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A Derivative Matrix-Based Covert Communication Method in Blockchain

computer Systems Science & Engineering 2023年第7期46卷 225-239页

作者： Xiang Zhang Xiaona Zhang Xiaorui Zhang Wei Sun Ruohan Meng Xingming Sun School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China School of Hydrology and Water Resources Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Key Laboratory of Hydrometeorological Disaster Mechanism andWarning ofMinistry ofWater Resources Nanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) NanjingUniversity of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Science Engineering Nanyang Technological UniversitySingapore

The data in the blockchain cannot be tampered with and the users are anonymous,which enables the blockchain to be a natural carrier for covert ***,the existing methods of covert communication in blockchain suffer from the predefined channel structure,the capacity of a single transaction is not high,and the fixed transaction behaviors will lower the concealment of the communication ***,this paper proposes a derivation matrix-based covert communication method in *** uses dual-key to derive two types of blockchain addresses and then constructs an address matrix by dividing addresses into multiple layers to make full use of the redundancy of ***,to solve the problem of the lack of concealment caused by the fixed transaction behaviors,divide the rectangular matrix into square blocks with overlapping regions and then encrypt different blocks sequentially to make the transaction behaviors of the channel addresses match better with those of the real ***,the linear congruence algorithm is used to generate random sequence,which provides a random order for blocks encryption,and thus enhances the security of the encryption *** results show that this method can effectively reduce the abnormal transaction behaviors of addresses while ensuring the channel transmission efficiency.

关键词： Covert communication blockchain concealment security capacity derivation

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Data Collection and Trajectory Planning for Energy-constrained UAV Swarm:A Q-Learning Based Approach

Data Collection and Trajectory Planning for Energy-constrain...

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第42届中国控制会议

作者： Yuan Gao Heng Zhang Jianwei Sun Xin Wang Ming Yang Xueli Wang Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences) School of Computer Engineering Jiangsu Ocean University School of Electronic Engineering Jiangsu Ocean University

This paper studies the energy-constrained path planning of multiple unmanned aerial vehicles(UAVs) based on the data collection of ground sensors in large-scale farms to achieve crop data *** reduce the energy consumption of the UAV swarm and improve the task completion rate,when all nodes are connected,the relay UAV is dynamically allocated according to the location information of the UAV *** relay UAV collects all data and flies to the base station for data *** to this scenario,a multi-UAV trajectory optimization problem including task time,energy consumption and node access constraints is ***,an optimization algorithm for UAV swarm trajectory design is *** algorithm is based on Q-learning and takes the changes in the distance between the UAV and the node,the UAV's flight angle,and the safe distance as the reward *** Q table is updated according to the awards to guide the flight route of the *** analysis shows that the proposed algorithm can effectively improve the completion rate of data collection tasks and reduce the overall energy consumption of the UAV swarm.

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Rotational Doppler effect using ultra-dense vector perfect vortex beams

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Photonics Research 2025年第2期13卷 468-476页

作者： JIANBO GAO XINGYUAN LU XUECHUN ZHAO ZHUOYI WANG JUNAN ZHU ZHIQUAN HU JINGJING HE QIWEN ZHAN YANGJIAN CAI CHENGLIANG ZHAO School of Physical Science and Technology Jiangsu Key Laboratory of Frontier Material Physics and DevicesSoochow UniversitySuzhou 215006China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shandong Provincial Engineering and Technical Center of Light Manipulations&Shandong Provincial Key Laboratory of Optics and Photonic Device School of Physics and ElectronicsShandong Normal UniversityJinan 250358China Shandong Joint Research Center of Light Manipulation Science and Photonics Integrated Chip of East China Normal University and Shandong Normal University East China Normal UniversityShanghai 200241China

The rotational Doppler effect holds significant potential for remote sensing of rotating objects due to its real-time performance and non-contact advantages.A single-ring beam is used to measure rotation *** enhance the signal-to-noise ratio and measure additional parameters,multiple rings are introduced in the context of a rotational Doppler ***,the interference between these rings poses a challenge for multitasking detection *** this study,cross-polarization superposition was applied to generate an ultra-dense vector perfect vortex beam that exhibited sensitivity to spatial position and object size,and flexibility in designing topological charge combinations for generating frequency combs.A proof-of-principle experiment was conducted to demonstrate its capability in improving the signal-to-noise ratio,and accurately perceiving both the radius of rotation and radial *** ultra-dense vector perfect vortex beam provides a general strategy for beam construction and the multi-parameter perception of rotating objects,thereby enabling potential applications in the measurement of velocity gradient measurement of fluids.

关键词： multitasking detection rotating objects ultra dense vector perfect vortex beam remote sensing frequency combs rotational doppler effect rotational Doppler effect cross polarization superposition

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A New and Efficient Dormitory Management System 1st

A New and Efficient Dormitory Management System

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1st International Conference on Applied Intelligence, ICAI 2023

作者： Meng, Boyuan Zhang, Yushu Li, Zhujun Yu, Wendong Wei, Hongwei Yuan, Lin Shandong Jinan250353 China Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250353 China School of Art Nantong University Jiangsu Nantong226019 China Jinan Springs Patent and Trademark Office Shandong Jinan250014 China Shandong Tianyi Information Technology Co. Ltd. Shandong Jinan250101 China

ISBN: (纸本)9789819708260

Today’s information trend is unstoppable, and the manual management mode of college dormitory management cannot meet the trend of increasing demand for dormitories year by year. It not only wastes many manpower and material resources, but also is easy to appear many unnecessary low-level errors. A system about student dormitory management not only saves many unnecessary repeated working time, but also reduces the generation of various low - level errors in information management, which makes the grasp and operation of information more convenient. The system adopts B/S architecture (browser / server architecture), mainly based on the development and implementation of JAVA language. The system has 3 user roles: administrator, student, and host administrator. The functional module of the administrator includes user management, student management, dormitory management, grade management and authority management, which can realize the management of dormitory information and other information. Students’ function module includes student personal details management, online pre-selection dormitory and view of absence records, students can pre-select the dormitory, and view personal information and absence information. The dormitory manager function module includes dormitory management, pre-selection setting and absence management, dormitory details management and absence management, and pre-selection time settings. After testing, the college dormitory management system can effectively realize the above functions and is interface friendly and easy to operate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Tourist Attraction Recommendation System Based on Django and Collaborative Filtering 1st

Tourist Attraction Recommendation System Based on Django and...

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1st International Conference on Applied Intelligence, ICAI 2023

作者： Jiang, Yufeng Zhang, Yushu Li, Zhujun Yu, Wendong Wei, Hongwei Yuan, Lin Shandong Jinan250353 China Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250353 China School of Art Nantong University Jiangsu Nantong226019 China Jinan Springs Patent and Trademark Office Shandong Jinan250014 China Shandong Tianyi Information Technology Co. Ltd. Jinan Shandong250101 China

ISBN: (纸本)9789819708260

Recently, with the development of social and economic levels and people's pursuit of quality of life, tourism has become the first choice for more and more people. However, the traditional travel agency-based tourism method has also begun to expose some problems. However, with the development of machine learning and big data technology, personalized recommendation systems have become more and more important, which also provides us with new ideas to solve this problem. This paper therefore explores the application of spatial clustering algorithm and collaborative filtering algorithm in tourist attraction recommendation. This algorithm can not only mine the geographical location information of tourist attractions, but also analyze and study the data connections between users and attractions, and use these data to make personalized recommendations, providing more considerate and convenient services for travel planning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Collaborative filtering

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Topology Uncertainty Modeling For Imbalanced Node Classification on Graphs

Topology Uncertainty Modeling For Imbalanced Node Classifica...

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

作者： Jiayi Gao Jiaxing Li Ke Zhang Youyong Kong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

Most existing graph neural networks work under a class-balanced assumption, while ignoring class-imbalanced scenarios that widely exist in real-world graphs. Although there are many methods in other fields that can alleviate this issue, they do not consider the special topology of the non-Euclidean graph. Hence, we propose Graph Topology Uncertainty (GraphTU), a novel probabilistic class-imbalanced solution specifically for graphs. Firstly, an invisible "uncertain gap" between under-represented minorities in training set and authentic minorities in unseen set is modeled by estimating statistical variances in topology. We extend the training distribution for minorities by sampling in this gap through a non-parametric way. Moreover, a gradient-guided mask is introduced to prevent biased statistics. Extensive experiments demonstrate the superior performance of GraphTU.

关键词： Training Uncertainty network topology Signal processing Probabilistic logic Graph neural networks Acoustics

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Reputation based Adaptive Federated Learning in Medical Scenarios

Reputation based Adaptive Federated Learning in Medical Scen...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Lianhai Wang Tianrui Liu Yingxiaochun Wang Xinlei Wang Qi Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Federated Learning (FL) is a distributed machine learning paradigm designed to address data silos and protect data privacy. However, in medical scenarios, the heterogeneity in data quality and the non-independent and identically distributed (Non-IID) nature of medical data from different hospitals can lead to degraded model performance in FL. To tackle this challenge, this paper proposes an adaptive federated learning framework based on reputation. By using a reputation evaluation model, the framework adaptively adjusts the aggregation weights of the models to enhance the performance of the global model. Additionally, our reputation evaluation model, integrated with blockchain technology, can rapidly identify malicious clients and effectively prevent poisoning attacks. Experimental results on two medical datasets demonstrate that this framework achieves higher model accuracy and faster convergence.

关键词： Adaptation models Data privacy Federated learning Hospitals Data integrity Computational modeling Evaluation models Data models Security Convergence

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ID-YOLOv7: A Method for Steel Surface Defect Detection Combining Residual Attention and Dynamic Upsampling

ID-YOLOv7: A Method for Steel Surface Defect Detection Combi...

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Smart World Congress (SWC), IEEE

作者： Shanshan Liu Xiangzhi Liu Jiazheng Man Bingyang Cao Yunfeng Dong Xiaoming Wu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Industrial defect detection is a crucial challenge in computer vision. The accuracy of detecting defects on steel surfaces is hindered by spatially varying noise. To address this challenge, we propose an efficient method for steel surface defect detection. Firstly, considering the impact of the shape and proportions of bounding boxes on localization accuracy, we developed an Adaptive Shape-Inner Intersection over Union (ASIIoU) loss function. This function dynamically adjusts auxiliary bounding boxes and takes into account their shape and scale to optimize the loss, thereby improving the accuracy of bounding box regression loss. Secondly, to enhance the ability of the model to learn local features, we designed a Content-Guided Residual Attention Block (CGRAB). It captures rich local features across different convolutional layers via skip connections while discarding irrelevant information. Lastly, to avoid the potential issue of detail blurring caused by traditional upsampling methods, we optimized the upsampling process to preserve key details and structural information in the image, reducing unnecessary noise amplification. Experimental validation demonstrates the effectiveness of our method, which achieved a $76.7 \% \mathrm{mAP}$ on the NEU-DET dataset.

关键词： Location awareness Accuracy Shape Noise Focusing Object detection Feature extraction Steel Surface treatment Defect detection

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Topgformer: Topological-Based Graph Transformer for Mapping Brain Structural Connectivity to Functional Connectivity

Topgformer: Topological-Based Graph Transformer for Mapping ...

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

作者： Dalu Guo Ke Zhang Jiaxing Li Youyong Kong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

Exploring the mapping between structural connectivity (SC) and functional connectivity (FC) is of essential importance to understanding the working mechanism of the human brain. Traditional methods are difficult to represent the complex relationship of high-order interaction between SC and FC. Recent learning-based methods can not well capture the important long-range interactions and edge information of brain connectivity. To address the issue, we propose a novel Topological-based Graph Transformer (TopGFormer) to generate functional connectivity from the structure connectivity with sufficient consideration of topological properties of brain connectivity. We propose a Topological Multi-Head Attention block that simultaneously uses centrality encoding and adjacency matrix to capture node and edge importance respectively. The centrality encoding reflects the importance of brain regions while the adjacency matrix captures the linking relationship of different regions. Experiments on HCP resting and emotion task datasets demonstrate the performance of the proposed method.

关键词： Learning systems Brain Correlation Signal processing Transformers Encoding Acoustics

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LCLB: Low Cross-Chain Transaction Ratio and Load Balancing for Blockchain Sharding

LCLB: Low Cross-Chain Transaction Ratio and Load Balancing f...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Yu Wang Xiaoming Wu Qile Yuan Yunfeng Dong Xiangzhi Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

As blockchain technology becomes widely applied, handling high volumes of transactions has become a critical challenge. Sharding has emerged as an effective solution to enhance the throughput of blockchain systems. Yet, this approach introduces challenges related to the frequency of cross-shard transactions and workload balancing. High rates of cross-shard transactions can slow overall processing speed and reduce throughput, while uneven load distribution can overburden some shards and underutilize others, impacting overall efficiency. To address these issues, this paper proposes a sharding method named LCLB. This method employs the concept of label propagation to pre-shard accounts with frequent transactions into a designated shard, ensuring a lower ratio of cross-shard transactions. It also uses an elastic load balancing algorithm to dynamically adjust the distribution of accounts, ensuring a relatively balanced processing load across all shards. Our results indicate that LCLB significantly enhances blockchain sharding performance,not only enhancing transaction throughput but also significantly reducing transaction confirmation latency, while ensuring a balanced load across various shards.

关键词： Sharding Heuristic algorithms Throughput Load management Blockchains Partitioning algorithms Delays Resource management Robots Intelligent control

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