OpenRaiser/Intern-Atlas huggingface dataset:
How to Make Museums More Interactive? Case Study of Artistic Chatbot
Authors: Filip J. Kucia, Anna Wróblewska, Bartosz Grabek, Szymon D. Trochimiak
Venue: CIKM
Year: 2025

Conversational agents powered by Large Language Models (LLMs) are increasingly utilized in educational settings, in particular in individual closed digital environments, yet their potential adoption in the physical learning environments like cultural heritage sites, museums, and art galleries remains relatively unexplored. In this study, we present Artistic Chatbot, a voice-to-voice RAG-powered chat system to support informal learning and enhance visitor engagement during a live art exhibition celebrating the 15th anniversary of the Faculty of Media Art at the Warsaw Academy of Fine Arts, Poland. The question answering (QA) chatbot responded to free-form spoken questions in Polish using the context retrieved from a curated, domain-specific knowledge base consisting of 226 documents provided by the organizers, including faculty information, art magazines, books, and journals. We describe the key aspects of the system architecture and user interaction design, as well as discuss the practical challenges associated with deploying chatbots at public cultural sites. Our findings, based on interaction analysis, demonstrate that chatbots such as Artistic Chatbot effectively maintain responses grounded in exhibition content (60% of responses directly relevant), even when faced with unpredictable queries outside the target domain, showing their potential for increasing interactivity in public cultural sites. During the demo presentation, the audience will be invited to query our Artistic Chatbot, which adopts the persona of an artificial art curator, a role that involves responding to questions while simultaneously assessing their relevance to the exhibition. The link for the demo video is available here https://github.com/cinekucia/artistic-chatbot-cikm2025.
On Evaluating Loss Functions for Stock Ranking: An Empirical Analysis with Transformer Model
Authors: Jan Kwiatkowski, Jaroslaw A. Chudziak
Venue: CIKM
Year: 2025

Quantitative trading strategies rely on accurately ranking stocks to identify profitable investments. Effective portfolio management requires models that can reliably order future stock returns. Transformer models are promising for understanding financial time series, but how different training loss functions affect their ability to rank stocks well is not yet fully understood. Financial markets are challenging due to their changing nature and complex relationships between stocks. Standard loss functions, which aim for simple prediction accuracy, often aren't enough. They don't directly teach models to learn the correct order of stock returns. While many advanced ranking losses exist from fields such as information retrieval, there hasn't been a thorough comparison to see how well they work for ranking financial returns, especially when used with modern Transformer models for stock selection. This paper addresses this gap by systematically evaluating a diverse set of advanced loss functions including pointwise, pairwise, listwise for daily stock return forecasting to facilitate rank-based portfolio selection on S&P 500 data. We focus on assessing how each loss function influences the model's ability to discern profitable relative orderings among assets. Our research contributes a comprehensive benchmark revealing how different loss functions impact a model's ability to learn cross-sectional and temporal patterns crucial for portfolio selection, thereby offering practical guidance for optimizing ranking-based trading strategies.
NLP-QA: A Large-scale Benchmark for Informative Question Answering over Natural Language Processing Documents
Authors: Avishek Lahiri, Debarshi Kumar Sanyal, Imon Mukherjee
Venue: CIKM
Year: 2025

The exponential growth of research literature across AI domains necessitates efficient information extraction via Question Answering (QA). However, scholarly QA development is hindered by the scarcity of large-scale, expertly-annotated datasets, that are needed for modern deep learning models. To address this gap and advance scholarly QA, we introduce NLP-QA, a new dataset of question-answer pairs derived from NLP research documents. We overcome the challenge of costly expert annotation by proposing a novel, automated construction method that leverages content from conference presentation slides. We create two versions of our dataset - one by extracting QA pairs from individual slides (NLP-QA-SS) and the other by extracting QA pairs from the collection of slides for a paper as a whole (NLP-QA-MS). We benchmark several Large Language Models (LLMs) on NLP-QA in zero-shot settings, with and without finetuning, to establish performance baselines, demonstrating the challenging nature as well as the utility of the dataset. We demonstrate the challenging nature of our dataset in zero-shot long-context reasoning of LLMs without additional finetuning. We show that there is a significant jump in the LLMs' performance after finetuning with NLP-QA. The dataset and code are publicly available at https://github.com/AvishekLahiri/NLP-QA.git
FairRegBoost: An End-to-End Data Processing Framework for Fair and Scalable Regression
Authors: Nico Lässig, Melanie Herschel
Venue: CIKM
Year: 2025

Fairness-aware machine learning has gained significant attention due to the growing demand for ethical decision-support systems. This paper introduces FairRegBoost, a novel fairness-aware regression framework that takes a holistic data management perspective by integrating automated data preparation, uncertainty modeling, and post-processing adjustments using optimal transport techniques into effective and efficient solutions. Our approach effectively balances predictive accuracy and fairness by minimizing the output distribution distance between protected groups, leveraging uncertainty and sample similarities guiding the transport. We conduct extensive experiments on real-world datasets with both single and multiple protected attributes. Results demonstrate that FairRegBoost consistently achieves superior fairness-accuracy trade-offs compared to state-of-the-art approaches. Moreover, our scalability analysis highlights the computational efficiency, making it a practical choice for large-scale applications.
THEME: Enhancing Thematic Investing with Semantic Stock Representations and Temporal Dynamics
Authors: Hoyoung Lee, Wonbin Ahn, Suhwan Park, Jaehoon Lee, Minjae Kim, Sungdong Yoo, Taeyoon Lim, Woohyung Lim, Yongjae Lee
Venue: CIKM
Year: 2025

Thematic investing, which aims to construct portfolios aligned with structural trends, remains a challenging endeavor due to overlapping sector boundaries and evolving market dynamics. A promising direction is to build semantic representations of investment themes from textual data. However, despite their power, general-purpose LLM embedding models are not well-suited to capture the nuanced characteristics of financial assets, since the semantic representation of investment assets may differ fundamentally from that of general financial text. To address this, we introduce THEME, a framework that fine-tunes embeddings using hierarchical contrastive learning. THEME aligns themes and their constituent stocks using their hierarchical relationship, and subsequently refines these embeddings by incorporating stock returns. This process yields representations effective for retrieving thematically aligned assets with strong return potential. Empirical results demonstrate that THEME excels in two key areas. For thematic asset retrieval, it significantly outperforms leading large language models. Furthermore, its constructed portfolios demonstrate compelling performance. By jointly modeling thematic relationships from text and market dynamics from returns, THEME generates stock embeddings specifically tailored for a wide range of practical investment applications.
Advanced Privacy Protection in Federated Learning using Server-initiated Homomorphic Encryption
Authors: Cameron Lee, Matthew L. Daggitt, Yansong Gao, Jin B. Hong
Venue: CIKM
Year: 2025

Federated learning (FL) has been widely adopted to provide machine learning (ML) privacy, protecting sensitive user data from leakage. However, there are still attacks that could exploit FL to access users' sensitive data, such as model inversion attacks, property inference attacks, and membership inference attacks. Various solutions were proposed to secure FL using various privacy-preserving techniques, such as differential privacy, homomorphic encryption, and multi-party encryption. However, existing solutions often add noise to the model that hinders the accuracy, or introduce large computational overhead that makes them impractical to use. In this paper, we propose a new privacy protection scheme for FL that uses homomorphic encryption (HE), noise, and secret sharing to protect users' sensitive data from up to n-2 adversarial clients and the server colluding. The computational overhead is minimised by transferring expensive computations of HE to the server, requiring only the encryption and homomorphic addition to be carried out by clients. We provide proof sketches to validate the security of our scheme, and experimental results to demonstrate the practicality of our proposed scheme. The results show that our scheme adds only up to 8% overhead without losing any accuracy to base FL models, showing minimal overhead without losing accuracy, regardless of the data used.
Deceptive Synthetic Updates: Stealth Free-Rider Attack on Model Aggregation in Federated Learning
Authors: Youngjoon Lee, Jinu Gong, Joonhyuk Kang
Venue: CIKM
Year: 2025

Federated Learning (FL) allows multiple clients to collaboratively train shared models without exchanging raw data, thereby preserving privacy. However, FL systems are vulnerable to malicious participants known as free-riders who exploit the collaborative nature without providing genuine data contributions. To expose this critical security threat, we introduce a novel stealth free-rider attack that leverages pre-trained forecasting models to generate highly realistic synthetic time-series data. Our approach enables malicious clients to deceive FL systems while obtaining benefits from fair participants' contributions, thereby undermining the integrity of federated networks. Numerical results on EEG-based sleep stage classification demonstrate that our attack maintains comparable performance with free-rider ratios up to 70% while causing catastrophic degradation when all clients are free-riders.
Mitigating Distribution Shift in Stock Price Data via Return-Volatility Normalization for Accurate Prediction
Authors: Hyunwoo Lee, Jihyeong Jeon, Jaemin Hong, U Kang
Venue: CIKM
Year: 2025

How can we address distribution shifts in stock price data to improve stock price prediction accuracy? Stock price prediction has attracted attention from both academia and industry, driven by its potential to uncover complex market patterns and enhance decisionmaking. However, existing methods often fail to handle distribution shifts effectively, focusing on scaling or representation adaptation without fully addressing distributional discrepancies and shape misalignments between training and test data. We propose ReVol (Return-Volatility Normalization for Mitigating Distribution Shift in Stock Price Data), a robust method for stock price prediction that explicitly addresses the distribution shift problem. ReVol leverages three key strategies to mitigate these shifts: (1) normalizing price features to remove sample-specific characteristics, including return, volatility, and price scale, (2) employing an attention-based module to estimate these characteristics accurately, thereby reducing the influence of market anomalies, and (3) reintegrating the sample characteristics into the predictive process, restoring the traits lost during normalization. Additionally, ReVol combines geometric Brownian motion for long-term trend modeling with neural networks for short-term pattern recognition, unifying their complementary strengths. Extensive experiments on real-world datasets demonstrate that ReVol enhances the performance of the state-of-the-art backbone models in most cases, achieving an average improvement of more than 0.03 in IC and over 0.7 in SR across various settings.
EdgeSLU: 1.58-bit Voice Control Framework
Authors: Seungeon Lee, Junuk Jung, Sanghyun Jung, Changbeom Kang, Jaeyoon Yoo
Venue: CIKM
Year: 2025

Natural-language voice interfaces promise ubiquitous smart environment control, yet cloud dependence incurs latency, connectivity, and privacy costs that are intolerable in safety-critical or bandwidth-limited settings. We introduce EdgeSLU, an entirely on-device speech pipeline that marries an extremely low 1.58-bit mixed-precision quantization scheme with an auto-tuned SIMD centric kernel, allowing deployment on commodity edge hardware with only tens of MB of memory. On a Raspberry Pi 5 (Arm Cortex A76), the Speech-To-Text (STT) engine achieves 6.37% WER in 35 MB RAM and 2.1s latency, while the quantized Natural Language Understanding (NLU) model delivers 93.33% intent accuracy in 0.48s and 23 MB RAM, yielding end-to-end interaction time of 2.6s. A lightweight paraphrase-based data generator bootstraps rich training sets from few examples, eliminating prohibitive annotation overhead. Demonstrated through offline control of Philips Hue lamps, EdgeSLU shows that aggressive mixed-precision quantization plus hardware-aware inference enables practical, privacy preserving voice control on off-the-shelf edge devices.
Context-aware Sequential Bundle Recommendation via User-specific Representations
Authors: Jaeri Lee, U Kang
Venue: CIKM
Year: 2025

How can we recommend bundles that reflect users' changing preferences over time? Sequential bundle recommendation aims to recommend bundles of items while capturing users' evolving preferences over time. Unlike traditional bundle or sequential recommendation, this task requires modeling both the structural composition of bundles and the temporal dynamics of user behavior. We identify three major challenges: (1) dynamic user preferences across bundle interactions, (2) user-dependent attention to different items in the same bundle, and (3) users' diverse preferences on bundling strategies. To address these, we propose CoReSBR (Contextualized Representation for Sequential Bundle Recommendation), an adaptive framework that constructs bundle representations contextualized by time-aware user preferences. CoReSBR encodes recent user interactions to reflect preference shifts, assigns attention-based weights on items in bundles using user embedding as query, and integrates multiple bundling strategies through user-specific combination. Extensive experiments on real-world datasets demonstrate that CoReSBR outperforms the state-of-the-art methods, achieving up to 8.91% higher nDCG and 8.05% higher recall.
Learning Graph Edit Distance via Node Matching Patterns
Authors: Junkyu Lee, Jongik Kim
Venue: CIKM
Year: 2025

Graph edit distance (GED) is a general and versatile measure of graph similarity. Many combinatorial algorithms have been proposed for computing exact GED, but they suffer from the high computational cost due to the NP-hardness of GED computation. To address this challenge, approximate GED computation techniques have been extensively studied. These techniques are generally twofold: early work is based on combinatorial algorithms that restrict search space for efficient computation, while more recent approaches employ machine learning techniques to estimate GED. Although learning-based approaches generally achieve higher estimation accuracy than combinatorial approximations, they often rely on smoothed node embeddings to model node-to-node interactions, which may limit their ability to capture fine-grained structural differences. To alleviate this limitation, we exploit the insight that sequential variations in node interactions across GNN layers exhibit informative patterns. In this paper, we design a novel neural model, Grasp, that learns to extract and leverage these patterns to predict pairwise node matching probabilities and their associated costs. By aggregating these estimates from the perspectives of both input graphs, Grasp effectively and accurately computes an approximate GED. Experimental results on real-world datasets demonstrate that Grasp significantly improves estimation accuracy over existing approximate methods.
Anomaly Detection for Advanced Driver Assistance System with NCDE-based Normalizing Flow
Authors: Kangjun Lee, Minha Kim, Youngho Jun, Simon S. Woo
Venue: CIKM
Year: 2025

For electric vehicles, the Adaptive Cruise Control (ACC) in Advanced Driver Assistance Systems (ADAS) is designed to assist braking based on driving conditions and user patterns. However, the driving data collected during development are limited and lack diversity, leading to late or aggressive braking. Moreover, it is necessary to effectively identify anomalies in braking patterns, which is critical for self-driving autonomous vehicles. We propose Graph Neural Controlled Differential Equation Normalizing Flow (GDFlow), which leverages Normalizing Flow (NF) with Neural Controlled Differential Equations (NCDE) to learn the distribution of normal driving patterns. Our approach captures spatio-temporal information from sensor data and accurately models continuous changes in driving patterns. Additionally, we introduce a quantile-based maximum likelihood objective to improve the likelihood estimate of normal data at the margin of the distribution. We validate GDFlow using real-world electric vehicle driving data that we collected from Hyundai IONIQ5 and GV80EV. Our model achieves state-of-the-art (SOTA) performance compared to nine baselines across four dataset configurations of different vehicle types and drivers. Furthermore, our model outperforms the latest anomaly detection methods across four time series benchmark datasets. Our approach demonstrates superior efficiency in inference time compared to existing methods.
Spectral Edge Encoding - SEE: Does Structural Information Really Enhance Graph Transformer Performance?
Authors: Seungjun Lee, San Kim, Johyeon Kim, Jaekwang Kim
Venue: CIKM
Year: 2025

We propose Spectral Edge Encoding (SEE), a parameter-free framework that quantifies each edge's contribution to the global structure by measuring spectral shifts in the Laplacian eigenvalues. SEE captures the low-frequency sensitivity of edges and integrates these scores into graph Transformer attention logits as a structure-aware bias. When applied to the Moiré Graph Transformer (MoiréGT) and evaluated on seven MoleculeNet classification benchmarks, SEE consistently improves ROC-AUC performance. In particular, MoiréGT+SEE achieves an average ROC-AUC of 85.3%, approximately 7.1 percentage points higher than the previous state-of-the-art model UniCorn (78.2%). Moreover, SEE preserves molecular topology and enables edge-level interpretability, offering a practical alternative to sequence-based chemical language models. These results demonstrate that spectrum-informed attention can simultaneously enhance performance and transparency in graph-based molecular modeling.
Pedagogy-R1: Pedagogical Large Reasoning Model and Well-balanced Educational Benchmark
Authors: Unggi Lee, Jaeyong Lee, Jiyeong Bae, Yeil Jeong, Junbo Koh, Gyeonggeon &apos, Boaz&apos, Lee, Gunho Lee, Taekyung Ahn, Hyeoncheol Kim
Venue: CIKM
Year: 2025

Recent advances in large reasoning models (LRMs) have demonstrated impressive capabilities in highly structured domains such as mathematics and programming. However, their application to education-where effective reasoning must be pedagogically meaningful, context-sensitive, and responsive to real student needs-remains relatively unexplored. Existing large language models (LLMs) often struggle to deliver instructional coherence, formative feedback, or simulate sophisticated teacher decision-making, limiting their practical utility in educational settings. To fill this gap, we present Pedagogy-R1, a comprehensive pedagogical reasoning framework designed to adapt LLMs for authentic classroom tasks. Our approach features three key innovations: (1) a distillation-based training pipeline that uses pedagogically filtered outputs for instruction tuning, (2) the Well-balanced Educational Benchmark (WBEB), which systematically evaluates models across five dimensions-subject knowledge, pedagogical knowledge, knowledge tracing, essay scoring, and real-world teacher decision-making-and (3) the Chain-of-Pedagogy (CoP) prompting strategy, employed both to generate pedagogically enriched training data and to elicit teacher-like reasoning during inference. We conduct a mixed-methods evaluation, combining fine-grained quantitative analyses of model performance with qualitative insights into the model's pedagogical reasoning patterns.
BOVIS: Bias-Mitigated Object-Enhanced Visual Emotion Analysis
Authors: Yubeen Lee, Sangeun Lee, Junyeop Cha, Jufeng Yang, Eunil Park
Venue: CIKM
Year: 2025

Visual emotion analysis is a promising field that aims to predict emotional responses elicited by visual stimuli. While recent advances in deep learning have significantly improved emotion detection capabilities, existing methods often fall short because of their exclusive focus on either holistic visual features or semantic content, thereby neglecting their interplay. To address this limitation, we introduce BOVIS, a Bias-Mitigated Object-Enhanced Visual Emotion Analysis framework. To capture the subtle relationships between visual and semantic features and enrich the understanding of emotional contexts, BOVIS leverages pre-trained models to extract comprehensive image features, integrate object-level semantics, and enhance contextual information. Moreover, BOVIS incorporates a bias mitigation strategy that involves an adjusted Mean Absolute Error loss function alongside an Inverse Probability Weighting method to address dataset imbalances and enhance fairness in emotion prediction. Comprehensive evaluations across various benchmark datasets demonstrate the effectiveness of the BOVIS framework in enhancing visual emotion analysis. The results reveal that the synergy between object-specific features and holistic visual representations improves the accuracy and interpretability of emotion analysis, while optimizing bias mitigation enhances fairness and increases reliability. The code is available at https://github.com/leeyubin10/BOVIS.git.
D-HAT: Dynamic Hypergraph Representation Learning with Attention-Based Multi-Level Hypergraph Sampling
Authors: Ah-Hyun Lee, Gordon Euhyun Moon
Venue: CIKM
Year: 2025

Hypergraph Neural Networks (HNNs) leverage higher-order interactions in graph-structured data to enable effective representation learning across a wide range of applications. Due to the issue of incorporating irrelevant relations in full hypergraphs, it is crucial to adopt a hypergraph sampling method that efficiently captures substructures while preserving representational quality. However, existing hypergraph sampling methods that target only nodes or hyperedges suffer from subgraph disconnection issues and neglect of node importance due to the randomness in sampling and use of static computational sub-hypergraphs. In this paper, we propose D-HAT, a hypergraph learning framework that dynamically constructs representative sub-hypergraphs through a novel attention-based multi-level hypergraph sampling strategy during the training of HNNs. To prioritize informative neighbors and enhance the representational quality of sub-hypergraphs during training, we develop a new attention-based HNNs incorporating attention-guided aggregation and dense skip connections. To the best of our knowledge, this paper is the first to quantitatively compare various hypergraph sampling methods for hypergraph representation learning. Experiments on real-world graph datasets demonstrate the effectiveness of D-HAT, which consistently achieves higher accuracy compared to existing hypergraph sampling methods.