Reviewerly/exHarmony huggingface dataset:
Geonews: timely geoscience educational YouTube videos about recent geologic events

Abstract. Geologic events like volcanic eruptions, earthquakes, and tsunamis hurt nearby people and stimulate the curiosity of people farther away, thus providing opportunities to engage the public to be more interested to learn about Earth processes. Geoscientists are increasingly using social media such as Twitter to explain to the public what caused these events, and videos provide an especially vivid way to reach this audience. However, it is still unclear how to create, evaluate, and disseminate videos on timely natural events to communicate geosciences. To address this challenge and opportunity, we analyzed the impact of 33 short geoscience educational (GeoEd) videos that we created and posted on YouTube between 2018 and 2020. These include 12 videos on timely geologic events (denoted Geonews videos) and 21 videos that are not specially about timely geologic topics (denoted General GeoEd videos), all of which were similarly advertised and have similar lengths. By comparing the performance of the Geonews and General GeoEd videos, we conclude the following points: (1) the YouTube audience is consistently interested in Geonews videos, but some General GeoEd videos are more popular; (2) Geonews videos may trigger more meaningful dialogues than General GeoEd videos, especially for local audiences; (3) the “golden period” of Geonews videos engaging YouTube audiences is within 3 weeks after posting; (4) the Geonews audience tends to be younger and more diverse than the General GeoEd video audience; (5) creating Geonews videos can be a promising strategy for geoscientists to engage public audiences on YouTube-like social media.
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation

Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning. However, the proposed methods are often accompanied by plenty of parameters, which greatly limits their application scenarios. Different from previous works that blindly increase the depth of the network, we explore the degradation mechanism of the noisy image and propose a lightweight Multiple Degradation and Reconstruction Network (MDRN) to progressively remove noise. Meanwhile, we propose two novel Heterogeneous Knowledge Distillation Strategies (HMDS) to enable MDRN to learn richer and more accurate features from heterogeneous models, which make it possible to reconstruct higher-quality denoised images under extreme conditions. Extensive experiments show that our MDRN achieves favorable performance against other SID models with fewer parameters. Meanwhile, plenty of ablation studies demonstrate that the introduced HMDS can improve the performance of tiny models or the model under high noise levels, which is extremely useful for related applications.
Detection of Oral Cavity Squamous Cell Carcinoma from Normal Epithelium of the Oral Cavity using Microscopic Images

The most common and widely known type of head and neck cancer is the Oral or mouth neoplasm, of which Oral Cavity Squamous Cell Carcinoma (OCSCC) is the most popular. Despite its impact on Mortality, it is always diagnosed at a late stage due to the inefficiency of the screening models at the early detection stage. Early detection of OCSCC has more than 83% survival rate, although the rate of early detection currently is 29%. Partnering with OCSCC early detection, the deep learning model aids in detecting patterns of oral cancer cells. Sequel to that, this paper proposes using ensemble pretrained deep learning models while unifying the ensemble heads with more shared layers for the early detection of OCSCC from microscopic images. Various pre-trained deep learning models are evaluated using transfer learning while using the Augmentor library to establish high-quality microscopic oral cancer image datasets. The proposed approach obtained a 0.1-0.6% improvement compared with transfer learning methods using 100x magnification and 400x magnification, thus illustrating the robustness of the model for low-quality and high-quality images. Noting that the dataset used in this paper is a newly released competition dataset, a comparison was made with only the article that used the same data when writing this paper. The result obtained proves that the proposed methodology is a promising method for detecting and classifying OCSCC.
Probabilistic Permutation Graph Search

There are several measures for fairness in ranking, based on different underlying assumptions and perspectives. PL optimization with the REINFORCE algorithm can be used for optimizing black-box objective functions over permutations. In particular, it can be used for optimizing fairness measures. However, though effective for queries with a moderate number of repeating sessions, PL optimization has room for improvement for queries with a small number of repeating sessions. In this paper, we present a novel way of representing permutation distributions, based on the notion of permutation graphs. Similar to PL, our distribution representation, called PPG, can be used for black-box optimization of fairness. Different from PL, where pointwise logits are used as the distribution parameters, in PPG pairwise inversion probabilities together with a reference permutation construct the distribution. As such, the reference permutation can be set to the best sampled permutation regarding the objective function, making PPG suitable for both deterministic and stochastic rankings. Our experiments show that PPG, while comparable to PL for larger session repetitions (i.e., stochastic ranking), improves over PL for optimizing fairness metrics for queries with one session (i.e., deterministic ranking). Additionally, when accurate utility estimations are available, e.g., in tabular models, the performance of PPG in fairness optimization is significantly boosted compared to lower quality utility estimations from a learning to rank model, leading to a large performance gap with PL. Finally, the pairwise probabilities make it possible to impose pairwise constraints such as "item $d_1$ should always be ranked higher than item $d_2$." Such constraints can be used to simultaneously optimize the fairness metric and control another objective such as ranking performance.
Text-Free Non-Parallel Many-To-Many Voice Conversion Using Normalising Flow

Non-parallel voice conversion (VC) is typically achieved using lossy representations of the source speech. However, ensuring only speaker identity information is dropped whilst all other information from the source speech is retained is a large challenge. This is particularly challenging in the scenario where at inference-time we have no knowledge of the text being read, i.e., text-free VC. To mitigate this, we investigate information-preserving VC approaches.Normalising flows have gained attention for text-to-speech synthesis, however have been under-explored for VC. Flows utilize invertible functions to learn the likelihood of the data, thus provide a lossless encoding of speech. We investigate normalising flows for VC in both text-conditioned and text-free scenarios. Furthermore, for text-free VC we compare pre-trained and jointly-learnt priors. Flow-based VC evaluations show no degradation between text-free and text-conditioned VC, resulting in improvements over the state-of-the-art. Also, joint-training of the prior is found to negatively impact text-free VC quality.
Towards Reducing the Need for Speech Training Data to Build Spoken Language Understanding Systems

The lack of speech data annotated with labels required for spoken language understanding (SLU) is often a major hurdle in building end-to-end (E2E) systems that can directly process speech inputs. In contrast, large amounts of text data with suitable labels are usually available. In this paper, we propose a novel text representation and training methodology that allows E2E SLU systems to be effectively constructed using these text resources. With very limited amounts of additional speech, we show that these models can be further improved to perform at levels close to similar systems built on the full speech datasets. The efficacy of our proposed approach is demonstrated on both intent and entity tasks using three different SLU datasets. With text-only training, the proposed system achieves up to 90% of the performance possible with full speech training. With just an additional 10% of speech data, these models significantly improve further to 97% of full performance.
SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization

Image harmonization aims to achieve visual consistency in composite images by adapting a foreground to make it compatible with a background. However, existing methods always only use the real image as the positive sample to guide the training, and at most introduce the corresponding composite image as a single negative sample for an auxiliary constraint, which leads to limited distortion knowledge, and further causes a too large solution space, making the generated harmonized image distorted. Besides, none of them jointly constrain from the foreground selfstyle and foreground-background style consistency, which exacerbates this problem. Moreover, recent region-aware adaptive instance normalization achieves great success but only considers the global background feature distribution, making the aligned foreground feature distribution biased. To address these issues, we propose a self-consistent style contrastive learning scheme (SCS-Co). By dynamically generating multiple negative samples, our SCS-Co can learn more distortion knowledge and well regularize the generated harmonized image in the style representation space from two aspects of the foreground self-style and foreground-background style consistency, leading to a more photorealistic visual result. In addition, we propose a background-attentional adaptive instance normalization (BAIN) to achieve an attention-weighted background feature distribution according to the foreground-background feature similarity. Experiments demonstrate the superiority of our method over other state-of-the-art methods in both quantitative comparison and visual analysis.
Augmentation Strategy Optimization for Language Understanding

This paper presents a new language processing and understanding where an adaptive data augmentation strategy for individual documents is proposed instead of using one universal policy for the whole dataset. Importantly, a reinforcement learning and understanding method is exploited for document classification where the document encoder, augmenter and classifier are jointly optimized. In particular, a new reward function based on the consistency loss maximization is presented to assure the diversity of the generated documents. Using this method, the reward for adaptive augmentation policy is immediately calculated for every augmented instance without the need of waiting the child model performance metrics as the reward. The experiments on various classification tasks with a strong baseline model show that the augmentation strategy optimization can improve the model training process by providing meaningful augmentation data which eventually result in desirable evaluation performance. Furthermore, the extensive studies on the behavior of policy in different settings are provided in order to assure the diversity of the augmented data that was obtained by the proposed method.
Importantaug: A Data Augmentation Agent for Speech

We introduce ImportantAug, a technique to augment training data for speech classification and recognition models by adding noise to unimportant regions of the speech and not to important regions. Importance is predicted for each utterance by a data augmentation agent that is trained to maximize the amount of noise it adds while minimizing its impact on recognition performance. The effectiveness of our method is illustrated on version two of the Google Speech Commands (GSC) dataset. On the standard GSC test set, it achieves a 23.3% relative error rate reduction compared to conventional noise augmentation which applies noise to speech without regard to where it might be most effective. It also provides a 25.4% error rate reduction compared to a baseline without data augmentation. Additionally, the proposed ImportantAug outperforms the conventional noise augmentation and the baseline on two test sets with additional noise added.
Hyper-Laplacian Regularized Nonconvex Low-Rank Representation for Multi-View Subspace Clustering

Multi-view subspace clustering methods used consensus and supplementary principles to learn the shared self-representation matrix or tensor have been applied to multiple fields. The existing advanced multi-view subspace clustering methods are mainly based on the extension of low-rank representation from matrix to tensor. However, the tensor optimization methods have two limitations: they cannot retain the local geometric structure of data features residing in multiple nonlinear subspaces; they represent the low-rank structure based on the tensor nuclear norm, which will cause undesirable low-rank approximation. To solve these problems, we propose a hyper-Laplacian regularized Nonconvex Low-rank Representation (HNLR) method for multi-view subspace clustering. HNLR uses hyper-Laplacian regularizer to capture the high-order local geometry structure of each view. In addition, by introducing a nonconvex Laplace function to replace the tensor nuclear norm, HNLR can greatly improve the approximate performance of the global low-rank structure. Based on the alternating direction method of multiplier, we design an effective alternate iteration strategy to optimize HNLR model. Experimental results on eight real datasets have proved the superiority of our proposed method.
3D face expression recognition with ensemble deep learning exploring congruent features among expressions

Abstract Automatic and accurate 3D face expression categorization has been a challenging task in many computer vision applications. This article presents a novel approach named 3D face expression recognition with ensemble deep learning (3D‐FER‐EDL). The framework comprises three levels. Each of the first two levels gives preference to a subset of labels, considering the congruent features among expressions. Each 3D expressive face is represented as a CDS features vector, which combines corner, distance, and slope features. CDS feature vector is given as input to the first level learners. Expression probability vectors from the first level learners are jointly given as the input to the second level learners. The probability vectors from level one and two are given as the input to the third level, which is the meta classifier. The experiments are conducted on the Bosphorus database and the CASIA 3D face database to evaluate the effectiveness of the proposed approach.
Eye-Tracking Beyond Peripersonal Space in Virtual Reality: Validation and Best Practices

Recent developments in commercial virtual reality (VR) hardware with embedded eye-tracking create tremendous opportunities for human subjects researchers. Accessible eye-tracking in VR opens new opportunities for highly controlled experimental setups in which participants can engage novel 3D digital environments. However, because VR embedded eye-tracking differs from the majority of historical eye-tracking research, in both providing for relatively unconstrained movement and stimulus presentation distances, there is a need for greater discussion around methods for implementation and validation of VR based eye-tracking tools. The aim of this paper is to provide a practical introduction to the challenges of, and methods for, 3D gaze-tracking in VR with a focus on best practices for results validation and reporting. Specifically, first, we identify and define challenges and methods for collecting and analyzing 3D eye-tracking data in VR. Then, we introduce a validation pilot study with a focus on factors related to 3D gaze tracking. The pilot study provides both a reference data point for a common commercial hardware/software platform (HTC Vive Pro Eye) and illustrates the proposed methods. One outcome of this study was the observation that accuracy and precision of collected data may depend on stimulus distance, which has consequences for studies where stimuli is presented on varying distances. We also conclude that vergence is a potentially problematic basis for estimating gaze depth in VR and should be used with caution as the field move towards a more established method for 3D eye-tracking.
ORCAS-I

User intent classification is an important task in information retrieval. In this work, we introduce a revised taxonomy of user intent. We take the widely used differentiation between navigational, transactional and informational queries as a starting point, and identify three different sub-classes for the informational queries: instrumental, factual and abstain. The resulting classification of user queries is more fine-grained, reaches a high level of consistency between annotators, and can serve as the basis for an effective automatic classification process. The newly introduced categories help distinguish between types of queries that a retrieval system could act upon, for example by prioritizing different types of results in the ranking.We have used a weak supervision approach based on Snorkel to annotate the ORCAS dataset according to our new user intent taxonomy, utilising established heuristics and keywords to construct rules for the prediction of the intent category. We then present a series of experiments with a variety of machine learning models, using the labels from the weak supervision stage as training data, but find that the results produced by Snorkel are not outperformed by these competing approaches and can be considered state-of-the-art. The advantage of a rule-based approach like Snorkel's is its efficient deployment in an actual system, where intent classification would be executed for every query issued. The resource released with this paper is the ORCAS-I dataset: a labelled version of the ORCAS click-based dataset of Web queries, which provides 18 million connections to 10 million distinct queries.
To Interpolate or not to Interpolate

Current pre-trained language model approaches to information retrieval can be broadly divided into two categories: sparse retrievers (to which belong also non-neural approaches such as bag-of-words methods, e.g., BM25) and dense retrievers. Each of these categories appears to capture different characteristics of relevance. Previous work has investigated how relevance signals from sparse retrievers could be combined with those from dense retrievers via interpolation. Such interpolation would generally lead to higher retrieval effectiveness. In this paper we consider the problem of combining the relevance signals from sparse and dense retrievers in the context of Pseudo Relevance Feedback (PRF). This context poses two key challenges: (1) When should interpolation occur: before, after, or both before and after the PRF process? (2) Which sparse representation should be considered: a zero-shot bag-of-words model (BM25), or a learnt sparse representation? To answer these questions we perform a thorough empirical evaluation considering an effective and scalable neural PRF approach (Vector-PRF), three effective dense retrievers (ANCE, TCTv2, DistillBERT), and one state-of-the-art learnt sparse retriever (uniCOIL). The empirical findings from our experiments suggest that, regardless of sparse representation and dense retriever, interpolation both before and after PRF achieves the highest effectiveness across most datasets and metrics.
Dynamic Sepsis Prediction for Intensive Care Unit Patients Using XGBoost-Based Model With Novel Time-Dependent Features

Sepsis is a systemic inflammatory response caused by pathogens such as bacteria. Because its pathogenesis is not clear, the clinical manifestations of patients vary greatly, and the alarming incidence and mortality pose a great threat to patients and medical systems, especially in the ICU (Intensive Care Unit). The traditional judgment criteria have the problem of low specificity. Artificial intelligence models could greatly improve the accuracy of sepsis prediction and judgment. Based on the XGBoost machine learning framework taking demographic, vital signs, laboratory tests and medical intervention data as input, this paper proposes a novel model for dynamically predicting sepsis and assessing risk. To realize the model, two methods for feature construction are introduced. For the observed time-series data of vital signs and laboratory tests, the time-dependent method performs to construct the time-dependent characteristics after the statistical screening. For the clinical intervention data, the statistical counting method is applied to construct count-dependent characteristics. Moreover, a new objective function is proposed for the XGBoost framework, and the first-order and second-order gradients of the objective function are also given for model training. Compared with the state-of-the-art methods at present, the proposed model has the best performance, with AUROC improved by 5.4% on the MIMIC-III dataset and 2.1% on PhysioNet Challenge 2019 dataset. The data processing and training methods of this model can be conveniently applied in different electronic health record systems and has a wide application prospect.
Classifying papers into subfields using Abstracts, Titles, Keywords and <scp>KeyWords</scp> Plus through pattern detection and optimization procedures: An application in Physics

Abstract Classifying papers according to the fields of knowledge is critical to clearly understand the dynamics of scientific (sub)fields, their leading questions, and trends. Most studies rely on journal categories defined by popular databases such as WoS or Scopus, but some experts find that those categories may not correctly map the existing subfields nor identify the subfield of a specific article. This study addresses the classification problem using data from each paper (Abstract, Title, Keywords, and the KeyWords Plus) and the help of experts to identify the existing subfields and journals exclusive of each subfield. These “exclusive journals” are critical to obtain, through a pattern detection procedure that uses machine learning techniques (from software NVivo), a list of the frequent terms that are specific to each subfield. With that list of terms and with the help of optimization procedures, we can identify to which subfield each paper most likely belongs. This study can contribute to support scientific policy‐makers, funding, and research institutions—via more accurate academic performance evaluations—, to support editors in their tasks to redefine the scopes of journals, and to support popular databases in their processes of refining categories.