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Secure and Trustworthy Machine Learning (SaTML), IEEE Conference on

作者： Patrik Joslin Kenfack Adín Ramírez Rivera Adil Mehmood Khan Manuel Mazzara Machine Learning and Knowledge Representation Lab Innopolis University Innopolis Russia Department of Informatics Digital Signal Processing and Image Analysis (DSB) group University of Oslo Oslo Norway School of Computer Science University of Hull Hull UK Institute of Software Development and Engineering Innopolis University Innopolis Russia

Machine Learning (ML) models trained on biased data can reproduce and even amplify these biases. Since such models are deployed to make decisions that can affect people's lives, ensuring their fairness is critical. One approach to mitigate possible unfairness of ML models is to map the input data into a less-biased new space by means of training the model on fair representations. Several methods based on adversarial learning have been proposed to learn fair representation by fooling an adversary in predicting the sensitive attribute (e.g., gender or race). However, adversarial-based learning can be too difficult to optimize in practice; besides, it penalizes the utility of the representation. Hence, in this research effort we train bias-free representations from the input data by inducing a uniform distribution over the sensitive attributes in the latent space. In particular, we propose a probabilistic framework that learns these representations by enforcing the correct reconstruction of the original data, plus the prediction of the attributes of interest while eliminating the possibility of predicting the sensitive ones. Our method leverages the inability of Deep Neural Networks (DNNs) to generalize when trained on a noisy label space to regularize the latent space. We use a network head that predicts a noisy version of the sensitive attributes in order to increase the uncertainty of their predictions at test time. Our experiments in two datasets demonstrated that the proposed model significantly improves fairness while maintaining the prediction accuracy of downstream tasks.

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Generalized Tensor-Aided Channel Estimation for Hardware Impaired Device Identification

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IEEE Transactions on Vehicular Technology 2025年

作者： Wu, Qi Sui, Zeping Ngo, Hien Quoc Wan, Qun Matthaiou, Michail School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China National Key Laboratory on Blind Signal Processing Chengdu610041 China Queen’s University Belfast BelfastBT3 9DT United Kingdom School of Computer Science and Electronics Engineering University of Essex ColchesterCO4 3SQ United Kingdom Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of

In this paper, we investigate the joint generalized channel estimation and device identification problem in Internet of Things (IoT) networks under multipath propagation. To fully utilize the received signal, we decompose the generalized channel into three components: transmitter hardware characteristics, path gains, and angles of arrival. By modeling the received signals as parallel factor (PARAFAC) tensors, we develop alternating least squares (ALS)-based algorithms to simultaneously estimate the generalized channels and identify the transmitters. Simulation results show that the proposed scheme outperforms both Khatri-Rao Factorization (KRF) and the conventional least squares (LS) method in terms of channel estimation accuracy and achieves performance close to the derived Cramér–Rao lower bound. © 2025 IEEE. All rights reserved,

关键词： Tensors

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Comparing Steam and PGSE Diffusion MRI signal of RAT Lymph Nodes Using In-Silico Simulations

Comparing Steam and PGSE Diffusion MRI Signal of RAT Lymph N...

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IEEE International Symposium on Biomedical Imaging

作者： Rémy Gardier Arnaud Savoy Juan Luis Villarreal Haro Gabriel Girard Erick J. Canales-Rodríguez Elda Fischi-Gomez Andreea Hertanu Ileana O. Jelescu Jonathan Rafael-Patino Jean-Philippe Thiran Signal Processing Laboratory (LTS5) École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Department of Computer Science Université de Sherbrooke Sherbrooke Canada Radiology Department Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL) Lausanne Switzerland CIBM Center for Biomedical Imaging Switzerland School of Biology and Medicine University of Lausanne (UNIL) Lausanne Switzerland

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Diffusion magnetic resonance imaging (dMRI) has demonstrated great capabilities to quantify tissue microstructure using biophysical diffusion models. Over the years, various gradient sequences have been proposed to provide sensitivity and specificity to various features of the tissue, using the pulse-gradient spin echo sequence (PGSI). In lymph node imaging, the long diffusion times required to estimate large characteristic length scales are achievable with the stimulated echo acquisition mode (STIAM). However, the butterfly gradients inherent to the STIAM protocol result in significant cross-terms in the diffusion-weighting matrix. If overlooked, these cross-terms can introduce biases in the microstructure estimations. Addressing these cross-terms within the analytical solution of the biophysical model is challenging. In this context, in-silico Monte-Carlo diffusion simulations in realistic numerical tissue substrates are a powerful tool to study diffusion without any assumptions on the analytical form of the signal. Using simulations and preclinical dMRI data, this work highlights (1) the STIAM bias over the PGSI sequence in free isotropic medium (agar), and (2) distinct regions of a rat lymph node correlate best with simulation in numerical substrates with distinct sphere densities. In summary, this work highlights the advantages of in-silico simulations for investigating MRI sequence effects on the DWI signal, yet it remains a challenging field.

关键词： Analytical models Biological system modeling Magnetic resonance imaging Estimation Data models Numerical models Microstructure

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Differentiable Interacting Multiple Model Particle Filtering

arXiv

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arXiv 2024年

作者： Brady, John-Joseph Luo, Yuhui Wang, Wenwu Elvira, Víctor Li, Yunpeng Computer Science Research Centre University of Surrey Guildford Surrey United Kingdom Data Science Department National Physical Laboratory Teddington Greater London United Kingdom Centre for Vision Speech and Signal Processing University of Surrey Guildford Surrey United Kingdom School of Mathematics University of Edinburgh Edinburgh United Kingdom Centre for Oral Clinical & Translational Sciences King’s College London London United Kingdom

We propose a sequential Monte Carlo algorithm for parameter learning when the studied model exhibits random discontinuous jumps in behaviour. To facilitate the learning of high dimensional parameter sets, such as those associated to neural networks, we adopt the emerging framework of differentiable particle filtering, wherein parameters are trained by gradient descent. We design a new differentiable interacting multiple model particle filter to be capable of learning the individual behavioural regimes and the model which controls the jumping simultaneously. In contrast to previous approaches, our algorithm allows control of the computational effort assigned per regime whilst using the probability of being in a given regime to guide sampling. Furthermore, we develop a new gradient estimator that has a lower variance than established approaches and remains fast to compute, for which we prove consistency. We establish new theoretical results of the presented algorithms and demonstrate superior numerical performance compared to the previous state-of-the-art *** Codes 62M20, 62F12 © 2024, CC BY.

关键词： Wiener filtering

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Time series numerical association rule mining variants in smart agriculture

arXiv

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arXiv 2022年

作者： Fister, Iztok Fister, Dušan Fister, Iztok Podgorelec, Vili Salcedo-Sanz, Sancho Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 MariborSI-2000 Slovenia Department of Signal Processing and Communications Universidad de Alcalá Alcalá de Henares Madrid28805 Spain

Numerical association rule mining offers a very efficient way of mining association rules, where algorithms can operate directly with categorical and numerical attributes. These methods are suitable for mining different transaction databases, where data are entered sequentially. However, little attention has been paid to the time series numerical association rule mining, which offers a new technique for extracting association rules from time series data. This paper presents a new algorithmic method for time series numerical association rule mining and its application in smart agriculture. We offer a concept of a hardware environment for monitoring plant parameters and a novel data mining method with practical experiments. The practical experiments showed the method’s potential and opened the door for further extension. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

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Iterative Random Training Sampling Convolutional Neural Network for Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2023年 61卷

作者： Chang, Chein-I Liang, Chia-Chen Hu, Peter Fuming University of Maryland Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan Providence University Department of Computer Science and Information Management Taichung02912 Taiwan University of Maryland School of Medicine R. A. Cowley Shock Trauma Center Shock Trauma Anesthesia Organized Research Center Department of Anesthesia BaltimoreMD21201 United States

Convolutional neural network (CNN) has received considerable interest in hyperspectral image classification (HSIC) lately due to its excellent spectral-spatial feature extraction capability. To improve CNN, many approaches have been directed at exploring the infrastructure of its network by introducing different paradigms. This article takes a rather different approach by developing an iterative CNN that extends a CNN by including a feedback system to repeatedly process the same CNN in an iterative manner. Its idea is to take advantage of a recently developed iterative training sampling spectral-spatial classification (IRTS-SSC) that allows CNN to update its spatial information of classification maps through a feedback spatial fil- tering system via IRTS. The resulting CNN is called iterative random training sampling CNN (IRTS-CNN) with several unique features. First, IRTS-CNN combines CNN and IRTS-SSC into one paradigm, an architecture that has never been investigated in the past. Second, it implements a series of spatial filters to capture spatial information of classified data samples and further feeds this information back via an iterative process to expand the current input data cube for the next iteration. Third, it utilizes the expanded data cube to randomly reselect training samples and then to reimplement CNN iteratively. Last but not least, IRTS-CNN provides a general framework that can implement any arbitrary CNN as an initial classifier to improve its performance through an iterative process. Extensive experiments are conducted to demonstrate that IRTS-CNN indeed significantly improves CNN, specifically when only a small size of limited training samples is used. © 1980-2012 IEEE.

关键词： Iterative methods

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MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition

arXiv

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arXiv 2021年

作者： Wu, Shuang Song, Xiaoning Feng, Zhenhua School of Artificial Intelligence and Computer Science Jiangnan University China Department of Computer Science University of Surrey United Kingdom Centre for Vision Speech and Signal Processing University of Surrey United Kingdom

Recently, word enhancement has become very popular for Chinese Named Entity Recognition (NER), reducing segmentation errors and increasing the semantic and boundary information of Chinese words. However, these methods tend to ignore the information of the Chinese character structure after integrating the lexical information. Chinese characters have evolved from pictographs since ancient times, and their structure often reflects more information about the characters. This paper presents a novel Multi-metadata Embedding based Cross-Transformer (MECT) to improve the performance of Chinese NER by fusing the structural information of Chinese characters. Specifically, we use multi-metadata embedding in a two-stream Transformer to integrate Chinese character features with the radical-level embedding. With the structural characteristics of Chinese characters, MECT can better capture the semantic information of Chinese characters for NER. The experimental results obtained on several well-known benchmarking datasets demonstrate the merits and superiority of the proposed MECT method.1 © 2021, CC BY-NC-SA.

关键词： Embeddings

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Adopting Artificial Intelligence Algorithms for Remote Fetal Heart Rate Monitoring and Classification using Wearable Fetal Phonocardiography

TechRxiv

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TechRxiv 2023年

作者： Abburi, Radha Hatai, Indranil Martinek, Radek Jaros, Rene Babu, T. Arun Babu, Sharmila Arun Samanta, Sibendu Department of Electronics and Communication Engineering SRM University AP Andhra Pradesh India Signal Processing and FPGA Mathworks Bangalore India Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu Ostrava708 00 Czech Republic Andhra Pradesh India Andhra Pradesh India

Fetal phonocardiography (FPCG) is a non-invasive Fetal Heart Rate (FHR) monitoring technique that can detect vibrations and murmurs in heart sounds. However, acquiring fetal heart sounds from a wearable FPCG device is challenging due to noise and artefacts. This research contributes a resilient solution to overcome the conventional issues by adopting Artificial Intelligence (AI) with FPCG for automated FHR monitoring in an end-to-end manner, named (AI-FHR). Four sequential methodologies were used to ensure reliable and accurate FHR monitoring. The proposed method removes low-frequency noises and high-frequency noises by using Chebyshev II high-pass filters and Enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ECEEMDAN) in combination with Phase Shifted Maximal Overlap Discrete Wavelet Transform (PS-MODWT) filters, respectively. The denoised signals are segmented to reduce complexity, and the segmentation is performed using multi-agent deep Q-learning (MA-DQL). The segmented signal is provided to reduce the redundancies in cardiac cycles using the Artificial Hummingbird Optimisation (AHBO) algorithm. The segmented and non-redundant signals are converted into 3D spectrograms using a machine learning algorithm called variational autoencoder-general adversarial networks (VAE-GAN). The feature extraction and classification are carried out by adopting a hybrid of the bidirectional gated recurrent unit (BiGRU) and the multi-boosted capsule network (MBCapsNet). The proposed method was implemented and simulated using MATLAB R2020a and validated by adopting effective validation metrics. The results demonstrate that the proposed method performed better than the current method. © 2023, CC BY.

关键词： Biomedical signal processing

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signal structure information-based data association for maneuvering targets with a convolutional Siamese network

Signal structure information-based data association for mane...

引用

IEEE International Conference on Radar

作者： Chang Gao Hongwei Liu Pramod K. Varshney Junkun Yan Penghui Wang National Laboratory of Radar Signal Processing Xidian University Xi'an China Department of Electrical Engineering and Computer Science Syracuse University NY USA

ISBN: (纸本)9781665468893

Traditional data association methods usually suffer performance degradation caused by model mismatch when target maneuvers occur. In addition, the signal structure information (SSI) available from the employed signal processing techniques is rarely used in data association due to the lack of appropriate tools. To overcome the model mismatch problem brought by the target maneuvers, we resort to a data driven method to design the data association algorithm. To make full use of the SSI, a convolutional neural network is used to transform the SSI into a feature space, where the similarity of the SSI between two observations can be calculated using the Euclidean distance. The resulting structure is the convolutional Siamese network (CSN). By comparing the similarity with a predetermined margin parameter, the decision about association can be made. Simulation results are presented to show that the use of SSI in the designed CSN can improve the association probability of maneuvering targets.

关键词： Correlation Convolution Simulation Radar Euclidean distance Transforms Radar tracking

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Zero-Shot Audio Captioning Using Soft and Hard Prompts

arXiv

引用

arXiv 2024年

作者： Zhang, Yiming Xu, Xuenan Du, Ruoyi Liu, Haohe Dong, Yuan Tan, Zheng-Hua Wang, Wenwu Ma, Zhanyu The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Electronic Systems Aalborg University Aalborg9220 Denmark The Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

In traditional audio captioning methods, a model is usually trained in a fully supervised manner using a human-annotated dataset containing audio-text pairs and then evaluated on the test sets from the same dataset. Such methods have two limitations. First, these methods are often data-hungry and require time-consuming and expensive human annotations to obtain audio-text pairs. Second, these models often suffer from performance degradation in cross-domain scenarios, i.e., when the input audio comes from a different domain than the training set, which, however, has received little attention. We propose an effective audio captioning method based on the contrastive language-audio pre-training (CLAP) model to address these issues. Our proposed method requires only textual data for training, enabling the model to generate text from the textual feature in the cross-modal semantic space. In the inference stage, the model generates the descriptive text for the given audio from the audio feature by leveraging the audio-text alignment from CLAP. We devise two strategies to mitigate the discrepancy between text and audio embeddings: a mixed-augmentation-based soft prompt and a retrieval-based acoustic-aware hard prompt. These approaches are designed to enhance the generalization performance of our proposed model, facilitating the model to generate captions more robustly and accurately. Extensive experiments on AudioCaps and Clotho benchmarks show the effectiveness of our proposed method, which outperforms other zero-shot audio captioning approaches for in-domain scenarios and outperforms the compared methods for cross-domain scenarios, underscoring the generalization ability of our method. © 2024, CC BY.

关键词： Semantics

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