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research on Cross-Domain Heterogeneous Information Interaction System in Complex Environment Based on Blockchain technology 1

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Ma, BaoQuan Cheng, YeJian Zhang, Ni Wang, Peng Lei, XuHua Huai, XiaoYong Li, JiaXin Jia, ShuJuan Wang, ChunXia National Computer System Engineering Research Institute of China Beijing102200 China School of Computer Science and Technology Xidian University Xi’an710071 China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

In the field of information interaction, when a project involves a large amount of cross-domain heterogeneous information, it is difficult to transmit and update the required information in a timely, accurate, reliable and safe manner in such a complex environment, thereby maintaining synchronization. The decentralization and immutability of blockchain technology provide new ideas for solving this problem. Therefore, this paper takes this as a starting point and proposes a cross-domain heterogeneous information exchange system based on blockchain, which calls smart contracts to realize data transmission and other functions. This paper states the design idea of the system, introduces the system software framework and network topology in detail, shows the flow chart of the system operation, and conducts simple software testing and verification. Finally, this work is summarized, and the development direction of future work is prospected, hoping to provide inspiration for the solution of such problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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Inflate Agriculture Crop Yield Prophesy through Deep Neural Network Schemes

Inflate Agriculture Crop Yield Prophesy through Deep Neural ...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Kumar, Thangiah Sathish Roy, Dharmendra Kumar Partheeban, Pachaivannan Ponnusamy, R. Hemamalini, Renganathan Rani Ettappan, M. Department of Computer Science and Engineering India Chennai Institute of Technology Department of Civil Engineering Chennai Kundrathur India Chennai Institute of Technology Department of Computer Science and Engineering Chennai Kundrathur India St. Peters Institute of Higher Education and Research Department of Electrical and Electronics Engineering Chennai Avadi India Chennai Institute of Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350348910

Recent advancements in DL (Deep Learning) have unlocked the potential for more accurate predictions in various fields, including agricultural crop yield forecasting. This paper presents the NeuroDendritic Networks (NDN) methodology, a biologically inspired approach that emulates the dynamic growth and retraction of dendrites in neurons. This novel method hinges on the NeuroDendritic Units (NDUs) and their ability to grow and prune dendritic branches adaptively based on data stimuli, ensuring optimal processing and information storage. As a result, NDN offers dynamic model adaptation, intrinsic regularization through dendritic retraction, and enhanced focus on pertinent data patterns. Preliminary tests have shown an expected improvement of up to 40% in prediction accuracy compared to traditional methods. This dynamic adaptation ensures the model remains attuned to the most influential factors affecting yield, leading to more informed and timely interventions. The versatility and adaptability of the NDN method not only redefine the standards in agricultural predictive modeling but also hint at its expansive potential across various domains, heralding a promising direction for future research. © 2023 IEEE.

关键词： Dynamic models

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A Federated Named Entity Recognition Model with Explicit Relation for Power Grid

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computers, Materials & Continua 2023年第5期75卷 4207-4216页

作者： Jingtang Luo Shiying Yao Changming Zhao Jie Xu Jim Feng State Grid Sichuan Economic Research Institute ChengduChina School of Computer Science Chengdu University of Information TechnologyChengduChina School of Information and Communication Engineering University of Electronic Science and Technology of China ChengduChina Amphenol Global Interconnect Systems San JoseCA 95131USA

The power grid operation process is complex,and many operation process data involve national security,business secrets,and user ***,labeled datasets may exist in many different operation platforms,but they cannot be directly shared since power grid data is highly *** to use these multi-source heterogeneous data as much as possible to build a power grid knowledge map under the premise of protecting privacy security has become an urgent problem in developing smart ***,this paper proposes federated learning named entity recognition method for the power grid field,aiming to solve the problem of building a named entity recognition model covering the entire power grid process training by data with different security *** decompose the named entity recognition(NER)model FLAT(Chinese NER Using Flat-Lattice Transformer)in each platform into a global part and a local *** local part is used to capture the characteristics of the local data in each platform and is updated using locally labeled *** global part is learned across different operation platforms to capture the shared NER *** local gradients fromdifferent platforms are aggregated to update the global model,which is further delivered to each platform to update their global *** on two publicly available Chinese datasets and one power grid dataset validate the effectiveness of our method.

关键词： Power grid named entity recognition federal learning

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Secrecy Capacity Analysis with Imperfect Channel State Information and Varying Interference for 6G C-V2X Communication 10

Secrecy Capacity Analysis with Imperfect Channel State Infor...

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10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Ayaz, Hina Abbas, Ghulam Abbas, Ziaul Haq Waqas, Muhammad Gik Institute of Engineering Sciences and Technology Facutly of Computer Science and Engineering Topi23460 Pakistan Research Center Topi23460 Pakistan University of Bahrain College of Information Technology Computer Engineering Department 32038 Bahrain

ISBN: (纸本)9798350329674

It has been observed that the use of radio-frequency fingerprinting (RF-FP) for location estimation (LE) of vehicles can significantly improve secrecy capacity (SC) for urban scenarios in 6th generation cellular vehicle-to-everything (6G C-V2X) communication. However, in most of the literature, interference is considered constant for simplicity. Thus, there is a need to investigate the impact of varying levels of interference and incomplete channel state information (CSI) on the performance of SC. This study examines the impact of shadowing and interference in the presence of heavy traffic and obstructions. We have proposed a technique that analyzes the effects of varying levels of interference for LE via RF-FP to improve SC with incomplete CSI. Simulation results demonstrate that interference has a significant impact on SC which depends on the distance and location of the eavesdropping vehicle. However, a decrease in SC is not solely caused by the increase in interference, since other factors, such as the speed and relative position of the illegitimate vehicles as well as incomplete CSI, can also significantly affect the SC performance, as demonstrated through simulations. © 2023 IEEE.

关键词： Vehicle to Everything

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Forecasting Diabetes: A Machine Learning Approach for Predictive Analysis 15

Forecasting Diabetes: A Machine Learning Approach for Predic...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sable, Nikita Manohar Kamble, Vijaya Chaudhari, Poonam Gogte, Purva Yeshwantrao Chavan college of Engineering Department of Computer Science & Engineering Nagpur India S.B.Jain Institute of Technology Management & Research Department of Computer Science & Engineering Nagpur India GES's R. H. Sapat College of Engineering Mangement Studies and Research Department of MCA Engineering Nashik India Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798331300579

Diabetes is a serious condition that is primarily brought on by aging, high blood pressure, pregnancy, insulin, body mass index, and the diabetes gene. Remember that if diabetes is not treated, it can cause damage to other organs and lead to major health issues like high blood pressure, kidney, eye, and heart damage. For the best possible care, diabetes must be detected early. Our research uses a range of machine learning techniques to improve the accuracy of early diabetes identification in patients or persons. To anticipate when diabetes may manifest, we would employ machine learning, ensemble classification algorithms, and a dataset. Naïve Bayesian network, logistic regression (LR), K-Nearest Neighbor, random forests (RF), and support vector machines (SVM). The research results in a model that clearly demonstrates its ability to predict diabetes. Our results show that the Random Forest model performs more accurately than other machine learning techniques. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Deep Reinforcement Learning for Unmanned Aerial Vehicles Cluster Task Allocation 6

Deep Reinforcement Learning for Unmanned Aerial Vehicles Clu...

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6th International Conference on computer Information science and Application technology, CISAT 2023

作者： Lyu, Yichen Zhang, Wangcheng Zhang, Yaoxin Ma, Jianhua School of Computer Science Beijing Institute of Technology Beijing100081 China CASIC Research Institute of Intelligent Decision Engineering Wuhan430000 China

ISBN: (纸本)9781510668546

Real-time task allocation for unmanned aerial vehicle (UAV) clusters is a complex decision problem, particularly in dynamic and uncertain environments. In practice, it can be difficult to predict enemy information, which is often obtained gradually through UAV reconnaissance. Traditional algorithms may not perform satisfactorily in such scenarios. In this study, we develop a deep reinforcement learning (DRL) algorithm that combines proximal policy optimization (PPO) with long short-term memory (LSTM) to address this challenge. By predicting the enemy's positions and behavior, the algorithm performs real-time task allocation using a pre-trained model. Comparative experiments with basic DRL algorithms validate the convergence, effectiveness, and scalability of our approach. © 2023 SPIE.

关键词： Reinforcement learning

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Design of Provably Secure Authentication Protocol for Edge-Centric Maritime Transportation System

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 14536-14545页

作者： Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Ghaffar, Zahid Khan, Muhammad Khurram Das, Ashok Kumar National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan King Saud University Center of Excellence in Information Assurance Riyadh11653 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The epidemic growth of the Internet of Things (IoT) objects have revolutionized Maritime Transportation Systems (MTS). Though, it becomes challenging for the centralized cloud-centric framework to fulfil the application requirements such as low latency and power utilization. The introduction of the distributed edge-centric framework has recently helped the IoT-enabled MTS to meet these requirements by manipulating the tasks at the edge of the networks. Despite the fact that MTS leverages mobile subscribers by overcoming inherent cloud computing limitations, data security and user privacy requirements in establishing the MTS setup are still non-trivial challenges. In this article, we develop a key agreement solution for mobile users to realize mutual authentication in a single round. Our protocol offers user anonymity to maintain user privacy, and it can prevent physical attacks by physically unclonable functions. Initially, the security analysis is conferred to substantiate our protocol's security persistence or strength. Later, its performance correlation is observed under the assumption of diverse metrics in a predefined empirical setup. The meticulous performance correlation endorses the precedence of our protocol over specified related protocols. © 2000-2011 IEEE.

关键词： Hash functions

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Heterogeneous System Data Storage and Retrieval Scheme Based on Blockchain 7th

Heterogeneous System Data Storage and Retrieval Scheme Based...

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Zhang, Ni Ma, BaoQuan Wang, Peng Lei, XuHua Cheng, YeJian Li, JiaXin Huai, XiaoYong Shen, ZhiWei Song, NingNing Wang, Long National Computer System Engineering Research Institute of China Beijing102200 China School of Computer Science and Technology Xidian University Xi’an710071 China

ISBN: (纸本)9783031281235

In the field of information interaction, when a project involves a large amount of heterogeneous information, it is difficult to transmit and update the required information timely, accurately, reliably and securely in such a complex environment to maintain synchronization. At present, blockchain itself has problems such as high storage pressure of nodes, low access efficiency, and simple query. Therefore, this paper takes this as a starting point and proposes a data mapping method of physical resources based on node attributes and heterogeneous nodes, which provides a general method for the data mapping from actual physical resources to information domain. This method can define the attributes of heterogeneous physical information nodes and support unified expression of various physical information resources on the same platform in the real physical world, then connect heterogeneous physical information nodes and improve the sharing ability of data resources between nodes. When accessing data, the corresponding content can be found in the off-chain database by obtaining the index information of the off-chain location stored on the chain. This method takes advantage of the large space and high access efficiency of the off-chain storage system to share the pressure of on-chain data storage. This paper expounds the design idea of the system, introduces the design objective and method in detail, gives the flow chart of the system operation, and carries out a simple software test and verification. Finally, this paper summarizes the work and prospects the development direction of future work, hoping to provide inspiration for solving such problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Optimal Equilibrium Selection of Price-maker Agents in Performance-based Regulation Market

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 204-212页

作者： Mohammad Rayati Aras Sheikhi Ali Mohammad Ranjbar Wei Sun Department of Electrical Engineering Sharif University of TechnologyTehranIran Sharif Energy Research Institute(SERI) Sharif University of TechnologyTehranIran Department of Electrical Engineering and Computer Science University of Central FloridaOrlandoFL 32816USA

This paper analyzes the oligopolistic equilibria of multiple price-maker agents in performance-based regulation(PBR)*** these markets,there are price-maker agents representing some frequency regulation(FR)providers and a number of independent price-taker FR providers.A model of equilibrium problem with equilibrium constraints(EPECs)is employed in this paper to study the equilibria of a PBR market in the presence of price-maker agents and price-taker FR *** to the incorporation of the FR providers’dynamics,the proposed model is reformulated as a mixed-integer linear programming(MILP)problem over innovative mathematical *** optimal equilibrium point is also selected for the market,where none of the agents is the unique deviator and the dynamic performance of power system is improved *** effectiveness of the proposed optimal equilibrium point is evaluated by comparing the outputs with the conventional optimal dispatches of the FR providers.

关键词： Performance-based regulation(PBR)market multi-agent system equilibrium problem with equilibrium constraint(EPEC) frequency regulation(FR) mixed-integer linear programming(MILP)

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ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation

ICON: Improving Inter-Report Consistency in Radiology Report...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Hou, Wenjun Cheng, Yi Xu, Kaishuai Hu, Yan Li, Wenjie Liu, Jiang Department of Computing The Hong Kong Polytechnic University Hong Kong Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798891761681

Previous research on radiology report generation has made significant progress in terms of increasing the clinical accuracy of generated *** this paper, we emphasize another crucial quality that it should possess, i.e., inter-report consistency, which refers to the capability of generating consistent reports for semantically equivalent *** quality is even of greater significance than the overall report accuracy in terms of ensuring the system's credibility, as a system prone to providing conflicting results would severely erode users' ***, existing approaches struggle to maintain inter-report consistency, exhibiting biases towards common patterns and susceptibility to lesion *** address this issue, we propose ICON, which Improves the inter-report CONsistency of radiology report *** to enhance the system's ability to capture similarities in semantically equivalent lesions, our approach first involves extracting lesions from input images and examining their ***, we introduce a lesion-aware mixup technique to ensure that the representations of the semantically equivalent lesions align with the same attributes, achieved through a linear combination during the training *** experiments on three publicly available chest X-ray datasets verify the effectiveness of our approach, both in terms of improving the consistency and accuracy of the generated reports. © 2024 Association for Computational Linguistics.

关键词： Radiology

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