Reviewerly/exHarmony huggingface dataset:
Fair Labeled Clustering

The widespread use of machine learning algorithms in settings that directly affect human lives has instigated significant interest in designing variants of these algorithms that are provably fair. Recent work in this direction has produced numerous algorithms for the fundamental problem of clustering under many different notions of fairness. Perhaps the most common family of notions currently studied is group fairness, in which proportional group representation is ensured in every cluster. We extend this direction by considering the downstream application of clustering and how group fairness should be ensured for such a setting. Specifically, we consider a common setting in which a decision-maker runs a clustering algorithm, inspects the center of each cluster, and decides an appropriate outcome (label) for its corresponding cluster. In hiring for example, there could be two outcomes, positive (hire) or negative (reject), and each cluster would be assigned one of these two outcomes. To ensure group fairness in such a setting, we would desire proportional group representation in every label but not necessarily in every cluster as is done in group fair clustering. We provide algorithms for such problems and show that in contrast to their NP-hard counterparts in group fair clustering, they permit efficient solutions. We also consider a well-motivated alternative setting where the decision-maker is free to assign labels to the clusters regardless of the centers' positions in the metric space. We show that this setting exhibits interesting transitions from computationally hard to easy according to additional constraints on the problem. Moreover, when the constraint parameters take on natural values we show a randomized algorithm for this setting that always achieves an optimal clustering and satisfies the fairness constraints in expectation. Finally, we run experiments on real world datasets that validate the effectiveness of our algorithms.
Accelerated GNN Training with DGL and RAPIDS cuGraph in a Fraud Detection Workflow

Graph Neural Networks (GNNs) have gained the interest of industry with Relational Graph Convolutional Networks (R-GCNs) showing promise for fraud detection. Taking existing workflows that leverage graph features to train a gradient boosted decision tree (GBDT) and replacing the graph features with GNN produced embedding achieves an increase in accuracy. However, recent work has shown that the combination of graph attributes with GNN embeddings provides the biggest lift in accuracy.
Partial Label Learning with Semantic Label Representations

Partial-label learning (PLL) solves the problem where each training instance is assigned a candidate label set, among which only one is the ground-truth label. The core of PLL is to learn efficient feature representations to facilitate label disambiguation. However, existing PLL methods only learn plain representations by coarse supervision, which is incapable of capturing sufficiently distinguishable representations, especially when confronted with the knotty label ambiguity, i.e., certain candidate labels share similar visual patterns. In this paper, we propose a novel framework partial label learning with semantic label representations dubbed ParSE, which consists of two synergistic processes, including visual-semantic representation learning and powerful label disambiguation. In the former process, we propose a novel weighted calibration rank loss that has two implications. First, it implies a progressive calibration strategy that utilizes the disambiguated label confidence to weight the similarity between each image feature embedding and its corresponding semantic label representations of all candidates. Second, it also considers the ranking relationship between candidate and non-candidate ones. Based on learned visual-semantic representations, subsequent label disambiguation is desirably endowed with more powerful abilities. Experiments on benchmarks show that ParSE outperforms state-of-the-art counterparts.
Numerical Tuple Extraction from Tables with Pre-training

Tables are omnipresent on the web and in various vertical domains, storing massive amounts of valuable data. However, the great flexibility in the table layout hinders the machine from understanding this valuable data. In order to unlock and utilize knowledge from tables, extracting data as numerical tuples is the first and critical step. As a form of relational data, numerical tuples have direct and transparent relationships between their elements and are therefore easy for machines to use. Extracting numerical tuples requires a deep understanding of intricate correlations between cells. The correlations are presented implicitly in texts and visual appearances of tables, which can be roughly classified into Hierarchy and Juxtaposition. Although many studies have made considerable progress in data extraction from tables, most of them only consider hierarchical relationships but neglect the juxtapositions. Meanwhile, they only evaluate their methods on relatively small corpora. This paper proposes a new framework to extract numerical tuples from tables and evaluate it on a large test set. Specifically, we convert this task into a relation extraction problem between cells. To represent cells with their intricate correlations in tables, we propose a BERT-based pre-trained language model, TableLM, to encode tables with diverse layouts. To evaluate the framework, we collect a large finance dataset that includes 19,264 tables and 604K tuples. Extensive experiments on the dataset are conducted to demonstrate the superiority of our framework compared to a well-designed baseline.
CERAM: Coverage Expansion for Recommendations by Associating Discarded Models

Systems that utilize and manage predictive models have become increasingly significant in industry. In the services offered by Yahoo! JAPAN, once a predictive model is utilized for recommendations, it is thrown away. Such models could however be reused for expanding the coverage of other recommendations. Here, our goal is to construct recommendation systems that expand the coverage of recommendations by effectively utilizing models which would otherwise be discarded. Another goal is to deploy such a recommendation system on real services and make practical use of it. In this paper, we describe a recommendation system that achieves these two goals by overcoming the challenges facing its deployment on real services. Specifically, we developed an optimization method that alleviates the psychological barrier against using the recommendation system and clarified the performance of our method in making real recommendations. An offline test and a large-scale online test on making real recommendations showed that our method substantially expands the coverage of recommendations. As a highlight of the results, our method made recommendations to 76.9 times more users at the same level of recommendation performance as the currently used recommendation system by the service. Overall, the results show that our method has a huge impact on services and can be applied to real recommendations.
Frontiers of Graph Neural Networks with DIG

This tutorial is proposed based upon the recently released open-source library Dive into Graphs (DIG) along with hands-on code examples. DIG is a turnkey library that considers four frontiers in graph deep learning, including self-supervised learning of GNNs, 3D GNNs, explainability of GNNs, and graph generation. It provides data interfaces, common algorithms, and evaluation metrics for each direction. It has 255,000+ visitors, 11,000+ installations, and 1,100+ stars within a year and is becoming a robust and dominant ecosystem for graph neural network research. In this tutorial, we will review representative methodologies for these four directions and show hands-on code examples to demonstrate how to effortlessly implement benchmarks using DIG. This tutorial targets a broad audience working on or interested in various research themes. To encourage audience participation, we will promote our tutorial in advance on social media, reading groups, and library contribution community. We anticipate this tutorial would attract more researchers to these interesting and promising topics, leading to a more active community, eventually generating both scientific values and real-world impacts.
Anomaly Detection for Spatiotemporal Data in Action

Anomaly detection is becoming important in modern society as everything goes digital. Consumers are spending a lot more time online, and various digital sensors are placed into physical/chemical equipment for health monitoring. Such monitoring data is growing at an exponential rate, which enables automated anomaly detection in various high-impact domains. In this tutorial, we dive deep into several state-of-art methods for finding anomalies in spatiotemporal data (or more generally, correlated multivariate data), on a few specific use cases such as telecom network performance and user behavior monitoring. We identify suitable methods for specific data representation (i.e. spatial, temporal and categorical dimensionality) for specific use case, and present methods to convert raw data into the formats the existing methods require. Such survey and hands-on exercise is necessary as each use case has its own special data representation and requirements in live applications. Certain methods fit better than others for a specific data format under a specific scenario. With such investigation outlined in this tutorial, we hope attendees will be able to more efficiently choose the most appropriate method for their use case. To reach as wide an audience as possible, we investigate anomaly detection application in multiple domains: telecom network performance, user behavior monitoring, financial transactions, and industrial internet of things. The types of datasets range from univariate, multivariate time series of single or multiple entities, to transactional tabular data with timestamps or sequenced indexes. The types of anomalies in these datasets include contextual anomalies for one entity or collective anomalies aggregated among multiple entities in time series, and suspicious record or user in transactional tabular data. During this three-hour hands-on tutorial, we examine the suitability and performance of different methods for the 4 introduced use cases. The available choices of anomaly detection modeling framework ranges from sequential or time series models, to static graph neural network models and dynamic graph neural network models that learns coupled spatial, structural and temporal information. Among these models, anomaly detection is formulated as a forecast problem for some use cases in which anomaly severity is derived based on the observed value deviation from the predicted value. In other use cases, anomaly detection is solved as a classification problem (either as node, edge classification in graph, row-based classification in tabular data or sequential classification in sequence data). The tutorial combines an introduction of fundamental anomaly detection techniques with hands-on exercises. For the hands-on exercises, we focus on the time series based HTM method introduced in and graph methods introduced in. Participants learn to identify suitable methods, apply data transformation techniques to convert raw data into the formats the different methods assume, and study these methods on the aforementioned real-world or synthetic data sets.
Finding Meta Winning Ticket to Train Your MAML

The lottery ticket hypothesis (LTH) states that a randomly initialized dense network contains sub-networks that can be trained in isolation to the performance of the dense network. In this paper, to achieve rapid learning with less computational cost, we explore LTH in the context of meta learning. First, we experimentally show that there are sparse sub-networks, known as meta winning tickets, which can be meta-trained to few-shot classification accuracy to the original backbone. The application of LTH in meta learning enables the adaptation of meta-trained networks on various IoT devices with fewer computation. However, the status quo to identify winning tickets requires iterative training and pruning, which is particularly expensive for finding meta winning tickets. To this end, then we investigate the inter- and intra-layer patterns among different meta winning tickets, and propose a scheme for early detection of a meta winning ticket. The proposed scheme enables efficient training in resource-limited devices. Besides, it also designs a lightweight solution to search the meta winning ticket. Evaluations on standard few-shot classification benchmarks show that we can find competitive meta winning tickets with 20% weights of the original backbone, while incurring only 8%-14% (Conv-4) and 19%-29% (ResNet-12) computation overhead (measured by FLOPs) of the standard winning ticket finding scheme.
Reducing the Friction for Building Recommender Systems with Merlin

Recommender Systems (RecSys) are the engine of the modern internet and the catalyst for human decisions. The goal of a recommender system is to generate relevant recommendations for users from a collection of items or services that might interest them. Building a recommendation system is challenging because it requires multiple stages (item retrieval, filtering, ranking, ordering) to work together seamlessly and efficiently during training and inference. The biggest challenges faced by new practitioners are the lack of understanding around what RecSys look like in the real world and the difficulty in transitioning from the simple Matrix Factorization (MF) to more complex deep learning architectures with multiple input features, neural components and prediction heads.
Generating Examples from CLI Usage: Can Transformers Help?

Continuous evolution in modern software often causes documentation, tutorials, and examples to be out of sync with changing interfaces and frameworks. Relying on outdated documentation and examples can lead programs to fail or be less efficient or even less secure. In response, programmers need to regularly turn to other resources on the web, such as StackOverflow for examples to guide them in writing software. We recognize that this inconvenient, error-prone, and expensive process can be improved by using machine learning applied to software usage data. In this paper, we present a practical system, which uses machine learning on large-scale telemetry data and documentation corpora, generating appropriate and complex examples that can be used to improve documentation. We discuss both feature-based and transformer-based machine learning approaches and demonstrate that our system achieves 100% coverage for the used functionalities in the product, providing up-to-date examples upon every release and reduces the numbers of PRs submitted by software owners writing and editing documentation by >68%. We also share valuable lessons learnt during the 3 years that our production quality system has been deployed for Azure Cloud Command Line Interface (Azure CLI)
Scientist-Encyclopedist T.I. Rainov — the Invited Chief Bibliographer of the V.I. Lenin State Library

On the basis of archival materials introduced into scientific circulation for the first time, the author considers the activity of the scientist-encyclopedist Timofey Ivanovich Rainov (1890, Kalchevo village, Akkerman district, Bessarabian province of the Russian Empire — 1958, Moscow), worked as visiting specialist-bibliographer of highest qualification in the V.I. Lenin State Library of the USSR in 1938—1941 and in 1948—1950. The paper shows that before Rainov was invited to the V.I. Lenin State Library, he passed a long way, gaining skills and competencies in the field of library science and bibliography. Unable to work freely in Soviet Russia as a philosopher and specialist in the psychology of creativity, T.I. Rainov, like other Russian intellectuals, was seeking ways to realize his research potential. In 1923, the scientist moved to Moscow, where the Library of the Socialist (Communist) Academy (later the Fundamental Library of Social Sciences of the USSR Academy of Sciences, FBON) became his main place of work. Possessing encyclopedic knowledge and immense erudition, he took the place of leading bibliographer. T.I. Rainov changed several professions, but, remaining invariably a researcher of scientific ideas, he repeatedly returned to the field of book studies and bibliography and completed his service in this field at the FBON. The article examines the main life circumstances that led him to the V.I. Lenin State Library, and it was found that when concluding an employment contract with him, T.I. Rainov was obliged to provide consultations “on all branches of knowledge” to both employees and readers of the V.I. Lenin State Library of the USSR. The appendix presents the experience of compiling an article about the scientific bibliographer T.I. Rainov for the forthcoming reference book “Employees of the Russian State Library: Eminent Scientists and Workers in Culture”.
Retrospective Bibliography in the Largest Siberian Library: History, Projects, Prospects

The article shows the significance of retrospective indexes in the system of Siberian bibliography in the historical context. The authors highlight the history of formation and development of retrospective bibliography in the State Public Scientific Technological Library of the Siberian Branch of the Russian Academy of Sciences, starting from the 1960s. The article places the main emphasis on the implementation of major bibliographic projects of the State Public Scientific Technological Library of the Siberian Branch of the Russian Academy of Sciences: creation of a set of retrospective indexes and databases on bibliography of bibliographies, the history of book, librarianship and bibliography; preparation of the Union catalogue of Siberian and Far Eastern books as part of the national retrospective bibliography of Russia; creation of biobibliographic databases. The authors draw conclusions about the importance of various types of bibliographic products (electronic and printed) and outline the tasks on bibliographic activity of the State Scientific Technological Library of the Siberian Branch of the Russian Academy of Sciences in the field of retrospective bibliography.
Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks

Felicitas is a distributed cross-device Federated Learning (FL) framework to solve the industrial difficulties of FL in large-scale device deployment scenarios. In Felicitas, FL-Clients are deployed on mobile or embedded devices, while FL-Server is deployed on the cloud platform. We also summarize the challenges of FL deployment in industrial cross-device scenarios (massively parallel, stateless clients, non-use of client identifiers, highly unreliable, unsteady and complex deployment), and provide reliable solutions. We provide the source code and documents at https://www.mindspore.cn/. In addition, the Felicitas has been deployed on mobile phones in real world. At the end of the paper, we demonstrate the validity of the framework through experiments.
A Logic Aware Neural Generation Method for Explainable Data-to-text

The most notable neural data-to-text approaches generate natural language from structural data relying on the surface form of the structural content, which ignores the underlying logical correlation between the input data and the target text. Moreover, identifying such logical associations and explaining them in natural language is desirable but not yet studied. In this paper, we introduce a practical data-to-text method for the logic-critical scenario, specifically for anti-money laundering applications. It involves detecting risks from input data and explaining any abnormal behaviors in natural language. The proposed method is a Logic Aware Neural Generation framework (LANG), which is a preliminary attempt to explore the integration of logic modeling and text generation. Concretely, we first convert expert rules to a logic graph. Then, the model utilizes meta path based encoder to exploit the expert knowledge. Besides, a retriever module with the encoded logic knowledge is used to bridge the gap between numeric input and target text. Finally, a rule-constrained loss is leveraged to improve the generation probability of tokens in rule recalled statements to ensure accuracy. We conduct extensive experiments on anti-money laundering data. Results show that the proposed method significantly outperforms baselines in both objective measures with relative 35% improvements in F1 score and subjective measures with 30% improvement in human preference.
Arabic Named Entity Recognition in Arabic Tweets Using BERT-based Models

With the large amount of unstructured data being broadcasted every day, building powerful methods enabling information retrieval and extraction becomes necessary. Unfortunately, named entity recognition is a difficult classification task to classify data into predefined labels, which is further challenged by the Arabic language's particular characteristics and complex nature. This work trains six BERT-based models (Bidirectional Encoder Representations from Transformers) and uses a BiLSTM-CRF architecture for the NER task on dialectal Arabic. Our fine-tuning approach yields new state-of-the-art results on publicly available dialectal Arabic social media datasets.
Act-Cbir: Detecting and Retrieving Actions in Still Images

Most of the research on still image action recogniotion (SIAR) assumes that access to additional annotations is provided at both training and testing time, which may discourage practical applications. In this paper, we present the Act-CBIR, an action-based image retrieval system that only relies on still images, does not exploit additional information in inference time, since it uses an action detector. We report on experiments using public available SIAR datasets showing that our configuration based on action detection yields results of 83.91\% and 85.03\% in AP@10, surpassing state-of-the-art works on CBIR. Our results are up to 15\% better than using the full image and 35\% better than using person detectors in terms of AP@10. Our introduced binarization method for encoding is up to 25\% faster in our experiments with minimum precision decrease, indicating that it would be ideal for large-scale datasets.
Space-and-Speaker-Aware Acoustic Modeling with Effective Data Augmentation for Recognition of Multi-Array Conversational Speech

We propose a space-and-speaker-aware (SSA) approach to acoustic modeling (AM), denoted as SSA-AM, to improve system performances of automatic speech recognition (ASR) in distant multi-array conversational scenarios. In contrast to conventional AM which only uses spectral features from a target speaker as inputs, the inputs to SSA-AM consists of speech features from both the target and interfering speakers, which contain discriminative information from different speakers, including spatial information embedded in interaural phase differences (IPDs) between individual interfering speakers and the target speaker. In the proposed SSA-AM framework, we explore four acoustic model architectures consisting of different combinations of four neural networks, namely deep residual network, factorized time delay neural network, self-attention and residual bidirectional long short-term memory neural network. Various data augmentation techniques are adopted to expand the training data to include different options of beamformed speech obtained from multi-channel speech enhancement. Evaluated on the recent CHiME-6 Challenge Track 1, our proposed SSA-AM framework achieves consistent recognition performance improvements when compared with the official baseline acoustic models. Furthermore, SSA-AM outperforms acoustic models without explicitly using the space and speaker information. Finally, our data augmentation schemes are shown to be especially effective for compact model designs.