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Balanced-Simplified Spatiotemporal Memory Attention for Image Captioning

ieee ACCESS 2025年 13卷 63671-63689页

作者： Al-Qatf, Majjed Yahya, Ali Abdullah Hawbani, Ammar Alsamhi, Saeed Hamood Jiang, Junming Curry, Edward Univ Galway Insight Ctr Data Analyt Galway H91 TK33 Ireland Anqing Normal Univ Sch Comp & Informat Anqing 246011 Peoples R China Shenyang Aerosp Univ Sch Comp Sci Shenyang 110100 Peoples R China Korea Univ Coll Informat Dept Comp Sci & Engn Seoul 02841 South Korea IBB Univ Fac Engn Elect Engn Dept Ibb Yemen

Visual attention and memory-based attention methods have been effectively utilized in image captioning to focus on the most relevant areas of an image during the language generation process. Nevertheless, they face significant challenges, as they are solely guided by the hidden state of the LSTM, leading to attention focused on less relevant areas at various time steps. Furthermore, many approaches apply a uniform focus across all visual information within an image, lacking a mechanism for adjustment of focus intensity. Additionally, the complexity of memory-based attention methods highlights the necessity of developing a simplified memory-based attention mechanism for more efficient and effective image captioning. To address these challenges, a novel attention method for image captioning, named Balanced-Simplified Spatiotemporal Memory Attention (BS-STMA), is proposed. The proposed attention mechanism captures spatiotemporal relationships by combining the advantages of LSTM and visual attention in a simple and effective manner. The combination of LSTM memory and the attention mechanism significantly enhances the model's capacity to retain, convey, and utilize relevant visual information throughout the captioning process. Additionally, an Intensity Balancing Controller (IBC) is introduced and integrated into BS-STMA to enhance its efficiency. IBC allows for adjustments of attention intensity, enabling the model to capture visual information more accurately over time. Extensive experiments on the MSCOCO dataset demonstrate that the method significantly improves image captioning performance, surpassing recent approaches in various evaluation metrics by effectively capturing spatiotemporal.

关键词： visualization Attention mechanisms Long short term memory Spatiotemporal phenomena Feature extraction Microprocessors Accuracy Logic gates Memory management Faces Intensity balancing controller BS-STMA visual attention image captioning

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AI-Generated Content for Academic visualization and Communication in Maker Education 3

AI-Generated Content for Academic Visualization and Communic...

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3rd International Conference on Educational Technology, ICET 2023

作者： Xing, Qingqing Zheng, Chenghong Zhu, Nan Yip, David The Hong Kong University of Science and Technology College of Education Sciences Guangzhou China The Hong Kong University of Science and Technology Data Science and Analytics Thrust Guangzhou China The Hong Kong University of Science and Technology Innovation Policy and Entrepreneurship Thrust Guangzhou China The Hong Kong University of Science and Technology Computational Media and Arts Thrust Guangzhou China

ISBN: (纸本)9798350302332

Collaborative learning in higher education, such as the emerging makerspaces, has contributed to research on innovation and participant expertise. However, there is little research on knowledge management in makerspaces or how learners transfer their individual tacit knowledge in the collaborative space. In addition, the functionality of maker education to promote individualized and personalized learning still needs to be explored. This study is based on Chinese STEM graduate students' experiences with project-based learning in a Maker Education environment to test how AIGC tools help to acquire and transfer students' individual tacit knowledge. The sample was formed from 266 MPhil students taking the "Design Thinking and Effective Academic Communication"course in their first academic year at the world's first interdisciplinary university in southern China. The AIGC teaching intervention was based on the four-phase knowledge management model. In a cycle of socialization-externalization-combination-internalization,learners first explore expertise in a research area in project teams and formulate their research interest. Then, storytelling, analogy, and metaphor data were first used to assess students' abilities to communicate their research topic in text and images on an A4 paper. The subsequent AIGC-supported instructional intervention consisted of 20-minute instructional Sprints with two instructors from Computational Media Arts and Academic Communication over two consecutive weeks. The students' work shows that combining AIGC tools allows for a higher quality of visualization of tacit knowledge in the combination phase -. © 2023 ieee.

关键词： Knowledge management

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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

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ieee Access 2023年 11卷 74000-74020页

作者： Javed, Safdar Hussain Ahmad, Maaz Bin Asif, Muhammad Akram, Waseem Mahmood, Khalid Das, Ashok Kumar Shetty, Sachin College of Computing and Information Sciences Karachi75190 Pakistan Lahore Garrison University Department of Computer Science Lahore54810 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan International Institute of Information Technology at Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Department of Modeling SuffolkVA23435 United States Old Dominion University Center for Cybersecurity Education and Research SuffolkVA23435 United States

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm. © 2013 ieee.

关键词： Real time systems

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data Mining and visualization to Understand Employee Attrition and Work Performance 3

Data Mining and Visualization to Understand Employee Attriti...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Gollapalli, Mohammed Rahman, Ata-Ur Osama, Abdurrahman Alfaify, Ahmad Yassin, Muhammad Alabdullah, Ahmad Imam Abdulrahman Bin Faisal University College of Computer Science & Information Technology Department of Computer Information Systems Dammam31441 Saudi Arabia Imam Abdulrahman Bin Faisal University College of Computer Science & Information Technology Department of Computer Science Dammam31441 Saudi Arabia

ISBN: (纸本)9798350321487

Throughout the course of careers, a large number of employees encounter a variety of normal life events that can have an impact on their performance and job environment satisfaction. Under these circumstances, some employees are compelled to leave their positions, not because of issues with their work environment, but due to personal circumstances. This is what we refer to as Employee Attrition, a pervasive phenomenon affecting numerous businesses and work environments in the present day. Employee attrition could occur in a variety of ways and be determined by several entities. Therefore, we look into various conditions, causes, indicators, and entities in order to study the attrition problem and determine its answers and enhancements. This study investigated the attrition of employees based on IBM data repository, together with the machine learning based experimental analysis and knowledge discoveries, to identify the most effective approaches to the attrition problem. The experimental data contained 1,471 records with 35 distinct characteristics. We formally employed Nave Bayes Classifier in this study using WEKA datamining software, and adequate knowledge has been extracted to better comprehend employee turnover and their work performance facets. © 2023 ieee.

关键词： data mining

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Mask Wearing Detection System Based on YOLO and Raspberry Pi 3

Mask Wearing Detection System Based on YOLO and Raspberry Pi

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3rd International Conference on Consumer Electronics and Computer Engineering, ICCECE 2023

作者： Liang, Ruishi Chen, Yizhu Li, Shuaibing Yang, Huizhi School of Computer University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China

ISBN: (纸本)9798350331578

To identify mask wearing quickly and automatically in public places is particularly important for epidemic prevention and control. In this paper, we present a real-Time mask wearing detection algorithm based on improved YOLOv5s, which speeds up the reasoning speed by 510% and achieves a detection accuracy of more than 96%. The proposed algorithm can be easily deployed in Raspberry PI. We also design a Web-based mask wearing detection system consisting of two parts: The cloud subsystem and the edge subsystem. The cloud part mainly realizes data storage, model training, equipment monitoring, big data visualization and other functions. The edge part uses Raspberry Pie as the core deployment equipment to complete data collection, model reasoning, information early warning and other functions. Our system features the advantages of high real-Time, low cost and low network traffic. It can be widely deployed in the supermarket, parks, intelligent light poles and other open scenes, resulting in greater practical application value. © 2023 ieee.

关键词： Disease control

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A System Design for 3D visualization of Underwater Multi-Target Tracking Based on Unity 3D and GMPHD Filter

A System Design for 3D Visualization of Underwater Multi-Tar...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Seungho Lee, Scott Uk-Jin Hanyang University Bio-Artificial Intelligence Department of Computer Science and Engineering Ansan Korea Republic of Hanyang University Department of Computer Science and Engineering Ansan Korea Republic of

ISBN: (纸本)9798331510756

This paper proposes a design for a visualization system for underwater multi-target tracking based on the Gaussian Mixture Probability Hypothesis Density (GMPHD) filter using Unity 3D engine. Traditional analysis methods for underwater tracking algorithms rely primarily on two-dimensional graphs and numerical data, making it challenging to intuitively understand the complex behavior of tracking systems in three-dimensional underwater environments. Our proposed system design provides a framework for 3D visualization of target movements, sonar sensor detection areas, and filter estimation results, along with an interactive interface for parameter adjustment. The proposed architecture consists of four main components: simulation core, visualization module, environment simulation, and user interface, which would enable researchers to analyze algorithm performance under various scenarios effectively. Through real-time visualization and parameter adjustment capabilities, the system is designed to offer immediate feedback on how different parameters affect tracking performance. This architectural approach aims to provide researchers with a more intuitive understanding of the GMPHD filter's operation and facilitate the optimization of tracking algorithms for underwater environments. The proposed system design demonstrates the potential of 3D visualization tools in advancing underwater multi-target tracking research and algorithm development. © 2025 ieee.

关键词： Target tracking

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Electrical Appliance Detection and Fire Warning System for Student Dormitory Based on Internet of Things Technology

Electrical Appliance Detection and Fire Warning System for S...

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2024 International Symposium on Internet of Things and Smart Cities, ISITSC 2024

作者： Zhao, Xingqi Xing, Yanghuan Fan, Yiling Shi, Yunying Wu, Qiming School of big data and computer Hechi University Yizhou Guangxi 546300 China

ISBN: (纸本)9798331505516

This project designed an electrical appliance monitoring and fire warning system based on the Internet of Things technology to meet the safety needs of students' dormitories. With the help of ZigBee AD hoc networking and NB-IoT communication technology, real-time collection of dormitory environmental parameters and electricity consumption data is realized, and centralized storage and visualization of data is realized through Alibaba Cloud platform. In terms of data display, the system obtains data by calling the API of Alibaba Cloud Internet of Things platform, and uses PyQt5 technology to build the user interface, and the mobile phone uses wechat mini program for real-time data display. In addition, the system uses support vector machine algorithm (SVM) and Mysql database to analyze and store the collected power consumption data, so as to identify potential high-power electrical appliances and give early warning, so as to effectively prevent fire accidents. © 2024 ieee.

关键词： Zigbee

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Bearing Fault Diagnosis and Localization Method Based on WDCNN and Unity3D

Bearing Fault Diagnosis and Localization Method Based on WDC...

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2023 Panda Forum on Power and Energy, PandaFPE 2023

作者： Liu, Haoming Chen, Jianjun Zhu, Jiaoyiling Luo, Shihua Fan, Haoyang Xi, Xianpeng School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350321173

In large power generation enterprises with extensive land coverage and diverse equipment, traditional Electric Motor (EM) condition monitoring methods result in high labor costs and heavy workloads for field inspection personnel. This paper proposes a method using Deep Learning (DL) for fault diagnosis of EM bearings and displays the fault information on the frontend web page through a Unity WebGL application. Firstly, a Wide Convolutional Deep Neural Network (WDCNN) model is employed to pre-train and perform fault classification on a typical bearing sample dataset. The classified results are then stored in a local database. A Unity3D WebGL application, built on a frontend platform based on the *** framework, creates a 3D visualization model of the EM and displays the fault classification results on the interactive 3D model, achieving a visual presentation of real-time data and fault diagnosis results. Experimental results of displaying fault data under different load conditions in the dataset indicate that this method can accurately locate bearing faults, verifying the feasibility of visualizing bearing datasets through DL models for fault identification and 3D visualization. © 2023 ieee.

关键词： Failure analysis

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Destruction-Restoration Suppresses data Protection Perturbations against Diffusion Models 35

Destruction-Restoration Suppresses Data Protection Perturbat...

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35th ieee International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Qin, Tianrui Gao, Xitong Zhao, Juanjuan Ye, Kejiang Univ Chinese Acad Sci Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9798350342734

Diffusion models have become popular in computer vision applications due to their ability to generate high-quality images quickly, with some models achieving a high degree of realism. However, the use of these models for novel image manipulation and generation poses significant risks of being used for harmful purposes, such as the spread of misinformation or copyright infringement. Recent research has proposed techniques that involve adding imperceptible perturbations to images to mitigate privacy and copyright concerns. However, trivial image processing methods can largely destroy these protective perturbations. In this paper, we propose a new method called DiffCleaner, a destruction-restoration pipeline, that combines the advantages of image corruption and restoration methods. It can not only remove protective perturbations but also further preserve high quality generation, as well as fidelity of the original features in generated images. DiffCleaner produces specialized images with visualization and quantitative metrics closely matching unprotected results. We pitch a range of circumventing methods vs. two state-of-the-art data protection methods against diffusion models, and show that both are fragile and offer inadequate data protection. By providing a benchmark comprising a range of protective perturbations removal methods, we hope this paper will inspire and facilitate new research towards developing more resilient methods for data protection.

关键词： data privacy data protection Diffusion model Adversarial examples

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Threshold Based Real-Time Landslide Prediction System Using Low-Cost Sensor Networks 3

Threshold Based Real-Time Landslide Prediction System Using ...

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3rd ieee Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Kaushal, Arush Sehgal, Vivek Kumar Jaypee University of Information Technology Department of Computer Science and Engineering H.P. Waknaghat Solan India

ISBN: (纸本)9798350302288

Landslides pose a recurring threat in the Himalayan region, leading to devastating consequences in terms of human casualties and property damage. This research introduces a groundbreaking approach to real-time landslide monitoring and prediction, leveraging an affordable sensor network. The system employs a network of wireless sensors strategically placed to continuously monitor environmental conditions and gather pertinent data. This data is then transmitted to a cloud server via a WiFi module, where it is integrated into the PowerBI platform for visualization and analysis. To predict landslides in real-time, the collected data is fed into a machine learning model. The performance of three algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Decision Tree (DT), was evaluated using the acquired data. Among these algorithms, SVM demonstrated exceptional results, exhibiting low values for key error metrics such as Mean Squared Error (MSE=O.067), Mean Absolute Error (MAE=O.246), and Root Mean Squared Error (RMSE=0.204). Threshold values are determined based on the training model's output, enabling the generation of alerts and warnings at different levels. This innovative system significantly enhances preparedness and response strategies in landslide-prone areas, equipping stakeholders with timely information to effectively mitigate the impact on lives and infrastructure. © 2023 ieee.

关键词： Decision trees

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