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SLEEPWISE: A Continuous Sleep Monitoring and cognitive Evaluation Platform in Ambient Residential Environment

SLEEPWISE: A Continuous Sleep Monitoring and Cognitive Evalu...

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Annual international conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Shuwen Jin Danqiong Wang Xiao Ma Tao Li Feng Lin Lei Yu Qing Zhang Research Center for Frontier Fundamental Studies Zhejiang Lab Hangzhou China Shanxi Bethune Hospital Shanxi Academy of Medical Sciences Tongji Shanxi Hospital Third Hospital of Shanxi Medical University Taiyuan China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Key Laboratory of Complex Systems and Data Science of Ministry of Education Complex Systems Research Center Shanxi University China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Age-related cognitive decline presents a significant health challenge for older adults, with no specific medication available to counteract it. Early detection of cognitive decline is crucial for timely intervention and effective management. Sleep status plays a vital role in evaluating cognitive function, as disrupted sleep patterns are associated with cognitive decline and increased risk of neurodegenerative conditions. Existing methods for assessing sleep status, such as self-report questionnaires, actigraphy, and polysomnography, lack the ability to consistently monitor sleep status over a long period. In this paper we introduced SLEEPWISE, an AI-powered platform deployed in residential environments for continuous ambient sleep monitoring to assess the cognitive well-being of older adults. SLEEPWISE utilizes non-contact pads with highly sensitive piezoelectric sensors to monitor sleep parameters such as duration and efficiency, which are transmitted daily to the server. Moreover, we have also developed a gamified module for SLEEPWISE, allowing an objective assessment of daily cognitive functions. This module aids SLEEPWISE in establishing the correlation models between sleep and cognitive health. To evaluate the effectiveness of SLEEP-WISE, a pilot study was conducted with participants recruited from a local hospital. During the 30-days trials, SLEEPWISE identified a negative impact on congnitive performance associated with increased nocturnal body movements and getting out of bad. These findings emphasize the potential of SLEEPWISE as an early screening tool for evaluating daily cognitive health and well-being.

关键词： Correlation Hospitals Sensors Servers Biomedical monitoring Older adults Engineering in medicine and biology Monitoring

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Fusion data on Fuzzy Modality: From Algebraic Interpretations to Quantum Simulations via Qiskit Platform

Fusion Data on Fuzzy Modality: From Algebraic Interpretation...

引用

ieee international conference on Systems, Man and Cybernetics

作者： Juliano Buss Bruna Novack Cecilia Botelho Helida Santos Giancarlo Lucca Anderson Cruz Adenauer Yamin Renata Reiser CDTec Universidade Federal de Pelotas Pelotas-RS Brazil C3 Universidade Federal do Rio Grande Rio Grande-RS Brazil PPGC Universidade Católica de Pelotas Pelotas-Rs Brazil IMD Universidade Federal do Rio Grande do Norte Natal-RN Brazil PPGC Universidade Federal de Pelotas Pelotas-RS Brazil

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

This study is given at the intersection of three important areas: Modal Logic (ML), Fuzzy Logic (FL), and Quantum computing (QC), leveraging from their main features. On the one hand, we have the QC ability to handle complex data more efficiently, taking advantage of the quantum mechanics concepts. On the other hand, ML and FL allow us to express uncertainties by mathematically modeling the imprecision of the natural language. Therefore, we first provide an algebraic model to interpret modal operators on fuzzy logic, and then we represent them in a quantum computing environment. Besides, we present some case studies simulating the fuzzy modal connectives in the Qiskit platform, aiming to better understand the evolution of quantum circuits.

关键词： Fuzzy logic Hands Quantum computing Uncertainty Computational modeling Mathematical models Registers Integrated circuit modeling Quantum circuit Quantum simulation

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Geo-Distributed Analytical Streaming Architecture for IoT Platforms

Geo-Distributed Analytical Streaming Architecture for IoT Pl...

引用

ieee international conference on Cluster computing

作者： M. Reza Hoseiny Farahabady Albert Y. Zomaya Center for Distributed and High Performance Computing The University of Sydney School of Computer Science Sydney NSW Australia

ISBN: (数字)9798350358711

ISBN: (纸本)9798350358728

The surge in real-time IoT data introduces scalability and computational challenges, necessitating advanced architectural and technological solutions. Streamed data processing is increasingly adopted across industries to enhance operational efficiency by extracting insights from vast, unstructured datasets. However, complex analytical tasks, such as multi-join queries, often require stateful iterative calculations on high-volume, high-velocity data, which challenges conventional programming models like MapReduce. This paper introduces an architectural model enabling application developers to create intricate streaming computational logic within an IoT platform. Our architecture supports scalable applications across distributed edge-tier nodes, particularly for iterative analytical operations on streamed data. We discuss core concepts and a timestamp model (borrowed from the timely data-flow concept) attached to data items circulating between computational blocks, which can execute concurrently on different edge-tier nodes. Additionally, we detail a buffer management mechanism that dynamically adjusts memory size in each computational block on nodes with limited capacity. This mechanism considers application performance requirements and runtime conditions to optimize buffer sizes. Performance evaluation against cloud-tier alternatives confirms the effectiveness of our solution. Experimental results show a significant reduction in p-99 delay compared to cloud-tier deployment with a database engine for analytical applications involving multi-join operations.

关键词： Computational modeling Biological system modeling Scalability Distributed databases data models Sensors Logic Iterative methods Surges Engines

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A Survey on cognitive Radio Network Spectrum Sensing using Deep Learning Methods

A Survey on Cognitive Radio Network Spectrum Sensing using D...

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Applied Artificial Intelligence and computing (ICAAIC), international conference on

作者： P. Nandhini S. Vimalnath Dept of Electronics and Communication Engineering V.S.B. Engineering College (Autonomous) Karur Tamilnadu India Dept of Electronics and Communication Engineering M. Kumarasamy College of Engineering (Autonomous) Karur Tamilnadu India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

A potential novel strategy for addressing the increasing demand for spectrum sensing in cognitive radio networks (CRNs) involves utilizing underutilized frequency bands through spectrum sensing. Spectrum sensing, a fundamental component of CRNs, detects available spectrum bands without disrupting signals from licensed users. Spectral sensing has recently emerged as a domain for deep learning algorithms, which have the capability to autonomously differentiate complex patterns and representations from raw spectrum data. This study conducts a comprehensive review of the state-of-the-art in deep learning for CRN spectrum sensing. It begins with an introductory overview of spectral sensing in CRNs and proceeds to assess several attempts focusing on the integration of deep learning into spectrum sensing. Further, the paper explores into crucial research challenges and potential opportunities associated while employing deep learning for spectrum sensing in CRNs. These challenges encompass issues such as data scarcity, adaptability to diverse environments, energy efficiency, resilience to interference and noise, and real-time processing constraints. The utilization of deep learning for spectrum sensing in CRNs represents an emerging field, and this review includes current methodologies, challenges, and potential remedies in this domain.

关键词： Deep learning Surveys Sensitivity Reviews Noise Interference Real-time systems

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Interpretable Deep Learning for Alzheimer’s Disease Through Genetic data and Explainable Artificial Intelligence

Interpretable Deep Learning for Alzheimer’s Disease Through...

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ieee/ACM international Symposium on Big data computing (BDC)

作者： Rouaa Alzoubi Ayad Turky Abir Hussain Sebti Foufou University of Sharjah United Arab Emirates

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder causing cognitive decline and memory loss. With its significant impact on individuals’ lives, AD is the most prevalent form of dementia, contributing to 60-80% of all dementia cases. At the same time, symptoms may not surface until years later, making early detection vital for effective intervention. Thus, this work presents an approach to early AD detection by integrating Genome-Wide Association Studies (GWAS) with deep learning models and Explainable Artificial Intelligence (XAI). First, different classical machine learning models are developed for AD, and a Convolutional Neural Network (CNN) model is trained using the AD GWAS dataset obtained from the AD neuroimaging initiative. We then employ transfer learning to train our CNN model as a base model over the ADNI dataset. In addition, XAI methods are used to interpret the transfer learning model decision. Acknowledging the well-known limitation that classical machine learning is not inherently a generic model. The results from this study will help determine the most critical genetic markers associated with AD and provide transparency in understanding the deep learning model decisions.

关键词： Deep learning Neuroimaging Accuracy Explainable AI Computational modeling Transfer learning Predictive models data models Convolutional neural networks Alzheimer's disease

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Quantum-Enhanced Spiking Neural Networks

Quantum-Enhanced Spiking Neural Networks

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Quantum computing and Engineering (QCE), ieee international conference on

作者： Ria Khatoniar Debanjan Konar Vaneet Aggarwal Dept. of Comp. Sc. & Engg. Birla Inst. of Tech. & Sc. (BITS) India Purdue Quantum Sc. & Eng. Inst. (PQSEI) and School of ECE and IE Purdue Univ. West Lafayette USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

This article explores the integration of Quantum computing (QC) into classical Spiking Neural Networks (SNN) architectures to address limitations in encoding spiking signals into quantum circuits. Incorporating QC techniques to encode temporal dependencies within quantum circuits, the study aims to enhance the capabilities of SNN. It introduces a method lever-aging quantum systems' parallelism and high-dimensional state spaces to encode temporal information from classical spiking signals into a quantum framework relying on angle encoding and using two successive rotation gates. While the quantum circuit faced challenges in reconstructing complex temporal patterns, the proposed hybrid quantum-classical model, referred to as Quantum-enhanced Spiking Neural Networks (SQ-Net), significantly improved the efficiency and accuracy of processing temporal data using trigonometric functions. This advancement in temporal encoding within quantum circuits enables quantum systems to handle intricate temporal information effectively for sequential problems.

关键词： Quantum system Computational modeling Simulation Spiking neural networks Logic gates Encoding data models Quantum circuit Integrated circuit modeling Long short term memory

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HyPRE: Hybrid Proxy Re-Encryption for Secure Multimedia data Sharing on Mobile Devices

HyPRE: Hybrid Proxy Re-Encryption for Secure Multimedia Data...

引用

ieee international conference on Multimedia and Expo (ICME)

作者： Xinyu Feng Cong Li Qingni Shen Jisheng Dong Wenjun Qian Yuejian Fang Zhonghai Wu School of Software and Microelectronics Peking University National Engineering Research Center for Software Engineering Peking University PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University School of Computer Science Peking University

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Due to the rapid growth of mobile internet, massive multimedia data (e.g., movies, photos, notes, etc.) on mobile devices is synchronized and shared through the cloud. During this process, public key encryption plays an important role in ensuring the confidentiality of data. However, due to the bottleneck of computing and storage resources in mobile devices, it is difficult to execute complex cryptographic algorithms on them. In this paper, we present a novel Hybrid Proxy Reencryption (HyPRE) scheme for the sharing of multimedia data on mobile devices, which empowers a semi-trusted proxy to convert a ciphertext under an identity to a new one under an expressive policy without revealing the underlying plaintext. Our scheme allows mobile devices with limited resources to encrypt data efficiently, and then to share the encrypted data to multiple entities securely. We define the HRA security for our HyPRE scheme to improve the incompleteness of the security under chosen plaintext attacks (CPA) in traditional proxy re-encryption schemes and prove it selectively secure under HRA. Experimental analysis indicates that HyPRE achieves 2× to 3× improvement in terms of re-encryption performance compared with the state-of-the-art ones.

关键词： Performance evaluation Cloud computing Public key data collection Motion pictures Mobile handsets Encryption

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Wave Prediction Based on Long Short-Term Memory Neural Network

Wave Prediction Based on Long Short-Term Memory Neural Netwo...

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ieee international conference on Signal Processing, Communications and computing (ICSPCC)

作者： Yuan Zhang Fantai Meng Hongyuan Zheng Bingyong Guo Siya Jin Ocean Institute Northwestern Polytechnical University Taicang China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

Wave prediction is of great importance in the field of wave energy. Machine learning provides an efficient new idea for wave prediction Due to its strong adaptability and excellent ability to deal with complex data. In this work, the long short-term memory (LSTM) model is investigated under four typical wave cases, including two extreme waves hitting the 50-year contour line of European Marine Energy Centre (EMEC) site, UK with the largest significant wave height and two different wave spectral shapes; another two wave conditions at the targeted resonance condition of a designed wave energy converter (WEC) with two different wave spectral shapes. Physical wave data recorded from wave tank are used to train a LSTM model. Validated with the physical data, the results show that the developed LSTM model has great performance for the prediction of the different waves studied in this work.

关键词： Training Accuracy Spectral shape Signal processing Predictive models data models Resonance Wave energy conversion Forecasting Long short term memory

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Improving Preference Detection with Eye Movement Gaze and cognitive Diversity 20

Improving Preference Detection with Eye Movement Gaze and Co...

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20th ieee international conference on cognitive Informatics and cognitive computing, ICCI*CC 2021

作者： Schweikert, Christina Shimojo, Shinsuke Zhang, Zihan Tato, Jonida Hendsey, Rebecca Hsu, D. Frank Mathematics and Science St. John's University Division of Computer Science Queens NY United States California Institute of Technology Division of Biology/Computation and Neural Systems PasadenaCA United States Fordham University Laboratory of Informatics and Data Mining Department of Computer and Information Science New YorkNY United States

ISBN: (纸本)9781665421195

Preference choices of a subject when given two human face images is a complex cognitive process. One way to detect a subject's preference is by recording and examining the subject's eye movement gaze sequence before his/her decision. Combinatorial fusion analysis / algorithm (CFA) is a new approach for combining multiple scoring systems using rank-score characteristic (RSC) function and cognitive diversity (CD) measure. In this paper, we apply CFA to the study of the eye movement gaze sequences for preference detection. In particular, we use the RSC function to characterize each of the attributes and the CD to measure the diversity between attributes. Our results demonstrate that weighted combination of attributes using diversity strength, computed using average CD's, improves the preference detection. © 2021 ieee.

关键词： Eye movements

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Research on Named Entity Recognition Method Based on BERT Model

Research on Named Entity Recognition Method Based on BERT Mo...

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ieee international conference on Big data computing Service and Applications (BigdataService)

作者： Shaopeng Xie Shanghai Vocational College of Science & Technology Shanghai China

ISBN: (数字)9798350366389

ISBN: (纸本)9798350366396

Named Entity Recognition (NER), as a fundamental task in the field of natural language processing, directly deter-mines the efficiency and accuracy of semantic processing and inference in subsequent tasks. In previous studies, the Bi-LSTM model has demonstrated relatively mature and stable performance in entity extraction tasks. However, with the continuous evolution of technology, pre-trained models have now become the preferred choice for mainstream NLP tasks. In this study, based on five publicly annotated datasets, we conducted an in-depth comparison between the Bi-LSTM and BERT models in the task of named entity recognition. We found that in four out of the five datasets, the performance of the BERT model was superior to that of Bi-LSTM. This result fully demonstrates the excellent performance of pre-trained models in the field of named entity recognition, especially when dealing with large-scale and complex textual data, where their performance is even more outstanding. Thanks to pre-trained models, which are trained on large-scale corpora, they can learn rich language rules and knowledge, thereby more accurately capturing deep semantic information in text and improving the accuracy and efficiency of recognition.

关键词： Adaptation models Analytical models Accuracy Semantics Named entity recognition Bidirectional control Encoding data models Tuning Context modeling

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