Nia Poor, Jaime Campanelli, and Daryl Johnson, Department of Cybersecurity, Rochester Institute of Technology, Rochester, United States
This paper proposes a novel covert communication channel within the popular social media network Reddit. The usage of this covert channel is based on the prerequisite of using the older (but still popular) version of Reddit, ”Old Reddit”, to enable sharing of a user’s upvotes publicly. The channel upvotes Reddit posts in subreddits that begin with characters corresponding to a secret message. By upvoting ”hot” posts within top subreddit communities, and incorporating random timing between upvotes, we demonstrate the feasibility of transmitting secret information without arousing suspicion.
Reddit, Old Reddit, covert communications, encoding, decoding.
Damodar Selvam, Independent Researcher, Milton, Georgia, USA
Knowledge-based web systems (KBWS) play a crucial role in enhancing digital identity management by using advanced technologies such as machine learning (ML),artificial intelligence (AI) and semantic web technologies. These systems provide robust mechanisms for authentication, identity verification, and personalised user experiences while ensuring privacy and security. This paper explores the architecture, implementation, and benefits of KBWS in the digital identity realm, highlighting case studies and recent advancements. By examining key challenges and proposing innovative solutions, this study directs to the expansion of secure and efficient digital identity systems.
Digital Identity, Knowledge based access, blockchain, Homomorphic Encryption , AI and ML.
Eduardo F. Damasceno, Ricardo L. Pires and Armando P. da Silva, Federal University of Technology of Parana, Brazil
Handwriting development is a multifaceted process involving fine motor control, perception, and visualmotor integration skills, crucially developed during the literacy phase. This text emphasizes the role of psychopedagogical professionals in monitoring the psychogenesis of written language. Early childhood educators play a pivotal role in initiating literacy acquisition, necessitating continuous training to enhance their pedagogical practices. While the literacy environment significantly influences knowledge acquisition by fostering student interaction and curiosity, challenges such as school organization and imposed goals can divert educators focus from identifying learning evolution or failure due to cognitive disorders. Recognizing the im-portance of literacy, educators must undergo ongoing training to create conducive literacy environments for comprehensive student development. The daunting task of teaching many students to read and write requires educators to consider variations in language writing, incorporating psychological elements and cultural backgrounds. This work introduces a tool leveraging artificial intelligence and machine learning algorithms to assist educators in monitoring and quickly identifying the stage of language writing formation in 6 to 7-year-old beginners. The prototype aids early childhood educators in categorizing and identifying each childs stage while detecting potential motor or cognitive deficits.
Handwriting Development, Language Writing Formation, Machine Learning.
Prof. Mohamed M. Ghoneim Sywelem, Department of Educational Foundations – Suez University – Suez City – Egypt
The rapid advancement of artificial intelligence (AI) technologies has led to their increasing integration in educational settings. This paper offers an overview of the ethical considerations inherent in this integration. Drawing upon existing literature and ethical frameworks, the paper examines key ethical issues arising from AI adoption in education, including AI-driven decision-making, student data privacy, and algorithmic bias. It also explores the impact of AI on teaching and learning processes. Furthermore, the paper discusses various ethical frameworks and guidelines aimed at addressing these concerns and promoting responsible AI use in education. Through the use of case studies and examples, the paper illustrates real-world ethical challenges faced by educators, institutions, and policymakers. Finally, it provides recommendations for fostering ethical AI integration in education, emphasizing the importance of ethical awareness and proactive measures to ensure the ethical use of AI technologies in educational settings. This overview serves as a foundational resource for educators, policymakers, researchers, and stakeholders involved in navigating the complex intersection of AI and ethics in education.
Ethical Considerations - Artificial Intelligence - Education.
Feng-Hsu Wang, Department of Computer Science and Information Engineering, Ming Chuan University, Taoyuan, Taiwan
AI-based learning technologies, especially deep learning, hold significant promise for enhancing students’ learning experiences in educational systems. However, the application of deep learning in education faces challenges related to transparency and accountability of AI systems, necessitating the development of Explainable Artificial Intelligence (XAI). This study aims to investigate the efficacy of an eXplainable Artificial Intelligence (XAI) technique within the realm of programming education. Specifically, it examines the application of Gradient Integration, a method utilized to generate code segments associated with errors in object-oriented programming. This is achieved through the analysis of a Performer-based deep learning classification model. Thirty-six participants took part in the experiment assessing students’ trust in the system, their cognitive load, and learning performance. The controlled experiments revealed a significant difference in reducing learners cognitive load and improved exam scores, indicating the positive impact of XAI. This study contributes to the ongoing exploration of the efficacy of XAI in educational settings.
Explainable Artificial Intelligence, Deep Learning Technology, Human-Computer Collaborative Learning, Programming Education .
Karla Karina Ruiz Mendoza and Sooriyan Aliyoglu, University Autonomus of Baca California, MexicoCastle University, New City, Cyprus
This study explores the integration of generative artificial intelligence (AI gen), especially models such as ChatGPT, into teacher training, highlighting both the promises and challenges of adopting advanced technologies in an educational context. Through a course titled "Introduction to Generative Artificial Intelligence for Teachers," 97 educators from various universities in Baja California participated, spanning a wide range of ages. The qualitative methodology adopted allowed an in-depth exploration of teachers perceptions, experiences, and expectations regarding AI in education. Results showed a generally positive evaluation of the course, with significant emphasis on the importance of AI in transforming educational practices. Approximately 41.38% of the comments highlighted the potential of gen AI to support and enhance teaching and learning. Additionally, there was a clear interest in deepening knowledge about AI, as well as a need for ongoing training strategies. However, the study also emphasizes critical reflections on the ethical and practical challenges of integrating AI into education, underscoring the importance of a reflective and ethical approach. The demand for gen AI training by educational institutions indicates a global trend toward the adoption of these technologies. The study concludes with recognition of the potential of AI to enrich pedagogy, provided that the associated risks are considered, and ethical and effective adoption is promoted.
Generative Artificial Intelligence, Education, Teacher Training, Ethical Reflections, Technological Adoption.
SaliuAbdulfatai (cln), AfeBabalola University, Ado Ekiti, Ekiti state, Nigeria
The study examined the study habits, skills and attitude as predictors of academic performance among students of Kwara State College of Education Ilorin. Descriptive survey method was adopted and instrument used for data collection was structured questionnaires. The study discovered that there are different study habits exhibited by include reviewing notes, practice tests and reviewing textbook; and the study skills enhanced by the study habits of student to include reading skills, listening skills, memorization, writing skills, and note taking. Based on these finding it was recommended amongst other that: students should be appointed as group leader since they like being appointed as group leader in class assignments as it makes them study hard, revise frequently so as not to forget what they have learnt and when they have to do some study-work, before starting, they read a lot about the theme, organize the ideas and write an outline.
Study habits, Study Skills, Attitude, Academic Performance, Students.
Um Albaneen Jamali, Deena Institute of Technology, Muharraq, the Kingdom of Bahrain
The current paper explores the impact of IoT robotics programs on fostering creativity in elementary school pupils. A mixed methodology combines pre-post CAP testing, interviews, and observational analysis over four months. The study sample comprised 60 female students, aged 10 to 12, from a middle socio-economic background. The participants were randomly divided into control and treatment groups. The students in the treatment group were introduced to an Arduino robotics program involving artificial intelligence, whereas the participants in the control group did not join such a program. The findings from the pre-post-tests demonstrated that the Arduino robotics program significantly enhanced thinking skills of creativity including flexibility, fluency, elaboration, and originality. The paper offers insights for education policymakers and provides recommendations for future research in this field. The chapter recommends incorporating Arduino robotics in the curriculum while calling on educational policymakers to offer training and research opportunities in this field.
Creativity, IOT, Robotics, Elementary School, Arduino.
Madhu Varma1 and Ritika Sahai2, 1Department of Medical Education, California University of Science and Medicine Colton, California USA, 2Chaminade University of Honolulu, Honolulu Hawaii
Medical education has followed the same traditional, ritualistic physician patient interaction pedagogies for the last five decades. These interactions can sometimes pose the patient at risk. Cost and logistics of management, limit use of animals and cadavers in simulation. Artificial intelligence has advanced use of mannequins that simulate real life scenarios and enhance student learning. Our aim was to evaluate current data and evidence to support the use of simulation in medical education. Pub Med was searched for peer reviewed articles for existing information on simulation use in medical education, it effects on student’s confidence, medical knowledge and learning experience .Data was analyzed by themes such as confidence building, communication and medical knowledge. Our review demonstrated the recurring themes of improved medical knowledge, better learning experience, improved patient safety, improved confidence, improved communication and improved functionality under stressful situations. All data provide evidence for increased use of simulation in medical education.
Data Driven Decision Making, Simulation in Medicine, Educational Transformation, AI, &Medical Education.
Howard Greisdorf, Ph.D., Research Advisor, PsyOptik, LLC
The last two decades of advances in information technologies and systems have also produced ever-increasing attempts to penetrate or corrupt those entities for profitable gain. The criminals, their attacks, and the defenses have subsequently entered the research literature tagged as social engineers, social engineering, and cyber security respectively. This work seeks to clarify and model the process by which perpetrators design their attacks and the victim characteristics that make them vulnerable to those attacks. The supporting studies suggest that any attempt to curtail cyber intrusions should include a training element focused on user personality traits that make them susceptible to criminal intentions and methodologies. The conclusion points to greater user self-awareness as a key component of any cyber security training.
Social engineering, human factors, persuasion, personality traits.
Mohamed Abdelmalek1, Anis Harhoura1, Issam Elaloui1, Mahdi Madani2a andEl-Bay Bourennane2, 1Faculty of Science and Technology, University of Burgundy, Dijon, France, 2ImViA Laboratory (EA 7535), University of Burgundy, 21000 Dijon, France
This paper proposes a model based on Convolutional Neural Network (CNN) for image encryption and decryption. We used the CIFAR-10 dataset containing 60.000 color images of size 32×32 across ten classes to train and test the proposed network. Our focus lies in designing a convolutional autoencoder for image compression and reconstruction, utilizing an encoder-decoder architecture. During training, the autoencoder learns to encode essential image features into a reduced-dimensional latent space and reconstructs the image from this space. The implementation of the proposed encryption model demonstrates efficacy in preserving data integrity while reducing dimensionality. Experimental results show that the used CNN exhibits a proficient encryption process and acceptable decryption process.
Visually image protection, Deep Learning, Encryption, Encoder, Data security, Image Compression.
Oluwabunmi Falebita1 and Oluwafemi Famakinde2, 1Innovation and Technology Policy Department, Nigerian Institute of Social and Economic Research, 2Social Policy Department, Nigerian Institute of Social and Economic Research, Nigeria
This qualitative cross-sectional survey delves into the nuanced perceptions surrounding data privacy practices in the realm of digital credit in Nigeria. Through in-depth interviews (IDI) with Digital Credit Users (DCUs) across various economic hubs in Nigeria, we explore their attitudes and concerns regarding the sensitivity of personal information and their willingness to divulge it to Digital Credit Providers (DCPs). Employing a multi-stage sampling technique, clusters representing Nigerias six zones were purposively selected, with the South-West zone chosen for its economic significance. In this zone, Lagos, Oyo, and Ogun States were further sampled based on economic activity, with 40 DCUs interviewed per state, totalling 120 DCUs. Local Government Areas (LGAs) within these states were selected based on Central Business District (CBD), urban, rural, and peri-urban criteria. Thematic analysis of interview transcripts using NVIVO 14 software unveiled key findings indicating that DCUs’ Bank Verification Number (BVN), National Identification Number (NIN), and debit card details are perceived as the most sensitive information. They expressed a high level of obligation to disclose information to DCPs and identified perceived risks such as financial loss, data breaches, and unwanted contact. Additionally, DCUs exhibited a strong preference for retaining control over their information, with many expressing a reluctance to proceed with digital credit applications if privacy breaches were anticipated. These findings shed light on the complex interplay between data privacy perceptions, risk assessment, and individual autonomy in the digital credit landscape.
Digital Credits, Digital Data Privacy, Digital Credit Providers & Digital Credit Users, Nigeria.
Gayeong Kim1, Jongpil Youn1, Youngju Nam2 and Euisin Lee1, 1School of Information Communication Engineering, Chungbuk National University, 2Research Institute for Computer and Information Communication, Chungbuk National University
Delivering high-capacity mobile content in vehicle networks is challenging due to the limitations of Road Side Units (RSUs) and the significant distances between them. To overcome these issues, the concept of relaying vehicles has been introduced, which allows content delivery in areas where RSUs are not able to cover. However, existing studies only use a single vehicle in each direction as a relaying vehicle, limiting the amount of content that can be downloaded. This paper proposes a new approach that utilizes one same-direction vehicle and multiple opposite-direction vehicles as relaying vehicles. The selection of relaying vehicles is based on the available time of the same direction vehicle, relaying times between the vehicle and each opposite direction vehicle, and relaying times between the requester vehicle and each opposite direction vehicle. The proposed method has shown enhanced performance in simulations compared to existing methods.
Content-Centric Vehicular Networks, Outage Zone, Content Precaching, Multi-hop Relaying.
AtheerAlmoamary,Raghad Alsuwailem, Ruba Altawil, Samiah Alanazi,and Ala Alluhaidan, Department of Information Systems, College of Computer and Information Sciences,Princess Nourahbint Abdulrahman University, Riyadh, Saudi Arabia
Objective: to create an environment accessible through any device, that assesses in the ease of the journey of living with cancer disease. Methods: For the development of the project, we followed the iterative waterfall method. A simple SDLC method that allows for continuous feedback and adjustments. Results: This idea was designed to take advantage of technology and help people with cancer or those recovering from it, their symptoms need to be tracked by a doctor and reminded of appointments, medications, or chemotherapy sessions, It is expected to advance patient adherence to their overall wellbeing, and motivate them during the treatment journey. Conclusion: “Cancer Ease” has been designed to empower individuals dealing with cancer by providing a user-friendly interface for information, personalized health tracking, easily accessible, and ultimately striving to enhance the quality of life throughout their journey with the disease.
PLWC, Cancer, Mental Symptoms, Physical Symptoms.
Maya Trutschl1, Urska Cvek2, Andrea L. Brumley3, Steven A. Conrad3, and Marjan Trutschl2, 1Caddo Parish Magnet High School, Shreveport, USA 2LSU Shreveport, Department of Computer Science, Shreveport, USA 3LSU Health Sciences Center, Department of Emergency Medicine and Department of General Surgery, Shreveport, USA
Pressure ulcers, or pressure injuries, are localized areas of skin and/or underlying tissue necrosis that typically occur over bony prominences due to a prolonged pressure or friction. They can lead to serious morbidity and mortality, emphasizing the need for prevention. This project utilizes a database of demographic and clinical features of a large patient data set and applies machine learning to determine the higher risk patients, coupled with a complimentary device to assist in the prevention of pressure injuries. A complementary monitoring system is built based on an inertial wearable utilizing an inexpensive microcontroller and a gyroscope. The combined approach is evaluated, fine-tuned, and assessed based on different performance metrics.
Pressure ulcers, Machine Learning,Inertial Wearable
Ali Akin and Habil Kalkan, Computer Engineering Department, Gebze Technical University, Kocaeli, TURKIYE
Traffic accidents, causing millions of deaths and billions of dollars in economic losses each year globally, have become a significant issue. One of the main causes of these accidents is drivers being sleepy or fatigued. Recently, various studies have focused on detecting drivers sleep/wake states using camera-based solutions that do not require physical contact with the driver, thereby enhancing ease of use. In this study, besides the eye blink frequency, a driver adaptive eye blink behavior feature set have been evaluated to detect thefatigue status. It is observed from the results that behavior of eye blink carries useful information on fatigue detection. The developed image-based system provides a solution that can work adaptively to the physical characteristics of the drivers and their positions in the vehicle.
Driver, Fatigue, Blinking, Head Movements, Face Landmark.
Indra Kumari and Minho Lee, Department of Machine Learning Data Research, Applied AI, Korea Institute of Science and Technology Information (KISTI), Korea National University of Science and Technology (UST), Daejeon, 34113, South Korea
Advanced Persistent Threats (APTs) pose a significant challenge for gov ernments, businesses, and organizations due to their dynamic nature. Identifying the exact type of attack is difficult, making traditional detection methods ineffective. While the MITRE ATT&CK framework provides valuable information on APT groups, it lacks real-world network traffic and system logs. This work addresses this limitation by leveraging DAPT 2020, a dataset mimicking real-world APT attacks. The aim is to detect APT attack phases through a deep learning-based framework, ultimately map ping them to the MITRE ATT&CK framework for better prevention. Traditional ma chine learning techniques are insufficient for this complex task, prompting the use of intelligent optimization and deep learning-based classification methods. The proposed framework consists of four modules: preprocessing, feature extraction, optimization, and classification. Preprocessing involves data cleaning and normalization to improve quality. Relevant features are then extracted using the Pearson Correlation Coefficient. Feature selection with Principal Component Analysis optimizes the data, reducing pro cessing time and boosting classification accuracy. Finally, a combined Recurrent Neu ral Network (RNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) units classifies the attack phases. This framework outperforms both deep learn ing and machine learning baselines, offering a promising solution for early APT detec tion and prevention, ultimately safeguarding organizations.
Advanced Persistent Threat (APT); MITRE ATT&CK; DAPT2020 dataset; Pearson Correlation Coefficient (PCC); Principal Components Analysis (PCA); and RNN-LSTM-GRU.
Zheng Li, School of Electrical, Computer and Telecommunication Engineering, University of Wollongong, Wollongong 2522 NSW, Australia
We study the problem of optimal prediction for output of multiple input multiple output stochastic systems and developed a minimum variance predictor for a linear time-varying autoregressive moving average model. It is shown that this predictor ensures minimum variance prediction of output without requiring a covariance matrix of the stochastic process even when the speed of parameter variation in the system is fast.
Linear systems, minimum variance prediction, optimal prediction, stochastic systems, time-varying systems.
Ekrem Duman1, Aydın Ulucan2 and Hakan Gursoy2, 1Department of Industrial Engineering, Ozyegin University, Istanbul, Turkey, 2Hacettepe University, Business Administration, Ankara, Turkey
When bank customers fail to make their payments with a due date (loan, credit card etc.) they are taken to a collection pool and with the help of several actions they are led to make their payments as soon as possible. If the delinquency exceeds 90 days, the loan is named as non-performing loan (NPL). NPLs are non-desired for banks since they need to spare the full loan amount from the working capital. In that regard, collection before litigation is very essential however, taking harsh actions may also offend customers and they may stop working with the bank. In such a case the loss of the bank will be the future potential profits from the customer. This makes the collection management a very complicated problem that has conflicting objectives and a lot of operational constraints to satisfy. In this study, we aim at developing a naïve AI based solution approach to this complicated problem.
Collection optimization, risk management, intelligent systems
Kamuru Sameena and Akram Pasha, School of Computer Science and Engineering, REVA University, Bengaluru, India
Precision agriculture leverages advanced technolo- gies to optimize crop yields and resource utilization. This re- search explores the integration of data science and federated learning (FL) to enhance precision crop management. By utilizing a comprehensive dataset comprising environmental and soil parameters, we developed predictive models using Artificial Neural Networks (ANN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) networks. The FL paradigm was employed to train the models in a decentralized manner, ensuring data privacy and enhancing model robustness. Model interpretability was achieved through Local Interpretable Model- Agnostic Explanations (LIME) and SHapley Additive exPlana- tions (SHAP), providing insights into the factors influencing crop recommendations. The results demonstrate significant improve- ments in prediction accuracy and resource optimization, validating the potential of FL to revolutionize agricultural practices. This study highlights the importance of collaborative learning frameworks and advanced machine learning (ML) techniques in fostering sustainable agricultural practices amidst evolving climatic conditions.
Precision Agriculture, Federated Learning, Crop Management, Data-Driven Agriculture, Machine Learning, Sustainable Farming, Smart Farming, Agricultural Data Analyt- ics, Distributed Learning, IoT in Agriculture
Hicham Maadan and Jaafar Elkarkri, LERMA Laboratory, Mohammadia school of engineers, University Mohammed 5 in Rabat, Morocco
The main aim of this paper is to study the significant applications of Digital Twins (DT) for Industry 4.0 in public health. In fact, we study Digital Twins and its need and we discuss the process used in their application for Industry 4.0. So, it can reduce time to specialists by designing and evaluating the processes in virtual environments before acting and operating. Comprehensive simulation platforms can be presented using Digital Twins to simulate and evaluate product and service performances in terms of analysis and modification of produced parts. In addition, basing on the mathematical models, we give the supportive features of (DT) for Industry 4.0 in order to identify some related applications in healthcare and then we discuss the advantages and challenges related to this innovative virtual tool.
Digital twin; Industry 4.0; Digital health; IoT& AI; data maths model; Optimization & Simulation.
