Reviewerly/exHarmony huggingface dataset:
When Does Translation Require Context? A Data-driven, Multilingual Exploration

Although proper handling of discourse significantly contributes to the quality of machine translation (MT), these improvements are not adequately measured in common translation quality metrics.Recent works in context-aware MT attempt to target a small set of discourse phenomena during evaluation, however not in a fully systematic way.In this paper, we develop the Multilingual Discourse-Aware (MUDA) benchmark, a series of taggers that identify and evaluate model performance on discourse phenomena in any given dataset.The choice of phenomena is inspired by a novel methodology to systematically identify translations requiring context.We confirm the difficulty of previously studied phenomena while uncovering others that were previously unaddressed.We find that common context-aware MT models make only marginal improvements over context-agnostic models, which suggests these models do not handle these ambiguities effectively.We release code and data for 14 language pairs to encourage the MT community to focus on accurately capturing discourse phenomena. 1* Equal contribution 1 Code available at https://github.com/CoderPat/MuDA .
Acute myeloid leukemias in Batna Epidemiological and cytological profile of acute myeloid leukemias:proposed 126 college cases at the Batna Anti-Cancer Center

Background Acute myeloid leukemias (AMLs) are heterogeneous clonal hematologic malignancies caused by the transformation of a hematopoietic stem cell or an already-committed progenitor to the myeloid lineage. The objective of this work is to describe the epidemiological and cytological characteristics of peripheral blood and marrow in 126 patients with AML at the Batna Anti-Cancer Center between 2016 and 2019. Methods The diagnostic criteria are based on the morphology of cells under the microscope, cytochemistry, clinical presentation, immunophenotyping of blasts by flow cytometry, cytogenetics, and the search for certain molecular markers. Results The age of the patients ranged from 15 to 88 years. Morphological examination of blood and marrow smears and myeloperoxidase reaction allowed AML to be classified according to the criteria of the French–American–British group: (1) 9.5% of AML0, (2) 17.5% of AML1, (3) 11.1% of LAM2, (4) 13.5% of LAM3, (5) 15.1% of LAM4, (6) 16.7% of LAM5, (7) 3.2% of LAM6, and (8) 0% of LAM7. There were 12.7% of cases difficult to classify, and 0.8%biphenotypic cases. Conclusions Discrepancies in terms of percentages are noted between the subtypes M0, M1, and M2. These results must be supplemented by studies of cytogenetics and molecular biology.
Grammar-based Decoding for Improved Compositional Generalization in Semantic Parsing

Sequence-to-sequence (seq2seq) models have achieved great success in semantic parsing tasks, but they tend to struggle on out-of-distribution (OOD) data.Despite recent progress, robust semantic parsing on large-scale tasks that combine challenges from both compositional generalization and natural language variations remains an unsolved issue.To encourage research in this area, this work introduces CUDON, a large-scale dialogue dataset in the Chinese language, specifically created to evaluate the compositional generalization of semantic parsing.The dataset contains about ten thousand multi-turn complex queries, and provides multiple splits with different degrees of train-test distribution divergence.We have investigated improving compositional generalization through grammar-based decoding on this dataset.With specially designed grammars that leverage program schema, we are able to significantly improve the accuracy of seq2seq semantic parsers on OOD splits: a LSTM-based parser using a Context-free Grammar (CFG) achieves over 25% higher accuracy than a standard seq2seq baseline; a parser using Tree-Substitution Grammar (TSG) improves parsing speed by five to seven times over the CFG parser with only a small accuracy loss.The grammar-based LSTM parsers also outperforms BART-and T5-based seq2seq parsers on the OOD splits, despite having less than one tenth of the parameters and no pretraining.We also validated our approach on the SMCalflow-CS dataset, specifically on the zero-shot learning task.
Deep Encoders with Auxiliary Parameters for Extreme Classification

The task of annotating a data point with labels most relevant to it from a large universe of labels is referred to as Extreme Classification (XC). State-of-the-art XC methods have applications in ranking, recommendation, and tagging and mostly employ a combination architecture comprised of a deep encoder and a high-capacity classifier. These two components are often trained in a modular fashion to conserve compute. This paper shows that in XC settings where data paucity and semantic gap issues abound, this can lead to suboptimal encoder training which negatively affects the performance of the overall architecture. The paper then proposes a lightweight alternative DEXA that augments encoder training with auxiliary parameters. Incorporating DEXA into existing XC architectures requires minimal modifications and the method can scale to datasets with 40 million labels and offer predictions that are up to 6% and 15% more accurate than embeddings offered by existing deep XC methods on benchmark and proprietary datasets, respectively. The paper also analyzes DEXA theoretically and shows that it offers provably superior encoder training than existing Siamese training strategies in certain realizable settings. Code for DEXA is available at https://github.com/Extreme-classification/dexa.
Graph Learning in Physical-informed Mesh-reduced Space for Real-world Dynamic Systems

Recent machine learning approaches have demonstrated their ability to extract information from data that could be translated into knowledge about the underlying dynamic systems. However, these learning-based models suffer from scalability issues when training on high-dimensional and high-resolution simulation data generated for real-world applications. In this work, we aim to tackle this challenge by deliberately prioritizing certain aspects of dynamic systems, while allocating relatively less attention and computational resources to others. Specifically, we concentrate on improving the predictive accuracy of crucial properties or regions that significantly impact these dynamic systems, while comparatively reducing emphasis on the remaining aspects. By employing graph learning schemes and custom-designed modules, we have developed a two-stage prediction model that incorporates prior knowledge of the systems. This approach enables us to place a heightened emphasis on the region of interest (ROI) during the learning process where the model operates in a reduced-dimensional mesh space, resulting in reduced computational costs while preserving crucial physical properties. To test and evaluate our method, we utilized two simulation datasets: lid-driven cavity and cylinder flow. The results show that even under reduced operational space, our method still achieves desirable performance on accuracy and generalizability of both prediction and physical consistency over region of interest.
UI/UX Analysis of Project Management Information System (PMIS) Website Using User-Centered Design Method

In Industry 4.0, a lot of data has been digitized and is no longer stored manually. In helping to record all the projects that were worked on initially stored in outstanding Project Management applications such as Trello. However, the Trello application has limitations and compatibility with business processes, including employees not writing down projects that have been done because they are not in accordance with procedures, causing many projects to go unrecorded. So because of these limitations a Project Management Information System (PMIS) website is needed which makes it easy to record all projects so that there is no more unrecorded data. Furthermore, there are problems in conducting an analysis that can make it easier for users and how to design a User Interface and User Experience on the PMIS website using the User Centered Design (UCD) method, which in the manufacturing process will continue to make changes according to needs. The results of the User Interface design that has been made will be tested for User Experience using the Single Ease Questionnaire (SEQ) method as a measure of the success of the User Interface that has been made. Based on the results of the User Experience test using the Single Ease Questionnaire method for 5 respondents, an average value of 6.3 was obtained, which means that it can be concluded that the User Interface that has been created has a level of convenience that is in accordance with the User Experience.
Fair Allocation Over Time, with Applications to Content Moderation

In today's digital world, interaction with online platforms is ubiquitous, and thus content moderation is important for protecting users from content that do not comply with pre-established community guidelines. Given the vast volume of content generated online daily, having an efficient content moderation system throughout every stage of planning is particularly important. We study the short-term planning problem of allocating human content reviewers to different harmful content categories. We use tools from fair division and study the application of competitive equilibrium and leximin allocation rules for addressing this problem. On top of the traditional Fisher market setup, we additionally incorporate novel aspects that are of practical importance. The first aspect is the forecasted workload of different content categories, which puts constraints on the allocation chosen by the planner. We show how a formulation that is inspired by the celebrated Eisenberg-Gale program allows us to find an allocation that not only satisfies the forecasted workload, but also fairly allocates the remaining working hours from the content reviewers among all content categories. A fair allocation of oversupply provides a guardrail in cases where the actual workload deviates from the predicted workload. The second practical consideration is time dependent allocation that is motivated by the fact that partners need scheduling guidance for the reviewers across days to achieve efficiency. To address the time component, we introduce new extensions of the various fair allocation approaches for the single-time period setting, and we show that many properties extend in essence, albeit with some modifications. Lastly, related to the time component, we additionally investigate how to satisfy markets' desire for smooth allocation (i.e, an allocation that does not vary much from time to time) so that the switch in staffing is minimized. We demonstrate the performance of our proposed approaches through real-world data obtained from Meta.
3rd Workshop on Online and Adaptive Recommender Systems (OARS)

Recommender systems (RecSys) play important roles in helping users navigate, discover, and consume large and highly dynamic information. Today, many RecSys solutions deployed in the real world rely on categorical user-profiles and/or pre-calculated recommendation actions that stay static during a user session. However, recent trends suggest that RecSys need to model user intent in real time and constantly adapt to meet user needs at the moment or change user behavior in situ. There are three primary drivers for this emerging need of online adaptation. First, in order to meet the increasing demand for a better personalized experience, the personalization dimensions and space will grow larger and larger. It would not be feasible to pre-compute recommended actions for all personalization scenarios beyond a certain scale. Second, in many settings the system does not have user prior history to leverage. Estimating user intent in real time is the only feasible way to personalize. As various consumer privacy laws tighten, it is foreseeable that many businesses will reduce their reliance on static user profiles. Therefore, it makes the modeling of user intent in real time an important research topic. Third, a user's intent often changes within a session and between sessions, and user behavior could shift significantly during dramatic events. A RecSys should adapt in real time to meet user needs and be robust against distribution shifts. The online and adaptive recommender systems (OARS) workshop offers a focused discussion of the study and application of OARS, and will bring together an interdisciplinary community of researchers and practitioners from both industry and academia. KDD, as the premier data science conference, is an ideal venue to gather leaders in the field to further research into OARS and promote its adoption. This workshop is complementary to several sessions of the main conference (e.g., recommendation, reinforcement learning, etc.) and brings them together using a practical and focused application.
DGI: An Easy and Efficient Framework for GNN Model Evaluation

While many systems have been developed to train graph neural networks (GNNs), efficient model evaluation, which computes node embedding according to a given model, remains to be addressed. For instance, using the widely adopted node-wise approach, model evaluation can account for over 90% of the time in the end-to-end training process due to neighbor explosion, which means that a node accesses its multi-hop neighbors. The layer-wise approach avoids neighbor explosion by conducting computation layer by layer in GNN models. However, layer-wise model evaluation takes considerable implementation efforts because users need to manually decompose the GNN model into layers, and different implementations are required for GNN models with different structures.
AI Explainability 360 Toolkit for Time-Series and Industrial Use Cases

With the growing adoption of AI, trust and explainability have become critical which has attracted a lot of research attention over the past decade and has led to the development of many popular AI explainability libraries such as AIX360, Alibi, OmniXAI, etc. Despite that, applying explainability techniques in practice often poses challenges such as lack of consistency between explainers, semantically incorrect explanations, or scalability. Furthermore, one of the key modalities that has been less explored, both from the algorithmic and practice point of view, is time-series. Several application domains involve time-series including Industry 4.0, asset monitoring, supply chain or finance to name a few.
RelKD 2023: International Workshop on Resource-Efficient Learning for Knowledge Discovery

Modern machine learning techniques, especially deep neural networks, have demonstrated excellent performance for various knowledge discovery and data mining applications. However, the development of many of these techniques still encounters resource constraint challenges in many scenarios, such as limited labeled data (data-level), small model size requirements in real-world computing platforms (model-level), and efficient mapping of the computations to heterogeneous target hardware (system-level). Addressing all of these metrics is critical for the effective and efficient usage of the developed models in a wide variety of real systems, such as large-scale social network analysis, large-scale recommendation systems, and real-time anomaly detection. Therefore, it is desirable to develop efficient learning techniques to tackle challenges of resource limitations from data, model/algorithm, or (and) system/hardware perspectives. The proposed international workshop on "Resource-Efficient Learning for Knowledge Discovery (RelKD 2023)" will provide a great venue for academic researchers and industrial practitioners to share challenges, solutions, and future opportunities of resource-efficient learning.
Training Large-scale Foundation Models on Emerging AI Chips

Foundation models such as ChatGPT and GPT-4 have garnered significant interest from both academia and industry due to their emergent capabilities, such as few-shot prompting, multi-step reasoning, instruction following, and model calibration. Such capabilities were previously only attainable with specially designed models, such as those using knowledge graphs, but can now be achieved on a much larger scale with foundation models. As the capabilities of foundation models have increased, so too have their sizes at a rate much faster than Moore's law. For example, the BERT large model was initially released as a 334M model in 2018, and by 2023, the largest GPT-4 models are estimated to range between 200-300B, representing an increase of three orders of magnitude in just five years. The training of foundation models requires massive computing power. For instance, training a BERT model on a single state-of-the-art GPU machine with multi-A100 chips can take several days, while training GPT-3 models on a large multi-instance GPU cluster can take several months to complete the estimated 3 X 1023 flops.
International Workshop on Federated Learning for Distributed Data Mining

The past decade has witnessed wide applications of machine learning to various domains for decision-making, including crime detection, urban planning, drug discovery, and health monitoring, which benefited from surging data resources. As data collection in real-world applications is often done in different locations, being able to mine and discover knowledge from distributed data sources is an essential requirement for building powerful predictive models. However, directly uploading all data sources to an untrustworthy centralized data server for learning will lead to risks of privacy leakage. Federated Learning (FL) emerges as a decentralized learning framework that aggregates knowledge from distributed data without centralizing them, hence mitigating privacy risks. By hosting this workshop, we aim to attract a broad spectrum of audiences, including researchers and practitioners from academia and industry interested in the latest advances in FL. As an effort to advance the fundamental development of FL in data mining, this workshop will encourage ideas exchange on the trustworthiness, scalability, robustness, and broad applications of FL.
QUERT: Continual Pre-training of Language Model for Query Understanding in Travel Domain Search

In light of the success of the pre-trained language models (PLMs), continual pre-training of generic PLMs has been the paradigm of domain adaption. In this paper, we propose QUERT, A Continual Pre-trained Language Model for QUERy Understanding in Travel Domain Search. QUERT is jointly trained on four tailored pre-training tasks to the characteristics of query in travel domain search: Geography-aware Mask Prediction, Geohash Code Prediction, User Click Behavior Learning, and Phrase and Token Order Prediction. Performance improvement of downstream tasks and ablation experiment demonstrate the effectiveness of our proposed pre-training tasks. To be specific, the average performance of downstream tasks increases by 2.02% and 30.93% in supervised and unsupervised settings, respectively. To check on the improvement of QUERT to online business, we deploy QUERT and perform A/B testing on Fliggy APP. The feedback results show that QUERT increases the Unique Click-Through Rate and Page Click-Through Rate by 0.89% and 1.03% when applying QUERT as the encoder. Resources are available at https://github.com/hsaest/QUERT
Discovering Dynamic Causal Space for DAG Structure Learning

Discovering causal structure from purely observational data (i.e., causal discovery), aiming to identify causal relationships among variables, is a fundamental task in machine learning.The recent invention of differentiable score-based DAG learners is a crucial enabler, which reframes the combinatorial optimization problem into a differentiable optimization with a DAG constraint over directed graph space. Despite their great success, these cutting-edge DAG learners incorporate DAG-ness independent score functions to evaluate the directed graph candidates, lacking in considering graph structure. As a result, measuring the data fitness alone regardless of DAG-ness inevitably leads to discovering suboptimal DAGs and model vulnerabilities.
Extreme Multi-Label Classification for Ad Targeting using Factorization Machines

Applications involving Extreme Multi-Label Classification (XMLC) face several practical challenges with respect to scale, model size and prediction latency, while maintaining satisfactory predictive accuracy. In this paper, we propose a Multi-Label Factorization Machine (MLFM) model, which addresses some of the challenges in XMLC problems. We use behavioral ad targeting as a case study to illustrate the benefits of the MLFM model. Predicting user qualifications for targeting segments plays a major role in both personalization and real-time bidding. Considering the large number of segments and the prediction time requirements of real-world production systems, building scalable models is often difficult and computationally burdensome. To cope with these challenges, we (1) reformulate the problem of assigning users to segments as a multi-label classification (XMLC) problem, and (2) leverage the benefits of the conventional FM model and generalize its capacity to joint prediction across a large number of targeting segments. We have shown that the MLFM model is both effective and computationally efficient compared to several baseline models on publicly available datasets in addition to the targeting use case.
PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce

Re-ranking draws increased attention on both academics and industries, which rearranges the ranking list by modeling the mutual influence among items to better meet users' demands. Many existing re-ranking methods directly take the initial ranking list as input, and generate the optimal permutation through a well-designed context-wise model, which brings the evaluation-before-reranking problem. Meanwhile, evaluating all candidate permutations brings unacceptable computational costs in practice. Thus, to better balance efficiency and effectiveness, online systems usually use a two-stage architecture which uses some heuristic methods such as beam-search to generate a suitable amount of candidate permutations firstly, which are then fed into the evaluation model to get the optimal permutation. However, existing methods in both stages can be improved through the following aspects. As for generation stage, heuristic methods only use point-wise prediction scores and lack an effective judgment. As for evaluation stage, most existing context-wise evaluation models only consider the item context and lack more fine-grained feature context modeling.
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs

In-Batch contrastive learning is a state-of-the-art self-supervised method that brings semantically-similar instances close while pushing dissimilar instances apart within a mini-batch. Its key to success is the negative sharing strategy, in which every instance serves as a negative for the others within the mini-batch. Recent studies aim to improve performance by sampling hard negatives within the current mini-batch, whose quality is bounded by the mini-batch itself. In this work, we propose to improve contrastive learning by sampling mini-batches from the input data. We present BatchSampler\footnoteThe code is available at BatchSampler to sample mini-batches of hard-to-distinguish (i.e., hard and true negatives to each other) instances. To make each mini-batch have fewer false negatives, we design the proximity graph of randomly-selected instances. To form the mini-batch, we leverage random walk with restart on the proximity graph to help sample hard-to-distinguish instances. BatchSampler is a simple and general technique that can be directly plugged into existing contrastive learning models in vision, language, and graphs. Extensive experiments on datasets of three modalities show that BatchSampler can consistently improve the performance of powerful contrastive models, as shown by significant improvements of SimCLR on ImageNet-100, SimCSE on STS (language), and GraphCL and MVGRL on graph datasets.