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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Tian, Meng Liu, Zhicheng Qiu, Chao Wang, Xiaofei Niyato, Dusit Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin China Nanyang Technological University College of Computing and Data Science Singapore Shenzhen University College of Computer Science and Software Engineering Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350351255

Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained edge devices presents considerable challenges, primarily due to their high computational energy demands and stringent quality requirements. In response to these challenges, we introduce a collaborative inference system tailored for green edge networks. First, we propose a multi-exit U-ViT model that achieves the balance between inference quality and processing speed by allowing adaptive selection of exit points during diffusion for efficient processing. Next, we develop a novel generative AI-assisted reinforcement learning algorithm that controls the exit selections and offloading decisions of the device to achieve maximum global gain. Furthermore, we design a novel policy network incorporating an attention-based state-embedded policy network to enhance the algorithm's ability to perceive and make decisions about the state of the environment. Experimental results demonstrate that our system achieves energy-efficient inference while ensuring the quality of the generated content. © 2024 IEEE.

关键词： Generative adversarial networks

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Approximate data mining in very large relational data

Approximate data mining in very large relational data

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17th Australasian Database Conference, ADC 2006

作者： Bezdek, James C. Hathaway, Richard J. Leckie, Christopher Kotagiri, Ramamohanarao Department of Computer Science University of West Florida Pensacola FL 32514 United States 2Department of Mathematical Sciences Georgia Southern University Statesboro GA 30460 United States Department of Computer Science and Software Engineering University of Melbourne Victoria 3010 Australia

In this paper we discuss eNERF, an extended version of non-Euclidean relational fuzzy c-means (NERFCM) for approximate clustering in very large (unloadable) relational data. The eNERF procedure consists of four parts: (i) selection of distinguished features by algorithm DF to be monitored during progressive sampling;(ii) progressively sampling a square N×N relation matrix RN by algorithm PS until an n×n sample relation Rn passes a goodness of fit test;(iii) Clustering Rn using algorithm LNERF;and (iv), extension of the LNERF results to RN-Rn by algorithm xNERF, which uses an iterative procedure based on LNERF to compute fuzzy membership values for all of the objects remaining after LNERF clustering of the accepted sample. Three of the four algorithms are new - only LNERF (called NERFCM in the original literature) precedes this article. © 2006, Australian computer Society, Inc.

关键词： Cluster analysis

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Impact of transfer learning compared to convolutional neural networks on fruit detection

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 7791-7803页

作者： Salem, Dina Ahmed Hassan, Nesma Abdelaziz Hamdy, Razan Mohamed Computer and Software Engineering Department Misr University for Science and Technology Giza Egypt

Smart farming, also known as precision agriculture or digital farming, is an innovative approach to agriculture that utilizes advanced technologies and data-driven techniques to optimize various aspects of farming operations. One smart farming activity, fruit classification, has broad applications and impacts across agriculture, food production, health, research, and environmental conservation. Accurate and reliable fruit classification benefits various stakeholders, from farmers and food producers to consumers and conservationists. In this study, we conduct a comprehensive comparative analysis to assess the performance of a Convolutional Neural Network (CNN) model in conjunction with four transfer learning models: VGG16, ResNet50, MobileNet-V2, and EfficientNet-B0. Models are trained once on a benchmark dataset called Fruits360 and another time on a reduced version of it to study the effect of data size and image processing on fruit classification performance. The original dataset reported accuracy scores of 95%, 93%, 99.8%, 65%, and 92.6% for these models, respectively. While accuracy increased when trained on the reduced dataset for three of the employed models. This study provides valuable insights into the performance of various deep learning models and dataset versions, offering guidance on model selection and data preprocessing strategies for image classification tasks. © 2024-IOS Press. All rights reserved.

关键词： Fruits

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End-to-end delay analysis for networked systems

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Frontiers of Information Technology & Electronic engineering 2015年第9期16卷 732-743页

作者： Jie SHEN Wen-bo HE Xue LIU Zhi-bo WANG Zhi WANG Jian-guo YAO Department of Control Science and Engineering Zhejiang University School of Computer Science Mc Gill University School of Computer Wuhan University Su zhou Institute of Wuhan University School of Software Shanghai Jiao Tong University

End-to-end delay measurement has been an essential element in the deployment of real-time services in networked systems. Traditional methods of delay measurement based on time domain analysis, however, are not efficient as the network scale and the complexity increase. We propose a novel theoretical framework to analyze the end-to-end delay distributions of networked systems from the frequency domain. We use a signal flow graph to model the delay distribution of a networked system and prove that the end-to-end delay distribution is indeed the inverse Laplace transform of the transfer function of the signal flow graph. ~vo efficient methods, Cramer＇s rule-based method and the Mason gain rule-based method, are adopted to obtain the transfer function. By analyzing the time responses of the transfer function, we obtain the end-to-end delay distribution. Based on our framework, we propose an efficient method using the dominant poles of the transfer function to work out the bottleneck links of the network. Moreover, we use the framework to study the network protocol performance. Theoretical analysis and extensive evaluations show the effectiveness of the proposed approach.

关键词： Networked system End-to-end Delay distribution

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Learning automaton based context oriented middleware architecture for precision agriculture

Learning automaton based context oriented middleware archite...

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International Conference on computer, Information and Telecommunication Systems, IEEE CITS 2015

作者： Venkata Krishna, P. Sivanesan, S. Misra, Sudip Obaidat, Mohammad S. Department of Computer Science Sri Padmavathi Mahila Visvavidyalayam Tirupati India School of Computing Science and Engineering VIT University Vellore India School of Information Technology Indian Institute of Technology Kharagpur India Dept. of Computer Science and Software Engineering Monmouth University NJ United States

ISBN: (纸本)9781467369459

The continuous alteration of living landscape in earth due to the greatest impact of information technology has converted the whole earth into a small digital village. That means anyone could share information in any form with any other person residing anywhere in the earth. With the availability of information and communication technology like cloud computing, heterogeneous networking, crowd sensing, web services and data mining, anywhere and anytime information sharing is possible, but this will bring out lot of challenges like incompatibility in standards, data portability, data aggregation, data dissemination, differential context and communication overhead. The ICT has changed many aspects of human lifestyle, work places and living spaces. However, there is one sector namely agriculture which has been deprived of the real advantages of ICT. Hence, there exists a digital divide between farming industry and other industries. Farming industry consumes large amount of natural resources such as water, energy and fertilizers that could escalate issues such as global warming, soil degradation and depletion of ground water to the next level. To reduce the global warming effect, farming industry needs to be integrated with relevant technologies. This paper proposes ubiquitous context oriented middleware architecture for precision agriculture (LA-COMPa) to solve major issues such as waste of water, improper application of fertilizer, choice of wrong crops and season, poor yield and lack of marketing. © 2015 IEEE.

关键词： Middleware

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SAMMS: Multi-modality Deep Learning with the Foundation Model for the Prediction of Cancer Patient Survival

SAMMS: Multi-modality Deep Learning with the Foundation Mode...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhu, Wen Chen, Yiwen Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China National University of Singapore Center for Continuing and Lifelong Education Singapore The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

Cancer survival prediction is pivotal in tailoring individualized treatment strategies and guiding clinician decision-making. Yet, existing methodologies grapple with efficiently harnessing the intricate distribution of medical data spanning various modalities. In response, we present SAMMS, an advanced multi-omics multimodal deep learning framework tailored for survival prediction. SAMMS leverages the robust image segmentation model, "Segment Anything"to adeptly characterize pathological images. This prowess is further enhanced by integrating multi-omics data and clinical insights, facilitating holistic modeling across a diverse modal spectrum. The framework weaves a modality-specific subnetwork with a cross-modality common subnetwork, meticulously capturing intra-modality nuances and inter-modality correlations. SAMMS eclipsed its contemporaries by delivering remarkable performance on TCGA's LGG and KIRC tumor datasets. A battery of analyses underscored SAMMS's unparalleled capability to distill multifaceted insights from multimodal datasets, yielding richer and more integrative multimodal representations. Such strides promise significant advancements in cancer survival analytics, bolstering the precision and efficacy of patient-centric treatments, disease oversight, and clinical decision processes. © 2023 IEEE.

关键词： Multi-omics Data Pathological Images Segment Anything Survival Analysis

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Incorporating software agents in automated personal software process (PSP) tools

Incorporating software agents in automated personal software...

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2009 9th International Symposium on Communications and Information Technology, ISCIT 2009

作者： Hassan, Hazrina Nasir, Mohd Hairul Nizam Md. Fauzi, Shukor Sanim Mohd Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia Faculty of Information Technology and Quantitative Science University Technology Mara Perlis Malaysia

ISBN: (纸本)9781424445219

The PSP-Expert Visualization Agent (EVA) is designed for software engineers specifically to introduce the agent-oriented concept, as well as to automate all the processes and fields involved in PSP. Currently, some developers of PSP automated tools include the agent's element in their system that focuses on sensor-based tracking of lines of code. However, none of them fully utilize this element for other purposes or provide the agent with a personal assistant for the software engineer. This sensor-based tracking is limited to Eclipse environments such as Java and is not compatible with the use of web platforms as a PSP automated tool. The PSP-EVA system is required to perform several tasks. There are three agents that exist within the system: Interface Agent (IA), TaskAgent (TA), and SearchAgent (SA). By incorporating the multi-software agent in this automation system, PSP-EVA is able to have ready-access to applications and also to provide a visual representation of performance to the user. Additionally, evaluation of software engineers and team performance can be easily derived and evaluated statistically. ©2009 IEEE.

关键词： software agents

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A novel transformer-based graph generation model for vectorized road design

A novel transformer-based graph generation model for vectori...

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作者： Zhou, Peichi Li, Chen Zhang, Jian Wang, Changbo Qin, Hong Liu, Long School of Software Engineering East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China Department of Computer Science StonyBrook University Stony BrookNY United States

Road network design, as an important part of landscape modeling, shows a great significance in automatic driving, video game development, and disaster simulation. To date, this task remains labor-intensive, tedious and time-consuming. Many improved techniques have been proposed during the last two decades. Nevertheless, most of the state-of-the-art methods still encounter problems of intuitiveness, usefulness and/or interactivity. As a rapid deviation from the conventional road design, this paper advocates an improved road modeling framework for automatic and interactive road production driven by geographical maps (including elevation, water, vegetation maps). Our method integrates the capability of flexible image generation models with powerful transformer architecture to afford a vectorized road network. We firstly construct a dataset that includes road graphs, density map and their corresponding geographical maps. Secondly, we develop a density map generation network based on image translation model with an attention mechanism to predict a road density map. The usage of density map facilitates faster convergence and better performance, which also serves as the input for road graph generation. Thirdly, we employ the transformer architecture to evolve density maps to road graphs. Our comprehensive experimental results have verified the efficiency, robustness and applicability of our newly-proposed framework for road design. © 2024 John Wiley & Sons Ltd.

关键词： Roads and streets

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Querying linguistic annotations

ADCS 2008 - Proceedings of the Thirteenth Australasian Docum...

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ADCS 2008 - Proceedings of the Thirteenth Australasian Document Computing Symposium 2008年 69-72页

作者： Ghodke, Sumukh Bird, Steven Department of Computer Science and Software Engineering University of Melbourne VIC 3010 Australia

Over the past decade, a variety of expressive linguistic query languages have been developed. The most scalable of these have been implemented on top of an existing database engine. However, with the arrival of efficient, wide-coverage parsers, it is feasible to parse text on a scale that is several orders of magnitude larger. We show that the existing database approach will not scale up, and speculate on a new approach that leverages proximity search in the context of an IR engine. We also propose a simple syntax for querying linguistic annotations, avoiding the usability problems with existing tree query languages.

关键词： Engines

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MushroomDetect: An Investigation into Local Mushroom Classification in Bangladesh Using Transfer Learning 15

MushroomDetect: An Investigation into Local Mushroom Classif...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Adhikari, Bornita Hossain, Md. Mikail Akter, Sumaia Ahamed, Md. Sazzadur Daffodil International University Department. of Computer Science and Engineering Dhaka Bangladesh Bangladesh Agricultural University Department. of Agricultural Chemistry Mmensingh Bangladesh Daffodil International University Department. of software Engineering Dhaka Bangladesh

ISBN: (纸本)9798350370249

When it comes to achieving food security, one of the main obstacles is ensuring that the diet includes high-quality foods. The development of scientific technologies has made manual local identification completely impractical because it takes so long and is so imprecise. To address this problem Using a machine vision-based approach to swiftly identify the most popular and farmed mushrooms from photos raises the accuracy level to our desired level. Pictures of our chosen species were carefully clicked to compile the dataset. We carefully selected new circumstances for seven varieties of selected mushrooms, including 'Ear mushroom,' 'Golden Oyster,' 'Milky Mushroom,' 'Oyster mushroom(Po2),' 'Oyster mushroom(Po10),' 'Pink Oyster,' and 'Reishi Mushroom,' to generate a bespoke dataset. We collected the 500 basic photographs and divided them into 7 groups. To get a position where training a dataset will be simple, we sought to increase the amount of raw dataset with augmentation. InceptionV3, InceptionResNetV2, and DenseNet201 were the transfer learning models employed in the initial stage. After putting DenseNet201 into practice, we received the intended outcome. All of these models provide us with a respectable level of accuracy, but after training DenseNet201, we were able to reach an accuracy of 96.72%. Accidental mushroom identification may also be possible in this study. © 2024 IEEE.

关键词： Shellfish

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