检索结果-内蒙古大学图书馆

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

作者： Zi-Qiang Shen Yu-Yi Tang Jun-Feng Yan Yang Li Guo-Ying Zhao Hunan University of Chinese Medicine China Hunan Digital Chinese Medicine Engineering Technology Research Center China Department of Computer Science and Engineering Center for Machine Vision Research University of Oulu Finland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the emergence of the Transformer architecture, the accuracy of deep learning within the domain of facial emotion recognition has seen further enhancement. However, Transformer comes with increased training complexity and time due to the large parameter count. Additionally, the global receptive field in Transformer's attention leads to unnecessary computations for features with limited spatial extent in image sentiment analysis. In this paper we presents a MSRFormer model, which combines Hybrid-scale self-attention and local fast convolution to address existing issues. The Hybrid-scale self-attention enables precise focus on salient regions of the image, where key features are located. A Local Fast Convolution module was integrated into the MLP head of the Transformer model, enhancing training speed and reducing parameters while maintaining feature learning. we conducted experiments on FERPlus, RAF-DB, and KDEF datasets and confirming its strong performance and broad applicability in facial expression recognition.

关键词： Training Representation learning Sentiment analysis Accuracy Head Convolution Face recognition Speech recognition Transformers Speech processing

来源：评论

学校读者我要写书评

暂无评论

Transfer Route Recommendation for Metro Systems Based on Multi-source Data Fusion 24

Transfer Route Recommendation for Metro Systems Based on Mul...

引用

24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Yuan, Tailan Xiong, Wen Yun, Lijun School of Information Science and Technology Yunnan Normal University Kunming China Engineering Research Center of Computer Vision and Intelligent Control Technology Department of Education of Yunnan Province Kunming China

ISBN: (纸本)9798350319934

As the network-structure complexity and the passenger volume continuously grow, how to accurately perform passenger flow guidance is a crucial issue for metro operators. Transfer route recommendation is a core approach for passenger flow guidance. Existing works are limited in obtaining route attributes (such as transfer walking time and waiting time, etc.). Furthermore, most of existing methods may not work well in the scenario of sudden passenger flow, because of their limited capability to accurately measure the real-time crowding. To address these issues, a data fusion based transfer route recommendation method (DFTRR) is proposed. Specifically, we first propose a spatio-temporal diversity-based method (STD) to measure the trajectory similarity. Based on that, we fuse two kinds of heterogeneous data, i.e., swiping records of smart cards and Wi-Fi signal records of mobile devices to extract a passenger's complete trip. And then we present the historical median travel time to alleviate the computation issue of route attributes. Meanwhile the mean crowding level is introduced to accurately measure the real-time crowding of routes. Finally, a generalized travel cost based on median travel time, number of transfers and mean crowding level is constructed for transfer route recommendation. The experimental results illustrate that the accuracy of STD algorithm is higher than the baseline 34.92% at maximum;DFTRR can offer the optimal transfer route according to passengers' preferences under sudden passenger flow. © 2022 IEEE.

关键词： Smart cards

来源：评论

学校读者我要写书评

暂无评论

Mobilizing Waldo: Evaluating Multimodal AI for Public Mobilization

arXiv

引用

arXiv 2024年

作者： Cebrian, Manuel Holme, Petter Pescetelli, Niccolò Center for Automation and Robotics Spanish National Research Council Spain Department of Computer Science Aalto University Finland The London Interdisciplinary School United Kingdom

Advancements in multimodal Large Language Models (LLMs), such as OpenAI’s GPT-4o, present significant potential for mediating human interactions across various contexts. However, their capabilities in controversial areas, such as persuasion, influence, and recruitment, raise substantial ethical and security concerns. To ethically evaluate these models in situational public influence and persuasion scenarios, we developed a prompting strategy utilizing "Where’s Waldo?" images as proxies for complex, crowded real-world gatherings. This approach provides a controlled and replicable environment to assess the model’s ability to process intricate visual information, comprehend social dynamics, and propose engagement strategies without infringing on individual privacy. By positioning Waldo as a hypothetical agent tasked with face-to-face mobilization, we analyzed the model’s performance in identifying key individuals and formulating mobilization tactics. Our findings reveal that while the model can generate vivid descriptions and creative strategies, it currently lacks the ability to accurately identify individuals or reliably assess social dynamics within these scenarios. Nevertheless, our methodology offers a valuable framework for testing and benchmarking the evolving capabilities of multimodal LLMs in social contexts. © 2024, CC BY.

关键词： Ability testing

来源：评论

学校读者我要写书评

暂无评论

Blockchain Technology and Artificial Intelligence's Future Applications in Cyber Security 3

Blockchain Technology and Artificial Intelligence's Future A...

引用

3rd International Conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Gupta, Richa Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Raj Kumar Goel Institute of Technology Department of Electronics & Communication Engineering India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

Artificial intelligence and blockchain are quickly integrating in daily life and business applications. When numerous information systems must access and analyze data in real-time in centralized systems and applications, such as healthcare, a bottleneck develops. This issue would be resolved by blockchain's decentralized database architecture, safe data storage, data exchange, and authentication. Additionally, AI can be present at the very top of the blockchain and produce insights from the shared data that is created and applied to forecasts. Blockchain is a cutting-edge Cybersecurity technology that creates chains by mutual consent between nodes that chronologically link new blocks to those previously stored in nodes. A number of industries, including banking, insurance, cybersecurity, forecasting, healthcare, and cryptocurrencies, among others, are growing more quickly as a result of technology convergence. The likelihood of these systems being hacked increases as more digital technologies are used and these businesses provide services. Blockchain technology and artificial intelligence can work together to create a strong defense against these dangers and security problems. We'll examine how blockchain and AI are merged in cybersecurity in this chapter. We'll go into more detail about how they help secure cyber-physical systems. © 2023 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Terahertz Communications and Sensing Overview 3

Terahertz Communications and Sensing Overview

引用

3rd International Conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Salama, Ramiz Altrjman, Chadi Al-Turjman, Fadi Yadav, Satya Prakash Vats, Satvik Gupta, Richa Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Raj Kumar Goel Institute of Technology Department of Electronics & Communication Engineering India

ISBN: (纸本)9798350330724

Terahertz (THz) (0.1-10 THz) wireless communication is one of the cornerstones of the next 6G wireless networks. THz frequencies have the ability to dramatically increase wireless capacity performance and enable high-resolution environment sensing if applied properly due to the enormous quantity of accessible bandwidth. However, the usage of wireless devices in high-frequency bands like THz is constrained by a very unpredictable and dynamic channel. The ultimate result is essentially unreliable intermittent connections since these channel constraints have a naturally restricted communication range and a high susceptibility to blocking and chemical absorption. Because of this, the THz band's potential for high-rate communications and high-resolution sensing may be hindered. This study thoroughly examines the steps necessary to build up and operate next-generation THz wireless networks that will work together to deliver a variety of communication and sensing services in this environment. We first lay the groundwork for this by defining the THz frequency range's fundamentals. Using these fundamentals as a foundation, we outline and carefully investigate seven specific qualities that characterize THz wireless systems: Some of the subjects discussed include the quasi-opticality of the band, wireless architectures suited for THz, synergy with lower frequency bands, cooperative sensing and communication systems, PHY-layer protocols, spectrum access techniques, and real-time network optimization. These seven distinctive features enable our understanding of how to re-engineer wireless systems as we know them today to fit THz bands and their specific settings. On the one hand, THz systems make use of its quasi-optimality and may turn any sensing opportunity into a communication problem, aiding in the development of a new breed of flexible wireless systems that can do many jobs beyond straightforward communications. THz systems can alternatively use intelligent surfaces, lower

关键词： Internet of Everything

来源：评论

学校读者我要写书评

暂无评论

CoBaIR: A Python Library for Context-Based Intention Recognition in Human-Robot-Interaction

CoBaIR: A Python Library for Context-Based Intention Recogni...

引用

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Adrian Lubitz Lisa Gutzeit Frank Kirchner Department of Computer Science Robotics Research Group University of Bremen Bremen Germany German Research Center for Artificial Intelligence Bremen Germany

Human-Robot Interaction (HRI) becomes more and more important in a world where robots integrate fast in all aspects of our lives but HRI applications depend massively on the utilized robotic system as well as the deployment environment and cultural differences. Because of these variable dependencies it is often not feasible to use a data-driven approach to train a model for human intent recognition. Expert systems have been proven to close this gap very efficiently. Furthermore, it is important to support understandability in HRI systems to establish trust in the system. To address the above-mentioned challenges in HRI we present an adaptable python library in which current state-of-the-art Models for context recognition can be integrated. For Context-Based Intention Recognition a two-layer Bayesian Network (BN) is used. The bayesian approach offers explainability and clarity in the creation of scenarios and is easily extendable with more modalities. Additionally, it can be used as an expert system if no data is available but can as well be fine-tuned when data becomes available

关键词：

来源：评论

学校读者我要写书评

暂无评论

Attendance System via Internet of Things, Blockchain and Artificial Intelligence Technology: Literature Review 37th

Attendance System via Internet of Things, Blockchain and Art...

引用

37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Abidemi, Sarumi Usman Mubarak, Auwalu Saleh Akanni, Olukayode Ameen, Zubaida Said Cacciagrano, Diletta Al-turjman, Fadi Artifiical Intelligence Engineering Department AI and Robotics Institue Near East University Mersin 10 Nicosia Turkey Computer Science Division School of Science and Technology Camerino University Camerino62032 Italy Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Kyrenia Turkey

ISBN: (纸本)9783031286933

Many researchers have make efforts on creating several attendance systems to keep track of student attendance in school which is also part of academic curriculum that will have great impact in student academic performance. There have been different technologies such as RFID, face recognition, biometric, web-based attendance system etc. To capture attendance in a way that’s better than the traditional method of pen and paper or roll call system. Making this attendance record available on blockchain network and applying artificial intelligence algorithm for prediction or classification in correlation with the data captured from student attendance purpose. Several articles, papers and journals has been reviewed from different research plethora of research publishers’ database through internet such as Google scholar, IEEExplore, Springerslink etc. This will make it easy for comparing several attendance systems with respect to internet of things, blockchain and artificial intelligence to address the research gap in this aspect. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

IL-NeRF: Incremental Learning for Neural Radiance Fields with Camera Pose Alignment

arXiv

引用

arXiv 2023年

作者： Zhang, Letian Li, Ming Chen, Chen Xu, Jie Department of Computer Science Middle Tennessee State University United States Center for Research in Computer Vision University of Central Florida United States Department of Electrical and Computer Engineering University of Miami United States

Neural radiance fields (NeRF) is a promising approach for generating photorealistic images and representing complex scenes. However, when processing data sequentially, it can suffer from catastrophic forgetting, where previous data is easily forgotten after training with new data. Existing incremental learning methods using knowledge distillation assume that continuous data chunks contain both 2D images and corresponding camera pose parameters, pre-estimated from the complete dataset. This poses a paradox as the necessary camera pose must be estimated from the entire dataset, even though the data arrives sequentially and future chunks are inaccessible. In contrast, we focus on a practical scenario where camera poses are unknown. We propose IL-NeRF, a novel framework for incremental NeRF training, to address this challenge. IL-NeRF’s key idea lies in selecting a set of past camera poses as references to initialize and align the camera poses of incoming image data. This is followed by a joint optimization of camera poses and replay-based NeRF distillation. Our experiments on real-world indoor and outdoor scenes show that IL-NeRF handles incremental NeRF training and outperforms the baselines by up to 54.04% in rendering quality. The project page is https://***/. © 2023, CC BY.

关键词： Cameras

来源：评论

学校读者我要写书评

暂无评论

Neural P3M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs 38

Neural P3M: A Long-Range Interaction Modeling Enhancer for G...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Yusong Cheng, Chaoran Li, Shaoning Ren, Yuxuan Shao, Bin Liu, Ge Heng, Pheng-Ann Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China University of Illinois Urbana-Champaign United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong University of Science and Technology of China China Microsoft Research AI4Science United States

Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range interactions in large molecular systems due to the localization assumption of GNN. To address this challenge, we introduce Neural P3M, a versatile enhancer of geometric GNNs to expand the scope of their capabilities by incorporating mesh points alongside atoms and reimaging traditional mathematical operations in a trainable manner. Neural P3M exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces, outperforming on benchmarks such as the MD22 dataset. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures. Codes are available at https://***/OnlyLoveKFC/Neural_P3M. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model

arXiv

引用

arXiv 2023年

作者： Iqbal, Hasan Khalid, Umar Chen, Chen Hua, Jing Department of Computer Science Wayne State University DetroitMI United States Center for Research in Computer Vision University of Central Florida OrlandoFL United States

It can be challenging to identify brain MRI anomalies using supervised deep-learning techniques due to anatomical heterogeneity and the requirement for pixel-level labeling. Unsupervised anomaly detection approaches provide an alternative solution by relying only on sample-level labels of healthy brains to generate a desired representation to identify abnormalities at the pixel level. Although, generative models are crucial for generating such anatomically consistent representations of healthy brains, accurately generating the intricate anatomy of the human brain remains a challenge. In this study, we present a method called the masked-denoising diffusion probabilistic model (mDDPM), which introduces masking-based regularization to reframe the generation task of diffusion models. Specifically, we introduce Masked Image Modeling (MIM) and Masked Frequency Modeling (MFM) in our self-supervised approach that enables models to learn visual representations from unlabeled data. To the best of our knowledge, this is the first attempt to apply MFM in denoising diffusion probabilistic models (DDPMs) for medical applications. We evaluate our approach on datasets containing tumors and numerous sclerosis lesions and exhibit the superior performance of our unsupervised method as compared to the existing fully/weakly supervised baselines. Project website: https://mddpm. ***/. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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：