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Crop Classification Methods Based on Siamese CBMM-CNN architectures Using Hyperspectral Remote Sensing Data 24th

Crop Classification Methods Based on Siamese CBMM-CNN Archi...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Xie, Bin Zhang, Jiahao Li, Yuling Li, Yusong Dong, Xinyu College of Computer and Cyber Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Shijiazhuang China

ISBN: (纸本)9789819615278

Amidst global population growth and escalating food demands, real-time agricultural monitoring is crucial for ensuring food security. During the initial stages of crop growth, however, it faces significant challenges in obtaining accurate ground truth data via remote sensing, thereby impeding effective crop classification. Traditional Convolutional Neural Network (CNN) models, although effective in image processing and feature extraction within computer vision, are notably limited in their ability to handle the high dimensionality and complex spectral information inherent in hyperspectral non-image remote sensing data. To address this problem, we designed an enhanced CNN model, CBM-CNN. the CBM-CNN incorporates additional convolutional layers (C), batch normalization (B), and MLP(M) to convert one-dimensional data into two-dimensional data for classification, thus improving the processing efficiency of highly nonlinear data features. Building on CBM-CNN, we add multiple attention mechanisms (CBMM-CNN) to enhance feature extraction capabilities. the Siamese CBMM-CNN (SCBMM-CNN) is employed for in-depth learning of data features, with supervised learning precisely adjusting the model to improve classification performance. Experimental results on hyperspectral remote sensing datasets demonstrate that SCBMM-CNN outperforms traditional machine learning models (PCA-RF, GBDT, GMO-SVM, Random Forest, Decision Tree) and deep learning models (CNN, CNN-LSTM) in terms of accuracy, recall rate, F1 score, and kappa coefficient. these results confirm that SCBMM-CNN significantly improves crop classification accuracy and provides a novel method for early crop growth classification. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Deep Learning architectures Based Sentiment Analysis Systematic Literature Review 5

Deep Learning Architectures Based Sentiment Analysis Systema...

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5th international conference on Control, Communication and Computing, ICCC 2023

作者： Anjana thampy, S. Jane Rubel Angelina, Jeyaraj Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Srivilliputtur India Lourdes Matha College of Science and Technology Department of Computer Science and Engineering Kerala Thiruvananthapuram India

ISBN: (纸本)9798350334128

Recently, sentiment analysis has attracted a good deal of interest from researchers. Each day, FB, Twitter, Weibo, and other social media, in addition to huge e-trade websites, generate big feedback, which can be used to investigate humans' critiques or emotions. Sentiment analysis will correctly analyze subjective records from this information. Deep learning becomes an effective machine learning approach which learns from a couple of layers of representations or features of the records and outputs a cutting-edge prediction effect. Deep learning overcomes all other machine learning techniques for sentiment analysis as well as offers different architectures for sentiment evaluation. Recently, RNN abbreviated as Recurrent Neural Networks as well as CNN abbreviated as Convolutional Neural Networks has been replaced with Transformer Language models in deep learning *** survey deals with contemporary architectures and challenges in that are in Sentiment Analysis as well as Natural Language processing. © 2023 IEEE.

关键词： Sentiment analysis

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Data Poisoning Attack Against Reinforcement Learning from Human Feedback in Robot Control Tasks 24th

Data Poisoning Attack Against Reinforcement Learning from H...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Zhou, Zihui Gao, Yutong Qi, Minfeng Wuhan430074 China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University of China Beijing100081 China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan572499 China City University of Macau 999078 China

ISBN: (纸本)9789819615247

Reinforcement Learning from Human Feedback (RLHF) technology provides a method for agents to learn human preferences and perform actions that satisfy human desires. this technology was originally used to complete robot control tasks in situations where it was difficult to design a reward function. However, in the process of collecting human feedback, malicious human annotators may launch attacks on RLHF, posing a significant challenge to the technology. Previous research has mostly focused on studying the harm caused by different attacks on RLHF in fine-tuning large language models (LLMs). However, our study is focused on the field of robot control. We designed two data poisoning attacks against human feedback datasets and implemented our attacks in three different offline reinforcement learning (RL) environments. the experimental results show that RLHF is vulnerable to data poisoning attacks in robot control tasks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Increased throughput and Power Efficient Multi-Path 1024-Point Radix-22 FFT Processor for Signal processing Applications 7

Increased Throughput and Power Efficient Multi-Path 1024-Poi...

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7th international conference on Computing in Engineering and Technology, ICCET 2022

作者： thokala, Mohan Rao Gopi, Telagamalla Department of Electronics and Communication Engineering Siddhartha Institute of Engineering and Technology Telangana State Hyderabad501506 India

ISBN: (纸本)9781839537042

FFT processor used for high-speed signal processing applications, such that to meet the required high-speed applications, FFT processor uses pipeline and parallel processing algorithms to provide such high-speed applications, pipelining techniques such as delay feedback and delay commutation technique are used, for this radix -2k algorithm based FFT processor to provide parallel path signal processing implemented pipeline architectures are used. For high throughput radix-22 radix-23 radix-24 algorithms are used. they reduce both the number of adders and the number of *** processor occupies an area of 3.6mm2 and operating supply voltage of 0.4V-1V, and at a frequency of 600MHz. By taking advantage of Radix-4 algorithm techniques, the processor is implemented with four parallel path signal processing without overhead of hardware compared to the two-path processor. the implemented processor takes less power, approximately 14%, compared to the two-path FFT processor, and the area reduces by 20% using Radix-22 algorithm techniques. Simulation results are observed using Xilinx ISE 14.7 Design Suite using Verilog-HDL coding. © 2022 the Institution of Engineering and Technology.

关键词： Fast Fourier transforms

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Traffic Sign Recognition by Image Preprocessing and Deep Learning 15th

Traffic Sign Recognition by Image Preprocessing and Deep Lea...

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15th international conference on Intelligent Human-Computer Interaction (IHCI)

作者： Khamdamov, U. R. Umarov, M. A. Khalilov, S. P. Kayumov, A. A. Abidova, F. Sh. Univ Management & Future Technol Tashkent Uzbekistan Tashkent Univ Informat Technol Samarkand Branch Tashkent Uzbekistan Tashkent Univ Informat Technol Tashkent Uzbekistan

ISBN: (纸本)9783031538292;9783031538308

Due to the improvement in the car manifacture, the rate of road traffic accidents is increasing. To solve these problems, there is loads of attention in research on the development of driver assistance systems, where the main innovation is traffic sign recognition (TSR). In this article, a special convolutional neural network model with high accuracy compared to traditional models is used for TSR. the Uzbek Traffic Sign Dataset (UTSD) applied in the zone of Uzbekistan was created, consisting of 21.923 images belonging to 56 classes. We proposed a parallel computing method for real-time processing of video haze removal. Our utilization can process the 1920 x 1080 video series with 176 frames per second for the dark channel prior (DCP) algorithm. 8.94 times reduction of calculation time compared to the Central processing Unit (CPU) was achieved by performing the TSR process on the Graphics processing Unit (GPU). the algorithms used to detect traffic signs are improved YOLOv5. the results showed a 3.9% increase in accuracy.

关键词： Traffic sign recognition deep learning image dehazing UTSD dataset parallel processing data augmentation

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A digital twin concept to prototype multi-drone based applications 24

A digital twin concept to prototype multi-drone based applic...

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14th international conference on the Internet of things, IoT 2024

作者： Carramiñana, David Braga, Lucas Rey Bernardos, Ana M. Dobrzycki, Andrzej D. Bergesio, Luca Besada, Juan A. Casar, José R. Universidad Politécnica de Madrid Information Processing and Telecommunications Center ETSI Telecomunicación Madrid Spain

ISBN: (纸本)9798400712852

this article presents a digital twin that aims to serve as a prototyping platform for algorithms and services integrating Unmanned Aerial Vehicles (UAVs, a.k.a. drones). the digital twin enables to manage a real indoor "operation space", equipped with a cohort of aerial (drones) and ground robots, and an advanced tracking system. this is done from a virtual scene (Unity-based) that can be configured to simulate different application scenarios. thus, real robots are connected into the virtual environment as IoT platforms. Bidirectional communication enables to both govern the physical space and receive feedback of physical changes into the virtual environment. In a demonstrative use-case, a surveillance and tracking mission for ground target detection in a security set up is assessed. Once the user configures the mission in the virtual tool, it is executed and tracked in the real environment. Connected drones provide their video input, which is processed for target detection and localization based on the simultaneous estimation of depth from the drones’ monocular cameras. the digital twin concept enables to test architectures for 5G (e.g. on-board, edge, cloud approaches), swarming algorithms and object detection and tracking algorithms, at the same time that facilitates the simulation of complex operational scenarios that are difficult to replicate in real conditions (e.g. for maintenance, defense, surveillance, etc.), facilitating the posterior successful deployment in the real world. © 2024 Copyright held by the owner/author(s).

关键词： Drones

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FlexGEMM: A Flexible Micro-kernel Generation Framework 24

FlexGEMM: A Flexible Micro-kernel Generation Framework

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5th international conference on Computer Information and Big Data Applications, CIBDA 2024

作者： Wang, Shunhong School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9798400718106

Deep learning (DL) has progressed by leaps and bounds in the past decade. Due to the immense computational cost of DL workloads, industry and academia have developed DL libraries with highly-specialized kernels for certain workloads/architectures, leading to numerous complex code bases that strive for performance. However, they are hard to maintain and not universal. In this paper, we propose a general-purpose, performance-portable micro-kernel FlexGEMM based on DPC++ SYCL implementation. By using parametric adjustment, FlexGEMM can easily adapt to different workloads and heterogeneous devices like GPUs and CPUs, and keep high-performance in the meantime. Task mapping schemes improve the cache and atomic efficiency. And if the workload and GPU meet the conditions, the data-racefree writing method can improve the GEMM performance by taking advantage of parallelism as much as possible. On Intel®Iris®Xe MAX Graphics (96EU), comparing FlexGEMM with the state-of-the-art library oneMKL, we demonstrate that our performance is on par with, and in some cases even surpasses it. In the case of tall-and-skinny matrices, FlexGEMM can achieve 2× to 20× speedup compared to oneMKL. © 2024 ACM.

关键词： Graphics processing unit

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On the Performance Portability of OpenACC, OpenMP, Kokkos and RAJA 2022

On the Performance Portability of OpenACC, OpenMP, Kokkos an...

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5th international conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2022

作者： Marowka, Ami Parallel Research Lab Petach-Tikva Israel

ISBN: (纸本)9781450384988

Performance Portability frameworks are becoming more central and essential in heterogeneous computing systems. However, the developer toolbox lacks the tools to assess the performance portability degree of these frameworks. this article presents a new definition and a metric for evaluating the performance portability of high-level parallel programming models. Using the new metric, the performance portability of OpenACC, OpenMP, Kokkos and RAJA were evaluated based on 324 case studies in various application domains, CPUs and GPUs architectures, and high-performance compilers. the results show that the four performance portability frameworks achieve impressive performance portability of over 80% with no significant differences between different architectures and compilers. © 2022 ACM.

关键词： parallel programming

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Development of a Decision Support System for Helpdesk-Service Based on the Processor for Natural Language Text Classification 1

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5th international Russian Automation conference, RusAutoCon 2023

作者： Biryukova, E.D. Mironov, A.S. Pacific National University 136 Pacific Street Khabarovsk680035 Russia

ISBN: (数字)9783031511271

ISBN: (纸本)9783031511264

Text is one of the most common sources of information. Due to this, there is an urgent need for automated processing of text data. In vast majority of cases, classifiers are applied in text data processing. the object of this work is to develop a text classification processor for a decision support system in the Helpdesk-service-like applications. the modern approach to the text classification based on machine learning method with the engagement of artificial neural network is widely known. the article describes the most common methods for text processing and neural networks architecture. Considered are such text processing methods as: stemming, lemmatization, bag-of-words, N-gram count, TF-IDF. the following neural network architectures: multilayer perceptron, convolutional neural network and long short-term memory network. To find the most optimal algorithm for building up a processor there were considered 36 processors with the use of various combinations of the above methods and neural networks architectures. the article has an analysis and a comparison of a work quality of various classification processors conducted on such characteristic as accuracy. It was established that the processors learned on the data processed though stemming and N-gram count show the highest accuracy. the processor, at the construction of which were used stemming, N-gram count and a convolutional neural network, showed a maximum accuracy of multiclass classification. the accomplished work found an optimum choice of a classifier to be used in form of a module in decision support systems in the Helpdesk-service-like applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Text processing

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TP-BFT: A Faster Asynchronous BFT Consensus with parallel Structure 24th

TP-BFT: A Faster Asynchronous BFT Consensus with Parallel S...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Ye, Shunliang Chen, Qi Xiao, Fuan Ke, Zhihui Yang, Guoyu Ma, Huawei Institute of Artificial Intelligence Guangzhou University Guangzhou510006 China Guangdong Provincial Key Laboratory of Blockchain Security Guangzhou University Guangzhou510006 China

ISBN: (纸本)9789819615445

the massive success of blockchains has significantly catalyzed interest in the extensive deployment of practical asynchronous Byzantine fault-tolerant (BFT) consensus protocols across wide area networks. However, existing asynchronous consensus protocols suffer from high communication cost and low throughput, which can’t meet the high-performance requirements of blockchain platforms. We identify two key factors that hinder the efficiency of asynchronous BFT protocols: (1) Reliable broadcast protocols incur high communication costs;(2) the broadcast and agreement phases are executed strictly sequentially, resulting in wasted bandwidth resources. To address above issues, we propose the lightweight provable broadcast and a novel asynchronous BFT protocol called TP-BFT. TP-BFT implements a lightweight broadcast followed by a recovery mechanism and supports parallel broadcast, agreement, and recovery phases. this approach offers two key advantages: (1) it mitigates the impact of low-bandwidth nodes on the efficiency of the broadcast phase;(2) it fully utilizes the bandwidth during the agreement phase to enhance the protocol’s throughput. We implement TP-BFT and conduct experiments with HoneyBadger and Dumbo. the results demonstrate that TP-BFT improves throughput by 106% and reduces latency by 64% compared to Dumbo. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bandwidth

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