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Symmetry-aware recursive image similarity exploration for materials microscopy

npj Computational Materials 2021年第1期7卷 1508-1521页

作者： Tri N.M.Nguyen Yichen Guo Shuyu Qin Kylie S.Frew Ruijuan Xu Joshua C.Agar Department of Materials Science and Engineering Lehigh UniversityBethlehemPA 18015USA Department of Computer Science and Engineering Lehigh UniversityBethlehemPA 18015USA Department of Mechanical Engineering Lehigh UniversityBethlehemPA 18015USA Department of Applied Physics Stanford UniversityStanfordCA 94305USA

In pursuit of scientific discovery,vast collections of unstructured structural and functional images are acquired;however,only an infinitesimally small fraction of this data is rigorously analyzed,with an even smaller fraction ever being *** method to accelerate scientific discovery is to extract more insight from costly scientific experiments already ***,data from scientific experiments tend only to be accessible by the originator who knows the experiments and ***,there are no robust methods to search unstructured databases of images to deduce correlations and ***,we develop a machine learning approach to create image similarity projections to search unstructured image *** improve these projections,we develop and train a model to include symmetry-aware *** an exemplar,we use a set of 25,133 piezoresponse force microscopy images collected on diverse materials systems over five *** demonstrate how this tool can be used for interactive recursive image searching and exploration,highlighting structural similarities at various length *** tool justifies continued investment in federated scientific databases with standardized metadata schemas where the combination of filtering and recursive interactive searching can uncover synthesis-structure-property *** provide a customizable open-source package(https://***/m3-learning/Recursive_Symmetry_Aware_Materials_Microstructure_Explorer)of this interactive tool for researchers to use with their data.

关键词： interactive image similarity

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Secure Energy Aware Power Control in Consumer Internet of Things With Semi Grant Free NOMA

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Abbas, Sohail Fayaz, Muhammad Ghandoura, Abdulrahman Khan, Muhammad Zahid Rehman, Ateeq Ur Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah UAE Department of Computer Science and IT University of Malakand Pakistan Applied College Department of Engineering and Applied Sciences Umm Al-Qura University Makkah Saudi Arabia Department of computing Staffordshire University UK

The Consumer Internet of Things (CIoT), a key aspect of the IoT, aims to integrate smart technologies into everyday life. In order to improve the spectral efficiency and provide massive connectivity to IoT networks, non-orthogonal multiple access (NOMA) variants like semi-grant-free (SGF) NOMA are employed. This paper aims to maximize secrecy energy efficiency (EE) for SGF-NOMA enabled CIoT in the presence of untrusted users (eavesdroppers) by utilizing a single-agent multi-agent deep reinforcement learning (SAMA-DRL) algorithm to overcome scalability and expensive learning issues. Given the limited long-distance transmission capabilities of CIoT devices, which typically have low transmit power, relay nodes are used to decode and forward data from grant-free (GF) users to the base station. Moreover, to enhance the coverage for GF users, the K-nearest neighbors (KNN) algorithm is utilized to place the relay nodes at an optimal positions. Furthermore, we design a collaborative contribution reward system to discourage user (agent) laziness. Simulation results show that the proposed SAMA-DRL-based SGF-NOMA algorithm for CIoT networks is more effective than baseline algorithms, achieving a 20% increase in secrecy EE compared to DRL-based SGF-NOMA without KNN. Moreover, the proposed scheme outperforms benchmark schemes in terms of EE across different radii. Additionally, we show that the proposed algorithm with quality of service based successive interference cancellation (SIC) is more power efficient as compared to conventional SIC decoding order. IEEE

关键词： Quality of service

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Edge-Cloud Computing Systems for Unmanned Aerial Vehicles Capable of Optimal Work Offloading with Delay 2

Edge-Cloud Computing Systems for Unmanned Aerial Vehicles Ca...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Narayana Tinnaluri, V.S. Vyankatesh Ghamande, Manasi Singh, Shashank Kuchipudi, Ramu Dattatraya Bhosale, Minakshi Dharani, R. Koneru Lakshmaiah Education Foundation Department of Computer Science Andhra Pradesh Vijayawada India Vishwakarma Institute of Information Technology Engineering and Applied Sciences Pune411037 India Integral University Lucknow Department of Computer Science and Engineering Uttar Pradesh India Chaitanya Bharathi Institute of Technology Department of Information Technology Telangana Hyderabad India Faculty of Mca Yashoda Technical Campus Satara Maharashtra Wadhe India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Tamilnadu Karur India

ISBN: (纸本)9798350346640

Mobile terminals' limited processing power and memory make it challenging to meet the needs of increasingly complex applications like autonomous vehicles and augmented reality. That's why there's been a rise in the demand for edge cloud computing power from endpoint gadgets. Due to its adaptability and proximity to the user, an unmanned aerial vehicle (UAV) may be used to support mobile edge computing (MEC) via job offloading, potentially relieving strain on edge clouds. Due to limitations in processing power and battery life, Unmanned Aerial Vehicles (UAV) devices face a huge issue with the rise of Applications and services for mobile devices that can't afford to be delayed and that need a lot of processing power. Task offloading is one way that mobile cloud computing may help you get around these restrictions. The biggest difficulties with this paradigm, however, are the significant latency and security concerns. The edge-cloud computing model was then proposed and has since seen widespread adoption as a means of dealing with these complications. However, the present task offloading models allow UAVs to perform their heavy jobs at the linked edge server, leading to unnecessary loads owing to the huge amount of UAVs and, in turn, increasing the latency. For this reason, this study has presented a delay-optimal task offloading strategy for multi-tier edge-cloud computing with many users. In this research, a system model has been proposed to assist an intelligent and perceptive agent in determining the best computational offloading strategy, minimizing both the time it takes to complete a job and the amount of power it needs to do so. The efficacy of an agent was shown, and simulation results revealed that its adoption may greatly cut delay and energy usage. © 2023 IEEE.

关键词： Mobile edge computing

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Interpretable Arabic Hate Speech Detection using Large Language Model

Interpretable Arabic Hate Speech Detection using Large Langu...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Majed Alwateer Ibrahim Gad Mahmoud Elmarhomy Ghada Elmarhomy Hasan Hashim Malik Almaliki El-Sayed Atlam Department of Computer Science College of Computer Science and Engineering Taibah University Saudi Arabia Computer Science Department Faculty of Science Tanta University Tanta Egypt Computer Science and Information College of Applied Taibah University Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Social media platforms serve as significant spaces for users to have conversations, discussions and express their opinions. However, anonymity provided to users on these platforms allows the spread of hate speech and other offensive material. Due to the wide-ranging nature of these platforms, there is a critical need to automatically detect and report occurrences of hate speech. There are various detection methods, but many of them operate as black boxes, lacking interpretability and explainability by design. To address the lack of interpretability, this study explores the development of an interpretable framework to detect hate speech in Arabic using large language models (LLMs). The proposed approach combines advanced natural language processing techniques with interpretable machine learning methods to enhance understanding of model decisions. The experimental results demonstrate that the model achieves high accuracy while maintaining interpretability, enabling users to understand the reasoning behind the detections. The proposed method achieves an accuracy of 0.846%, with a precision of 0.843% and a recall of 0.846%, outperforming existing Arabic hate speech detection models. These results show the effectiveness of combining LLM with interpretability for this critical task, providing a reliable and transparent solution for automated moderation of harmful content.

关键词： Technological innovation Accuracy Social networking (online) Smart cities Large language models Hate speech Text categorization Writing Reliability Stakeholders

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An on-chip platform for multi-degree-of-freedom control of two-dimensional quantum and nonlinear materials

An on-chip platform for multi-degree-of-freedom control of t...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Tang, Haoning Wang, Yiting Ni, Xueqi Watanabe, Kenji Taniguchi, Takashi Fan, Shanhui Mazur, Eric Yacoby, Amir Cao, Yuan School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States National Institute for Materials Science Namiki 1-1 Ibaraki Tsukuba305-0044 Japan Department of Applied Physics Ginzton Laboratory Stanford University StanfordCA94305 United States

We introduce the first on-chip, microelectromechanical system for the in situ tuning of twisted 2D materials, enabling tunable interfacial properties, synthetic topological singularities, and adjustable-polarization light sources for advanced quantum material manipulation in 2D-3D devices. © Optica Publishing Group 2024

关键词： Degrees of freedom (mechanics)

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An on-chip platform for multi-degree-of-freedom control of two-dimensional quantum and nonlinear materials

An on-chip platform for multi-degree-of-freedom control of t...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Degrees of freedom (mechanics)

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AI Based Prediction for Heart Disease: A Comparative Analysis and an Improved Machine Learning Approach 6

AI Based Prediction for Heart Disease: A Comparative Analysi...

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6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Raval, Jay Verma, Jai Prakash Islam, Sardar N.M. Jain, Rachna Thakur, Narina Institute of Technology Nirma University Computer Science Engineering Department Ahmedabad India Victoria University Applied Informatics Research Melbourne Australia Bhagwan Parshuram Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665453110

Heart disease problems are growing day by day in the world. Many factors are responsible for increasing the chance of heart attack and any other disease. Many countries have a low level of cardiovascular competence in predicting heart disease-related issues. Finding the best accurate machine learning classifiers for various diagnostic uses by data mining and machine learning techniques aids in predicting whether or not the heart disease-related issue will occur. To predict heart disease, a number of supervised machine-learning algorithms are used and their effectiveness are evaluated. With the exceptionof MLP and KNN, all applied algorithms had their estimated feature significance scores for each feature. This helps to find the main factors affecting heart disease and the accuracy of the model, which helps to get the best prediction. At the end of the research the support vector machine gives us 87.91 % highest testing accuracy compare with all applied machine learning algorithm. © 2022 IEEE.

关键词： Data mining

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Improving Performance and Explainability of Sensor-Based Human Activity Recognition 9

Improving Performance and Explainability of Sensor-Based Hum...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350318241

Significant progress has been made in developing systems that can automatically recognize human activities, largely thanks to deep learning models and sensor data. However, high performance and comprehensibility are often at odds in these systems. The aim of this research is to enhance both the accuracy and understandability of models that use sensors for human activity recognition (HAR). We present a new convolutional neural network design which takes input from accelerometers and gyroscopes across multiple data streams. This allows efficient capture of time and frequency characteristics. Additionally, we put forward an attention module that identifies informative patterns in the sensor signals to shed light on the model's predictions. Experiments were conducted on a benchmark dataset of common daily movements gathered from ubiquitous sensors. Our model achieves state-of-the-art accuracy exceeding prior deep learning techniques. The attention visuals provide transparency into which sensor segments are most relevant for accurate activity classification. This validates improved model interpretability. Our multi-channel architecture pushes sensor-based HAR forward through heightened predictive performance. We offer original perspectives to create reliable, precise systems for HAR. © 2024 IEEE.

关键词： Wearable sensors

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Active twistoptics: on-chip multidimensional dynamic control for data hypercube reconstruction

Active twistoptics: on-chip multidimensional dynamic control...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Tang, Haoning Lou, Beicheng Du, Fan Gao, Guangqi Zhang, Mingjie Ni, Xueqi Yacoby, Amir Cao, Yuan Fan, Shanhui Mazur, Eric School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States National Institute for Materials Science Namiki 1-1 Ibaraki Tsukuba305-0044 Japan Department of Applied Physics Ginzton Laboratory Stanford University StanfordCA94305 United States

Our MEMS-integrated twistoptics device enables precise control of interlayer gaps and twist angles in photonic crystals, achieving high-accuracy, multidimensional light manipulation with significant potential in recon... 详细信息

ISBN: (纸本)9781957171395

关键词： Nanophotonics

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Customer Churn Prediction in the Telecom Sector with Machine Learning and Adaptive k-Means Cluster using Imbalance Data 2

Customer Churn Prediction in the Telecom Sector with Machine...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Aravinda, N.L. Reddy, R. Archana Bala Dhandayuthapani, V. Nayak, Jithendra P. R. Husseen, Ahmad Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Engineering Sr University Telangana Warangal India University of Technology and Applied Sciences Shinas Branch Department of It College of Computing and Information Sciences Oman Srinivas Institute of Technology Department of Cse Valachil Mangalore India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

A crucial aspect of maintaining a customer-oriented business in the telecommunications sector with machine learning (ML) is understanding the reasons and factors that lead to customer churn. However, the dataset is difficult by noise, misclassifications, duplicated data, and imbalanced customer information complicating the process of identifying ways to split the dataset based on events. The under-sampling method address the imbalance data by decreasing the data quantity of majority class, thereby achieving a balanced dataset. This method, utilizing Adaptive k-means clustering, directly determines the reduction based on that data quality in each class. The datasets are combined with class labels to generate a new balanced dataset used for classification in the SVM algorithm, which distinguishes between customer churn and non-churn in the prediction for the telecom sector. The SVM algorithm can perform linear and non-linear classification with a kernel function to control overfitting improve generalization and handle high-dimensional data. The obtained results demonstrate that the proposed method achieves better accuracy of 95.70%, precision of 56.01%, Recall of 55.05%, and F1-score of 96.05% on the telecom churn dataset. These results ensure superior detection and classification performance compared to other existing methods, such as Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbour (KNN). © 2024 IEEE.

关键词： Decision trees

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