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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Zhu, Jiajing Liu, Yongguo Wen, Chuanbiao Wu, Xindong University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Chengdu University of Traditional Chinese Medicine College of Medical Information Engineering Chengdu China Mininglamp Technology Mininglamp Academy of Sciences Beijing China

ISBN: (纸本)9798350322279

Adverse drug-drug interaction (ADDI) is a significant life-threatening issue for public health. The current methods for ADDI prediction usually work in a "nondiscriminatory"manner by treating each feature without discrimination and equally employing all features into ADDI modeling. Driven by this issue, we propose a Dependence Guided Discriminative Feature Selection (DGDFS) model for ADDI prediction, in which molecular structure and side effect are adopted with the incorporation of l2,0-norm equality constraints to select discriminative molecular substructures and side effects and three dependence based terms among molecular structure, side effect, and ADDIs to guide feature selection. Extensive experiments demonstrate the superior performance of DGDFS compared with fourteen state-of-the-art ADDI prediction and feature selection models. © 2023 IEEE.

关键词： Forecasting

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Investigating the Potential of VR in Language Education: A Study of Cybersickness and Presence Metrics 13

Investigating the Potential of VR in Language Education: A S...

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13th International Conference on Educational and information technology, ICEIT 2024

作者： Hasan, Sakib Wang, Jing Anwar, Muhammad Shahid Zhang, Hanqi Liu, Yang Yang, Lidong School of Information and Electronics Beijing Institute of Technology Beijing100081 China Gachon University Department of Ai and Software Seongnam-si Korea Republic of Sch. of Foreign Languages Cognition and Computation Ministry of Industry and Information Technology Beijing Institute of Technology Beijing China School of Information Engineering Inner Mongolia University of Science and Technology Baotou China

ISBN: (纸本)9798350372663

This study highlights the vital importance of assessing the Simulator Sickness Questionnaire and presence measures as virtual reality (VR) incorporation into language teaching gains popularity. To address user discomfort, which prevents efficient learning in VR environments, the measurement of SSQ becomes crucial. Additionally, evaluating presence metrics is essential to determine the level of engagement and immersion, both crucial for rich language learning experiences. This paper designs a VR-based Chinese language application and proposes a thorough test technique aimed at systematically analyzing SSQ and presence measures Subjective tests and data analysis were carried out to highlight the significance of addressing user discomfort in VR language education. The results of this study shed light on the difficulties posed by user discomfort in VR language learning and offer insightful advice on how to improve VR language learning applications. Furthermore, the outcome of the research explores 'VR-based language education,' 'inclusive language learning platforms,' and 'cross-cultural communication,' highlighting the potential for VR to facilitate language learning across diverse cultural backgrounds. Overall, the analysis results contribute to the enrichment of language learning experiences in the virtual realm and underscore the need for continued exploration and improvement in this field. © 2024 IEEE.

关键词： Virtual reality

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Establishing Pattern Sequences Using Artificial Neural Networks with an Application to Organizational Patterns 10

Establishing Pattern Sequences Using Artificial Neural Netwo...

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10th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Matovič, Viktor Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava84216 4 Slovakia

Explicit relationships included in textual description of patterns are insufficient to establish pattern sequences. This article brings a new method of establishing pattern sequences using artificial neural networks. The method is based on extracting implicit relationships between organizational patterns using the softmax regression model by Zhang et al., which are learned by the artificial neural network and encoded in its weights. The implementation of the method is based on the mini-batch stochastic gradient descent method by Zhang et al. and the Keras application programming interface of the TensorFlow framework. To evaluate the method, we conducted four experiments on selected organizational patterns documented by Coplien and Harrison. Two pattern sequences were established in each experiment. For each pattern sequence, a pattern story was created in order to check its meaningfulness. © 2023 Copyright for this paper by its authors.

关键词： artificial neural networks organizational pattern pattern relationships pattern sequence softmax regression

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Graph-in-Graph Convolutional Network for Hyperspectral Image Classification

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IEEE Transactions on Neural Networks and Learning Systems 2024年第1期35卷 1157-1171页

作者： Jia, Sen Jiang, Shuguo Zhang, Shuyu Xu, Meng Jia, Xiuping College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Key Laboratory for Geo-Environmental Monitoring of Coastal Zone Shenzhen University Ministry of Natural Resources Shenzhen518060 China School of Engineering and Information Technology University of New South Wales CanberraACT 2612 Australia

With the development of hyperspectral sensors, accessible hyperspectral images (HSIs) are increasing, and pixel-oriented classification has attracted much attention. Recently, graph convolutional networks (GCNs) have been proposed to process graph-structured data in non-Euclidean domains and have been employed in HSI classification. But most methods based on GCN are hard to sufficiently exploit information of ground objects due to feature aggregation. To solve this issue, in this article, we proposed a graph-in-graph (GiG) model and a related GiG convolutional network (GiGCN) for HSI classification from a superpixel viewpoint. The GiG representation covers information inside and outside superpixels, respectively, corresponding to the local and global characteristics of ground objects. Concretely, after segmenting HSI into disjoint superpixels, each one is converted to an internal graph. Meanwhile, an external graph is constructed according to the spatial adjacent relationships among superpixels. Significantly, each node in the external graph embeds a corresponding internal graph, forming the so-called GiG structure. Then, GiGCN composed of internal and External graph convolution (EGC) is designed to extract hierarchical features and integrate them into multiple scales, improving the discriminability of GiGCN. Ensemble learning is incorporated to further boost the robustness of GiGCN. It is worth noting that we are the first to propose the GiG framework from the superpixel point and the GiGCN scheme for HSI classification. Experiment results on four benchmark datasets demonstrate that our proposed method is effective and feasible for HSI classification with limited labeled samples. For study replication, the code developed for this study is available at https://***/ShuGuoJ/***. © 2012 IEEE.

关键词： Convolution

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Short-Term Forecasting of High-Altitude Wind Fields Along Flight Routes Based on CAM-ConvLSTM-3DCNN 2

Short-Term Forecasting of High-Altitude Wind Fields Along Fl...

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2nd International Conference on Algorithm, Image Processing and Machine Vision, AIPMV 2024

作者： Chen, Siyu Luo, Fei Du, Jianhua Cao, Liang School of Software Engineering Chengdu University of Information Technology Chengdu China Hainan Province Meteorological Bureau Hainan China Chengdu University of Information Technology Hainan South China Sea Key Laboratory of Meteorological Disaster Prevention and Reduction Chengdu China

ISBN: (纸本)9798350390254

The short-term forecasting of high-altitude wind fields along flight routes is crucial for aviation safety and flight quality. However, due to the complexity and strong spatiotemporal correlations of high-altitude wind fields, these correlated features lead to rapid changes in the wind field at different temporal and spatial scales. This complexity is further compounded by the influence of multiple meteorological factors such as temperature, humidity, and pressure, forming intricate interactions. Existing forecasting models often struggle to capture the spatiotemporal relationships of the wind field, resulting in limited prediction accuracy. To address this challenge, this paper proposes a short-term forecasting model for high-altitude wind fields along flight routes based on CAM-ConvLSTM-3DCNN. This model dynamically adjusts the influence of meteorological factors on wind field changes through a channel-adaptive down-sampling method and utilizes 3D convolution to maintain spatiotemporal consistency, effectively extracting and integrating the spatiotemporal features of meteorological elements. Experimental results demonstrate that the proposed model significantly outperforms traditional numerical weather prediction methods and existing deep learning models in terms of U wind and V wind prediction accuracy, validating its superior performance in high-altitude wind field forecasting. © 2024 IEEE.

关键词： Wind forecasting

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques 5

Novel Approach for Enhancing Mental Well-Being Through Machi...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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B-PesNet: Smoothly Propagating Semantics for Robust and Reliable Multi-Scale Object Detection for Secure Systems

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Computer Modeling in engineering & Sciences 2022年第9期132卷 1039-1054页

作者： Yunbo Rao Hongyu Mu Zeyu Yang Weibin Zheng Faxin Wang Jiansu Pu Shaoning Zeng School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou313000China

Multi-scale object detection is a research hotspot,and it has critical applications in many secure *** the object detection algorithms have constantly been progressing recently,how to perform highly accurate and reliable multi-class object detection is still a challenging task due to the influence of many factors,such as the deformation and occlusion of the object in the actual *** more interference factors,the more complicated the semantic information,so we need a deeper network to extract deep ***,deep neural networks often suffer from network *** prevent the occurrence of degradation on deep neural networks,we put forth a new model using a newly-designed Pre-ReLU,which inserts a ReLU layer before the convolution layer for the sake of preventing network degradation and ensuring the performance of deep *** structure can transfer the semantic information more smoothly from the shallow to the deep ***,the deep networks will encounter not only degradation,but also a decline in ***,to speed up the two-stage detector,we divide the feature map into many groups so as to diminish the number of ***,calculation speed has been enhanced,achieving a balance between speed and *** mathematical demonstration,a Balanced Loss(BL)is proposed by a balance factor to decrease the weight of the negative sample during the training phase to balance the positives and ***,our detector demonstrates rosy results in a range of experiments and gains an mAP of 73.38 on PASCAL VOC2007,which approaches the requirement of many security systems.

关键词： Object detection Pre-ReLU CNN Balanced loss

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Towards Modeling Functional Requirements From Tacit Knowledge

Towards Modeling Functional Requirements From Tacit Knowledg...

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2023 Congress in Computer Science, Computer engineering, and Applied Computing, CSCE 2023

作者： Jafar, Rameen Almufareh, Maram Fahaad Ashraf, Sundas Khan, Bushra Butt, Wasi Haider Humayun, Mamoona National University of Sciences and Technology Department of Computer and Software Engineering Pakistan College of Computer and Information Sciences Jouf University Department of Information Systems Sakakah Saudi Arabia

ISBN: (纸本)9798350327595

Requirements Elicitation is the process of identifying system needs by talking with stakeholders who have a direct or indirect effect on the requirements. Requirements may be derived from several sources and are one of the most important phases of requirement engineering. We need to perform different elicitation techniques to find out the user's needs. Pre-requirements tracing is the process of determining the origins of a specific demand. Typically, certain needs arise that have no apparent source, but stakeholders will testify to their importance. Such criteria, however, are most likely dependent on tacit or tacit-like information entrenched in the issue domain. Tacit knowledge is one of the most common challenges for requirement analysts. This study contributes to constructing a systematic literature review for exploring multiple ways to model functional requirements using tacit knowledge. We have identified and analyzed 10 studies. In these research papers, different authors have discussed different methods to gather functional requirements from tacit knowledge. © 2023 IEEE.

关键词： Elicitation Techniques Requirement engineering Tacit-knowledge

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Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

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Computers, Materials & Continua 2025年第5期83卷 2889-2905页

作者： Meng Zhu Xingyue Wang Honge Ren Abeer Hakeem Linda Mohaisen College of Information Engineering Harbin UniversityHarbin150086China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin UniversityHarbin150086China College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China Heilongjiang Forestry Intelligent Equipment Engineering Research Center Northeast Forestry UniversityHarbin150040China Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.

关键词： Person reidentification ReID computer vision self-attention spatial-temporal information

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KAN-HyperpointNet for Point Cloud Sequence-Based 3D Human Action Recognition

KAN-HyperpointNet for Point Cloud Sequence-Based 3D Human Ac...

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

作者： Chen, Zhaoyu Li, Xing Huang, Qian Geng, Qiang Yang, Tianjin Han, Shihao College of Information Science and Technology & Artificial Intelligence Nanjing Forestry University China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9798350368741

Point cloud sequence-based 3D action recognition has achieved impressive performance and efficiency. However, existing point cloud sequence modeling methods cannot adequately balance the precision of limb micro-movements with the integrity of posture macro-structure, leading to the loss of crucial information cues in action inference. To overcome this limitation, we introduce D-Hyperpoint, a novel data type generated through a D-Hyperpoint Embedding module. D-Hyperpoint encapsulates both regional-momentary motion and global-static posture, effectively summarizing the unit human action at each moment. In addition, we present a D-Hyperpoint KANsMixer module, which is recursively applied to nested groupings of D-Hyperpoints to learn the action discrimination information and creatively integrates Kolmogorov-Arnold Networks (KAN) to enhance spatio-temporal interaction within D-Hyperpoints. Finally, we propose KAN-HyperpointNet, a spatio-temporal decoupled network architecture for 3D action recognition. Extensive experiments on two public datasets: MSR Action3D and NTU-RGB+D 60, demonstrate the state-of-the-art performance of our method. © 2025 IEEE.

关键词： 3D action recognition D-Hyperpoint KAN-HyperpointNet point cloud sequence

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