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A sharding blockchain-based UAV system for search and rescue missions

Frontiers of computer science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalability,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalability brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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Detecting Metadata-Related Logic Bugs in Database Systems via Raw Database Construction 50th

Detecting Metadata-Related Logic Bugs in Database Systems vi...

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50th International Conference on Very Large Data Bases, VLDB 2024

作者： Song, Jiansen Dou, Wensheng Gao, Yu Cui, Ziyu Zheng, Yingying Wang, Dong Wang, Wei Wei, Jun Huang, Tao Key Lab of System Software at CAS State Key Lab of Computer Science at Institute of Software at CAS University of CAS Beijing China Nanjing Institute of Software Technology University of CAS Nanjing China Institute of Software at CAS China

Database Management Systems (DBMSs) are widely used to efficiently store and retrieve data. DBMSs usually support various metadata, e.g., integrity constraints for ensuring data integrity and indexes for locating data. DBMSs can further utilize these metadata to optimize query evaluation. However, incorrect metadata-related optimizations can introduce metadata-related logic bugs, which can cause a DBMS to return an incorrect query result for a given query. In this paper, we propose a general and effective testing approach, Raw database construction (Radar), to detect metadata-related logic bugs in DBMSs. Given a database db containing some metadata, Radar first constructs a raw database rawDb, which wipes out the metadata in db and contains the same data as db. Since db and rawDb have the same data, they should return the same query result for a given query. Any inconsistency in their returned query results indicates a metadata-related logic bug. To effectively detect metadata-related logic bugs, we further propose a metadata-oriented testing optimization strategy to focus on testing previously unseen metadata, thus detecting more metadata-related logic bugs quickly. We implement and evaluate Radar on five widely-used DBMSs, and have detected 42 bugs, of which 38 have been confirmed as new bugs and 16 have been fixed by DBMS developers. © 2024, VLDB Endowment. All rights reserved.

关键词： Metadata

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Sparse Representation-Based Robust Channel Estimation in XL-RIS-Assisted Systems 100

Sparse Representation-Based Robust Channel Estimation in XL-...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Bian, Xuechun Xu, Wenbo Wang, Yue Beijing University of Posts and Telecommunications Key Lab of Universal Wireless Communications Beijing China Georgia State University Department of Computer Science AtlantaGA United States

ISBN: (纸本)9798331517786

The extremely large reconfigurable intelligent surface (XL-RIS) is an architecture that shows potential in expanding the transmission region and meeting the high transmission requirements. With the deployment of XL-RIS, users transmit signals through the spherical-wavefront near-field channel, which presents challenges in channel estimation with low pilot overhead. Additionally, XL-RIS is a non-stationary system whose elements are prone to failure due to accidental damages or blockages, thereby further complicating the channel state. To address these issues, this paper proposes a two-stage sparse representation-based channel estimation (SRCE) scheme to jointly recover the near-field channel state and diagnose RIS element failures with low pilot overhead. The first stage utilizes a proposed double-domain filter (DDF) method to eliminate part of the received noise based on the sparse representation in the angle and polar domain. Then, a robust failure-aware double sparsity orthogonal matching pursuit (FA-OMP) algorithm is proposed by iteratively reconstructing the channel state, as well as eliminating the perturbation invoked by RIS element failures. The efficiency and robustness of our proposed scheme are validated through simulations. © 2024 IEEE.

关键词： Channel estimation

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Rethinking Exploration in Reinforcement Learning with Effective Metric-Based Exploration Bonus 38

Rethinking Exploration in Reinforcement Learning with Effect...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Yiming Zhao, Kaiyan Liu, Furui Hou, Leong U. State Key Laboratory of Internet of Things for Smart City University of Macau China School of Computer Science Wuhan University Wuhan China Zhejiang Lab Hangzhou China

Enhancing exploration in reinforcement learning (RL) through the incorporation of intrinsic rewards, specifically by leveraging state discrepancy measures within various metric spaces as exploration bonuses, has emerged as a prevalent strategy to encourage agents to visit novel states. The critical factor lies in how to quantify the difference between adjacent states as novelty for promoting effective exploration. Nonetheless, existing methods that evaluate state discrepancy in the latent space under L1 or L2 norm often depend on count-based episodic terms as scaling factors for exploration bonuses, significantly limiting their scalability. Additionally, methods that utilize the bisimulation metric for evaluating state discrepancies face a theory-practice gap due to improper approximations in metric learning, particularly struggling with hard exploration tasks. To overcome these challenges, we introduce the Effective Metric-based Exploration-bonus (EME). EME critically examines and addresses the inherent limitations and approximation inaccuracies of current metric-based state discrepancy methods for exploration, proposing a robust metric for state discrepancy evaluation backed by comprehensive theoretical analysis. Furthermore, we propose the diversity-enhanced scaling factor integrated into the exploration bonus to be dynamically adjusted by the variance of prediction from an ensemble of reward models, thereby enhancing exploration effectiveness in particularly challenging scenarios. Extensive experiments are conducted on hard exploration tasks within Atari games, Minigrid, Robosuite, and Habitat, which illustrate our method's scalability to various scenarios. The project website can be found at https://***/view/effective-metric-exploration. © 2024 Neural information processing systems foundation. All rights reserved.

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Practical model with strong interpretability and predictability: An explanatory model for individuals' destination prediction considering personal and crowd travel behavior

Practical model with strong interpretability and predictabil...

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作者： Zhao, Juanjuan Ye, Jiexia Xu, Minxian Xu, Chengzhong Shenzhen Institutes of Advanced Technology China Academic of Science Shenzhen China State Key Lab of IOTSC Department of Computer Science University of Macau SAR China

Real-time individuals' destination prediction is of great significance for real-time user tracking, service recommendation and other related applications. Traditional technology mainly used statistical methods based on the travel patterns mined from personal history travel data. However, it is not clear how to predict the destinations of individuals with only limited personal historical data. In this paper, taking the public transportation metro systems as example, we design a practical method called practical model with strong interpretability and predictability to predict each passenger's destination. Our main novelties are two aspects: (1) We propose to predict individuals' destination by combining personal and crowd behavior under certain context. (2) An explanatory model combining discrete choice model and neural network model is proposed to predict individuals' stochastic trip's destination, which can be applied to other transportation analysis scenarios about individuals' choice behavior such as travel mode choice or route choice. We validate our method based on extensive experiments, using smart card data collected by automatic fare collection system and weather data in Shenzhen, China. The experimental results demonstrate that our approach can achieve better performance than other baselines in terms of prediction accuracy. © 2020 John Wiley & Sons, Ltd.

关键词： Forecasting

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Incremental Object Detection With Image-Level labels

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2331-2341页

作者： Yu, Qianzi Zhu, Kai Wang, Wen Cao, Yang Kang, Yu University of Science and Technology of China Department of Automation School of Information Science and Technology Hefei230026 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230039 China Zhejiang University College of Computer Science and Technology State Key Lab of CAD&CG Hangzhou310027 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China

Incremental object detection (IOD) aims to achieve simultaneous prediction of old and new samples on localization and classification when new concepts are provided. It is a challenging task due to the need for a joint interpretation of semantic and spatial information. While the existing work accomplishes the detector generalization with the help of annotated sample classes and bounding boxes, this article presents one astonishing finding on the unnecessity of new bounding boxes, which will significantly reduce the annotation cost and condition constraints in IOD. To enable the incremental detection process with image-level labels, we propose a multibranch decoupling scheme in which the representation, classification, and regression branches are customized to accommodate new concepts with class labels with different semantic levels. The regression branch is first frozen to ensure generalizable localization while maintaining the stability of representation optimization. Then, the representation branch is gradient-inverted to estimate the distribution drift from the old object-level set to the new image-level one, improving the effectiveness of feature updates in the absence of homogeneous semantic and spatial supervision. Finally, the classification branch is calibrated to balance the contribution of different images to the gradient, in which the frequency ratios of old instances and new images are calculated as a postprocessing factor for the logits margin. Extensive experiments on two standard incremental detection benchmarks demonstrate the above-par recognition and localization performance while reducing the burden on new class annotation, outperforming state-of-the-art methods by 2% and 3%, respectively. © 2023 IEEE.

关键词： Feature extraction

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SOFTENED SYMBOL GROUNDING FOR NEUROSYMBOLIC SYSTEMS 11

SOFTENED SYMBOL GROUNDING FOR NEUROSYMBOLIC SYSTEMS

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11th International Conference on Learning Representations, ICLR 2023

作者： Li, Zenan Yao, Yuan Chen, Taolue Xu, Jingwei Cao, Chun Ma, Xiaoxing Lü, Jian State Key Lab of Novel Software Technology Nanjing University China Department of Computer Science Birkbeck University of London United Kingdom

Neuro-symbolic learning generally consists of two separated worlds, i.e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in *** paper presents a novel, softened symbol grounding process, bridging the gap between the two worlds, and resulting in an effective and efficient neuro-symbolic learning ***, the framework features (1) modeling of symbol solution states as a Boltzmann distribution, which avoids expensive state searching and facilitates mutually beneficial interactions between network training and symbolic reasoning;(2) a new MCMC technique leveraging projection and SMT solvers, which efficiently samples from disconnected symbol solution spaces;(3) an annealing mechanism that can escape from sub-optimal symbol *** with three representative neuro-symbolic learning tasks demonstrate that, owing to its superior symbol grounding capability, our framework successfully solves problems well beyond the frontier of the existing proposals. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Boltzmann equation

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Elastic Aggregation for Federated Optimization

Elastic Aggregation for Federated Optimization

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Dengsheng Chen Jie Hu Vince Junkai Tan Xiaoming Wei Enhua Wu Meituan State Key Laboratory of Computer Science ISCAS University of Chinese Academy of Sciences Bytedance Inc. University of Macau

Federated learning enables the privacy-preserving training of neural network models using real-world data across distributed clients. FedAvg has become the preferred optimizer for federated learning because of its simplicity and effectiveness. FedAvg uses naïve aggregation to update the server model, interpolating client models based on the number of instances used in their training. However, naïve aggregation suffers from client drift when the data is heterogenous (non-IID), leading to unstable and slow convergence. In this work, we propose a novel aggregation approach, elastic aggregation, to overcome these issues. Elastic aggregation interpolates client models adaptively according to parameter sensitivity, which is measured by computing how much the overall prediction function output changes when each parameter is changed. This measurement is performed in an unsupervised and online manner. Elastic aggregation reduces the magnitudes of updates to the more sensitive parameters so as to prevent the server model from drifting to any one client distribution, and conversely boosts updates to the less sensitive parameters to better explore different client distributions. Empirical results on real and synthetic data as well as analytical results show that elastic aggregation leads to efficient training in both convex and nonconvex settings while being fully agnostic to client heterogeneity and robust to large numbers of clients, partial participation, and imbalanced data. Finally, elastic aggregation works well with other federated optimizers and achieves significant improvements across the board.

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IVE: Accelerating Enumeration-Based Subgraph Matching via Exploring Isolated Vertices 40

IVE: Accelerating Enumeration-Based Subgraph Matching via Ex...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Jiang, Zite Zhang, Shuai Hou, Xingzhong Yuan, Mengting You, Haihang Institute of Computing Technology CAS State Key Lab of Processors Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijinz China Zhongguancun Laboratory Beijing China School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798350317152

The performance of the enumeration-based sub-graph matching, which searches all isomorphic subgraphs in the data graph, is crucial to various applications. The upper bound of the complexity for the enumeration-based method is exponential to the number of query graph vertices, denoted as n. We propose a novel subgraph matching algorithm called the Isolated Vertices Exploration (IVE). The IVE leverages isolated vertices during the reordering and enumeration phases, thereby significantly accelerating the subgraph matching process. During the enumeration, the isolated vertices can be matched by using a quick bipartite graph matching algorithm. Consequently, the complexity of matching the remaining non-isolated vertices is exponential to the number of non-isolated vertices, denoted as n'. For the reordering, we designed the Maximum Deleted Edges (MDE) to minimize n'. MDE iteratively selects the query vertex with the maximum edges. According to the experimental results, n' is less than 0.8n for 99.8% of arbitrary graphs. Moreover, IVE outperforms the state-of-the-art algorithms in various scenarios with different sizes, sparsities and fields, achieving a performance speedup of up to 80.3x. © 2024 IEEE.

关键词： Iterative methods

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