检索结果-内蒙古大学图书馆

4th International Conference on computing and Communications Technologies, ICCCT 2021

作者： Karthikeyan, B. Gowri, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Information Technology Chennai India

ISBN: (纸本)9781665414470

After a rapid spread of Coronavirus (COVID-19) in Wuhan-China in December 2019, the World Health Organization (WHO) confirmed that this was a dangerous virus that could spread from person to person through droplets and airborne contaminants. To prevent the spread of the Covid19, people should wear a mask during the epidemic. During this pandemic, it is becoming increasingly difficult to keep track of human beings the one who wears a mask as a usual practice or not. It will not solely depend on human efforts to keep track the whole world so there is a need to build software that automatically detects whether people in public places wearing a mask or not. Many new models are developed utilizing convolutional Neural Network to build a model as accurately as possible. The method proposed in this paper uses the ResNet model to obtain multiple faces with a single (SSD-Single Shot Multibox Detector) image using a network (model) and MobileNetV2 Architecture used as face mask detectors. This proposed model has 99% more accuracy than most other face recognition models. This mask detector model uses a dataset of hidden morphed masked images to obtain more accurate model. This system should be used in Real-Time applications which require face mask discovery for protection purpose due to the sudden happening of Covid-19. © 2021 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Mesh Watermark Removal Attack and Mitigation: A Novel Perspective of Function Space

arXiv

引用

arXiv 2023年

作者： Zhu, Xingyu Ye, Guanhui Dong, Chengdong Luo, Xiapu Zhang, Shiyao Wei, Xuetao Department of Computer Science and Engineering Southern University of Science and Technology China Department of Computing Hong Kong Polytechnic University Hong Kong

Mesh watermark embeds secret messages in 3D meshes and decodes the message from watermarked meshes for ownership verification. Current watermarking methods directly hide secret messages in vertex and face sets of meshes. However, mesh is a discrete representation that uses vertex and face sets to describe a continuous signal, which can be discretized in other discrete representations with different vertex and face sets. This raises the question of whether the watermark can still be verified on the different discrete representations of the watermarked mesh. We conduct this research in an attack-then-defense manner by proposing a novel function space mesh watermark removal attack FU N CEV A D E and then mitigating it through function space mesh watermarking FU N CMA R K. In detail, FU N CEV A D E generates a different discrete representation of a watermarked mesh by extracting it from the signed distance function of the watermarked mesh. We observe that the generated mesh can evade ALL previous watermarking methods. FU N CMA R K mitigates FU N CEV A D E by watermarking signed distance function through message-guided deformation. Such deformation can survive isosurfacing and thus be inherited by the extracted meshes for further watermark decoding. Extensive experiments demonstrate that FU N CEV A D E achieves 100% evasion rate among all previous watermarking methods while achieving only 0.3% evasion rate on FU N CMA R K. Besides, our FU N CMA R K performs similarly on other metrics compared to state-of-the-art mesh watermarking methods. © 2023, CC BY.

关键词： Mesh generation

来源：评论

学校读者我要写书评

暂无评论

Retraction Note: Optimizing radio access in 5G vehicle networks using novel machine learning-driven resource management

引用

Optical and Quantum Electronics 2024年第10期56卷 1-2页

作者： Natarajan, Rajesh Mahadev, Natesh Alfurhood, Badria Sulaiman Ranjith, Christodoss Prasanna Zaki, John Manu, M. N. Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Shinas Oman Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysore India Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Computer and Systems Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Information Science and Engineering SJBIT Bengaluru India

来源：评论

学校读者我要写书评

暂无评论

RAHP: A Redundancy-aware Accelerator for High-performance Hypergraph Neural Network

RAHP: A Redundancy-aware Accelerator for High-performance Hy...

引用

IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Hui Yu Yu Zhang Ligang He Yingqi Zhao Xintao Li Ruida Xin Jin Zhao Xiaofei Liao Haikun Liu Bingsheng He Hai Jin National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Warwick United Kingdom National University of Singapore Singapore

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Hypergraph Neural Network (HyperGNN) has emerged as a potent methodology for dissecting intricate multilateral connections among various entities. Current software/hardware solutions leverage a sequential execution model that relies on hyperedge and vertex indices for conducting standard matrix operations for HyperGNN inference. Yet, they are impeded by the dual challenges of redundant computation and irregular memory access overheads. This is primarily due to the frequent and repetitive access and updating of a number of feature vectors corresponding to the same hyperedges and vertices. To address these challenges, we propose the first redundancy-aware accelerator, RAHP, which enables high performance execution of HyperGNN inference. Specifically, we present a redundancy-aware asynchronous execution approach into the accelerator design for HyperGNN to reduce redundant computations and off-chip memory accesses. To unveil opportunities for data reuse and unlock the parallelism that existing HyperGNN solutions fail to capture, it prioritizes vertices with the highest degree as roots, prefetching other vertices along the hypergraph structure to capture the common vertices among multiple hyperedges, and synchronizing the computations of hyperedges and vertices in real-time. By such means, this facilitates the concurrent processing of relevant hyperedge and vertex computations of the common vertices along the hypergraph topology, resulting in smaller redundant computations overhead. Furthermore, by efficiently caching intermediate results of the common vertices, it curtails memory traffic and off-chip communications. To fully harness the performance potential of our proposed approach in the accelerator, RAHP incorporates a topology-driven data loading mechanism to minimize off-chip memory accesses on the fly. It is also endowed with an adaptive data synchronization scheme to mitigate the effects of conflicting updates of both hyperedges and vertices. Moreover, RAHP emplo

关键词： Computational modeling Neural networks Memory management Loading Energy conservation Vectors Software Topology Synchronization Standards

来源：评论

学校读者我要写书评

暂无评论

Secure Image Steganography Technique based on Hybrid Transforms and Enhanced AES

Secure Image Steganography Technique based on Hybrid Transfo...

引用

2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Shaik, Syed Basha Devi, D. Sudha Sujithra, M. Sarika, G. Tripathi, Vikas Kapila, Dhiraj K L University Department of Computer Science and Engineering Vijayawada India Coimbatore Institute of Technology Department of Computing-Data Science Coimbatore India Baselios Mathews II College of Engineering Department of Electronics and Communication Engineering Kerala Sasthamkotta India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Lovely Professional University Department of Computer Science and Engineering Phagwara India

ISBN: (纸本)9781665489621

This, work uses a hybrid technique that combines the Modified Advanced Encryption Standard (MAES) and Elliptic Curve Cryptography to encrypt medical images (ECC). This article describes a modified AES for image encryption. For the global cryptosystem, a co-design approach is employed, with MAES processing on the NIOS II processor and hardware-accelerated scalar ECC multiplication. The suggested cryptographic system executes in a significantly shorter amount of time, which is a critical consideration for real implementation. This security assessment was effective, and our trials reveal that our proposed technique simplifies and corrects cryptography's essentials. In actuality, the results of the evaluation reveal that the proposed method is efficient, rapid, and secure. © 2022 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Leveraging SDN: Empirical Evaluation of Privacy Enriched Data Communication over VANET Using Software Defined Network Assistance

Leveraging SDN: Empirical Evaluation of Privacy Enriched Dat...

引用

International Conference on Science technology engineering and Management (ICONSTEM)

作者： R. Ratheesh S. Vijayalakshmi B. Arunsundar M. Swarna G. Saranya Department of ECE Agni College of Technology Chennai Department of ECE R.M.K. Engineering College Chennai Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of ECE Dr. M.G.R. Educational and Research Institute University Chennai Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In the realm of modern transportation systems, Vehicular Ad Hoc Networks (VANETs) play a pivotal role in enabling efficient communication among vehicles and infrastructure elements for enhanced road safety and traffic management. However, the inherent vulnerabilities in VANETs, such as privacy breaches and security threats, necessitate robust frameworks to mitigate risks and ensure secure data transmission. This paper presents a comprehensive architecture for Privacy-Enriched Data Communication over VANETs using Software-Defined Network (SDN) assistance. The proposed framework integrates key components including vehicles, Roadside Units (RSUs), and SDN controllers to facilitate secure and privacy-aware communication. Vehicles act as data sources, employing pseudonyms and encryption mechanisms to protect user privacy. RSUs serve as access points, collecting and relaying data while enforcing access control policies. SDN controllers provide centralized management, dynamically allocating resources and enforcing privacy policies through encryption, pseudonymization, and access control mechanisms. Communication protocols such as IEEE 802.11p and LTE enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, while the OpenFlow protocol facilitates communication between SDN controllers and network devices. Additionally, trust management mechanisms evaluate the trustworthiness of network entities based on behavioral analysis, reliability metrics, and security considerations, ensuring the integrity and resilience of the network. By amalgamating SDN-based management with privacy enhancement mechanisms and trust management protocols, the proposed framework offers a robust solution for addressing privacy and security challenges in VANETs, thereby fostering safer and more secure transportation ecosystems.

关键词： Access control Data privacy Protocols Vehicle-to-infrastructure Vehicular ad hoc networks Transportation Encryption

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Framework for Multi-Service Multi-Modal Devices in NextG ORAN Systems

arXiv

引用

arXiv 2025年

作者： Gain, Mrityunjoy Kim, Kitae Raha, Avi Deb Adhikary, Apurba Saad, Walid Han, Zhu Hong, Choong Seon Department of Artificial Intelligence School of Computing Kyung Hee University Yongin17104 Korea Republic of Department of Computer Science and Engineering School of Computing Kyung Hee University Yongin17104 Korea Republic of Department of Computer Science and Engineering School of Computing Kyung Hee University Yongin-si17104 Korea Republic of Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States Electrical and Computer Engineering Department University of Houston HoustonTX77004 United States

Next-generation (NextG) networks will adopt cloud-based, disaggregated designs with open interfaces, thereby enabling tailored data-driven control. The open radio access network (ORAN) paradigm enhances RAN optimization by enabling intelligent and flexible network management. The ORAN Alliance standardizes open and virtualized architectures, supporting services such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (uRLLC), and massive machine-type communication (mMTC). Traditionally, eMBB, uRLLC, and mMTC services are tied to specific use equipment (UE) optimized for one service. NextG networks, however, aim to enable multiple services on a single device, crucial for applications like the metaverse. To fill this, in this paper, an artificial intelligence (AI)driven efficient RAN management framework is proposed. This framework introduces the concept of the multi-service-modal UE (MSMU) system, which allows a single UE to handle both eMBB and uRLLC services. The proposed framework integrates traffic demand prediction, route optimization, RAN slicing, service identification, and radio resource management under uncertainty. The challenge of dynamic environments in such a system is addressed by decomposing the optimization problem into long-term (L-SP) and short-term (S-SP) subproblems. Using a long short-term memory (LSTM) model, the proposed approach allows the prediction of eMBB and uRLLC traffic demands and optimal routes for RAN slicing in the L-SP. For the S-SP, another LSTM model is employed to handle real-time service type identification and resource management based on long-term predictions. To support continuous adaptation, continual learning is incorporated into the S-SP framework, allowing the model to learn new service types while retaining prior knowledge. Experimental results show that the proposed framework efficiently manages dual-mode UEs, achieving low mean square error for traffic demand (0.003), resource block prediction

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Milk Grade Detection and Analysis of Quality Factors for Dairy Farm Management

Milk Grade Detection and Analysis of Quality Factors for Dai...

引用

Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Anish Gogna Abhishek Rajpal B. Yamini Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science & Technology (SRMIST) Chengalpattu District Tamil Nadu India

Milk is a very important drink that is required by individuals of all the ages. Milk is the source of various vitamins and minerals for our body as well as a good source of protein. However, it is equally important to make good quality of milk to be available to the consumer. Milk produced in dairies often carry various disease causing germs due to improper care of the cattle. So, milk quality check is performed. However it is not possible for a human being to go through the large volume of numerical data. So we have to design an intelligent system that is capable of scanning through large volumes of the milk data and extract the parameters. It must also be able to predict with very high accuracy the presence of any kind of anomalies or substances that can harm the quality of the milk that is sold and at the same time help in the E-commerce aspect by farming management with milk testing labs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Internet of Medical Things: A CT Image Denoising in Tetrolet Domain 5

Internet of Medical Things: A CT Image Denoising in Tetrolet...

引用

5th International Conference on Information Systems and Computer Networks, ISCON 2021

作者： Diwakar, Manoj Pandey, Richa Sharma, Ritik Saun, Sapna Singh, Prabhishek Pandey, Neeraj Kumar Graphic Era Deemed to Be University Department of Computer Science Engineering Uttarakhand Dehradun India Graphic Era Hill University Campus-Haldwani Department of Computer Science Engineering Uttarakhand India Amity University Uttar Pradesh Amity School of Engineering Technology Department of Computer Science Engineering Noida India DIT University School of Computing Dehradun India

ISBN: (纸本)9781665403412

The Internet of Medical Things (IoMT) is a huge community of linked medical equipment and technologies that communicate with a large number of Servers over the internet to provide a variety of services such as medical image denoising. Projection-based reconstructed images in computed tomography (CT) are noisy due to thermal noise and electrical noise, both of which are approximately AWGN in magnitude. Because of the variety of clinical features and textures included in CT reconstructed images, denoising is a difficult process. It is possible to estimate the true noise of a single image, and then to minimize both the real noise and the additional noise by reconstructing CT images. A unique modified approach is proposed in this work, in which the noise will be calculated actual as well as added, followed by a proposed method for denoising, which is based on thresholding in tetrolet domain. Different denoising schemes are used in the experiments, and the results are compared to one another. Experimentation has revealed that both in terms of PSNR and visual quality, the suggested technique yields promising results that are favorable to both. © 2021 IEEE.

关键词： Image denoising

来源：评论

学校读者我要写书评

暂无评论

Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

引用

Computers, Materials & Continua 2022年第12期73卷 4641-4656页

作者： Mubashar Hussain Toqir A.Rana Aksam Iftikhar M.Usman Ashraf Muhammad Waseem Iqbal Ahmed Alshaflut Abdullah Alourani School of Systems and Technology University of Management and TechnologyLahore54770Pakistan Department of Computer Science and It The University of LahoreLahore54000Pakistan School of Computer Sciences Universiti Sains Malaysia(USM)Penang11800Malaysia Department of Computer Science Comsats University IslamabadLahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Software Engineering The Superior UniversityLahore53700Pakistan Faculty of Computing and It Albaha UniversityAlbahaSaudi Arabia Department of Computer Science Majmaah UniversitySaudi Arabia

In the field of sentiment analysis,extracting aspects or opinion targets fromuser reviews about a product is a key *** the polarity of an opinion is much more useful if we also know the targeted Aspect or *** based approaches,like dependency-based rules,are quite popular and effective for this ***,they are heavily dependent on the authenticity of the employed parts-of-speech(POS)tagger and dependency *** popular rule based approach is to use sequential rules,wherein the rules formulated by learning from the user’s ***,in general,the sequential rule-based approaches have poor generalization ***,existing approaches mostly consider an aspect as a noun or noun phrase,so these approaches are unable to extract verb *** this article,we have proposed a multi-layered rule-based(ML-RB)technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun ***,after rigorous analysis,we have also constructed rules for the extraction of verb *** verb rules primarily based on the association between verb and opinion *** proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review *** F1 score for both the datasets are 0.90 and 0.88,respectively,which are better than existing *** improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.

关键词： Explicit aspect aspect extraction opinion mining rule-based verb aspects

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：