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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Zhou, Fengyu Liu, Jun Zhang, Dazhi Huang, Zengxi School of Computer Science and Software Engineering Xihua University Chengdu China Sichuan Police College Criminal Science and Technolog Department Luzhou China

ISBN: (纸本)9798350350890

Skeleton-based action recognition is a crucial task in computer vision, aiming to perform pattern recognition on a skeleton sequence to identify the actions represented by the skeleton's semantics. However, current mainstream skeleton-based methods primarily rely on global edge information. Unimportant edges may influence this approach, limiting the model's performance in action recognition tasks. Additionally, because these methods treat all information equally, they may lead to resource wastage. This paper proposes a Hypergraph Transformer-based action recognition method using Adaptive EdgeConv Module(AEM) to address these issues effectively. In this method, we introduce Adaptive Edge Graph Convolution Networks(AEGCNs), which utilize the AEM to adaptive obtain edge sets and edge set weights, thereby adaptive identifying key edge sets. With this information, the model can dynamically adjust the importance of different edge sets based on various samples. This approach improves the accuracy of action recognition and reduces the loss of computational resources. Our experimental results on two challenging benchmark datasets (i.e., NTU RGB+D and NTU RGB+D 120) show that the proposed method is comparable to state-of-the-art methods. © 2024 IEEE.

关键词： Musculoskeletal system

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A Reduced State-Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia University College of Computer Science the Engineering Research Center of Ecological Big Data Ministry of Education the Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software the Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model-checking method traverses the full state space, which suffers from the state-space explosion problem. A reduced state-space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state-space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model-checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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An application on forecasting for stock market prices: hybrid of some metaheuristic algorithms with multivariate adaptive regression splines

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International Journal of Intelligent Computing and Cybernetics 2023年第4期16卷 847-866页

作者： Dilek Sabancı Serhat Kılıçarslan Kemal Adem Department of Mathematics Tokat Gaziosmanpas¸a UniversityTokatTurkiye Department of Software Engineering Bandırma Onyedi Eylul UniversityBalıkesirTurkiye Department of Computer Engineering Sivas University of Science and TechnologySivasTurkiye

Purpose:Borsa Istanbul 100 Index,known as BIST100,is the main indicator to measure the performance of the 100 highest stocks publicly traded in Borsa Istanbul concerning market and trading *** 100 index prediction is a popular research domain for its complex data structure caused by stock price,commodity,interest rate and exchange rate *** study proposed hybrid models using both Genetic,Particle Swarm Optimization,Harmony Search and Greedy algorithms from metaheuristic algorithms approach for dimension reduction,and MARS for ***/methodology/approach:This paper aims to model in the simplest way through metaheuristic algorithms hybridized with the MARS model the effects of stock,commodity,interest and exchange rate variables on BIST 100 during the Covid-19 pandemic period(in the process of closing)between January 2020 and June ***:The most suitable hybrid model was chosen as PSO&MARS by calculating the RMSE,MSE,GCV,MAE,MAD,MAPE and R2 measurements of training,test and overall dataset to check every model’s *** results demonstrated that the proposed PSO&MARS hybrid modeling procedure gave results both as good as the MARS model and a simpler and non-complex model ***/value:Using metaheuristic algorithms as a supporting tool for variable selection can help to identify important independent variables and contribute to the establishment of more non-complex ***,test and overall dataset to check every model’s efficiency.

关键词： Dimension reduction Metaheuristic algorithms MARS Feature selection BIST 100

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Optimization of Interactive Videos Empowered the Experience of Learning Management System

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computer Systems science & engineering 2023年第7期46卷 1021-1038页

作者： Muhammad Akram Muhammad Waseem Iqbal M.Usman Ashraf Erssa Arif Khalid Alsubhi Hani Moaiteq Aljahdali Department of Computer Science Superior University Lahore54000Pakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Computer Science King Abdulaziz UniversityJeddahSaudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia

The Learning management system(LMS)is now being used for uploading educational content in both distance and blended *** platform has two types of users:the educators who upload the content,and the students who have to access the *** students,usually rely on text notes or books and video tutorials while their exams are conducted with formal *** assessments and examination criteria are ineffective with restricted learning space which makes the student tend only to read the educational contents and videos instead of interactive *** aim is to design an interactive LMS and examination video-based interface to cater the issues of educators and *** is designed according to Human-computer interaction(HCI)principles to make the interactive User interface(UI)through User experience(UX).The interactive lectures in the form of annotated videos increase user engagement and improve the self-study context of users involved in *** interface design defines how the design will interact with users and how the interface exchanges *** findings show that interactive videos for LMS allow the users to have a more personalized learning experience by engaging in the educational *** result shows a highly personalized learning experience due to the interactive video and quiz within the video.

关键词： User interface user experience learning management system linear nonlinear video interactive video visual design

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Synthetic Aircraft Trajectory Generation Using Time-Based VQ-VAE

Synthetic Aircraft Trajectory Generation Using Time-Based VQ...

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2025 Integrated Communications, Navigation and Surveillance Conference, ICNS 2025

作者： Murad, Abdulmajid Ruocco, Massimiliano SINTEF Digital Department of Software Engineering Safety and Security Trondheim Norway Norwegian University of Science and Technology Department of Computer Science Trondheim Norway

ISBN: (纸本)9798331534738

In modern air traffic management, generating synthetic flight trajectories has emerged as a promising solution for addressing data scarcity, protecting sensitive information, and supporting large-scale analyses. In this paper, we propose a novel method for trajectory synthesis by adapting the Time-Based Vector Quantized Variational Autoencoder (TimeVQVAE). Our approach leverages time-frequency domain processing, vector quantization, and transformer-based priors to capture both global and local dynamics in flight data. By discretizing the latent space and integrating transformer priors, the model learns long-range spatiotemporal dependencies and preserves coherence across entire flight paths. We evaluate the adapted TimeVQVAE using an extensive suite of quality, statistical, and distributional metrics, as well as a flyability assessment conducted in an open-source air traffic simulator. Results indicate that TimeVQVAE outperforms a temporal convolutional VAE baseline, generating synthetic trajectories that mirror real flight data in terms of spatial accuracy, temporal consistency, and statistical properties. Furthermore, the simulator-based assessment shows that most generated trajectories maintain operational feasibility, although occasional outliers underscore the potential need for additional domain-specific constraints. Overall, our findings underscore the importance of multi-scale representation learning for capturing complex flight behaviors and demonstrate the promise of TimeVQVAE in producing representative synthetic trajectories for downstream tasks such as model training, airspace design, and air traffic forecasting. © 2025 IEEE.

关键词： Aerobraking

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No-reference Image Contrast Quality Assessment based on the Degree of Uniformity in Probability Distribution

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IEIE Transactions on Smart Processing and Computing 2022年第2期11卷 85-91页

作者： Kim, Wonvin Yim, Changhoon Department of Computer Software Korean Bible University Seoul Korea Republic of Department of Computer Science and Engineering Konkuk University Seoul Korea Republic of

We propose a novel contrast-based no-reference image quality assessment (NR-IQA) method. The proposed method quantifies the degree of uniformity in the probability distribution of the pixel values of an image to measure contrast quality without the need for a reference image. To quantify the degree of symmetric uniformity in the distribution of pixel values for image contrast assessment, we divide the intensity levels into left and right ranges recursively at each depth as a binary tree. This symmetric hierarchical recursive calculation of the degree of uniformity for contrast measurement provides a good metric for NR-IQA. Experimental results show that the proposed method is more consistent with the levels of contrast decrement and is superior in assessing contrast-enhanced and dehazed images compared with state-of-the-art and classic NR-IQA methods. The proposed method is more suited to real-time applications due to its lower computational complexity compared with previous methods. © 2022 The Institute of Electronics and Information Engineers.

关键词： Probability distributions

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Enhancing Pakistan Rice Plant Disease Detection: A Highly Effective Pre-Trained CNN Model 25

Enhancing Pakistan Rice Plant Disease Detection: A Highly Ef...

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25th International Multi Topic Conference, INMIC 2023

作者： Arshed, Muhammad Asad Akram, Zeeshan Bajwa, Haroon Zahid Azeem, Muhammad Huzaifa Dawood, Muhammad Ahmad Riaz, Faisal Department of Software Engineering Lahore Pakistan Department of Computer Science Lahore Pakistan Department of Infs Lahore Pakistan

ISBN: (纸本)9798350317701

Rice, a crucial global food source, faces numerous challenges impacting its quality and quantity. Pakistan is a significant rice producer and needs more agricultural technological advancements. Conventional disease detection methods for rice plants are time-consuming and ineffective. However, recent progress in agricultural technology, particularly machine learning & deep learning, holds great promise for improved disease identification. In this study, different datasets were considered for preparing a dataset that is specifically related to Pakistan's environmental rice diseases. This study uses a prepared dataset to train the proposed fine-tuned ResNet101V2 model and for the robustness of the proposed model comparison with ResNet101, ResNet50V2, and DenseNet121 also performed in this study. Among the models evaluated, ResNet101V2 exhibited superior performance with a test accuracy of 81%, demonstrating high accuracy in identifying six distinct classes, including five rice leaf diseases (Leaf Blight, Leaf Smut, Hispa, Leaf Blast, and Brown Spot) and one healthy category specific to Pakistan environments. This study represents a significant advancement toward developing more efficient disease detection and management systems in rice farming, ultimately leading to reduced financial losses and improved agricultural practices. © 2023 IEEE.

关键词： Agricultural technology

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ESL127: Emirate Sign Language Dataset(s) and e-Dictionary (Version 2.0) Utilizing Deep Learning Recognition Models 15

ESL127: Emirate Sign Language Dataset(s) and e-Dictionary (V...

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15th International Conference on Innovations in Information Technology, IIT 2023

作者： Ahmed, Ahmed Abdelhadi Gochoo, Munkhjargal Taghizadeh, Mohamad Otgonbold, Munkh-Erdene Batnasan, Ganzorig Department of Computer Science and Software Engineering Al Ain United Arab Emirates Iran University of Science and Technology Department of Electrical Engineering Iran

ISBN: (纸本)9798350382396

As stated by the United Arab Emirates's (UAE) Community Development Authority (CDA), there are around 3,065 individuals with hearing disabilities in the country. These individuals often struggle to communicate with broader society and rely on scarce sign language (SL) interpreters. Moreover, Arabic's dialects diversity compounds the issue by causing dialects in the Arabic Sign Language (ArSL). Hence, the call for a standardized reference for ArSL in the region is a priority. To address these challenges, we've developed an Emirate Sign Language (ESL) electronic dictionary (e-dictionary) with a dataset of 127 signs and 50 sentences, recorded by hearing-impaired individuals in the UAE with various degrees of deafness. Supervised by certified interpreters and validated by ESL's department head at CDA in Dubai, the recordings were made using Azure Kinect DK, resulting in 708 recordings. The dataset is then processed to 10fps. The e-dictionary offers features such as webcam-based sign recognition using YOLOv8 technology, voice-based signing via Arabic Automatic Speech Recognition, text-based signing, and words spelling in ArSL. © 2023 IEEE.

关键词： Character recognition

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Understanding the Impact of Unexpected Cobot Movements on Human Stress Levels: A Time Series Classification Task

Understanding the Impact of Unexpected Cobot Movements on Hu...

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2024 Workshop on Embracing Human-Aware AI in Industry 5.0, HAII5.0 2024

作者： Shilon, Lior Shevtsov, Vladyslav Schillreff, Nadia Rother, Anne Mark, Shlomo Spiliopoulou, Myra Department of Software Engineering Sami Shamoon College of Engineering Ashdod Israel Faculty of Computer Science Otto-von-Guericke-University Magdeburg Germany

This study investigates the impact of unexpected stimuli on participants’ stress levels during human-robot interactions (HRI). We designed an experiment, where a cobot performs writing tasks, as well as some unexpected actions, intended to surprise the human observer. During this experiment, we are continuously monitoring physiological responses through Electrodermal Activity (EDA) and Electrocardiogram (ECG) sensors. Our goal was to capture the relationship between surprising actions by the cobot and indicators of change in the human stress levels. We employed time series classification techniques, namely ROCKET, Shapelet Transform and WEASEL, along with Support Vector Machine (SVM) analysis on temporal segments. This approach enabled us to develop a binary classification model on stress levels. Our research contributes to the design of AI systems that can detect changes in the stress levels of humans who interact with cobots that perform unexpected actions. Thus, we work towards the improvement of safety protocols in HRI. © 2024 Copyright for this paper by its authors.

关键词： Collaborative robots

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Limited Resources Usage of Deep-Learning-Based IoT Devices in Healthcare 2

Limited Resources Usage of Deep-Learning-Based IoT Devices i...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Gao, Xian He, Peixiong Zhou, Yi Qin, Xiao Columbus State University Tsys School of Computer Science ColumbusGA United States Auburn University Department of Computer Science and Software Engineering AuburnAL United States

ISBN: (纸本)9798331529529

Deep learning models are widely used in healthcare. Given the large amount of data and the need for real-time processing, running these models on IoT devices is a viable solution. This study examines the impact of deep learning models on the limited resources of IoT devices. We selected three models introduced at the 2023 CVPR conference and executed them on a Raspberry Pi with different dataset sizes to measure CPU utilization, memory utilization, and energy consumption. The results show that for the pruned Yolov5 model, CPU utilization has the strongest positive correlation with energy consumption (5.43 × 10=4 Joules), while memory utilization shows the most significant negative correlation (-4.27 × 10=4 Joules). In the FasterNet-T0 model, dataset size has the strongest positive correlation with energy consumption (2.75 × 10=5 Joules). For the Lite-Mono model, dataset size shows the most significant positive correlation (1.84 × 10=4 Joules), while memory utilization has the strongest negative correlation with energy consumption (- 2.52 × 10=4 Joules). These results highlight the key factors correlated with energy consumption across different models, providing valuable insights for deploying deep learning models on IoT devices. © 2024 IEEE.

关键词： Deep Learning Energy Consumption Hardware Utilization Healthcare IoT

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