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arXiv 2025年

作者： Zhang, Ruichen Tang, Shunpu Liu, Yinqiu Niyato, Dusit Xiong, Zehui Sun, Sumei Mao, Shiwen Han, Zhu College of Computing and Data Science Nanyang Technological University Singapore Computer Science and Design Pillar University of Technology and Design Singapore Institute for Infocomm Research Agency for Science Technology and Research Singapore Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446701 Korea Republic of

—The increasing complexity and scale of modern telecommunications networks demand intelligent automation to enhance efficiency, adaptability, and resilience. Agentic AI has emerged as a key paradigm for intelligent communications and networking, enabling AI-driven agents to perceive, reason, decide, and act within dynamic networking environments. However, effective decision-making in telecom applications, such as network planning, management, and resource allocation, requires integrating retrieval mechanisms that support multi-hop reasoning, historical cross-referencing, and compliance with evolving 3GPP standards. This article presents a forward-looking perspective on generative information retrieval-inspired intelligent communications and networking, emphasizing the role of knowledge acquisition, processing, and retrieval in agentic AI for telecom systems. We first provide a comprehensive review of generative information retrieval strategies, including traditional retrieval, hybrid retrieval, semantic retrieval, knowledge-based retrieval, and agentic contextual retrieval. We then analyze their advantages, limitations, and suitability for various networking scenarios. Next, we present a survey about their applications in communications and networking. Additionally, we introduce an agentic contextual retrieval framework to enhance telecom-specific planning by integrating multi-source retrieval, structured reasoning, and self-reflective validation. Experimental results demonstrate that our framework significantly improves answer accuracy, explanation consistency, and retrieval efficiency compared to traditional and semantic retrieval methods. Finally, we outline future research directions. © 2025, CC BY.

关键词： Generative adversarial networks

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Automated Bioacoustic Monitoring for South African Bird Species on Unlabeled data 32

Automated Bioacoustic Monitoring for South African Bird Spec...

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32nd International Conference in Central Europe on computer Graphics, Visualization and computer Vision, WSCG 2024

作者： Doell, Michael Kuehn, Dominik Suessle, Vanessa Burnett, Matthew J. Downs, Colleen T. Weinmann, Andreas Hergenroether, Elke Department of Computer Science University of Applied Sciences Darmstadt Schoefferstrasse 3 Darmstadt Germany Centre for Functional Biodiversity School of Life Sciences University of KwaZulu-Natal P/Bag X01 Scottsville Pietermaritzburg3209 South Africa Algorithms for Computer Vision Imaging and Data Analysis Group University of Applied Sciences Darmstadt Schoefferstrasse 3 Darmstadt Germany

Analyses for biodiversity monitoring based on passive acoustic monitoring (PAM) recordings is time-consuming and challenged by the presence of background noise in recordings. Existing models for sound event detection (SED) worked only on certain avian species and the development of further models required labeled data. The developed framework automatically extracted labeled data from available platforms for selected avian species. The labeled data were embedded into recordings, including environmental sounds and noise, and were used to train convolutional recurrent neural network (CRNN) models. The models were evaluated on unprocessed real world data recorded in urban KwaZulu-Natal habitats. The Adapted SED-CRNN model reached a F1 score of 0.73, demonstrating its efficiency under noisy, real-world conditions. The proposed approach to automatically extract labeled data for chosen avian species enables an easy adaption of PAM to other species and habitats for future conservation projects. © 2024 University of West Bohemia. All rights reserved.

关键词： Biotic

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Towards Graph-Based Semi-Supervised Learning on Audio Embeddings for Label Classification

Towards Graph-Based Semi-Supervised Learning on Audio Embedd...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Rishabh Chandaliya Mohan Timilsina John Breslin Martin Serrano Department of Computer Science University of Galway Ireland Insight SFI Research Centre for Data Analytics Galway Ireland

The increasing importance of audio-based healthcare diagnostics, particularly in chronic respiratory problems, has prompted the development of novel approaches for disease classification using the full spectrum analysis of cough sounds. This paper presents an innovative study exploring the potential of employing supervised and semi-supervised learning methodologies for disease categorization based on cough audio samples. Specifically, this study focuses on scenarios characterized by a shortage of annotated data related to chronic diseases. The objectives of our study involved the utilization of standard machine learning algorithms for direct classification based on embeddings, as well as the integration of Graph Neural Networks (GNNs) on the KNN graph. Preliminary results indicate that GNN models consistently outperformed traditional classifiers. For instance, with just 1 % of the data, GAT and GCN achieved AUC PR values of 0.84 and 0.87, respectively, surpassing all traditional methods. The superiority mentioned above was sustained even when the data fraction was augmented to 3% and 5%. The usefulness of graph neural networks (GNNs) was further supported by comprehensive performance measures, wherein the graph convolutional network (GCN) exhibited exceptional preci-sion and PR (precision-recall). In summary, the AudioVecDiagnosis framework presents a promising opportunity for further investigation in audio-based healthcare diagnostics. It provides an optimum approach for situations with a limited availability of labeled data.

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OldVisOnline: Curating a dataset of Historical Visualizations

arXiv

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arXiv 2023年

作者： Zhang, Yu Jiang, Ruike Xie, Liwenhan Zhao, Yuheng Liu, Can Ding, Tianhong Chen, Siming Yuan, Xiaoru The Department of Computer Science University of Oxford United Kingdom the School of Intelligence Science and Technology Peking University China The School of Data Science Fudan University China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fundamental Software Innovation Lab Huawei Technologies China

With the increasing adoption of digitization, more and more historical visualizations created hundreds of years ago are accessible in digital libraries online. It provides a unique opportunity for visualization and history research. Meanwhile, there is no large-scale digital collection dedicated to historical visualizations. The visualizations are scattered in various collections, which hinders retrieval. In this study, we curate the first large-scale dataset dedicated to historical visualizations. Our dataset comprises 13K historical visualization images with corresponding processed metadata from seven digital libraries. In curating the dataset, we propose a workflow to scrape and process heterogeneous metadata. We develop a semi-automatic labeling approach to distinguish visualizations from other artifacts. Our dataset can be accessed with OldVisOnline, a system we have built to browse and label historical visualizations. We discuss our vision of usage scenarios and research opportunities with our dataset, such as textual criticism for historical visualizations. Drawing upon our experience, we summarize recommendations for future efforts to improve our dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Digital libraries

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IoT Based Radar System using Ultrasonic Sensor for Enhanced Object Detection and Tracking

IoT Based Radar System using Ultrasonic Sensor for Enhanced ...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Jadhav, Balasaheb Wanjale, Pooja Gavli, Renuka Thopate, Kaushalya Maharnawar, Niranjan Karmankar, Nikhil Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Technology Department of Engineering Sciences and Humanities Pune India Ajeenkya D. Y. Patil School of Engineering Department-Computer Engineering India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Sagitech Solutions Pvt. Ltd. India

ISBN: (纸本)9798350308167

a radar system built on the Internet of Things (IoT) that uses ultrasonic sensors to find things within 150 degrees. The system's rotation is controlled by a servo motor, and a Graphical User Interface (GUI) built using Java Swing and applets displays the results of detection. In addition, the system has a feature that, upon the observation of an object, sends notifications to a user's mobile device. The proposed system can be used to detect objects in its vicinity efficiently and effectively in a variety of scenarios, such as security surveillance and environmental monitoring. The system's hardware and software components are covered in the article along with the testing and validation processes applied to gauge the system's performance. The suggested IoT-based radar system can, in general, improve object recognition abilities and give users real-time notifications, making it a desirable complement to the current range of IoT devices. © 2023 IEEE.

关键词： Arduino IoT object detection Radar tracking system Ultrasonic Sensor

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SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals

arXiv

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arXiv 2024年

作者： Thapa, Rahul He, Bryan Kjær, Magnus Ruud Moore, Hyatt Ganjoo, Gauri Mignot, Emmanuel Zou, James Department of Biomedical Data Science Stanford University United States Department of Computer Science Stanford University United States Department of Health Technology Technical University of Denmark Denmark Department of Psychiatry and Behavioral Sciences Stanford University United States

Sleep is a complex physiological process evaluated through various modalities recording electrical brain, cardiac, and respiratory activities. We curate a large polysomnography dataset from over 14,000 participants comprising over 100,000 hours of multi-modal sleep recordings. Leveraging this extensive dataset, we developed SleepFM, the first multi-modal foundation model for sleep analysis. We show that a novel leave-one-out approach for contrastive learning significantly improves downstream task performance compared to representations from standard pairwise contrastive learning. A logistic regression model trained on SleepFM’s learned embeddings outperforms an end-to-end trained convolutional neural network (CNN) on sleep stage classification (macro AUROC 0.88 vs 0.72 and macro AUPRC 0.72 vs 0.48) and sleep disordered breathing detection (AUROC 0.85 vs 0.69 and AUPRC 0.77 vs 0.61). Notably, the learned embeddings achieve 48% top-1 average accuracy in retrieving the corresponding recording clips of other modalities from 90,000 candidates. This work demonstrates the value of holistic multi-modal sleep modeling to fully capture the richness of sleep recordings. SleepFM is open source and available at https://***/rthapa84/sleepfm-codebase. © 2024, CC BY.

关键词： Embeddings

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Faster Adaptive Optimization via Expected Gradient Outer Product Reparameterization

arXiv

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arXiv 2025年

作者： DePavia, Adela Charisopoulos, Vasileios Willett, Rebecca University of Chicago United States Data Science Institute University of Chicago United States Department of Statistics Department of Computer Science University of Chicago United States NSF-Simons National Institute for Theory and Mathematics in Biology United States

Adaptive optimization algorithms—such as Adagrad, Adam, and their variants—have found widespread use in machine learning, signal processing and many other settings. Several methods in this family are not rotationally equivariant, meaning that simple reparameterizations (i.e. change of basis) can drastically affect their convergence. However, their sensitivity to the choice of parameterization has not been systematically studied;it is not clear how to identify a "favorable" change of basis in which these methods perform best. In this paper we propose a reparameterization method and demonstrate both theoretically and empirically its potential to improve their convergence behavior. Our method is an orthonormal transformation based on the expected gradient outer product (EGOP) matrix, which can be approximated using either full-batch or stochastic gradient oracles. We show that for a broad class of functions, the sensitivity of adaptive algorithms to choice-of-basis is influenced by the decay of the EGOP matrix spectrum. We illustrate the potential impact of EGOP reparameterization by presenting empirical evidence and theoretical arguments that common machine learning tasks with "natural" data exhibit EGOP spectral decay. © 2025, CC BY.

关键词： Stochastic systems

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Penetration of Deep Learning in Human Health Care and Pharmaceutical Industries;the Opportunities and Challenges 3

Penetration of Deep Learning in Human Health Care and Pharma...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Raman, Ramakrishnan Radha, H.R. Inbamalar, T.M. Subhahan, D. Abdus Kumar, Ashok Bathrinath, S. Sarkar, Swagata Symbiosis Institute of Business Management Pune & Symbiosis International Maharashtra Pune India K. S. School of Engineering and Management Department of Chemistry Karnataka Bangalore India R.M.K. College of Engineering Department of Electronics and Communication Tamil Nadu Thiruvallur India B v Raju Institute of Technology Department of Computer Science Telangana Narsapur India Banasthali Vidyapith Department of Computer Science Rajasthan India Kalasalingam Academy of Research Department of Mechanical Engineering Tamil Nadu Krishnankoil India Sri Sai Ram Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350336238

Computational medicine has emerged as a result of the advancement of medical technology, which has led to the emergence of the big data era in the biomedical area, which is supported by artificial intelligence technology. To advance the development of precision medicine, people must be able to extract the valuable information from this vast biomedical data. In the past, professionals in the field of feature engineering and domain knowledge were typically utilised to extract the features from the biological data using machine learning techniques, which took a lot of time and resources. Modern machine learning techniques like deep learning (DL) have an advantage over them in that they can automatically find strong, complex features from fresh data without the necessity for succeeding engineering. The study of DL's applications in the fields of genomics, drug development, electronic health records, and medical imaging suggests that deep learning has clear advantages in maximising the use of biomedical data. Deep learning is becoming increasingly important in the field of medicine and health due to its large range of potential applications. The lack of data, interpretability, data privacy, and heterogeneity are some of the limitations of deep learning in computational medical health. A resource for improving the use of deep learning in medical health is provided by the analysis and discussion of these difficulties. © 2023 IEEE.

关键词： Health care

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A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction

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Frontiers of Information Technology & Electronic Engineering 2023年第9期24卷 1316-1331页

作者： Dawen XIA Jian GENG Ruixi HUANG Bingqi SHEN Yang HU Yantao LI Huaqing LI College of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang 550025China Department of Automotive Engineering Guizhou Traffic Technician and Transportation CollegeGuiyang 550008China College of Computer Science Chongqing UniversityChongqing 400044China College of Electronic and Information Engineering Southwest UniversityChongqing 400715China

To address the imbalance problem between supply and demand for taxis and passengers,this paper proposes a distributed ensemble empirical mode decomposition with normalization of spatial attention mechanism based bi-directional gated recurrent unit(EEMDN-SABiGRU)model on Spark for accurate passenger hotspot *** focuses on reducing blind cruising costs,improving carrying efficiency,and maximizing ***,the EEMDN method is put forward to process the passenger hotspot data in the grid to solve the problems of non-smooth sequences and the degradation of prediction accuracy caused by excessive numerical differences,while dealing with the eigenmodal ***,a spatial attention mechanism is constructed to capture the characteristics of passenger hotspots in each grid,taking passenger boarding and alighting hotspots as weights and emphasizing the spatial regularity of passengers in the ***,the bi-directional GRU algorithm is merged to deal with the problem that GRU can obtain only the forward information but ignores the backward information,to improve the accuracy of feature ***,the accurate prediction of passenger hotspots is achieved based on the EEMDN-SABiGRU model using real-world taxi GPS trajectory data in the Spark parallel computing *** experimental results demonstrate that based on the four datasets in the 00-grid,compared with LSTM,EMDLSTM,EEMD-LSTM,GRU,EMD-GRU,EEMD-GRU,EMDN-GRU,CNN,and BP,the mean absolute percentage error,mean absolute error,root mean square error,and maximum error values of EEMDN-SABiGRU decrease by at least 43.18%,44.91%,55.04%,and 39.33%,respectively.

关键词： Passenger hotspot prediction Ensemble empirical mode decomposition(EEMD) Spatial attention mechanism Bi-directional gated recurrent unit(BiGRU) GPS trajectory Spark

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PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain 23

PipeEdge: A Trusted Pipelining Collaborative Edge Training b...

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2023 World Wide Web Conference, WWW 2023

作者： Yuan, Liang He, Qiang Chen, Feifei Dou, Ruihan Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Mathematics University of Waterloo Canada National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads. © 2023 ACM.

关键词： Deep neural networks

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