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2024 IEEE International Conference on Big Data, BigData 2024

作者： Rojas, Fredy Madanian, Samaneh Templeton, John Michael Poellabauer, Christian Schneider, Sandra L. Department of Computer Science and Software Engineering Auckland New Zealand University of South Florida Department of Computer Science and Engineering TampaFL United States Florida International University School of Computing and Information Sciences MiamiFL United States Saint Mary's College Department of Communicative Sciences & Disorders Notre DameIN United States

ISBN: (纸本)9798350362480

Mild traumatic brain injury (mTBI) is challenging to diagnose due to its subtle and transient symptoms, making noninvasive diagnostic tools crucial for early detection. This study explores the use of a custom ResNet deep learning model combined with the Grad-CAM interpretability technique for mTBI detection via speech analysis. Speech data were transformed into Mel-spectrograms and fed into the model for binary classification between concussed and control individuals. The Grad-CAM method provided insights into which frequency regions of the Mel-spectrogram were most important for the model's predictions, with higher-frequency regions identified as significant for the model in detecting mTBI. Using Monte Carlo Cross-Validation (MCCV), we evaluated 50 different subject train-test split configurations to gain insights into the model's performance stability and variability. This analysis can assess the model's ability to learn consistent patterns within the dataset and suggest potential generalization tendencies. The variability observed in the performance metrics distribution underscores the importance of robust evaluation methods, particularly when working with small datasets. The combination of deep learning, robust evaluation technique and interpretability in this study contributes to the development of clinically viable, speech-based tools for mTBI detection, with potential applications in sports and healthcare settings. © 2024 IEEE.

关键词： Speech analysis

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A Secure Key Agreement Scheme for Unmanned Aerial Vehicles-Based Crowd Monitoring System

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computers, Materials & Continua 2022年第3期70卷 6141-6158页

作者： Bander Alzahrani Ahmed Barnawi Azeem Irshad Areej Alhothali Reem Alotaibi Muhammad Shafiq Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Software Engineering International Islamic University IslamabadPakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Unmanned aerial vehicles(UAVs)have recently attractedwidespread attention in civil and commercial *** example,UAVs(or drone)technology is increasingly used in crowd monitoring solutions due to its wider air footprint and the ability to capture data in real ***,due to the open atmosphere,drones can easily be lost or captured by attackers when reporting information to the crowd management *** addition,the attackers may initiate malicious detection to disrupt the crowd-sensing communication ***,security and privacy are one of the most significant challenges faced by drones or the Internet of Drones(IoD)that supports the Internet of Things(IoT).In the literature,we can find some authenticated key agreement(AKA)schemes to protect access control between entities involved in the IoD ***,the AKA scheme involves many vulnerabilities in terms of security and *** this paper,we propose an enhancedAKAsolution for crowdmonitoring applications that require secure communication between drones and controlling *** scheme supports key security features,including anti-forgery attacks,and confirms user *** security characteristics of our scheme are analyzed byNS2 simulation and verified by a random oracle *** simulation results and proofs show that the proposed scheme sufficiently guarantees the security of crowd-aware communication.

关键词： IoT unmanned aerial vehicles authentication crowd monitoring

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Design and Implementation of a Following Vehicle Based on ROS and UWB 7

Design and Implementation of a Following Vehicle Based on RO...

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7th International Conference on Automation, Control and Robots, ICACR 2023

作者： Yu, Yun-Shuai Chen, Chin-Wei National Formosa University Department of Computer Science and Information Engineering Yunlin Taiwan Software Research & Development Center New Taipei City Taiwan

ISBN: (纸本)9798350302882

Autonomous vehicle technology has gained significant attention in various industries recently. This paper presents the design and implementation of a following vehicle that utilizes the Robot Operating System (ROS) and Ultra-Wideband (UWB) RF transceiver modules to achieve precise target localization, map the surrounding environment, and navigate accordingly. A mobile application has also been developed to track the following vehicle's position. © 2023 IEEE.

关键词： Radio transceivers

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Affective Computing: A Topic-Based SER Approach on Collaborative Discussions in Academic Setting 53

Affective Computing: A Topic-Based SER Approach on Collabora...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Dehbozorgi, Nasrin Kunuku, Mourya Teja Kennesaw State University Department of Software Engineering MariettaGA United States Kennesaw State University Department of Computer Science MariettaGA United States

ISBN: (纸本)9798350336429

One of the biggest concerns in the modern day especially in the educational domain centers on the student's mental health. High rates of anxiety and depression have especially brought the attention of researchers in engineering education to apply affective computing to help with students' academic performance. It is known that a person's emotional states cause physiological and physical changes in the body. Emotions may impact facial expression, tone of speech, blood pressure, pulse, etc. Since visual and auditory signals are two variables that can be measured without the need to attach any physical device to the individuals, they are most studied in this field. Speech in particular has been known as a means that transfers much information about the mental and emotional states of the person. Speech Emotion Recognition (SER) is a growing field that has been applied in several domains including engineering education. Recent advancements in AI, Natural Language Understanding (NLU), and Large Language Models (LLM) have significantly streamlined this line of research. In this work which is a continuation of our prior work, we propose a speech analysis model that extracts both the emotions and topics from verbal discussions in a computer science classroom to understand if the expressed emotions were mostly about the course related topics or not. The goal of this research is to develop a tool that helps educators gain insights into the students' emotional states in teamwork and also understand the context of their conversations. We further analyze if the expressed emotions in the verbal class discussions are mostly about the course content or other subjects outside class setting. To expand the emotion analysis module we added a new layer to our developed pipeline by passing the speech data into the ChatGPT API to generate summarized scripts and extract additional classes of emotion. The preliminary results from this study are promising, indicating the potential value of th

关键词： Affective computing ChatGPT API engineering education Large Language Models (LLM) NLP Speech Emotion Recognition (SER) Teamwork Topic modeling

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Decentralized Medical Image Sharing: A Blockchain Based Approach with Subject Sensitive Hashing for Enhanced Privacy and Integrity

IET Blockchain

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IET Blockchain 2025年第1期5卷

作者： Arafat, Yeasir Siam, Abu Sayem Md. Chowdhury, Md Muzadded Hasan, Md Mehedi Jobayer, Sayed Hossain Shatabda, Swakkhar Islam, Salekul Mukta, Saddam Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Electrical and Computer Engineering Department North South University Dhaka Bangladesh Department of Software Engineering LUT University Lappeenranta Finland

This research presents a blockchain-based framework for secure and efficient medical image sharing, prioritizing data integrity and privacy. The framework involves two key phases: image compression with feature extraction and image encryption with storage on the InterPlanetary File System (IPFS). Medical images are compressed using the JPEG algorithm to reduce file size while maintaining diagnostic value. A deep neural network-based subject sensitive hashing (SSH) algorithm ensures feature map integrity by extracting consistent features from both original and compressed images. Encrypted images, along with SSH-generated hashes, are securely stored in the IPFS server. The encryption key and hash sequence are used for secure image retrieval, with smart contracts validating access requests based on the hash sequence. This multi-stage feature extraction approach demonstrates robust image integrity, security, and privacy, as verified by experimental results. Achieving an average correctness rate of 98% across multiple datasets, the framework significantly enhances healthcare data management by addressing the challenges of secure, scalable, and private medical image sharing. This research contributes to the development of more efficient, reliable, and privacy-conscious solutions for medical image handling in healthcare systems. © 2025 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Data integrity

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SegIt: Empowering Sensor Data Labeling with Enhanced Efficiency and Security 24

SegIt: Empowering Sensor Data Labeling with Enhanced Efficie...

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7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Zhang, Zhen Abraham, Samuel Lee, Alex Li, Yichen Morota, Gota Ha, Dong Sam Shin, Sook BlacksburgVA United States The Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan China School of Animal Sciences Virginia Tech BlacksburgVA United States

ISBN: (纸本)9798400717833

SegIt is a novel, user-friendly, and highly efficient sensor data labeling tool designed to tackle critical challenges such as data privacy, synchronization accuracy, and memory efficiency inherent in existing labeling tools. While many current sensor data labeling tools provide free online services, they typically necessitate users to upload unlabeled sensor data, alongside video or audio references, to cloud storage for labeling. Nevertheless, such third-party storage exposes user data to potential security risks. SegIt, an innovative open-source tool, provides a software solution for tagging unlabeled sensor data directly on a local computer, ensuring enhanced accuracy, convenience, and, most importantly, data security. © 2024 Copyright held by the owner/author(s).

关键词： Data accuracy

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Toponym Identification in Epidemiology Articles – A Deep Learning Approach 20th

Toponym Identification in Epidemiology Articles – A Deep L...

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20th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2019

作者： Davari, MohammadReza Kosseim, Leila Bui, Tien D. Department of Computer Science and Software Engineering Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9783031243394

When analyzing the spread of viruses, epidemiologists often need to identify the location of infected hosts. This information can be found in public databases, such as GenBank [3], however, information provided in these databases are usually limited to the country or state level. More fine-grained localization information requires phylogeographers to manually read relevant scientific articles. In this work we propose an approach to automate the process of place name identification from medical (epidemiology) articles. The focus of this paper is to propose a deep learning based model for toponym detection and experiment with the use of external linguistic features and domain specific information. The model was evaluated using a collection of 105 epidemiology articles from PubMed Central [33] provided by the recent SemEval task 12 [28]. Our best detection model achieves an F1 score of 80.13 %, a significant improvement compared to the state of the art of 69.84 %. These results underline the importance of domain specific embedding as well as specific linguistic features in toponym detection in medical journals. © 2023, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Bridging Machine Learning and Formal Concept Analysis for Effective Crowd Detection 25

Bridging Machine Learning and Formal Concept Analysis for Ef...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Oraiqat, Anas M. Drieiev, Oleksandr Samara, Ghassan Almatarneh, Sattam Al-Oraiqat, Karim A. Alrawashdeh, Hazim Drieieva, Hanna Elrashidi, Ali Hasan, Yassin M. Y. Zarqa University Faculty of Information Technology Department of Computer Science Jordan Central Ukrainian National Technical University Department of Cybersecurity and Software Kropyvnytskyi Ukraine Zarqa University Faculty of Information Technology Department of Data Science and Artificial Intelligence Jordan Zarqa University Faculty of Information Technology Department of Software Engineering Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Assiut University Electrical Engineering Department Egypt

ISBN: (纸本)9798331540012

Crowd detection and prevention systems have become essential for managing densely populated areas. Modern systems leverage the combined power of machine learning, data mining, and image processing to extract and analyse features from crowded zones, enabling the identification of behavioural patterns and anomalies. However, most current solutions primarily focus on detection and lack robust decision-making and recommendation mechanisms for selecting appropriate crowd-prevention strategies. To address this gap, we integrate the predictive capabilities of machine learning models with the analytical and clustering strengths of Fuzzy Formal Concept Analysis (fuzzy FCA). Machine learning is employed to extract valuable insights from images of the area, with a neural network used to identify human figures, track individuals' positions, and predict crowd levels. This data is fed into an FFCA-based decision system, where crowd information is structured and clustered using lattice theory. This latter helped exclude low-crowd zones and cluster the rest of the monitored areas based on their crowd features. Additionally, we define bottom-up parsing algorithms to recommend suitable crowd-prevention plans based on crowd density levels within fuzzy formal concepts. Experiments confirmed not only the ability of fuzzy FCA to "exclude"low-crowd zones thanks to a used crowd threshold but also the efficient "feature-based clustering"of crowded zones into hierarchical formal concepts and these latter's "bottom-up parsing"to finally identify the dense zones. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis of Learning Outcomes in software engineering: an Automated Reflection Analysis Tool 53

Analysis of Learning Outcomes in Software Engineering: an Au...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Dehbozorgi, Nasrin Dindu, Koushik Goud Kennesaw State University Department of Software Engineering MariettaGA United States Science Kennesaw State University Department of Computer MariettaGA United States

ISBN: (纸本)9798350336429

The rapid advancements in artificial intelligence (AI) have been transforming various domains, including engineering education. The availability of AI-based content and easy access to information have made students more dependent on these technologies. With the rise of online courses, there is growing concern about student engagement, poor learning outcomes, and low retention rates in higher education. Engagement plays a critical role in student success and can be achieved through formative assessment, critical thinking, and reflective thinking strategies. Reflection plays a key role in developing critical thinking and meta-cognitive skills. According to the constructive alignment framework which is an outcome-driven approach, the teaching and assessment methods should be shaped around fulfilling the course learning outcomes. Although the idea of constructive alignment is not a new topic, the higher education sector has recently emphasized it at a large scale due to the diversity of new required skills for students to enter the 5th industrial revolution. In earlier work, we proposed an AI-based reflection analysis model that combines both aspects of learning outcome-based assessment and student engagement by applying 'Minute Paper'. Minute paper is a formative assessment tool that helps the instructors identify the muddy points of the lesson and students' learning gaps as well as their learning outcomes by asking two questions at the end of each lecture (i.e., what they learned and what they didn't). In this work, we propose a more advanced version of the reflection analysis tool by applying transformer-based language models to analyze students' responses to the Minute Paper reflections with higher accuracy in the course context. For this purpose, we train the BERTopic model with the course syllabus and lecture material to get more accurate data in the context of the given courses. The proposed system aids instructors in future course development by adding additiona

关键词： Automated Reflection Analysis BERTopic Learning out-comes Natural Language Processing software engineering

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Prediction of Motion Trajectory of a Moving Object 31

Prediction of Motion Trajectory of a Moving Object

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2023 31st National Conference with International Participation, TELECOM 2023

作者： Nikolov, Ventsislav Rakitin, Ivo Technical University of Varna Computer Science and Engineering Departments Varna Bulgaria Technical University of Varna Software and Internet Technologies Department Varna Bulgaria

ISBN: (纸本)9798350303292

In this paper experimental result of prediction of a moving object in a net of discrete points is presented. The prediction is done by autoregressive model of X-axis and Y-axis independently. Such kind of prediction could be valuable for prediction of moving of autonomous robots, drones and other similar artificial objects which can have implemented some kind of intelligent behavior. Thus the moving object can predict reaching a dangerous area or erupting points in its path. The results are analyzed and conclusions are summarized. © 2023 IEEE.

关键词： Forecasting

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