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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

IEEE Internet of Things Journal 2025年第12期12卷 21784-21792页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Few-shot Link Prediction on Hyper-relational Facts 30

Few-shot Link Prediction on Hyper-relational Facts

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wei, Jiyao Guan, Saiping Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Hyper-relational facts, which consist of a primary triple (head entity, relation, tail entity) and auxiliary attribute-value pairs, are widely present in real-world Knowledge Graphs (KGs). Link Prediction on Hyper-relational Facts (LPHFs) is to predict a missing element in a hyper-relational fact, which helps populate and enrich KGs. However, existing LPHFs studies usually require an amount of high-quality data. They overlook few-shot relations, which have limited instances, yet are common in real-world scenarios. Thus, we introduce a new task, Few-Shot Link Prediction on Hyper-relational Facts (FSLPHFs). It aims to predict a missing entity in a hyper-relational fact with limited support instances. To tackle FSLPHFs, we propose MetaRH, a model that learns Meta Relational information in Hyper-relational facts. MetaRH comprises three modules: relation learning, support-specific adjustment, and query inference. By capturing meta relational information from limited support instances, MetaRH can accurately predict the missing entity in a query. As there is no existing dataset available for this new task, we construct three datasets to validate the effectiveness of MetaRH. Experimental results on these datasets demonstrate that MetaRH significantly outperforms existing representative models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Knowledge graph

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From Missing Pieces to Masterpieces: Image Completion with Context-Adaptive Diffusion

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 6073-6087页

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Zhou, Huiyu Felsberg, Michael Tao, Dacheng Li, Xuelong University of York Department of Computer Science United Kingdom University of Leicester School of Computing and Mathematical Sciences United Kingdom Linkoping University Computer Vision Laboratory Sweden Nanyang Technological University College of Computing & Data Science Singapore China Northwestern Polytechnical University Key Laboratory of Intelligent Interaction and Applications Ministry of Industry and Information Technology Xi'an China

Image completion is a challenging task, particularly when ensuring that generated content seamlessly integrates with existing parts of an image. While recent diffusion models have shown promise, they often struggle with maintaining coherence between known and unknown (missing) regions. This issue arises from the lack of explicit spatial and semantic alignment during the diffusion process, resulting in content that does not smoothly integrate with the original image. Additionally, diffusion models typically rely on global learned distributions rather than localized features, leading to inconsistencies between the generated and existing image parts. In this work, we propose ConFill, a novel framework that introduces a Context-Adaptive Discrepancy (CAD) model to ensure that intermediate distributions of known and unknown regions are closely aligned throughout the diffusion process. By incorporating CAD, our model progressively reduces discrepancies between generated and original images at each diffusion step, leading to contextually aligned completion. Moreover, ConFill uses a new Dynamic Sampling mechanism that adaptively increases the sampling rate in regions with high reconstruction complexity. This approach enables precise adjustments, enhancing detail and integration in restored areas. Extensive experiments demonstrate that ConFill outperforms current methods, setting a new benchmark in image completion. © 1979-2012 IEEE.

关键词： computer aided design

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The Double Contrast for Unsupervised Person Re-Identification 24

The Double Contrast for Unsupervised Person Re-Identificatio...

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2024 International Conference on computer Vision and Deep Learning, CVDL 2024

作者： Liu, Daocheng Fu, Yanyun Shi, Wenxi Zhu, Zhansheng Wang, Deyong School of Computer Science and Technology Urumqi China Beijing Academy of Science and Technology Beijing China Key Laboratory of Big Data of Xinjiang Social Security Risk Urumqi China

ISBN: (纸本)9798400718199

At present, the most popular unsupervised person re-identification(Re-ID) research mainly uses some clustering methods to gather samples with similar features and generate a pseudo-label for each cluster, and uses the parameterless contrast loss to feedback correlation information between *** previous methods used the memory dictionary to store all instance-dependent features, but This approach does not take into account the different number of positive samples in each cluster, and the progress of each cluster update of the memory dictionary is not the *** order to improve the consistency of the update progress, a method is implemented to store the central feature vector in each cluster, but using a hard-sample feature vector in the cluster to update it, so that a single instance feature cannot represent the central feature well. In this article, we study out a method to combine hard instance contrast and cluster contrast, that is, the central feature vector in each cluster is stored in the memory dictionary, and the hard instance feature vector in each cluster is also stored *** addition, in terms of momentum update, the central feature vector and hard instance feature vector of each cluster in mini-batch are used for *** not only solves the problem of inconsistent update progress, but also better learns the similarity and difference of instances in the same cluster, and better aggregates instances within cluster and instances between clusters. Through three dataset experiments, it is concluded that this method has a good improvement. © 2024 ACM.

关键词： Vectors

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Selective Temporal Knowledge Graph Reasoning 30

Selective Temporal Knowledge Graph Reasoning

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Hou, Zhongni Jin, Xiaolong Li, Zixuan Bai, Long Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Temporal Knowledge Graph (TKG), which characterizes temporally evolving facts in the form of (subject, relation, object, timestamp), has attracted much attention recently. TKG reasoning aims to predict future facts based on given historical ones. However, existing TKG reasoning models are unable to abstain from predictions they are uncertain, which will inevitably bring risks in real-world applications. Thus, in this paper, we propose an abstention mechanism for TKG reasoning, which helps the existing models make selective, instead of indiscriminate, predictions. Specifically, we develop a confidence estimator, called Confidence Estimator with History (CEHis), to enable the existing TKG reasoning models to first estimate their confidence in making predictions, and then abstain from those with low confidence. To do so, CEHis takes two kinds of information into consideration, namely, the certainty of the current prediction and the accuracy of historical predictions. Experiments with representative TKG reasoning models on two benchmark datasets demonstrate the effectiveness of the proposed CEHis. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Forecasting

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Class-Incremental Few-Shot Event Detection 30

Class-Incremental Few-Shot Event Detection

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhao, Kailin Jin, Xiaolong Bai, Long Guo, Jiafeng Cheng, Xueqi Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Event detection is one of the fundamental tasks in information extraction and knowledge graph. However, a realistic event detection system often needs to deal with new event classes constantly. These new classes usually have only a few labeled instances as it is time-consuming and labor-intensive to annotate a large number of unlabeled instances. Therefore, this paper proposes a new task, called class-incremental few-shot event detection. Nevertheless, this task faces two problems, i.e., old knowledge forgetting and new class overfitting. To solve these problems, this paper further presents a novel knowledge distillation and prompt learning based method, called Prompt-KD. Specifically, to handle the forgetting problem about old knowledge, Prompt-KD develops an attention based multi-teacher knowledge distillation framework, where the ancestor teacher model pre-trained on base classes is reused in all learning sessions, and the father teacher model derives the current student model via adaptation. On the other hand, in order to cope with the few-shot learning scenario and alleviate the corresponding new class overfitting problem, Prompt-KD is also equipped with a prompt learning mechanism. Extensive experiments on two benchmark datasets, i.e., FewEvent and MAVEN, demonstrate the superior performance of Prompt-KD. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Distillation

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Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

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Digital Communications and Networks 2024年第1期10卷 109-116页

作者： Xiaoming He Yingchi Mao Yinqiu Liu Ping Ping Yan Hong Han Hu The College of Computer and Information Hohai UniversityNanjingChina The Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjing China The School of Computer Science and Engineering Nanyang Technological UniversitySingapore The College of Textile and Clothing Engineering Soochow UniversitySuzhouChina The College of Internet of Things Nanjing University of Posts and TelecomurunicationsNanjingChina

In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference ***,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable *** the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign ***,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power ***,agents in the collection stage slow down,which hinders the learning of other ***,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler *** experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

关键词： B5G Heterogeneous edge networks PPO Channel assignment Power allocation Throughput

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Language-agnostic Zero-shot Entity Linking via Entity Prototype Network 15

Language-agnostic Zero-shot Entity Linking via Entity Protot...

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Yang, Haihong Qin, Ming Zhang, Qiang Xu, Renjun Zhejiang University College of Computer Science and Technology Hangzhou China Zhejiang University School of Software Technology Hangzhou China Zhejiang University Center for Data Science Hangzhou China

ISBN: (纸本)9798331508821

Following the recent trend of language understanding, we introduce a novel approach for constructing a general-purpose entity linking system by learning entity prototypes. Our model, the Entity Prototype Network (EPN), generates language-agnostic entity prototypes by reading entity descriptions and aligning diverse surface forms in different languages to the corresponding entity prototype in a unified vector space. To validate our core assumption of learnable language-agnostic entity prototypes, we define a new Zero-shot Cross-lingual Entity Linking (ZXEL) task. Additionally, we propose a simple yet effective auxiliary task termed Entity Identification, which enhances our model's ability to capture both intra-entity similarity and inter-entity dissimilarity. We evaluate our model on the new task in both classic and generalized settings. Experimental results demonstrate consistent improvements (exceeding 30% on average) over competitive baselines. An ablation study confirms the necessity of our model design and highlights the impact of the proposed auxiliary task. © 2024 IEEE.

关键词： Zero-shot learning

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Enhancing medical vision-language contrastive learning via inter-matching relation modelling

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IEEE Transactions on Medical Imaging 2025年第6期44卷 2463-2476页

作者： Li, Mingjian Meng, Mingyuan Fulham, Michael Feng, David Dagan Bi, Lei Kim, Jinman University of Sydney Biomedical data analysis and visualization lab School of Computer Science NSW Australia Royal Prince Alfred Hospital Department of Molecular Imaging NSW Australia Shanghai Jiao Tong University Institute of Translational Medicine Shanghai China

Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-text alignment. These learned image representations can be transferred to and benefit various downstream medical vision tasks such as disease classification and segmentation. Recent mVLCL methods attempt to align image sub-regions and the report keywords as local-matchings. However, these methods aggregate all local-matchings via simple pooling operations while ignoring the inherent relations between them. These methods therefore fail to reason between local-matchings that are semantically related, e.g., local-matchings that correspond to the disease word and the location word (semantic-relations), and also fail to differentiate such clinically important local-matchings from others that correspond to less meaningful words, e.g., conjunction words (importance-relations). Hence, we propose a mVLCL method that models the inter-matching relations between local-matchings via a relation-enhanced contrastive learning framework (RECLF). In RECLF, we introduce a semantic-relation reasoning module (SRM) and an importance-relation reasoning module (IRM) to enable more fine-grained report supervision for image representation learning. We evaluated our method using six public benchmark datasets on four downstream tasks, including segmentation, zero-shot classification, linear classification, and cross-modal retrieval. Our results demonstrated the superiority of our RECLF over the state-of-the-art mVLCL methods with consistent improvements across single-modal and cross-modal tasks. These results suggest that our RECLF, by modelling the inter-matching relations, can learn improved medical image representations with better generalization capabilities. © 2025 IEEE.

关键词： Contrastive Learning

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Provenance model for Lattice QCD 40

Provenance model for Lattice QCD

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40th International Symposium on Lattice Field Theory, LATTICE 2023

作者： Auge, Tanja Bali, Gunnar Klettke, Meike Ludäscher, Bertram Söldner, Wolfgang Weishäupl, Simon Wettig, Tilo University of Regensburg Faculty of Computer Science and Data Science Germany University of Regensburg Department of Physics Germany University of Illinois at Urbana-Champaign School of Information Sciences United States

Workflow management has become an important topic in many research communities. Here, we focus on the particular aspect of provenance tracking. We follow the W3C PROV standard and formulate a provenance model for Lattice QCD that includes the ensemble-generation and measurement parts of the Lattice QCD workflow. Since many important provenance questions in our community require extensions of this model, we propose a multi-layered provenance approach that combines prospective and retrospective elements. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Lattice theory

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