OpenRaiser/Intern-Atlas huggingface dataset:
Adversarially Attacking Graph Properties and Sparsification in Graph Learning
Authors: Chunjiang Zhu, Blake Gaines, Jing Deng, Jinbo Bi
Venue: CIKM
Year: 2025

Graph neural networks and graph transformers explicitly or implicitly rely on fundamental properties of the underlying graph, such as spectral properties and shortest-path distances. However, it is still not clear how these graph properties are vulnerable to adversarial attacks and what impacts this has on the downstream graph learning. Moreover, while graph sparsification has been used to improve computational cost of learning over graphs, its susceptibility to adversarial attacks has not been studied. In this paper, we study adversarial attacks on graph properties and graph sparsification and their impacts on downstream graph learning, paving the way for how to protect against these potential attacks. Our proposed methods are effective in attacking spectral properties, shortest distances, and graph sparsification as demonstrated in our experimental evaluation.
Asymmetric Diffusion Recommendation Model
Authors: Yongchun Zhu, Guanyu Jiang, Jingwu Chen, Feng Zhang, Xiao Yang, Zuotao Liu
Venue: CIKM
Year: 2025

Recently, motivated by the outstanding achievements of diffusion models, the diffusion process has been employed to strengthen representation learning in recommendation systems. Most diffusion-based recommendation models typically utilize standard Gaussian noise in symmetric forward and reverse processes in continuous data space. Nevertheless, the samples derived from recommendation systems inhabit a discrete data space, which is fundamentally different from the continuous one. Moreover, Gaussian noise has the potential to corrupt personalized information within latent representations. In this work, we propose a novel and effective method, named Asymmetric Diffusion Recommendation Model (AsymDiffRec), which learns forward and reverse processes in an asymmetric manner. We define a generalized forward process that simulates the missing features in real-world recommendation samples. The reverse process is then performed in an asymmetric latent feature space. To preserve personalized information within the latent representation, a task-oriented optimization strategy is introduced. In the serving stage, the raw sample with missing features is regarded as a noisy input to generate a denoising and robust representation for the final prediction. By equipping base models with AsymDiffRec, we conduct online A/B tests, achieving improvements of +0.131% and +0.166% in terms of users' active days and app usage duration respectively. Additionally, the extended offline experiments also demonstrate improvements. AsymDiffRec has been implemented in the Douyin Music App.
LatentExplainer: Explaining Latent Representations in Deep Generative Models with Multimodal Large Language Models
Authors: Mengdan Zhu, Raasikh Kanjiani, Jiahui Lu, Andrew Choi, Qirui Ye, Liang Zhao
Venue: CIKM
Year: 2025

Deep generative models like VAEs and diffusion models have advanced various generation tasks by leveraging latent variables to learn data distributions and generate high-quality samples. Despite the field of explainable AI making strides in interpreting machine learning models, understanding latent variables in generative models remains challenging. This paper introduces LatentExplainer, a framework for automatically generating semantically meaningful explanations of latent variables in deep generative models. LatentExplainer tackles three main challenges: inferring the meaning of latent variables, aligning explanations with inductive biases, and handling varying degrees of explainability. Our approach perturbs latent variables, interprets changes in generated data, and uses multimodal large language models (MLLMs) to produce human-understandable explanations. We evaluate our proposed method on several real-world and synthetic datasets, and the results demonstrate superior performance in generating high-quality explanations for latent variables. The results highlight the effectiveness of incorporating inductive biases and uncertainty quantification, significantly enhancing model interpretability.
FunLoc: A Novel Function-level Bug Localization Framework Enhanced by Contrastive and Active Learning Strategies
Authors: Ziye Zhu, Liangliang Peng, Yu Wang, Yun Li, Xianzhong Long
Venue: CIKM
Year: 2025

The increasing complexity of software systems has made them more prone to bugs, prompting the development of automated bug localization techniques to ensure software reliability. Despite these techniques having demonstrated notable success at the file level, their application and optimization at the function level often encounter serious performance cliffs. This limitation underscores the urgent need for a dedicated framework for function-level bug localization, which we address through FunLoc, a novel framework that takes coarse-grained source files as input units and identifies fine-grained buggy functions as output. To address the critical challenges of handling domain-specific bug reports and managing vast function-level sample space, we introduce two key innovations that are seamlessly integrated into FunLoc. First, we design a contrastive learning-based domain-adaptive language model to enhance the framework's ability to process and interpret specialized bug reports effectively. Second, we propose an active learning-based dynamic negative sampling strategy to address the scalability issues arising from the extensive function-level sample space. To evaluate the effectiveness of our approach, we extend and release a function-level bug localization dataset derived from large-scale real-world projects. Extensive experiments demonstrate that our approach outperforms state-of-the-art techniques.
TSD-CT: A Benchmark Dataset for Truthfulness Stance Detection
Authors: Zhengyuan Zhu, Haiqi Zhang, Zeyu Zhang, Chengkai Li
Venue: CIKM
Year: 2025

We present TSD-CT (Truthfulness Stance Detection-Claim and Tweet), a benchmark dataset designed to advance research in truthfulness stance detection. While prior stance detection datasets focus primarily on political figures, topics, or events, TSD-CT targets truthfulness stance of social media posts toward factual claims. Truthfulness stance reflects whether a post endorses a claim as true, rejects it as false, or expresses no clear position. This focus is particularly valuable for tracking public reactions to misinformation and for enabling applications that analyze belief dynamics in online discourse. TSD-CT comprises 5,331 claim-tweet pairs, each annotated into one of five classes: positive, negative, neutral/no stance, topically different, or problematic. To ensure annotation quality, we introduce a strategy that uses gold-standard labels to compute error scores, evaluate annotator performance, and filter out low-quality contributions. The resulting dataset achieves strong inter-annotator agreement. An error analysis further highlights frequent sources of confusion, particularly between neutral/no stance and other classes. The dataset, along with the annotation interface and codebase, is publicly released to facilitate further research.
TrustMap: Mapping Truthfulness Stance of Social Media Posts on Factual Claims for Geographical Analysis
Authors: Zhengyuan Zhu, Haiqi Zhang, Zeyu Zhang, Chengkai Li
Venue: CIKM
Year: 2025

Factual claims and misinformation circulate widely on social media, shaping public opinion and decision-making. The concept of truthfulness stance refers to whether a text affirms a claim as true, rejects it as false, or takes no clear position. Capturing such stances is essential for understanding how the public engages with and propagates misinformation. We present TrustMap, an application that identifies and visualizes stances of tweets toward factual claims. Users may input factual claims or select claims from a curated set. For each claim, TrustMap retrieves relevant social media posts and applies a retrieval-augmented approach with fine-tuned language models to classify stance. Posts are classified as positive, negative, or neutral/no stance. These classifications are then aggregated by location to reveal regional variations in public opinion. To enhance interpretability, TrustMap uses large language models to generate stance explanations for individual posts and to produce regional stance summaries. By integrating retrieval-augmented truthfulness stance detection with geographical visualization, TrustMap provides the first tool of its kind for exploring how belief in factual claims varies across regions.
Frequency-Decoupled Distillation for Efficient Multimodal Recommendation
Authors: Ziyi Zhuang, Hongji Li, Junchen Fu, Jiacheng Liu, Joemon M. Jose, Youhua Li, Yongxin Ni
Venue: CIKM
Year: 2025

Multimodal recommender systems (MMRec) leverage multimodal features, such as visual and textual data, to improve recommendation performance, playing a key role in platforms like online shopping and short videos. However, the large modality encoders and complex processing modules of MMRec significantly reduce its efficiency. A promising solution is compressing MMRec into an ID-based MLP model (MLPRec), which has a simpler structure and avoids complex modality handling. However, traditional knowledge distillation methods struggle to transfer knowledge effectively from MMRec to MLPRec, due to differences in their model structure and capacity. To address this, we propose a frequency-decoupled knowledge distillation framework-FDRec-to efficiently transfer knowledge from MMRec to MLPRec. By analyzing graph signals from a signal processing perspective, we propose decoupling the distillation process into low-frequency and high-frequency components, ensuring effective transmission of challenging high-frequency knowledge while preventing it from being overshadowed by monotonous low-frequency signals. To address the instability and fragmentation issues of KL divergence in traditional distillation approaches, we introduce the Wasserstein distance, which captures geometric structure and provides stable gradients. Additionally, FDRec incorporates an embedding-level contrastive learning method, further enhancing the transfer of refined knowledge from MMRec and injecting graph structure information into MLPRec for more effective distillation. Extensive experiments on four benchmark datasets and five popular MMRec models show that FDRec not only significantly reduces the computational costs and improves the inference efficiency, but also achieves comparable or even superior performance compared to MMRec. Our code is available at: https://github.com/Suehn/FDRec_
MGSTDN: Multi-Granularity Spatial-Temporal Diffusion Network for Next POI Recommendation
Authors: Zhuang Zhuang, Haitao Yuan, Shanshan Feng, Heng Qi, Yanming Shen, Baocai Yin
Venue: CIKM
Year: 2025

Next Point-of-Interest (POI) prediction is important to various human mobility applications, such as route planning and location-based advertising. To address the spatial-temporal sparsity issues arising from users' irregular and inconsistent visit times to different POIs, multi-granular structures can be incorporated to enhance feature representation through hierarchical relationships. However, existing methods often fall short in capturing the comprehensive multi-granularity spatial-temporal correlations due to three primary limitations: (1) users' complex mobility patterns entangled in single trajectory data, (2) limited mobility patterns details due to independent modeling at each granularity, and (3) low inference efficiency in cascaded multi-granularity predictions. To tackle these challenges, we propose a novel approach that models transformations across different granularities in both spatial regions and temporal periods as a diffusion process, leading to the development of the Multi-Granularity Spatial-Temporal Diffusion Network (MGSTDN). In particular, this model adopts a multi-task architecture, where predictions at varying spatial-temporal granularities (i.e., different diffusion steps) are treated as distinct tasks. By employing a multi-granularity diffusion mechanism in both spatial and temporal dimensions, it captures more nuanced spatial-temporal correlations, enhancing the physical constraints and behavioral pattern dependencies across granularities. During the diffusion process's forward stage, coarser-grained regions and periods are derived based on fine-grained features. In the reverse stage, finer-grained regions and periods are recovered from coarse-grained features, guided by encoded historical trajectory information, until the next POI is determined. To improve computational efficiency, we introduce a multi-granularity mapping propagation matrix, enabling parallel computation and accelerating the prediction process across different granularities. We evaluated the effectiveness of MGSTDN through extensive experiments on three datasets, demonstrating significant improvements over existing methods.
Active Recommendation for Email Outreach Dynamics
Authors: Cenek Zid, Rodrigo Alves, Pavel Kordík
Venue: CIKM
Year: 2025

Email outreach remains a cornerstone of modern marketing, enabling direct, timely communication. However, this strategy faces significant personalization challenges, since new campaigns typically lack historical interaction data and rich side information. In this work, we propose a framework that combines collaborative-filtering (CF) signals derived from a shallow autoencoder (SAE) with a Thompson Sampling-based multi-armed bandit to dynamically select small batches of recipients for each email template. We show SAEs help balance exploration and exploitation by quantifying recipient informativeness and confidence, enabling efficient personalization without retraining during active learning. To facilitate reproducibility and future research, we release a large dataset of almost 15 million recipient-message interactions, offering new insights into email outreach dynamics for CF. Our experiments show that our method outperforms multiple baselines in retrieval metrics while retaining interpretable model components.
Vulnerability-Aware Hardening for Secure Privacy-Preserving Record Linkage
Authors: Sumayya Ziyad, Peter Christen, Anushka Vidanage, Charini Nanayakkara, Rainer Schnell
Venue: CIKM
Year: 2025

Privacy-Preserving Record Linkage (PPRL) aims to link records across multiple data sources without revealing any sensitive information about the entities whose records are being linked. However, recent studies have identified attacks that exploit multiple vulnerabilities in popular PPRL methods. To address such vulnerabilities and prevent possible reidentification, hardening techniques have been proposed to perturb patterns in encodings. Most such hardening techniques are either specific to bit array based encodings (such as Bloom filters), or they rely on randomness which can negatively affect linkage quality. Here we propose a novel hardening technique that addresses the frequency, similarity, and co-occurrence vulnerabilities, and is applicable on any PPRL method that uses character q-grams. Our technique identifies and hardens only those q-grams that are vulnerable, and modifies them using a non-random, context-aware approach that ensures these q-grams are not vulnerable after hardening. We evaluate our technique using real and synthetic data sets, and show that it substantially reduces the vulnerabilities of PPRL encoding methods and makes them more secure.
Relational Multi-Path Enhancement for Extrapolative Relation Reasoning in Temporal Knowledge Graph
Authors: Linlin Zong, Chi Ma, Jiahui Zhou, Xinyue Liu, Wenxin Liang, Xianchao Zhang, Bo Xu
Venue: CIKM
Year: 2025

Relation reasoning in temporal knowledge graph infers unknown or emerging relational dependencies from historical structured data. Traditional approaches face inherent limitations in capturing complex semantic correlations and structural patterns among relations. To tackle this problem, we propose the Relational Multi-path Enhancement network (RME), which primarily focuses on relation modeling to enrich relation representations through comprehensive multi-path analysis. RME consists of five key components: (1) Controlled random walk module creates multi-hop head-to-tail paths using an adaptive stopping rule that balances short- and long-term connections. (2) Shared path extraction module identifies both shared-head paths and shared-tail paths. (3) Time-decayed path encoding module processes these paths differently. (4) Gated information aggregation module combines path information to determine which parts matter most. (5) Attention decoding module makes the final prediction by focusing on the most relevant path features. Experiments on multiple TKG benchmark datasets demonstrate that RME outperforms the state-of-the-art methods in relation multi-path reasoning.
Structuring Video Semantics with Temporal Triplets for Zero-Shot Video Question Answering
Authors: Linlin Zong, Xinyu Zhai, Xinyue Liu, Wenxin Liang, Xianchao Zhang, Bo Xu
Venue: CIKM
Year: 2025

Current large vision-language models (VLMs) exhibit remarkable performance in basic video understanding tasks. However, existing VLMs are still limited to surface-level perception and lack fine-grained spatio-temporal understanding and combinatorial reasoning capabilities. Existing methods typically rely on expensive human annotations or subtitle extraction, yet they struggle to effectively model temporal relations between frames. This paper proposes a structured representation based on temporal triplets to address two major challenges in traditional approaches: temporal fragmentation and entity reference ambiguity. By modeling objects, attributes, and relationships within the video and incorporating temporal information, we convert semantic content from keyframes into a sequence of temporal triplets. This structured representation is then used as input for zero-shot video question answering (VideoQA). Experiments were conducted on four benchmark VideoQA datasets: NExT-QA, STAR, MSVD-QA, and MSRVTT-QA, showing that our method achieves competitive performance without requiring fine-tuning, validating its generality and effectiveness.
ExplorAct: Context-Aware Next Action Recommendations for Interactive Data Exploration
Authors: Dinuka Manohara de Zoysa, James Bailey, Renata Borovica-Gajic
Venue: CIKM
Year: 2025

Modern data analysis platforms, such as Tableau, Microsoft Power BI, Google Looker Studio, Kibana, and Splunk, have democratized data exploration by enabling users to interact with data through intuitive visual interfaces, eliminating the need for proficiency in query languages like SQL. These platforms allow both experts and non-experts to perform high-level operations and incrementally construct complex analysis workflows. As the volume and complexity of data grow, assisting users in navigating these workflows becomes increasingly important. One promising direction is to provide intelligent next-action recommendations that guide users through meaningful and efficient exploration paths. In this paper, we present ExplorAct, a context-aware next-action recommendation framework that leverages historical session logs to predict and suggest relevant next steps during data exploration. Unlike existing approaches that suffer from scalability issues due to log-size-dependent retrieval, ExplorAct achieves constant-time inference by employing a deep learning architecture that models both the structural and sequential aspects of exploration sessions. Through extensive experiments on four real-world datasets, we show that ExplorAct consistently outperforms state-of-the-art (SOTA) baselines across three core recommendation tasks, while maintaining stable and low-latency inference regardless of log size.
Parallel bit-level pipelined VLSI processing unit for the histogramming operation
Authors: Mahdi Abdelguerfi, Arun K. Sood, S. Khalaf
Venue: CVPR
Year: 1988

Using the odd-even network topology, a parallel bit-level pipelined VLSI processing unit is designed for the histogramming operation. In this approach, histogramming is divided into two stages, the counting and marking process and the filtering process. The filtering process is computationally inexpensive compared to the counting and marking phase. The proposed processing unit is composed of one type of bit-serial structure (processing element) operating in parallel. The architecture and VLSI implementation of the processing unit is considered. The performance of the design is compared with the implementation of the histogramming operation on the Massively Parallel Processor. The comparative analysis shows that the odd-even network based approach has significant advantages in terms of both processing speed and performance/cost ratio.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Recovery of non-rigid motion and structure
Authors: Bradley Horowitz, Alex Pentland
Venue: CVPR
Year: 1991

The elastic properties of real materials provide constraint on the types of non-rigid motion that can occur, and thus allow overconstrained estimates of 3-D non-rigid motion from optical flow data. It is shown that by modeling and simulating the physics of non-rigid motion it is possible to obtain good estimates of both object shape and velocity. Examples using grey-scale and X-ray imagery are presented, including an example of tracking a complex articulated figure.<<ETX>>
Multi-dimensional robust edge detection
Authors: Linnan Liu, Brian G. Schunck, Charles R. Meyer
Venue: CVPR
Year: 1991

A multidimensional edge model is established and a first-order estimation for multidimensional edge profiles is proposed. An optimal edge location detection algorithm is developed. The advantages of the algorithm are that (1) it has little dependence on assumptions of edge models, noise models, or smoothing filters, (2) it has better abilities for detecting very weak edges and making less edge orientation errors than other edge detectors, (3) it can handle corners and complicated multidimensional image structures, and (4) it detects different edge types at the same time.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Using collinear points to compute egomotion and detect nonrigidity
Authors: Niels da Vitoria Lobo, John K. Tsotsos
Venue: CVPR
Year: 1991

A novel approach to computing egomotion and detecting points not moving rigidly with the scene when an observer moves with unrestricted motion is presented. The approach, using collinear image points, is based on an exact method for cancelling effects of the observer's rotation from optic flow. For each point only the component of velocity normal to the direction of the line joining the collinear points is needed. The algorithm is simple, appears robust, and is ideal for parallel implementation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Topological segmentation of discrete surfaces
Authors: Grégoire Malandain, Nicholas Ayache, Gilles Bertrand 0001
Venue: CVPR
Year: 1991

An approach to the segmentation of a discrete 3-D object into a structure of characteristic topological primitives with attached qualitative features is proposed. This structure can be seen as a qualitative description of the object. The approach concentrates on topological properties of discrete surfaces. These surfaces may correspond to the external surface of the objects, or to the skeleton surface. The labeling algorithm is based on very local computations, allowing massively parallel and real-time computations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>