检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Li, Haopeng Deng, Andong Liu, Jun Rahmani, Hossein Guo, Yulan Schiele, Bernt Bennamoun, Mohammed Ke, Qiuhong School of Computing and Information Systems University of Melbourne Australia Center for Research in Computer Vision University of Central Florida United States Pillar Singapore University of Technology and Design Singapore School of Computing and Communications Lancaster University United Kingdom School of Electronics and Communication Engineering Sun Yat-sen University China Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Saarland Informatics Campus Germany School of Physics Maths and Computing University of Western Australia Australia Department of Data Science & AI Monash University Australia

Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not been explored due to the lack of relevant datasets and the challenging nature it presents. Most datasets for video question answering (VideoQA) focus mainly on general and coarse-grained understanding of daily-life videos, which is not applicable to sports scenarios requiring professional action understanding and fine-grained motion analysis. In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. The Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions, covering multiple sports. Furthermore, to address the characteristics of the sports VideoQA task, we propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering. We conduct extensive experiments on Sports-QA, including baseline studies and the evaluation of different methods. The results demonstrate that our AFT achieves state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

来源：评论

学校读者我要写书评

暂无评论

A Disaster Management System Using Cloud computing

A Disaster Management System Using Cloud Computing

引用

ACS/IEEE International Conference on Computer Systems and Applications

作者： Saurabh Singhal Ashish Sharma Mahendra Kumar Gourisaria Bhisham Sharma Imed Ben Dhaou Department of Computer Engineering and Applications GLA University Mathura India School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Hekma School of Engineering Computing and Design Dar Al Hekma Univerity Jeddah Saudi Arabia

Natural disasters cause immense hardship for communities around the world, necessitating effective and coordinated responses to mitigate the consequences on infrastructure and human life. Cloud computing is a breakthrough technology that has enormous promise for improving disaster management tactics. This study investigates the critical role of cloud computing in disaster management, showing its multiple benefits during the phases of planning, response, and recovery. The study investigates how cloud computing improves disaster preparedness by allowing stakeholders to simulate and prepare for various disaster scenarios using data-gathering capabilities, collaborative tools, and simulation models. The paper discusses the role of cloud computing in preparation of disaster management and how an organization can use the latest technology to minimize the impact of disaster on it.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Survey on a Novel Cryptojacking Covert Attack

A Survey on a Novel Cryptojacking Covert Attack

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Yogesh M. Gajmal Pranav More Kiran Dhanaji Kale Arvind Jagtap Information Technology Finolex Academy of Management and Technology Ratnagiri India School of AI & Future Technologies Universal AI University Karjat India Electronics and Communication Engineering Presidency University Bangalore Bengalore India MIT School of Computing MIT Art Design and Technology University Pune India

New technologies based on cryptocurrencies and blockchain are revolutionizing how we conduct commercials online. Nowadays, a wide range of blockchain as well as cryptocurrency systems, apps, also technologies are publicly accessible in the direction of businesses, end users, as well as even malicious attackers that seek towards deploy cryptojacking malware to abuse the computational resources of common people. Without the victims' knowledge, this type of malware operates on their machines. In order to mine cryptocurrency for the cybercriminal, it frequently infects browsers and performs CPU-intensive computations. The cybercriminal then steals the revenues without compensating for the resources used. However, current detection techniques, for instance browser extensions that designed to defend users using blacklist protection or antivirus programmes by different analysis techniques, can only partially solve the problem of emerging cryptojacking because attackers can simply get around them via using obfuscation methods or else frequently altering their domains or else scripts. As a result, numerous researches in the literature suggested employing different dynamic/behavioral indicators to identify cryptojacking malware. However, a systematic study using a deep grasp of the new cryptojacking attack as well as a thorough assessment of studies into the literature is lacking in the literature. Therefore, using data from the web and journal articles, we give a systematic study of cryptojacking in this paper. Finally, we offer some prevention advice as well as warning signs that you might have been a victim of cryptojacking.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Urban Living - A Comprehensive Solution Using Machine Learning and IoT

Enhancing Urban Living - A Comprehensive Solution Using Mach...

引用

Emerging Technology (INCET), International Conference for

作者： Atharva Puranik Vishwanath Ghanekar Satyajit Pangaonkar Reena Gunjan Prakash Rokade School of Computer Science and Engineering Avantika University Ujjain Madhya Pradesh India MIT School of Computing MIT Art Design and Technology University Pune Maharashtra India Cloud and Infrastructure Services Infosys Ltd Pune Maharashtra India

The urban population is increasing rapidly, leading to challenges in managing daily life activities such as transportation, healthcare, and accessing local services. The developed "At a Glance," is a comprehensive mobile application and digital billboard that provides real-time solutions to several problems faced by city dwellers. The app uses machine learning and IoT to offer features such as real-time traffic monitoring, local news updates, resource people contacts, medical and health information, among others. It aims to promote sustainable living, reduce traffic congestion and air pollution, and make the city a better place to live. "At a Glance" uses various technologies such as Flutter, Firebase Realtime Database and Storage, TensorFlow, IoT, geolocator, geocoder, OpenStreetMaps, and Raspberry Pi. The app offers features such as traffic density monitoring, real-time weather updates, local news and event notifications, and a resource people directory. The app also uses machine learning algorithms to recommend relevant services and resources to users based on their preferences. "At a Glance" has the potential to revolutionize urban living by providing a one-stop solution for all daily life activities. The app can help people make informed decisions, save time and resources, and reduce their carbon footprint. The app's user-friendly interface and accessibility make it a promising solution for cities worldwide.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Plant Pathologist- A Machine Learning Diagnostician for the Plant Disease

Plant Pathologist- A Machine Learning Diagnostician for the ...

引用

Emerging Technology (INCET), International Conference for

作者： Vidushi Yadav Satyajit Pangaonkar Reena Gunjan Prakash Rokade School of Computer Science and Engineering Avantika University Ujjain Madhya Pradesh India MIT School of Computing MIT Art Design and Technology University Pune Maharashtra India Cloud and Infrastructure Services Infosys Ltd Pune Maharashtra India

Crop infection in terms of bacteria, fungus and other viruses is an unrelenting issue and loos of food due to this is a key challenge faced by farmers for many decades. Proper identification of this disease may prevent this loss. This study describes precision agriculture in terms of a mobile application called ‘plant pathologist’ for detecting plant diseases that forecast their occurrence using machine learning and artificial intelligence. A combination of computer vision and deep learning algorithms were used to evaluate images of plant leaves and compared them to a database of known diseases in order to make a quick and accurate diagnosis. The development process involved the identification of the research issue and the objectives, the gathering and pre-processing of data, the choice and training of the model, and the model evaluation through this quantitative methodology. The convolutional neural network (CNN) was trained on a sizable dataset of annotated plant disease photos as part of the machine learning model utilized in the mobile app. The CNN was trained to recognise the visual characteristics of photos of healthy and diseased plants and classify them according to several disease types. To ensure adaptability and precise identification of plant diseases, the app has been trained on a specific type. With hyperparameter tuning and cross-validation, the best-performing CNN model was chosen for the model selection procedure. The mobile application, which offers thorough information on plant disease diagnosis is then updated with the chosen model. This offers farmers, botanists, and plant enthusiasts an invaluable resource to accurately identify plant diseases through machine learning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Bridging Gaps in HCI: Advancing Education, Research, and Careers in Asia

Bridging Gaps in HCI: Advancing Education, Research, and Car...

引用

2025 CHI Conference on Human Factors in computing Systems, CHI EA 2025

作者： Gamage, Dilrukshi Cheng, Shiwei Mudliar, Preeti Lu, Zhicong Zhao, Shengdong Ahmed, Nova Ma, Xiaojuan Lee, Uichin Wang, Ding Department of Computation and Intelligent Systems University of Colombo School of Computing Colombo Sri Lanka Zhejiang University of Technology Hangzhou China International Institute of Information Technology Bangalore Bangalore India Department of Computer Science City University of Hong Kong Hong Kong School of Creative Media City University of Hong Kong Hong Kong Design Inclusion and Access Lab. Department of Electrical and Computer Engineering North South University Dhaka Dhaka Bangladesh Hong Kong University of Science and Technology Hong Kong Hong Kong School of Computing KAIST Daejeon South Korea Google Atlanta GA United States

ISBN: (纸本)9798400713958

Asia’s Human-Computer Interaction (HCI) landscape is rapidly evolving, yet it faces distinct challenges in curriculum development, research establishment, and career navigation. This panel discussion, hosted by Asia SIGCHI Committee (ASC) at CHI2025, will bring together distinguished experts to address these challenges and explore strategies for fostering a robust and inclusive HCI community in Asia. Panelists will discuss the integration of culturally relevant content in HCI education, approaches to enhancing research visibility on international platforms, and initiatives to bridge gaps between academic training and industry expectations. Through an engaging discussion and audience interaction, the panel aims to identify actionable solutions, promote collaboration, and inspire future initiatives that strengthen HCI’s growth across the region. Participants will gain valuable insights into overcoming systemic barriers while building sustainable and impactful HCI programs tailored to Asia’s unique sociocultural dynamics. This panel seeks to advance the global relevance and contributions of HCI in Asia. © 2025 Copyright held by the owner/author(s).

关键词： Asia Curricula HCI HCI Education HCI Teaching

来源：评论

学校读者我要写书评

暂无评论

Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

引用

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5520-5537页

作者： Jian Liu Wei Sun Hui Yang Pengchao Deng Chongpei Liu Nicu Sebe Hossein Rahmani Ajmal Mian National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics and the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computing and Communications Lancaster University Lancaster U.K. Department of Computer Science The University of Western Australia Crawley WA Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance.

关键词： Shape Solid modeling Three-dimensional displays Pose estimation Training Robots Grasping Diffusion models Real-time systems Point cloud compression

来源：评论

学校读者我要写书评

暂无评论

MLFuse: Multi-Scenario Feature Joint Learning for Multi-Modality Image Fusion

引用

IEEE Transactions on Multimedia 2025年

作者： Lei, Jia Li, Jiawei Liu, Jinyuan Wang, Bin Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these methods have difficulty breaking fusion barriers across various modalities owing to the lack of targeted learning routes. In this work, we propose a multi-scenario feature joint learning architecture, MLFuse, that employs the commonalities of multi-modality images to deconstruct the fusion progress. Specifically, we construct a cross-modal knowledge reinforcing network that adopts a multipath calibration strategy to promote information communication between different images. In addition, two professional networks are developed to maintain the salient and textural information of fusion results. The spatial-spectral domain optimizing network can learn the vital relationship of the source image context with the help of spatial attention and spectral attention. The edge-guided learning network utilizes the convolution operations of various receptive fields to capture image texture information. The desired fusion results are obtained by aggregating the outputs from the three networks. Extensive experiments demonstrate the superiority of MLFuse for infrared-visible image fusion and medical image fusion. The excellent results of downstream tasks (i.e., object detection and semantic segmentation) further verify the high-quality fusion performance of our method. The code is publicly available at https://***/jialei-sc/MLFuse. © 1999-2012 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

DCET-Net: Dual-Stream Convolution Expanded Transformer for Breast Cancer Histopathological Image Classification

DCET-Net: Dual-Stream Convolution Expanded Transformer for B...

引用

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zou, Ying Chen, Shannan Sun, Qiule Liu, Bin Zhang, Jianxin Dalian University Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian China Dalian University of Technology School of Information and Communication Engineering Dalian China Dalian China Dalian Minzu University School of Computer Science and Engineering Dalian China

ISBN: (纸本)9781665401265

Researches on breast cancer histopathological image classification have achieved a great breakthrough using deep backbones of Convolutional Neural Networks (CNNs) in recent years. However, due to the inductive bias of locality, CNNs are unable to effectively extract the global feature information of breast cancer histopathological images, limiting the improvement of the classification results. To overcome this shortcoming, this paper reasonably introduces an extra backbone stream of a pure transformer, which consists of a self-attention mechanism to capture global receptive fields of histopathological images, thereby compensating the locality characteristic of CNNs backbone. Based on two backbone streams of CNN and transformer, a dual-stream network called DCET-Net is proposed, which considers local features and global ones simultaneously, and progressively combines them from these two streams to form the final representations for classification. DCET-Net is extensively evaluated on the representative BreakHis histopathological image dataset, and experimental results demonstrate that it is highly competitive with the state-of-the-art CNN methods in breast cancer histopathological image classification task. © 2021 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Exploring Equilibrium Strategies in Network Games with Generative AI

arXiv

引用

arXiv 2024年

作者： Yang, Yaoqi Du, Hongyang Sun, Geng Xiong, Zehui Niyato, Dusit Han, Zhu The College of Communications Engineering Army Engineering University PLA Nanjing210000 China The School of Computer Science and Engineering NTU Singapore The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States

Game theory offers a powerful framework for analyzing strategic interactions among decision-makers, providing tools to model, analyze, and predict their behavior. However, implementing game theory can be challenging due to difficulties in deriving solutions, understanding interactions, and ensuring optimal performance. Traditional non-AI and discriminative AI approaches have made valuable contributions but struggle with limitations in handling large-scale games and dynamic scenarios. In this context, generative AI emerges as a promising solution because of its superior data analysis and generation capabilities. This paper comprehensively summarizes the challenges, solutions, and outlooks of combining generative AI with game theory. We start with reviewing the limitations of traditional non-AI and discriminative AI approaches in employing game theory, and then highlight the necessity and advantages of integrating generative AI. Next, we explore the applications of generative AI in various stages of the game theory lifecycle, including model formulation, solution derivation, and strategy improvement. Additionally, from game theory viewpoint, we propose a generative AI-enabled framework for optimizing machine learning model performance against false data injection attacks, supported by a case study to demonstrate its effectiveness. Finally, we outline future research directions for generative AI-enabled game theory, paving the way for its further advancements and development. © 2024, CC BY.

关键词： Game theory

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：