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2024 IEEE International Conference on Big Data, BigData 2024

作者： Gupta, Deepti Elluri, Lavanya Jain, Avi Moni, Shafika Showkat Aslan, Omer Texas A&m University - Central Texas Dept. of Computer Information Systems TX United States College of Business University of Tampa FL United States Embry-Riddle Aeronautical University Dept. of Electrical Engineering and Computer Science FL United States Bandirma Onyedi Eylül University Department of Software Engineering Bandirma Turkey

ISBN: (纸本)9798350362480

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamperproof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls - such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture - this approach strengthens an organization's defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth's transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, e

关键词： Smart contract

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Facial Surface Anthropometric Features and Measurements With an Emphasis on Rhinoplasty (vol 42, pg 133, 2022)

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AESTHETIC SURGERY JOURNAL 2022年第2期42卷 230-230页

作者： Celikoyar, M. Mazhar Perez, Michael F. Akbas, M. Ilhan Topsakal, Oguzhan Department of Otolaryngology Istanbul Florence Nightingale Hospital Istanbul Turkey Computer Science Department Florida Polytechnic University Lakeland FL USA Electrical Computer Software and Systems Engineering Department Embry-Riddle Aeronautical University Daytona Beach FL USA

Background Facial features and measurements are utilized to analyze patients’ faces for various reasons, including surgical planning, scientific communications, patient-surgeon communications, and post-surgery evaluations. Objectives There are numerous descriptions regarding these features and measurements scattered throughout the literature, and the authors did not encounter a current compilation of these parameters in the medical literature. Methods A narrative literature review of the published medical literature for facial measurements used for facial analysis in rhinoplasty was conducted through the electronic databases MEDLINE/PubMed and Google Scholar, along with a citation search. Results A total of 61 facial features were identified: 45 points (25 bilateral, 20 unilateral), 5 lines (3 bilateral, 2 unilateral), 8 planes, and 3 areas. A total of 122 measurements were identified: 48 distances (6 bilateral, 42 unilateral), 57 angles (13 bilateral, 44 unilateral), and 17 ratios. Supplemental figures were created to depict all features and measurements utilizing a frontal, lateral, or basal view of the face. Conclusions This paper provides the most comprehensive and current compilation of facial measurements to date. The authors believe this compilation will guide further developments (methodologies and software tools) for analyzing nasal structures and assessing the objective outcomes of facial surgeries, in particular rhinoplasty. Moreover, it will improve communication as a reference for facial measurements of facial surface anthropometry, in particular rhinoplasty.

关键词： anthropometry face rhinoplasty software surgical procedures operative nose facial features

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Leveraging Prompt engineering on ChatGPT for Enhanced Learning in CEC330: Digital System Design in Aerospace

Leveraging Prompt Engineering on ChatGPT for Enhanced Learni...

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IEEE Education engineering (EDUCON)

作者： Alessia Tripaldelli George Pozek Brian Butka Electrical Engineering and Computer Science Dept. Embry-Riddle Aeronautical University Daytona Beach USA

ISBN: (数字)9798350394023

ISBN: (纸本)9798350394030

This research examines the transformative role of prompt engineering in optimizing ChatGPT's utility in a digital-system-design class at embry-riddle aeronautical university. By exploring various prompt engineering strategies, the authors investigate how to tailor prompts to significantly enhance ChatGPT's ability to assist students in complex coding and debugging tasks. The study highlights the pivotal role of prompt engineering in elevating ChatGPT from a basic informational tool to an integral component of the educational process. The chosen course centers on using FPGAs to generate and apply state machines. One course activity involves generating musical tones and sequentially playing them to produce the specified songs. Another activity challenges students to construct the foundational elements of a simple reduced instruction set processor, which includes defining a custom instruction set and conforming to a 16-bit design specification. This process entails developing an arithmetic logic unit, creating a general register array, and formulating the processor's specific instruction set. Therefore, this research is a pilot study with a small sample of students using ChatGPT to examine what prompts work best when used in a technical programming class.

关键词： Adaptation models Codes Instruction sets Refining Chatbots Encoding Iterative methods

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Can Artificial Intelligence Pass a Sophomore Level Digital Design Laboratory?

Can Artificial Intelligence Pass a Sophomore Level Digital D...

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IEEE Southeastcon

作者： Casey Elder George Pozek Samuel Horine Alessia Tripaldelli Brian Butka Electrical Engineering and Computer Science Dept. Embry-Riddle Aeronautical University Daytona Beach USA

Artificial intelligence technology is rapidly growing new capabilities with the introduction of natural language processing and generative pretrained transformer models such as chatGPT3. This research investigates whether this model can complete a sophomore level digital design laboratory. This work took a set of existing laboratories for the class CEC222 at embry-riddle aeronautical university and tasked ChatGPT with completing while not being able to physically wire the circuits it was able describe the wiring, write the programs required to execute the laboratories and answer the laboratory questions. When the labs were independently graded, ChatGPT achieved a grade of 73%. This paper describes the labs, gives examples of the results, and discusses the successes and failures of ChatGPT as applied to this laboratory class. The paper concludes with a discussion of ways that ChatGPT can be integrated into the curriculum.

关键词： Wiring Education Wires Chatbots Transformers Peer-to-peer computing Artificial intelligence

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Learn to Trace Odors: Robotic Odor Source Localization via Deep Learning Methods with Real-world Experiments

Learn to Trace Odors: Robotic Odor Source Localization via D...

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IEEE Southeastcon

作者： Lingxiao Wang Ziyu Yin Shuo Pang Dept. of Electrical Engineering Louisiana Tech University Ruston LA Dept. of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona Beach FL

This paper presents an olfactory-based navigation algorithm via deep learning (DL) methods. The object is to obtain a neural network that navigates a mobile robot to find an odor source without explicating search strategies. Two deep neural networks (DNNs), including feedforward and long short-term memory neural networks (FNN and LSTM), are devised to calculate robot heading commands based on onboard sensor readings. The training dataset is obtained by implementing traditional olfactory-based navigation algorithms, namely moth-inspired and Bayesian-inference methods, on a mobile robot in hundreds of odor source localization (OSL) tests. After the supervised learning, DNNs are validated in real-world experiments with unseen odor source locations and airflow fields. Experiment results show that both FNN and LSTM can imitate the moth-inspired method but cannot effectively learn the complex Bayesian-inference method. In terms of the averaged search time in repeated tests, the proposed FNN and LSTM outperform the Bayesian-inference method by 18% and 14%, respectively, and both networks achieve a comparable search performance with the moth-inspired method.

关键词： Deep learning Location awareness Training Navigation Supervised learning Position measurement Robot sensing systems

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Blockchain-Enhanced Framework for Secure Third-Party Vendor Risk Management and Vigilant Security Controls

Blockchain-Enhanced Framework for Secure Third-Party Vendor ...

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IEEE International Conference on Big Data

作者： Deepti Gupta Lavanya Elluri Avi Jain Shafika Showkat Moni Omer Aslan Dept. of Computer Information Systems Texas A&M University - Central Texas Texas USA College of Business University of Tampa Florida USA Dept. of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Florida USA Department of Software Engineering Bandırma Onyedi Eylül University Bandırma Turkey

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamperproof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls—such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture—this approach strengthens an organization’s defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth’s transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, enabl

关键词： Multi-factor authentication Smart contracts Organizations Real-time systems Regulation Blockchains Security Time factors Risk management Resilience

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SWOT Analysis of Artificial Intelligence in Education

SWOT Analysis of Artificial Intelligence in Education

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IEEE World engineering Education Conference (EDUNINE)

作者： Jorge Ortega-Moody Kouroush Jenab Saeid Moslehpour Lizeth Del Carmen Molina Acosta Edward Jhohan Marin Garcia José Neftalí Torres Marin School of Engineering Embry-Riddle Aeronautical FL USA Dept. of Engineering Sciences Morehead State University KY USA Dept. of Electrical and Computer Engineering University of Hartford CT USA Dept.of Electronic Engineering Universidad del Valle Cartago Colombia

ISBN: (数字)9798331542788

ISBN: (纸本)9798331542795

Artificial Intelligence (AI) is a transformative technology with significant potential in the field of education. However, its integration into educational systems has sparked debates over its benefits and drawbacks. This paper employs a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis to explore the implications of AI in education. The analysis identifies AI’s ability to personalize learning, improve efficiency, and expand access to education as key strengths. Conversely, weaknesses include the risks of dependency, ethical concerns, and inaccuracies. Opportunities lie in the advancement of adaptive learning systems and the development of ethical guidelines. Threats include potential misuse, equity challenges, and resistance to adoption. This study provides insights into the responsible implementation of AI in education and outlines strategies to maximize its benefits while addressing its challenges.

关键词： Ethics Adaptive learning Prevention and mitigation Education Collaboration Transforms Strategic planning Stakeholders Artificial intelligence Guidelines

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Blockchain-Enhanced Framework for Secure Third-Party Vendor Risk Management and Vigilant Security Controls

arXiv

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arXiv 2024年

作者： Gupta, Deepti Elluri, Lavanya Jain, Avi Moni, Shafika Showkat Aslan, Omer Dept. of Computer Information Systems Texas A&M University - Central Texas TX United States College of Business University of Tampa Florida United States Dept. of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Florida United States Department of Software Engineering Bandırma Onyedi Eylül University Bandırma Turkey

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamper-proof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls—such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture—this approach strengthens an organization’s defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth’s transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, enab

关键词： Blockchain

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Multi-Scale U-Shape MLP for Hyperspectral Image Classification

arXiv

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arXiv 2023年

作者： Lin, Moule Jing, Weipeng Di, Donglin Chen, Guangsheng Song, Houbing The College of Information and Computer Engineering Northeast Forestry University Harbin150040 China The Baidu Co. Ltd Beijing100085 China The Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona BeachFL32114 United States

Hyperspectral images have significant applications in various domains, since they register numerous semantic and spatial information in the spectral band with spatial variability of spectral signatures. Two critical challenges in identifying pixels of the hyperspectral image are respectively representing the correlated information among the local and global, as well as the abundant parameters of the model. To tackle this challenge, we propose a Multi-Scale U-shape Multi-Layer Perceptron (MUMLP) a model consisting of the designed MSC (Multi-Scale Channel) block and the UMLP (U-shape Multi-Layer Perceptron) structure. MSC transforms the channel dimension and mixes spectral band feature to embed the deep-level representation adequately. UMLP is designed by the encoder-decoder structure with multi-layer perceptron layers, which is capable of compressing the large-scale parameters. Extensive experiments are conducted to demonstrate our model can outperform state-of-the-art methods across-the-board on three wide-adopted public datasets, namely Pavia university, Houston 2013 and Houston 2018. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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Cache-Enabled in Cooperative Cognitive Radio Networks for Transmission Performance

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Tsinghua Science and Technology 2020年第1期25卷 1-11页

作者： Jiachen Yang Chaofan Ma Jiabao Man Huifang Xu Gan Zheng Houbing Song School of Electrical and Information Engineering Tianjin UniversityTianjin 300000China Wolfson School of Mechanical Electrical and Manufacturing EngineeringLoughborough UniversityLoughboroughLE11 3TUUK Department of Electrical ComputerSoftwareand Systems EngineeringEmbry-Riddle Aeronautical UniversityDaytona BeachFL 32114USA.

The proliferation of mobile devices that support the acceleration of data services(especially smartphones)has resulted in a dramatic increase in mobile traffic. Mobile data also increased exponentially, already exceeding the throughput of the backhaul. To improve spectrum utilization and increase mobile network traffic, in combination with content caching, we study the cooperation between primary and secondary networks via content caching. We consider that the secondary base station assists the primary user by pre-caching some popular primary ***, the secondary base station can obtain more licensed bandwidth to serve its own user. We mainly focus on the time delay from the backhaul link to the secondary base station. First, in terms of the content caching and the transmission strategies, we provide a cooperation scheme to maximize the secondary user’s effective data transmission rates under the constraint of the primary users target rate. Then, we investigate the impact of the caching allocation and prove that the formulated problem is a concave problem with regard to the caching capacity allocation for any given power allocation. Furthermore, we obtain the joint caching and power allocation by an effective bisection search algorithm. Finally, our results show that the content caching cooperation scheme can achieve significant performance gain for the primary and secondary systems over the traditional two-hop relay cooperation without caching.

关键词： cooperative cognitive radio network content caching power allocation

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