OpenRaiser/Intern-Atlas huggingface dataset:
SparseBF: Enhancing Scalability and Efficiency for Sparsely Filled Privacy-Preserving Record Linkage
Authors: Han Xu, Yuhong Shao, Kareem Benaissa, Yutong Li
Venue: CIKM
Year: 2024

Bloom filter (BF) encodings are a proven method for comparing the similarity of records from multiple databases while maintaining privacy. This process, known as privacy-preserving record linkage (PPRL), is computationally expensive, especially with large datasets. To address this challenge, we have observed that BF encodings often exhibit sparsely filled patterns. Leveraging this insight, we introduce SparseBF, a scalable data structure that is space-optimized and maintains fast computation speed for PPRL. Compared to typical BF encodings, SparseBF brings in three improvements. First, SparseBF employs a hybrid storage scheme that selects the optimal storage component for BF encodings. Second, SparseBF utilizes its adaptive compressed sparse row storage to achieve lossless space compression, both when BFs are sparsely occupied and when they are densely populated. Third, SparseBF supports SIMD vector instructions to optimize the record linkage speed. Experiments show that, in sparsely filled scenarios, SparseBF outperforms the existing solution by up to 2.1x record linkage computation speed, while delivering up to 70.5% savings in storage space.
The 8th Workshop on Graph Techniques for Adversarial Activity Analytics (GTA3 2024)
Authors: Jiejun Xu, Hanghang Tong, Andrea L. Bertozzi
Venue: CIKM
Year: 2024

Graphs are powerful analytic tools for modeling adversarial activities across a wide range of domains and applications. Examples include identifying and responding to cybersecurity systems' threats and vulnerabilities, strengthening critical infrastructure's resilience and robustness, and combating covert illicit activities that span various domains like finance, communication, and transportation. With the rapid development of generative AI, the lifecycle and throughput of adversarial activities, such as generating attacks or synthesizing deceptive signals, have accelerated significantly. For instance, a malicious actor can generate a large number of malware variants to flood defense systems or create agents to disseminate misleading signals, obscuring their activities. Consequently, there is a pressing need for novel and effective technology to autonomously handle these adversarial activities and keep pace with the evolving threats. The purpose of this workshop is to provide a forum to discuss emerging research problems and novel approaches in graph analysis for modeling adversarial activities in the age of generative AI.
Sequence-level Semantic Representation Fusion for Recommender Systems
Authors: Lanling Xu, Zhen Tian, Bingqian Li, Junjie Zhang, Daoyuan Wang, Hongyu Wang, Jinpeng Wang, Sheng Chen, Wayne Xin Zhao
Venue: CIKM
Year: 2024

With the rapid development of recommender systems, there is increasing side information that can be employed to improve the recommendation performance. Specially, we focus on the utilization of the associated textual data of items (e.g., product title) and study how text features can be effectively fused with ID features in sequential recommendation. In this paper, we propose a novel Te xt-I D semantic fusion approach for sequential Rec ommendation, namely TedRec. The core idea is to conduct a sequence-level semantic fusion approach by integrating global contexts. The key strategy lies in that we transform the text and ID embeddings by Fourier Transform from time domain to frequency domain. In the frequency domain, the global sequential characteristics of the original sequences are inherently aggregated into the transformed representations, so that we can employ simple multiplicative operations to effectively fuse the two kinds of item features. Our fusion approach can be proved to have the same effects of contextual convolution, so as to achieving sequence-level semantic fusion. Further, we propose to enhance the discriminability of the text embeddings from the text encoder, by adaptively injecting positional information via a mixture-of-experts (MoE) modulation method. Both offline and online experiments demonstrate the effectiveness of our approach.
Towards Energy-Efficient Llama2 Architecture on Embedded FPGAs
Authors: Han Xu, Xingyuan Wang, Shihao Ji
Venue: CIKM
Year: 2024

Large language models (LLMs) have shown immense potential for applications in information retrieval and knowledge management, but their computational and memory demands pose challenges for resource-constrained devices. In response, this work introduces an FPGA-based accelerator designed to improve LLM inference performance on embedded devices. We leverage quantization techniques, asynchronous computation, and a fully-pipelined accelerator to enhance efficiency. Our empirical evaluations, conducted using the TinyLlama 1.1B model on a Xilinx ZCU102 platform, demonstrate a 14.3-15.8x speedup and a 6.1x energy efficiency improvement over running exclusively on the ZCU102 processing system (PS).
Generative AI and Retrieval-Augmented Generation (RAG) Systems for Enterprise
Authors: Anbang Xu, Tan Yu, Min Du, Pritam Gundecha, Yufan Guo, Xinliang Zhu, May Wang, Ping Li, Xinyun Chen
Venue: CIKM
Year: 2024

This workshop introduces generative AI applications for enterprise, with a focus on retrieval-augmented generation (RAG) systems. Generative AI is a field of artificial intelligence that can create new content and solve complex problems. RAG systems are a novel generative AI technique that combines information retrieval with text generation to generate rich and diverse responses. RAG systems can leverage enterprise data, which is often specific, structured, and dynamic, to provide customized solutions for various domains. However, enterprise data also poses challenges such as scalability, security, and data quality. This workshop convenes researchers and practitioners to explore RAG and other generative AI systems in real-world enterprise scenarios, fostering knowledge exchange, collaboration, and identification of future directions. Relevant to the CIKM community, the workshop intersects with core areas of data science and machine learning, offering potential benefits across various domains.
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
Authors: Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Wanyu Wang, Yuyang Ye, Xiangyu Zhao, Enhong Chen, Yefeng Zheng
Venue: CIKM
Year: 2024

Model editing aims to precisely alter the behaviors of large language models (LLMs) in relation to specific knowledge, while leaving unrelated knowledge intact. This approach has proven effective in addressing issues of hallucination and outdated information in LLMs. However, the potential of using model editing to modify knowledge in the medical field remains largely unexplored, even though resolving hallucination is a pressing need in this area. Our observations indicate that current methods face significant challenges in dealing with specialized and complex knowledge in medical domain. Therefore, we propose MedLaSA, a novel Layer-wise Scalable Adapter strategy for medical model editing. MedLaSA harnesses the strengths of both adding extra parameters and locate-then-edit methods for medical model editing. We utilize causal tracing to identify the association of knowledge in neurons across different layers, and generate a corresponding scale set from the association value for each piece of knowledge. Subsequently, we incorporate scalable adapters into the dense layers of LLMs. These adapters are assigned scaling values based on the corresponding specific knowledge, which allows for the adjustment of the adapter's weight and rank. The more similar the content, the more consistent the scale between them. This ensures precise editing of semantically identical knowledge while avoiding impact on unrelated knowledge. To evaluate the editing impact on the behaviours of LLMs, we propose two model editing studies for medical domain: (1) editing factual knowledge for medical specialization and (2) editing the explanatory ability for complex knowledge. We build two novel medical benchmarking datasets and introduce a series of challenging and comprehensive metrics. Extensive experiments on medical LLMs demonstrate the editing efficiency of MedLaSA, without affecting unrelated knowledge.
Tutorial on Landing Generative AI in Industrial Social and E-commerce Recsys
Authors: Da Xu, Danqing Zhang, Lingling Zheng, Bo Yang, Guangyu Yang, Shuyuan Xu, Cindy Liang
Venue: CIKM
Year: 2024

Over the past two years, GAI has evolved rapidly, influencing various fields including social and e-commerce Recsys. Despite exciting advances, landing these innovations in real-world Recsys remains challenging due to the sophistication of modern industrial product and systems. Our tutorial begins with a brief overview of building industrial Recsys and GAI fundamentals, followed by the ongoing efforts and opportunities to enhance personalized recommendations with foundation models. We then explore the integration of curation capabilities into Recsys, such as repurposing raw content, incorporating external knowledge, and generating personalized insights/explanations to foster transparency and trust. Next, the tutorial illustrates how AI agents can transform Recsys through interactive reasoning and action loops, shifting away from traditional passive feedback models. Finally, we shed insights on real-world solutions for human-AI alignment and responsible GAI practices. A critical component of the tutorial is detailing the AI, Infrastructure, LLMOps, and Product roadmap (including the evaluation and responsible AI practices) derived from the production solutions in LinkedIn, Amazon, TikTok, and Microsoft. While GAI in Recsys is still in its early stages, this tutorial provides valuable insights and practical solutions for the Recsys and GAI communities.
Predicting Scientific Impact Through Diffusion, Conformity, and Contribution Disentanglement
Authors: Zhikai Xue, Guoxiu He, Zhuoren Jiang, Sichen Gu, Yangyang Kang, Star Zhao, Wei Lu
Venue: CIKM
Year: 2024

The scientific impact of academic papers is influenced by intricate factors such as dynamic popularity and inherent contribution. Existing models typically rely on static graphs for citation count estimation, failing to differentiate among its sources. In contrast, we propose distinguishing effects derived from various factors and predicting citation increments as estimated potential impacts within the dynamic context. In this research, we introduce a novel model, DPPDCC, which Disentangles the Potential impacts of Papers into Diffusion, Conformity, and Contribution values. It encodes temporal and structural features within dynamic heterogeneous graphs derived from the citation networks and applies various auxiliary tasks for disentanglement. By emphasizing comparative and co-cited/citing information and aggregating snapshots evolutionarily, DPPDCC captures knowledge flow within the citation network. Afterwards, popularity is outlined by contrasting augmented graphs to extract the essence of citation diffusion and predicting citation accumulation bins for quantitative conformity modeling. Orthogonal constraints ensure distinct modeling of each perspective, preserving the contribution value. To gauge generalization across publication times and replicate the realistic dynamic context, we partition data based on specific time points and retain all samples without strict filtering. Extensive experiments on three datasets validate DPPDCC's superiority over baselines for papers published previously, freshly, and immediately, with further analyses confirming its robustness. Our codes and supplementary materials can be found at GitHub (https://github.com/ECNU-Text-Computing/DPPDCC).
CLR2G: Cross modal Contrastive Learning on Radiology Report Generation
Authors: Hongchen Xue, Qingzhi Ma, Guanfeng Liu, Jianfeng Qu, Yuanjun Liu, An Liu
Venue: CIKM
Year: 2024

The automatic generation of radiological imaging reports aims to produce accurate and coherent clinical descriptions based on X-ray images. This facilitates clinicians in completing the arduous task of report writing and advances clinical automation. The primary challenge in radiological imaging report generation lies in accurately capturing and describing abnormal regions in the images under data bias conditions, resulting in the generation of lengthy texts containing image details. Existing methods mostly rely on prior knowledge such as medical knowledge graphs, corpora, and image databases to assist models in generating more precise textual descriptions. However, these methods still struggle to identify rare anomalies in the images. To address this issue, we propose a two-stage training model, named CLR2G, based on cross-modal contrastive learning. This model delegates the task of capturing anomalies, particularly those challenging for the generative model trained with cross-entropy loss under data bias conditions, to a specialized abnormality capture component. Specifically, we employ a semantic matching loss function to train additional abnormal image and text encoders through cross-modal contrastive learning, facilitating the capture of 13 common anomalies. We utilize the anomalous image features, text features and their confidence probabilities as a posteriori knowledge to help the model generate accurate image reports. Experimental results demonstrate the state-of-the-art performance of our method on two widely used public datasets, IU-Xray and MIMIC-CXR.
Learning Counterfactual Explanations with Intervals for Time-series Classification
Authors: Akihiro Yamaguchi, Ken Ueno, Ryusei Shingaki, Hisashi Kashima
Venue: CIKM
Year: 2024

The need for explainability in time-series classification models has been increasing. Counterfactual explanations recommend how to modify the features of an original instance so that the prediction by a given classifier flips to the desired class. Since features in the time series are temporally dependent, interpretability is improved by considering intervals where the counterfactual can deviate from the original instance. In this study, we propose a model-agnostic counterfactual generation method (CEI) that jointly learns these intervals and the counterfactual. Furthermore, CEI can generate a counterfactual tailored to the directly specified limited number of intervals. We mathematically formulate CEI as a continuous optimization and demonstrate its effectiveness on the UCR datasets.
Buffalo: Biomedical Vision-Language Understanding with Cross-Modal Prototype and Federated Foundation Model Collaboration
Authors: Bingjie Yan, Qian Chen, Yiqiang Chen, Xinlong Jiang, Wuliang Huang, Bingyu Wang, Zhirui Wang, Chenlong Gao, Teng Zhang
Venue: CIKM
Year: 2024

Federated learning (FL) enables collaborative learning across multiple biomedical data silos with multimodal foundation models while preserving privacy. Due to the heterogeneity in data processing and collection methodologies across diverse medical institutions and the varying medical inspections patients undergo, modal heterogeneity exists in practical scenarios, where severe modal heterogeneity may even prevent model training. With privacy considerations, data transfer cannot be permitted, restricting knowledge exchange among different clients. To trickle these issues, we propose a cross-modal prototype imputation method for visual-language understanding (Buffalo) with only a slight increase in communication cost, which can improve the performance of fine-tuning general foundation models for downstream biomedical tasks. We conducted extensive experiments on medical report generation and biomedical visual question-answering tasks. The results demonstrate that Buffalo can fully utilize data from all clients to improve model generalization compared to other modal imputation methods in three modal heterogeneity scenarios, approaching or even surpassing the performance in the ideal scenario without missing modality.
Multi-Task Recommendation with Task Information Decoupling
Authors: Ruiran Yan, Rui Fan, Defu Lian
Venue: CIKM
Year: 2024

Multi-task learning (MTL) has become increasingly prevalent in e-commerce recommender systems. However, existing MTL methods, particularly those utilizing the Multi-gate Mixture-of-Experts (MMoE) architecture, face challenges due to their implicit routing mechanisms. These mechanisms can inadvertently lead to negative knowledge transfer, failing to resolve conflicts among tasks and resulting in gradient contradictions on shared parameters. Such issues undermine the generalization capability of MTL models across various tasks. To address these limitations, we introduce the Task Information Decoupling Model (TIDM), designed to alleviate negative transfer by decoupling task knowledge. TIDM incorporates two innovative modules following the expert layer: the Maximize Information Aggregation Module (MIA) and the Automatic Information Selection Module (AIS). The MIA module employs an auxiliary loss to filter out irrelevant task information and aggregates task-specific knowledge using a dissimilar self-attention network. Subsequently, the AIS module automatically selects the most pertinent task-specific information to facilitate task tower learning. Our experiments demonstrate that TIDM outperforms five contemporary MTL models across two datasets, showcasing its effectiveness in extracting task-specific information. This advancement is crucial for enhancing the performance of recommender systems in e-commerce and other complex domains.
Attacking Visually-aware Recommender Systems with Transferable and Imperceptible Adversarial Styles
Authors: Shiyi Yang, Chen Wang, Xiwei Xu, Liming Zhu, Lina Yao
Venue: CIKM
Year: 2024

The inclusion of the images opens up a security vulnerability of visually-aware recommender systems (VARSs). It can be exploited by unscrupulous parties to upload well-crafted adversarial images for certain malicious purposes (e.g., promoting their own products for profits). Some studies have focused on attacking VARSs to gain insights into their robustness, while they are still far from practical, i.e., the attacks often 1) lack diversity in perturbations, 2) are easily perceived and 3) have limited transferability, which may lead to overestimation of defenses in practice. To tackle the problems, we propose to perturb the style of the product, which is an unnoticeable but important property of visual recommendations. Specifically, we propose a novel Style perturbation-based Practical Attack Framework (SPAF). Unlike existing attacks that change pixels within l∞ -norm constraints, SPAF interferes with styles in latent feature space so that the attack becomes unbounded in the pixel space to reflect possible actual perturbations. SPAF formulates attack objectives as an optimization problem and adopts an adaptive adversarial style transfer network to solve it so that transferable and imperceptible attacks can be generated. Comprehensive experiments on real-world datasets demonstrate that SPAF significantly outperforms state-of-the-art attacks.
Breaking State-of-the-Art Poisoning Defenses to Federated Learning: An Optimization-Based Attack Framework
Authors: Yuxin Yang, Qiang Li, Chenfei Nie, Yuan Hong, Binghui Wang
Venue: CIKM
Year: 2024

Federated Learning (FL) is a novel client-server distributed learning framework that can protect data privacy. However, recent works show that FL is vulnerable to poisoning attacks. Many defenses with robust aggregators (AGRs) are proposed to mitigate the issue, but they are all broken by advanced attacks. Very recently, some renewed robust AGRs are designed, typically with novel clipping or/and filtering strategies, and they show promising defense performance against the advanced poisoning attacks. In this paper, we show that these novel robust AGRs are also vulnerable to carefully designed poisoning attacks. Specifically, we observe that breaking these robust AGRs reduces to bypassing the clipping or/and filtering of malicious clients, and propose an optimization-based attack framework to leverage this observation. Under the framework, we then design the customized attack against each robust AGR. Extensive experiments on multiple datasets and threat models verify our proposed optimizationbased attack can break the SOTA AGRs. We hence call for novel defenses against poisoning attacks to FL. Code is available at: https: //github.com/Yuxin104/BreakSTOAPoisoningDefenses.
Robust Heterophily Graph Learning via Uniformity Augmentation
Authors: Xusheng Yang, Zhengyu Chen, Yuexian Zou
Venue: CIKM
Year: 2024

Graphs serve as fundamental representations for a diverse array of complex systems, capturing intricate relationships and interactions between entities. In many real-world scenarios, graphs exhibit non-homophilous, or heterophilous, characteristics, challenging traditional graph analysis methods rooted in homophily assumptions. Recent heterophilous methods frequently struggle with noise in node attributes, which can degrade the quality of graph representations and affect downstream task performance. Common graph augmentations, while useful, often introduce bias and irrelevant noise. This paper proposes a novel method, Robust Heterophily Graph Learning via Uniformity Augmentation (RHGL-UA), which incorporates uniformity in the augmentation process through controlled random perturbations. This approach ensures a more uniform distribution of representations across different layers of the model. By adapting to data variations and learning more diverse information, RHGL-UA significantly improves performance on downstream tasks and stands out as the first practical robust heterophily graph method using representation augmentation with a theoretical guarantee. Extensive experiments demonstrate the merit of our proposed method.
BART-based Hierarchical Attentional Network for Sentence Ordering
Authors: Yiping Yang, Baiyun Cui, Yingming Li
Venue: CIKM
Year: 2024

In this paper, we introduce a novel BART-based Hierarchical Attentional Ordering Network (BHAONet), aiming to address the coherence modeling challenge within paragraphs, which stands as a cornerstone in comprehension, generation, and reasoning tasks. By leveraging the pre-trained BART model to encode the entire sequence, we can effectively exploit global semantic and contextual information. Moreover, the token-level and sentence-level hierarchical attentional layers are incorporated to encourage the model to focus on features at various levels of granularity. In addition, a transformer-guided pointer network is developed for decoding. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness and superiority of our proposed model.
CAG: A Consistency-Adaptive Text-Image Alignment Generation for Joint Multimodal Entity-Relation Extraction
Authors: Xinjie Yang, Xiaocheng Gong, Binghao Tang, Yang Lei, Yayue Deng, Huan Ouyang, Gang Zhao, Lei Luo, Yunling Feng, Bin Duan, Si Li, Yajing Xu
Venue: CIKM
Year: 2024

Joint Multimodal Entity-Relation Extraction (JMERE) aims to extract entity-relationship triples in texts from given image-text pairs. As a joint multimodal information extraction task, it has attracted increasing research interest. Previous works of JMERE typically utilize graph networks to align textual entities and visual objects and achieve promising performance. However, these methods do not pay attention to the inconsistency between text and image and the straight alignment could limit the performance of JMERE models. In this paper, we propose a Consistency-adaptive text-image Alignment Generation (CAG) framework for various text-image consistency scenarios. Specifically, we propose a Consistency Factor (CF) to measure the consistency between images and texts. We also design consistency-adaptive contrastive learning based on CF, which can reduce the impact of inconsistent visual and textual information. Additionally, we adopt JMERE-specifical instruction tuning for better entity-relationship triplet generation. Experimental results on the JMERE dataset demonstrate that our proposed CAG is effective and achieves state-of-the-art performance.