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A multi-agents visual federated learning framework for underground coal mines 24

A multi-agents visual federated learning framework for under...

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7th International Conference on Signal Processing and Machine Learning, SPML 2024

作者： Ji, Yucheng Yuan, Hongyong Liu, Yongqiang Fang, Yudong Chen, Wei Ministry of Emergency Management Big Data Center China School of Safety Science Tsinghua University China School of Computer Science and Technology China University of Mining and Technology China

ISBN: (纸本)9798400717192

The video safety monitoring and analysis is a critical problem in underground coal mines. Due to the complex environment in the underground coal mine and the requirement of perception and decision-making abilities from the coal mine robots, single intelligent agent(robot) is difficult to complete complicated tasks. This paper proposes a framework of federated learning for multiple robots(agents) in underground coal mines to achieve video monitor and analysis tasks. Three federated learning models are raised include asynchronous federation optimization algorithm;visual federation method for heterogeneous image data and coal mine video equipment federated model aggregation. The visual federated learning framework can significantly improve the safety monitoring, accelerate the development of automation in the coal mine production field, and constructing and improving smart mines. © 2024 ACM.

关键词： Risk management

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Comparative Model Evaluation of Lightweight Transformer-Based and CNN-Based Architechture for Degenerative Lumbar Spine Classification

Comparative Model Evaluation of Lightweight Transformer-Base...

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Advancement in data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Jennifer Ardelia Limicia Wilbert Chandra Alexander Agung Santoso Gunawan Jeffrey Junior Tedjasulaksana Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Low back pain is a leading cause of disability globally, is often associated with degenerative lumbar spine conditions. Accurate diagnosis of these conditions is critical but challenging due to the subjective nature of MRI interpretation and the weak correlation between imaging findings and symptoms. Thus, the purpose of this study is to evaluate the performance of CNN-based architectures (VGG-16, EfficientNetB0, EfficientNetV2) and transformer-based architecture specifically Vision Transformers (ViT) in classifying lumbar spine conditions as normal, moderate, or severe. Using the RSNA 2024 Lumbar Spine Degenerative Classification dataset. The models are evaluated using the ROC-AUC and PRAUC as the performance metrics due to the dataset imbalance. Results indicate that EfficientNetB0 achieved the highest overall performance, with an average ROC-AUC of 0.784 and PR-AUC of 0.528, demonstrating strong adaptability to imbalanced datasets. EfficientNetV2 also performed competitively, while VGG-16 showed moderate effectiveness. The Vision Transformer (ViT), however, underperformed due to its reliance on larger datasets and challenges in capturing fine-grained spatial features. The findings highlight the potential of EfficientNet-based models for accurate and efficient lumbar spine diagnostics. This study underscores the potential of advanced deep learning approaches in improving diagnostic workflows for degenerative lumbar spine conditions.

关键词： computer vision Adaptation models Accuracy Magnetic resonance imaging Soft sensors Spine Transforms Transformers data models Convolutional neural networks

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Pap Smear Medical Image Classification Using Deep Learning: A Systematic Review 6

Pap Smear Medical Image Classification Using Deep Learning: ...

引用

6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Selvano, Elvan Elwiehardja, Gregorius Natanael Pardamean, Bens Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia

ISBN: (纸本)9798350345162

Cervical cancer has been known as one of the most prevalent medical disorders globally and a leading cause of death. Early detection, particularly through Pap tests, plays a vital role in its prevention. Previous studies have leveraged machine learning and deep learning techniques to classify the medical images obtained from Pap tests. In this study, a Systematic Literature Review methodology was used to examine 15 relevant papers that have been filtered from queries to Google Scholar which have gone through 4 stages of filtering that include: identification, screening, eligibility, and inclusion. This study addresses two research questions regarding the datasets and deep learning techniques for classifying pap smear images in recent years. The performance of the models was analyzed and potential areas for improvements are suggested. The findings of this study reveal that the Herlev University Hospital and SIPaKMed datasets are the most used. The methodologies used by researchers range from machine learning techniques, transfer learning using Convolutional Neural Networks, and utilize state-of-the-art models with novel optimizing methodology. While there are exciting opportunities in the field, challenges include model generalization and interpretability. © 2023 IEEE.

关键词： cervical cancer deep learning machine learning pap test

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Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility data with Large Language Models 38

Mobility-LLM: Learning Visiting Intentions and Travel Prefer...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Gong, Letian Lin, Yan Zhang, Xinyue Lu, Yiwen Han, Xuedi Liu, Yichen Guo, Shengnan Lin, Youfang Wan, Huaiyu School of Computer Science and Technology Beijing Jiaotong University China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China Department of Computer Science Aalborg University Denmark

Location-based services (LBS) have accumulated extensive human mobility data on diverse behaviors through check-in sequences. These sequences offer valuable insights into users' intentions and preferences. Yet, existing models analyzing check-in sequences fail to consider the semantics contained in these sequences, which closely reflect human visiting intentions and travel preferences, leading to an incomplete comprehension. Drawing inspiration from the exceptional semantic understanding and contextual information processing capabilities of large language models (LLMs) across various domains, we present Mobility-LLM, a novel framework that leverages LLMs to analyze check-in sequences for multiple tasks. Since LLMs cannot directly interpret check-ins, we reprogram these sequences to help LLMs comprehensively understand the semantics of human visiting intentions and travel preferences. Specifically, we introduce a visiting intention memory network (VIMN) to capture the visiting intentions at each record, along with a shared pool of human travel preference prompts (HTPP) to guide the LLM in understanding users' travel preferences. These components enhance the model's ability to extract and leverage semantic information from human mobility data effectively. Extensive experiments on four benchmark datasets and three downstream tasks demonstrate that our approach significantly outperforms existing models, underscoring the effectiveness of Mobility-LLM in advancing our understanding of human mobility data within LBS contexts. © 2024 Neural information processing systems foundation. All rights reserved.

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Multi-Modal Fusion with Semantic Supervision for Radiology Report Generation 26

Multi-Modal Fusion with Semantic Supervision for Radiology R...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Jia, Xing Xiong, Yun Zhang, Yao Luo, Li Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Public Health Fudan University China

ISBN: (纸本)9781643684369

Radiology report generation, one way of analyzing radiology images, is to generate a textual report automatically for the given image, and it is of great significance to assist diagnosis and alleviate the workload of radiologists. Some report generation methods have been therefore proposed. However, these methods suffer from the problem of low-quality generation, because of the visual and textual bias and training with text similarity oriented objective. To solve this problem, we propose a novel radiology report generation model with multi-modal fusion and semantic supervision, namely MS-Gen. MS-Gen consists of two main components, i.e., the semantic-visual fusion module and the semantic weighted contrastive loss. Specifically, the main idea of the semantic-visual fusion module is to make use of the domain-specific prior knowledge contained in a large pre-trained visual-language model and also the complementary nature between the image and text modalities. Moreover, a novel optimization term, i.e., the semantic weighted contrastive loss, is proposed to guide the optimization process with semantic similarity objective, and further enforce the generated reports with higher clinical accuracy. Extensive experiments conducted on two real datasets of IU X-Ray and MIMIC-CXR demonstrate the effectiveness of MS-Gen. © 2023 The Authors.

关键词： Semantics

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Performance Analysis of Distributed database System in Cloud Computing Environment 9

Performance Analysis of Distributed Database System in Cloud...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Prasetio, Nicholas Ferdinand, Daniel Wijaya, Brandon Christopher Anggreainy, Maria Susan Kumiawan, Afdhal Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program School of Computer Science Jakarta11530 Indonesia

ISBN: (纸本)9798350370126

This research discusses the performance evaluation of distributed database systems in a cloud computing environment Cloud computing environments allow data and applications to be stored and deployed on infrastructure located in different parts of the world. However, the use of distributed database systems in cloud computing environments can cause performance issues, such as complex data access and factors such as network latency, security, and scalability that affect system performance. Therefore, performance evaluation of distributed database systems is necessary to ensure effective data management across the infrastructure. The purpose of this research is to measure and understand the performance of distributed database systems in a cloud computing environment. This is important because proper performance evaluation is needed to ensure distributed database systems can operate effectively and efficiently in such environments. This research will analyze the features of distributed database systems, factors that affect performance, how to measure and compare system performance, and how to improve system performance. Analyzing the performance of distributed database systems in a cloud computing environment can help users choose the most appropriate and efficient cloud computing platform for their business needs and improve operational efficiency. © 2023 IEEE.

关键词： Distributed database systems

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Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation 41

Learning-Efficient Yet Generalizable Collaborative Filtering...

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41st International Conference on Machine Learning, ICML 2024

作者： Pu, Yuanhao Chen, Xiaolong Huang, Xu Chen, Jin Lian, Defu Chen, Enhong School of Artificial Intelligence and Data Science University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China School of Business and Management Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Cognitive Intelligence China

The weighted squared loss is a common component in several Collaborative Filtering (CF) algorithms for item recommendation, including the representative implicit Alternating Least Squares (iALS). Despite its widespread use, this loss function lacks a clear connection to ranking objectives such as Discounted Cumulative Gain (DCG), posing a fundamental challenge in explaining the exceptional ranking performance observed in these algorithms. In this work, we make a breakthrough by establishing a connection between squared loss and ranking metrics through a Taylor expansion of the DCG-consistent surrogate loss-softmax loss. We also discover a new surrogate squared loss function, namely Ranking-Generalizable Squared (RG2) loss, and conduct thorough theoretical analyses on the DCG-consistency of the proposed loss function. Later, we present an example of utilizing the RG2 loss with Matrix Factorization (MF), coupled with a generalization upper bound and an ALS optimization algorithm that leverages closed-form solutions over all items. Experimental results over three public datasets demonstrate the effectiveness of the RG2 loss, exhibiting ranking performance on par with, or even surpassing, the softmax loss while achieving faster convergence. Copyright 2024 by the author(s)

关键词： Collaborative filtering

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MAHSNet: Multi-Scale Adaptive Hybrid Semantic Network for Medical Image Segmentation 3

MAHSNet: Multi-Scale Adaptive Hybrid Semantic Network for Me...

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3rd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2024

作者： Xu, Zijing Wang, Lei Zhu, Ge Heilongjiang University School of Computer Science and Big Data Harbin China

ISBN: (纸本)9798350355413

Benefiting from self-attention, the Transformer-based methods excel in global context modeling. However, these methods have limitations in capturing local dependencies between pixels, making it difficult to accurately segment small lesions, small tissues, and complex target boundaries. To address this issue, we propose the Multi-Scale Adaptive Hybrid Semantic Network (MAHSNet) for medical image segmentation. The core of MAHSNet is the Efficient Pure Attention Cascaded Decoder (EPACD). Unlike traditional CNN-Transformer decoder architectures, EPACD introduces gate attention, coordinate attention, and boundary attention to accurately segment local details and target boundaries, while significantly reducing the model's parameters and computational complexity. Experimental results on six datasets across two modalities demonstrate that MAHSNet outperforms existing state-of-the-art methods in multiple medical segmentation tasks. For example, the Dice scores on the Synapse datasets reached 83.76%. The parameters and computational complexity were reduced by 73% and 82% compared to TransUnet, achieving dual improvements in segmentation performance and efficiency. © 2024 IEEE.

关键词： component computer Vision Deep Neural Networks Medical Image Segmentation Transformer

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Factor Analysis: Uncovering Factor Structure in the Indonesian Teacher Engagement Index (ITEI) Survey Results data 11

Factor Analysis: Uncovering Factor Structure in the Indonesi...

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11th International Conference on computer, Control, Informatics and its Applications, IC3INA 2024

作者： Cam, Grace Shirley Sasmoko Indrianti, Yasinta Manalu, Sonya Rapinta Moniaga, Jurike V. Data Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Primary Teacher Education Department Faculty Humanities Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Podomoro University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331542313

This study aims to explore the latent structure of the Indonesian Teacher Engagement Index (ITEI) based on the ITEI survey results of primary school teachers in various regions of Indonesia. Exploration of the ITEI structure is obtained by identifying the main factors hidden in the ITEI survey data with machine learning. The machine learning method used is Exploratory factor analysis (EFA) with Principal Component Analysis (PCA). The ITEI dataset consists of 10,365 records, covering 44 items from 6 ITEI dimensions. The machine learning analysis yielded 4 main factors that became new dimensions, explaining the variation of 29 items. The four new factors found are summarized into the dimensions of Values and Ethics in Education, Personal and Social Development in Education, Character and Leadership in Education, and Social Competence and Justice in Education. This exploration was conducted to show the latent structure of the ITEI that matches the real situation and provide a deeper understanding of the main components that make up the teacher engagement index in Indonesia, so that conclusions can be drawn accurately from the survey results. The implications of these findings can be used to improve understanding and development of more effective education policies in the future. © 2024 IEEE.

关键词： Adversarial machine learning

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VREP Platform: A Virtual Reality-Based Human-computer Interaction Experimental Platform 3

VREP Platform: A Virtual Reality-Based Human-Computer Intera...

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3rd International Conference on Robotics, Automation and Intelligent Control, ICRAIC 2023

作者： Tan, Bozhi Liu, Shikai He, Yuqi Zhu, Yi Nanjing Audit University School of Computer Science Nanjing China Nanjing Audit University School of Statistics and Data Science Nanjing China

ISBN: (纸本)9798350360813

The deployment of human-computer interaction (HCI) experimental platforms across various domains is of paramount importance. Presently, conventional HCI platforms continue to be rooted in Personal computer (PC), exhibiting limitations such as low immersion, reduced real-time responsiveness, and diminished accuracy. In light of these constraints, this research advocates for a Virtual Reality (VR)-based HCI experimental platform, with the aim of overcoming these limitations. The platform seeks to enhance user experiences, evaluate emerging interaction technologies, and facilitate scholarly research. Integrating VR technologies, this platform offers a simulated, realistic interactive environment supporting multimodal inputs. The architecture includes three main components: users, processing, and the computer, with submodules facilitating scene construction, tracking, and connectivity. The platform showcases its potential for immersive interaction through an intelligent collaboration experiment between humans and unmanned aerial vehicles. © 2023 IEEE.

关键词： Virtual reality

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