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Web Application Development using Flask - Faculty Directory Management 14

Web Application Development using Flask - Faculty Directory ...

引用

14th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Tarunika, R. Rohit Ram, S.S. Sujithra, M. Sudha Devi, D. Department of Computing-Data Science Coimbatore Institute of Technology India Department of Computing-Data Science Coimbatore Institute of Technology India

The main goal of web development is to create, build and maintain websites. It is what allows the user to experience seamless performance when accessing a website. The web applications landscape has evolved tremendously over the years using various web development frameworks. A web development framework is a set of resources and tools for software developers to build and manage web applications, web services and websites. One such framework is Flask written in Python. The objective is to create a faculty directory using Flask with a goal to provide a centralized resource that can be used by students, staff, and administrators to easily find and connect with faculty members. By improving access to information about faculty members, the directory project aims to promote collaboration and facilitate the sharing of knowledge within the academic community. © Grenze Scientific Society, 2023.

关键词： Python

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Resource Allocation and Secure Wireless Communication in the Large Model based Mobile Edge computing System 24

Resource Allocation and Secure Wireless Communication in the...

引用

2024 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile computing, MobiHoc 2024

作者： Wang, Zefan Wang, Yitong Zhao, Jun College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400705212

With the rapid advancement of large models and mobile edge computing, transfer learning, particularly through fine-tuning, has become crucial for adapting models to downstream tasks. Traditionally, this requires users to share their data with model owners for fine-tuning, which is not only costly but also raises significant privacy concerns. Furthermore, fine-tuning large-scale models is computationally intensive and often impractical for many users. To tackle these challenges, we introduce a system that combines offsite-tuning with physical-layer security, which provides local data owners with a lightweight adapter and a compressed emulator. data owners then fine-tune the adapter locally and securely send it back to the model owners through a confidential channel for integration, ensuring privacy and resource conservation. Our paper focuses on optimizing computational resource allocation among data owners and the large model owner deployed on edge, and on the compression ratio of adapters. We incorporate a secrecy uplink channel to maximize the utility that we defined while minimizing system costs like energy consumption and delay. The optimization uses the Dinkelbach algorithm, fractional programming, successive convex approximation and alternating optimization. Experiments demonstrate our algorithm’s superiority over baseline methods. © 2024 Copyright held by the owner/author(s).

关键词： Mobile edge computing

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data Processing Efficiency Aware User Association and Resource Allocation in Blockchain Enabled Metaverse over Wireless Communications 24

Data Processing Efficiency Aware User Association and Resour...

引用

2024 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile computing, MobiHoc 2024

作者： Qian, Liangxin Zhao, Jun College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400705212

In the rapidly evolving landscape of the Metaverse, enhanced by blockchain technology, the efficient processing of data has emerged as a critical challenge, especially in wireless communication systems. Addressing this need, our paper introduces the innovative concept of data processing efficiency (DPE), aiming to maximize processed bits per unit of resource consumption in blockchain-empowered Metaverse environments. To achieve this, we propose the DPE-Aware User Association and Resource Allocation (DAUR) algorithm, a tailored solution for these complex systems. The DAUR algorithm transforms the challenging task of optimizing the sum of DPE ratios into a solvable convex optimization problem. It uniquely alternates the optimization of key variables like user association, work offloading ratios, task-specific computing resource distribution, bandwidth allocation, user power usage ratios, and server computing resource allocation ratios. Our extensive numerical results demonstrate the DAUR algorithm’s effectiveness in DPE. © 2024 Copyright held by the owner/author(s).

关键词： Convex optimization

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Improved Particle Approximation Error for Mean Field Neural Networks 38

Improved Particle Approximation Error for Mean Field Neural ...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Nitanda, Atsushi Singapore College of Computing and Data Science Nanyang Technological University Singapore

Mean-field Langevin dynamics (MFLD) minimizes an entropy-regularized nonlinear convex functional defined over the space of probability distributions. MFLD has gained attention due to its connection with noisy gradient descent for mean-field two-layer neural networks. Unlike standard Langevin dynamics, the nonlinearity of the objective functional induces particle interactions, necessitating multiple particles to approximate the dynamics in a finite-particle setting. Recent works (Chen et al., 2022;Suzuki et al., 2023b) have demonstrated the uniform-in-time propagation of chaos for MFLD, showing that the gap between the particle system and its mean-field limit uniformly shrinks over time as the number of particles increases. In this work, we improve the dependence on logarithmic Sobolev inequality (LSI) constants in their particle approximation errors which can exponentially deteriorate with the regularization coefficient. Specifically, we establish an LSI-constant-free particle approximation error concerning the objective gap by leveraging the problem structure in risk minimization. As the application, we demonstrate improved convergence of MFLD, sampling guarantee for the mean-field stationary distribution, and uniform-in-time Wasserstein propagation of chaos in terms of particle complexity. © 2024 Neural information processing systems foundation. All rights reserved.

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Deep Convolutional Neural Network for Enhancing Traffic Sign Recognition Developed on Yolo V5 2

Deep Convolutional Neural Network for Enhancing Traffic Sign...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2024

作者： Liu, Aofan Liu, Yutong Kifah, Saif School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia

ISBN: (纸本)9798350370188

In today's era, deep learning neural networks with multiple hidden layers have been widely used in many fields. The deep learning method has more powerful features that enhance the method's performance by a learning process. With the development of the logistics industry and the prevalence of autonomous driving, traffic sign recognition has gained rising attention. This study uses a YOLO CNN to classify traffic signs. To improve model performance, we used MSRCR image augmentation during preprocessing. In the improvement phase, we used YOLOv5 to automate traffic sign categorization and improved training methods and network architecture. GTSRB and CCTSDB were used to assess the proposed technique. The experimental results show that the YOLOv5 model outperforms other methods. It has a 99.8% accuracy rate in the GTSRB dataset and 98.4% precision in the CCTSDB. © 2024 IEEE.

关键词： Object recognition

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Preserving Privacy in Healthcare Analytics: Federated Learning for Breast Cancer Prediction in a Collaborative Learning Framework 15

Preserving Privacy in Healthcare Analytics: Federated Learni...

引用

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

作者： Anuvarshini, G.B. Varshaa, D. Sujithra, M. Devi, D. Sudha Department of Computing- Data Science Coimbatore Institute of Technology Coimbatore India

ISBN: (纸本)9798331300579

Federated learning has emerged as a promising technique in machine learning, enabling collaborative training across distributed datasets. Particularly in fields like healthcare, where data privacy is paramount, federated learning offers a means to improve privacy and security. This paper addresses the critical need for safeguarding sensitive medical data by proposing a privacy-preserving federated learning algorithm. Leveraging homomorphic encryption, the proposed algorithm ensures that the learning process protects both data and model integrity. By employing a secure multi-party computation protocol, the algorithm defends against potential adversaries seeking to compromise the deep learning model. Evaluation on real-world medical datasets demonstrates the effectiveness of the approach in preserving privacy and security amidst collaborative learning environments. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Renewing Systems Analysis: The Impact of Game-Based Learning on Repeating Students 18

Renewing Systems Analysis: The Impact of Game-Based Learning...

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18th European Conference on Games Based Learning, ECGBL 2024

作者： Smit, Imelda Unit for Data Science and Computing North-West University South Africa

ISBN: (纸本)9781917204194

Systems Analysis and Design, a second-year subject offered over two semesters, is experienced as challenging. The subject is offered at a South African university and form part of several baccalaureus courses, of which the BSc. Information Technology course is the most prominent. Several reasons contribute to the situation where a substantial percentage of students struggle to pass the first of two modules;the extensive theoretical concepts covered, the necessity to understand their implementation, and the ability to apply the theory in real-life scenarios simulated in a learning context. To address this issue, the class was split, to afford repeating students inverted learning with a gaming focus. The selection of games was aiming to suit the type of learning that was expected to take place. In support, students competed in their project groups. Students new to the module were taught as per the status quo. The mixed method enquiry of this changed setting focused on the group of repeating students. Quantitative data extracted focused on the success of students as repeaters, and their evaluation of the module compared to newcomer students. Qualitative data was gathered using interviews with purposively selected participants who passed the first semester as a repeating student and improved much based on the difference in participation marks, before and after the intervention. Qualitative data analysis focused on identifying codes to allow the categorization of responses into categories to elucidate the value of this endeavour. The participants on which reporting of this paper focuses, formed part of a larger study with a wider perspective. In this paper the focus is on the impressions of the individual regarding this experience. The intervention reported on occurred over a four-year period and was concluded six years ago. This allowed participants to complete their studies and start a career. Results show that the intervention did have a substantial impact on the prog

关键词： Students

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Prompt Engineering for Curriculum Design 4

Prompt Engineering for Curriculum Design

引用

4th International Conference on Educational Technology, ICET 2024

作者： Leung, Jonathan Shen, Zhiqi Nanyang Technological University College of Computing and Data Science Singapore Singapore

ISBN: (纸本)9798350376944

Large Language Models (LLMs) have recently demonstrated successes in a broad range of areas that require language understanding and generation. The design of course curricula is a key part of the educational process, as it helps students understand expectations and learning goals, and teachers to maintain consistency when conducting the course. Thus, there would be benefits if educators could leverage the capabilities of LLMs. The first benefit is the potential for improving course content, which would lead to better learning outcomes for students. Secondly, educators can save time by having an LLM assist in creating course material or assessments. However, many methods of using LLMs in specific applications fine-tune LLMs, which requires task-specific data as well as technical knowledge to perform the fine-tuning. In this work we use Prompt Engineering, a set of methods that aims to improve generated responses from LLMs by altering the input prompt to the LLM. Often, however, advice for designing prompts is very broad, such as "be concise". We utilize prompt patterns to create and propose three reusable, specific, and customizable prompts that can be used to assist in the design of a course syllabus, lesson material, and assessment questions. The use of prompt patterns additionally aims to produce reliable and consistent results from LLMs. © 2024 IEEE.

关键词： Students

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Enhancing AI Security: A Review of Blockchain-Integrated Solutions 4

Enhancing AI Security: A Review of Blockchain-Integrated Sol...

引用

4th International Conference on Computer science and Blockchain, CCSB 2024

作者： Wei, Cai School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia

ISBN: (纸本)9798350356502

Artificial Intelligence (AI) technology is advancing rapidly, driving innovation across various industries while also presenting significant security challenges. These challenges include protecting model ownership, ensuring data privacy, and defending against malicious attacks. Blockchain technology, recognized for its decentralized, transparent, and tamper-proof nature, offers a promising solution to address these issues. This review article explores three key research questions: How can blockchain technology protect AI model ownership? In what ways does blockchain safeguard data privacy within AI systems? What other security-related concerns arise with blockchain-driven AI? Through exploring these questions, the article addresses discussions on ownership, data privacy, attacks, scalability, quantum threats, and ethical security challenges, Through a comprehensive analysis of the existing literature, this article identifies the benefits and limitations of combining blockchain with AI, highlights technical and practical challenges, and proposes directions for future research to enhance the security, effectiveness, and ethical governance of blockchain-integrated AI systems. © 2024 IEEE.

关键词： Federated learning

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Neurosymbolic AI for Mining Key Aspects of Socially Responsible Investing 24

Neurosymbolic AI for Mining Key Aspects of Socially Responsi...

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24th IEEE International Conference on data Mining Workshops, ICDMW 2024

作者： Van Der Heever, Wihan Satapathy, Ranjan Cambria, Erik Goh, Rick Siow Mong Nanyang Technological University College of Computing and Data Science Singapore Singapore

ISBN: (纸本)9798331530631

Environmental, Social, and Governance (ESG) factors have become critical for assessing corporate sustainability and ethical responsibility. However, the vast volume of unstructured data available across corporate reports, social media, and news sources poses a challenge for systematic ESG analysis. This paper explores the application of neurosymbolic AI, which combines neural networks' pattern recognition capabilities with the structured reasoning of symbolic AI, to mine key aspects of ESG from large-scale, diverse data sources. By leveraging SenticNet for concept parsing and deep learning for sentiment analysis, we extract relevant ESG metrics, classify corporate practices, and identify trends. This hybrid approach enhances both the interpretability and scalability of ESG analysis, providing more accurate insights into corporate behaviors and their impact on sustainability goals. Results demonstrate that neurosymbolic AI not only improves the extraction of meaningful ESG aspects but also enables real-time monitoring, supporting data-driven decision-making for investors, regulators, and stakeholders. © 2024 IEEE.

关键词： Decision making

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