检索结果-内蒙古大学图书馆

27th International Conference on Pattern Recognition, ICPR 2024

作者： Mishra, Aakansha Anand, Ashish Guha, Prithwijit Department of Computer Science and Engineering IIT Guwahati Guwahati India Department of Electrical Engineering IIT Guwahati Guwahati India

ISBN: (纸本)9783031784941

Recent advancements in Visual Question Answering (VQA) have been driven by the integration of complex attention mechanisms. This work introduces a novel approach aimed at enhancing multi-modal representations through dense interactions between visual and textual inputs in an alternating fashion. The proposed model features an attention framework that incorporates both self-attention and co-attention mechanisms, strategically applied to image and text modalities. Self-attention modules capture contextual dependencies among objects in images and words in questions, crucial for accurate inference of answers. Meanwhile, co-attention mechanisms facilitate effective cross-modal interactions between images and text. To extract fine-grained information from both modalities, we introduce a Cascade of Self- and Co-Attention blocks (CSCA). This architecture is evaluated extensively on prominent benchmarks including VQA2.0, TDIUC, and GQA datasets. Experimental results, including comprehensive ablation studies, highlight the effectiveness of the model’s key components and the cascading nature of attention mechanisms in enhancing performance across diverse VQA tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Question answering

来源：评论

学校读者我要写书评

暂无评论

A Data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems

引用

CSEE Journal of Power and Energy Systems 2022年第3期8卷 789-800页

作者： Hongjun Gao Zhenyu Liu Youbo Liu Lingfeng Wang Junyong Liu College of Electrical Engineering Sichuan UniversityChengdu 610065China Department of Electrical Engineering and Computer Science University of Wisconsin-MilwaukeeMilwaukeeWI 53211USA

A multi-energy conversion can effectively increase the utilization of renewable energy in the urban integrated energy system(UIES).Meanwhile,the uncertainties of renewable energy resources(e.g.,wind energy)also bring increased challenges to the operation of *** this study,a typical two-stage datadriven distributionally robust operation(DDRO)model based on finite scenarios is proposed for UIES including power,gas and heat networks to obtain a salient strategy from both an economic and robustness *** the first stage,the forecasted information for wind power is especially included to improve the economic aspect of robust *** worst probability distribution for the selected known real-time wind power scenarios can be identified in the second stage where the power differences caused by the real-time uncertainties of wind power can be mitigated by flexible regulation of energy purchasing and coupling units(such as gas turbine,power to gas equipment,electric boiler and gas boiler).Moreover,norm-1 and norm-inf co-constraints are utilized to construct a confidence set for the probability distributions of uncertain wind *** whole two-stage model is solved by the column-and-constraint generation(CCG)***,case studies are conducted to show the performance of the proposed model and various *** Terms-Data-driven methods,distributionally robust optimization,urban integrated energy system,wind power.

关键词： Data-driven methods distributionally robust optimization urban integrated energy system wind power

来源：评论

学校读者我要写书评

暂无评论

A Conditional GAN and Dual-Channel Hybrid Deep Feature Framework for Robust Sensor Fault Detection in WSNs

A Conditional GAN and Dual-Channel Hybrid Deep Feature Frame...

引用

2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Khan, Rehan Saeed, Umer Koo, Insoo University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of School of Computer Science and Technology University of Bedfordshire Luton United Kingdom

ISBN: (纸本)9798331510756

Sensor-generated data is vital to the operation of numerous systems and services in the rapidly growing field of the Internet of Things. Wireless Sensor Networks, as an essential setup for these systems, are frequently deployed in large, diverse, and often harsh environments. However, these networks are highly vulnerable to various faults, potentially leading to improper data transmission, reliability, and financial stability of the systems. To address these challenges, we propose a hybrid model for sensor fault detection that integrates a machine learning classifier with the deep learning (DL) model, specifically VGG-16 and ResNet-50. Synthetic samples are generated using a Conditional Generative Adversarial Network and common sensor faults, such as hardover, drift, spike, erratic, and stuck fault are introduced by leveraging a publicly available temperature sensor dataset. Time-series data is transformed into Gramian Angular Field images, from which deep features are extracted using VGG-16 and ResNet-50. These extracted features are then fused to form a hybrid feature pool. Our framework effectively addresses problems related to data imbalance and enhances accuracy. The proposed model outperforms the individual feature sets, VGG-16 (89.22%) and ResNet-50 (84.21%), achieving notable accuracy of 92.55% with the fused feature set, underscoring its potential for robust sensor fault detection. © 2025 IEEE.

关键词： Gallium nitride

来源：评论

学校读者我要写书评

暂无评论

Exploring Deep Learning Architectures for Enhanced Cyber Threat Detection: A Survey

Exploring Deep Learning Architectures for Enhanced Cyber Thr...

引用

2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Oyinloye, Toluwase Sunday Arowolo, Micheal Olaolu Ogundoku, Roseline Oluwaseun Prasad, Rajesh African University of Science and Technology Department of Computer Science Abuja Nigeria University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO65201 United States Landmark University Department of Computer Science Omu-Aran Nigeria

ISBN: (纸本)9798350358155

As new organizational innovations and ideal model implementations take place;current methods of interruption detection and protection are becoming outdated. Due to the next flexible innovation, which will transmit incredibly rapid data volumes, new problems will arise for network safety frameworks. Apps on mobile devices are gradually displacing more conventional methods of managing data and information that depend on the web. As a result, there are several threats that could compromise or steal these data and information. A complete system shutdown is possible in the event of one of these attacks. This study introduces a deep learning-based cyber-attack detection system for wireless sensor networks (WSN). This method considers both the actions of the WSN nodes, and the data transmissions made possible by the MQTT protocol. By switching from a traditional machine learning model to a deep learning one, this strategy achieves much better detection accuracy. Using deep learning models that combine real information gathered from network streams, cyberthreats can be identified as digital risks in the network. © 2024 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Automated Visualization of NOTAMs with Natural Language Processing

Automated Visualization of NOTAMs with Natural Language Proc...

引用

AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Salyer, Owen G. Phelps, Brady Mourning, Chad Russ College of Engineering and Technology Ohio University School of Electrical Engineering and Computer Science AthensOH United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

NOTAMs (Notices to Air Missions) are crucial in aviation, providing essential information about flight operations such as airport conditions, airspace restrictions, and navigational aids;however, their complex format poses challenges for aviation professionals and laypersons alike. This complexity necessitates advanced technologies for automated information extraction and visualization of the data contained in NOTAMs. Natural language processing (NLP), particularly, named-entity recognition (NER), offers a promising solution by automating the identification and classification of critical information within these notices. This work focuses on employing NER to extract relevant entities from NOTAMs. We then use these extracted entities and a data visualization engine to create 3D representations of the extracted NOTAMs for specific regions. Building on our previous success with Obstruction class NOTAMs, we apply NER to multiple NOTAM classes in order to visualize them in 3D space, with the final goal of creating an automated system which allows for visualization of NOTAMs on a particular flight path or near a queried location. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Exploring Genetic Programming in Image Processing: Challenges and Enhanced Techniques 19

Exploring Genetic Programming in Image Processing: Challenge...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Chang, Feng-Cheng Huang, Hsiang-Cheh Department of Computer Science and Information Engineering Tamkang University Taiwan Department of Electrical Engineering National University of Kaohsiung Taiwan

ISBN: (纸本)9798331507817

In recent years, the advancement of AI has been primarily driven by neural networks, which, despite their success, pose challenges in terms of explainability and high-power consumption. Genetic Programming (GP) offers an interpretable alternative, although its complexity has limited its practical application. This paper explores the potential of GP through experiments on a simple image filtering task, aiming to understand its properties and limitations. We also investigate the integration of transformer concepts into the GP process. Preliminary results suggest that while GP faces convergence challenges, the introduction of symbolic transformers may enhance its effectiveness in image processing tasks. These findings open up new possibilities for optimizing GP in future applications. © 2025 IEEE.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

Project or Factorize? A Case Study of Multiview CCA and PARAFAC2 Tensor Factorization 58

Project or Factorize? A Case Study of Multiview CCA and PARA...

引用

58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Chen, Jia Papalexakis, Evangelos E. University of California Dept. of Electrical and Computer Engineering Riverside United States Uuniversity of Californiav Dept. of Computer Science and Engineering Riverside United States

ISBN: (纸本)9798350354058

Consider learning the shared representations from multiple unlabeled views. Previous work either projects different views to the same space while enforcing the agreement among the projected views such as multiview canonical correlation analysis (MCCA), or factorizes different views while ensuring the common latent components across the views such as PARAFAC2, a tensor decomposition method. In this paper we first investigate a fundamental question: 'Do these two approaches learn different representations?' Preliminary numerical results suggest that in practice they do, which, in turn, begs the question of how we can leverage this observation in order to compute a superior representation. In this paper, we present a simple proof-of-concept scheme which augments MCCA with PARAFAC2 representations and vice-versa, and we demonstrate on multiple real datasets that such scheme can improve upon the baseline representation, paving the way for future research on optimally combining the strengths of projection and factorization methods for multiview representation learning. © 2024 IEEE.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

iStream: A Flexible Container-Based Testbed for Multimedia Streaming 6

iStream: A Flexible Container-Based Testbed for Multimedia S...

引用

6th IEEE International Conference on Multimedia Information Processing and Retrieval, MIPR 2023

作者： Akbari, Navid Majdabadi, Reza Hedayati Ansari, Akram Wang, Mea Krishnamurthy, Diwakar University of Calgary Department of Computer Science Canada University of Calgary Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350307818

Multimedia streaming is growing at a phenomenal rate, especially with the emergence of augmented/virtual reality applications and the growing demand for YouTube-like services. This stimulates research in all aspects of streaming systems, including but not limited to real-time and resource-efficient transcoding, server efficiency, network optimization, and client adaptation. Research and development of any part of the system require repetitive and tedious tasks in setting up the rest of the system for validation and evaluation of new ideas. This not only complicates the research work but also challenges the reproducibility and sharing of the research results. Existing proposed testbeds all have a specific focus and do not address all components of multimedia streaming. In this paper, we propose iStream, a flexible testbed for multimedia streaming system that integrates various components relevant to such systems. The components are plug-&-playable and customizable to compose various streaming systems and setups. iStream provides multimedia system researchers and developers with a flexible playground to explore their innovative ideas. Along with the design, we present a diverse set of case studies demonstrating the wide-range use of iStream. © 2023 IEEE.

关键词： Testbeds

来源：评论

学校读者我要写书评

暂无评论

A Neural Computing-Based Access Control Protocol for AI-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

引用

IEEE Transactions on Consumer Electronics 2024年第1期70卷 3573-3581页

作者： Mahmood, Khalid Tariq, Tayyaba Sangaiah, Arun Kumar Ghaffar, Zahid Saleem, Muhammad Asad Shamshad, Salman National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology International Graduate School of AI Douliu64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China The University of Lahore Department of Software Engineering Lahore54590 Pakistan

The continuous revolution in Artificial Intelligence (AI) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In practice, autonomous and intelligent robots and flying vehicles (i.e., drones) are also becoming the integral part of Industry 5.0. These vehicles are attaining significant importance nowadays because of their applications in consumer electronics. However, there may be numerous privacy issues in such an infrastructure. Nevertheless, neural computing-driven security solutions have played a significant role in overcoming the security and privacy issues of AI-driven intelligent flying vehicles. Motivated by these facts, this article presents a neural computing-based access control protocol for AI-driven flying vehicles to prevent all possible security threats. A detailed security evaluation of the devised protocol is done formally using the extensively recognized Real-Or-Random (ROR) oracle model. Similarly, the informal security analysis ensures the resistance of our scheme against various potential attacks under the assumptions of CK and DY adversarial models. Moreover, detailed performance scrutiny of the devised protocol with various existing schemes shows the robustness and supremacy of our scheme in terms of computation, communication overhead, and security attributes. © 1975-2011 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Blockchain based federated learning for intrusion detection for Internet of Things

引用

Frontiers of computer science 2024年第5期18卷 109-119页

作者： Nan SUN Wei WANG Yongxin TONG Kexin LIU School of Cyber Science and Technology Beihang UniversityBeijing 100191China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100094China School of Computer Science and Engineering Beihang UniversityBeijing 100191China

In Internet of Things(loT),data sharing among different devices can improve manufacture efficiency and reduce workload,and yet make the network systems be more vulnerable to various intrusion *** has been realistic demand to develop an efficient intrusion detection algorithm for connected *** of existing intrusion detection methods are trained in a centralized manner and are incapable to identify new unlabeled attack *** this paper,a distributed federated intrusion detection method is proposed,utilizing the information contained in the labeled data as the prior knowledge to discover new unlabeled attack ***,the blockchain technique is introduced in the federated learning process for the consensus of the entire *** results are provided to show that our approach can identify the malicious entities,while outperforming the existing methods in discovering new intrusion attack types.

关键词： intrusion detection federated learning new attacks discovering blockchain

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：