Lana Do and Tehmina Amjad, Khoury College of Computer Sciences, Northeastern University, Silicon Valley Campus, California, USA
Fine-grained sentiment analysis captures subtle emotional tones in text, offering insights beyond positive and negative classifications. It helps users make informed decisions by revealing nuanced opinions and sentiment intensities in textual data. This paper introduces Sentiment-Enhanced Fine-Tuned DeBERTaV3 (FiTSent DeBERTaV3), a classification model designed for both sentence-level and document-level sentiment analysis. Built upon the DeBERTaV3 architecture, our model incorporates tailored fine-tuning strategies to address the unique characteristics of each dataset. On the Stanford Sentiment Treebank (SST-5), fine-tuning addresses shorter, nuanced texts, while for Yelp Reviews, strategies are adapted for longer, narrative-style reviews. Additionally, the use of attention pooling allows the model to prioritize sentiment-critical tokens, enhancing its ability to capture subtle sentiment distinctions. FiTSent DeBERTaV3 achieved competitive performance, outperforming baselines on both tasks. These results highlight the effectiveness of our approach and its versatility in handling datasets with varying lengths and complexities, which have not been jointly evaluated before.
Fine-Grained Sentiment Analysis, DeBERTaV3, Dataset-specific Fine-Tuning, Sentiment-Focused Attention Pooling, Sentence-level analysis & Document-level analysis.
Xinyi Zhou1, Zihao Luo2, 1The Derryfield School, 2108 River Road, Manchester, NH 03104, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
This research presents an AI-driven mental health platform designed to provide personalized emotional support, journaling, and geolocation-based resource navigation [1]. The app integrates an empathetic chatbot, customizable journaling tools, and a resource locator to bridge gaps in mental health accessibility [2]. User surveys with 10 participants revealed high satisfaction with the chatbot’s emotional support and the journaling feature’s guided prompts, while the resource locator faced occasional accuracy issues. Key challenges, including NLP accuracy, geolocation reliability, and advanced customization, were identified as areas for improvement. By leveraging user feedback and iterative updates, this app has the potential to revolutionize accessible mental health support.
Mental Health, AI-Driven Chatbot, Personalized Journaling, Geolocation Resources, User-Centered Design
Lee Seo-jun1, Choi Seo-yeon1, Oh Ji-soo1, and Gyu Tae Bae2, 1High School of Korea, Seoul, 2University of California, Berkeley
Frequent wildfires are becoming an increasing menace to environments, buildings and people especially in places that have large areas of forest cover. In South Korea around 63% of the total land is forested which renders it an area that is continually and severely threatened by wildfires. Usual methodologies of fire detection using satellite images or ground based observation are uncomfortable since they come with drawbacks such as high costs, long response time, and weather interference. In this paper we describe an efficient fire detection system for UNMANNED AERIAL VEHICLE (VTOL) incorporating Convolutional Neural Networks. This makes it possible to greatly enhance detection performance of the models even in complicated conditions that were in the original design. In simulations that approximate the field situations as closely as possible, real time operations of the optimized algorithm yielded 93 percent of the target detection percentage with 20 percent of false positives and a frame latency of 1.2 seconds. Furthermore, the implementation of the model on a Raspberry Pi within a VTOL drone proved the ability of the system for on demand forest fire surveillance and control. This work delineates the promise of drone based systems for fire detection to supplement existing systems for wildland fire prevention.
Fire Detection, VTOL Drones, Convolutional Neural Networks, Real-Time Processing, Wildfire Surveillance.
Aisheng Wang1, Carlos Gonzalez2, 1Crean Lutheran High School, 12500 Sand Canyon Ave, Irvine, CA 92618, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
The YouTube Summarizer is an AI-powered system designed to extract key insights from lengthy YouTube videos using GPT-4o-mini, Retrieval-Augmented Generation (RAG), and FAISS embeddings [1]. The system retrieves transcripts via the YouTube API, processes them using LangChain, and generates concise summaries optimized for gaming strategies, educational content, and technology reviews [2]. Experiments comparing AI-generated summaries with human-written summaries showed that while the system performed well in structured content, it struggled with highly technical topics. Identified challenges include incomplete transcripts, oversimplification, and domain-specific accuracy gaps, which could be addressed through fine-tuning AI models, improving transcript retrieval, and integrating query refinement options [3]. The results demonstrate the efficiency and potential of AI-driven video summarization, paving the way for future enhancements in accuracy, adaptability, and user interactivity.
YouTube Summarization, Artificial Intelligence (AI), GPT-4o and RAG, Video Transcript Processing, FAISS Embeddings.
Abol Froushan, Fellow of the Royal Society of Arts, London, UK
The advent of AI-assisted creativity introduces new possibilities for poetic generation, yet it remains unclear how AI poetry compares to human-authored verse in depth, resonance, and complexity. This paper presents the Poetic Intensity Measurement Framework (PIMF), a structured method for assessing poetic intensity across 15 dimensions, including creative imagination, unpredictability, emotional intensity, and sonic quality. By applying vector space modelling, we compare AI and human poetry, revealing key deficiencies in AI-generated verse, particularly in metaphorical depth, emotional resonance, and unpredictability, which hinder its ability to achieve poetic transcendence. The framework extends beyond poetry, offering applications in music, visual arts, and cinematic expression, demonstrating how poetic intensity manifests across artistic disciplines. Our findings reveal the limits of AI in replicating poetic transcendence while showcasing its potential as a collaborative tool for creative exploration. This research bridges quantitative analysis and humanistic interpretation, contributing to computational poetics, AI creativity, and machine-assisted literary analysis.
Computational Poetics, Poetic Intensity Measurement, AI-Generated Poetry, Creative Imagination in AI, Human-AI Collaboration in Literature.
Peter Luh1, Alan Wilhelm2, 1Retired Physicist, San Jose, California, USA, 2CTO @ Referential.ai, San Francisco, California USA
AI chatbots promise indispensability, yet no standard measures this quality beyond innovation or ethics. Inspired by Samuel Johnsons quip, "Im lost without my Boswell, often attributed via Holmes to Watson, we propose the Boswell Test, a framework assessing AI companions indispensability through mentor-level expertise and intimate user insight. Our initial test, probing complex AI policy queries, reveals strengths in knowledge delivery (such as U.S., China) but gaps in personalization (such as EUs broad ethics, Indias scale focus). We automate such queries via multiple chatbots with cross-assessment of grading each others responses. Automation easily extends to multiple domains. True indispensability where users feel lost without my chatbot, however, requires understanding the human host, an elusive frontier for todays AI, constrained by data and algorithmic limits. .
Boswell Test, Boswell Quotient, Chatbot, Indispensability, Turing Test.
T. Charaa1, T. Hamdeni2, and I. Abdeljaoued-Tej2, 1University of Carthage, Tunisia, 2University Tunis-El-Manar, Tunisia
Text-guided image editing on real images, particularly in the context of fashion, presents a highly versatile yet challenging task. This process requires that the editing system take as input only the original image and a textual instruction specifying the desired modifications. The system must autonomously identify the regions of the image to be altered while preserving the other characteristics of the original image. In this paper, we present our approach, which leverages state-of-the-art artificial intelligence techniques, including deep neural networks, large language models (LLMs), and advanced methods for image generation and editing, such as Stable Diffusion and InstructPix2Pix. By integrating these models, our system achieves precise interpretation of textual instructions and ensures consistent application of modifications, while maintaining the visual integrity and authenticity of the original image. This framework offers a robust and scalable solution for text-guided image editing in fashion and other domains.
Artificial Intelligence, Computer Vision, Image Editing, Neural Models, Text-Guided Image Editing, Deep Learning, Large Language Models (LLMs).
Abdeen Mustafa Omer, Energy Research Institute (ERI), Nottingham, United Kingdom
The use of renewable energy sources is a fundamental factor for a possible energy policy in the future. Taking into account the sustainable character of the majority of renewable energy technologies, they are able to preserve resources and to provide security, diversity of energy supply and services, virtually without environmental impact. Sustainability has acquired great importance due to the negative impact of various developments on environment. The rapid growth during the last decade has been accompanied by active construction, which in some instances neglected the impact on the environment and human activities. Policies to promote the rational use of electric energy and to preserve natural non-renewable resources are of paramount importance. Low energy design of urban environment and buildings in densely populated areas requires consideration of wide range of factors, including urban setting, transport planning, energy system design and architectural and engineering details. The focus of the world’s attention on environmental issues in recent years has stimulated response in many countries, which have led to a closer examination of energy conservation strategies for conventional fossil fuels. One way of reducing building energy consumption is to design buildings, which are more economical in their use of energy for heating, lighting, cooling, ventilation and hot water supply. However, exploitation of renewable energy in buildings and agricultural greenhouses can, also, significantly contribute towards reducing dependency on fossil fuels. This will also contribute to the amelioration of environmental conditions by replacing conventional fuels with renewable energies that produce no air pollution or greenhouse gases. This study describes various designs of low energy buildings. It also, outlines the effect of dense urban building nature on energy consumption, and its contribution to climate change. Measures, which would help to save energy in buildings, are also presented.
Renewable technologies, Built environment, Sustainable development, Mitigation measures.
Shangchen Sun1, Han Tun Oo2, 1Andrews College, 15800 Yonge Street, Aurora, ON L4G 3H7 Canada, 2California State Polytechnic University, Pomona, CA 91768
This research evaluates a fencing training application that leverages AI and CV2 for pose estimation and real-time feedback. Two experiments were conducted to address critical performance challenges. The first experiment tested the impact of camera angles on pose estimation accuracy, finding optimal results with a front-facing camera and notable deviations at non-ideal angles [1]. The second experiment assessed the system’s ability to detect head positions when users wore fencing helmets, with accuracy dropping from 100% to 87% due to occlusions. Key findings emphasize the need for user guidance in camera placement and algorithmic improvements for handling occlusions. Proposed solutions include incorporating multi-angle calibration, adaptive algorithms, and potential hardware upgrades like depth-sensing cameras [2]. These improvements aim to enhance accessibility, affordability, and precision, making the application a valuable tool for beginners and advanced fencers alike. Overall, this study highlights the potential of AI-driven solutions in democratizing sports training.
Fencing Training, Pose Estimation, Artificial Intelligence (AI), CV2 Detection, Real-Time Feedback.
Varad Joshi and Kesani Hanirvesh, KL University, India
This paper introduces an AI-driven framework for optimizing Continuous Integration (CI) and Continuous Deployment (CD) pipelines in healthcare software systems. These systems demand high reliability, compliance, and operational efficiency due to their critical nature. Traditional DevOps workflows face challenges such as deployment failures, compliance complexity, and human intervention. Our framework leverages machine learning algorithms for anomaly detection, automated compliance verification, and intelligent monitoring to enhance CI/CD pipelines. Key findings from a case study on a hospital management system reveal a 40% reduction in deployment errors and improved system reliability by 25%. This study demonstrates how AI can bridge critical gaps in healthcare DevOps, enabling safer and more efficient software delivery.
DevOps, Continuous Integration, Continuous Deployment, Healthcare Systems, Artificial Intelligence.
Junade Ali, Engprax Ltd, Edinburgh, Scotland, UK
Case studies have shown that software disasters snowball from technical issues to catastrophes through humans covering up problems rather than addressing them and empirical research has found the psychological safety of software engineers to discuss and address problems to be foundational to improving project success. However, the failure to do so can be attributed to psychological factors like loss aversion. We conduct a large-scale study of the experiences of 600 software engineers in the UK and USA on project success experiences. Empirical evaluation finds that approaches like ensuring clear requirements before the start of development, when loss aversion is at its lowest, correlated to 97% higher project success. The freedom of software engineers to discuss and address problems correlates with 87% higher success rates. The findings support the development of software development methodologies with a greater focus on human factors in preventing failure.
Software development methodologies, Agile software, loss aversion, socio-technical systems.
Hye-Young Kim, Department of Games, Hongik University, Sejong-si, South Korea
NFT (Non-Fungible Token) has considerable potential in the field of intellectual property. It can not only improve the efficiency of copyright registration but also promote the improvement of transaction transparency and liquidity. However, existing copyright protection schemes of NFT image relied on the NFTs itself minted by third-party platforms. Also, the widespread use of NFTs has introduced new complexities to copyright protection due to their unique nature. Therefore, we have proposed a multi-layered blockchain security framework to resolve security vulnerabilities by protecting users from threats such as illegal copying, intellectual property rights infringement, and malware infection that may occur during the process of acquiring NFT assets through analysis of smart contracts, metadata, and digital assets that constitute NFTs.
Blockchain, Non-Fungible Token, copyright protection, smart contract.
Anthony Chidi Nzomiwu, Krakow University of Economics, Poland
The integration of Artificial Intelligence (AI) and blockchain technology presents transformative opportunities for enhancing security and efficiency in financial risk management. This paper examines the convergence of these technologies through a security-first lens, proposing a novel framework that leverages the computational capabilities of AI and the cryptographic properties of blockchain to strengthen security in financial risk assessment, monitoring, and mitigation. We analyze potential threats in current financial risk management systems and demonstrate how AI-blockchain integration can address these vulnerabilities while enabling more sophisticated risk analytics. Our research contributes to existing literature by: (1) establishing a security architecture for AI-blockchain integration in financial contexts, (2) evaluating cryptographic mechanisms for preserving data integrity in AI-driven risk assessment, and (3) developing a threat model specific to automated financial risk systems. Through case study analysis of implementations in credit risk assessment and fraud detection, we provide empirical evidence of security improvements and identify implementation challenges. The proposed framework offers financial institutions a structured approach to deploying these technologies while maintaining robust security controls and regulatory compliance.
Artificial Intelligence, Blockchain Technology, Financial Risk Management, Cryptographic Security, Trust Mechanisms, Financial Fraud Detection.
Calla Huayapa Maxgabriel Alexis1, Huaillapuma Santa Cruz Luis Martin1, Maldonado Mamani Ricardo Anibal2, Zapana Yucra Franklyn1, 1Universidad Nacional de Juliaca, Perú, 2Universidad Andina Néstor Cáceres Velásquez, Puno, Perú
The objective is to determine the impact of three work methods on the assembly times of production lines L1, L2, and L3, and thereby establish a standardized method that ensures the most efficient and uniform performance in the production process. The research methodology is based on an experimental design model where three types of work methods are applied under controlled conditions. Assembly times are evaluated through 10 repetitions per production line. The information obtained is analyzed using analysis of variance (ANOVA), which determines the significant differences between the times of each method applied to the production lines. The results indicate significant differences among the production lines, with a p-value of 0.000, which is less than 0.05. In conclusion, the work method has had an important impact, as production line A presented better strategies and thus improved efficiency.
Work Methods, Production Times, Production Lines, Assembly, Industrial.
Ancia Katjiteo and Enock Limbo Simasiku, Department of Applied Education Sciences, Faculty of Education and Human Sciences, University of Namibia, Namibia
This is a fact that needs to be understood why the subject of entrepreneurship is important in preparations of the young and upcoming generation of learner to face the job market opportunity. Traditional assessment approaches are not effective in identifying various and numerous skills and competencies required for entrepreneurship success. To fill this gap, this systematic review synthesizes the existing research on alternative assessments that have been proposed for use in the context of entrepreneurship education with the aim of informing new assessment methods which could enhance students’ achievement of learning outcomes and readiness for entrepreneurship. In the past, the Namibian assessment practices have been more of norm referenced whereby medium of assessment was mostly paper and pencil tests and written examinations which seem to go against the grain of learners’ diverse learning styles, cultural endowments and multiple intelligences. The advantage of the use of alternative evaluation is that it flexible and comprehensive and allows for showcasing knowledge in a range of ways. This research proceeds through a comprehensive literature review and analysis of peer-reviewed publications to select various multiple assessment approaches. Such methods include, but are not limited to, the following: experiential learning projects, business simulations, case analyses, portfolio presentation and pitching. The review analyses the effect of these other forms of assessments in developing the evaluation of self-critical thinking, creativity problem solving skills and the development of an entrepreneurial spirit. Moreover, it examines the various factors that affect the utilisation as well as the acceptance of the other forms of assessment such as preparation of the teacher, availability of resources, and students. From this analysis, the understanding of how alternative assessment could improve the entrepreneurial education curriculum and future studies.
Alternative assessments; Entrepreneurship education; Assessment methods; Experiential learning; Active learning; Student engagement; Simulation games.
Haleema Azra and Iffath Zeeshan, Department of Education, American College of Education, Indiana, USA
The increasing adoption of big data analytics in higher education presents both opportunities for enhancing student outcomes and significant challenges regarding privacy protection and ethical data governance. This qualitative study investigated how educational institutions can effectively balance the implementation of data analytics while maintaining robust privacy protections and ethical standards. Through semi-structured interviews with 30 participants at Greenfield University, including faculty, administrators, and data privacy officers, the research explored current practices, challenges, and potential solutions in educational data analytics. Using thematic analysis, four major themes emerged: ethical framework challenges, privacy protection measures, student rights and consent, and institutional policy implementation. The findings revealed significant tensions between leveraging data for educational improvement and protecting student privacy, particularly in areas of predictive analytics and early intervention strategies. Participants highlighted the inadequacy of traditional consent mechanisms and the need for more transparent data practices. The study identified critical gaps in existing ethical frameworks and emphasized the importance of developing comprehensive guidelines that balance technological innovation with privacy protection. Key recommendations include implementing more robust data governance frameworks, enhancing transparency in data collection and usage, and developing more effective mechanisms for obtaining meaningful informed consent from students. This research contributes to the growing body of literature on ethical considerations in educational data analytics and provides practical insights for institutions seeking to implement data-driven approaches while maintaining ethical integrity and student privacy.
big data analytics, higher education, student privacy, ethical frameworks, data governance, educational technology.
Majlinda KETA1, Valentina SINAJ2, 1Faculty of Social Sciences, University of Tirana, Albania, 2Faculty of Economy, University of Tirana, Albania
This study aims to examine the impact of gender on digital ethics and the use of technology by academic staff at the University of Tirana (UT), with a focus on differences across faculties. With a sample of 315 lecturers from six faculties of UT, this research uses the questionnaire method to collect data regarding the academic staff’s knowledge of digital ethics, use of technology, trust in digital standards and perception of the impact of digital ethics on institutional culture. Data analysis, descriptive statistics and regression analysis were used to test the hypotheses raised. Data processing was carried out using R software. The results of this study will provide valuable insight into identifying factors that may help or hinder digital transformation and the use of digital ethics in the academic environment. The study can also contribute to the development of strategies to improve awareness and implementation of digital ethics within universities.
Digital ethics, gender, Higher education; academic staff
Yuhan Wu2, Ang Li2, 1Margaret’s Episcopal School, San Juan Capistrano, CA 92675, 2California State Polytechnic University, Pomona, CA 91768
This research explores the development of an AI-powered journaling application designed to improve teen mental health by combining wellness journaling, AI-generated feedback, and trend analysis. The study investigates whether frequent journaling and AI-driven insights contribute to emotional awareness, stress management, and long-term mental well-being [10]. The application uses ChatGPT API to analyze journal entries, providing structured feedback and generating weekly mental health trend reports. Two experiments were conducted: one measuring pre- and post-survey results and another comparing frequent vs. infrequent journaling. Results showed significant improvements in stress reduction and emotional expression among frequent journalers, validating AI-assisted journaling as a promising tool. However, challenges such as AI personalization, user engagement, and external influences require further refinement [11]. This research suggests that AI-powered journaling can serve as a valuable complement to traditional mental health resources, fostering self-awareness and proactive emotional management among teenagers [12].
AI-assisted journaling, Mental health support, Teen well-being, Emotional self-reflection, Hybrid mental health solutions.
Soubhik Baral, Meenakshi Ghai, Kanchan Mali, Nidhi Jain, Central Research Laboratory, Bangalore, India
The demand for smart agriculture is burgeoning worldwide, driven by the increasing need for Internet of Things (IoT) solutions. With India’s status as the world’s most populous nation, there is a significant demand for agricultural advancements. Despite historical advancements in labor efficiency, population growth continues to challenge the balance of supply and demand over time. This has led to a desire for “smart agriculture” which makes use of computer programs for field data analysis and monitoring in addition to sensor-based network deployment. The use of IoT-based smart agriculture technology may benefit family farming and organic agriculture. In this paper, LoRa-based wireless smart network solution was designed and developed. The primary focus of the network solution is the proprietary IoT gateway and sensors, which are interfaced on a Single Board Computers (SBC) to give a wide range of field data collecting capabilities. The study’s key component is its ability to use LoRa-based IoT gateways to gather sensor data from the farm or agricultural field, send it to the localhost LoRaWAN server, and onward via internet to our backhaul network for detailed analysis. To enhance agricultural efficiency, this project developed an Android application capable of monitoring temperature, humidity, wetness, water level, and soil conditions. The network solution will enable farmers and governments to make more informed decisions and optimize the use of resources.
LoRa, IoT gateway, SBC, Sensors.
Yongning Tang1, Feng Wang2, and Zutong Hu2, 1School of Information Technology, Illinois State University, 2 School of Engineering, Liberty University
The growing demand for diverse 5G applications requires efficient network slicing to meet strict QoS needs. This paper proposes the AI-Orchestrated Slicing Framework (AOSF), integrating AI-driven traffic prediction, resource allocation, and real-time QoS monitoring across network domains. AOSF utilizes a multimode LSTM for traffic forecasting, a Deep Q-Learning Network (DQN) for resource management, and a Statistical-Autoencoder for anomaly detection, balancing elasticity and utilization in dynamic environments. Implemented on a 5G testbed with OpenAirInterface, ONAP, and Kubernetes, AOSF demonstrates superior resource efficiency, elasticity, and scalability in extensive evaluations. This work establishes AOSF as a scalable and high-performance solution for network slicing in 5G and beyond.
network Slicing, resource allocation, elasticity, utilization, AI, 5G and beyond.
Muhammad Junaid Iqbal and Jordi Serra-Ruiz, Universitat Oberta de Catalunya (UOC),CYBERCAT-Center for Cybersecurity Research of Catalonia, Rambla del Poblenou, 154-156, 08018 Barcelona, Spain
Ransomware has emerged as a critical and rapidly evolving cybersecurity threat, significantly impacting sectors such as healthcare, finance, and government infrastructures. This paper presents a comprehensive survey of contemporary ransomware detection techniques, focusing on machine learning (ML) and deep learning (DL) methodologies, which have shown promise in adapting to the rapidly changing landscape of ransomware attacks. The survey includes a detailed comparative analysis of static, dynamic, and hybrid detection models, highlighting their respective advantages and limitations. The key findings from the survey show that ML and DL-based methods have a better detection capabilities but still having challenges such as large and diverse datasets, the computational cost of advanced techniques, and model adaptability across various platforms still exist. We also delve into some up-and-coming trends, like quantum computing and federated learning, both of which have the potential to overcome present limitations in computation efficiency and privacy concerns, respectively. It also points to the increasing attention being paid to adversarial defenses, which seek to make models more robust against complex evasion attempts.
Ransomware; Machine learning; Deep Learning; Cybersecurity; Hybrid Detection; Adversarial Defense.
Amal Abdulaziz Alrasheed, Amal Nasser Aldosari, Reema Fahad Alharthy, Dena Adel Alfaiz and Shatha Ibrahim Almalki, Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Temporary disabilities such as fractures are common injuries that may affect humans. The most susceptible parts of fractures are legs and arms, especially in children, which make them need physical therapy sessions after removing the cast. However, children face difficulties and challenges in adhering to physical therapy sessions in health centers due to their tension and fear of the center environment, which may affect their performance and commitment to these sessions. The proposed system “Mareen” solves these challenges by providing an innovative digital game that transforms physical therapy into an attractive and accessible experience using augmented reality, body tracking, and rule-based programming to provide interactive physical therapy sessions and immediate feedback on the childs session performance. The proposed system falls within studies that promote the activation of technology to achieve realistic and effective medical solutions. It aims to improve the physical therapy experience for children and enhance their commitment to exercise by providing rewards and encouraging points during play. It also seeks to decrease the financial burden on parents and reduce the density of appointments in health centers.
Fractures, Augmented Reality, Body Tracking, Rule-based Programming, Gamification.
Nursulu Kapalova, Dilmukhanbet Dyusenbayev, Haumen Armyanbek and Oleg Lizunov, Information Security Laboratory, Institute of Information and Computing Technologies, Almaty, Kazakhstan
Cryptographic encryption algorithm AL04 was developed, featuring an architecture that includes nonlinear substitution blocks and a round key schedule algorithm. It is symmetric block encryption algorithm that provide an optimal balance between security, performance, and minimal resource requirements for securing voice communications over HF radio channels. The developed algorithm wase evaluated for reliability using established cryptanalysis methods. It was tested for randomness using statistical tests from the NIST and D. Knuth test suites. The results indicate that the binary sequence produced after encryption with the proposed algorithm closely approximates randomness. Additionally, the S-boxes and the encryption algorithms themselves were examined for the presence of the avalanche effect. Based on the conducted tests and studies, it was determined that it effectively exhibit a strong avalanche effect. It was also shown that the developed encryption algorithm provides the necessary level of resistance to linear, differential, and algebraic cryptanalysis.
radio communication, data transmission, information protection, cryptography, encryption algorithm, cryptanalysis.
Wang Yan, Zhao Dawei, Xu Hong, ZhengXiyu, GuiZhou Light Industry Technical College, Guiyang, GuiZhou, PRC
This study proposes a model-free robotic manipulator joint position and velocity tracking control algorithm with a simple structure for controlling a robotic manipulator. The proposed design guarantees the responses of the tracking error within preset bounds using joint position and velocity feedback. Moreover, this controller does not require either the robotic dynamic model or any estimation of dynamic model. Theintelligent control algorithm is designed to achieve joint position and velocity tracking of a desired trajectory with prescribed performance (PP) bounds automatically. Simulations of a three-degree-of-freedom spatial manipulator with the PP controller confirm the conclusions. Results show that the PP controller outperforms the traditional position and derivative (PD) trajectory tracking controller without requiring excessive control effort.
intelligent control; prescribed performance;velocity tracking.
Ruilai Yang1, Rodrigo Onate2, 1Tesoro High School, 1 Tesoro Creek Rd, Rancho Santa Margarita, CA 92688, 2California State Polytechnic University, Pomona, CA 91768
In the field of chess education, coaches often face significant challenges in efficiently grading large volumes of student assignments, which limits the time they can dedicate to personalized instruction and strategic development [5]. To address this problem, we developed a mobile application designed to streamline and automate the grading process for chess homework. Our solution integrates user authentication, an intuitive user interface, and robust database management to create an accessible and effective platform for both coaches and students [11]. By leveraging Firebase services and Flutter development tools, we built an application that allows coaches to assign, collect, and grade student work quickly, while providing students with immediate feedback to enhance their learning [6]. Throughout the project, we navigated challenges related to data organization, user interface design, and the integration of a functional chessboard. This application ultimately empowers coaches to focus more on mentorship and less on administrative tasks, while helping students better track their progress and target areas for improvement. Our approach demonstrates the importance of combining thoughtful system architecture with user-centered design in educational technology solutions.
Assignment, Chess, Grading, Learning Management, Automation Tools
Raymond Bierens, Abbas Shahim and Svetlana Khapova, Vrije Universiteit Amsterdam, Netherlands
In 2024, the NIS2 Directive came into effect in Europeintroducing specific measures, reporting obligations and personal liabilitiesto mitigate risks for societal digital disruption. We conducted an inductive study and interviewed 29 CISO’s and IT or C-level executives from large, NIS2 affected, organizations in The Netherlands and validated the outcomes with 300+ cybersecurity professionals through various workshops. Our study reveals interrelated tensions in organizational behavior driven by the regulatory changes. It stimulates more compliance behavior amongst organizations and their suppliers which is being reinforced by its accountability and liability clauses. However, our study confirms that considerable residual risks remain due to the dynamic nature of technology, shifting security risks up the supply chain, and dependencies on global technology companies. Government is perceived as challenged in their ability to govern while being a critical factor to succesfullly transforming compliance behavior into digital security risk management to reduce residual risks.
NIS2 Directive, Policy, Organizational Risk, Personal Risk, Liability, Technology, Information Security, Cyber Security, Digital Risk, Risk Management
Xiang Li1, Ang Li2, 1Northwood High School, 7545 Portola Pkwy, Irvine, CA 92618, 2Computer Science Department, California State University, Long Beach, CA 90840
Mathemagics is a novel educational game designed to engage K-12 students by embedding mathematical principles directly into gameplay [14]. This research investigates how integrating math into game mechanics—using a roguelike framework—can overcome common challenges such as math anxiety and disinterest. Developed in C# with the Unity engine, the game incorporates a dynamic health system represented by arithmetic expressions, magic ability to cast arithmetic operations, and adaptive difficulty tailored to individual performance [1]. Experimental evaluations measure both shifts in students’ attitudes towards math and improvements in mathematical, or more specifically, arithmetic proficiency. Preliminary results indicate increased motivation and arithmetic foundation. A comparative analysis with existing educational methods reveals that Mathemagics not only supplements traditional learning but also aligns with the ongoing learning principle by fostering continuous conceptual growth. Although challenges such as visual design remain, the findings suggest that game-based learning offers a promising supplementary tool to enrich math education and stimulate a lifelong interest in the subject [15].
Game Design, Education Purpose, Integrating Mathematics into Game Mechanisms, Electronic Learning
KHEMIS Salim, AMARA Yacine, BENATIA Mohamed Akrem, aEcole Militaire Polytechnique EMP, BP 17, Bordj El Bahri, 16046, Algiers, Algeria
Adversarial attacks expose significant vulnerabilities in machine learning and deep learning models across various domains, undermining their reliability. In Natural Language Processing (NLP), such attacks subtly manipulate input texts to alter victim model predictions while remaining nearly imperceptible to human readers. Although numerous studies have proposed defense and attack strategies in the field of NLP, substantial room for improvement remains. Existing defenses that rely solely on embeddings often fail to capture the hidden features indicative of adversarial manipulations. This paper contributes on the following points: (a) word frequency analysis between clean and adversarial attacks, (b) Proposing an improved , and (c) , and evaluates their combined efficacy in detecting adversarial samples. By integrating these features, we propose a robust detection model capable of identifying adversarial attacks at the word level and enhancing the robustness of NLP models. Experiments conducted in the Bert-Base-Uncased model, using the Ag News and IMDB datasets, demonstrate that the proposed method outperforms state-of-the-art techniques in most cases, achieving superior detection accuracy without compromising performance on clean inputs.
Adversarial Attacks, Adversarial Defenses, Natural Language Processing, Robustness, Deep Learning, NLP Models.
Qiaoman Cai1, Qiaoqian Cai2, Ang Li3, 1Crean Lutheran High School, 12500 Sand Canyon Ave, Irvine, CA 92618, 2Crean Lutheran High School, 12500 Sand Canyon Ave, Irvine, CA 92618, 3California State University, Long Beach,1250 Bellflower Blvd, Long Beach, CA 90840
This paper presents CareBridge Health, an AI-powered mobile application designed to improve womens healthcare access through symptom analysis and educational support [1]. The app integrates an intuitive body mapping interface, GPT-4-based AI analysis architecture, and a curated educational database [2]. It addresses gaps in symptom recognition and health literacy among women. Two experiments with ten participants evaluated the app’s effectiveness. The usability experiment resulted in an average rating of 4.6 out of 5 for ease of use, while the health education experiment achieved an average score of 2.7 out of 3 in a knowledge test. These results demonstrate the apps success in providing an intuitive and educational experience. The project builds upon previous mobile health and AI diagnostic research but uniquely targets women’s health with a holistic, personalized approach. Limitations include reliance on internal logic instead of a live AI backend and the need for broader clinical content. Overall, CareBridge Health represents a promising innovation in women’s health technology by empowering users with actionable insights and accessible education.
Womens Health Technology, Mobile Health (mHealth), AI Symptom Analysis, Health Literacy, Patient Education.
Paulin Tchumtcha Wembe, RMIT University, Blockchain Innovation HUB
Blockchain or Distributed Ledger Technology’s (DLT) disruptive architecture will revolutionise both economic activity and social structure. Institutional crypto economics is a new analytic framework for studying that evolutionary process in general, and bitcoin in particular, it presents us with a new method of organising the world, just like the Internet did. Bitcoin will have a similar effect on economy, money and finance. Developing countries face multiple problems such as lack of financial services and infrastructure (road, railways, telecommunication, and others). Developing countries are well positioned to gain from the Blockchain or Distributed Ledger Technology’s disruptive architecture. This will be very well visible in the implementation and usage of Internet of Thing (IoT) in emerging services. The nature of innovation in service-sector-based technology in developing countries is distinct, and the nature of IoT as a potentially disruptive emergent service product technology enabler emphasises this distinction. The conventional product-process innovation divide may no longer be applicable: the true value in IoT rests in neither. It is present in the system as well as the data collected by all devices everywhere in the world. The services income, which is generated by a combination of intelligent apps, analytics, and system integration services, represents a considerably greater revenue possibility for both developers and consumers of IoT enabled use cases. This paper presents how a peer-to-peer network that provides coverage for low-power IoT devices bringing a new viewpoint to the cellular telecommunications market. The network is a decentralised IoT infrastructure that is built on a Blockchain or Directed Acyclic Graph (DAG) by the people, communities, and individuals to offer hotspots wireless to the communities that help creates opportunities in financial freedom, helps supply chains traceability, forestry control and others. The paper makes a unique and significant contribution to the deployment of IoT on the Blockchain or Distributed Ledger Technology and his potential to reduce poverty while also increasing the efficiency and effectiveness of existing processes in various industries in developing countries.
Internet of Things, Decentralised IoT infrastructure, Blockchain.
Alberto Bucciero1, Davide Zecca1, Francesco Valentino Taurino1, Mohamed Ali Jaziri1, Riccardo Colella1, Andrea Pandurino1, Matteo Greco1, Alessandra Chirivì1, Irene Muci2, Mohamed Sami Saad Emara1, 1Institute of Heritage Science, National Council of Research, Lecce,Italy, 2Università La Sapienza, Rome, Italy
Environmental monitoring in heritage sites is essential to ensure the long-term preservation of culturally significant structures, particularly those exposed to fluctuating microclimatic conditions. This paper presents the deployment and evaluation of the SENNSE platform, an IoT-based solution combining distributed environmental sensors, edge computing, and 3D digital twin integration. SENNSE was implemented in the Grotta degli Animali, a semiconfined 16th-century grotto in Florence, Italy. The platform continuously monitored temperature, humidity, and CO₂ levels, enabling spatially contextualized data analysis through immersive visualization. Over several weeks, data revealed strong vertical gradients, visitor-induced CO₂ spikes, and correlations with external weather. Adaptive strategies ensured sensor reliability despite architectural constraints. SENNSE demonstrates how real-time sensing, linked to interactive spatial models, can enhance conservation decision-making and support proactive heritage management.
Cultural Heritage, IoT, Wireless Sensor Network, Real-Time Monitoring, Digital Twin, Environmental Sensing, Microclimate Analysis.
