Generative Adversarial Networks (DGANs) and Discrete Hidden Markov Models (DHMMs) to achieve superior detection precision across various DoS attack types, including application-level, protocol-level, and data volume attacks. Implemented in a highly interactive honeypot environment with distributed server and virtual machine configurations, ADHDN demonstrates remarkable adaptability and resilience. Performance evaluation using the IoTID20 dataset reveals that ADHDN consistently outperforms contemporary models, such as RBMD, BNDH, and AHDL. ADHDN achieves a true positive rate of 99.7% for protocol-level attacks, 99.4% for application-level attacks, and 97.5% for data volume attacks under low attack volumes, maintaining robust performance even as attack intensity scales. These results underscore ADHDN’s potential to redefine DoS detection in dynamic and high-interaction environments, offering a scalable and efficient solution to contemporary cybersecurity challenges.

关键词： Denial of service Distributed denial of service Intrusion detection systems Generative adversarial networks Real-time attack detection Deep learning

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MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results 17th

MIPI 2022 Challenge on Under-Display Camera Image Restorati...

引用

17th European Conference on computer Vision, ECCV 2022

作者： Feng, Ruicheng Li, Chongyi Zhou, Shangchen Sun, Wenxiu Zhu, Qingpeng Jiang, Jun Yang, Qingyu Loy, Chen Change Gu, Jinwei Zhu, Yurui Wang, Xi Fu, Xueyang Hu, Xiaowei Hu, Jinfan Liu, Xina Chen, Xiangyu Dong, Chao Zhang, Dafeng Huang, Feiyu Liu, Shizhuo Wang, Xiaobing Jin, Zhezhu Jiang, Xuhao Shao, Guangqi Wang, Xiaotao Lei, Lei Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Huang, Tao Fang, Zhenxuan Huang, Mengluan Xu, Junwei Zhang, Yong Yang, Yuechi Shu, Qidi Yang, Zhiwen Li, Shaocong Yao, Mingde Xu, Ruikang Guan, Yuanshen Huang, Jie Xiong, Zhiwei Zhu, Hangyan Liu, Ming Liu, Shaohui Zuo, Wangmeng Jia, Zhuang Song, Binbin Song, Ziqi Mao, Guiting Hou, Ben Liu, Zhimou Ke, Yi Ouyang, Dengpei Han, Dekui Zhang, Jinghao Zhu, Qi Zheng, Naishan Zhao, Feng Jin, Wu Conde, Marcos Nathan, Sabari Timofte, Radu Xu, Tianyi Xu, Jun Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Jiang, Biao Ding, Yuhan Li, WanZhang Feng, Xiaoyue Chen, Sijing Zhong, Tianheng Lu, Jiyang Chen, Hongming Fan, Zhentao Chen, Xiang Nanyang Technological University Singapore Singapore SenseBrain San Jose United States Shanghai AI Laboratory Shanghai China SenseTime Research and Tetras.AI Beijing China University of Science and Technology of China Hefei China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Samsung Research China Beijing China Xiaomi Beijing China Hefei University of Technology Hefei China School of Artificial Intelligence Xidian University Xi’an China School of Remote Sensing and Information Engineering Wuhan University Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Harbin Institute of Technology Harbin China University of Macau China Changsha Research Institute of Mining and Metallurgy Changsha China Tianjin University Tianjin China Computer Vision Lab University of Wurzburg Würzburg Germany Couger Inc. Tokyo Japan School of Statistics and Data Science Nankai University Tianjin China Founding Minds Software Thiruvananthapuram India Department of Electronics and Communication SRM University AP Amaravati India Fudan University Shanghai China Shenyang Aerospace University Shenyang China Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9783031250712

Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://***/mipi-challenge/MIPI2022. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image reconstruction

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Multiplexed structured illumination super-resolution imaging with time-domain upconversion nanoparticles

arXiv

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arXiv 2021年

作者： Liu, Baolei Liao, Jiayan Song, Yiliao Lu, Jie Zhou, Jiajia Wang, Fan Faculty of Science University of Technology SydneyNSW2007 Australia Centre for Artificial Intelligence Faculty of Engineering and IT University of Technology SydneyNSW2007 Australia School of Electrical and Data Engineering Faculty of Engineering and IT University of Technology SydneyNSW2007 Australia

The emerging optical multiplexing within nanoscale shows super-capacity in encoding information by using the time-domain fingerprints from uniform nanoparticles. However, the optical diffraction limit compromises the decoding throughput and accuracy of the nanoparticles during wide-field imaging. This, in turn, challenges the quality of nanoparticles to afford the modulated excitation condition, and further to retain the multiplexed optical fingerprints for super-resolution multiplexing. Here we report a tailor-made time-domain super-resolution method with the lifetime-engineered upconversion nanoparticles for multiplexing. We demonstrate that the nanoparticles are bright, uniform, and stable under structured illumination, which supports a lateral resolution of 186 nm, less than 1/4th of excitation wavelength. We further develop a deep learning algorithm to coordinate with super-resolution images for more accurate decoding compared to a numeric algorithm. We demonstrate a three-channel sub-diffraction-limit imaging-based optical multiplexing with decoding accuracies above 93% for each channel, and larger than 60% accuracies for potential seven-channel multiplexing. The improved resolution provides high throughput by resolving the particles within the optical limit, which enables higher multiplexing capacity in space. This time-domain super-resolution multiplexing opens a new horizon for handling the growing amount of information content, diseases source, and security risk in modern society. Copyright © 2021, The Authors. All rights reserved.

关键词： Nanoparticles

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Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy

arXiv

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arXiv 2022年

作者： Bergmeir, Christoph de Nijs, Frits Sriramulu, Abishek Abolghasemi, Mahdi Bean, Richard Betts, John Bui, Quang Dinh, Nam Trong Einecke, Nils Esmaeilbeigi, Rasul Ferraro, Scott Galketiya, Priya Genov, Evgenii Glasgow, Robert Godahewa, Rakshitha Kang, Yanfei Limmer, Steffen Magdalena, Luis Montero-Manso, Pablo Peralta, Daniel Kumar, Yogesh Pipada Sunil Rosales-Pérez, Alejandro Ruddick, Julian Stratigakos, Akylas Stuckey, Peter Tack, Guido Triguero, Isaac Yuan, Rui Department of Data Science and Artificial Intelligence Monash University Melbourne Australia School of Mathematics and Physics University of Queensland Brisbane Australia Centre for Energy Data Innovation School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia School of Electrical and Electronics Engineering University of Adelaide Adelaide Australia Honda Research Institute Europe GmbH Offenbach am Main63073 Germany School of Information Technology Deakin University Melbourne Australia Building and Property Division Monash University Melbourne Australia EVERGi MOBI Vrije Universiteit Brussel Brussels Belgium School of Economics and Management Beihang University Beijing China E.T.S. Ingenieros Informáticos Universidad Politécnica de Madrid Madrid28660 Spain Disciple of Business Analytics University of Sydney Australia IDLab Department of Information Technology Ghent University - imec Belgium Department of Computer Science Centro de Investigación en Matem áticas Monterrey66629 Mexico Mines Paris PSL University Sophia Antipolis06904 France DaSCI Andalusian Institute in Data Science and Computational Intelligence Granada Spain Department of Computer Science and Artificial Intelligence University of Granada Granada Spain

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the "IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Deep Knowledge Tracing Based on Bayesian Neural Network 4th

Deep Knowledge Tracing Based on Bayesian Neural Network

引用

4th International Conference on Intelligent, Interactive Systems and Applications, IISA2019

作者： Donghua, Li Yanming, Jia Jian, Zhou Wufeng, Wang Ning, Xu School of Computer Science and Technology Wuhan University of Technology Wuhan China Artificial Intelligence and Big Data Department Beijing Global Wisdom Inc Beijing China School of Information Engineering Wuhan University of Technology Wuhan China Zhongshan Institute of Advanced Engineering Technology of WUT Zhongshan China

ISBN: (纸本)9783030343866

Knowledge Tracing (KT) is the de-facto standard for inferring student knowledge from performance data, modeling students’ changing knowledge state during skill acquisition. The existing knowledge tracing models have problems such as incomplete features consideration, poor effect and easy over-fitting in the process of simulating students’ knowledge acquisition. In order to better model students’ learning and track the process of students’ knowledge acquisition, a Bayesian neural network Deep Knowledge Tracing (BDKT) is proposed. The model combines Bayesian neural network with Deep Knowledge Tracing (DKT), it can not only model feature-rich students’ behavior data, but also effectively prevent over-fitting, enhance the generalization ability of the model, and accelerate the convergence speed of the model. The above performances of the model are tested by the experiments on the data sets of KDD Cup and Assistments. © 2020, Springer Nature Switzerland AG.

关键词： Students

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Multi-stage image denoising with the wavelet transform

arXiv

引用

arXiv 2022年

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Bob Zhang, Yanning Zhang, David School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Department of Computer and Information Science University of Macau 999078 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China Guangdong Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. However, most of existing CNNs depend on enlarging depth of designed networks to obtain better denoising performance, which may cause training difficulty. In this paper, we propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and a residual block (RB). DCB uses a dynamic convolution to dynamically adjust parameters of several convolutions for making a tradeoff between denoising performance and computational costs. WEB uses a combination of signal processing technique (i.e., wavelet transformation) and discriminative learning to suppress noise for recovering more detailed information in image denoising. To further remove redundant features, RB is used to refine obtained features for improving denoising effects and reconstruct clean images via improved residual dense architectures. Experimental results show that the proposed MWDCNN outperforms some popular denoising methods in terms of quantitative and qualitative analysis. Codes are available at https://***/hellloxiaotian/MWDCNN. © 2022, CC BY-NC-ND.

关键词： Wavelet transforms

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MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

arXiv

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arXiv 2024年

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Jinan250353 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Jinan250014 China The Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

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Hierarchical progressive learning for zero-shot peptide-HLA binding prediction and automated antigenic peptide design

引用

Cell reports 2025年第6期44卷 115763页

作者： Xinyuan Zhu Jiadong Lu Xinting Hu Tengchuan Jin Shan Lu Fuli Feng School of Information Science and Technology University of Science and Technology of China Hefei Anhui China. School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei Anhui China. School of Computer Science and Engineering Nanyang Technological University Singapore Singapore. Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui China. Department of Cellular and Molecular Medicine University of California San Diego San Diego CA USA. Electronic address: ms.shanlu@***. School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei Anhui China. Electronic address: fengfl@***.

Predicting the binding of peptides to human leukocyte antigen (HLA) alleles is critical for identifying epitopes initiating immune responses in vaccine development and immunotherapies. Despite the progress in predicting binding peptides of 1% HLA class-I alleles with in silico prediction tools, it remains challenging to predict binding peptides for alleles lacking epitopes. To achieve the binding prediction on those zero-shot alleles, we developed a hierarchical progressive learning (HPL) framework that progressively learns sequence patterns of specific peptide-HLA complexes. HPL models improve the prediction performance for zero-shot HLA class-I alleles by 60.8% (1,414.0% for non-classical ones). We further developed an automated peptide mutation search program, which automatically identifies binding peptides for any target HLA class-I allele and mutates weak-binding or non-binding peptides to enhance binding affinity under the guidance of HPL models. Our program generated high-affinity binding candidates of any target allele with limited mutations in more than 38.1% of testing cases.

关键词： CP: Immunology HLA-peptide binding immunotherapy protein language model

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DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images

arXiv

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arXiv 2023年

作者： Qian, Bo Chen, Hao Wang, Xiangning Che, Haoxuan Kwon, Gitaek Kim, Jaeyoung Choi, Sungjin Shin, Seoyoung Krause, Felix Unterdechler, Markus Hou, Junlin Feng, Rui Li, Yihao Daho, Mostafa El Habib Wu, Qiang Zhang, Ping Yang, Xiaokang Cai, Yiyu Li, Huating Jia, Weiping Sheng, Bin Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Department of Ophthalmology Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China VUNO Inc. Seoul Korea Republic of AI/DX Convergence Business Group KT Korea Republic of Johannes Kepler University Linz Linz Austria School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China LaTIM UMR 1101 Inserm Brest France University of Western Brittany Brest France Department of Computer Science and Engineering The Ohio State University Ohio United States Department of Biomedical Informatics The Ohio State University Ohio United States Translational Data Analytics Institute The Ohio State University Ohio United States MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Shanghai China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Department of Endocrinology and Metabolism Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China

computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomography angiography (UW-OCTA) is a non-invasive and safe imaging modality in DR diagnosis system, but there is a lack of publicly available benchmarks for model development and evaluation. To promote further research and scientific benchmarking for diabetic retinopathy analysis using UW-OCTA images, we organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI 2022). The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams from geographically diverse institutes submitting different solutions in these three tasks, respectively. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge. The obtained results from top algorithms indicate the importance of data augmentation, model architecture and ensemble of networks in improving the performance of deep learning models. These findings have the potential to enable new developments in diabetic retinopathy analysis. The challenge remains open for post-challenge registrations and submissions for benchmarking future methodology developments. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

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Robust Fault Diagnosis of Drilling Machinery Under Complex Working Conditions Based on Carbon Intelligent Industrial Internet of Things

引用

IEEE Internet of Things Journal 2025年

作者： Fang, Kai Tong, Lianghuai Xu, Xiaojie Cai, Jijing Peng, Xueyuan Omar, Marwan Bashir, Ali Kashif Wang, Wei Xi'an Jiaotong University College of Energy and Power Engineering Xi'an710049 China Quzhou Academy of Metrology and Quality Inspection Quzhou324000 China Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Illinois Institute of Technology Information Technology and Management United States Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen518172 China Beijing Institute of Technology School of Medical Technology Beijing100081 China

As sustainable development gains attention, integrating carbon-intelligent computing into fault diagnosis systems has emerged as a critical strategy to reduce energy consumption and carbon footprints. This approach uses artificial intelligence (AI) and the Internet of Things (IoT) to optimize task scheduling, aligning it with low-carbon energy sources based on time and location. In fault diagnosis, energy-intensive tasks such as data processing and model inference can be scheduled during periods of abundant renewable energy, thereby minimizing emissions. However, drilling machines operate under complex conditions that generate non-stationary noise, which distorts signals and complicates fault diagnosis. Therefore, this paper combines Bidirectional Long Short-Term Memory (BiLSTM) with the Kolmogorov-Arnold Network (KAN) and integrates Wavelet Transform and Convolutional Autoencoder, proposing a highly robust fault diagnosis model for drilling machines, named WCBK. The Wavelet Transform converts pressure timeseries data, which contains fault information, into time-frequency images, facilitating the detection of fault frequency components. The Convolutional Autoencoder preserves essential features while removing noise by learning low-dimensional representations of the signal, effectively capturing local features in time-frequency images through local connections to enhance denoising performance. Finally, the composite deep learning network, which combines BiLSTM and KAN, achieves highly robust fault diagnosis under complex working conditions. The effectiveness of the proposed WCBK model was validated through ablation experiments, experiments on different individuals, experiments on different parts, and model adaptability evaluations. In experiments involving different individuals and parts, the WCBK model improved fault diagnosis accuracy by 10.9% and 8.8%, respectively, compared to existing models. © 2014 IEEE.

关键词： Drilling machines (machine tools)

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