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arXiv 2024年

作者： Trachu, Thanapat Piansaddhayanon, Chawan Chuangsuwanich, Ekapol Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand Center of Excellence in Computational Molecular Biology Chulalongkorn University Thailand

Diffusion-based speech enhancement has shown promising results, but can suffer from a slower inference time. Initializing the diffusion process with the enhanced audio generated by a regression-based model can be used to reduce the computational steps required. However, these approaches often necessitate a regression model, further increasing the system's complexity. We propose Thunder, a unified regression-diffusion model that utilizes the Brownian bridge process which can allow the model to act in both modes. The regression mode can be accessed by setting the diffusion time step closed to 1. However, the standard score-based diffusion modeling does not perform well in this setup due to gradient instability. To mitigate this problem, we modify the diffusion model to predict the clean speech instead of the score function, achieving competitive performance with a more compact model size and fewer reverse steps. © 2024, CC BY-NC-SA.

关键词： Diffusion

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Attribute-Aware Amplification of Facial Feature Sequences for Facial Emotion Recognition

Attribute-Aware Amplification of Facial Feature Sequences fo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tagon Sompong Chawan Piansaddhayanon Ekapol Chuangsuwanich Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand Center of Excellence in Computational Molecular Biology Chulalongkorn University Thailand

Many works have been proposed to automatically recognize facial human emotions, yet distinguishing subtle emotions still remains a challenge. However, just amplifying these facial movements as a whole does not accurately reflect the actual expressions as action intensity is different for each facial part. We propose an attributionaware amplification of facial descriptors that considers gender, facial components, and emotions to alleviate this issue. The amplifications are also continuously updated in an evolutionary manner during training using Population Based Augmentation (PBA). We found that applying different amplifications for each facial region/unit is crucial for the success of our method, outperforming the competing approaches on both the RAVDESS and THAI-SER datasets.

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Revisiting kernel attention with correlated Gaussian process representation 24

Revisiting kernel attention with correlated Gaussian process...

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Long Minh Bui Tho Tran Huu Duy Dinh Tan Minh Nguyen Trong Nghia Hoang FPT Software AI Center and School of Electrical Engineering and Computer Science Washington State University FPT Software AI Center and Department of Mathematics National University of Singapore FPT Software AI Center Department of Mathematics National University of Singapore School of Electrical Engineering and Computer Science Washington State University

Transformers have increasingly become the de facto method to model sequential data with state-of-the-art performance. Due to its widespread use, being able to estimate and calibrate its modeling uncertainty is important to understand and design robust transformer models. To achieve this, previous works have used Gaussian processes (GPs) to perform uncertainty calibration for the attention units of transformers and attained notable successes. However, such approaches have to confine the transformers to the space of symmetric attention to ensure the necessary symmetric requirement of their GP's kernel specification, which reduces the representation capacity of the model. To mitigate this restriction, we propose the Correlated Gaussian Process Transformer (CGPT), a new class of transformers whose self-attention units are modeled as cross-covariance between two correlated GPs (CGPs). This allows asymmetries in attention and can enhance the representation capacity of GP-based transformers. We also derive a sparse approximation for CGP to make it scale better. Our empirical studies show that both CGP-based and sparse CGP-based transformers achieve better performance than state-of-the-art GP-based transformers on a variety of benchmark tasks.

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Application of Hyperledger Blockchain Technology to Logistics Supply Chain with IoT 4

Application of Hyperledger Blockchain Technology to Logistic...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sayar, Ahmet Kurtbas, Muhammet Cuneyd Sancaktar, Caglayan Kabak, Rana Dudu Cakmak, Sukru Department of Computer Engineering Kocaeli University Kabaoglu Baki Komsuoglu Avenue No:515 Umuttepe Kocaeli İzmit41010 Turkey Atez Software Technologies R&D Center Skyland B Blok No: 4B İstanbul Turkey

Product movement tracking stands as a pivotal aspect of logistics operations. The ever-expanding application of IoT technologies holds significant potential to enhance product tracking in logistics. However, the rapid expansion of IoT usage has also introduced several challenges, particularly concerning data privacy, security, and transparency. Blockchain technology, through distributed ledger systems, offers a secure and transparent means to manage transactions within a network. In this study, the Hyperledger Fabric blockchain network, chosen for its private and permissioned nature, leverages authentication features to ensure the confidentiality, integrity, and security of data from IoT devices. By integrating the widely adopted MQTT protocol, IoT devices involved in logistics processes communicate effectively with the blockchain network. This study presents a system where IoT devices enable the secure and automated tracking of data through blockchain's registration and distribution mechanisms, all of which are demonstrated within a simulation environment. Developed specifically for the logistics sector, this system holds promising potential to address challenges across various fields. © 2025 The Authors. Published by Elsevier B.V.

关键词： Distributed ledger

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Preface:Security and safety of data in cloud computing

Security and Safety

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Security and Safety 2025年第1期 4-5页

作者： Dengguo Feng Jian Ren Yang Zhang Institute of Software Chinese Academy of Sciences Department of Electrical and Computer Engineering Michigan State University CISPA Helmholtz Center for Information Security

As a technology integrating distributed computing,network computing and virtualization,cloud computing has attracted much attention since its *** offering flexible and reliable resource services,cloud computing has been widely applied in various domains and has also enabled the advancement of transformative IT technologies such as big data and artificial ***,the extensive use of cloud computing also brings a large number of data security issues such as data theft and privacy *** data,especially sensitive data during computation and processing,are increasingly exposed to risks of security and privacy breaches,which pose great challenges to both industry and academia.

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SEMU5G: an Emulator for Improving Model Accuracy and Performance Evaluation of 5G Network Core Architectures based on SDN 10

SEMU5G: an Emulator for Improving Model Accuracy and Perform...

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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Zaki Hamidi, Eki Ahmad Lazuardi, Arga Valen Rahmansyah, Muhammad Litfan Novel Bajri, Salman Alfarizi Tulloh, Rohmat Negara, Ridha Muldina Ahdan, Syaiful Jupriyadi UIN Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia Telkom University Telecommunication Technology School of Applied Science Bandung Indonesia The University Center of Excellence Intelligent Sensing-IoT Telkom University Indonesia Telkom University School of Electrical Engineering Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia

ISBN: (纸本)9798350377057

The rapid advancement of 5G Radio Access Network (RAN) architecture is facilitating the construction of 5G networks, marking a significant milestone in telecommunications evolution. Given the complexity of the 5G core architecture, traditional simulation methods are insufficient, necessitating novel approaches. Emulator systems are crucial for creating dynamic, controlled environments that enable exploration of real-world scenarios without physical constraints. SEMU5G, a 5G core emulator, SDN controller, and RAN simulator, utilizes Open5GS implemented in Docker Container for test system flexibility and isolation. Additionally, an SDN controller is integrated to monitor data flows in the User Plane Function (UPF) and gNB simulated by UERANSIM in Mininet-WiFi. This comprehensive integration facilitates effective and flexible real-world exploration, providing a dynamic and controlled test environment for 5G core research. Scenario testing comprises two stages: firstly, a fixed network topology is employed to compare resource usage and confirm successful SEMU5G integration. Secondly, a mobile network topology is utilized to implement a mobile device scenario and compare the Quality of Service (QoS) of SEMU5G with other available emulators. These stages ensure thorough evaluation of SEMU5G's performance and its comparative advantage over existing solutions. © 2024 IEEE.

关键词： Wi-Fi

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A Review on the Advancement of Renewable Natural Fiber Hybrid Composites: Prospects, Challenges, and Industrial Applications

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Journal of Renewable Materials 2024年第7期12卷 1237-1290页

作者： Mohammed Mohammed Jawad K.Oleiwi Aeshah M.Mohammed Anwar Ja’afar Mohamad Jawad Azlin F.Osman Tijjani Adam Bashir O.Betar Subash C.B.Gopinath Center of Excellence Geopolymer&Green Technology(CEGeoGTech) Universiti Malaysia PerlisArau02600Malaysia Faculty of Chemical Engineering Technology Universiti Malaysia Perlis(UniMAP)Arau02600Malaysia Department of Materials Engineering University of TechnologyBaghdad10070Iraq Department of Chemical Materials University of Bagdad College of Education for Pure Science Ibn-AlhaithamBaghdad10001Iraq Department of Computer Techniques Engineering Al-Rafidain University CollegeBaghdad10001Iraq Faculty of Electronics Engineering Technology Universiti Malaysia PerlisKampus Uniciti Alam Sg.ChuchuhPerlis02100Malaysia Research Center(NANOCAT) University of MalayaKuala Lumpur50603Malaysia Institute of Nano Electronic Engineering Universiti Malaysia PerlisPerlis01000Malaysia Center for Global Health Research Saveetha Medical College&HospitalSaveetha Institute of Medical and Technical Sciences(SIMATS)Tamil Nadu602105India

Naturalﬁbre(NFR)reinforced functional polymer composites are quickly becoming an indispensable sustainable material in the transportation industry because of their lightweight,lower cost in manufacture,and adaptability to a wide variety of ***,the major difﬁculties of using theseﬁbres are their existing poor dimensional stability and the extreme *** assessing the mechanical properties(MP)of composites,the interfacial bonding(IB)happening between the NFR and the polymer matrix(PM)plays an incredibly signiﬁcant *** compared to NFR/syntheticﬁbre hybrid composites,hybrid composites(HC)made up of two separate NFR are less prevalent;yet,these hybrid composites also have the potential to be valuable materials in terms of environmental issues.A new dimension to theﬂexibility of composites reinforced with NFR is added by the cost-effective manufacture of hybrid composites utilising *** purpose of this study is to offer an over-view of the keyﬁndings that were presented on hybrid *** emphasis was focused on the factors that inﬂuence the performance of the naturalﬁber composites,diverse approaches to enhancing MP,physical,electri-cal,and thermal characteristics of the *** study in polymer science gains interest for applications in con-struction and automotive industries.

关键词： Renewable naturalﬁber hybrid composites sustainability nanomaterial greener material

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Machine learning-based models for predicting calibration cost of flowmeters in the oil industry

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Neural Computing and Applications 2024年第4期37卷 2537-2556页

作者： Mohammed, Awsan Javaid, Abdullah Ghaithan, Ahmed AlDhafer, Osamah Al-shaibani, Maged S. Alshibani, Adel Architectural Engineering and Construction Management Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Interdisciplinary Research Center for Smart Mobility and Logistics King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Software Engineering University of Prince Mugrin Madinah40202 Saudi Arabia Department of Computer Science King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Interdisciplinary Research Center of Construction and Building Materials King Fahd University of Petroleum and Minerals Dhahran34463 Saudi Arabia

Accurate measurement of liquid volume in pipelines is a crucial and sensitive process in the petroleum industry. Flowmeter is a common device used to measure the volume of the liquid passing through the pipeline and to invoice clients. Improving flowmeter calibration preserves functionality, identifies issues, and ensures accurate market valuation and pricing. Estimating the cost of third-party calibration is a challenging task for decision-makers in the petroleum industry. Thus, this paper proposes machine-learning algorithms to predict and analyze the cost of flowmeter calibration in the petroleum industry. These techniques include artificial neural networks, SGDRegressor, XGBoost, AdaBoost, support vector machine, K-nearest neighbors, and random forest. Prior to developing the model, the factors impacting the cost calibration of the flowmeters are identified from the literature and finalized by oil and gas experts. In addition, the relationship between the input factors and the output is examined to ensure the quality of the data. The analysis indicated that the most crucial factors affecting the calibration cost are the flowmeter size, flange class, flowmeter type, calibration method, and calibration factor. Furthermore, the developed machine learning-based models are validated by using 153 new additional data. The results revealed that the random forest is the best technique for estimating calibration costs, with an accuracy of 99%, followed by AdaBoost with an accuracy of 97%, and artificial neural network with an accuracy of 96%. The proposed models provide superior accuracy and efficiency which will significantly contribute to estimating calibration costs, assisting decision-makers in establishing a budget for flowmeter calibration. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Budget control

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A systematic literature review to implement large language model in higher education: issues and solutions

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Discover Education 2025年第1期4卷 1-25页

作者： Guizani, Sghaier Mazhar, Tehseen Shahzad, Tariq Ahmad, Wasim Bibi, Afsha Hamam, Habib College of Engineering Alfaisal University Riyadh 11533 Saudi Arabia School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Department of Computing School of Arts and Creative Technology University of Greater Manchester Manchester BL15AB United Kingdom Department of Computer and Software Technology University of Swat Swat 19200 Pakistan Faculty of Engineering Uni de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Artificial intelligence-driven Chatbots, especially large language models (LLMs) like GPT-4, represent significant progress in digital education. These models excel in mimicking human-like text and transforming learning and teaching methods. This study examines the development, application, and impact of LLMs in education. It highlights their role in automating instructional tasks and promoting personalized learning experiences. Despite integration concerns and ethical debates, LLMs showcase the potential of AI to improve educational practices. Our research concludes that LLMs offer transformative opportunities for education. However, their incorporation requires careful ethical considerations, data privacy measures, and a balance between human educators and AI technologies. The findings suggest strategies for integrating LLMs into educational frameworks to enhance learning outcomes while preserving educational integrity. © The Author(s) 2025.

关键词： Artificial intelligence Education Large language models Learning systems Natural language processing systems Neural networks

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Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

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computer Systems Science & engineering 2023年第10期47卷 873-889页

作者： Mohamed Yacin Sikkandar K.Hemalatha M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Biomedical Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its *** researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their *** of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)*** the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical ***,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest *** the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly *** study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and *** major intention of the IFFO-DLEF model is to investigate the presence or absence of *** do so,the presented IFFODLEF model applies image pre-processing at the initial *** addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature ***,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify *** parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification *** experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.

关键词： COVID-19 computer vision deep learning image classification fusion model

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