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Understanding Brain Network Stimulation for Emotion Analyzing Connectivity Feature Map from Electroencephalography

International Journal of Advanced computer science and Applications 2025年第4期16卷 542-551页

作者： Maria, Mahfuza Akter Akhand, M.A.H. Kamal, Abdus Samad Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Graduate School of Science and Technology Gunma University Kiryu376-8515 Japan

In understanding brain functioning by Electroencephalography (EEG), it is essential to be able to not only identify more active brain areas but also understand connectivity among different areas. The functional and efficient connectivity networks of the brain have been examined in this study by constructing a connectivity feature map (CFM) with four widely used connectivity methods from the Database for Emotion Analysis Using Physiological Signals (DEAP) emotional EEG data to research how this connectivity’s patterns are influenced by emotion. According to the investigation results, emotions are mainly related to the parietal, central, and frontal regions. The parietal region is more responsible for emotion alteration among these three regions. Positive emotions are associated with more direct correlations and dependencies than negative ones. When experiencing negative emotions, the regions of the brain function more synchronously as well as there are less flow of information. Whether direct or inverse, there is less correlation between brain regions in the higher frequency band than in the lower frequency band. Higher frequencies are associated with increased dependence and directed information transfer between brain regions. Generally, the electrodes in the same lobe show stronger connectivity than those in different lobes. At a glance, the present study is a comprehensive analysis to understand brain network stimulation for emotion from EEG, and it significantly differs from the existing emotion recognition studies typically focused on recognition proficiency. © (2025), (science and Information Organization). All rights reserved.

关键词： Emotion Recognition

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Synthesis of Variable Gain Robust State Observer for Linear Systems with Structured Uncertainties

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IEEE Access 2025年 13卷 75323-75331页

作者： Hirasawa, Kazuhiro Sawato, Hayate Asada, Daiki Oya, Hidetoshi Tokyo City University Graduate School of Integrative Science and Engineering Tokyo Japan Tokyo City University The Department of Computer Science Tokyo Japan

This paper proposes a new variable gain robust state observer for a class of linear uncertain systems. The variable gain robust state observer proposed in this paper consists of a fixed observer gain matrices and nonlinear modification functions which are determined by appropriate updating rules. It is shown that sufficient conditions for the existence of the proposed variable gain robust state observer can be reduced to solvability of Linear Matrix Inequalities (LMIs). Finally, we give simple numerical examples so as to illustrate the effectiveness of the proposed variable gain robust state observer. © 2013 IEEE.

关键词： Linear matrix inequalities

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Low Cost Active Learning Framework for Short Answer Scoring 20th

Low Cost Active Learning Framework for Short Answer Scoring

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20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Hori, Tatsuya Yamauchi, Koichiro Graduate school of Engineering Department of Computer Science Chubu University Kasugai Japan

ISBN: (纸本)9789819600250

Short answer scoring (SAS) that automatically scores learner’ answers based on given rubric. Because SAS uses different rubrics for different prompts, it is necessary to generate training data for each prompt, which is time-consuming and labor-intensive. We propose a human-in-the-loop framework that achieves SAS with a minimal amount of training data through active data creation and efficient training. We fine-tuned the Sentence BERT model with a small dataset by using SetFit (Lewis Tunstall, et al. (2022).) and then apply active learning (AL) to an unlabeled data pool. The grader scores the data selected by AL, creating new training data that are then used to further fine-tune the models. For the experiments, we used six prompts from a Japanese SAS dataset. Our experimental results demonstrate that the proposed method outperforms previous studies in most cases. We also examined the potential challenges when applying this framework in practical scenarios. This framework significantly reduces the graders’ time and effort by enabling automatic scoring with minimal training data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Active learning

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Virtual Learning Machine for Tiny Devices 20th

Virtual Learning Machine for Tiny Devices

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20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Kitagawa, Nozomi Yamauchi, Koichiro Graduate school of Engineering Department of Computer Science Chubu University Kasugai Japan

ISBN: (纸本)9789819600250

Recent developments in artificial intelligence (AI) have increased the demand for high-performance computational devices. However, edge devices are highly restricted in terms of computational power and memory capacity. To address this, a learning engine called swap kernel regression (SKR) [Masaharu et al. 2019] was developed that enables both inference and learning using an edge device. The key feature of SKR is ability to extend beyond the real physical capacity by collaborating with a second storage device. SKR thereby enables the virtual construction of a large-scale learning machine on a tiny device. We developed an improved version of SKR that maximizes performance by solving the thrashing problem and further reduces the computational cost. Using this improved version, we aim to achieve few-shot class-incremental learning (FSCIL), which continuously adapts to new tasks with limited samples, envisioned for on-site implementation. The proposed method uses a kernel machine that operates in a restricted environment in collaboration with a secondary storage system. The kernel parameters, which are not essential for calculating the output values for upcoming inputs, are stored in secondary storage to create a space in the main memory. The essential kernel parameters stored in secondary storage are loaded into main memory when required. Using this strategy, the system can realize recognition or regression tasks without compromising its generalization capability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Privacy-Preserving Keyword Search with Access Control for Secret Sharing-Based Data Outsourcing

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IEEE Access 2025年 13卷 73625-73651页

作者： Kamal, Ahmad Akmal Aminuddin Mohd Okada, Masamune Fujisawa, Masaya Tokyo University of Science Department of Information and Computer Technology 125 Tokyo85858 Japan Tokyo University of Science Graduate School of Engineering Tokyo125-8585 Japan

Searchable encryption, also known as secure search, is a technology that enables search operations on encrypted data while maintaining its confidentiality. Extensive research has been conducted on searchable encryption utilizing public-key and symmetric encryptions. However, public-key encryption involves significant computational expenses and is deemed inefficient for querying large databases, particularly in cloud settings. Hence, our attention is on searchable encryption using a secret sharing scheme (also known as searchable secret sharing), which is recognized for its minimal computational complexity. A secret sharing scheme is a method to transform a secret input into several distinct values known as shares. Kamal et al. introduced a simple searchable encryption utilizing secret sharing schemes, yet did not include user access control capabilities. In this study, we introduce a secure search method with user access control employing a secure computation based on a (k, n) threshold secret sharing scheme, where every data stored in the cloud has an owner, and owners grant access to users at their *** assume a client-server model to perform a secure computation between the owner, the player, and the n ≥ k cloud servers. Furthermore, we provide an in-depth analysis of the security aspects of our proposed distribution, query generation, and search processes, demonstrating that our approach is resilient against an honest-but-curious adversary with information from up to k−1 computing servers. Moreover, we also include an improved and efficient method by using a (n, n) additive secret sharing scheme when assuming n = k computing servers. We provide discussion comparing both our proposed methods in terms of their computation and communication costs. Finally, we also include a detailed performance analysis using Python, including a comparison with conventional secret sharing-based methods. © 2013 IEEE.

关键词： Cryptography Cloud computing Encryption Servers Access control Keyword search Data privacy Outsourcing Indium tin oxide Homomorphic encryption

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A Lightweight Authentication Protocol for RFID-Assisted Supply Chain Management System

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IEEE Internet of Things Journal 2025年第9期12卷 12845-12852页

作者： Tariq, Tayyaba Kuo, Wen-Chung Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Alenazi, Mohammed J. F. National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia

In the evolving landscape of supply chain management, the integration of radio-frequency identification (RFID) technology has marked a significant milestone. This development has led to the emergence of a new system in RFID-based supply chain management, which is intricately linked with the advances in the Internet of Things (IoT). RFID technology employs electromagnetic fields to identify and track tags on objects and revolutionizes the management and tracking of items in the supply chain. However, the public communication among RFID tags, RFID readers, and supply chain infrastructure predominantly escalates security and privacy challenges. Several authentication protocols have been proposed to overcome these challenges. However, the vulnerability of most proposed protocols to numerous security attacks renders them inefficient. Therefore, to address these crucial challenges, we devised an RFID-based authentication protocol for supply chain management systems. The incorporation of a physically unclonable function (PUF) into the protocol fortifies the system against physical tampering attacks. To validate the security and effectiveness of the devised protocol, both informal and formal security analysis are conducted. The formal security analysis is conducted using the widely used random oracle model. The informal security analysis reveals that the devised protocol provides enhanced and efficient security features. Furthermore, we perform a comparative analysis with related protocols, focusing on critical performance metrics like communication cost, computation cost, and overall security features. The results of the comparative analysis are promising, indicating a substantial 30.92% reduction in computational cost and a 23.98% reduction in communication cost in comparison to related protocols, thus highlighting the protocol’s superior performance and resource efficiency. © 2014 IEEE.

关键词： Authentication Protocol

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Enhancing Privacy in IoT Networks: A Comparative Analysis of Classification and Defense Methods

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IEEE Access 2025年 13卷 71611-71646页

作者： Emre Ergun, Ahmet Can, Ozgu Kantarcioglu, Murat Ege University Graduate School of Natural and Applied Sciences Bornova35100 Turkey Izmir Katip Celebi University Faculty of Engineering and Architecture Department of Computer Engineering Cigli35620 Turkey Ege University Department of Computer Engineering Bornova35100 Turkey Virginia Tech Department of Computer Science BlacksburgVA24061 United States

The rapid proliferation of Internet of Things (IoT) devices has led to a substantial increase in network packet traffic, raising significant privacy concerns. Although traffic encryption is employed to protect the privacy of IoT devices, attackers can still leverage Machine Learning (ML) and Deep Learning (DL) techniques to classify device types by analyzing packet characteristics, such as size and timing. The main challenges in the state of the art are the lack of effective methods for exposing privacy violations in encrypted IoT traffic, and the absence of robust defense mechanisms to mitigate privacy breaches caused by network traffic analysis. Considering these challenges, this study presents two key contributions: (i) a novel vector-based classification method that enhances device-type identification from encrypted IoT traffic using advanced ML and DL techniques, and (ii) a robust defense mechanism based on Differential Privacy (DP) and advanced padding techniques against traffic analysis attacks. Therefore, the study examines privacy risks associated with sequential IoT device data and evaluates the effectiveness of ML algorithms using two datasets. The results demonstrate that the proposed vector-based classification method significantly improves the attacker’s classification accuracy, even when privacy-preserving techniques, such as padding, are used to obscure device-type classification. For this purpose, the study evaluates eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) for IoT traffic classification, achieving an accuracy rate of 99.61% with XGBoost, 96.74% with LSTM, and 96.94% with GRU. Additionally, the Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (kNN), and GRU classification algorithms are also evaluated and compared with the XGBoost and LSTM classifiers for the proposed attack model. As a defense mechanism, DP is applied using the Fourier Perturbation Algorithm (FPA) to optimize padd

关键词： Decision trees

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RobustFace: a novel image restoration technique for face adversarial robustness improvement

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Multimedia Tools and Applications 2025年第11期84卷 8965-8993页

作者： Sadu, Chiranjeevi Das, Pradip K. Yannam, V Ramanjaneyulu Nayyar, Anand Department of Computer Science and Engineering RGUKT Nuzvid Andhra Pradesh Nuzvid India Department of Computer Science and Engineering IIT Guwahati Assam Guwahati India School of Technology Woxsen University Telangana Hyderabad India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

Machine Learning (ML) models, particularly Deep Learning (DL), have made rapid progress and achieved significant milestones across various applications, including numerous safety-critical contexts. However, these models have recently been discovered to be susceptible to adversarial attacks, which are well-crafted input images. The primary objective of this paper is to propose a novel methodology titled "RobustFace", which is based on deep image restoration networks, that significantly improves the facial adversarial robustness of various image-classification models. Adversarial images are created using the Private Fast Gradient Sign Method (P-FGSM), StyleGAN and Fast Landmark Manipulation (FLM) methods. The adversarial images are then enhanced using deep image restoration networks to bring back them into the original space. The encoded weighted local magnitude patterns (WLMP) are extracted and provided to different types of classifiers to detect facial adversarial images from the clean images. The effectiveness of RobustFace has been demonstrated on two real-world datasets and experimental outcomes show that it significantly improves facial adversarial robustness on all evaluating classifiers. It improves the highest classification accuracy from 98.75% to 99.00% on P-FGSM attacks, from 77.94% to 85.25% on adversarial attacks generated by StyleGAN and from 65.52% to 69.50% for FLM attacks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image reconstruction

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Paths Optimization by Jointing Link Management and Channel Estimation Using Variational Autoencoder With Attention for IRS-MIMO Systems

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 381-394页

作者： Wu, Meng-Hsun Chen, Hong-Yunn Yang, Ta-Wei Hsu, Chih-Chuan Huang, Chih-Wei Chou, Cheng-Fu National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan National Pingtung University Department of Computer Science and Information Engineering Pingtung91201 Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei10617 Taiwan Texas A&M University Department of Computer Science and Engineering College StationTX77840 United States National Central University Department of Communication Engineering Taoyuan320 Taiwan

In massive MIMO systems, achieving optimal end-to-end transmission encompasses various aspects such as power control, modulation schemes, path selection, and accurate channel estimation. Nonetheless, optimizing resource allocation remains a significant challenge. In path selection, the direct link is a straightforward link between the transmitter and the receiver. On the other hand, the indirect link involves reflections, diffraction, or scattering, often due to interactions with objects or obstacles. Relying exclusively on one type of link can lead to suboptimal and limited performance. Link management (LM) is emerging as a viable solution, and accurate channel estimation provides essential information to make informed decisions about transmission parameters. In this paper, we study LM and channel estimation that flexibly adjust the transmission ratio of direct and indirect links to improve generalization, using a denoising variational autoencoder with attention modules (DVAE-ATT) to enhance sum rate. Our experiments show significant improvements in IRS-assisted millimeter-wave MIMO systems. Incorporating LM increased the sum rate and reduced MSE by approximately 9%. Variational autoencoders (VAE) outperformed traditional autoencoders in the spatial domain, as confirmed by heatmap analysis. Additionally, our investigation of DVAE-ATT reveals notable differences in the temporal domain with and without attention mechanisms. Finally, we analyze performance across varying numbers of users and ranges. Across various distances—5m, 15m, 25m, and 35m—performance improvements averaged 6%, 11%, 16%, and 22%, respectively. © 2023 CCBY.

关键词： Resource allocation

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Task-Adapted Learnable Embedded Quantization for Scalable Human-Machine Image Compression

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4768-4783页

作者： Li, Shaohui Ma, Shuoyu Dai, Wenrui Kan, Nuowen Cheng, Fan Li, Chenglin Zou, Junni Xiong, Hongkai Tsinghua University Shenzhen International Graduate School Guangdong Shenzhen518055 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

Image compression for both human and machine vision has become prevailing to accommodate to rising demands for machine-machine and human-machine communications. Scalable human-machine image compression is recently emerging as an efficient alternative to simultaneously achieve high accuracy for machine vision in the base layer and obtain high-fidelity reconstruction for human vision in the enhancement layer. However, existing methods achieve scalable coding with heuristic mechanisms, which cannot fully exploit the inter-layer correlations and evidently sacrifice rate-distortion performance. In this paper, we propose task-adapted learnable embedded quantization to address this problem in an analytically optimized fashion. We first reveal the relationship between the latent representations for machine and human vision and demonstrate that optimal representation for machine vision can be approximated with post-training optimization on the learned representation for human vision. On such basis, we propose task-adapted learnable embedded quantization that leverages learnable step predictor to adaptively determine the optimal quantization step for diverse machine vision tasks such that inter-layer correlations between representations for human and machine vision are sufficiently exploited using embedded quantization. Furthermore, we develop a human-machine scalable coding framework by incorporating the proposed embedded quantization into pre-trained learned image compression models. Experimental results demonstrate that the proposed framework achieves state-of-the-art performance on machine vision tasks like object detection, instance segmentation, and panoptic segmentation with negligible loss in rate-distortion performance for human vision. © 1991-2012 IEEE.

关键词： Machine vision

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