检索结果-内蒙古大学图书馆

DP-GenFL: a local differentially private federated learning system through generative data

Science China(information Sciences) 2023年第8期66卷 275-276页

作者： Jun LI Kang WEI Chuan MA Feng SHU School of Electronic and Optical Engineering Nanjing University of Science and Technology Zhejiang Lab Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education School of Information and Communication Engineering Hainan University

With the rapid development of the Internet of Things(Io T),the amount of data from intelligent devices is propagating at unprecedented scales. Meanwhile, machine learning(ML),which relies heavily on such data, is revolutionizing many aspects of our lives [1]. However, conventional centralized ML offers little scalability for efficiently processing this huge amount of data.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

T-Sorokin: A General Mobility Model in Opportunistic networks 26

T-Sorokin: A General Mobility Model in Opportunistic Network...

引用

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

作者： Tu, Jinbin Li, Qing Wang, Yun Southeast University Key Lab of Computer Network and Information Integration Moe School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350331684

The opportunistic networks are a kind of ad hoc networks that rely on the chance of nodes meeting to transmit messages. Acting as an effective supplement to 4G and 5G networks in some special scenarios where hardware devices are limited, the opportunistic networks have a significant application in health monitoring, warning broadcasting, disaster relief, and so on. The mobility model is one of the research focuses on the opportunistic networks. On the basis of the social mobility theory proposed by Sorokin, a general mobility model, which is suited for various scenarios, called T-Sorokin is proposed. This model is described as a seven-tuple and implemented on the Opportunistic network Environment simulator and fits both Infocom06 and Rome taxi data set, which includes different areas ranging from hotel to city and different mobile units ranging from person to taxi. The results of experiments demonstrate that the T-Sorokin model has the advantage of generality, simplicity, and accuracy. It can simply establish movement tracks close to real data under different scenarios. © 2023 IEEE.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

Joint Optimization of UAV Deployment and Task Computation Offloading Decision in UAV-assisted Edge Computing network 25

Joint Optimization of UAV Deployment and Task Computation Of...

引用

25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Liu, Yichuan Tu, Jinbin Wang, Yun School of Computer Science and Engineering Southeast University Key Lab of Computer Network and Information Integration Moe Nanjing China

ISBN: (纸本)9798350330014

The UAVs' deployment decision and task computation offloading decision in the UAV-assisted edge computing network significantly impact the operating efficiency of edge network. On the basis of this, the Optimization Model for UAV Cluster Deployment and Computation Offloading Decision (OMUCDCOD) is established. The model jointly optimizes the number, location of UAV s, and task computation offloading decision. Different from previous studies, this model regards the terminal devices in the edge network as virtual MEC servers, and introduces the collaborative computing mode. The task computation offloading decision made can effectively utilize the computing capabilities offered by the edge network. Considering that the two problems of UAV deployment decision and task computation offloading decision are intricately interconnected, we propose a two-layer optimization algorithm combining K-Means and ant colony algorithm (ToKmAc) to solve OMUCDCOD. ToKmAc is divided into upper and lower layers to solve this optimization problem. The upper layer uses K-Means to solve the UAV deployment decision, that is, the number and location of UAVS;the lower layer employs ant colony algorithm to solve computation offloading decision. Finally, extensive experiments verify the effectiveness of OMUCDCOD and ToKmAc. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

SEAVER: Attention Reallocation for Mitigating Distractions in Language Models for Conditional Semantic Textual Similarity Measurement

SEAVER: Attention Reallocation for Mitigating Distractions i...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Baixuan Fan, Yunlong Gao, Zhiqiang School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

ISBN: (纸本)9798891761681

Conditional Semantic Textual Similarity (C-STS) introduces specific limiting conditions to the traditional Semantic Textual Similarity (STS) task, posing challenges for STS models. Language models employing cross-encoding demonstrate satisfactory performance in STS, yet their effectiveness significantly diminishes in C-STS. In this work, we argue that the failure is due to the fact that the redundant information in the text distracts language models from the required condition-relevant information. To alleviate this, we propose SElf-Augmentation Via SElf-Reweighting (SEAVER), which, based solely on models' internal attention and without the need for external auxiliary information, adaptively reallocates the model's attention weights by emphasizing the importance of condition-relevant tokens. On the C-STS-2023 test set, SEAVER consistently improves performance of all million-scale fine-tuning baseline models (up to around 3 points), and even surpasses performance of billion-scale few-shot prompted large language models (such as GPT-4). Our code is available at https://***/BaixuanLi/SEAVER. © 2024 Association for Computational Linguistics.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Multi-Label Open Set Recognition 38

Multi-Label Open Set Recognition

引用

38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Wang, Yi-Bo Hang, Jun-Yi Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

In multi-label learning, each training instance is associated with multiple labels simultaneously. Traditional multi-label learning studies primarily focus on closed set scenario, i.e. the class label set of test data is identical to those used in training phase. Nevertheless, in numerous real-world scenarios, the environment is open and dynamic where unknown labels may emerge gradually during testing. In this paper, the problem of multi-label open set recognition (MLOSR) is investigated, which poses significant challenges in classifying and recognizing instances with unknown labels in multi-label setting. To enable open set multi-label prediction, a novel approach named SLAN is proposed by leveraging sub-labeling information enriched by structural information in the feature space. Accordingly, unknown labels are recognized by differentiating the sub-labeling information from holistic supervision. Experimental results on various datasets validate the effectiveness of the proposed approach in dealing with the MLOSR problem. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning 41

Binary Decomposition: A Problem Transformation Perspective f...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Hang, Jun-Yi Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

Semi-supervised learning (SSL) is a classical machine learning paradigm dealing with labeled and unlabeled data. However, it often suffers performance degradation in real-world open-set scenarios, where unlabeled data contains outliers from novel categories that do not appear in labeled data. Existing studies commonly tackle this challenging open-set SSL problem with detect-and-filter strategy, which attempts to purify unlabeled data by detecting and filtering outliers. In this paper, we propose a novel binary decomposition strategy, which refrains from error-prone procedure of outlier detection by directly transforming the original open-set SSL problem into a number of standard binary SSL problems. Accordingly, a concise yet effective approach named BDMatch is presented. BDMatch confronts two attendant issues brought by binary decomposition, i.e. class-imbalance and representation-compromise, with adaptive logit adjustment and label-specific feature learning respectively. Comprehensive experiments on diversified benchmarks clearly validate the superiority of BDMatch as well as the effectiveness of our binary decomposition strategy. Copyright 2024 by the author(s)

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

A common gateway for interoperability in multi-architecture LVC simulation 6

A common gateway for interoperability in multi-architecture ...

引用

6th International Conference on computer information Science and Application Technology, CISAT 2023

作者： Lin, Shichao Wang, Yun School of Computer Science and Engineering Southeast University Nanjing China Key Lab of Computer Network and Information Integration MOE China

ISBN: (纸本)9781510668546

With the development of Live-Virtual-Constructive (LVC) simulation technology, numerous LVC simulation resources have been developed. To construct more large-scale LVC simulation, it is necessary to integrate existing simulation resources. Due to the heterogeneity of LVC resources, the interoperability between heterogeneous simulation resources becomes a major concern. This paper proposes a common gateway in multi-architecture LVC simulation environment that converts messages between different simulation architectures and achieves a level of semantic interoperability. The gateway provides a collection of protocol-independent interfaces for simulation systems to access co-simulation conveniently. To maintain timing consistency in heterogeneous simulation environments, a time synchronization method is employed to coordinate logical time and real time. Experimental results show that the scheme is flexible and can maintain data transmission latency at a millisecond level in LVC simulation environment, which includes High Level Architecture (HLA), Data Distribution Service (DDS), and Distributed Interactive Simulation (DIS). © 2023 SPIE.

关键词： Interoperability

来源：评论

学校读者我要写书评

暂无评论

Prompting Large Language Models with Knowledge-Injection for Knowledge-Based Visual Question Answering

引用

Big Data Mining and Analytics 2024年第3期7卷 843-857页

作者： Zhongjian Hu Peng Yang Fengyuan Liu Yuan Meng Xingyu Liu School of Computer Science and Engineering Southeast University the Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education of the People’s Republic of ChinaNanjing 211189China Southeast University-Monash University Joint Graduate School(Suzhou) Southeast UniversitySuzhou 215125China

Previous works employ the Large Language Model(LLM)like GPT-3 for knowledge-based Visual Question Answering(VQA).We argue that the inferential capacity of LLM can be enhanced through knowledge *** methods that utilize knowledge graphs to enhance LLM have been explored in various tasks,they may have some limitations,such as the possibility of not being able to retrieve the required *** this paper,we introduce a novel framework for knowledge-based VQA titled“Prompting Large Language Models with Knowledge-Injection”(PLLMKI).We use vanilla VQA model to inspire the LLM and further enhance the LLM with knowledge *** earlier approaches,we adopt the LLM for knowledge enhancement instead of relying on knowledge ***,we leverage open LLMs,incurring no additional *** comparison to existing baselines,our approach exhibits the accuracy improvement of over 1.3 and 1.7 on two knowledge-based VQA datasets,namely OK-VQA and A-OKVQA,respectively.

关键词： visual question answering knowledge-based visual question answering large language model knowledge injection

来源：评论

学校读者我要写书评

暂无评论

Integrating Syntactic and Semantic Knowledge in AMR Parsing with Heterogeneous Graph Attention network 48

Integrating Syntactic and Semantic Knowledge in AMR Parsing ...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sataer, Yikemaiti Shi, Chuanqi Gao, Miao Fan, Yunlong Li, Bin Gao, Zhiqiang Southeast University School of Computer Science and Engineering Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China

ISBN: (纸本)9781728163277

Meaning Representation (AMR) parsing is the task of translating a sentence to an AMR semantic graph which captures the basic meaning of the sentence, and is empowered by pre-trained Transformer models recently. These models encode the syntactic and semantic knowledge implicitly through self-supervised pre-trained tasks. We argue that encoding the syntactic and semantic knowledge explicitly is beneficial to AMR parsing and can improve data efficiency. Specifically, syntactic dependency and semantic role labeling (SRL) have similar sub-structures with AMR. In this work, we propose a novel linguistic knowledge-enhanced AMR parsing model, which augments the pre-trained Transformer with syntactic dependency and semantic role labeling structures of sentences. By applying a heterogeneous graph attention network, we can obtain syntactically and semantically augmented word representation, which can be integrated using an attentive integration layer and gating mechanism. Experimental results show that our model achieves state-of-the-art performance on different benchmarks, especially in out-of-domain and low-resource scenarios. © 2023 IEEE.

关键词： Syntactics

来源：评论

学校读者我要写书评

暂无评论

Advances and challenges in artificial intelligence text generation

引用

Frontiers of information Technology & Electronic Engineering 2024年第1期25卷 64-83页

作者： Bing LI Peng YANG Yuankang SUN Zhongjian HU Meng YI School of Computer Science and Engineering Southeast UniversityNanjing 210000China Key Laboratory of Computer Network and Information Integration Ministry of EducationSoutheast UniversityNanjing 210000China

Text generation is an essential research area in artificial intelligence(AI)technology and natural language processing and provides key technical support for the rapid development of AI-generated content(AIGC).It is based on technologies such as natural language processing,machine learning,and deep learning,which enable learning language rules through training models to automatically generate text that meets grammatical and semantic *** this paper,we sort and systematically summarize the main research progress in text generation and review recent text generation papers,focusing on presenting a detailed understanding of the technical *** addition,several typical text generation application systems are ***,we address some challenges and future directions in AI text *** conclude that improving the quality,quantity,interactivity,and adaptability of generated text can help fundamentally advance AI text generation development.

关键词： AI text generation Natural language processing Machine learning Deep learning

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：