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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Miny, Torben Heppner, Sebastian Garmaev, Igor Ristin, Marko Otto, Björn Braunisch, Nico Jacoby, Michael Kleinert, Tobias Van De Venn, Hans Wernher Wollschlaeger, Martin Information and Automation Systems Rwth Aachen University Aachen Germany Zurich Switzerland Institute for Automation and Communication Otto von Guericke University Magdeburg Germany Institute of Applied Computer Science Tu Dresden Dresden Germany Karlsruhe Germany

ISBN: (纸本)9798350361230

As Industrie 4.0 (I4.0) technologies continue to advance, ensuring the reliability and robustness of emerging standards like Asset Administration Shells (AAS) becomes paramount. This paper introduces a comprehensive testing approach for reactive (a.k.a. Type 2) AAS implementations, addressing the challenges posed by their intricate and dynamic nature. Our methodology adopts a design-by-contract approach to formalize API behavior. We design the client as a test oracle, where code contracts extensively validate the AAS operations, their interactions and effects. The related test cases cover various operation chains, including superpaths, service specifications, and serialization modifiers. The objective of this approach is to enhance the reliability and effectiveness of testing in the evolving landscape of AAS Type 2 implementations. © 2024 IEEE.

关键词： Software testing

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A Sentiment Analysis Study of Twitter Users' Reactions to the COVID-19 Vaccine 14

A Sentiment Analysis Study of Twitter Users' Reactions to th...

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14th International Conference on information and Communication systems, ICICS 2023

作者： Alkhushayni, Suboh Alomari, Zakaria Al-Zaleq, Du'a Yarmouk University Faculty of Information Technology and Computer Science Dept. Information Systems Irbid Jordan New York Institute of Technology Dept. Computer Science VancouverBC Canada Al-Ahliyya Amman University Dept. Robotics and Artificial Intelligence Engineering Amman Jordan

ISBN: (纸本)9798350307870

This study investigates public attitudes towards the COVID-19 vaccine through Twitter data analysis. Using the Twitter API, tweets were collected, preprocessed, and labeled. Features were extracted using the Bag of Words representation, and sentiment analysis was conducted using Text Blob and Vader. Machine learning and deep learning models were trained and tested, revealing that deep learning models achieved the highest accuracy and F1 score. The research underscores the efficacy of machine learning and deep learning in analyzing COVID-19 vaccine-related tweets, shedding light on factors influencing vaccine resistance. These insights are crucial for pharmaceutical companies and public health officials, enabling them to address barriers to vaccine acceptance and enhance the societal benefits of widespread vaccination, contributing to the pandemic's resolution. © 2023 IEEE.

关键词： COVID-19

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Privacy framework for context-aware robot development

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Paladyn 2021年第1期12卷 468-480页

作者： Heuer, Tanja Schiering, Ina Gerndt, Reinhard Department of Computer Science Ostfalia University of Applied Sciences Institute of Information Engineering Wolfenbüttel Germany Department of Computer Science Ostfalia University of Applied Sciences Institute of Distributed Systems Wolfenbüttel Germany

Privacy is an essential topic in (social) robotics and becomes even more important when considering interactive and autonomous robots within the domestic environment. Robots will collect a lot of personal and sensitive information about the users and their environment. Thereby, privacy does consider the topic of (cyber-)security and the protection of information against misuse by involved service providers. So far, the main focus relies on theoretical concepts to propose privacy principles for robots. This article provides a privacy framework as a feasible approach to consider security and privacy issues as a basis. Thereby, the proposed privacy framework is put in the context of a user-centered design approach to highlight the correlation between the design process steps and the steps of the privacy framework. Furthermore, this article introduces feasible privacy methodologies for privacy-enhancing development to simplify the risk assessment and meet the privacy principles. Even though user participation plays an essential role in robot development, this is not the focus of this article. Even though user participation plays an essential role in robot development, this is not the focus of this article. The employed privacy methodologies are showcased in a use case of a robot as an interaction partner contrasting two different use case scenarios to encourage the importance of context awareness. © 2021 Tanja Heuer et al., published by De Gruyter.

关键词： Machine design

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LieVis: A Visual Interactive Dashboard for Lie Detection Using Machine Learning and Deep Learning Techniques 26

LieVis: A Visual Interactive Dashboard for Lie Detection Usi...

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26th International Conference on computer and information Technology, ICCIT 2023

作者： Prome, Shanjita Akter Rafiqul Islam, Md Asirvatham, David Hossain Sakib, Md. Kowsar Ari Ragavan, Neethiahnanthan Australian Institute of Higher Education Business Information Systems Australia Taylors University School of Computer Science Malaysia Taylors University Faculty of Social Sciences & Leisure Management Malaysia

ISBN: (纸本)9798350359015

Lying poses a significant concern in our everyday lives, impacting social interactions. The skill of detecting lies or deceitful statements is of immense value, mainly due to the elusive nature of the underlying patterns associated with lying. Numerous studies have extensively explored lie detection, employing various advanced techniques. Researchers have devoted their attention to devising more effective and efficient solutions for lie detection. However, there remain ongoing challenges, particularly in achieving higher accuracy levels. There has been relatively limited focus on developing visually interpretable and interactive systems for lie detection. Therefore, we leveraged artificial intelligence (AI), incorporating machine learning and deep learning techniques to augment the existing system. Our experiment shows that deep learning models, specifically BERT, achieve the highest accuracy, whereas machine learning models such as Random Forest (RF), and K-Nearest Neighbors (KNN) exhibit superior accuracy across both sets of features. To enhance result exploration, we introduced a dedicated visual interactive system/dashboard called LieVis, meticulously designed to facilitate comprehensive exploration of the obtained results. The evaluation of LieVis showcases its superiority over existing systems. The source code and the live dashboard with result visualization are available on our GitHub repository https://***/KHSakib/LieVis/tree/master. © 2023 IEEE.

关键词： Deep Learning Lie Detection Machine Learning Visual Dashboard

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Artificial Intelligence in Health Care: Various Applications

Artificial Intelligence in Health Care: Various Applications

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Proceedings of the International Convention MIPRO

作者： Nikola Protrka Blerton Abazi University of Applied Sciences in Criminal Investigation and Public Security Zagreb Croatia Faculty of Information Systems and Computer Science University for Business and Technology Prishtina Kosovo

ISBN: (数字)9798350382501

ISBN: (纸本)9798350382518

Artificial intelligence (AI) has become a rapidly growing field with the potential to revolutionize many industries, including healthcare. Integrating AI into healthcare has the potential to transform patient care and disease management, leading to improved outcomes and reduced costs. Our research explores the various applications of AI in healthcare, including predictive analytics, medical image analysis, drug discovery, and clinical decision making, caregiver or virtual assistant via AI chatbot. It also explores the challenges and limitations associated with the use of AI in healthcare, such as ethical concerns, data privacy issues, and algorithmic bias. Finally, this research paper concludes by highlighting the potential of AI to improve patient outcomes, increase efficiency in healthcare delivery and ultimately transform the future of healthcare.

关键词： Ethics Accuracy Medical services Transforms Radiology Prediction algorithms Security

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LowResIR-Net: Lightweight Residual Network for ADL Recognition Using Low-Resolution Infrared Sensor Data 16

LowResIR-Net: Lightweight Residual Network for ADL Recogniti...

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16th International Conference on ENTERprise information systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care information systems and Technologies, HCist 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand

Detecting activities of daily living (ADL) is crucial for supported living and medical monitoring. Traditional approaches rely on high-resolution sensors and computationally intensive algorithms, limiting their scalability and practical use in real-world settings. This research introduces LowResIR-Net, a compact deep residual network architecture specifically tailored for recognizing ADL utilizing low-resolution infrared sensor data. LowResIR-Net efficiently extracts key features from low-dimensional data by leveraging the advantages of infrared sensing, such as privacy protection and light resilience. Its architecture combines residual connections with depth-wise convolutions, enhancing feature learning while maintaining a compact model size. The model’s performance is evaluated using the Coventry-2018 dataset, containing low-resolution infrared data from three residential sensor locations. Our tests have shown that in all three cases, LowResIR-Net consistently achieves superior performance compared to other cutting-edge deep learning models, such as CNN, LSTM, BiLSTM, GRU, and BiGRU. LowResIR-Net attains the most excellent accuracies of 90.18%, 93.75%, and 93.30%, and F1-scores of 81.81%, 89.55%, and 88.92% for scenarios I, II, and III, respectively. Furthermore, it achieves these results with fewer parameters than most other models. LowResIR-Net’s strong performance highlights its ability to accurately capture complex patterns in low-resolution infrared data for ADL detection. This study demonstrates the potential of lightweight deep learning and low-resolution infrared sensors in developing privacy-focused and computationally efficient wellness monitoring systems. © 2025 The Author(s).

关键词： Infrared detectors

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PrAViC: Probabilistic Adaptation Framework for Real-Time Video Classification

arXiv

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arXiv 2024年

作者： Trędowicz, Magdalena Struski, Lukasz Mazur, Marcin Janusz, Szymon Lewicki, Arkadiusz Tabor, Jacek Faculty of Mathematics and Computer Science Jagiellonian University Kraków Poland Faculty of Applied Computer Science University of Information Technology and Management Rzeszów Poland

Video processing is generally divided into two main categories: processing of the entire video, which typically yields optimal classification outcomes, and real-time processing, where the objective is to make a decision as promptly as possible. The latter is often driven by the need to identify rapidly potential critical or dangerous situations. These could include machine failure, traffic accidents, heart problems, or dangerous behavior. Although the models dedicated to the processing of entire videos are typically well-defined and clearly presented in the literature, this is not the case for online processing, where a plethora of hand-devised methods exist. To address this, we present PrAViC, a novel, unified, and theoretically-based adaptation framework for dealing with the online classification problem for video data. The initial phase of our study is to establish a robust mathematical foundation for the theory of classification of sequential data, with the potential to make a decision at an early stage. This allows us to construct a natural function that encourages the model to return an outcome much faster. The subsequent phase is to demonstrate a straightforward and readily implementable method for adapting offline models to online and recurrent operations. Finally, by comparing the proposed approach to the non-online state-of-the-art baseline, it is demonstrated that the use of PrAViC encourages the network to make earlier classification decisions without compromising accuracy. © 2024, CC BY.

关键词： Classification (of information)

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An Attention Mechanism Based AVOD Network for 3D Vehicle Detection

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-13页

作者： Yuan, Di Zhang, Haiping Shu, Xiu Liu, Qiao Chang, Xiaojun He, Zhenyu Shi, Guangming Guangzhou Institute of Technology Xidian University Xian China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China National Center for Applied Mathematics Chongqing Normal University Chongqing China Faculty of Engineering and Information Technology University of Technology Sydney Broadway New South Wales Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

With the continuous advancement of autonomous driving technology, 3D vehicle detection has become of widespread interest. The traditional aggregate view object detection (AVOD) framework has achieved some good results in 3D vehicle detection tasks. However, the complexity of the 3D vehicle detection scenario makes the current detection methods still not meet the actual requirements. To enhance the detection accuracy of 3D vehicle targets, we propose to equip an attention mechanism to improve the representation capability of feature maps, thereby further increasing the precision of 3D vehicle detection. Specifically, we have added the channel attention ECANet, spatial attention SANet, and mixed attention ECANet+SANet respectively into the image-based feature pyramid network of the AVOD detection framework, which can enhance the feature maps representation and improve the detection accuracy observably. The improved AVOD network is verified using the KITTI dataset. By showing the detection results of these attention mechanisms, it is found that the feature pyramid networks (FPN) module in the AVOD network based on Image has the best performance when integrating a mixed attention mechanism. In comparison to the original AVOD network, the detection results on the average precision index of the proposed method have improved by 2.29%, 2.81%, and 1.32% in the three indexes of simple, medium, and difficult, respectively. Extensive experiments have confirmed the practicality and efficacy of the AVOD network to equip the attention mechanisms for 3D vehicle detection. IEEE

关键词： Semantics

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An Enhanced Secure Authentication Scheme for the Internet of Things

An Enhanced Secure Authentication Scheme for the Internet of...

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2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology, ICSEIET 2023

作者： Neetu Srivastava, Animesh Barik, Lal Bihari Dhondiyal, Shiv Ashish College of Smart Computing Coer University Roorkee India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350329186

The Internet of Things (IoT) has led to the proliferation of interconnected devices, including smart appliances and industrial sensors. Nevertheless, the rapid expansion of the IoT ecosystem has given rise to apprehensions regarding security, as compromised devices have the potential to result in data breaches and cyber assaults. This paper presents an enhanced secure authentication scheme for the Io'T, which aims to tackle the issues above by building upon existing authentication methodologies. The multi-step system commences with an initial setup phase wherein each loT device is assigned a unique device ID and a confidential pre-shared key. A secure key exchange protocol generates a session key for each authenticated device. The authentication server generates a random attempt to join the network or execute specific tasks. The machine then signs the data with its private key to authenticate the challenge. The server verifies the answer by comparing it with the public key signature of the device. The proposed methodology enables devices to update their session keys periodically to safeguard against potential attacks. © 2023 IEEE.

关键词： Authentication

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Prediction of COVID-19 Confirmed Cases Using Gradient Boosting Regression Method

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computers, Materials & Continua 2021年第1期66卷 315-329页

作者： Abdu Gumaei Mabrook Al-Rakhami Mohamad Mahmoud Al Rahhal Fahad Raddah H.Albogamy Eslam Al Maghayreh Hussain AlSalman College of Computer and Information Sciences King Saud UniversityRiyadh11362Saudi Arabia Computer Science Department Faculty of Applied ScienceTaiz UniversityTaizYemen College of Applied Computer Sciences King Saud UniversityRiyadh11362Saudi Arabia

The fast spread of coronavirus disease(COVID-19)caused by SARSCoV-2 has become a pandemic and a serious threat to the *** of May 30,2020,this disease had infected more than 6 million people globally,with hundreds of thousands of ***,there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare *** study uses gradient boosting regression(GBR)to build a trained model to predict the daily total confirmed cases of *** GBR method can minimize the loss function of the training process and create a single strong learner from weak *** are conducted on a dataset of daily confirmed COVID-19 cases from January 22,2020,to May 30,*** results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR *** results reveal that the GBR model achieves 0.00686 root mean square error,the lowest among several comparative models.

关键词： COVID-19 coronavirus disease SARS-CoV-2 machine learning gradient boosting regression(GBR)method

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