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Federating Unlabeled Samples: A Semi-supervised Collaborative Framework for Whole Slide Image Analysis 23rd

Federating Unlabeled Samples: A Semi-supervised Collaborativ...

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23rd international conference on intelligent data engineering and automated learning (IDEAL)

作者： Launet, Laetitia del Amor, Rocio Colomer, Adrian Mosquera-Zamudio, Andres Moscardo, Anais Monteagudo, Carlos Zhao, Zhiming Naranjo, Valery Univ Politecn Valencia I3B Valencia Spain Univ Valencia Hosp Clin Univ Valencia Dept Pathol Valencia Spain Univ Amsterdam Multiscale Networked Syst Amsterdam Netherlands

ISBN: (纸本)9783031217524;9783031217531

Over the last decades, deep learning-based algorithms have witnessed tremendous progress in the medical field to assist pathologists in clinical decisions and reduce their workload. For these models to reach their full potential, access to large and diverse datasets is essential, but collaborations between hospitals are highly limited by privacy-related regulations. At the same time, medical institutions do not always have specialized pathologists to diagnose biopsies and label local data. To address these limitations, federated learning gained traction to enable multi-institution model training without sharing sensitive patient data. However, this technique is still in its infancy when it comes to digital pathology applications, and does not consider institutions with unlabeled data in federations. In this paper, we introduce a novel semi-supervised federated learning approach that promotes multi-institutional training of deep learning models while integrating unlabeled collaborating data sources into the federated setup. The experimental results show a better performance for models trained under a federated setting with both labeled and unlabeled data. Optimally, this framework will also bring the promise of assisting clinical decisions in hospitals that do not have specialized pathologists.

关键词： Federated learning Self-training Whole slide images Multiple instance learning

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Economical Electricity Supplement for Mobile Collectors in Large-Scale Multitask WSNs 21

Economical Electricity Supplement for Mobile Collectors in L...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhai, Xin Xu, Lijie Zhang, Jialei Wang, Kun Xu, Jia Xu, Bei Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9798350329223

For large-scale multitask wireless sensor networks (LSM-WSNs), the traditional data collection mode could suffer low energy-efficiency on data transmission, since the large-scale multitask scenarios could result in much higher packet collision probability, especially for harsh environments. Mobile data collection is an efficient data acquisition way to prolong network lifetime for LSM-WSNs. However, the mobile collectors could suffer electricity shortage problem, since the limited battery capacity of any mobile collector could not afford the energy consumption of its long-distance movement and massive data collection in large-scale multitask scenarios. Deploying wireless chargers to supplement the energy of mobile collectors is a feasible solution to electricity shortage problem, but will incur extra charger deployment cost. In this paper, we focus on the problem that how to optimize such charger deployment cost, which is NP-hard. By transforming it into minimum-cost submodular cover problem, we devise an efficient approximation algorithm with a provable approximation ratio. The extensive simulation results reveal that our solution always outperforms the other solutions under whatever configurations. © 2023 IEEE.

关键词： Wireless sensor networks

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Enhancing Negation Scope Detection using Multitask learning 21

Enhancing Negation Scope Detection using Multitask Learning

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Patel, Harsh Zhang, Xulang Liu, Qian Indian Inst Technol Gandhinagar Palaj India Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781665424271

Negation is a linguistic phenomenon that usually occurs in a text for denial or refute of some occasion. Detection of such negative assertions is an essential sub-task in various applications of information extraction and data mining. In this paper, we present a deep multitask learning (MTL) framework to enhance the performance of Negation Scope detection using part-of-speech (POS) tagging as an auxiliary task. We show how the relationship between these two tasks, which do not seem to be easily linked from a linguistic point of view, is mutually beneficial.

关键词： sentiment analysis multitask learning negation scope detection pos-tagging deep learning

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Preserving Privacy of Input Features Across All stages of Collaborative learning 21

Preserving Privacy of Input Features Across All Stages of Co...

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作者： Lu, Jianrong Xue, Lulu Wan, Wei Li, Minghui Zhang, Leo Yu Hu, Shengqing Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology School of Software Engineering Wuhan China Griffith University School of Information and Communication Technology Brisbane Australia Huazhong University of Science and Technology Union Hospital Tongji Medical College Department of Nuclear Medicine Wuhan China

ISBN: (纸本)9798350329223

Collaborative learning is a widely used privacy-preserving distributed training framework where users participate in global training using gradients instead of disclosing their private data. However, gradient inversion attacks have challenged the privacy of this approach by reconstructing private inputs from gradients. While prior works have proposed various defenses against gradient inversion attacks, their privacy assessments have mainly focused on untrained models, lacking consideration for the trained model, which should be the primary focus in collaborative learning. In this context, we first conduct a comprehensive privacy evaluation across all stages of collaborative learning. We uncover the limitations of existing defenses in providing sufficient privacy protection for trained models. To address this challenge, we introduce GradPrivacy, a novel framework tailored to safeguard the privacy of trained models without compromising their performance. GradPrivacy comprises two key components: the amplitude perturbation module, which perturbs gradient parameters associated with critical features to thwart attackers from reconstructing essential input feature information, and the deviation correction module, which effectively maintains model performance by correcting deviations in model update directions from previous rounds. Extensive evaluations demonstrate that GradPrivacy successfully achieves effective privacy preservation, surpassing state-of-the-art methods in terms of the privacy-accuracy trade-off. © 2023 IEEE.

关键词： Economic and social effects

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LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection 25

LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Qi, Jiaxing Huang, Shaohan Luan, Zhongzhi Yang, Shu Fung, Carol Yang, Hailong Qian, Depei Shang, Jing Xiao, Zhiwen Wu, Zhihui Sino-German Joint Software Institute Beihang University Beijing100191 China The Concordia Institute for Information Systems Engineering Concordia University Quebec Canada The China Mobile Information Technology Center China

ISBN: (纸本)9798350330014

The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-based anomaly detection. These methods face several challenges such as high-dimensional and noisy log data, class imbalance, generalization, and model interpretability. Recently, ChatGPT has shown promising results in various domains. However, there is still a lack of study on the application of ChatGPT for log-based anomaly detection. In this work, we proposed LogGPT, a log-based anomaly detection framework based on ChatGPT. By leveraging ChatGPT's language interpretation capabilities, LogGPT aims to explore the transferability of knowledge from large-scale corpora to log-based anomaly detection. We conduct experiments to evaluate the performance of LogGPT and compare it with three deep learning-based methods on BGL and Spirit datasets. LogGPT shows promising results and has good interpretability. This study provides preliminary insights into prompt-based models, such as ChatGPT, for the log-based anomaly detection task. © 2023 IEEE.

关键词： Anomaly detection

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Braintransformer: Subject-Wise Patch Embed Transformer for Cross-Subject Brain Visual Information Decoding 21

Braintransformer: Subject-Wise Patch Embed Transformer for C...

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Zongxin, Du Lin, Li University of Electronic Science and Technology of China Faculty of Computer Science and Engineering Chengdu610000 China

ISBN: (纸本)9798331519254

Visual information decoding aims to infer the visual content perceived by a subject based on their brain responses, representing a cutting-edge area of neuroscience research. Functional magnetic resonance imaging (fMRI) provides millimeter-level spatial resolution and a relatively high signal-to-noise ratio, making it an ideal tool for neural information decoding. However, practical applications remain limited, primarily due to the reliance on subject-specific models. In this work, we propose BrainTransformer, a novel framework for cross-subject visual information decoding. To overcome the limitations, we develop a self-supervised patch embedding transformer trained using a masked signal modeling approach on large-scale fMRI data, enabling the learning of robust and generalizable neural representations across subjects. By aligning these learned fMRI representations with CLIP's image and text embeddings, BrainTransformer leverages rich semantic information from both visual and textual modalities. This alignment allows us to employ a multimodal latent diffusion model guided by CLIP's embeddings to generate images consistent with brain activity. Experimental results demonstrate BrainTransformer's effectiveness in decoding visual information across subjects, marking a significant step toward scalable and generalizable brain decoding. © 2024 IEEE.

关键词： Signal to noise ratio

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A Deep learning Based Approach for strawberry Yield Prediction via Semantic Graphics 21

A Deep Learning Based Approach for Strawberry Yield Predicti...

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21st international conference on Control, Automation and Systems (ICCAS)

作者： Ilyas, Talha Kim, Hyongsuk Jeonbuk Natl Univ Intelligent Robots Res Ctr Dept Elect Engn Jeonju 54896 South Korea Jeonbuk Natl Univ Dept Elect & Informat Engn Jeonju 54896 South Korea

ISBN: (纸本)9788993215212

In Korea, strawberry producers lack efficient and precise yield forecasts, which would allow them to deploy optimal manpower, equipment, and other resources for harvesting, shipping, and marketing. Reliable estimation of the quantity of strawberry fruit with respect to their ripeness level is critical for forecasting the upcoming strawberry production. Typically, the quantity and ripeness of fruits are estimated manually, which is labor-intensive and time-consuming. In this case, automated yield prediction based on robotic agriculture is a realistic option. We provide in this study an automated strawberry fruit recognition and counting system for accurate and reliable yield prediction. This paper proposes a unique neural network training approach for strawberry fruit counting and ripeness detection that combines semantic graphics for data annotation with a fully convolutional neural network (FCN). Semantic graphics, the suggested data annotation approach, is straightforward to apply, and the desired targets can be quickly tagged with little effort. Moreover, the proposed FCN is an enhanced encoder-decoder network designed specifically for efficient learning of semantic graphics. Quantitative analysis of proposed algorithm showed 4.47% increase in detection accuracy over prior techniques while running at higher frames per second.

关键词： semantic graphics yield forecasting crop management segmentation deep learning encoder-decoder cnn

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Unseen Named Entity Recognition for Thai Language 21

Unseen Named Entity Recognition for Thai Language

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21st international System-on-Chip Design conference, ISOCC 2024

作者： Kuntichod, Watcharet Lee, Wilaiporn Prayote, Akara King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9798350377088

Most common challenges in Named-entity recognition (NER) require suitable and annotated datasets, tailored to the specific domain of the documents under analysis, for example, legal, medical, and financial documents each require distinct datasets. Reliable identification of named entities within the training set is crucial. Annotations is a vital role in building an effective mechanism. However, creating training sets demands significant human effort, contributing to the high costs associated with NER. High quality training materials are indispensable for evolving machine-learning algorithms, underscoring the importance of dependable training data. Thus, the primary challenge lies in acquiring and managing data rather than in the algorithms themselves. To overcome most common situations of finding new unseen names, this paper proposes a mechanism to detect a new undiscovered name in Thai language based on sentence contexts. The method uses BI-LstM model to learn sentence structures and predict a new unseen name from its context. Our framework comprises two main tasks. The first one is word entity extraction. The second task is using the model to identify unrecognized named entities. Evaluation of the proposed method is described with a successful rate of accuracy in discovering newly identified entities. This contributes to the expansion of entity recognition capabilities in Thai language processing. © 2024 IEEE.

关键词： BI-LstM Herb Entity Unseen Named Entity

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AREDAPPS: Mobile Augmented Reality Development and learning Framework Based on Augmented Reality Technology for engineering Drawing Course 13th

AREDAPPS: Mobile Augmented Reality Development and Learning ...

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13th European-Alliance-for-Innovation (EAI) international conference on intelligent Technologies for Interactive Entertainment (INTETAIN)

作者： Ali, Dayana Farzeeha Omar, Marlissa Sunar, Mohd Shahrizal Zaid, Norasykin Mohd Ibrahim, Nor Hasniza Surif, Johari Univ Teknol Malaysia Johor Baharu 81310 Johor Malaysia Univ Kebangsaan Malaysia Bangi 43600 Selangor Malaysia

ISBN: (纸本)9783030991883;9783030991876

Technology in teaching and learning is crucial as we are now focusing on 21st Century learning. Moreover, the Covid-19 pandemic has affected many sectors, including education sectors, where most educators are continuously looking for approach and tools to enhance their teaching deliveries to ensure students can still learn effectively even though most of the classes are currently conducted online. Other than that, students also have problems with their visualization skills when learning engineering drawing, which caused a lack of understanding of the content, thus making it even harder for them to learn in this situation. Technology such as augmented reality is a well-received technology application due to its benefit in enhancing users' learning experiences, enhancing visualization skills, and supplementing materials more effectively. Thus, this paper aims to describe Augmented Reality engineering Drawing Apps (AREDApps) development process, a mobile augmented reality apps for orthographic projection topic in engineering drawing subject, highlight the students' perception when using AREDApps during learning and introduce the engineering drawing learning framework based on AREDApps implementation in the classroom. The result indicates that students were mostly satisfied with augmented reality technology to learn orthographic projection. This is because AREDApps helps them supplement the information that cannot be delivered by using modules or PowerPoint presentation such as the three-dimensional representations of the task given to them. The findings of this paper strengthen the facts that augmented reality is a suitable technology to teach complex technical subjects, which requires supplementary materials that can help students to be able to visualize the task effectively.

关键词： AREDApps Visualization Orthographic projection engineering drawing Perception Framework

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Proceedings of the 15th international conference on Biomedical engineering Systems and Technologies, BIOstEC 2022

Proceedings of the 15th International Conference on Biomedic...

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Proceedings of the 15th international conference on Biomedical engineering Systems and Technologies, BIOstEC 2022

ISBN: (纸本)9783031388538

The proceedings contain 21 papers. The special focus in this conference is on Biomedical engineering Systems and Technologies. The topics include: Mobile Tele-Dermatology Use Among University students: A Pilot study at Saint Joseph University (USJ);predictive Alarm Prevention by Forecasting Threshold Alarms at the Intensive Care Unit;st-Segment Anomalies Detection from Compressed Sensing Based ECG data by Means of Machine learning;A Proof-of-Concept Implementation Based on the Framework of AI-Enabled Proactive mHealth: Health Promotion with Motivation;improved Blood Vessels Segmentation of Infant Retinal Image;on the Impact of the Vocabulary for Domain-Adaptive Pretraining of Clinical Language Models;a Systematic Literature Review of Extended Reality Exercise Games for the Elderly;simulating the Vital Signs of a Digital Patient Undergoing Surgery, for the Purpose of Training Anaesthetists;A Multi-Modal dataset (MMSD) for Acute stress Bio-Markers;analysis of Extracellular Vesicle data on Fluorescence and Atomic Force Microscopy Images;on the Use of WebAssembly for Rendering and Segmenting Medical Images;referable Diabetic Retinopathy Detection Using Deep Feature Extraction and Random Forest;automated Segmentation of Patterned Cells in Micropatterning Microscopy Images;automated data Adaptation for the Segmentation of Blood Vessels;Security Analysis of the Internet of Medical Things (IoMT): Case study of the Pacemaker Ecosystem;Parallel Lossy Compression for Large FAstQ Files;comparing Different Dictionary-Based Classifiers for the Classification of Volatile Compounds Measured with an E-nose;high-Level Features for Human Activity Recognition and Modeling;data Augmentation Based on Virtual Wrist Devices for Fall Detection.

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