检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

NEURON-ENHANCED AUTOENCODER MATRIX COMPLETION AND COLLABORATIVE FILTERING: THEORY AND PRACTICE 12

NEURON-ENHANCED AUTOENCODER MATRIX COMPLETION AND COLLABORAT...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Fan, Jicong Chen, Rui Zhang, Zhao Ding, Chris H.Q. School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science & Information Engineering Hefei University of Technology China

Neural networks have shown promising performance in collaborative filtering and matrix completion but the theoretical analysis is limited and there is still room for improvement in terms of the accuracy of recovering missing values. This paper presents a neuron-enhanced autoencoder matrix completion (AEMC-NE) method and applies it to collaborative filtering. Our AEMC-NE adds an element-wise autoencoder to each output of the main autoencoder to enhance the reconstruction capability. Thus it can adaptively learn an activation function for the output layer to approximate possibly complicated response functions in real data. We provide theoretical analysis for AEMC-NE as well as AEMC to investigate the generalization ability of autoencoder and deep learning in matrix completion, considering both missing completely at random and missing not at random. We show that the element-wise neural network has the potential to reduce the generalization error bound, the data sparsity can be useful, and the prediction performance is closely related to the difference between the numbers of variables and samples. The numerical results on synthetic data and five benchmark datasets demonstrated the effectiveness of AEMC-NE in comparison to many baselines. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Edge Coalition Construction Based on a Novel Market Equilibrium Price

引用

China Communications 2025年第3期22卷 288-305页

作者： Wang Xiaolong Dang Jianwu Zhao Shuxu Wang Yangping Zhang Zhanping Hao Zhanjun School of Electronic and Information Engineering Lanzhou Jiaotong UniversityLanzhou 730070China Big Data Center of Gansu Province Lanzhou 730000China School of Mathematics and Information Engineering Longdong UniversityLanzhou 745000China College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China

The emergence of multi-access edge computing(MEC)aims at extending cloud computing capabilities to the edge of the radio access *** the large-scale internet of things(IoT)services are rapidly growing,a single edge infrastructure provider(EIP)may not be sufficient to handle the data traffic generated by these *** of the existing work addressed the computing resource shortage problem by optimizing tasks schedule,whereas others overcome such issue by placing computing resources on ***,when considering a multiple EIPs scenario,an urgent challenge is how to generate a coalition structure to maximize each EIP’s gain with a suitable price for computing resource block corresponding to a *** this end,we design a scheme of EIPs collaboration with a market price for containers under a scenario that considers a collection of service providers(SPs)with different budgets and several EIPs distributed in geographical ***,we bring in the net profit market price model to generate a more reasonable equilibrium price and select the optimal EIPs for each SP by a convex *** we use a mathematical model to maximize EIP’s profits and form stable coalitions between EIPs by a distributed coalition formation *** results demonstrate that our proposed collaborative scheme among EIPs enhances EIPs’gain and increases users’surplus.

关键词： coalition formation computing resource block market equilibrium multi-access edge computing surplus

来源：评论

学校读者我要写书评

暂无评论

Application of improved virtual sample and sparse representation in face recognition

引用

CAAI Transactions on Intelligence Technology 2023年第4期8卷 1391-1402页

作者： Yongjun Zhang Zewei Wang Xuexue Zhang Zhongwei Cui Bob Zhang Jinrong Cui Lamin LJanneh College of Computer Science and Technology Guizhou UniversityGuiyangChina Big Data Science and Intelligent Engineering Research Institute Guizhou Education UniversityGuiyangChina School of Mathematics and Big Data Guizhou Education UniversityGuiyangChina Department of Computer and Information Science University of MacaoAvenida da UniversidadeTaipaMacaoChina School of Mathematics and Information South China Agricultural UniversityGuangzhouChina

Sparse representation plays an important role in the research of face *** a deformable sample classification task,face recognition is often used to test the performance of classification *** face recognition,differences in expression,angle,posture,and lighting conditions have become key factors that affect recognition ***,there may be significant differences between different image samples of the same face,which makes image classification very ***,how to build a robust virtual image representation becomes a vital *** solve the above problems,this paper proposes a novel image classification ***,to better retain the global features and contour information of the original sample,the algorithm uses an improved non‐linear image representation method to highlight the low‐intensity and high‐intensity pixels of the original training sample,thus generating a virtual ***,by the principle of sparse representation,the linear expression coefficients of the original sample and the virtual sample can be calculated,*** obtaining these two types of coefficients,calculate the distances between the original sample and the test sample and the distance between the virtual sample and the test *** two distances are converted into distance ***,a simple and effective weight fusion scheme is adopted to fuse the classification scores of the original image and the virtual *** fused score will determine the final classification *** experimental results show that the proposed method outperforms other typical sparse representation classification methods.

关键词： representation sample image

来源：评论

学校读者我要写书评

暂无评论

Probabilistic time series forecasting with deep non-linear state space models

引用

CAAI Transactions on Intelligence Technology 2023年第1期8卷 3-13页

作者： Heming Du Shouguo Du Wen Li School of Computer Science College of Engineering and Computer ScienceAustralian National UniversityCanberraAustralian Capital TerritoryAustralia Shanghai Municipal Big Data Center ShanghaiChina Department of Economic Statistics School of Statistics and InformationShanghai University of International Business and EconomicsShanghaiChina

Probabilistic time series forecasting aims at estimating future probabilistic distributions based on given time series *** is a widespread challenge in various tasks,such as risk management and decision *** investigate temporal patterns in time series data and predict subsequent probabilities,the state space model(SSM)provides a general *** of SSM achieve considerable success in many fields,such as engineering and ***,since underlying processes in real-world scenarios are usually unknown and complicated,actual time series observations are always irregular and ***,it is very difficult to determinate an SSM for classical statistical *** this paper,a general time series forecasting framework,called Deep Nonlinear State Space Model(DNLSSM),is proposed to predict the probabilistic distribution based on estimated underlying unknown processes from historical time series *** fuse deep neural networks and statistical methods to iteratively estimate states and network parameters and thus exploit intricate temporal patterns of time series *** particular,the unscented Kalman filter(UKF)is adopted to calculate marginal likelihoods and update distributions recursively for non-linear *** that,a non-linear Joseph form covariance update is developed to ensure that calculated covariance matrices in UKF updates are symmetric and positive ***,the authors enhance the tolerance of UKF to round-off errors and manage to combine UKF and deep neural *** this manner,the DNLSSM effectively models non-linear correlations between observed time series data and underlying dynamic *** in both synthetic and real-world datasets demonstrate that the DNLSSM consistently improves the accuracy of probability forecasts compared to the baseline methods.

关键词： Artificial Intelligence machine learning time series

来源：评论

学校读者我要写书评

暂无评论

Revealing Security Flaws in Cross-Modal Retrieval Models through Video Poisoning

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 6184-6194页

作者： Jin, Ming Hu, Wenbo Hong, Richang Zhu, Lei Hefei University of Technology School of Computer and Information Hefei China Data Space Research Institute Hefei Comprehensive National Science Center China Tongji University School of Electronic and Information Engineering Shanghai China

Video-text cross-modal retrieval is widely studied to improve retrieval accuracy. However, the security of video-text cross-modal retrieval models receives little attention. If attackers exploit the security vulnerabilities in these models, it poses a significant threat to the retrieval models. Thus, identifying security flaws in video-text cross-modal retrieval models becomes the focus of our research. We are the first to design a video poisoning model to uncover security vulnerabilities in retrieval models. Existing poisoning models have certain limitations when it comes to exploiting vulnerabilities in retrieval models. These include failing to comprehensively embed malicious information into the original video and being unable to maintain visual consistency between the original and poisoned videos. These limitations can result in unsuccessful attacks on retrieval models and an inability to effectively identify security flaws within them. To address these shortcomings, we design an efficient poisoning model that embeds malicious information thoroughly into the original clean data to attack video-text cross-modal retrieval models. We are the first to use a poisoning model to attack retrieval models, thereby uncovering their security vulnerabilities. Second, we introduce a bi-level poisoning module to ensure that malicious information is thoroughly embedded into the original video, thereby enhancing the attack capability of the poisoning model. Finally, we design an adversarial module to improve visual consistency between the original and poisoned videos, thus enhancing the concealment of malicious information within the training data of retrieval models. Our poisoning model can identify security flaws in video-text cross-modal retrieval models, providing insights into improving the security of retrieval models. The effectiveness of our model is validated on the MSR-VTT, LSMDC, and MSVD datasets. © 1991-2012 IEEE.

关键词： Content based retrieval

来源：评论

学校读者我要写书评

暂无评论

Scalable Transactional Stream Processing on Multicore Processors

引用

IEEE Transactions on Knowledge and data engineering 2025年第7期37卷 4254-4269页

作者： Zhao, Jianjun Mao, Yancan Yang, Zhonghao Liu, Haikun Zhang, Shuhao Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China National University of Singapore 119077 Singapore Nanyang Technological University 639798 Singapore

Transactional stream processing engines (TSPEs) are central to modern stream applications handling shared mutable states. However, their full potential, particularly in adaptive scheduling, remains largely unexplored. We present MorphStream, a TSPE designed to optimize parallelism and performance for transactional stream processing on multicores. Through a unique three-stage execution paradigm (i.e., planning, scheduling, and execution), MorphStream enables adaptive scheduling under varying workload characteristics. Building on this foundation, MorphStream is further enhanced with support for non-deterministic state access, employing a stateful task precedence graph to handle undefined read/write sets at runtime while guaranteeing transaction semantics. Additionally, MorphStream incorporates a generalized framework for managing window-based operations, enabling efficient tracking and maintenance of overlapping windows using multi-versioned state management. These extensions enhance the system’s ability to process dynamic and irregular workloads. Experimental results demonstrate up to 3.4 times higher throughput and 69.1% lower latency compared to state-of-the-art TSPEs, validating its scalability and adaptability in real-world streaming scenarios. © 1989-2012 IEEE.

关键词： Multicore programming

来源：评论

学校读者我要写书评

暂无评论

AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading 23

AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage...

引用

23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Duan, Zhuohui Feng, Hao Liu, Haikun Liao, Xiaofei Jin, Hai Li, Bangyu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133458

The key-value separation is renowned for its significant mitigation of the write amplification inherent in traditional LSM trees. However, KV separation potentially increases performance overhead in the management of Value region, especially for garbage collection (GC) operation that is used to reduce the redundant space occupation. In response, many efforts have been made to optimize the GC mechanism for KV separation. However, our analysis indicates that such solution based on trade-offs between CPU and I/O overheads cannot simultaneously satisfy the three requirements of KV separated systems in terms of throughput, tail latency, and space usage. This limitation hinders their real-world application. In this paper, we introduce AegonKV, a "three-birds-one-stone" solution that comprehensively enhances the throughput, tail latency, and space usage of KV separated systems. AegonKV first proposes a SmartSSD-based GC offloading mechanism to enable asynchronous GC operations without competing with LSM read/write for bandwidth or CPU. AegonKV leverages offload-friendly data structures and hardware/software execution logic to address the challenges of GC offloading. Experiments demonstrate that AegonKV achieves the largest throughput improvement of 1.28-3.3 times, a significant reduction of 37%-66% in tail latency, and 15%-85% in space overhead compared to existing KV separated systems. © 2025 FAST. All Rights Reserved.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

Pre-Training and Fine-Tuning for Efficient Routing in Opportunistic Networks

Pre-Training and Fine-Tuning for Efficient Routing in Opport...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hao, Jia Wu, Xiaorui Seah, Winston K. G. Zhang, Feng Xu, Gang College of Computer Science Engineering Research Center of Ecological Big Data Inner Mongolia University Ministry of Education Hohhot China School of Engineering & Computer Science Victoria University of Wellington Wellington New Zealand School of Computer and Information Technology Shanxi University Taiyuan China

ISBN: (纸本)9781665410205

In opportunistic networks, it is a challenge to find the best relay node instead of blindly selecting from among available nodes to forward messages and effectively transmit them to their destinations. By comparing the similarity between nodes, the next hop node that is most similar to the destination node is found in time. Existing node similarity-based opportunistic networks routing algorithms only calculate similarity between nodes according to nodes' properties that can be directly obtained from the network, such as the historical meeting record between nodes, etc. However, this superficial similarity calculation is obviously inadequate to describe the inherent dynamic nature of opportunistic networks at both spatial and temporal levels, and ignores the salient movement characteristics of nodes, resulting in poor routing performance. Therefore, this paper proposes an opportunistic network routing strategy based on pre-training and fine-tuning (PTFT) model. Firstly, an autoencoder is added to the graph neural network model to encode node movement behavior. Then, a pre-training graph neural network model in large scale opportunistic network scenarios is applied to learn potential features of nodes through fine-tuning. Finally, we calculate the similarity between nodes based on the node potential feature, thereby assisting the nodes to achieve an efficient routing decision. The simulation results show that our PTFT-based algorithm not only has superior performance compared to traditional routing algorithms, but also faster learning speed than other machine learning-based routing algorithms. © 2024 IEEE.

关键词： Routing algorithms

来源：评论

学校读者我要写书评

暂无评论

Incentive Mechanism of Selfish Nodes Based on Energy Optimization and Game Theory

Incentive Mechanism of Selfish Nodes Based on Energy Optimiz...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Dong, XiangJia Cheng, Yuan Seah, Winston K. G. Zhang, Feng Xu, Gang College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China School of Engineering & Computer Science Victoria University of Wellington Wellington New Zealand School of Computer and Information Technology Shanxi University Taiyuan China

ISBN: (纸本)9781665410205

With the proliferation of mobile intelligent terminals, opportunistic networks have attracted widespread attention as a complementary technology to multi-network convergence. Different from traditional wireless networks, message delivery in opportunistic networks does not rely on a fixed infrastructure, but rather storing messages in a cache and utilizing the movement and encounters of nodes to relay messages. However, in practical application scenarios, nodes have limited storage space and energy and will easily exhibit selfishness. An increase in the number of selfish nodes will drastically degrade the performance of the network. To solve the problem of significant network performance degradation when the number of selfish nodes is high, this paper proposes an Incentive mechanism of Selfish nodes based on Energy optimization and Game Theory (ISEGT). The mechanism abstracts the process of forwarding messages by nodes into a bargaining game process, and selectively forwards messages based on nodes' remaining energy and other circumstances. The experimental results show that the ISEGT mechanism can motivate selfish nodes to actively participate in message forwarding, which improves the success rate of message delivery and the survival rate of nodes, and optimizes the overall performance of the network. © 2024 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：