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Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

computers, Materials & Continua 2024年第6期79卷 4741-4762页

作者： Zhenyu Qian Yizhang Jiang Zhou Hong Lijun Huang Fengda Li Khin Wee Lai Kaijian Xia School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China Department of Medical Imaging The Changshu Affiliated Hospital of Soochow UniversitySuzhou215500China Department of Neurosurgery The Changshu Affiliated Hospital of Soochow UniversityChangshu215500China Department of Scientific Research The Changshu Affiliated Hospital of Soochow UniversitySuzhou215500China Changshu Key Laboratory of Medical Artificial Intelligence and Big Data Suzhou215500China Department of Biomedical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical *** deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical *** MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature ***,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering *** address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss *** experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC *** results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these ***,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

关键词： Deep subspace clustering multiscale network structure automatic hyperparameter tuning semi-supervised medical image clustering

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Cross-Lingual Summarization for Overseas Applications Using Multilingual Pre-Trained Models and Knowledge Distillation

Cross-Lingual Summarization for Overseas Applications Using ...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Shunmuga Sundari. M Sanjay Agal Adusupalle Muni Raju Ashok Patil Artificial Intelligence and Data Science Chennai Institute of Technology Computer science and Engineering Faculty of Engineering and Technology Parul University Balaji Institute of International Business (BIIB) Symbiosis Institute of Business Management Nagpur Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Cross-lingual summarization (CLS) has emerged as an essential tool for bridging linguistic gaps in global communication, facilitating the extraction of concise and meaningful content in a target language from source documents written in different languages. This work introduces a novel framework for CLS, leveraging state-of-the-art multilingual pre-trained models alongside knowledge distillation techniques to ensure efficient and scalable performance. The approach is built on transformer-based architectures, such as mBART and mT5, which are fine-tuned to address the challenges of semantic coherence, fluency, and context preservation across diverse language pairs. To optimize these models for deployment in resource-constrained environments, knowledge distillation is employed, transferring knowledge from larger, computationally expensive models to lightweight student models while maintaining high accuracy. Extensive experimentation on multilingual datasets showcases the robustness of the proposed method, achieving significant improvements over traditional pipeline approaches that rely on separate translation and summarization steps. Evaluation metrics, including ROUGE and BLEU scores, demonstrate superior performance across multiple language pairs. Moreover, the proposed framework achieves a threefold reduction in computational overhead compared to full-scale transformer models, making it highly suitable for real-world overseas applications. This research provides a scalable, efficient, and effective solution for generating high-quality summaries across languages, contributing to enhanced multilingual accessibility and information sharing on a global scale.

关键词： Measurement Translation Computational modeling Scalability Semantics Coherence Transformers Smart systems Robustness Multilingual

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Defending Against Backdoor Attacks through Causality-Augmented Diffusion Models for dataset Purification

Defending Against Backdoor Attacks through Causality-Augment...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yuefeng Lai Lizhao Wu Hui Lin Xiaokang Zhou College of Computer and Cyber Security Fujian Normal University FuZhou China Faculty of Business Data Science Kansai University Osaka Japan RIKEN Center for AIP Tokyo Japan

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

dataset-Based Defense is a common proactive defense method aimed at countering backdoor attacks by filtering and removing poisoned samples from the training dataset. Recent research has utilized diffusion models to purify contaminated samples. However, during both forward and backward denoising processes, the inherent noise and distribution characteristics of the data can also affect the purification of poisoned samples, making the process unreliable. To address this issue, this paper re-examines the purification process from the perspective of causal graphs, revealing how backdoor triggers and noise act as confounding factors, generating false associations between contaminated images and labels. To eliminate this false association, we propose a Causality-Augmented Diffusion Model-Based Defense (CADMBD) method. CADMBD applies multiple iterations of forward noise addition and backward denoising to the data input in the diffusion model. During the backward denoising process, residual calculation and causal layer adjustment are used to eliminate backdoor pathways, thereby establishing a true causal relationship between benign features and labels. Finally, the most suitable generated image is selected as the final purified image based on pixel and feature distances from each iteration. Experimental results demonstrate that CADMBD effectively addresses various complex attacks, significantly enhancing the purification effect while minimizing the impact on benign sample accuracy, outperforming baseline defense methods.

关键词： Training data privacy Accuracy Purification Filtering Noise reduction Noise Diffusion models Security

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Soft Real-time Emotion-aware System through Human Physiological Signals using Pervasive Devices

Soft Real-time Emotion-aware System through Human Physiologi...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Feri Setiawan Sunder Ali Khowaja Computer Science School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia School of Computing Faculty of Computing Digital and Data Technological University Dublin Dublin Ireland

ISBN: (数字)9798350368086

ISBN: (纸本)9798350368093

The concept of connected things that involves emotional aspects has been raised as a new research issue known as “emotional IoT”. One of the challenges is to recognize the emotion in real-time without interfering the user’s task or activity. However, most of the existing works on emotion recognition deal with high sampling rate devices and thus need complex arrangement. In this study, we present a framework to recognize four emotions based on human physiological signals using pervasive devices with low sampling rate. The employment of low sampling rate devices may degrade the classification accuracy due to the data offsets or calibration errors. Our study proposes a set of energy deviation features to overcome the accuracy degradation caused by low sampling rate devices. The recursive feature elimination method is applied to avoid overfitting by selecting the most important features and reduce the computational overhead in real-world implementation. Experimental results show that the proposed approach achieves 92.01% and 91.75% accuracy in terms of valence and arousal, respectively. The realization of the proposed approach is done in an end-to-end soft real-time emotion recognition system using smartphone and smartwatch devices. The performance of our system was evaluated under laboratory setup, resulting in 46.3 milliseconds of response time on the average for recognizing emotions.

关键词： Performance evaluation Emotion recognition Wearable Health Monitoring Systems Accuracy Satellites Feature extraction Real-time systems Physiology Time factors Overfitting

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Multi-Modal Vision Transformers for Crop Mapping from Satellite Image Time Series

Multi-Modal Vision Transformers for Crop Mapping from Satell...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Theresa Follath David Mickisch Jan Hemmerling Stefan Erasmi Marcel Schwieder Begüm Demir Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany Thünen Institute of Farm Economics BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Using images acquired by different satellite sensors has shown to improve classification performance in the framework of crop mapping from satellite image time series (SITS). Existing state-of-the-art architectures use self-attention mechanisms to process the temporal dimension and convolutions for the spatial dimension of SITS. Motivated by the success of purely attention-based architectures in crop mapping from single-modal SITS, we introduce several multi-modal multi-temporal transformer-based architectures. Specifically, we investigate the effectiveness of Early Fusion, Cross Attention Fusion and Synchronized Class Token Fusion within the Temporo-Spatial Vision Transformer (TSViT). Experimental results demonstrate significant improvements over state-of-the-art architectures with both convolutional and self-attention components.

关键词： Image sensors computer vision Satellites Time series analysis Crops Geoscience and remote sensing Transformers

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Capability Level of SPBE Application and Infrastructure Audit Tools Using COBIT 2019

Capability Level of SPBE Application and Infrastructure Audi...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Nindy Irzavika Friyogi Resvy Mahda Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia Research Center of Data and Information Sciences Badan Riset dan Inovasi Nasional Jakarta Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

SPBE, an electronic government system, uses IT infrastructure and applications to deliver services. IT audits, conducted using tools like “SPBE Audit Tools,” ensure effective service delivery. COBIT 2019 can improve governance and align IT with business goals. This study aims to evaluate its capability level using COBIT 2019. The research flow involves problem identification, literature review, data analysis, goal alignment, design, and recommendation. This process ensures that the governance system effectively supports the organization's strategic goals and addresses the identified research problem. Gap analysis is conducted to indicate areas where improvements are necessary to align with desired capabilities. It compares the current capability levels to the target levels. The domains APO02, APO04, and BAI03 are below respective targets. Based on the gap analysis results, recommendations will be formulated to minimize the gap and improve the capability levels.

关键词： data analysis Multimedia systems Informatics Systematic literature review Electronic government Business

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A Robust Framework for Epileptic Seizure Diagnosis: Utilizing GRU-CNN Architectures in EEG Signal Analysis

A Robust Framework for Epileptic Seizure Diagnosis: Utilizin...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Maryam Nikoupour Mohammad Reza Keyvanpour Seyed Vahab Shojaedini Department of Computer Engineering Faculty of Engineering Data Mining Laboratory Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

Seizures that take place repeatedly and without provocation are referred to as epilepsy. Epilepsy can be diagnosed with electroencephalography (EEG). One of the most influential challenges of the past few years has been the use of deep learning algorithms to replace manual inspection of medical signals by specialists, such as epilepsy signal classification. This paper presents a multi-label classification approach for epileptic seizures using deep learning. UCI machine learning repository’s epileptic seizure dataset has been used to classify epileptic seizure patients. 178 features are present in each of the 11500 samples in the dataset. Based on a variety of criteria, the proposed method may have a positive impact on epilepsy diagnosis, in most cases by approximately 6% compared with existing methods utilizing long short-term memory (LSTM) and autoencoder. It is possible thus to develop and apply gated recurrent unit-based methods with good potential for categorizing EEG signals for epilepsy diagnosis based on gated recurrent unit (GRU)-CNN based methods.

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Deep Perspective Transformation Based Vehicle Localization on Bird’s Eye View

arXiv

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arXiv 2023年

作者： Mahyar, Abtin Motamednia, Hossein Rahmati, Dara Department of Computer and Data Sciences Shahid Beheshti University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Faculty of Computer Science and engineering Shahid Beheshti University Tehran Iran

An accurate understanding of a self-driving vehicle’s surrounding environment is crucial for its navigation system. To enhance the effectiveness of existing algorithms and facilitate further research, it is essential to provide comprehensive data to the routing system. Traditional approaches rely on installing multiple sensors to simulate the environment, leading to high costs and complexity. In this paper, we propose an alternative solution by generating a top-down representation of the scene, enabling the extraction of distances and directions of other cars relative to the ego vehicle. We introduce a new synthesized dataset that offers extensive information about the ego vehicle and its environment in each frame, providing valuable resources for similar downstream tasks. Additionally, we present an architecture that transforms perspective view RGB images into bird’s-eye-view maps with segmented surrounding vehicles. This approach offers an efficient and cost-effective method for capturing crucial environmental information for self-driving cars. 1 © 2023, CC BY.

关键词： Birds

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Implementation of expert systems in potassium deficiency in cocoa plants using forward chaining method

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Procedia computer science 2023年 216卷 136-143页

作者： Muhammad Titan Hafizal Dian Pratama Putra Herry Wirianata Nanda Satya Nugraha Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Faculty of Forestry STIPER Institute of Agriculture Yogyakarta 55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta 11480 Indonesia

As one of the largest exporters in the world, cocoa (Theobroma cacao L.) production in Indonesia provides an important contribution to the plantation sector that can, directly and indirectly, attribute to the national economic development. However, recent data shows a decline in cocoa productivity in Indonesia. This is arguably caused by various factors, including poor agricultural management and practices in cocoa plantations, that may lead to lower fertility in the soil and an increased risk of diseases and pests. Potassium deficiency is a major contributing factor to the low soil fertility that affects cocoa yields. Therefore, in this work, we implement an application with an expert system utilizing the forward chaining method to detect potassium deficiency in cocoa plants and then give a fertilization-based recommendation based on the plants’ condition. The system employs a set of rules to identify symptoms related to the deficiency on the sample photo of a cocoa leaf according to the channels of red, green, and blue of the image. The sample images of cocoa leaves are submitted to the application with an easy-to-use interface that can show the scanning result and proceed to display the suggested quantity of fertilizers to prevent potassium deficiency. Implementing the system can contribute constructive impacts to improve current practices in the overall cropping system of cocoa plants.

关键词： agriculture cocoa expert system forward chaining recommendation system

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Remote Patient Monitoring Systems Network Adaptation Scenarios

Remote Patient Monitoring Systems Network Adaptation Scenari...

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2022 IEEE International Conference on Current Development in Engineering and Technology, CCET 2022

作者： Khujamatov, Halimjon Akhmedov, Nurshod Lazarev, Amir Arsheen, Sobiya Tashkent University of Information Technologies Dean of the Faculty of Computer Engineering Tashkent Uzbekistan Tashkent University of Information Technologies Data Transmission Networks and Systems Department Tashkent Uzbekistan Maulana Azad National Urdu University Department of Computer Science and Information Technology India

ISBN: (纸本)9781665454155

This article is devoted to the actual topic of studying scenarios for adapting a network infrastructure to a network of remote patient monitoring systems (PCMS). The paper presents an overview and analysis of scenarios for adapting the network infrastructure of monitoring systems. The stages of scenario execution have been studied and simulation models of the RPMS network infrastructure have been developed that implement various adaptation scenarios. A comparative analysis of adaptation scenarios was carried out according to several criteria, such as the speed of request delivery, delivery time, and network load. Simulation models built in the Anylogic environment, a powerful simulator of mass service system, showed the effectiveness of the mesh topology in relation to the ad-hoc topology according to several criteria. The theoretical significance of the work lies in carrying out analytical work on scenarios for adapting the network infrastructure of the RPMS, as well as in studying the network architecture. The practical significance of the work lies in the development of a simulation model that allows you to explore scenarios for adapting the network architecture. © 2022 IEEE.

关键词： Network architecture

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