检索结果-内蒙古大学图书馆

10th International Congress on Advanced Applied Informatics, IIAI-AAI 2021

作者： Fan, Zheming Ohashi, Takeshi Hayashi, Toyohiro Graduate School of Computer Science and System Engineering Kyushu Institute of Technology Fukuoka Japan Kyushu Institute of Technology Information Science and Technology Center Fukuoka Japan

ISBN: (纸本)9781665424202

Along with the electrification, there are many electrical equipment are used in our daily life. Using those equipment we need cables to charge and connect them. But, sometimes cables will become problems, like when we clean the room the cables will be annoying. We think if we can resolve this problem with robot, it will save lots of time for people. But, unfortunately, there is no system which can manipulate cables with robot. The key point is cable can deform it's shape. Because of this, we can't recognize and record cables position correctly from picture. And if we want to manipulate cables with robot, the 3D information of cables is far batter than 2D information. Therefore, in this paper, we made a system which can build a 3D voxels model base on 2D image as a beforehand for use robot to manipulate cables. In our system, we cut input image into a series of small pictures and make 3D model based on each pictures then we assemble those 3D model together. So we can get a complete 3D model(output of this system) which is made base on input image and can represent the 3D cables position. Our method can transfer 2D data to 3D data, this let us can get 3D information from image and we think this is useful in robot science. © 2021 IEEE.

关键词： Cables

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Data-Driven Learning Models for Emerging Communication Networks: A Comprehensive Survey

引用

IEEE Communications Surveys and Tutorials 2024年

作者： Fouda, Mostafa M. Fadlullah, Zubair Md Ibrahem, Mohamed I. Kato, Nei Idaho State University College of Science and Engineering Department of Electrical and Computer Engineering PocatelloID83209 United States Western University Department of Computer Science LondonONN6G 2V4 Canada Augusta University School of Computer and Cyber Sciences AugustaGA30912 United States Tohoku University Graduate School of Information Sciences Sendai980-8579 Japan

With the proliferation of Beyond 5G (B5G) communication systems and heterogeneous networks, mobile broadband users are generating massive volumes of data that undergo fast processing and computing to obtain actionable insights. While analyzing this huge amount of data typically involves machine and deep learning-based data-driven Artificial Intelligence (AI) models, a key challenge arises in terms of providing privacy assurances for user-generated data. Even though data-driven techniques have been widely utilized for network traffic analysis and other network management tasks, researchers have also identified that applying AI techniques may often lead to severe privacy concerns. Therefore, the concept of privacy-preserving data-driven learning models has recently emerged as a hot area of research to facilitate model training on large-scale datasets while guaranteeing privacy along with the security of the data. In this paper, we first demonstrate the research gap in this domain, followed by a tutorial-oriented review of data-driven models, which can be potentially mapped to privacy-preserving techniques. Then, we provide preliminaries of a number of privacy-preserving techniques (e.g., differential privacy, functional encryption, Homomorphic encryption, secure multi-party computation, and federated learning) that can be potentially adopted for emerging communication networks. The provided preliminaries enable us to showcase the subset of data-driven privacy-preserving models, which are gaining traction in emerging communication network systems. We provide a number of relevant networking use cases, ranging from the B5G core and Radio Access Networks (RANs) to semantic communications, adopting privacy-preserving data-driven models. Based on the lessons learned from the pertinent use cases, we also identify several open research challenges and hint toward possible solutions. © 1998-2012 IEEE.

关键词： communication networks data-driven models deep learning federated learning machine learning Privacy preservation

来源：评论

学校读者我要写书评

暂无评论

Cardiovascular Disease Analysis Using Correlational Analysis and Association Rules Mining for In-depth Analysis to Identify Predominant Variables

Cardiovascular Disease Analysis Using Correlational Analysis...

引用

computer science, information Technology and engineering (ICCoSITE), International Conference on

作者： Boby Siswanto Haryono Soeparno Nesti F. Sianipar Widodo Budiharto Computer Science Department BINUS Graduate Program- Computer Science Bina Nusantara University Jakarta Indonesia Food Technology Department Engineering Faculty Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short (<165 cm), and age=middle (45-60 years) are more likely to be affected by cardiovascular disease. The variable gender=woman with height=short indicates a 76.07% probability of developing cardiovascular disease.

关键词： computer science Correlation Itemsets Cardiovascular diseases Data mining information technology

来源：评论

学校读者我要写书评

暂无评论

Improving Land Cover Segmentation Using Multispectral Dataset

Improving Land Cover Segmentation Using Multispectral Datase...

引用

International Conference on Informatics and Computing (ICIC)

作者： Herlawati Herlawati Rahmadya Trias Handayanto Yaya Heryadi Edi Abdurachman Haryono Soeparno Edy Irwansyah Eka Miranda Informatics Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Computer Engineering Universitas Islam 45 Bekasi Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Trisakti Institute of Transportation and Logistic Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

computer vision has been used in many areas such as medical, transportation, military, geography, etc. The fast development of sensor devices inside camera and satellite provides not only red-greed-blue (RGB) images but also multispectral dataset with some channels including RGB, infrared, short-wave, and thermal wave. Most of the dataset is panchromatic (black and white) and RGB, for example Google Map and other satellite-based map applications. This study examines the effects of multispectral dataset for semantic segmentation of land cover. The comparison between RGB with band 2 to band 7 of Landsat 8 Satellite shows an improvement of accuracy from 90.283 to 94.473 for U-Net and from 91.76 to 95.183 for DeepLabV3+. In addition, this research also compares two well-known semantic segmentation methods, namely U-Net and DeepLabV3+, that shown that DeepLabV3+ outperformed U-Net regarding to speed and accuracy. Testing was conducted in the Karawang Regency area, West Java, Indonesia.

关键词：

来源：评论

学校读者我要写书评

暂无评论

In-Group Love, Out-Group Hate: A Framework to Measure Affective Polarization via Contentious Online Discussions 25

In-Group Love, Out-Group Hate: A Framework to Measure Affect...

引用

34th ACM Web Conference, WWW 2025

作者： Nettasinghe, Buddhika Rao, Ashwin Jiang, Bohan Percus, Allon G. Lerman, Kristina University of Iowa Tippie College of Business Iowa CityIA United States USC Information Sciences Institute Department of Computer Science Marina del ReyCA United States Arizona State University School of Engineering TempeAZ United States Claremont Graduate University Institute of Mathematical Sciences ClaremontCA United States

ISBN: (纸本)9798400712746

Affective polarization, the emotional divide between ideological groups marked by in-group love and out-group hate, has intensified in the United States, driving contentious issues like masking and lockdowns during the COVID-19 pandemic. Despite its societal impact, existing models of opinion change fail to account for emotional dynamics nor offer methods to quantify affective polarization robustly and in real-time. In this paper, we introduce a discrete choice model that captures decision-making within affectively polarized social networks and propose a statistical inference method estimate key parameters—in-group love and out-group hate—from social media data. Through empirical validation from online discussions about the COVID-19 pandemic, we demonstrate that our approach accurately captures real-world polarization dynamics and explains the rapid emergence of a partisan gap in attitudes towards masking and lockdowns. This framework allows for tracking affective polarization across contentious issues has broad implications for fostering constructive online dialogues in digital spaces. © 2025 Copyright held by the owner/author(s).

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

Harmonious Music-driven Group Choreography with Trajectory-Controllable Diffusion

arXiv

引用

arXiv 2024年

作者： Dai, Yuqin Zhu, Wanlu Li, Ronghui Ren, Zeping Zhou, Xiangzheng Ying, Jixuan Li, Jun Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Shenzhen International Graduate School Tsinghua University Shenzhen China

Creating group choreography from music is crucial in cultural entertainment and virtual reality, with a focus on generating harmonious movements. Despite growing interest, recent approaches often struggle with two major challenges: multi-dancer collisions and single-dancer foot sliding. To address these challenges, we propose a Trajectory-Controllable Diffusion (TCDiff) framework, which leverages non-overlapping trajectories to ensure coherent and aesthetically pleasing dance movements. To mitigate collisions, we introduce a Dance-Trajectory Navigator that generates collision-free trajectories for multiple dancers, utilizing a distance-consistency loss to maintain optimal spacing. Furthermore, to reduce foot sliding, we present a footwork adaptor that adjusts trajectory displacement between frames, supported by a relative forward-kinematic loss to further reinforce the correlation between movements and trajectories. Experiments demonstrate our method’s superiority. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-based Risk Prediction Model for Recurrence-free Survival in Patients with Hepatocellular Carcinoma Using Multi-phase CT Image 11

Deep Learning-based Risk Prediction Model for Recurrence-fre...

引用

11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Wang, Weibin Wang, Fang Yang, Yunjun Li, Yinhao Liu, Jing Han, Xianhua Lin, Lanfen Tong, Ruofeng Hu, Hongjie Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Kusatsu Japan Zhejiang University Department of Radiology Sir Run Run Shaw Hospital Hangzhou China Wenzhou Medical University Department of Radiology the First Affiliated Hospital Wenzhou China Ritsumeikan University College of Information Science and Engineering Kusatsu Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China College of Information Science and Engineering Yamaguchi University Yamaguchi Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9781665492324

Risk prediction for recurrence is a critical task for patients with hepatocellular carcinoma (HCC). Effective prediction can evaluate treatment options and guide personalized medicine. Traditional approaches use clinical data to construct cox-regression models to predict the risk of recurrence. Recently, prognosis analysis based on image information of patient often has better performance compared with traditional approaches. In particular, deep learning has demonstrated its superiority in medical image processing. In this paper, we propose a deep learning-based model to predict the risk of recurrence. We collected 292 patients with HCC from two independent centers, which were divided into a training set, an internal validation set, and an external validation set. Our models were compared with traditional clinical models and better performance was obtained. Our proposed method achieves a C-index performance of 0.627 and 0.630 for the internal validation set and external validation set, respectively. © 2022 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

MuLeS: A Multi-Client Learning-Based MPQUIC Scheduler

MuLeS: A Multi-Client Learning-Based MPQUIC Scheduler

引用

Consumer Communications and Networking Conference, CCNC IEEE

作者： Thanh Trung Nguyen Minh Hai Vu Thi Ha Ly Dinh Phi Le Nguyen Kien Nguyen Faculty of Computer Science Phenikaa University Hanoi Vietnam School of Information and Communication Technology Hanoi University of Science and Technology Hanoi Vietnam Institute for Advanced Academic Research Chiba University Japan Graduate School of Engineering Chiba University Japan

Multipath QUIC (MPQUIC) is an emerging multi-path transport protocol that lets a mobile client simultaneously use several wireless networks (e.g., Wi-Fi and cellular) in 5G and beyond. MPQUIC's performance heavily relies on its scheduler, which determines a path or several ones for sending packets in the upcoming time slot. Despite numerous efforts, the traditional design of MPQUIC schedulers can not handle wireless networks' dynamicity. Recently, a learning-based approach has shown the potential to bypass such limitations of the MPQUIC scheduler with various learning-based schedulers proposed in the literature. However, the existing works only consider the scheduling task in a single client context. When applying such a scheduler to multiple client scenarios (likely to occur in practice), they suffer from a so-called rush scheduling phenomenon. More specifically, the packet forwarding decisions made by a scheduler are only accountable to one client, resulting in conflicts of interest with other clients' schedulers. Consequently, it may harm the network performance. This paper addresses the issue and designs a learning-based MPQUIC scheduler considering the existence of multiple clients. To the best of our knowledge, this is the first work to do so. We propose MuLeS, a learning-based scheduler for MPQUIC in the multi-client scenario. MuLeS uses a central controller, which allows it to observe the state of all flows in the network. Our evaluation results show that MuLeS outperforms contemporary schedulers in terms of various metrics, including download time and loss rate. Notably, MuLeS reduces the average download time by 7%-16% compared to the other schedulers.

关键词：

来源：评论

学校读者我要写书评

暂无评论

InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception

arXiv

引用

arXiv 2024年

作者： Li, Haijie Wu, Yanmin Meng, Jiarui Gao, Qiankun Zhang, Zhiyao Wang, Ronggang Zhang, Jian School of Electronic and Computer Engineering Peking University China Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology Peking University Shenzhen Graduate School China College of Information Science and Engineering Northeastern University China

3D scene understanding is vital for applications in autonomous driving, robotics, and augmented reality. However, scene understanding based on 3D Gaussian Splatting faces three key challenges: (i) an imbalance between appearance and semantics, (ii) inconsistencies in object boundaries, and (iii) difficulties with top-down instance segmentation. To address these challenges, we propose InstanceGaussian, a method that jointly learns appearance and semantic features while adaptively aggregating instances. Our contributions are as follows: (i) a new Semantic-Scaffold-GS representation to improve feature representation and boundary delineation, (ii) a progressive training strategy for enhanced stability and segmentation, and (iii) a category-agnostic, bottom-up instance aggregation approach for better segmentation. Experimental results demonstrate that our approach achieves state-of-the-art performance in category-agnostic, open-vocabulary 3D point-level segmentation, validating the effectiveness of our proposed method. Project page: https://***/InstanceGaussian/ Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Proposal of Close Contact Confirmation Method in Cyberspace for Influencer Marketing

Proposal of Close Contact Confirmation Method in Cyberspace ...

引用

IEEE Global Conference on Life sciences and Technologies (LifeTech)

作者： Atsushi Isozaki Naoki Suyama Tetsuo Imai Kenichi Arai Toru Kobayashi Computer and Information Science Program Graduate School of Engineering Nagasaki University Nagasaki Japan Computer and Information Science Program School of Engineering Nagasaki University Nagasaki Japan Division of Electrical Engineering and Computer Science Graduate School of Engineering Nagasaki University Nagasaki Japan

ISBN: (数字)9781665418751

ISBN: (纸本)9781665448086

We have developed the system to measure the effectiveness of influencer marketing by a general-purpose method using web browsers and web servers. This system uses the “COVID-19 Contact-Confirming Application” (COCOA) as a reference model. COCOA was developed by the Ministry of Health, Labor and Welfare in response to the epidemic of COVID-19, to detect suspicions of close contact while maintaining anonymity. The same model was applied to the cyberspace, and the “close contact confirmation method in the cyberspace” was considered in this study. We implemented a prototype and confirmed its functional validity while solving implementation problems using the Cross-Storage library.

关键词： COVID-19 Cyberspace Prototypes Web servers Libraries Browsers Proposals

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：