Reviewerly/exHarmony huggingface dataset:
PaperLM: A Pre-trained Model for Hierarchical Examination Paper Representation Learning

Representation learning of examination papers is significantly crucial for online education systems, as it benefits various applications such as estimating paper difficulty and examination paper retrieval. Previous works mainly explore the representation learning of individual questions in an examination paper, with limited attention given to the examination paper as a whole. In fact, the structure of examination papers is strongly correlated with paper properties such as paper difficulty, which existing paper representation methods fail to capture adequately. To this end, we propose a pre-trained model namely PaperLM to learn the representation of examination papers. Our model integrates both the text content and hierarchical structure of examination papers within a single framework by converting the path of the Examination Organization Tree (EOT) into embedding. Furthermore, we specially design three pre-training objectives for PaperLM, namely EOT Node Relationship Prediction (ENRP), Question Type Prediction (QTP) and Paper Contrastive Learning (PCL), aiming to capture features from text and structure effectively. We pre-train our model on a real-world examination paper dataset, and then evaluate the model with three down-stream tasks: paper difficulty estimation, examination paper retrieval, and paper clustering. The experimental results demonstrate the effectiveness of our method.
Search Result Diversification Using Query Aspects as Bottlenecks

We address some of the limitations of coverage-based search result diversification models, which often consist of separate components and rely on external systems for query aspects. To overcome these challenges, we introduce an end-to-end learning framework called DUB. Our approach preserves the intrinsic interpretability of coverage-based methods while enhancing diversification performance. Drawing inspiration from the information bottleneck method, we propose an aspect extractor that generates query aspect embeddings optimized as information bottlenecks for the task of diversified document re-ranking. Experimental results demonstrate that DUB outperforms state-of-the-art diversification models.
SAILOR: Structural Augmentation Based Tail Node Representation Learning

Graph neural networks (GNNs) have achieved state-of-the-art performance in representation learning for graphs recently. However, the effectiveness of GNNs, which capitalize on the key operation of message propagation, highly depends on the quality of the topology structure. Most of the graphs in real-world scenarios follow a long-tailed distribution on their node degrees, that is, a vast majority of the nodes in the graph are tail nodes with only a few connected edges. GNNs produce inferior node representations for tail nodes due to the lack of sufficient structural information. In the pursuit of promoting the performance of GNNs for tail nodes, we explore how the deficiency of structural information deteriorates the performance of tail nodes and propose a general structural augmentation based tailno de representation learning framework, dubbed as øurs, which can jointly learn to augment the graph structure and extract more informative representations for tail nodes. Extensive experiments on six public benchmark datasets demonstrate that øurs outperforms the state-of-the-art methods for tail node representation learning.
AutoMRM: A Model Retrieval Method Based on Multimodal Query and Meta-learning

With more and more Deep Neural Network (DNN) models are publicly available on model sharing platforms (e.g., HuggingFace), model reuse has become a promising way in practice to improve the efficiency of DNN model construction by avoiding the costs of model training. To that end, a pivotal step for model reuse is model retrieval, which facilitates discovering suitable models from a model hub that match the requirements of users. However, the existing model retrieval methods have inadequate performance and efficiency, since they focus on matching user requirements with the model names, and thus cannot work well for high-dimensional data such as images. In this paper, we propose a user-task-centric multimodal model retrieval method named AutoMRM. AutoMRM can retrieve DNN models suitable for the user's task according to both the dataset and description of the task. Moreover, AutoMRM utilizes meta-learning to retrieve models for previously unseen task queries. Specifically, given a task, AutoMRM extracts the latent meta-features from the dataset and description for training meta-learners offline and obtaining the representation of user task queries online. Experimental results demonstrate that AutoMRM outperforms existing model retrieval methods including the state-of-the-art method in both effectiveness and efficiency.
Content-Based Email Classification at Scale

Understanding the content of email messages can enable new features that highlight what matters to users, making email a more useful tool for people to manage their lives. We present work from a consumer email platform to build multilabel models to classify messages according to a mail-specific, content-based taxonomy that represents the topic, type, and objective of an email. While state-of-the-art Transformer-based language models can achieve impressive results for text classification, these models are too costly to deploy at the scale of email. Using a knowledge distillation framework, we first build a complex, accurate teacher model from limited human-labeled training data and then use a large amount of teacher-labeled data to train lightweight student models that are suitable for deployment. The student models retain up to 91% of the predictive performance of the teacher model while reducing inference cost by three orders of magnitude. Deployed to production in Yahoo Mail, these models classify billions of emails every day and power features that help people tackle their inboxes.
Teamwork in Cybersecurity: Evaluating the Cooperative Board Game [d0x3d!] as an Experimental Testbed

It is crucial to identify the knowledge, skills, and attitudes (KSAs) that contribute to success in cybersecurity teams. We introduce a board game, [d0x3d!], as an experimental testbed designed to create a controlled environment and set of manageable tasks aimed at exploring teamwork competencies that may be relevant to the cybersecurity workforce. [d0x3d!] requires players to work together and share information to retrieve stolen digital assets. The authors aim to improve the efficacy of cybersecurity team training by incorporating modern teamwork theory and measurement. This testbed provides a low-cost and user-friendly platform for training, evaluation, and research.
A System for Interviewing and Collecting Statements Based on Intent Classification and Named Entity Recognition Using Augmentation

In cases of child sexual abuse, interviewing and obtaining trustworthy statements from victims and witnesses is essential because their statements are the only evidence. It is crucial to ascertain objectively the credibility of the victim’s statements, which may vary based on the nature of the questions posed by the forensic interviewer. Therefore, interview skills that eliminate subjective opinions require a high level of training for forensic interviewers. To reduce high-risk subjective interviews, objectively analyzing statements is essential. Understanding the victim’s intent and named entity recognition (NER) in the statements is necessary to give the victim open-ended questions and memory recall. Therefore, the system provides an intent classification and NER method that follows the National Institute of Child Health and Human Development Investigative Interview Protocol, which outlines the collection of objective statements. Large language models such as BERT and KoBERT, along with data augmentation techniques, were proposed using a restricted training dataset of limited size to achieve effective intent classification and NER performance. Additionally, a system that can collect objective statements with the proposed model was developed and it was confirmed that it could assist statement analysts. The verification results showed that the model achieved average F1-scores of 95.5% and 97.8% for intent classification and NER, respectively, which improved the results of the limited data by 3.4% and 3.7%, respectively.
Leveraging Sequential Episode Mining for Session-Based News Recommendation

News recommender systems aim to help users find interesting and relevant news stories while mitigating information overload. Over the past few decades, various challenges have emerged in developing effective algorithms for real-world scenarios. These challenges include capturing evolving user preferences and addressing concept drift during reading sessions. Additionally, ensuring the freshness and timeliness of news content poses significant obstacles. To address these issues, we utilize an innovative sequential pattern mining approach known as Marbles to capture user behavior. Marbles leverages frequent episodes to generate a collection of association rules, where a frequent episode is a partially ordered pattern that occurs frequently in the input sequence. The recommendation process involves identifying relevant rules extracted from these patterns and weighting them. Subsequently, a heuristic procedure assesses candidate rules and generates a list of recommendations for users based on their most recent reading session. Notably, we conduct our evaluation in a streaming scenario, simulating real-world usage, where both our algorithm and baselines dynamically improve their models with each user click. Through our empirical evaluation in this streaming-based scenario, which closely models real-world usage, we demonstrate the applicability of the Marbles algorithm in session-based recommendation. Our proposed approach outperforms baseline algorithms on two real-world data sets, effectively addressing the challenges specific to the news domain.
Hierarchical Episodic Control

Deep reinforcement learning is one of the research hotspots in artificial intelligence and has been successfully applied in many research areas; however, the low training efficiency and high demand for samples are problems that limit the application. Inspired by the rapid learning mechanisms of the hippocampus, to address these problems, a hierarchical episodic control model extending episodic memory to the domain of hierarchical reinforcement learning is proposed in this paper. The model is theoretically justified and employs a hierarchical implicit memory planning approach for counterfactual trajectory value estimation. Starting from the final step and recursively moving back along the trajectory, a hidden plan is formed within the episodic memory. Experience is aggregated both along trajectories and across trajectories, and the model is updated using a multi-headed backpropagation similar to bootstrapped neural networks. This model extends the parameterized episodic memory framework to the realm of hierarchical reinforcement learning and is theoretically analyzed to demonstrate its convergence and effectiveness. Experiments conducted in four-room games, Mujoco, and UE4-based active tracking highlight that the hierarchical episodic control model effectively enhances training efficiency. It demonstrates notable improvements in both low-dimensional and high-dimensional environments, even in cases of sparse rewards. This model can enhance the training efficiency of reinforcement learning and is suitable for application scenarios that do not rely heavily on exploration, such as unmanned aerial vehicles, robot control, computer vision applications, and so on.
VFedAD: A Defense Method Based on the Information Mechanism Behind the Vertical Federated Data Poisoning Attack

In recent years, federated learning has achieved remarkable results in the medical and financial fields, but various attacks have always plagued federated learning. Data poisoning attack and defense research in horizontal federated learning are sufficient, yet vertical federated data poisoning attack and defense remains an open area due to two challenges: (1) Complex data distributions lead to immense attack possibilities, and (2) defense methods are insufficient for complex data distributions. We have discovered that from the perspective of information theory, the above challenges can be addressed elegantly and succinctly with a solution. We first reveal the information-theoretic mechanisms underlying vertical federated data poisoning attacks and then propose an unsupervised vertical federated data poisoning defense method (VFedAD) based on information theory. VFedAD learns semantic-rich client data representations through contrastive learning task and cross-client prediction task to identify anomalies. Experiments show VFedAD effectively detects vertical federated anomalies, protecting subsequent algorithms from vertical federated data poisoning attacks.
Improving Query Correction Using Pre-train Language Model In Search Engines

Query correction is a task that automatically detects and corrects errors in what users type into a search engine. Misspelled queries can lead to user dissatisfaction and churn. However, correcting a user query accurately is not an easy task. One major challenge is that a correction model must be capable of high-level language comprehension. Recently, pre-trained language models (PLMs) have been successfully applied to text correction tasks, but few works have been done on query correction. However, it is nontrivial to directly apply these PLMs to query correction in large-scale search systems due to the following challenging issues: 1) Expensive deployment. Deploying such a model requires expensive computations. 2) Lacking domain knowledge. A neural correction model needs massive training data to activate its power.
WOT-Class: Weakly Supervised Open-world Text Classification

State-of-the-art weakly supervised text classification methods, while significantly reduced the required human supervision, still requires the supervision to cover all the classes of interest. This is never easy to meet in practice when human explore new, large corpora without complete pictures. In this paper, we work on a novel yet important problem of weakly supervised open-world text classification, where supervision is only needed for a few examples from a few known classes and the machine should handle both known and unknown classes in test time. General open-world classification has been studied mostly using image classification; however, existing methods typically assume the availability of sufficient known-class supervision and strong unknown-class prior knowledge (e.g., the number and/or data distribution). We propose a novel framework øur that lifts those strong assumptions. Specifically, it follows an iterative process of (a) clustering text to new classes, (b) mining and ranking indicative words for each class, and (c) merging redundant classes by using the overlapped indicative words as a bridge. Extensive experiments on 7 popular text classification datasets demonstrate that øur outperforms strong baselines consistently with a large margin, attaining 23.33% greater average absolute macro-F1 over existing approaches across all datasets. Such competent accuracy illuminates the practical potential of further reducing human effort for text classification.
Network Embedding with Adaptive Multi-hop Contrast

\beginabstract Graph neural networks (GNNs) have shown strong performance in graph-based analysis tasks. Despite their remarkable success, the inherent homophilic message-passing mechanism (MP) makes GNNs challenging to generalize to heterophilic graphs. In addition, the MP explicitly exploits the connection relationships between local neighbor nodes making GNNs unable to maintain stable performance in the face of adversarial perturbation attacks. In this paper, we propose a new method to explore graph structure by removing explicit message-passing mechanisms and present a network embedding framework AMCNE with Adaptive Multi-hop Contrast loss (AMCLoss) to address these challenges. AMCNE only relies on a simple autoencoder to obtain node representations for classification and uses elaborate contrastive loss to drive nodes capturing complex structural information on heterophilic graphs. The comprehensive experiments show that AMCNE outperforms state-of-the-art baseline models on homophilic and heterophilic graphs and is more robust in the node classification task. \endabstract
Reconciling Training and Evaluation Objectives in Location Agnostic Surrogate Explainers

Transparency in AI models is crucial to designing, auditing, and deploying AI systems. However, 'black box' models are still used in practice for their predictive power despite their lack of transparency. This has led to a demand for post-hoc, model-agnostic surrogate explainers which provide explanations for decisions of any model by approximating its behaviour close to a query point with a surrogate model. However, it is often overlooked how the location of the query point in the decision surface of the black box model affects the faithfulness of the surrogate explainer. Here, we show that when using standard techniques, there is a decrease in agreement between the black box and the surrogate model for query points towards the edge of the test dataset and when moving away from the decision boundary. This originates from a mismatch between the data distributions used to train and evaluate surrogate explainers. We address this by leveraging knowledge about the test data distribution captured in the class labels of the black box model. By addressing this and encouraging users to take care in understanding the alignment of training and evaluation objectives, we empower them to construct more faithful surrogate explainers.
G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems

Recently, a new paradigm, meta learning, has been widely applied to Deep Learning Recommendation Models (DLRM) and significantly improves statistical performance, especially in cold-start scenarios. However, the existing systems are not tailored for meta learning based DLRM models and have critical problems regarding efficiency in distributed training in the GPU cluster. It is because the conventional deep learning pipeline is not optimized for two task-specific datasets and two update loops in meta learning. This paper provides a high-performance framework for large-scale training for Optimization-based Meta DLRM models over the G PU cluster, namely G -Meta. Firstly, G-Meta utilizes both data parallelism and model parallelism with careful orchestration regarding computation and communication efficiency, to enable high-speed distributed training. Secondly, it proposes a Meta-IO pipeline for efficient data ingestion to alleviate the I/O bottleneck. Various experimental results show that G-Meta achieves notable training speed without loss of statistical performance. Since early 2022, G-Meta has been deployed in Alipay's core advertising and recommender system, shrinking the continuous delivery of models by four times. It also obtains 6.48% improvement in Conversion Rate (CVR) and 1.06% increase in CPM (Cost Per Mille) in Alipay's homepage display advertising, with the benefit of larger training samples and tasks.
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation

Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of items, overlooking the collaborative relationships among them. Graph-based methods incorporate collaborative information by utilizing the user-item interaction graph. However, these methods sometimes face challenges in terms of time complexity and computational efficiency. To address these limitations, this paper presents AutoSeqRec, an incremental recommendation model specifically designed for sequential recommendation tasks. AutoSeqRec is based on autoencoders and consists of an encoder and three decoders within the autoencoder architecture. These components consider both the user-item interaction matrix and the rows and columns of the item transition matrix. The reconstruction of the user-item interaction matrix captures user long-term preferences through collaborative filtering. In addition, the rows and columns of the item transition matrix represent the item out-degree and in-degree hopping behavior, which allows for modeling the user's short-term interests. When making incremental recommendations, only the input matrices need to be updated, without the need to update parameters, which makes AutoSeqRec very efficient. Comprehensive evaluations demonstrate that AutoSeqRec outperforms existing methods in terms of accuracy, while showcasing its robustness and efficiency.
Good Intentions: Adaptive Parameter Management via Intent Signaling

Model parameter management is essential for distributed training of large machine learning (ML) tasks. Some ML tasks are hard to distribute because common approaches to parameter management can be highly inefficient. Advanced parameter management approaches---such as selective replication or dynamic parameter allocation---can improve efficiency, but they typically need to be integrated manually into each task's implementation and they require expensive upfront experimentation to tune correctly. In this work, we explore whether these two problems can be avoided. We first propose a novel intent signaling mechanism that integrates naturally into existing ML stacks and provides the parameter manager with crucial information about parameter accesses. We then describe AdaPM, a fully adaptive, zero-tuning parameter manager based on this mechanism. In contrast to prior parameter managers, our approach decouples how access information is provided (simple) from how and when it is exploited (hard). In our experimental evaluation, AdaPM matched or outperformed state-of-the-art parameter managers out of the box, suggesting that automatic parameter management is possible.
GraphERT-- Transformers-based Temporal Dynamic Graph Embedding

Dynamic temporal graphs evolve over time, adding and removing nodes and edges between time snapshots. The tasks performed on such graphs are diverse and include detecting temporal trends, finding graph-to-graph similarities, and graph visualization and clustering. For all these tasks, it is necessary to embed the entire graph in a low-dimensional space by using graph-level representations instead of the more common node-level representations. This embedding requires handling the appearance of new nodes over time as well as capturing temporal patterns of the entire graph. Most existing methods perform temporal node embeddings and focus on different methods of aggregating them for a graph-based representation. In this work, we propose an end-to-end architecture that captures both the node embeddings and their influence in a structural context during a specific time period of the graph. We present GraphERT (Graph Embedding Representation using Transformers), a novel approach to temporal graph-level embeddings. Our method pioneers the use of Transformers to seamlessly integrate graph structure learning with temporal analysis. By employing a masked language model on sequences of graph random walks, together with a novel temporal classification task, our model not only comprehends the intricate graph dynamics but also unravels the temporal significance of each node and path. This novel training paradigm empowers GraphERT to capture the essence of both the structural and temporal aspects of graphs, surpassing state-of-the-art approaches across multiple tasks on real-world datasets.