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engineering graduate attribute attainment measurement models

Australasian Journal of engineering Education 2022年第2期27卷 77-87页

作者： Ngu, LockHei Sia, Charlie ChinVoon Lee, MingHa Lakshmanan, Rajeswari Lai, JiaChi Ling, TingSoon Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Malaysia

Various outcome-based education (OBE) performance measurement approaches demonstrate students’ attainment of a set of Graduate Attributes (GA) in the engineering programme. This paper presents four measurement models, namely the indicative, explicit, GA assessment, and culminating models, to measure GA attainment. The indicative model uses the total assessment mark as an indicator for GA attainment, whereas the explicit model utilises a specific mark from an assessment. The GA assessment and culminating models utilise the explicit approach from assessments that fulfil the GA requirement. The culminating model only consists of assessments from advanced and capstone courses. This research aims to determine whether the indicative model that uses an indicator approach can significantly represent GA attainment. This research also explores assessment selection differences in GA attainment. Statistical analysis was used to determine if the models were significantly different through the paired two-sample means t-test. The indicative model could not represent the GA attainment as it differed substantially from the explicit model. Both GA assessment and culminating models can be used to represent the attainment of students’ GA. GA assessment model provides monitoring of students’ progress in GA attainment, while the culminating models enable measurement at capstones and in advanced years. © 2023, Engineers Australia.

关键词： Students

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Data Encryption in 6G Networks: A Zero- Knowledge Proof Model 8

Data Encryption in 6G Networks: A Zero- Knowledge Proof Mode...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Khamesra, Aryan Selvaraj, P. Singh, Isha Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computing Technologies Chennai Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350376425

This work presents an innovative algorithm demonstrating the effectiveness of zero-knowledge proofs (ZKPs) in network security. By integrating Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) for key generation, the algorithm showcases their applicability in enhancing security measures within 6G networks. It highlights the utility of ZKPs in bolstering data privacy and security by enabling entities to validate knowledge without compromising sensitive information. The algorithm shows its capability to ensure robust communication security through comprehensive simulations, thereby laying the groundwork for dependable next-generation communication infrastructures. © 2024 IEEE.

关键词： Ciphertext

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Peer To Peer Fund Raising using Blockchain 15

Peer To Peer Fund Raising using Blockchain

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Reddy, Boyapati Jane Prakeerth Vasu, Aaruni Mohankumar, Pradeep K. Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331300579

Crowdfunding has emerged as a transformative financial model in the contemporary business landscape, providing a decentralized avenue for entrepreneurs, creatives, and innovators to secure funding for a wide range of projects. This Project explores the multifaceted nature of crowdfunding, encompassing its origins, evolution, and the diverse platforms that facilitate this alternative form of financing. The study delves into the key mechanisms and principles that underpin successful crowdfunding campaigns, emphasizing the role of social media, community engagement, and trust-building in fostering financial support. Furthermore, the project investigates the impact of crowdfunding on traditional funding channels, examining how it has disrupted conventional investment models and empowered individuals and small businesses. The project also addresses the challenges and risks associated with crowdfunding, including issues of accountability, regulatory concerns, and the potential for project failure. © Grenze Scientific Society, 2024.

关键词： Decentralized finance

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Cloud Detection and Nowcasting for Ship Navigation 15

Cloud Detection and Nowcasting for Ship Navigation

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sreenivasan, Mahalakshmi Sai Kumar, Kanuri S.V.S. Selvaraj, P. Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331300579

Navigating through oceans is challenging. Sailors are disadvantaged in determining the most appropriate and safe path, free from hurdles and extreme weather conditions, especially on pitch-dark nights. Ships rely on their communication with satellites for navigation. When this connection breaks, the ship loses its ability to navigate and follow the required path. In such scenarios, the Geographical Information System (GIS) and Satellite Imagery-based algorithms are best suited to assist. It is necessary to focus on cloud behavior to ensure the success of ocean endeavors. Cloud Detection is performed by the methods of K-Means clustering and MRCNN. The approach of Mean Path Adjustment (MPA) is used to trace the center of mass to predict Cloud Motion;thereby, building a convolutional LSTM model for Cloud Nowcasting. Collectively, these methods help in detecting clouds, predicting their motion, and helping sailors decide the route to follow, based on the weather conditions. This system works well when the communication between the ship and satellite breaks. A high similarity index of 0.8363 is achieved with the test images. © Grenze Scientific Society, 2024.

关键词： Satellite imagery

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Comparative study on thermogravimetric analysis of banana fibers treated with chemicals

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Materials Today: Proceedings 2023年 78卷 458-461页

作者： Lai, Tasha Sie Ming Jayamani, Elammaran Soon, Kok Heng Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Jalan Simpang Tiga Sarawak Kuching 93350 Malaysia

Natural fibers have shown to have exceptional properties and as excellent reinforcing agents in composite materials, both of which are solutions to environmental problems. However, due to its hydrophilic nature, it tends to swell and degrade when exposed to moisture. Therefore, it will result in poor interfacial bonding with polymer matrix, and poor mechanical and thermal properties. Surface chemical treatment of fibers improved the properties of the fibers by destroying the hydrogen bond in the fiber network. An investigation has been made to study the effects of thermogravimetric analysis (TGA) on three different chemically treated banana fibers from Island of Borneo. TGA is an effective technique for performing thermal analysis. In this technique, the mass of a certain material that needs to be analyzed is observed in relation to temperature. The banana fibers obtained were subjected to chemical treatment of 5 wt% of sodium hydroxide (NaOH), 5 wt% of potassium hydroxide (KOH), and 5 wt% of calcium carbonate (CaCO3) for 24 h. The thermogravimetric analysis shows an improved thermal stability after the fibers were being treated. Besides that, it shows higher thermal stability with sodium hydroxide treated banana fiber and the least weight loss of 48% compared to other treated banana fibers. © 2022

关键词： Thermogravimetric analysis

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CFD Simulation of Smart Adaptive HVAC Vents for Efficient Cooling in Buildings

CFD Simulation of Smart Adaptive HVAC Vents for Efficient Co...

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ASEAN Australian engineering Congress, AAEC 2022

作者： Chan, Phil S. T. Wong, Basil T. Bong, Victor N. S. Chong, Kok-Hing Boniface, Christopher J. Faculty of Engineering Computing and Science Swinburne University of Technology Kuching Sarawak Malaysia

ISBN: (纸本)9789819955466

In recent years, various novel control methods have been shown to improve the energy efficiency of HVAC systems significantly, utilizing feedback from the cooled location to control the cooling effects. Despite this, the inflexibility of standard HVAC systems limits the potential energy savings. In this work, the performance of an easily retro-fitted smart adaptive vent system which was designed to overcome this inflexibility is studied. Computational fluid dynamics (CFD) was used to evaluate and compare the smart adaptive vent system against a standard diffuser. The directed airflow possible with the smart adaptive vent improved occupant thermal comfort under the same airflow rates at a cost of increased draft rates. The average PMV for all occupants were noted to reach a comfortable value at ~58% faster timeframe. The results demonstrate that the smart adaptive vent is capable of achieving greater thermal comfort at a similar or lower airflow rate, consequently improving the energy efficiency of the HVAC system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational fluid dynamics

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A Task-Driven Visualization Framework for Diabetes Management

A Task-Driven Visualization Framework for Diabetes Managemen...

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ASEAN Australian engineering Congress, AAEC 2022

作者： Low, Lip Shen Vong, Wan Tze Loh, Brian Chung Shiong Then, Patrick Hang Hui Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Kuching Malaysia

ISBN: (纸本)9789819955466

Diabetes are chronic diseases that require patients to regularly manage their own health conditions. The goal is to have their blood glucose levels under control by making frequent changes to their diabetes therapy, diet, and lifestyle behaviour. As a result, patients are advised to use personal health devices such as glucometers and blood pressure meters to collect their own health data. These multi-source health data are presented by patients to their health care providers during clinical visits in separate visualizations and are often complicated by the variety of data sources and formats. To address this issue, TaskVisDMa conceptual task-driven visualizations framework for diabetes management—is proposed in this paper to produce advanced visualizations that can provide both informative and actionable insights for health care providers to make correct reasoning for treatment plan adjustments and to better communicate with patients. The framework is composed of a collection of data pre-processing, design choices and visualization strategies to handle multi-source and multi-dimensional patient-generated health data, and is divided into five modules: an input module that receives multiple health datasets, a data pre-processing module that processes and integrates the input datasets, an data-idiom-task linking module that uses a task base to determine the data attributes and visualization idioms that best represent the data-analysis tasks identified from the pre-processed dataset, a visualization generation module that uses a strategy base to generate a holistic visual representation of the multi-source and multi-dimensional datasets, and an output layer that presents an interactive and holistic visual representation to users. Our future work will focus on: (1) expanding the task base and strategy base to formulate if–then rules for the automated selection of design choices and visual and analytic strategies;and (2) advancing the framework as a tool for creating visu

关键词： Data visualization

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Comparative Analysis of Graph Representation Techniques for Code Vulnerability Detection 9

Comparative Analysis of Graph Representation Techniques for ...

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9th International Conference on engineering and Emerging Technology, ICEET 2023

作者： Elahee, Muhammad Irfan Elahee Bin Mohd Illzam Yong, Kelvin Sheng Chek Then, Patrick Hang Hui Yong, Sim Kwan Swinburne University of Technology Sarawak Faculty of Engineering Computing and Science Kuching Malaysia

ISBN: (纸本)9798350316926

The surge in software development and products that use software have intensified the risk of software vulnerabilities. The difficulty of maintaining code security has been exacerbated by the growing software ecosystem and complexity of contemporary software systems. Code vulnerability detection is important in preventing these vulnerabilities, but current methods are time-consuming and may overlook certain vulnerabilities. Deep learning, specifically tree/graph-based neural network, may be a promising solution as it preserves hierarchical and structural information in code, leading to better performance in vulnerability detection. This research engages in a more comprehensive investigation in discussing different methods of code representation, including Abstract Syntax Trees (AST), Control Flow Graphs (CFG) and Program Dependence Graphs (PDG). These graphs undergo thorough analysis and are examined to pinpoint the critical information they extract and how it contributes to vulnerability detection. Notably, it is discovered that distinct graph representations excel at identifying different types of vulnerabilities. The information captured from each graph representations can be used to identify the type of vulnerability and differentiate vulnerable code from non-vulnerable code. Such finding lays the foundation for a more advanced software vulnerability detection tool based on graph representation of codes, offering software vulnerabilities can be efficiently addressed. © 2023 IEEE.

关键词： Flow graphs

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Insights into research on blockchain for smart contracts: a bibliometric analysis

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Multimedia Tools and Applications 2024年 1-25页

作者： Singh, Renu Gupta, Ashlesha Mittal, Poonam Faculty of Informatics and Computing J.C. Bose University of Science and Technology YMCA Faridabad India Department of Computer Science & amp Engineering Indraprastha Institute of Information Technology IIIT New Delhi India

Over the past few years, blockchain technology has gained significant attention. This surge in popularity can be attributed to the emergence of cryptocurrencies and the development of smart contracts. Cryptocurrency is a digital currency that eliminates the problem of double spending. Cryptocurrencies like Bitcoin, Ethereum, Litecoin, Stellar, Zcash, Maker, Aave, etc. become popular and are preferred for money transfers. Smart contracts are the next popular technology on the blockchain after cryptocurrency. It can be considered a piece of code that can execute automatically when the predefined conditions are fulfilled. Researchers believe that the potential of blockchain with smart contracts is only in its initial stages and that its true potential has yet to be fully discovered. Hence, an extensive bibliometric analysis is conducted to understand blockchain trends for smart contracts and to give future directions in this field. For this analysis, various steps are followed, starting with formulating the research question, defining the scope of our research, extracting and analyzing data, answering the research question, and finally, drawing a conclusion. This research paper will be fruitful for scholars and researchers, providing an extensive statistical and network analysis of extracted smart contracts publications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Bibliometric analysis Blockchain Crypto contract Scientometrics Scopus Smart contracts VOSViewer

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Angular Position Estimation for Human-Following and Robot Navigation 6th

Angular Position Estimation for Human-Following and Robot Na...

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6th International Conference on Smart Grid and Internet of Things, SGIoT 2022

作者： Asante, Isaac Theng, Lau Bee Tsun, Mark Tee Kit Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Kuching Malaysia

ISBN: (纸本)9783031312748

Mobile robot navigation and human following are two related areas under the field of robotics that have garnered a lot of interest over the years, due to their advantages in the real world, in various settings. Modern techniques depending exclusively on computer vision for environmental data input commonly rely on state-of-the-art object detection methods to obtain data about the location of detected objects in a scene. However, these detection frameworks do not directly provide the angular position of obstacles and human targets in images. In this research project, the Mask R-CNN instance segmentation framework detects static objects and humans in an environment. A chain of algorithms is then used to transform the image’s content and pixel information into a one-dimensional array that can be mapped to the robot’s field of view. The findings show that the result can aid a mobile robot in estimating the angular position of obstacles and a human in a scene, which is necessary for collision-free navigation and robot-human following in an unknown environment. The proposed method also shows adaptivity as it works outdoors and indoors under poor lighting conditions. It can be used as a standalone algorithm in robotics simulations with webcams and static images. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Object detection

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