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2023 Advances in science and Engineering technology International Conferences, ASET 2023

作者： Riaz, Sadia Mushtaq, Arif S.P. Jain School of Global Management Information Technology Dubai Campus United Arab Emirates City University Ajman Artificial Intelligence & Data Science United Arab Emirates

ISBN: (纸本)9781665454742

Given the growing popularity of Massive Open Online Courses (MOOCs) due to openness to support learning needs of massive audience, we cannot overlook the fact that MOOCs also suffer from poor completion and satisfaction rate. A 2018 Columbia University's Teachers College study on edX and Coursera courses shows that MOOC Certificate programs have a completion rate of 15% or less. It might seem easier, but creating MOOCs is anything but simple and MOOCs design and research face substantial hurdles in understanding individuals' technology-mediated epistemic practices. In this study, we attempted to reconstruct the episodic memories of members of Generations Y and Z, based on their unique interaction with MOOC platforms and gathered qualitative insights. The data was collected using semi-structured interviews based on purposeful sampling, and analysed thematically in Atlas software to translate usability perceptions and challenges faced by two population groups. The findings of the study suggest that MOOCs openness be redesigned by instructional designers to include personalization, content validation and automation processes for better retention, satisfaction and completion rates. © 2023 IEEE.

关键词： Population statistics

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Structure-preserving finite element methods for computing dynamics of rotating Bose–Einstein condensate

arXiv

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arXiv 2024年

作者： Li, Meng Wang, Junjun Guan, Zhen Du, Zhijie School of Mathematics and Statistics Zhengzhou University Henan Academy of Big Data Zhengzhou450001 China School of Mathematics and Statistics Pingdingshan University Pingdingshan467000 China School of Science Wuhan University of Technology Wuhan430070 China

This work is concerned with the construction and analysis of structure-preserving Galerkin methods for computing the dynamics of rotating Bose–Einstein condensate (BEC) based on the Gross-Pitaevskii equation with angular momentum rotation. Due to the presence of the rotation term, constructing finite element methods (FEMs) that preserve both mass and energy remains an unresolved issue, particularly in the context of nonconforming FEMs. Furthermore, in comparison to existing works, we provide a comprehensive convergence analysis, offering a thorough demonstration of the methods’ optimal and high-order convergence properties. Finally, extensive numerical results are presented to check the theoretical analysis of the structure-preserving numerical method for rotating BEC, and the quantized vortex lattice’s behavior is scrutinized through a series of numerical tests. Copyright © 2024, The Authors. All rights reserved.

关键词： Finite element method

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Intuitive UAV Operation: A Novel dataset and Benchmark for Multi-Distance Gesture Recognition

Intuitive UAV Operation: A Novel Dataset and Benchmark for M...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhenpeng Xu Pan Sun Yu Lu Huilin Ge Meng Li Yingjian Qi College of Big Data and Internet Shenzhen Technology University Shenzhen China College of Applied Science Shenzhen University Shenzhen China College of Automation Jiangsu University Science and Technology Zhenjiang China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

UAV gesture recognition, a novel human-computer interaction form, offers an intuitive approach to controlling UAVs in various environments. However, there is a lack of comprehensive datasets for AI-powered UAV gesture recognition. This paper contributes in several ways: (i) We introduce MD-UHGRD, a unique UAV static gesture dataset with 20, 000 images and annotations, collected from a diverse group of participants in different environmental conditions. This dataset is expected to bridge a significant gap in UAV gesture recognition algorithms. (ii) We propose SA-YOLO, a multifunctional UAV gesture recognition method that not only enables gesture recognition but also includes face and pedestrian tracking, optimizing UAV control in complex scenarios. SA-YOLO incorporates the Spatial Asymptotic Feature Pyramid Network (SAFPN), Scale Pyramid Pooling with Cross Stage Partial Networks Convolution (SPPCSPC), and Space-to-Depth Convolution (SPD-Conv). (iii) Extensive evaluation of SAYOLO on MD-UHGRD establishes it as a benchmark in this domain. Our method demonstrates high accuracy, processing speed, and a compact model size, achieving a 93.2% mean Average Precision (mAP) with 10.3 million parameters and 48 frames per second (FPS). Among competing models, SA-YOLO not only achieves the highest mAP but also maintains a balance in model size and FPS. The database and code are available at: https://***/ijcnn2024/SA-YOLO.

关键词： Pedestrians Accuracy Convolution Tracking Face recognition Neural networks Gesture recognition

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Cross-Modal Knowledge Graph Construction for Multiple Food Additives 18th

Cross-Modal Knowledge Graph Construction for Multiple Food ...

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18th Chinese Intelligent Systems Conference, CISC 2022

作者： Wang, Di Li, Haisheng Wang, Wei Qiao, Libing School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China National Engineering Laboratory for Agri-product Quality Traceability Beijing100048 China

ISBN: (纸本)9789811962257

Aiming at the food safety problems caused by the improper use of food additives, a cross-modal knowledge graph for multiple food additives is constructed based on the data obtained from the Internet. Firstly, we design a model of the Resource Description Framework graph to extract the knowledge from structured text. Subsequently, semantic role tagging and dependency syntactic parsing are used to extract knowledge from unstructured text. Finally, we add images to the knowledge graph as cross-modal attributes by semantic label sets. Overall, we propose a novel cross-modal knowledge graph construction framework, which lays the foundation for intelligent question answering systems and supervision platforms for multiple food additives. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Breakthrough in breast tumor detection and diagnosis: a noise-resilient, rotation-invariant framework

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Multimedia Tools and Applications 2025年 1-27页

作者： Nosheen, Fariha Khan, Salabat Sharif, Muhammad Kim, Do Hyeun Alkanhel, Reem AbdelSamee, Nagwan Department of Computer Science COMSATS University Islamabad Attock Campus Attock43600 Pakistan Department of Computer Engineering Jeju National University Jeju Special Self-Governing Province Jeju-Si63243 Korea Republic of Big Data Research Center Jeju National University Jeju63243 Korea Republic of Department of Information Technology College of Computer and Information Sciences Princess Nourahbint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The fact that early-stage breast cancer typically presents no signs, poses a global risk to the lives of women. Digital mammography is just one method among many that can detect breast cancer in its early stages. Despite extensive research, most methods for detecting breast cancers still produce a large number of false positives. The difficulty in improving detection accuracy is in reducing false positives by differentiating masses from normal tissues. Using the textural properties of the masses, this study aims to develop a computer-aided diagnosis system that reduces the number of false positive and negative mammography results. The suggested method initially partitions regions of interest (ROI) into small patches to extract an abnormality region and micro-pattern, which permits the extraction of specific information about the image's content in targeted areas. Reducing the computational complexity of an image analysis operation is accomplished by partitioning a ROI into smaller patches rather than processing the complete image at once. Due to the significant impact the noise has on detection accuracy in mammograms, a textural descriptor that is insensitive to noise is introduced to describe image features such as lines, spots, flat areas, and edges. By expanding the rotation-invariant and noise-tolerant descriptor histograms, we generate an improved histogram that preserves the original regional patterns and spatial relationships between masses. To distinguish between "normal" and "mass" and "benign" and "malignant", Support Vector Machines (SVM) is employed with grid search based hyperparameter optimization. Our proposed approach was evaluated using the Digital database for Screening Mammography (DDSM), which contains over 1024 ROI cases. This database is a well-established benchmark for testing new mammography analysis methods. Each case in the DDSM database has been analyzed and interpreted by qualified radiologists, with comprehensive information provided thr

关键词： Diseases

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An integrative profiling of metabolome and transcriptome in the plasma and skeletal muscle following an exercise intervention in diet-induced obese mice

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Journal of Molecular Cell Biology 2023年第3期15卷 18-31页

作者： Shuang Han Qingqian Wu Mengying Wang Miqi Yang Chen Sun Jiaqi Liang Xiaozhen Guo Zheyu Zhang Jingya Xu Xinyuan Qiu Cen Xie Siyu Chen Yue Gao Zhuo-Xian Meng Department of Pathology and Pathophysiology and Department of Cardiology of the Second Affiliated Hospital Zhejiang University School of MedicineHangzhou 310058China Department of Geriatrics Affiliated Hangzhou First People’s HospitalZhejiang University School of MedicineHangzhou 310006China Key Laboratory of Disease Proteomics of Zhejiang Province Zhejiang University School of MedicineHangzhou 310058China Department of Big Data in Health Science School of Public Health Zhejiang University School of MedicineHangzhou 310003China State Key Laboratory of Natural Medicines and School of Life Science and Technology China Pharmaceutical UniversityNanjing 211198China State Key Laboratory of Drug Research Shanghai Institute of Material MedicalChinese Academy of SciencesShanghai 201203China Department of Biology and Chemistry College of Liberal Arts and SciencesNational University of Defense TechnologyChangsha 410073China

Exercise intervention at the early stage of type 2 diabetes mellitus(T2DM)can aid in the maintenance of blood glucose homeostasis and prevent the development of macrovascular and microvascular ***,the exercise-regulated pathways that prevent the development of T2DM remain largely *** this study,two forms of exercise intervention,treadmill training and voluntary wheel running,were conducted for high-fat diet(HFD)-induced obese *** observed that both forms of exercise intervention alleviated HFD-induced insulin resistance and glucose *** muscle is recognized as the primary site for postprandial glucose uptake and for responsive alteration beyond exercise *** profiling of the plasma and skeletal muscle in Chow,HFD,and HFD-exercise groups revealed robust alterations in metabolic pathways by exercise intervention in both *** analysis identified nine metabolites,including beta-alanine,leucine,valine,and tryptophan,which were reversed by exercise treatment in both the plasma and skeletal *** analysis of gene expression profiles in the skeletal muscle revealed several key pathways involved in the beneficial effects of exercise on metabolic *** addition,integrative transcriptomic and metabolomic analyses uncovered strong correlations between the concentrations of bioactive metabolites and the expression levels of genes involved in energy metabolism,insulin sensitivity,and immune response in the skeletal *** work established two models of exercise intervention in obese mice and provided mechanistic insights into the beneficial effects of exercise intervention on systemic energy homeostasis.

关键词： type 2 diabetes exercise intervention metabolomics transcriptomics

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Separate and Integrate Different Level Reasoning for Event Causality Identification

Separate and Integrate Different Level Reasoning for Event C...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yuchang Deng Wenti Huang Tingxuan Chen Jun Long School of Software Xinjiang University Urumqi China School of Computer Science and Engineering Hunan University of Science and Technology Changsha China School of Computer Science and Engineering Central South University Changsha China Big Data Institute Central South University Changsha China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Document-level event causality identification (DECI) aims to discern the causal relationship between two event mentions within a document, which is a more challenging task compared to sentence-level event causality identification. However, the conventional DECI model applies the same method to all pairs of events without considering the distinctions and connections between intra-sentence and inter-sentence event pairs. This paper introduces a novel Separate and Integrate Different Level reasoning model (SIDL) to extract and fuse text features of events. Specifically, we separately encode intra-sentence and inter-sentence event pairs and leverage attention from pre-trained language models to identify the relevant context for aiding in the determination of causality. Additionally, we propose a causal reasoning strategy that leverages intra-sentence causal relationships to assist in the inference of inter-sentence causal relationships. We combine different features of event pairs by discriminating the characters and effects of intra-sentence and inter-sentence reasoning. Experimental results show that our approach achieves superior performance compared to previous models in two benchmarks EventStoryLine and CausalTimeBank.

关键词： Knowledge engineering Correlation Fuses Neural networks Cause effect analysis Benchmark testing Feature extraction

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Adaptive Split Balancing for Optimal Random Forest

arXiv

引用

arXiv 2024年

作者： Zhang, Yuqian Ji, Weijie Bradic, Jelena Institute of Statistics and Big Data Renmin University of China China School of Statistics and Management Shanghai University of Finance and Economics China Department of Mathematics and Halicioglu Data Science Institute University of California San Diego United States

In this paper, we propose a new random forest algorithm that constructs the trees using a novel adaptive split-balancing method. Rather than relying on the widely-used random feature selection, we propose a permutation-based balanced splitting criterion. The adaptive split balancing forest (ASBF), achieves minimax optimality under the Lipschitz class. Its localized version, which fits local regressions at the leaf level, attains the minimax rate under the broad Hölder class Hq,β of problems for any q ∈ N and β ∈ (0, 1]. We identify that over-reliance on auxiliary randomness in tree construction may compromise the approximation power of trees, leading to suboptimal results. Conversely, the proposed less random, permutation-based approach demonstrates optimality over a wide range of models. Although random forests are known to perform well empirically, their theoretical convergence rates are slow. Simplified versions that construct trees without data dependence offer faster rates but lack adaptability during tree growth. Our proposed method achieves optimality in simple, smooth scenarios while adaptively learning the tree structure from the data. Additionally, we establish uniform upper bounds and demonstrate that ASBF improves dimensionality dependence in average treatment effect estimation problems. Simulation studies and real-world applications demonstrate our methods' superior performance over existing random forests. Copyright © 2024, The Authors. All rights reserved.

关键词： Random forests

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral Image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Pei Wang, Dong Wu, Chanyue Yang, Jing Kang, Lei Bai, Zongwen Li, Ying Shen, Qiang School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Yan'an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd. Qingdao China Department of Computer Science Aberystwyth University Aberystwyth United Kingdom

ISBN: (纸本)9798350368741

Hyperspectral Image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques. © 2025 IEEE.

关键词： Cross-scene classification Diffusion model Hyperspectral image Transformer

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Genetic algorithm-based deep reinforcement learning model for estimating chlorophyll-a concentration from remote sensing data

Journal of Biotech Research

引用

Journal of Biotech Research 2023年 15卷 204-218页

作者： Lan, Xinmei Ye, Yang An, Jianfeng Fang, Xinxin Liu, Yuanda Xue, Guokun School of Geomatics Information Service Liaoning Technical University Liaoning Fuxin China School of Geospatial Information Service Liaoning Technical University Liaoning Fuxin China Dalian Huangbohai Marine Surveying Data Information Co. Ltd Liaoning Dalian China College of Geomatics Xi'an University of Science and Technology Shaanxi Xi'an China

ChlorophyII concentration relies on the plankton present on the ocean surface that is affected by climatic changes. Remote sensing applications deploy sensing devices on the ocean surface for detecting the characteristics of plankton behaviors. Such a process generates time-dependent data for analyzing the chlorophyII concentrations. This article introduced a Hybrid ChlorophyII Concentration Estimation Scheme (HC2ES) using remote sensing application data. The hybrid process was employed by using a genetic algorithm and deep reinforcement learning for data segregation and concentration analysis. The genetic process assimilated the low and high chlorophyII (sensed) data for identifying the climatic impact on vegetation. The no-concentration data was segregated by using the mutation operation of the genetic process. The mutated data was recurrently analyzed by using current and previous concentration levels (data) in the reinforcement layers, which provided further classifications on high and low chlorophyII concentrations for providing insights into remote sensing expansions. The joint hybrid process filtered the fewer concentration data preventing its analysis from identifying chlorophyII and its associated inputs. The proposed scheme was validated by using analysis rate, classification, concentration detection, and computation time. © (), (). All Rights Reserved.

关键词： Remote sensing

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