检索结果-内蒙古大学图书馆

2024 International Conference on Future of Medicine and Biological Information Engineering, MBIE 2024

作者： Pei, Chengdan Zhang, Wenqian She, Yifei Xu, Shengzhou Network Information Center Wuhan Institute of Technology Wuhan430205 China College of Computer Science South-Central Minzu University Wuhan430074 China

ISBN: (纸本)9781510682825

Esophageal cancer is the sixth leading cause of cancer-related mortality worldwide. Barium esophagram is an inexpensive, noninvasive, and widely available procedure. However, the variation in tumor size, the interference of surrounding tissues, and esophageal deformation make tumor detection very challenging. This paper proposes a novel algorithm for detecting esophageal cancer, consisting of a detection network and a classification network. A deformable residual bottleneck block is proposed to replace the residual bottleneck block in ResNet50 to sample the deformed esophagus adaptively. The improved ResNet50 is used as the backbone network of the detection network, which is combined with FPN network to predict tumors of various sizes. Then, regions of interest detected from each image of the barium esophagram by the detection network are used as the input of the classification network. The attention module CBAM is introduced into the classification network to enhance the significance of the esophageal region, and improve the classification accuracy. The algorithm is evaluated on 40 positive (1166 images) and 53 negative cases (1547 images), the accuracy of the algorithm on positive and negative cases are 100% (40/40) and 84.90% (45/53) respectively. The experiment demonstrates that our proposed method yields promising results with the barium esophagram dataset. © 2024 SPIE.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem

引用

IEEE Access 2024年 12卷 14703-14718页

作者： Ho, Kuo-Hao Cheng, Jui-Yu Wu, Ji-Han Chiang, Fan Chen, Yen-Chi Wu, Yuan-Yu Wu, I-Chen National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

Job-shop scheduling problem (JSP) is a mathematical optimization problem widely used in industries like manufacturing, and flexible JSP (FJSP) is also a common variant. Since they are NP-hard, it is intractable to find the optimal solution for all cases within reasonable times. Thus, it becomes important to develop efficient heuristics to solve JSP/FJSP. A kind of method of solving scheduling problems is construction heuristics, which constructs scheduling solutions via heuristics. Recently, many methods for construction heuristics leverage deep reinforcement learning (DRL) with graph neural networks (GNN). In this paper, we propose a new approach, named residual scheduling, to solving JSP/FJSP. In this new approach, we remove irrelevant machines and jobs such as those finished, such that the states include the remaining (or relevant) machines and jobs only. Our experiments show that our approach reaches state-of-the-art (SOTA) among all known construction heuristics on most well-known open JSP and FJSP benchmarks. In addition, we also observe that even though our model is trained for scheduling problems of smaller sizes, our method still performs well for scheduling problems of large sizes in terms of makespan. Interestingly in our experiments, our approach even reaches zero makespan gap for 49 among 60 JSP instances whose job numbers are more than 100 on 15 machines. © 2013 IEEE.

关键词： Heuristic algorithms

来源：评论

学校读者我要写书评

暂无评论

Based-on computer Vision Applications for Bus Stop Passenger Detection System 3

Based-on Computer Vision Applications for Bus Stop Passenger...

引用

3rd IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Chang, Yu-Cheng Tsai, Hua-Wen Huang, Chao-Yi Wu, Zong-Rong Department of Computer Information and Network Engineer Lunghwa University of Science and Technology Taiwan Department of Bachelor's Program in Intelligent Robot National Yunlin University of Science and Technology Taiwan

ISBN: (纸本)9798350333862

Passenger transport is one of the most common ways of commuting in Taiwan. It plays an important role in the transportation system due to its large number of stations, dense frequency, and cheap transportation. Due to the unfriendly transportation environment and a large number of passengers, a blind spot of passenger transportation exists, which leads to traffic accidents at the station. We research to make the Bus Stop Passenger Detection System. Taking the object detection of Wheelchairs into consideration, it is more convenient to assist the disabled to find the passenger transportation system, which makes Taiwan's transportation system more convenient. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Exploring the Characteristics of Popular Deep Learning GitHub Repositories∗ 2

Exploring the Characteristics of Popular Deep Learning GitHu...

引用

2nd International Conference on Intelligent Computing and Next Generation networks, ICNGN 2023

作者： Zhou, Yiren Qiao, Yu Xu, Tao University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Network Center Zaozhuang University Zaozhuang China

ISBN: (纸本)9798350329070

Deep learning (DL) repositories on GitHub platform have a significant growth following the successful application of DL for different domain tasks. How to capture widespread and continued attention from such many DL repositories becomes an urgent concern for researchers and practitioners of DL. While some studies have attempted to explore popular GitHub repositories, they mainly focused on traditional software or academic AI repositories on GitHub. DL repositories are different from traditional software or academic AI repositories, it is necessary to explore the characteristics of popular DL GitHub repositories. Therefore, through a case study of 652 popular and unpopular DL repositories, we study the growth patterns and characteristics of popular DL repositories and the impact of four types of factors (i.e., basic information, DL-related information, README information, and repositories' owner information) on the popularity of DL repositories. We find that: i) different from the four growth patterns in the traditional software repositories, DL repositories have three growth patterns: Faster-then-Slower, Linear, and Explosive growth patterns;ii) various features exert diverse influences on the popularity of repositories within distinct domains;ii) basic information of the GitHub repositories (e.g., number of contributions, and number of topics) and README information (e.g., has-quickexample, has-photo) are more helpful in attracting attention compared with DL-related information. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

An Improved Deadlock Recovery Policy of Flexible Manufacturing Systems Based on Resource Flow Graphs

引用

IEEE Access 2024年 12卷 65202-65212页

作者： Tseng, Ching-Yun Chen, Ju-Chin Pan, Yen-Liang National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung807618 Taiwan R.O.C. Air Force Academy Mathematics and Physics Division General Education Center Gangshan Kaohsiung820009 Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung807618 Taiwan Chaoyang University of Technology Department of Aeronautical Engineering Taichung413310 Taiwan

The system deadlock problem of the flexible manufacturing system (FMS) needs to be solved urgently. Deadlocks may occur when resources are repeatedly requested by multiple working processes, where such competition causes a lack of resources, and the system cannot get into subsequent processes smoothly. In this paper, we build FMS by using a particular class of Petri Net (PN) model called a system of simple sequential processes with resources (S3PR) net, which features a high utilization rate of resources. When parts of resources are held by related processes and these processes wait for others to release resources to move on, a circular waiting situation occurs. Circular waiting is one of the conditions for system deadlocks occurring, where the system under this situation has a high probability of causing system deadlocks. The resources being held have to be released to break the waiting states. This paper develops a novel control transition-based deadlock recovery policy by combining graph theory and reachability graph analysis technique. The proposed approach firstly builds a subnet of the PN model called resource flow graph (RFG) and finds out all its hold and request circuits (HRCs), which may lead to a circular waiting situation. In addition, we design a new algorithm to obtain the minimal set of control transitions further. We introduce three PN models of FMS to demonstrate the proposed method. Through experimental results and comparison between related works, it is known that the proposed deadlock recovery uses fewer controllers and has the same performance. © 2013 IEEE.

关键词： Frequency modulation

来源：评论

学校读者我要写书评

暂无评论

Shuffled Autoregression for Motion Interpolation 48

Shuffled Autoregression for Motion Interpolation

引用

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

作者： Huang, Shuo Jia, Jia Yang, Zongxin Wang, Wei Wu, Haozhe Yang, Yi Xing, Junliang Tsinghua University Department of Computer Science and Technology Beijing100084 China Beijing National Research Center for Information Science and Technology China Zhejiang University Ccai College of Computer Science and Technology China

ISBN: (纸本)9781728163277

This work aims to provide a deep-learning solution for the motion interpolation task. Previous studies solve it with geometric weight functions. Some other works propose neural networks for different problem settings with consecutive pose sequences as input. However, motion interpolation is a more complex problem that takes isolated poses (e.g., only one start pose and one end pose) as input. When applied to motion interpolation, these deep learning methods have limited performance since they do not leverage the flexible dependencies between interpolation frames as the original geometric formulas do. To realize this interpolation characteristic, we propose a novel framework, referred to as Shuffled AutoRegression, which expands the autoregression to generate in arbitrary (shuffled) order and models any inter-frame dependencies as a directed acyclic graph. We further propose an approach to constructing a particular kind of dependency graph, with three stages assembled into an end-to-end spatial-temporal motion Transformer. Experimental results on one of the current largest datasets show that our model generates vivid and coherent motions from only one start frame to one end frame and outperforms competing methods by a large margin. The proposed model is also extensible to multiple keyframes' motion interpolation tasks and other areas' interpolation. © 2023 IEEE.

关键词： animation human motion motion interpolation neural networks transformer

来源：评论

学校读者我要写书评

暂无评论

Leveraging Social Media for Real-Time Interpretable and Amendable Suicide Risk Prediction with Human-in-The-Loop

引用

IEEE Transactions on Affective Computing 2024年第2期16卷 1128-1145页

作者： Dai, Yi Liu, Jinlei Cao, Lei Xue, Yuanyuan Wang, Xin Ding, Yang Tian, Junrui Feng, Ling Tsinghua University Department of Computer Science Technology Beijing China Beijing Normal University Faculty of Psychology Beijing China China Transport Telecommunications & Information Center Beijing China

Suicide presents a global health challenge, prompting the development of diverse prevention strategies. Among them, timely identification of individuals at risk of suicide remains challenging. Although social media offers potential for tracking users' mental status, harnessing collaboration between AI and human experts for real-time prediction of suicide risk is inadequately explored. This study presents a human-in-the-loop framework for real-time suicide risk prediction based on social media. Once a user made a new post on social media, the AI model assesses user's suicide risk within the next month with explanation based on the historic and new posts plus domain knowledge. Human experts on the other side look into the explanation to confirm/clarify uncertain information as feedback, enabling consistent evolution of the model. Experiments on the constructed dataset, containing 66 suicidal users and 66 non-suicidal users, show that our method achieved 82.58% prediction accuracy, outperforming competitive baselines by 6.57%. Leveraging human feedback improved prediction accuracy by 4.12%. Consultation with 18 experts (including 6 medical staff and 12 psychologists) was conducted to examine the validity of our method. Ethics considerations, as well as potential and limitations of large language models in mental condition prediction, are also discussed at the end of the paper © 2010-2012 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Opportunistic Relay Strategy for Body Area networks

Opportunistic Relay Strategy for Body Area Networks

引用

2024 IEEE International Conference on E-Health networking, Application and Services, HealthCom 2024

作者： Wu, Hongbo Jia, Yukuan Yan, Jintao Zhou, Sheng Niu, Zhisheng Chang, Zheng Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350350548

In wireless body area networks (WBANs), the deep channel fading between the nodes and the hub significantly impairs the reliability of end-to-end signal transmission. However, some nodes in WBANs necessitate high-priority data transmission with stringent latency and accuracy requirements. Retransmission is ineffective against channel fading and can result in increased communication overhead and extended transmission delays. This paper proposes an opportunistic relay strategy tailored to the characteristics of WBAN nodes with varying priorities. This strategy converts suitable low-priority nodes as relays during high-priority time slots to forward high-priority data to the hub when deep fading occurs. The relay can be opportunistically selected based on the channel condition and the power usage. With Lyanupov optimization, we maximize the transmission reliability while ensuring the extra power consumption of low-priority nodes are acceptable. Subsequently, simulations are conducted in the network Simulator 3 (NS3) to validate the proposed relay selection strategy, showing that the proposed strategy effectively improves the reliability from 90.2% to 99.1% with an additional power consumption of 30%. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

来源：评论

学校读者我要写书评

暂无评论

BPCoach: Exploring Hero Drafting in Professional MOBA Tournaments via Visual Analytics

引用

Proceedings of the ACM on Human-computer Interaction 2024年第CSCW1期8卷 1-31页

作者： Liu, Shiyi Ma, Ruofei Zhao, Chuyi Li, Zhenbang Xiao, Jianpeng Li, Quan School of Information Science and Technology ShanghaiTech University Shanghai China Department of Computer Science Tufts University MedfordMA United States Guangzhou Qucheng Culture Media Co. Guangzhou China School of Information Science and Technology ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai China

Hero drafting for multiplayer online arena (MOBA) games is crucial because drafting directly affects the outcome of a match. Both sides take turns to "ban"/"pick" a hero from a roster of approximately 100 heroes to assemble their drafting. In professional tournaments, the process becomes more complex as teams are not allowed to pick heroes used in the previous rounds with the "best-of-N" rule. Additionally, human factors including the team's familiarity with drafting and play styles are overlooked by previous studies. Meanwhile, the huge impact of patch iteration on drafting strengths in the professional tournament is of concern. To this end, we propose a visual analytics system, BPCoach, to facilitate hero drafting planning by comparing various drafting through recommendations and predictions and distilling relevant human and in-game factors. Two case studies, expert feedback, and a user study suggest that BPCoach helps determine hero drafting in a rounded and efficient manner. © 2023 Copyright held by the owner/author(s).

关键词： Visualization

来源：评论

学校读者我要写书评

暂无评论

Multi-Task Learning for UAV Trajectory and Caching With Federated Cloud-Assisted Knowledge Distillation

引用

IEEE Transactions on network and Service Management 2025年第3期22卷 2516-2533页

作者： Tietaa Maale, Gerald Arden Elorm Kuadey, Noble Arafat, Yeasin Thomas, Kwantwi Sun, Guolin Liu, Guisong Research Institute of Intelligent Computing University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China McCoy University College of Education Department of Mathematics and ICT Nadowli Ghana Research Institute of Intelligent Computing at the University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China Ho Technical University Department of Computer Science Ho Ghana University of Mines and Technology Department of Computer Science and Engineering Tarkwa Ghana Southwestern University of Finance and Economics Complex Laboratory of New Finance and Economics School of Computing and Artificial Intelligence Chengdu610074 China Ministry of Education Engineering Research Center of Intelligent Finance Chengdu610030 China

The proliferation of Internet of Things (IoT) technologies and ubiquitous connectivity has led to uncrewed aerial vehicles (UAVs) playing key role as edge servers, revolutionizing the wireless communications landscape by facilitating computing and caching resources closer to ground users (GUs). This advancement significantly alleviates core network loads, reduces latency, and guarantees content availability even in congested or remote areas. However, jointly optimizing UAV caching strategies and trajectories gives rise to a multi-task optimization (MTO) problem. This paper introduces a novel multi-task geo-temporal caching (MT-GTC) framework that addresses the interplay between UAV caching mechanisms and trajectory optimization in a cohesive manner. Leveraging a proposed multi-task learning (MTL) model for joint optimization of UAV caching and trajectory design, we develop a federated learning cloud-assisted knowledge distillation (FL-CAKD) scheme to preserve data privacy and adapt to data heterogeneity. FL-CAKD transfers knowledge from a cloud model orchestrator (CMO), which houses a large and sophisticated teacher model, to a lightweight on-device MTL student models using soft target distributions instead of large model parameters, significantly reducing communication costs. MT-GTC optimizes caching and trajectories to maximize cache hits and minimize latency. Evaluations on real-world mobility datasets demonstrate up to 95% cache hit rates and 21% lower delays compared to baselines. © 2004-2012 IEEE.

关键词： Multi-task 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：