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2023 Winter Summit on Smart Computing and Networks, WiSSCoN 2023

作者： Mohan, Ramya Hasson, Feras N Fadhil, Hilal A. Arunmozhi, Rama Rajinikanth, Venkatesan Department of Computer Science Engineering Tamil Nadu Chennai602105 India National University of Science and Technology Department of Electrical and Computer Engineering Oman Sohar University Department of Electrical and Computer Engineering Oman

ISBN: (纸本)9798350334050

The brain tumor (BT) is a severe condition caused by abnormal cell growth. If left untreated, the BT may result in a variety of harsh conditions, including death. As a consequence of the significance of automatic BT detection, several schemes have been developed and implemented in the literature to accurately assess the BT. We propose a method for accurately segmenting brain images from MRI slices in this study. This proposal includes a number of phases, including;(i) collecting and resizing images, (ii) enhancing the image using a selected scheme, (iii) segmenting using ResUnet, and (iv) evaluating and validating the performance. This study examines several brain MRi slices with or without the skull region, and the results are evaluated separately. Based on these outcomes, it is concluded that the proposed ResUnet together with CLAHE provides a significant improvement over other methods concerning Jaccard (>92%), Dice (>95%), and accuracy (>98%). © 2023 IEEE.

关键词： Tumors

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Benchmarking Machine Learning based Intrusion Detection for IoT Edge Devices 12

Benchmarking Machine Learning based Intrusion Detection for ...

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12th IEEE International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2024

作者： Alsharif, Maram Rawat, Danda B. Department of Electrical Engineering & Computer Science WashingtonDC20059 United States

ISBN: (纸本)9798331510633

Machine learning-based Intrusion detection (ML-IDS) gained a lot of interest from the research community as security threats facing the IoT are on the rise. Researchers focused on improving intrusion detection efficiency based on machine learning. However, the feasibility of applying proposed ML algorithms to resource limited IoT devices limited. We present a proof of concepts (POC) for ML-IDS for IoT edge device using actual devices. Furthermore, we envisage the lack of virtual environments that mimics the realistic IoT world, puts research completeness on bay. In this paper, we extend the benchmarks of intrusion detection, applied to IoT edge devices of a varying of computing resources. The benchmarks are based on machine learning techniques endorsed by the research community, and adopt popular intrusion datasets for training and testing models. We also utilize the benchmarks to calibrate Virtual-box environment in a novel way to produce virtual machines that closely mimic realistic IoT edge devices. Thus accurate virtualization would become a reliable tool available to researchers to test their approaches. © 2024 IEEE.

关键词： Intrusion Detection IoT Security Machine Learning

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SmartAgroChain: Revolutionizing Agricultural Supply Chains with Blockchain Technology 1

SmartAgroChain: Revolutionizing Agricultural Supply Chains w...

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1st IEEE International Conference on Building a Secure and Empowered Cyberspace, BuildSEC 2024

作者： Sarker, Shuvro Israq, Mobashera Abdal, Labib Mim, Umma Hani Sourov, Nahian Islam, Salekul Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Electrical and Computer Engineering North South University Dhaka Bangladesh

ISBN: (纸本)9798331516727

Agricultural crop and its supply chains are a fundamental component of our life and means of subsistence, influencing the lives of farmers and consumers alike and guaranteeing the continuous flow of important food supplies. It is plagued with inefficiencies, a lack of transparency and unfair pricing, all of which have an adverse effect on both farmers and consumers. This article introduces SmartAgroChain, a blockchain-based decentralized strategy, which will transform the agricultural supply chain by ensuring equality, trust and transparency among all parties involved. In this system, farmers publish their produced products together with their predicted prices, which starts a community-driven bidding or auction process with retailers. All price related activities take place through blockchain transactions. Our research results demonstrate that SmartAgroChain successfully removes intermediaries, provides fair pricing and builds users' confidence. In comparison to traditional marketplaces, field implementations in semi-urban and rural areas exhibit significant adoption rates and transaction volumes. Both farmers and consumers gain from the competitive price that is ensured by the open bidding procedure. Moreover, we have developed a escrow system for payment that removes the fraudulent activities. We have designed the necessary algorithms for bidding and payment processes and implemented through Ethereum smart contracts. Our performance study shows feasibility the scalability of SmartAgroChain system. © 2024 IEEE.

关键词： Smart contract

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log-Sigmoid Activation-Based Long Short-Term Memory for Time-Series Data Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 672-683页

作者： Ranjan, Priyesh Khan, Pritam Kumar, Sudhir Das, Sajal K. Department of Computer Science Missouri University of Science and Technology RollaMO65409 United States Department of Electrical Engineering Indian Institute of Technology Patna Patna801106 India

With the enhanced usage of artificial-intelligence-driven applications, the researchers often face challenges in improving the accuracy of data classification models, while trading off the complexity. In this article, we address the classification of time-series data using the long short-term memory (LSTM) network while focusing on the activation functions. While the existing activation functions, such as sigmoid and tanh, are used as LSTM internal activations, the customizability of these activations stays limited. This motivates us to propose a new family of activation functions, called log-sigmoid, inside the LSTM cell for time-series data classification and analyze its properties. We also present the use of a linear transformation (e.g., log tanh) of the proposed log-sigmoid activation as a replacement of the traditional tanh function in the LSTM cell. Both the cell activation and recurrent activation functions inside the LSTM cell are modified with log-sigmoid activation family while tuning the log bases. Furthermore, we report a comparative performance analysis of the LSTM model using the proposed and the state-of-the-art activation functions on multiple public time-series databases. © 2023 IEEE.

关键词： Classification (of information)

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Evaluation of software aging in Hyperledger Fabric 35

Evaluation of software aging in Hyperledger Fabric

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35th IEEE International Symposium on Software Reliability engineering Workshops, ISSREW 2024

作者： Soeda, Kojiro Xiao, Xiao Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350367041

Software aging is the gradual decline in performance that occurs with continuous software operation. As the demands on software systems increase, assessing the risk of software aging becomes crucial, especially in modern systems requiring high availability, such as blockchain platforms. Herein, we quantitatively evaluated the risk of software aging in Hyperledger Fabric, a permissioned blockchain. We subjected Hyperledger Fabric to various loads in an experimental environment and monitored memory and CPU usage fluctuations. Through statistical tests - including the Mann-Kendall test, modified Cox-Stuart test, and Sen's slope estimation - we identified the risk of software aging in terms of memory and CPU usage across all peers. © 2024 IEEE.

关键词： Software testing

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Optimal Function Block Distribution for IEC 61499 Distributed Control Application 3

Optimal Function Block Distribution for IEC 61499 Distribute...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Bojnurdi, Vahid Ehteshami Lyu, Tuojian Atmojo, Udayanto Dwi Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Lulea Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798331540319

The efficient distribution of function blocks across programmable logic controllers (PLCs) is critical to improving the performance of distributed automation systems. This paper presents a novel randomized greedy optimization algorithm to minimize communication latency and maximize resource utilization by optimizing the allocation of function blocks across multiple PLCs in compliance with the IEC 61499 standard. Simulations demonstrate the effectiveness of the approach in achieving substantial performance improvements in distributed control applications. The results highlight the flexibility and scalability of the proposed optimization method, particularly in environments with varying PLC configurations. © 2024 IEEE.

关键词： Resource allocation

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Evaluating Cloud Assisted Machine Learning for Internet-of-Things Edge Device Security 12

Evaluating Cloud Assisted Machine Learning for Internet-of-T...

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12th IEEE International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2024

作者： Alsharif, Maram Rawat, Danda B. Department of Electrical Engineering & Computer Science WashingtonDC20059 United States

ISBN: (纸本)9798331510633

Security solutions based on machine learning enabled Intrusion detection systems (ML-IDS) have been popular for IoT edge device security. The cloud support has been considered to leverage heavy workloads off IoT edge devices such as learning, model fitting and updating. In this paper, we apply ML model optimization techniques as 3 distinct key roles of the cloud layer in Machine Learning as a Service (MaaS) architecture. In order to influence the performance of IoT edge devices, the optimization would require: a) device benchmarking that quantifies the available computing resources and network traffic for the edge device;b) feature weighing that determines the contribution of each extracted feature to the overall ML-IDS operation;and c) dimensionality reduction of important features in the training dataset. We examine feature importance and dimensionality reduction techniques on 3 public IDS datasets and present how they affect ML model performance. © 2024 IEEE.

关键词： Cloud security

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Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis

Beyond Human Vision: The Role of Large Vision Language Model...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Verma, Prateek Van, Minh-Hao Wu, Xintao University of Arkansas Department of Electrical Engineering and Computer Science Fayetteville United States

ISBN: (纸本)9798350362480

Vision language models (VLMs) such as LLaVA, ChatGPT-4, and Gemini have recently emerged and gained the spotlight for their ability to comprehend the dual modality of image and textual data showing impressive performance on tasks such as natural image captioning, visual question answering, and spatial reasoning. Additionally, a universal segmentation model by Meta AI, Segment Anything Model (SAM) shows unprecedented performance at isolating objects from unforeseen images. Because medical experts, biologists, and materials scientists routinely examine microscopy or medical images in conjunction with textual information in the form of captions, literature, or reports, and draw conclusions of great importance and merit, it is essential to evaluate their performance on these images. In this study, we charge ChatGPT, LLaVA, Gemini, and SAM quantitatively with classification, segmentation and counting tasks. We observed that ChatGPT and Gemini were impressively able to comprehend the visual features in microscopy images, while SAM was quite capable at isolating artifacts in a general sense. However, the performance was not close to that of a domain expert - the models were readily encumbered by the introduction of impurities, defects, object overlaps and diversity present in the images. © 2024 IEEE.

关键词： biology electron microscopy images materials science vision language models zero-shot evaluation

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A Novel Blockchain-based System for Service Quality Improvement in Multi-Tenant O-RANs

A Novel Blockchain-based System for Service Quality Improvem...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Jiarui Shang, Xiaojun Zeng, Yiming Yang, Yuanyuan Stony Brook University Department of Electrical and Computer Engineering United States University of Texas Department of Computer Science and Engineering Arlington United States

ISBN: (纸本)9798350351255

Open Radio Access Networks (O-RANs) are transforming the landscape of telecommunications to better performance and higher cost-efficiency by enabling network operators to integrate diverse vendor components. Nevertheless, the involvement of multiple Network Service Providers (NSPs) and Mobile Network Operators (MNOs) also brings new challenges in the management of computation and network resources. To resolve this challenge, we propose a blockchain-based framework to guarantee secure, transparent, and decentralized resource allocation in O-RAN systems. Our resource allocation mainly considers the tradeoff of cost and service quality. Our design facilitates real-time adjustments to resource distribution based on dynamic network conditions and incorporates user feedback to optimize service quality continuously. By integrating a Proof-of-Reputation (PoR) consensus mechanism, the framework enhances the reliability and integrity of transactions among competing vendors without central oversight. We evaluate the performance of our design through extensive simulations, which demonstrate significant improvements over the baselines in resource utilization and service delivery across various network scenarios. © 2024 IEEE.

关键词： Radio access networks

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ACHIEVING SAMPLE AND COMPUTATIONAL EFFICIENT REINFORCEMENT LEARNING BY ACTION SPACE REDUCTION VIA GROUPING 12

ACHIEVING SAMPLE AND COMPUTATIONAL EFFICIENT REINFORCEMENT L...

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12th International Conference on Learning Representations, ICLR 2024

作者： Li, Yining Ju, Peizhong Shroff, Ness Department of Electrical and Computer Engineering United States Department of Computer Science and Engineering The Ohio State University ColumbusOH43210 United States

Reinforcement learning often needs to deal with the exponential growth of states and actions when exploring optimal control in high-dimensional spaces (often known as the curse of dimensionality). In this work, we address this issue by learning the inherent structure of action-wise similar MDP to appropriately balance the performance degradation versus sample/computational complexity. In particular, we partition the action spaces into multiple groups based on the similarity in transition distribution and reward function, and build a linear decomposition model to capture the difference between the intra-group transition kernel and the intra-group rewards. Both our theoretical analysis and experiments reveal a surprising and counter-intuitive result: while a more refined grouping strategy can reduce the approximation error caused by treating actions in the same group as identical, it also leads to increased estimation error when the size of samples or the computation resources is limited. This finding highlights the grouping strategy as a new degree of freedom that can be optimized to minimize the overall performance loss. To address this issue, we formulate a general optimization problem for determining the optimal grouping strategy, which strikes a balance between performance loss and sample/computational complexity. We further propose a computationally efficient method for selecting a nearly-optimal grouping strategy, which maintains its computational complexity independent of the size of the action space. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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