Reviewerly/exHarmony huggingface dataset:
Search a Great Leveler? Ensuring More Equitable Information Acquisition

Abstract The ubiquitous search box promised to democratize knowledge access by making information universally accessible. But while many search engines cater well for certain user groups, information tasks and content types, they cater poorly for others. Poorly‐served users include those with certain types of impairment (e.g. dyslexia), and weakly‐supported tasks include highly exploratory goals, where it can be difficult to express information needed as a query. Furthermore, the overdominance of search functionality in many information environments has restricted support for other important forms of information acquisition, such as serendipitous information encountering and creative “inspiration hunting.” Search results and recommendations can also promote certain types of content due to algorithmic bias. Rather than act as a great leveler by making information acquisition effective, efficient and enjoyable for all, search engines often unfairly favor some types of user, task or content over others. In short, search is not always equitable. This panel discussion will elucidate the inequity of search as an information acquisition paradigm from multiple perspectives and propose design principles to ensure more equitable information acquisition.
Video Representation Learning with Graph Contrastive Augmentation

Contrastive-based self-supervised learning for image representations has significantly closed the gap with supervised learning. A natural extension of image-based contrastive learning methods to the video domain is to fully exploit the temporal structure presented in videos. We propose a novel contrastive self-supervised video representation learning framework, termed Graph Contrastive Augmentation (GCA), by constructing a video temporal graph and devising a graph augmentation that is designed to enhance the correlation across frames of videos and developing a new view for exploring temporal structure in videos. Specifically, we construct the temporal graph in the video by leveraging the relational knowledge behind the correlated sequence video features. Afterwards, we apply the proposed graph augmentation to generate another graph view by cooperating random corruption of the original graph to enhance the diversity of the intrinsic structure of the temporal graph. To this end, we provide two different kinds of contrastive learning methods to train our framework using temporal relationships concealed in videos as self-supervised signals. We perform empirical experiments on downstream tasks, action recognition and video retrieval, using the learned video representation, and the results demonstrate that with the graph view of temporal structure, our proposed GCA remarkably improves performance against or on par with the recent methods.
Stylegan-Induced Data-Driven Regularization for Inverse Problems

Recent advances in generative adversarial networks (GANs) have opened up the possibility of generating high-resolution photo-realistic images that were impossible to produce previously. The ability of GANs to sample from high-dimensional distributions has naturally motivated researchers to leverage their power for modeling the image prior in inverse problems. We extend this line of research by developing a Bayesian image reconstruction framework that utilizes the full potential of a pre-trained StyleGAN2 generator, which is the currently dominant GAN architecture, for constructing the prior distribution on the underlying image. Our proposed approach, which we refer to as learned Bayesian reconstruction with generative models (L-BRGM), entails joint optimization over the style-code and the input latent code, and enhances the expressive power of a pre-trained StyleGAN2 generator by allowing the style-codes to be different for different generator layers. Considering the inverse problems of image inpainting and super-resolution, we demonstrate that the proposed approach is competitive with, and sometimes superior to, state-of-the-art GAN-based image reconstruction methods.
Multi-Institution Encrypted Medical Imaging AI Validation Without Data Sharing

Adoption of artificial intelligence medical imaging applications is often impeded by barriers between healthcare systems and algorithm developers given that access to both private patient data and commercial model IP is important to perform pre-deployment evaluation. This work investigates a framework for secure, privacy-preserving and AI-enabled medical imaging inference using CrypTFlow2, a state-of-the-art end-to-end compiler allowing cryptographically secure 2-party Computation (2PC) protocols between the machine learning model vendor and target patient data owner. A common DenseNet-121 chest x-ray diagnosis model was evaluated on multi-institutional chest radiographic imaging datasets both with and without CrypTFlow2 on two test sets spanning seven sites across the US and India, and comprising 1,149 chest x-ray images. We measure comparative AUROC performance between secure and insecure inference in multiple pathology classification tasks, and explore model output distributional shifts and resource constraints introduced by secure model inference. Secure inference with CrypTFlow2 demonstrated no significant difference in AUROC for all diagnoses, and model outputs from secure and insecure inference methods were distributionally equivalent. The use of CrypTFlow2 may allow off-the-shelf secure 2PC between healthcare systems and AI model vendors for medical imaging, without changes in performance, and can facilitate scalable pre-deployment infrastructure for real-world secure model evaluation without exposure to patient data or model IP.
Forward Engineering Relational Schemas and High-Level Data Access from Conceptual Models

Forward engineering relational schemas based on conceptual models is an established practice, with a number of commercial tools available and widely used in production settings. These tools employ automated transformation strategies to address the gap between the primitives offered by conceptual modeling languages (such as ER and UML) and the relational model. Despite the various benefits of automated transformation, once a database schema is obtained, data access is usually undertaken by relying on the resulting schema, at a level of abstraction lower than that of the source conceptual model. Data access then requires both domain knowledge and comprehension of the (non-trivial) technical choices embodied in the resulting schema. We address this problem by forward engineering not only a relational schema, but also creating an ontology-based data access mapping for the resulting schema. This mapping is used to expose data in terms of the original conceptual model, and hence queries can be written at a high level of abstraction, independently of the transformation strategy selected.
Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering

Due to the complexity of the various waveforms of microseismic data, there are high requirements on the automatic multi-classification of such data; an accurate classification is conducive for further signal processing and stability analysis of surrounding rock masses. In this study, a microseismic multi-classification (MMC) model is proposed based on the short time Fourier transform (STFT) technology and convolutional neural network (CNN). The real and imaginary parts of the coefficients of microseismic data are inputted to the proposed model to generate three classes of targets. Compared with existing methods, the MMC has an optimal performance in multi-classification of microseismic data in terms of Precision, Recall, and F1-score, even when the waveform of a microseismic signal is similar to that of some special noise. Moreover, semisynthetic data constructed by clean microseismic data and noise are used to prove the low sensitivity of the MMC to noise. Microseismic data recorded under different geological conditions are also tested to prove the generality of the model, and a microseismic signal with Mw ≥ 0.2 can be detected with a high accuracy. The proposed method has great potential to be extended to the study of exploration seismology and earthquakes.
Multi-Task Transfer Learning with Data Augmentation for Recognizing Question Entailment in the Medical Domain

Recognizing Question Entailment (RQE) is an important task for Question Answering (QA) as it infers the logical relation between two questions. Existing research efforts on this topic using multi-task transfer learning models have proven to be successful. However, the relative scarcity of labeled data has impeded the exploitation of this family of high-capacity approaches. In this paper, we propose a multi-task transfer learning-based method with data augmentation for RQE in medical QA. The proposed method first generates new training data from existing RQE examples based on contextual word embeddings and QA data. It then uses multi-task transfer learning that combines multi-task learning and language-model pre-training. Experimental evaluations performed on the RQE test set of the 2019 MEDIQA challenge show that our data augmentation method led to a substantial increase of an average of 18.5% and that the proposed RQE approach is more effective than the state-of-the-art systems.
Generally Boosting Few-Shot Learning with HandCrafted Features

Existing Few-Shot Learning (FSL) methods predominantly focus on developing different types of sophisticated models to extract the transferable prior knowledge for recognizing novel classes, while they almost pay less attention to the feature learning part in FSL which often simply leverage some well-known CNN as the feature learner. However, feature is the core medium for encoding such transferable knowledge. Feature learning is easy to be trapped in the over-fitting particularly in the scarcity of the training data, and thereby degenerates the performances of FSL. The handcrafted features, such as Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP), have no requirement on the amount of training data, and used to perform quite well in many small-scale data scenarios, since their extractions involve no learning process, and are mainly based on the empirically observed and summarized prior feature engineering knowledge. In this paper, we intend to develop a general and simple approach for generally boosting FSL via exploiting such prior knowledge in the feature learning phase. To this end, we introduce two novel handcrafted feature regression modules, namely HOG and LBP regression, to the feature learning parts of deep learning-based FSL models. These two modules are separately plugged into the different convolutional layers of backbone based on the characteristics of the corresponding handcrafted features to guide the backbone optimization from different feature granularity, and also ensure that the learned feature can encode the handcrafted feature knowledge which improves the generalization ability of feature and alleviate the over-fitting of the models. Three recent state-of-the-art FSL approaches are leveraged for examining the effectiveness of our method. Extensive experiments on miniImageNet, CIFAR-FS and FC100 datasets show that the performances of all these FSL approaches are well boosted via applying our method on all three datasets. Our codes and models have been released.
Semantic segmentation for remote sensing images via dense feature extraction and companion loss neural network

Semantic segmentation models with good performance are crucial for the practical application of high-resolution remote-sensing images (RSI). Compared with nature images, in most cases the RSI dataset has the problem of unbalanced sample distribution between classes and unbalanced target size ratio. Using semantic segmentation to pixel-wise classify and identification RSI can solve this problem to some extent. At present, most semantic segmentation models based on mainstream networks solve these problems from the object scale and super-pixel perspective, whereas the accuracy still needs to be improved. To enhance the quality of the predicted feature maps, the Dense-Inception Net (D-INet) model is proposed based on the idea of DenseNet feature reuse and combined with the attention mechanism, which enables the network to maintain depth while widening the width to obtain more advanced semantic information. The connection of contextual information is strengthened by connecting RFB multi-scale modules at the shallow level, and shallow features with more spatial features are extracted for feature fusion with the decoder. To further lift the segmentation accuracy, a companion loss is introduced in the encoder, and the model is trained to have better segmentation performance for small sample objects by adaptively adjusting the loss coefficients. Experimental results show that the proposed method significantly increases the accuracy and mean Intersection Over Union (mIOU) scores.
Financial Credit Default Forecast Based on Big Data Analysis

How to effectively evaluate and identify the potential default risk of borrowers and calculate the default probability of borrowers before issuing loans is the basis and important link of the credit risk management of modern financial institutions.This paper mainly studies the statistical analysis of historical loan data of banks and other financial institutions with the help of the idea of non-balanced data classification, and uses machine learning algorithms (not statistical algorithms) such as random forest, logical regression and decision tree to establish loan default prediction model.The experimental results show that neural network and random forest algorithm outperform decision tree and logistic regression classification algorithm in prediction performance.In addition, by using the random forest algorithm to rank the importance of features, the features that have a greater impact on the final default can be obtained, so as to make a more effective judgment on the loan risk in the financial field.
Comparing AI-Based and Traditional Prospect Generating Methods

This contribution deals with a comparison of one AI based data mining tool and two traditional approaches utilized to collect and interpret data for prospect generation. Traditional prospect generation methods, like manual web search or using purchased data from external providers may involve high costs and efforts and are subject to failures and waste through outdated and untargeted data. In contrast, AI based methods claim to provide better results at lower costs. Based on a real case, the authors compare effects of these three prospect generation methods. AI based data mining tools compensate for some weaknesses of other methods, especially because they do not need pre-defined selection criteria which might bias the results. In addition, they involve less effort from the researcher. However, the results in generating concrete prospects may be still weaker than with traditional methods if web crawling activities are influenced by underlying databases. For academic research in the field of prospect generation, this study provides a fact-based comparison of approaches. Implications for businesses include the advice to combine methods rather than to rely on a single approach. The time available for research and the complexity of the target market have an influence on the selection of the prospect generation approach.
Shapir: Standardizing and Democratizing Access to Web APIs

Today, many web sites offer third-party access to their data through web APIs. But manually encoding URLs with arbitrary endpoints, parameters, authentication handshakes, and pagination, among other things, makes API use challenging and laborious for programmers, and untenable for novices. In addition, each site offers its own idiosyncratic data model, properties, and methods that a new user must learn, even when the sites manage the same common types of information as many others.
Isolation Distributional Kernel A New Tool for Point &amp; Group Anomaly Detection

We introduce Isolation Distributional Kernel as a new way to measure the similarity between two distributions. Existing approaches based on kernel mean embedding, which convert a point kernel to a distributional kernel, have two key issues: the point kernel employed has a feature map with intractable dimensionality; and it is {\em data independent}. This paper shows that Isolation Distributional Kernel (IDK), which is based on a {\em data dependent} point kernel, addresses both key issues. We demonstrate IDK's efficacy and efficiency as a new tool for kernel based anomaly detection for both point and group anomalies. Without explicit learning, using IDK alone outperforms existing kernel based point anomaly detector OCSVM and other kernel mean embedding methods that rely on Gaussian kernel. For group anomaly detection,we introduce an IDK based detector called IDK$^2$. It reformulates the problem of group anomaly detection in input space into the problem of point anomaly detection in Hilbert space, without the need for learning. IDK$^2$ runs orders of magnitude faster than group anomaly detector OCSMM.We reveal for the first time that an effective kernel based anomaly detector based on kernel mean embedding must employ a characteristic kernel which is data dependent.
Revealing Disocclusions in Temporal View Synthesis through Infilling Vector Prediction

We consider the problem of temporal view synthesis, where the goal is to predict a future video frame from the past frames using knowledge of the depth and relative camera motion. In contrast to revealing the disoccluded regions through intensity based infilling, we study the idea of an infilling vector to infill by pointing to a non-disoccluded region in the synthesized view. To exploit the structure of disocclusions created by camera motion during their infilling, we rely on two important cues, temporal correlation of infilling directions and depth. We design a learning framework to predict the infilling vector by computing a temporal prior that reflects past infilling directions and a normalized depth map as input to the network. We conduct extensive experiments on a large scale dataset we build for evaluating temporal view synthesis in addition to the SceneNet RGB-D dataset. Our experiments demonstrate that our infilling vector prediction approach achieves superior quantitative and qualitative infilling performance compared to other approaches in literature.
Continuous Transition: Improving Sample Efficiency for Continuous Control Problems via MixUp

Although deep reinforcement learning (RL) has been successfully applied to a variety of robotic control tasks, it&#x2019;s still challenging to apply it to real-world tasks, due to the poor sample efficiency. Attempting to overcome this shortcoming, several works focus on reusing the collected trajectory data during the training by decomposing them into a set of policy-irrelevant discrete transitions. However, their improvements are somewhat marginal since i) the amount of the transitions is usually small, and ii) the value assignment only happens in the joint states. To address these issues, this paper introduces a concise yet powerful method to construct Continuous Transition, which exploits the trajectory information by exploiting the potential transitions along the trajectory. Specifically, we propose to synthesize new transitions for training by linearly interpolating the consecutive transitions. To keep the constructed transitions authentic, we also develop a discriminator to guide the construction process automatically. Extensive experiments demonstrate that our proposed method achieves a significant improvement in sample efficiency on various complex continuous robotic control problems in MuJoCo and outperforms the advanced model-based / model-free RL methods. The source code is available<sup>1</sup>.
A Simple Baseline for Cross-Domain Few-Shot Text Classification

Few-shot text classification has been largely explored due to its remarkable few-shot generalization ability to in-domain novel classes. Yet, the generalization ability of existing models to cross-domain novel classes has seldom be studied. To fill the gap, we investigate a new task, called cross-domain few-shot text classification (XFew) and present a simple baseline that witnesses an appealing cross-domain generalization capability while retains a nice in-domain generalization capability. Experiments are conducted on two datasets under both in-domain and cross-domain settings. The results show that current few-shot text classification models lack a mechanism to account for potential domain shift in the XFew task. In contrast, our proposed simple baseline achieves surprisingly superior results in comparison with other models in cross-domain scenarios, confirming the need of further research in the XFew task and providing insights for possible directions. (The code and datasets are available at https://github.com/GeneZC/XFew).
The impact of text presentation on translator performance

Abstract Widely used computer-aided translation (CAT) tools divide documents into segments, such as sentences, and arrange them side-by-side in a spreadsheet-like view. We present the first controlled evaluation of these design choices on translator performance, measuring speed and accuracy in three experimental text-processing tasks. We find significant evidence that sentence-by-sentence presentation enables faster text reproduction and within-sentence error identification compared to unsegmented text, and that a top-and-bottom arrangement of source and target sentences enables faster text reproduction compared to a side-by-side arrangement. For revision, on the other hand, we find that presenting unsegmented text results in the highest accuracy and time efficiency. Our findings have direct implications for best practices in designing CAT tools.
Detecting Covariate Drift with Explanations

Detecting when there is a domain drift between training and inference data is important for any model evaluated on data collected in real time. Many current data drift detection methods only utilize input features to detect domain drift. While effective, these methods disregard the model's evaluation of the data, which may be a significant source of information about the data domain. We propose to use information from the model in the form of explanations, specifically gradient times input, in order to utilize this information. Following the framework of Rabanser et al. [], we combine these explanations with two-sample tests in order to detect a shift in distribution between training and evaluation data. Promising initial experiments show that explanations provide useful information for detecting shift, which potentially improves upon the current state-of-the-art.
Temporally stable video segmentation without video annotations

Temporally consistent dense video annotations are scarce and hard to collect. In contrast, image segmentation datasets (and pre-trained models) are ubiquitous, and easier to label for any novel task. In this paper, we introduce a method to adapt still image segmentation models to video in an unsupervised manner, by using an optical flow-based consistency measure. To ensure that the inferred segmented videos appear more stable in practice, we verify that the consistency measure is well correlated with human judgement via a user study. Training a new multi-input multi-output decoder using this measure as a loss, together with a technique for refining current image segmentation datasets and a temporal weighted-guided filter, we observe stability improvements in the generated segmented videos with minimal loss of accuracy.