检索结果-内蒙古大学图书馆

Artificial Intelligence (CAI), IEEE Conference on

作者： Cheng Chen Yueming Lyu Xingrui Yu Jing Li Ivor W Tsang The Australian Artificial Intelligence Institute (AAII) University of Technology Sydney Australia CFAR Agency for Science Technology and Research Singapore IHPC Agency for Science Technology and Research Singapore School of Computer Science and Engineering College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Partial Label Learning (PLL) involves associating each instance with a candidate label set, which includes the true label along with false positive labels. Traditional PLL approaches typically assume that these false positive labels follow either a uniform or a non-uniform structure, based on the true label of an instance. However, these assumptions do not fully encompass all aspects of real-world scenarios, especially in cases of finegrained objects with subtle differences in appearance. Therefore, we introduce a fine-grained PLL task specifically designed to handle candidate sets that comprise the true label, partially nonuniform fine-grained labels manifesting in a hierarchical structure, and other false positive labels. Nevertheless, the incorporation of these partially non-uniform, fine-grained labels can result in an intractable posterior, leading to an inconsistent classifier. To address this issue, we propose a Specific Classes-Hierarchical (SCH) regularisation that ensures classifier consistency. Subsequently, the Global Label-Hierarchical-wise Embedding (GLHE) regularisation is introduced, using the refined pseudo labels of positive samples from the obtained consistent classifier. This allows the model to learn more distinctive representations from finegrained instances. Our extensive experiments on datasets such as CIFAR-10, CIFAR-100, and CUB-200 have demonstrated the effectiveness of our approach.

关键词： Complexity theory Phase locked loops Task analysis Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Behaviour of Tree-Based Neural Networks in Regression Tasks

arXiv

引用

arXiv 2024年

作者： Samoaa, Peter Farahani, Mehrdad Longa, Antonio Leitner, Philipp Chehreghani, Morteza Haghir Data Science and AI Division Chalmers University of Technology Gothenburg Sweden Interaction Design and Software Engineering Division Chalmers University of Technology Gothenburg Sweden Department of Information Engineering and Computer Science University of Trento Trento Italy

The landscape of deep learning has vastly expanded the frontiers of source code analysis, particularly through the utilization of structural representations such as Abstract Syntax Trees (ASTs). While these methodologies have demonstrated effectiveness in classification tasks, their efficacy in regression applications, such as execution time prediction from source code, remains underexplored. This paper endeavours to decode the behaviour of tree-based neural network models in the context of such regression challenges. We extend the application of established models—tree-based Convolutional Neural Networks (CNNs), Code2Vec, and Transformer-based methods—to predict the execution time of source code by parsing it to an AST. Our comparative analysis reveals that while these models are benchmarks in code representation, they exhibit limitations when tasked with regression. To address these deficiencies, we propose a novel dual-transformer approach that operates on both source code tokens and AST representations, employing cross-attention mechanisms to enhance interpretability between the two domains. Furthermore, we explore the adaptation of Graph Neural Networks (GNNs) to this tree-based problem, theorizing the inherent compatibility due to the graphical nature of ASTs. Empirical evaluations on real-world datasets showcase that our dual-transformer model outperforms all other tree-based neural networks and the GNN-based models. Moreover, our proposed dual transformer demonstrates remarkable adaptability and robust performance across diverse datasets. © 2024, CC BY-NC-SA.

关键词： Regression analysis

来源：评论

学校读者我要写书评

暂无评论

Movable Superdirective Pairs: A Phase Shifter-Free Approach to mmWave Communications

引用

IEEE Wireless Communications Letters 2025年

作者： Han, Liangcheng Yin, Haifan Gao, Mengying Zhang, Rui Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China The Chinese University of Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore

In this letter, we propose a novel Movable Superdirective Pairs (MSP) approach that combines movable antennas with superdirective pair arrays to enhance millimeter-wave (mmWave) communications. By controlling the rotation angles and positions of superdirective antenna pairs via low-power actuators on the user side, the proposed MSP approach maximizes the received signal-to-noise ratio (SNR) of multipath signals without the need for phase shifters, thereby reducing hardware costs and insertion losses. An efficient algorithm based on alternating optimization and gradient projection is developed to solve the non-convex rotation–position design problem. Moreover, MSP is shown to be robust in both line-of-sight (LOS) and non-line-ofsight (NLOS) channels. Simulation results demonstrate that MSP achieves significant performance gains over fixed-position arrays, while maintaining low system complexity. © 2012 IEEE.

关键词： Microwave antennas

来源：评论

学校读者我要写书评

暂无评论

Imaging Detection and Classification for Near-space Dim Targets 7

Imaging Detection and Classification for Near-space Dim Targ...

引用

7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Yuan, Yupu Xi, Runping Chen, Qian School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Province Key Laboratory of Speech & Image Processing Xi'an China National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China

ISBN: (纸本)9781839539848

The motion mode of near-space targets is complex due to their high threat level. The target imaging faces low SNR and susceptibility to background noise. The existing detection and classification algorithms struggle to meet the demands of performance and speed on such targets. To address these issues, a YOLOv5-based target detection algorithm utilizes CARAFE upsampling and the small detection head to extract internal features and edge information of dim target. The algorithm adeptly captures dim target features, enhancing real-time performance and detection accuracy while overcoming subtle feature granularity and extraction difficulties. Aiming at the problem that there is no existing trajectory classification algorithm in the near-space field, we propose an infrared dim target trajectory classification algorithm. The improved attention module is used to enhance the extraction of motion features, and the target category is accurately identified by the prediction module, improving the capacity to extract dependencies between trajectory fragments. We execute comparative experiments on common infrared dim target dataset, near-space infrared dim target dataset and real dataset to confirm the viability of our methods. The outcomes indicate that our proposed method can be applied to the above three types of dim targets and significantly outperforms state-of-the-art methods. © The Institution of engineering & Technology 2023.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

Unlabelled Sensing with Priors: Algorithm and Bounds

Unlabelled Sensing with Priors: Algorithm and Bounds

引用

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

作者： Garweet Sresth Ajit Rajwade Satish Mulleti Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay India Department of Computer Science and Engineering Indian Institute of Technology Bombay India Department of Electrical Engineering Indian Institute of Technology Bombay Mumbai India

In this study, we consider a variant of unlabelled sensing where the measurements are sparsely permuted, and additionally, a few correspondences are known. We present an estimator to solve for the unknown vector. We derive a theoretical upper bound on the ℓ 2 reconstruction error of the unknown vector. Through numerical experiments, we demonstrate that the additional known correspondences result in a significant improvement in the reconstruction error. Additionally, we compare our estimator with the classical robust regression estimator and we find that our method outperforms it on the normalized reconstruction error metric by up to 20% in the high permutation regimes (> 30%). Lastly, we showcase the practical utility of our framework on a non-rigid motion estimation problem. We show that using a few manually annotated points along point pairs with the key-point (SIFT-based) descriptor pairs with unknown or incorrectly known correspondences can improve motion estimation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Edge-AI: IoT Request Service Provisioning in Federated Edge Computing Using Actor-Critic Reinforcement Learning

引用

IEEE Transactions on engineering Management 2024年 71卷 12519-12528页

作者： Baghban, Hojjat Rezapour, Amir Hsu, Ching-Hsien Nuannimnoi, Sirapop Huang, Ching-Yao National Yang Ming Chiao Tung University Department of Electronics Engineering and Institute of Electronics Hsinchu30010 Taiwan Asia University Department of Computer Science and Information Engineering Taichung41354 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Logitech Inc. Hsinchu30010 Taiwan China Medical University Department of Medical Research and China Medical University Hospital Taichung404 Taiwan Foshan University Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Mathematics and Big Data Foshan528000 China National Yang-Ming Chiao Tung University Department of Electronics Engineering Institute of Electronics Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Department of Electronics Engineering Institute of Electronics Hsinchu30010 Taiwan

Edge computing plays a critical role in the Internet of Things (IoT) environment as it potentially supports the time-critical IoT applications' resources as well as latency requirements. In the near future, most edge service providers are envisioned to receive revenue from deploying these applications with the expenditures proportional to placing the offloaded requests from IoT devices and allocating the required resources. One way to maximize the edge profit and minimize the response latency is to integrate the edge nodes and form the edge federation. Therefore, edge service providers can have interoperability to distribute the IoT requests on the appropriate edge nodes in the light of providing satisfactory service levels to meet their objectives. Since the edge nodes are volatile and IoT time-critical applications are increasing, the edge nodes are envisioned to face massive traffic from IoT devices. Therefore, exploiting the traditional dynamic requests placement approaches cannot meet the SLA requirement of both IoT devices and edge service providers. In this article, we designed an intelligent reinforcement learning-based request service provisioning system (i.e., here, we call Edge-AI) as part of a smart edge orchestrator in the edge federation. We implement the proposed method, which is called DRL-Dispatcher, and compare it with greedy and random algorithms in edge federation. The experimental results show that the proposed DRL-Dispatcher performs better in terms of profit and low response latency as compared with the baseline approaches. © 1988-2012 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture 26

A Graph Sequence Generator and Multi-head Self-attention Mec...

引用

26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wu, Yuejia Zhou, Jian-Tao College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center Intelligent Info. Proc. Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models. © 2023 IEEE.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

EMPA-YOLO:A Lightweight Real-time Weed Detection Method Suitable for Natural

EMPA-YOLO:A Lightweight Real-time Weed Detection Method Suit...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Yuzhe Wang Jiahao Chen Xiaodong Duan Zhuohui Li College of Mechanical and Electronic Engineering Dalian China College of Computer Science and Engineering Dalian China SEAC Key Laboratory of Big Data Applied Technology Dalian China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Weed detection is crucial for the healthy growth of crops, yet existing detection models struggle to perform high-accuracy real-time detection of weeds in natural environments on edge computing devices. This paper introduces EMPA-YOLO, a model designed for rapid, accurate, real-time weed detection on low-performance edge computing devices. It incorporates an efficient multi-scale convolutional structure, C3EMSC, and a lightweight, adaptive weight subsampling layer, LAWDS, into YOLOv5s. Additionally, a logical distillation algorithm, AlignSoftTarget, is proposed for knowledge distillation. Validation on a mixed dataset of crops and weeds showed that EMPA-YOLO improved mAP50 by 11.2%, reduced parameter count by 4.8M, decreased computational load by 8.7GFLOPs, and increased inference frame rate by 58% compared to the original YOLOv5s algorithm. When compared to YOLOv3, YOLOv5s, YOLOv6, YOLOv8s, and RT-DETR, inference speed improved by 89.2%, 60.8%, 77.5%, 76.5%, and 94.7%, respectively, with mean accuracy enhancements of 7.3%, 11.2%, 16.7%, 0.9%, and 1.2%. Real-world testing on edge computing devices met real-time detection requirements, proving its efficacy and practicality in weed detection.

关键词： YOLO Performance evaluation Adaptation models Accuracy Computational modeling Crops Real-time systems Inference algorithms data models Edge computing

来源：评论

学校读者我要写书评

暂无评论

Covert Communication in RSMA-Assisted Ambient Backscatter Communication Systems

引用

IEEE Transactions on Wireless Communications 2025年

作者： Zhang, Zhuo Yang, Liang Lei, Hongjiang Li, Xingwang Niyato, Dusit Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering China Jishou University School of Communication and Electronic Engineering China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering The Chongqing Key Laboratory of Mobile Communications Technology Chongqing400065 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

In this paper, we investigate covert communication in rate-splitting multiple access (RSMA)-assisted ambient backscatter communication (AmBC) systems. Specifically, in the presence of illegitimate users, a base station applies RSMA to the downlink transmission of covert users and common users in the the primary system of ambient backscatter. To evaluate the covertness of the system, we derive closed-form expressions for the detection error probability (DEP), outage probability (OP), and covert rate (CR), where the DEP is applied to evaluate the ability of the warden to detect the presence or absence of communication behaviors between the base station and the covert user. Based on these expressions, we also conduct an asymptotic analysis of the outage probability, an upper bound analysis of the covert rate, and an analysis of the impact of the power allocation factor on system performance. Numerical results show that the RSMA scheme has a higher covert rate and a higher minimum DEP value compared to the non-orthogonal multiple access (NOMA) scheme, but it may reduce its outage performance. Additionally, an appropriate use of the power allocation factor can enhance the covertness of the system. Furthermore, we find that the co-channel interference between the primary and secondary systems in the ambient backscatter system can degrade the covert performance of the primary system. © 2002-2012 IEEE.

关键词： Backscattering

来源：评论

学校读者我要写书评

暂无评论

Efficient Transmission of Holographic Images: A Novel Approach Toward 6G Telepresence Services 1

引用

26th International Conference on Distributed computer and Communication Networks: Control, Computation, Communications, DCCN 2023

作者： Svechnikov, Daniil Pankov, Bogdan Nesterova, Yana Volkov, Artem Ateya, Abdelhamied A. Koucheryavy, Andrey The Bonch-Bruevich Saint-Petersburg State University of Telecommunications St. Petersburg193232 Russia EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Communications Engineering Zagazig University Sharqia Zagazig44519 Egypt

ISBN: (数字)9783031504822

ISBN: (纸本)9783031504815

In recent years, advancements in technology have brought forth a new frontier in visual communication. Holography is a technique that captures and reproduces three-dimensional (3D) images with an unprecedented level of realism and depth, has emerged as a groundbreaking method for conveying visual information. Unlike traditional images and videos, holography recreates scenes with full parallax, enabling viewers to perceive objects from various angles. The transmission of holographic images presents both exciting possibilities and unique challenges. To this end, this article investigates a novel method for transmitting holographic images efficiently across communication networks. This innovative approach involves transmitting data related to control point movements rather than the entire image. The technique focuses on capturing the dynamic 3D movements of a person and transmitting information about the shifts in their control points. To achieve this, a system comprising three distinct applications has been devised. These applications are responsible for recording object motion data, converting it into key point coordinates, transmitting it through communication networks, and rendering the data as movements of a 3D object within an augmented reality (AR) application. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： 5G mobile communication systems

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：