June 22 ~ 23, 2024, Sydney, Australia
Kamronbek Yusupov1, Md Rezanur Islam1, Insu Oh2, Mahdi Sahlabadi2, and Kangbin Yim2, 1Software Convergence, Soonchunhyang University, Asan-si, South Korea, 2Department of Information Security Engineering, Soonchunhyang University, Asan-si, South Korea
This research focuses on evaluating the security of an intrusion detection system in a CAN bus-based vehicle control network. A series of studies were conducted to evaluate the performance of models proposed by previous researchers, testing their effectiveness in real-world scenarios as opposed to those on which they were trained. The article demonstrates that models trained and tested on the same dataset can only sometimes be considered adequate. An approach that included models trained only on CAN ID, Payload, or full data was chosen. The research results show that such methods are ineffective enough in real-world attack scenarios because they cannot distinguish between new scenarios not presented during training. The results of testing the models in various attack scenarios are presented, and their limitations are identified. In addition, a new method is proposed explicitly for attack scenarios that may occur in the real-world use of an in-vehicle CAN communication system.
Intrusion Detection System, Controller Area Network, In-Vehicle Network, LSTM.
Sara Abbaspour Asadollah, School of Innovation, Design and Engineering, Mälardalen University Västerås, Sweden
Ensuring digital privacy is critical to protecting sensitive information and guarding against malicious actors in todays interconnected world. This work-in-progress paper explores the concept of digital privacy and its importance in maintaining online security. We highlight the importance of robust strategies to protect information by examining the consequences of failing to prioritize digital privacy, including identity attack scenarios in industrial applications by proposing a systematic approach to improving digital privacy. Our methodology includes creating a Data Flow Diagram (DFD) to visualize the data flow within the system and applying the STRIDE threat modeling framework to identify potential threats, with a focus on privacy-related aspects. We then extract privacy-related threats and create attack scenarios to guide testers to validate system security. To validate our methodology, we plan to conduct a case study in an industrial application, an automated train control system. By analyzing the data flow and identifying potential attack scenarios, we want to demonstrate the effectiveness of our approach in real-world applications. We also want to automate the process and collaborate with more industries to ensure scalability and practical applicability.
Privacy-related threats, Cyberattack scenario, Information security, Data protection.
Mahmuda Akter and Nour Moustafa, The School of Systems and Computing, University of New South Wales Canberra, ACT 2612, Australia
The Internet of Things (IoT) forms intelligent systems, such as smart cities and factories, to enhance intellectual productivity and provide revolutionary and automated services to end-users and organisations. However, a future IoT ecosystem requires more dynamicity and heterogeneity with advanced privacy preservation. Federated Learning (FL) addresses the challenge of maintaining data privacy by using a privacy-preserving sharing mechanism instead of transmitting raw data. However, the latest cyber threats cause privacy breaches in existing Federated Learning schemes. This study presents a systematic analysis of Federated Learning-based privacypreserving methods in IoT systems. A standard IoT architecture with possible privacy threats is illustrated. Also, Federated Learning schemes and their taxonomies are discussed in a privacypreserving manner, with initial experiments proving the significance of Federated Learning-based privacy preservation in IoT environments and finding acceptable noise addition in differential privacy by keeping higher testing accuracy in different settings to enhance privacy preservation of federated learning. Various Federated Learning schemes, challenges and future research directions are covered.
Federated Learning, Privacy Preserving, Internet of Things (IoT), Privacy Threats.
Waleed A Al Maamari1, Muhammad R Ahmed2, 3, Rusmawati Binti Said1, Mohammed H Marhaban2, 1School of Business and Economics, University Putra Malaysia, Selangor, Malaysia, 2Faculty of Engineering, University Putra Malaysia, Selangor, Malaysia, 3Military Technological College, Muscat, Oman
In the current cybersecurity environment, incident response holds paramount importance for organizations concerned with the need to maintain security process and mitigate potential breaches. Therefore, this paper analyzes the emerging trends and persistent challenges as shaping incident response practices. These emerging trends—ransomware attacks, the integration of AI and ML, proactive threat hunting, cloud security incident response, and threat intelligence sharing—also bring with them new opportunities for the development of incident response. However, current challenges to Incident response effectiveness include resource constraints, skill shortages, issues of regulatory compliance, organizational silos, and cultural barriers. These can only be addressed through investment in advanced technologies, training on an ongoing basis, collaborative partnerships, and proactive efforts in regulatory compliance. Organizations can strengthen their incident response postures and effectively mitigate cyber risks by making a priority of leadership commitment, fostering a cybersecurity-aware culture, and embracing proactive measures to meet the exigencies of the changing cybersecurity landscape.
Incident Response, Cybersecurity, Emerging Trends, Persistent Challenges, Organizational Resilience.
Mikhail Diyachkov1, Arkadi Yakubov1, Hadassa Daltrophe1, and Kiril Danilchenko2, 1Shamoon College of Engineering, Ashdod, Israel, 2University of Waterloo, Waterloo, Ontario, Canada
With the proliferation of deep learning, steganography techniques can now leverage neural networks to imperceptibly hide secret information within digital media. This presents potential risks of propagating malware covertly. We present an innovative deep-learning framework that embeds malware within images for stealthy distribution. Our methodology transforms malware programs into image representations using a specialized neural network. These image representations are then embedded seamlessly within innocuous cover images using an encoding network. The resulting stego images appear unmodified to the naked eye. We develop a separate network to extract the malware from stego images. This attack pipeline allows the malware to bypass traditional signature-based detection. We experimentally demonstrate the efficacy of our approach and discuss its implications. Our framework achieves high-fidelity reconstruction of embedded malware programs with minimal distortions in the cover images. We also analyze the impact of loss functions on concealment and extraction capacity. The proposed technique represents a significant advancement in AI-driven steganography. By highlighting an intriguing attack vector, our work motivates research into more robust defensive solutions. Our study promotes responsible disclosure by releasing the attack implementation as open-source
Waleed A Al Maamari1, Muhammad R Ahmed2, 3, Rusmawati Binti Said1, Mohammed H Marhaban2, 1School of Business and Economics, University Putra Malaysia, Selangor, Malaysia, 2Faculty of Engineering, University Putra Malaysia, Selangor, Malaysia, 3Military Technological College, Muscat, Oman
In the current cybersecurity environment, incident response holds paramount importance for organizations concerned with the need to maintain security process and mitigate potential breaches. Therefore, this paper analyzes the emerging trends and persistent challenges as shaping incident response practices. These emerging trends—ransomware attacks, the integration of AI and ML, proactive threat hunting, cloud security incident response, and threat intelligence sharing—also bring with them new opportunities for the development of incident response. However, current challenges to Incident response effectiveness include resource constraints, skill shortages, issues of regulatory compliance, organizational silos, and cultural barriers. These can only be addressed through investment in advanced technologies, training on an ongoing basis, collaborative partnerships, and proactive efforts in regulatory compliance. Organizations can strengthen their incident response postures and effectively mitigate cyber risks by making a priority of leadership commitment, fostering a cybersecurity-aware culture, and embracing proactive measures to meet the exigencies of the changing cybersecurity landscape.
Incident Response, Cybersecurity, Emerging Trends, Persistent Challenges, Organizational Resilience.
Mikhail Diyachkov1, Arkadi Yakubov1, Hadassa Daltrophe1, and Kiril Danilchenko2, 1Shamoon College of Engineering, Ashdod, Israel, 2University of Waterloo, Waterloo, Ontario, Canada
With the proliferation of deep learning, steganography techniques can now leverage neural networks to imperceptibly hide secret information within digital media. This presents potential risks of propagating malware covertly. We present an innovative deep-learning framework that embeds malware within images for stealthy distribution. Our methodology transforms malware programs into image representations using a specialized neural network. These image representations are then embedded seamlessly within innocuous cover images using an encoding network. The resulting stego images appear unmodified to the naked eye. We develop a separate network to extract the malware from stego images. This attack pipeline allows the malware to bypass traditional signature-based detection. We experimentally demonstrate the efficacy of our approach and discuss its implications. Our framework achieves high-fidelity reconstruction of embedded malware programs with minimal distortions in the cover images. We also analyze the impact of loss functions on concealment and extraction capacity. The proposed technique represents a significant advancement in AI-driven steganography. By highlighting an intriguing attack vector, our work motivates research into more robust defensive solutions. Our study promotes responsible disclosure by releasing the attack implementation as open-source
Nikhil Ghadge, Software Architect, Workforce Identity Cloud, Okta.Inc
Identity management has become increasingly critical in todays digital landscape, where sensitive data is exposed to frequent breaches and unauthorized access. This research aims to investigate the best practices for governance and administration to strengthen identity management systems, focusing on security, privacy, and usability. By examining current industry standards, regulations, and technological advancements, the study seeks to provide valuable insights for organizations looking to enhance their identity management capabilities. The research methodology employs a mixed-methods approach, combining quantitative surveys and data analysis with qualitative interviews to gather a comprehensive understanding of existing practices and challenges in identity governance and administration. The study explores key components such as authentication, authorization, and access control, offering practical recommendations to improve the effectiveness of identity management strategies. The research highlights the importance of adopting role-based access control (RBAC), continuous monitoring and compliance, identity lifecycle management, and the integration of identity governance with IT infrastructure. It also emphasizes the significance of effective password management, authentication measures, and the implementation of Single Sign-On (SSO) solutions to enhance security and user experience. Furthermore, the study underscores the critical role of data encryption and protection measures in safeguarding sensitive information and mitigating the risk of data breaches. By adhering to best practices in identity management, organizations can improve their overall cybersecurity posture, ensure compliance with regulations, and foster trust among stakeholders in an increasingly complex digital environment.
Identity and Access Management, Digital Identity, Authentication, Authorization, Governance.
Carlos Olea, Holly Tucker, Jessica Phelan, Cameron Pattison, Shen Zhang, Maxwell Lieb, Doug Schmidt, Jules White, Department of Computer Science, Vanderbilt University, Nashville, TN, United States
Prompt engineering for the effective use of LLMs has become an important and useful area of research since the advent of highly performant LLMs. Various patterns of prompting have proven effective, among them chain of thought, self consistency and personas. In this paper we measure the effect of both single and multi-agent personas in various knowledge-testing environ- ments, multiple choice and short answer environments. We present our methodology and find that the use of single and multi-agent personas have nominal effects in summation multiple choice tasks and that single-agent expert personas have benefits in short answer tasks. We then hypothesize a spectrum of effect for both single and multi-agent persona usage given our findings.
Prompt Engineering, Large Language Models, Question Answering.
Agampreet Saini,Dr. Rahul Kumar Singh, Dr. Manoj Kumar Sachan, UPES, Dehradun, India
The scope of financial time series prediction has evolved significantly, driven by the surging availability of data and computational advancements. This transition has seen the traditional statistical methods give way to present-day machine learning and deep learning techniques. Despite these strides, challenges continue to exist, with market unpredictability, dimensionality issues, and overfitting in high-frequency data standing as primary obstacles. Newer machine learning models are better than old ones at predicting stocks. When electronic trading and yield optimisation come together, they make these models even better. Deep learning has completely changed machine learning, especially in forecasting financial trends over time. Complex deep learning models lack interpretability, hindering real-world adoption. Considering this we have used two deep learning models which are Long Short-Term Memory and Convolutional Neural Network and trained these on apple stock market data over the years along with one machine learning model i.e. Linear Regression. Then we have compared the accuracy of all the models based on their R-squared Score value.
Financial time series, machine learning, deep learning, market unpredictability, R-squared Score.
Janga Bussaja, Independent Researcher, USA
The system outlined in this proposal exists in a conceptual phase, awaiting the necessary resources for implementation. The theoretical framework presented herein lays the foundation for the development and deployment of Smoky, an innovative artificial intelligence system designed to confront systemic racism. Grounded in African-centered scholarship and equipped with sophisticated monitoring capabilities, Smoky stands as a pioneering endeavor in the realm of leveraging technology for social equity. This scholarly exploration delves into the conceptualization, development, and potential applications of Smoky as a formidable asset in the ongoing struggle against racial injustice. As with any transformative idea, securing funding and support is paramount to transitioning from theory to tangible action. This paper serves as a call to philanthropists and potential collaborators to join in the realization of this vision, contributing to the advancement of technology-driven solutions for social justice.
African-centered Language Model (LLM), Systemic Racism, White Supremacy, Social Justice, Artificial Intelligence (AI) Algorithms, Knowledge Representation, Neural Networks, Data Mining, Machine Learning, Information Retrieval, Natural Language Processing (NLP), Multimedia Analysis, Pattern Recognition, Cognitive Informatics, Planetary Chess, Counter-racist Scholars, Biases in AI, Hybrid Intelligent Systems, Evolutionary Computing, Technical Challenges in AI Development.
JHaocheng Yang1, Tyler Boulom2, 1Immersion academy, 4010 Barranca Parkway, Irvine, CA 92604, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
This paper addresses the challenge of disengagement and lack of motivation among students learning history, often attributed to traditional teaching methods that fail to captivate their interest. We propose an innovative solution through an educational role-playing game (RPG) that integrates adaptive AI to create a personalized, immersive learning environment [1]. This game allows students to explore historical events as active participants rather than passive recipients, enhancing engagement and retention of information. Key technologies include an adaptive AI system that tailors content to individual learning styles and a game-based platform that encourages exploration and decision-making [2]. Challenges such as ensuring historical accuracy, maintaining user engagement, and addressing diverse educational needs were addressed through iterative design and testing, incorporating feedback from real-world classroom settings. Experimental application across various educational scenarios demonstrated significant improvements in student engagement and historical knowledge comprehension. Results indicated that students not only enjoyed learning about history through this interactive platform but also developed a deeper understanding of the subject matter. Ultimately, this RPG-based educational tool offers a compelling alternative to traditional history education, promoting active learning and sustained interest [3]. Its ability to adapt to individual learner profiles and engage students in meaningful ways makes it an invaluable resource in educational environments striving to enhance both learning outcomes and student motivation.
Interactive Learning, Educational Role-Playing Games, Unity, Artificial Intelligence.
Chenyue Shao1, Garret Washburn2, 1Campion School, Agiou Ioulianis street, Pallini, 15351, Athens, Greece, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
In the modern computing age, where it seems that almost everyone has a computer, the organization of individuals file structures has become increasingly more and more unorganized [1]. In an effort to solve this problem, the File Sorter application proposed in this paper enables a user to easily sort their files with the help of an AI model designed to sort files accurately and efficiently. The File Sorter application utilizes AI to predict where a file should go given a dataset [2]. To test this application, we employed experiments to design how time efficient the File Sorter was as well as how accurately it sorted files, in which both experiments displayed no inefficiency. The public should use the File Sorter application because it accurately and efficiently sorts files in varying manners, and it is good practice for an individual to maintain a healthy file structure because it enables a productive file structure workflow.
File Organization, AI Sorting, File Management, Productivity Tools.
Rani Aher, Dr. Mandaar Pande, Department of Symbiosis Centre for Information Technology Symbiosis International University, Plot No:15, Rajiv Gandhi Infotech Park, MIDC, Hinjewadi, Phase 1,Pune, Maharashtra 411057, India
A development of systems capable of compressing audio files is a very appealing research topic. This is due to the necessity to improve storage utilisation and accelerate data transfer over restricted communication channels. As a result, numerous investigators have explored and developed many methods, also to compress audio information with several approaches as well as ways, every one of them have negative consequences such as excessive time consumption or complex computations and remains a key problem. Recent improvements in deep learning has prompted researchers to use unified deep network models to investigate challenges needing highly organised data. The building and design of such models for compressing audio signals has been problematic due to the demand for discrete representations that are difficult to train. This research focuses on quantum neural networks for audio file compression. Our method is based on an innovative encoding process that embeds audio signals in quantum states. For the BBC sound dataset, our framework can compress larger files than previously possible while achieving a best compression ratio than classical neural networks.
Audio compression, Quantum Theory, Quantum Machine Learning, Deep Learning, Quantum States, Q-bit.
Omar Hujran, Department of Statistics and Business Analytics, United Arab Emirates University, Al Ain City, United Arab Emirates
The rapid advancement of smart technologies has significantly bolstered the governments capabilities for digital transformation. The integration of these technologies in various government functions is expanding, showcasing dynamic capabilities that have the potential to fully unlock the transition from digital to data-driven smart government. Despite the widespread acknowledgment of the importance of smart government services, there is a notable dearth of research in this area. Additionally, the existing e-government literature lacks a comprehensive criterion for evaluating smart government, serving as a single reference to guide governments in this transformative journey. Thus, the aim of this study is to fill these gaps in the literature. Results from this study reveal that Abu Dhabi government employs emerging technologies, including artificial intelligence, cloud computing, open-access government data, mobile applications, sensors, and social networking. The significance of the current study lies in the inclusion of specific measures to assess smart government maturity.
Smart Government, E-government, Maturity Model, Digital Transformation, UAE.
Yuanshou Chang1, Yu Sun2, 1Arizona State University, 1151 S Forest Ave, Tempe, AZ 85281, 2Computer Science Department, California State Polytechnic University, Pomona, CA91768
In the burgeoning landscape of artificial intelligence, Large Language Models (LLMs) such as GPT have surgedinpopularity, embedding themselves into the fabric of daily digital interactions [1]. As these models assume a pivotal role in shaping discourse, understanding their inherent political biases becomes crucial. This paper delves intothepolitical stance of GPT, examining its consistency and the potential for modification through prompt engineering. Our investigation reveals that GPT exhibits a consistent left-libertarian stance, a finding that underscores theimportance of recognizing and addressing the ideological underpinnings of AI technologies [2]. Furthermore, weexplore the feasibility of altering GPTs political stance towards neutral and right-authoritarian positions throughstrategic prompt design. This research not only illuminates the political dimensions of LLMs but also opens avenuesfor more balanced and controlled AI interactions, of ering insights into the complex interplay between technology, ideology, and user agency.
Prompt Engineering, Artificial Intelligence, Political Bias, Large Language Models (LLMs).
Anurag Yadav1, Neeraj Yadav2, Major Singh Goraya3, 1Sant Longowal Institute of Engineering and Technology, Sangrur, India, 2Bennett University, Greater Noida, India, 3Sant Longowal Institute of Engineering and Technology, Sangrur, India
Selecting a cloud service provider (CSP) from a wide range of options with varying offerings poses a crucial challenge for service requesting consumers (SRCs). The decision criteria outlined in Service Level Agreement (SLA), which defines the Quality of Service (QoS) offered by the CSP, help in selecting a suitable provider. However, relying solely on the SLA for evaluating the merits of a CSP is insufficient. Therefore, the Quality of Experience (QoE) has gained significance in assessing the overall satisfaction of the (SRC). Additionally, monitoring consumer buying behaviour is essential from a business perspective, as all aspects of a business transaction should be mutually beneficial for both the CSP and SRC. Therefore, a new framework is proposed to determine the relative rankings of CSPs and SRCs. Effectiveness of the framework is established through experiments on the QWS cloud dataset, which reveal positive results in terms of implementation complexity and time required.
Cloud Computing, Cloud Service Selection, Quality of Experience, Quality of Service, Multi Criteria Decision making.
Muhammad R Ahmed1, 2, Woshan Srimal1, Thirein Myo1, Mohammed A aseeri3, T. Raja Rani1, Mohammed H Marhaban2, 1Military Technological College, Muscat, Oman, 2Faculty of Engineering, University Putra Malaysia, Selangor, Malaysia, 3King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
Computer vision has been increasingly interested in image classification over the past couple of decades. A manual classification of an image can be a very difficult task and time-consuming process; however, automated image classification can provide a highly accurate result using different methods of image classification. It is very challenging to analyze images automatically by a system when compared to the manual classification with human vision. The basic idea behind classification systems is to capture images, and to process those images simultaneously for categorizing them. Basically, image classification involves classifying an image on the basis of similarities and features. If the images are noisy, blurry, have clusters in the background, or are of poor quality, the results may be inaccurate or false. Considering this, it is necessary to develop an accurate algorithm for classifying images. In this work, we have implemented Convolutional Neural Network (CNN) to classify the images. CNN is a deep learning based machine learning algorithm which mimics the human biological vision system and it can automatically detect the features without the human interaction. In future, we would like to combine with method with another algorithm.
Deep Learning, ANN, CNN, Keras, Image Classification.
Andrew Liu1, Andrew Park2, 1Crean Lutheran High School, 12500 Sand Canyon Ave, Irvine, CA 92618, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
This research paper illuminates the growing necessity for innovative educational platforms in the biomedical field, given the burgeoning population and consequent demand for medical professionals. Proposing a novel solution, the project introduces a Virtual Reality (VR) application, designed using the Unity game engine, to foster scientific exploration within a simulated forensic laboratory environment [1]. Key components of the program include a VR Control System, accurate emulations of scientific equipment, and a cohesive puzzle design to drive user engagement and analytical thinking. Challenges such as VR controls comfort, environment scaling, and asset acquisition were navigated, enhancing the user experience. The application was subjected to user experimentation, particularly with high school students, through two distinct surveys assessing user experience and feedback for enhancements. Results indicated a notable engagement among the younger demographic with a demand for additional levels and specific improvements on equipment like the centrifuge [2]. The feedback gathered underscores the project’s potential in revolutionizing biomedical education, providing an immersive, interactive, and enjoyable learning platform. By addressing the feedback and expanding the project further, the VR application holds promise in significantly contributing to the educational domain, preparing the next generation of medical professionals in a cost-effective and engaging manner.
VR Puzzles, Forensics, Investigations, Unity.
Muhammad Garba, Muhammad Abdurrahman Usman and Anas Muhammad Gulumbe, Department of Computer Science, Faculty of Physical Sciences, Kebbi State University of Science & Technology, Aliero. Nigeria
The study focuses on predicting breast cancer survival using naïve bayes techniques and compares several machine learning models across large dataset of 310,000 patient records. The survival and non-survival classes were the two main categories. The objective of the study was to assess the effectiveness of the Naïve Bayes classifier in the data mining area and to attain noteworthy outcomes for survival classification that were consistent with the body of existing literature. Naive Bayes achieved an average accuracy of 91.08%, indicating reliable performance but with some variability across folds. Logistic Regression achieved an accuracy of 94.84%, excelling in identifying instances of class 1 but struggling with class 0. Decision Tree model, with an accuracy of 93.42%, showed similar performance trends. At 95.68% accuracy, Random Forest outperformed Decision Tree. However, all models faced challenges in classifying instances of class 0 accurately. The Naive Bayes algorithm was compared with K-Nearest Neighbors (KNN) and Support Vector Machines (SVM). Future research will enhance prediction models with new methods and address the challenge of accurately identifying instances of class 0. .
Machine Learning, Data Mininig,Naïve Bayes, Cancer, random survival forest.
Najlae Jennan and El Mehdi Mellouli, Laboratory of Engineering, Systems and Applications, Sidi Mohamed Ben, Abdellah University, Fez, Morocco
This paper focuses on the modelling and control of a bicopter system, an advanced unmanned aerial vehicle (UAV), through a direct Takagi-Sugeno (T-S) fuzzy logic controller based on a fast terminal sliding mode control (FTSMC) technique combined with Particle Swarm Optimization (PSO). The objective of this approach is to mitigate the challenges posed by high nonlinearity and uncertainties in the design of the bicopter system. The process begins with the elaboration of a mathematical model of the bicopter system, followed by the implementation of a fuzzy system adapted to the FTSMC method for the direct control of the bicopter. The control laws controlling the system states are developed based on a stability analysis using the Lyapunov method. Furthermore, the coefficients influencing system stability are optimised using PSO approach in order to enhance the controller’s performance. The efficacy and robustness of the proposed control strategy are validated through simulations, which demonstrate accurate flight attitude tracking of the bicopter. .
Bicopter, fast terminal sliding mode, fuzzy logic, Lyapunov, particle swarm optimization, unmanned aerial vehicle.
Ilhem Feddaoui1 Hela Limam2 Hashim Jarrar3 Jalel Akaichi4, 1ECE Paris_Lyon, 2Institut Supérieur d’Informatique, Université de Tunis El Manar, Tunisia and Laboratoire BestMod, Institut Supérieur de Gestion de Tunis, Tunis, Tunisia, 3Department of Information Systems, Bisha University, Saudi Arabia, 4Department of Computer Science, Bisha University, Saudi Arabia
In the contemporary online environment, the increasing prevalence of distributed and accessible web services presents a challenge, as users struggle to locate services that are suitable for their specific needs. In this context, it is essential to categorize web services that offer the same functionality to facilitate the selection of appropriate options. The imprecision of current web service selection methods can be attributed to the disregard for users feedback and past selections. Furthermore, users encounter difficulties in selecting the most suitable web services when using conventional keyword-based search strategies. Our research introduces a novel hybrid approach that combines syntactic and semantic methods for selecting appropriate web services based on collaborative filtering, ontology-based querying, and QoS. Experiments demonstrate the effectiveness of our proposed selection technique in accurately recommending the required web services. .
Ontology, Query, QoS, Collaborative Filtering, Web service.
Vinicius De Souza Santos, Federal Institute of Education, Science, and Technology of S˜ao Paulo (IFSP) - Campus Birigui, Brazil
The global prevalence of diabetes is increasing at an alarming rate, making early and accurate detection a critical area of interest. This study employs Machine Learning techniques to predict the incidence of diabetes in a population of women from the Pima heritage, known for their predisposition to the disease. Using a database of diagnostic measures, multiple algorithms were applied, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Decision Trees, and Random Forest, to develop predictive models. Principal Component Analysis (PCA) was implemented for dimensionality reduction and highlighting of key diagnostic variables, optimizing algorithm performance. The results highlighted the superiority of the Random Forest, which showed higher accuracy and precision, suggesting its viability as a clinical diagnostic tool. This study contributes to the emerging field of artificial intelligence applications in health, providing valuable insights for the prevention and early treatment of diabetes.
Machine Learning, Diabetes, Principal Component Analysis, Random Forest.
Lulu Pi, Xiaoyong Zheng, Roderick M. Rejesus, North Carolina State University, USA
In this study, using experimental data from agricultural experiment stations in Wis- consin from 1990 to 2010 and the recently developed causal forest double machine learning method (CFDML), we estimate the heterogeneous effects of GM corn on yield and yield risk. We find that there is significant heterogeneity in the yield effect of GM corn adoption, but under most growing conditions, the yield effect is positive and sta- tistically significant. Furthermore, we also find significant heterogeneity in the effect of GM corn adoption on yield risk. However, unlike the yield effect, under most growing conditions, the yield risk effect is statistically insignificant. Only under a few scenarios, adopting GM corn has a statistically significant effect, either negative or positive, on yield risk.
GM Corn, Yield and Yield Risk, Causal Forest Double Machine Learning.
Priyanshu Sharma, VIT Bhopal University, India
Wearable medical devices, crafted to monitor and collect health-related information when worn, strive to empower individuals and their healthcare teams by providing insights into health status, facilitating well-informed treatment decisions. The incorporation of artificial intelligence (AI) and machine learning (ML) further enhances these capabilities. AI involves the development and deployment of computer systems capable of executing tasks traditionally associated with human intelligence, such as creating algorithms and models that enable machines to analyze data, engage in reasoning, learn, and make decisions or predictions. Machine learning, a foundational aspect of AI, involves training algorithms on data to recognize patterns and make predictions or classifications without explicit programming. Through iterative learning processes, machine learning models continually improve their performance and adapt to new data. While the potential of AI and machine learning in wearable medical devices is just beginning to be explored, it is evident that these components offer a diverse range of benefits. Nevertheless, it is crucial to acknowledge that they also present specific challenges and considerations that require careful attention.
Machine learning, IoMT, Wearable devices, Monitoring, Healthcare applications.
Forrest McKee and David Noever, 4901-D Corporate Drive, Huntsville, AL, USA
The widespread adoption of voice-activated systems has modified routine human-machine interaction but has also introduced new vulnerabilities. This paper investigates the susceptibility of automatic speech recognition (ASR) algorithms in these systems to interference from near-ultrasonic noise. Building upon prior research that demonstrated the ability of near-ultrasonic frequencies (16 kHz - 22 kHz) to exploit the inherent properties of microelectromechanical systems (MEMS) microphones, our study explores alternative privacy enforcement means using this interference phenomenon. We expose a critical vulnerability in the most common microphones used in modern voice-activated devices, which inadvertently demodulate near-ultrasonic frequencies into the audible spectrum, disrupting the ASR process. Through a systematic analysis of the impact of near-ultrasonic noise on various ASR systems, we demonstrate that this vulnerability is consistent across different devices and under varying conditions, such as broadcast distance and specific phoneme structures. Our findings highlight the need to develop robust countermeasures to protect voice-activated systems from malicious exploitation of this vulnerability. Furthermore, we explore the potential applications of this phenomenon in enhancing privacy by disrupting unauthorized audio recording or eavesdropping. This research underscores the importance of a comprehensive approach to securing voice-activated systems, combining technological innovation, responsible development practices, and informed policy decisions to ensure the privacy and security of users in an increasingly connected world.
Cybersecurity, Voice-activated systems, Automatic speech recognition, Near-ultrasonic frequencies, MEMS microphones, privacy, Acoustic interference, Internet of things, Dgital signal processing, Audio forensics.
Ruoxuan Dong1, Austin Amakye Ansah2, Shuyu Wang3, 1Shanghai Soong Ching Ling School, No.2 Yehui Road, Qingpu District, Shanghai, China, 2The University of Texas at Arlington, 701 S Nedderman Dr, Arlington, TX 76019, 3California State Polytechnic University, Pomona, CA 91768
Most people listen to music to regulate mood, achieve self-awareness, and for socialization [9]. 95.6 percent ofAmericans aged 13 and older listen to some form of audio in their lives. According to a study, people stop findingnew music by age 30. Our solution, Vocodex, provides a way for people to find new music related to their moodthrough emotional state description. Our solution is built with Flutter, which provides the tools needed to build a cross-platform app for Android, Ios, and the web [10]. Our app classifies emotions of up to four different classesand uses that information to fetch songs from a database. Songs are then played with our in-app music player. Themodel trained to analyze text achieved up to 94% accuracy when retrained on a heavier dataset.
Flutter, AI , Music, SVM.
Chunming Zhu1, Zachary Andow2, 1Beckman High School, 3588 Bryan Ave, Irvine, CA, 2Computer Science Department, California State Polytechnic University, Pomona, CA
In recent years, environmental and technological concerns have rapidly approached, emphasizing the importance of energy consumption. The paper details the design and implementation of a motion detection-based lamp system, capable of switching a lamp on and off based on predefined conditions, leading to significant energy savings. The project includes two-way communication between devices, a user-friendly control interface, and an accurate way to save energy. Users can control their system through the application and specify the requirements to their likening. This application can help ease of life along with a reduced energy consumption. Careful testing has been done along with control cases to compare the findings and shows the amount of precision that must be used for intelligent control systems. The results show that automated lights or electronics can be extremely useful along with the ability to remotely control your devices with the application. This application is suitable for consumption and offers a great opportunity for those invested to save energy with minimal effort.
Energy Consumption, PIR sensor, Motion Detection, Adafruit ESP32-S2.
Abreu Andre Boavida1, Shan Lu2, FUKAI Hidekazu3 and Ferdinando da Conceição Soares4, 1Department of Informatics Engineering, National University of Timor-Leste Dili, Timor-Leste, 2Information and Communication Engineering Nagoya University Nagoya, Japan, 3Intelligent Science and Engineering Gifu University Gifu, Japan, 4Department of Informatics Engineering, National University of Timor-Leste Dili, Timor-Leste
Recent advancements in computer vision technology, specifically focusing on its impact on transportation management and security in the context of the proliferation of microlet vehicles in Dili. This study presents a comprehensive approach to improving the detection and classification of microlite vehicles in urban environments using state-of-the-art deep learning and computer vision techniques, addressing challenges such as similar color patterns. The primary objective is to identify microlet vehicle lanes based on existing color code numbers corresponding to routes, with a secondary goal of assessing and detecting the location of the microlet routes based on color code numbers. Various computer vision algorithms, including YOLOv8, YOLO- NAS, Faster RCNN, and HoG, are explored, considering lighting conditions and vehicle interactions. Real-world applications in urban traffic management and autonomous vehicle navigation are tested. The study evaluates the performance of different deep learning models and object detection techniques on a dataset of 14,713 microlit vehicle images, with Mobilenetv2 and Yolov8 identified as the most proficient models achieving high accuracy in color path classification and object detection, respectively. The research provides valuable insights for applying advanced computer vision models to address challenges in microlet vehicle path detection and classification, ultimately enhancing urban transportation management.
Microlet vehicles, object detection, classification, deep learning, computer vision and image processing.
Yves Spencer Catuday, Mark Jerald De Torres and Godwin Emmanuel Tayas, Department of Electronics Engineering, Batangas State University, Batangas City, Philippines
This study presents the development of an attendance monitoring system that utilizes face recognition technology. The system is built using Python libraries and aims to provide an efficient method for tracking student attendance in educational institutions. The study discusses the rapid advancements in face recognition technology and its growing application in various fields, including security, authentication, and identification. Traditional attendance methods are often tedious and time-consuming, leading to the exploration of automated systems like the one proposed in this study. The system works by initializing a web camera and detecting student’s faces in real time. Once a face is recognized, the system marks the student’s attendance. The system has been tested with a dataset of 25 student images, achieving a recognition rate of 92% and an overall accuracy of 84%. Despite some challenges, such as the complexity of installing Python libraries and factors affecting recognition accuracy, the system demonstrates the potential for real-world application. The study concludes that face recognition libraries in Python can successfully locate and identify faces from a database, making them suitable for attendance monitoring scenarios. For future research, the study suggests adding features to adjust video quality based on surrounding conditions and incorporating a stabilizer to improve the accuracy and stability of the recognition phase. The researchers believe these enhancements could improve system performance and broader applicability. This study contributes to the growing body of research on the practical applications of face recognition technology and offers a novel approach to attendance monitoring in educational.
Face Recognition, Python, Attendance Monitoring System, Face Recognition Library.
Mujiba Shaima1, Md Nasir Uddin Rana1, Md Tanvir Islam1, Norun Nabi2, Mazharul Islam Tusher1, Estak Ahmed1, Sushanta Saha1, 1Department of Computer Science, Monroe College, New Rochelle, New York, USA, 2Master of Science in Information Technology (MSIT)- Washington University of Science and Technology (WUST), Alexandria, Virginia, USA
IoT, or the Internet of Things, describes a network of networked objects that are equipped with software, sensors, and other technologies to gather and share data. However, blockchain is a distributed ledger technology that makes it possible to record transactions over a network of computers in a safe, transparent, and unchangeable. The way that blockchain and IoT can enhance each others advantages is how they are connected: Blockchain technology, with its decentralized and impenetrable ledger, offers safe and effective storage and transfer of the massive volumes of data generated by Internet of Things devices. Organizations may guarantee the security and integrity of IoT data by incorporating blockchain technology into IoT systems. This will allow for reliable and open communications and transactions between users and devices. Here, we summarize the current body of research and draw attention to the main cybersecurity issues facing blockchain-based Internet of Things platforms. These problems are divided into three primary categories: (i) security of IoT devices; (ii) security of blockchains; and (iii) integration of IoT devices with blockchain (network security). To further address a little about these issues and improve the cybersecurity of blockchain-based IoT systems, we also analysis future research directions.
IoT, Blockchain, Blockchain Layer, Hash Identification.
Masoomeh Zeinalnezhad and Somaieh Alavi, Mahdieh Kiani Peikani, Radman Rahimi Yeganeh
Background: The prevalence of diabetes is alarming in Iran. While thyroid diseases harm diabetes control, the possibility of having thyroid is uncertain among Iranian people with diabetes. This research aims to design an intelligent system to predict thyroid disorders, including hypothyroidism, hyperthyroidism, subclinical hyperthyroidism, and subclinical hypothyroidism, among diabetic patients with hyperlipidemia. Methods: The study comprised 249 medical records collected from two laboratories over one month in Tehran, Iran, annotated with nine features, including age, gender, Cholesterol, Triglyceride, Thyroid stimulating hormone (TSH), Triiodothyronine (T3), Tetra iodothyronine (T4), Fast Blood Sugar (FBS), and High-Density Lipoprotein (HDL). As the performance of machine learning (ML) models can vary from one dataset to another based on the characteristics of the outcome and features, several algorithms were assessed in this study. In particular, Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM) were applied and improved by Genetic Algorithm (GA) through hyper-parameter optimization. The models were evaluated with Accuracy, Precision, Recall, and F-measure indices. Results: The proposed GA-DT model achieved an accuracy rate of 99.12%, with better prediction performance. Conclusion: The output of this study confirms that the proposed integrated approach of data mining and meta-heuristic algorithms could be used for the reliable design of a clinical decision support system for diagnosing thyroid disorders among diabetic patients.
Diabetes, Thyroid Disorders, Hyperlipidemia, Data mining, Genetic Algorithm, Tehran.
Zixuan Chen, Garret Washburn
In the current digital age, the capability of a business or workforce team to send out or receive a mass amount of emails is increasingly becoming more important. However, with this becoming more and more of a necessity, the tools to do so are not developing as rapidly. The method this paper seeks to provide as a solution to this problem is the Email Sender and Receiver applications that are described within this paper [1]. The main technologies used to create these Python applications behind the scenes are the Mailgun API, the Gmail API, and the PySimpleGui Python library [2]. One of the challenges that we faced during the development of the Email Sender and Receiver applications was the scheduling of emails for recipients in dif erent timezones, as it is important that the sending of emails be accurate to their time across the globe. Another challenge faced during development was the configuration and maintenance of the Google Project in order to use the Gmail API, as the backend for the Email Receiver application relies on the Gmail API to grab from the user account [3]. To ensure the functionality and efficiency of the Email Sender and Receiver applications, we conducted experiments on both applications to test their limits. For the Email Sender application, an experiment was conducted to see how the size of the email being sent would af ect how the application runs, and we found the application to be quite stable. Additionally, for the Email Receiver application, another experiment was conducted to test and see how far into the past we could grab emails and not impact the application. As a result, we found the Gmail API limits how many emails you may grabinone request. To conclude, the Email Sender and Receiver applications are viable tools for companies and individuals alike, as both applications provide an easy-to-use bulk email tool that doesn’t compromise functionality.
Bulk Email, Email System, Software, Bulk Email System.
Zerihun Olana Asefa and Admas Abtew, Jimma University, Department of Information Technology
News recommendation schemes utilize features of the news itself and information about users to suggest and recommend relevant news items to the users towards the interest they have. However, the effectiveness of the existing news recommendation scheme is limited in the occurrence of new user cold start problems. Therefore, we designed a news recommender system using hybrid approaches to address new user cold start problems to ease and suggest more related news articles for new users. To achieve the objective mentioned above, user demographic data with a hybrid recommendation system that contains the scheme of both content-based and collaborative filtering approaches is proposed. To evaluate the effectiveness of the proposed model, an extensive experiment is conducted using a dataset of news articles with user rating value and user demographic data. The performance of the proposed model is done by two ways of experiment. So, the performance of the proposed model performs around 68.05% of Precision, 42.46% of Recall and 52.1% of the average F1 score for the experiment based on individual user similarity in the system. And also performs around 93.75% of precision, 40.25% of recall and 56.31% F1-score for the similarity of users based on the similarity of users within the same category which is better than the first experiment.
news recommendation system; cold start problem; hybrid approach; demographic information; new users; popular news.
Kota Shamanth Ramanath Nayak and Leila Kosseim, Computational Linguistics at Concordia (CLaC) Laboratory Department of Computer Science and Software Engineering Concordia University, Montr´eal, Qu´ebec, Canada
This paper describes our approach to hierarchical multi-label detection of persuasion techniques in meme texts. Our model, developed as a part of the recent SemEval task is based on fine-tuning individual language models (BERT, XLM-RoBERTa, and mBERT) and leveraging a mean-based ensemble model in addition to dataset augmentation through paraphrase generation from ChatGPT. Results with the SemEval 2024 data show that training with paraphrases significantly enhances the model performance but using a balanced training set is more beneficial than a larger unbalanced one. Moreover, analysis underscores the potential pitfalls of indiscriminate incorporation of paraphrases from diverse distributions as they can introduce substantial noise in the model.
Language Models, Multi-label Classification, Persuasion Techniques.
1Institut Supérieur d Informatique, Université de Tunis El Manar, Laboratoire BestMod, Institut Supérieur de Gestion de Tunis University of Tunis, Tunis, Tunisia, 2Department of Information Systems, University of Bisha, Saudi Arabi, 3Computer Science Department, University of Clermont Ferrand, Clermont Ferrand, France, 4Ecole Supérieure de Commerce de Tunis University of Manouba, Laboratoire BestMod, Institut Supérieur de Gestion de Tunis University of Tunis
Saving patients’ lives in peril is a crucial task; therefore, it is the primary concern of physicians and/or caregivers. While heading to an adequate medical institution which must be determined with precision, late response, information overload, and poor decisions may lead to serious even catastrophic consequences on a patient’s health. It is even worse if we know that some areas do not have good medical coverage such as in rural regions. A good solution is to enhance mobile physicians, which is an ordinary physician in move with any adequate vehicle, functionalities. She/he has the role to visit the patient when requested, providing initial aid, making an initial diagnosis, and giving care of a patient while moving to a medical institution when involved. To facilitate, for the mobile physician, efficient on-road decision-making, we propose a medical decision support system, mainly, based on an Ontology-driven approach for effective emergency management. It permits ascertaining and ranking the appropriate medical institutions, as quickly as possible, including healthcare resources institutions according to the patient’s condition determined initially thanks to the first diagnosis, and evaluated, continuously, in real-time.
Emergency management, Mobile physician, Routing algorithm, Ontology, Location-based services, Healthcare Pervasive systems, Ranking.