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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yu, Hanzhi Liu, Yuchen Chen, Mingzhe University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States

ISBN: (纸本)9798350351255

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT) that is a virtual representation of the physical network. The considered network includes a physical network where a base station (BS) serves a set of users, and a DNT that evolves with the status of both DNT and the physical network. The BS must use its limited spectrum resources to serve the users, as well as transmit the physical network information to the cloud server for DNT synchronization. Since the DNT can predict the physical network status, the BS may not need to transmit physical network information to the server at each time slot thus saving spectrum resources to serve users. However, if the BS does not transmit physical information to the DNT over a long period of time, the DNT may not be able to represent the physical network accurately. To this end, the BS must determine whether to send physical network information to the server to update DNT and the spectrum resources used for physical network information transmission and serving users. We formulate this resources allocation problem as an optimization problem aiming to maximize the sum of data rates of all users, while minimizing the gap between the states of the physical network and the DNT. The formulated problem is challenging to solve by conventional optimization methods, since the BS may not be able to know the future status of the DNT. To solve this problem, we design a gate recurrent unit (GRU) and soft action-critic (SAC) based algorithm. The GRU enables the DNT to predict its future states by using historical state data, and updating the DNT when the BS does not transmit physical network information. The SAC based algorithm enables the BS to learn the relationship between the physical network information transmission and the future status estimation accuracy of the DNT thus determining whether to transmit physical network information to the cloud server, ensuring an accurac

关键词： Resource allocation

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Automated Counting System Based on Watershed Algorithm for Tilapia Fish Egg 1

Automated Counting System Based on Watershed Algorithm for T...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Hassan, Mohamad Ghozali Harun, Nor Hazlyna Md Azhar, Amiera Syazlin Md Pauzi, Siti Naquiah Ja'afar, Noor Ashri Mansor, Nur Suhaili School of Technology Management and Logistics Universiti Utara Malaysia Malaysia School of Computing Universiti Utara Malaysia Data Science Research Lab Malaysia Institute for Advanced and Smart Digital Opportunities School of Computing Sintok Malaysia

ISBN: (纸本)9798350306156

Aquaculture is a significant component of Malaysia's fishery sector and makes a substantial contribution to the country's economy. It is the source of income and employment for the nation. Tilapia fish is one of the main species cultured in the aquaculture industry, as eggs produced by this fish are considered very valuable both in terms of quantity and quality. A standard egg selection procedure to calculate the fish egg properly has not yet been implemented. The process of fish egg selection plays a vital role in determining the accurate count of fish eggs. This study focuses on developing an automated counting system for tilapia fish eggs using an image processing technique concentrating on the watershed algorithm. This algorithm enables the image of the fish eggs to be clustered in shaded colors, thus enabling the total number of fish eggs to be automatically counted. The results show that the automated counting system for tilapia fish eggs works successfully as the accuracy obtained was above 90%. It is believed that an automated counting system can help fish breeders to perform fish egg counting automatically and more effectively and efficiently. © 2023 IEEE.

关键词： Fish

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Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental Study [Extended Abstract] 40

Matching Knowledge Graphs in Entity Embedding Spaces: An Exp...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zeng, Weixin Zhao, Xiang Tan, Zhen Tang, Jiuyang Cheng, Xueqi National University of Defense Technology Laboratory for Big Data and Decision China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China Institute of Computing Technology CAS China

ISBN: (纸本)9798350317152

Entity alignment (EA) identifies equivalent entities that locate in different knowledge graphs (KGs), and has attracted growing research interests over the last few years with the advancement of KG embedding techniques. Although a pile of embedding-based EA frameworks have been developed, they mainly focus on improving the performance of entity representation learning, while largely overlook the subsequent stage that matches KGs in entity embedding spaces. Nevertheless, accurately matching entities based on learned entity representations is crucial to the overall alignment performance, as it coordinates individual alignment decisions and determines the global matching result. Hence, it is essential to understand how well existing solutions for matching KGs in entity embedding spaces perform on present benchmarks, as well as their strengths and weaknesses. To this end, in this article we provide a comprehensive survey and evaluation of matching algorithms for KGs in entity embedding spaces in terms of effectiveness and efficiency on both classic settings and new scenarios that better mirror real-life challenges. Based on in-depth analysis, we provide useful insights into the design trade-offs and good paradigms of existing works, and suggest promising directions for future development. © 2024 IEEE.

关键词： Embeddings

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Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷 1-16页

作者： Hsu, Chih-Chung Jian, Chih-Yu Tu, Eng-Shen Lee, Chia-Ming Chen, Guan-Lin National Cheng Kung University Institute of Data Science The Department of Statistics The Miin Wu School of Computing Tainan70101 Taiwan National Cheng Kung University Department of Computer Science Tainan70101 Taiwan MobileDrive Artificial Intelligence R&d Department New Taipei City23679 Taiwan

This article addresses the challenges associated with hyperspectral image (HSI) reconstruction from miniaturized satellites, which often suffer from stripe effects and are computationally resource-limited. We propose a real-time compressed sensing (RTCS) network designed to be lightweight and require only relatively few training samples for efficient and robust HSI reconstruction in the presence of the stripe effect and under noisy transmission conditions. The RTCS network features a simplified architecture that reduces the required training samples and allows for easy implementation on integer-8-based encoders, facilitating rapid compressed sensing for stripe-like HSI, which exactly matches the moderate design of miniaturized satellites on push broom scanning mechanism. This contrasts optimization-based models that demand high-precision floating-point operations, making them difficult to deploy on edge devices. Our encoder employs an integer-8-compatible linear projection for stripe-like HSI data transmission, ensuring RTCS. Furthermore, based on the novel two-streamed architecture, an efficient HSI restoration decoder is proposed for the receiver side, allowing for edge-device reconstruction without needing a sophisticated central server. This is particularly crucial as an increasing number of miniaturized satellites necessitates significant computing resources on the ground station. Extensive experiments validate the superior performance of our approach, offering new and vital capabilities for existing miniaturized satellite systems. © 1980-2012 IEEE.

关键词： Real time systems

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A Systematic Method of Stroke Prediction Model based on Big data and Machine Learning 2

A Systematic Method of Stroke Prediction Model based on Big ...

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2nd International Conference on Smart Technologies, Communication and Robotics, STCR 2022

作者： Viswapriya, S.E. Rajeswari, D. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business System Tamilnadu India

ISBN: (纸本)9781665460477

There is an enormous increase in number of diseases worldwide. The non-communicable diseases such as cardio vascular disease will leads to death. The second major reason of death in people worldwide occurs due to stroke. It affects any portion of brain due to interruption or reduction of Blood supply. The brain damage can be reduced if required actions taken earlier. So there is necessary requirement to build stroke predictive models. The combined techniques of Machine Learning (ML) and Deep Learning (DL) techniques play the vital role in Disease Prediction. There are many researches has been done for stroke prediction using various ML Algorithms. In order to improve accuracy, the proposed model will work on the hybrid ANNRF (Artificial Neural Network-Random Forest). The proposed method can be reached 94% in classification accuracy. © 2022 IEEE.

关键词： Forecasting

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An Extended-Isomap for high-dimensional data accuracy and efficiency: a comprehensive survey

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Multimedia Tools and Applications 2024年第38期83卷 85523-85574页

作者： Yousaf, Mahwish Shakoor Khan, Muhammad Saadat Ullah, Shamsher Institute of Intelligence Machine Hefei Institutes of Physical Science Chinese Academy of Sciences Anhui Hefei230027 China Key Laboratory of Materials Physics Institute of Solid-State Physics Chinese Academy of Sciences Anhui Hefei230027 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China

Manifold learning is a widely adopted nonlinear dimensionality reduction technique employed to discover low-dimensional representations from high-dimensional data and to explore the intrinsic data structure. It encompasses a range of nonlinear techniques, including Isomap, Local Linear Embedding (LLE), Local Tangent Space Alignment (LTSA), and Multidimensional Scaling (MDS), among others. Isomap, as a popular method in manifold learning, has exhibited promising results. However, it encounters several challenges, such as the Topological Stability Problem, Shortest Path Distance, and noise sensitivity, which compromise its accuracy and elevate its computational demands. This survey paper presents three novel existing approaches to address these challenges and enhance the performance of Isomap. Firstly, the FastIsomap method, an existing approach that combines state-of-the-art algorithms like the KD tree and NN-descent to increase graph accuracy while reducing computational cost. Secondly, the FastIsomapVis method, also an existing technique, employs a hierarchical divide-and-conquer strategy employing the KD tree and Dijkstra Buckets Double, which enhances accuracy and reduces computational time in Isomap. Lastly, the NR-Isomap method, another existing approach, used the Local Tangent Space Alignment algorithm to remove noise sensitivity and short-circuit edge problems. The existing methods improve the accuracy of Isomap but also streamline its computational requirements. Furthermore, this survey paper discusses the open issues and future research directions for Isomap and manifold learning methods. It can be a valuable resource for researchers seeking insights across diverse domains. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Clustering algorithms

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MHGRL: An Effective Representation Learning Model for Electronic Health Records 30

MHGRL: An Effective Representation Learning Model for Electr...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Feiyan Li, Liangzhi Wang, Xiaoli Liu, Chang Luo, Feng Su, Jinsong Qian, Yiming School of Informatics Xiamen University Xiamen China Meetyou AI Lab Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China RMIT University Melbourne Australia Institute of High Performance Computing Agency for Science Technology and Research Singapore

ISBN: (纸本)9782493814104

Electronic health records (EHRs) serve as a digital repository storing comprehensive medical information about patients. Representation learning for EHRs plays a crucial role in healthcare applications. In this paper, we propose a Multimodal Heterogeneous Graph-enhanced Representation Learning, denoted as MHGRL, aimed at learning effective EHR representations. To address the challenge posed by data insufficiency of EHRs, MHGRL utilizes a multimodal heterogeneous graph to model an EHR. Specifically, we construct a heterogeneous graph for each EHR and enrich it by incorporating multimodal information with medical ontology and textual notes. With the integration of pre-trained model, graph neural network, and attention mechanism, MHGRL effectively incorporates both node attributes and structural information across a multimodal heterogeneous graph. Moreover, we employ contrastive learning to ensure the consistency of representations for similar EHRs and improve the model robustness. The experimental results show that MHGRL outperforms all baselines on two real clinical datasets in downstream tasks, including EHR clustering and disease prediction. The code is available at https://***/emmali808/MHGRL. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Records management

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Is Score Matching Suitable for Estimating Point Processes? 38

Is Score Matching Suitable for Estimating Point Processes?

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Cao, Haoqun Meng, Zizhuo Ke, Tianjun Zhou, Feng Center for Applied Statistics School of Statistics Renmin University of China China Data Science Institute University of Technology Sydney Australia Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing China

Score matching estimators have gained widespread attention in recent years partly because they are free from calculating the integral of normalizing constant, thereby addressing the computational challenges in maximum likelihood estimation (MLE). Some existing works have proposed score matching estimators for point processes. However, this work demonstrates that the incompleteness of the estimators proposed in those works renders them applicable only to specific problems, and they fail for more general point processes. To address this issue, this work introduces the weighted score matching estimator to point processes. Theoretically, we prove the consistency of our estimator and establish its rate of convergence. Experimental results indicate that our estimator accurately estimates model parameters on synthetic data and yields results consistent with MLE on real data. In contrast, existing score matching estimators fail to perform effectively. Codes are publicly available at https://***/KenCao2007/WSM_TPP. © 2024 Neural information processing systems foundation. All rights reserved.

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Large Model for Small data: Foundation Model for Cross-Modal RF Human Activity Recognition 24

Large Model for Small Data: Foundation Model for Cross-Modal...

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22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024

作者： Weng, Yuxuan Wu, Guoquan Zheng, Tianyue Yang, Yanbing Luo, Jun Department of Computer Science and Engineering Southern University of Science and Technology China Smart City Center Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China College of Computer Science Sichuan University China College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400706974

Radio-Frequency (RF)-based Human Activity Recognition (HAR) rises as a promising solution for applications unamenable to techniques requiring computer visions. However, the scarcity of labeled RF data due to their non-interpretable nature poses a significant obstacle. Thanks to the recent breakthrough of foundation models (FMs), extracting deep semantic insights from unlabeled visual data become viable, yet these vision-based FMs fall short when applied to small RF datasets. To bridge this gap, we introduce FM-Fi, an innovative cross-modal framework engineered to translate the knowledge of vision-based FMs for enhancing RF-based HAR systems. FM-Fi involves a novel cross-modal contrastive knowledge distillation mechanism, enabling an RF encoder to inherit the interpretative power of FMs for achieving zero-shot learning. It also employs the intrinsic capabilities of FM and RF to remove extraneous features for better alignment between the two modalities. The framework is further refined through metric-based few-shot learning techniques, aiming to boost the performance for predefined HAR tasks. Comprehensive evaluations evidently indicate that FM-Fi rivals the effectiveness of vision-based methodologies, and the evaluation results provide empirical validation of FM-Fi's generalizability across various environments. © 2024 Copyright is held by the owner/author(s).

关键词： Zero-shot learning

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3D Based CNN for Improved Segmentation from the Hyperspectral Images 1

3D Based CNN for Improved Segmentation from the Hyperspectra...

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1st International Conference on Computer science and Emerging Technologies, CSET 2023

作者： Narmadha, T. Arockia Raj, A. Saravanan, P. Kumar, A. Suresh Sekaran, Ramesh Gowri Shankar, J. School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering - Data Science Madanapalle India School of Computing Science and Engineering Galgotias University Delhi NCR India

ISBN: (纸本)9798350341737

In this paper, we develop a 3D based CNN for Improved Segmentation of Paddy Fields from the HSI. Within the scope of this research, we will investigate a unique deep learning model, specifically 3D-CNN. In order to evaluate the validity of the model, it is applied to three separate datasets, each of which is then subjected to one of four distinct dimensionality reduction strategies. The purpose of this is to determine which of these four models is the most effective at reducing the number of dimensions. According to the findings, the 3D-CNN model is superior to both the AlexNet and CNN models in terms of the accuracy of its segmentations. In addition to this, the findings demonstrate that the 3D-CNN models perform better than the other approaches in terms of segmentation accuracy with a smaller number of datasets. © 2023 IEEE.

关键词： Image enhancement

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