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Automated object recognition in high-resolution optical remote sensing imagery

National science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

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On Evaluation of Bangla Word Analogies

On Evaluation of Bangla Word Analogies

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2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023

作者： Akter, Mousumi Sarkar, Souvika Karmaker, Shubhra Kanti Lab Department of Computer Science & Software Engineering Auburn University AL United States

ISBN: (纸本)9798891760608

This paper presents a benchmark dataset of Bangla word analogies for evaluating the quality of existing Bangla word embeddings. Despite being the 7th largest spoken language in the world, Bangla is still a low-resource language and popular NLP models often struggle to perform well on Bangla data sets. Therefore, developing a robust evaluation set is crucial for benchmarking and guiding future research on improving Bangla word embeddings, which is currently missing. To address this issue, we introduce a new evaluation set of 16,678 unique word analogies in Bangla as well as a translated and curated version of the original Mikolov dataset (10,594 samples) in Bangla. Our experiments with different state-of-the-art embedding models reveal that current Bangla word embeddings struggle to achieve high accuracy on both data sets, demonstrating a significant gap in multilingual NLP research. ©2023 Association for Computational Linguistics.

关键词： Embeddings

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ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification 22

ViT Enhanced Privacy-Preserving Secure Medical Data Sharing ...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Amin, Al Hasan, Kamrul Ullah, Sharif Hossain, M. Shamim College of Engineering Tennessee State University Department of Electrical & Computer Engineering TN United States College of Science and Engineering University of Central Arkansas Department of Computer Science & Engineering ConwayAR United States College of Computer and Information Sciences King Saud University Department of Software Engineering Riyadh Saudi Arabia

ISBN: (纸本)9798331508050

Privacy-preserving and secure data sharing are critical for medical image analysis while maintaining accuracy and minimizing computational overhead are also crucial. Applying existing deep neural networks (DNNs) to encrypted medical data is not always easy and often compromises performance and security. To address these limitations, this research introduces a secure framework consisting of a learnable encryption method based on block-pixel operation to encrypt the data and subsequently integrate it with the Vision Transformer (ViT). The proposed framework ensures data privacy and security by creating unique scrambling patterns per key, providing robust performance against leading bit attacks and minimum difference attacks. © 2025 IEEE.

关键词： Deep neural networks

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Fast Implementation of Eta-T pairing on FPGAs

Fast Implementation of Eta-T pairing on FPGAs

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2024 International Conference on Horizons of Information Technology and engineering, HITE 2024

作者： Aslam, Waseem Raza, Asim University of Central Punjab Department of Software Engineering Lahore Pakistan University of Central Punjab Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798331516055

Key distribution as a core feature of any security system is one of the challenging tasks in an online transaction. Pairing is used to share the key between the users as an answer to the underlying security problem. Diffie-Hellman key exchange protocol computes pairing in two rounds whereas Eta-T is the most efficient and widely used pairing algorithm in the field of elliptic curve cryptosystems. After careful analysis of existing Eta-T pairing techniques, this work proposes a modification from serial computation scheme to parallel for better overall performance. Parallel Karatsuba multiplier and Most Significant reduction algorithm is used to speed up and optimize the pairing algorithm. Consequently, proposed pairing accelerator calculates Eta-T pairing in just 36.4μs and takes only 3597 clock cycles with maximum clock frequency of 98.7 MHz on Virtex-II. Further, by increasing the number of coefficients processed per cycle up to 6, this work shows reduction in the calculation time and clock cycles of Eta-T pairing up to 26.16μs and 1997 respectively. It is also shown that calculation time starts increasing after reaching D = 14. Similarly, it is shown that the fastest calculation of Eta-T pairing in characteristics three over the field F397 uses significantly less clock cycles than previous known implementations at the cost of less clock frequency. © 2024 IEEE.

关键词： Electric clocks

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Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network

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Neural Computing and Applications 2024年第20期36卷 12357-12370页

作者： Abid, Fazeel Rasheed, Jawad Hamdi, Mohammed Alshahrani, Hani Al Reshan, Mana Saleh Shaikh, Asadullah Department of Information Systems Dr. Hassan Murad School of Management University of Management and Technology Lahore Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Software Engineering Istanbul Nisantasi University Istanbul34394 Turkey Department Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Department Information Systems College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Sentiment analysis

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Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1518-1520页

作者： Lei Xue Jianfeng Ye Yongbao Wu Jian Liu D.C.Wunsch Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096 School of Automation Southeast UniversityNanjing 210096China Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA IEEE

Dear Editor,This letter is concerned with prescribed-time Nash equilibrium(PTNE)seeking problem in a pursuit-evasion game(PEG)involving agents with second-order *** order to achieve the prior-given and user-defined convergence time for the PEG,a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global ***,it is theoretically proved that the prescribedtime convergence of the designed algorithm for achieving Nash equilibrium of ***,the effectiveness of the PTNE method was validated by numerical simulation results.A PEG consists of two groups of agents:evaders and *** pursuers aim to capture the evaders through cooperative efforts,while the evaders strive to evade *** is a classic noncooperative *** has attracted plenty of attention due to its wide application scenarios,such as smart grids[1],formation control[2],[3],and spacecraft rendezvous[4].It is noteworthy that most previous research on seeking the Nash equilibrium of the game,where no agent has an incentive to change its actions,has focused on asymptotic and exponential convergence[5]-[7].

关键词： seeking prescribed convergence

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Smart Cities Paradigm with AI-Enabled Effective Requirements engineering 18

Smart Cities Paradigm with AI-Enabled Effective Requirements...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Bashir, Asima Latif, Niha Khan, Arisha Masood Sohail, Muhammad Mcs National University of Science and Technology Department of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350328172

Effective requirements gathering and management tools are key factors for sustainable smart cities. These tools enable stakeholders including government officials, private sector partners, and citizens to identify and prioritize requirements, manage changes, and make sure that the product is as per their expectations. In the context of smart cities, these tools must be able to capture the requirements for a wide range of systems, including transport, energy sector, citizens safety, and environment maintenance. Overall, effective requirements gathering and management tools are critical to the success of smart cities, enabling them to be more efficient, sustainable and responsive. With the advent of new technologies, there is a growing need for advanced tools to support the RE process for smart cities. This paper investigates different artificial in-telligence (AI) based requirements gathering and management tools. Amongst those techniques dependent on natural language processing (NLP) and machine learning (ML) in recommendation systems for requirements gathering and ReQtest, ReqSuite, Jama Connect, Visure, Xabrio, and IBM DOORS as management tools have been proposed. Their summarized performance revealed different scores across parameters highlighting their impact as effective tools for smart city paradigm. The study concludes that AI-based techniques and tools can be employed in smart cities, with selection based on project requirements and constraints. © 2023 IEEE.

关键词： Recurrent neural networks

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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Plant Disease Detection in Precision Agriculture: Deep Learning Approaches 2

Plant Disease Detection in Precision Agriculture: Deep Learn...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Dey, Puja Mahmud, Tanjim Nahar, Sultana Rokeya Hossain, Mohammad Shahadat Andersson, Karl University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Department of Computer Science and Engineering Dhaka1212 Bangladesh Luleå University of Technology Pervasive and Mobile Computing Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350327533

Plants affect economy, agriculture industry and climate of any country. Therefore it is very important to take care of plants. Like human being, plants are also infected by various disease that are resulted from virus, bacteria, and fungi. It is necessary to identify these diseases in time and cure them immediately to counteract the destruction of whole plant. In recent times, deep learning networks have attained tremendous improvement in classifying image and detecting object. Therefore, in our research, some pre-Trained Convolution Neural Networks (AlexNet, VGG16, VGG19) have been utilised along with transfer learning for detecting plant diseases. First of all, pre-processing technique has been applied to improve image quality and increase the accuracy. After the training process, testing has been done for validating the results. To train these models, PlantVillage dataset has been used which contains both diseased and healthy leaves. 80% data has been utilised in training step and other 20% has been used in testing. In this research, precision, recall and f1-score are also computed besides the accuracy. Using AlexNet, VGG16, and VGG19 96.63%, 95.05%, and 95.22% testing accuracy have been achieved respectively. As we can see that among all models the modified AlexNet performed best and got 96.63% as accuracy, 92 % as precision, 91 % as recall, and 91 % as f1-score. © 2024 IEEE.

关键词： Deep Learning Fully Connected Histogram equalization Pooling RELU

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K Semantics for Security Policy Enforcement on Android Applications with Practical Cases 2nd

K Semantics for Security Policy Enforcement on Android Appli...

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2nd EAI International Conference on Computational Intelligence and Communications, CICom 2021

作者： Ziadia, Marwa Mejri, Mohamed Fattahi, Jaouhar Department of Computer Science and Software Engineering Laval University QuébecQC Canada

ISBN: (纸本)9783031195228

As much as they have positioned Android among the most widely used operating systems, Android applications have helped malware creators to break in and infect its devices. A long list of threats caused by downloaded applications targets the integrity of the system and the privacy of the users. To protect Android users from such applications, we propose a formal environment for security policy enforcement. The main idea is to establish a synergy between the aspect-oriented paradigm and formal methods such as the program rewriting technique. The purpose of our approach is to rewrite the application program by adding control tests at certain carefully selected points to ensure that the security policy is respected. The new version of the program preserves all the good behaviors of the original version that comply with the security policy and acts against the bad ones. The particularity of this approach is that all the steps for security policy enforcement are performed automatically using the K-framework. This semantic engineering framework handles the whole process, from the formal specification of the policy and the program to the enforcement technique. Two practical cases are fully discussed in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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