OpenRaiser/Intern-Atlas huggingface dataset:
FastSimiFeat: A Fast and Generalized Approach Utilizing k-NN for Noisy Data Handling
Authors: Jungi Lee, Hwiwoo Park, Myounghwan Kim, Jiseong Yoon, Kwangsun Yoo, Seok-Joo Byun
Venue: CIKM
Year: 2024

As deep learning technologies continue to evolve, the challenge of training neural networks with noisy data becomes increasingly critical. Incorrect labels, which often result in the model's tendency to memorize incorrect data--a phenomenon known as the memorization effect--significantly undermine both performance and the ability to generalize. Traditional methods to address noisy labels typically involve extensive modifications during training, leading to prolonged refinement processes. Although some recent approaches eliminate the need for retraining by using pre-trained models, they still face challenges with lengthy refinement times and inaccurate noise ratio estimations. In response, we introduce FastSimiFeat, a novel algorithm that utilizes the k-nearest neighbors (k-NN) technique on feature vectors derived from pre-trained models efficiently. This training-free method incorporates a new confusion matrix-based noise ratio estimator that significantly reduces the need for iterative refinement by adapting the number of k-NN cycles based on the detected noise level. Additionally, we propose an innovative label correction method that leverages potentially noisy data to enhance model robustness and generality. Our extensive evaluations on both synthetic and real-world datasets demonstrate that FastSimiFeat not only minimizes refinement time but also consistently outperforms existing methods in terms of accuracy. These results confirm the suitability of FastSimiFeat for industrial applications where reliable data processing is paramount. By leveraging inherent features of neural networks pre-trained on large datasets, FastSimiFeat sets a new standard for minimal-dependency approaches in noisy data environments, facilitating the deployment of more reliable and efficient deep learning models across various sectors.
HypMix: Hyperbolic Representation Learning for Graphs with Mixed Hierarchical and Non-hierarchical Structures
Authors: Eric Wonhee Lee, Bo Xiong, Carl Yang, Joyce C. Ho
Venue: CIKM
Year: 2024

Heterogeneous networks contain multiple types of nodes and links, with some link types encapsulating hierarchical structure over entities. Hierarchical relationships can codify information such as subcategories or one entity being subsumed by another and are often used for organizing conceptual knowledge into a tree-structured graph. Hyperbolic embedding models learn node representations in a hyperbolic space suitable for preserving the hierarchical structure. Unfortunately, current hyperbolic embedding models only implicitly capture the hierarchical structure, failing to distinguish between node types, and they only assume a single tree. In practice, many networks contain a mixture of hierarchical and non-hierarchical structures, and the hierarchical relations may be represented as multiple trees with complex structures, such as sharing certain entities. In this work, we propose a new hyperbolic representation learning model that can handle complex hierarchical structures and also learn the representation of both hierarchical and non-hierarchic structures. We evaluate our model on several datasets, including identifying relevant articles for a systematic review, which is an essential tool for evidence-driven medicine and node classification.
XploitSQL: Advancing Adversarial SQL Injection Attack Generation with Language Models and Reinforcement Learning
Authors: Daniel Leung, Omar Tsai, Kourosh Hashemi, Bardia Tayebi, Mohammad A. Tayebi
Venue: CIKM
Year: 2024

SQL injection (SQLi) compromises database-driven applications by enabling attackers to insert malicious SQL commands via input fields, potentially leading to unauthorized access, data manipulation, or system compromise. In recent years, alongside the development of various rule-based Web Application Firewalls (WAFs) aimed at mitigating SQL injection attacks, there has also been a notable rise in the utilization of machine learning and deep learning techniques to address this issue. Although significant progress has been made in these studies, detecting and mitigating SQLi-related attacks continues to present a significant challenge. A crucial factor contributing to the lack of extensive SQLi detection solutions is the absence of a comprehensive testing methodology. In this work, we introduce XploitSQL-an innovative approach to advance adversarial SQL injection generation by leveraging language models and reinforcement learning. Our model is trained to produce evasive SQLi samples, enhancing the robustness of SQLi detection models and offering opportunities for more comprehensive detection strategies. To assess the efficacy of the proposed method, we employed state-of-the-art SQL injection detection models in conjunction with commercially available web-based firewalls. Across all tested detection models, detection rates declined when faced with evasive samples generated by XploitSQL. Furthermore, our model outperforms existing methods for generating attack samples.
RL-ISLAP: A Reinforcement Learning Framework for Industrial-Scale Linear Assignment Problems at Alipay
Authors: Hanjie Li, Yue Ning, Yang Bao, Changsheng Li, Boxiao Chen, Xingyu Lu, Ye Yuan, Guoren Wang
Venue: CIKM
Year: 2024

Industrial-scale linear assignment problems (LAPs) are frequently encountered in various industrial scenarios, e.g., asset allocation within the domain of credit management. However, optimization algorithms for such problems (e.g., PJ-ADMM) are highly sensitive to hyper-parameters. Existing solving systems rely on empirical parameter selection, which is challenging to achieve convergence and extremely time-consuming. Additionally, the resulting parameter rules are often inefficient. To alleviate this issue, we propose RL-ISLAP, an efficient and lightweight Reinforcement Learning framework for Industrial-Scale Linear Assignment Problems. We formulate the hyper-parameter selection for PJ-ADMM as a sequential decision problem and leverage reinforcement learning to enhance its convergence. Addressing the sparse reward challenge inherent in learning policies for such problems, we devise auxiliary rewards to provide dense signals for policy optimization, and present a rollback mechanism to prevent divergence in the solving process. Experiments on OR-Library benchmark demonstrate that our method is competitive to SOTA stand-alone solvers. Furthermore, the scale-independent design of observations enables us to transfer the acquired hyper-parameter policy to a scenario of LAPs in varying scales. On two real-world industrial-scale LAPs with up to 10 millions of decision variables, our proposed RL-ISLAP achieves solutions of comparable quality in 2/3 of the time when compared to the SOTA distributed solving system employing fine-tuned empirical parameter rules.
Improving Prompt-based News Recommendation with Individual Template and Customized Answer
Authors: Yijiang Li, Jun Wu
Venue: CIKM
Year: 2024

Prompt learning plays a key role in aligning the task of news recommendation (NR) with the Pre-trained Language Models (PLMs). However, current prompt-based NR methods utilize fixed templates and answer words, ignoring the personalization of user's demand and the diversity between news topics. To this end, we propose an Automatic Prompt based NR (AutoPNR) scheme, which automatically generates individual templates for users according to their potential interests, and customized answer words w.r.t. the topics of candidate news. Concretely, such an individual template utilizes several specific tokens to encode a user's interest extracted from her/his reading history, while a pair of customized answer words are retrieved from a large vocabulary (often existing alongside PLMs) based on the topic of candidate news. Through extensive experiments on the real-world datasets, we show that our AutoPNR works well with different PLMs, and considerably outperforms state-of-the-art NR techniques.
Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation
Authors: Shiyuan Li, Yixin Liu, Qingfeng Chen, Geoffrey I. Webb, Shirui Pan
Venue: CIKM
Year: 2024

Unsupervised graph representation learning (UGRL) based on graph neural networks (GNNs), has received increasing attention owing to its efficacy in handling graph-structured data. However, existing UGRL methods ideally assume that the node features are noise-free, which makes them fail to distinguish between useful information and noise when applied to real data with noisy features, thus affecting the quality of learned representations. This urges us to take node noisy features into account in real-world UGRL. With empirical analysis, we reveal that feature propagation, the essential operation in GNNs, acts as a "double-edged sword" in handling noisy features - it can both denoise and diffuse noise, leading to varying feature quality across nodes, even within the same node at different hops. Building on this insight, we propose a novel UGRL method based on Multi-hop feature Quality Estimation (MQE for short). Unlike most UGRL models that directly utilize propagation-based GNNs to generate representations, our approach aims to learn representations through estimating the quality of propagated features at different hops. Specifically, we introduce a Gaussian model that utilizes a learnable "meta-representation" as a condition to estimate the expectation and variance of multi-hop propagated features via neural networks. In this way, the ''meta representation'' captures the semantic and structural information underlying multiple propagated features but is naturally less susceptible to interference by noise, thereby serving as high-quality node representations beneficial for downstream tasks. Extensive experiments on multiple real-world datasets demonstrate that MQE in learning reliable node representations in scenarios with diverse types of feature noise.
Ocean Significant Wave Height Estimation with Spatio-temporally Aware Large Language Models
Authors: Zhe Li, Ronghui Xu, Jilin Hu, Zhong Peng, Xi Lu, Chenjuan Guo, Bin Yang
Venue: CIKM
Year: 2024

Significant wave height (SWH) is a vital metric in marine science, and accurate SWH estimation is crucial for various applications, e.g., marine energy development, fishery, early warning systems for potential risks, etc. Traditional SWH estimation methods that are based on numerical models and physical theories are hindered by computational inefficiencies. Recently, machine learning has emerged as an appealing alternative to improve accuracy and reduce computational time. However, due to limited observational technology and high costs, the scarcity of real-world data restricts the potential of machine learning models. To overcome these limitations, we propose an ocean SWH estimation framework, namely Orca. Specifically, Orca enhances the limited spatio-temporal reasoning abilities of classic LLMs with a novel spatiotemporal aware encoding module. By segmenting the limited buoy observational data temporally, encoding the buoys' locations spatially, and designing prompt templates, Orca capitalizes on the robust generalization ability of LLMs to estimate significant wave height effectively with limited data. Experimental results on the Gulf of Mexico demonstrate that Orca achieves state-of-the-art performance in SWH estimation.
Coresets for Deletion-Robust k-Center Clustering
Authors: Ruien Li, Yanhao Wang, Michael Mathioudakis
Venue: CIKM
Year: 2024

The k-center clustering problem is of fundamental importance for a broad range of machine learning and data science applications. In this paper, we study the deletion-robust version of the problem. Specifically, we aim to extract a small subset of a given data set, referred to as a coreset, that contains a provably good set of k centers even after an adversary deletes up to z arbitrarily chosen points from the data set. We propose a 4-approximation algorithm that provides a coreset of size O(kz). To our knowledge, this is the first algorithm for deletion-robust k-center clustering with a theoretical guarantee. Moreover, we accompany our theoretical results with extensive experiments, demonstrating that our algorithm achieves significantly better robustness than non-trivial baselines against three heuristic gray-box and white-box adversarial deletion attacks.
Privacy-preserving Spatial Dataset Search in Cloud
Authors: Pengyue Li, Hua Dai, Sheng Wang, Wenzhe Yang, Geng Yang
Venue: CIKM
Year: 2024

The development of cloud computing has met the growing demand for dataset search in the era of massive data. In the field of spatial dataset search, the high prevalence of sensitive information in spatial datasets underscores the necessity of privacy-preserving search processing in the cloud. However, existing spatial dataset search schemes are designed on plaintext datasets and do not consider privacy protection in search processing. In this paper, we first propose a privacy-preserving spatial dataset search scheme. The density distribution-based similarity model is proposed to measure the similarity between spatial datasets, and then the order-preserving encrypted similarity is designed to achieve secure similarity calculation. With the above idea, the baseline search scheme (PriDAS) is proposed. To improve the search efficiency, a two-layer index is designed to filter candidate datasets and accelerate the similarity calculation between datasets. By using the index, the optimized search scheme (PriDAS+) is proposed. To analyze the security of the proposed schemes, the game simulation-based proof is presented. Experimental results on three real-world spatial data repositories with 100,000 spatial datasets show that PriDAS+ only needs less than 0.4 seconds to accomplish the search processing.
LLM-Empowered Few-Shot Node Classification on Incomplete Graphs with Real Node Degrees
Authors: Yun Li, Yi Yang, Jiaqi Zhu, Hui Chen, Hongan Wang
Venue: CIKM
Year: 2024

Graphs constructed from real-world scenarios are often incomplete due to privacy restrictions or resource limitations, posing significant challenges for node classification, especially when labeled data are scarce. In many scenarios of incomplete graphs, the real node degrees, such as the number of followers in social networks or publications' references in citation networks, are easily accessible and informative, which could indicate the degree of incompleteness. However, most of existing researches of incomplete graphs focus on edge completion, but ignore the node completion with known node degrees. In this paper, we propose a new few-shot node classification problem on incomplete graphs with real node degrees. To deal with node completion, edge completion and label completion of this problem, we develop an effective Large Language Models (LLMs) empowered Graph Convolutional Network (GCN) model utilizing the real node Degrees, namely LLMDGCN. First, we leverage LLMs to initially fill in the missing nodes and labels. Next, we design an edge prediction module that employs the real node degrees and inter-category probability matrix to recover the missing edges for each node. We then iteratively train the GCN and the edge prediction module. The GCN generates pseudo labels, which the edge prediction module uses to restore edges, and these edges are fed back into the GCN to improve accuracy. Extensive experiments on four benchmark datasets demonstrate the effectiveness and robustness of our proposed method for the few-shot node classification on incomplete graphs with real node degrees.
Design Element Aware Poster Layout Generation
Authors: Yinan Li, Jia Chen, Yin Bai, Jia Cheng, Jun Lei
Venue: CIKM
Year: 2024

Despite the recent significant advancements in poster layout generation, existing works are mainly unaware of the given design elements (i.e., text, logo, and underlay), which leads to undesirable layouts or visual artifacts. The visual artifacts we refer to include (1) improper sizes, e.g., placing a short piece of text into a large textbox or long texts into small text boxes, and (2) image distortion, e.g., the stretched logo in Fig. 1. To advance research in this field, we propose a new design-element aware poster layout generation task, which require the generated layouts to not only have harmonic relationships but also fit well with the design elements. To address this task, we propose Design Element aware Transformer (DET), an encoder-decoder based transformer network, to generate reasonable layouts that fit not only the background images but also the design elements. The encoder extracts a fine-grained multi-scale representation from the background image and its saliency map. The decoder receives the background features and produces layouts conditioned on the content and desired sizes of the design elements. Adopting the multi-scale representation and the deformable attention in both the encoder and decoder enables our method to accurately understand/generate the spatial relationships between the background objects and design elements. We adapted three public poster layout generation datasets to fit our task and conducted experiments on them. In the meantime, we propose a new evaluation metric called AspDiff to measure whether the generated layout matches the given design elements. Quantitative and qualitative evaluation on three datasets demonstrates that DET yields superior results compared to other layout generation methods. Our code and datasets will be released.
ChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image Generation
Authors: Peiyu Li, Xiaobao Huang, Yijun Tian, Nitesh V. Chawla
Venue: CIKM
Year: 2024

Significant work has been conducted in the domain of food computing, yet these studies typically focus on single tasks such as t2t (instruction generation from food titles and ingredients), i2t (recipe generation from food images), or t2i (food image generation from recipes). None of these approaches integrate all modalities simultaneously. To address this gap, we introduce a novel food computing foundation model that achieves true multimodality, encompassing tasks such as t2t, t2i, i2t, it2t, and t2ti. By leveraging large language models (LLMs) and pre-trained image encoder and decoder models, our model can perform a diverse array of food computing-related tasks, including food understanding, food recognition, recipe generation, and food image generation. Compared to previous models, our foundation model demonstrates a significantly broader range of capabilities and exhibits superior performance, particularly in food image generation and recipe generation tasks. We open-sourced ChefFusion at https://github.com/Peiyu-Georgia-Li/ChefFusion-Multimodal-Foundation-Model-Integrating-Recipe-and-Food-Image-Generation.git.
LagCNN: A Fast yet Effective Model for Multivariate Long-term Time Series Forecasting
Authors: Linsen Li, Chunfei Jian, Feng Wan, Dongdong Geng, Ziquan Fang, Lu Chen, Yunjun Gao, Weihao Jiang, Jiang Zhu
Venue: CIKM
Year: 2024

Long-term time series forecasting has gained significant attention in recent years due to its widely-application in various fields. Transformer-based models have gained popularity for the ability to capture long-sequence interactions. However, these models are limited in real-world use because of the memory consumption and computation explosion. The CNN-based models are also one of the main models used for time series prediction, but their performance has always been inferior to the transformer-based models in previous works. We have reconsidered the role of CNN components and redefined the way CNN basic components are used for time series prediction. In addition, the time lags information between periods in the time series is important. Unfortunately, existing works lack consideration of this classic but important information. Motivated by these factors, we propose a fast yet effective CNN model with time lags for multivariate long-term time series forecasting, named LagCNN. Specifically, the time series is transformed into lag-patches to capture the correlation between periods. Then, a fast CNN model is performed in the feature dimension rather than the time dimension like most previous works do. Meanwhile, information aggregation is performed in the time dimension to extract complex temporal patterns. LagCNN significantly outperforms state-of-the-art on multiple publicly available datasets. One step further, LagCNN exhibits significant efficiency advantages over the most efficient Transformer model (PatchTST), resulting in a significant reduction in memory usage (4.4×) and runtime (10.7×).
Privacy-Preserving Graph Embedding based on Local Differential Privacy
Authors: Zening Li, Rong-Hua Li, Meihao Liao, Fusheng Jin, Guoren Wang
Venue: CIKM
Year: 2024

Graph embedding has become a powerful tool for learning latent representations of nodes in a graph. Despite its superior performance in various graph-based machine learning tasks, serious privacy concerns arise when the graph data contains personal or sensitive information. To address this issue, we investigate and develop graph embedding algorithms that satisfy local differential privacy (LDP). We introduce a novel privacy-preserving graph embedding framework, named PrivGE, to protect node data privacy. Specifically, we propose an LDP mechanism to obfuscate node data and utilize personalized PageRank as the proximity measure to learn node representations. Furthermore, we provide a theoretical analysis of the privacy guarantees and utility offered by the PrivGE framework. Extensive experiments on several real-world graph datasets demonstrate that PrivGE achieves an optimal balance between privacy and utility, and significantly outperforms existing methods in node classification and link prediction tasks.
Explainable and Coherent Complement Recommendation Based on Large Language Models
Authors: Zelong Li, Yan Liang, Ming Wang, Sungro Yoon, Jiaying Shi, Xin Shen, Xiang He, Chenwei Zhang, Wenyi Wu, Hanbo Wang, Jin Li, Jim Chan, Yongfeng Zhang
Venue: CIKM
Year: 2024

A complementary item is an item that pairs well with another item when consumed together. In the context of e-commerce, providing recommendations for complementary items is essential for both customers and stores. Current models for suggesting complementary items often rely heavily on user behavior data, such as co-purchase relationships. However, just because two items are frequently bought together does not necessarily mean they are truly complementary. Relying solely on co-purchase data may not align perfectly with the goal of making meaningful complementary recommendations. In this paper, we introduce the concept of "coherent complement recommendation", where "coherent" implies that recommended item pairs are compatible and relevant. Our approach builds upon complementary item pairs, with a focus on ensuring that recommended items are well used together and contextually relevant. To enhance the explainability and coherence of our complement recommendations, we fine-tune the Large Language Model (LLM) with coherent complement recommendation and explanation generation tasks since LLM has strong natural language explanation generation ability and multi-task fine-tuning enhances task understanding. Experimental results indicate that our model can provide more coherent complementary recommendations than existing state-of-the-art methods, and human evaluation validates that our approach achieves up to a 48% increase in the coherent rate of complement recommendations.