OpenRaiser/Intern-Atlas huggingface dataset:
COKE: Causal Discovery with Chronological Order and Expert Knowledge in High Proportion of Missing Manufacturing Data
Authors: Ting-Yun Ou, Ching Chang, Wen-Chih Peng
Venue: CIKM
Year: 2024

Understanding causal relationships between machines is crucial for fault diagnosis and optimization in manufacturing processes. Real-world datasets frequently exhibit up to 90% missing data and high dimensionality from hundreds of sensors. These datasets also include domain-specific expert knowledge and chronological order information, reflecting the recording order across different machines, which is pivotal for discerning causal relationships within the manufacturing data. However, previous methods for handling missing data in scenarios akin to real-world conditions have not been able to effectively utilize expert knowledge. Conversely, prior methods that can incorporate expert knowledge struggle with datasets that exhibit missing values. Therefore, we propose COKE to construct causal graphs in manufacturing datasets by leveraging expert knowledge and chronological order among sensors without imputing missing data. Utilizing the characteristics of the recipe, we maximize the use of samples with missing values, derive embeddings from intersections with an initial graph that incorporates expert knowledge and chronological order, and create a sensor ordering graph. The graph-generating process has been optimized by an actor-critic architecture to obtain a final graph that has a maximum reward. Experimental evaluations in diverse settings of sensor quantities and missing proportions demonstrate that our approach compared with the benchmark methods shows an average improvement of 39.9% in the F1-score. Moreover, the F1-score improvement can reach 62.6% when considering the configuration similar to real-world datasets, and 85.0% in real-world semiconductor datasets. The source code is available at https://github.com/OuTingYun/COKE.
The Impact of External Sources on the Friedkin-Johnsen Model
Authors: Charlotte Out, Sijing Tu, Stefan Neumann, Ahad N. Zehmakan
Venue: CIKM
Year: 2024

To obtain a foundational understanding of timeline algorithms and viral content in shaping public opinions, computer scientists started to study augmented versions of opinion formation models from sociology. In this paper, we generalize the popular Friedkin--Johnsen model to include the effects of external media sources on opinion formation. Our goal is to mathematically analyze the influence of biased media, arising from factors such as manipulated news reporting or the phenomenon of false balance. Within our framework, we examine the scenario of two opposing media sources, which do not adapt their opinions like ordinary nodes, and analyze the conditions and the number of periods required for radicalizing the opinions in the network. When both media sources possess equal influence, we theoretically characterize the final opinion configuration. In the special case where there is only a single media source present, we prove that media sources which do not adapt their opinions are significantly more powerful than those which do. Lastly, we conduct the experiments on real-world and synthetic datasets, showing that our theoretical guarantees closely align with experimental simulations.
Cross-contextual Sequential Optimization via Deep Reinforcement Learning for Algorithmic Trading
Authors: Kaiming Pan, Yifan Hu, Li Han, Haoyu Sun, Dawei Cheng, Yuqi Liang
Venue: CIKM
Year: 2024

High-frequency algorithmic trading has consistently attracted attention in both academic and industrial fields, which is formally modeled as a near real-time sequential decision problem. DRL methods are treated as a promising direction compared with the traditional approaches, as they have shown great potential in chasing maximum accumulative return. However, the financial data gathered from volatile market change rapidly, which makes it dramatically difficult to grasp crucial factors for effective decision-making. Existing works mainly focus on capturing temporal relations while ignoring deriving essential factors across features. Therefore, we propose a DRL-based cross-contextual sequential optimization (CCSO) method for algorithmic trading. In particular, we employ a convolution module in the first stage to derive latent factors via inter-sequence aggregation and apply a well-designed self-attention module in the second stage to capture market dynamics by aggregating temporal intra-sequence details. With the two-stage extractor as encoder and a RNN-based decision-maker as decoder, an Encoder-Decoder module is established as the policy network to conduct potent feature analysis and suggest action plans. Then, we design a dynamic programming based learning method to address the challenge of complex network updates in reinforcement learning, leading to considerable enhancement in learning stability and efficiency. To the best of our knowledge, this is the first work that solves the sequential optimization problem by joint representation of trading data across time and features in the DRL framework. Extensive experiments demonstrate the superior performance of our method compared to other state-of-the-art algorithmic trading approaches in various widely-used metrics.
Graph-theoretical Approach to Enhance Accuracy of Financial Fraud Detection Using Synthetic Tabular Data Generation
Authors: Dae-Young Park
Venue: CIKM
Year: 2024

Tabular data synthesis has become crucial for financial applications including fraud detection, especially where there are data privacy regulations such as General Data Protection Regulation (GDPR) restrict access to original data. Despite its importance, current generative models inadequately address key challenges in financial fraud detection (FFD) data, namely extreme class imbalance, high data sparsity, and non-normal attribute distributions. My research introduces novel graph-theoretical generative models, SeparateGGM and SignedGGM, designed to tackle these challenges. By integrating graph neural network-based feature engineering, graph topology and connectivity analysis, and novel graph centrality indicators, my models achieve optimal graph settings for enhanced fraud detection accuracy. This approach is pioneering in its application of diverse graph-theoretical methods to improve FFD performance. Preliminary results demonstrate my models' superiority over competing methods on multiple FFD benchmark datasets. The goal of this research is to significantly advance real-world financial fraud detection techniques and to show that several graph-theoretical methodologies can significantly contribute to the generation of high-quality tabular synthetic data for enhancing fraud detection accuracy to the data science community.
Progressive Label Disambiguation for Partial Label Learning in Homogeneous Graphs
Authors: Rajat Patel, Aakarsh Malhotra, Sudipta Modak, Siddharth Yerramsetty
Venue: CIKM
Year: 2024

Many existing Graph Neural Networks (GNN) methods assume that labels are reliable and sufficient, which may not be the case in real-world scenarios. This paper addresses one such problem of Partial Label Learning (PLL) on graph-structured data. In the PLL for graphs, each node is represented by a candidate set of labels, where only one is true while the others are inaccurate. Despite advancements with PLL in tabular and vision domains, the graph-structured data still needs to be explored. In this work, we first define PLL for graphs. Subsequently, we propose a new PLD-Graph algorithm for PLL in homogeneous graphs with scarce labels. We utilize graph augmentation to reduce the effects of inexact labels and provide additional supervision from unlabeled nodes. Progressive label disambiguation is performed based on the model's ability to predict correct classes. Furthermore, an additional loss estimates the label corruption matrix to capture associations between correct and incorrect labels. We show the effectiveness of the proposed algorithm on multiple graph datasets, with two types of noise and varying levels of ambiguous labels. Overall, the proposed PLD-Graph algorithm outperforms state-of-the-art PLL methods.
Advancing Misinformation Awareness in Recommender Systems for Social Media Information Integrity
Authors: Royal Pathak
Venue: CIKM
Year: 2024

Recommender systems play an essential role in determining the content users encounter on social media platforms and in uncovering relevant news. However, they also present significant risks, such as reinforcing biases, over-personalizing content, fostering filter bubbles, and inadvertently promoting misinformation. The spread of false information is rampant across various online platforms, such as Twitter (now X), Meta, and TikTok, especially noticeable during events like the COVID-19 pandemic and the US Presidential elections. These instances underscore the critical necessity for transparency and regulatory oversight in the development of recommender systems. Given the challenge of balancing free speech with the risks of outright removal of fake news, this paper aims to address the spread of misinformation from algorithmic biases in recommender systems using a social science perspective.
Towards Deconfounded Visual Question Answering via Dual-causal Intervention
Authors: Daowan Peng, Wei Wei
Venue: CIKM
Year: 2024

The Visual Question Answering (VQA) task has recently become notorious because models are prone to predicting well-educated "guesses" as answers rather than deriving them through visual understanding. The main culprit for this is that VQA models memorize the shortcut biases in the dataset during the training process. While a variety of solutions have been proposed, they solely focus on the shortcuts in the language modality, leaving other kinds of shortcut biases untouched. In this paper, we shift our lens to all kinds of shortcuts and resort to causal inference to circumvent these issues. Causal inference methods can discover the causal effect (P(Y|do(X))) [27] rather than statistic-based spurious correlations (P(Y|X)) in the dataset, making them naturally suitable for debiasing learning. To deconfound these shortcut biases, we propose a causality-aware method, coined as Dual Causal Intervention (DCI), to endow VQA models with better generalization by combining two components: linguistic backdoor intervention and visual front-door intervention. To be specific, we harness backdoor intervention to cut off the effects of confounders in the language modality and employ front-door intervention to eliminate the impact of confounders in the visual modality. We conducted extensive experiments on two challenging Out-of-Distribution (OOD) benchmarks, including VQA-VS and VQA-CE, which are designed to assess the robustness of VQA models under different shortcut biases. The experimental results show the effectiveness of our method. Specifically, our approach outperforms the current state-of-the-art debiasing methods on the IID metric and all nine OOD metrics of the VQA-VS dataset, and also surpasses the performance of the best-performing methods on all metrics of the VQA-CE dataset.
GARF: A Self-supervised Data Cleaning System with SeqGAN
Authors: Jinfeng Peng, Hanghai Cui, Derong Shen, Yue Kou, Tiezheng Nie, Tianlong Guo
Venue: CIKM
Year: 2024

High-quality data is essential for data science and machine learning applications, but unfortunately, real-world data often contains significant amounts of errors, such as typos, missing values, and data inconsistencies. Despite all the efforts in cleaning data using either logical or learning-based methods, in practice, data cleaning still requires high human cost, for either manually providing data repairing rules or preparing labeled datasets for training machine learning models. In this paper, we introduce GARF, a novel data cleaning system based on sequence generative adversarial networks (SeqGAN). One key information GARF tries to learn is data repair rules. To automatically extracts data repair rules from dirty data, GARF employs a SeqGAN to capture the dependency relationships, and converts the information learned by machine to interpretable data repair rules for humans. Additionally, considering that both generated rules and data may not be fully trusted, GARF provides a co-cleaning process to iteratively update inaccurate rules and repair dirty data until there is no tuple violating rules. We have implemented and deployed GARF as an open-sourced system, and demonstrated its usability on data cleaning in real-world scenarios.
Improving German News Clustering with Contrastive Learning
Authors: Piriyakorn Piriyatamwong, Saikishore Kalloori, Fabio Zünd
Venue: CIKM
Year: 2024

Automatic news articles clustering is one of the most important tasks for news publishers. Traditional unsupervised models exploit generic text representation (e.g., BERT) and typically do not consider the relationships between each paragraph in news articles. Such depth learning from news articles is important for clustering full-length articles. Recently contrastive learning (CL) has shown to be a popular method for representation learning that uses positive and negative data pairs generated using data augmentation techniques to improve the representation in the latent space. In this work, we propose text augmentation methods and use contrastive learning to cluster daily growing full-length German news articles. Our experiments on four German news article datasets (one labeled and three unlabeled datasets) demonstrate that contrastive learning and our text augmentation methods significantly improve the representation of news articles compared to generic pre-trained text representation and have high performance for clustering tasks.
Unified Argument Retrieval System from German News Articles Using Large Language Models
Authors: Piriyakorn Piriyatamwong, Saikishore Kalloori, Fabio Zünd
Venue: CIKM
Year: 2024

The rapid growth in the number of news articles published daily can create challenges for users to explore specific topics and gather different perspectives around the topics to make neutral and unbiased conclusions. The system's ability to intelligently cluster news articles from multiple sources and retrieve concise (pro/con) relevant arguments is necessary for users' well-informed decision-making. In this paper, we introduce our unified argument retrieval system that uses our clustering model to cluster news articles and subsequently extracts the core arguments from news articles using the argument prediction model. We conducted a user study to understand the system's usability and users' satisfaction with the quality of clusters and arguments extracted.
Bi-directional Learning of Logical Rules with Type Constraints for Knowledge Graph Completion
Authors: Kunxun Qi, Jianfeng Du, Hai Wan
Venue: CIKM
Year: 2024

Knowledge graph completion (KGC) aims to infer missing facts from existing facts. Learning logical rules plays a pivotal role in KGC, as logical rules excel in explaining why a missing fact is inferred. Most existing rule learning methods focus merely on learning chain-like rules, neglecting type constraints on entities. In practice, type constraints are crucial in expressing precise rules. Therefore, we propose a novel formalism for logical rules named TC-rules, which complements chain-like rules with both explicit and implicit type constraints on entity variables. Accordingly, we propose an end-to-end approach to effectively learn TC-rules, by parameterizing a neural model to simulate the inference of TC-rules. Considering that existing end-to-end methods learn two different sets of logical rules to respectively answer a head query (?,rnew, t) and a tail query (h,rrnew, ?), leading to confusing explanations for supporting a new fact (h,rnew, t), we propose a bi-directional learning mechanism to ensure that the TC-rules learnt for answering (?,rnew, t) are the same as the TC-rules learnt for answering (h,rnew, ?). Experimental results on eight benchmark datasets demonstrate that the proposed method outperforms state-of-the-art rule learners in both the link prediction task and the triple classification task. Furthermore, our case study confirms that expressive TC-rules can be extracted from the parameter assignment of the learnt neural model.
Hol-Light: A Holistic framework for Efficient and Dynamic Traffic Signal Management
Authors: Siyao Qiao, Jia Wu
Venue: CIKM
Year: 2024

Numerous reinforcement learning-based traffic control methods have been proposed to enhance transportation efficiency and alleviate traffic congestion. However, the existing solutions predominantly address only specific facets of the challenge. For instance, some focus on enhancing control performance, while others aim to address control issues within heterogeneous road networks, explore model generalizability, or consider model compression for practical deployment scenarios. The question arises: Can a single approach effectively tackle all these issues concurrently? We propose a holistic framework, Hol-Light, that can effectively solve all these issues. It delves deeply into the feature representations of traffic phase considering the interplay between phase relationships and traffic flow dynamics, and meticulously captures the interphase relationships through an elegantly designed model that is both parameter-efficient and minimal. To substantiate the efficacy of our approach, we conducted comprehensive experiments utilizing two well-established traffic simulators: CityFlow and SUMO. The experiment results indicate that our method excels in terms of high performance, rapid training speed, robust generalization capabilities, and adaptability to diverse road network configurations.
ILTS: Inducing Intention Propagation in Decentralized Multi-Agent Tasks with Large Language Models
Authors: Xihe Qiu, Haoyu Wang, Xiaoyu Tan, Chao Qu
Venue: CIKM
Year: 2024

Achieving coordination while avoiding suboptimal equilibria poses a significant challenge in decentralized multi-agent reinforcement learning (MARL) systems operating under limited global information. Conventional decentralized approaches have struggled to effectively induce cooperative behaviors between agents. We propose a novel hierarchical framework that synergistically combines large language models (LLMs) and deep reinforcement learning to address this challenge. Our proposed learning-to-share (ILTS) method decomposes the global objective into a two-level hierarchy: high-level LLM policy determines how to share rewards between neighboring agents to shape emergent collaboration, while low-level policies optimize the induced localized objectives using Q value networks. A meta-learning sophisticated dynamic reward-sharing scheme via LLMs is developed to facilitate decentralized cooperation without explicit communication. Experimental results demonstrate that ILTS outperforms prior MARL algorithms across cooperative multi-agent tasks by inducing collaborative strategies and performing intention propagation from lightweight learned signals. This hierarchical framework avoids the need for hand-engineered rewards or explicit communication while promoting scalable learning of intricate symbiotic behaviors between agents perceiving only local observations.
EASE: Learning Lightweight Semantic Feature Adapters from Large Language Models for CTR Prediction
Authors: Zexuan Qiu, Jieming Zhu, Yankai Chen, Guohao Cai, Weiwen Liu, Zhenhua Dong, Irwin King
Venue: CIKM
Year: 2024

Recent studies highlight the potential of large language models (LLMs) to enhance content integration in recommender systems by leveraging their semantic understanding capabilities. However, directly incorporating LLMs into an online inference pipeline significantly increases computation costs for large-scale deployment, posing a practical challenge in balancing their benefits and costs. In this work, we propose the EASE framework, which enriches and aligns semantic feature embeddings using LLMs during the training phase while establishing a lightweight inference pipeline that does not directly involve LLMs. Specifically, we train a semantic adapter to align item features with LLMs and simultaneously enrich semantic embeddings through reconstruction tasks from LLMs. During inference, we retain only the item feature encoder and lightweight semantic adapter, thereby eliminating the computation overhead of resource-intensive LLMs. Our EASE framework is flexible, supporting not only text and visual features but also other pre-processed embedding features. Extensive experiments on both public and industrial datasets demonstrate that enriching semantic feature embeddings with our EASE framework yields consistent improvements in downstream click-through rate prediction tasks.
SGFL-Attack: A Similarity-Guidance Strategy for Hard-Label Textual Adversarial Attack Based on Feedback Learning
Authors: Panjia Qiu, Guanghao Zhou, Mingyuan Fan, Cen Chen, Yaliang Li, Wenming Zhou
Venue: CIKM
Year: 2024

Hard-label black-box textual adversarial attack presents a challenging task where only the predictions of the victim model are available. Moreover, several constraints further complicate the task of launching such attacks, including the inherent discrete and non-differentiable nature of text data and the need to introduce subtle perturbations that remain imperceptible to humans while preserving semantic similarity. Despite the considerable research efforts dedicated to this problem, existing methods still suffer from several limitations. For example, algorithms based on complex heuristic searches necessitate extensive querying, rendering them computationally expensive. The introduction of continuous gradient strategies into discrete text spaces often leads to estimation errors. Meanwhile, geometry-based strategies are prone to falling into local optima. To address these limitations, in this paper, we introduce SGFL-Attack, a novel approach that leverages a Similarity-Guidance strategy based on Feedback Learning for hard-label textual adversarial attack, with limited query budget. Specifically, the proposed SGFL-Attack utilizes word embedding vectors to assess the importance of words and positions in text sequences, and employs a feedback learning mechanism to determine reward or punishment based on changes in predicted labels caused by replacing words. In each iteration, SGFL-Attack guides the search based on knowledge acquired from the feedback learning mechanism, generating more similar samples while maintaining low perturbations. Moreover, to reduce the query budget, we incorporate local hash mapping to avoid redundant queries during the search process. Extensive experiments on seven widely used datasets show that the proposed SGFL-Attack method significantly outperforms state-of-the-art baselines and defenses over multiple language models.
ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification
Authors: Umair Qudus, Michael Röder, Daniel Vollmers, Axel-Cyrille Ngonga Ngomo
Venue: CIKM
Year: 2024

Detecting the veracity of a statement automatically is a challenge the world is grappling with due to the vast amount of data spread across the web. Verifying a given claim typically entails validating it within the framework of supporting evidence like a retrieved piece of text. Classifying the stance of the text with respect to the claim is called stance classification. Despite advancements in automated fact-checking, most systems still rely on a substantial quantity of labeled training data, which can be costly. In this work, we avoid the costly training or fine-tuning of models by reusing pre-trained large language models together with few-shot in-context learning. Since we do not train any model, our approach ExPrompt is lightweight, demands fewer resources than other stance classification methods and can serve as a modern baseline for future developments. At the same time, our evaluation shows that our approach is able to outperform former state-of-the-art stance classification approaches regarding accuracy by at least 2 percent. Our scripts and data used in this paper are available at https://github.com/factcheckerr/ExPrompt.