检索结果-内蒙古大学图书馆

5th International Conference on computer Vision and Data Mining, ICCVDM 2024

作者： Huang, Shan Liu, Yanan Wu, Yufan Wang, Jinlan Duan, Xiaodong College of Computer Science and Engineering Dalian Minzu University Dalian116600 China State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian116600 China

ISBN: (纸本)9781510682863

In social recommendation, user's interest preference is influenced by multiple factors. It is a challenging problem to mine data information effectively in multiple types of interaction behaviors between users and items to obtain more accurate embedding of user items. Existing social recommendation models tend to take a homogeneous approach to the interaction effects of neighbourhood nodes on target nodes. We propose a social recommendation model based on deep representation learning of hypergraphs. The model aims to learn embedded representations of users and items from multi-type user-item interaction data efficiently. An attention mechanism that integrating time interval information is introduced for computing the preference weights of each item to the user. Messages are generated with time-interval attention weights the inter-user and inter-item hypergraph information is combined to update the messages and aggregate the information from multiple layers. Extensive experiments on two real datasets show that the proposed models are superior to other advanced recommendation methods in improving the recommendation performance. © 2024 SPIE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Towards Depth-continuous Scene Representation with Displacement Field for Robust Light Field Depth Estimation

引用

IEEE Transactions on Multimedia 2025年 27卷 3637-3649页

作者： Chen, Rongshan Sheng, Hao Yang, Da Cong, Ruixuan Cui, Zhenglong Wang, Sizhe Wang, Tun Zhao, Mingyuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing International Innovation Institute 166 Shuanghongqiao Street Pingyao Town Yuhang District Hangzhou31 1115 China Macao Polytechnic University Faculty of Applied Sciences Macao999078 China

The light field (LF) captures both the spatial and angular information of scenes, enabling accurate depth estimation. However, previous deep learning methods typically model surface depth only while ignoring the continuous nature of depth in 3D scenes. In this paper, we use the displacement field (DF) to describe this continuous property, and propose a novel depth-continuous scene representation for robust LF depth estimation. Experiments demonstrate that our representation enables the network to generate highly detailed depth maps with fewer parameters and faster speeds. Specifically, inspired by the signed distance field in 3D object descriptions, we aim to exploit the intrinsic depth-continuous property of 3D scenes via DF and define a novel depth-continuous scene representation. Then, we introduce a simple, yet general, learning framework for depth-continuous scene embedding, and the proposed network, DepthDF, achieves state-of-the-art performance on both synthetic and real-world LF datasets, ranking 1st on the HCI 4D light field benchmark. Furthermore, previous LF depth estimation methods can also be seamlessly integrated into this framework. Finally, we extend this framework beyond LF depth estimation to various tasks, including multiview stereo depth inference, LF superresolution, and LF salient object detection. Our experiments demonstrate an improved performance when continuous scene representation is applied, suggesting that our framework can potentially provide insights for more fields. © 1999-2012 IEEE.

关键词： Stereo image processing

来源：评论

学校读者我要写书评

暂无评论

Development of an Immersive Learning Environment for Fundamentals of Nursing Labs 10

Development of an Immersive Learning Environment for Fundame...

引用

10th International Conference on Virtual Reality, ICVR 2024

作者： Obeid, Mohammad F. Ewais, Ahmed Asia, Mohammad Shenandoah University Division of Applied Technology Winchester United States Arab American University Palestine Computer Science Department Jenin Palestine Arab American University Palestine Health Sciences Department Jenin Palestine

ISBN: (纸本)9798350364231

The field of nursing is no stranger to the use of simulation and training systems for education and skill acquisition. Virtual reality (VR) has been utilized as an immersive learning environment (ILV) for many years as it facilitates repetitive training, scenario introduction, and information retention with no disturbance of the real system/clinic. In this work, the development of an ILV for fundamentals of nursing laboratory courses is presented. The project is carried out cross-institutionally to create a modular platform that allows the continuous addition of VR modules for nursing and medical procedures. An initial user study is conducted with faculty and senior nursing students hypothesizing the utility of this platform for efficient, scalable, and effective training;as well as investigating development decisions related to user interface design and user experience. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Digital Transformation in Mechatronics: ICT Applications in Automation and Robotics

Digital Transformation in Mechatronics: ICT Applications in ...

引用

International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Deepshikha Vajpayee Nagesh Gupta Vaishali Joshi School of Computer Science and Information Technology Symbiosis University of Applied Sciences Indore India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Mechatronics, a multidisciplinary field and transformed the conventional methods to dynamic and autonomous systems. The emergence of robotics and technology in various sectors marks a significant revolution in the industry specially in manufacturing, construction, food, and pharmaceutical industries. This ensured quality, efficiency and precision in the concerned industries. Based on this background, it is from this perspective that the paper addresses transformatory work in collaborative robots, automation technologies, and robotic manipulators through object recognition, human-robot interaction, and machine learning applications. Reviewing the key technologies- machine vision, thermography etc. that transformed the quality control across the industries, enhancing the reliability and precision and efficiency of the *** robots and manipulators in conjunction with the da Vinci Surgical System represent huge promise for surgical purposes and therapeutic interventions within medicine. Discuss some of the challenges in the path planning for system adaptability and insist on the need for robust data in medical robotics, besides the key success factors for adopting robotics in every other sector. This research marks how the growing role of robotics, which includes changing industries that are driving the Fourth Industrial Revolution, offset labor shortages, and growing global demands, is being found.

关键词： Industries Mechatronics Service robots Infrared imaging Quality control Robot sensing systems Fourth Industrial Revolution Quality assessment Robots Testing

来源：评论

学校读者我要写书评

暂无评论

Empower Hub: Sustainable Innovative Navigation and Smart Interior Design 3

Empower Hub: Sustainable Innovative Navigation and Smart Int...

引用

3rd International Conference on Sustainable Mobility Applications, Renewables and technology, SMART 2024

作者： Abdalla, Manoon Nasri Al Falasi, Mohammed El-Messiry, Haythem School of Architecture and Interior Design Canadian university Dubai United Arab Emirates School of Engineering Applied science and Technology Canadian university Dubai United Arab Emirates The Department of Computer Science Canadian University Dubai United Arab Emirates

ISBN: (纸本)9798331532925

This paper will conclude the integration between smart interior design and Artificial Intelligence including sustainability within design principles and functions. Presenting "Empower hub" a center that is specifically devoted to people with physical disabilities that concludes AI in the infrastructure. Using sustainable materials and energy-efficient lighting are the objectives. This can include solar power smart systems and adaptive technology, which greatly assist people with various types of impairments, and it is not adequately integrated in many sectors. The design focus is to address the special needs of people with visual, auditory, or mobility impairments, utilize state-of-the-art adaptive technology. Therefore, navigation assistance and motion detection systems are being proposed for the center. The smart prototypes will process data and sensors that will detect movements for smooth access. In addition to proximity sensors that can be vocal to ensure audio assistance. Most of those sensors will be powered by solar energy to ensure the sustainable factor of the project in addition to reducing energy consumption. Overall, the research will conclude the integration of AI and interior design while applying sustainable design principles in interior design with the integration of machine learning to benefit people of determination in order of accessibility and function to allow them to give back to society by giving them easy access to ensure their safety and wellbeing. © 2024 IEEE

关键词： Architectural design

来源：评论

学校读者我要写书评

暂无评论

Assisted diagnosis of breast cancer based on federated learning and improved DenseNet 5

Assisted diagnosis of breast cancer based on federated learn...

引用

5th International Conference on computer Vision and Data Mining, ICCVDM 2024

作者： Huang, Shan Xiang, Hexia Li, Jingjie Sun, Yongkai Duan, Xiaodong College of Computer Science and Engineering Dalian Minzu University Dalian116600 China State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian116600 China

ISBN: (纸本)9781510682863

Breast cancer is one of the common cancers worldwide, and how to identify breast cancer as early as possible plays an important role in its later diagnosis and treatment. However, relying only on data from a single organisation can limit the ability of trained neural networks to meet the performance demands of medical diagnostic services, and centralised learning that collects data from decentralised organisations can lead to privacy leakage. To address the above challenges, this paper proposes an assisted diagnosis model for breast cancer combining federated learning and improved DenseNet. Specifically, in contrast to mainstream centralised learning, this paper introduces federated learning to integrate multiparty data and effectively prevent privacy leakage. Each client sends the local model to the central server for aggregation, and then the global model obtained from the aggregation is sent to each client for training, thus achieving collaborative training of neural network models. In addition, in order to enhance the ability to capture feature information, this paper incorporates a Dynamic Convolution module into the densely connected network to improve the diagnostic accuracy without significantly increasing the size of the network model. The experimental results show that the proposed method has better diagnostic performance in breast cancer diagnosis compared with other methods. © 2024 SPIE.

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish 30

Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets fo...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Lango, Marta Naglik, Borys Lango, Mateusz Naglik, Iwo Poznan University of Technology Faculty of Computing and Telecommunications Poland Adam Mickiewicz University Faculty of Mathematics and Computer Science Poland Charles University Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Czech Republic

ISBN: (纸本)9782493814104

Aspect-Sentiment Triplet Extraction (ASTE) is one of the most challenging and complex tasks in sentiment analysis. It concerns the construction of triplets that contain an aspect, its associated sentiment polarity, and an opinion phrase that serves as a rationale for the assigned polarity. Despite the growing popularity of the task and the many machine learning methods being proposed to address it, the number of datasets for ASTE is very limited. In particular, no dataset is available for any of the Slavic languages. In this paper, we present two new datasets for ASTE containing customer opinions about hotels and purchased products expressed in Polish. We also perform experiments with two ASTE techniques combined with two large language models for Polish to investigate their performance and the difficulty of the assembled datasets. The new datasets are available under a permissive licence and have the same file format as the English datasets, facilitating their use in future research. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

Dp-graph2vec: Learning Differential Privacy Representations of Graphs 7

Dp-graph2vec: Learning Differential Privacy Representations ...

引用

7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Su, Hengdi Sang, Yingpeng Shen, Hong School Of Computer Science And Engineering Sun Yat-sen University Guangzhou China School Of Engineering And Technology Central Queensland University Brisbane Australia Macao Polytechnic University Faculty Of Applied Sciences Macau China

ISBN: (纸本)9798350361445

Many machine learning tasks based on graph embedding achieved significant results in a wide range of fields, such as node clustering and link prediction. However, the original graph data may contain confidential information about individuals or organizations, which could be revealed through the publication of graphs or even via embeddings of graphs. While datasets contain highly private sensitive information, graph embedding methods which do not consider any privacy protection measures become impractical. Researchers recently have explored how differential privacy can be employed to a number of machine learning tasks, but differential privacy embedding methods for graphs have not been considered yet to the best of our knowledge. To address this problem, we propose a differential privacy embedding methods for graphs, named dp-graph2vec, based on gradient perturbation. In dp-graph2vec, we design a training mechanism called Average-Negative Sampling Update(ANSU) to solve the problem of privacy accounting. We proved that the mechanism satisfies the definition of differential privacy. Finally, we demonstrated results on various public datasets. Experiments and theory show that dp-graph2vec can strike a balance between privacy protection and data utility. © 2024 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

IMPRECISE COVERING RING STAR PROBLEM

Decision Making: Applications in Management and Engineering

引用

Decision Making: Applications in Management and Engineering 2023年第1期6卷 303-320页

作者： Mukherjee, Anupam Barma, Partha Sarathi Dutta, Joydeep Das, Sujit Pamucar, Dragan School of Applied Science and Humanities Haldia Institute of Technology Haldia India Center for Distance and Online Education The University of Burdwan Burdwan India Dept. of Computer Science Kazi Nazrul University Asansol India National Institute of Technology Warangal India Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia

In this paper, we formulate and solve an Imprecise Covering Ring Star Problem (ICRSP), a generalization of the Ring Star Problem (RSP). For a given network, the objective of this problem is to find a subset of nodes that forms a cycle, and other nodes are left-out nodes. This problem minimizes the total routing cost due to the cycle itself and assignment costs from the left-out nodes assigned to their nearest concentrators (i.e., nodes on the cycle). No assigned node exceeds a predetermined covering distance from its concentrator. The covering distance and the routing and assignment costs are considered fuzzy in the proposed ICRSP. A Modified Genetic Algorithm (MGA) has been developed and used to solve this model for different confidence levels depending on the corresponding imprecise parameters, reducing it to a deterministic form with fuzzy possibility and necessity approaches. Some comparisons with TSP benchmark problems are presented to justify the algorithm's performance. As individual cases, some practical ICRSPs are also solved and presented numerically. © 2023 by the authors.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

IMAN: An Adaptive Network for Robust NPC Mortality Prediction with Missing Modalities

IMAN: An Adaptive Network for Robust NPC Mortality Predictio...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yejing Huo Guoheng Huang Lianglun Cheng Jianbin He Xuhang Chen Xiaochen Yuan Guo Zhong Chi-Man Pun School of Computer Science and Technology Guangdong University of Technology School of Computer Science and Engineering Huizhou University Department of Computer and Information Science University of Macau Faculty of Applied Sciences Macao Polytechnic University School of Information Science and Technology Guangdong University of Foreign Studies

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex malignancy particularly challenging in advanced stages, is crucial for optimizing treatment strategies and improving patient outcomes. However, this predictive process is often compromised by the high-dimensional and heterogeneous nature of NPC-related data, coupled with the pervasive issue of incomplete multi-modal data, manifesting as missing radiological images or incomplete diagnostic reports. Traditional machine learning approaches suffer significant performance degradation when faced with such incomplete data, as they fail to effectively handle the high-dimensionality and intricate correlations across modalities. Even advanced multi-modal learning techniques like Transformers struggle to maintain robust performance in the presence of missing modalities, as they lack specialized mechanisms to adaptively integrate and align the diverse data types, while also capturing nuanced patterns and contextual relationships within the complex NPC data. To address these problem, we introduce IMAN: an adaptive network for robust NPC mortality prediction with missing modalities. IMAN features three integrated modules: the Dynamic Cross-Modal Calibration (DCMC) module employs adaptive, learnable parameters to scale and align medical images and field data; the Spatial-Contextual Attention Integration (SCAI) module enhances traditional Transformers by incorporating positional information within the self-attention mechanism, improving multi-modal feature integration; and the Context-Aware Feature Acquisition (CAFA) module adjusts convolution kernel positions through learnable offsets, allowing for adaptive feature capture across various scales and orientations in medical image modalities. Extensive experiments on our proprietary NPC dataset demonstrate IMAN’s robustness and high predictive accuracy, even with missing data. Compared to existing methods, IMAN consistently outperforms in scenarios with inc

关键词： Adaptive systems Accuracy Convolution Mortality Transformers Robustness Planning Medical diagnosis Kernel Medical diagnostic imaging

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：