Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces

Abstract: Identifying meaningful concepts in large data sets can provide valuable insights into engineering design problems. Concept identification aims at identifying non-overlapping groups of design instances that are similar in a joint space of all features, but which are also similar when considering only subsets of features. These subsets usually comprise features that characterize a design with respect to one specific context, for example, constructive design parameters, performance values, or operation modes. It is desirable to evaluate the quality of design concepts by considering several of these feature subsets in isolation. In particular, meaningful concepts should not only identify dense, well separated groups of data instances, but also provide non-overlapping groups of data that persist when considering pre-defined feature subsets separately. In this work, we propose to view concept identification as a special form of clustering algorithm with a broad range of potential applications beyond engineering design. To illustrate the differences between concept identification and classical clustering algorithms, we apply a recently proposed concept identification algorithm to two synthetic data sets and show the differences in identified solutions. In addition, we introduce the mutual information measure as a metric to evaluate whether solutions return consistent clusters across relevant subsets. To support the novel understanding of concept identification, we consider a simulated data set from a decision-making problem in the energy management domain and show that the identified clusters are more interpretable with respect to relevant feature subsets than clusters found by common clustering algorithms and are thus more suitable to support a decision maker.

Felix Lanfermann
Sebastian Schmitt
Patricia Wollstadt

2022 IEEE International Conference on Data Mining Workshops (ICDMW)


Data-centric AI: Perspectives and Challenges

Abstract: The role of data in building AI systems has recently been significantly magnified by the emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model advancements to ensuring data quality and reliability. Although our community has continuously invested efforts into enhancing data in different aspects, they are often isolated initiatives on specific tasks. To facilitate the collective initiative in our community and push forward DCAI, we draw a big picture and bring together three general missions: training data development, inference data development, and data maintenance. We provide a top-level discussion on representative DCAI tasks and share perspectives. Finally, we list open challenges. More resources are summarized at https://github.com/daochenzha/data-centric-AI

Daochen Zha
Zaid Pervaiz Bhat
Kwei-Herng Lai
Fan Yang
Xia Hu


Synthetic data generation method for data-free knowledge distillation in regression neural networks

Abstract: Knowledge distillation is the technique of compressing a larger neural network, known as the teacher, into a smaller neural network, known as the student, while still trying to maintain the performance of the larger neural network as much as possible. Existing methods of knowledge distillation are mostly applicable for classification tasks. Many of them also require access to the data used to train the teacher model. To address the problem of knowledge distillation for regression tasks under the absence of original training data, previous work has proposed a data-free knowledge distillation method where synthetic data are generated using a generator model trained adversarially against the student model. These synthetic data and their labels predicted by the teacher model are then used to train the student model. In this study, we investigate the behavior of various synthetic data generation methods and propose a new synthetic data generation strategy that directly optimizes for a large but bounded difference between the student and teacher model. Our results on benchmark and case study experiments demonstrate that the proposed strategy allows the student model to learn better and emulate the performance of the teacher model more closely.


A review of clustering models in educational data science towards fairness-aware learning

Abstract: Ensuring fairness is essential for every education system. Machine learning is increasingly supporting the education system and educational data science (EDS) domain, from decision support to educational activities and learning analytics. However, the machine learning-based decisions can be biased because the algorithms may generate the results based on students' protected attributes such as race or gender. Clustering is an important machine learning technique to explore student data in order to support the decision-maker, as well as support educational activities, such as group assignments. Therefore, ensuring high-quality clustering models along with satisfying fairness constraints are important requirements. This chapter comprehensively surveys clustering models and their fairness in EDS. We especially focus on investigating the fair clustering models applied in educational activities. These models are believed to be practical tools for analyzing students' data and ensuring fairness in EDS.


Significant Digits: Using Large-Scale Blockchain Data to Predict Fraudulent Addresses

Abstract: Blockchain systems and cryptocurrencies have exploded in popularity over the past decade, and with this growing user base, the number of cryptocurrency scams has also surged. Given the graphical structure of blockchain networks and the abundance of data generated on these networks, we use graph mining techniques to extract essential information on transactions and apply Benford's Law to extract distributional information on address transactions. We then apply a gradient-boosting tree model to predict fraudulent addresses. Our results show that our method can detect scams with reasonable accuracy and that the features generated based on Benford's Law are the most significant features.


Algorithms for Massive Data -- Lecture Notes

Abstract: These are the lecture notes for the course CM0622 - Algorithms for Massive Data, Ca' Foscari University of Venice. The goal of this course is to introduce algorithmic techniques for dealing with massive data: data so large that it does not fit in the computer's memory. There are two main solutions to deal with massive data: (lossless) compressed data structures and (lossy) data sketches. These notes cover both topics: compressed suffix arrays, probabilistic filters, sketching under various metrics, Locality Sensitive Hashing, nearest neighbour search, algorithms on streams.


DMOps: Data Management Operation and Recipes

Abstract: Data-centric AI has shed light on the significance of data within the machine learning (ML) pipeline. Recognizing its significance, academia, industry, and government departments have suggested various NLP data research initiatives. While the ability to utilize existing data is essential, the ability to build a dataset has become more critical than ever, especially in the industry. In consideration of this trend, we propose a "Data Management Operations and Recipes" to guide the industry in optimizing the building of datasets for NLP products. This paper presents the concept of DMOps which is derived from real-world experiences with NLP data management and aims to streamline data operations by offering a baseline.


Towards Assessing Data Bias in Clinical Trials

Abstract: Algorithms and technologies are essential tools that pervade all aspects of our daily lives. In the last decades, health care research benefited from new computer-based recruiting methods, the use of federated architectures for data storage, the introduction of innovative analyses of datasets, and so on. Nevertheless, health care datasets can still be affected by data bias. Due to data bias, they provide a distorted view of reality, leading to wrong analysis results and, consequently, decisions. For example, in a clinical trial that studied the risk of cardiovascular diseases, predictions were wrong due to the lack of data on ethnic minorities. It is, therefore, of paramount importance for researchers to acknowledge data bias that may be present in the datasets they use, eventually adopt techniques to mitigate them and control if and how analyses results are impacted. This paper proposes a method to address bias in datasets that: (i) defines the types of data bias that may be present in the dataset, (ii) characterizes and quantifies data bias with adequate metrics, (iii) provides guidelines to identify, measure, and mitigate data bias for different data sources. The method we propose is applicable both for prospective and retrospective clinical trials. We evaluate our proposal both through theoretical considerations and through interviews with researchers in the health care environment.

Chiara Criscuolo
Tommaso Dolci
Mattia Salnitri


Seeing through Things: Exploring the Design Space of Privacy-Aware Data-Enabled Objects

Abstract: Increasing amounts of sensor-augmented research objects have been used in design research. We call these objects Data-Enabled Objects, which can be integrated into daily activities capturing data about people's detailed whereabouts, behaviours and routines. These objects provide data perspectives on everyday life for contextual design research. However, data-enabled objects are still computational devices with limited privacy awareness and nuanced data sharing. To better design data-enabled objects, we explore privacy design spaces by inviting 18 teams of undergraduate design students to re-design the same type of sensor-enabled home research camera. We developed the Connected Peekaboo Toolkit (CPT) to support the design teams in designing, building, and directly deploying their prototypes in real home studies. We conducted Thematic Analysis to analyse their outcomes which led us to interpret that privacy is not just an obstacle but can be a driver by unfolding an exploration of possible design spaces for data-enabled objects.

Yu-Ting Cheng
Mathias Funk
Rung-Huei Liang
Lin-Lin Chen

ACM Trans. Comput.-Hum. Interact. 1, 1, Article 1 (January 2022)


GenSyn: A Multi-stage Framework for Generating Synthetic Microdata using Macro Data Sources

Abstract: Individual-level data (microdata) that characterizes a population, is essential for studying many real-world problems. However, acquiring such data is not straightforward due to cost and privacy constraints, and access is often limited to aggregated data (macro data) sources. In this study, we examine synthetic data generation as a tool to extrapolate difficult-to-obtain high-resolution data by combining information from multiple easier-to-obtain lower-resolution data sources. In particular, we introduce a framework that uses a combination of univariate and multivariate frequency tables from a given target geographical location in combination with frequency tables from other auxiliary locations to generate synthetic microdata for individuals in the target location. Our method combines the estimation of a dependency graph and conditional probabilities from the target location with the use of a Gaussian copula to leverage the available information from the auxiliary locations. We perform extensive testing on two real-world datasets and demonstrate that our approach outperforms prior approaches in preserving the overall dependency structure of the data while also satisfying the constraints defined on the different variables.

Angeela Acharya
Siddhartha Sikdar
Sanmay Das
Huzefa Rangwala


VISEM-Tracking, a human spermatozoa tracking dataset

Abstract: A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-assisted sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet sperm preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa.

Vajira Thambawita
Steven A. Hicks
Andrea M. Storås
Thu Nguyen
Jorunn M. Andersen
Oliwia Witczak
Trine B. Haugen
Hugo L. Hammer
Pål Halvorsen
Michael A. Riegler


CLIP: Train Faster with Less Data

Abstract: Deep learning models require an enormous amount of data for training. However, recently there is a shift in machine learning from model-centric to data-centric approaches. In data-centric approaches, the focus is to refine and improve the quality of the data to improve the learning performance of the models rather than redesigning model architectures. In this paper, we propose CLIP i.e., Curriculum Learning with Iterative data Pruning. CLIP combines two data-centric approaches i.e., curriculum learning and dataset pruning to improve the model learning accuracy and convergence speed. The proposed scheme applies loss-aware dataset pruning to iteratively remove the least significant samples and progressively reduces the size of the effective dataset in the curriculum learning training. Extensive experiments performed on crowd density estimation models validate the notion behind combining the two approaches by reducing the convergence time and improving generalization. To our knowledge, the idea of data pruning as an embedded process in curriculum learning is novel.

Muhammad Asif Khan
Ridha Hamila
Hamid Menouar

International Conference on Big Data and Smart Computing


Noisy Label Classification using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning

Abstract: As the size of the dataset used in deep learning tasks increases, the noisy label problem, which is a task of making deep learning robust to the incorrectly labeled data, has become an important task. In this paper, we propose a method of learning noisy label data using the label noise selection with test-time augmentation (TTA) cross-entropy and classifier learning with the NoiseMix method. In the label noise selection, we propose TTA cross-entropy by measuring the cross-entropy to predict the test-time augmented training data. In the classifier learning, we propose the NoiseMix method based on MixUp and BalancedMix methods by mixing the samples from the noisy and the clean label data. In experiments on the ISIC-18 public skin lesion diagnosis dataset, the proposed TTA cross-entropy outperformed the conventional cross-entropy and the TTA uncertainty in detecting label noise data in the label noise selection process. Moreover, the proposed NoiseMix not only outperformed the state-of-the-art methods in the classification performance but also showed the most robustness to the label noise in the classifier learning.

Hansang Lee
Haeil Lee
Helen Hong
Junmo Kim


Generating Realistic Synthetic Relational Data through Graph Variational Autoencoders

Abstract: Synthetic data generation has recently gained widespread attention as a more reliable alternative to traditional data anonymization. The involved methods are originally developed for image synthesis. Hence, their application to the typically tabular and relational datasets from healthcare, finance and other industries is non-trivial. While substantial research has been devoted to the generation of realistic tabular datasets, the study of synthetic relational databases is still in its infancy. In this paper, we combine the variational autoencoder framework with graph neural networks to generate realistic synthetic relational databases. We then apply the obtained method to two publicly available databases in computational experiments. The results indicate that real databases' structures are accurately preserved in the resulting synthetic datasets, even for large datasets with advanced data types.

Ciro Antonio Mami
Andrea Coser
Eric Medvet
Alexander T. P. Boudewijn
Marco Volpe
Michael Whitworth
Borut Svara
Gabriele Sgroi
Daniele Panfilo
Sebastiano Saccani


Evaluating Synthetic Tabular Data Generated To Augment Small Sample Datasets

Abstract: This work proposes a method to evaluate synthetic tabular data generated to augment small sample datasets. While data augmentation techniques can increase sample counts for machine learning applications, traditional validation approaches fail when applied to extremely limited sample sizes. Our experiments across four datasets reveal significant inconsistencies between global metrics and topological measures, with statistical tests producing unreliable significance values due to insufficient sample sizes. We demonstrate that common metrics like propensity scoring and MMD often suggest similarity where fundamental topological differences exist. Our proposed normalized Bottleneck distance based metric provides complementary insights but suffers from high variability across experimental runs and occasional values exceeding theoretical bounds, showing inherent instability in topological approaches for very small datasets. These findings highlight the critical need for multi-faceted evaluation methodologies when validating synthetic data generated from limited samples, as no single metric reliably captures both distributional and structural similarity.


Data Privacy in Multi-Cloud: An Enhanced Data Fragmentation Framework

Abstract: Data splitting preserves privacy by partitioning data into various fragments to be stored remotely and shared. It supports most data operations because data can be stored in clear as opposed to methods that rely on cryptography. However, majority of existing data splitting techniques do not consider data already in the multi-cloud. This leads to unnecessary use of resources to re-split data into fragments. This work proposes a data splitting framework that leverages on existing data in the multi-cloud. It improves data splitting mechanisms by reducing the number of splitting operations and resulting fragments. Therefore, decreasing the number of storage locations a data owner manages. Broadcasts queries locate third-party data fragments to avoid costly operations when splitting data. This work examines considerations for the use of third-party fragments and application to existing data splitting techniques. The proposed framework was also applied to an existing data splitting mechanism to complement its capabilities.

Conference on Privacy, Security and Trust


Permutation-Invariant Tabular Data Synthesis

Abstract: Tabular data synthesis is an emerging approach to circumvent strict regulations on data privacy while discovering knowledge through big data. Although state-of-the-art AI-based tabular data synthesizers, e.g., table-GAN, CTGAN, TVAE, and CTAB-GAN, are effective at generating synthetic tabular data, their training is sensitive to column permutations of input data. In this paper, we first conduct an extensive empirical study to disclose such a property of permutation invariance and an in-depth analysis of the existing synthesizers. We show that changing the input column order worsens the statistical difference between real and synthetic data by up to 38.67% due to the encoding of tabular data and the network architectures. To fully unleash the potential of big synthetic tabular data, we propose two solutions: (i) AE-GAN, a synthesizer that uses an autoencoder network to represent the tabular data and GAN networks to synthesize the latent representation, and (ii) a feature sorting algorithm to find the suitable column order of input data for CNN-based synthesizers. We evaluate the proposed solutions on five datasets in terms of the sensitivity to the column permutation, the quality of synthetic data, and the utility in downstream analyses. Our results show that we enhance the property of permutation-invariance when training synthesizers and further improve the quality and utility of synthetic data, up to 22%, compared to the existing synthesizers.

Yujin Zhu
Zilong Zhao
Robert Birke
Lydia Y. Chen


Execution-based Evaluation for Data Science Code Generation Models

Abstract: Code generation models can benefit data scientists' productivity by automatically generating code from context and text descriptions. An important measure of the modeling progress is whether a model can generate code that can correctly execute to solve the task. However, due to the lack of an evaluation dataset that directly supports execution-based model evaluation, existing work relies on code surface form similarity metrics (e.g., BLEU, CodeBLEU) for model selection, which can be inaccurate. To remedy this, we introduce ExeDS, an evaluation dataset for execution evaluation for data science code generation tasks. ExeDS contains a set of 534 problems from Jupyter Notebooks, each consisting of code context, task description, reference program, and the desired execution output. With ExeDS, we evaluate the execution performance of five state-of-the-art code generation models that have achieved high surface-form evaluation scores. Our experiments show that models with high surface-form scores do not necessarily perform well on execution metrics, and execution-based metrics can better capture model code generation errors. Source code and data can be found at https://github.com/Jun-jie-Huang/ExeDS

Junjie Huang
Chenglong Wang
Jipeng Zhang
Cong Yan
Haotian Cui
Jeevana Priya Inala
Colin Clement
Nan Duan
Jianfeng Gao


Scalable Modular Synthetic Data Generation for Advancing Aerial Autonomy

Abstract: One major barrier to advancing aerial autonomy has been collecting large-scale aerial datasets for training machine learning models. Due to costly and time-consuming real-world data collection through deploying drones, there has been an increasing shift towards using synthetic data for training models in drone applications. However, to increase widespread generalization and transferring models to real-world, increasing the diversity of simulation environments to train a model over all the varieties and augmenting the training data, has been proved to be essential. Current synthetic aerial data generation tools either lack data augmentation or rely heavily on manual workload or real samples for configuring and generating diverse realistic simulation scenes for data collection. These dependencies limit scalability of the data generation workflow. Accordingly, there is a major challenge in balancing generalizability and scalability in synthetic data generation. To address these gaps, we introduce a scalable Aerial Synthetic Data Augmentation (ASDA) framework tailored to aerial autonomy applications. ASDA extends a central data collection engine with two scriptable pipelines that automatically perform scene and data augmentations to generate diverse aerial datasets for different training tasks. ASDA improves data generation workflow efficiency by providing a unified prompt-based interface over integrated pipelines for flexible control. The procedural generative approach of our data augmentation is performant and adaptable to different simulation environments, training tasks and data collection needs. We demonstrate the effectiveness of our method in automatically generating diverse datasets and show its potential for downstream performance optimization.

Mehrnaz Sabet
Praveen Palanisamy
Sakshi Mishra


Adaptive Data Depth via Multi-Armed Bandits

Abstract: Data depth, introduced by Tukey (1975), is an important tool in data science, robust statistics, and computational geometry. One chief barrier to its broader practical utility is that many common measures of depth are computationally intensive, requiring on the order of $n^d$ operations to exactly compute the depth of a single point within a data set of $n$ points in $d$-dimensional space. Often however, we are not directly interested in the absolute depths of the points, but rather in their relative ordering. For example, we may want to find the most central point in a data set (a generalized median), or to identify and remove all outliers (points on the fringe of the data set with low depth). With this observation, we develop a novel and instance-adaptive algorithm for adaptive data depth computation by reducing the problem of exactly computing $n$ depths to an $n$-armed stochastic multi-armed bandit problem which we can efficiently solve. We focus our exposition on simplicial depth, developed by Liu (1990), which has emerged as a promising notion of depth due to its interpretability and asymptotic properties. We provide general instance-dependent theoretical guarantees for our proposed algorithms, which readily extend to many other common measures of data depth including majority depth, Oja depth, and likelihood depth. When specialized to the case where the gaps in the data follow a power law distribution with parameter $α<2$, we show that we can reduce the complexity of identifying the deepest point in the data set (the simplicial median) from $O(n^d)$ to $\tilde{O}(n^{d-(d-1)α/2})$, where $\tilde{O}$ suppresses logarithmic factors. We corroborate our theoretical results with numerical experiments on synthetic data, showing the practical utility of our proposed methods.


Efficacy of MRI data harmonization in the age of machine learning. A multicenter study across 36 datasets

Abstract: Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we showed the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage.

Chiara Marzi
Marco Giannelli
Andrea Barucci
Carlo Tessa
Mario Mascalchi
Stefano Diciotti

Medicine
Computer Science
Engineering
Biology


Proceedings of Principle and practice of data and Knowledge Acquisition Workshop 2022 (PKAW 2022)

Abstract: Over the past two decades, PKAW has provided a forum for researchers and practitioners to discuss the state-of-the-arts in the area of knowledge acquisition and machine intelligence (MI, also Artificial Intelligence, AI). PKAW2022 will continue the above focus and welcome the contributions on the multi-disciplinary approach of human and big data-driven knowledge acquisition, as well as AI techniques and applications.


DatChain -- Blockchain implementation in Data transfer for IoT Devices

Abstract: Currently, the IoT ecosystem is comprised of fully connected smart devices that exchange data to provide more automated, precise, and fast decisions. This idealised situation can only be accomplished if a system for data transactions is processed efficiently and security is ensured with high scalability and practicability. The integrity of data must be maintained during the exchange or transfer of data between entities. We propose to make a application called DatChain that responds to the above situation. The application stores data sensed by the Iot sensors in the backend after encrypting it and when the data is required for any purpose it can be exchanged using a suitable blockchain network that can keep up with the transfer rate even at high traffic in a secure environment.

Om Rajput
Suyash Nigam
M. J. Chowdhury
Kayalvizhi Jayavel


The Tensor Data Platform: Towards an AI-centric Database System

Abstract: Database engines have historically absorbed many of the innovations in data processing, adding features to process graph data, XML, object oriented, and text among many others. In this paper, we make the case that it is time to do the same for AI -- but with a twist! While existing approaches have tried to achieve this by integrating databases with external ML tools, in this paper we claim that achieving a truly AI-centric database requires moving the DBMS engine, at its core, from a relational to a tensor abstraction. This allows us to: (1) support multi-modal data processing such as images, videos, audio, text as well as relational; (2) leverage the wellspring of innovation in HW and runtimes for tensor computation; and (3) exploit automatic differentiation to enable a novel class of "trainable" queries that can learn to perform a task. To support the above scenarios, we introduce TDP: a system that builds upon our prior work mapping relational queries to tensors. Thanks to a tighter integration with the tensor runtime, TDP is able to provide a broader coverage of new emerging scenarios requiring access to multi-modal data and automatic differentiation.

Apurva Gandhi
Yuki Asada
Victor Fu
Advitya Gemawat
Lihao Zhang
Rathijit Sen
Carlo Curino
Jesús Camacho-Rodríguez
Matteo Interlandi

Conference on Innovative Data Systems Research


Haven't I Seen You Before? Assessing Identity Leakage in Synthetic Irises

Abstract: Generative Adversarial Networks (GANs) have proven to be a preferred method of synthesizing fake images of objects, such as faces, animals, and automobiles. It is not surprising these models can also generate ISO-compliant, yet synthetic iris images, which can be used to augment training data for iris matchers and liveness detectors. In this work, we trained one of the most recent GAN models (StyleGAN3) to generate fake iris images with two primary goals: (i) to understand the GAN's ability to produce "never-before-seen" irises, and (ii) to investigate the phenomenon of identity leakage as a function of the GAN's training time. Previous work has shown that personal biometric data can inadvertently flow from training data into synthetic samples, raising a privacy concern for subjects who accidentally appear in the training dataset. This paper presents analysis for three different iris matchers at varying points in the GAN training process to diagnose where and when authentic training samples are in jeopardy of leaking through the generative process. Our results show that while most synthetic samples do not show signs of identity leakage, a handful of generated samples match authentic (training) samples nearly perfectly, with consensus across all matchers. In order to prioritize privacy, security, and trust in the machine learning model development process, the research community must strike a delicate balance between the benefits of using synthetic data and the corresponding threats against privacy from potential identity leakage.

Patrick Tinsley
Adam Czajka
Patrick Flynn


Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers

Abstract: Analysis of the fairness of machine learning (ML) algorithms recently attracted many researchers' interest. Most ML methods show bias toward protected groups, which limits the applicability of ML models in many applications like crime rate prediction etc. Since the data may have missing values which, if not appropriately handled, are known to further harmfully affect fairness. Many imputation methods are proposed to deal with missing data. However, the effect of missing data imputation on fairness is not studied well. In this paper, we analyze the effect on fairness in the context of graph data (node attributes) imputation using different embedding and neural network methods. Extensive experiments on six datasets demonstrate severe fairness issues in missing data imputation under graph node classification. We also find that the choice of the imputation method affects both fairness and accuracy. Our results provide valuable insights into graph data fairness and how to handle missingness in graphs efficiently. This work also provides directions regarding theoretical studies on fairness in graph data.

Haris Mansoor
Sarwan Ali
Shafiq Alam
Muhammad Asad Khan
Umair ul Hassan
Imdadullah Khan


AI Assistants: A Framework for Semi-Automated Data Wrangling

Abstract: Data wrangling tasks such as obtaining and linking data from various sources, transforming data formats, and correcting erroneous records, can constitute up to 80% of typical data engineering work. Despite the rise of machine learning and artificial intelligence, data wrangling remains a tedious and manual task. We introduce AI assistants, a class of semi-automatic interactive tools to streamline data wrangling. An AI assistant guides the analyst through a specific data wrangling task by recommending a suitable data transformation that respects the constraints obtained through interaction with the analyst. We formally define the structure of AI assistants and describe how existing tools that treat data cleaning as an optimization problem fit the definition. We implement AI assistants for four common data wrangling tasks and make AI assistants easily accessible to data analysts in an open-source notebook environment for data science, by leveraging the common structure they follow. We evaluate our AI assistants both quantitatively and qualitatively through three example scenarios. We show that the unified and interactive design makes it easy to perform tasks that would be difficult to do manually or with a fully automatic tool.

Tomas Petricek
Gerrit J. J. van den Burg
Alfredo Nazábal
Taha Ceritli
Ernesto Jiménez-Ruiz
Christopher K. I. Williams

IEEE Transactions on Knowledge and Data Engineering


Application of Knowledge Distillation to Multi-task Speech Representation Learning

Abstract: Model architectures such as wav2vec 2.0 and HuBERT have been proposed to learn speech representations from audio waveforms in a self-supervised manner. When they are combined with downstream tasks such as keyword spotting and speaker verification, they provide state-of-the-art performance. However, these models use a large number of parameters, the smallest version of which has 95 million parameters. This constitutes a challenge for edge AI device deployments. In this paper, we investigate the application of knowledge distillation to speech representation learning (SRL) models followed by joint fine-tuning with multiple downstream voice-activated tasks. In our experiments on two such tasks, our approach results in nearly 75% reduction in model size while suffering only 0.1% accuracy and 0.9% equal error rate degradation compared to the full-size model. In addition, we show that fine-tuning the SRL models results in a significant performance boost compared to using frozen SRL models.

Mine Kerpicci
Van Nguyen
Shuhua Zhang
Erik Visser


Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data

Abstract: Several approaches have been developed to mitigate algorithmic bias stemming from health data poverty, where minority groups are underrepresented in training datasets. Augmenting the minority class using resampling (such as SMOTE) is a widely used approach due to the simplicity of the algorithms. However, these algorithms decrease data variability and may introduce correlations between samples, giving rise to the use of generative approaches based on GAN. Generation of high-dimensional, time-series, authentic data that provides a wide distribution coverage of the real data, remains a challenging task for both resampling and GAN-based approaches. In this work we propose CA-GAN architecture that addresses some of the shortcomings of the current approaches, where we provide a detailed comparison with both SMOTE and WGAN-GP*, using a high-dimensional, time-series, real dataset of 3343 hypotensive Caucasian and Black patients. We show that our approach is better at both generating authentic data of the minority class and remaining within the original distribution of the real data.

Raffaele Marchesi
Nicolo Micheletti
Giuseppe Jurman
Venet Osmani


Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings

Abstract: In this paper, we introduce the Vehicle Claims dataset, consisting of fraudulent insurance claims for automotive repairs. The data belongs to the more broad category of Auditing data, which includes also Journals and Network Intrusion data. Insurance claim data are distinctively different from other auditing data (such as network intrusion data) in their high number of categorical attributes. We tackle the common problem of missing benchmark datasets for anomaly detection: datasets are mostly confidential, and the public tabular datasets do not contain relevant and sufficient categorical attributes. Therefore, a large-sized dataset is created for this purpose and referred to as Vehicle Claims (VC) dataset. The dataset is evaluated on shallow and deep learning methods. Due to the introduction of categorical attributes, we encounter the challenge of encoding them for the large dataset. As One Hot encoding of high cardinal dataset invokes the "curse of dimensionality", we experiment with GEL encoding and embedding layer for representing categorical attributes. Our work compares competitive learning, reconstruction-error, density estimation and contrastive learning approaches for Label, One Hot, GEL encoding and embedding layer to handle categorical values.


Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation

Abstract: Knowledge distillation is one of the primary methods of transferring knowledge from large to small models. However, it requires massive task-specific data, which may not be plausible in many real-world applications. Data augmentation methods such as representation interpolation, token replacement, or augmentation with models are applied to tackle this problem. However, these data augmentation methods either potentially cause shifts in decision boundaries (representation interpolation), are not expressive enough (token replacement), or introduce too much computational overhead (augmentation with models). To this end, we propose AugPro (Augmentation with Projection), an effective and efficient data augmentation method for distillation. Our method builds on top of representation interpolation augmentation methods to maintain the diversity of expressions and converts the augmented data to tokens to avoid shifting decision boundaries. It uses simple operations that come with little computational overhead. The results on multiple GLUE tasks show that our methods can improve distillation performance by a large margin at a low time cost. Codes are available at https://github.com/google-research/google-research/tree/master/augpro.

Ziqi Wang
Yuexin Wu
Frederick Liu
Daogao Liu
Le Hou
Hongkun Yu
Jing Li
Heng Ji

International Conference on Learning Representations


Nuclear data activities for medium mass and heavy nuclei at Los Alamos

Abstract: Nuclear data is critical for many modern applications from stockpile stewardship to cutting edge scientific research. Central to these pursuits is a robust pipeline for nuclear modeling as well as data assimilation and dissemination. We summarize a small portion of the ongoing nuclear data efforts at Los Alamos for medium mass to heavy nuclei. We begin with an overview of the NEXUS framework and show how one of its modules can be used for model parameter optimization using Bayesian techniques. The mathematical framework affords the combination of different measured data in determining model parameters and their associated correlations. It also has the advantage of being able to quantify outliers in data. We exemplify the power of this procedure by highlighting the recently evaluated 239-Pu cross section. We further showcase the success of our tools and pipeline by covering the insight gained from incorporating the latest nuclear modeling and data in astrophysical simulations as part of the Fission In R-process Elements (FIRE) collaboration.

M. R. Mumpower
T. M Sprouse
T. Kawano
M. W. Herman
A. E. Lovell
G. W. Misch
D. Neudecker
H. Sasaki
I. Stetcu
P. Talou


Causal Structural Hypothesis Testing and Data Generation Models

Abstract: A vast amount of expert and domain knowledge is captured by causal structural priors, yet there has been little research on testing such priors for generalization and data synthesis purposes. We propose a novel model architecture, Causal Structural Hypothesis Testing, that can use nonparametric, structural causal knowledge and approximate a causal model's functional relationships using deep neural networks. We use these architectures for comparing structural priors, akin to hypothesis testing, using a deliberate (non-random) split of training and testing data. Extensive simulations demonstrate the effectiveness of out-of-distribution generalization error as a proxy for causal structural prior hypothesis testing and offers a statistical baseline for interpreting results. We show that the variational version of the architecture, Causal Structural Variational Hypothesis Testing can improve performance in low SNR regimes. Due to the simplicity and low parameter count of the models, practitioners can test and compare structural prior hypotheses on small dataset and use the priors with the best generalization capacity to synthesize much larger, causally-informed datasets. Finally, we validate our methods on a synthetic pendulum dataset, and show a use-case on a real-world trauma surgery ground-level falls dataset.

Jeffrey Jiang
Omead Pooladzandi
Sunay Bhat
Gregory Pottie


DAGAD: Data Augmentation for Graph Anomaly Detection

Abstract: Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Receiving increasing attention from both academia and industry, yet existing research on this task still suffers from two critical issues when learning informative anomalous behavior from graph data. For one thing, anomalies are usually hard to capture because of their subtle abnormal behavior and the shortage of background knowledge about them, which causes severe anomalous sample scarcity. Meanwhile, the overwhelming majority of objects in real-world graphs are normal, bringing the class imbalance problem as well. To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes. A series of experiments on three datasets prove that DAGAD outperforms ten state-of-the-art baseline detectors concerning various mostly-used metrics, together with an extensive ablation study validating the strength of our proposed modules.

Fanzhen Liu
Xiaoxiao Ma
Jia Wu
Jian Yang
Shan Xue
Amin Beheshti
Chuan Zhou
Hao Peng
Quan Z. Sheng
Charu C. Aggarwal


From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data

Abstract: While large-scale sequence modeling from offline data has led to impressive performance gains in natural language and image generation, directly translating such ideas to robotics has been challenging. One critical reason for this is that uncurated robot demonstration data, i.e. play data, collected from non-expert human demonstrators are often noisy, diverse, and distributionally multi-modal. This makes extracting useful, task-centric behaviors from such data a difficult generative modeling problem. In this work, we present Conditional Behavior Transformers (C-BeT), a method that combines the multi-modal generation ability of Behavior Transformer with future-conditioned goal specification. On a suite of simulated benchmark tasks, we find that C-BeT improves upon prior state-of-the-art work in learning from play data by an average of 45.7%. Further, we demonstrate for the first time that useful task-centric behaviors can be learned on a real-world robot purely from play data without any task labels or reward information. Robot videos are best viewed on our project website: https://play-to-policy.github.io

Zichen Jeff Cui
Yibin Wang
Nur Muhammad Mahi Shafiullah
Lerrel Pinto


Dynamic Topological Data Analysis of Functional Human Brain Networks

Abstract: Developing reliable methods to discriminate different transient brain states that change over time is a key neuroscientific challenge in brain imaging studies. Topological data analysis (TDA), a novel framework based on algebraic topology, can handle such a challenge. However, existing TDA has been somewhat limited to capturing the static summary of dynamically changing brain networks. We propose a novel dynamic-TDA framework that builds persistent homology over a time series of brain networks. We construct a Wasserstein distance based inference procedure to discriminate between time series of networks. The method is applied to the resting-state functional magnetic resonance images of human brain. We demonstrate that our proposed dynamic-TDA approach can distinctly discriminate between the topological patterns of male and female brain networks. MATLAB code for implementing this method is available at https://github.com/laplcebeltrami/PH-STAT.


AMC 12 atomic mass compilation data extrapolated for atomic masses of nuclei far from the valley of stability

Abstract: The experimental mass data from the Atomic Mass Compilation - 2012 (AMC12) has been analyzed for two-neutron separation energies (S$_{2n}$), two-proton separation energies (S$_{2p}$), double-beta decay energies (Q$_{2β^-}$), and four-beta decay energies (Q$_{4β^-}$) and plotted against neutron number and mass number, respectively. A new weighted slope method of extrapolation, tested for known and new mass measurements, has been used to obtain the extrapolated mass values with better precision for more than 1100 nuclei far from the valley of stability, out of which more than 100 are being reported for the first time. A comparison has been made with five of the popular mass models with reference to experimental extrapolated masses from the present work and the Atomic Mass Evaluation 2016 (AME16). The extrapolated experimental atomic mass data will be very useful for both experimentalists and mass-model theoreticians, as well as in simulations of astrophysical r-processes.

K. Venkataramaniah
Shreesha Rao D. S.
C. Scheidenberger


Data-Efficient Augmentation for Training Neural Networks

Abstract: Data augmentation is essential to achieve state-of-the-art performance in many deep learning applications. However, the most effective augmentation techniques become computationally prohibitive for even medium-sized datasets. To address this, we propose a rigorous technique to select subsets of data points that when augmented, closely capture the training dynamics of full data augmentation. We first show that data augmentation, modeled as additive perturbations, improves learning and generalization by relatively enlarging and perturbing the smaller singular values of the network Jacobian, while preserving its prominent directions. This prevents overfitting and enhances learning the harder to learn information. Then, we propose a framework to iteratively extract small subsets of training data that when augmented, closely capture the alignment of the fully augmented Jacobian with labels/residuals. We prove that stochastic gradient descent applied to the augmented subsets found by our approach has similar training dynamics to that of fully augmented data. Our experiments demonstrate that our method achieves 6.3x speedup on CIFAR10 and 2.2x speedup on SVHN, and outperforms the baselines by up to 10% across various subset sizes. Similarly, on TinyImageNet and ImageNet, our method beats the baselines by up to 8%, while achieving up to 3.3x speedup across various subset sizes. Finally, training on and augmenting 50% subsets using our method on a version of CIFAR10 corrupted with label noise even outperforms using the full dataset. Our code is available at: https://github.com/tianyu139/data-efficient-augmentation


Data augmentation on-the-fly and active learning in data stream classification

Abstract: There is an emerging need for predictive models to be trained on-the-fly, since in numerous machine learning applications data are arriving in an online fashion. A critical challenge encountered is that of limited availability of ground truth information (e.g., labels in classification tasks) as new data are observed one-by-one online, while another significant challenge is that of class imbalance. This work introduces the novel Augmented Queues method, which addresses the dual-problem by combining in a synergistic manner online active learning, data augmentation, and a multi-queue memory to maintain separate and balanced queues for each class. We perform an extensive experimental study using image and time-series augmentations, in which we examine the roles of the active learning budget, memory size, imbalance level, and neural network type. We demonstrate two major advantages of Augmented Queues. First, it does not reserve additional memory space as the generation of synthetic data occurs only at training times. Second, learning models have access to more labelled data without the need to increase the active learning budget and / or the original memory size. Learning on-the-fly poses major challenges which, typically, hinder the deployment of learning models. Augmented Queues significantly improves the performance in terms of learning quality and speed. Our code is made publicly available.

Kleanthis Malialis
Dimitris Papatheodoulou
Stylianos Filippou
Christos G. Panayiotou
Marios M. Polycarpou

IEEE Symposium Series on Computational Intelligence


Non-fungible token transactions: data and challenges

Abstract: Non-fungible tokens (NFT) have recently emerged as a novel blockchain hosted financial asset class that has attracted major transaction volumes. Investment decisions rely on data and adequate preprocessing and application of analytics to them. Both owing to the non-fungible nature of the tokens and to a blockchain being the primary data source, NFT transaction data pose several challenges not commonly encountered in traditional financial data. Using data that consist of the transaction history of eight highly valued NFT collections, a selection of such challenges is illustrated. These are: price differentiation by token traits, the possible existence of lateral swaps and wash trades in the transaction history and finally, severe volatility. While this paper merely scratches the surface of how data analytics can be applied in this context, the data and challenges laid out here may present opportunities for future research on the topic.

Jason B. Cho
Sven Serneels
David S. Matteson


Secure Multiparty Computation for Synthetic Data Generation from Distributed Data

Abstract: Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, finance, and education. Synthetic data generation algorithms with privacy guarantees are emerging as a paradigm to break this data logjam. Existing approaches, however, assume that the data holders supply their raw data to a trusted curator, who uses it as fuel for synthetic data generation. This severely limits the applicability, as much of the valuable data in the world is locked up in silos, controlled by entities who cannot show their data to each other or a central aggregator without raising privacy concerns. To overcome this roadblock, we propose the first solution in which data holders only share encrypted data for differentially private synthetic data generation. Data holders send shares to servers who perform Secure Multiparty Computation (MPC) computations while the original data stays encrypted. We instantiate this idea in an MPC protocol for the Multiplicative Weights with Exponential Mechanism (MWEM) algorithm to generate synthetic data based on real data originating from many data holders without reliance on a single point of failure.

Mayana Pereira
Sikha Pentyala
Anderson Nascimento
Rafael T. de Sousa
Martine De Cock


How Do Data Science Workers Communicate Intermediate Results?

Abstract: Data science workers increasingly collaborate on large-scale projects before communicating insights to a broader audience in the form of visualization. While prior work has modeled how data science teams, oftentimes with distinct roles and work processes, communicate knowledge to outside stakeholders, we have little knowledge of how data science workers communicate intermediately before delivering the final products. In this work, we contribute a nuanced description of the intermediate communication process within data science teams. By analyzing interview data with 8 self-identified data science workers, we characterized the data science intermediate communication process with four factors, including the types of audience, communication goals, shared artifacts, and mode of communication. We also identified overarching challenges in the current communication process. We also discussed design implications that might inform better tools that facilitate intermediate communication within data science teams.

Rock Yuren Pang
Ruotong Wang
Joely Nelson
Leilani Battle


Increasing Data Equity Through Accessibility

Abstract: This position statement is a response to the Office of Science and Technology Policy's Request for Information on "Equitable Data Engagement and Accountability." This response considers data equity specifically for people with disabilities. The RFI asks "how Federal agencies can better support collaboration with other levels of government, civil society, and the research community around the production and use of equitable data." We argue that one critically underserved community in the context of data equity is people with disabilities. Today's tools make it extremely difficult for disabled people to (1) interact with data and data visualizations and (2) take jobs that involve working with and visualizing data. Yet access to such data is increasingly critical, and integral, to engaging with government and civil society. We must change the standards and expectations around data practices to include disabled people and support the research necessary to achieve those goals.

Frank Elavsky
Jennifer Mankoff
Arvind Satyanarayan


Nonstationary data stream classification with online active learning and siamese neural networks

Abstract: We have witnessed in recent years an ever-growing volume of information becoming available in a streaming manner in various application areas. As a result, there is an emerging need for online learning methods that train predictive models on-the-fly. A series of open challenges, however, hinder their deployment in practice. These are, learning as data arrive in real-time one-by-one, learning from data with limited ground truth information, learning from nonstationary data, and learning from severely imbalanced data, while occupying a limited amount of memory for data storage. We propose the ActiSiamese algorithm, which addresses these challenges by combining online active learning, siamese networks, and a multi-queue memory. It develops a new density-based active learning strategy which considers similarity in the latent (rather than the input) space. We conduct an extensive study that compares the role of different active learning budgets and strategies, the performance with/without memory, the performance with/without ensembling, in both synthetic and real-world datasets, under different data nonstationarity characteristics and class imbalance levels. ActiSiamese outperforms baseline and state-of-the-art algorithms, and is effective under severe imbalance, even only when a fraction of the arriving instances' labels is available. We publicly release our code to the community.

Kleanthis Malialis
Christos G. Panayiotou
Marios M. Polycarpou


The current state of single-cell proteomics data analysis

Abstract: Sound data analysis is essential to retrieve meaningful biological information from single-cell proteomics experiments. This analysis is carried out by computational methods that are assembled into workflows, and their implementations influence the conclusions that can be drawn from the data. In this work, we explore and compare the computational workflows that have been used over the last four years and identify a profound lack of consensus on how to analyze single-cell proteomics data. We highlight the need for benchmarking of computational workflows, standardization of computational tools and data, as well as carefully designed experiments. Finally, we cover the current standardization efforts that aim to fill the gap and list the remaining missing pieces, and conclude with lessons learned from the replication of published single-cell proteomics analyses.


FAIR for AI: An interdisciplinary and international community building perspective

Abstract: A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.

E. A. Huerta
Ben Blaiszik
L. Catherine Brinson
Kristofer E. Bouchard
Daniel Diaz
Caterina Doglioni
Javier M. Duarte
Murali Emani
Ian Foster
Geoffrey Fox
Philip Harris
Lukas Heinrich
Shantenu Jha
Daniel S. Katz
Volodymyr Kindratenko
Christine R. Kirkpatrick
Kati Lassila-Perini
Ravi K. Madduri
Mark S. Neubauer
Fotis E. Psomopoulos
Avik Roy
Oliver Rübel
Zhizhen Zhao
Ruike Zhu


Label driven Knowledge Distillation for Federated Learning with non-IID Data

Abstract: In real-world applications, Federated Learning (FL) meets two challenges: (1) scalability, especially when applied to massive IoT networks; and (2) how to be robust against an environment with heterogeneous data. Realizing the first problem, we aim to design a novel FL framework named Full-stack FL (F2L). More specifically, F2L utilizes a hierarchical network architecture, making extending the FL network accessible without reconstructing the whole network system. Moreover, leveraging the advantages of hierarchical network design, we propose a new label-driven knowledge distillation (LKD) technique at the global server to address the second problem. As opposed to current knowledge distillation techniques, LKD is capable of training a student model, which consists of good knowledge from all teachers' models. Therefore, our proposed algorithm can effectively extract the knowledge of the regions' data distribution (i.e., the regional aggregated models) to reduce the divergence between clients' models when operating under the FL system with non-independent identically distributed data. Extensive experiment results reveal that: (i) our F2L method can significantly improve the overall FL efficiency in all global distillations, and (ii) F2L rapidly achieves convergence as global distillation stages occur instead of increasing on each communication cycle.

Minh-Duong Nguyen
Quoc-Viet Pham
Dinh Thai Hoang
Long Tran-Thanh
Diep N. Nguyen
Won-Joo Hwang


Benchmarking Apache Spark and Hadoop MapReduce on Big Data Classification

Abstract: Most of the popular Big Data analytics tools evolved to adapt their working environment to extract valuable information from a vast amount of unstructured data. The ability of data mining techniques to filter this helpful information from Big Data led to the term Big Data Mining. Shifting the scope of data from small-size, structured, and stable data to huge volume, unstructured, and quickly changing data brings many data management challenges. Different tools cope with these challenges in their own way due to their architectural limitations. There are numerous parameters to take into consideration when choosing the right data management framework based on the task at hand. In this paper, we present a comprehensive benchmark for two widely used Big Data analytics tools, namely Apache Spark and Hadoop MapReduce, on a common data mining task, i.e., classification. We employ several evaluation metrics to compare the performance of the benchmarked frameworks, such as execution time, accuracy, and scalability. These metrics are specialized to measure the performance for classification task. To the best of our knowledge, there is no previous study in the literature that employs all these metrics while taking into consideration task-specific concerns. We show that Spark is 5 times faster than MapReduce on training the model. Nevertheless, the performance of Spark degrades when the input workload gets larger. Scaling the environment by additional clusters significantly improves the performance of Spark. However, similar enhancement is not observed in Hadoop. Machine learning utility of MapReduce tend to have better accuracy scores than that of Spark, like around 3%, even in small size data sets.

International Conference on Cloud and Big Data Computing


Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation

Abstract: Data-free Knowledge Distillation (DFKD) has attracted attention recently thanks to its appealing capability of transferring knowledge from a teacher network to a student network without using training data. The main idea is to use a generator to synthesize data for training the student. As the generator gets updated, the distribution of synthetic data will change. Such distribution shift could be large if the generator and the student are trained adversarially, causing the student to forget the knowledge it acquired at previous steps. To alleviate this problem, we propose a simple yet effective method called Momentum Adversarial Distillation (MAD) which maintains an exponential moving average (EMA) copy of the generator and uses synthetic samples from both the generator and the EMA generator to train the student. Since the EMA generator can be considered as an ensemble of the generator's old versions and often undergoes a smaller change in updates compared to the generator, training on its synthetic samples can help the student recall the past knowledge and prevent the student from adapting too quickly to new updates of the generator. Our experiments on six benchmark datasets including big datasets like ImageNet and Places365 demonstrate the superior performance of MAD over competing methods for handling the large distribution shift problem. Our method also compares favorably to existing DFKD methods and even achieves state-of-the-art results in some cases.

Kien Do
Hung Le
Dung Nguyen
Dang Nguyen
Haripriya Harikumar
Truyen Tran
Santu Rana
Svetha Venkatesh


Exploring Inconsistent Knowledge Distillation for Object Detection with Data Augmentation

Abstract: Knowledge Distillation (KD) for object detection aims to train a compact detector by transferring knowledge from a teacher model. Since the teacher model perceives data in a way different from humans, existing KD methods only distill knowledge that is consistent with labels annotated by human expert while neglecting knowledge that is not consistent with human perception, which results in insufficient distillation and sub-optimal performance. In this paper, we propose inconsistent knowledge distillation (IKD), which aims to distill knowledge inherent in the teacher model's counter-intuitive perceptions. We start by considering the teacher model's counter-intuitive perceptions of frequency and non-robust features. Unlike previous works that exploit fine-grained features or introduce additional regularizations, we extract inconsistent knowledge by providing diverse input using data augmentation. Specifically, we propose a sample-specific data augmentation to transfer the teacher model's ability in capturing distinct frequency components and suggest an adversarial feature augmentation to extract the teacher model's perceptions of non-robust features in the data. Extensive experiments demonstrate the effectiveness of our method which outperforms state-of-the-art KD baselines on one-stage, two-stage and anchor-free object detectors (at most +1.0 mAP). Our codes will be made available at \url{https://github.com/JWLiang007/IKD.git}.

Jiawei Liang
Siyuan Liang
Aishan Liu
Ke Ma
Jingzhi Li
Xiaochun Cao


How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events

Abstract: Social media moderation policies are often at the center of public debate, and their implementation and enactment are sometimes surrounded by a veil of mystery. Unsurprisingly, due to limited platform transparency and data access, relatively little research has been devoted to characterizing moderation dynamics, especially in the context of controversial events and the platform activity associated with them. Here, we study the dynamics of account creation and suspension on Twitter during two global political events: Russia's invasion of Ukraine and the 2022 French Presidential election. Leveraging a large-scale dataset of 270M tweets shared by 16M users in multiple languages over several months, we identify peaks of suspicious account creation and suspension, and we characterize behaviours that more frequently lead to account suspension. We show how large numbers of accounts get suspended within days from their creation. Suspended accounts tend to mostly interact with legitimate users, as opposed to other suspicious accounts, often making unwarranted and excessive use of reply and mention features, and predominantly sharing spam and harmful content. While we are only able to speculate about the specific causes leading to a given account suspension, our findings shed light on patterns of platform abuse and subsequent moderation during major events.

Francesco Pierri
Luca Luceri
Emily Chen
Emilio Ferrara


Rule-adhering synthetic data -- the lingua franca of learning

Abstract: AI-generated synthetic data allows to distill the general patterns of existing data, that can then be shared safely as granular-level representative, yet novel data samples within the original semantics. In this work we explore approaches of incorporating domain expertise into the data synthesis, to have the statistical properties as well as pre-existing domain knowledge of rules be represented. The resulting synthetic data generator, that can be probed for any number of new samples, can then serve as a common source of intelligence, as a lingua franca of learning, consumable by humans and machines alike. We demonstrate the concept for a publicly available data set, and evaluate its benefits via descriptive analysis as well as a downstream ML model.


Online Continual Learning via the Meta-learning Update with Multi-scale Knowledge Distillation and Data Augmentation

Abstract: Continual learning aims to rapidly and continually learn the current task from a sequence of tasks. Compared to other kinds of methods, the methods based on experience replay have shown great advantages to overcome catastrophic forgetting. One common limitation of this method is the data imbalance between the previous and current tasks, which would further aggravate forgetting. Moreover, how to effectively address the stability-plasticity dilemma in this setting is also an urgent problem to be solved. In this paper, we overcome these challenges by proposing a novel framework called Meta-learning update via Multi-scale Knowledge Distillation and Data Augmentation (MMKDDA). Specifically, we apply multiscale knowledge distillation to grasp the evolution of long-range and short-range spatial relationships at different feature levels to alleviate the problem of data imbalance. Besides, our method mixes the samples from the episodic memory and current task in the online continual training procedure, thus alleviating the side influence due to the change of probability distribution. Moreover, we optimize our model via the meta-learning update resorting to the number of tasks seen previously, which is helpful to keep a better balance between stability and plasticity. Finally, our experimental evaluation on four benchmark datasets shows the effectiveness of the proposed MMKDDA framework against other popular baselines, and ablation studies are also conducted to further analyze the role of each component in our framework.

Engineering applications of artificial intelligence


Online Updating Huber Robust Regression for Big Data Streams

Abstract: Big data streams are grasping increasing attention with the development of modern science and information technology. Due to the incompatibility of limited computer memory to high volume of streaming data, real-time methods without historical data storage is worth investigating. Moreover, outliers may occur with high velocity data streams generating, calling for more robust analysis. Motivated by these concerns, a novel Online Updating Huber Robust Regression algorithm is proposed in this paper. By extracting key features of new data subsets, it obtains a computational efficient online updating estimator without historical data storage. Meanwhile, by integrating Huber regression into the framework, the estimator is robust to contaminated data streams, such as heavy-tailed or heterogeneous distributed ones as well as cases with outliers. Moreover, the proposed online updating estimator is asymptotically equivalent to Oracle estimator obtained by the entire data and has a lower computation complexity. Extensive numerical simulations and a real data analysis are also conducted to evaluate the estimation and calculation efficiency of the proposed method.


Data Augmentation for Deep Receivers

Abstract: Deep neural networks (DNNs) allow digital receivers to learn to operate in complex environments. To do so, DNNs should preferably be trained using large labeled data sets with a similar statistical relationship as the one under which they are to infer. For DNN-aided receivers, obtaining labeled data conventionally involves pilot signalling at the cost of reduced spectral efficiency, typically resulting in access to limited data sets. In this paper, we study how one can enrich a small set of labeled pilots data into a larger data set for training deep receivers. Motivated by the widespread use of data augmentation techniques for enriching visual and text data, we propose dedicated augmentation schemes that exploits the characteristics of digital communication data. We identify the key considerations in data augmentations for deep receivers as the need for domain orientation, class (constellation) diversity, and low complexity. Following these guidelines, we devise three complementing augmentations that exploit the geometric properties of digital constellations. Our combined augmentation approach builds on the merits of these different augmentations to synthesize reliable data from a momentary channel distribution, to be used for training deep receivers. Furthermore, we exploit previous channel realizations to increase the reliability of the augmented samples.

IEEE Transactions on Wireless Communications


FAKD: Feature Augmented Knowledge Distillation for Semantic Segmentation

Abstract: In this work, we explore data augmentations for knowledge distillation on semantic segmentation. To avoid over-fitting to the noise in the teacher network, a large number of training examples is essential for knowledge distillation. Imagelevel argumentation techniques like flipping, translation or rotation are widely used in previous knowledge distillation framework. Inspired by the recent progress on semantic directions on feature-space, we propose to include augmentations in feature space for efficient distillation. Specifically, given a semantic direction, an infinite number of augmentations can be obtained for the student in the feature space. Furthermore, the analysis shows that those augmentations can be optimized simultaneously by minimizing an upper bound for the losses defined by augmentations. Based on the observation, a new algorithm is developed for knowledge distillation in semantic segmentation. Extensive experiments on four semantic segmentation benchmarks demonstrate that the proposed method can boost the performance of current knowledge distillation methods without any significant overhead. Code is available at: https://github.com/jianlong-yuan/FAKD.

Jianlong Yuan
Qian Qi
Fei Du
Zhibin Wang
Fan Wang
Yifan Liu

IEEE Workshop/Winter Conference on Applications of Computer Vision


Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentation

Abstract: Cost-sensitive classification is critical in applications where misclassification errors widely vary in cost. However, over-parameterization poses fundamental challenges to the cost-sensitive modeling of deep neural networks (DNNs). The ability of a DNN to fully interpolate a training dataset can render a DNN, evaluated purely on the training set, ineffective in distinguishing a cost-sensitive solution from its overall accuracy maximization counterpart. This necessitates rethinking cost-sensitive classification in DNNs. To address this challenge, this paper proposes a cost-sensitive adversarial data augmentation (CSADA) framework to make over-parameterized models cost-sensitive. The overarching idea is to generate targeted adversarial examples that push the decision boundary in cost-aware directions. These targeted adversarial samples are generated by maximizing the probability of critical misclassifications and used to train a model with more conservative decisions on costly pairs. Experiments on well-known datasets and a pharmacy medication image (PMI) dataset made publicly available show that our method can effectively minimize the overall cost and reduce critical errors, while achieving comparable performance in terms of overall accuracy.

Qiyuan Chen
Raed Al Kontar
Maher Nouiehed
Jessie Yang
Corey Lester


Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV

Abstract: Clinical data usually cannot be freely distributed due to their highly confidential nature and this hampers the development of machine learning in the healthcare domain. One way to mitigate this problem is by generating realistic synthetic datasets using generative adversarial networks (GANs). However, GANs are known to suffer from mode collapse thus creating outputs of low diversity. This lowers the quality of the synthetic healthcare data, and may cause it to omit patients of minority demographics or neglect less common clinical practices. In this paper, we extend the classic GAN setup with an additional variational autoencoder (VAE) and include an external memory to replay latent features observed from the real samples to the GAN generator. Using antiretroviral therapy for human immunodeficiency virus (ART for HIV) as a case study, we show that our extended setup overcomes mode collapse and generates a synthetic dataset that accurately describes severely imbalanced class distributions commonly found in real-world clinical variables. In addition, we demonstrate that our synthetic dataset is associated with a very low patient disclosure risk, and that it retains a high level of utility from the ground truth dataset to support the development of downstream machine learning algorithms.

Nicholas I-Hsien Kuo
Federico Garcia
Anders Sönnerborg
Maurizio Zazzi
Michael Böhm
Rolf Kaiser
Mark Polizzotto
Louisa Jorm
Sebastiano Barbieri


TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation

Abstract: Collecting and annotating task-oriented dialog data is difficult, especially for highly specific domains that require expert knowledge. At the same time, informal communication channels such as instant messengers are increasingly being used at work. This has led to a lot of work-relevant information that is disseminated through those channels and needs to be post-processed manually by the employees. To alleviate this problem, we present TexPrax, a messaging system to collect and annotate problems, causes, and solutions that occur in work-related chats. TexPrax uses a chatbot to directly engage the employees to provide lightweight annotations on their conversation and ease their documentation work. To comply with data privacy and security regulations, we use an end-to-end message encryption and give our users full control over their data which has various advantages over conventional annotation tools. We evaluate TexPrax in a user-study with German factory employees who ask their colleagues for solutions on problems that arise during their daily work. Overall, we collect 202 task-oriented German dialogues containing 1,027 sentences with sentence-level expert annotations. Our data analysis also reveals that real-world conversations frequently contain instances with code-switching, varying abbreviations for the same entity, and dialects which NLP systems should be able to handle.

Lorenz Stangier
Ji-Ung Lee
Yuxi Wang
Marvin Müller
Nicholas Frick
Joachim Metternich
Iryna Gurevych


Local Differentially Private Fuzzy Counting in Stream Data using Probabilistic Data Structure

Abstract: Privacy-preserving estimation of counts of items in streaming data finds applications in several real-world scenarios including word auto-correction and traffic management applications. Recent works of RAPPOR and Apple's count-mean sketch (CMS) algorithm propose privacy preserving mechanisms for count estimation in large volumes of data using probabilistic data structures like counting Bloom filter and CMS. However, these existing methods fall short in providing a sound solution for real-time streaming data applications. In this work, we propose a novel (local) Differentially private mechanism that provides high utility for the streaming data count estimation problem with similar or even lower privacy budgets while providing: a) fuzzy counting to report counts of related or similar items (for instance to account for typing errors and data variations), and b) improved querying efficiency to reduce the response time for real-time querying of counts. We provide formal proofs for privacy and utility guarantees and present extensive experimental evaluation of our algorithm using real and synthetic English words datasets for both the exact and fuzzy counting scenarios. Our privacy preserving mechanism substantially outperforms the prior work in terms of lower querying time, significantly higher utility (accuracy of count estimation) under similar or lower privacy guarantees, at the cost of communication overhead.

Dinusha Vatsalan
Raghav Bhaskar
Mohamed Ali Kaafar


Cryptoeconomic Security for Data Availability Committees

Abstract: Layer 2 systems have received increasing attention due to their potential to scale the throughput of L1 blockchains. To avoid the cost of putting data on chain, these systems increasingly turn to off-chain data availability solutions such as data availability committees (DACs). However, placing trust on DACs conflicts with the goal of obtaining an L2 architecture whose security relies solely on the L1 chain. To eliminate such trust assumptions, we propose a DAC protocol that provides financial incentives to deter the DAC nodes from adversarial behavior such as withholding data upon request. We then analyze the interaction of rational DAC nodes and clients as a dynamic game, with a Byzantine adversary that can corrupt and bribe the participants. We also define a notion of optimality for the DAC protocols, inspired by fairness and economic feasibility. Our main result shows that our protocol is optimal and guarantees security with the highest possible probability under reasonable assumptions on the adversary.


Teaching Visual Accessibility in Introductory Data Science Classes with Multi-Modal Data Representations

Abstract: Although there are various ways to represent data patterns and models, visualization has been primarily taught in many data science courses for its efficiency. Such vision-dependent output may cause critical barriers against those who are blind and visually impaired and people with learning disabilities. We argue that instructors need to teach multiple data representation methods so that all students can produce data products that are more accessible. In this paper, we argue that accessibility should be taught as early as the introductory course as part of the data science curriculum so that regardless of whether learners major in data science or not, they can have foundational exposure to accessibility. As data science educators who teach accessibility as part of our lower-division courses in two different institutions, we share specific examples that can be utilized by other data science instructors.


Investigation of robustness and numerical stability of multiple regression and PCA in modeling world development data

Abstract: Popular methods for modeling data both labelled and unlabeled, multiple regression and PCA has been used in research for a vast number of datasets. In this investigation, we attempt to push the limits of these two methods by running a fit on world development data, a set notorious for its complexity and high dimensionality. We assess the robustness and numerical stability of both methods using their matrix condition number and ability to capture variance in the dataset. The result indicates poor performance from both methods from a numerical standpoint, yet certain qualitative insights can still be captured.

Highlights in Science Engineering and Technology


Model based clustering of multinomial count data

Abstract: We consider the problem of inferring an unknown number of clusters in replicated multinomial data. Under a model based clustering point of view, this task can be treated by estimating finite mixtures of multinomial distributions with or without covariates. Both Maximum Likelihood (ML) as well as Bayesian estimation are taken into account. Under a Maximum Likelihood approach, we provide an Expectation--Maximization (EM) algorithm which exploits a careful initialization procedure combined with a ridge--stabilized implementation of the Newton--Raphson method in the M--step. Under a Bayesian setup, a stochastic gradient Markov chain Monte Carlo (MCMC) algorithm embedded within a prior parallel tempering scheme is devised. The number of clusters is selected according to the Integrated Completed Likelihood criterion in the ML approach and estimating the number of non-empty components in overfitting mixture models in the Bayesian case. Our method is illustrated in simulated data and applied to two real datasets. An R package is available at https://github.com/mqbssppe/multinomialLogitMix.

Advances in Data Analysis and Classification


Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures

Abstract: The order/dimension of models derived on the basis of data is commonly restricted by the number of observations, or in the context of monitored systems, sensing nodes. This is particularly true for structural systems (e.g., civil or mechanical structures), which are typically high-dimensional in nature. In the scope of physics-informed machine learning, this paper proposes a framework -- termed Neural Modal ODEs -- to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems. Neural Ordinary Differential Equations -- Neural ODEs are exploited as the deep learning operator. In this initiating exploration, we restrict ourselves to linear or mildly nonlinear systems. We propose an architecture that couples a dynamic version of variational autoencoders with physics-informed Neural ODEs (Pi-Neural ODEs). An encoder, as a part of the autoencoder, learns the abstract mappings from the first few items of observational data to the initial values of the latent variables, which drive the learning of embedded dynamics via physics-informed Neural ODEs, imposing a modal model structure on that latent space. The decoder of the proposed model adopts the eigenmodes derived from an eigen-analysis applied to the linearized portion of a physics-based model: a process implicitly carrying the spatial relationship between degrees-of-freedom (DOFs). The framework is validated on a numerical example, and an experimental dataset of a scaled cable-stayed bridge, where the learned hybrid model is shown to outperform a purely physics-based approach to modeling. We further show the functionality of the proposed scheme within the context of virtual sensing, i.e., the recovery of generalized response quantities in unmeasured DOFs from spatially sparse data.

Zhilu Lai
Wei Liu
Xudong Jian
Kiran Bacsa
Limin Sun
Eleni Chatzi


Beyond Hard Labels: Investigating data label distributions

Abstract: High-quality data is a key aspect of modern machine learning. However, labels generated by humans suffer from issues like label noise and class ambiguities. We raise the question of whether hard labels are sufficient to represent the underlying ground truth distribution in the presence of these inherent imprecision. Therefore, we compare the disparity of learning with hard and soft labels quantitatively and qualitatively for a synthetic and a real-world dataset. We show that the application of soft labels leads to improved performance and yields a more regular structure of the internal feature space.

Vasco Grossmann
Lars Schmarje
Reinhard Koch


A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy

Abstract: A distinguishing characteristic of federated learning is that the (local) client data could have statistical heterogeneity. This heterogeneity has motivated the design of personalized learning, where individual (personalized) models are trained, through collaboration. There have been various personalization methods proposed in literature, with seemingly very different forms and methods ranging from use of a single global model for local regularization and model interpolation, to use of multiple global models for personalized clustering, etc. In this work, we begin with a generative framework that could potentially unify several different algorithms as well as suggest new algorithms. We apply our generative framework to personalized estimation, and connect it to the classical empirical Bayes' methodology. We develop private personalized estimation under this framework. We then use our generative framework for learning, which unifies several known personalized FL algorithms and also suggests new ones; we propose and study a new algorithm AdaPeD based on a Knowledge Distillation, which numerically outperforms several known algorithms. We also develop privacy for personalized learning methods with guarantees for user-level privacy and composition. We numerically evaluate the performance as well as the privacy for both the estimation and learning problems, demonstrating the advantages of our proposed methods.

Kaan Ozkara
Antonious M. Girgis
Deepesh Data
Suhas Diggavi


Gaia Data Release 3: Cross-match of Gaia sources with variable objects from the literature

Abstract: Context. In the current ever increasing data volumes of astronomical surveys, automated methods are essential. Objects of known classes from the literature are necessary for training supervised machine learning algorithms, as well as for verification/validation of their results. Aims.The primary goal of this work is to provide a comprehensive data set of known variable objects from the literature cross-matched with \textit{Gaia}~DR3 sources, including a large number of both variability types and representatives, in order to cover as much as possible sky regions and magnitude ranges relevant to each class. In addition, non-variable objects from selected surveys are targeted to probe their variability in \textit{Gaia} and possible use as standards. This data set can be the base for a training set applicable in variability detection, classification, and validation. MethodsA statistical method that employed both astrometry (position and proper motion) and photometry (mean magnitude) was applied to selected literature catalogues in order to identify the correct counterparts of the known objects in the \textit{Gaia} data. The cross-match strategy was adapted to the properties of each catalogue and the verification of results excluded dubious matches. Results.Our catalogue gathers 7\,841\,723 \textit{Gaia} sources among which 1.2~million non-variable objects and 1.7~million galaxies, in addition to 4.9~million variable sources representing over 100~variability (sub)types. Conclusions.This data set served the requirements of \textit{Gaia}'s variability pipeline for its third data release (DR3), from classifier training to result validation, and it is expected to be a useful resource for the scientific community that is interested in the analysis of variability in the \textit{Gaia} data and other surveys.

P. Gavras
L. Rimoldini
K. Nienartowicz
G. Jevardat de Fombelle
B. Holl
P. Ábrahám
M. Audard
M. Carnerero
G. Clementini
J. De Ridder
E. Distefano
P. Garcia-Lario
A. Garofalo
Á. Kóspál
K. Kruszyńska
M. Kun
I. Lecoeur-Taïbi
G. Marton
T. Mazeh
N. Mowlavi
C. Raiteri
V. Ripepi
L. Szabados
S. Zucker
L. Eyer


Proteus: A Self-Designing Range Filter

Abstract: We introduce Proteus, a novel self-designing approximate range filter, which configures itself based on sampled data in order to optimize its false positive rate (FPR) for a given space requirement. Proteus unifies the probabilistic and deterministic design spaces of state-of-the-art range filters to achieve robust performance across a larger variety of use cases. At the core of Proteus lies our Contextual Prefix FPR (CPFPR) model - a formal framework for the FPR of prefix-based filters across their design spaces. We empirically demonstrate the accuracy of our model and Proteus' ability to optimize over both synthetic workloads and real-world datasets. We further evaluate Proteus in RocksDB and show that it is able to improve end-to-end performance by as much as 5.3x over more brittle state-of-the-art methods such as SuRF and Rosetta. Our experiments also indicate that the cost of modeling is not significant compared to the end-to-end performance gains and that Proteus is robust to workload shifts.

Eric R. Knorr
Baptiste Lemaire
Andrew Lim
Siqiang Luo
Huanchen Zhang
Stratos Idreos
Michael Mitzenmacher

SIGMOD'22: Proceedings of the 2022 International Conference on Management of Data, June 2022, 1670-1684


Protoformer: Embedding Prototypes for Transformers

Abstract: Transformers have been widely applied in text classification. Unfortunately, real-world data contain anomalies and noisy labels that cause challenges for state-of-art Transformers. This paper proposes Protoformer, a novel self-learning framework for Transformers that can leverage problematic samples for text classification. Protoformer features a selection mechanism for embedding samples that allows us to efficiently extract and utilize anomalies prototypes and difficult class prototypes. We demonstrated such capabilities on datasets with diverse textual structures (e.g., Twitter, IMDB, ArXiv). We also applied the framework to several models. The results indicate that Protoformer can improve current Transformers in various empirical settings.

Ashkan Farhangi
Ning Sui
Nan Hua
Haiyan Bai
Arthur Huang
Zhishan Guo

Pacific-Asia Conference on Knowledge Discovery and Data Mining


KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP

Abstract: This paper focuses on the data augmentation for low-resource NLP tasks where the training set is limited. The existing solutions either leverage task-independent heuristic rules (e.g., Synonym Replacement) or fine-tune general-purpose pre-trained language models (e.g., GPT2) using the limited training instances to produce new synthetic data. Consequently, they have trivial task-specific knowledge and are limited to yielding low-quality synthetic data. To combat this issue, we propose Knowledge Mixture Data Augmentation Model (KnowDA) which is an Seq2Seq language model pre-trained on a mixture of diverse NLP tasks under a novel framework of Knowledge Mixture Training (KoMT). The goal of KoMT is to condense diverse NLP task-specific knowledge into the single KnowDA model (i.e., all-in-one) such that KnowDA could utilize these knowledge to quickly grasp the inherent synthesis law of the target task through limited training instances. Specifically, KoMT reformulates input examples from various heterogeneous NLP tasks into a unified text-to-text format, and employs denoising training objectives in different granularity to learn to reconstruct partial or complete samples. To the best of our knowledge, we are the first attempt to apply 100+ NLP multi-task training for data augmentation. Extensive experiments show that i) the synthetic data produced by KnowDA successfully improves performance of the strong pre-trained language models (i.e., Bert, ALBert and Deberta) by a large margin on the low-resource NLP benchmark FewGLUE, CoNLL'03 and WikiAnn; ii) KnowDA successfully transfers the task knowledge to NLP tasks whose types are seen and unseen in KoMT.

Yufei Wang
Jiayi Zheng
Can Xu
Xiubo Geng
Tao Shen
Chongyang Tao
Daxin Jiang


Augmented Imagefication: A Data-driven Fault Detection Method for Aircraft Air Data Sensors

Abstract: In this paper, a novel data-driven approach named Augmented Imagefication for Fault detection (FD) of aircraft air data sensors (ADS) is proposed. Exemplifying the FD problem of aircraft air data sensors, an online FD scheme on edge device based on deep neural network (DNN) is developed. First, the aircraft inertial reference unit measurements is adopted as equivalent inputs, which is scalable to different aircraft/flight cases. Data associated with 6 different aircraft/flight conditions are collected to provide diversity (scalability) in the training/testing database. Then Augmented Imagefication is proposed for the DNN-based prediction of flying conditions. The raw data are reshaped as a grayscale image for convolutional operation, and the necessity of augmentation is analyzed and pointed out. Different kinds of augmented method, i.e. Flip, Repeat, Tile and their combinations are discussed, the result shows that the All Repeat operation in both axes of image matrix leads to the best performance of DNN. The interpretability of DNN is studied based on Grad-CAM, which provide a better understanding and further solidifies the robustness of DNN. Next the DNN model, VGG-16 with augmented imagefication data is optimized for mobile hardware deployment. After pruning of DNN, a lightweight model (98.79% smaller than original VGG-16) with high accuracy (slightly up by 0.27%) and fast speed (time delay is reduced by 87.54%) is obtained. And the hyperparameters optimization of DNN based on TPE is implemented and the best combination of hyperparameters is determined (learning rate 0.001, iterative epochs 600, and batch size 100 yields the highest accuracy at 0.987). Finally, a online FD deployment based on edge device, Jetson Nano, is developed and the real time monitoring of aircraft is achieved. We believe that this method is instructive for addressing the FD problems in other similar fields.

Hang Zhao
Jinyi Ma
Zhongzhi Li
Yiqun Dong
Jianliang Ai