Artificial Intelligence and Robotics Commons^™

Website Owner Identification Through Multi-Level Contrastive Representation Learning, Cheng Tu, Yunshan Ma, Yang Li, Min Zhang, Miao Hu, Fan Shi, Xiang Wang

Research Collection School Of Computing and Information Systems

Website owner identification aims to recognize the organization or individual who owns a given website that is served on the web. It is a crucial step for cyberspace surveying and mapping, playing a significant role in cyberspace administration and governance. Existing widely employed solutions for website owner identification mainly fall into two paradigms: (1) querying the public information databases such as WHOIS, which store the Internet resource’s registered users or assignees; and (2) directly extracting the organization or individual name of the website owner from the webpage using the technique of named entity recognition. However, the former is less reliable …

Unsupervised Visual Chain-Of-Thought Reasoning Via Preference Optimization, Kesen Zhao, Beier Zhu, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Chain-of-thought (CoT) reasoning greatly improves the interpretability and problem-solving abilities of multimodal large language models (MLLMs). However, existing ap proaches focus on text CoT, limiting their ability to lever age visual cues. Visual CoT remains underexplored, and the only work [35] is based on supervised fine-tuning that relies on extensive labeled bounding-box data and is hard to generalize to unseen cases. In this paper, we introduce Unsupervised Visual CoT (UV-CoT), a novel framework for image-level CoT reasoning via preference optimization. UV-CoTperforms preference comparisons between model generated bounding boxes (one is preferred and the other is dis-preferred), eliminating the need for …

Genwardrobe: A Fully Generative System For Travel Fashion Wardrobe Construction, Peng Jin, Yilin Wen, Mingzhe Yu, Yunshan Ma, Rong Zheng, Jin‑Tu Fan, Chong Wah Ngo

Research Collection School Of Computing and Information Systems

With the increasing demand for outfit planning in real-world travel scenarios, the need for constructing a travel fashion wardrobe, a series of outfits tailored to a user's personalization and destination-specific context over a short travel period, has grown significantly. However, existing systems or works often focus on isolated factors and rely on retrieval-based methods, with insufficient utilization of generative models, limiting their adaptability to real-world travel scenarios. To address this issue, this study introduces GenWardrobe, a fully generative system for travel fashion wardrobe construction. GenWardrobe consists of three key modules: user query analysis, fashion knowledge retrieval via retrieval-augmented generation and …

Contrastrepair: Enhancing Conversation-Based Automated Program Repair Via Contrastive Test Case Pairs, Jiaolong Kong, Xiaofei Xie, Mingfei Cheng, Shangqing Liu, Xiaoning Du, Qi Guo

Research Collection School Of Computing and Information Systems

Automated Program Repair (APR) aims to automatically generate patches for rectifying software bugs. Recentstrides in Large Language Models (LLM), such as ChatGPT, have yielded encouraging outcomes in APR,especially within the conversation-driven APR framework. Nevertheless, the efficacy of conversation-drivenAPR is contingent on the quality of the feedback information. In this article, we propose ContrastRepair, anovel conversation-based APR approach that augments conversation-driven APR by providing LLMs withcontrastive test pairs. A test pair consists of a failing test and a passing test, which offer contrastive feedback tothe LLM. Our key insight is to minimize the difference between the generated passing test and the …

Explainabledetector: Exploring Transformer-Based Language Modeling Approach For Sms Spam Detection With Explainability Analysis, Mohammad Amaz Uddin, Muhammad Nazrul Islam, Leandros Maglaras, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

Short Message Service (SMS) is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages - commonly known as SMS spam. With the rapid adoption of smartphones and increased Internet connectivity, SMS spam has emerged as a prevalent threat. Spammers have recognized the critical role SMS plays in today's modern communication, making it a prime target for abuse. As cybersecurity threats continue to evolve, the volume of SMS spam has increased substantially in recent years. Moreover, the unstructured format of SMS data creates significant challenges for SMS spam detection, making it more difficult …

Optimizing Cybersecurity Through Ai Predictive Analytics And Human Expertise, Cathy Mae C. Dutong

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Hideki Takei, DBA

As cybersecurity threats evolve in complexity and scale, the reliance on artificial intelligence (AI) has become increasingly prevalent across both public and private sectors. This study examines the dual role of AI-driven predictive analytics in strengthening organizational cybersecurity, while addressing the ongoing need for human oversight. Through a mixed-method approach, combining survey data from cybersecurity professionals with an extensive literature review, this research analyzes AI's capacity to detect emerging threats, the systemic challenges associated with AI integration, and the indispensable role of human expertise in interpreting AI outputs. Findings indicate that while AI enhances proactive …

Global Trends In Ai-Driven Product Development: A Cross-Country Analysis, Shilpa Dhananjayan

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Hideki Takei, DBA

Artificial Intelligence (AI) is transforming industries and accelerating global innovation, yet its benefits remain unevenly distributed. A nation’s AI readiness—its capacity to adopt and implement AI technologies—plays a crucial role in economic growth and technological advancement. Key determinants of AI readiness include digital infrastructure, data accessibility, government policies, research and development (R&D) investment, and workforce development. This study examines the relationship between AI readiness, AI adoption, innovation, R&D investment of a nation, Digital Infrastructure Index (DII) and Human Capital Index (HCI) using a Random Forest regression model. Findings reveal a strong correlation between AI adoption …

A Feature Engineering Technique For Enhancing The Generalization Of Machine Learning Models In Estimating Crop Evapotranspiration, Gaku Yokoyama, Sohta Harigai, Shigehiro Kubota, Koichi Nomura, Gregory R. Goldsmith, Daisuke Yasutake, Tomoyoshi Hirota, Masaharu Kitano

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Accurate and precise estimation of evapotranspiration (ET) is crucial for understanding the terrestrial carbon, water, and energy cycles. While process-based models of ET, such as the Penman–Monteith model offer robust generalization capabilities, they are limited by the need for detailed parameters (e.g., stomatal conductance,) that are challenging to measure continuously. On the other hand, machine learning models can estimate ET by capturing relationships between ET and environmental variables without experimentally measuring model parameters. However, machine learning models face the challenge of limited generalizability. This issue is particularly significant given the uncertainty introduced by changing climatic …

Solution Of Fractional Order Diffusion Equations With Clique Neural Network, Merve Zeynep Kaya, Mesut Karabacak, Ercan Çelik

Mathematical Modelling and Numerical Simulation with Applications

In this paper, the clique artificial neural network method is used to solve the fractional diffusion equation, which is a subclass of partial differential equations. The clique neural network architecture is constructed using input, hidden, and output layers. Several degrees of clique polynomials were used as activation functions, and the output layer was obtained by multiplying them with weight coefficients. Subsequently, the optimization equation was derived, and the exact solution, numerical solution, and error function graphs were obtained using a specialized algorithm. Analysis of the results demonstrates that the clique artificial neural network method provides quicker and more accurate results …

Synergistic Modeling Of Hydrogel Gelation Via Time-Delay Dynamics And Machine Learning Algorithms, Mine Babaoglu, Dipesh ., Pankaj Kumar, Jagjit Singh Dhatterwal, Mansoor Alsulami

Mathematical Modelling and Numerical Simulation with Applications

This paper presents an integrated framework in which delay differential equation (DDE) modeling and machine learning (ML) approaches are coupled to study hydrogel formation kinetics, with emphasis on delayed crosslinker addition. Conventional mechanistic models disclose many physical and kinetic complexities of reacting mixtures; they seldom depict the nonlinear and time-evolving complexities inherent in developing polymer networks. To address this, a mathematical model is developed that examines how the insertion of crosslinkers affects system stability and equilibrium. Analytical and numerical results show that delays nearing critical levels cause bifurcation behavior with substantial implications on gelation kinetics. Sophisticated machine learning systems, including …

Ai Exposure And The Future Of Work: Linking Task-Based Measures To U.S. Occupational Employment Projections, Erik Vasilauskas, Michael Horrigan

Reports

No abstract provided.

Non-Invasive Detection Of Choroidal Melanoma Via Tear-Derived Protein Corona On Gold Nanoparticles: A Machine Learning Approach, Hakimeh Rakhshandeh, Ahmad Nasiraei, Hamid Riazi-Esfahani, Babak Masoomian, Fariba Ghassemi, Mojtaba Arjmand, Saeed Heidari Keshel, Fatemeh Atyabi, Rassoul Dinarvand

Wills Eye Hospital Papers

This study investigates the feasibility of using tear sample analysis, based on protein corona formation on gold nanoparticles combined with electrospray ionization mass spectrometry (ESI-MS) and machine learning techniques, as a non-invasive approach for the detection of choroidal melanoma. The aim is to assess whether protein-nanoparticle interactions can support early and reliable identification of this ocular condition. Tear samples were collected using Schirmer strips from six healthy individuals and six patients diagnosed with choroidal melanoma, with subsequent augmentation to 18 samples per group. Gold nanoparticles (AuNPs, ~ 20 nm) were synthesized via citrate reduction and incubated with tear samples to …

Heuristic Weight Initialization For Transfer Learning In Classification Problems, Musulmon Lolaev, Anand Paul, Jeonghong Kim

School of Public Health Faculty Publications

Transfer learning is the predominant method for adapting pre-trained models on another task to new domains while preserving their internal architectures and augmenting them with requisite layers in Deep Neural Network models. Training intricate pre-trained models on a sizable dataset requires significant resources to fine-tune hyperparameters carefully. Most existing initialization methods mainly focus on gradient flow-related problems, such as gradient vanishing or exploding, or other existing approaches that require extra models that do not consider our setting, which is more practical. To address these problems, we suggest employing gradient-free heuristic methods to initialize the weights of the final new-added fully …

Retracted: Idea Density And Grammatical Complexity As Neurocognitive Markers, Diego Iacono, Gloria Feltis

Department of Neurology Faculty Papers

Language, a uniquely human cognitive faculty, is fundamentally characterized by its capacity for complex thoughts and structured expressions. This review examines two critical measures of linguistic performance: idea density (ID) and grammatical complexity (GC). ID quantifies the richness of information conveyed per unit of language, reflecting semantic efficiency and conceptual processing. GC, conversely, measures the structural sophistication of syntax, indicative of hierarchical organization and rule-based operations. We explore the neurobiological underpinnings of these measures, identifying key brain regions and white matter pathways involved in their generation and comprehension. This includes linking ID to a distributed network of semantic hubs, like …

Optimization Of Multi-Target Interception Scheme Based On Performance Simulation Modeling, Hanwen Liu, Zhimin Zhuo, Xue Yang

Journal of System Simulation

Abstract: The air attack scenarios faced by air defense weapons and equipment show the trend of saturation, diversification and intelligence. It is very important to establish multi-target interception efficiency model and optimize interception scheme according to simulation. The current intercepting efficiency index mainly considers the whole operation process, and can not guide the optimization of the intercepting scheme of specific intercepting rounds. The generation of interception schemes mainly relies on experience and simple mathematical model, which is difficult to cope with the increasingly complex and changeable battlefield environment. Therefore, an interception scheme advantage index that comprehensively considers interception probability and …

Design And Prediction Of Deep Fuzzy Neural Network, Chengbiao Wei, Taoyan Zhao, Jiangtao Cao, Ping Li

Journal of System Simulation

Abstract: A deep fuzzy neural network (DFNN) is proposed to solve the problem that the deep neural network has poor interpretability and the correction of the model is not targeted when dealing with the big data regression prediction problem. The proposed deep fuzzy neural network adopts an adaptive fuzzy Cmeans (AFCM) clustering algorithm in structural learning. The structure of the model, namely the number of rules and the antecedent parameters of the rules, is determined by calculating the introduced validity function. The identification of consequent parameters uses an improved grey wolf optimization (IGWO) algorithm. By replacing the linear decreasing strategy …

Lane Detection In Dark Light Based On Instance Association, Yanji Jiang, Yingyang Zhang, Hao Dong, Xiaoguang Zhang, Meihui Wang

Journal of System Simulation

Abstract: In current research on lane detection, existing algorithms can efficiently detect lane lines under good lighting conditions. However, lane detection in low light still faces the challenge of a high false negative rate. A detection algorithm called Instance Association Net(IANet) is proposed to address this issue by utilizing the structural relationships between lane lines, which is helpful for low light conditions. The algorithm first generates unique masks for different lane lines using features at the starting points of the lane lines and a global feature map, achieving instance-level feature separation of the lane lines. It employs an instance-level attention …

Station Layout Optimization Method And Simulation For Non-Cooperative Target In Angle Of Arrival Positioning, Yida Ning, Jiongqi Wang, Juhui Wei, Zhenzu Bai, Zhangming He

Journal of System Simulation

Abstract: In the context of angle of arrival (AOA) positioning system for non-cooperative target tracking and positioning, accurately determining true location of the target poses a significant challenge. Conventional station deployment indicators like geometric dilution of precision (GDOP) fail to provide effective guidance for optimization station layout. To address the issue, this study introduces a novel indicator for station optimization and evaluation based on factors that influence positioning accuracy within an angle measurement system. These factors encompass angular differencing, baseline intersection angles, and the observer-target line distance. Moreover, this indicator encompasses the challenges associated with data conformity in "air to …

Research On Strong Real-Time Synchronisation Algorithm For Lvc Co-Simulation, Junhui Li, Songtao Sun, Fei Liu

Journal of System Simulation

Abstract: Live, virtual, and constructive(LVC) joint simulation has become a hot research topic of current military simulation; however, existing time management strategies usually fail to meet the needs of strict real-time performance of LVC. A LVC joint simulation synchronization algorithm is proposed that starts with a window sliding-based median smoothing strategy and real time drift rate-based clock compensation strategy for effective node synchronization. A novel hybrid timing strategy is introduced combining long and short cycles implemented in software, which balances precision and efficiency. A simulation catch-up strategy is proposed to address software delays, which combined with the highprecision timing strategy, …

Wingtip Docking Control Of Composite Aircraft Based On Adrc Theory, Chunlei Xie, Hongxia Hu, Weibo Han

Journal of System Simulation

Abstract: The process of wingtip docking in composite aircraft is challenged by significant unsteady vortex aerodynamic disturbances arising from the close-range coupling of wingtips, thereby posing considerable constraints on docking precision and flight safety. This study endeavors to address the intricate task of airborne wingtip docking control amidst wingtip vortex disturbances through a comprehensive investigation of airborne wingtip docking control technology, grounded in the tenets of active disturbance rejection control (ADRC) theory. Initially, a mathematical model encapsulating the dynamics of three-channel attitude/displacement during the docking operation, incorporating both the wingtip docking mechanism and the wingtip vortex model, is established. …