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International Journal of Cognitive Computing in Engineering 2025年 6卷 170-182页

作者： Vaidya, Hanumesh Prasad, K.V. Rajashekhar, C. Tripathi, D. S, Renuka Shetty, Jyoti Swamy, Kumar Y, Shobha Department of Studies in Mathematics Vijayanagara Sri Krishnadevaraya University Karnataka Ballari India Department of Mathematics Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India Department of Studies in Computer Science Vijayanagara Sri Krishnadevaraya University Karnataka Ballari India Karnataka Ballari India Department of Studies in Computer Science Kishkinda University Karnataka Ballari India Department of Mathematics National Institute of Technology Uttarakhand Srinagar India

Rice classification plays a critical role in global agriculture, impacting the food security of over 3.5 billion people worldwide. As the complexity of this task has grown, advanced Machine Learning (ML) and Deep Learning (DL) models have emerged as powerful tools for accurate variety identification. These sophisticated algorithms excel in processing extensive datasets, leveraging key characteristics such as grain size, color, and texture to make precise predictions. However, a persistent challenge in this field is a class imbalance, where certain rice varieties are significantly underrepresented in datasets. This imbalance can severely impact model performance, particularly for minority classes, leading to biased predictions that favor more abundant varieties. Many existing models struggle to effectively address this issue, often prioritizing majority classes at the expense of overall generalization across all rice types. To tackle this critical gap in rice classification, we introduce a novel hybrid model: the XGBoost Multi-Layer 33Perceptron (XGB-MLP). This innovative approach is specifically designed to handle class imbalance, ensuring fair and accurate classification across all rice varieties, regardless of their representation in the dataset. Our model demonstrates remarkable versatility, effectively accommodating binary and multi-class scenarios while maintaining robust performance in the face of imbalanced data. As part of our contribution, we have also developed a new dataset intentionally incorporating class imbalance. This dataset serves as a rigorous benchmark for evaluating our model's performance against existing works in the field. The results of our comprehensive evaluation are compelling, with the XGB-MLP model achieving outstanding accuracy across various classification tasks: 99.86 % for binary class, 99.95 % for multi-class, and 98.46 % for a challenging multi-class scenario using a merged dataset. These impressive results not only surpass the pe

关键词： Adversarial machine learning

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Social Argumentation Systems

Journal of Applied Logics

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Journal of Applied Logics 2025年第3期12卷 251-294页

作者： Bikakis, Antonis Flouris, Giorgos Leite, Joao Patkos, Theodore Department of Information Studies University College London United Kingdom Institute of Computer Science FORTH Heraklion Greece NOVA LINCS NOVA University Lisbon Portugal

While a lot of research has been conducted on understanding and formalising the interplay of arguments within the context of Computational Argumentation, this research is not fully applicable to the types of arguments that populate the Social Web. In that context, arguments usually have the form of comments, opinions or reviews, and are the main ingredients of online discussion forums, social networks, online rating and review sites, debate portals and other online communities-the electronic version of word-of-mouth communication. As a result, voting and other forms of reaction to the provided comments or arguments (other than just "attacks") are allowed, features that are not normally considered in the classical literature on Computational Argumentation. In this chapter, we study extensions of argumentation frameworks that have been proposed to describe and understand the more complex types of interactions among arguments that can be found in the Social Web, and present the current state-of-the-art, as well as open problems. © 2025, College Publications. All rights reserved.

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Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation 31

Extending LLMs to New Languages: A Case Study of Llama and P...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Sani, Samin Mahdizadeh Sadeghi, Pouya Vu, Thuy-Trang Yaghoobzadeh, Yadollah Haffari, Gholamreza Department of Electrical and Computer Engineering University of Tehran Iran Tehran Institute for Advanced Studies Khatam University Iran Department of Data Science & AI Monash University Australia

ISBN: (纸本)9798891761964

Large language models (LLMs) have made great progress in classification and text generation tasks. However, they are mainly trained on English data and often struggle with low-resource languages. In this study, we explore adding a new language, i.e., Persian, to Llama (a model with a limited understanding of Persian) using parameter-efficient fine-tuning. We employ a multi-stage approach involving pretraining on monolingual Persian data, aligning representations through bilingual pretraining and instruction datasets, and instruction-tuning with task-specific datasets. We evaluate the model's performance at each stage on generation and classification tasks. Our findings suggest that incorporating the Persian language, through bilingual data alignment, can enhance classification accuracy for Persian tasks, with no adverse impact and sometimes even improvements on English tasks. Additionally, the results highlight the model's initial strength as a critical factor when working with limited training data, with cross-lingual alignment offering minimal benefits for the low-resource language. Knowledge transfer from English to Persian has a marginal effect, primarily benefiting simple classification tasks. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Deep Clustering: A Comprehensive Survey

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IEEE Transactions on Neural Networks and Learning Systems 2025年第4期36卷 5858-5878页

作者： Ren, Yazhou Pu, Jingyu Yang, Zhimeng Xu, Jie Li, Guofeng Pu, Xiaorong Yu, Philip S. He, Lifang University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518000 China University of Illinois Chicago Department of Computer Science ChicagoIL60607 United States Lehigh University Department of Computer Science and Engineering BethlehemPA18015 United States

Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering (DC), which can learn clustering-friendly representations using deep neural networks (DNNs), has been broadly applied in a wide range of clustering tasks. Existing surveys for DC mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering. To address this issue, in this article, we provide a comprehensive survey for DC in views of data sources. With different data sources, we systematically distinguish the clustering methods in terms of methodology, prior knowledge, and architecture. Concretely, DC methods are introduced according to four categories, i.e., traditional single-view DC, semi-supervised DC, deep multiview clustering (MVC), and deep transfer clustering. Finally, we discuss the open challenges and potential future opportunities in different fields of DC. © 2012 IEEE.

关键词： Self-supervised learning

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A Music Recommender System to Identify the Emotional State of a Person Using Machine Learning Algorithms 4

A Music Recommender System to Identify the Emotional State o...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Muruganantham, A. Malarvizhi, C. Divya Vahini, S. Saranya, D. Uthiramoorthy, A. Karnataka Bengaluru India Psg Institute of Advanced Studies Department of Computer Science Coimbatore India Dr.Mahalingam College of Engineering and Technology Department of Computer Applications Tamil Nadu Pollachi India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Rathinam College of Arts and Science Department of Computer Science India

ISBN: (纸本)9798331523923

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. The system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods - with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. This study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be - make tailored entertainment paths in the digital era. © 2025 IEEE.

关键词： Convolutional neural networks

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Buffer and Splitter Insertion for Adiabatic Quantum-Flux-Parametron Circuits

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 975-988页

作者： Fu, Rongliang Wang, Mengmeng Kan, Yirong Chen, Olivia Yoshikawa, Nobuyuki Yu, Bei Ho, Tsung-Yi The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan Nara Institute of Science and Technology Division of Information Science Nara630-0192 Japan Kyushu University Department of Advanced Information Technology Fukuoka819-0395 Japan

The extremely low-bit energy characteristic of the adiabatic quantum-flux-parametron (AQFP) circuit makes it a promising candidate for highly energy-efficient computing systems. However, in contrast with conventional circuit design, general logic synthesis tools can not make sure that the circuit functionality of generated AQFP circuits is correct. AQFP circuits require buffer and splitter insertion for dataflow synchronization at all clock phases of the circuit and multifan-out driving. Notably, buffers and splitters inserted take up much area and delay in AQFP circuits, also causing a significant increase in energy dissipation. To address this problem, this article analyses in detail why buffer and splitter insertion is necessary for AQFP circuits and proposes a global optimization framework for this purpose. This framework consists of three parts: 1) logic level assignment;2) splitter tree generation;and 3) buffer insertion. An integer linear programming algorithm is proposed for the logic level assignment to estimate the globally optimal number of inserted buffers and splitters. Subsequently, a dynamic programming-based multiway search tree generation algorithm is proposed to construct an optimal splitter tree for each net of the input circuit. Moreover, three optimization strategies are proposed to further enhance the effectiveness and efficiency of our framework. Experimental results on ISCAS'85 and EPFL benchmarks demonstrate the effectiveness and efficiency of our proposed framework compared with the state-of-the-art, particularly with significant advantages on large circuits. © 2024 IEEE.

关键词： Global optimization

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Transformative Transfer Learning for MRI Brain Tumor Precision: Innovative Insights

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IEEE Access 2025年 13卷 31749-31761页

作者： Anwar, Raja Waseem Abrar, Mohammad Ullah, Faizan German University of Technology in Oman Department of Computer Science Muscat130 Oman Arab Open University Faculty of Computer Studies Muscat122 Oman Institute of Neuroscience and Medicine Forschungszentrum Jülich Jülich52428 Germany

Accurate brain tumor detection is essential for effective treatment and improved patient outcomes, yet conventional MRI classification methods often struggle with complex and variable imaging data. This study introduces the Transformative Transfer Learning (TTL) model, leveraging pre-trained deep neural networks fine-tuned for MRI brain tumor classification, using datasets from Nickparvar and Cheng. High-quality inputs were achieved through preprocessing, including resizing, normalization, noise reduction, and data augmentation. The TTL model, employing architectures such as VGG16, ResNet-50, InceptionV3, and DenseNet-121, demonstrated a substantial improvement over traditional methods, achieving an accuracy of 94.5%, sensitivity of 92.8%, and specificity of 93.1%. Additionally, the model yielded a precision of 93.2%, recall of 92.8%, F1-score of 93.0%, and true positive and true negative rates of 92.8% and 93.1%, respectively. Training and validation loss values of 0.15 and 0.18 confirm effective model convergence. These metrics underscore the model’s potential as a robust tool in clinical diagnostics, providing a pathway to more accurate and efficient MRI-based brain tumor detection. © 2025 The Authors.

关键词： Deep neural networks

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14-Bit SAR ADC with on-Chip Digital Bubble Sorting Calibration Technology

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Chinese Journal of Electronics 2025年第1期34卷 125-136页

作者： Hua Fan Zhuorui Chen Tongrui Xu Franco Maloberti Qi Wei Quanyuan Feng School of Integrated Circuit Science and Engineering(Exemplary School of Microelectronics) University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chongqing Institute of Microelectronics Industry Technology University of Electronic Science and Technology of China Department of Electrical Computer and Biomedical Engineering University of Pavia Department of Precision Instrument Tsinghua University Institute of Microelectronics Southwest Jiaotong University

This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

关键词： Semiconductor device measurement Capacitors Linearity Switches Calibration System-on-chip Frequency measurement Time-domain analysis Sorting Signal to noise ratio

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Unsupervised Meteorological Downscaling Based on Dual Learning and Subgrid-scale Auxiliary Information

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Advances in Atmospheric sciences 2025年第1期42卷 53-66页

作者： Jing HU Jialing MU Xiaomeng HUANG Xi WU Department of Computer Science Chengdu University of Information TechnologyChengdu 610225China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China

Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable *** deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale *** article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate *** a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the *** dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping *** findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and ***,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation *** summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.

关键词： downscaling unsupervised deep learning dual learning auxiliary information

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