检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

260 篇 会议
175 篇 期刊文献
2 册 图书

馆藏范围

437 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

298 篇 工学
- 205 篇 计算机科学与技术...
- 159 篇 软件工程
- 109 篇 信息与通信工程
- 57 篇 生物工程
- 41 篇 控制科学与工程
- 35 篇 电气工程
- 29 篇 生物医学工程（可授...
- 23 篇 机械工程
- 20 篇 电子科学与技术（可...
- 17 篇 光学工程
- 17 篇 仪器科学与技术
- 16 篇 建筑学
- 13 篇 交通运输工程
- 12 篇 土木工程
- 12 篇 网络空间安全
- 9 篇 动力工程及工程热...
140 篇 理学
- 71 篇 数学
- 58 篇 生物学
- 29 篇 统计学（可授理学、...
- 28 篇 物理学
- 16 篇 系统科学
- 9 篇 化学
63 篇 管理学
- 34 篇 管理科学与工程(可...
- 34 篇 图书情报与档案管...
- 13 篇 工商管理
28 篇 医学
- 26 篇 临床医学
- 25 篇 基础医学(可授医学...
- 16 篇 药学(可授医学、理...
- 13 篇 公共卫生与预防医...
19 篇 法学
- 19 篇 社会学
4 篇 文学
3 篇 经济学
2 篇 教育学
2 篇 农学
1 篇 艺术学

主题

18 篇 pervasive comput...
14 篇 computer science
14 篇 mobile computing
13 篇 laboratories
13 篇 semantics
12 篇 feature extracti...
11 篇 deep learning
10 篇 protocols
10 篇 computer network...
9 篇 quality of servi...
9 篇 computational mo...
9 篇 middleware
9 篇 training
8 篇 servers
8 篇 wireless sensor ...
8 篇 internet
8 篇 context
8 篇 mobile communica...
8 篇 accuracy
8 篇 telecommunicatio...

机构

37 篇 department of co...
31 篇 university of ch...
28 篇 beijing key labo...
22 篇 key laboratory o...
18 篇 key laboratory o...
15 篇 national mobile ...
12 篇 key laboratory o...
11 篇 institute of com...
11 篇 the national mob...
11 篇 beijing key labo...
9 篇 school of comput...
9 篇 key laboratory o...
8 篇 tsinghua univers...
8 篇 department of co...
8 篇 state key labora...
8 篇 department of co...
7 篇 pervasive and mo...
7 篇 internet and mob...
6 篇 college of infor...
6 篇 school of comput...

作者

34 篇 jia jia
28 篇 shi yuanchun
26 篇 lai yu-kun
22 篇 gao lin
19 篇 liu yong-jin
18 篇 yu chun
17 篇 andersson karl
16 篇 wang yuntao
15 篇 lianhong cai
13 篇 karl andersson
13 篇 hossain mohammad...
11 篇 mohammad shahada...
10 篇 yi ran
10 篇 yuanchun shi
9 篇 chen yiqiang
9 篇 helal sumi
9 篇 wang wei
9 篇 yiqiang chen
9 篇 yaoxue zhang
9 篇 cao jiannong

语言

348 篇 英文
85 篇 其他
4 篇 中文

检索条件"机构=Pervasive and Mobile Computing Laboratory Department of Computer Science"

共 437 条记录，以下是11-20 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Explainable AI for Tomato Leaf Disease Detection: Insights into Model Interpretability 26

Explainable AI for Tomato Leaf Disease Detection: Insights i...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Mahmud, Tanjim Barua, Koushick Barua, Anik Basnin, Nanziba Das, Sudhakar Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Leeds Beckett University Department of Computer Science and Engineering United Kingdom University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Pervasive and Mobile Computing Laboratory Luleå971 87 Sweden

ISBN: (纸本)9798350359015

Plant diseases pose a significant threat to global food security by limiting access to safe and abundant food sources while impacting agricultural productivity and food safety. To address this challenge, innovative disease detection techniques are crucial. This paper presents a novel approach to tomato leaf disease detection grounded in deep transfer learning. We employ convolutional neural networks (CNNs) to recognize and categorize diseases, leveraging pre-trained models such as EfficientNetB3, Xception, and mobileNetV2. Our experimental results highlight the efficacy of these models, with the CNN achieving an accuracy of 0.92, mobileNetV2 at 0.93, Xception at 0.94 and EfficientNetB3 at 0.993. By combining these machine learning techniques with image processing methods, we create a system capable of diagnosing tomato leaf diseases promptly. The system employs image processing techniques to facilitate disease recognition, enabling automated disease identification as soon as symptoms manifest on leaves or plants. This empowers farmers with timely information and appropriate treatment options, ultimately leading to precise disease management. Furthermore, our research delves into model interpretability by employing Saliency maps and Grad-CAM techniques, providing valuable insights into the decision-making process of our deep learning models. This transparency enhances the trustworthiness of the AI-driven disease detection system, fostering its practical application in agriculture. © 2023 IEEE.

关键词： Convolutional neural network Image diagnosis Image processing Machine learning techniques

来源：评论

学校读者我要写书评

暂无评论

Model Trip: Enhancing Privacy and Fairness in Model Fusion Across Multi-Federations for Trustworthy Global Healthcare 40

Model Trip: Enhancing Privacy and Fairness in Model Fusion A...

引用

40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Chen, Qian Chen, Yiqiang Yan, Bingjie Jiang, Xinlong Zhang, Xiaojin Kang, Yan Zhang, Teng Huang, Wuliang Gao, Chenlong Fan, Lixin Yang, Qiang Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Beijing Key Laboratory of Mobile Computing and Pervasive Device China Peng Cheng Laboratory China School of Computer Science and Technology Huazhong University of Science and Technology China Webank Hong Kong Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798350317152

Federated Learning has emerged as a revolutionary innovation in the evolving landscape of global healthcare, fostering collaboration among institutions and facilitating collaborative data analysis. As practical applications continue to proliferate, numerous federations have formed in different regions. The optimization and sustainable development of federation-pretrained models have emerged as new challenges. These challenges primarily encompass privacy, population shift and data dependency, which may lead to severe consequences such as the leakage of sensitive information within models and training samples, unfair model performance and resource burdens. To tackle these issues, we propose FairFusion, a cross-federation model fusion approach that enhances privacy and fairness. FairFusion operates across federations within a Model Trip paradigm, integrating knowledge from diverse federations to continually enhance model performance. Through federated model fusion, multi-objective quantification and optimization, FairFusion obtains trustworthy solutions that excel in utility, privacy and fairness. We conduct comprehensive experiments on three public real-world healthcare datasets. The results demonstrate that FairFusion achieves outstanding model fusion performance in terms of utility and fairness across various model structures and subgroups with sensitive attributes while guaranteeing model privacy. © 2024 IEEE.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

PlacidDreamer: Advancing Harmony in Text-to-3D Generation 24

PlacidDreamer: Advancing Harmony in Text-to-3D Generation

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Huang, Shuo Sun, Shikun Wang, Zixuan Qin, Xiaoyu Xiong, Yanmin Zhang, Yuan Wan, Pengfei Zhang, Di Jia, Jia Tsinghua University Beijing China Kuaishou Technology Beijing China Department of Computer Science and Technology BNRist Tsinghua University Key Laboratory of Pervasive Computing Ministry of Education Beijing China

ISBN: (纸本)9798400706868

Recently, text-to-3D generation has attracted significant attention, resulting in notable performance enhancements. Previous methods utilize end-to-end 3D generation models to initialize 3D Gaussians, multi-view diffusion models to enforce multi-view consistency, and text-to-image diffusion models to refine details with score distillation algorithms. However, these methods exhibit two limitations. Firstly, they encounter conflicts in generation directions since different models aim to produce diverse 3D assets. Secondly, the issue of over-saturation in score distillation has not been thoroughly investigated and solved. To address these limitations, we propose PlacidDreamer, a text-to-3D framework that harmonizes initialization, multi-view generation, and text-conditioned generation with a single multi-view diffusion model, while simultaneously employing a novel score distillation algorithm to achieve balanced saturation. To unify the generation direction, we introduce the Latent-Plane module, a training-friendly plug-in extension that enables multi-view diffusion models to provide fast geometry reconstruction for initialization and enhanced multi-view images to personalize the text-to-image diffusion model. To address the over-saturation problem, we propose to view score distillation as a multi-objective optimization problem and introduce the Balanced Score Distillation algorithm, which offers a Pareto Optimal solution that achieves both rich details and balanced saturation. Extensive experiments validate the outstanding capabilities of our PlacidDreamer. The code is available at https://***/HansenHuang0823/PlacidDreamer. © 2024 ACM.

关键词： Digital elevation model

来源：评论

学校读者我要写书评

暂无评论

Embedding an Ethical Mind: Aligning Text-to-Image Synthesis via Lightweight Value Optimization 24

Embedding an Ethical Mind: Aligning Text-to-Image Synthesis ...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Xingqi Yi, Xiaoyuan Xie, Xing Jia, Jia Department of Computer Science and Technology Tsinghua University Beijing China Microsoft Research Asia Beijing China Dcst BNRist Tsinghua University Key Laboratory of Pervasive Computing Beijing China

ISBN: (纸本)9798400706868

Recent advancements in diffusion models trained on large-scale data have enabled the generation of indistinguishable human-level images, yet they often produce harmful content misaligned with human values, e.g., social bias, and offensive content. Despite extensive research on Large Language Models (LLMs), the challenge of Text-to-Image (T2I) model alignment remains largely unexplored. Addressing this problem, we propose LiVO (Lightweight Value Optimization), a novel lightweight method for aligning T2I models with human values. LiVO only optimizes a plug-and-play value encoder to integrate a specified value principle with the input prompt, allowing the control of generated images over both semantics and values. Specifically, we design a diffusion model-tailored preference optimization loss, which theoretically approximates the Bradley-Terry model used in LLM alignment but provides a more flexible trade-off between image quality and value conformity. To optimize the value encoder, we also develop a framework to automatically construct a text-image preference dataset of 86k (prompt, aligned image, violating image, value principle) samples. Without updating most model parameters and through adaptive value selection from the input prompt, LiVO significantly reduces harmful outputs and achieves faster convergence, surpassing several strong baselines and taking an initial step towards ethically aligned T2I models. Warning: This paper involves descriptions and images depicting discriminatory, pornographic, bloody, and horrific scenes. © 2024 ACM.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Exploring BERT and ELMo for Bangla Spam SMS Dataset Creation and Detection 26

Exploring BERT and ELMo for Bangla Spam SMS Dataset Creation...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Khan, Fariba Mustafa, Rashed Tasnim, Farzana Mahmud, Tanjim Hossain, Mohammad Shahadat Andersson, Karl University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh International Islamic University Chittagong Department of Computer Science and Engineering Chittagong4318 Bangladesh Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Luleå University of Technology Pervasive and Mobile Computing Laboratory Luleå971 87 Sweden

ISBN: (纸本)9798350359015

The proliferation of digital communication and social media platforms has led to an upsurge in spam messages across various languages, including Bangla. These intrusive messages not only cause user annoyance but also elevate the risk of important messages going unnoticed. To address this concern, we harnessed the power of natural language processing (NLP) techniques in conjunction with machine-learning models. This study delves into the efficacy of diverse machine-learning and deep-learning models in detecting spam messages in the Bangla language. Particularly, deep-learning methodologies such as ELMo and BERT have exhibited promising results in spam detection across other languages. However, the realm of Bangla spam detection remains underexplored, with a dearth of deep-learning approaches. To bridge this gap, we constructed a unique Bangla SMS dataset, given the scarcity of available Bangla Spam SMS datasets, and proceeded to evaluate the performance of ELMo, BERT, and conventional machine learning models. The findings from our experiments underscore the superior performance of deep learning-based models over traditional methods, with Elmo surpassing BERT in Bangla spam detection, achieving an impressive accuracy of approximately 94%. This endeavor bears the potential to not only enhance SMS utilization efficiency but also bolster cybersecurity measures against phishing attacks. © 2023 IEEE.

关键词： BERT ELMo Machine Learning Spam SMS Word Embedding

来源：评论

学校读者我要写书评

暂无评论

Research on Error Handling Techniques for Speech Interaction 3

Research on Error Handling Techniques for Speech Interaction

引用

3rd IEEE International Conference on computer Systems, ICCS 2023

作者： Ma, Rui Zou, Xuechao Tao, Pin Tu, Yixiang Qinghai University Department of Computer Technology and Applications Xining China Ministry of Education Key Laboratory of Pervasive Computing Beijing China Tsinghua University Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350313666

With the rapid advancement of speech recognition and semantic understanding, speech interaction systems have gained widespread usage. This paper investigates the use of intelligent error handling approaches in speech interaction systems when faced with recognition or comprehension challenges. A case study focusing on the common task of 'electronic spreadsheet editing' is conducted, wherein an experimental environment is developed by combining an open-source neural network code repository with a third-party speech recognition interface. Drawing from historical experience, a 'triplet' paradigm is constructed to parse user command texts, and an improved approach for intelligent selection of error recovery strategies is proposed. This approach automatically determines the appropriate strategy for different speech interaction errors based on user logs. Experiments on 20 users demonstrate that our proposed intelligent error handling techniques for speech interaction, exhibit performance similar to empirically designed systems in practical tasks. Moreover, they significantly enhance user satisfaction and task completion efficiency when utilizing the system. © 2023 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

QoE Assessment of Cloud-Based Social Extended Reality Applications Over Heterogeneous Access Networks

QoE Assessment of Cloud-Based Social Extended Reality Applic...

引用

Consumer Communications and Networking Conference, CCNC IEEE

作者： Karan Mitra Henrique Souza Rossi Justin Gavrell Christer Åhlund Department of Computer Science Electrical and Space Engineering Pervasive and Mobile Computing Luleå University of Technology Skellefteå Sweden

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

The next generation of immersive applications, such as eXtended reality (XR), will likely be cloud-based and streamed over mobile networks using myriad technologies such as WiFi and 6th-generation mobile networks. mobile networks promise ubiquitous connectivity but are prone to stochastic network conditions that may be detrimental to end users' quality of experience (QoE). The impact of network conditions on QoE has been studied extensively by industry and academia regarding various multimedia services such as audio, video, and gaming. However, the impact of network conditions on users' QoE for XR-based social applications has yet to be thoroughly investigated. This paper presents novel results assessing the impact of network conditions $(\boldsymbol{N}=\mathbf{20})$ involving factors such as round trip time (RTT), jitter (RJ), and packet losses (PL) on users' QoE via realistic subjective tests $(\boldsymbol{N}=\mathbf{28})$ regarding social XR application. Our results show that social XR applications require stringent QoS conditions. In particular, our results show that increasing RTT values do not significantly affect users' QoE up to 77ms. Combined PL and RTT cases cause significant QoE degradation from 77ms onward with greater than 2% PL. Most importantly, results show that a very small jitter value with one standard deviation beyond 52 milliseconds can lead to significant QoE degradation. Further, jitter values beyond three standard deviations for 27ms RTT and beyond should be avoided.

关键词： Degradation Cloud computing Extended reality Packet loss Quality of service Jitter Streaming media Quality of experience Standards Wireless fidelity

来源：评论

学校读者我要写书评

暂无评论

A Demonstration of QoE Assessment for Cloud-based Social XR Applications over mobile Networks

A Demonstration of QoE Assessment for Cloud-based Social XR ...

引用

Consumer Communications and Networking Conference, CCNC IEEE

作者： Henrique Souza Rossi Karan Mitra Justin Gavrell Christer Åhlund Pervasive and Mobile Computing Department of Computer Science Electrical and Space Engineering Luleå University of Technology Skellefteå Sweden

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Cloud-based social eXtended Reality (XR) services are the cornerstone for realizing the promises of the Metaverse. These services hosted either on datacenters or edge, will demand stringent mobile network quality of service (QoS) to operate effectively and provide an acceptable user quality. It becomes fundamental to study how mobile networks QoS factors round-trip time (RTT), packet loss (PL), and jitter affect these services by measuring their effect on users' perceived quality of experience (QoE). Subjective QoE assessment involves carefully controlled laboratory environments to generate the desired conditions between a large set of users. The requirements for a cloud-based social XR service lab-setup are complex: Identify a reliable streaming service, a customizable VR application, emulate network conditions, define activities or tasks for users to perform, collect their data, label it; all while mitigating possible human mistakes. To address these requirements, we present an effective technical setup that can consistently repeat the same conditions between users and that can be easily replicated to other labs conducting cloud-based social XR research.

关键词： Cloud computing Metaverse Extended reality Laboratories Packet loss Quality of service Jitter Time measurement Quality of experience Reliability

来源：评论

学校读者我要写书评

暂无评论

Weighted Poisson-disk Resampling on Large-Scale Point Clouds 39

Weighted Poisson-disk Resampling on Large-Scale Point Clouds

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jiao, Xianhe Lv, Chenlei Zhao, Junli Yi, Ran Wen, Yu-Hui Pan, Zhenkuan Wu, Zhongke Liu, Yong-Jin College of Computer Science and Technology Qingdao University China College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Computer Science & Technology Beijing Jiaotong University China School of Artificial Intelligence Beijing Normal University China Department of Computer Science and Technology MOE-Key Laboratory of Pervasive Computing Tsinghua University China

ISBN: (纸本)157735897X

For large-scale point cloud processing, resampling takes the important role of controlling the point number and density while keeping the geometric consistency. However, current methods cannot balance such different requirements. Particularly with large-scale point clouds, classical methods often struggle with decreased efficiency and accuracy. To address such issues, we propose a weighted Poisson-disk (WPD) resampling method to improve the usability and efficiency for the processing. We first design an initial Poisson resampling with a voxel-based estimation strategy. It is able to estimate a more accurate radius of the Poisson-disk while maintaining high efficiency. Then, we design a weighted tangent smoothing step to further optimize the Voronoi diagram for each point. At the same time, sharp features are detected and kept in the optimized results with isotropic property. Finally, we achieve a resampling copy from the original point cloud with the specified point number, uniform density, and high-quality geometric consistency. Experiments show that our method significantly improves the performance of large-scale point cloud resampling for different applications, and provides a highly practical solution. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Geometry

来源：评论

学校读者我要写书评

暂无评论

FedTG: Text-guided Federated Domain Generalization

FedTG: Text-guided Federated Domain Generalization

引用

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

作者： Yiming Chen Nan He Lifeng Sun Department of Computer Science and Technology Tsinghua University Key Laboratory of Pervasive Computing Ministry of Education

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated Domain Generalization is aimed at learning a global model that generalizes well to new clients with domain shift in a privacy-preserving manner. Inspired by the powerful generation capability of Large language models (LLMs), we propose a novel method for this problem, named Text-guided Federated Domain Generalization (FedTG), which utilizes text generated by LLMs to guide the training of visual models. In the client, we align the image embeddings to the text embeddings, which provides strong generalization knowledge. In the server, we generate abundant textual descriptions about different classes, subsequently converting them into embeddings for fine-tuning the global classifier, which helps the global classifier not overfit to a particular domain. Moreover, we utilize the powerful image generation capabilities of LLMs to generate a cross-domain validation dataset to help select the most generalized model. We conduct experiments on several datasets, and the results prove that FedTG outperforms relevant baselines.

关键词： Training Visualization computer vision Image synthesis Large language models Computational modeling Signal processing Acoustics Servers Speech processing

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共44页 << < 1 2 3 4 5 6 7 8 9 10 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：