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检索条件"任意字段=IEEE International Conference on Data Science and Data Intensive Systems"

共 86302 条记录，以下是251-260 订阅

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A Hybrid Model for Real-Time Traffic Prediction with Augmented Historical data using Traffic Simulation 26

A Hybrid Model for Real-Time Traffic Prediction with Augment...

引用

ieee 26th international conference on Intelligent Transportation systems (ITSC)

作者： Tak, Hye-Young Kim, Yeeun Kim, Sunghoon Yeo, Hwasoo Korea Adv Inst Sci & Technol Dept Civil & Environm Engn Daejeon South Korea Korea Transport Inst Dept Mobil Transformat Ctr Smart City & Transport Sejong South Korea

ISBN: (纸本)9798350399462

Real-time traffic prediction offers valuable tools for efficient traffic operation through timely and accurate information to road users and agencies. Despite the prevalence of data-driven approaches in traffic prediction with the advent of big data, these methods struggle to respond to unforeseen traffic situations during real-time prediction. To address this issue, we propose a hybrid model that integrates both data-driven and model-based approaches. Our model augments historical data using traffic simulation for unexpected traffic situations such as sudden speed drops on a road section, which can cause significant prediction errors. An artificial neural network is used to balance between the collected historical data and the augmented data. The proposed model was evaluated by predicting traffic conditions of an actual road section and significantly improved prediction accuracy. Our proposed method represents a state-of-the-art solution for enhancing realtime traffic prediction under the hybrid framework.

关键词： real-time traffic prediction hybrid model machine learning traffic simulation deep neural networks

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Robustness of ML-Based Seizure Prediction Using Noisy EEG data From Limited Channels 20

Robustness of ML-Based Seizure Prediction Using Noisy EEG Da...

引用

20th Annual international conference on Distributed Computing in Smart Sensor systems and the Internet of Things (DCOSS-IoT)

作者： Mohammad, Umair Saeed, Fahad Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA

ISBN: (纸本)9798350369458;9798350369441

Seizures pose a significant health hazard for over 50 million individuals with epilepsy worldwide, with approximately 56% experiencing uncontrollable seizures according to the CDC. Predicting seizures is challenging even with the availability of various sensors (gyroscopes, pulse rate sensors, heart rate monitors, etc). Electroencephalography (EEG) data can directly measure the activity of the brain and has been the choice of leveraging deep learning (DL) models for seizure prediction. Despite DL models achieving over 95% accuracy on retroactive clinical-grade EEG data, this performance fails to translate in real-world settings where the accuracy goes down to 66% which warrants further investigation. Moreover, consumer-grade wearable EEG headsets, characterized by lower data quality and a varying number of channels across brands, present additional challenges. In this paper, we estimate the robustness of DL models which are trained on clinical-grade EEG data but tested on the type of data expected from consumer-grade wearable EEG headsets. We select the previously published model SPERTL to estimate its robustness when: (1) predicting with data from less leads/channels, (2) predicting when faced with streaming data, (3) evaluating performance on imbalanced data with more interictal segments. Our results are compared against baseline results from the SPERTL model which we have re-configured to operate independently of the number of channels with an average baseline area under the curve (AUC) score of 98.56%. Our results demonstrate that though the model is surprisingly resilient to streaming and noisy data, reducing the number of channels and a higher class imbalance have a more severe degradation. The AUC across all cross-validation sets degrades only by 2% and 3% on average for noisy and streaming data, respectively. However, a performance reduction, on average, is observed by 32% when imbalance is increased with higher percentage of interictal samples, and up to 16

关键词： Electroencephalography (EEG) Wearable Seizure Prediction Robustness Lower Quality data

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IAQ Monitoring System Optimizing data-Driven Sensor Placement 20

IAQ Monitoring System Optimizing Data-Driven Sensor Placemen...

引用

20th Annual international conference on Distributed Computing in Smart Sensor systems and the Internet of Things (DCOSS-IoT)

作者： Filios, Gabriel Nikoletseas, Sotiris Stivaros, Ioannis Univ Patras Dept Comp Engn & Informat Patras Greece Comp Technol Inst & Press Diophantus Patras Greece Univ Patras Dept Elect & Comp Engn Patras Greece

ISBN: (纸本)9798350369458;9798350369441

Indoor Air Quality (IAQ) significantly impacts people's health and comfort in buildings. Although IAQ research spans two decades, a comprehensive assessment of factors affecting indoor air pollution remains elusive. Recent efforts focus on real-time monitoring using virtual sensing, a computational technique in engineering and data science. This paper presents a novel IAQ monitoring system emphasizing dynamic sensor placement for enhanced efficiency. The system employs random sensor positions and calculates measurement predictability, allowing identification and removal of less useful sensors, reducing data volume, and saving energy. Multiple reduction strategies are available, depending on the target number of edge devices or the desired maximum prediction error. Importantly, the system operates locally, without relying on internet connectivity. It consists of edge devices using air quality sensors, a gateway for data gathering and algorithm initiation, by training and evaluating multiple different machine learning techniques to determine point combination predictability. Deployed in two indoor settings, one with HVAC and the other naturally ventilated, the system's effectiveness is assessed, shortcomings identified, and conclusions drawn for future work.

关键词： data Driven Sensor Placement Indoor Air Quality Monitoring System Machine Learning Virtual Sensing Sensor Reduction

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Quantum Run-length Encoding: Optimizing data Compression on Quantum Computers with Exponential Resource Efficiency

Quantum Run-length Encoding: Optimizing Data Compression on ...

引用

2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Jiale Che, Xilong Jin, Shiyong Pan, Kaifan Peng, Shun Hu, Juncheng College of Computer Science and Technology Jilin University Changchun China Computer Network Information Center University of Chinese Academy of Science Beijing China

ISBN: (纸本)9798350368741

Quantum computers, leveraging superposition and entanglement, offer significant qubit efficiency for data processing compared to classical systems. However, encoding classical data into quantum states, given the current limitations of quantum hardware, often results in higher runtime complexity than classical methods, thus limiting the perceived quantum advantage. Previous quantum data compression methods, primarily based on Amplitude Encoding and mixed-state systems, result in lossy data recovery and necessitate extensive preprocessing. In this work, we propose Quantum Run-Length Encoding (QRLE), a novel lossless quantum data compression method that integrates Basic Encoding with Run-Length Encoding principles. By encoding repeated data sequences with their run lengths, QRLE achieves efficient and accurate data recovery on quantum computers, while exponentially reducing both qubit costs and runtime complexity compared to existing quantum data storage models. We further explore QRLE's application in image processing, where it significantly optimizes quantum resource utilization over recent quantum image representation techniques. Experiments conducted on both quantum simulators and IBM's superconducting quantum computer validate the efficiency of QRLE and confirm its compatibility with current quantum hardware. © 2025 ieee.

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Content-based Art Recommendation Using Multimodal Graph Neural Networks 15

Content-based Art Recommendation Using Multimodal Graph Neur...

引用

15th ieee international conference on Knowledge Graph, ICKG 2024

作者： Dutta, Haimonti Tiwari, Anushka The State University of New York at Buffalo School of Management and Institute for Artificial Intelligence and Data Science Department of Management Science and Systems BuffaloNY14260 United States The State University of New York at Buffalo Institute for Artificial Intelligence and Data Science BuffaloNY14260 United States

ISBN: (纸本)9798331508821

Graph Neural Network (GNN)-based recommendation systems have become very popular in recent years. Their popularity stems from the fact that nodes can access higher-order neighbor information and there are well-designed algorithms for embedding, message passing, and propagation. In this paper, we present the design of a GNN-based recommendation system on a novel data set collected from field research. Designed for an endangered performing art form, the recommendation system uses multimodal text and image data to suggest similar panels (paintings) to the end-user of the system. We show that multimodal data is particularly helpful in graph representation learning, message passing, and propagation and the panels recommended to users using content-based features are similar to those obtained from systems using user-item bipartite graphs such as MMGCN and GRCN. © 2024 ieee.

关键词： Graph neural networks

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DecTest: A Decentralised Testing Architecture for Improving data Accuracy of Blockchain Oracle

DecTest: A Decentralised Testing Architecture for Improving ...

引用

2024 ieee international conference on systems, Man, and Cybernetics, SMC 2024

作者： Zeng, Xueying Xian, Youquan Li, Chunpei Hu, Zhengdong Zhou, Aoxiang Liu, Peng School of Computer Science and Engineering Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China

ISBN: (纸本)9781665410205

Blockchain technology ensures secure and trust-worthy data flow between multiple participants on the chain, but interoperability of on-chain and off-chain data has always been a difficult problem that needs to be solved. To solve the problem that blockchain systems cannot access off-chain data, oracle is introduced. However, existing research mainly focuses on the consistency and integrity of data, but ignores the problem that oracle nodes may be externally attacked or provide false data for selfish motives, resulting in the unresolved problem of data accuracy. In this paper, we introduce a new Decentralized Testing architecture (DecTest) that aims to improve data accuracy. A blockchain oracle random secret testing mechanism is first proposed to enhance the monitoring and verification of nodes by introducing a dynamic anonymized question-verification committee. Based on this, a comprehensive evaluation incentive mechanism is designed to incentivize honest work performance by evaluating nodes based on their reputation scores. The simulation results show that we successfully reduced the discrete entropy value of the acquired data and the real value of the data by 61.4 %. © 2024 ieee.

关键词： data integrity

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Poster: Multi-Writer Searchable Encryption with Fast Search and Post-Quantum Security 43

Poster: Multi-Writer Searchable Encryption with Fast Search ...

引用

43rd ieee international conference on Distributed Computing systems (ICDCS)

作者： Wang, Jiafan Liu, Dongxi CSIRO Data61 Canberra ACT Australia

ISBN: (纸本)9798350339864

Searchable encryption enables secure searches over encrypted data in the cloud. Among all paradigms, public key encryption with keyword search (PEKS) is particularly desirable by privacy-preserving distributed computing and IoT applications, since it allows multiple parties (i.e., writers) to independently contribute encrypted data. However, a PEKS search usually requires a linear scan over the entire dataset for keyword search, causing unacceptable latency when facing a large amount of data. All existing efforts to speed up multi-writer searchable encryption are based on conventional hardness assumptions for security, which can be broken provided the advent of quantum computers. In this work, we propose a lattice-based multi-writer searchable encryption scheme, which lets writers build indices for their outsourced data to make keyword searches faster. Meanwhile, the security of the proposed solution relies on the learning with errors assumption, which is known to withstand the potential attack from quantum computers.

关键词： Large dataset

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Convolutional Auto-encoder-based Packet-Loss-and-Error-Resilient data Collection Method for Underwater Sensors 13

Convolutional Auto-encoder-based Packet-Loss-and-Error-Resil...

引用

13th ieee international conference on Communications, Circuits and systems (ICCCAS)

作者： Yang, Fan Chen, Huifang Xie, Lei Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netw Hangzhou 310027 Peoples R China Zhejiang Univ Zhoushan Ocean Res Ctr Zhoushan 316021 Peoples R China

ISBN: (纸本)9798350386288;9798350386271

In recent years, the underwater acoustic sensor network (UASN) is emerging as an effective means for marine data collection. However, due to the limited bandwidth of acoustic channel, the limited constrained energy supply of underwater sensors and data redundancy, it is impossible to deliver all the raw data generated by underwater sensors. Hence, to compress time-redundant data at a sensor node is of great significance for data collection from underwater sensors. Moreover, the underwater acoustic transmission link is unreliable, the issue of data collection with certain packet error resilience should be resolved. In this paper, a packet-loss-and-error-resilient data collection method based on convolutional auto-encoder (CAE) is proposed to collect the time-series data from underwater sensors, named as the PLER-CAE data collection method. In the proposed method, the auto-encoder is used to reduce data redundancy of the time-series data. The encoder deployed at the collection end compresses the data for transmission through the underwater channel, and the decoder deployed at the receiving end reconstructs the original data. Moreover, the proposed method complements the retransmission mechanism and the error correction technique in order to enhance data reconstruction quality. Numerical results show that the proposed PLER-CAE data collection method is effective.

关键词： convolutional auto-encoder data collection Packet-loss-and-error-resilient method underwater

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A Review of Research on data Empowerment 9

A Review of Research on Data Empowerment

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9th international conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Xia, Xu Shanghai Institute of Technology School of Economics and Management Shanghai China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363043

With the development of cloud computing, Internet of Things, big data and other digital technologies, more and more scholars have begun to pay attention to the research related to data empowerment, but the existing research has problems such as confusing concepts and confusing systems. This paper uses the literature research method to clarify the conceptual basis of data Empowerment, including the development history of data Empowerment, the definition, form and main object of data Empowerment, the definition and differentiation of related concepts, and data Empowerment and related theoretical research, and finally, in view of the current status of the research, it looks forward to the future direction of the data Empowerment research in terms of the basic research, the research methodology and the model. © 2024 ieee.

关键词： data centers

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Exploring data-Efficient Physics-Informed Neural Networks for Fluid Simulation 4

Exploring Data-Efficient Physics-Informed Neural Networks fo...

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4th international conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Wang, Bohan New York University Tandon School of Engineering Department of Computer Science and Engineering BrooklynNY United States

ISBN: (纸本)9798350394375

Computational Fluid Dynamics (CFD) is crucial in engineering applications such as aerospace and bioengineering, but it often require substantial computational resources. Learning-based simulation methods have the potential to accelerate the computation process but may require a large amount of data. This paper addresses the challenge of data-intensive demands in learning physics-based fluid simulation model by investigating the data efficiency of Physics-Informed Neural Networks (PINNs) for fluid dynamics. It quantifies the minimal data requirements for training PINNs, highlighting their potential to overcome the limitations of traditional CFD methods in resource-constrained environments. By establishing data efficiency benchmarks, our research supports the broader applicability of PINNs across various industries, including aerospace and environmental monitoring. © 2024 ieee.

关键词： Machine learning

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