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4th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2023

作者： Lai, Mingzheng Wang, Pengjie Zeng, Yifan Lv, Wei Alibaba Cloud College of Big Data Application Nation's Research Laboratory Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9798350326444

With the rapid development of drones. The traditional 'manual feature extraction + classifier-based' object detection algorithm can no longer meet the accuracy requirements. Aiming at the problem that the reasoning speed is slowed down due to the complex background of UAV aerial images, we propose a target detection model based on a multi-head attention mechanism: Swim-Transformer. This model introduces a multi-head attention mechanism based on the YOLOv5, and introduces a decoupling head method in the head to improve detection accuracy and speed up network convergence. Experiments show that the new target detection framework is superior to the traditional target detection algorithm on the UAV aerial photography data set VisDrone, and increasing mAP by about 0.0067%. © 2023 IEEE.

关键词： Drones

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A novel approach for calculating single-source shortest paths of weighted digraphs based on rough sets theory

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Mathematical Biosciences and Engineering 2024年第2期21卷 2626-2645页

作者： Hua, Mingfeng Xu, Taihua Yang, Xibei Chen, Jianjun Yang, Jie School of Computer Jiangsu University of Science and Technology Zhenjiang212100 China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China School of Physics and Electronic Science Zunyi Normal University Zunyi563002 China

Calculating single-source shortest paths (SSSPs) rapidly and precisely from weighted digraphs is a crucial problem in graph theory. As a mathematical model of processing uncertain tasks, rough sets theory (RST) has been proven to possess the ability of investigating graph theory problems. Recently, some efficient RST approaches for discovering different subgraphs (e.g. strongly connected components) have been presented. This work was devoted to discovering SSSPs of weighted digraphs by aid of RST. First, SSSPs problem was probed by RST, which aimed at supporting the fundamental theory for taking RST approach to calculate SSSPs from weighted digraphs. Second, a heuristic search strategy was designed. The weights of edges can be served as heuristic information to optimize the search way of k-step R-related set, which is an RST operator. By using heuristic search strategy, some invalid searches can be avoided, thereby the efficiency of discovering SSSPs was promoted. Finally, the W3SP@R algorithm based on RST was presented to calculate SSSPs of weighted digraphs. Related experiments were implemented to verify the W3SP@R algorithm. The result exhibited that W3SP@R can precisely calculate SSSPs with competitive efficiency. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Heuristic algorithms

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

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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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A potential game-based outsourcing computation framework for multiple calculators

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Digital Communications and Networks 2022年第5期8卷 654-663页

作者： Duo Zhang Youliang Tian Linjie Wang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Mathematics and Statistics Guizhou UniversityGuiyang550025China

Outsourcing computation enables a computationally weak client to outsource the computation of a function f to a more powerful but untrusted *** traditional outsourcing computation model forbids communication between players,but it has little *** on the game theory,this paper establishes an outsourcing computation model which is more in line with the actual ***,we construct a structural mapping relationship between security outsourcing computation and the optimization ***,by designing the individual potential function and the global potential function,the individual goal is consistent with the global goal to ensure the correctness of the calculation ***,in the information exchange environment between calculators,we construct a Zero-determinant strategy to ensure that the calculator chooses the strategy according to the predetermined target.

关键词： Outsourcing computation Game theory Potential function Zero-determinant strategy Information exchange

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Aspect-level sentiment analysis model fused with GPT and multi-layer attention 3

Aspect-level sentiment analysis model fused with GPT and mul...

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3rd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2022

作者： Lai, Mingzheng Hong, Zelin Lu, Jiadong Lv, Wei Nation's Research Laboratory: Alibaba Cloud College of Big Data Application Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9781510663473

With the popularization of the Internet and the rapid development of artificial intelligence, the requirements for the accuracy of sentiment analysis on text data have been continuously improved, and aspect-level sentiment analysis has emerged as the times require. This paper analyzes and combines the research results of aspect-level sentiment analysis in recent years, and proposes an aspect-level sentiment analysis model integrating GPT and multi-layer attention. (GPT and Multi-Layer Attention Network, GPT-MAN). The model first combines GPT and aspect coding to obtain a new word vector representation method, and then inputs the word vector with rich semantics into the network with a multi-layer attention mechanism. Sentiment Analysis Accuracy. In the experiment, the GPT-MAN model is analyzed from the selection of word embedding model and the optimization of hyperparameters using the datasets of Restaurant, Laptop and Twitter, and the effectiveness of this method is verified. And in comparison with different models, the accuracy of the model in the three datasets increased by 2.57, 3.34, and 1.19 percentage points, respectively. © 2023 SPIE.

关键词： Embeddings

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GrammarGPT: Exploring Open-Source LLMs for Native Chinese Grammatical Error Correction with Supervised Fine-Tuning 12th

GrammarGPT: Exploring Open-Source LLMs for Native Chinese G...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Fan, Yaxin Jiang, Feng Li, Peifeng Li, Haizhou School of Data Science The Chinese University of Hong Kong Shenzhen China School of Computer Science and Technology Soochow University Suzhou China Shenzhen Research Institute of Big Data Guangdong Shenzhen China School of Information Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9783031446986

Grammatical error correction aims to correct ungrammatical sentences automatically. Recently, some work has demonstrated the excellent capabilities of closed-source Large Language Models (LLMs, e.g., ChatGPT) in grammatical error correction. However, the potential of open-source LLMs remains unexplored. In this paper, we introduced GrammarGPT, an open-source LLM, to preliminary explore its potential for native Chinese grammatical error correction. The core recipe of GrammarGPT is to leverage the hybrid dataset of ChatGPT-generated and human-annotated. For grammatical errors with clues, we proposed a heuristic method to guide ChatGPT to generate ungrammatical sentences by providing those clues. For grammatical errors without clues, we collected ungrammatical sentences from publicly available websites and manually corrected them. In addition, we employed an error-invariant augmentation method to enhance the ability of the model to correct native Chinese grammatical errors. We ultimately constructed about 1k parallel data and utilized these data to fine-tune open-source LLMs (e.g., Phoenix, released by The Chinese University of Hong Kong, Shenzhen) with instruction tuning. The experimental results show that GrammarGPT outperforms the existing SOTA system significantly. Although model parameters are 20x larger than the SOTA baseline, the required amount of data for instruction tuning is 1200x smaller, illustrating the potential of open-source LLMs on native CGEC. Our GrammarGPT ranks 3 rd on NLPCC2023 SharedTask1, demonstrating our approach’s effectiveness. The code and data are available at https://***/FreedomIntelligence/GrammarGPT. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Error correction

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Readout electronics for the gamma detector of the HIRFL-CSR external target facility

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Nuclear science and Techniques 2025年第2期36卷 71-81页

作者： Xian-Qin Li Hai-Bo Yang Xiao-Meng Ma Chao-Jie Zou Tao Liu Xian-Cai Zhou Duo Yan Yang-Zhou Su Shu-Wen Tang Shi-Tao Wang Yu-Hong Yu Zhi-Yu Sun Cheng-Xin Zhao Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China School of Nuclear Science and Technology University of Chinese Academy of SciencesBeijing 100049China School of Physics and Electronic Engineering Northwest Normal UniversityLanzhou 730070China School of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China

The Cooling Storage Ring of the Heavy Ion Research Facility in Lanzhou(HIRFL-CSR)was constructed to study nuclear physics,atomic physics,interdisciplinary science,and related *** External Target Facility(ETF)is located in the main ring of the *** gamma detector of the ETF is built to measure emitted gamma rays with energies below 5 MeV in the center-of-mass frame and is planned to measure light fragments with energies up to 300 *** readout electronics for the gamma detector were designed and *** readout electronics consist of thirty-two front-end cards,thirty-two readout control units(RCUs),one common readout unit,one synchronization&clock unit,and one sub-trigger *** using the real-time peak-detection algorithm implemented in the RCU,the data volume can be significantly *** addition,trigger logic selection algorithms are implemented to improve the selection of useful events and reduce the data *** test results show that the integral nonlinearity of the readout electronics is less than 1%,and the energy resolution for measuring the 60 Co source is better than 5.5%.This study discusses the design and performance of the readout electronics.

关键词： HIRFL-CSR Gamma detector External target facility Readout electronics Readout control unit Common readout unit Peak-detection algorithm

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An Integration Deep Learning Model for the Oil-tea Camellia Disease and Pest Recognition 2

An Integration Deep Learning Model for the Oil-tea Camellia ...

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2nd International Conference on Automation, Robotics and Computer Engineering, ICARCE 2023

作者： Yang, Fang Chen, Junde Ren, Fuping Wang, Zihao Li, Ziyuan Jiang, Shuyu Business School Xiamen Institute of Technology Xiamen China Xiangtan University Department of Electronic Commerce Xiangtan China Shenzhen Comtech Technology Co. Ltd Shenzhen China Zhuhai College of Science and Technology Data Science and Big Data Technology Langfang China

ISBN: (纸本)9798350308341

Oil-tea camellia is recognized as a healthy edible oil in the world. It has the functions of lowering blood pressure, lowering blood lipids and softening blood vessels, and it has high comprehensive utilization value. However, oil-tea camellia is often invaded by various disease and pest in the process of planting, which reduces its yield, how to use intelligent information technology to identify disease and pest in oil-tea camellia is of great significance. Recent developments in deep learning-based Convolutional Neural Network (CNN) have shown prominent performance in the identification of crop pests and diseases. In order to solve the problems of low precision and weak generalization ability of traditional methods for oil-tea camellia disease and pest identification, this paper introduces an efficient CNN model based on mobile terminal that accurately identifies the common types of crop pest and diseases. The strategy of two-phase transfer learning (TL) is done during the training process. And an integration deep learning model based on the lightweight convolutional network architecture, feature extraction and fusion, and model compression is developed. The proposed model exhibits promising performance in comparison to other state-of-the-art models. Notably, the model achieves an average recognition accuracy of 99.78% for Camellia Oleifera diseases. The proposed model also shows strong generalization ability, it attains an average recognition accuracy of 99.55% on the widely utilized Plant village dataset. The model achieves recognition accuracies of 68.15% and 95.09% on the open-source IP102 dataset and the Real-world dataset respectively. The experimental results conclusively affirm the feasibility and efficacy of the proposed approach for the precise identification of Camellia Oleifera diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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Subpart Suppression Network for Few-Shot Object Counting

Subpart Suppression Network for Few-Shot Object Counting

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Lanxin Liu, Xinyan Li, Guorong School of Computer Science and Technology Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China School of Computer Science and Technology Harbin Institute of Technology Weihai China

ISBN: (纸本)9798350368741

Few-shot object counting and detection aim to count objects along with their bounding boxes specified by exemplar bounding boxes. Current mainstream methods predict density maps by applying similarity between exemplar and image features to get counting results and detecting peak points from the density map as the positions of objects. However, the sub-parts of objects can also have high similarity to the exemplars, harming the counting and detection performance. To address these issues, we propose a two-stage Subpart Suppression Network (SSN), consisting of a Subpart Suppression Density map Predictor (SSDP), which enforces the model focus on whole objects rather than subparts, and a SAM-based Detector and Verifier, which refines the final prediction by a clustering method. Extensive experiments on two popular datasets show an advantage in performance over the state-of-the-art methods and prove the components' effectiveness. © 2025 IEEE.

关键词： Counting Detection Few-Shot Localization

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VFLF: A verifiable federated learning framework against malicious aggregators in Industrial Internet of Things

VFLF: A verifiable federated learning framework against mali...

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作者： Zhou, Zhou Tian, Youliang Peng, Changgen Yang, Nan Long, Shigong State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Guiyang China Institute of Cryptography and Data Security Guizhou University Guizhou Guiyang China

As an important approach to overcome data silos and privacy concerns in deep learning, federated learning, which can jointly train the global model and keep data local, has shown remarkable performance in a range of industrial applications. However, federated learning still suffers from the problem that shared gradients may be subject to tampering, inference functions, and falsification. To address this issue, we propose a verifiable federated learning framework to deal with malicious aggregators. Initially, we propose a reputation calculation mechanism to solve the problem of selecting a reliable aggregator based on a multiweight subjective logic model. Furthermore, we design a verifiable federated learning scheme to ensure data confidentiality, integrity, and verifiability, as well as support the client's dynamic withdrawal. Security analyses indicate that our framework is secure against malicious adversaries. Furthermore, experimental results on real datasets show that our verifiable federated learning has high accuracy and feasible efficiency. © 2022 John Wiley & Sons, Ltd.

关键词： Internet of things

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