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2023 IEEE Region 10 Symposium, TENSYMP 2023

作者： Evangelista, Ivan Roy S. Cabatuan, Melvin K. Milagrosa, Lorelyn Joy T. Bandala, Argel A. Concepcion, Ronnie Dadios, Elmer P. De la Salle University Department of Electronics and Computer Engineering Manila Philippines Sultan Kudarat State University Electronics Engineering Department Sultan Kudarat Philippines University of Southern Mindanao Department of Crop Science North Cotabato Philippines De la Salle University Department of Manufacturing Engineering and Management Manila Philippines

ISBN: (纸本)9781665482585

Despite the advances achieved in crop protection and management, crop diseases remain a problem for corn farmers. Numerous studies have presented the efficacy of corn disease detection and classification using machine learning-based vision detectors. However, many of these models rely on datasets with lab-based images that do not depict the real-world accurately. In this study, a vision-based corn disease detector and classifier is developed with EfficientNetV2 as base architecture. In addition, an algorithm for evaluating the severity of the disease has also been created. Two datasets were built to train the models, the common corn diseases (CCD) and corn disease severity (CDS). The EfficientNetV2-B0, B1, B2, B3, and S models, pre-trained on ImageN et, were explored for feature extraction. A custom classifier head is incorporated into the EfficientNetV2-based model to complete the architecture. It consists of a single convolutional neural network (CNN) and two fully connected layers. Transfer learning and fine-tuning were employed to improve the performance. The models were evaluated based on accuracy, cross-entropy loss, precision, recall, and F1-score. The EfficientNetV2B2 model performed best on the disease classification task, with an accuracy of 95.74%. The EfficientNetV2B3 is the top performer in the disease severity assessment task, with an accuracy of 98.73%. The EfficientNetV2S also surpassed other proposed models in PlantVillage (PV) dataset with an accuracy of 99.52%. © 2023 IEEE.

关键词： computer vision

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Slack Time Analysis for APB Timer Using Genus Synthesis Tool 8th

Slack Time Analysis for APB Timer Using Genus Synthesis Tool

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8th International Conference on ICT for Sustainable Development, ICT4SD 2023

作者： Madhura, R. Krishnappa, Krutthika Hirebasur Manasa, R. Yashaswini, K.P. Visvesvaraya Technological University Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Karnataka Bengaluru India Department of Computer Science Southern University and A&M College Baton RougeLA United States

ISBN: (纸本)9789819949311

Synthesis plays an important role in the complex world of chip design, as constant push to improve chip—to get more performance, lower power, and improved area. In an effort to emphasize more exact results while using the genus tool, the Advanced Peripheral Bus (APB) timer—a design that is regarded to be a standard framework—is implemented in this research. Logical synthesis is then performed using Synopsys Design Constraints (SDC), fast lib, and slow lib. Fast lib was used to enhance setup check slack time and minimize hold check slack time. It is capable to resolve timing issues with regards to setup and hold time and comply with deadlines utilizing late external delay assertions for setup and early external delay assertions for hold time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Timing circuits

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Classifying Whole Slide Images: What Matters?

arXiv

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arXiv 2023年

作者： Nguyen, Long Nibali, Aiden Millward, Joshua He, Zhen Department of Computer Science and Information Technology La Trobe University BundooraVIC3086 Australia

Recently there have been many algorithms proposed for the classification of very high resolution whole slide images (WSIs). These new algorithms are mostly focused on finding novel ways to combine the information from small local patches extracted from the slide, with an emphasis on effectively aggregating more global information for the final predictor. In this paper we thoroughly explore different key design choices for WSI classification algorithms to investigate what matters most for achieving high accuracy. Surprisingly, we found that capturing global context information does not necessarily mean better performance. A model that captures the most global information consistently performs worse than a model that captures less global information. In addition, a very simple multi-instance learning method that captures no global information performs almost as well as models that capture a lot of global information. These results suggest that the most important features for effective WSI classification are captured at the local small patch level, where cell and tissue micro-environment detail is most pronounced. Another surprising finding was that unsupervised pre-training on a larger set of 33 cancers gives significantly worse performance compared to pre-training on a smaller dataset of 7 cancers (including the target cancer). We posit that pre-training on a smaller, more focused dataset allows the feature extractor to make better use of the limited feature space to better discriminate between subtle differences in the input patch. © 2023, CC BY.

关键词： Image classification

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Dynamic Resource Management with Graphic Deep Reinforcement Learning in Space-Air-Ground Integrated Networks

Dynamic Resource Management with Graphic Deep Reinforcement ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yue Cai Peng Cheng Zhuo Chen Wei Xiang Branka Vucetic Yonghui Li School of Electrical and Information Engineering The University of Sydney Australia Department of Computer Science and Information Technology La Trobe University Australia CSIRO Data61 Australia

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Space-Air-Ground integrated network (SAGIN) is a crucial component of the 6G, enabling global and seamless communication coverage. An efficient task offloading and resource allocation scheme is key in SAGIN to maximize resource utilization efficiency, meeting the stringent quality of service (QoS) requirements for different service types. In this paper, we introduce a dynamic SAGIN model featuring diverse antenna configurations, two timescale types, different channel models for each segment, and dual service types. We formulate a problem of sequential decision-making task offloading and resource allocation. Our proposed solution is an innovative online approach referred to as graphic deep reinforcement learning (GDRL). This approach utilizes a graph neural network (GNN)-based feature extraction network to identify the inherent dependencies within the graphical structure of the states. Simulation results validate that our proposed GDRL significantly outperforms state-of-the-art deep reinforcement learning (DRL) approaches by achieving the highest reward and lowest overall latency.

关键词： Graphics Simulation Quality of service Space-air-ground integrated networks Feature extraction Deep reinforcement learning Real-time systems Graph neural networks Resource management Mirrors

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A Classification of Cross-layer Optimization Approaches in LoRaWAN for Internet of Things 14

A Classification of Cross-Layer Optimization Approaches in L...

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14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Chaguile, Carl Christian Alipio, Melchizedek Bures, Miroslav De la Salle University Department of Electronics and Computer Engineering 2401 Taft Avenue Malate Manila1004 Philippines Fee Czech Technical University in Prague Department of Computer Science Karlovo namesti 13 Prague 2121 35 Czech Republic

ISBN: (纸本)9798350335385

The Internet of Things (IoT) uses Low-Power Wide Area Networks (LPWAN) for applications that require long-range, energy-efficient, and low-cost end devices. LoRaWAN is one of the most popular LPWAN technologies because of its key features and openness making it highly suitable for IoT. Despite its exceptional features, some challenges faced by this technology are optimizing the protocol used for scheduling, low data rate, and duty cycle restrictions. One possible way to address these challenges is by using cross-layer optimization. This optimization technique violates the restrictions of the traditional OSI protocol stack giving freedom to its protocol layers. However, there is currently no summary of cross-layer methods implemented in LoRaWAN. This paper presents a classification of state-of-the-art cross-layer approaches that were used in optimizing the LoRaWAN technology in IoT. The cross-layer techniques were classified based on the merging of adjacent layers, direct communication between layers, and completely new abstractions. In addition, this paper identified the issues and challenges featured in these state-of-the-art cross-layer approaches. Finally, this paper serves as an overview of the performance of cross-layer optimization in LoRaWAN technology for IoT applications. © 2023 IEEE.

关键词： Internet of things

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Multi-Agent RAG Chatbot Architecture for Decision Support in Net-Zero Emission Energy Systems

Multi-Agent RAG Chatbot Architecture for Decision Support in...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Gihan Gamage Nishan Mills Daswin De Silva Milos Manic Harsha Moraliyage Andrew Jennings Damminda Alahakoon Centre for Data Analytics and Cognition La Trobe University Melbourne Australia Department of Computer Science Virginia Commonwealth University Richmond USA

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Modern energy platforms are increasingly leveraging Artificial Intelligence (AI) for effective decision-making and efficient operations. This has led to the development of expansive data spaces that comprise both structured and unstructured energy data in various modalities. Conversational agents with the most recent advancements in large language Models (LLM) are primed to facilitate the efficient retrieval of this diverse information for decision support. In this paper, we propose a multi-agent chatbot architecture for decision support in net-zero emissions energy systems, leveraging LLMs and Retrieval-Augmented Generation (RAG). This architecture consists of a Chatbot User Interface (UI), an advanced Natural language Understanding (NLU) module for precise entity and intent recognition, a robust Chatbot Core with four specialized agents: Observer, Knowledge Retriever, Behavior Analyzer, and Visualizer and Response Construction Module. These components work together to address diverse decision support needs in energy environments, specifically for net zero carbon emissions initiatives that need to consider diverse parameters and large volumes of data. We showcase the chatbot's successful integration and evaluation for decision support in the net-zero emissions energy system of a large tertiary education institution.

关键词： Intent recognition Net zero Education Decision making Data visualization Collaboration Observers

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Hands-On Learning: Teaching Parallel and Distributed Computing through Unplugged Activities in Undergraduate CS Courses 24

Hands-On Learning: Teaching Parallel and Distributed Computi...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis

作者： Anurag Dasgupta Venkat Margapuri Simon Shamoun Shubbhi Taneja Matthew Toups Computer Science and Engineering Technology Valdosta State University Valdosta GA Department of Computing Sciences Villanova University Villanova PA Department of Computer Science Hofstra University Hempstead NY Department of Computer Science Worcester Polytechnic Institute Worcester Massachusetts Computer Science Department Tulane University New Orleans LA

ISBN: (纸本)9798350355543

The authors present and evaluate an unplugged activity to introduce parallel computing concepts to undergraduate students. Students in five CS classrooms used a deck of playing cards in small groups to consider how parallelization can improve performance and how improvement decreases with increased parallelization. Before and after the activity, students took a short survey about their solution and their ideas about parallelism. The authors carried out this activity in seven courses at five institutions in the 2023-2024 academic year. The results showed that students had an increased appreciation for parallelization and this type of activity.

关键词： CS

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Incomplete multi-view clustering via local and global co-regularization

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science China(Information sciences) 2022年第5期65卷 96-111页

作者： Jiye LIANG Xiaolin LIU Liang BAI Fuyuan CAO Dianhui WANG Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Artificial Intelligence Research Institute China University of Mining and Technology Department of Computer Science and Computer Engineering La Trobe University

The incompleteness of multi-view data is a phenomenon associated with real-world data mining applications, which brings a huge challenge for multi-view clustering. Although various types of clustering methods, which try to obtain a complete and consensus clustering result from a latent subspace, have been developed to overcome this problem, most methods excessively rely on views-public instances to bridge the connection with view-private instances. When lacking sufficient views-public instances, existing methods fail to transmit the information among incomplete views effectively. To overcome this limitation, we propose an incomplete multi-view clustering algorithm via local and global co-regularization(IMVC-LG). In this algorithm, we define a new objective function that is composed of two terms: local clustering from each view and global clustering from multiple views, which constrain each other to exploit the local clustering information from different incomplete views and determine a global consensus clustering result, ***, an iterative optimization method is proposed to minimize the objective function. Finally, we compare the proposed algorithm with other state-of-the-art incomplete multi-view clustering methods on several benchmark datasets to illustrate its effectiveness.

关键词： incompleteness multi-view clustering local clustering global clustering co-regularization

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COVID19 detection in chest x-ray using vision-transformer with different patch dimensions 2

COVID19 detection in chest x-ray using vision-transformer wi...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kadry, Seifedine Abualigah, laith González Crespo, Rubén Verdú, Elena Damasevicius, Robertas Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays. © 2024 Elsevier B.V.. All rights reserved.

关键词： Diagnosis

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Enhancing Peer-to-Peer Energy Trading Efficiency and Fairness Through a Hybrid Auction Mechanism in Decentralized Smart Grids

Enhancing Peer-to-Peer Energy Trading Efficiency and Fairnes...

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Annual Conference of Industrial Electronics Society

作者： Mayank Arora Gururaj M V Ankush Sharma Naveen Chilamkurti Department of Electrical Engineering Indian Institute of Technology Kanpur India Department of Computer Science and IT La Trobe University Melbourne VIC Australia

This paper explores hybrid auction mechanism to incentivize peer-to-peer (P2P) energy trading in decentralized smart grid system to maximize societal welfare. P2P energy trading is a promising approach for leveraging renewable energy sources, allowing users to generate and exchange energy within local networks. In this context, buyers represent consumers, while sellers are prosumers, acting as both energy producers and consumers over time. Buyers are incentivized to procure energy from sellers due to the lower cost and greener source of energy compared to the centralized grid. In addition, the production and dissemination of renewable energy on a smaller scale mitigates the burden on transmission infrastructure, thereby decreasing reliance on the primary power grid. To optimize energy trading, We propose a hybrid auction that takes into account the constraints of grid infrastructure, such as transmission line capacity and related expenses, in order to increase the profitability of energy trading. The mechanism ensures individual rationality, truthfulness, computational efficiency, and fairness in the bidding process. Through mathematical examination and simulation experiments, the hybrid auction mechanism is assessed for its efficacy in enhancing energy trading efficiency and facilitating equitable resource allocation. This research contributes to the advancement of P2P energy trading mechanisms, enabling efficient use of renewable energy resources in decentralized smart grid environment.

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