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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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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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A Provably Secure and Lightweight Access Control Protocol for EI-Based Vehicle to Grid Environment

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IEEE Internet of Things Journal 2023年第18期10卷 16650-16657页

作者： Shamshad, Salman Mahmood, Khalid Shamshad, Usman Hussain, Ibrar Hussain, Shafiq Das, Ashok Kumar The University of Lahore Department of Software Engineering Lahore54590 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan Riphah International University Riphah School of Computing and Innovation Lahore54000 Pakistan University of Sahiwal Department of Computer Science Sahiwal57000 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Virginia Modeling Analysis and Simulation Center SuffolkVA23435 United States

The energy Internet (EI) presents a novel paradigm for renewable energy distribution that utilizes communication and computing technologies to revolutionize the conventional intelligent transportation systems (ITSs) and power grid into a structure that provides assistance to open innovation. The EI offers sustainable and bidirectional transmission for the improvement and analysis of energy convention among electric vehicles (EVs) and service providers. To ensure efficient, reliable, and secure operation, EI must be safe from security attacks. Therefore, efficient and secure key negotiation is a significant issue for the EI-based Vehicle-to-Grid (V2G) architecture. Thus, to ensure the system's security, we have devised a robust scheme to facilitate a secure key agreement between the entities involved for the secure and efficient renewable energy distribution for the EI-based energy vehicles in V2G. The devised scheme's robustness is solicited through the widely accepted formal random or real (RoR) model. In addition, the informal security analysis is conducted on the devised scheme, which is evident that the designed scheme achieves all the required security features of EI-based ITS. Moreover, the performance evaluation results endorse that the designed scheme achieves the desired security and minimizes the communication, computation, and energy overhead by 29.33%, 27.94%, and 28.08% in comparison to the existing competitive schemes. © 2014 IEEE.

关键词： Vehicle-to-grid

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Exploring the Effectiveness of Deep Learning in Audio Compression and Restoration 2

Exploring the Effectiveness of Deep Learning in Audio Compre...

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2nd IEEE International Conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Pushparani, S. Rekha, K. Sashi Sivagami, V.M. Usharani, R. Jothi, M. Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology SriperumbudurTamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Tamil Nadu Kattankulathur India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India

ISBN: (纸本)9798350384277

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case. © 2024 IEEE.

关键词： Audio streaming

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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An Ensemble Deep Learning Model for Diabetic Retinopathy Identification 9

An Ensemble Deep Learning Model for Diabetic Retinopathy Ide...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Hussain, Mohammad Manzoor Shanmugam, P. Moorthi, K. Sakthivelu, U. Rajasekar, A. Kumar, R. Naresh B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India Rajalakshmi Engineering College Department Computer Science and Engineering Chennai India Sairam Engineering College Department Electronics and Communication Engineering Chennai India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Tamilnadu Kattankulathur India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350384208

Diabetic Retinopathy (DR) is an eye disease that can potentially cause significant injury in those diagnosed with diabetes. The condition arises due to issues with blood circulation to the retina, the delicate tissue in the posterior part of the eye. DR is recognized as the leading factor contributing to visual impairment among those affected by diabetes. However, timely identification and intervention might mitigate the occurrence of this condition. Conversely, the timely identification of DR poses challenges, while the diagnostic process can be laborious and time-intensive. The automated detection of DR necessitates categorizing colour retinal fundus pictures. This article presents a computer-aided diagnostic strategy that utilizes Deep Learning (DL) algorithms. In order to classify retinal fundus pictures, this study employed a combination of DL models, specifically ResNet50 and Inception V3. The studies are being conducted on the DIARETDB1 dataset. An analysis is conducted to compare the results of the suggested methodology. The comparison study results show that the proposed approach has more significant accuracy levels than the other methods. A high degree of accuracy, that is, 98.37%, has been attained by utilising the proposed techniques. © 2023 IEEE.

关键词： Deep learning

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Cloud-Enhanced Machine Learning Models for Predictive Maintenance in Industrial IoT 2

Cloud-Enhanced Machine Learning Models for Predictive Mainte...

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2nd IEEE International Conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Usharani, R. Sivagami, V.M. Saravanan, K. Pushparani, S. Rekha, K. Sashi Department of Computational Intelligence School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Care College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798350384277

This study examines the implementation and impact of Cloud-Enhanced Machine Learning Models in the realm of Predictive Maintenance within Industrial Internet of Things (IIoT) settings. Emphasizing the integration of cloud computing with machine learning, the research focuses on how this synergy improves the accuracy and efficiency of maintenance predictions in industrial environments. Key metrics evaluated include prediction accuracy, operational efficiency, cost savings, and reduction in equipment downtime. The study demonstrates a significant increase in prediction accuracy, from 65% to 88%, attributed to the advanced data processing and analytical capabilities of cloud-enhanced machine learning. Operational efficiency saw marked improvements, with a 50% reduction in response times and a 25% increase in maintenance scheduling efficiency, leading to a 20% rise in asset utilization rates. Financially, the implementation led to a 37% reduction in annual maintenance costs and a 40% decrease in both the cost per downtime incident and long-term repair expenses. Most notably, the study recorded a 62.5% reduction in both the duration and frequency of downtime incidents. These results highlight the transformative potential of integrating cloud computing and machine learning in IIoT for predictive maintenance, showcasing significant advancements in predictive accuracy, operational efficiency, cost-effectiveness, and overall reduction in downtime. The study underscores the critical role of technological innovation in advancing industrial maintenance strategies, leading to more proactive, efficient, and economically viable operations. © 2024 IEEE.

关键词： Predictive maintenance

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Generative Adversarial Network with Soft-Dynamic Time Warping and Parallel Reconstruction for Energy Time Series Anomaly Detection

arXiv

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arXiv 2024年

作者： Prabhu, Hardik Valadi, Jayaraman Arjunan, Pandarasamy Robert Bosch Centre for Cyber Physical Systems Indian Institute of Science Bengaluru India School of Computing and Data Sciences FLAME University Pune India

In this paper, we employ a 1D deep convolutional generative adversarial network (DCGAN) for sequential anomaly detection in energy time series data. Anomaly detection involves gradient descent to reconstruct energy sub-sequences, identifying the noise vector that closely generates them through the generator network. Soft-DTW is used as a differentiable alternative for the reconstruction loss and is found to be superior to Euclidean distance. Combining reconstruction loss and the latent space’s prior probability distribution serves as the anomaly score. Our novel method accelerates detection by parallel computation of reconstruction of multiple points and shows promise in identifying anomalous energy consumption in buildings, as evidenced by performing experiments on hourly energy time series from 15 buildings. © 2024, CC BY.

关键词： Anomaly detection

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Design and Development of an IoT Based Smart Irrigation System to Enhance Agricultural Productivity Using ESP8266 Wi-Fi Enabled Controller 5

Design and Development of an IoT Based Smart Irrigation Syst...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Parashar, Vivek Singh, Megha Senthamarai, M. Poonkuzhali, P. Vadar, Parashuram Shankar Swapna, B. School of Computing Science and Engineering Vit Bhopal University Bhopal India Oriental University Department of Computer Science and Engineering Indore India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Erode India R.M.D. Engineering College Department of Ece Kavaraipettai India Yashwantrao Chavan School of Rural Development Shivaji University Maharashtra Kolhapur India Dr Mgr Educational and Research Institute Chennai India

ISBN: (纸本)9798350379945

Innovative solutions in many fields, including agriculture, have been made possible by the widespread use of Internet of Things (IoT) technology. Our goal in this research was to increase crop yields by developing a smart irrigation system that would use the IoT. An ESP8266 Wi-Fi-enabled controller serves as the center of the system, collecting data and making decisions all at once. Important parts include sensors for soil moisture, air temperature and humidity, light intensity, water level, a pump to move water, and the ability to connect to a cloud service for data storage and analysis. Water level, soil moisture, temperature, humidity, and light intensity are some of the environmental characteristics that must be continuously monitored in order for the system to function. The data is sent to the main controller in real-time by the sensors using Wi-Fi. Based on predefined thresholds and algorithms, the system dynamically adjusts irrigation schedules to optimize water usage and enhance crop growth. To evaluate the system's performance, sensor readings from multiple trials were analyzed. Results indicate that the system effectively maintains optimal environmental conditions for plant growth, leading to improved agricultural productivity. The integration with a cloud platform enables remote monitoring and control, facilitating efficient water resource management and decision-making for farmers. © 2024 IEEE.

关键词： Soil moisture

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