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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Ng, Hu Chong, Wing Kin Chia, Yu Zhang Vun Yap, Timothy Tzen Goh, Vik Tor Wong, Lai Kuan Teck Tan, Ian Kim Cher, Dong Theng Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia Faculty of Engineering Multimedia University Cyberjaya Malaysia School of Mathematical & Computer Sciences Heriot-Watt Univeristy Putrajaya Malaysia SIRIM Berhad Shah Alam Malaysia

ISBN: (纸本)9798350353464

Aspect-based subjectivity analysis stands as an important task in natural language processing, seeking to identify the subjectivity of various aspects or features within a text. A new method for aspect-based subjectivity analysis using BERT is introduced in this paper. BERT has demonstrated impressive performance across various NLP tasks, and its capabilities are utilized to accurately ascertain the subjectivity of specific aspects within a given text. The approach involves fine-tuning BERT on a sizable dataset annotated with aspect-level subjectivity labels, enabling the model to grasp the subtleties of aspect-based subjectivity analysis. Extensive experiments on benchmark datasets are conducted to showcase the effectiveness of this approach and compare it with existing methods. The results reveal that this proposed approach surpasses state-of-the-art techniques in aspect-based subjectivity analysis, underscoring the potential of leveraging BERT for such purposes. ©2024 IEEE.

关键词： Aspect-based NLP Subjectivity Analysis

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Estimating Chlorophyll Content from Hyperspectral Data Using Gradient Features 23rd

Estimating Chlorophyll Content from Hyperspectral Data Usin...

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23rd International Conference on Computational Science, ICCS 2023

作者： Ruszczak, Bogdan Wijata, Agata M. Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Opole Poland Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Gliwice Poland KP Labs Gliwice Poland

ISBN: (纸本)9783031360206

Non-invasive estimation of chlorophyll content in plants plays an important role in precision agriculture. This task may be tackled using hyperspectral imaging that acquires numerous narrow bands of the electromagnetic spectrum, which may reflect subtle features of the plant, and inherently offers spatial scalability. Such imagery is, however, high-dimensional, therefore it is challenging to transfer from the imaging device, store and investigate. We propose a machine learning pipeline for estimating chlorophyll content from hyperspectral data. It benefits from the Savitzky-Golay filtering to smooth the (potentially noisy) spectral curves, and from gradient-based features extracted from such a smoothed signal. The experiments revealed that our approach significantly outperforms the state of the art according to the widely-established estimation quality metrics obtained for four chlorophyll-related parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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On Min-Max Graph Balancing with Strict Negative Correlation Constraints 34

On Min-Max Graph Balancing with Strict Negative Correlation ...

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34th International Symposium on Algorithms and Computation, ISAAC 2023

作者： Kuo, Ting-Yu Chen, Yu-Han Frosini, Andrea Hsieh, Sun-Yuan Tsai, Shi-Chun Kao, Mong-Jen Dept. of Computer Science National Yang-Ming Chiao-Tung University Hsinchu Taiwan Dept. of Computer Science and Information Engineering National Cheng-Kung University Tainan Taiwan Dept. of Mathematics and Informatics University of Florence Italy Dept. of Computer Science and Information Engineering National Chi-Nan University Puli Taiwan

ISBN: (纸本)9783959772891

We consider the min-max graph balancing problem with strict negative correlation (SNC) constraints. The graph balancing problem arises as an equivalent formulation of the classic unrelated machine scheduling problem, where we are given a hypergraph G = (V, E) with vertex-dependent edge weight function p: E × V 7→ Z≥0 that represents the processing time of the edges (jobs). The SNC constraints, which are given as edge subsets C1, C2, . . ., Ck, require that the edges in the same subset cannot be assigned to the same vertex at the same time. Under these constraints, the goal is to compute an edge orientation (assignment) that minimizes the maximum workload of the vertices. In this paper, we conduct a general study on the approximability of this problem. First, we show that, in the presence of SNC constraints, the case with maxe∈E |e| = maxi |Ci| = 2 is the only case for which approximation solutions can be obtained. Further generalization on either direction, e.g., maxe∈E |e| or maxi |Ci|, will directly make computing a feasible solution an NP-complete problem to solve. Then, we present a 2-approximation algorithm for the case with maxe∈E |e| = maxi |Ci| = 2, based on a set of structural simplifications and a tailored assignment LP for this problem. We note that our approach is general and can be applied to similar settings, e.g., scheduling with SNC constraints to minimize the weighted completion time, to obtain similar approximation guarantees. Further cases are discussed to describe the landscape of the approximability of this prbolem. For the case with |V | ≤ 2, which is already known to be NP-hard, we present a fully-polynomial time approximation scheme (FPTAS). On the other hand, we show that the problem is at least as hard as vertex cover to approximate when |V | ≥ 3. © Ting-Yu Kuo, Yu-Han Chen, Andrea Frosini, Sun-Yuan Hsieh, Shi-Chun Tsai, and Mong-Jen Kao;licensed under Creative Commons License CC-BY 4.0.

关键词： Computational complexity

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Retain and Adapt: Online Sequential EEG Classification With Subject Shift

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4479-4492页

作者： Duan, Tiehang Wang, Zhenyi Shen, Li Doretto, Gianfranco Adjeroh, Donald A. Li, Fang Tao, Cui Mayo Clinic Department of Artificial Intelligence and Informatics JacksonvilleFL32216 United States University of Maryland College ParkMD20740 United States Jd Explore Academy Beijing100101 China West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States

Large variance exists in Electroencephalogram (EEG) signals with its pattern differing significantly across subjects. It is a challenging problem to perform online sequential decoding of EEG signals across different subjects, where a sequence of subjects arrive in temporal order and no signal data is jointly available beforehand. The challenges include the following two aspects: 1) the knowledge learned from previous subjects does not readily fit to future subjects, and fast adaptation is needed in the process;and 2) the EEG classifier could drastically erase information of learnt subjects as learning progresses, namely catastrophic forgetting. Most existing EEG decoding explorations use sizable data for pretraining purposes, and to the best of our knowledge we are the first to tackle this challenging online sequential decoding setting. In this work, we propose a unified bi-level meta-learning framework that enables the EEG decoder to simultaneously perform fast adaptation on future subjects and retain knowledge of previous subjects. In addition, we extend to the more general subject-agnostic scenario and propose a subject shift detection algorithm for situations that subject identity and the occurrence of subject shifts are unknown. We conducted experiments on three public EEG datasets for both subject-aware and subject-agnostic scenarios. The proposed method demonstrates its effectiveness in most of the ablation settings, e.g. an improvement of 5.73% for forgetting mitigation and 3.50% for forward adaptation on SEED dataset for subject agnostic scenarios. © 2020 IEEE.

关键词： Brain computer interface

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Neutrosophic Model for Evaluation Healthcare Security Criteria for Powerful and Lightweight Secure Storage System in Cloud-Based E-Healthcare Services

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Neutrosophic Sets and Systems 2023年 58卷 367-378页

作者： El-Douh, Ahmed A. Lu, SongFeng Abdelhafeez, Ahmed Ali, Ahmed M. Aziz, Alber S. School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Faculty of Information Systems and Computer Science October 6th University Giza12585 Egypt Faculty of Computers and Informatics Zagazig University Sharqiyah Zagazig44519 Egypt

Large data volumes make manually keeping and preserving health records for future reference problematic. Most unusual is the coronavirus epidemic's overcrowding of hospitals. The Secure Pattern Electronic Healthcare Records (SPEHR) scheme provides a powerful and lightweight secure storage system for cloud-based E-healthcare services that meets remote healthcare security criteria. So we proposed a neutrosophic model with a multi-criterion decision-making (MCDM) method for the analysis and evaluation of these criteria. The neutrosophic model is used to deal with vague and uncertain data. Then we integrated the neutrosophic model with the AHP method to rank and compute the weights of the criteria. Offering stakeholders a secure interface and avoiding unwanted access to cloud-stored data mitigates E-healthcare risks. The key derivation ensures end-to-end data. ciphering to prevent unauthorized use (KDF). This work provides robust security solutions for various environments. This work can meet security needs for people and secure-communicating organizations. We found the data privacy is the best criterion. © (2023). All Rights Reserved.

关键词： Access control

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A Contend Node-Restricted Receiver-Initiated MAC Protocol With Consecutive Packet Transmission

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Liu, Yuxin Ota, Kaoru Dong, Mianxiong Wang, Ruoyu Changsha University College of Computer Science and Engineering Changsha410022 China Muroran Institute of Technology Department of Sciences and Informatics Japan Central South University School of Computer Science and Engineering Changsha410083 China

The medium access control (MAC) protocol plays a crucial role in wireless communications, influencing key performance metrics, such as energy consumption, delay, collision, and throughput. This paper introduces the Contend Node Restricted Joint Consecutive Packet Transmissions Receiver-Initiated (CNR-CPT-RI) protocol, specifically designed to minimize collisions and reduce energy consumption in wake-up radio-enabled wireless sensor networks (WSNs). The CNR-CPT-RI protocol is an effective communication protocol that is mainly applied with nodes in the one-hop range of the sink. Since the sink has the ability to control each node, it becomes feasible to manage the nodes in a way that effectively reduces collisions and enhances network lifetime as well as throughput. Moreover, the CNR-CPT-RI protocol can adaptively adjust the number of nodes participating in channel contention, maintaining it within an optimal range to balance reduced collisions, decreased energy consumption, and sustained high throughput. Furthermore, the winning node in channel contention employs Consecutive Packet Transmissions (CPTs) method to further diminish competition. Comprehensive theoretical and experimental results demonstrate that the number of collisions of the CNR-CPT-RI protocol is only 42.4%, 35.9%, and 32.5% of the baseline protocol when the number of nodes is 10, 20, and 30, respectively. The energy consumption of the CNR-CPT-RI protocol is reduced by 5.7%, 15%, and 22.6%, and the delay is decreased by 6.7%, 16%, and 32%, respectively, compared with that of the existing protocol. © 2017 IEEE.

关键词： Internet protocols

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Characteristics of Accuracy Function on Multiclass Classification Based on Best, Average, and Worst (BAW) Subset of Random Forest Model 24

Characteristics of Accuracy Function on Multiclass Classific...

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24th International Electronics Symposium, IES 2022

作者： Susetyoko, Ronny Yuwono, Wiratmoko Purwantini, Elly Iman, Budi Nur Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Department of Electrical Engineering Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

This study aims to determine the effect of percentage of training data and number of features on average accuracy of classification model. Characteristics of the accuracy function of 2 datasets with different feature data types were also studied. All possible random forest models were calculated for accuracy based on some percentage level of training data and number of features. The accuracy function was constructed based on subsets of best accuracy (Best), average accuracy (Average), and worst accuracy (Worst) using polynomial regression. There were no significant difference in the average accuracy of various percentage levels of training data. However, there were significant difference in the average accuracy based on the number of features. The best polynomial model for average accuracy function of the 2 datasets had R2 99.50% and R2 96.99%, respectively. Based on individual testing (with a 95% confidence level), there was a linear effect of the percentage of training data on the average accuracy, and there were linear and quadratic effects on the number of features on the average accuracy. In the first dataset, classification model using 7 features obtain classification accuracies ranging from 96.9%-97.6%. In the second dataset using 7 features, the classification accuracies range from 74.0%-75.0%. Characteristics of the Best, Average, and Worst accuracy (BAW) functions could be an important description of the accuracy function characteristics and to determine the number of features. © 2022 IEEE.

关键词： Decision trees

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Equipment Parameter Investigation for a Proposed Power Conductor Hot Spot Identification system

Equipment Parameter Investigation for a Proposed Power Condu...

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International Conference on Modern Power Systems (MPS)

作者： Alexis Polycarpou Michael Komodromos Kyriacos Kalli Maria C. Argyrou Andreas Ioannou Deparment of Electrical Computer Engineering and Informatics Frederick University Frederick Research Center Nicosia Cyprus Department of Electrical Engineering / Computer Engineering and Informatics PhOSLab Cyprus University of Technology Limassol Cyprus

Reliability, efficiency and continuity of power energy supplied is an area which receives increasing attention as the main infrastructure of power transmission and distribution systems in many countries is ageing. Hot spot related cable failures and power interruptions have a big financial impact on the power provider. The operational parameters of a method to identify in real time hot spots on cables is investigated in this paper, which can result to timely fault prevention. This can be achieved through exploiting the optical sensing capabilities of the existing optical fibre on-grid network. The resulting parameters aim to be advantageous for the system as they increase accuracy and will enable the Remote, automated, continuous, and real time monitoring of the grid infrastructure integrity.

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An Initial Acquisition Scheme with Reduced Complexity for Modified Walsh-Hadamard Code Division Multiplexing 15

An Initial Acquisition Scheme with Reduced Complexity for Mo...

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15th International Conference on Advanced Technologies for Communications, ATC 2022

作者： Kojima, Toshiharu Yoshinaka, Yuto Graduate School of Informatics and Engineering The University of Electro-Communications Department of Computer and Network Engineering 1-5-1 Chofugaoka Tokyo182-8585 Japan

ISBN: (数字)9781665451888

ISBN: (纸本)9781665451888

Modified Walsh-Hadamard code division multiplexing (MWHCDM) has an anti-blockage capability. It is thus suitable for helicopter satellite communications where the transmission channel is described as the periodic blockage channel. This paper addresses the computational complexity reduction of the initial acquisition for MWHCDM. The conventional initial acquisition scheme employs the sliding Walsh-Hadamard transform (WHT). A narrowing function based on the redundancy of MWHCDM signals is proposed to reduce the sliding WHT. computer simulation shows that the proposed narrowing function reduces the sliding WHT by about 70% without acquisition performance degradation. © 2022 IEEE.

关键词： Code division multiple access

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University Department Recommendations Using Subject-Score-Based Skyline Queries

University Department Recommendations Using Subject-Score-Ba...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Damarjati, Cahya Wicaksana, Galang Suripto, Slamet Wijayanto, Heri Setyawan, Haris Chen, Hsing-Chung Universitas Muhammadiyah Yogyakarta Department of Information Technology Yogyakarta Indonesia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Yogyakarta Indonesia Universitas Mataram Department of Informatics Engineering Mataram Indonesia Asia University China Medical University Department of Computer Science and Information Engineering Department of Medical Research Taichung Taiwan

ISBN: (纸本)9798350379839

Selecting the right university department is a critical decision-making problem for high school graduates, significantly influenced by limited guidance and personal preferences. Traditional counseling methods often do not fully utilize available academic data, resulting in less effective recommendations. This study proposed a Subject-Score-Based Skyline Queries Algorithm to enhance decision-making by recommending university departments based on candidates' high school subject scores. It used the score differences and minimum score rule in addition to the native Skyline Queries. The Algorithm identifies departments where a candidate's grades meet or exceed minimum requirements across multiple academic subjects, thereby suggesting optimal matches. This approach aims to mitigate uncertainty and enhance decision confidence among prospective students, potentially reducing dropout rates and improving academic outcomes. Experimental results demonstrate the method's effectiveness in providing personalized recommendations, underscoring its applicability beyond university admissions to various decision-making contexts. Future research could extend this approach by incorporating non-academic factors for more comprehensive guidance. © 2024 IEEE.

关键词： Students

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