Reviewerly/exHarmony huggingface dataset:
Federated Competing Risk Analysis

Conducting survival analysis on distributed healthcare data is an important research problem, as privacy laws and emerging data-sharing regulations prohibit the sharing of sensitive patient data across multiple institutions. The distributed healthcare survival data often exhibit heterogeneity, non-uniform censoring and involve patients with multiple health conditions (competing risks), which can result in biased and unreliable risk predictions. To address these challenges, we propose employing federated learning (FL) for survival analysis with competing risks. In this work, we present two main contributions. Firstly, we propose a simple algorithm for estimating consistent federated pseudo values (FPV) for survival analysis with competing risks and censoring. Secondly, we introduce a novel and flexible FPV-based deep learning framework named Fedora, which jointly trains our proposed transformer-based model, TransPseudo, specific to the participating institutions (clients) within the Fedora framework without accessing clients' data, thus, preserving data privacy. We conducted extensive experiments on both real-world distributed healthcare datasets characterized by non-IID and non-uniform censoring properties, as well as synthetic data with various censoring settings. Our results demonstrate that our Fedora framework with the TransPseudo model performs better than the federated learning frameworks employing state-of-the-art survival models for competing risk analysis.
Multi-modal Mixture of Experts Represetation Learning for Sequential Recommendation

Within online platforms, it is critical to capture the dynamic user preference from the sequential interaction behaviors for making accurate recommendation over time. Recently, significant progress has been made in sequential recommendation with deep learning. However, existing neural sequential recommender often suffer from the data sparsity issue in real-world applications.
Quad-Tier Entity Fusion Contrastive Representation Learning for Knowledge Aware Recommendation System

Knowledge graph (KG) has recently emerged as a powerful source of auxiliary information in the realm of knowledge-aware recommendation (KGR) systems. However, due to the lack of supervision signals caused by the sparse nature of user-item interactions, existing supervised graph neural network (GNN) models suffer from performance degradation. Moreover, the over-smoothing issue further limits the number of GNN layers or hops required to propagate messages - these models ignore the non-local information concealed deep within the knowledge graph. We propose the Quad-Tier Entity Fusion Contrastive Representation Learning (QTEF-CRL) knowledge-aware framework to achieve learning of deep user preferences from four perspectives: the collaborative, semantic, preference, and structural view. Unlike existing methods, the proposed tri-local and single-global quad-tier architecture exploits the knowledge graph holistically to achieve effective self-supervised representation learning. The newly-introduced preference view constructed from the collaborative knowledge graph (CKG) comprises a preference graph and preference-guided GNN that are specifically designed to capture non-local information explicitly. Experiments conducted on three datasets highlight the efficacy of our proposed model.
Diversity-aware Deep Ranking Network for Recommendation

Diversity is a vital factor in recommendation systems.Improving the diversity in recommendations helps broaden users' horizons, bring good user experience and promote the enterprises' sales. In the past years, many efforts have been devoted to optimizing the diversity in the matching stage and the re-ranking stage of the recommendation system, but few in the ranking stage. The ranking stage is the intermediate stage of the recommendation system. Improving the diversity of the ranking stage can preserve the diversity of the matching stage, and provide a more diversified list for the re-ranking stage. Besides, the ranking models are able to achieve a better balance between accuracy and diversity. In this paper, we aim to improve the diversity in the ranking stage. To address the diversity challenges posed by the pointwise ranking model and biased user interaction history, we propose a Diversity-aware Deep Ranking Network by carefully designing two diversity-aware components that are diversity-aware listwise information fusion and balanced weighting loss. We conduct both offline and online experiments, and the results demonstrate that our proposed model effectively improves the recommendation diversity in the ranking stage while maintaining the accuracy. Moreover, the new model achieves 1.27%, 2.30% and 1.98% improvements in VBR, GMV and Coverage in Meituan, one of the world's largest E-commerce platforms.
A Model-Agnostic Method to Interpret Link Prediction Evaluation of Knowledge Graph Embeddings

In link prediction evaluation, an embedding model assigns plausibility scores to unseen triples in a knowledge graph using an input partial triple. Performance metrics like mean rank are useful to compare models side by side, but do not shed light on their behavior. Interpreting link prediction evaluation and comparing models based on such interpretation are appealing. Current interpretation methods have mainly focused on single predictions or other tasks different from link prediction. Since knowledge graph embedding methods are diverse, interpretation methods that are applicable only to certain machine learning approaches cannot be used. In this paper, we propose a model-agnostic method for interpreting link prediction evaluation as a whole. The interpretation consists of Horn rules mined from the knowledge graph containing the triples a model deems plausible. We combine precision and recall measurements of mined rules using Fβ score to quantify interpretation accuracy. To maximize interpretation accuracy when comparing models, we study two approximations to the hard problem of merging rules. Our quantitative study shows that interpretation accuracy serves to compare diverse models side by side, and that these comparisons are different from those using ranks. Our qualitative study shows that several models globally capture expected semantics, and that models make a common set of predictions despite of redundancy reduction.
ELTRA: An Embedding Method based on Learning-to-Rank to Preserve Asymmetric Information in Directed Graphs

Double-vector embedding methods capture the asymmetric information in directed graphs first, and then preserve them in the embedding space by providingtwo latent vectors, i.e., source and target, per node. Although these methods are known to besuperior to the single-vector ones (i.e., providing asingle latent vector per node), wepoint out their three drawbacks as inability to preserve asymmetry on NU-paths, inability to preserve global nodes similarity, and impairing in/out-degree distributions. To address these, we first proposeCRW, anovel similarity measure for graphs that considers contributions ofboth in-links and out-links in similarity computation,without ignoring their directions. Then, we proposeELTRA, aneffective double-vector embedding method to preserve asymmetric information in directed graphs. ELTRA computesasymmetry preserving proximity scores (AP-scores) by employing CRW in which the contribution of out-links and in-links in similarity computation isupgraded anddowngraded, respectively. Then, for every node u, ELTRA selects its top-tclosest nodes based on AP-scores andconforms theranks of their corresponding target vectors w.r.t u's source vector in the embedding space to theiroriginal ranks. Our extensive experimental results withseven real-world datasets andsixteen embedding methods show that (1) CRWsignificantly outperforms Katz and RWR in computing nodes similarity in graphs, (2) ELTRAoutperforms the existing state-of-the-art methods in graph reconstruction, link prediction, and node classification tasks.
Relevance-based Infilling for Natural Language Counterfactuals

Counterfactual explanations are a natural way for humans to gain understanding and trust in the outcomes of complex machine learning algorithms. In the context of natural language processing, generating counterfactuals is particularly challenging as it requires the generated text to be fluent, grammatically correct, and meaningful. In this study, we improve the current state of the art for the generation of such counterfactual explanations for text classifiers. Our approach, named RELITC (Relevance-based Infilling for Textual Counterfactuals), builds on the idea of masking a fraction of text tokens based on their importance in a given prediction task and employs a novel strategy, based on the entropy of their associated probability distributions, to determine the infilling order of these tokens. Our method uses less time than competing methods to generate counterfactuals that require less changes, are closer to the original text and preserve its content better, while being competitive in terms of fluency. We demonstrate the effectiveness of the method on four different datasets and show the quality of its outcomes in a comparison with human generated counterfactuals.
Graph Learning for Exploratory Query Suggestions in an Instant Search System

Search systems in online content platforms are typically biased toward a minority of highly consumed items, reflecting the most common user behavior of navigating toward content that is already familiar and popular. Query suggestions are a powerful tool to support query formulation and to encourage exploratory search and content discovery. However, classic approaches for query suggestions typically rely either on semantic similarity, which lacks diversity and does not reflect user searching behavior, or on a collaborative similarity measure mined from search logs, which suffers from data sparsity and is biased by highly popular queries. In this work, we argue that the task of query suggestion can be modelled as a link prediction task on a heterogeneous graph including queries and documents, enabling Graph Learning methods to effectively generate query suggestions encompassing both semantic and collaborative information. We perform an offline evaluation on an internal Spotify dataset of search logs and on two public datasets, showing that node2vec leads to an accurate and diversified set of results, especially on the large scale real-world data. We then describe the implementation in an instant search scenario and discuss a set of additional challenges tied to the specific production environment. Finally, we report the results of a large scale A/B test involving millions of users and prove that node2vec query suggestions lead to an increase in online metrics such as coverage (+1.42% shown search results pages with suggestions) and engagement (+1.21% clicks), with a specifically notable boost in the number of clicks on exploratory search queries (+9.37%).
Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries

We present ConceptEvo, a unified interpretation framework for deep neural networks (DNNs) that reveals the inception and evolution of learned concepts during training. Our work addresses a critical gap in DNN interpretation research, as existing methods primarily focus on post-training interpretation. ConceptEvo introduces two novel technical contributions: (1) an algorithm that generates a unified semantic space, enabling side-by-side comparison of different models during training, and (2) an algorithm that discovers and quantifies important concept evolutions for class predictions. Through a large-scale human evaluation and quantitative experiments, we demonstrate that ConceptEvo successfully identifies concept evolutions across different models, which are not only comprehensible to humans but also crucial for class predictions. ConceptEvo is applicable to both modern DNN architectures, such as ConvNeXt, and classic DNNs, such as VGGs and InceptionV3.
Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease Prediction

Positive-Unlabeled (PU) Learning is a challenge presented by binary classification problems where there is an abundance of unlabeled data along with a small number of positive data instances, which can be used to address chronic disease screening problem. State-of-the-art PU learning methods have resulted in the development of various risk estimators, yet they neglect the differences among distinct populations. To address this issue, we present a novel Positive-Unlabeled Learning Tree (PUtree) algorithm. PUtree is designed to take into account communities such as different age or income brackets, in tasks of chronic disease prediction. We propose a novel approach for binary decision-making, which hierarchically builds community-based PU models and then aggregates their deliverables. Our method can explicate each PU model on the tree for the optimized non-leaf PU node splitting. Furthermore, a mask-recovery data augmentation strategy enables sufficient training of the model in individual communities. Additionally, the proposed approach includes an adversarial PU risk estimator to capture hierarchical PU-relationships, and a model fusion network that integrates data from each tree path, resulting in robust binary classification results. We demonstrate the superior performance of PUtree as well as its variants on two benchmarks and a new diabetes-prediction dataset.
GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation

Despite the recent emergence of video captioning models, how to generate vivid, fine-grained video descriptions based on the background knowledge (i.e., long and informative commentary about the domain-specific scenes with appropriate reasoning) is still far from being solved, which however has great applications such as automatic sports narrative. Based on soccer game videos and synchronized commentary data, we present GOAL, a benchmark of over 8.9k soccer video clips, 22k sentences, and 42k knowledge triples for proposing a challenging new task setting as Knowledge-grounded Video Captioning (KGVC). We experimentally test existing state-of-the-art (SOTA) methods on this resource to demonstrate the future directions for improvement in this challenging task. We hope that our data resource (now available at https://github.com/THU-KEG/goal) can serve researchers and developers interested in knowledge-grounded cross-modal applications.
SAFE: Sequential Attentive Face Embedding with Contrastive Learning for Deepfake Video Detection

The emergence of hyper-realistic deepfake videos has raised significant concerns regarding their potential misuse. However, prior research on deepfake detection has primarily focused on image-based approaches, with little emphasis on video. With the advancement of generation techniques enabling intricate and dynamic manipulation of entire faces as well as specific facial components in a video sequence, capturing dynamic changes in both global and local facial features becomes crucial in detecting deepfake videos. This paper proposes a novel sequential attentive face embedding, SAFE, that can capture facial dynamics in a deepfake video. The proposed SAFE can effectively integrate global and local dynamics of facial features revealed in a video sequence using contrastive learning. Through a comprehensive comparison with the state-of-the-art methods on the DFDC (Deepfake Detection Challenge) dataset and the FaceForensic++ benchmark, we show that our model achieves the highest accuracy in detecting deepfake videos on both datasets.
KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Tasks

In recent years, countless research papers have addressed the topics of knowledge graph creation, extension, or completion in order to create knowledge graphs that are larger, more correct, or more diverse. This research is typically motivated by the argumentation that using such enhanced knowledge graphs to solve downstream tasks will improve performance. Nonetheless, this is hardly ever evaluated. Instead, the predominant evaluation metrics - aiming at correctness and completeness - are undoubtedly valuable but fail to capture the complete picture, i.e., how useful the created or enhanced knowledge graph actually is. Further, the accessibility of such a knowledge graph is rarely considered (e.g., whether it contains expressive labels, descriptions, and sufficient context information to link textual mentions to the entities of the knowledge graph). To better judge how well knowledge graphs perform on actual tasks, we present KGrEaT - a framework to estimate the quality of knowledge graphs via actual downstream tasks like classification, clustering, or recommendation. Instead of comparing different methods of processing knowledge graphs with respect to a single task, the purpose of KGrEaT is to compare various knowledge graphs as such by evaluating them on a fixed task setup. The framework takes a knowledge graph as input, automatically maps it to the datasets to be evaluated on, and computes performance metrics for the defined tasks. It is built in a modular way to be easily extendable with additional tasks and datasets.
Robust Graph Clustering via Meta Weighting for Noisy Graphs

How can we find meaningful clusters in a graph robustly against noise edges? Graph clustering (i.e., dividing nodes into groups of similar ones) is a fundamental problem in graph analysis with applications in various fields. Recent studies have demonstrated that graph neural network (GNN) based approaches yield promising results for graph clustering. However, we observe that their performance degenerates significantly on graphs with noise edges, which are prevalent in practice. In this work, we propose MetaGC for robust GNN-based graph clustering. MetaGC employs a decomposable clustering loss function, which can be rephrased as a sum of losses over node pairs. We add a learnable weight to each node pair, and MetaGC adaptively adjusts the weights of node pairs using meta-weighting so that the weights of meaningful node pairs increase and the weights of less-meaningful ones (e.g., noise edges) decrease. We show empirically that MetaGC learns weights as intended and consequently outperforms the state-of-the-art GNN-based competitors, even when they are equipped with separate denoising schemes, on five real-world graphs under varying levels of noise. Our code and datasets are available at https://github.com/HyeonsooJo/MetaGC.
Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations

Recent work on knowledge graph completion (KGC) focuses on acquiring embeddings of entities and relations in knowledge graphs. These embedding methods necessitate that all test entities be present during the training phase, resulting in a time-consuming retraining process for out-of-knowledge-graph (OOKG) entities. To tackle this predicament, current inductive methods employ graph neural networks (GNNs) to represent unseen entities by aggregating information of the known neighbors, and enhance the performance with additional information, such as attention mechanisms or logic rules. Nonetheless, Two key challenges continue to persist: (i) identifying inter-rule correlations to further facilitate the inference process, and (ii) capturing interactions among rule mining, rule inference, and embedding to enhance both rule and embedding learning.
KID34K: A Dataset for Online Identity Card Fraud Detection

Though digital financial systems have provided users with convenient and accessible services, such as supporting banking or payment services anywhere, it is necessary to have robust security to protect against identity misuse. Thus, online digital identity (ID) verification plays a crucial role in securing financial services on mobile platforms. One of the most widely employed techniques for digital ID verification is that mobile applications request users to take and upload a picture of their own ID cards. However, this approach has vulnerabilities where someone takes pictures of the ID cards belonging to another person displayed on a screen, or printed on paper to be verified as the ID card owner. To mitigate the risks associated with fraudulent ID card verification, we present a novel dataset for classifying cases where the ID card images that users upload to the verification system are genuine or digitally represented. Our dataset is replicas designed to resemble real ID cards, making it available while avoiding privacy issues. Through extensive experiments, we demonstrate that our dataset is effective for detecting digitally represented ID card images, not only in our replica dataset but also in the dataset consisting of real ID cards. Our dataset is available at https://github.com/DASH-Lab/idcard_fraud_detection.
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models

Causal Neural Network models have shown high levels of robustness to adversarial attacks as well as an increased capacity for generalisation tasks such as few-shot learning and rare-context classification compared to traditional Neural Networks. This robustness is argued to stem from the disentanglement of causal and confounder input signals. However, no quantitative study has yet measured the level of disentanglement achieved by these types of causal models or assessed how this relates to their adversarial robustness.
Investigating the Impact of Multimodality and External Knowledge in Aspect-level Complaint and Sentiment Analysis

Automated complaint analysis is vital for generating critical insights, which in turn enhance customer satisfaction, product quality, and overall business performance. Nevertheless, conventional methods frequently fail to capture the nuances of aspect-level complaints and inadequately utilize external knowledge, thus creating a gap in effective complaint detection and analysis. In response to this issue, we proactively explore the role of external knowledge and multimodality in this domain. This leads to the development of MGasD (Multimodal Generative framework for aspect-based complaint and sentiment Detection), a multimodal knowledge-infused unified framework. MGasD diverges from traditional methods by reframing the complaint detection problem as a multimodal text-to-text generation task. Significantly, our research includes the development of a novel aspect-level dataset. Annotated for both complaint and sentiment categories across diverse domains such as books, electronics, edibles, fashion, and miscellaneous, this dataset provides a comprehensive platform for the concurrent study of complaints and sentiment. This resource facilitates a more robust understanding of consumer feedback. Our proposed methodology establishes a benchmark performance in the novel aspect-based complaint and sentiment detection tasks based on extensive evaluation. We also demonstrate that our model consistently outperforms all other baselines and state-of-the-art models in both full and few-shot settings (The dataset and code are available at:https://github.com/appy1608/CIKM2023).
Generating Product Insights from Community Q&amp;A

In e-commerce sites, customer questions on the product details-page express the customers' information needs about the product. The answers to these questions often provide the necessary information. In this work, we present and address the novel task of generating product insights from community questions and answers (Q&A). These insights can be presented to customers to assist them in their shopping journey. Our method first generates concise, self-contained sentences based on the information in the Q&A. Then insights are selected based on the prominence of their associated questions. Empirical evaluation attests to the effectiveness of our approach in generating well-formed, objective, and helpful insights that are often not available in the product description or in summaries of customer reviews.