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Proximity-induced magnetic order in topological insulator on ferromagnetic semiconductor

science China(Information sciences) 2023年第12期66卷 267-274页

作者： Hangtian WANG Koichi MURATA Weiran XIE Jing LI Jie ZHANG Kang L.WANG Weisheng ZHAO Tianxiao NIE School of Integrated Circuit Science and Engineering and Advanced Innovation Center for Big Data and Brain Computing Beihang University Beihang-Goertek Joint Microelectronics Institute Qingdao Research InstituteBeihang University Department of Electrical Engineering University of California

Introducing magnetic order into topological insulator（TI） to break the time-reversal symmetry can yield numerous fascinating physical phenomena,which brings new hope for the emerging spintronic *** proximity effect is regarded as a promising strategy that could advance the step for realistic application by choosing a suitable ferromagnetic layer with the merits of high Curie temperature and high compatibility with mainstream semiconductor ***,we prepare a Bi2Se3thin film on Sicompatible ferromagnetic semiconductor（FMS） of MnxGe1-xby molecular beam *** integration,the nonmagnetic Bi2Se3exhibits an anomalous Hall signal and a clear weak localization cusp in magnetoresistance until 150 K,confirming that a high-temperature magnetism can be induced by the proximity *** investigation of the magnetoconductance quantitatively indicates that the Bi2Se3conductance suffers a transition from weak antilocalization to weak localization behavior after integrating with MnxGe1-x,and an 80 meV bandgap is predicted to be opened in the surface states in Bi2Se3layer due to the proximityinduced *** results prove that the proximity effect could be an important method to achieve topological magnetism at high temperatures,and reveals its potential for the manipulation of the topological surface states.

关键词： topological insulator ferromagnetic semiconductor proximity effect quantum interference

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Verifiable Machine Learning Models in Industrial IoT via Blockchain 14th

Verifiable Machine Learning Models in Industrial IoT via B...

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12th International Advanced computing Conference, IACC 2022

作者： Stodt, Jan Ghajar, Fatemeh Ghovanlooy Reich, Christoph Clarke, Nathan Institute for Data Science Cloud Computing and IT Security Furtwangen University of Applied Sciences Furtwangen Germany Offenburg University of Applied Sciences Offenburg Germany Plymouth University Portland Square PlymouthPL4 8AA United Kingdom

ISBN: (纸本)9783031356438

The importance of machine learning (ML) has been increasing dramatically for years. From assistance systems to production optimisation to healthcare support, almost every area of daily life and industry is coming into contact with machine learning. Besides all the benefits ML brings, the lack of transparency and difficulty in creating traceability pose major risks. While solutions exist to make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge, as unnoticed modification of a model is also a danger when using ML. This paper proposes to create an ML Birth Certificate and ML Family Tree secured by blockchain technology. Important information about training and changes to the model through retraining can be stored in a blockchain and accessed by any user to create more security and traceability about an ML model. © 2023, Springer Nature Switzerland AG.

关键词： Blockchain

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Colour Correction and Detail Enhancement in Underwater Images using Hybrid Real-ESRGAN

Colour Correction and Detail Enhancement in Underwater Image...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Vijay Chandar Vishnu G Nair D Rajeswari Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

The motive of underwater image enhancement is to improve and enhance the quality of photographs taken underwater. Various traditonal image processing techniques and deep learning models have been developed to work on this task. But most often the deep learning models and conventional methods of image processing work in silos, leading to subpar results. This research proposes a strategy that utilizes a combination of both approaches to solve this issue. Initially, traditional image processing techniques like colour correction and image stretching are used to enhance the colour of the images. The enhanced images are then fed into a custom Real-ESRGAN model that has been fine-tuned on an underwater image dataset to optimize performance in this context. This hybrid approach has the potential to outperform the models utilizing either traditional or deep learning methods in isolation by leveraging the strengths of both. In addition, batch processing is used in the pipeline to efficiently handle multiple images at the same time. This effectively addresses the scalability problem that arises frequently in image enhancement tasks. With eye-catching visuals, the proposed methodology achieves a Peak Signal-to-Noise Ratio (PSNR) of 23.66 and a Mean Squared Error (MSE) of 0.28, suggesting excellent image restoration and noise reduction. The qualitative assessments show that the improved image graphs have better colour, contrast, and clarity than the originals.

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Multi-Agent System Based data Mining Technique for Supplier Selection

Multi-Agent System Based Data Mining Technique for Supplier ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Dhatterwal, Jagjit Singh Kaswan, Kuldeep Singh Singh, Jay Naruka, Mahaveer Singh Govardhan, D. Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation AP Guntur India School of Computing Science and Engineering Galgotias University UP Greater Noida India G L Bajaj Institute of Technology and Management Department of Electrical and Electronics Engineering Greater Noida India Institute of Aeronautical Engineering Department of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798350357769

Different target knowledge representations, which correspond to different hypothesis spaces, are suitable for learning various types of target functions. A distinct learning algorithm, capitalizing on an underlying structure to guide the exploration of the hypothesis space, leverages each of these hypothesis representations. Consequently, making decisions on these matters entails navigating an expansive landscape of alternative approaches to determine the one that best aligns with the defined learning problem. To select an appropriate learning algorithm that performs optimally for the given problem and target function, it is essential to analyze the interplay between the size and completeness of the hypothesis space, the availability of training examples, the learner's prior knowledge, and the confidence in a hypothesis's ability to generalize correctly to unseen data points. This challenge often arises in the context of poorly integrated systems. The objective of this paper is to introduce a specific approach to addressing these challenges, involving the use of enhanced elicitation techniques and tools, improved development paradigms, knowledge modeling languages, ontologies, and advanced methods for system maintenance. However, in the past decade, an alternative reasoning paradigm and computational problem-solving approach have garnered significant attention, involving the use of Multi-Agent System (MAS) based data Mining (DM) techniques. This paper proposes the integration of MAS and DM for supplier selection, with a focus on partitioning-based algorithms such as K-means, Farthest First, Expectation Maximization, and Filtered Clusterer. These algorithms are compared based on factors such as dataset size, the number of clusters, and the time required for cluster formation. Finally, within this integrated model, MAS-DM is exemplified through an illustrative result-matching process. © 2023 IEEE.

关键词： Cluster data Mining JADE Multi-Agent Ontologies

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Music Recommendation based on Facial Expressions using data Augmentation

Music Recommendation based on Facial Expressions using Data ...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Tanisha Kapoor Arnaja Ganguly D Rajeswari Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

Emotion recognition from facial expressions plays a pivotal role in human-computer interaction, psychology, marketing, and various other domains. This research paper presents a comprehensive study and implementation of an automated emotion recognition system based on deep learning techniques. The project addresses the challenge of accurately classifying a wide range of emotions from facial images in real-time. Leveraging Convolutional Neural Networks (CNNs) and modern deep learning approaches, the system demonstrates high accuracy in identifying emotions, including happiness, sadness, anger, and more. This project used the image dataset FER-2013 for the mood prediction portion of the project, and Spotify music data to predict emotions for music recommendation. Both the datasets utilized are freely available on Kaggle. The proposed system can achieve an accuracy of 91.23% in predicting the users’ emotion based on their facial expressions.

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OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System

arXiv

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arXiv 2023年

作者： Yang, Mingtao Ng, See-Kiong Fu, Jinlan The Department of Computer Science School of Computing National University of Singapore Singapore Institute of Data Science National University of Singapore 117602 Singapore

Pre-trained conversation models (PCMs) have demonstrated remarkable results in task-oriented dialogue (TOD) systems. Many PCMs focus predominantly on dialogue management tasks like dialogue state tracking, dialogue generation tasks like response generation, or both. However, the existing PCMs seldom consider dialogue comprehension tasks, such as dialogue question answering and summarization tasks. These tasks allow PCMs to glean dialogue context from various angles. This observation naturally raises the question: Can the performance of downstream dialogue tasks be enhanced if a PCM is pre-trained on dialogue management, generation, and comprehension tasks? To investigate this, we proposed an Omnipotent Dialogue pre-training model (OmniDialog). It unifies these three dialogue tasks into a monolithic framework by multi-task learning, fostering inter-task communication. The pre-training corpus of OmniDialog spans 7 dialogue-focused tasks, drawing from 15 datasets and encompassing over 3.2 million dialogue utterances. To our knowledge, OmniDialog is a pioneering PCM pre-trained across dialogue management, generation, and comprehension domains. We evaluated its performance across four tasks: dialogue summarization, end-to-end dialogue modeling, dialogue state tracking, and intent classification. The results underscore its efficacy in domain transfer learning, low-resource, and full-dataset scenarios. Furthermore, to glean a nuanced understanding of OmniDialog’s strengths and potential pitfalls, we designed a fine-grained analysis framework for dialogue-centric tasks. Experimental results show that the OmniDialog is good at hard samples, such as long dialogues and lengthy responses. Copyright © 2023, The Authors. All rights reserved.

关键词： Speech processing

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Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

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Intelligent Automation & Soft computing 2023年第1期35卷 957-969页

作者： T.Shanthi M.Ramprasath A.Kavitha T.Muruganantham Department of Electronics and Communication Engineering Sona College of TechnologySalem636005Tamil NaduIndia Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirapalli621112TamilnaduIndia Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112TamilnaduIndia

The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems(IATS).Despite the IATS's benefits,security remains a significant *** technology has grown in popularity as a means of implementing safe,dependable,and decentralised independent IATS systems,allowing for more utilisation of legacy IATS infrastructures and resources,which is especially advantageous for crowdsourcing *** technology can be used to address security concerns in the IATS and to aid in logistics *** light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems,this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching(BDL-IATS).The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment ***,the deep belief network(DBN)model is used to select suitable vehicles and goods for ***,the chaotic krill herd technique is used to tune the DBN model’s *** performance of the BDL-IATS technique is validated,and the findings are inspected under a variety of *** simulationfindings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.

关键词： Blockchain ethereum intelligent autonomous transport system security deep belief network

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Cloud computing based Smart Traffic Management System with Priority Switching for Health Care Services 2

Cloud Computing based Smart Traffic Management System with P...

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2nd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2023

作者： Shiny, X.S. Asha Ravikumar, D. Chinnasamy, A. Hemavathi, S. Department of Computer Science and Engineering Telangana Hyderabad India Kings Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Chennai India Sri Sairam Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665456302

Regular cities can be transformed into intelligent structures by leveraging information and communication technologies. Innovative city development could be significantly impacted by the Internet of Things paradigm, commonly called cloud computing. All urban locations have a lot of traffic. In this study, Internet-of- Things (IoT) based system is proposed for health care services to organize and to establish the traffic signaling and pick up a route under which road congestion can be administrated. Vehicle accident contraventions are identified by the traffic officers using an online service that is hierarchically carefully monitored or constrained. However, the suggested approach is generic and may be utilized in any major metropolis without losing its generality. Traffic lights linked to cameras in metropolitan areas can be upgraded by connecting to IoT. During a pandemic, this approach is precious. The police can easily regulate traffic from their homes using their cell phones and identify defaulters. The suggested technique aids in the separation of ambulance and rescue engines from ordinary traffic. © 2023 IEEE.

关键词： Internet of things

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic

Guardians of Truth: Exploring Techniques for Veracity Identi...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： P. Suthanthiradevi G Revathy P Sathish Kumar B Karthikeyan Lakshmi.V Muthu Data Science and Business Systems School of Computing SRM institute of Science and Technology India Department of Computer science and engineering Vels Institute of science Technology and Advanced studies India Computer and Communication Engineering Rajalakshmi institute of technology India School of Computing SASTRA Deemed University India Department of Computer Science and Engineering St. Joseph’s Institute of Technology India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score.

关键词： COVID-19 Social networking (online) Pandemics Blogs Predictive models Transformers Prediction algorithms

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Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges

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Computers in Biology and Medicine 2025年 189卷 109834-109834页

作者： Ibrahim, Mahmoud Khalil, Yasmina Al Amirrajab, Sina Sun, Chang Breeuwer, Marcel Pluim, Josien Elen, Bart Ertaylan, Gökhan Dumontier, Michel Institute of Data Science Faculty of Science and Engineering Maastricht University Maastricht Netherlands Department of Advanced Computing Sciences Faculty of Science and Engineering Maastricht University Maastricht Netherlands VITO Belgium Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands

This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic, mammographic, ultrasound, CT, MRI, and X-ray), text, time-series, and tabular data (EHR). Unlike previous narrowly focused reviews, our study encompasses a broad array of medical data modalities and explores various generative models. Our aim is to offer insights into their current and future applications in medical research, particularly in the context of synthesis applications, generation techniques, and evaluation methods, as well as providing a GitHub repository as a dynamic resource for ongoing collaboration and innovation. Our search strategy queries databases such as Scopus, PubMed, and ArXiv, focusing on recent works from January 2021 to November 2023, excluding reviews and perspectives. This period emphasizes recent advancements beyond GANs, which have been extensively covered in previous reviews. The survey also emphasizes the aspect of conditional generation, which is not focused on in similar work. Key contributions include a broad, multi-modality scope that identifies cross-modality insights and opportunities unavailable in single-modality surveys. While core generative techniques are transferable, we find that synthesis methods often lack sufficient integration of patient-specific context, clinical knowledge, and modality-specific requirements tailored to the unique characteristics of medical data. Conditional models leveraging textual conditioning and multimodal synthesis remain underexplored but offer promising directions for innovation. Our findings are structured around three themes: (1) Synthesis applications, highlighting clinically valid synthesis applications and significant gaps in using synthetic data beyond augmentation, such as for validation and evaluation;(2) Generation techniques, identifying gaps in personalization and cross-modality innovation;and (3) Evalu

关键词： Generative adversarial networks

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