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Whether human-induced activities could change the gradient pattern of coastal land use along the sea-land direction:a case study in Manila Bay,Philippines

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Acta Oceanologica Sinica 2023年第2期42卷 163-174页

作者： Zhi Ding Fenzhen Su Yanan Chen Ying Liu Xue Feng Wenqiu Hu Fengqin Yan He Li Pujia Yu Xuguang Tang Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station School of Geographical ScienceSouthwest UniversityChongqing400715China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing100101ChinaState Key Laboratory of Resources and Environmental Information SystemInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing100101China Key Laboratory of Reservoir Aquatic Environment Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing400714China

Increasing intense human activities have largely changed the coastal landscape and caused many environmental ***,whether human-induced activities could change the coastal land use gradient pattern,an important coastal zonal characteristic along the sea-land direction,remains *** Bay was selected as the study area in this *** to the distance of the land use and land cover(LULC)to the coastline,we clustered the typical coastal land use sequence patterns(CLUSPs)along the sea-land direction between 1988 and 2016 in Manila Bay and found the following.(1)Four typical CLUSPs,including the natural CLUSP dominated by forest land and grassland,the agricultural CLUSP dominated by dry farm and paddy field,the urbanised CLUSP dominated by construction land and the fishery CLUSP dominated by fishing farm,were mined in *** typical CLUSPs(a natural CLUSP,an intermediate CLUSP between the agricultural and urbanised CLUSPs,and a fishery CLUSP)were mined in 2016.(2)Affected by the dominant LULC,these typical CLUSPs showed a regular spatial pattern along the sea-land *** example,the typical natural CLUSP showed a landward pattern due to the long distance between the forest land and grassland and the coastline.(3)However,influenced by urban and aquaculture expansion,the land intensification of the CLUSP exhibited an obvious increase and caused the decrease of the CLUSP diversity from 1988 to *** increase in the area of LULC coverage showed no obvious correlation with its distance from the coastline(DFC),but the net increase rate of LULC coverage had a significant negative correlation with the ***,human-induced activities have a large impact on the gradient pattern of coastal land use along the sea-land direction.

关键词： coastal zone land use gradient sea-land human activities Manila Bay

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Human-Computer Interaction and Knowledge Transfer: Study of E-Learning Achievement 7

Human-Computer Interaction and Knowledge Transfer: Study of ...

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7th International Conference of Computer and Informatics Engineering, IC2IE 2024

作者： Alamsyah, Doni Purnama Othman, Norfaridatul Ahkmaliah Kerta, Johan Muliadi Morika, Doni Ramdhani, Yudi Bina Nusantara University Binus Business School Creativepreneurship Department Jakarta Indonesia Universiti Teknikal Malaysia Melaka Department of Management Technology Melaka Malaysia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Design Design Interior Department Jakarta Indonesia Satu University Information System Program Bandung Indonesia

ISBN: (纸本)9798331505530

Human-computer interaction influences the effectiveness of e-learning knowledge transfer, so it is necessary to pay attention to user engagement and achievement. The aim of this research is to examine the factors that support e-learning knowledge transfer through achievement and user engagement (self-efficacy, self-confidence, self-esteem). The research method was carried out by surveying e-learning users, namely students, data from students was collected through quantitative questionnaires. The data is then processed using the SmartPLS tool through two tests, namely the PLS Algorithm and Boostrapping. The model was tested using inner and outer tests, and the data was analyzed based on the research hypothesis design. Student interactions with e-learning influence several behaviors that can support e-learning knowledge transfer performance. The research results prove that student e-learning achievement has a positive impact on e-learning knowledge transfer. Several supporting factors play a role in increasing e-learning achievement, namely self-efficacy, self-confidence and self-esteem of students. However, the supporting factors for students are only self-efficacy and self-confidence which are directly able to increase e-learning knowledge transfer. It is known that students who are less able to respect themselves in interacting with e-learning are unable to support knowledge transfer. The rules of human-computer interaction play a role in supporting e-learning knowledge transfer, studies on user behavior need to be considered, especially self-efficacy and self-confidence to support online learning academic performance. © 2024 IEEE.

关键词： Knowledge transfer

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A Comparative Study of Machine Learning Models for Sentiment Analysis on User Reviews of the Desa Wisata Nusantara Application in the Google Play Store 11

A Comparative Study of Machine Learning Models for Sentiment...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Maulana, Fairuz Iqbal Rahayu, Anita Widartha, Vandha Pradwiyasma Adi, Puput Dani Prasetyo Purnomo, Agung Pramono, Andi School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Pukyong National University Department of Information System Busan Korea Republic of Bandung Indonesia Binus Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia

ISBN: (纸本)9798350389654

In the swiftly changing digital environment, user reviews on platforms like the Google Play Store have emerged as a crucial factor influencing an application's success. These reviews, abundant in user experiences and perceptions, serve as a valuable asset for sentiment analysis. However, the sheer volume of these reviews poses a challenge for manual analysis, thereby necessitating the deployment of automated sentiment analysis methods. We utilize the Bernoulli Naive Bayes (BNB) and Support Vector Machine (SVM) models, selected for its simplicity, efficiency, and capability to handle large datasets with high dimensionality, characteristic of text data such as user reviews. Based on the analysis and conclusions derived from the research inquiries in this study, it is evident that a significant proportion of customer reviews, classifying 42.39% (or 142 reviews) as positive, 45.07% (or 151 reviews) as negative, and 12.54 % (or 42 reviews) as Neutral sentiments regarding the Desa Wisata Nusantara Application on the Google Play Store. We found that the Bernoulli Naive Bayes model had better accuracy than the Support Vector Machine. The accuracy level of the Bernoulli Naive Bayes model is 73 % and the Support Vector Machine is 70%. We present the outcomes of our analysis, discuss their implications, and propose directions for future research. Our research signifies a promising advancement towards harnessing machine learning for sentiment analysis, setting the stage for future investigations in this intriguing field. © 2024 IEEE.

关键词： Contrastive Learning

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Nonreciprocity of energy transfer in a nonlinear asymmetric oscillator system with various vibration states

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Applied Mathematics and Mechanics(English Edition) 2023年第5期44卷 727-744页

作者： Jian’en CHEN Jianling LI Minghui YAO Jun LIU Jianhua ZHANG Min SUN Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin 300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of TechnologyTianjin 300384China School of Aeronautics and Astronautics Tiangong UniversityTianjin 300387China Agricultural Information Institute of Chinese Academy of Agricultural Sciences Key Laboratory of Agricultural Big DataMinistry of Agriculture and Rural AffairsBeijing 100081China School of Science Tianjin Chengjian UniversityTianjin 300384China

The nonreciprocity of energy transfer is constructed in a nonlinear asymmetric oscillator system that comprises two nonlinear oscillators with different parameters placed between two identical linear *** slow-flow equation of the system is derived by the complexification-averaging *** semi-analytical solutions to this equation are obtained by the least squares method,which are compared with the numerical solutions obtained by the Runge-Kutta *** distribution of the average energy in the system is studied under periodic and chaotic vibration states,and the energy transfer along two opposite directions is *** effect of the excitation amplitude on the nonreciprocity of the system producing the periodic responses is analyzed,where a three-stage energy transfer phenomenon is *** the first stage,the energy transfer along the two opposite directions is approximately equal,whereas in the second stage,the asymmetric energy transfer is *** energy transfer is also asymmetric in the third stage,but the direction is reversed compared with the second ***,the excitation amplitude for exciting the bifurcation also shows an asymmetric *** vibrations are generated around the resonant frequency,irrespective of which linear oscillator is *** excitation threshold of these chaotic vibrations is dependent on the linear oscillator that is being *** addition,the difference between the energy transfer in the two opposite directions is used to further analyze the nonreciprocity in the *** results show that the nonreciprocity significantly depends on the excitation frequency and the excitation amplitude.

关键词： nonreciprocity strong nonlinearity energy transfer chaotic vibration higher branch of response

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A Multi Target Drug Design Method Based on Target Protein Sequence and Feature Similarity

A Multi Target Drug Design Method Based on Target Protein Se...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haoran Liu Xiaoli Lin Jing Hu Xiaolong Zhang Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

This paper describes a multi target drug design method based on features of the target proteins. The multi target drug which inhibit multiple proteins have prospective applications, but design is difficult. In this study, the target protein sequences are embedded to obtain target features. The features of each target are independently encoded as target latent vectors, and the features of multiple targets are jointly encoded as target similarity latent vectors. Based on the features of targets and the similarity features among the targets, multi target drugs are efficiently designed. In the experiments, the designed multi target drugs can be docked with target proteins with a proprietary molecular structure according to the requirements of different target pocket structures. The excellent fitness among the molecular structure of multi target drugs and the protein structure of multiple targets confirms the performance of the proposed method. In the molecular docking part of the experi¬ments, the binding affinity of designed multi target drugs is far better than that in the previous studies, which administrates the better performance of this work.

关键词： Drugs Design methodology Vectors Protein sequence Encoding Bioinformatics

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Scheduling Production in the Flexible Packaging Industry Using Mathematical Models and Genetic Algorithms

Scheduling Production in the Flexible Packaging Industry Usi...

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2023 International Conference on Informatics Engineering, science and Technology, INCITEST 2023

作者： Tarigan, Masmur Gaol, Ford Lumban Mauritsius, Tuga Budiharto, Widodo Esa Unggul University Bina Nusantara University Computer Science Department / Computer Science Graduate Program Jakarta Indonesia Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia Management Bina Nusantara University Department of Information System Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350317862

Minimizing time wastage is a crucial objective for all production operations. Reliable manufacturing scheduling The objective is to mitigate time-based production waste caused by excessive processing, waiting, and transportation. Industry 4.0 promotes technological innovation across all industrial sectors to increase efficiency and effectiveness while preserving high competitiveness. The implementation of artificial intelligence in Industry 4.0 is expected to result in a reduction in time wastage. Non-dominance sequencing genetic algorithms (NSGA), genetic algorithms (GA), and evolutionary algorithms (EA) are the three most common scheduling approaches, according to literature-indexed research summaries from the past five years. The examination of production scheduling in the flexible packaging business, which involves the utilization of different machines and processes and is produced on demand, had not been conducted before in this study. A mathematical scheduling model will be developed in this research to ascertain the shortest production time span in the flexible packaging industry. As a form of technological innovation, this mathematical model will be utilized in experiments employing a genetic algorithm approach. For the genetic algorithm to generate a minimum makespan in the flexible packaging industry's production scheduling process. © 2023 IEEE.

关键词： Genetic algorithms

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Multi Task Classification Using Deep Learning Approaches for Big Five Personality Traits Prediction: A Review 11

Multi Task Classification Using Deep Learning Approaches for...

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11th International Conference on information Technology, Computer and Electrical Engineering, ICITACEE 2024

作者： Nudin, Salamun Rohman Gernowo, Rahmat Somantri, Maman Wibowo, Adi Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Diponegoro University Faculty of Engineering Department of Electrical Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350389289

This paper presents a comprehensive review of deep learning methods for the Big Five personality traits prediction using multi-task classification. The purpose of the review is to determine the performance of models developed in previous research to describe research opportunities and challenges in the future. The Big Five model is a framework in psychology that categorizes human personality into five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The review begins by analyzing 22 articles from the science Direct, IEEE Xplore, and Scopus databases by applying screening using the Big Five Personality keywords. Then study the concepts of deep learning for model development such as: input, architecture, method, dataset, and performance to identify the strengths and limitations of each approach. The review results of the highest average accuracy by modality such as unimodal text with essay dataset (80.98%), unimodal image with ImageNet dataset (95.36%), unimodal audio with SSPNet dataset (79.48%), bimodal image and audio with Chalearn Video First Impression dataset (91.67%), multimodal text, image, and audio with Chalearn Video First Impression dataset (91.88%). The average accuracy of the Big Five Personality traits was highest (95.36%) and lowest (59%). This highlights the need for standard benchmarks, interpretable models and interdisciplinary collaboration to improve the accuracy, reliability and ethical implications of personality prediction. Future research is expected to pave the way for advancements in understanding human personality through deep learning approaches using other modalities such as EEG, GSR, and PPG. © 2024 IEEE.

关键词： Contrastive Learning

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A Comparative Study on Adversarial Attacks for Visible and Infrared Images

A Comparative Study on Adversarial Attacks for Visible and I...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Guo, Haiwang Tan, Wei Zhao, Jiajia School of Electronics and Information Northwestern Polytechnical University Shaanxi Xi'an710072 China Agent Cooperation Laboratory Science and Technology on Complex System Control and Intelligent Beijing100074 China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

In recent years, the booming development of artificial intelligence has driven the development and progress of society. With its excellent performance, deep learning has been widely used in many fields, including some security-sensitive fields such as unmanned systems, auto-driving and so on. Research has found that deep neural networks are vulnerable to adversarial samples, which can lead to misclassification of neural networks and can cause serious safety incidents. Hence, the study of adversarial samples has received increasing attention, and researchers have conducted studies from different perspectives, and relevant research results have emerged. However, most of these studies have focused on visible images, and few have studied infrared images, which play an important role in military and other important fields. In this paper, a comparative study on adversarial attacks for visible and infrared images is presented. A number of adversarial attack experiments are conducted on ResNet18 and VGG16 models for visible and infrared images. By comparing the experimental results, the characteristics of infrared adversarial samples are derived relative to those with visible adversarial samples. Then, a gradient attack method from a frequency domain perspective is proposed. The adversarial perturbation noise is added to the low frequency component of images. It is verified experimentally that the proposed method improves the transferability of infrared adversarial samples compared to some baseline methods. © 2022 IEEE.

关键词： Deep neural networks

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Resource Allocation for Fairness Enhancement in Multi-Cell Vehicular VLC system With Optical IRS: A Cooperative Transmission Approach

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IEEE Internet of Things Journal 2025年第11期12卷 16931-16946页

作者： An, Nan Yang, Fang Liu, Chang Cheng, Ling Song, Jian Han, Zhu Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Center of National Railway Intelligent Transportation System Engineering and Technology Beijing100081 China China Academy of Railway Sciences Corporation Limited Signal and Communication Research Institute Beijing100081 China University of the Witwatersrand School of Electrical and Information Engineering Johannesburg2050 South Africa Tsinghua University Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Beijing100084 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

In multi-cell downlink vehicular visible light communication (VLC) systems, vehicles at a cell edge experience lower achievable data rates compared with those at a cell center, primarily due to the weaker channel gain and inter-cell interference, leading to unfairness among vehicles. In this paper, a cooperative transmission approach is proposed for a vehicular VLC system with optical intelligent reflecting surface (OIRS) to enhance the max-min fairness of the system by leveraging the additional OIRS-reflected channels and the interference-mitigating capabilities of cooperative transmission. To this end, the system model is established, followed by the formulation of a resource allocation problem aiming at enhancing max-min fairness, in which the minimum achievable data rate among vehicles is optimized. Then, an effective resource algorithm is proposed, transforming the original problem into an equivalent form and subsequently decomposing it into three subproblems, focusing on OIRS assignment, subchannel allocation, and power adjustment, respectively. By employing a block coordinate descent algorithm, the three subproblems are solved iteratively until convergence. In addition, simulation results confirm the improvement in max-min fairness brought by OIRS and the proposed cooperative transmission approach. Moreover, compared with various baselines, the proposed resource allocation algorithm significantly enhances in the max-min fairness for the vehicular VLC system, crucial for this application. © 2014 IEEE.

关键词： Resource allocation

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An end-to-end instance segmentation method based on improved ConvNeXt V2

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International Journal of Wireless and Mobile Computing 2024年第4期26卷 384-392页

作者： Wang, Wenlu Sun, Ying Xu, Manman Bai, Dongxu Huang, Li Zou, Chunlong Chen, Baojia Tang, Dalai Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance China Three Gorges University Hubei Yichang China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot China

In order to improve the efficiency of indoor mobile robots in locating and segmenting environmental instances, an instance segmentation method based on RTMDet is proposed. Firstly, the more powerful ConvNeXt V2 is used as the backbone network of the model, which improves the performance of the existing RTMDet model. Subsequently, NAS-FPN is used as a converged network to detect the network at any given time. Finally, AdamW is used as the model optimiser, which effectively solves the problem of excessive memory occupation when updating parameters and improves the performance of the model. The proposed method obtains the highest evaluation index in all cases. After training and testing on the Cityscapes data set, the recognition accuracy reaches 65.0%. © 2024 Inderscience Enterprises Ltd.

关键词： Statistical tests

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