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3rd International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2023

作者： Inoue, Shuichi Nobukawa, Sou Nishimura, Haruhiko Watanabe, Eiji Isokawa, Teijiro Graduate School of Information and Computer Science Chiba Institute of Technology Narashino Japan Yamato University Faculty of Informatics Osaka Japan National Institute for Basic Biology Okazaki Japan Graduate School of Engineering University of Hyogo Himeji Japan

ISBN: (纸本)9798350300604

Deep echo state network (Deep-ESN) model consists of multiple reservoir layers, that can respond to layer-specific different time-scales. This dynamical characteristic leads to enhance performance of ESN. However, neither the design guidelines for the hyperparameters of the network and individual neurons nor the mechanism to produce the diverse dynamical response have been clarified. In this study, we proposed an approach to generate the dynamical responses with different time-scales in each layer by adjusting the leaking rate of neurons. Through the evaluations time-series prediction task for different leaking rates, multiscale entropy analysis for each reservoir layer, and cross-correlation between adjacent layers, we found that when the leaking rate is set to low, the layer-specific dynamics with different time-scales are generated, as well as a mechanism whereby the signal propagates to subsequent layers with a delay, i.e., queue characteristic. These characteristics produce a high memory capacity. Consequently, diverse responses with delay leads to an enhancement of Deep-ESN functionality. © 2023 IEEE.

关键词： Deep learning

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GITANet: Group Interactive Threshold-based Attention Network for Hyperspectral Image Classification

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IEEE Transactions on Multimedia 2025年 27卷 3571-3584页

作者： Duan, Yule Chen, Chuang Fu, Maixia Gong, Xiuwen Niu, Yingying Luo, Fulin The University of Sydney Faculty of Engineering NSW2006 Australia Henan University of Technology Key Laboratory of Grain Information Processing and Control Ministry of Education Zhengzhou450001 China Henan University of Technology Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001 China Chongqing University College of Computer Science Chongqing400044 China

Group convolution networks have shown great potential in hyperspectral image (HSI) classification because of their ability to divide total spectral bands into multiple groups and focus on fine discrimination within different spectral ranges. Most group convolution networks process parallel spectral groups independently;however, they neglect the important relevance of nearby spectral ranges. Moreover, the feature maps from different spectral groups are not considered for recalibration in the existing attention. To address these issues, we propose a novel group interactive threshold attention network (GITANet). In the network, a stratified-split-concatenation strategy, which not only splits all bands into multiple groups for intragroup convolution but also propagates the intergroup information via the stratified concatenation operation between different groups, is designed for bandwise group convolution. Relying on the high dependencies among nearby spectra, the cross-group interactive attention block is designed to encourage significant spectral features. Subsequently, from different spectral ranges, a learnable threshold generation block is built to estimate the information validity of each pixel. On the basis of this threshold, soft threshold spatial attention is developed in the bandwise encoderdecoder architecture, which emphasizes high-value spatial areas during the fusion of group convolutional features. Therefore, complementary and discriminative spectral-spatial features are obtained to improve the performance of HSI classification. The experimental results on three HSI datasets illustrate that GITANet is superior to several state-of-the-art networks. © 1999-2012 IEEE.

关键词： Convolution

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Design and Implementation of IoT-Based Smart Fuel Monitoring System 21

Design and Implementation of IoT-Based Smart Fuel Monitoring...

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21st IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2025

作者： Ahmed Abdalmaged, Islam Mohamedalfatih Saud Al-Humairi, Safaa Najah Hezam Al-Hakimi, Asmaa Mahfoudh Kumari, Aparna Management and Science University School of Graduate Studies Selangor Shah Alam40100 Malaysia Management and Science University Faculty of Information Sciences and Engineering Selangor Shah Alam40100 Malaysia Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad India

ISBN: (纸本)9798331522193

The fast growth of the 'Internet of Things' (IoT) has impacted on several sectors, including fuel management. This paper describes the design, fabrication, and implementation of an IoT-based smart fuel monitoring system to improve the effectiveness of using fuel resources while minimizing losses and optimizing processes. A Rasp berry Pi controller with various sensors tracks essential parameters such as fuel level, temperature, pressure, pH, and gas levels. The system also provides for processing and presenting the collected data through a mobile application and a website, making it possible to manage the system further from the users via remote control. The system was tested using two fuel types, Benzene and Diesel, and it was effective in fuel parameters management. Results established that the pressure of Benzene was reasonably constant, and it was about 14.8 psi after reaching equilibrium. In contrast, the pressure returned to 14.7 psi value for Diesel, which was its initial value. The temperature readings were constant throughout the experiment, with the benzene temperature being higher than Diesel. The system also differentiated between pH levels, keeping Benzene at 6.9 while Diesel at around 7. These results prove the capabilities and efficiency of this system in anomaly detection, fuel adulteration, and fuel theft prevention, thus solving modern solutions in fuel management. © 2025 IEEE.

关键词： Decision making

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Revalidating the Encoder-Decoder Depths and Activation Function to Find Optimum Vanilla Transformer Model

Revalidating the Encoder-Decoder Depths and Activation Funct...

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2023 International Conference on computer science, information technology and Engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Fitria Murad, Dina Tho, Cuk Hashimoto, Kiyota Bina Nusantara University Graduate Program- Computer Science Computer Science Department BINUS Jakarta11480 Indonesia Bina Nusantara University Online Learning Computer Science Department BINUS Jakarta11480 Indonesia Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

The transformer model has become a state-of-the-art model in Natural Language Processing. The initial transformer model, known as the vanilla transformer model, is designed to improve some prominent models in sequence modeling and transduction problems such as language modeling and machine translation. The initial transformer model has 6 stacks of identical encoder-decoder layers with an attention mechanism whose aim is to push limitations of common recurrent language models and encoder-decoder architectures. Its outstanding performance has inspired many researchers to extend the architecture to improve its performance and computation efficiency. Despite many extensions to the vanilla transformer, there is no clear explanation of the encoder-decoder set out depth in the vanilla transformer model. This paper presents exploration results on the effect of combination encoder-decoder layer depth and activation function in the feed-forward layer of the vanilla transformer model on its performance. The model is tested to address a downstream task: text translation from Bahasa Indonesia to the Sundanese language. Although the value difference is not significantly large, the empirical results show that the combination of depth = 2 with Sigmoid, Tanh, and ReLU activation function;and d = 6 with ReLU activation shows the highest average training accuracy. Interestingly, d = 6 and ReLU show the lowest average training and validation loss. However, statistically, there is no significant difference between depth and activation functions. © 2023 IEEE.

关键词： Neural machine translation

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Systematic Literature Review Langchain Proposed 25

Systematic Literature Review Langchain Proposed

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25th International Electronics Symposium, IES 2023

作者： Asyrofi, Rakha Dewi, Mutia Rahmi Lutfhi, Muhammad Irfan Wibowo, Prasetyo Faculty of Marine Science and Engineering Universitas Hangtuah Surabaya Indonesia Department of Computer Science and Information Engineering National Central University Taiwan Department of Information Technology Politeknik Negeri Padang Padang Indonesia Faculty of Engineering Universitas Negeri Yogyakarta Indonesia Graduate Institute of Network Learning Technology National Central University Taiwan Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (纸本)9798350314731

While systematic literature reviews are frequently carried out within software engineering research, performing them in a rigorous and reproducible manner can be difficult. This paper proposes some new methods for evaluating and validating systematic literature reviews. Our approach consists of several steps, such as: Selecting a set of relevant scientific papers to analyze, Developing a list of questions and criteria to evaluate each literature review, and Determining what types of functionality and performance should be evaluated. We tested our method by having multiple experts evaluate the literature reviews based on our questions and criteria. We measured the similarity in scores between each expert to determine the reliability of the evaluations. The average similarity index between experts was 0.58 to 0.83, indicating a reasonable level of agreement in their assessments. This shows our evaluation method can produce fairly consistent results, even when different experts are involved. The relatively high level of agreement is notable considering each expert brings their perspectives and opinions in analyzing literature reviews. By providing concrete questions, criteria, and evaluation methods, we aimed to guide the experts toward more uniform evaluations. In summary, we developed and tested a new approach for evaluating and validating systematic literature reviews in software engineering. By assessing reliability via interrater agreement, we showed that consistent and reproducible results are possible using our evaluation framework and methodology. Our methods could help researchers gain more insight into what makes for an effective and high-quality literature review. © 2023 IEEE.

关键词： Software testing

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GAF-GAN: Gated Attention Feature Fusion Image Inpainting Network Based on Generative Adversarial Network 29

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Liang, Hu Liu, Yuchen Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures. © 2023 IEEE.

关键词： Dense feature fusion module Gated attention mechanism Image inpainting Underwater environment

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Decomposition and Reorganization of Phonetic information for Speaker Embedding Learning

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 1745-1757页

作者： Hong, Qian-Bei Wu, Chung-Hsien Wang, Hsin-Min National Cheng Kung University and Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701 Taiwan Academia Sinica Taipei115 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan

Speech content is closely related to the stability of speaker embeddings in speaker verification tasks. In this paper, we propose a novel architecture based on self-constraint learning (SCL) and reconstruction task (RT) to remove the influence of phonetic information on speaker embedding generation. First, SCL is used to reduce the divergence of frame-level features, which can avoid ambiguity between the resulting embeddings of the two utterances being compared. Second, RT is used to further remove phonetic information in frame-level layers, focusing on speaker-discriminative feature transformation. In our experiments, the speaker embedding models were trained on the VoxCeleb2 dataset and evaluated on the VoxCeleb1, Librispeech, SITW and VoxMovies datasets. Experimental results on VoxCeleb1 show that the proposed DROP-TDNN system reduced the EER by 7.5%, compared to the state-of-the-art ECAPA-TDNN system. Furthermore, the proposed DROP-TDNN system also outperformed the ECAPA-TDNN system in the experiments on SITW, Librispeech and VoxMovies under cross-dataset conditions. In the experiments on SITW, the proposed system reduced the EER by 3.4% compared to the ECAPA-TDNN system. In the experiments on Librispeech, the proposed system demonstrated the advantage of removing phonetic information under the clean speech condition, with a significant reduction of 25.5% in EER compared to the ECAPA-TDNN system. In the experiments on VoxMovies, the proposed system reduced the EER by up to 7.9% compared to the ECAPA-TDNN system under different pronunciation and background conditions. © 2014 IEEE.

关键词： Speech recognition

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Robustness Aware SFC Placement in NFV-Enabled Multi-Domain Networks Under Resource Demand Uncertainty 11

Robustness Aware SFC Placement in NFV-Enabled Multi-Domain N...

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11th International Conference on Dependable Systems and Their Applications, DSA 2024

作者： Zhang, Chuangchuang Lee, Shou-Yu Liu, Pan Yang, Hongyong Chu, William Cheng-Chung Ludong University School of Information and Electrical Engineering Yantai264025 China Tunghai University Department of Computer Science Taiwan Shanghai Business School Faculty of Business Information Shanghai201400 China Fuyao University of Science and Technology School of Computing and Artificial Intelligence China

ISBN: (纸本)9798331532390

The increasing service demands in 5G networks have led to the emergence of Network Function Virtualization (NFV) technology. Through the virtualization technique, NFV enables on-demand and flexible service provisioning and deployment by implementing networks functions in forms of software instances, called Virtual Network Functions (VNFs). With the rapid development and implementation of 5G, the Service Function Chain (SFC) placement problem in multi-domain network scenarios is gaining significant attentions due to the sudden and dynamic patterns of resource demands from a diversity of service requests. Most existing proposals attempt to optimize the SFC placement in multi-domain networks, under the assumption that resource demand required each service request is given in prior and keeps unchanged during the SFC execution period. However, it is impractical in real network scenarios due to the evolving dynamic changes of service requests. The uncertainty of service demands has an important effect on service reliability, service quality and network performance. To this end, in this paper, we study the problem of robust SFC placement in multi-domain networks under the assumption that resource demands of SFC requests are uncertain. We first formulated the problem of robust SFC placement in multi-domain networks as an integer programming model with the aim to minimize the SFC placement cost while considering the Quality of Service (QoS) guarantee. Then, we develop a three-stage heuristic placement algorithm to solve the problem. The extensive simulation results demonstrate that our proposed heuristic algorithm is efficient and can save more placement cost than comparison mechanisms. © 2024 IEEE.

关键词： 5G mobile communication systems

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Indonesian-Sundanese Language Machine Translation using Bidirectional Long Short-term Memory Model

Indonesian-Sundanese Language Machine Translation using Bidi...

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2023 International Conference on computer science, information technology and Engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Fitria Murad, Dina Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program - Doctor of Computer Science Computer Science Department Bina Nusantara University Jakarta11480 Indonesia BINUS Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Bina Nusantara University Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. However, the language translation is not an easy computation task when there is a language-resource gap. This paper presents empirical results on the performance of two models: the Long Short-term Memory and the Bidirectional Long Short-term Memory models as machine language translation models involving Bahasa Indonesia and the Sundanese language. The empiric results showed that the Bidirectional Long Short-term Memory model achieves higher performance as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.95 and 0.95 average training accuracy respectively;and 0.90 and 0.89 average testing BLEU scores respectively) than the Long Short-term Memory model as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.93 and 0.92 average training accuracy respectively;and 0.91 and 0.88 average testing BLEU scores). These results validate some previously reported studies that claim the Bidirectional Long Short-term Memory model potentially outperform the Long Short-term Memory model when it is used to process a sequence dataset. © 2023 IEEE.

关键词： Computational linguistics

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Two-Stage Sampling: A Framework for Imbalanced Classification With Overlapped Classes

Two-Stage Sampling: A Framework for Imbalanced Classificatio...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Firdausanti, Neni Alya Fatyanosa, Tirana Noor Data, Mahendra Mendonca, Israel Aritsugi, Masayoshi Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Computer Science Brawijaya University Malang Indonesia Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

ISBN: (纸本)9781665480451

Class imbalance and overlapping instances problems have long been recognized as one of the major causes of the performance deterioration of the classification model. Moreover, the majority class may have an irrelevant and noisy instance that shifts the decision boundary of the classification far away from the ideal one. We propose a framework for balancing the class distribution and mitigating the class overlap problem in a dataset. The key feature of our framework is its ability to detect the overlapping instances between classes and then remove the problematic instances from the majority class. Thus, it will have more precise information for the oversampling method to generate the synthetic minority instances. We evaluated the proposed framework using the Lending club and ten other datasets from the KEEL repository. We demonstrate the implementations of our framework using Tomek and Edited Nearest Neighbor for removing the overlapping instances from the majority class and SWIM-MD for generating the synthetic minority instances. Also, we used eight well-known classifiers to show that our proposed framework can improve the performance of various classifiers. Lastly, we present a detailed analysis of the experimental result that shows the superiority of our proposed framework. Our proposed framework outperformed the state-of-the-art methods in terms of geometry mean classification performance metric. © 2022 IEEE.

关键词： Machine learning

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