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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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EG-SAN: Evolving Graph Self-attention Networks for Detecting Illicit Activities in Cryptocurrency 15th

EG-SAN: Evolving Graph Self-attention Networks for Detectin...

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15th EAI International Conference on Digital Forensics and Cyber Crime, ICDF2C 2024

作者： Cui, Bo Zhang, Jia Engineering Research Center of Ecological Big Data Ministry of Education Beijing China College of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)9783031893599

In recent years, illicit activities in cryptocurrency transactions have posed a severe threat to financial systems and society. Current detection methods mainly rely on machine learning and graph neural networks but often overlook the dynamic evolution of transaction graphs over time. To address this, we propose a novel Evolutionary Graph Self-Attention Network (EG-SAN) that can simultaneously capture the spatial relationships among nodes as well as their temporal evolution features through a joint self-attention mechanism across structural and temporal dimensions. Specifically, the graph attention network aggregates features from node neighborhoods in the graph data, while gated recurrent units dynamically update its layer weights to capture the evolution of the graph over the temporal dimension. Subsequently, a self-attention mechanism further optimizes these weights, enabling the model to more accurately reflect the salient features of the data at different time steps. We evaluate EG-SAN on a Bitcoin transaction dataset, and the results demonstrate its superiority over existing state-of-the-art methods. Further application to graph datasets from other domains also confirms the effectiveness and robustness of EG-SAN, showcasing its broad application potential. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Graph neural networks

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Logic-Aware Knowledge Graph Reasoning for Structural Sparsity under Large Language Model Supervision 25

Logic-Aware Knowledge Graph Reasoning for Structural Sparsit...

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34th ACM Web Conference, WWW 2025

作者： Pan, Yudai Hong, Jiajie Zhao, Tianzhe Song, Lingyun Liu, Jun Shang, Xuequn School of Computer Science Northwest Polytechnical University Shaanxi Xi’an China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical University Shaanxi Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Shaanxi Xi’an China

ISBN: (纸本)9798400712746

Knowledge Graph (KG) reasoning aims to predict missing entities in incomplete triples, which requires adequate structural information to derive accurate embeddings. However, KGs in the real world are not as dense as the idealized benchmarks, where sparse graph structures restrict the comprehensive structural information for superior performance. Although the logical semantics in KGs shows its potential in alleviating the impact of structural sparsity, there still exist some challenges. The deficient supervision and the semantic gap of logic make it difficult to introduce logical semantics in sparse KG reasoning. To this end, we propose a novel KG reasoning approach LoLLM injecting logic with the supervised information supplied by the Large Language Model (LLM), which is proved to be effective in evaluating and scoring. Firstly, LoLLM derives structural embeddings employing a graph convolutional network (GCN) with relation-aware and triple-aware attention. LoLLM secondly constructs reasoning paths instantiated from the first-order logic rules extracted from sparse KGs, and injects the logical semantics by a designed LLM-enhanced tuning strategy. We propose a textual loss (TL) and a logical loss (LL) in the optimization and obtain logical tuning embeddings of KG in this process. Finally, LoLLM fuses structural embeddings from the GCN and logical tuning embeddings from the LLM-enhanced tuning for scoring and incomplete triple prediction. Extensive experiments on two sparse KGs and a benchmark show that LoLLM outperforms state-of-the-art structure-based and Language Model (LM)-augmented baselines. Moreover, the logic rules with corresponding confidences provide explicit explanations as an interpretable paradigm. © 2025 Copyright held by the owner/author(s).

关键词： Structural analysis

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Multimodal Supported Chatbot Framework for People with Aphasia 10

Multimodal Supported Chatbot Framework for People with Aphas...

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10th International Multidisciplinary engineering Research Conference on Moratuwa engineering Research, MERCon 2024

作者： Ayesha, R.L.A. Sanduni Vijayarathna, L.B. Hasini M. Vithanage, N.P.A. Thayasivam, Uthayasanker Alahakoon, Damminda Adikari, Achini Pallewela, Nuwan University of Moratuwa Department of Computer Science and Engineering Katubedda Sri Lanka La Trobe University Research Centre for Data Analytics and Cognition Australia

ISBN: (纸本)9798331529048

Chatbots have become a trending topic with emerging platforms like ChatGPT, Gemini, and Copilot, for conversation assistance. Current chatbots mainly focus on the general public assuming a natural flow of conversation. However, there is a need for a chatbot that supports people with various communication disabilities. This research fills this gap by offering a novel technique for a chatbot that assists people with Aphasia, a condition characterised by difficulties with language. We propose a multi-modal chatbot that is customised and designed to assist users with communication disabilities in navigating a website. Unlike typical chatbots, which rely on one form of communication, our architecture combines multiple modalities to improve comprehension and promote effective communication for people with Aphasia. We focus on gathering multimodal inputs by recognising and combining user intents from diverse sources. The use of Txtai, an all-in-one embeddings database for semantic search improves our chatbot's capacity to process various inputs efficiently. We leverage specialised models like Whisper for audio transcription and MediaPipe Gesture Recognizer for gesture detection to enhance user interactions. Additionally, Rasa Core integration improves conversational experiences for users. We propose that this new approach will make communication more accessible and inclusive for individuals with Aphasia. © 2024 IEEE.

关键词： Gesture recognition

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A Method of Emergency Volunteer Team Internal Participation in Rescue Decision-Making Considering Psychological Behavior

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Journal of Systems science and Information 2024年第1期12卷 64-80页

作者： Jie BAI Shengqun CHEN College of Computer and Information Sciences Fujian Agriculture and Forestry UniversityFuzhou 350002China School of Information Engineering Fujian Business UniversityFuzhou 350506China Engineering Research Center of Big Data Analytics for Business Intelligence Fujian Province UniversityFuzhou 350506China

A method for internal participation in rescue decision-making of emergency volunteer teams considering psychological behavior is proposed to address the time sequence of rescue ***,the problem of multi-tasking and multi-operation within the emergency volunteer team is ***,considering that task leaders are influenced by behavioral and psychological factors in the evaluation,the required time for the job is used as a reference point,and the expected time that volunteers can complete the job is used as an attribute *** task leader's prospect satisfaction value for each volunteer is calculated based on prospect theory,and the perceived utility values of disappointment theory and regret theory are calculated to measure the task leader's satisfaction with each ***,a multilayer coded genetic algorithm is used to construct an optimization model for emergency volunteer decision-making with the objective of maximizing the satisfaction ***,the feasibility and effectiveness of this method are illustrated by an example *** result shows that the efficiency of rescue tasks can be improved through decision optimization within the volunteer team.

关键词： emergency volunteers rescue task multi-layer coding genetic algorithm satisfaction value psychological behavior

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A Real-Time Sentiment Analysis Model of Students by Fusing Attention Mechanism 13

A Real-Time Sentiment Analysis Model of Students by Fusing A...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Hu, Pengyun Tang, Xianpiao Yang, Liu Xia, Daoxun School of Big Data and Computer Science Guizhou Normal University China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350319156

By recognizing students' facial expressions in a classroom, it is possible to determine in real-time whether students are confused, dissatisfied or happy. Understanding students' emotional states helps teachers adjust teaching strategies and methods, thus improving the quality and effect of teaching. However, classroom scenarios are often complex, and most previous expression recognition methods suffer from the problems of missing local features and low accuracy. Additionally, the imbalanced distribution of datasets has an impact on the recognition accuracy during model training. Therefore, in this paper, based on deep learning theory, an LCANet model, which incorporates an attention mechanism, is proposed to realize the recognition and analysis of students' expressions during classroom teaching. To optimize the global feature extraction capability of the network, the model uses ConvNeXt V2 as the backbone. Moreover, to enable the model to better focus on the important parts of facial expressions, we add an improved channel space attention (CSA) module to extract local features. In addition, we introduce the LF loss function to alleviate the class imbalance problem in the facial expression dataset. The experimental results show that our LCANet model achieves good recognition rate on the public FERPlus dataset with 91.43% accuracy. Therefore, the proposed LCANet model has good robustness and expression recognition performance. © 2023 IEEE.

关键词： Students

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IGED:Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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Cooperation and Contract Design in Project Management with Outsourcing

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Journal of Systems science and Systems engineering 2023年第1期32卷 34-70页

作者： Xiaoqiang Cai Nicholas G.Hall Siyi Wang Feng Zhang School of Data Science The Chinese University of Hong KongShenzhen518172China Shenzhen Research Institute of Big Data Shenzhen518172China Department of Operations&Business Analytics Fisher College of BusinessOhio State UniversityColumbusOhioUSA School of Science and Engineering The Chinese University of Hong KongShenzhen518172China Suning Bank Nanjing210019China

We study a project management problem where the prime contractor needs to outsource tasks to subcontractors with the required *** execution of the project requires proper coordination among the subcontractors,as well as contract design by the prime contractor to incentivize the *** modeling the subcontractors’coordination problem as a cooperative game,we develop a profit sharing scheme to facilitate the subcontractors’*** consider two contract designs for the prime contractor:a uniform contract across all subcontractors,and a nonuniform one that customizes incentives for each *** propose efficient algorithms to solve the implicit optimization problems for optimal contract *** experiments show that the pooling effect of subcontractors’cooperation mitigates the negative impact of poor estimates about the crashing cost and resource *** observe three unexpected results through the randomized computation experiments:(i)the subcontractors’profits may decrease if they provide false information;(ii)it is safer for the prime contractor to overestimate subcontractors’crashing costs than underestimate them;and(iii)uniform contracts deliver more project profit for the subcontractors in the coalitions.

关键词： Project management with outsourcing subcontracting cooperative game contract design

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High Dynamic Collaborative Team Query via Multi-Fuzzy-Constrained Graph Pattern Matching 9

High Dynamic Collaborative Team Query via Multi-Fuzzy-Constr...

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9th International Conference on Cloud Computing and big data analytics, ICCCBDA 2024

作者： Hu, Tao Zhang, Zan Bu, Chenyang Li, Lei Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350373554

Graph pattern matching is a technique widely used in various fields such as protein structure analysis, social group querying, and expert localization. This technique involves finding matching subgraphs in large social networks that align with the patterns specified in the pattern graph. In this paper, we focus on a specific sub-problem in social group querying, known as the cooperative team query, which arises from practical applications, where the nodes in the pattern graph and the data graph represent team member entities, while the edges represent their social relationships. We note that the requirements of many teams in the real world are dynamic, necessitating iterative computation for graph pattern matching using traditional methods. To address this challenge in highly dynamic systems, we propose a graph pattern matching method based on core pattern graph matching cache. This approach involves extracting the core pattern graph, and comprising core team members based on the characteristics of cooperative teams. The core graph-based matching cache enables the second half of the algorithm to operate on an order-of-magnitude smaller graph, significantly improving efficiency. Additionally, the multi-threaded approach fully leverages hardware resources, synchronizing multiple matching result of the core pattern graph to reduce matching time. Experimental results on three real social network datasets demonstrate that our proposed algorithm, Core Pattern Graph Matching Cache-based Multi-threaded Exploration (CCMTE), significantly outperforms existing methods in terms of efficiency. © 2024 IEEE.

关键词： Efficiency

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Analysis of Bitcoin Prediction using Transparent and Opaque Machine Learning Algorithms 15

Analysis of Bitcoin Prediction using Transparent and Opaque ...

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

作者： Singh, Kavita R. Jamar, Anushri V. Khadgi, Pracheta P. Kapse, Smita Kumar, Ajay Department of Computer Technology Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India School of Big Data and Computer Science Guizhou Province Guiyang City China

ISBN: (纸本)9798331300579

The unpredictable nature of cryptocurrency markets, particularly Bitcoin, has attracted significant attention from investors, researchers, and financial institutions seeking to understand and predict price movements. This study aims to analyse the effectiveness of both transparent and opaque machine learning algorithms in predicting Bitcoin prices. Transparent algorithms, such as linear regression and decision trees, provide clear insights into the factors influencing predictions. On the other hand, opaque algorithms, like ensemble methods, offer complex, non-linear models that may capture intricate patterns in the data. The main analysis is to lower the mean absolute error and enhance the r2 square, both key indicators of forecast accuracy. The study presents that linear regression and random forest outperformed others, achieving the smallest error and highest variance explanation, suggesting their effectiveness in Bitcoin price prediction with and without outliers respectively. In contrast, the support vector machine underperforms compared to all other machine learning algorithms under consideration. This paper sheds light on the most capable machine-learning techniques for Bitcoin forecasting, aiding trader investment choices and guiding academic study in the field of prediction. © Grenze Scientific Society, 2024.

关键词： Prediction models

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