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27th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2023

作者： Nesbit, Trevor Department of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

ISBN: (纸本)9781713880325

Many higher education institutions adapted to the Covid-19 pandemic by switching their teaching into online mode making use of online synchronous sessions using technologies such as Zoom. It was common for lecturers to find it disconcerting that many students were not turning on their cameras and microphones and how this made it difficult to ascertain whether their students were engaged in the sessions at all. This paper examines the experiences of teaching staff in eight courses relating to the use of audience response systems (ARS) to improve the experiences of students and teaching staff when conducting synchronous online teaching sessions or hybrid sessions when they had some face-face students and some online students joining sessions synchronously. Literature is examined that shows the benefits relating to the use of ARS in synchronous online teaching sessions to include anonymity of student responses;enhancement of feedback between teaching staff and students;and teaching staff getting a better sense of student engagement during a session. An analysis of the eight cases presented confirms these benefits in the literature from the perspective of the teaching staff. The findings apply irrespective of the ARS being used and will be of relevance and interest to any teaching staff seeking to improve the experiences of students and teaching staff involved in synchronous online teaching sessions. © 2023 Proceedings of World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI. All rights reserved.

关键词： Students

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La-substituted W-type barium-nickel ferrites for tunable and high-performance electromagnetic wave absorption

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International Journal of Minerals,Metallurgy and Materials 2025年第3期32卷 645-656页

作者： Long Wang Jiurong Liu Shenghui Xie Yanli Deng Zhou Wang Shanyue Hou Shengying Yue Gang Wang Na Wu Zhihui Zeng Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials(Ministry of Education) School of Materials Science and EngineeringShandong UniversityJinan 250061China Electronic Materials Research Laboratory Key Laboratory of the Ministry of Education and International Center for Dielectric ResearchSchool of Electronic Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China Army Engineering University of PLA College of Field Engineering Nanjing 210007China Laboratory for Multiscale Mechanics and Medical Science State Key Laboratory for Strength and Vibration of Mechanical StructuresSchool of AerospaceXi’an Jiaotong UniversityXi’an 710049China Department of Electrical and Computer Engineering Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbana 61801USA School of Chemistry and Chemical Engineering Shandong UniversityJinan 250100China

W-type barium-nickel ferrite(BaNi_(2)Fe_(16)O_(27))is a highly promising material for electromagnetic wave(EMW)absorption be-cause of its magnetic loss capability for EMW,low cost,large-scale production potential,high-temperature resistance,and excellent chemical ***,the poor dielectric loss of magnetic ferrites hampers their utilization,hindering enhancement in their EMW-absorption *** efficient strategies that improve the EMW-absorption performance of ferrite is highly desired but re-mains ***,an efficient strategy substituting Ba^(2+)with rare earth La^(3+)in W-type ferrite was proposed for the preparation of novel La-substituted ferrites(Ba_(1-x)LaxNi_(2)Fe_(15.4)O_(27)).The influences of La^(3+)substitution on ferrites’EMW-absorption performance and the dissipative mechanism toward EMW were systematically explored and ***^(3+)efficiently induced lattice defects,enhanced defect-induced polarization,and slightly reduced the ferrites’bandgap,enhancing the dielectric properties of the ***^(3+)also enhanced the ferromagnetic resonance loss and strengthened magnetic *** effects considerably improved the EMW-absorption perform-ance of Ba_(1-x)LaxNi_(2)Fe_(15.4)O_(27)compared with pure W-type *** x=0.2,the best EMW-absorption performance was achieved with a minimum reflection loss of-55.6 dB and effective absorption bandwidth(EAB)of 3.44 GHz.

关键词： electromagnetic wave absorption W-type barium ferrite rare earth dielectric

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Deep Semantic and Attentive Network for Unsupervised Video Summarization

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ACM Transactions on Multimedia Computing, Communications and Applications 2022年第2期18卷 1-21页

作者： Zhong, Sheng-Hua Lin, Jingxu Lu, Jianglin Fares, Ahmed Ren, Tongwei College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical Engineering Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Cairo Egypt State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

With the rapid growth of video data, video summarization is a promising approach to shorten a lengthy video into a compact version. Although supervised summarization approaches have achieved state-of-the-art performance, they require frame-level annotated labels. Such an annotation process is time-consuming and tedious. In this article, we propose a novel deep summarization framework named Deep Semantic and Attentive Network for Video Summarization (DSAVS) that can select the most semantically representative summary by minimizing the distance between video representation and text representation without any frame-level labels. Another challenge associated with video summarization tasks mainly originates from the difficulty of considering temporal information over a long time. Long Short-Term Memory (LSTM) performs well for temporal dependencies modeling but does not work well with long video clips. Therefore, we introduce a self-attention mechanism into our summarization framework to capture the long-range temporal dependencies among the frames. Extensive experiments on two popular benchmark datasets, i.e., SumMe and TVSum, show that our proposed framework outperforms other state-of-the-art unsupervised approaches and even most supervised methods. © 2022 Association for Computing Machinery.

关键词： Semantics

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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A Security Model of Multihospital FHIR Database Authorization Based on Secret Sharing and Blockchain

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IEEE Internet of Things Journal 2024年第6期11卷 10325-10335页

作者： Yang, Ching-Nung Li, Peng Cheng, Hsiang-Han Kuo, Hsin-Chuan Lu, Ming-Chan Xiong, Lizhi National Dong Hwa University Department of Computer Science and Information Engineering Hualien97401 Taiwan North China Electric Power University Department of Mathematics and Physics Hebei Key Laboratory of Physics and Energy Technology Baoding071003 China Nanjing University of Information Science and Technology School of Computer and Software Nanjing210044 China

Secret sharing (SS) is a threshold technology that shares a secret value by generating and distributing n shares in the way that a set of any k shares can recover the secret. On the other hand, blockchain is a decentralized system establishing a secure data structure for storing data in the form of a hash chain. Combining SS and blockchain is definitely a promising paradigm in the data security field. Recently, the fast healthcare interoperability resources (FHIRs) standard is defined for medical data exchange across various medical institutions. In this article, we combine SS, ElGamal's encryption/decryption, and blockchain to propose a multihospital FHIR database authorization (MFDA). To achieve a secure MFDA, the group-based SS (GSS) and the notion using GSS on exponent (referred to as EGSS) are proposed. The secrets of GSS and EGSS have the discrete logarithm problem (DLP) relation, on which the secrets can be used as ElGamal's private key and public key, respectively. Also, their shares will have DLP relation. By uploading public shares of EGSS on blockchain and using private shares of GSS in a private network, we can recover ElGamal's public key from a deployed smart contract on blockchain and recover ElGamal's private key from a qualified set. Meantime, when recovering private key, the private shares of GSS can be authenticated by public shares on blockchain to check tampering and corrupted of private shares during the communication in a private network. Theorems, examples, and experiments demonstrate that the proposed MFDA has efficient and secure database authorization. © 2014 IEEE.

关键词： Blockchain

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Using Deep Learning and Object-Oriented Metrics to Identify Critical Components in Object-Oriented Systems 23

Using Deep Learning and Object-Oriented Metrics to Identify ...

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5th World Symposium on software engineering, WSSE 2023

作者： Tete, Akpedje Touré, Fadel Badri, Mourad Software Engineering Research laboratory Department of Mathematics and Computer Science University of Quebec Trois-RivièresQC Canada

ISBN: (纸本)9798400708053

This paper aims at studying the ability of deep machine learning to predict software faults based on object-oriented metrics. This research investigated software faults from the perspective of fault-proneness, faults number and faults frequency, and used data collected from several versions of a Java open-source software system. This study relied on Chidamber and Kemerer suite of metrics as proxy to capture various software characteristics. In this study, the deep learning regression and classification results were compared to linear and logistic regressions. Auto-Encoders (AE) and Principal Components Analysis (PCA) have been used to reduce redundant information from the dataset. To evaluate the prediction ability of the models, this research used the inter-version validation strategy. The results showed that the models can achieve a significant average performance up to 89%. © 2023 Copyright held by the owner/author(s).

关键词： Principal component analysis

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Optimizing auditory brainstem response detection through NSGA-II guided feature selection

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Neural Computing and Applications 2025年第15期37卷 8925-8943页

作者： Majidpour, Jafar Hassanzadeh, Hiwa Khezri, Edris Arabi, Hossein Department of Software Engineering University of Raparin Kurdistan Region Rania Iraq Department of Computer Science University of Raparin Kurdistan Region Rania Iraq Department of Computer Engineering Islamic Azad University Boukan Branch Boukan Iran Division of Nuclear Medicine and Molecular Imaging Department of Medical Imaging Geneva University Hospital Geneva1211 Switzerland

Hearing-impaired people undergo auditory brainstem response (ABR) testing to assess their peripheral auditory nerve system. Audiologists apply diagnostic labels to ABR data using reference-based indicators such as peak latency, waveform shape, amplitude, and others. ABR test scoring errors may invalidate auditory nerve system integrity results. Machine learning, especially deep learning, may reduce human error in ABR analysis. This work offers a complete methodology to handle ABR analysis problems. First, extract ABR test images from PDF reports to create a dataset. The next step uses Elastic Distortion approach to create high-quality ABR images while conserving data. ResNet-50 deep learning extracts ABR image characteristics in the third phase. The fourth step finds the most relevant features using a trainable multi-objective non-dominated sorting genetic algorithm II optimizer. Finally, ABR images are classified using machine learning algorithms. The proposed model achieved significant performance metrics: an accuracy of 99.46%, precision of 99.33%, specificity of 99.57%, sensitivity of 99.57%, F1-score of 99.43%, and a mean squared error of 0.0054 when using a support vector machine classifier with both augmented and original images. These findings demonstrate the effectiveness of trainable feature selection and high-quality ABR images in improving ABR analysis accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Mean square error

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Prediction of three-dimensional ocean temperature in the South China Sea based on time series gridded data and a dynamic spatiotemporal graph neural network

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Acta Oceanologica Sinica 2024年第7期43卷 26-39页

作者： Feng Nan Zhuolin Li Jie Yu Suixiang Shi Xinrong Wu Lingyu Xu Department of Computer Engineering and Science Shanghai UniversityShanghai 200444China Key Laboratory of Digital Ocean National Marine Data and Information ServiceTianjin 300171China

Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related ***,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean *** graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among *** this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior *** and spatial dependencies in the time series were then captured using temporal and graph *** also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid *** this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea *** compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.

关键词： dynamic associations three-dimensional ocean temperature prediction graph neural network time series gridded data

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CMCF-Net: An End-to-End Context Multiscale Cross-Fusion Network for Robust Copy-Move Forgery Detection

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IEEE Transactions on Multimedia 2024年 26卷 6090-6101页

作者： Xiong, Lizhi Xu, Jianhua Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanjing University of Information Science and Technology Software Engineering Nanjing210044 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan Fudan University School of Computer Science Shanghai200433 China

Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multiple scales of features in the encoder part may not effectively aggregate contextual information, resulting in poor performance. In this paper, an end-to-end context multiscale cross-fusion network (CMCF-Net) is proposed to detect image copy-move forgery. The proposed network consists of a multiscale feature extraction fusion (MSF) module and a multi-information fusion decoding (MFD) module. Multiscale information is efficiently extracted and fused in the MSF module utilizing stacked-scale feature fusion, which improves the network's forgery localization ability on objects of different scales. The MFD module employs contextual information combination and weighted fusion of multiscale information to guide the network in obtaining relevant clues from correlated information at multiple different scales. Experimental results and analysis have demonstrated that the proposed CMCF-Net achieves the best localization results with higher robustness. © 1999-2012 IEEE.

关键词： Feature extraction

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An Integrated Machine Learning Framework Based Liver Disease Diagnosis System

An Integrated Machine Learning Framework Based Liver Disease...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Imtiaz, Irsa Qaiser, Ayesha Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Sciences and Technology Department of Computer Science Kuwait

ISBN: (纸本)9798350349719

Viral hepatitis increases the likelihood of developing liver cancer, which is a critical public health concern. It is a severe disorder that can be fatal if not recognized and treated promptly. The most common causes of liver cancer are hepatitis viruses. Early-stage diagnosis of HCC is crucial for improving patient outcomes, particularly when hepatitis virus infection is the underlying cause. This study looks into the use of ML algorithms for early detection of liver cancer in hepatitis virus-infected patients. The study will present a unified approach for the early detection of liver cancer that is highly accurate. Early detection of liver cancer is critical in reducing mortality and assisting practitioners in treating patients on time based on results. Machine learning algorithms have been intensively researched and applied to the diagnosis of liver cancer, especially in recent years. Researchers employed a variety of machine learning techniques to create models for detecting liver cancer at different stages, including early diagnosis. To enhance the model's flexibility and efficiency, We used three datasets that contained all of the information required for diagnosing liver cancer. Several machine-learning approaches were used to improve the model's accuracy. Our proposed model acquired a 98.10% accuracy rate with Bagging. Using several datasets in machine learning for liver disease detection enhances model performance by diversifying the training examples, resulting in a more robust and generalizable model with lower overfitting risk. This study aims to improve the quality of life for people who are at risk of or have been diagnosed with liver cancer. © 2024 IEEE.

关键词： Viruses

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