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15th International Conference on computer science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Roh, Kyoungeui Park, Ji Su Kang, Jungho Shon, Jin Gon Advanced Convergence Technology Laboratory Korea National Open University Seoul Korea Republic of Department of Computer Science and Engineering Jeonju University Jeonju-si Korea Republic of Department of Software Engineering Baewha Woman University Seoul Korea Republic of Department of Computer Science Graduate School Korea National Open University Seoul Korea Republic of

ISBN: (纸本)9789819724468

There is a concern about the learner effectiveness in video lecture. The ability of learners to effectively engage is influenced by the time they can sustain their attention. However, learners watching video lectures often struggle to maintain their attention after a certain period of time. This is primarily because video lectures are created without taking into consideration the time learners can sustain their attention. This study aims to investigate what kind of stimuli could be presented to learners during video lectures to help them sustain their attention. This study designs five experimental stimuli to be presented during video lectures playback and measures the time learners sustain attention for each stimulus. The experimental results show that when video lecture learners are received simultaneous visual stimulus in the form of dynamic text and auditory stimulus in the form of voiceover, the time they sustain attention in video lectures is the longest. This suggests that presenting dynamic text and voiceover simultaneously during video lectures can be the most effective strategy to sustain learners’ attention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Broadcasting in Split Graphs 13th

Broadcasting in Split Graphs

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13th International Symposium on Algorithms and Complexity, CIAC 2023

作者： Harutyunyan, Hovhannes A. Hovhannisyan, Narek Department of Computer Science and Software Engineering Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9783031304477

Broadcasting is an information dissemination primitive where a message is passed from one node (called originator) to all other nodes in the network. With the increasing interest in interconnection networks, an extensive amount of research was dedicated to broadcasting. Two main research goals of this area are finding inexpensive network structures that maintain efficient broadcasting and finding the broadcast time for well-known and widely used network topologies. In the scope of this paper, we will mainly focus on determining the broadcast time and the optimal broadcasting scheme for graphs. Determination of the broadcast time of a node x in an arbitrary network G is known to be NP-hard. Polynomial time solutions are known only for a few classes of networks. There also exist various heuristic and approximation algorithms for different network topologies. In this paper, we will consider networks that can be represented as split graphs. We will present a polynomial time 2-approximation algorithm for the broadcast time problem and will introduce an algorithm for generating optimal or near-optimal broadcast schemes in split graphs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information dissemination

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Metaheuristic Algorithms for Proof Searching in HOL4 34

Metaheuristic Algorithms for Proof Searching in HOL4

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34th International Conference on software engineering and Knowledge engineering, SEKE 2022

作者： Nawaz, M. Saqib Nawaz, M. Zohaib Hasan, Osman Fournier-Viger, Philippe School of Computer Science and Software Engineering Shenzhen University China Department of Computer Science and IT University of Sargodha Sargodha Pakistan Islamabad Pakistan

ISBN: (纸本)1891706543

User guided proof development in interactive theorem proving is a manual and time consuming activity. For automating proof searching and optimization in a higher-order logic proof assistant, we provide two metaheuristic algorithms that are based on Fitness Dependent Optimizer (FDO) and Bat Algorithm (BA). In both metaheuristic algorithms, random proof sequences are first created from a population of frequently occurring proof steps that are discovered using pattern mining techniques. Created proof sequences are then evolved till their fitness matches the fitness of the original (or target) proof sequences. Experiments are performed to investigate the performance of the proposed algorithms on different HOL4 theories. Moreover, the proposed FDO and BA-based proof searching approaches are compared with Simulated Annealing (SA) and Genetic Algorithm (GA)based methods. Results show that BA performs best, followed by FDO and SA for proof finding and optimization in HOL4. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Simulated annealing

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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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Client Segmentation and Customization in E-Commerce: Applications of Machine Learning from a Management Perspective

Client Segmentation and Customization in E-Commerce: Applica...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Sriprasadh, K. Palit, Shamik Pravallika, B. Manjunatha Lenka, Reena Singla, Atul Srm Institute Of Science And Technology Faculty Of Science And Humanities Department Of Computer Science And Applications Chennai Vadapalani India University Of Stirling Rak Campus Department Of Computing Science And Software Engineering United Arab Emirates Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Mechanical Engineering Bangalore India University Pune India Lovely Professional University Phagwara India

ISBN: (纸本)9798350366846

A machine learning-based e-commerce personalised recommendation system helps address the issue of information overload that inevitably arises when consumers have more and more options in e-commerce, leading to an increasingly complex structure. Customer segmentation is a crucial component of contemporary marketing strategy since it enables companies to effectively adjust their advertising campaigns and customise their communications with customers. Through the analysis of enormous quantities of consumer behaviour data and the discovery of trends that can be utilised for dividing customers into segments, ML algorithms provide a potent tool for automating the procedure of customer segmentation. We report our research and results for identifying a customer's segment. In order to perform machine learning, we integrate the Python Tensorflow library with Pandas for manipulating data frames. To address any discrepancies or gaps in the collected client data, it is refined. Key features are found, and to find hidden patterns and relationships, an exploratory analysis of the data is done. After that, we decide on a suitable machine learning technique to divide the clientele into various groups. We also experiment with several clustering models, including conventional machine learning techniques. © 2024 IEEE.

关键词： Python

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EEG Spectral Analysis for Inattention Detection in Academic Domain 53

EEG Spectral Analysis for Inattention Detection in Academic ...

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

作者： Gopi, Sreekanth Dehbozorgi, Nasrin Kennesaw State University Department of Computer Science KennesawGA United States Kennesaw State University Department of Software Engineering KennesawGA United States

ISBN: (纸本)9798350336429

The increasing rate of attention deficit among students, attributed to social media, has far-reaching consequences on their academic performance. Inattention or lack of attention is a state of absent-mindedness or not paying enough attention to the details. A rich body of literature suggests that it is highly associated with underachievement in the academic context. In particular, students who have been clinically diagnosed with Attention Deficit Hyperactivity Disorder(ADHD), a neuro-developmental disorder characterized by inattention, hyperactivity, and impulsivity symptoms are more at risk of under-performance and retention, with some leaving school without a terminal degree. In addition to the academic domain, inattention generally impacts the quality of life and future occupations. Research suggests that ADHD is also attributed to societal and unemployment excess costs as well as productivity loss and healthcare expenses which are estimated to be over $14K per adult in the United States (US). Although more than eight million adults were identified with ADHD in the US by 2018, not all inattention cases are associated with ADHD. Inattention could be caused by several other factors such as stress or anxiety and its early detection and timely intervention is critical, especially in the academic domain. There are existing studies that analyze brain signals by Electroencephalogram (EEG) scans to identify individuals who have ADHD. In this study, we developed a Machine learning (ML) pipeline model that is trained on both ADHD and inattention data to determine if a person is having an attention problem. In the first phase of developing the model, we trained a few different classifiers on a 19-channel public EEG dataset of 60 ADHD and 61 non-ADHD participants. Data analysis showed K-Nearest Neighbor (KNN) classifier outperformed other classifiers with an accuracy of 89%. While many of the existing papers focus on ADHD data, in this work we expand our model to analyze the

关键词： Academic performance Attention deficit EEG engineering education Machine Learning

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Exploring Deep Learning and Grad-CAM for Speech-Based Detection of Mild Traumatic Brain Injury

Exploring Deep Learning and Grad-CAM for Speech-Based Detect...

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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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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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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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