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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Chen, Jie You, Xuetong Zhao, Shu Xiao, Jingxuan Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Province Hefei 230601 China School of Computer Science and Technology Anhui University Anhui Province Hefei 230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Province Hefei 230601 China Faculty of Innovation Engineering Department of Computer Science Macau University of Science and Technology Macau China

ISBN: (纸本)9783031927409

Depression has become a major global public health challenge. Social media texts reflect users’ emotional and psychological states, providing a valuable data source for depression detection through natural language analysis. However, current research still faces limitations in modeling the collaborative representation of user texts and dynamic behaviors. To address this, we propose MVDep, a model that integrates multi-view features to enhance social text representation for depression detection. Specifically, we enhance text representation by investigating depression from multi-view, including social media texts, user profiles, and social behaviors, enabling more effective depression detection. First, we conduct initial filtering of social media texts using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and then screen out the uncertain domain of data samples based on the three-way decision (3WD). Next, we extract quantified user profiles and social behavioral features to enhance the representation of depression tendencies for uncertain domain samples. Finally, we fuse the multi-view features derived from social media texts, user profiles and social behaviors, improving the model’s ability to detect depression. We evaluate our model on the SWDD dataset, demonstrating its superiority over state-of-the-art baseline models in depression detection tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3WD Depression Detection Multi-View Feature Fusion Social Media Texts

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One-bit Compressed Sensing using Generative Models

arXiv

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arXiv 2025年

作者： Kafle, Swatantra Joseph, Geethu Varshney, Pramod K. ANDRO Computational Solutions NY United States Electrical Engineering and Computer Science Department Syracuse University NY United States Signal Processing Systems group Electrical Engineering Mathematics and Computer Science faculty the Delft University of Technology Netherlands

This paper addresses the classical problem of one-bit compressed sensing using a deep learning-based reconstruction algorithm that leverages a trained generative model to enhance the signal reconstruction performance. The generator, a pretrained neural network, learns to map from a low-dimensional latent space to a higher-dimensional set of sparse vectors. This generator is then used to reconstruct sparse vectors from their one-bit measurements by searching over its range. The presented algorithm provides an excellent reconstruction performance because the generative model can learn additional structural information about the signal beyond sparsity. Furthermore, we provide theoretical guarantees on the reconstruction accuracy and sample complexity of the algorithm. Through numerical experiments using three publicly available image datasets, MNIST, Fashion-MNIST, and Omniglot, we demonstrate the superior performance of the algorithm compared to other existing algorithms and show that our algorithm can recover both the amplitude and the direction of the signal from one-bit measurements. © 2025, CC BY.

关键词： Compressed sensing

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STARESNET: A NETWORK IN SPACETIME ALGEBRA TO SOLVE MAXWELL’S PDES

arXiv

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arXiv 2024年

作者： Pepe, Alberto Lasenby, Joan Buchholz, Sven Signal Processing and Communications Lab University of Cambridge Cambridge United Kingdom Department of Computer Science and Media Technische Hochschule Brandenburg University of Applied Sciences Brandenburg Germany

We introduce STAResNet, a ResNet architecture in Spacetime Algebra (STA) to solve Maxwell’s partial differential equations (PDEs). Recently, networks in Geometric Algebra (GA) have been demonstrated to be an asset for truly geometric machine learning. In [1], GA networks have been employed for the first time to solve partial differential equations (PDEs), demonstrating an increased accuracy over real-valued networks. In this work we solve Maxwell’s PDEs both in GA and STA employing the same ResNet architecture and dataset, to discuss the impact that the choice of the right algebra has on the accuracy of GA networks. Our study on STAResNet shows how the correct geometric embedding in Clifford Networks gives a mean square error (MSE), between ground truth and estimated fields, up to 2.6 times lower than than obtained with a standard Clifford ResNet with 6 times fewer trainable parameters. STAREsNet demonstrates consistently lower MSE and higher correlation regardless of scenario. The scenarios tested are: sampling period of the dataset;presence of obstacles with either seen or unseen configurations;the number of channels in the ResNet architecture;the number of rollout steps;whether the field is in 2D or 3D space. This demonstrates how choosing the right algebra in Clifford networks is a crucial factor for more compact, accurate, descriptive and better generalising pipelines. © 2024, CC BY.

关键词： Mean square error

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Unfolding for Joint Channel Estimation and Symbol Detection in MIMO Communication Systems

arXiv

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arXiv 2023年

作者： Bhattacharya, Swati Hari, K.V.S. Eldar, Yonina C. Statistical Signal Processing Lab Department of Electrical Communication Engineering Indian Institute of Science Bangalore India Faculty of Math and Computer Science Weizmann Institute of Science Israel

This paper proposes a Joint Channel Estimation and Symbol Detection (JED) scheme for Multiple-Input Multiple-Output (MIMO) wireless communication systems. Our proposed method for JED using Alternating Direction Method of Multipliers (JED-ADMM) and its model-based neural network version JED using Unfolded ADMM (JED-U-ADMM) markedly improve the symbol detection performance over JED using Alternating Minimization (JED-AM) for a range of MIMO antenna configurations. Both proposed algorithms exploit the non-smooth constraint, that occurs as a result of the Quadrature Amplitude Modulation (QAM) data symbols, to effectively improve the performance using the ADMM iterations. The proposed unfolded network JED-U-ADMM consists of a few trainable parameters and requires a small training set. We show the efficacy of the proposed methods for both uncorrelated and correlated MIMO channels. For certain configurations, the gain in SNR for a desired BER of 10−2 for the proposed JED-ADMM and JED-U-ADMM is upto 4 dB and is also accompanied by a significant reduction in computational complexity of upto 75%, depending on the MIMO configuration, as compared to the complexity of JED-AM. © 2023, CC BY.

关键词： Channel estimation

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Unified Bayesian Representation for High-Dimensional Multi-Modal Biomedical Data for Small-Sample Classification

SSRN

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SSRN 2024年

作者： Belenguer-Llorens, Albert Sevilla-Salcedo, Carlos Tohka, Jussi Gómez-Verdejo, Vanessa Department of Signal Processing and Communications Universidad Carlos III de Madrid Leganés28911 Spain Department of Computer Science Aalto University Espoo02150 Finland Madrid28009 Spain

We present BALDUR, a novel Bayesian algorithm designed to deal with multi-modal datasets and small sample sizes in high-dimensional settings while providing explainable solutions. To do so, the proposed model combines within a common latent space the different data views to extract the relevant information to solve the classification task and prune out the irrelevant/redundant features/data views. Furthermore, to provide generalizable solutions in small sample size scenarios, BALDUR efficiently integrates dual kernels over the views with a small sample-to-feature ratio. Finally, its linear nature ensures the explainability of the model outcomes, allowing its use for biomarker identification. This model was tested over two different neurodegeneration datasets, outperforming the state-of-the-art models and detecting features aligned with markers already described in the scientific literature. © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Unified Bayesian representation for high-dimensional multi-modal biomedical data for small-sample classification

arXiv

引用

arXiv 2024年

关键词： Adversarial machine learning

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Integrating HL7 FHIR into Clinical Decision Support Systems: A Real-World Application with Pepper Humanoid Robot in Hospital During Doctor Visits 9

Integrating HL7 FHIR into Clinical Decision Support Systems:...

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9th International Conference on Mathematics and computers in sciences and Industry, MCSI 2024

作者： Safran, Valentino Horvat, Samo Ilijevec, Bojan Roj, Igor Rober Flis, Vojko Mlakar, Izidor University of Maribor Faculty of Electrical Engineering and Computer Science Laboratory of Digital Signal Processing Maribor Slovenia University Medical Centre Maribor Department of Thoracic Surgery Maribor Slovenia University Medical Centre Maribor Clinical Department of Abdominal and General Surgery Maribor Slovenia University Medical Centre Maribor Department of Nurses in Clinic for Surgery Maribor Slovenia University Medical Centre Maribor Clinical Department of Cardiac Surgery Maribor Slovenia

ISBN: (纸本)9798350377224

This study explores the integration of HL7 Fast Healthcare Interoperability Resources (FHIR) into a Clinical Decision Support System (CDSS) utilizing a Pepper robot to enhance doctor visits in a hospital setting. We evaluate a real-world implementation where the Pepper robot acts as an interface for data collection from patients, clinical monitors, and hospital information systems. The CDSS leverages FHIR to seamlessly integrate this data and present it visually, aiming to improve clinical decision-making processes for doctors. Our research investigates the potential of this FHIR-based CDSS with a robot interface to optimize healthcare delivery and potentially improve patient outcomes. © 2024 IEEE.

关键词： Anthropomorphic robots

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Himalayan Glacier and the Possibility of Past Ice Streams on Martian Outflow Channels using Remote Sensing Imagery

Himalayan Glacier and the Possibility of Past Ice Streams on...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Indranil Misra Mukesh Kumar Rohil S. Manthira Moorthi Debajyoti Dhar Signal and Image Processing Area Space Applications Centre Indian Space Research Organisation (ISRO) Ahmedabad India Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Martian outflow channels are extensive, long features on the planet's surface, running for thousands of kilometers and up to a kilometer deep. Scientific consensus attributes the formation of Martian outflow channels to catastrophic floods from overflowing lakes during Mars' early history. However, we present an alternative perspective, suggesting the possibility of past glacier ice streams on Mars, influenced by recent scientific studies indicating movements in Amazonian ice streams as potential carvers of Martian outflow channels. The paper proposes a hypothesis of a past cryosphere on Mars resulting in ice stream outflow channels. The study compares and analyzes Martian outflow channels and Himalayan glaciers using a novel remote sensing image processing workflow developed to support this proposed idea. The methodology involves topographic correction to normalize radiance measurements in uneven terrain, Principal Component Analysis (PCA) to extract maximum variance information, and feature matching using Scale Invariant Feature Transform (SIFT) with Random Sample Consensus (RANSAC) outlier removal to assign Feature Similarity Metric (FSM) scores for determining analogous sites. The paper highlights intriguing observations from Himalayan glaciers, which lend credence to the idea of an ice flow mechanism for the origin of Martian outflow channels.

关键词： Mars Visualization Transforms Feature extraction Glaciers Surface topography Streams

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Image Registration of High Resolution Chandrayaan-2 Data

Image Registration of High Resolution Chandrayaan-2 Data

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Ridhi Makharia Jai G. Singla Amitabh Nitant Dube Harish Sharma Department of AIML School of Computer Science Engineering Manipal University Jaipur Rajasthan India Signal and Image Processing Area Space Applications Centre ISRO Ahmedabad Gujarat India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

Accurate and reliable detection of features, along with effective mapping and registration, has been a topic of considerable discussion. Detecting and identifying craters on the lunar surface poses significant challenges due to subtle intensity variations and shadow effects. Conventional corner and edge detection methods often struggle to outline these craters, complicating the image registration. This paper proposes a novel strategy for data preprocessing to enhance edge and corner detection for OHRC and NAC datasets. Utilizing SIFT (Scale-Invariant Feature Transform) and ASIFT (Affine Scale-Invariant Feature Transform) algorithms, the study identifies and maps key points between the OHRC (data to be registered) and NAC (reference) datasets, achieving high pixel-level registration accuracy (within the range of 0.2-2.5 pixels). The application of image stitching and warping, results in georeferenced images that are both accurate and precise, contributing to improved navigation and enhanced image quality.

关键词： Image quality Image registration Accuracy Navigation Image edge detection Moon Transforms Feature extraction Reliability Image stitching

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Monitoring Riparian Vegetation Change Dynamics using Kullback-Leibler Divergence: A Case Study in the Vicinity of Sabarmati River, Gandhinagar, India

Monitoring Riparian Vegetation Change Dynamics using Kullbac...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Riparian Vegetation monitoring using remote sensing images involves using satellite or aerial imagery to assess the health, extent, and changes in vegetation along water bodies such as rivers and streams. This monitoring is important to balance the aquatic ecosystems. The paper presents a novel methodology to detect Riparian Vegetation change dynamics using multi-temporal satellite imagery. The workflow performs the image pre-processing task that includes multi-temporal image registration, top-of-atmosphere reflectance computation, and NDVI geophysical parameters retrieved to quantify the amount and health of vegetation in a Riparian zone buffer. The changes in Riparian Vegetation are detected using the Kullback-Leibler (KL) divergence technique and a change detection map is generated. The proposed approach is evaluated using recent INS-2B Nano-MX and historical IRS-1C LISS-3 imagery near Sabarmati River, Gandhinagar, India. It is found from the visual assessment that there is an increase in healthy Riparian Vegetation around the Sabarmati River. The results are cross-validated with Landsat NDVI time series analysis in the Riparian Vegetation Region of Interest (ROI) for the last two decades and it is found that vegetation change estimated around the river stream closely follows the trend with the proposed method.

关键词： Visualization Normalized difference vegetation index Time series analysis Vegetation mapping Landsat Rivers Satellite images

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