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Design and Implementation of E-Commerce Thiqah Web Platform Based on *** and MySQL 13th

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Design and Implementation of E-Commerce Thiqah Web Platform ...

13th National Technical Symposium on Unmanned System technology, NUSYS 2023

作者： Alhuraibi, Mohammed Ameen Shamsan Algeelani, Nasir Ahmed Sariff, Nohaidda Ismail, Zool Hilmi Farea, Mazen Mohammed Al-Gailani, Samir Ahmed Faculty of Computer and Information Technology Al-Madinah International University Kuala Lumpur57100 Malaysia School of Engineering Faculty of Innovation and Technology Taylor’s University Selangor Subang Jaya47500 Malaysia Centre for Artificial Intelligence and Robotics Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Faculty of Finance and Administrative Science Al-Madinah International University Kuala Lumpur57100 Malaysia Johor Bahru81310 Malaysia

ISBN: (纸本)9789819720262

E-commerce has taken the world by a storm, opening a new world of business and consumption at one’s fingertips. It is if not the most turned to, then one of the most relied-on methods of consumption in today’s era. Since its peak, an uncountable number of businesses have turned to e-commerce as a mode of business. To a certain extent, this shift is necessary and inevitable, considering the rate at which digitalization is developing and incorporated into lives in the long haul. Web development is an integral part of e-commerce and is key in ensuring its success, making it an inseparable aspect of e-commerce. With the many methods of development at hand, this article seeks to shed light on how *** can be optimized in the front-end development of a web application, demonstrating its use in Thiqah, the web application developed for this purpose. At the end of the study, Thiqah was successfully developed utilizing ***, ensuring a seamless coding system that is impenetrable and organized, best suited for the convenience of web development. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Marketplaces

Image-based Gait Spatiotemporal Parameters Estimation using a Single Camera and CNN-Transformer Hybrid Network

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Image-based Gait Spatiotemporal Parameters Estimation using ...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Ankhzaya Jamsrandorj Quynh Hoang Ngan Nguyen Dawoon Jung Min Seok Baek Kyung-Ryoul Mun Jinwook Kim Department of Human Computer Interface & Robotics Engineering KIST School University of Science & Technology Seoul Republic of Korea Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Republic of Korea Department of AI Robotics KIST School University of Science & Technology Seoul Republic of Korea Department of Neurology Wonju Severance Christian Hospital Yonsei University Wonju College of Medicine Wonju Republic of Korea

Vision-based gait analysis can play an important role in the remote and continuous monitoring of the elderly’s health conditions. However, most vision-based approaches compute gait spatiotemporal parameters using human pose information and provide average parameters. This study aimed to propose a straightforward method for stride-by-stride gait spatiotemporal parameters estimation. A total of 160 elderly individuals participated in this study. Data were gathered with a GAITRite system and a mobile camera simultaneously. Three deep learning networks were trained with a few RGB frames as input and a continuous 1D signal containing both spatial and temporal gait parameters as output. The trained networks estimated the stride lengths with correlations of 0.938 and more and detected gait events with F 1 -scores of 0.914 and *** relevance— The proposed method showed excellent agreements with the GAITRite system in analyzing spatiotemporal gait parameters. Our approach can be applied to monitor the elderly’s health conditions based on their gait parameters for early diagnosis of diseases, proper treatment, and timely intervention.

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Active Generalized Category Discovery

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arXiv 2024年

作者： Ma, Shijie Zhu, Fei Zhong, Zhun Zhang, Xu-Yao Liu, Cheng-Lin MAIS Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics HKISI-CAS China School of Computer Science and Information Engineering Hefei University of Technology China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task, which endeavors to cluster unlabeled samples from both novel and old classes, leveraging some labeled data of old classes. Given that knowledge learned from old classes is not fully transferable to new classes, and that novel categories are fully unlabeled, GCD inherently faces intractable problems, including imbalanced classification performance and inconsistent confidence between old and new classes, especially in the low-labeling regime. Hence, some annotations of new classes are deemed necessary. However, labeling new classes is extremely costly. To address this issue, we take the spirit of active learning and propose a new setting called Active Generalized Category Discovery (AGCD). The goal is to improve the performance of GCD by actively selecting a limited amount of valuable samples for labeling from the oracle. To solve this problem, we devise an adaptive sampling strategy, which jointly considers novelty, informativeness and diversity to adaptively select novel samples with proper uncertainty. However, owing to the varied orderings of label indices caused by the clustering of novel classes, the queried labels are not directly applicable to subsequent training. To overcome this issue, we further propose a stable label mapping algorithm that transforms ground truth labels to the label space of the classifier, thereby ensuring consistent training across different active selection stages. Our method achieves state-of-the-art performance on both generic and fine-grained datasets. Our code is available at https://github. com/mashijie1028/ActiveGCD Copyright © 2024, The Authors. All rights reserved.

关键词： Conformal mapping

Opinion-based Strategy for Distributed Multi-Robot Task Allocation in Swarms of Robots

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Opinion-based Strategy for Distributed Multi-Robot Task Allo...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Ziqiao Zhang Shengkang Chen Scott Mayberry Fumin Zhang School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA United States Cheng Kar-Shun Robotics Institute Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Opinions of individuals in large groups evolve through interactions with neighbors and the environment, which can be modeled with opinion dynamics. In this paper, we propose a distributed opinion-based strategy for large-scale multi-robot task allocation utilizing the convergence behaviors of opinion dynamics. The strategy relies on the specialized opinion dynamics on the unit sphere for robot task selection. We investigate the convergence behaviors of opinion dynamics in the context of regions of attraction. Simulation results with a swarm of 200 homogeneous robots validate the effectiveness of our proposed strategy.

关键词： Simulation Dynamic scheduling Resource management Intelligent robots Convergence

OUS: Bridging Scene Context and Facial Features to Overcome the Rigid Cognitive Problem 39

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OUS: Bridging Scene Context and Facial Features to Overcome ...

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Mai, Xinji Wang, Haoran Tao, Zeng Lin, Junxiong Yan, Shaoqi Wang, Yan Yu, Jiawen Tong, Xuan Li, Yating Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University China School of Electrical and Electronic Engineering Shanghai Insittute of Techonlogy China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Engineering Research Center of AI & Robotics Ministry of Education Fudan University China

ISBN: (纸本)157735897X

Dynamic Facial Expression Recognition (DFER) is crucial for affective computing but often overlooks the impact of scene context. We have identified a significant issue in current DFER tasks: human annotators typically integrate emotions from various angles, including environmental cues and body language, whereas existing DFER methods tend to consider the scene as noise that needs to be filtered out, focusing solely on facial information. We refer to this as the Rigid Cognitive Problem. The Rigid Cognitive Problem can lead to discrepancies between the cognition of annotators and models in some samples. To align more closely with the human cognitive paradigm of emotions, we propose an Overall Understanding of the Scene DFER method (OUS). OUS effectively integrates scene and facial features, combining scene-specific emotional knowledge for DFER. Extensive experiments on the two largest datasets in the DFER field, DFEW and FERV39k, demonstrate that OUS significantly outperforms existing methods. By analyzing the Rigid Cognitive Problem, OUS successfully understands the complex relationship between scene context and emotional expression, closely aligning with human emotional understanding in real-world scenarios. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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A3lign-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP

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arXiv 2024年

作者： Tao, Zeng Wang, Yan Lin, Junxiong Wang, Haoran Mai, Xinji Yu, Jiawen Tong, Xuan Zhou, Ziheng Yan, Shaoqi Zhao, Qing Han, Liyuan Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China School of Information Science and Technology Fudan University Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

The performance of CLIP in dynamic facial expression recognition (DFER) task doesn’t yield exceptional results as observed in other CLIP-based classification tasks. While CLIP’s primary objective is to achieve alignment between images and text in the feature space, DFER poses challenges due to the abstract nature of text and the dynamic nature of video, making label representation limited and perfect alignment difficult. To address this issue, we have designed A3lign-DFER, which introduces a new DFER labeling paradigm to comprehensively achieve alignment, thus enhancing CLIP’s suitability for the DFER task. Specifically, our A3lign-DFER method is designed with multiple modules that work together to obtain the most suitable expanded-dimensional embeddings for classification and to achieve alignment in three key aspects: affective, dynamic, and bidirectional. We replace the input label text with a learnable Multi-Dimensional Alignment Token (MAT), enabling alignment of text to facial expression video samples in both affective and dynamic dimensions. After CLIP feature extraction, we introduce the Joint Dynamic Alignment Synchronizer (JAS), further facilitating synchronization and alignment in the temporal dimension. Additionally, we implement a Bidirectional Alignment Training Paradigm (BAP) to ensure gradual and steady training of parameters for both modalities. Our insightful and concise A3lign-DFER method achieves state-of-the-art results on multiple DFER datasets, including DFEW, FERV39k, and MAFW. Extensive ablation experiments and visualization studies demonstrate the effectiveness of A3lign-DFER. The code will be available in the future. © 2024, CC BY.

关键词： Alignment

Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey

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arXiv 2023年

作者： Chen, Shengchao Long, Guodong Jiang, Jing Liu, Dikai Zhang, Chengqi Australian Artificial Intelligence Institute School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia Robotics Institute University of Technology Sydney SydneyNSW2007 Australia

As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric sciences is increasingly adopting data-driven models, powered by progressive developments in deep learning (DL). Specifically, DL techniques are extensively utilized to decode the chaotic and nonlinear aspects of Earth systems, and to address climate challenges via understanding weather and climate data. Cutting-edge performance on specific tasks within narrower spatio-temporal scales has been achieved recently through DL. The rise of large models, specifically large language models (LLMs), has enabled fine-tuning processes that yield remarkable outcomes across various downstream tasks, thereby propelling the advancement of general AI. However, we are still navigating the initial stages of crafting general AI for weather and climate. In this survey, we offer an exhaustive, timely overview of state-of-the-art AI methodologies specifically engineered for weather and climate data, with a special focus on time series and text data. Our primary coverage encompasses four critical aspects: types of weather and climate data, principal model architectures, model scopes and applications, and datasets for weather and climate. Furthermore, in relation to the creation and application of foundation models for weather and climate data understanding, we delve into the field’s prevailing challenges, offer crucial insights, and propose detailed avenues for future research. This comprehensive approach equips practitioners with the requisite knowledge to make substantial progress in this domain. Our survey encapsulates the most recent breakthroughs in research on large, data-driven models for weather and climate data understanding, emphasizing robust foundations, current advancements, practical applications, crucial resources, and prospective research opportunities. Copyright © 2023, The Authors. All rights reserved.

关键词： Time series

Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation

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arXiv 2023年

作者： Liu, Jiaming Xu, Ran Yang, Senqiao Zhang, Renrui Zhang, Qizhe Chen, Zehui Guo, Yandong Zhang, Shanghang National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China AI2Robotics MMLab CUHK Hong Kong University of Science and Technology of China China

Continual Test-Time Adaptation (CTTA) is proposed to migrate a source pre-trained model to continually changing target distributions, addressing real-world dynamism. Existing CTTA methods mainly rely on entropy minimization or teacher-student pseudo-labeling schemes for knowledge extraction in unlabeled target domains. However, dynamic data distributions cause miscalibrated predictions and noisy pseudo-labels in existing self-supervised learning methods, hindering the effective mitigation of error accumulation and catastrophic forgetting problems during the continual adaptation process. To tackle these issues, we propose a continual self-supervised method, Adaptive Distribution Masked Autoencoders (ADMA), which enhances the extraction of target domain knowledge while mitigating the accumulation of distribution shifts. Specifically, we propose a Distribution-aware Masking (DaM) mechanism to adaptively sample masked positions, followed by establishing consistency constraints between the masked target samples and the original target samples. Additionally, for masked tokens, we utilize an efficient decoder to reconstruct a hand-crafted feature descriptor (e.g., Histograms of Oriented Gradients), leveraging its invariant properties to boost task-relevant representations. Through conducting extensive experiments on four widely recognized benchmarks, our proposed method attains state-of-the-art performance in both classification and segmentation CTTA tasks. Our project page: https://***/view/continual-mae/home Copyright © 2023, The Authors. All rights reserved.

关键词： Extraction

Happy: A Debiased Learning Framework for Continual Generalized Category Discovery

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arXiv 2024年

作者： Ma, Shijie Zhu, Fei Zhong, Zhun Liu, Wenzhuo Zhang, Xu-Yao Liu, Cheng-Lin MAIS Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics HKISI-CAS China School of Computer Science and Information Engineering Hefei University of Technology China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Constantly discovering novel concepts is crucial in evolving environments. This paper explores the underexplored task of Continual Generalized Category Discovery (C-GCD), which aims to incrementally discover new classes from unlabeled data while maintaining the ability to recognize previously learned classes. Although several settings are proposed to study the C-GCD task, they have limitations that do not reflect real-world scenarios. We thus study a more practical C-GCD setting, which includes more new classes to be discovered over a longer period, without storing samples of past classes. In C-GCD, the model is initially trained on labeled data of known classes, followed by multiple incremental stages where the model is fed with unlabeled data containing both old and new classes. The core challenge involves two conflicting objectives: discover new classes and prevent forgetting old ones. We delve into the conflicts and identify that models are susceptible to prediction bias and hardness bias. To address these issues, we introduce a debiased learning framework, namely Happy, characterized by Hardness-aware prototype sampling and soft entropy regularization. For the prediction bias, we first introduce clustering-guided initialization to provide robust features. In addition, we propose soft entropy regularization to assign appropriate probabilities to new classes, which can significantly enhance the clustering performance of new classes. For the harness bias, we present the hardness-aware prototype sampling, which can effectively reduce the forgetting issue for previously seen classes, especially for difficult classes. Experimental results demonstrate our method proficiently manages the conflicts of C-GCD and achieves remarkable performance across various datasets, e.g., 7.5% overall gains on ImageNet-100. Our code is publicly available at https://***/mashijie1028/Happy-CGCD. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning