Reviewerly/exHarmony huggingface dataset:
Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach

Abstract The interpretation of feature importance in machine learning models is challenging when features are dependent. Permutation feature importance (PFI) ignores such dependencies, which can cause misleading interpretations due to extrapolation. A possible remedy is more advanced conditional PFI approaches that enable the assessment of feature importance conditional on all other features. Due to this shift in perspective and in order to enable correct interpretations, it is beneficial if the conditioning is transparent and comprehensible. In this paper, we propose a new sampling mechanism for the conditional distribution based on permutations in conditional subgroups. As these subgroups are constructed using tree-based methods such as transformation trees, the conditioning becomes inherently interpretable. This not only provides a simple and effective estimator of conditional PFI, but also local PFI estimates within the subgroups. In addition, we apply the conditional subgroups approach to partial dependence plots, a popular method for describing feature effects that can also suffer from extrapolation when features are dependent and interactions are present in the model. In simulations and a real-world application, we demonstrate the advantages of the conditional subgroup approach over existing methods: It allows to compute conditional PFI that is more true to the data than existing proposals and enables a fine-grained interpretation of feature effects and importance within the conditional subgroups.
Voice for the Voiceless: Active Sampling to Detect Comments Supporting the Rohingyas

The Rohingya refugee crisis is one of the biggest humanitarian crises of modern times with more than 700,000 Rohingyas rendered homeless according to the United Nations High Commissioner for Refugees. While it has received sustained press attention globally, no comprehensive research has been performed on social media pertaining to this large evolving crisis. In this work, we construct a substantial corpus of YouTube video comments (263,482 comments from 113,250 users in 5,153 relevant videos) with an aim to analyze the possible role of AI in helping a marginalized community. Using a novel combination of multiple Active Learning strategies and a novel active sampling strategy based on nearest-neighbors in the comment-embedding space, we construct a classifier that can detect comments defending the Rohingyas among larger numbers of disparaging and neutral ones. We advocate that beyond the burgeoning field of hate speech detection, automatic detection of help speech can lend voice to the voiceless people and make the internet safer for marginalized communities.
Understanding blockchain for insurance use cases

Abstract Insurance industry practitioners have deep knowledge of their industry, but there is a lack of a simple-to-understand, practical blueprint on applying distributed ledger technology solutions, including blockchain. This paper provides a practical guide for actuaries, risk professionals, insurance companies and their Boards on blockchain, including an education piece to provide an understanding of the technology. Examples of real-world applications and use cases in insurance are provided to illustrate the capability of the technology. The current risks and challenges in adopting the technology are also considered. Finally, a checklist of issues to consider in adopting a blockchain solution for insurance business problems is provided.
End-to-End Transition-Based Online Dialogue Disentanglement

Dialogue disentanglement aims to separate intermingled messages into detached sessions. The existing research focuses on two-step architectures, in which a model first retrieves the relationships between two messages and then divides the message stream into separate clusters. Almost all existing work puts significant efforts on selecting features for message-pair classification and clustering, while ignoring the semantic coherence within each session. In this paper, we introduce the first end-to- end transition-based model for online dialogue disentanglement. Our model captures the sequential information of each session as the online algorithm proceeds on processing a dialogue. The coherence in a session is hence modeled when messages are sequentially added into their best-matching sessions. Meanwhile, the research field still lacks data for studying end-to-end dialogue disentanglement, so we construct a large-scale dataset by extracting coherent dialogues from online movie scripts. We evaluate our model on both the dataset we developed and the publicly available Ubuntu IRC dataset [Kummerfeld et al., 2019]. The results show that our model significantly outperforms the existing algorithms. Further experiments demonstrate that our model better captures the sequential semantics and obtains more coherent disentangled sessions.
What makes instance discrimination good for transfer learning

Contrastive visual pretraining based on the instance discrimination pretext task has made significant progress. Notably, recent work on unsupervised pretraining has shown to surpass the supervised counterpart for finetuning downstream applications such as object detection and segmentation. It comes as a surprise that image annotations would be better left unused for transfer learning. In this work, we investigate the following problems: What makes instance discrimination pretraining good for transfer learning? What knowledge is actually learned and transferred from these models? From this understanding of instance discrimination, how can we better exploit human annotation labels for pretraining? Our findings are threefold. First, what truly matters for the transfer is low-level and mid-level representations, not high-level representations. Second, the intra-category invariance enforced by the traditional supervised model weakens transferability by increasing task misalignment. Finally, supervised pretraining can be strengthened by following an exemplar-based approach without explicit constraints among the instances within the same category.
Premise Selection in Natural Language Mathematical Texts

The discovery of supporting evidence for addressing complex mathematical problems is a semantically challenging task, which is still unexplored in the field of natural language processing for mathematical text.The natural language premise selection task consists in using conjectures written in both natural language and mathematical formulae to recommend premises that most likely will be useful to prove a particular statement.We propose an approach to solve this task as a link prediction problem, using Deep Convolutional Graph Neural Networks.This paper also analyses how different baselines perform in this task and shows that a graph structure can provide higher F1-score, especially when considering multi-hop premise selection.
Opportunities for Cost Savings with In-Transit Visualization

We analyze the opportunities for in-transit visualization to provide cost savings compared to in-line visualization. We begin by developing a cost model that includes factors related to both in-line and in-transit which allows comparisons to be made between the two methods. We then run a series of studies to create a corpus of data for our model. We run two different visualization algorithms, one that is computation heavy and one that is communication heavy with concurrencies up to 32, 768 cores. Our primary results are in exploring the cost model within the context of our corpus. Our findings show that in-transit consistently achieves significant cost efficiencies by running visualization algorithms at lower concurrency, and that in many cases these efficiencies are enough to offset other costs (transfer, blocking, and additional nodes) to be cost effective overall. Finally, this work informs future studies, which can focus on choosing ideal configurations for in-transit processing that can consistently achieve cost efficiencies.
Patterns for Blockchain Data Migration

With the rapid evolution of technological, economic, and regulatory landscapes, contemporary blockchain platforms are all but certain to undergo major changes. Therefore, the applications that rely on them will eventually need to migrate from one blockchain instance to another to remain competitive and secure, as well as to enhance the business process, performance, cost efficiency, privacy, and regulatory compliance. However, the differences in data and smart contract representations, modes of hosting, transaction fees, as well as the need to preserve consistency, immutability, and data provenance introduce unique challenges over database migration. We first present a set of blockchain migration scenarios and data fidelity levels using an illustrative example. We then present a set of migration patterns to address those scenarios and the above data management challenges. Finally, we demonstrate how the effort, cost, and risk of migration could be minimized by choosing a suitable set of data migration patterns, data fidelity level, and proactive system design. Practical considerations and research challenges are also highlighted.
Understanding action recognition in still images

Action recognition in still images is closely related to various other computer vision tasks such as pose estimation, object recognition, image retrieval, video action recognition and frame tagging in videos. This problem is focused on recognizing a person's action or behavior using a single frame. Unlike action recognition in videos - a relatively very well established area of research where spatio-temporal features are used, these are not available for still images, making the problem more challenging. In the present work only actions that involve objects are considered. A complex action is broken down into components based on semantics. The importance of each of these components in action recognition is systematically studied.
Hour-Ahead Photovoltaic Power Forecasting Using an Analog Plus Neural Network Ensemble Method

The common analog approach and ensemble methods in photovoltaic (PV) power forecasting are based on the forecasts from several numerical weather prediction (NWP) models. These may be not applicable to the very-short-term PV power forecasting, since forecasts based on NWP models are reliable in horizons longer than six hours. In this paper, a methodology for one-hour-ahead PV power forecasting is proposed. Instead of the NWP models, the persistence method is applied in the analog approach to produce meteorological forecasts. The historical data with meteorological predictions similar to the target forecast hour are identified to train the forecast model. Then, the feed forward neural networks (FNNs) act as the base predictors of the neural network ensemble method to replace the NWP-based PV power prediction methods. The forecast results produced by the FNNs are combined by the random forest (RF) algorithm. The performance of the proposed method is evaluated on a real grid-connected PV plant located in Southeast China. Results show that the proposed method outperforms six benchmark models: the persistence model, the support vector regression (SVR) model, the linear regression model, the RF model, the gradient boosting model, and XGBoost model. The improvements reach up to over 40% for the standard error metrics.
Sketch-Guided Scenery Image Outpainting

The outpainting results produced by existing approaches are often too random to meet users' requirement. In this work, we take the image outpainting one step forward by allowing users to harvest personal custom outpainting results using sketches as the guidance. To this end, we propose an encoder-decoder based network to conduct sketch-guided outpainting, where two alignment modules are adopted to impose the generated content to be realistic and consistent with the provided sketches. First, we apply a holistic alignment module to make the synthesized part be similar to the real one from the global view. Second, we reversely produce the sketches from the synthesized part and encourage them be consistent with the ground-truth ones using a sketch alignment module. In this way, the learned generator will be imposed to pay more attention to fine details and be sensitive to the guiding sketches. To our knowledge, this work is the first attempt to explore the challenging yet meaningful conditional scenery image outpainting. We conduct extensive experiments on two collected benchmarks to qualitatively and quantitatively validate the effectiveness of our approach compared with the other state-of-the-art generative models.
Implementing the Adaptive Learning Techniques

The concept of adaptive learning emerged a few decades ago, but most theoretical findings have never been put into practice, and software solutions had no significant reach for a long time due to insufficient e-learning technology development and coverage. The recent advancements of information technology allow the elaboration of complex big data analytics and artificial intelligence solutions, in adaptive learning in particular. This article investigates exploitation of adaptive learning technology and techniques.The solutions proposed allow mapping optimal individualized learning paths for students in online courses, using the ratio of the level of knowledge at course completion to time spent on the course as an optimality criterion. A genetic algorithm is used to solve this optimization problem. A model based on the speed of forgetting was applied to extrapolate the level of retained knowledge. Practical implementation of the technology proposed involves a set of tools to expand the adaptive learning opportunities of distance learning systems and a module to operate the genetic algorithm. We developed a few options of software architecture using different technologies and programming languages and either one or two servers. The solution was tested during the design of adaptive learning courses for National University of Science and Technology MISIS (NUST MISIS) and Tomsk State University of Control Systems and Radioelectronics (TUSUR).
Normalized Loss Functions for Deep Learning with Noisy Labels

Robust loss functions are essential for training accurate deep neural networks (DNNs) in the presence of noisy (incorrect) labels. It has been shown that the commonly used Cross Entropy (CE) loss is not robust to noisy labels. Whilst new loss functions have been designed, they are only partially robust. In this paper, we theoretically show by applying a simple normalization that: any loss can be made robust to noisy labels. However, in practice, simply being robust is not sufficient for a loss function to train accurate DNNs. By investigating several robust loss functions, we find that they suffer from a problem of underfitting. To address this, we propose a framework to build robust loss functions called Active Passive Loss (APL). APL combines two robust loss functions that mutually boost each other. Experiments on benchmark datasets demonstrate that the family of new loss functions created by our APL framework can consistently outperform state-of-the-art methods by large margins, especially under large noise rates such as 60% or 80% incorrect labels.
Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters

We study methods for reconstructing Bayesian uncertainties on dynamical mass estimates of galaxy clusters using convolutional neural networks (CNNs). We discuss the statistical background of approximate Bayesian neural networks and demonstrate how variational inference techniques can be used to perform computationally tractable posterior estimation for a variety of deep neural architectures. We explore how various model designs and statistical assumptions impact prediction accuracy and uncertainty reconstruction in the context of cluster mass estimation. We measure the quality of our model posterior recovery using a mock cluster observation catalog derived from the MultiDark simulation and UniverseMachine catalog. We show that approximate Bayesian CNNs produce highly accurate dynamical cluster mass posteriors. These model posteriors are log-normal in cluster mass and recover $68\%$ and $90\%$ confidence intervals to within $1\%$ of their measured value. We note how this rigorous modeling of dynamical mass posteriors is necessary for using cluster abundance measurements to constrain cosmological parameters.
Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup

Distinguishing informative and actionable messages from a social media platform like Twitter is critical for facilitating disaster management.For this purpose, we compile a multilingual dataset of over 130K samples for multilabel classification of disaster-related tweets.We present a masking-based loss function for partially labeled samples and demonstrate the effectiveness of Manifold Mixup in the text domain.Our main model is based on Multilingual BERT, which we further improve with Manifold Mixup.We show that our model generalizes to unseen disasters in the test set.Furthermore, we analyze the capability of our model for zero-shot generalization to new languages.Our code, dataset, and other resources are available on Github. 1
Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture

The objective of this work is to detect Alzheimer’s disease using Magnetic Resonance Imaging. For this, we use a three-dimensional densenet-121 architecture. With the use of only freely available tools, we obtain good results: a deep neural network showing metrics of 87% accuracy, 87% sensitivity (micro-average), 88% specificity (micro-average), and 92% AUROC (micro-average) for the task of classifying five different classes (disease stages). The use of tools available for free means that this work can be replicated in developing countries.
Searchable encryption cloud storage with dynamic data update to support efficient policy hiding

Ciphertext policy attribute based encryption (CP-ABE) can provide high finegrained access control for cloud storage. However, it needs to solve problems such as property privacy protection, ciphertext search and data update in the application process. Therefore, based on CP-ABE scheme, this paper proposes a dynamically updatable searchable encryption cloud storage (DUSECS) scheme. Using the characteristics of homomorphic encryption, the encrypted data is compared to achieve efficient hiding policy. Meanwhile, adopting linked list structure, the DUSECS scheme realizes the dynamic data update and integrity detection, and the search encryption against keyword guessing attacks is achieved by combining homomorphic encryption with aggregation algorithm. The analysis of security and performance shows that the scheme is secure and efficient.
Probabilistic Data-Driven Sampling via Multi-Criteria Importance Analysis

Although supercomputers are becoming increasingly powerful, their components have thus far not scaled proportionately. Compute power is growing enormously and is enabling finely resolved simulations that produce never-before-seen features. However, I/O capabilities lag by orders of magnitude, which means only a fraction of the simulation data can be stored for post hoc analysis. Prespecified plans for saving features and quantities of interest do not work for features that have not been seen before. Data-driven intelligent sampling schemes are needed to detect and save important parts of the simulation while it is running. Here, we propose a novel sampling scheme that reduces the size of the data by orders-of-magnitude while still preserving important regions. The approach we develop selects points with unusual data values and high gradients. We demonstrate that our approach outperforms traditional sampling schemes on a number of tasks.
Meta-Learning Symmetries by Reparameterization

Many successful deep learning architectures are equivariant to certain transformations in order to conserve parameters and improve generalization: most famously, convolution layers are equivariant to shifts of the input. This approach only works when practitioners know the symmetries of the task and can manually construct an architecture with the corresponding equivariances. Our goal is an approach for learning equivariances from data, without needing to design custom task-specific architectures. We present a method for learning and encoding equivariances into networks by learning corresponding parameter sharing patterns from data. Our method can provably represent equivariance-inducing parameter sharing for any finite group of symmetry transformations. Our experiments suggest that it can automatically learn to encode equivariances to common transformations used in image processing tasks. We provide our experiment code at https://github.com/AllanYangZhou/metalearning-symmetries.
Elaborating on Learned Demonstrations with Temporal Logic Specifications

Most current methods for learning from demonstrations assume that those demonstrations alone are sufficient to learn the underlying task. This is often untrue, especially if extra safety specifications exist which were not present in the original demonstrations. In this paper, we allow an expert to elaborate on their original demonstration with additional specification information using linear temporal logic (LTL). Our system converts LTL specifications into a differentiable loss. This loss is then used to learn a dynamic movement primitive that satisfies the underlying specification, while remaining close to the original demonstration. Further, by leveraging adversarial training, our system learns to robustly satisfy the given LTL specification on unseen inputs, not just those seen in training. We show that our method is expressive enough to work across a variety of common movement specification patterns such as obstacle avoidance, patrolling, keeping steady, and speed limitation. In addition, we show that our system can modify a base demonstration with complex specifications by incrementally composing multiple simpler specifications. We also implement our system on a PR-2 robot to show how a demonstrator can start with an initial (sub-optimal) demonstration, then interactively improve task success by including additional specifications enforced with our differentiable LTL loss.