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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Peng, Peizhen Wu, Yansheng Yang, Wanqi Wei, Wenxin Yang, Ming School of Computer and Electronic Information The School of Artificial Intelligence Nanjing Normal University Nanjing China

ISBN: (纸本)9798350368741

The seizure early warning devices based on multichannel EEG signals is one of the most used assisted-living strategies for drug-resistant epileptic patients. One of the challenges in the development of these devices is that existing algorithms cannot avoid the effects of electrode loosening. To alleviate such problem, a seizure prediction model robust to corrupted EEG recordings is proposed in this paper. This method intends to learn a stable feature space adapted to different corruption versions via constructing a jointly-optimized autoencoder. Robust information is captured during the reconstruction process by developing a corruption module, while an adversarial training procedure circumvents overfitting using variational inference. Moreover, a regularization term is designed to minimize the distance among various corruption versions in the embedding space with the Riemannian manifold-based distribution alignment. Both raw EEG data and corrupted EEG data are utilized to evaluate prediction performance. Experimental results indicate that this method can predict seizures effectively and stably under the condition of electrode looseness. © 2025 IEEE.

关键词： autoencoder Electroencephalography(EEG) Riemannian manifold seizure prediction time series classification

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Reconstruction of EEG and ECG from Single Channel Mixture using Branched autoencoder based Separable Representations

Reconstruction of EEG and ECG from Single Channel Mixture us...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Datta, Shreyasi Gubbi, Jayavardhana Pal, Arpan TCS Research India

ISBN: (纸本)9798350368741

The growing use of wearable devices requires accurate and compact representations of high dimensional physiological signals. This work presents a UNet inspired autoencoder to represent and reconstruct multiple neuro-physiological signals from single channel data. The architecture comprises single-encoder/dual-branched decoders to obtain self-attention enabled compact embeddings of mixed ExG (EEG/ECG) signals through decaying encoder-decoder skip connections, for improved representation capability. The embeddings are separable into individual ExG components enabling simultaneous reconstruction of high fidelity EEG and ECG sources. The pretrained encoder can be used for a complex downstream task with minimum fine-tuning. Using the proposed method on a large corpus of single-channel mixed ExG generated from overnight Polysomnography (PSG) recordings, we show subject- and class- independent EEG/ECG reconstructions validated by multiple domain-specific metrics, and evaluate the classification performance of the encoded EEG embeddings into five sleep stages as a downstream task. © 2025 IEEE.

关键词： attention autoencoder ECG EEG reconstruction representation

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autoencoder-Based DIFAR Sonobuoy Signal Transmission and Reception Method Incorporating Residual Vector Quantization and Compensation Module: Validation Through Air Channel Modeling

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APPLIED SCIENCES-BASEL 2025年第1期15卷

作者： Park, Yeonjin Hong, Jungpyo Changwon Natl Univ Dept Informat & Commun Engn Chang Won 51140 South Korea

This paper proposes a novel autoencoder-based neural network for compressing and reconstructing underwater acoustic signals collected by Directional Frequency Analysis and Recording sonobuoys. To improve both signal compression rates and reconstruction performance, we integrate Residual Vector Quantization and a Compensation Module into the decoding process to effectively compensate for quantization errors. Additionally, an unstructured pruning technique is applied to the encoder to minimize computational load and parameters, addressing the battery limitations of sonobuoys. Experimental results demonstrate that the proposed method reduces the data transmission size by approximately 31.25% compared to the conventional autoencoder-based method. Moreover, the spectral mean square errors are reduced by 60.58% for continuous wave signals and 55.25% for linear frequency modulation signals under realistic air channel simulations.

关键词： DIFAR sonobuoy autoencoder air channel modeling signal reconstruction

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autoencoder-based physical layer authentication in a real indoor environment

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PHYSICAL COMMUNICATION 2025年 70卷

作者： Senigagliesi, Linda Ciattaglia, Gianluca Gambi, Ennio Univ Politecn Marche Dept Informat Engn Via Brecce Bianche 12 I-60131 Ancona Italy

Authentication of wireless nodes, as in fifth-generation (5G) and Internet of Things (IoT) networks, is an increasingly pressing issue, in order to limit the required computational effort and the necessary overhead. A simplification of the authentication process may therefore be of interest to achieve the satisfaction of stringent performance requirements, such as those envisaged for sixth-generation (6G) networks. This paper provides a study on the feasibility of physical layer authentication (PLA) in a real indoor environment, as an alternative solution to the traditional authentication schemes. To ensure the reliability of the proposed approach a simulated scenario is firstly tested. Subsequently, real-world data are collected through a laboratory setup using a Vectorial Signal Transceiver (VST) and two Universal Software Radio Peripherals (USRPs) to emulate the behavior of the receiver, the legitimate transmitter, and the potential adversary. A machine learning (ML) algorithm is then exploited to act as authenticator. This means that channel fingerprint is extracted from signals to create a dataset used to train a sparse autoencoder. To emulate areal authentication scenario, the autoencoder is trained only on the class of the legitimate user. Once anew message arrives, the autoencoder task is to discern authentic signals from those forged by the adversary. It is shown that a geometric mean of accuracy of more than 90%, with corresponding low levels of false alarm and missed detection, is achievable irrespective of the nodes location, underlining the robustness and versatility of the proposed ML-based PLA approach.

关键词： Machine learning autoencoder Physical layer authentication Channel measurements Universal Software Radio Peripherals

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Abstractive text summarization from Amazon food review dataset using modified attention based Bi-LSTM autoencoder model

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2025年第3期48卷 268-282页

作者： Prabha, P. L. Parvathy, M. Sethu Inst Technol Dept Comp Sci & Engn Kariapatti 626115 India

Automatic Text Summarization (ATS) is extensively performed to obtain insights from long texts or documents through Natural Language Processing (NLP). Even though various DL and ML-based algorithms have been used to perform text summarization, there is a lack of extensive studies on abstractive text summarization. The attention mechanisms used in traditional text summarization methods generate a fixed length vector irrespective of input sequence length. Hence, this study enhances the summarization efficiency through the implementation of a modified attention mechanism to generate dynamic weights for the input sequence. The proposed Modified Attention-based Bi-directional LSTM with Auto Encoder (Matt BiLSTM-AE) model is implemented for abstractive text summarization. The Amazon food review dataset is loaded as an input dataset, which is pre-processed to remove stop words, punctuation, and special characters. The optimized weight adjustment is obtained through the dynamic attention mechanism in the proposed model. The performance of the proposed MAtt-Bi-LSTM-AE text summarization is evaluated through ROUGE, accuracy, and loss metrics. From performance analysis, the accuracy rate was found to be 74.6%, while the loss rate was found to be 1.1751.

关键词： Abstractive text summarization dynamic attention natural language processing automatic text summarization autoencoder Bi-LSTM ROUGE

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Few-shot cyberviolence intent classification with Meta-learning autoencoder based on adversarial domain adaptation

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NEUROCOMPUTING 2025年 620卷

作者： Yang, Shun Du, Yajun Du, Shangyi Li, Xianyong Chen, Xiaoliang Li, Yanli Xie, Chunzhi Liu, Jia Xihua Univ Sch Comp & Software Engn Chengdu 610065 Sichuan Peoples R China McGill Univ Sch Stat & Comp Sci Montreal PQ H3A 0G4 Canada

The phenomenon of cyberviolence has become a critical issue in online security, drawing attention from various stakeholders. A major shortcoming in the previous works is the limitation of using simple methods like "yes" or "no" to evaluate cyberviolence utterances, which can significantly restrict netizens' free speech. Therefore, we provide a novel strategy for detecting cyberviolence utterances based on the user's real intent. Fine-grained cyberviolence intents are complex, leading to texts that share similar syntactic structures and semantics but differ in intent category. The previous method did not consider this issue. To address this, in this paper, we propose a Meta-learning A uto E ncoder (MetaAE) based on adversarial domain adaptation. The goal is to comprehend and learn the inherent logical rules and important semantic knowledge of cyberviolence utterances, specifically targeting fine-grained cyberviolence intent problems. Specifically, we use the autoencoder structure to help the model implement self-supervised learning. This enables the model to comprehend the inherent logical structure of texts with different intent categories and helps the model learn important semantic knowledge of the text during the encoder compression process. At the same time, to solve the problem of overfitting in small samples and multidomain cyberviolence utterance, we introduce domain adversarial learning to align domain features and enhance model robustness. Experimental results on both areal cyberviolence intent classification dataset and a public dataset demonstrate significant improvements. On 5-way 1-shot and 5-shot Chinese and English cyberviolence datasets, MetaAE improved the accuracy by approximately 7.23%, 8.27%, 7.22%, and 5%, respectively. In the public dataset, MetaAE improved accuracy by approximately 2.53% on 5-way 5-shot.1

关键词： Few-shot learning Meta-learning Intent classification Cyberviolence autoencoder

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Sensory-biased autoencoder enables prediction of texture perception from food rheology

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FOOD RESEARCH INTERNATIONAL 2025年 205卷 116007页

作者： Kraessig, Paul M. Singh, Shyamvanshikumar P. Lu, Jiakai Corvalan, Carlos M. Purdue Univ Dept Food Sci Transport Phenomena Lab W Lafayette IN USA Univ Massachusetts Dept Food Sci Ahmerst MA USA

Understanding how the physical properties of food affect sensory perception remains a critical challenge for food design. Here, we present an innovative machine learning strategy to decode the complex relationships between non-Newtonian rheological attributes of liquid foods and their perceived texture. A unique and key aspect of our approach is the implementation of an autoencoder neural network that incorporates sensory scores as a decoder bias during training. This enables the autoencoder to effectively identify non-linear, non-injective relationships between shear-thinning properties and perceived thickness, even when trained on a small dataset. This strategy offers a promising approach for advancing food product development by aiding the design of carefully tailored sensory experiences.

关键词： Food rheology Food design Machine learning Neural network autoencoder Non-Newtonian fluid

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LLM-AE-MP: Web Attack Detection Using a Large Language Model with autoencoder and Multilayer Perceptron

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 274卷

作者： Yang, Jing Wu, Yuangui Yuan, Yuping Xue, Haozhong Bourouis, Sami Abdel-Salam, Mahmoud Prajapat, Sunil Por, Lip Yee Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur 50603 Malaysia Jilin Univ Sch Software Changchun 130012 Jilin Province Peoples R China Radio Film & Televis Design & Res Inst Co Ltd Informat & Network Inst Beijing 100045 Peoples R China NYU Tandon Sch Engn New York NY 11101 USA Taif Univ Coll Comp & Informat Technol Dept Informat Technol POB 11099 Taif 21944 Saudi Arabia Mansoura Univ Fac Comp & Informat Sci Mansoura 35516 Egypt Cent Univ Himachal Pradesh Srinivasa Ramanujan Dept Math Dharamshala 176215 India

Web applications store sensitive data, making them prime targets for cybercriminals and posing national security risks. This study introduces a new approach to distinguishing legitimate and malicious hypertext transfer protocol (HTTP) requests using an autoencoder (AE). The integration of AE allows for efficient feature distillation, enhancing the sensitivity of the model to anomalies in HTTP traffic. The AE framework is combined with a transductive long short-term memory (TLSTM) network, which is trained with an advanced generative adversarial network (GAN). Using GAN promotes an adaptive learning environment, significantly boosting the robustness and generalizability of our method against evolving web attack vectors. TLSTM uses transductive learning to focus on data points near the test set, improving the adaptability of the model to outperform traditional LSTM models. In our GAN, the generator purposely excludes gradients from the most influential batch elements, improving the ability of the model to generate diverse and generalized outputs. After training the AE, its latent representations are passed to a multilayer perceptron (MLP) for detection tasks. To address the imbalanced classification in MLP, we use a reinforcement learning (RL) strategy. The RL approach strategically adjusts incentives, enhancing the performance of the model in identifying less frequent but critical malicious instances, thereby supporting a balanced security assessment. Our evaluations using the CSIC 2010 (Spanish National Research Council 2010), FWAF (web application firewall), and HttpParams datasets show that our method outperforms existing techniques, achieving (Accuracy, F-measure, geometric mean (G-means), and area under the curve (AUC)) reaching (90.937%, 89.755%, 88.446%, 0.838), (89.055, 90.663%, 88.334%, 0.847) and (92.242%, 93.774%, 91.356%, 0.897), respectively. Moreover, our model achieves efficient runtime and memory usage across the datasets, providing a practical soluti

关键词： Web attack Anomalous request autoencoder Long short-term memory Generative adversarial network Reinforcement learning

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An Efficient Workflow Scheduling Using Genetically Modified Golden Jackal Optimization With Recurrent autoencoder in Cloud Computing

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INTERNATIONAL JOURNAL OF NETWORK MANAGEMENT 2025年第1期35卷

作者： Tripathi, Saurav Tripathi, Sarsij MNNIT Allahabad Dept Comp Sci & Engn Prayagraj Uttar Pradesh India

In this paper, a novel workflow scheduling framework is proposed using genetically modified golden jackal optimization (GM-GJO) with recurrent autoencoder. An integrated autoencoder and bidirectional gated recurrent unit (iAE-BiGRU) are used to forecast the number of virtual machines (VMs) needed to manage the system's present workload. The following step involves assigning the tasks of several workflows to cloud VMs through the use of the GM-GJO method for multiworkflow scheduling. GM-GJO provides optimal workflow scheduling by considering minimal maximizing utilization rate, minimizing makespan, and minimizing the number of deadline missed workflows. The proposed approach attempts to allocate the best possible set of resources for the workflows based on objectives such as deadline, cost, and quality of service (QoS). Extensive experiments were conducted with the CloudSIM tool, and the performance is evaluated in terms of scheduling length ratio, cost, QoS, etc. The execution time of 513.45 ms is achieved with a Sipht workflow of 30 tasks. When comparing the suggested strategy to the current methodologies, the suggested approach performs better.

关键词： workflow prediction autoencoder bidirectional gated recurrent unit workflow scheduling genetically modified golden jackal optimization

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Fault detection for high-speed train traction system using autoencoder-Fréchet inception distance

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第4期36卷 046205-046205页

作者： Zhang, Cheng Lao, Yu-Yu Deng, Cheng-Long Li, Yuan Shenyang Univ Chem Technol Coll Sci Shenyang Liaoning Peoples R China Shenyang Univ Chem Technol Coll Comp Sci & Technol Shenyang Liaoning Peoples R China Shenyang Univ Chem Technol Coll Informat Engn Shenyang Liaoning Peoples R China

The detection of the process state in the traction system plays a crucial role in ensuring the safe operation of high-speed trains. However, the running data in the traction system exhibits nonlinear and dynamic nature, which leads to the failure of traditional detection methods. To address the above problems, a novel fault detection method based on autoencoder-Fr & eacute;chet inception distance (AE-FID) is proposed in this paper. Firstly, autoencoder (AE) is used for feature extraction to capture the inherent nonlinear structure in the data. Secondly, a novel fault detection statistic is developed by combining sliding window technology and Fr & eacute;chet inception distance (FID) to eliminate the influence of data dynamics. Finally, the proposed statistic is utilized to detect faults in the traction system, and the occurrence of faults is measured in terms of structural differences to mitigate the influence of the masking effects. The proposed method is evaluated through a numerical example that simulates dynamic nonlinear systems and the Traction Drive Control System-Fault Injection Benchmark simulation platform of a high-speed train. The experimental results demonstrate the effectiveness of the proposed method in fault detection for traction systems.

关键词： traction system fault detection autoencoder Fr & eacute chet inception distance sliding window

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