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arXiv 2024年

作者： Danopoulos, Dimitrios Zervakis, Georgios Soudris, Dimitrios Henkel, Jörg The School of Electrical and Computer Engineering National Technical University of Athens Athens15780 Greece The Department of Computer Engineering & Informatics University of Patras Patras Greece Karlsruhe Institute of Technology Karlsruhe76131 Germany

Vision Transformer (ViT) models which were recently introduced by the transformer architecture have shown to be very competitive and often become a popular alternative to Convolutional Neural Networks (CNNs). However, the high computational requirements of these models limit their practical applicability especially on low-power devices. Current state-of-the-art employs approximate multipliers to address the highly increased compute demands of DNN accelerators but no prior research has explored their use on ViT models. In this work we propose TransAxx, a framework based on the popular PyTorch library that enables fast inherent support for approximate arithmetic to seamlessly evaluate the impact of approximate computing on DNNs such as ViT models. Using TransAxx we analyze the sensitivity of transformer models on the ImageNet dataset to approximate multiplications and perform approximate-aware finetuning to regain accuracy. Furthermore, we propose a methodology to generate approximate accelerators for ViT models. Our approach uses a Monte Carlo Tree Search (MCTS) algorithm to efficiently search the space of possible configurations using a hardware-driven hand-crafted policy. Our evaluation demonstrates the efficacy of our methodology in achieving significant trade-offs between accuracy and power, resulting in substantial gains without compromising on performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Economic and social effects

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Editorial:Special Topic on Federated Learning for IoT and Edge Computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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Advancing Color Perception: An IoT-driven Pedagogical System for Visually Impaired Children

Advancing Color Perception: An IoT-driven Pedagogical System...

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Advances in Signal Processing, Power, Communication, and Computing (ASPCC), IEEE International Conference on

作者： Prasandika Balage Nuwan Jayawardene School of Computer Science and Engineering University of Westminster London United Kingdom Department of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

Visual impairment is characterized by the loss of vision, encompassing both complete blindness and partial vision loss. Studies reveal a notable prevalence of visual impairment among school-aged children. The significance of colors in acquiring and discerning visual information is crucial, aiding children in effectively perceiving and comprehending their surroundings. This study presents an Internet of Things (IoT) system designed for teaching colors to visually impaired children. The system achieved an accuracy rate surpassing 77.8%, and it is operated effectively regardless of surrounding lighting conditions. Given that the target users of the system are visually impaired children, this research was conducted in close alignment with Human-computer Interaction (HCI) principles.

关键词： Human computer interaction Visualization Accuracy Visual impairment Education Lighting Color Blindness Signal processing Internet of Things

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Distributed Gradient Clustering: Convergence and the Effect of Initialization 58

Distributed Gradient Clustering: Convergence and the Effect ...

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

作者： Armacki, Aleksandar Sharma, Himkant Bajović, Dragana Jakovetić, Dušan Chakraborty, Mrityunjoy Kar, Soummya Carnegie Mellon University PittsburghPA United States Indian Institute of Technology Kharagpur Kharagpur India University of Novi Sad Faculty of Technical Sciences Department of Power Electronics and Computer Engineering Novi Sad Serbia University of Novi Sad Faculty of Sciences Department of Mathematics and Informatics Novi Sad Serbia

ISBN: (纸本)9798350354058

We study the effects of center initialization on the performance of a family of distributed gradient-based clustering algorithms introduced in [1], that work over connected networks of users. In the considered scenario, each user contains a local dataset and communicates only with its immediate neighbours, with the aim of finding a global clustering of the joint data. We perform extensive numerical experiments, evaluating the effects of center initialization on the performance of our family of methods, demonstrating that our methods are more resilient to the effects of initialization, compared to centralized gradient clustering [2]. Next, inspired by the K-means++ initialization [3], we propose a novel distributed center initialization scheme, which is shown to improve the performance of our methods, compared to the baseline random initialization. © 2024 IEEE.

关键词： K-means clustering

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Automated Estimation of Left Myocardial Strain from Cine CTA: A Comparison with Cine MRI

Automated Estimation of Left Myocardial Strain from Cine CTA...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Sun, An-Yu Hsu, Li-Yueh Chang, Lin-Ching Bandettini, W Patricia Chen, Marcus Y. National Institutes of Health National Heart Lung and Blood Institute BethesdaMD United States Catholic University of America Department of Electrical Engineering and Computer Science WashingtonDC United States National Institutes of Health Clinical Center BethesdaMD United States New Jersey Institute of Technology Department of Informatics NewarkNJ United States

ISBN: (纸本)9798350362480

Cardiovascular disease, the leading cause of death in the U.S., affects both the heart and blood vessels. Contrast-enhanced cardiac computed tomography angiography (CTA) is a prominent imaging modality for assessing heart and coronary artery morphology, aiding in the diagnosis of cardiovascular disease. Cine CTA, which captures multiple 3D heart images throughout the cardiac cycle, is particularly useful for evaluating nonischemic cardiomyopathy, a common cause of heart failure unrelated to coronary artery disease. Key biomarkers, such as left ventricular ejection fraction, which measures the amount of blood pumped from the heart's lower chambers per contraction, and myocardial strain, which evaluates the deformation of the heart muscle during contraction and relaxation, are vital for assessing heart function. We developed an automated artificial intelligence (AI) framework to segment various cardiac structures across all image volumes representing different phases of the cardiac cycle. The framework also automatically extracts three short-axis slices to calculate the myocardial strain in the left ventricle. The results are compared with strain measurements obtained from cardiovascular magnetic resonance (CMR) cine imaging. Our study shows both radial and circumferential strains from cine CTA are comparable to those from cine CMR. The proposed AI framework facilitates a comprehensive evaluation of myocardial function throughout the cine CTA, supporting improved diagnostic accuracy. © 2024 IEEE.

关键词： computerized tomography

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Sentiment Polarity Analysis of Bangla Food Reviews Using Machine and Deep Learning Algorithms

arXiv

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arXiv 2024年

作者： Amin, Al Sarkar, Anik Islam, Md Mahamodul Miazee, Asif Ahammad Islam, Md Robiul Hoque, Md Mahmudul Department of Computer Science and Engineering Uttara University Dhaka Bangladesh National Institute of Technology Silchar India Department of Informatics University of Oslo Oslo Norway Department of Computer Science Maharishi International University Iowa United States Department of Computer Science and Engineering CCN University of Science & Technology Comilla Bangladesh

The Internet has become an essential tool for people in the modern world. Humans, like all living organisms, have essential requirements for survival. These include access to atmospheric oxygen, potable water, protective shelter, and sustenance. The constant flux of the world is making our existence less complicated. A significant portion of the population utilizes online food ordering services to have meals delivered to their residences. Although there are numerous methods for ordering food, customers sometimes experience disappointment with the food they receive. Our endeavor was to establish a model that could determine if food is of good or poor quality. We compiled an extensive dataset of over 1484 online reviews from prominent food ordering platforms, including Food Panda and HungryNaki. Leveraging the collected data, a rigorous assessment of various deep learning and machine learning techniques was performed to determine the most accurate approach for predicting food quality. Out of all the algorithms evaluated, logistic regression emerged as the most accurate, achieving an impressive 90.91% accuracy. The review offers valuable insights that will guide the user in deciding whether or not to order the food. © 2024, CC BY.

关键词： Sentiment analysis

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Learning Features Using an optimized Artificial Neural Network for Breast Cancer Diagnosis

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SN computer Science 2022年第3期3卷 229页

作者： AlShourbaji, Ibrahim Kachare, Pramod Zogaan, Waleed Muhammad, L.J. Abualigah, Laith Department of Computer and Network Engineering Jazan University Jazan 45142 Saudi Arabia Department of Electronics and Telecommunication Engineering Ramrao Adik Institute of Technology Nerul Maharashtra Navi Mumbai 400706 India Department of Computer Science Jazan University Jazan 45142 Saudi Arabia Department of Computer Science Faculty of Science Federal University of Kashere P.M.B. 0182 Gombe Nigeria Faculty of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan

Breast cancer (BC) has been one of the significant causes of death worldwide, and its early detection can play a vital role in increasing the survival rate of this disease. This paper suggests a novel feature learning method for BC diagnosis using the artificial neural network (ANN) by optimizing the hidden layers. It uses a systematic hyper-parameter search method to optimize number of hidden layers, neurons in the hidden layers, activation functions, learning rate, batch size, and training epochs of the ANN. The number of hidden layers and the number of neurons in the hidden layer are investigated using t-distributed stochastic neighbor embedding (t-SNE) technique to specify the best hyper-parameters that achieved the highest testing accuracy while avoiding model overfitting. Comparative analysis showed that the proposed method achieved higher performance than the ANN-based models that utilizes particle swarm optimization (PSO), multi-verse optimizer (MVO), BAT algorithm, and firefly algorithm (FFA) as feature selection (FS) methods, with the best classification accuracy of 0.9948, sensitivity of 0.9815, and specificity of 0.9882 for 10 iterations of holdout cross-validation. The performance of the proposed method is also comparable with several earlier reported state-of-the-art models in the literature. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： ANN Brest cancer Classification Feature selection Machine learning

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CoDescribe: An Intelligent Code Analyst for Enhancing Productivity and Software Quality 10th

CoDescribe: An Intelligent Code Analyst for Enhancing Produ...

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10th International Symposium on Applied Computing for Software and Smart systems, ACSS 2023

作者： Das, Souvick Deb, Novarun Cortesi, Agostino Chaki, Nabendu Department of Environmental Science Informatics and Statistics Ca’ Foscari University Venezia30172 Italy Indian Institute of Information Technology Vadodara Gujrat Gandhinagar382028 India Department of Computer Science and Engineering University of Calcutta 67 Sukanta Park Kolkata700101 India

ISBN: (纸本)9789819977826

Automating the documentation, explanation, and modification of source code has been an area of intense research interest for several decades. This paper proposes a novel framework for analyzing source codes to generate natural language summaries, answer complex user queries by reasoning, and even support syntactic error checking and code conversion to other languages. The framework incorporates transformer-based models and knowledge graphs to understand the context of the code and the user’s queries. The contributions of this proposed approach represent a significant leap forward by overcoming the limitations of the most advanced ChatGPT model. The framework ensures the accuracy and reliability of the responses while handling multiple source code files efficiently, making it a valuable asset for software developers and stakeholders. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Efficient Cache Performance Equivalent 2D-Texel to Memory Mapping Identification for Embedded GPUs

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IEEE Access 2025年 13卷 95094-95121页

作者： Ahmed El-Mahdy Marwa K. Elteir Kholoud Shata School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Department of Computer Science and Engineering Egypt-Japan University of Science and Technology New Borg El-Arab City Egypt Department of Computer and Systems Engineering Alexandria University Alexandria Egypt Informatics Research Institute (IRI) City of Scientific Research and Technological Applications (SRTA-City) New Borg El-Arab City Egypt

With the increasing trend of utilizing smart devices, exploiting embedded GPUs becomes plausible to provide for intense computations that approach workstation performance, opening the way for complex, server-grade applications on such devices. Although general-purpose development frameworks exist, they generally abstract implementation details making it difficult for further performance exploitation. One such critical aspect is the texture memory hierarchy design parameters, which are primarily trade secrets. Unfortunately, standard cache hierarchy identification methods are not applicable, due to utilizing logical 2D-texel-to-memory mapping to exploit the 2D locality of access prior to physical cache access. This paper presents, for the first time, a parameterized model capable of describing the underlying multidimensional tiling layouts governing this mapping. Although it can be shown that reverse-engineering the model to obtain the exact parameters is an undecidable problem, we strive to obtain a corresponding set of parameters that results in the same cache behavior. In particular, such parameters define the texture order resulting in a linear order that traverses the caches in memory contiguous blocks. This study proposes a reverse-engineering observation method that exploits the contiguity of tiled cached regions and cache set conflicts to efficiently reveal such parameters. The complexity of the method is $O(n^{2})$ , where n is the number of bits in one of the texture buffer dimensions, ensuring practical applicability. Furthermore, optimization of a benchmark workload—via input data and memory access pattern alignment with the inferred multidimensional tiling layout—yields up to a $2.22\times $ speedup.

关键词： Layout Graphics processing units Performance evaluation Electronic mail Urban areas Systems architecture Object recognition Three-dimensional displays Standards Smart devices

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An Artificial Intelligence (AI) Based X-AODV Routing Protocol in Wireless Networks 4

An Artificial Intelligence (AI) Based X-AODV Routing Protoco...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Hussain, Khalid Ullah Sheikh, Md Amin Jazri, Husin Bin Muzzaffar, Saira Chowdhury, Sohini Kavita School of Computing and Informatics Albukhary International University Kedah Alor Setar Malaysia School of Computer Science Taylor's University Faculty of Innovation and Technology Subang Jaya Malaysia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India Chandigarh Group of Colleges Jhanjeri Department of Computer Science and Engineering Punjab Mohali140307 India

ISBN: (纸本)9798350353266

The multi-hopping mechanism features in the wireless network environment, in which all nodes contain the information of link and connectivity in any infrastructure less manner. When mobile devices either jump from their original network or change their location without describing any fashion, it is named mobile ad hoc network MANET. To ensure the up to date information related to the location and to provide the routing traffic of the node, numerous protocols have been used to remain efficient in achieving this for instance, AODV, DSR, DSDV etc. While we, the peer-to-peer networking, have challenges in many places, security is something that warrants attention more than any other things. The signal corruption hampers data transmission in bedwire networks when the attacks take place. This kind of activity can touch anyone including the layers lying above the physical and below the network layer. In the current discussion we developed, proposition of diversified, efficient and secure AI-based X-AODV routing protocol. We have investigated our proposed protocol against the recent modified two protocols which are SAODV and ARAN. The outcome of simulation reveals that our proposed routing protocol takes place in successfully performing of attack detection and is also the best when it comes to providing security and performance. The Simulation outcomes exhibit the Networks' performance improvement. © 2024 IEEE.

关键词： Network layers

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