OpenRaiser/Intern-Atlas huggingface dataset:
Solar Forecasting with Causality: A Graph-Transformer Approach to Spatiotemporal Dependencies
Authors: Yanan Niu, Demetri Psaltis, Christophe Moser, Luisa Lambertini
Venue: CIKM
Year: 2025

Accurate solar forecasting underpins effective renewable energy management. We present SolarCAST, a causally informed model predicting future global horizontal irradiance (GHI) at a target site using only historical GHI from site X and nearby stations S---unlike prior work that relies on sky-camera or satellite imagery requiring specialized hardware and heavy preprocessing. To deliver high accuracy with only public sensor data, SolarCAST models three classes of confounding factors behind X-S correlations using scalable neural components: (i) observable synchronous variables (e.g., time of day, station identity), handled via an embedding module; (ii) latent synchronous factors (e.g., regional weather patterns), captured by a spatio-temporal graph neural network; and (iii) time-lagged influences (e.g., cloud movement across stations), modeled with a gated transformer that learns temporal shifts. It outperforms leading time-series and multimodal baselines across diverse geographical conditions, and achieves a 25.9% error reduction over the top commercial forecaster, Solcast. SolarCAST offers a lightweight, practical, and generalizable solution for localized solar forecasting.. Code available at https://github.com/YananNiu/SolarCAST
Uncovering the Persuasive Fingerprint of LLMs in Jailbreaking Attacks
Authors: Havva Alizadeh Noughabi, Julien Serbanescu, Fattane Zarrinkalam, Ali Dehghantanha
Venue: CIKM
Year: 2025

Despite recent advances, Large Language Models (LLMs) remain vulnerable to jailbreak attacks that bypass alignment safeguards and elicit harmful outputs. While prior research has proposed various attack strategies differing in human readability and transferability, little attention has been paid to the linguistic and psychological mechanisms that may influence a model's susceptibility to such attacks. In this paper, we examine an interdisciplinary line of research that leverages foundational theories of persuasion from the social sciences to craft adversarial prompts capable of circumventing alignment constraints in LLMs. Drawing on well-established persuasive strategies, we hypothesize that LLMs, having been trained on large-scale human-generated text, may respond more compliantly to prompts with persuasive structures. Furthermore, we investigate whether LLMs themselves exhibit distinct persuasive fingerprints that emerge in their jailbreak responses. Empirical evaluations across multiple aligned LLMs reveal that persuasion-aware prompts significantly bypass safeguards, demonstrating their potential to induce jailbreak behaviors. This work underscores the importance of cross-disciplinary insight in addressing the evolving challenges of LLM safety. The code and data are available. https://github.com/CyberScienceLab/Our-Papers/tree/main/PersuasiveJailbreaking/.
POLAR: Policy Optimization for Literature Analysis under Review Constraints
Authors: Giorgio Maria Di Nunzio
Venue: CIKM
Year: 2025

Systematic reviews are vital for evidence-based decision-making but remain resource-intensive due to the volume of literature requiring expert screening. Technology-Assisted Review (TAR) systems offer a solution by ranking documents for review, yet questions remain about how best to allocate limited human effort across multiple review topics. In this paper, we explore the problem of effort distribution by comparing alternative screening policies under fixed effort constraints. Using real-world data from the CLEF eHealth 2017-2019 TAR tasks, we evaluate both baseline and adaptive policies that account for topic size, screening depth, and residual uncertainty. We introduce effort-aware evaluation metrics to measure trade-offs between review effectiveness and resource use. Our results show that simple, topic-sensitive policies can significantly improve the yield of relevant documents discovered, offering practical insights for scalable and equitable systematic review workflows.
Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations
Authors: Yongkyung Oh, Seungsu Kam, Dongyoung Lim, Sungil Kim
Venue: CIKM
Year: 2025

Astronomical time series from large-scale surveys like LSST are often irregularly sampled and incomplete, posing challenges for classification and anomaly detection. We introduce a new framework based on Neural Stochastic Delay Differential Equations (Neural SDDEs) that combines stochastic modeling with neural networks to capture delayed temporal dynamics and handle irregular observations. Our approach integrates a delay-aware neural architecture, a numerical solver for SDDEs, and mechanisms to robustly learn from noisy, sparse sequences. Experiments on irregularly sampled astronomical data demonstrate strong classification accuracy and effective detection of novel astrophysical events, even with partial labels. This work highlights Neural SDDEs as a principled and practical tool for time series analysis under observational constraints.
Neural Differential Equations for Continuous-Time Analysis
Authors: Yongkyung Oh, Dongyoung Lim, Sungil Kim
Venue: CIKM
Year: 2025

Modeling complex, irregular time series is a critical challenge in knowledge discovery and data mining. This tutorial introduces Neural Differential Equations (NDEs)--a powerful paradigm for continuous-time deep learning that intrinsically handles the non-uniform sampling and missing values where traditional models falter. We provide a comprehensive review of the theory and practical application of the entire NDE family: Neural Ordinary (NODEs), Controlled (NCDEs), and Stochastic (NSDEs) Differential Equations. The tutorial emphasizes robustness and stability and culminates in a hands-on session where participants will use key open-source libraries to solve real-world tasks like interpolation and classification. Designed for AI researchers and practitioners, this tutorial equips attendees with essential tools for time series analysis.
TANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification
Authors: Yongkyung Oh, Dongyoung Lim, Sungil Kim, Alex A. T. Bui
Venue: CIKM
Year: 2025

Handling missing data in time series classification remains a significant challenge in various domains. Traditional methods often rely on imputation, which may introduce bias or fail to capture the underlying temporal dynamics. In this paper, we propose TANDEM (Temporal Attention-guided Neural Differential Equations for Missingness), an attention-guided neural differential equation framework that effectively classifies time series data with missing values. Our approach integrates raw observation, interpolated control path, and continuous latent dynamics through a novel attention mechanism, allowing the model to focus on the most informative aspects of the data. We evaluate TANDEM on 30 benchmark datasets and a real-world medical dataset, demonstrating its superiority over existing state-of-the-art methods. Our framework not only improves classification accuracy but also provides insights into the handling of missing data, making it a valuable tool in practice.
FairSplit: Mitigating Bias in Graph Neural Networks through Sensitivity-based Edge Partitioning
Authors: Indranil Ojha, Kushal Bose, Swagatam Das
Venue: CIKM
Year: 2025

Fairness in machine learning has become increasingly crucial, particularly in graph-based models where biased representations can reinforce societal inequalities. Traditional fairness-aware learning methods on graphs focus on graph rewiring, debiasing node embeddings, adversarial learning, and additional fairness constraints. However, these approaches often struggle to balance fairness and task performance. We propose a novel edge partitioning strategy that creates two distinct subgraphs, maintaining a balance between bias and diversity. We categorize edges as homophilic or heterophilic depending on the sensitive attribute of the corresponding node pairs. An edge is s-homophilic if it joins two nodes with the same sensitivity value, otherwise s-heterophilic. The partition splits the input graph into two subgraphs, both containing all nodes, one with only s-homophilic edges and the other with s-heterophilic ones. Using a Graph Neural Network (GNN), we obtain independent node representations from both graphs, which are then aggregated into a unified node embedding. To enforce fairness, we jointly optimize a primary task loss and a fairness loss, ensuring predictive accuracy and bias mitigation. We evaluate our approach on three benchmark datasets and find that it achieves improved fairness metrics while maintaining accuracy comparable to that of existing state-of-the-art methods.
S2Cap: A Benchmark and a Baseline for Singing Style Captioning
Authors: Hyunjong Ok, Jaeho Lee
Venue: CIKM
Year: 2025

Singing voices contain much richer information than common voices, including varied vocal and acoustic properties. However, current open-source audio-text datasets for singing voices capture only a narrow range of attributes and lack acoustic features, leading to limited utility towards downstream tasks, such as style captioning. To fill this gap, we formally define the singing style captioning task and present S2Cap, a dataset of singing voices with detailed descriptions covering diverse vocal, acoustic, and demographic characteristics. Using this dataset, we develop an efficient and straightforward baseline algorithm for singing style captioning. The dataset is available at https://zenodo.org/records/15673764.
Revisiting Pre-processing Group Fairness: A Modular Benchmarking Framework
Authors: Brodie Oldfield, Ziqi Xu, Sevvandi Kandanaarachchi
Venue: CIKM
Year: 2025

As machine learning systems become increasingly integrated into high-stakes decision-making processes, ensuring fairness in algorithmic outcomes has become a critical concern. Methods to mitigate bias typically fall into three categories: pre-processing, in-processing, and post-processing. While significant attention has been devoted to the latter two, pre-processing methods, which operate at the data level and offer advantages such as model-agnosticism and improved privacy compliance, have received comparatively less focus and lack standardised evaluation tools. In this work, we introduce FairPrep, an extensible and modular benchmarking framework designed to evaluate fairness-aware pre-processing techniques on tabular datasets. Built on the AIF360 platform, FairPrep allows seamless integration of datasets, fairness interventions, and predictive models. It features a batch-processing interface that enables efficient experimentation and automatic reporting of fairness and utility metrics. By offering standardised pipelines and supporting reproducible evaluations, FairPrep fills a critical gap in the fairness benchmarking landscape and provides a practical foundation for advancing data-level fairness research.
QueryBridge: One Million Annotated Questions with SPARQL Queries - Dataset for Question Answering over Knowledge Graphs
Authors: Abdelghny Orogat, Ahmed El-Roby
Venue: CIKM
Year: 2025

Question answering over knowledge graphs (QAKG) involves interpreting natural language questions and linking them to structured knowledge graphs. Existing benchmark datasets (e.g., QALD, LC-QuAD) are limited in size and annotation, hindering QAKG model generalization. To address this, we present QueryBridge, a dataset with over one million annotated questions paired with SPARQL queries. Each question is tagged with essential elements (e.g., entities, relationships) and annotated by query shape (e.g., chain, star) to support complex reasoning.
Disentangling Complex Questions in LLMs via Multi-Hop Dependency Graphs
Authors: Roland Oruche, Alphaeus Dmonte, Vani Seth, Zian Zeng, Yuanxun Zhang, Marcos Zampieri, Prasad Calyam
Venue: CIKM
Year: 2025

While Large language models (LLMs) have shown to exhibit remarkable performance in a wide range of NLP tasks, they often struggle to interpret and reason over multi-hop questions in open-domain question answering (ODQA) settings. While popular prompt approaches such as Chain-of-Thought and Plan-and-Solve facilitate more manageable questions for OQDA via task decomposition, these approaches are prone to generating erroneous and redundant intermediate steps in multi-hop queries due to limited capacity for modeling complex entity relationships. In this paper, we introduce a novel prompt approach for multi-hop QA viz., MoDeGraph (Multi-Hop Dependency Graphs), that is designed to steer LLMs to extract and model entity relationships in complex questions. MoDeGraph constructs a dependency graph from LLM-generated entity-relation triples to enable more coherent and human-like multi-step reasoning. Experimental results in knowledge-intensive tasks for multi-hop QA demonstrate our approach produces more coherent and faithful reasoning chains as well as consistent increase in QA performance across several benchmark datasets.
Portuguese post-OCR Resources for Text Optimisation
Authors: Tomás Freitas Osório, Henrique Lopes Cardoso
Venue: CIKM
Year: 2025

Optical Character Recognition (OCR) systems are designed to extract text from images. While typically optimised for modern documents, they often struggle when applied to historical documents due to older fonts, complex layouts, and physical degradation, which can result in noisy outputs. To reduce OCR errors, post-OCR algorithms are commonly used, however, their development and evaluation require image-transcription pairs. Compared to other European languages, there is a lack of transcribed documents for historical Portuguese, especially for texts predating the 19th century. To address this gap, we introduce Portuguese post-OCR Resources for Text Optimisation (PORTO), a dataset that spans from the 17th to the 20th centuries. PORTO contains 3,782 image-transcription pairs, along with OCR outputs from four different systems, providing a valuable resource for the development and evaluation of OCR and post-OCR methods tailored to historical Portuguese.
Multimodal Sentiment Analysis via Progressive Fusion of Audio-Visual Affective Descriptions
Authors: Lisong Ou, Zhixin Li
Venue: CIKM
Year: 2025

Multimodal Sentiment Analysis (MSA) holds significant research value in the fields of intelligent human-computer interaction and affective computing. Although existing MSA approaches have made considerable progress, challenges remain in identifying subtle emotional distinctions within audio and visual expressions. In particular, conventional fusion methods have not effectively addressed the difficulty of integrating heterogeneous modality information. To tackle these challenges, we propose a progressive fusion framework based on audio-visual affective descriptions for MSA. Specifically, we design an audio-visual emotional description generator that transforms raw audiovisual data into textual emotional descriptions, thereby effectively highlighting affective features. Subsequently, this emotional description is integrated with the original multimodal features to obtain a richer feature representation. Building upon this, we introduce a three-stage progressive fusion architecture. First, we employ cross-modal transformers to facilitate interactions among modalities and to learn inter-modal dependencies. Second, a gated fusion mechanism is incorporated to effectively eliminate redundant information and further promote interaction and compatibility among features. Finally, an attention mechanism is utilized to dynamically adjust the weights of features from different modalities, enabling effective multimodal sentiment information fusion. Experimental results on widely used sentiment analysis benchmark datasets, including MOSI, MOSEI, and CH-SIMS, underscore significant enhancements compared to state-of-the-art models.
Building Safer Sites: A Large-Scale Multi-Level Dataset for Construction Safety Benchmark
Authors: Zhenhui Ou, Dawei Li, Zhen Tan, Wenlin Li, Huan Liu, Siyuan Song
Venue: CIKM
Year: 2025

Construction safety research is a critical field in civil engineering, aiming to mitigate risks and prevent injuries through the analysis of site conditions and human factors. However, the limited volume and lack of diversity in existing construction safety datasets pose significant challenges to conducting in-depth analyses. To address this research gap, this paper introduces the Construction Safety Dataset (CSDataset), a well-organized comprehensive multi-level dataset that encompasses incidents, inspections, and violations recorded sourced from the Occupational Safety and Health Administration (OSHA). This dataset uniquely integrates structured attributes with unstructured narratives, facilitating a wide range of approaches driven by machine learning and large language models. We also conduct a preliminary approach benchmarking and various cross-level analyses using our dataset, offering insights to inform and enhance future efforts in construction safety. For example, we found that complaint-driven inspections were associated with a 17.3% reduction in the likelihood of subsequent incidents. Our dataset and code are released at https://github.com/zhenhuiou/Construction-Safety-Dataset-CSDataset.
STKGNN: Scalable Spatio-Temporal Knowledge Graph Reasoning for Activity Recognition
Authors: Gözde Ayse Tataroglu Özbulak, Yash Raj Shrestha, Jean-Paul Calbimonte
Venue: CIKM
Year: 2025

The emergence of dynamic, high-volume data streams demands advanced reasoning frameworks to capture complex spatio-temporal relationships that are essential for enabling contextual understanding. However, current approaches often lack scalable and adaptable semantic representations in dynamic and spatio-temporal scenarios. To answer this need, we introduce a novel Spatio-Temporal Knowledge approach based on Graph Neural Networks (STKGNN) for activity recognition. This framework performs graph-based reasoning over semantically enriched Spatio-Temporal Knowledge Graphs (STKGs) constructed from open-source video datasets. By leveraging these custom STKGs, we propose three advanced Graph Neural Network (GNN) based architectures to recognize various activities. Accordingly, we establish a comprehensive approach for spatio-temporal reasoning that adapts to diverse Knowledge Graph structures by addressing adaptability, scalability, and temporal complexities. This framework enhances activity recognition and provides a foundation for wider dynamic or real-time applications in different domains including healthcare, autonomous systems, video surveillance, and various other fields.
Trustworthy AI Psychotherapy: Multi-Agent LLM Workflow for Counseling and Explainable Mental Disorder Diagnosis
Authors: Mithat Can Ozgun, Jiahuan Pei, Koen V. Hindriks, Lucia Donatelli, Qingzhi Liu, Junxiao Wang
Venue: CIKM
Year: 2025

LLM-based agents have emerged as transformative tools capable of executing complex tasks through iterative planning and action, achieving significant advancements in understanding and addressing user needs. Yet, their effectiveness remains limited in specialized domains such as mental health diagnosis, where they underperform compared to general applications. Current approaches to integrating diagnostic capabilities into LLMs rely on scarce, highly sensitive mental health datasets, which are challenging to acquire. These methods also fail to emulate clinicians' proactive inquiry skills, lack multi-turn conversational comprehension, and struggle to align outputs with expert clinical reasoning. To address these gaps, we propose DSM5AgentFlow, the first LLM-based agent workflow designed to autonomously generate DSM-5 Level-1 diagnostic questionnaires. By simulating therapist-client dialogues with specific client profiles, the framework delivers transparent, step-by-step disorder predictions, producing explainable and trustworthy results. This workflow serves as a complementary tool for mental health diagnosis, ensuring adherence to ethical and legal standards. Through comprehensive experiments, we evaluate leading LLMs across three critical dimensions: conversational realism, diagnostic accuracy, and explainability. Our datasets and implementations are fully open-sourced.
terazi: AI Fairness Tool for Doubly Imbalanced Data
Authors: Asli Umay Öztürk, Yigit Sever, Ata Yalcin, Viktoria Pauw, Stephan Hachinger, Ismail Hakki Toroslu, Pinar Karagoz
Venue: CIKM
Year: 2025

The field of Artificial Intelligence (AI) fairness focuses on developing unbiased approaches for machine learning problems, with many contributions and ready-to-use tools. However, existing solutions fall short when both sensitive attributes and target labels have imbalanced representations in a given dataset. Our proposed algorithm and its tool, terazi, aim to propose a fair AI solution for this doubly imbalanced case. The proposed solution is based on finding the optimal distribution within the imbalanced data to balance fairness and classification performance, and the tool facilitates using this solution. In this demonstration, we showcase the capabilities of our algorithm, and the easy-to-use GUI of our web application for data scientists, researchers, and AI practitioners.
On the Gap Between Diffusion and Transformer Multi-Tabular Generation
Authors: Gijs Paardekooper, Jeroen M. Galjaard, Lydia Y. Chen
Venue: CIKM
Year: 2025

Shareable tabular data is of high importance in industry and research. While generating synthetic records is well-studied, research has only recently extended to relational data synthesis. In the tabular generation setting, diffusion and transformer models exhibit superior performance over prior art. However, in the relational setting, diffusion models outperform transformers. This work focuses on the performance gap between tabular transformers and diffusion models in single (tabular) and multi-table (relational) settings, using REaLTabformer and ClavaDDPM as representative state-of-the-art models. We evaluate these architectures on a set of single- and multi-table datasets, highlighting the gap's root causes between the methods. In our experiments, we attribute this difference to the influence of contextual information and data representation. To bridge the gap in the relational setting, we propose two seemingly simple strategies: layer sharing and contextual cues. This work1 offers insights into key design considerations for single- and multitable generative models, including the incorporation of contextual information and the reuse of existing knowledge. With the proposed methods, we achieve improvements of 1.52× and 1.94× for the Logistic Detection and Discriminator Measure metrics, respectively.