OpenRaiser/Intern-Atlas huggingface dataset:
pyPANTERA: A Python PAckage for Natural language obfuscaTion Enforcing pRivacy & Anonymization
Authors: Francesco Luigi De Faveri, Guglielmo Faggioli, Nicola Ferro
Venue: CIKM
Year: 2024

Privacy is critical when dealing with user-generated text, as common in Natural Language Processing (NLP) and Information Retrieval (IR) tasks. Documents, queries, posts, and reviews might pose a risk of inadvertently disclosing sensitive information. Such exposure of private data is a significant threat to user privacy, as it may reveal information that users prefer to keep confidential. The leading framework to protect user privacy when handling textual information is represented by the ε-Differential Privacy (DP). However, the research community lacks a unified framework for comparing different DP mechanisms. This study introduces pyPANTERA, an open-source Python package developed for text obfuscation. The package is designed to incorporate State-of-the-Art DP mechanisms within a unified framework for obfuscating data. pyPANTERA is not only designed as a modular and extensible library for enriching DP techniques, thereby enabling the integration of new DP mechanisms in future research, but also to allow reproducible comparison of the current State-of-the-Art mechanisms. Through extensive evaluation, we demonstrate the effectiveness of pyPANTERA, making it an essential resource for privacy researchers and practitioners. The source code of the library and for the experiments is available at: https://github.com/Kekkodf/pypantera **REMOVE 2nd URL**://github.com/Kekkodf/pypantera.
Federated Heterogeneous Contrastive Distillation for Molecular Representation Learning
Authors: Jinjia Feng, Zhen Wang, Zhewei Wei, Yaliang Li, Bolin Ding, Hongteng Xu
Venue: CIKM
Year: 2024

With the increasing application of deep learning to solve scientific problems in biochemistry, molecular federated learning has become popular due to its ability to offer distributed privacy-preserving solutions. However, most existing molecular federated learning methods rely on joint training with public datasets, which are difficult to obtain in practice. These methods also fail to leverage multi-modal molecular representations effectively. To address the above issues, we propose a novel framework, Federated Heterogeneous Contrastive Distillation (FedHCD), which enables to jointly train global models from clients with heterogeneous data modalities, learning tasks, and molecular models. To aggregate data representations of different modalities in a data-free manner, we design a global multi-modal contrastive strategy to align the representation of clients without public dataset. Utilizing intrinsic characteristics of molecular data in different modalities, we tackle the exacerbation of local model drift and data Non-IIDness caused by multi-modal clients. We introduce a multi-view contrastive knowledge transfer to extract features from atoms, substructures, and molecules, solving the issue of information distillation failure due to dimensional biases in different data modalities. Our evaluations on eight real-world molecular datasets and ablation experiments show that FedHCD outperforms other state-of-the-art FL methods, irrespective of whether or not they use public datasets.
CHDAER: Consistent Hashing-based Data Allocation for Efficient Recommendation in Edge Environment
Authors: Zhikang Feng, Chao Yan, Rong Jiang, Xiaolong Xu, Xuyun Zhang, Xiaokang Zhou, Wanchun Dou, Lianyong Qi
Venue: CIKM
Year: 2024

With the rapid development of the mobile internet, there is an increasing demand for quick access to effective data. Consequently, more research is focusing on data processing and optimization of recommendation systems in edge computing environments. However, in traditional edge computing environments, recommendation systems typically depend on frequent data query interactions among all edge servers to obtain results, which increases time delays. This issue is further exacerbated by the need to process large amounts of data within edge storage systems. To address this challenge, we propose an efficient recommendation method based on data allocation. Specifically, during the data allocation process, we first extract similar features of users, ensuring that the characteristics of some user data align as closely as possible with the overall user feature distribution. Then, using an improved consistent hashing algorithm, we achieve a uniform data layout, allowing the recommendation system to efficiently and accurately provide recommendations by querying data from the nearest edge server to the user. Finally, extensive experiments on real datasets show that our method significantly reduces time delays and improves the accuracy of recommendation results.
A Generative Benchmark Creation Framework for Detecting Common Data Table Versions
Authors: Daniel C. Fox, Aamod Khatiwada, Roee Shraga
Venue: CIKM
Year: 2024

Multiple versions of the same dataset can exist in a data repository (e.g., data warehouses, data lakes, etc.), mainly because of the interactive and collaborative nature of data science. Data creators generally update existing datasets and upload them as new datasets to data repositories without proper documentation. Identifying such versions helps in data management, data governance, and making better decisions using data. However, there is a dearth of benchmarks to develop and evaluate data versioning techniques, which requires a lot of human effort. Thus, this work introduces a novel framework to generate benchmarks for data versioning using Generative AI (specifically Large Language Models). The proposed framework offers properties that existing benchmarks do not have, including proper documentation, version lineage, and complex transformations generated by an LLM. We also share VerLLM-v1, the first version of the benchmark that features these properties, and compare it to existing benchmarks.
GraphWeaver: Billion-Scale Cybersecurity Incident Correlation
Authors: Scott Freitas, Amir Gharib
Venue: CIKM
Year: 2024

In the dynamic landscape of large enterprise cybersecurity, accurately and efficiently correlating billions of security alerts into comprehensive incidents is a substantial challenge. Traditional correlation techniques often struggle with maintenance, scaling, and adapting to emerging threats and novel sources of telemetry. We introduce GraphWeaver, an industry-scale framework that shifts the traditional incident correlation process to a data-optimized, geo-distributed graph based approach. GraphWeaver introduces a suite of innovations tailored to handle the complexities of correlating billions of shared evidence alerts across hundreds of thousands of enterprises. Key among these innovations are a geo-distributed database and PySpark analytics engine for large-scale data processing, a minimum spanning tree algorithm to optimize correlation storage, integration of security domain knowledge and threat intelligence, and a human-in-the-loop feedback system to continuously refine key correlation processes and parameters. GraphWeaver is integrated into the Microsoft Defender XDR product and deployed worldwide, handling billions of correlations with a 99% accuracy rate, as confirmed by customer feedback and extensive investigations by security experts. This integration has not only maintained high correlation accuracy but reduces traditional correlation storage requirements by 7.4x. We provide an in-depth overview of the key design and operational features of GraphWeaver, setting a precedent as the first cybersecurity company to openly discuss these critical capabilities at this level of depth.
Retrogressive Document Manipulation of US Federal Environmental Websites
Authors: Lesley Frew, Michael L. Nelson, Michele C. Weigle
Venue: CIKM
Year: 2024

Changes made to webpages can affect their retrievability. Often this is done with the intention of increasing the page's search engine ranking to improve overall access to information on the page. The Environmental Data and Governance Initiative (EDGI) created a dataset that describes changes on US federal environmental webpages between 2016 and 2020. EDGI noted that many environmental terms were deleted from the pages, but without user data, claims that page retrievability and public information access were lowered are only anecdotal. The Open Resource for Click Analysis in Search (ORCAS) dataset was created during the same time frame, from 2017 to 2020, and enables high quality user intent analysis without compromising on user privacy protection. We present an analysis of the intersection of the EDGI dataset and the ORCAS dataset, matching changes on federal environmental webpages with their associated queries. We use web archives and a change-text indexing system to link changes in term frequency on the pages with the queries. We find that the pages contain fewer query terms in 2020 than in 2016, lowering the pages' retrievability. The analysis provides substantive support of EDGI's claim that federal environmental pages were made less accessible between 2016 and 2020.
DCAI: The 4th International Workshop on Data-Centric AI
Authors: Yanjie Fu, Kunpeng Liu, Dongjie Wang
Venue: CIKM
Year: 2024

Machine learning traditionally emphasizes developing models for given datasets, but real-world data is often messy, making model improvement insufficient for enhancing performance. Data-Centric AI (DCAI) is an emerging field that systematically improves datasets, leading to significant practical ML advancements. While experienced data scientists have manually refined datasets through trial-and-error and intuition, DCAI approaches data enhancement as a systematic engineering discipline. DCAI represents a shift from focusing on models to the underlying data used for training and evaluation. Despite the dominance of common model architectures and predictable scaling rules, building and using datasets remain labor-intensive and costly, lacking infrastructure and best practices. The DCAI movement aims to develop efficient, high-productivity open data engineering tools for modern ML systems. This workshop seeks to foster an interdisciplinary DCAI community to address practical data challenges, including data collection, generation, labeling, preprocessing, augmentation, quality evaluation, debt, and governance. By defining and shaping the DCAI movement, this workshop aims to influence the future of AI and ML, inviting interested parties to contribute through paper submissions.
SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model
Authors: Lingyue Fu, Hao Guan, Kounianhua Du, Jianghao Lin, Wei Xia, Weinan Zhang, Ruiming Tang, Yasheng Wang, Yong Yu
Venue: CIKM
Year: 2024

Knowledge Tracing (KT) aims to determine whether students will respond correctly to the next question, which is a crucial task in intelligent tutoring systems (ITS). In educational KT scenarios, transductive ID-based methods often face severe data sparsity and cold start problems, where interactions between individual students and questions are sparse, and new questions and concepts consistently arrive in the database. In addition, existing KT models only implicitly consider the correlation between concepts and questions, lacking direct modeling of the more complex relationships in the heterogeneous graph of concepts and questions. In this paper, we propose a Structure-aware INductive Knowledge Tracing model with large language model (dubbed SINKT), which, for the first time, introduces large language models (LLMs) and realizes inductive knowledge tracing. Firstly, SINKT utilizes LLMs to introduce structural relationships between concepts and constructs a hetero- geneous graph for concepts and questions. Secondly, by encoding concepts and questions with LLMs, SINKT incorporates semantic information to aid prediction. Finally, SINKT predicts the student's response to the target question by interacting with the student's knowledge state and the question representation. Experiments on four real-world datasets demonstrate that SINKT achieves state-of-the-art performance among 12 existing transductive KT models. Additionally, we explore the performance of SINKT on the inductive KT task and provide insights into various modules.
Tabular Data-centric AI: Challenges, Techniques and Future Perspectives
Authors: Yanjie Fu, Dongjie Wang, Hui Xiong, Kunpeng Liu
Venue: CIKM
Year: 2024

Tabular data are the most widely used data formats in almost every application domain, such as, biology, ecology, and material science. The purpose of tabular data-centric AI is to use AI to augment the predictive power of tabular data to get better AI. Tabular data-centric AI is essential because it can reconstruct distance measures, reshape discriminative patterns, and improve data AI readiness (structural, predictive, interaction, and expression levels), which is significant in industries and real-world deployments. Therefore, our tutorial is designed to capture the interest of professionals with expertise in artificial intelligence, machine learning, and data mining, as well as researchers engaged in specific application areas and interdisciplinary studies. Examples of such applications include quality control, predictive maintenance, supply chain optimization, process efficiency improvements, biomarker identification, material performance screening. In this tutorial, we will explore the emerging field of Tabular Data-Centric AI. Our discussion will provide a comprehensive overview of this domain: (1) We will demonstrate the different settings within this research domain based on distinct application scenarios. (2) We will identify and explain the significant challenges encountered in tabular data-centric AI. (3) We will highlight existing methods and benchmarks. (4) We will discuss future potential directions for this domain and examine its interconnections with other research areas. To enhance the learning experience, this tutorial will include a hands-on section designed to teach participants the fundamental aspects of developing, evaluating and visualizing techniques in tabular data-centric AI. After this tutorial, attendees will have a deep understanding of tabular data-centric AI research, including its key challenges, seminal techniques, and insights into integrating tabular data-centric AI into their own research.
ACDM: An Effective and Scalable Active Clustering with Pairwise Constraint
Authors: Xun Fu, Wen-Bo Xie, Bin Chen, Tao Deng, Tian Zou, Xin Wang
Venue: CIKM
Year: 2024

Clustering is fundamentally a subjective task: a single dataset can be validly clustered in various ways, and without further information, clustering systems cannot determine the appropriate clustering to perform. This underscores the importance of integrating constraints into clustering, enabling users to convey their preferences to the system. Active constraint-based clustering approaches prioritize the identification of the most valuable constraints to inquire about, striving to achieve effective clustering with the minimal number of constraints needed. We propose an A ctive C lustering with D iffusion M odel (ACDM). ACDM applies the nearest-neighbor technique to construct a diffusion graph, and utilizes an online framework to refine the clustering result iteratively. In each iteration, (a) nodes with high uncertainty and representativeness are selected in batch mode, (b) then a novel neighborhood-set-based query is used for categorizing the selected nodes, using pairwise constraints, and (c) the categorized nodes are used as source nodes in the diffusion model for cluster refinement. We experimentally demonstrate that ACDM outperforms state-of-the-art methods in terms of clustering quality and scalability.
HierRec: Scenario-Aware Hierarchical Modeling for Multi-scenario Recommendations
Authors: Jingtong Gao, Bo Chen, Menghui Zhu, Xiangyu Zhao, Xiaopeng Li, Yuhao Wang, Yichao Wang, Huifeng Guo, Ruiming Tang
Venue: CIKM
Year: 2024

Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations contributes to strengthening information sharing and improving overall performance. However, existing multi-scenario models only consider coarse-grained explicit scenario modeling that depends on pre-defined scenario identification from manual prior rules, which is biased and sub-optimal. To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively, and conducts explicit and implicit scenario modeling jointly. In particular, HierRec designs a basic scenario-oriented module based on the dynamic weight to capture scenario-specific representations. Then the hierarchical explicit and implicit scenario-aware modules are proposed to model hybrid-grained scenario information, where the multi-head implicit modeling design contributes to perceiving distinctive patterns from different perspectives. Our experiments on two public datasets and real-world industrial applications on a mainstream online advertising platform demonstrate that HierRec outperforms existing models significantly. The implementation code is available for reproducibility.
Compositional and Hierarchical Semantic Learning Model for Hospital Readmission Prediction
Authors: Weiting Gao, Xiangyu Gao, Yi Chen
Venue: CIKM
Year: 2024

Clinical notes provide a wealth of patient information that is valuable for predicting clinical outcomes. In particular, predicting hospital 30-day readmission is important to improve healthcare outcomes and reduce cost. Previous works on outcome prediction using clinical notes overlook complex semantic compositions and syntactic structure when learning the note level embedding, which may fail to capture the note semantics and make accurate predictions. To address these limitations, we propose a Compositional and Hierarchical Semantic Learning Model (CHSLM). It formulates the semantic learning of clinical notes into three hierarchies: word, composition, and note, and aggregates the semantics in a bottom-up manner. To aggregate the semantics from words to compositions, we construct heterogeneous medical-composition graphs to represent word interactions within and between medical compositions and use Graph Neural Networks to learn the composition embedding. To aggregate the semantics from composition- to note-level, we incorporate a mutual BiAffine transformation process. The experimental results on 30-day readmission prediction using two types of clinical notes demonstrate the effectiveness of our method over the state-of-the-art clinical prediction models.
Demystifying Financial Texts Using Natural Language Processing
Authors: Sohom Ghosh
Venue: CIKM
Year: 2024

Human beings aspire for a better life. Financial well-being enables this. However, lack of financial literacy, ever-growing wealth inequality, and persuading illicit information floating in social media inhibit one's progress towards a good fortune. In this paper, we discuss four pillars where Natural Language Processing can help improve financial literacy, reduce wealth disparity, ensure a sustainable future, and economic prosperity. These pillars are: Inclusive investing, Improved investing, Impactful (green) investing, and Informed investing. Additionally, we focus to specifically cater to the Indian market (Indic investing) and present several resources to enhance comprehensibility of financial texts. Inclusive investing deals with enhancing the readability and reachability of financial texts. Improved investing addresses the need to simplify investors' journey by providing them with hypernyms and relations between entities. Impactful investing is associated with focusing on sustainable pathways. Improved investing is about eradicating finance related misinformation from social media, like evaluating trustworthiness of posts by executives, detecting in-claim and exaggerated numerals, etc. In most cases, we are able to demonstrate the efficacies of our approaches by benchmarking them with existing state-of-the-art methods.
Tiled Bit Networks: Sub-Bit Neural Network Compression Through Reuse of Learnable Binary Vectors
Authors: Matt Gorbett, Hossein Shirazi, Indrakshi Ray
Venue: CIKM
Year: 2024

Binary Neural Networks (BNNs) enable efficient deep learning by saving on storage and computational costs. However, as the size of neural networks continues to grow, meeting computational require- ments remains a challenge. In this work, we propose a new form of quantization to tile neural network layers with sequences of bits to achieve sub-bit compression of binary-weighted neural networks. The method learns binary vectors (i.e. tiles) to populate each layer of a model via aggregation and reshaping operations. During in- ference, the method reuses a single tile per layer to represent the full tensor. We employ the approach to both fully-connected and convolutional layers, which make up the breadth of space in most neural architectures. Empirically, the approach achieves near full- precision performance on a diverse range of architectures (CNNs, Transformers, MLPs) and tasks (classification, segmentation, and time series forecasting) with up to an 8x reduction in size compared to binary-weighted models. We provide two implementations for Tiled Bit Networks: 1) we deploy the model to a microcontroller to assess its feasibility in resource-constrained environments, and 2) a GPU-compatible inference kernel to facilitate the reuse of a single tile per layer in memory.
Beyond Language Bias: Overcoming Multimodal Shortcut and Distribution Biases for Robust Visual Question Answering
Authors: Jingliang Gu, Zhixin Li
Venue: CIKM
Year: 2024

Recent studies have found that many VQA models are influenced by biases, preventing them from effectively using multimodal information for reasoning. Consequently, these methods, which perform well on standard VQA datasets, exhibit underwhelming performance on the bias-sensitive VQA-CP dataset. Although numerous studies in the past have focused on mitigating biases in VQA models, most have only considered language bias. In this paper, we address the issue of bias in VQA task by targeting the various sources of bias. Specifically, to counteract shortcut biases, we integrate a bias detector capable of capturing both vision and language biases, and we reinforce its ability to capture biases using a generative adversarial network and knowledge distillation. To combat distribution bias, we use a cosine classifier to obtain a cosine feature branch from the base model, training it with an adaptive angular margin loss based on answer frequency and difficulty, along with a supervised contrastive loss to enhance the model's classification ability in the feature space. In the prediction stage, we fuse the cosine features with the prediction of the base model to obtain the final prediction of our model. Finally, extensive experiments demonstrate that our approach SD-VQA achieves state-of-the-art performance on the VQA-CPv2 dataset without using any data balancing, and achieves competitive results on the VQAv2 dataset.