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Effect of design-based learning on elementary students computational thinking skills in visual programming maker course

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Interactive Learning Environments 2023年第6期31卷 3633-3646页

作者： Matere, Isaac M. Weng, Cathy Astatke, Melese Hsia, Chih-Hsien Fan, Chen-Guo Department of digital learning and Education National Taiwan university of Science and Technology Taipei Taiwan Department of Computer Science and Information Engineering National Ilan University Yilan City Taiwan

Recently there has been a change in elementary curricula to focus on computational thinking, however, a good curriculum needs to be matched with good learning or teaching strategies. This study explores design-based learning (DBL) in elementary students to examine the development of computational thinking and to support knowledge construction in designing final products. An experimental design was used with 64 students;divided into experimental and control groups. The experimental group used a DBL approach with worksheets aimed at students designing their own final product design, while the control group was taught by the traditional approach with worksheets for redoing, practicing and designing their final product. Pre and posttests were used for data collection and evaluation scheme developed to score the final product. The finding showed no significant difference in the development of computational thinking between the two groups. However, there was a significant difference in designing the final product between the two groups. Therefore, DBL supported elementary students in designing the final product although redesigning curricula by integrating both teaching methods is suggested for the greater achievement of computational thinking. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： computational-thinking design-based learning Elementary students final product teaching methods

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PrivNUD: Effective Range Query Processing under Local Differential Privacy 39

PrivNUD: Effective Range Query Processing under Local Differ...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Wang, Ning Wang, Yaohua Wang, Zhigang Nie, Jie Wei, Zhiqiang Tang, Peng Gu, Yu Yu, Ge Ocean University of China School of Computer Science and Engineering China Ministry of Education Shandong University Key Lab of Cryptologic Technology and Information Security China Northeastern University School of Computer Science and Engineering China

ISBN: (纸本)9798350322279

Local differential privacy (LDP) has been established as a strong privacy standard for collecting sensitive information from users. Although it has attracted much research attention in recent years, the majority of existing works focus on applying LDP to frequency distribution estimation for each individual value in a discrete domain. This paper concerns the important range queries involving multiple discrete values. Till now, only a few works target this problem. They all rely on the B-ary tree to construct a uniform and hierarchical decomposition, so as to decrease the error when answering large range queries. However, the uniform splitting manner ignores the properties of decomposed sub-domains and processes them equally without preferences, which leads to significant performance *** this paper, we tackle the problem head on: our proposal, privNUD, is a novel domain hierarchical decomposition mechanism. It dynamically decomposes each domain with a tailored granularity into some sub-domains, which sensitively considers the potential chances to answer one range query. The issue of granularity is carefully analyzed for better performance. It also can smartly prune the sub-domains with small frequencies. Besides, an adaptive user allocation technique is designed to dynamically decide the scale of users that are involved in each sub-domain's frequency estimation. Extensive experiments using real and synthetic datasets demonstrate that privNUD achieves significantly higher result accuracy compared to the up-to-date solutions. © 2023 IEEE.

关键词： Frequency estimation

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and information Organization). All rights reserved.

关键词： Quantum cryptography

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Radiative cooling for passive thermal management towards sustainable carbon neutrality

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National Science Review 2023年第1期10卷 87-97页

作者： Jun Liang Jiawei Wu Jun Guo Huagen Li Xianjun Zhou Sheng Liang Cheng-Wei Qiu Guangming Tao Wuhan National Laboratory for Optoelectronics and Optics Valley Laboratory Huazhong University of Science and Technology Department of Electrical and Computer Engineering National University of Singapore MOE Key Laboratory of Thermo-Fluid Science and Engineering School of Energy and Power Engineering Xi'an Jiaotong University Key Laboratory of Education Ministry on Luminescence and Optical Information Technology National Physical Experiment Teaching Demonstration CenterDepartment of PhysicsSchool of Physical Science and Engineering Beijing Jiaotong University State Key Laboratory of Material Processing and Die &Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology

Photonic structures at the wavelength scale offer innovative energy solutions for a wide range of applications,from high-efficiency photovoltaics to passive cooling,thus reshaping the global energy *** cooling based on structural and material design presents new opportunities for sustainable carbon neutrality as a zero-energy,ecologically friendly cooling *** this review,in addition to introducing the fundamentals of the basic theory of radiative cooling technology,typical radiative cooling materials alongside their cooling effects over recent years are summarized and the current research status of radiative cooling materials is outlined and ***,technical challenges and potential advancements for radiative cooling are forecast with an outline of future application scenarios and development *** the future,radiative cooling is expected to make a significant contribution to global energy saving and emission reduction.

关键词： photonic structures radiative cooling energy saving and emission reduction carbon neutrality

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Developing Automated Feature Selection Methods with Artificial Intelligence 1

Developing Automated Feature Selection Methods with Artifici...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Dehankar, Pooja Mandal, Sanjeev Kumar Hemelatha, S. Vijayaraghavan, P. Junnarkar, Aparna A. Bhambu, Pawan Ajeenkya D. Y. Patil University School of Engineering Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350359756

This studies article examines the capability of using artificial Intelligence (AI) to improve automatic feature choice strategies (AFSM). AFSM is a procedure that determines the maximum essential capabilities in a dataset, which allows to reduce fees and improve version accuracy. By using combining AI and AFSM strategies, its miles feasible to broaden greater sensible and effective characteristic selection algorithms that may discover the highest quality function combos. The research focuses in the main on supervised gaining knowledge of algorithms, together with assist Vector Machines (SVMs), to conduct the evaluation. It explains the idea of characteristic selection, provides an outline of existing AFSM strategies, and then explores capacity improvements that may come from combining AI and AFSM. It additionally offers an example of an AI-enabled AFSM set of rules and discusses viable advantages and concerns. The studies in the long run concludes that AI-enabled AFSM should provide better feature selection accuracy and better performance than traditional techniques when implemented to numerous datasets. © 2023 IEEE.

关键词： Support vector machines

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A Novel NSGA-II Approaches for Combating Advanced Persistent Threats with Machine Learning 12th

A Novel NSGA-II Approaches for Combating Advanced Persistent...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Soni, Monika Pawar, Avinash M. Khubalkar, Deepti Tanjeja, Ankit Kumar Mishra, V.A. Waykole, Tushar Satish Auricle Global Society of Education and Research Bikaner India Bharati Vidyapeeth’s College of Engineering for Women Maharashtra Pune India Pune India Information Technology Arya College of Engineering Jaipur India Computer Engineering VIIT Pune India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9789819601387

Advanced persistent threats (APTs) are becoming more advanced, requiring the creation of strong cybersecurity measures. This study suggests a new method to address advanced persistent threats (APT) by utilizing machine learning, particularly by implementing the "Non-dominated Sorting Genetic Algorithm" II (NSGA-II). The proposed model attains a true-positive rate (TPR) of 98.62% and a precision of 97.59%. Extensive experiments were conducted using a diverse dataset containing APT and benign data samples to assess the effectiveness of NSGA-II compared to other popular machine learning algorithms such as random forest, isolation forest, and LightGBM. The NSGA-II model consistently surpassed other algorithms in terms of true-positive rate (TPR) and precision, demonstrating its superiority in detecting and mitigating advanced persistent threats (APT). The study explores the structure and functionality of NSGA-II, highlighting its ability to enhance the efficiency of machine learning models for APT detection. The algorithm's efficiency in exploring the solution space, balancing multiple objectives, and identifying nondominated solutions greatly contributes to its success in dealing with the complex nature of APT. This study emphasizes the practical implications of using NSGA-II for APT detection in real-world situations. The model shows a high level of accuracy in differentiating malicious activities from normal network behavior, offering an effective defense against APT. The suggested method employing NSGA-II for APT detection provides a promising solution to the changing cybersecurity threat environment. The high true-positive rate (TPR) and precision rates of NSGA-II exceed those of random forest, isolation forest, and LightGBM, demonstrating its potential as a strong and effective tool in addressing advanced persistent threats (APT) and improving cybersecurity resilience. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity

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A Novel Heart Disease Monitoring and Prediction Using Machine Learning Algorithm 8th

A Novel Heart Disease Monitoring and Prediction Using Machin...

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8th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Senbagavalli, M. Karpagalakshmi, R.C. Sumathi, D. Lenin, J. Prasad, G.R.K. Manikandan, A. Department of Computer Science and Information Technology Alliance College of Engineering and Design Alliance University Bangalore India Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Kattankulathur Chennai603203 India

ISBN: (纸本)9789819757855

Background: Continuous monitoring of patient health statistics becomes a difficult task in hospitals. Manually, it is difficult to monitor the health of the patients in the hospital continuously. Older and unconscious older people in particular need to be monitored regularly, and their relatives want to be informed about their health at every time. We, therefore, propose a revolutionary system that easily computerizes this task. Our device provides an intelligent device for monitoring patient status, which uses sensors to track hospital patients’ health status and informs their relatives in the event of a problem via the Internet. Our system uses temperature, glucose levels and heart rate detection to track health. This task has been proposed to improve the monitoring system by using the Internet of Things (IoT) for hospital applications. Result: The suggested system was developed by MAX30100, LM35, ultrasonic sensor and nodeMCU connected to the Internet. BLYNK IoT Android app has already been used to send the notification via Android Application. The remote healthcare monitoring system is also proposed with cloud service and data analytics as to the aiding features. The readings are captured by mobile phone, which acts as the graphical user interface to get the status of the patient’s health. The implemented hardware results illustrate that the suggested model can continuously observe the physiologic parameters and save lives promptly. Conclusion: ML classification algorithms like Regression tree, SVM, and RF analysis for prediction accuracy of patient health data. The proposed RF algorithm’s simulation results provide minimum prediction error and high accuracy compared with the existing Regression tree and SVM algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ultrasonic sensors

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Text Analysis Based on Natural Language Processing (NLP) 2

Text Analysis Based on Natural Language Processing (NLP)

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2nd International Conference on Advances in engineering Science and technology, AEST 2022

作者： Flayeh, Azhar Kassem Hamodi, Yaser Issam Zaki, Nashwan Dheyaa Ministry of Higher Education & Scientific Research Department of Computer Engineering & Information Technology Baghdad Iraq Ministry of Higher Education & Scientific Research Computer Engineering College of Electrical and Electronic Techniques Baghdad Iraq University of Information Technology and Communications College of Engineering Media Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350334906

In the modern era of information explosion, where there is enormous information including thousands and millions of texts loaded on the Internet of over 1.2 million terabytes, there are no accurate statistics on the size of texts within this huge amount of data. Given the multiplicity of websites on the Internet and the spread of social networking services like Twitter, for example, it is stated that about 6000 tweets are posted per second in the year 2019. This work aims to analyze these texts through resorting to artificial intelligence to help solve this problem. In this article, a review is presented on the latest concepts in the field of word processing, information extraction, and implementing an interface for text analysis using the C++ programming language and Visual Studio 2015 edition. A comparison is made between two articles in terms of the number of verbs, prepositions, article strength, number of letters, with the possibility of sorting the text into verbs, nouns, object, and adjectives, as will be displayed in the program interface. © 2022 IEEE.

关键词： Sentiment analysis

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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge Computing

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Min Northeastern University School of Computer Science and Engineering The Engineering Research Center of Security Technology of Complex Network System Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system *** first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multiper-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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An Imperceptible Adversarial Attack Against 3D Object Detectors in Autonomous Driving

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IEEE Internet of Things Journal 2025年第12期12卷 21404-21414页

作者： Wang, Yizhou Wu, Libing Jin, Jiong Wang, Enshu Zhang, Zhuangzhuang Zhao, Yu Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Swinburne University of Technology School of Engineering MelbourneVIC3122 Australia Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

As LiDAR-based 3D object detection gains attention, existing research on point cloud adversarial attacks has exposed vulnerabilities in 3D neural network models, which can further impact the reliability of perception systems in autonomous driving. However, current adversarial attacks primarily focus on the point cloud classification tasks, while detection tasks are more challenging to attack because they involve the localization of multiple targets and the sparse distribution of objects. Existing methods often implement attacks by adding global perturbations to the point clouds, which results in poor attack performance and lack of imperceptibility. In this paper, we investigate the robustness of 3D object detectors against adversarial examples. We propose a novel imperceptible attack method to generate 3D adversarial point clouds. First, we introduce the saliency map for the point clouds, assigning the unique value to each point to measure its importance to the model's discrimination results. These salient points are then aggregated and adaptively matched to different attack areas corresponding to different targets. Next, we design an optimization-based attack algorithm to generate adversarial point clouds, which are supervised by a dual-loss function consisting of the detection loss to ensure attack effectiveness and the distance loss to limit gap with the original point cloud. Finally, we conducted experiments on two real-world datasets, the KITTI and Waymo Open datasets, to evaluate the proposed attack method. Extensive experiments demonstrate that our attack method achieves average attack success rates of 83.07% and 77.11% on two datasets against seven 3D object detectors with minimal perturbations. Additionally, the generated adversarial point clouds exhibit strong transferability across multiple mainstream detectors. © 2025 IEEE.

关键词： Object detection

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