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Virtual Laboratory Learning Experience in engineering: An Extended technology Acceptance Model (TAM)

Virtual Laboratory Learning Experience in Engineering: An Ex...

Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Ahmed Jamah Ahmed Alnagrat Khamiss M. S. Ahmed Muhannad Ismail Alkhallas Osama Abulaqasim Ibrahim Almakhzoom Syed Zulkarnain Syed Idrus Rizalafande Che Ismail Department of Computer Science and Information Technology Higher Institute of Science and Technology Wadi al-Shati Wadi al-Shati Fezzan Libya Department of Computer Science Faculty of Information Technology Sebha University Sebha Libya Department of Software Engineering Faculty of Engineering Technology in Zuwarah Zuwarah Libya Department of Communication Faculty of Business & Communication (FPK) Universiti Malaysia Perlis(UniMAP) Kangar Perlis Malaysia Faculty of Electronic Engineering Technology (FTKEN) Universiti Malaysia Perlis (UniMAP) Arau Malaysia

This research presents an enhanced version of the technology Acceptance Model (TAM), which integrates usability and learning objectives to evaluate the effect of adopting virtual laboratories on student performance. Data from 97 participants, gathered via a survey, was used to examine the correlation between perceived usefulness (PU), perceived ease of use (PEOU), and intention to use (IU) concerning their experiences and outcomes with a simulation-based virtual laboratory. A custom questionnaire was designed to assess TAM components, namely PU, PEOU, and IU, specifically in the context of virtual laboratory learning environments. Findings indicate that the integration of specific laboratory learning objectives fosters the creation of a more credible and authentic simulation tool. This, in turn, optimizes the learning process and enhances students' learning outcomes. The refined TAM model provides valuable insights into the determinants of user acceptance of virtual laboratories and offers practical guidance for educators and instructional designers to construct more effective and efficient virtual laboratory environments in educational contexts.

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Information geometry of bosonic Gaussian thermal states

arXiv

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arXiv 2024年

作者： Huang, Zixin Wilde, Mark M. School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States

Bosonic Gaussian thermal states form a fundamental class of states in quantum information science. This paper explores the information geometry of these states, focusing on characterizing the distance between two nearby states and the geometry induced by a parameterization in terms of their mean vectors and Hamiltonian matrices. In particular, for the family of bosonic Gaussian thermal states, we derive expressions for their Fisher–Bures and Kubo–Mori information matrices with respect to their mean vectors and Hamiltonian matrices. An important application of our formulas consists of fundamental limits on how well one can estimate these parameters. We additionally establish formulas for the derivatives and the symmetric logarithmic derivatives of bosonic Gaussian thermal states. The former could have applications in gradient descent algorithms for quantum machine learning when using bosonic Gaussian thermal states as an ansatz, and the latter in formulating optimal strategies for single parameter estimation of bosonic Gaussian thermal states. Finally, the expressions for the aforementioned information matrices could have additional applications in natural gradient descent algorithms when using bosonic Gaussian thermal states as an ansatz. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

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Mobile Medical Assistant System for Laboratory Report Analysis and Medical Drug Identification

Mobile Medical Assistant System for Laboratory Report Analys...

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Advancements in Computing (ICAC), International Conference on

作者： Deshani Warnakulasuriya Tharushi Dewangi Navodya Sewwandi Minoli Rathnayake Nuwan Kodagoda Kushanra Suriyawansha Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

It is quite common for medical drugs and prescriptions to be misidentified by hospitals and after drugs are being dispensed to the patients. Misidentification of medical drugs is more common among elderly and visually impaired patients. In hospital organizations, the leading medical error is adverse drug events. Another most common issue patients face is keeping track of medical lab reports. Our proposed mobile medical assistant system uses image processing to identify drugs with or without packaging, identifying prescription and medical lab reports. Furthermore, the mobile application will identify the trends of medical lab reports and predict next month’s results of the medical lab report of the patient using machine learning.

关键词： Drugs Hospitals Image processing Organizations Machine learning Packaging Market research

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Rubber Buddy: A Mobile Application to Empower Rubber Planters of Sri Lanka

Rubber Buddy: A Mobile Application to Empower Rubber Planter...

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Advancements in Computing (ICAC), International Conference on

作者： Aparna Jayawardena Kasuni Ganegoda Sakuni Imbulana Gavin Gunapala Nuwan Kodagoda Thilini Jayasinghe Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

This research was conducted to develop a mobile application that provides expert solutions for the common problems faced by rubber planters in Sri Lanka. The application developed consists of four components, namely, identification of pests in immature rubber plantations and rubber nurseries; leaf disease identification; cover crop identification; and weed identification. Images taken using the mobile phone cameras are recognized using machine learning models developed using several convolutional neural network (CNN) architectures such as mobile net version 2 (MobileNet v2), VGG 16, VGG19, and residual networks (ResNet). After the images were recognized, the application will provide expert solutions and management strategies to the rubber planters. As most of the rubber plantations are located in areas with low network coverage, the application was designed to be operated in offline mode using TensorFlow lite technology.

关键词： Image recognition Crops Machine learning computer architecture Mobile handsets Mobile applications Rubber

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Semi-definite optimization of the measured relative entropies of quantum states and channels

arXiv

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arXiv 2024年

The measured relative entropies of quantum states and channels find operational significance in quantum information theory as achievable error rates in hypothesis testing tasks. They are of interest in the near term, as they correspond to hybrid quantum-classical strategies with technological requirements far less challenging to implement than required by the most general strategies allowed by quantum mechanics. In this paper, we prove that these measured relative entropies can be calculated efficiently by means of semi-definite programming, by making use of variational formulas for the measured relative entropies of states and semi-definite representations of the weighted geometric mean and the operator connection of the logarithm. Not only do the semi-definite programs output the optimal values of the measured relative entropies of states and channels, but they also provide numerical characterizations of optimal strategies for achieving them, which is of significant practical interest for designing hypothesis testing protocols. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimal systems

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Facial Data Minimization: Shallow Model as Your Privacy Filter

arXiv

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arXiv 2023年

作者： Pu, Yuwen Chen, Jiahao Pan, Jiayu Yan, Diqun Zhang, Xuhong Ji, Shouling College of Computer Science and Technology Zhejiang University Hangzhou Zhejiang310027 China College of Software Technology Zhejiang University NingBo Zhejiang315048 China Faculty of Electrical Engineering and Computer Science Ningbo University NingBo Zhejiang315211 China

Face recognition service has been used in many fields and brings much convenience to people. However, once a user's facial data is transmitted to a service provider, the user will lose control of his/her private data. In recent years, there are various security and privacy issues due to the leakage of facial data. Although many privacy-preserving methods have been proposed, they usually fail when they are not accessible to adversaries' strategies or the complete face recognition model. Hence, in this paper, by fully considering two cases of uploading facial images and facial features, which are very typical in practical face recognition service systems, we proposed a data privacy minimization transformation (PMT) method. This method can process the original facial data based on the shallow model of the authorized face recognition model to obtain the obfuscated data. The obfuscated data cannot only maintain satisfactory performance on authorized models and restrict the performance on other unauthorized models but also prevent original privacy data from leaking by AI methods and human visual theft. Additionally, since a service provider may execute preprocessing operations on the received data, we also propose an enhanced perturbation method to improve the robustness of PMT. Besides, to authorize one facial image to multiple service models simultaneously, a multiple-restriction mechanism is proposed to improve the scalability of PMT. Finally, we conduct extensive experiments and evaluate the effectiveness of the proposed PMT in defending against face reconstruction, data abuse, and face attribute estimation attacks. These experimental results demonstrate that PMT performs well in preventing facial data abuse and privacy leakage while maintaining high face recognition accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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Aerial Vehicle Detection and Classification Through Fusion of Multi-Domain Features of Acoustic Signals

Aerial Vehicle Detection and Classification Through Fusion o...

引用

engineering & Computing Technologies (ICECT), International Conference on

作者： Malaika Sumble Sumair Aziz Muhammad Umar Khan Khushbakht Iqtidar Raul Fernandez-Rojas Department of Electronics Engineering University of Engg. & Tech. Taxila Taxila Pakistan Faculty of Science and Technology University of Canberra Canberra ACT Australia Dept. of Computer & Software Engineering National University of Sciences & Tech. Islamabad Pakistan

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

Detection of aerial vehicles is a challenging task. Over time, the significance of developing such vehicles has increased rapidly. However, an utmost concern arises from the potential misuse of these small-sized vehicles in illicit activities such as unauthorized surveillance, smuggling contraband, or disrupting critical infrastructure significant security threats. Recognizing the gravity of this issue, we have opted to introduce an innovative approach to automate the detection processes for aerial vehicles. This initiative aims to deploy effective drone detection systems to mitigate risks and safeguard against illegal activities. We put use to two datasets from GitHub depot. Cepstral, spectral, and time domain features were extracted from the data, followed by classification. We conducted two experiments, with the first (Drone, No-Drone) yielding 98.6% accuracy, using the Ensemble (Bagged Trees) classifier. The second set of experimentation addressed five classes: Background Noises, Bebop Drone, Drone, Helicopter, and Mambo Drone. Ensemble (Bagged trees) again outperformed all other classifiers and achieved 98.3% accuracy. The results highlight that our proposed framework gives effective results based on audio signals of different aerial vehicles.

关键词： Machine learning algorithms Accuracy Cepstral analysis Surveillance Signal processing algorithms Feature extraction Time-domain analysis

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Efficient Byzantine-Robust Federated Learning Based on the Multi-Message Shuffle Protocol for Consumer Internet of Things

引用

IEEE Internet of Things Journal 2025年

作者： Jin, Chunhua Li, Lulu Wang, Jiahao Li, Jinbiao Liu, Junyi Chen, Guanhua Zhang, Hao Weng, Jian Faculty of Computer Software Engineering with Huaiyin Institute of Technology Huai’an China Huaiyin Institute of Technology Faculty of Computer Software Engineering Huai’an China Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China Jinan University College of Information Science and Technology China

The accelerated advancement of consumer Internet of Things (CIoT) devices has resulted in their pervasive integration into the fabric of contemporary lifestyles. As these devices are capable of collecting vast quantities of data pertaining to users, they present a significant risk to the privacy and security of individuals. Federated learning is an advanced machine learning paradigm that prioritises the preservation of user data privacy and enhances security by eliminating the need for centralised data collection. As a result, it is a promising approach to address user data privacy and security concerns. However, federated learning still faces challenges such as privacy attacks, Byzantine attacks and high communication overheads. To address these challenges, we propose an efficient Byzantine-robust federated learning method based on multi-message shuffle protocol. To achieve privacy protection and Byzantine robustness, we introduce an innovative aggregation strategy inspired by the multi-message shuffle protocol for differential privacy. To improve communication efficiency, we develop an adaptive gradient compression scheme. Theoretical analysis indicates that our multi-message shuffle protocol is capable of satisfying the requisite differential privacy conditions. Compared with the state-of-the-art methods, our proposed scheme demonstrates superior convergence capabilities on the real-world dataset when approximately 40% of participants are Byzantine, achieving accuracies of 96.78%, which illustrates its strong robustness. Upon reduction of the transmitted model parameters to 1% of the original size, the proposed scheme exhibits a maximum accuracy degradation of 0.35%. which significantly reduces the communication overhead while achieving high accuracy. In addition, the scheme also achieves an accuracy of 88.73% on the Non-iid MotionSense dataset, which is superior to other schemes. © 2014 IEEE.

关键词： Federated learning

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A Three-Step Procedural Paradigm for Domain-Specific Social Media Slang Analytics

A Three-Step Procedural Paradigm for Domain-Specific Social ...

引用

Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Aishwarya Sundaram Hema Subramaniam Siti Hafizah Ab Hamid Azmawaty Mohamad Nor Institute for Advanced Studies Universiti Malaya Kuala Lumpur Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Educational Psychology and Counselling Faculty of Education Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Social media is a crucial aspect of modern society, shaping global communication and information exchange. Social media slang encompasses informal language expressions, abbreviations, and unconventional words on these platforms, serving purposes such as concise communication and identity establishment. Data analytics from social media slang plays a vital role in providing real-time trends, enhancing sentiment analysis, and improving the accuracy of predictive models by capturing evolving linguistic trends. This study aims to address gaps in existing social media slang analytics by proposing a three-step process for extracting and validating domain-specific social media slang terms, specifically focusing on the domain of anxiety prediction in the context of mental health. The proposed approach involves the identification of relevant users, extraction of social media slang terms, and validation by subject matter experts. A pilot study is conducted in the domain of anxiety prediction, employing a self-prepared questionnaire and involving participants aged 13-14 from schools in Selangor, Malaysia. The pilot study identifies ten social media slang terms associated with anxiety, validated by subject matter experts who are licensed mental health practitioners. The slang terms exhibit varying prevalence levels among adolescents, with some terms absent from popular slang dictionaries like Urban Dictionary. The findings highlight the need for extracting slang terms from pertinent users and subject matter expert validation in incorporating social media slang into more appropriate predictive analytics.

关键词： Sentiment analysis Dictionaries Social networking (online) Soft sensors Anxiety disorders Mental health Predictive models

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The Necessity of Emotion Recognition from Speech Signals for Natural and Effective Human-Robot Interaction in Society 5.0

The Necessity of Emotion Recognition from Speech Signals for...

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Yeş im Ü lgen Sö nmez Asaf Varol Faculty of Technology Software Engineering F&#x0131 rat University Elaz&#x0131 &#x011F Turkey College of Engineering and Natural Sciences Computer Engineering Maltepe University &#x0130 stanbul Turkey

The history of humanity has reached Industry 4.0 that aims to the integration of information technologies and especially artificial intelligence with all life-sustaining mechanisms in the 21st century, and consecutively, the transformation of Society 5.0 has begun. Society 5.0 means a smart society in which humans share life with physical robots and software robots as well as smart devices based on augmented reality. Industry 4.0 contains main structures such as the internet of things, big data analytics, digital transformation, cyber-physical systems, artificial intelligence, and business processes optimization. It is impossible to consider the machines to be without emotions and emotional intelligence within the transformation of smart tools and artificial intelligence, in addition, while it is planned to give most of the commands with voice and speaking, it became more important to develop algorithms that can detect emotions. In the smart society, new and rapid methods are needed for speech recognition, emotion recognition, and speech emotion recognition areas to maximize human-computer (HCI) or human-robot interaction (HRI) and collaboration. In this study, speech recognition and speech emotion recognition studies in robot technology are investigated and developments are revealed.

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