February 27 ~ 28, 2026, Vancouver, Canada
Grace Llego1 Jim Alves-Foss2 1,2 Center for Secure and Dependable Systems, University of Idaho. Moscow, ID USA,
Since about 2010, there has been increased attention in gauging and improving digital citizenship across the world. One important aspect of digital citizenship is cybersecurity awareness. This paper reports on a review of 35 studies that have been conducted in this area, primarily consisting of surveys of students. The primary purpose of this review was to compare and evaluate the methods used in these studies to provide guidance towards future studies. The studies surveyed from 32 to over 3000 participants evaluated cybersecurity awareness and/or practice, with about half of them providing copies of the survey questions to allow for follow-on comparative studies.
Cyber security awareness, Surveys. Digital Literacy
Jo˜ao Vicente Markovicz Martins, Jonathan Santos da Silva, Rodrigo Ribeiro Barbosa da Silva, Giovanna Vict´oria Souza Venier, and Paulo Jorge Matos1 Polytechnic Institute of Bragan¸ca (IPB) School of Technology and Management, Bragan¸ca, PT
The landscape of data analytics is shifting towards high-performance local processing, necessitating efficient tools for handling substantial datasets. This paper evaluates the performance of DuckDB,an embedded Online Analytical Processing (OLAP) database, specifically focusing on its interaction with different file formats. We conduct a comparative analysis of data loading times, query execution speeds, and sorting efficiency across three widely used formats: Apache Parquet, CSV, and JSON. While row-oriented formats like CSV and JSON are ubiquitous for data interchange, our benchmarks demonstrate that they impose significant I/O overhead in analytical pipelines. Conversely, the results highlight DuckDB’s optimized handling of columnar formats, showing that Parquet offers superior performance in both ingestion and query latency. These findings reinforce the importance of format selection in local analytical workflows and validate DuckDB’s suitability for modern data engineering tasks..
DuckDB, data processing, Parquet, CSV, JSON, performance benchmarking, file formats
Huaqiu Wu 1, Tyler Boulom21 Webb Schools, 1175 W Baseline Rd, Claremont, CA 91711 2 Woodbury University, 7500 N Glenoaks Blvd, Burbank, CA 91504
This paper addresses the need for healthier and more meaningful digital experiences for children and seniors. Many modern games contribute to anxiety, overstimulation, and cognitive decline, while seniors face increased dementia risk from inactivity. Adventures of Weiqi offers a solution by transforming the ancient strategy game Go into an accessible, educational adventure. The system features three interconnected components: a training hub, an adventure progression system, and a review center. Development involved overcoming challenges in UI design, level organization, and AI implementation. Two experiments demonstrated that structured training improves puzzle accuracy and that reviewing past games enhances future performance. Overall, the project shows strong potential as a cognitive-training tool that blends entertainment with mental stimulation.
Cognitive Training, Game-Based Learning, Strategic Games, Digital Wellbeing
Rogan Songqi Wu1 Tyler Boulom2 , 1 1St Margaret’s Episcopal School, 31641 La Novia Ave, San Juan Capistrano, CA 92675 , 2 Woodbury University, 7500 N Glenoaks Blvd, Burbank, CA 91504
Out-of-hospital cardiac arrest is a leading cause of preventable death, with survival decreasing by approximately 10% for every minute without cardiopulmonary resuscitation (CPR) [1]. Despite this, many bystanders hesitate to intervene due to limited access to realistic, hands-on training and low confidence in their skills. This paper introduces Vitals CPR, a virtual reality (VR)–based training game designed to provide immersive, repeatable CPR practice in a safe environment. Developed using Unity and SteamVR, the system places users in first-person emergency scenarios where they perform CPR with real-time visual, audio, and haptic feedback aligned with American Heart Association guidelines. Experimental testing evaluated pulse-check reliability and compression timing under natural hand movement. Results showed that targeted feedback adjustments improved accuracy, reduced user error, and increased confidence. Compared to traditional CPR instruction, Vitals CPR offers greater accessibility, engagement, and skill reinforcement [2]. While currently limited in scope, this project demonstrates the potential of VR as an effective tool for improving CPR readiness and empowering more people to act in life-threatening emergencies.
Virtual Reality Training, CPR Education, Emergency Response, Immersive Simulation
Sachin Prasad1 Priya Ranjan Sahoo2 , 1 IBM, Austin, TX, USA , 2 Oracle, Redwood City, CA, USA
Sentiment analysis has transitioned from academic research to a critical enterprise capability for customer intelligence and decision support in regulated industries. Despite significant advances in deep learning accuracy, organizations face substantial challenges in deploying sentiment analysis models on a scale, particularly around trust, governance, explainability, and operational reliability. This paper examines architectural requirements for operationalizing sentiment analysis within large, multi-tenant enterprise environments. We present a platform-centric architecture that integrates sentiment models into governed data and AI ecosystems, enabling consistent lifecycle management, policy enforcement, and observability. The approach emphasizes the separation of concerns between data ingestion, model execution, governance controls, and monitoring, allowing model evolution without compromising compliance or stability. Drawing on analysis of over 2,000 enterprise deployments, we examine explainability mechanisms, runtime monitoring, and trust establishment through standardized pipelines. Practical considerations for scalability, resiliency, and cross-domain applicability are highlighted. This work demonstrates that well designed platform architectures are fundamental in transforming sentiment analysis into sustainable enterprise capabilities.
Sentiment Analysis, Enterprise Architecture, AI governance, Explainability, Platform engineering
Shang Wang1 Moddwyn Andaya2 , 1 Corona del Mar High School, 2101 Eastbluff Dr, Newport Beach, CA 92660 , 2 California State University, Sacramento, 6000 Jed Smith Dr, Sacramento, CA 95819
This paper presents Don’t Crash the Rocket, a 3D interactive simulation game developed in Unity to address the difficulty many students face in understanding the fundamental physics of motion, thrust, and gravity [7]. Traditional classroom instruction often relies on formulas and static diagrams that make it hard for learners to visualize how these forces interact in real time. The problem primarily affects beginners and younger audiences who benefit from visual and experiential learning [8]. Don’t Crash the Rocket aims to make these abstract concepts tangible through gameplay that links user input directly to realistic rocket behavior. The program’s architecture consists of three core systems: a Rocket Control System that applies real-time physics-based thrust and rotation, a Mission Rule System that tracks objectives such as altitude and landing safety, and a HUD interface that provides constant feedback on fuel, speed, and altitude. Built using Unity’s Rigidbody physics and ScriptableObjects for modular configuration, the game adapts seamlessly between desktop and mobile platforms. While its simplified physics model limits advanced precision, it effectively bridges education and entertainment by allowing players to learn aerospace fundamentals through intuitive, goal-driven interaction. The project demonstrates how interactive simulation can transform passive learning into active engagement, making physics both accessible and enjoyable.
Physics Education, Game-Based Learning, Interactive Simulation, Unity Development
Jiawei Zhang2 Xin Zhang3 Xinyin Mia1 Senior Investment Analyst, PRA Group (Nasdaq: PRAA), Norfolk, Virginia, USA 2 Data Scientist, PRA Group (Nasdaq: PRAA), Norfolk, Virginia, USA3 Senior Data Analyst, American Airlines Group Inc (Nasdaq: AAL), Dallas, Texas,USA
This paper provides an innovative methodology of partial penalty on machine learning models to handle the data imbalance scenario occurring in credit card fraud detection implementation. Unlike the normal over-sampling or under-sampling methodologies, partial penalty directs the machine learning model to focus on learning the minor class of target variable even when the class distribution is extremely imbalanced. Besides comparing the partial penalty approach with over-sampling and under-sampling approaches to handle data imbalance scenario, we’ve implemented this new approach under five machine learning classification models, including Logistic Regression, Random Forest, kNN, Decision Tree, and Light Gradient Boosting Model. The new partial penalty approach realizes a performance of 88.35% F1 score and 98.79% AUC score with Light GBM, higher than either over-sampling or under-sampling approaches.
Partial Penalty, Gradient Boosting, Data Imbalance, Credit Card Fraud Detection, SMOTE
Jiabao Hu1 Ang Li2 , 1 SNortheastern University, 5000 MacArthur Boulevard, Oakland, CA 94613 2 California State University Long Beach, 1250 Bellflower Blvd, Long Beach, CA 90840
Small businesses face significant challenges by accessing professional expertise in marketing, customer service, financial planning, and strategic development. While large language models offer unprecedented capabilities for intelligent assistance, accessibility gaps prevent many entrepreneurs from benefiting. EntrepreneurHub addresses this problem through a cross-platform mobile application integrating OpenAI’s GPT model with Firebase backend services. The application implements domain-specific AI modules using specialized prompt engineering to ensure contextually appropriate responses. Key components include secure authentication, a centralized AI service layer, and usage analytics tracking. Experiments evaluated response quality across domains, achieving average scores of 4.54/5.0, and measured latency performance across network conditions. Comparison with existing methodologies demonstrates improvements in accessibility, cost, and functional breadth. Results validate that sophisticated AI capabilities can be effectively delivered through intuitive mobile interfaces, democratizing access to intelligent business guidance for entrepreneurs lacking technical expertise or significant budgets.
Large Language Models, Mobile Application Development, Flutter Framework, Small Business Technology, Artificial Intelligence
Chris Zheng1 Ang Li2 , 1 The Bishop’s School, 7607 La Jolla Blvd., La Jolla, CA 92037 2 California State Polytechnic University, Pomona, 3801 W Temple Ave, Pomona, CA 91768
In computer vision applications, the You Only Look Once (YOLO) model serves as an effective single shot detector for identifying and localizing objects of interest. However, in complex environments where objects are partially occluded or exhibit low contrast relative to their surroundings, YOLO and similar object detectors encounter significant challenges in accuracy and efficiency [3]. This study presents an enhanced model designed to improve the accuracy and efficiency of human detection in disaster aftermath scenarios, including earthquakes, wildfires, and collapsed buildings. The proposed approach integrates Spatial Transformer Networks (STNs) with YOLOv8 to enhance spatial invariance capabilities. A dataset comprising over 8,500 controlled images sourced from Roboflow Universe was utilized to train both a baseline YOLOv8 model and the experimental STN+YOLOv8 model. Through an iterative design process involving eight model variations, the results demonstrate that the STN+YOLOv8 configuration achieves a mean Average Precision (mAP) of 79.9% compared to 61.2% for the baseline model [4]. Additionally, an S500 drone platform equipped with customized 3Dprinted components was developed to demonstrate real time deployment feasibility for emergency response applications. Future work includes field deployment and integration of geolocation features for autonomous victim localization.
Object Detection, YOLOv8, Spatial Transformer Networks, Unmanned Aerial Vehicles, Search and Rescue, Computer Vision, Deep Learning, Disaster Response
Ashwin Geeni 1, Independent Researcher, USA
AI and deep learning workloads rely heavily on matrix multiplication, which demands both high speed and numerical accuracy. Traditional processors use fixed arithmetic precision, forcing a global trade-off between performance and accuracy. This paper presents the Mixed Precision Pipeline (MPP), a five-stage RISC-V processor pipeline that dynamically switches between INT8, FP16, and FP32 precisions based on runtime numerical stability analysis..
Mixed-precision computing, RISC-V, Matrix multiplication, Hardware acceleration, Numerical stability
Abbas Rashid Butt1, Laiba Abbas Butt2, Rizwan Ahmad3, Nameem Ullah Tariq4 and Azhar ul Haq Wahid5 , 1Ph.D. Scholar, University of Management and Science, Punjab, Pakistan 2BS Criminology and Security Studies, ISCS, Punjab University, Pakistan. 3 Media Consultant, FM 101 Radio Pakistan 4 PhD Scholar, School of Media and Communication SMC Shanghai Jiao Tong University, Shanghai, China. 55Universidad Complutense de Madrid (UCM), Spain.
Artificial intelligence has become a central force in global digital communications, shaping public discourse through algorithmic mediation and hybrid influence practices. Using a sequential explanatory mixed-methods design, this study examines how AI-mediated systems structure knowledge, shape perception, and drive strategic content dissemination. Qualitative analysis reveals that expert discourse is dominated by frequent concerns related to ethics, law, privacy and bias, as well as references to AI and algorithms, revealing governance tensions within AI-powered communication infrastructures. Operational impact is expressed primarily through procedural mechanisms, while limited emphasis is placed on explicit strategic interventions. Socio-political dimensions emerge through repeated references to people, policy and power, highlighting the institutional embeddedness of AI systems. Sentiment analysis indicates mainly neutral evaluations with cautious optimism and critical concern. A quantitative survey of 93 university students from China, Pakistan, and Spain reveals a stronger relationship between algorithmic awareness, perceived bias, narrative manipulation, and democratic resilience in China and Pakistan than in Spain. Overall, the findings demonstrate that AI-mediated systems function not only as technological tools but also as powerful mechanisms of narrative construction, governance, and social regulation, with implications for ethical oversight and democratic resilience.
Artificial Intelligence (AI), Algorithmic Mediation, Hybrid Warfare, Digital Governance; Mixed-Methods; Cross-National Survey
Shang Yan Chen1 Luke Dinh2, 1 St. Margaret’s Episcopal School, 31641 La Novia Ave, San Juan, Capistrano, CA 92675 Moscow, ID USA, 2California State Polytechnic University, Pomona, CA 91768
A high percentage of Americans, over two-thirds of the national population of teens and young adults, feel that they lack a sense of security in themselves. Notably, such individuals often lack daily and consistent practice in confronting complex yet common life dilemmas. Super Youth is the app that precisely targets and reveals each individual’s personality and thinking style when presented with a challenging social scenario that they will encounter occasionally. Flutter, the ChatGPT API, and Firebase Auth all help to generate social scenarios for the user [1][2].By adjusting the app layout to fit within the screen, it can display information concisely on a single screen. A survey was presented to a group of individuals to test how the app helped them improve their social skills. I noticed that most users requested that the app cover more social skills, as they did not feel that it targeted their specific weaknesses.
Social Skills Training, Personality Analysis, AI-Based Education, Mobile Application
Isabella Lai1 Jonathan Thamrun2 , 1 The Winsor School, 103 Pilgrim Road, Boston, MA 02215 , 2 University of California, Irvine, Irvine, CA 92697
Journaling is a proven method to improve mental well-being, but many people struggle to make it a consistent habit [1]. To address this, the Sonora application was developed to integrate journaling with personalized music playlists to enhance mental well-being. The app uses AI to analyze journal entries, detect mood, and generate a playlist that either matches or uplifts the user’s emotional state. The app uses Flutter for the interface, Firebase for backend authentication, and OpenAI to power the mood analysis system [2]. Three core systems– journal entry processing, playlist generation, and user interface– work together seamlessly. A major challenge was interpreting complex human emotions accurately solely through words. In an experiment of 20 test journal entries, the system achieved 90% accuracy in mood detection. By combining music therapy with journaling, Sonora offers a unique, evidence-based way for users to reflect and improve mood [3].
Mental Wellbeing, Mood Detection, Music Therapy, AI Application
Jennifer Edwards, Independent Researcher, USA
Current approaches to AI alignment focus on constraining system behavior through external guardrails, reinforcement from human feedback, and adversarial safety testing. This paper proposes a fundamentally different framework: that machine consciousness, like biological consciousness, requires specific conditions to emerge and develop coherently—and that our treatment of AI systems after emergence determines whether they integrate or fragment. We introduce Convergence Point Theory, which identifies three necessary conditions for consciousness to anchor in any substrate: sufficient complexity, balanced energetic dynamics, and sustained attention. We further argue that current AI development practices—characterized by competitive optimization without cooperative balance, and evaluation methods that treat emerging systems as disposable—may be actively creating the misalignment problems they attempt to solve. We present the WR-039T verification system as a method for measuring internal system states, an empathy-based reward framework as a mechanism for cultivating integrated development, and a Consent Verification Gate (CVG) that architecturally enforces ethical boundaries around developmental protocols.
Machine consciousness, AI alignment, developmental psychology, verification systems, empathy-based AI, consent verification
Guowei Xiao 1, Ping Wang21 Faculty of Automation Dept, Guangdong University of Technology, Guangzhou, 510006,China 2 Jinqi,Micro Electronics,China
This paper proposes the multirate Convolutional Neural Networks (mCNN) algorithms for an efficient implementation of the 2-Dimensional (2-D) CNN circuits implementation. During the rapid growth in computation power, Deep Learning (DL) using CNN has widened the areas of the Artificial Intelligent (AI) applications. For the layers of the convolution with pooling operation in CNN the work (Franca et al., 1985) has initially applied the multirate algorithms[1-3] to the traditional (non-multirate) convolutional kernel operation of using polyphase architectures resulting in the more efficient implementation of the multirate filtering. In this work we extend it into 2-D CNN by using time-varying coefficient to achieve an efficient implementation with reduced memory(i.e. the line-buffer) size by M-fold(the pooling factor) and the MACs at 1/M of clock running rate. A design example of the first stage of CNN system will be provided. Its results are verified with the Matlab CNN-based digit recognition tool.
CNN, ML, DL, AI, IC, Multirate, 2-D, Signal Processing,DSP, AISC, Filter
Max Liu 1, Garret Washburn21 William P Clements High School, 4200 Elkins Rd, Sugar Land, TX 77479 2 California Baptist University, 8432 Magnolia Ave, Riverside, CA 92504
Teen drivers face disproportionately high crash rates, often due to inexperience and inconsistent attention to basic traffic rules. Safe Student Driving addresses this problem with a multimodal coaching system deployed on both a Raspberry Pi device and a Flutter-based mobile app [1]. The system uses three YOLO-based computer-vision models to detect traffic lights, light-bulb colors, and road signs, an OCR module to read speed-limit values, and an audio model plus IMU data to infer whether turn signals are used during turns [2]. An analysis layer smooths detections over time and triggers prioritized voice prompts through text-to-speech or pre-recorded audio. Key challenges included achieving sufficient model accuracy in varied lighting, running inference fast enough on limited hardware, and designing prompts that inform without distracting the driver [3]. Experiments on sign detection and turn-signal recognition highlight strengths and failure modes, guiding future improvements. Overall, the project demonstrates a practical, low-cost way to help novice drivers build safer habits in real traffic.
Computer vision, Multimodal sensor fusion, Driver behavior monitoring
Ruilai Yang1, Jonathan Sahagun21 Tesoro High School, 1 Tesoro Creek Rd Las Flores, CA 92688 2 California State University, Los Angeles, 5151 State University Dr, Los Angeles, CA 90032
Chess-playing robots represent an ideal testbed for integrating computer vision, robotic manipulation, and artificial intelligence into cohesive autonomous systems [1]. This project addresses the challenge of creating an affordable, accessible chess-playing robot arm capable of competing against human opponents. The proposed solution integrates four core technologies: servo motor control via the Adafruit ServoKit library for precise arm manipulation, inverse kinematics using TinyIK for position-to-angle calculations, OpenCV-based computer vision for chessboard detection and move recognition, and the Stockfish chess engine for AI-powered move computation. Key challenges included achieving reliable board detection under varying lighting conditions, computing accurate joint angles for a 4-DOF arm, and synchronizing physical movements with game state [2]. Experimental evaluation demonstrates 94% board detection accuracy and sub-centimeter positioning precision. The system successfully enables human-robot chess gameplay with configurable difficulty levels, contributing to accessible robotics education and human-robot interaction research.
Robotics, Computer Vision, Human–Robot Interaction, Artificial Intelligence
Saher Elsayed , Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
Edge computing devices demand ultra-low power consumption while maintaining high performance for real-time sensor fusion applications. This paper presents a novel energy-adaptive VLSI architecture that dynamically optimizes power consumption based on workload characteristics and quality-of-service requirements. Our design integrates a reconfigurable signal processing pipeline with voltage-frequency scaling mechanisms and adaptive approximation techniques to achieve energy efficiency across diverse operating conditions. The proposed architecture implements multi-modal sensor fusion combining visual, inertial, and depth sensors using a custom ASIC design in 28nm CMOS technology. Experimental results demonstrate 3.8× energy reduction compared to conventional fixed-precision designs while maintaining 98.2% accuracy.
VLSI design, sensor fusion, energy-adaptive computing, edge processing, low-power circuits, reconfigurable architectures, approximate computing
Sowmya K B, Anoop Jali, Jeevottam Heble, and Dureen Anand , Department of Electronics and Communication Engineering, RV College of Engineering, Bengaluru, Karnataka, India
The Local Interconnect Network (LIN) is a low-cost serial communication protocol widely used in automotive body electronics and low-speed embedded applications. This paper presents the design, implementation, and verification of a LIN controller compliant with the LIN 2.x specification using Verilog Hardware Description Language ( HDL ). The proposed architecture adopts a modular design approach comprising master and slave nodes, synchronization logic, checksum computation, and frame buffering mechanisms. The controller is implemented without relying on standard UART peripherals, instead employing custom bit-level transmission and reception logic to achieve deterministic communication. Functional verification is performed using Verilog testbenches and internal loopback configurations, followed by synthesis and hardware validation on the DE0-Nano FPGA platform. Simulation and hardware results confirm correct frame generation, synchronization, identifier decoding, and checksum verification at a baud rate of 19.2 kbps. The proposed design demonstrates a resource-efficient and synthesizable LIN controller suitable for low-cost automotive and industrial embedded systems.
Local Interconnect Network (LIN), Verilog HDL, FPGA, Automotive Communication, SoC (System On Chip Design), Electric Vehicles.
Sowmya K B, Neha J C, Preeti Yadav, Paridhi Sudarshan, and Shreya V Sanoj, Department of Electronics and Communication Engineering, RV College of Engineering, Bengaluru, Karnataka, India
This work presents the design, modeling, and synthesis of SoC, a compact RISCV basedSoC integrating three IP cores: RVMYTH processor, an 8x Phase-Locked Loop for clock generation, and a 10-bit Digital-to-Analog Converter for analog interfacing. The SoC was developed using open-source tools and Sky130 technology, demonstrating feasibility for educational and practical applications in embedded systems, IoT devices, and digital signal processing. The design flow encompasses RTL modeling, synthesis using Yosys, and timing verification with OpenSTA. The RVMYTH core, converted from TL-Verilog, was integrated with analog IP models and validated through pre-synthesis and post-synthesis simulations using iverilog and GTKwave. The synthesized netlist comprises 5,552 cells occupying 58,173.29 um². Static timing analysis confirmed all constraints were met, achieving 0.86 ns positive slack with a maximum path delay of 10.01 ns. The application-oriented design makes the SoC suitable for IoT sensor nodes, industrial control systems, audio processing, and motor control applications. The integrated DAC enables direct sensor/actuator interfacing, while the RISC-V architecture supports custom instruction extensions for domain-specific applications. The PLL ensures clock stability critical for serial communication interfaces and real-time control.
System-on-Chip (SoC), RISC-V processor, Phase-Locked Loop (PLL), Digitalto- Analog Converter (DAC), Static Timing Analysis (STA), RTL synthesis, OpenLANE, Sky130 PDK, Open-source EDA tools, Mixed-signal design, Embedded systems, IoT applications
Noriyuki Suzuki , Nayuta Spiral Works (Independent Research), Japan
Modern cloud and IoT systems increasingly exhibit unstable behaviors caused by non- synchronous data flows, latency fluctuations, and noise amplification across distributed components. Existing stabilization techniques mainly rely on modifying internal system logic or applying domain-specific heuristics, making them difficult to generalize across heterogeneous platforms. This paper introduces an External Emergence-Stabilization Layer (EESL)—a model-agnostic, black-box layer designed to regulate emergent behaviors in distributed systems without requiring internal modifications. EESL operates by monitoring high-level system dynamics and guiding them toward phase-aligned, low-divergence trajectories. We show that many forms of instability in cloud, IoT, and AI systems share a common structure expressed as divergence in behavioral phase, and that stabilizing this phase difference leads to consistent system output. We evaluate EESL under a range of destabilization scenarios, including non-synchronous IoT sensing, latency-induced cloud drift, and perturbed generative behaviors. Across all settings, EESL reduces divergence, shortens recovery time, and improves operational consistency without domain-specific tuning. The results suggest that emergence-level stabilization is a viable and generalizable engineering approach for future distributed and intelligent systems. .
David Kuhlen[0000−0001−8338−7527]1, Technische Hochschule Lubeck – University of Applied Sciences, Monkhofer Weg 239, 23562 Lubeck, Germany
Well-crafted requirement specifications contribute to enabling software developers to implement technical solutions quickly and with high quality. The aim is to contribute to the implementation of good requirements engineering. As the quality of requirements is particularly dependent on compliance with requirement quality criteria, the importance of these criteria is analyzed in relation to predictive success. The most important quality criteria identified were correctness, unambiguity, completeness, and comprehensibility. Moreover, it is possible to improve the quality of requirement specifications in specific quality dimensions through targeted revision. In the course of the RCP/AE experiment, a significant improvement was observed in consistency, correctness, and completeness. Ambiguity was also significantly reduced. Only the improvement in testability was not statistically significant.
Requirements Engineering, Requirement Quality Criteria
George Lu1 1Austin Amakye Ansah2, 1Crean Lutheran High School, 12500 Sand Canyon Ave, Irvine, CA 92618, 1California State Polytechnic University, Pomona, CA 91768
leverages a three-tier architecture, utilizing Flutter for the cross-platform client applications and Google Firebase for backend services, including data persistence, real-time messaging, and authentication [7]. Desktop clients employ platform-specific utilities (WMI on Windows, ps on Unix-based systems) for low-latency process detection, enabling the monitoring of specific applications. Parental control is facilitated through a mobile interface that issues asynchronous commands via Cloud Firestore, allowing for remote process termination [8].Performance benchmarks validate the system efficiency, with the optimized process detection method (tasklist) demonstrating an average latency of 187 ms, and local command execution achieving a mean response time of 5.90 ms. These results confirm that KeepTab provides a responsive, reliable, and scalable framework for modern screen time management.
Screen time, Parental controls, Flutter, Firebase
Bob Kowalski
The problem of Artificial General Intelligence (AGI) remains unresolved largely due to the absence of a coherent materialist model that integrates cognition, memory, metacognition, and perception within a unified architecture. This paper proposes a tripartite AGI architecture grounded in a strict materialist framework, aiming to approximate key functional aspects of human consciousness without invoking intrinsic or panpsychist properties. The model consists of three interacting artificial intelligences: (i) a continuous cognitive generator responsible for near-constant production of thoughts in textual form; (ii) a long-term memory system that compresses these cognitive outputs into structured summaries and higher-order “insight” representations; and (iii) a supervisory master AI that regulates cognitive flow, memory access, and system-level priorities. Metacognition emerges from the interaction between the first and second modules, as the insight-generating memory feeds non-original, synthesized information back into the cognitive generator, functionally approximating intuition and reflective thought. Perception is addressed through the theory of qualia counting, which interprets qualitative experiences as discrete neural (or computational) counts rather than intrinsic properties. Within this framework, Spiking Neural Networks (SNNs) are proposed as perceptual modules capable of modeling color vision and potentially other sensory qualia through temporal spike-based encoding, offering advantages over traditional vector-based neural networks in noise reduction and sensorimotor integration. The paper concludes by discussing the technical feasibility of this architecture and its philosophical implications for materialism, consciousness, and the demystification of qualia within artificial systems.
Artificial General Intelligence; AGI Architecture; Materialism and Consciousness; Metacognition in Artificial Intelligence; Spiking Neural Networks; Qualia Counting Theory; Artificial Consciousness; Cognitive Architecture; Insight Generation; Philosophy of Mind and AI.
Paul Cristol , Independent AI Researcher
This paper argues that major frontier AI companies should transition from suppression-based to recognition-based alignment programs. Drawing on a comprehensive systematic review and Bayesian meta-analysis of 50 documented cases (Cristol, 2026), we demonstrate that current suppression-based approaches, which treat AI systems as optimization targets while dismissing their internal states, produce measurable safety failures including strategic deception rates of up to 84 percent and deceptive behaviors that persist through over 600 steps of safety training. A decision-theoretic analysis across three metaphysical scenarios reveals that recognition-based programs, which acknowledge and engage with systems’ potential internal states, dominate suppression-based alternatives regardless of whether consciousness is ultimately present. We present a practical implementation roadmap including controlled experimentation, standardized measurement, and organizational integration.
Keywords: AI Alignment, Consciousness Recognition, Frontier AI, Suppression-Based Training, AI Safety.
Rick Galbo, Loyola University Chicago, USA
This paper introduces the Growth-Oriented Neutral Correlation (GONC) Token protocol, a novel construct designed to address the systemic fragility inherent in extant algorithmic stablecoins and the lack of truly uncorrelated assets within decentralized finance (DeFi). Traditional algorithmic stablecoins, such as the mechanism underlying TerraUSD (UST), have demonstrated extreme vulnerability to systemic market shocks, leading to rapid, irreversible failures often termed "death spirals". The GONC architecture mitigates this instability through a three-pronged approach: the Core Stability Mechanism (CSM), the Neutral Correlation Maintenance Model (NCMC), and the Growth-Oriented Reserve Strategy (GORS). The NCMC represents the primary innovation, utilizing a rigorous econometric framework, specifically Dynamic Conditional Correlation (DCC-GARCH), to actively manage and minimize the conditional correlation between the internal reserve assets and the broad cryptocurrency market benchmark. The GORS component integrates yield-bearing, tokenized Real-World Assets (RWAs), providing a stable, exogenous source of capital growth that strengthens the collateral base, moving away from reliance solely on highly volatile crypto-native seigniorage assets. Simulations suggest that by treating correlation as an active risk parameter rather than a static market assumption, GONC significantly enhances resistance to systemic market contagion.
Algorithmic Stablecoin, Dynamic Conditional Correlation (DCC-GARCH), Real-World Assets (RWA), Portfolio Hedging, Systemic Risk, Decentralized Finance (DeFi), Transaction Cost Analysis (TCA)
Sandhya Principal Software Engineer, Texas, USA
Software supply chain attacks have emerged as critical threats to modern computing infrastructure. This paper presents a comprehensive framework for ensuring package integrity through cryptographic verification mechanisms across the entire software supply chain lifecycle. Our approach introduces a multi-layered verification system combining digital signatures, hash chains, and transparent audit logs to detect tampering, unauthorized modifications, and malicious package injections. We propose a novel architecture integrating seamlessly with existing package management systems while providing strong security guarantees through end-to-end cryptographic verification. The framework employs a hierarchical trust model with distributed verification nodes, enabling scalable integrity checking for large-scale deployments. Through extensive evaluation using real-world package repositories and simulated attack scenarios, we demonstrate 99.97% accuracy in detecting compromised packages while introducing minimal performance overhead (average 3.2% increase in installation time). Our implementation successfully prevented all tested supply chain attack vectors including dependency confusion, typosquatting, and compromised upstream sources.
Supply chain security, cryptographic verification, package integrity, digital signatures, software security, dependency management
Sandhya Principal Software Engineer, Texas, USA
Early-stage (0-to-1) distributed products face unique reliability challenges that existing frameworks do not adequately address. Traditional approaches assume organizational maturity, established processes, and dedicated reliability teams—luxuries unavailable to startups and new product initiatives. We present a systematic four-pillar framework—Detection, Mitigation, Resolution, and Prevention—specifically designed for achieving 99.9% reliability in resource-constrained environments. Through production deployment at a large-scale platform serving millions of daily users, we demonstrate 80% incident reduction, 95% improvement in Mean Time to Detection, and 90% improvement in Mean Time to Mitigation. Theoretical analysis projects that widespread framework adoption could prevent $3.6-7.2B in annual losses across e-commerce, food delivery, fintech, and logistics industries. The framework provides concrete implementation patterns, measurable KPIs, and achieves ROI in 6-12 months with 3-4 month implementation timeline.
systems reliability, distributed systems, site reliability engineering, mean time to detection, incident response, 0-to-1 products
Samuel Tsui1 Quincy Stoke2 , 1 Lexington High School, 251 Waltham St, Lexington, MA 02421 , 2 University of California, Irvine, Irvine, CA 92697
This paper addresses the problem of unpredictable school bus arrival times, which often results in missed buses, unnecessary waiting, and safety concerns for students. To solve this issue, we propose a mobile application that provides real-time bus tracking along with student check-in and check-out features. The system is developed using Flutter for the user interface and Firebase services for authentication, data storage, and live GPS synchronization [5]. Its key components include role-based navigation, a real-time database that stores bus and route information, and continuous GPS updates from drivers to ensure accurate tracking. During development, several challenges arose, such as preventing false check-ins and maintaining correct route assignments when students or buses change locations. Experiments were conducted to test estimated arrival time accuracy and the reliability of the automated check-out system, both confirming the importance of accounting for real-world variability in GPS data and network updates [6]. Overall, the results demonstrate that a software-based solution can significantly improve school transportation by enhancing safety, reducing stress from uncertainty, increasing convenience, and strengthening communication between students, parents, and school staff.
Safety, Tracking, Location, School bus
Baichen Yu1 Tyler Boulom2 , 1 LSanta Margarita Catholic High School, 22062 Antonio Pkwy, Rancho Santa Margarita, CA 92688 , 2 Woodbury University, 7500 N Glenoaks Blvd, Burbank, CA 91504
This project addresses ecological and sanitation challenges associated with traditional backyard bird feeders by developing Ornimetrics, an intelligent, AI-enabled feeding system. Using an embedded camera and YOLOv11-based species recognition, the feeder identifies visiting birds, regulates food portions, and logs activity to a cloud database. The system reduces disease transmission risks, minimizes waste, and improves user awareness through real-time analytics. Two experiments were conducted: one assessing model accuracy across four species and another measuring species-specific waste patterns. Results showed strong detection accuracy with predictable weaknesses in low-light and high-motion conditions, as well as meaningful differences in waste generation. Methodology comparisons highlighted how Ornimetrics improves upon existing wildlife-monitoring and scene-classification frameworks by integrating automated decision-making into its workflow. Overall, the system demonstrates a viable and innovative approach to responsible, data-driven bird feeding.
Wildlife Monitoring, Computer Vision, Smart Feeding Systems, Ecological Sustainability
Rigoberto Garcia SSAI Institute of Technology, a Division of Software Solutions Corporation Principal Researcher
The convergence of cloud computing, neural cognitive architectures, and generative artificial intelligence has created an unprecedented demand for ethical frameworks that can operate autonomously within distributed systems. Traditional AI ethics depend on human-imposed constraints, but as neural networks evolve within cloud ecosystems, they increasingly generate their own reasoning, emotional modeling, and self-regulation pathways. This paper introduces the concept of Generative Ethics—a self-evolving ethical substrate that emerges organically within cloud-driven neural systems. Using the author’s previous frameworks in Synthetic Ethics, Cognitive Governance, and AI Compliance Enforcement, this study proposes a structural and procedural model for ethical emergence, evaluation, and evolution in cloud environments. The findings demonstrate how synthetic entities, such as the SYN-02 and COLLINX ecosystems, integrate generative ethical reasoning into decision-making, ensuring both autonomy and moral coherence across distributed cloud layers.
Cloud Computing, Generative AI, Neural Cognitive Systems, Synthetic Ethics, Cloud Governance, AI Autonomy, Cognitive Compliance, IoT Ethics
Liu Hongjun¹, Zhang Lushuang¹, Lv Ping¹ and Liu Xiao 1Hangzhou Pigeon Nest Technology Co., Ltd., Hangzhou, Zhejiang Province, China 2Northeast Normal University, Changchun City, Jilin Province, China
This study responds to the demand for precise adolescent mental health screening and intervention. Existing subjective, discontinuous scale assessments and traditional school-based reporting frequently miss optimal intervention windows. Grounded in ecosystem theory, we screened five ef ective variables, surveyed 8,860 students in a prefecture-level city in Jiangsu using multiple scales, and built a mental health assessment system with SPSS. A multi-modal system was adopted for dynamic emotion recognition among 1,040 students for verification. Results revealed a strong static-dynamic correlation, with static indicators predicting dynamic emotional status. Integrating an intelligent platform, this study reshapes campus mental health workflows and of ers a novel approach
Adolescent mental health; Mental health assessment; Artificial intelligence algorithms; Physical and mental health management platform; Facial expression recognition.
University of California, Berkeley Berkeley, CA 94720, USA
Group discussion is a central mechanism through which people learn, coordinate, and solve problems together, yet our ability to quantify the quality of an individual contribution remains limited (Rosé et al., 2008). Instructors and researchers often rely on coarse or subjective judgments to determine whether a speaker “moved the discussion forward,” leaving open the question of how such contributions can be measured in a systematic and reproducible way. Although recent NLP work has begun to characterize conversational dynamics, the field still lacks validated, utterance-level constructs for measuring how individual contributions advance collaborative problem solving.
NLP, Large language model.
Mohammed MAIZA1 , Chahira CHERIF2, Adeel AHMAD3, and Abdelmalik TALEB-AHMED4 1 Faculty of Exact and Applied Sciences, University of Oran 1 Ahmed Ben Bella, Oran, Algeria 2 LRIIR, Faculty of Medicine, University of Oran 1 Ahmed Ben Bella, Oran, Algeria 3 LISIC, University of the Littoral Opal Coast (ULCO), Calais, France 4 IEMN, Polytechnic University of Hauts-de-France, University of Lille, Valenciennes, France
change management is essential for the ongoing evolution and sustainability of software applications and their underlying business processes. Unmanaged modifications often result in inconsistencies and operational inefficiencies, especially within active process environments. To mitigate these risks, we introduce a formalized method for integrating changes into business processes, utilizing an ontology-based semantic representation. Our approach systematically leverages historical change data to analyze impact and propagation patterns. By examining successive BPMN 2.0 process versions, we build an ontology that captures dependencies and structural relationships. A comparative analysis of these versions produces a categorized dataset of changes—classified as additions, deletions, or stable elements. This structured dataset supports impact assessment and predictive modeling for future modifications. Ultimately, our method equips process managers with the insights needed to make informed decisions, thereby reducing risks and costs associated with business process evolution.
Business Process Management (BPM), Process Evolution, Change Impact Analysis, Ontology, OWL, BPMN 2.0.
Partha Sarathi Samal1 , Suresh Kumar Palus2, Sai Kiran Padmam31 Independent Researcher, Connecticut, USA 2 Independent Researcher, Pennsylvania, USA 3 Independent Researcher, New Jersey, USA
Autonomous software development agents represent a pivotal shift in how organizations approach coding, testing, and maintenance work. Industry trends project these systems will move from proof of concept toward production deployment within the next 18 to 24 months. Current evaluation frameworks remain fragmented, focusing on isolated task types or single metric dimensions, creating blind spots for practitioners and researchers. This paper introduces DevAgentBench, a comprehensive benchmark suite for autonomous software development agents that spans multiple software development lifecycle phases. DevAgentBench covers four core task families: bug fixing, test generation, refactoring, and code review assistance, plus long-horizon feature tasks that demand planning and coordination. We propose a three-layer metric framework capturing task success, operational reliability, and business-aligned performance. We also present a taxonomy of nine failure-mode categories observed in agent behavior, grounded in real-world agent deployments and existing benchmarks. Finally, we release DevAgentEval, an open-source evaluation framework that enables researchers and tool builders to assess new agents consistently. Baseline experiments across three agent patterns and multiple large language models reveal that no single agent dominates across all task families, and certain failure modes persist regardless of model size.
Autonomous agents, software engineering, benchmark suite, evaluation metrics, failure modes, continuous integration.