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Corrections to "Reed Solomon Codes Against Adversarial Insertions and Deletions"

IEEE Transactions on Information Theory 2025年第4期71卷 3237-3238页

作者： Con, Roni Shpilka, Amir Tamo, Itzhak Technion – Israel Institute of Technology Department of Computer Science Haifa3200003 Israel Tel Aviv University Blavatnik School of Computer Science Tel Aviv6997801 Israel Tel Aviv University Department of Electrical Engineering-Systems Tel Aviv6997801 Israel

The purpose of this note is to correct an error made by Con et al. (2023), specifically in the proof of Theorem 9. Here we correct the proof but as a consequence we get a slightly weaker result. In Theorem9, we claimed that for integers k and n such that k q RS code that can decode from n - 2k + 1 insdel errors where (Formula presented). Here we prove the following. Theorem 1: For integers n and k q RS-code, where (Formula presented) is a prime power, that can decode from n - 2k + 1 adversarial insdel errors. Note that the exponent of n is 4k - 3 whereas in Theorem 9 it is 4k - 4. For constant dimensional codes, the field size is of order O(n4k-3) , and in particular, for k = 2 the field size is of order O(n5) . © 1963-2012 IEEE.

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Enhanced Classification of Turmeric Leaf Disease Severity Levels Using an Optimized Hybrid Deep Learning Model: LevelPrecisionRecallF1-scoreAccuracy

Enhanced Classification of Turmeric Leaf Disease Severity Le...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Tanwar, Vishesh Anand, Vatsala Chauhan, Rahul Rawat, Ramesh Singh Kumar, Gotte Ranjith Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science & Engineering Uttarakhand Dehradun248002 India School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (纸本)9798350394399

During a study that was conducted in India on turmeric fields, researchers found that various types of turmeric had a prevalence of turmeric leaf spot disease ranging from 6% to 28%. Traditional methods for disease prediction in turmeric crops have several limitations, including high costs for procuring the data input mandatory to run the model, a lack of regional data, and an insufficient dataset. In this study, we investigate the Turmeric problem while also using the hybrid convolutional neural networks (CNN) with SVM for Leaf spot severity level classification. This study looks at the infection that may be found on turmeric leaves. First, the unhealthy plant is captured on camera with the help of secondary sources. To extract and categorize the characteristics of the sickness, CNN -SVM deep learning algorithms are employed to their fullest potential. The results of the research revealed an accuracy of 97% for the Leaf spot Turmeric Disease severity level classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Energy-efficiency Analysis of Different Scheduling Algorithms for Mobile Crowd Computing 15

Energy-efficiency Analysis of Different Scheduling Algorithm...

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

作者： Pramanik, Pijush Kanti Dutta Biswas, Tarun Galgotias University School of Computer Applications and Technology Greater Noida India Indian Institute of Information Technology Department of Computer Science & Engineering Ranchi India

ISBN: (纸本)9798350370249

Mobile crowd computing (MCC) has emerged as a promising paradigm to leverage the underutilised computational resources of smart mobile devices (SMDs). However, the energy constraints of these devices pose a significant challenge in efficiently executing computational tasks. This study analyses the energy efficiency of various scheduling algorithms for MCC environments. The study compares the performance of seven scheduling algorithms: PPIA, RAS, PSO, GA, MaxMin, MinMin, and MCT. These algorithms were evaluated in terms of their energy efficiency when executing different sets of tasks with varied instruction lengths. The experiment used a simulated MCC environment with real data from five SMDs. The results demonstrate that the PSO algorithm consistently outperformed the other scheduling schemes regarding energy efficiency across all task sets. The GA algorithm also showed promising results. The MinMin algorithm exhibited the lowest energy efficiency. The findings of this study provide valuable insights for designing and implementing energy-efficient task scheduling strategies in MCC systems. Analysing the different algorithms under varying task loads can help MCC service providers make informed decisions when selecting appropriate scheduling techniques to optimise the energy utilisation of SMDs. © 2024 IEEE.

关键词： Crowd computing Energy-efficiency Mobile cloud Particle swarm optimisation Task scheduling

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A self-stabilizing distributed algorithm for the 1-MIS problem under the distance-3 model

A self-stabilizing distributed algorithm for the 1-MIS probl...

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作者： Kakugawa, Hirotsugu Kamei, Sayaka Shibata, Masahiro Ooshita, Fukuhito Faculty of Advanced Science and Technology Ryukoku University Shiga Otsu Japan Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashihiroshima Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka Japan Faculty of Engineering Fukui University of Technology Fukui Fukui Japan

Fault-tolerance and self-organization are critical properties in modern distributed systems. Self-stabilization is a class of fault-tolerant distributed algorithms which has the ability to recover from any kind and any finite number of transient faults and topology changes. In this article, we propose a self-stabilizing distributed algorithm for the 1-MIS problem under the unfair central daemon assuming the distance-3 model. Here, in the distance-3 model, each process can refer to the values of local variables of processes within three hops. Intuitively speaking, the 1-MIS problem is a variant of the maximal independent set (MIS) problem with improved local optimizations. The time complexity (convergence time) of our algorithm is (Formula presented.) steps and the space complexity is (Formula presented.) bits, where (Formula presented.) is the number of processes. Finally, we extend the notion of 1-MIS to (Formula presented.) -MIS for each nonnegative integer (Formula presented.), and compare the set sizes of (Formula presented.) -MIS ((Formula presented.)) and the maximum independent set. © 2024 John Wiley & Sons Ltd.

关键词： Consensus algorithm

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Deep Reinforcement Learning Based Energy Consumption Minimization for Intelligent Reflecting Surfaces Assisted D2D Users Underlaying UAV Network

Deep Reinforcement Learning Based Energy Consumption Minimiz...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Vishnoi, Vineet Consul, Prakhar Budhiraja, Ishan Gupta, Suneet Kumar, Neeraj School of Computer Science Engineering and Technology Bennett University U.P. Greater Noida India Thapar Institute of Engineering and Technology Computer Science and Engineering Punjab Patiala India

ISBN: (纸本)9781665494274

Device-to-device communication (D2D-C) is a leading-edge technique in 5G and forthcoming 6G networks due to its benefits for enhanced spectrum efficiency and energy-efficiency (EE). Despite these potential advantages, co-channel interference, cross-channel interference, and massive connectivity are the major issues in D2D-C. In this study, we explore the uplink RIS-assisted communication system in the presence of device-to-device pairs (D2DPs). Reconfigurable intelligent surface (RIS) is introduced into wireless communication networks with the assistance of an unmanned aerial vehicle (UAV) in order to solve the issues of co-channel and cross-channel interference. In order to estimate the UAV's trajectory and RIS's phase shift while ensuring that every user's data requirement is met, a centralized-declining deep-Q network (C-DDQN) method is proposed. In the C-DDQN method, a central controller acts as an agent. The UAV's trajectory and the RIS's phase shift are controlled by a central controller. Numerical outcomes showed that the suggested scheme overcomes state-of-the-art techniques in terms of results. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Multimodal Evidential Learning for Open-World Weakly-Supervised Video Anomaly Detection

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IEEE Transactions on Multimedia 2025年 27卷 3132-3143页

作者： Huang, Chao Huang, Weiliang Jiang, Qiuping Wang, Wei Wen, Jie Zhang, Bob Sun Yat-sen University School of Cyber Science and Technology Shenzhen Campus Shenzhen518000 China University of Macau PAMI Research Group Department of Computer and Information Science 999078 China Ningbo University Faculty of Information Science and Engineering Ningbo315211 China Harbin Institute of Technology Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen518055 China

Efforts in weakly-supervised video anomaly detection center on detecting abnormal events within videos by coarse-grained labels, which has been successfully applied to many real-world applications. However, a significant limitation of most existing methods is that they are only effective for specific objects in specific scenarios, which makes them prone to misclassification or omission when confronted with previously unseen anomalies. Relative to conventional anomaly detection tasks, Open-world Weakly-supervised Video Anomaly Detection (OWVAD) poses greater challenges due to the absence of labels and fine-grained annotations for unknown anomalies. To address the above problem, we propose a multi-scale evidential vision-language model to achieve open-world video anomaly detection. Specifically, we leverage generalized visual-language associations derived from CLIP to harness the full potential of large pre-trained models in addressing the OWVAD task. Subsequently, we integrate a multi-scale temporal modeling module with a multimodal evidence collector to achieve precise frame-level detection of both seen and unseen anomalies. Extensive experiments on two widely-utilized benchmarks have conclusively validated the effectiveness of our method. The code will be made publicly available. © 1999-2012 IEEE.

关键词： Self-supervised learning

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Novel approach to classify brain tumor based on transfer learning and deep learning

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International Journal of Information technology (Singapore) 2023年第4期15卷 2031-2038页

作者： Jain, Sachin Jain, Vishal Department of Computer Science and Engineering School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida 201306 India

Transfer learning strategies were used to develop a unique method in the field of medicine. Investigation in this study suggests an ensemble technique for early brain tumor detection utilizing tumor images. This investigation uses the BRATS dataset, which can be broken into two categories. A transfer learning strategy was used throughout initializing of the parameters and pre-training three neural networks named MobileNetV2, InceptionV3, and ResNet50. After these component models extracted characteristics from input photos, the Relu layer coupled to the Dense Layer was utilized as the classifier to train the models on the relevant dataset. The accuracy was verified using cross-validation of five, ten, and twenty folds. The final classifier was the Ensemble Deep Learning- Brain Tumor Classification (EDL-BTC), constructed by applying a weighted aggregate of the results of the component models. By obtaining 98.3%, 98.6%, and 98.6% at five, ten, and 20-fold cross-validation, it was shown that the EDL-BTC surpassed the most advanced pre-trained models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Deep learning Ensemble learning Transfer learning

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Transfer Learning-Based Method for Classifying Yoga Poses using Deep Convolutional Neural Networks

Transfer Learning-Based Method for Classifying Yoga Poses us...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Vallabhaneni, Nagalakshmi Panneer, Prabhavathy Sundaram, Venkatesan Meenakshi School of Computer Science Engineering and Information Systems VIT University Vellore India Department of Computer Science and Engineering National Institute of Technology Puducherry India

ISBN: (纸本)9798350325188

Yoga is a popular practice that aims to improve one's health and well-being through physical postures, breathing exercises, and meditation. The growing popularity of yoga has prompted researchers to focus on automating the process of classifying yoga poses. Using deep convolutional neural networks (CNNs) and transfer learning, we present a deep learning-based method for correctly categorizing yoga postures in this research. The primary goal of this study is to develop an efficient method for automatically recognizing and categorizing yoga poses in given photographs. The impressive success of deep CNNs in picture classification tasks is exploited here to accomplish this goal. We also use transfer learning, a method for customizing pre-trained CNN models for our unique yoga posture categorization task. This paper presents a Transfer Learning based Method for Yoga Poses classification using Deep Convolutional Neural Networks (TLMYPC-DCNN). The proposed model uses the Yoga Posture Dataset for classification of images. Initially, this model starts with pre-processing the input images by splitting input data into three groups such as training set, validation set and test data. The deep learning CNN model will be trained using the training data, and then its parameters will be tweaked using the validation data. Finally, the test data will be used to assess how well the data performs. A convolutional neural network (CNN) model called MobileNetV2 will be applied to the model photos. The experimental results show that the suggested TLMYPC-DCNN based technique with transfer learning is effective at accurately and reliably classifying yoga poses. © 2023 IEEE.

关键词： Deep Convolutional Neural Networks MobileNetV2 Transfer Learning Yoga Pose classification

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Human Gait Recognition Algorithm Based on MobileNetV1 with Attention Mechanism 3

Human Gait Recognition Algorithm Based on MobileNetV1 with A...

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3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Zhang, Jinsha Zhang, Xuedong School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9781510663473

For embedded modern equipment, the current gait recognition algorithm model is difficult to deploy on it due to a large amount of gait frame image data, slow network processing speed, complex structure and low computational efficiency. In this paper, a lightweight convolutional network model integrating the attention mechanism is proposed. The algorithm first performs morphological processing on the image, extracts the gait contour image, and calculates the gait energy image;Integrates the attention mechanism with MobileNetV1;The feature information of the image is effectively extracted, and the parameters of the network are reduced. A number of body method validation experiments are conducted in the CAISIAB gait database of the Chinese Academy of sciences, and the experimental results are significantly improved with other deep learning models. © 2023 SPIE.

关键词： Computational efficiency

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Bioinspired polysaccharide-based nanocomposite membranes with robust wet mechanical properties for guided bone regeneration

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National science Review 2024年第3期11卷 215-226页

作者： Jian-Hong Xiao Zhen-Bang Zhang JiaHao Li Si-Ming Chen Huai-Ling Gao YinXiu Liao Lu Chen ZiShuo Wang YiFan Lu YuanZhen Hou HengAn Wu DuoHong Zou Shu-Hong Yu Department of Chemistry New Cornerstone Science Laboratory Institute of Biomimetic Materials&Chemistry Anhui Engineering Laboratory of Biomimetic MaterialsDivision of Nanomaterials&Chemistry Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of China Department of Dental Implant Center Stomatologic Hospital and College Key Laboratory of Oral Diseases Research of Anhui Province Anhui Medical University CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Modern Mechanics CAS Center for Excellence in Complex System Mechanics University of Science and Technology of China Department of Oral Surgery College of Stomatology National Clinical Research Center for Oral DiseasesShanghai Key Laboratory of Stomatology Shanghai Research Institute of Stomatology Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Institute of Innovative Materials(I2M) Department of Chemistry Department of Materials Science and Engineering Southern University of Science and Technology

Polysaccharide-based membranes with excellent mechanical properties are highly desired. However, severe mechanical deterioration under wet conditions limits their biomedical applications. Here, inspired by the structural heterogeneity of strong yet hydrated biological materials, we propose a strategy based on heterogeneous crosslink-and-hydration（HCH） of a molecule/nano dual-scale network to fabricate polysaccharide-based nanocomposites with robust wet mechanical properties. The heterogeneity lies in that the crosslink-and-hydration occurs in the molecule-network while the stress-bearing nanofiber-network remains unaffected. As one demonstration, a membrane assembled by bacterial cellulose nanofiber-network and Ca2+-crosslinked and hydrated sodium alginate molecule-network is designed. Studies show that the crosslinked-and-hydrated molecule-network restricts water invasion and boosts stress transfer of the nanofiber-network by serving as interfibrous bridge. Overall, the molecule-network makes the membrane hydrated and flexible; the nanofiber-network as stress-bearing component provides strength and *** HCH dual-scale network featuring a cooperative effect stimulates the design of advanced biomaterials applied under wet conditions such as guided bone regeneration membranes.

关键词： bioinspiration heterogeneous crosslink-and-hydration dual-scale network nanocomposite membrane wet mechanical properties guided bone regeneration

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