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2023 International Conference on Optoelectronic Materials and Devices, ICOMD 2023

作者： Guan, Nianci Han, Qizhuo Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an710072 China The School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground- Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9781510674509

Efficient and stable Perovskite solar cells (PSCs) have been successfully produced using a dual-layer modified (DLM) perovskite thin film. This method simultaneously modifies both the bottom (SnO2/perovskite) and top (perovskite/SpiroOMeTAD) interfaces of the perovskite. The crystallinity of the perovskite film is enhanced after the DLM treatment. Additionally, thin barrier layers are formed on both its bottom and top surfaces, which reduce surface defect density and non-radiative recombination of carriers. The bottom and top modifier layers prevent direct contact between the hole transport layer and the electron transport layer, forming a high-energy barrier that promotes carrier transmission in PSCs. Consequently, the methylammonium acetate (MAAc)-PSC achieves an efficiency of 21.96%. © 2024 SPIE. All rights reserved.

关键词： Perovskite solar cells

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Regularized Multi-LLMs Collaboration for Enhanced Score-based Causal Discovery

arXiv

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arXiv 2024年

作者： Li, Xiaoxuan Liu, Yao Wang, Ruoyu Yao, Lina School of Computer Science and Engineering Northeastern University Shenyang110169 China University of New South Wales Sydney Australia School of Computing Macquarie University Sydney Australia CSIRO’s Data61 Australia

As the significance of understanding the cause-and-effect relationships among variables increases in the development of modern systems and algorithms, learning causality from observational data has become a preferred and efficient approach over conducting randomized control trials. However, purely observational data could be insufficient to reconstruct the true causal graph. Consequently, many researchers tried to utilise some form of prior knowledge to improve causal discovery process. In this context, the impressive capabilities of large language models (LLMs) have emerged as a promising alternative to the costly acquisition of prior expert knowledge. In this work, we further explore the potential of using LLMs to enhance causal discovery approaches, particularly focusing on score-based methods, and we propose a general framework to utilise the capacity of not only one but multiple LLMs to augment the discovery process. © 2024, CC BY.

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data Assimilation for Microstructure Evolution in Kinetic Monte Carlo 152nd

Data Assimilation for Microstructure Evolution in Kinetic Mo...

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152nd Annual Meeting and Exhibition of The Minerals, Metals and Materials Society, TMS 2023

作者： Tran, Anh Wang, Yan Rodgers, Theron Scientific Machine Learning Sandia National Laboratories AlbuquerqueNM United States Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States Computational Materials and Data Science Sandia National Laboratories AlbuquerqueNM United States

ISBN: (纸本)9783031225239

Modeling grain growth has been a subject of interest in computational material science, as it occurs in thermal-based processing methods such as annealing and sintering. Kinetic Monte Carlo with Potts model is often used as an integrated computational materials engineering (ICME) grain growth model and can generate high-fidelity synthetic microstructures. In this paper, we offer a data-driven stochastic calculus perspective on the kinetics of grain growth and model the microstructure evolution through the lens of stochastic differential equations, based on Langevin dynamics and Fokker-Planck equation to forecast the grain size distribution. We demonstrate that our proposed approach agrees reasonably well with the hybrid Potts-phase field model using SPPARKS in forecasting the long-term evolution of grain size distribution. © 2023, The Minerals, Metals & Materials Society.

关键词： Grain growth

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Swin-TCNet: A vision transformer-based method for automated chromosomal object detection

Swin-TCNet: A vision transformer-based method for automated ...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Kuo, Chih-En Lu, Chien-Hsing Huang, Li-Yuan National Chung Hsing University Department of Applied Mathematics Taichung Taiwan Taichung Veterans General Hospital Department of Obstetrics and Gynecology Taichung Taiwan National Chung Hsing University Institute of Data Science and Information Computing Taichung Taiwan

ISBN: (纸本)9781665480451

Chromosomes are intracellular aggregates carrying genetic information in genes, which are major objects of study in biological cytogenetics. An abnormal number or structure of chromosome causes chromosome disorders. Chromosome screening is an important part of prenatal the care. However, manual analysis of chromosomes is time-consuming. With the increasing popularity of prenatal diagnosis, manpower becomes overstretched. Therefore, an automatic approach for chromosome detection and recognition is urgently needed. Here, we proposed a vision transformer-based method for automated chromosomal object detection on metaphase cell images. The proposed method, named Swin Transformer Chromosomes Net (Swin-TCNet), that was mainly composed of Swin Transformer, region proposal network (RPN) and, multi-intersection over union (IoU) strategy. A large database formed by 5,000 metaphase cell images consisting of a total of 229,852 chromosomes was used to develop and evaluate the proposed method. The method uses the original cell meta images without pre-processing as input because that is clinically more friendly. The evaluation method used is the COCO API's mAP50, the testing set is up to 1250 original cell meta images (57,484 chromosomes in total), which is much more than the existing related literature. The accuracy and F1-score of our proposed method are 98.9% and 99.1%, respectively. Moreover, the practicality and robustness of Swin-TCNet in clinical practice can also be confirmed through a large amount of validation data. © 2022 IEEE.

关键词： Object detection

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Improving the Accuracy of Iris Recognition Based on Attention Mechanism

Improving the Accuracy of Iris Recognition Based on Attentio...

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International Conference on Computer Network, Electronic and Automation (ICCNEA)

作者： Zike Wang Jianguo Wang Lei Zhong School of Computer Science and Engineering Xi'an Technological University Xi’an China Research Institute of Artificial Intelligence and Data Science Xi'an Technological University Xi’an China

Iris recognition algorithms are becoming more and more widely used in daily life, and the accuracy requirements for iris recognition algorithms are getting higher and higher. Therefore, in this paper, an improved Shufflenetv2 algorithm is proposed as a way to improve the speed and accuracy of current iris recognition algorithms in daily life. In this paper, deep and shallow features are filtered by using the channel attention mechanism, which enables the model to acquire feature representations of both underlying and high-level semantics. In addition, in the loss function, this paper uses the focal loss function instead of the cross entropy loss function to achieve a solution to the imbalance between the number of positive and negative samples and prevent too much learning to negative sample features. By validating on the CASIA-Iris-Thousand dataset, which is commonly used in iris recognition tasks, the proposed model achieves an equal error rate (EER) of 1.66% compared to the Shufflenetv2 model, which has an EER of 5.3%, and optimizes by 3.64%. The model proposed in this paper can effectively improve the accuracy of iris recognition.

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Blockchain-Based Identity Management: Enhancing Privacy and Security in Digital Identity System

Blockchain-Based Identity Management: Enhancing Privacy and ...

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International Conference on Contemporary computing and Informatics (IC3I)

作者： S Saranya Monika K Manikandan Jangam Nagaraju S. Nagendiran B. T. Geetha Computer Science and Engineering Dr. N.G.P. Institute of Technology Coimbatore Mangalmay Institute of Management and Technology Greater Noida India School of Computer Science & Engineering (SCOPE) Vellore Institute of Technology (VIT) Vellore Tamilnadu India Department of Data Science MLR Institute of Technology Dundigal Hyderabad Department of Artificial Intelligence and Data Science R. M. K. Engineering College Department of ECE Saveetha School of Engineering (SIMATS) Saveetha University Tamil Nadu India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Privacy and security are major concerns when it comes to digital identities, particularly in centralized systems where data can be easily hacked or where people engage in identity fraud. These issues can be addressed using blockchain technology since it allows for the creation of a decentralized digital identity that is both secure and tamper-proof. This study employs Kaggle dataset to document the transactions and the devices involved to make the distinctions on how blockchain can boost privacy and security. Predominantly, various machine learning algorithms are used to analyze blockchain applications, and the results emphasize the fact blockchain systems greatly enhance transaction viability and diminish fraudulent actions. Therefore, the study argues that blockchain-based identity management systems offset the shortcomings of the centralized model by enhancing data ownership and combating fraud. Subsequent analysis expanded on the discussion of elaborating the scalability, interoperability, and application of algorithms to blockchains as well as how it can fundamentally change the way that certain industries, particularly identity management, would work with consumers at the center of reliable and secure blockchain solutions.

关键词： Machine learning algorithms Protocols Scalability Instruments Machine learning Blockchains Fraud Security Reliability Interoperability

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Deep Learning-Based EEG Signal Classification of Epileptic Patients 4

Deep Learning-Based EEG Signal Classification of Epileptic P...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Kumar, V. Senthil Karpagavalli, C. Pradeep, S. Divya, S. Taqui, Syed Noeman Vinayagam, Nadanakumar SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Tiruchirapalli621105 India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam626117 India S.A. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai600077 India Dr. N. G.P. Institute of Technology Department of Biomedical Engineering Tamil Nadu Coimbatore641048 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Hindustan Institute of Technology and Science Department of Automobile Engineering Padur Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350385793

Epilepsy is a neurological disorder characterized by repeated seizures throughout the day. Epilepsy affects around 65 million people worldwide. For people with focal epilepsy, surgery is a possibility but medicine is the standard treatment for generalized epilepsy. Unfortunately, these medications fail to control epileptic seizures in more than 30% of patients. Yet, this ends up in accidents and significantly shortens the patient's life expectancy. An electroencephalogram (EEG) is a critical diagnostic tool for epilepsy. The conventional method for detecting epileptic activity is expert human interpretation of multi-channel EEG readings. This is a complex and time-consuming procedure, thus many researchers have tried to automate it using Machine Learning (ML) and Deep Learning (DL) methods. Unfortunately, the investigation did not produce satisfactory results. This study aims to fill this research gap by proposing a new CNN-BLSTM (Convolutional Neural Network-Bidirectional Long Short-Term Memory) based hybrid DL model for epileptic detection. The EEG data from Kaggle was collected and processed. This study uses the same amount of data and epochs for training, validation, and testing all four DL models such as AlexNet, VGG-16, InceptionV3, and the proposed model. According to the experiment findings, the proposed model has a better level of accuracy (98.37%) than the other three models. © 2024 IEEE.

关键词： Electroencephalography

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A Detailed Analysis of Secrecy and Performance Metrics in Hybrid RF/ FSO Communication Systems: Implications of Composite Weibull and Log-normal Fading with M Turbulent Channels

A Detailed Analysis of Secrecy and Performance Metrics in Hy...

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Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

In this paper, we have analyzed the secrecy performance and system operation performance for hybrid RF/FSO systems against composite Weibull-log-normal fading channel with M turbulent channels. The performance is evaluated by carrying out extensive simulations to calculate Signal-to-Noise Ratio (SNR), Bit Error Rate (BER) and outage probability, we also re-evaluate channel capacity and secrecy capacity with respect of environmental conditions. Compared with RF only system, FSO only standalone scheme and some meaningful orthogonal-modulation multicarrier IM/DD-based schemes proposed by the literatures. Similarly, the utilization of adaptive modulation schemes improved resistance to fading and roughness in systems. The above results confirm the hybridization of both RF and FSO a comprehensive solution for dependable communication in different and even hostile surrounding conditions which set this work as reliable foundation point towards forthcoming research activity or application relevance within all range telecommunication sector.

关键词： Fading channels Radio frequency Communication systems Systems operation Simulation Bit error rate Modulation Probability Power system reliability Signal to noise ratio

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Collaborative Edge Service Placement for Maximizing QoS with Distributed data Cleaning 31

Collaborative Edge Service Placement for Maximizing QoS with...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Liang, Yuzhu Wang, Wenhua Zheng, Xi Liu, Qin Wang, Liang Wang, Tian School of Artificial Intelligence Beijing Normal University Beijing100875 China School of Computing Macquarie University NSW2109 Australia College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai519000 China BNU-HKBU United International College Guangdong Key Lab of Ai and Multi-Modal Data Processing Zhuhai519000 China

ISBN: (纸本)9798350399738

The proliferation of dirty data on Internet of Things (IoT) devices can undermine the accuracy of data-driven decision-making by affecting the distribution of original data. The Quality of Service (QoS) of data cleaning on these devices is heavily impacted by processing delay and accuracy. In this paper, we find that edge service placement is a key step aligned with data cleaning and consider the collaborative edge service placement with distributed data cleaning (SPDC) problem. To address this issue, we propose a novel distributed collaborative edge-based architecture that effectively balances the demands of storage, communication, computation, and load constraints. Experimental results show that the proposed approach significantly improves the accuracy of data cleaning by 0.31%-86.07% and reduces delay by 2.73%-58.71% compared to state-of-the-art baselines. © 2023 IEEE.

关键词： Edge computing

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Investigation of sex-related functional connectivity alterations in autism using class imbalance mitigation approach

Investigation of sex-related functional connectivity alterat...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jong Young Namgung Jongmin Mun Yeong Jun Park Jaeoh Kim Bo-Yong Park Department of Data Science Inha University Incheon Republic of Korea Marshall School of Business University of Southern California Los Angeles United States Department of Computer Engineering Inha University Incheon Republic of Korea Department of Data Science & Center for Neuroscience Imaging Research Inha University & Institute for Basic Science Incheon & Suwon Republic of Korea

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Autism spectrum disorder is primarily diagnosed in males, leading to the lack of understanding of brain disorganization in female individuals with autism. To fill the gap, we applied a Gaussian mixture model-based oversampling technique to the functional connectivity data to adjust for the sex imbalance in autism. Leveraging a dimensionality reduction technique, we generated a low-dimensional principal component (i.e., gradient) and assessed its between-group differences between the sexes. We observed significant sex-related differences in sensorimotor, attention, and default mode networks, which were associated with higher-order cognitive control processes. Transcriptomic association analysis provided a potential biological underpinning, specifying gene enrichment in the cortex, thalamus, and striatum. Finally, symptom severity prediction analysis suggested that the functional gradient was only associated with symptoms in female individuals with autism.

关键词： Dimensionality reduction Autism Prevention and mitigation Transcriptomics Process control Thalamus Reliability engineering Biology Gene expression Gaussian mixture model

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