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Cross-Chain Privacy-Preserving for BIoMT With Designated Verifier Proxy Signature

IEEE Internet of Things Journal 2025年

作者： Li, Chaoyang Jiang, Bohao Dong, Mianxiong Chen, Yuling Huang, Min Xin, Xiangjun Ota, Kaoru Zhengzhou University of Light Industry College of Software Engineering Zhengzhou450001 China Muroran Institution of Technology Muroran050-8585 Japan Guizhou University State Key Laboratory of Public Big Data Guizhou Guiyang550025 China

Blockchain-enabled Internet of Medical Things (BIoMT) has received extensive attention and in-depth research to solve the centralized, data island problems with the rapid developments of blockchain-related technologies. However, many different chains with different data structures, consensus protocols, and cryptographic algorithms are constructed, which brings a new »data island» problem. Meanwhile, the cryptographic algorithms used in most current BIoMT systems are weak against quantum attacks. In this paper, a cross-chain privacy-preserving (CCPP) model and a designated verifier proxy signature (DVPS) scheme have been proposed. This CCPP model is equipped with the relay chain technology and DVPS to achieve secure cross-chain medical data-sharing among different BIoMT systems. The DVPS scheme is constructed with lattice theory, which can achieve signer proxy, designated user verification, and anti-quantum attack. Then, the security proof shows that the proposed DVPS can capture the security properties of correctness, unforgeability, the signer's anonymity, and non-transferability. The performance evaluations show that the cross-chain transactions are efficient and stable with the transaction number increasing, and the proposed DVPS is efficient about the key size, time consumption, and energy consumption. This work can also improve the privacy security of system users and medical data in BIoMT systems and promote the value play of medical data. © 2014 IEEE.

关键词： Lattice theory

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-...

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

作者： Kaijun Deng Dezhi Zheng Jindong Xie Jinbao Wang Weicheng Xie Linlin Shen Siyang Song Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Department of Computer Science University of Exeter

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics Signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

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K-ON: Stacking knowledge On the Head Layer of Large Language Model

arXiv

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arXiv 2025年

作者： Guo, Lingbing Zhang, Yichi Bo, Zhongpu Chen, Zhuo Sun, Mengshu Zhang, Zhiqiang Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Ant Group Joint Lab of Knowledge Graph China Ant Group China School of Software Technology Zhejiang University China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Recent advancements in large language models (LLMs) have significantly improved various natural language processing (NLP) tasks. Typically, LLMs are trained to predict the next token, aligning well with many NLP tasks. However, in knowledge graph (KG) scenarios, entities are the fundamental units and identifying an entity requires at least several tokens. This leads to a granularity mismatch between KGs and natural languages. To address this issue, we propose K-ON, which integrates KG knowledge into the LLM by employing multiple head layers for next k-step prediction. K-ON can not only generate entity-level results in one step, but also enables contrastive loss against entities, which is the most powerful tool in KG representation learning. Experimental results show that K-ON outperforms state-of-the-art methods that incorporate text and even the other modalities. Copyright © 2025, The Authors. All rights reserved.

关键词： knowledge graph

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FrustumFusionNets: A Three-Dimensional Object Detection Network Based on Tractor Road Scene

arXiv

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arXiv 2025年

作者： Yang, Lili Chang, Mengshuai Guo, Xiao Feng, Yuxin Mei, Yiwen Wu, Caicong College of Information and Electrical Engineering China Agricultural University Beijing100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China

To address the issues of the existing frustum-based methods' underutilization of image information in road three-dimensional object detection as well as the lack of research on agricultural scenes, we constructed an object detection dataset using an 80-line Light Detection And Ranging (LiDAR) and a camera in a complex tractor road scene and proposed a new network called FrustumFusionNets (FFNets). Initially, we utilize the results of image-based two-dimensional object detection to narrow down the search region in the three-dimensional space of the point cloud. Next, we introduce a Gaussian mask to enhance the point cloud information. Then, we extract the features from the frustum point cloud and the crop image using the point cloud feature extraction pipeline and the image feature extraction pipeline, respectively. Finally, we concatenate and fuse the data features from both modalities to achieve three-dimensional object detection. Experiments demonstrate that on the constructed test set of tractor road data, the FrustumFusionNetv2 achieves 82.28% and 95.68% accuracy in the three-dimensional object detection of the two main road objects, cars and people, respectively. This performance is 1.83% and 2.33% better than the original model. It offers a hybrid fusion-based multi-object, high-precision, real-time three-dimensional object detection technique for unmanned agricultural machines in tractor road scenarios. On the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) Benchmark Suite validation set, the FrustumFusionNetv2 also demonstrates significant superiority in detecting road pedestrian objects compared with other frustum-based three-dimensional object detection methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Tractors (truck)

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“Weibing” in traditional Chinese medicine—biological basis and mathematical representation of disease-susceptible state

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Acta Pharmaceutica Sinica B 2025年第5期15卷 2363-2371页

作者： Sun, Wanyang Wang, Rong Ouyang, Shuhua Liang, Wanli Duan, Junwei Gong, Wenyong Hu, Lianting Chen, Xiujuan Li, Yifang Kurihara, Hiroshi Yao, Xinsheng Gao, Hao He, Rongrong Institute of Traditional Chinese Medicine and Natural Products College of Pharmacy/Guangdong Engineering Research Center of Traditional Chinese Medicine & Disease Susceptibility/Guangdong Engineering Research Center of Traditional Chinese Medicine & Health Products/International Cooperative Laboratory of TCM Modernization and Innovative Drug Development of Chinese Ministry of Education (MOE)/Guangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research/State Key Laboratory of Bioactive Molecules and Druggability Assessment Jinan University Guangzhou 510632 China State Key Laboratory of Quality Research in Chinese Medicine Macau University of Science and Technology Macau 999078 China College of Information Science and Technology Jinan University Guangzhou 510632 China Faculty of Data Science City University of Macau Macau 999078 China Medical Big Data Center Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou 510632 China The Data Center Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital) Tongji Medical College Huazhong University of Science and Technology Wuhan 430016 China

“Weibing” is a fundamental concept in traditional Chinese medicine (TCM), representing a transitional state characterized by diminished self-regulatory abilities without overt physiological or social dysfunction. This perspective delves into the biological foundations and quantifiable markers of Weibing, aiming to establish a research framework for early disease intervention. Here, we propose the “Health Quadrant Classification” system, which divides the state of human body into health, sub-health, disease-susceptible state, and disease. We suggest the disease-susceptible stage emerges as a pivotal point for TCM interventions. To understand the intrinsic dynamics of this state, we propose laboratory and clinical studies utilizing time-series experiments and stress-induced disease susceptibility models. At the molecular level, bio-omics technologies and bioinformatics approaches are highlighted for uncovering intricate changes during disease progression. Furthermore, we discuss the application of mathematical models and artificial intelligence in developing early warning systems to anticipate and avert the transition from health to disease. This approach resonates with TCM's preventive philosophy, emphasizing proactive health maintenance and disease prevention. Ultimately, our perspective underscores the significance of integrating modern scientific methodologies with TCM principles to propel Weibing research and early intervention strategies forward. © 2025 The Authors

关键词： Disease-susceptible state Mathematical modeling Preventive medicine Traditional Chinese medicine Weibing

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Oesophageal cancer-specific mortality risk and public health insurance: Prospective cohort study from China

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World Journal of Gastrointestinal Oncology 2025年第4期17卷 274-285页

作者： Xiang-Lin Wu Xiao-Sheng Li Jing-Han Cheng Lin-Xin Deng Zu-Hai Hu Jun Qi Hai-Ke Lei Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment Chongqing University Cancer HospitalChongqing 400030China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center Chongqing University Cancer HospitalChongqing 400030China Research Center for Medicine and Social Development Chongqing Medical UniversityChongqing 400016China Department of Health Statistics School of Public HealthChongqing Medical UniversityChongqing 400016China Department of Thoracic Surgery The People’s Hospital of Changshou ChongqingChongqing 401220China

BACKGROUND Oesophageal cancer is a significant health concern worldwide,with high inci-dence and mortality *** China,the disease burden is particularly high,accounting for a substantial proportion of oesophageal cancer cases and related deaths *** To explore the relationship between the mortality rate of oesophageal cancer patients and insurance type,out-of-pocket ratio,and the joint effects of insurance type and out-of-pocket *** The χ^(2) test was used to analyze patients’demographic and clinical *** logistic regression,the Cox proportional hazard model,and the competitive risk model were used to calculate the cumulative hazard ratios(HRs)of all-cause death and oesophageal cancer-specific death among patients with different types of insurance and out-of-pocket *** Compared with patients covered by basic medical insurance for urban and rural residents,patients covered by urban employee basic medical insurance for urban workers(UEBMI)had a 23.30%increased risk of oesophageal cancer-specific death[HR=1.233,95%confidence interval(CI):1.093-1.391,P<0.005].Compared with patients in the low out-of-pocket ratio group,patients in the high out-of-pocket ratio group had a 25.80%reduction in the risk of oesophageal cancer-specific death(HR=0.742,95%CI:0.6555-0.84,P<0.005).With each 10%increase in the out-of-pocket ratio,the risk of oesophageal cancer-specific death decreased by 10.10%in patients covered by ***,the risk of oesophageal cancer-specific death increased by 26.90%in patients in the high out-of-pocket ratio *** This study reveals the relationships of the specific mortality rate of patients with oesophageal cancer with the out-of-pocket ratio and medical insurance types as well as their combined *** study provides practical suggestions and guidance for the formulation of relevant policies in this area.

关键词： Oesophageal cancer Public health Insurance Prognosis Mortality risk

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Task Prioritization in Multiagent Environments: A Novel Approach Using Nash Q-Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Kexin Hu, Yu Wang, Jielei Li, Lin Zhang, Shuyuan Luo, Guangchun Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

—With the rapid proliferation of smartphones and various terminal devices, edge computing has gained increasing importance in overcoming computational limitations and enhancing service quality through task offloading. However, most existing approaches handle tasks on a first-come, first-served basis, neglecting task prioritization, which is critical in real-world scenarios where tasks have different levels of urgency and resource requirements. This paper addresses the challenge of effective task prioritization in multi-agent task offloading by proposing a Nash Q-learning-based strategy. Our methodology involves optimizing task allocation decisions using Nash Q-learning to integrate Nash equilibrium principles, thereby enhancing adaptability and dynamic decision-making capabilities within a multi-agent environment. The proposed algorithm is evaluated through extensive simulations, comparing total delay, resource utilization, and task success rates across various task offloading strategies. The results demonstrate that our Nash Q-learning approach outperforms conventional techniques by dramatically lowering overall latency, optimizing resource allocation, and maintaining high task success rates. This work demonstrates the efficacy of Nash Q-learning for dynamically setting priorities for tasks in multi-agent edge computing environments, providing a robust framework for effective resource management and collaborative task completion. And also highlight the potential of Nash Q-learning in advancing intelligent task management in edge computing, suggesting its applicability in real-world, resource-constrained environments. © 2025 IEEE. All rights reserved,

关键词： Computation offloading

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Predicting CRISPR Cas9 Off-Target Activities With a Two-Stage Deep Learning Framework

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 956-962页

作者： Tianshan Zhang Bei Jiang Guang Yang Zhijie Bao Jiajie Peng AI for Science Interdisciplinary Research Center School of Computer Science Northwestern Polytechnical University Xi'an China Key Laboratory of Big Data Storage and Management Ministry of Industry and Information Technology Northwestern Polytechnical University Shanxi China The National Engineering Laboratory for In tegrated Aerospace Ground Ocean Big Data Application Technology Shanxi China Tianjin second people's hospital Tianjin China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China

The CRISPR-Cas9 system, found across bacteria and archaea, enables efficient genome engineering in eukaryotic cells. There is a challenge that Cas9 guide RNA may cause off-target activities. Although numbers of methods have been proposed to predict off-target activities in guide RNA designing, this procedure involves numerous potential off-target sites, which causes label imbalance problem. To address this problem, we developed a deep learning framework, named CAF-Net (Cas9 Augmentation and Finetune Network), for predicting off-target activities of CRISPR-Cas9. First, we pretrain an embedding model to extract features from target and guide sequence pairs. Subsequently, data augmentation is applied to these features. And finally, the model is finetuned with synthetic samples. Evaluation results demonstrate its performance on published datasets.

关键词： RNA Bioinformatics Feature extraction DNA Genomics Convolutional neural networks Computational modeling Transformers Predictive models CRISPR

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A multip-level additive distortion method for security improvement in palette image steganography

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Journal of Visual Communication and Image Representation 2025年 110卷

作者： Chen, Yi Wang, Hongxia Cui, Yunhe Shen, Guowei Guo, Chun Liu, Yong Wu, Hanzhou State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Provincial Characteristic Key Laboratory of Software Engineering and Information Security Guizhou University Guiyang550025 China School of Cyber Science and Engineering Sichuan University Chengdu610065 China School of Artificial Intelligence Hubei University of Automotive Technology Shiyan442002 China School of Big Data and Computer Science Guizhou Normal University Guiyang550025 China School of Communication and Information Engineering Shanghai University Shanghai200444 China

With the rapid development of the Internet and communication technology, palette images have become a preferred media for steganography. However, the security of palette image steganography faces a big problem. To address this, we propose a multiple-level additive distortion method for security improvement in palette image steganography. The proposed multiple-level additive distortion method comprises an index-level cost method and a pixel-level cost method. The index-level and the pixel-level costs by the two methods can respectively reflect the relationship changes of adjacent indices and the pixels corresponding to the adjacent indices. Meanwhile, the index-level and the pixel-level costs can also reflect the modification impact of steganography. Therefore, the proposed method can improve the security of palette image steganography. We conducted extensive experiments on three datasets to verify the security improvement. Experiment results have shown our proposed multiple-level distortion method indeed has an advantage in security when compared with four state-of-the-art methods. © 2025 Elsevier Inc.

关键词： Additive costs Color table Index matrix Multiple-level distortions Palette images Security Steganography

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Bioclimatic zonation and spatial-scale dependence of lacustrine microbial assemblages

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Science Bulletin 2025年第7期70卷 1152-1164页

作者： Shuren Wang Qinglong L.Wu Huabing Li Rujia He Congcong Jiao Mengyu Qin Ye Deng Guoqing Zhang Dayong Zhao Jin Zeng Key Laboratory of Lake and Watershed Science for Water Security Nanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjing 211135China Joint International Research Laboratory of Global Change and Water Cycle the National Key Laboratory of Water Disaster PreventionHohai UniversityNanjing 210098China Center for Evolution and Conservation Biology Southern Marine Sciences and Engineering Guangdong Laboratory(Guangzhou)Guangzhou 511458China Sino-Danish Centre for Education and Research University of Chinese Academy of SciencesBeijing 100049China CAS Key Laboratory of Environmental Biotechnology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China National Genomics Data Center&Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghai 200031China

Bioclimatic zonation is critical for understanding how climate shapes biodiversity and biogeographic ***,existing studies have primarily focused on macroorganisms,leaving microbial communities largely *** study seeks to address this gap through extensive sampling of bacterial communities from 931 sediment samples across 199 lakes in *** on the obtained data,we identified five distinct lacustrine microbial bioclimatic zones,each showing significant differences in multiple facets of bacterial diversity(i.e.,alpha,beta,and gamma diversity)and clear bioclimatic zone-dependent microbial biogeographic ***,the alpha and beta diversity of the bacterial communities showed opposing patterns across bioclimatic *** environmental variables—specifically mean annual temperature,elevation,lake hydrological variables,and sediment pH—exerted contrasting effects on the alpha and beta diversity and played critical roles in shaping microbial community distribution at different spatial *** continental scales,predominant geographic and climatic variables dictated the patterns of bioclimatic zonation of lacustrine microbial *** regional scales,hydrological variables influenced the dispersal capacity of lake microbes,whereas sediment physicochemical variables were the most important selection factors shaping local microbial ***,our findings indicated that bioclimatic boundaries substantially enhanced the contribution of variable selection on bacterial community assembly and led to marked changes in distance-decay relationships in community ***,this study established a continental bioclimatic framework for lacustrine microbial communities,clarifying how environmental variables control microbial distributions across spatial scales,providing new insights into microbial biogeography,and advancing our knowledge about biodiversity under future climate change scenarios.

关键词： Bioclimatic zonation Lake sediments Microbial biogeography Scale dependence Hydrological variables

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