检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Tao, Ruijie Qian, Xinyuan Jiang, Yidi Li, Junjie Wang, Jiadong Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Computer Science and Technology University of Science and Technology Beijing Beijing100083 China Chinese University of Hong Kong Shenzhen518172 China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big data Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore University of Bremen 28359 Germany

Audio-visual target speaker extraction (AV-TSE) aims to extract the specific person’s speech from the audio mixture given auxiliary visual cues. Previous methods usually search for the target voice through speech-lip synchronization. However, this strategy mainly focuses on the existence of target speech, while ignoring the variations of the noise characteristics, i.e., interference speaker and the background noise. That may result in extracting noisy signals from the incorrect sound source in challenging acoustic situations. To this end, we propose a novel selective auditory attention mechanism, which can suppress interference speakers and non-speech signals to avoid incorrect speaker extraction. By estimating and utilizing the undesired noisy signal through this mechanism, we design an AV-TSE framework named Subtraction-and-ExtrAction network (SEANet) to suppress the noisy signals. We conduct abundant experiments by re-implementing three popular AV-TSE methods as the baselines and involving nine metrics for evaluation. The experimental results show that our proposed SEANet achieves state-of-the-art results and performs well for all five datasets. The code can be found in: https://***/TaoRuijie/*** © 2024, CC BY.

关键词： Background noise

来源：评论

学校读者我要写书评

暂无评论

A Rational Delegating Computation Protocol Based on Smart Contract 14th

A Rational Delegating Computation Protocol Based on Smart Co...

引用

14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Ma, Juan Chen, Yuling Liu, Guoxu Zhu, Hongliang Peng, Changgen State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and Technology Shouguang China Engineering Lab for Cloud Security and Information Security Center of BUPT Beijing China

ISBN: (纸本)9783030898137

The delegating computation has become an irreversible trend, together comes the pressing need for fairness and efficiency issues. To solve this problem, we leverage game theory to propose a smart contract-based solution. Firstly, according to the behavioral preferences of the participants, we design an incentive contract to describe the motivation of the participants. Secondly, to satisfy the fairness of the rational delegating computation, we propose a delegating computation protocol based on the smart contract. Specifically, rational participants are to gain the maximum utility and reach the Nash equilibrium in the protocol. Besides, we design a reputation mechanism with a reputation certificate, which measures the reputation from multiple dimensions. And the reputation is used to assure the client’s trust in the computing party to improve the efficiency of the protocol. Finally, the analysis results show that the proposed protocol solves the complex traditional verification problem. Meanwhile, we prove the fairness and correctness of the protocol. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Contrastive Learning for Adapting Language Model to Sequential Recommendation

Contrastive Learning for Adapting Language Model to Sequenti...

引用

IEEE International Conference on data Mining (ICDM)

作者： Fei-Yao Liang Wu-Dong Xi Xing-Xing Xing Wei Wan Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Contribution during internship at NetEase Games UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

With the explosive growth of information, recommendation systems have emerged to alleviate the problem of information overload. In order to improve the performance of recommendation systems, many existing methods introduce Large Language Models to extract textual information from description text. However, Large Language Models are trained on large-scale generic textual data and may face a semantic gap for downstream recommendation tasks. To address the above issues, we propose Contrastive Learning for Adapting Language Model to Sequential Recommendation (CLA-Rec). In CLA-Rec, we first extract text embeddings from description text using Large Language Models and align the text embeddings learned by Large Language Models with the collaborative information through contrastive learning to obtain high-quality item representations. Through semantic alignment, we bridge the semantic gap between Large Language Models and the recommendation task. To map textual information and collaborative information into user representations, we utilize a Transformer model to learn user representations and capture user preferences by combining the semantically aligned item representations. Extensive experiments on three public datasets demonstrate that our method outperforms state-of-the-art approaches on multiple evaluation metrics, illustrating the effectiveness of the CLA-Rec model in adapting Large Language Models to recommendation tasks.

关键词： Measurement Adaptation models Large language models Semantics Collaboration Contrastive learning Transformers data models data mining Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Impacts of Radiance Quantization on Surface Mapping: Comparisons among the Landsat Sensors 28

Impacts of Radiance Quantization on Surface Mapping: Compari...

引用

28th International Conference on Geoinformatics, Geoinformatics 2021

作者： Chen, Feng Zhang, Ziqun Zhang, Wenhao Song, Yuejun College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen361024 China Jiangxi Academy of Water Science and Engineering Jiangxi Key Laboratory of Soil Erosion and Prevention Nanchang330029 China

ISBN: (纸本)9781665420747

Radiance quantization and dynamic ranges are considered the basic characteristics of a sensor in radiometry. However, their impacts on surface mapping have not been fully investigated and understood (e.g., for different Landsat sensors), while much attention has been paid to the impacts related to other aspects (e.g., spatial resolution, the wavelength of spectral channels, and several spectral channels). In this paper, a synthesized data collection of multispectral reflectance and radiance (with and without considering actual quantization) for the Landsat sensors from hyperspectral records was generated. Based on the synthesized data, the impacts of radiance quantization were discussed in terms of channel reflectance and spectral indices. As the preliminary findings show, the quantization effects on channel reflectance vary among channels, presenting different impacts on spectral indices correspondingly, which affect the measures of between-sensor difference. It also suggests that the effects of quantization may relate to land surface conditions (e.g., land use and land cover) in addition to the sensor characteristics. © 2021 IEEE.

关键词： Landsat

来源：评论

学校读者我要写书评

暂无评论

Underwater Information Sensing Method Based on Improved Dual-Coupled Duffing Oscillator Under Lévy Noise Description 17th

Underwater Information Sensing Method Based on Improved Dual...

引用

17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Zhang, Hanwen Qin, Zhen Chen, Dajiang School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9783030926342

Sensing underwater information has become particularly important to obtain information about the marine environment and target characteristics. At present, most interference models for underwater information sensing tasks under substantial interference choose Gaussian noise models. However, it often contains a strong impact and does not conform to the Gaussian distribution. Moreover, in the current research on the sensing of underwater unknown frequency signals, there are problems that the sensing method cannot sufficiently estimate the parameters of the unknown frequency signal, and the signal-to-noise ratio threshold is too high. An underwater environment sensing method is proposed by using the Lévy noise model to describe the underwater natural environment interference and estimate its parameters, which can better describe the impact characteristics of the underwater environment. Then, the intermittent chaos theory and variable step method are leveraged to improve the existing dual-coupled Duffing oscillator method. The simulation results show that the proposed method can sense weak signals in the background of strong Lévy noise and estimate its frequency, with an estimation error as low as 0.1%. Compared with the original one, the minimum signal-to-noise ratio threshold is reduced by 3.098 dB, and the computational overhead is significantly reduced. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

MixUp-Based Hard Negative Sample for Self-Supervised Entity Alignment

MixUp-Based Hard Negative Sample for Self-Supervised Entity ...

引用

IEEE International Conference on Communication and Information Systems (ICCIS)

作者： Wenyuan Su Zicheng Su Shaojun Liu Xiaohan Fang Beijing Knowledge Atlas Technology Co. Ltd. Beijing China Fujian Institute of Scientific and Technological Information Fuzhou China Fujian Provincial Key Laboratory of Information and Network Fuzhou Fujian China School of Information Science and Engineering Yanshan University Qinhuangdao China

Entity alignment, aiming to discover the entities that refer to the same things in different knowledge graphs (KGs), and is a pivotal technology for the fusion of KGs, which has attracted wide attention in recent years. To apply supervised learning, we need enough labeled data. However, manually labeling data is, However, self-learning, as a new learning method, has achieved remarkable results and attracted increasing attention in representation learning in EA, such as SelfKG. Through neighborhood information aggregation, we can provide high quality negative examples in a targeted manner, speed up convergence, and let the model optimize in the direction. In this paper, we propose a new method named MSEA for negative sampling, which contains Positive Mixing strategy and Multi-head strategy. For the problem that SelfKG lacks information interaction between two KG, we propose Interactive Contrastive Learning Mechanism. Experimental results show that our approach outperforms the previous best self-supervised method by a large margin (over 4% Hits@l almost improvement on average) and performs on par with previous SOTA supervised counterparts, demonstrating the effectiveness of the interactive contrastive learning for self-supervised EA.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Model-enhanced vector index 23

Model-enhanced vector index

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Hailin Zhang Yujing Wang Qi Chen Ruiheng Chang Ting Zhang Ziming Miao Yingyan Hou Yang Ding Xupeng Miao Haonan Wang Bochen Pang Yuefeng Zhan Hao Sun Weiwei Deng Qi Zhang Fan Yang Xing Xie Mao Yang Bin Cui School of Computer Science & Key Lab of High Confidence Software Technologies Peking University and Microsoft Microsoft Microsoft and Aerospace Information Research Institute & Key Laboratory of Target Cognition and Application Technology Chinese Academy of Sciences Microsoft and Institute of Information Engineering Chinese Academy of Sciences Carnegie Mellon University National University of Singapore School of Computer Science & Key Lab of High Confidence Software Technologies Peking University and National Engineering Laboratory for Big Data Analysis and Applications Peking University

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with end-to-end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Model-enhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embedding-based models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Modeling and Risk Analysis of Cooperative Adaptive Cruise Control Systems Based on Petri Nets and Distributed Edge Intelligence

引用

IEEE Internet of Things Journal 2025年第12期12卷 19995-20006页

作者： Yu, Wangyang Cheng, Yumeng Fang, Xianwen Zhai, Xiaojun Wang, Yinglong Jing, Hongyuan Shaanxi Normal University Chang'an District Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an710119 China Shaanxi Normal University School of Computer Science Chang'an District Xi'an710119 China Anhui University of Science and Technology Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety Anhui232001 China University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom Beijing Union University College of Robotics Beijing100101 China

Fueled by advancements in intelligent transportation systems, the Internet of Vehicles (IoV) seeks to connect smart vehicles, road infrastructure, and users into a unified network, enhancing traffic efficiency and reducing accident risks. Centralized cloud data collection raises concerns about privacy and communication overhead. To address these, Distributed Edge Intelligence (DEI) reduces transmission costs and improves privacy by implementing machine learning at the network edge. In this context, Cooperative Adaptive Cruise Control (CACC) systems, combined with DEI in the IoV framework, enhance transportation system intelligence through real-time data processing and decentralized decision-making. This paper proposes a modeling and analysis method for CACC systems based on Petri nets. The datasets are automatically generated using tools developed by our team, and machine learning methods are utilized to perform risk prediction analysis on the CACC model. From the perspective of Petri nets synchronization, we propose risk mitigation strategies from a design standpoint. The research results show that the proposed method significantly reduces signal accumulation and enhances synchronization in CACC systems. This improvement provides new theoretical support and technical guidance for the design and implementation of CACC systems, ultimately enhancing their safety and reliability. © 2025 IEEE.

关键词： Adaptive cruise control

来源：评论

学校读者我要写书评

暂无评论

Digital Over-the-Air Federated Learning in Multi-Antenna Systems

arXiv

引用

arXiv 2023年

作者： Wang, Sihua Chen, Mingzhe Shen, Cong Yin, Changchuan Brinton, Christopher G. The Beijing Laboratory of Advanced Information Network The Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States The School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as an optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, an artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

来源：评论

学校读者我要写书评

暂无评论

Comprehensive effects of tea branch biochar on antibiotic resistance profiles and C/N/S cycling in the compost microbiota of animal manure

引用

Science of the Total Environment 2024年 957卷 177457页

作者： Ma, Jiawei Yang, Wenyan Li, Shuai Yang, Zhenyu Qiao, Chen Liu, Dan Wang, Mei College of Tea Science and Tea Culture Zhejiang A&F University Hangzhou311300 China Key Laboratory of Soil Pollution Bioremediation of Zhejiang Province State Key Laboratory of Subtropical Silviculture Zhejiang A&F University Hangzhou311300 China Key Laboratory of Applied Biotechnology on Animal Science & Veterinary Medicine of Zhejiang Province Zhejiang Engineering Research Center for Veterinary Diagnostics & Advanced Technology Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management Belt and Road International Joint Laboratory for One Health and Food Safety China-Australia Joint Laboratory for Animal Health Big Data Analytics College of Veterinary Medicine Zhejiang A&F University Hangzhou311300 China

The comprehensive effects of exogenous additives on microbial-driven antibiotic resistance profiles and C/N/S conversion in animal manure composting remains uncertain. This study examined whether tea branch biochar could regulate the microflora involved in antibiotic resistance and C/N/S conversion during pig and chicken manure composting. Compared with the control treatment, biochar addition prolonged the high-temperature period (>55 °C) for 1–2 days and raised the maximum temperature in chicken manure composting. Moreover, biochar addition reduced the prevalence of antibiotic resistance genes (ARGs) in both pig and chicken manure composting by up to 30 %, targeting various types of ARGs such as peptide, phenicol, and diaminopyrimidines. Additionally, the compost microbiota exhibited the overlaps of C/N/S conversion functions. Luteimonas (Xanthomonadaceae) was identified as a dominant bacterium responsible for C/N/S conversion in both pig and chicken manure composting, while also acting as a potential ARG carrier. Thus, Luteimonas is crucial in shaping antibiotic resistance profiles and C/N/S cycling in animal manure composting, indicating its role as a keystone genus. These findings suggest that tea branch biochar can mitigate the spread of ARGs from animal manure, as well as enhance nutrient cycling and compost quality. © 2024 Elsevier B.V.

关键词： Animal manure Antibiotic resistance genes C/N/S cycling Composting Tea branch biochar

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：