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arXiv 2022年

作者： Rajput, Om Nigam, Suyash Chowdhury, M.J. Jayavel, Kayalvizhi Department of Computer Science and Engineering SRM Institute of Science and Technology India Department of Computer Science and Engineering La Trobe University Australia

Currently, the IoT ecosystem is comprised of fully connected smart devices that exchange data to provide more automated, precise, and fast decisions. This idealised situation can only be accomplished if a system for data transactions is processed efficiently and security is ensured with high scalability and practicability. The integrity of data must be maintained during the exchange or transfer of data between entities. We propose to make a application called DatChain that responds to the above situation. The application stores data sensed by the Iot sensors in the backend after encrypting it and when the data is required for any purpose it can be exchanged using a suitable blockchain network that can keepup with the transfer rate even at high traffic in a secure environment. © 2022, CC BY.

关键词： Data transfer

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AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms

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computer Modeling in engineering & sciences 2024年第9期140卷 2315-2347页

作者： Lirong Yin Lei Wang Siyu Lu Ruiyang Wang Haitao Ren Ahmed AlSanad Salman A.AlQahtani Zhengtong Yin Xiaolu Li Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia College of Resource and Environment Engineering Guizhou UniversityGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China

At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalization *** this situation,this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block(AFB)for feature *** module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating *** with dynamic convolution with scale information,the network can extract more differentiated feature *** introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature *** convolution and pixel-based gating mechanisms enhance the module’s ***,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB *** results revealed that the network combined with the AFB module has stronger generalization ability and expression ability.

关键词： Super-resolution feature extraction dynamic convolution attention mechanism gate control

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Distances Between Immersed Graphs: Metric Properties

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Matematica 2023年第1期2卷 197-222页

作者： Buchin, Maike Chambers, Erin Fang, Pan Fasy, Brittany Terese Gasparovic, Ellen Munch, Elizabeth Wenk, Carola Department of Computer Science Ruhr University Bochum Bochum 44780 Germany Department of Computer Science Saint Louis University Saint Louis MO United States Department of Computer Science Tulane University New Orleans LA United States School of Computing & amp Department of Mathematical Sciences Montana State University Bozeman MT United States Department of Mathematics Union College Schenectady NY United States Department of Computational Mathematics Science and Engineering Michigan State University East Lansing MI United States Department of Mathematics Michigan State University East Lansing MI United States

Graphs in metric spaces appear in a wide range of data sets, and there is a large body of work focused on comparing, matching, or analyzing collections of graphs in different ambient spaces. In this survey, we provide an overview of a diverse collection of distance measures that can be defined on the set of finite graphs immersed (and in some cases, embedded) in a metric space. For each of the distance measures, we recall their definitions and investigate which of the properties of a metric they satisfy. Furthermore we compare the distance measures based on these properties and discuss their computational complexity. © The Author(s) 2022.

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In-situ fabrication of on-chip 1T'-MoTe_(2)/Ge Schottky junction photodetector for self-powered broadband infrared imaging and position sensing

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Nano Research 2024年第6期17卷 5587-5594页

作者： Menglei Zhu Kunxuan Liu Di Wu Yunrui Jiang Xue Li Pei Lin Zhifeng Shi Xinjian Li Ran Ding Yalun Tang Xuechao Yu Longhui Zeng School of Physics and Microelectronics and Key Laboratory of Material PhysicsZhengzhou UniversityZhengzhou 450052China Department of Electrical and Computer Engineering University of California San DiegoLa JollaCA 92093USA State Key Laboratory of Integrated Optoelectronics College of Electronic Science and EngineeringJilin UniversityChangchun 130012China Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China

High-sensitivity room-temperature multi-dimensional infrared(IR)detection is crucial for military and civilian ***,the gapless electronic structures and unique optoelectrical properties have made the two-dimensional(2D)topological semimetals promising candidates for the realization of multifunctional optoelectronic ***,we demonstrated the in-situ construction of high-performance 1T’-MoTe_(2)/Ge Schottky junction device by inserting an ultrathin AlOx passivation *** good detection performance with an ultra-broadband detection wavelength range of up to 10.6 micron,an ultrafast response time of~160 ns,and a large specific detectivity of over 109 Jones in mid-infrared(MIR)range surpasses that of most 2D materials-based IR sensors,approaching the performance of commercial IR *** on-chip integrated device arrays with 64 functional detectors feature high-resolution imaging capability at room *** these outstanding detection features have enabled the demonstration of position-sensitive detection *** demonstrates an exceptional position sensitivity of 14.9 mV/mm,an outstanding nonlinearity of 6.44%,and commendable trajectory tracking and optoelectronic demodulation *** study not only offers a promising route towards room-temperature MIR optoelectronic applications,but also demonstrates a great potential for application in optical sensing systems.

关键词： MoTe_(2) broadband photodetection Schottky junction imaging position sensitive detector

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Evaluation of Game Design Framework Using a Gamified Browser-Based Application

arXiv

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arXiv 2023年

作者： Alhazmi, Abdulrahman Hassan Arachchilage, Nalin Asanka Gamagedara The Department of Computer Science Italy La Trobe University Australia Jazan University Saudi Arabia The School of Computer Science The University of Auckland New Zealand

Privacy Policy under GDPR law helps users understand how software developers handle their personal data. GDPR privacy education must be considered a vital aspect of combating privacy threats. In this paper, we present the design and development of a gamified browser-based application aimed at motivating software developers to enhance their secure coding behavior. To evaluate the proposed game design framework through the developed framework, a think-aloud study was carried out along with pre-and post-test. There was an improvement in the software developers' secure coding behaviors through playing the game which had GDPR privacy laws incorporated to enhance their knowledge of privacy. © 2023, CC BY.

关键词： Application programs

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Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling 41

Multi-Fidelity Residual Neural Processes for Scalable Surrog...

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41st International Conference on Machine Learning, ICML 2024

作者： Niu, Ruijia Wu, Dongxia Kim, Kai Ma, Yi-An Watson-Parris, Duncan Yu, Rose Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Halıcıoglu Data Science Institute University of California La Jolla San DiegoCA United States Scripps Institution of Oceanography University of California La Jolla San DiegoCA United States

Multi-fidelity surrogate modeling aims to learn an accurate surrogate at the highest fidelity level by combining data from multiple sources. Traditional methods relying on Gaussian processes can hardly scale to high-dimensional data. Deep learning approaches utilize neural network based encoders and decoders to improve scalability. These approaches share encoded representations across fidelities without including corresponding decoder parameters. This hinders inference performance, especially in out-of-distribution scenarios when the highest fidelity data has limited domain coverage. To address these limitations, we propose Multi-fidelity Residual Neural Processes (MFRNP), a novel multi-fidelity surrogate modeling framework. MFRNP explicitly models the residual between the aggregated output from lower fidelities and ground truth at the highest fidelity. The aggregation introduces decoders into the information sharing step and optimizes lower fidelity decoders to accurately capture both in-fidelity and cross-fidelity information. We show that MFRNP significantly outperforms state-of-the-art in learning partial differential equations and a real-world climate modeling task. Our code is published at: ***/Rose-STL-lab/MFRNP. Copyright 2024 by the author(s)

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Music Emotion Recognition using Convolutional Neural Networks for Regional languages 1

Music Emotion Recognition using Convolutional Neural Network...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Shashidhar, R. Balivada, Dhanush Shalini, D.N. Hirebasur Krishnappa, Krutthika Roopa, M. Jss Science and Technology University Department of Electronics and Communication Engineering Mysuru India Sothern University and A&m College Baton Department of Computer Science Baton RougeLA United States Dayananda Sagar College of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350316469

Music emotion recognition is a challenging task that has been getting increasing kindness in the field of artificial intelligence. We propose a unique lD Convolutional Neural Network (lD CNN) method for music emotion recognition. Our proposed approach combines the advantages of convolutional neural networks with the lD temporal structure of music signals. Evaluate our model on two publicly available datasets and demonstrate its effectiveness in accurately recognizing different emotions from music signals. Our method presents a novel approach to abstracting raw audio signals into meaningful representations. It utilizes multiple lD convolutional and pooling layers to extract features from the raw audio signals, which can then be used for further analysis. We demonstrate the effectiveness of our approach by providing examples of applications that can benefit from this abstraction process, such as sound recognition and speech recognition. Additionally, we discuss the potential of this method to be applied in other areas such as natural language processing and music generation. We compare the performance of our model with traditional machine learning (ML) methods and demonstrate that our lD CNN outperforms these traditional ML methods in terms of accuracy. We also discuss potential use cases for this technology and its implications for future research in audio signal processing. We show that our model achieves superior results in terms of accuracy and computational efficiency, making it a promising solution for music emotion recognition tasks. Our results suggest that the lD CNN approach is an effective tool for recognizing musical emotions with high accuracy. © 2023 IEEE.

关键词： lD Convolutional Neural Networks Mel Frequency Cepstral Coefficients Music Information Retrieval Relu Weighted L2 Regularization

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Reactive Synthesis for Expected Impacts

arXiv

引用

arXiv 2024年

作者： Chini, Emanuele Sala, Pietro Simonetti, Andrea Zare, Omid Department of Computer Control and Management Engineering University La Sapienza Rome Italy Department of Computer Science University of Verona Verona Italy

As business processes become increasingly complex, effectively modeling decision points, their likelihood, and resource consumption is crucial for optimizing operations. To address this challenge, this paper introduces a formal extension of the Business Process Model and Notation (BPMN) that incorporates choices, probabilities, and impacts, referred to as BPMN+CPI. This extension is motivated by the growing emphasis on precise control within business process management, where carefully selecting decision pathways in repeated instances is crucial for conforming to certain standards of multiple resource consumption and environmental impacts. In this context we deal with the problem of synthesizing a strategy (if any) that guarantees that the expected impacts on repeated execution of the input process are below a given threshold. We show that this problem belongs to PSPACE complexity class;moreover we provide an effective procedure for computing a strategy (if present). © 2024, CC BY.

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A review of federated learning in renewable energy applications:Potential,challenges,and future directions

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Energy and AI 2024年第3期17卷 444-457页

作者： Albin Grataloup Stefan Jonas Angela Meyer Bern University of Applied Sciences School of Engineering and Computer ScienceQuellgasse 21Biel2501Switzerland Universitàdella Svizzera italiana Faculty of InformaticsVia la Santa 1Lugano-Viganello6962Switzerland Delft University of Technology Department of Geoscience and Remote SensingStevinweg 1Delft2628The Netherlands

Federated learning has recently emerged as a privacy-preserving distributed machine learning *** learning enables collaborative training of multiple clients and entire fleets without sharing the involved training *** preserving data privacy,federated learning has the potential to overcome the lack of data sharing in the renewable energy sector which is inhibiting innovation,research and *** paper provides an overview of federated learning in renewable energy *** discuss federated learning algorithms and survey their applications and case studies in renewable energy generation and *** also evaluate the potential and the challenges associated with federated learning applied in power and energy ***,we outline promising future research directions in federated learning for applications in renewable energy.

关键词： Federated learning Renewable energy Dis tributed learning Data heterogeneity Privacy Data sharing Wind energy Solar energy Buildings Review

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Ics-Ltu2022: A Dataset for Ics Vulnerabilities

SSRN

引用

SSRN 2023年

作者： Alanazi, Manar AHMED T. Mahmood, Abdun Chowdhury, Mohammad Jabed Morshed Department of Computer Science and Information Technology School of Engineering and Mathematical Science La Trobe University Bundoora MelbourneVIC3086 Australia

HighlightsICS-LTU2022: A dataset for ICS vulnerabilitiesManar Alanazi, Abdun Mahmood, Mohammad Jabed Morshed Chowdhury• The research is based on collecting vulnerability data from public sources, primarily the National Vulnerability Database and Common Vulnerabilities and Exposures.• The dataset compiled from these sources is comprehensive, encompassing a wide range of vulnerabilities in the SCADA and ICS domains. It includes details such as vulnerability descriptions, severity assessments, and affected products.• The primary objective of this research is to identify and analyse patterns and trends within ICS vulnerabilities to shed light on the evolving nature of security threats in this critical sector.• The findings from this analysis offer valuable insights into the state of cybersecurity in SCADA and ICS. Researchers and practitioners can use these insights to better understand the risks and challenges in this field.• The main findings, considerations for professionals, data quality evaluations, and threats to validity are discussed. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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