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Articles 631 - 660 of 63971

Full-Text Articles in Computer Sciences

Multi-Scale Modeling Method For Intelligent Unmanned Aerial Vehicle Swarm Combat, Yelei Zhu, Shoulin Shen, Jiang Zhu, Chuanhua Wen Jul 2026

Multi-Scale Modeling Method For Intelligent Unmanned Aerial Vehicle Swarm Combat, Yelei Zhu, Shoulin Shen, Jiang Zhu, Chuanhua Wen

Journal of System Simulation

Abstract: Intelligent unmanned aerial vehicle swarm combat exhibits multi-scale characteristics of microlevel tactical autonomy, meso-level resource constraints, and macro-level network collaboration. In view of the problem that a single-scale modeling method is difficult to simultaneously characterize individual decision-making details, resource flow process, and system collaboration mechanism, a multi-scale modeling method integrating multi-agent modeling, system dynamics, and complex network theory was proposed. By designing six formal coupling operators to achieve cross-layer information mapping, a time progression mechanism based on a master-slave clock and hybrid synchronization was established, and a proof of error boundedness was provided. A prototype system was implemented on …

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Research On Safety Monitoring System For Digital Twin Fully Mechanized Mining Face In Thin Coal Seams, Ya Liu, Zhaoyun Zhang, Cheng An, Canguang Zheng Jul 2026

Research On Safety Monitoring System For Digital Twin Fully Mechanized Mining Face In Thin Coal Seams, Ya Liu, Zhaoyun Zhang, Cheng An, Canguang Zheng

Journal of System Simulation

Abstract: To solve the problems of opaque production processes, difficult expression of production information, and high difficulty in real-time model driving and safety monitoring in fully mechanized mining faces of thin coal seams, studying the use of digital twin technology and risk early warning models to solve beyond-visual-range visual production operations and safety monitoring control of the working face has important theoretical and practical value. This paper studied the construction methods of the digital twin system for fully mechanized mining faces in thin coal seams and discussed the construction of high-precision three-dimensional models, the real-time collection and processing conversion of …

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Research On Multi-Objective Hybrid Flow-Shop Scheduling With Order Splitting, Junjie Yu, Weixi Ji, Chen Chen, Jingyu Lu, Chaoyang Zhang Jul 2026

Research On Multi-Objective Hybrid Flow-Shop Scheduling With Order Splitting, Junjie Yu, Weixi Ji, Chen Chen, Jingyu Lu, Chaoyang Zhang

Journal of System Simulation

Abstract: To address the order splitting multi-objective hybrid flow-shop scheduling problem (OSMOHFSP), a dual-objective optimization model was formulated with the objectives of minimizing makespan and total order tardiness. Fixed sub-batch specification constraints were incorporated to reflect common splitting limitations in actual production. A sub-batch generation strategy based on a candidate set of sub-batch specifications was designed to efficiently filter feasible splitting combinations, effectively reducing the search space dimensionality and computational complexity. A hybrid multi-objective metaheuristic algorithm integrating NSGA-II and SA was proposed. The global search capability was enhanced by constructing two crossover and four mutation operators. An improved SA embedding …

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Ultra-Short-Term Photovoltaic Power Prediction Method Based On Spatio-Temporal Feature Enhancement Of Ground-Based Cloud Images, Zhiwei Kou, Fengyue Xin, Xiaoming Cui, Yu Yin, Feifan Li, Yongsheng Qi Jul 2026

Ultra-Short-Term Photovoltaic Power Prediction Method Based On Spatio-Temporal Feature Enhancement Of Ground-Based Cloud Images, Zhiwei Kou, Fengyue Xin, Xiaoming Cui, Yu Yin, Feifan Li, Yongsheng Qi

Journal of System Simulation

Abstract: In view of the time lag in ground-based cloud image acquisition and the insufficient accuracy of existing photovoltaic power prediction models, an ultra-short-term photovoltaic power prediction method named spatial-temporal feature enhancement-ground-based cloud image-improved LSTM (STFE-GCI-ILSTM), which is based on spatio-temporal feature enhancement-ground-based cloud images-improved long short-term memory (LSTM) network was proposed. A ground-based cloud image prediction model with multi-scale spatio-temporal feature enhancement (STFE-GCI) was constructed. By spatial feature enhancement, temporal feature enhancement, and multi-scale feature fusion technologies, the deep spatio-temporal features of historical cloud image sequences were extracted to generate future predicted cloud image sequences, thereby eliminating the time …

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Prediction Of Industrial Concentration Parameters Based On Caudformer Model, Kaipeng Xu, Yan Wang, Xin Zhang, Yang Liu, Zhenzhong Wang, Xiang Liu, Zhicheng Ji Jul 2026

Prediction Of Industrial Concentration Parameters Based On Caudformer Model, Kaipeng Xu, Yan Wang, Xin Zhang, Yang Liu, Zhenzhong Wang, Xiang Liu, Zhicheng Ji

Journal of System Simulation

Abstract: To solve the problems in the industrial concentration process of traditional Chinese medicine that traditional prediction methods are difficult to deal with complex characteristics such as nonlinearity, high-dimensional coupling, and highly skewed distribution and that existing deep learning models insufficiently consider causal relationships among variables, ignore frequency-domain periodic characteristics, and lack long-range dependency modeling capabilities, an improved multivariate time series prediction model CauDformer was proposed in this paper. Based on the iTransformer architecture, a causal multi-head self-attention mechanism (C-MHSA) was introduced to compulsorily constrain the dependency direction among variables by utilizing a lower triangular causal mask, guiding the model …

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Accountable Agents In Software Engineering: An Analysis Of Terms Of Service And A Research Roadmap, Christoph Treude Jul 2026

Accountable Agents In Software Engineering: An Analysis Of Terms Of Service And A Research Roadmap, Christoph Treude

Research Collection School Of Computing and Information Systems

AI coding assistants and autonomous agents are becoming integral to software development workflows, reshaping how code is produced, reviewed, and maintained. While recent research has focused mainly on the capabilities and impacts of productivity of these systems, much less attention has been paid to accountability: who is responsible when agents generate, modify, or recommend code? In practice, accountability is defined through the Terms of Service (ToS) and related policy documents that govern the use of AI-powered development tools.In this vision paper, we present a comparative analysis of the Terms of Service for widely used AI coding assistants and agent-enabled development …

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A Framework For Top-K Queries With Constrained Preferences, Kyriakos Mouratidis, Nikolaos Chaloulakos, Bo Tang Jul 2026

A Framework For Top-K Queries With Constrained Preferences, Kyriakos Mouratidis, Nikolaos Chaloulakos, Bo Tang

Research Collection School Of Computing and Information Systems

Traditional rank-aware processing assumes a dataset that contains available options to cover a specific need (e.g., restaurants, hotels, etc) and users who browse that dataset via top-k queries with linear scoring functions, i.e., by ranking the options according to the weighted sum of their attributes, for a set of given weights. In practice, however, user preferences (weights) may only be estimated with bounded accuracy, or may be inherently imprecise due to the inability of a human user to specify exact weight values with absolute accuracy. Motivated by this, we define the constrained-preference top-k (CT) query. Given an approximate description of …

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Spatiotemporal Sycophancy: Negation-Based Gaslighting In Video Large Language Models, Ziyao Tang, Pengkun Jiao, Bin Zhu, Huiyan Qi, Jingjing Chen, Yu-Gang Jiang Jul 2026

Spatiotemporal Sycophancy: Negation-Based Gaslighting In Video Large Language Models, Ziyao Tang, Pengkun Jiao, Bin Zhu, Huiyan Qi, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Video Large Language Models (Vid-LLMs) have demonstrated remarkable performance in video understanding tasks, yet their robustness under conversational interaction remains largely underexplored. In this paper, we identify spatiotemporal sycophancy, a failure mode in which Vid-LLMs retract initially correct, visually grounded judgments and conform to misleading user feedback under negation-based gaslighting. Rather than merely changing their answers, the models often fabricate unsupported temporal or spatial explanations to justify incorrect revisions. To systematically investigate this phenomenon, we propose a negation-based gaslighting evaluation framework and introduce GasVideo-1000, a curated benchmark designed to probe spatiotemporal sycophancy with clear visual grounding and temporal reasoning requirements. …

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Cdt-1d Cnn Integration With Simpson-Sobolev Regularization For High-Frequency Options Trading: With Fem-Based Heston Option Pricing, Daniel M. Margolis, Johannes Tausch, Arthur K. Selender Jul 2026

Cdt-1d Cnn Integration With Simpson-Sobolev Regularization For High-Frequency Options Trading: With Fem-Based Heston Option Pricing, Daniel M. Margolis, Johannes Tausch, Arthur K. Selender

Mathematics Theses and Dissertations

This dissertation presents a computational framework for high-frequency options trading that combines Cross-Data-Type 1-D Convolutional Neural Networks (CDT-1D CNN) with Simpson-Sobolev regularization for directional prediction, and finite element methods (FEM) for realistic option pricing during backtesting. The core innovation lies in developing a mathematically rigorous regularization approach that maintains the adaptability of modern deep learning while enabling accurate evaluation through stochastic volatility models. The primary contribution is the Simpson-Sobolev regularization scheme, which extends traditional Sobolev regularization by incorporating Simpson’s rule for numerical integration. This approach achieves higher-order accuracy in approximating the Sobolev norms that control function smoothness. Simpson’s rule attains …

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Ethos: A Computational Framework For Material Culture Research, Chelsea L. Grafe Jul 2026

Ethos: A Computational Framework For Material Culture Research, Chelsea L. Grafe

Department of Textiles, Merchandising and Fashion Design: Dissertations, Theses, and Student Research

Museum catalog records make large collections searchable, but they do not always preserve the reasoning behind the claims they contain. A record may present a quilt's identity as settled while leaving the reasoning behind that claim invisible, making it difficult for later researchers to check, question, or reuse. This project asks whether museum catalog data can be analyzed in a way that keeps each analytical claim connected to the evidence that supports it. To test this question, the project applies a material-first computational framework named Ethos. Ethos formalizes an object-centered inquiry sequence in which material dependencies establish where analysis begins …

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Improving Vector Embedding Generation Throughput For Neural Information Retrieval Models, Conrad Tkacz Jul 2026

Improving Vector Embedding Generation Throughput For Neural Information Retrieval Models, Conrad Tkacz

Theses and Dissertations from DePaul University

For decades, advancements in information retrieval technologies have changed how individuals discover data with computer systems. More recent advancements in artificial intelligence (AI) have also made the benefits of neural information retrieval systems available to millions. These information retrieval systems help empower future discoveries, though this is not the case for scientific data and High-Performance Computing (HPC) systems. HPC systems can generate enormous amounts of data, and no tools are currently available that can ingest data at the rates required to efficiently build an information retrieval system to explore scientific data. This thesis explores the current state of constructing a …

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How Do Different Food Influents Affect Biogas Production, Process And Operational Stability, And System Performance In The Armfield W8, A Bench-Scale Anaerobic Digester, Jarko Rumbaoa, Anahita Adhikari Jul 2026

How Do Different Food Influents Affect Biogas Production, Process And Operational Stability, And System Performance In The Armfield W8, A Bench-Scale Anaerobic Digester, Jarko Rumbaoa, Anahita Adhikari

Rose-Hulman Undergraduate Research Publications

No abstract provided.

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Center Of Mass (Com) Shift In Electric Vertical Takeoff And Landing (Evtol) Aircraft, Irene Lema Madueno Jul 2026

Center Of Mass (Com) Shift In Electric Vertical Takeoff And Landing (Evtol) Aircraft, Irene Lema Madueno

Rose-Hulman Undergraduate Research Publications

No abstract provided.

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Sustainable Extraction Of A New Diketopiperazine, Anthony Chapman Jul 2026

Sustainable Extraction Of A New Diketopiperazine, Anthony Chapman

Rose-Hulman Undergraduate Research Publications

No abstract provided.

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Native Plants For Green Roofs In Indiana, Alasdair Curts Jul 2026

Native Plants For Green Roofs In Indiana, Alasdair Curts

Rose-Hulman Undergraduate Research Publications

No abstract provided.

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Advancing Social Media Analytics And Personalized Generation Via Transfer Learning, Discourse-Aware Modeling, And Collaborative Modeling, Gibson Nkhata Jul 2026

Advancing Social Media Analytics And Personalized Generation Via Transfer Learning, Discourse-Aware Modeling, And Collaborative Modeling, Gibson Nkhata

Graduate Theses and Dissertations

Social media platforms have become central to information exchange, shaping public opinion across social, political, and economic domains. However, the massive volume of user-generated content, combined with its informal, nuanced, and often noisy nature, presents significant challenges for automated analysis and generation. Tasks such as stance detection, rumor verification, and personalized content generation are further complicated by sarcasm, evolving discourse structures, and diverse user preferences. Addressing these challenges requires models that can effectively leverage linguistic nuance, conversational dynamics, and collaborative user signals. Transfer learning has emerged as a powerful paradigm for improving performance in low-resource and complex language understanding tasks. …

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Towards Audio-Driven Photorealistic Avatars For Teleconsultation: A Development And Evaluation Framework Under Constrained Bandwidth Environments, Fnu Himanshu Jul 2026

Towards Audio-Driven Photorealistic Avatars For Teleconsultation: A Development And Evaluation Framework Under Constrained Bandwidth Environments, Fnu Himanshu

Graduate Theses and Dissertations

Telehealth can improve access to healthcare services, especially for patients in rural and underserved areas. Many telehealth services use video communication to provide real-time interaction and a visible connection between patients and healthcare providers. However, video communication requires continuous transmission of audio and video data. When network conditions are limited or unreliable, audio and video interruptions can reduce communication quality. This thesis explores an audio-driven photorealistic avatar as a possible visual communication layer for teleconsultation without requiring continuous webcam video transmission. It develops two connected frameworks: the Audio-Driven Photorealistic Avatar Communication Framework and the Evaluation Framework. The Audio-Driven Photorealistic Avatar …

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Improved Pbs Algorithm For Multi-Agent Path Planning Based On Conflict Guidance And Punishment Mechanism, Jinbao Zhang, Jianlin Mao, Chengze Qian, Guimi Sun, Kaixin Tong Jul 2026

Improved Pbs Algorithm For Multi-Agent Path Planning Based On Conflict Guidance And Punishment Mechanism, Jinbao Zhang, Jianlin Mao, Chengze Qian, Guimi Sun, Kaixin Tong

Journal of System Simulation

Abstract: To address the bottleneck in which the priority-based search (priority-based search, PBS) algorithm for multi-agent path planning easily falls into conflict loops and generates invalid node expansions in complex scenarios, an improved algorithm based on conflict guidance and a punishment mechanism (improved PBS multi-agent path finding algorithm based on conflict guidance and punishment mechanism, CGP-PBS) was proposed. A conflict-guided node expansion mechanism was constructed; in high-level search, it comprehensively evaluated path cost and the number of conflicts, preferentially expanded child nodes with high potential for conflict resolution, and delayed the expansion of high-conflict nodes, thereby effectively compressing the search …

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Impartial Intelligence? Evidence Of Country-Label Sensitivity In Ai Financial Analysis, Fabio Motoki, Jedson Pinto Jul 2026

Impartial Intelligence? Evidence Of Country-Label Sensitivity In Ai Financial Analysis, Fabio Motoki, Jedson Pinto

School of Accountancy Faculty Publications

This study examines whether large language models exhibit systematic country-contingent differential treatment in financial fraud detection. Analyzing 30,000 synthetic transactions with identical statistical properties across three country attributions (United States, Great Britain, and China), we find LLMs assign significantly higher fraud probabilities to Chinese-attributed transactions (36.2%) compared to Western countries (≈30–31%), resulting in accuracy disparities of 67% versus 74%. The gap remains stable across five independent experimental replications and persists when using Chinese language prompts, ruling out linguistic effects. Bias mitigation strategies, such as requiring explanations or explicit country neutrality instructions, reduce but fail to eliminate these disparities. Testing across …

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Analysis Of Clinical Health Worker Notes With Explainable Artificial Intelligence For A Better Perspective In Preventing Readmission, Christopher Scott Klimp Jul 2026

Analysis Of Clinical Health Worker Notes With Explainable Artificial Intelligence For A Better Perspective In Preventing Readmission, Christopher Scott Klimp

Theses and Dissertations from DePaul University

Emergency Department (ED) readmissions remain a major challenge for healthcare systems, affecting both patient care quality and financial costs. Most prediction models depend largely on structured data from clinical tools that assign points to a small set of predefined factors – such as comorbidities, previous hospital visits, and behaviors such as smoking and drinking – and then sum those points to produce an overall risk score. These point-based tools leave out important information from Social Determinants of Health and a wealth of information from Community Health Workers (Community health workers (CHWs), trusted members of a community who help connect people …

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Using Recommender Systems To Help Revitalize Local News, Payam Pourashraf Jul 2026

Using Recommender Systems To Help Revitalize Local News, Payam Pourashraf

Theses and Dissertations from DePaul University

Local news outlets have experienced steep declines in readership as national and global online news sources have expanded and consolidated audience attention. Because many local media companies rely primarily on subscription-based business models, they must increase user engagement and provide clearer value to local subscribers. Personalization and recommender systems offer one promising path toward this goal. However, many recommender systems assume that a user’s past behavior can be summarized in a single unified profile. In practice, preferences are often context-dependent: the same person may prefer different content depending on geography, time, task, or situation. Local news offers a socially important …

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Advancing Imbalanced Classification Through Representation Learning, Morteza Mohammady Gharasuie Jul 2026

Advancing Imbalanced Classification Through Representation Learning, Morteza Mohammady Gharasuie

Computer Science Theses & Dissertations

Real-world datasets frequently exhibit severe class imbalance, where certain categories are significantly underrepresented relative to others, leading standard empirical risk minimization to bias learning toward majority classes and degrade performance on minority categories. This dissertation addresses imbalanced classification across both structured tabular data and long-tailed visual recognition by developing methods that improve representation learning and decision reliability under skewed distributions. For tabular data, it introduces Conditional Probability Representation (CPR), a target-based encoding framework that embeds feature–label relationships directly into the representation space, enhanced by a progressive feature upgrading mechanism for semi-supervised settings and a class-frequency–aware extension that improves robustness to …

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Operational Agency: A Permeable Legal Fiction For Tracing Culpability In Ai Systems, Anirban Mukherjee, Hannah H. Chang Jul 2026

Operational Agency: A Permeable Legal Fiction For Tracing Culpability In Ai Systems, Anirban Mukherjee, Hannah H. Chang

Research Collection Lee Kong Chian School Of Business

Modern artificial intelligence (AI) systems act with a high degree of independence yet lack legal personhood—a paradox that fractures doctrines grounded in human-centric notions of mens rea and actus reus. This Article introduces Operational Agency (OA)—a permeable legal fiction structured as an ex post evidentiary framework—and Operational Agency Graph (OAG)—a tool for mapping causal interactions among human actors, organizations, and AI systems. OA evaluates an AI’s observable operational characteristics: its goal-directedness (as a proxy for intent), predictive processing (as a proxy for foresight), and safety architecture (as a proxy for standard of care). OAG operationalizes that analysis by embedding these …

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Configuring Agentic Ai Coding Tools: An Exploratory Study, Matthias Galster, Seyedmoein Mohsenimofidi, Jai Lal Lulla, Muhammad Auwal Abubakar, Christoph Treude, Sebastian Baltes Jul 2026

Configuring Agentic Ai Coding Tools: An Exploratory Study, Matthias Galster, Seyedmoein Mohsenimofidi, Jai Lal Lulla, Muhammad Auwal Abubakar, Christoph Treude, Sebastian Baltes

Research Collection School Of Computing and Information Systems

Agentic AI coding tools increasingly automate software development tasks. Developers can configure these tools through versioned repository-level artifacts such as Markdown and JSON files. We present a systematic analysis of configuration mechanisms for agentic AI coding tools, covering Claude Code, GitHub Copilot, Cursor, Gemini, and Codex. We identify eight configuration mechanisms spanning from static context to executable and external integrations and, in an empirical study of 2,853 GitHub repositories, examine whether and how they are adopted, with a detailed analysis of Context Files, Skills, and Subagents. First, Context Files dominate the configuration landscape and are often the sole mechanism in …

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Semantic Context Improvisational Retrieval-Augmented Generation For Empathic Conversational Ai, Sharjeel Tahir, Judith Johnson, Jumana Abu-Khalaf, Syed Afaq Ali Shah Jul 2026

Semantic Context Improvisational Retrieval-Augmented Generation For Empathic Conversational Ai, Sharjeel Tahir, Judith Johnson, Jumana Abu-Khalaf, Syed Afaq Ali Shah

Research outputs 2022 to 2026

A fundamental limitation of modern conversational AI is its limited capacity to demonstrate sustained empathy in long-form interactions. We propose SCIRAG (Semantic Context Improvisational Retrieval-Augmented Generation), a feedback-driven retrieval framework for adaptive empathic dialogue. It employs a dual-loop retrieval framework, iteratively optimizing a static counseling dataset through user metadata and feedback memory refinement. To enhance contextual alignment, we deploy retrieval adaptation, enabling the model to retain and leverage past conversational cues based on user preferences. When integrated with Mixtral-8x7B, SCIRAG improves human-rated empathic understanding by +1.26 points and empathic response by +1.00 point on the RoPE scale, while increasing acceptability …

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Scattered Hypothesis Generation For Open-Ended Event Forecasting, He Chang, Zhulin Tao, Lifang Yang, Xianglin Huang, Yunshan Ma Jul 2026

Scattered Hypothesis Generation For Open-Ended Event Forecasting, He Chang, Zhulin Tao, Lifang Yang, Xianglin Huang, Yunshan Ma

Research Collection School Of Computing and Information Systems

Despite the importance of open-ended event forecasting for risk management, current LLM-based methods predominantly target only the most probable outcomes, neglecting the intrinsic uncertainty of real-world events. To bridge this gap, we advance open-ended event forecasting from pinpoint forecasting to scatter forecasting by introducing the proxy task of hypothesis generation. This paradigm aims to generate an inclusive and diverse set of hypotheses that broadly cover the space of plausible future events. To this end, we propose SCATTER, a reinforcement learning framework that jointly optimizes inclusiveness and diversity of the hypothesis. Specifically, we design a novel hybrid reward that consists of …

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Mab-Dqa: Addressing Query Aspect Importance In Document Question Answering With Multi-Armed Bandits, Yixin Xiang, Yunshan Ma, Xiaoyu Du, Yibing Chen, Yanxin Zhang, Jinhui Tang Jul 2026

Mab-Dqa: Addressing Query Aspect Importance In Document Question Answering With Multi-Armed Bandits, Yixin Xiang, Yunshan Ma, Xiaoyu Du, Yibing Chen, Yanxin Zhang, Jinhui Tang

Research Collection School Of Computing and Information Systems

Document Question Answering (DQA) involves generating answers from a document based on a user’s query, representing a key task in document understanding. This task requires interpreting visual layouts, which has prompted recent studies to adopt multimodal Retrieval-Augmented Generation (RAG) that processes page images for answer generation. However, in multimodal RAG, visual DQA struggles to utilize a large number of images effectively, as the retrieval stage often retains only a few candidate pages (e.g., Top-4), causing informative but less visually salient content to be overlooked in favor of common yet low-information pages. To address this issue, we propose a Multi-Armed Bandit–based …

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Avadclip: Audio-Visual Collaboration For Robust Video Anomaly Detection, Peng Wu, Wanshun Su, Guansong Pang, Yujia Sun, Qingsen Yan, Peng Wang, Yanning Zhang Jul 2026

Avadclip: Audio-Visual Collaboration For Robust Video Anomaly Detection, Peng Wu, Wanshun Su, Guansong Pang, Yujia Sun, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

With the increasing adoption of video anomaly detection in intelligent surveillance domains, conventional visual-only detection approaches often struggle with information insufficiency and high false-positive rates in complex environments. To address these limitations, we present a novel weakly supervised framework that leverages audio-visual collaboration for robust video anomaly detection. Capitalizing on the exceptional cross-modal representation learning capabilities of Contrastive Language-Image Pretraining (CLIP) across visual, audio, and textual domains, our framework introduces two major innovations: an efficient audio-visual fusion that enables adaptive cross-modal integration through lightweight parametric adaptation while maintaining the frozen CLIP backbone, and a novel audio-visual prompt that dynamically enhances …

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Robust Graph Learning On The Web: Challenges, Methods, And Applications, Ao Xiang, Yang Liu, Guansong Pang, Yuanhao Ding, Hezhe Qiao, Dawei Cheng, Qing He Jul 2026

Robust Graph Learning On The Web: Challenges, Methods, And Applications, Ao Xiang, Yang Liu, Guansong Pang, Yuanhao Ding, Hezhe Qiao, Dawei Cheng, Qing He

Research Collection School Of Computing and Information Systems

Graph learning is transforming web intelligence, powering applications from recommender systems to anomaly detection. However, most existing approaches implicitly assume ideal conditions where training and testing data are accurate, complete, and free from manipulation. In reality, web environments rarely exhibit such stability. Dynamic user behavior, incomplete or outdated content, adversarial interference, and sudden distribution shifts can all erode the reliability of even state-of-the-art models, leading to biased or unsafe outcomes. This tutorial provides a comprehensive survey of emerging strategies for robust graph learning on the web. We first present a structured taxonomy of the principal robustness threats specific to web …

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Verbalizing Lightgcn: Direct Learning Of Textual Representations From User-Item Interaction Graph Via Llms, Manh-Khanh Ngo Huu, Hady Wirawan Lauw Jul 2026

Verbalizing Lightgcn: Direct Learning Of Textual Representations From User-Item Interaction Graph Via Llms, Manh-Khanh Ngo Huu, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

In this work, we propose VerbaLightGCN, a novel LLM-based recommendation framework that integrates the semantic understanding of LLMs with user-item interaction modeling. Traditional collaborative filtering (CF) models typically embed user and item IDs into a latent space to capture interaction signals. However, pretrained LLMs cannot natively interpret these learned embeddings. To bridge this gap, VerbaLightGCN adopts a CF-as-text paradigm, in which collaborative signals are encoded in textual form and directly learned from the user–item interaction graph, and are then combined with semantic information to construct user and item profiles that function as latent embeddings. Inspired by LightGCN, our method retains …

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