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52nd Annual Conference of the European Society for engineering: Educating Responsible Engineers, SEFI 2024

作者： Neal, P.R. Daniel, S. Hassan, G.M. Grundy, S. Haque, R. Nikolic, S. Belkina, M. Lyden, S. Sandison, C. School of Chemical Engineering University of New South Wales Sydney Australia School of Professional Practice and Leadership University of Technology Sydney Sydney Australia School of Computer Science and Software Engineering University of Western Australia Perth Australia School of Science Technology and Engineering University of the Sunshine Coast Sippy Downs Australia Faculty of Engineering and Information Sciences University of Wollongong Wollongong Australia College Western Sydney University Sydney Australia School of Engineering University of Tasmania Hobart Australia

ISBN: (纸本)9782873520274

ChatGPT and other Generative AI tools (GenAI) have generated much commotion and confusion within academic circles. Many academics still need to understand the risk such tools pose on current assessment practices and how students can use them for improved grades/outcomes. Unfortunately, most studies are very generic, do not go into much detail, and are outside of the engineering education context. In addition, GenAI provides many opportunities to construct novel, authentic and/or personalised learning experiences for students. The workshop's facilitators have completed two comprehensive studies evaluating ChatGPT's impact on engineering education assessment (Nikolic et al. 2023;Nikolic et al. 2024). While strengths, weaknesses and opportunities were outlined, it is only the starting point for much-needed conversations. This workshop introduced participants to various GenAI tools in the context of a variety of engineering assessment types and engaged participants with a range of experience levels. We guided and engaged participants in exploring GenAI capabilities, in applying a framework for evaluating security and opportunities in assessment and through this demonstrated a model for professional development that they can adapt to their own institutions. © 2024 SEFI 2024 - 52nd Annual Conference of the European Society for engineering, Proceedings: Educating Responsible Engineers. All rights reserved.

关键词： Students

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Analyses of Parametric Relation in Ant Colony Approach for Robot Path Planning Problem

Analyses of Parametric Relation in Ant Colony Approach for R...

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IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO

作者： Sudeep Sharan Anh Tong Ngoc Minh Juan José Domínguez-Jiménez Peter Nauth Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Cádiz Spain

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

One of the most challenging areas of robotics research is the mobile robot vehicle in a complicated obstacle environment. Recently, algorithms for autonomous mobile vehicles have largely been influenced by nature-based algorithms. The swarm behavior of many different organisms, such as ants, fireflies, honey bees, and foxes, is simulated by several algorithms in mobile robot research, and the results are highly interesting and inspiring. In this paper, we took the inspired algorithm as an ant colony which is used to solve the path planning problems for any mobile vehicle/robots. While studying and using the ant colony approaches to solve the problem, we found that the parameter selections are one of the important expects to make the results of any application worse or better. Therefore, we decided to perform the study on the parametric relation in an ant colony approach. The importance of utilization of the Ant Colony optimization (ACO) approach is to control the parameters. In this paper, we analysed/investigated the relationship between the different control parameters of the ACO algorithm and their performance in the resolution of path planning problem. Simulation is performed with different parameter values and by the result analysis, a selection of parameter is presented.

关键词： Ant colony optimization Path planning Organisms Mobile robots

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Accurate Real-World Co-ordinate Finding and the Object Classification for the Robot Manipulator

Accurate Real-World Co-ordinate Finding and the Object Class...

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Embedded Systems and Artificial Intelligence (ESAI), International Conference on

作者： Sudeep Sharan Hung Do Thanh Peter Nauth Juan José Domínguez-Jiménez Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Cádiz Spain

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

This paper presents an object detection and classification of the objects using Deep Learning (DL). The integration of object detection algorithms and depth camera developed is capable of providing robots, such as self developed assistive intelligent robot called “ROSWITHA” (RObot System WITH Autonomy) with the ability to detect an object and interpret their surrounding environment. It also provides the real-world coordinates/position of the object in that environment, which helps the robot to perform some applications, such as grabbing and serving, etc. You Only Look Once (YOLO) is one of the most popular object detection algorithms which is treated as a single regression problem and is processed by a single neural network. In this work, the YOLOv5 (YOLO version 5) network is implemented on a depth camera “RealSense” for processing object detection and estimating the position of the objects. The model is trained on reduced/filtered application-required objects (e.g. bottle and cup) from the available COCO dataset, and the training outcome alongside the performance of the overall system was evaluated. The results exhibited a satisfactory performance of the model in accurately detecting and classifying the objects with a high confidence score ranging from the least to $\mathbf{7 2 \%}$ to the highest of $\mathbf{9 0 \%}$. The confidence score could also be adjusted by the user on the developed GUI to attain the most certain predictions.

关键词： YOLO Training Accuracy Robot kinematics Current measurement Robot vision systems Cameras Manipulators Distance measurement Robots

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Exact Quantum Sensing Limits for Bosonic Dephasing Channels

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PRX Quantum 2024年第2期5卷 020354-020354页

作者： Zixin Huang Ludovico Lami Mark M. Wilde School of Mathematical and Physical Sciences Centre for Quantum Software and Information Faculty of Engineering and Information Technology Science Park 123 Amsterdam 1098 XG the Netherlands Korteweg–de Vries Institute for Mathematics Institute for Theoretical Physics School of Electrical and Computer Engineering

Dephasing is a prominent noise mechanism that afflicts quantum information carriers, and it is one of the main challenges toward realizing useful quantum computation, communication, and sensing. Here, we consider discrimination and estimation of bosonic dephasing channels, when using the most general adaptive strategies allowed by quantum mechanics. We reduce these difficult quantum problems to simple classical ones based on the probability densities defining the bosonic dephasing channels. By doing so, we rigorously establish the optimal performance of various distinguishability and estimation tasks and construct explicit strategies to achieve this performance. To the best of our knowledge, this is the first example of a non-Gaussian bosonic channel for which there are exact solutions for these tasks.

关键词： Non-gaussian quantum states Quantum channels Quantum fluctuations & noise Quantum information theory Quantum parameter estimation Quantum sensing

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A Logical Evaluation of Predicting Immune System Strength by using Enhanced Deep Learning Strategy

A Logical Evaluation of Predicting Immune System Strength by...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Malathi, K. Ramkumar, M. Ruby, E. D. Kanmani Santhi, G.B. Korde, Vivek Aljawawdeh, Hamzeh J. Saveetha University Department of Cse Chennai India Veltech Rangarajan Dr Sagunthala R & D Institute of Science & Technology Department of Ece Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of information technology Department of software engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The immune system plays a crucial role in defending the body against diseases, making it vital to develop models that can accurately predict immune system strength. This study presents an Enhanced Deep Learning Strategy (EDLS) designed to predict immune system strength based on immune-related biomarkers such as White Blood Cell (WBC) count and Immunoglobulins. A dataset consisting of 23,654 records with 12 features was collected from clinical sources. The data underwent comprehensive preprocessing, including noise reduction using a Median Filter, feature selection with Random Forest, and data fusion using Kalman Filters. The EDLS model integrates AutoEncoder for dimensionality reduction and Cascaded Long Short-Term Memory (LSTM) for capturing temporal dependencies. The proposed model outperforms nine traditional and advanced machine learning models, including Logistic Regression, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), with an accuracy of 94.72%. Evaluation metrics such as Precision (92.01%), Recall (93.77%), and F1-Score (92.88%) highlight the model's effectiveness in predicting immune system strength. Feature importance analysis reveals that WBC count and Immunoglobulins are the most influential predictors. This study demonstrates the potential of EDLS for enhancing immune strength prediction, which can have significant implications for personalized healthcare and disease prevention. © 2024 IEEE.

关键词： Median filters

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Overlooked Aspects of Data Governance: Workflow Framework for Enterprise Data Deduplication

Overlooked Aspects of Data Governance: Workflow Framework fo...

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2023 International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2023

作者： Azeroual, Otmane Nikiforova, Anastasija Sha, Kewei Berlin Germany University of Tartu Faculty of Science and Technology Institute of Computer Science Chair of Software Engineering Tartu Estonia European Open Science Cloud Task Force 'Fair Metrics and Data Quality' Brussels Belgium University of Houston Clear Lake College of Science and Engineering Houston United States

ISBN: (纸本)9798350339291

Data quality in companies is decisive and critical to the benefits their products and services can provide. However, in heterogeneous IT infrastructures where, e.g., different applications for Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), product management, manufacturing, and marketing are used, duplicates, e.g., multiple entries for the same customer or product in a database or information system, occur. There can be several reasons for this, but the result of non-unique or duplicate records is a degraded data quality. This ultimately leads to poorer, inefficient, and inaccurate data-driven decisions. For this reason, in this paper, we develop a conceptual data governance framework for effective and efficient management of duplicate data, and improvement of data accuracy and consistency in large data ecosystems. We present methods and recommendations for companies to deal with duplicate data in a meaningful way. © 2023 IEEE.

关键词： Ecosystems

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Assessing Cybersecurity Awareness among the Hashemite University Students in Terms of computer Usage 16

Assessing Cybersecurity Awareness among the Hashemite Univer...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Aljammal, Ashraf H. Qawasmeh, Ahmad Taamneh, Salah Wedyan, Fadi Obiedat, Mamoon Salameh, Hani Bani The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Computer Science and Applications Zarqa Jordan Lewis University Department of Engineering Computing and Mathematical Sciences RomeovilleIL United States The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Information Technology Zarqa Jordan The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Software Engineering Zarqa Jordan

ISBN: (纸本)9798350371154

Internet usage among information technology users has expanded substantially, as have cybercrimes with potentially disastrous repercussions. Therefore, internet users have to employ the available security measures and policies during internet usage. Nowadays, University students make up a sizable portion of internet users. This study assesses the cybersecurity awareness among The Hashemite University students In terms of computer Usage. A questionnaire survey is used to assess students' cybersecurity knowledge while using their PCs and connecting to the internet, concentrating on their behaviors and use of security measures. © 2024 IEEE.

关键词： Cybersecurity

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Recognition of Handwritten Tamazight Characters Using ResNet, MobileNet and VGG Transfer Learning 23

Recognition of Handwritten Tamazight Characters Using ResNet...

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23rd International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2024

作者： Fattahi, Jaouhar Sghaier, Feriel Mejri, Mohamed Manai, Elyes Ghayoula, Ridha Department of Computer Science and Software Engineering Laval University 2325 rue de l'Université QuébecQCG1V 0A6 Canada Ecole Nationale d'Ingénieurs de Carthage Université de Carthage Tunis Tunisia Faculty of Engineering Information Systems Department University of Moncton New Brunswick Canada

ISBN: (纸本)9781643685380

The Tamazight civilization stands as a significant cultural entity, marked by its linguistic diversity, historical legacy, and scriptural traditions, which collectively enrich the cultural tapestry of North Africa. Among these traditions, the Tamazight handwritten script assumes particular importance, embodying centuries of cultural identity and artistic expression. Recognizing the imperative of safeguarding this cultural heritage, our study focuses on Tamazight handwritten character recognition. Leveraging the strategic application of Transfer Learning, we explore its efficacy in this domain. Transfer Learning presents a robust framework wherein pre-existing models are adapted for specific tasks despite limited data availability. Our research employs three prominent Transfer Learning architectures: VGG, ResNet, and MobileNet. Through a rigorous comparative analysis, we discern the efficacy of these methodologies in the context of Tamazight handwritten character recognition. Our findings underscore the potential of Transfer Learning to significantly augment the accuracy and efficiency of script recognition systems, thereby advancing the overarching objective of preserving and propagating the Tamazight cultural heritage. © 2024 IOS Press. All rights reserved.

关键词： Transfer learning

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YOLO-BTM: A Novel Shuttlecock Detection Method for Embedded Badminton Robots

YOLO-BTM: A Novel Shuttlecock Detection Method for Embedded ...

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Automation, Robotics and computer engineering (ICARCE), International Conference on

作者： Yimin Zhang Chuxuan Chen Ronglin Hu Faculty of Computer & Software Engineering Huaiyin Institute of Technology Huai’an China

ISBN: (纸本)9781665475495

Employing robots in badminton training contributes to a more accurate analysis of an athlete's movements and helps avoid injuries. Shuttlecock detection during the flying stage is a critical component of the badminton robot design. However, previous shuttlecock localization methods were unable to detect shuttlecock quickly and accurately in embedded device-based badminton robots, given scale variations, few extractable features, occlusion, and device limitation. In this paper, a deep learning-based shuttlecock localization method is proposed. First, an indoor shuttlecock dataset including 9548 shuttlecock images of various angles and scenes was constructed. Then a shuttlecock detection method YOLO-BTM is proposed, which is based on YOLOv4-Tiny. We proposed a new convolution block to replace the cross-stage partially block in the backbone, to improve the detection speed. To improve the network's ability to detect small objects, the efficient channel attention block is introduced in feature fusion. Finally, a comparative experiment on the accuracy of the method and the detection speed was conducted. The results show that the proposed YOLO-BTM has better performance in detection speed and accuracy compared to the existing state-of-the-art object detection methods on our own shuttlecock dataset. Our method enables real-time, accurate localization of shuttlecock and has the potential to be used in other embedded device based sports robots.

关键词： Location awareness Training Performance evaluation Visualization Object detection Feature extraction Real-time systems

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Voice Enabled Intelligent Programming Assistant 4

Voice Enabled Intelligent Programming Assistant

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Wataketiya, Ravindu Chandrasiri, Navinda Kithsiri, Ramesh Malwatta, Hirush Nadeeshani, Madhuka Siriwardana, Samanthi Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

In modern era where software development is of vital importance, software developers are challenged with conditions like Repetitive Strain Injury (RSI) which hinders their ability to work effectively. Furthermore, people with difficulties with using their hands also find it challenging to program in the traditional manner. As a solution, coding with one's voice has been experimented with, but current solutions lack interactivity and are harder to use and setup leaving much room for improvement in this domain. In this research work, by using input classifier models with accuracies over 90%, intent classifiers with accuracies over 70%, code parsing and various human computer interaction techniques, we developed a conversationally interactive, programming language agnostic, easy to setup and easy to use Voice Coding Assistant. This will potentially help a global audience of programmers to achieve their goals and improve productivity and lead a healthier life. We have named the system thus developed, "Venic". © 2022 IEEE.

关键词： Human computer interaction

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