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Enhanced Traffic Carbon Emissions Data Sharing and Modeling via Blockchain and Personalized Federated Learning in IIoT Ecosystem

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IEEE Internet of Things Journal 2025年

作者： Zhang, Xinhuan Liu, Hongjie Yang, Fan Zhejiang Normal University College of Engineering Jinhua321004 China Xi’an Jiaotong University School of Computer Science and Technology Xi’an710049 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Changchun130012 China Wuhan University of Technology Hubei Key Laboratory of Transportation Internet of Things China

The energy transition is essential for achieving sustainable development, and in this context, sharing traffic carbon emissions data within the IIoT ecosystem plays a pivotal role in incentivizing the reduction of greenhouse gas emissions and promoting cleaner transportation solutions. By leveraging interconnected IoT devices and data analytics, we can enhance transparency and collaboration, driving more effective strategies for sustainable transportation. However, managing traffic carbon emissions and facilitating the complex sharing of emission data across various entities present significant challenges. To address these issues, we propose a Blockchain-based Traffic Carbon Data Sharing and Modeling (BCSM) method that provides a secure, efficient, and reliable platform for carbon emissions trading. Our approach introduces a blockchain-based framework designed to ensure data integrity and user privacy in carbon markets, thereby enhancing transparency and trust among participants. We further develop a hierarchical aggregated carbon trading data modeling architecture that integrates hypernetworks with attention mechanisms, enabling the creation of high-quality, personalized models for federated learning clients. This architecture allows clients to benefit from collective insights while preserving data confidentiality. Additionally, we implement a blockchain-editable access control mechanism, facilitating fine-grained permissions management and maintaining a comprehensive, auditable trail of system interactions. The experimental results demonstrate that our BCSM approach not only enhances data security but also significantly improves model performance, offering a robust solution to the challenges in traffic carbon trading and contributing to the broader energy transition objectives. © 2014 IEEE.

关键词： Access control models

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UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks

arXiv

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arXiv 2024年

作者： Li, Hongjuan Kang, Hui Sun, Geng Li, Jiahui Wang, Jiacheng Wang, Xue Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Communication Engineering Jilin University Changchun130012 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems. Specifically, the UAV swarm forms a UAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a trade-off between the effectiveness of CB-based interference mitigation and the energy conservation of UAVs. Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic non-dominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by UAVs to non-receiving BSs. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Self-Ensembling Semi-Supervised Model for Bone X-ray Images Landmark Detection

Self-Ensembling Semi-Supervised Model for Bone X-ray Images ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tian Bai Shenyao Liu Yuzhao Wang Yu Wang Dong Dong College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China College of Software Jilin University Changchun China First hospital of Jilin University Changchun China

ISBN: (纸本)9781665429825

Bone image landmark detection plays a vital role in orthopedic diseases diagnosis and analysis. As a promising approach, the data-driven convolutional neural network has been widely applied in landmarks detection. However, the model’s performance relies heavily on numerous high-quality labeled data, which are difficult to access. A practical solution for this issue in bone images landmark detection has not been provided, so we propose a framework of semi-supervised bone images landmark detection encouraged by the recent success of the self-ensembling method. The student and teacher networks share the same structure in our framework, where unlabeled data are utilized by enforcing the prediction consistency of two networks under different perturbations to enhance the network’s performance. The two networks use a mutual learning approach to improve the efficiency of using unlabeled data. In addition, we also propose a transformative perturbation scheme to prevent model overfitting during training and enhance the generalization capability of the model on unseen data. Experimental results on an in-house hip dataset collected from the actual process of clinical diagnosis and a publicly available dataset demonstrate that our framework has excellent performance compared with other semi-supervised methods.

关键词： Training Perturbation methods Biological system modeling Predictive models Bones Reliability engineering Data models

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Incomplete graph learning: A comprehensive survey

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Neural Networks 2025年 190卷

作者： Riting Xia Huibo Liu Anchen Li Xueyan Liu Yan Zhang Chunxu Zhang Bo Yang College of Computer Science Inner Mongolia University Hohhot 010021 China School of Computer Science and Technology Jilin University Changchun Jilin 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China

Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods are non-robust and affected by missing attributes in graphs, resulting in sub-optimal outcomes. This has led to the emergence of incomplete graph learning, which aims to process and learn from incomplete graphs to achieve more accurate and representative results. In this paper, we conducted a comprehensive review of the literature on incomplete graph learning. Initially, we categorize incomplete graphs and provide precise definitions of relevant concepts, terminologies, and techniques, thereby establishing a solid understanding for readers. Subsequently, we classify incomplete graph learning methods according to the types of incompleteness: (1) attribute-incomplete graph learning methods, (2) attribute-missing graph learning methods, and (3) hybrid-absent graph learning methods. By systematically classifying and summarizing incomplete graph learning methods, we highlight the commonalities and differences among existing approaches, aiding readers in selecting methods and laying the groundwork for further advancements. In addition, we summarize the datasets, incomplete processing modes, evaluation metrics, and application domains used by the current methods. Lastly, we discuss the current challenges and propose future directions for incomplete graph learning, with the aim of stimulating further innovations in this crucial field. To our knowledge, this is the first review dedicated to incomplete graph learning, aiming to offer valuable insights for researchers in related fields. 1

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Grid Partition and Agglomeration for Bidirectional Hierarchical Clustering 2nd

Grid Partition and Agglomeration for Bidirectional Hierarchi...

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2nd EAI International Conference on Security and Privacy in New Computing Environments, SPNCE 2019

作者： Wu, Lei Chen, Hechang Yu, Xiangchun Chao, Sun Yu, Zhezhou Dou, RuiTing College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9783030213725

Clustering is an important data processing tool, which can be used to reveal the distribution structure of unfamiliar domain data, or as preprocess methods to magnify data object to accelerate subsequent processing or simplify models. However, the distribution of many real-world data in feature space is very complex or uneven. Besides, the similarity/distance is not easy to be properly defined in feature space with different dimensional quantity. Therefore, many existing clustering algorithms are not stable in real datasets, and better performance of different datasets relies on artificial special design, such as scale normalization. In this paper, we propose a bidirectional hierarchical clustering (BHC) algorithm with two phases. In the first phase (Top-down), based on the probability density function of data in different dimensions, the feature space is divided into over-segmented grids to adapt to the complex distribution of data. In the second phase (Bottom-up), based on statistical information, a robust distance instead of geometrical distance is defined to agglomerate the grids into a dendrogram. Compared with the individual data points, grids created in the first phase can carry more statistical information, and the magnified processing objects can accelerate the clustering process. The second phase enhances the algorithm’s ability by the ability of recognize arbitrary shape data clusters. The effectiveness of BHC is compared with 20 popular or recent clustering algorithms on 8 artificial datasets and 6 real-world datasets. And the results show that our algorithm can achieve good results on most datasets. In particular, BHC surpasses all the comparison algorithms involved in the experiment on all real-world datasets. In addition, in order to test the efficiency of the algorithm, we design an experiment which can test the influence of dimension and data size on the operation time. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommuni

关键词： Clustering algorithms

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A new SAT Encoding Scheme for Exactly-one Constraints 5

A new SAT Encoding Scheme for Exactly-one Constraints

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5th Annual International Conference on Network and Information Systems for Computers, ICNISC 2019

作者： Cai, Jiatong Su, Yating Yang, Xixi Min, Jionghao Lai, Yong College of Software Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

Exactly-one constraints have comprehensive applications for the fields of artificial intelligence and operations research. For many encoded SAT problems generated by the existing encoding schemes of exactly-one constraints, the state-of-the-art knowledge compilers cannot complete compilation. In this paper, we propose a new encoding scheme of exactly-one constraints. We introduce two-dimensional auxiliary variables (represented as a matrix) to denote the constraint that exactly one of some variables can be assigned as true. The clauses generated by our scheme is significantly less than those generated by three other existing encoding schemes. The experimental results on the exact cover problems show that the encoded CNF formulas generated by our scheme requires less compilation time, compared with the other three coding schemes. © Published under licence by IOP Publishing Ltd.

关键词： Operations research

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Reducing Fault-tolerant Overhead for Distributed Stream Processing with Approximate Backup

Reducing Fault-tolerant Overhead for Distributed Stream Proc...

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International Conference on Computer Communications and Networks (ICCCN)

作者： Yuan Zhuang Xiaohui Wei Hongliang Li Mingkai Hou Yundi Wang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun China

ISBN: (数字)9781728166070

ISBN: (纸本)9781728166087

The stream processing model continuously processes online data in an on-pass fashion that can be more vulnerable to failures than other offline-data processing schemes. Checkpoint-based fault-tolerant methods have been widely used to enhance the reliability of stream processing systems. To ensure exact data recoveries upon failures, full-backup mechanisms are used to store a complete copy of data, which introduces substantial runtime overhead and increases output latency. In the meantime, a wide range of online processing applications prefer quick-and-dirty results with a slight degradation inaccuracy to delayed exact results. This paper introduces a novel approximate fault-tolerant problem (OAFP) with the objective of reducing the failure-free fault-tolerant overhead and ensuring user-defiled output accuracy requirement upon failure at the same time. We present an approximate fault-tolerant scheme based on sampling backup mechanism and study the trade-off between fault-tolerant overhead and output accuracy in stream processing systems. We proposed two algorithms to compute backup plans for both single-node failure and correlated failure scenarios. Extensive experiments with different types of stream topologies are conducted on our simulator to verify the correctness and effectiveness of our approach. We prove our solution guarantees the output accuracy requirement with minimum FT latency for directed acyclic graph (DAG) stream topologies with single-node failures.

关键词： Task analysis Fault tolerance Fault tolerant systems Topology Checkpointing computational modeling Data models

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Understanding the Runtime Overheads of Deep Learning Inference on Edge Devices

Understanding the Runtime Overheads of Deep Learning Inferen...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Xiu Ma Guangli Li Lei Liu Huaxiao Liu Xiaobing Feng College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China SKL of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

With the growing ubiquity of the Internet of Things, in-the-edge inference of deep neural network models has been a major driver for promoting the widespread use of intelligent applications. As model inference characteristics are crucial for optimizing and deploying deep neural networks on hardware platforms, many studies focus on analyzing the performance of neural networks such as latency, accuracy, throughput, and energy consumption. However, few existing works have ever discussed the runtime overheads hidden in neural network inference, despite the overheads are non-negligible for edge applications. The lack of in-depth analysis of the overheads hinders the understanding of how hardware designs and model structures impact on-device inference performance. In this paper, we characterize the runtime overheads of deep learning inference on representative edge devices by leveraging state-of-the-art neural network models, performing a systematical analysis from the perspective of end-to-end performance, hardware platforms, memory bandwidth, and neural network model structures. Based on experimental results, the crucial insights are offered to facilitate the design and configure of resource-efficient networks and pick appropriate models on the specific platform, which provides a comprehensive view of runtime overheads of in-the- edge neural network inference for architects and developers.

关键词： Deep learning Performance evaluation Analytical models Runtime Roads Neural networks Throughput

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PepHarmony: A Multi-View Contrastive Learning Framework for Integrated Sequence and Structure-Based Peptide Encoding

arXiv

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arXiv 2024年

作者： Zhang, Ruochi Wu, Haoran Liu, Chang Li, Huaping Wu, Yuqian Li, Kewei Wang, Yifan Deng, Yifan Chen, Jiahui Zhou, Fengfeng Gao, Xin Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin Changchun130012 China School of Artificial Intelligence Jilin University Jilin Changchun130012 China Syneron Technology Guangzhou510700 China Beijing Life Science Academy Beijing102209 China School of Biomedical Sciences LKS Faculty of Medicine The University of Hong Kong Hong Kong Thuwal23955 Saudi Arabia Thuwal23955 Saudi Arabia College of Software Jilin University Jilin Changchun130012 China College of Computer Science and Technology Jilin University Jilin Changchun130012 China

Recent advances in protein language models have catalyzed significant progress in peptide sequence representation. Despite extensive exploration in this field, pre-trained models tailored for peptide-specific needs remain largely unaddressed due to the difficulty in capturing the complex and sometimes unstable structures of peptides. This study introduces a novel multi-view contrastive learning framework PepHarmony for the sequence-based peptide encoding task. PepHarmony innovatively combines both sequence- and structure-level information into a sequence-level encoding module through contrastive learning. We carefully select datasets from the Protein Data Bank (PDB) and AlphaFold database to encompass a broad spectrum of peptide sequences and structures. The experimental data highlights PepHarmony's exceptional capability in capturing the intricate relationship between peptide sequences and structures compared with the baseline and fine-tuned models. The robustness of our model is confirmed through extensive ablation studies, which emphasize the crucial roles of contrastive loss and strategic data sorting in enhancing predictive performance. The proposed PepHarmony framework serves as a notable contribution to peptide representations, and offers valuable insights for future applications in peptide drug discovery and peptide engineering. We have made all the source code utilized in this study publicly accessible via GitHub at https://***/zhangruochi/PepHarmony or http://***/supp/. © 2024, CC BY-NC-ND.

关键词： Peptides

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DeepSelective: Feature Gating and Representation Matching for Interpretable Clinical Prediction

arXiv

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arXiv 2025年

作者： Zhang, Ruochi Yang, Qian Wang, Xiaoyang Wu, Haoran Zhou, Qiong Wang, Yu Li, Kewei Wang, Yueying Fan, Yusi Zhang, Jiale Huang, Lan Liu, Chang Zhou, Fengfeng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Beijing Life Science Academy Beijing102209 China Syneron Technology Guangzhou510700 China

The rapid accumulation of Electronic Health Records (EHRs) has transformed healthcare by providing valuable data that enhance clinical predictions and diagnoses. While conventional machine learning models have proven effective, they often lack robust representation learning and depend heavily on expert-crafted features. Although deep learning offers powerful solutions, it is often criticized for its lack of interpretability. To address these challenges, we propose DeepSelective, a novel end to end deep learning framework for predicting patient prognosis using EHR data, with a strong emphasis on enhancing model interpretability. DeepSelective combines data compression techniques with an innovative feature selection approach, integrating custom-designed modules that work together to improve both accuracy and interpretability. Our experiments demonstrate that DeepSelective not only enhances predictive accuracy but also significantly improves interpretability, making it a valuable tool for clinical decision-making. The source code is freely available at http://***/supp/*** . © 2025, CC BY-NC-ND.

关键词： Electronic health record

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