Reviewerly/exHarmony huggingface dataset:
Multi-Stage Pre-training for Low-Resource Domain Adaptation

Transfer learning techniques are particularly useful in NLP tasks where a sizable amount of high-quality annotated data is difficult to obtain.Current approaches directly adapt a pretrained language model (LM) on in-domain text before fine-tuning to downstream tasks.We show that extending the vocabulary of the LM with domain-specific terms leads to further gains.To a bigger effect, we utilize structure in the unlabeled data to create auxiliary synthetic tasks, which helps the LM transfer to downstream tasks.We apply these approaches incrementally on a pre-trained Roberta-large LM and show considerable performance gain on three tasks in the IT domain: Extractive Reading Comprehension, Document Ranking and Duplicate Question Detection.
Parameters optimization method for the time-delayed reservoir computing with a nonlinear duffing mechanical oscillator

Abstract Reservoir computing (RC) is a recently introduced bio-inspired computational framework capable of excellent performances in the temporal data processing, owing to its derivation from the recurrent neural network (RNN). It is well-known for the fast and effective training scheme, as well as the ease of the hardware implementation, but also the problematic sensitivity of its performance to the optimizable architecture parameters. In this article, a particular time-delayed RC with a single clamped–clamped silicon beam resonator that exhibits a classical Duffing nonlinearity is presented and its optimization problem is studied. Specifically, we numerically analyze the nonlinear response of the resonator and find a quasi-linear bifurcation point shift of the driving voltage with the driving frequency sweeping, which is called Bifurcation Point Frequency Modulation (BPFM). Furthermore, we first proposed that this method can be used to find the optimal driving frequency of RC with a Duffing mechanical resonator for a given task, and then put forward a comprehensive optimization process. The high performance of RC presented on four typical tasks proves the feasibility of this optimization method. Finally, we envision the potential application of the method based on the BPFM in our future work to implement the RC with other mechanical oscillators.
An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.
Autofluorescence Image Reconstruction and Virtual Staining for In-Vivo Optical Biopsying

Modern photonic technologies are emerging, allowing the acquisition of in-vivo endoscopic tissue imaging at a microscopic scale, with characteristics comparable to traditional histological slides, and with a label-free modality.This raises the possibility of an 'optical biopsy' to aid clinical decision making.This approach faces barriers for being incorporated into clinical practice, including the lack of existing images for training, unfamiliarity of clinicians with the novel image domains and the uncertainty of trusting 'black-box' machine learned image analysis, where the decision making remains inscrutable.In this paper, we propose a new method to transform images from novel photonics techniques (e.g.autofluorescence microscopy) into already established domains such as Hematoxilyn-Eosin (H-E) microscopy through virtual reconstruction and staining.We introduce three main innovations: 1) we propose a transformation method based on a Siamese structure that simultaneously learns the direct and inverse transformation ensuring domain back-transformation quality of the transformed data.2) We also introduced an embedding loss term that ensures similarity not only at pixel level, but also at the image embedding description level.This drastically reduces the perception distortion trade-off problem existing in common domain transfer based on generative adversarial networks.These virtually stained images can serve as reference standard images for comparison with the already known H-E images.3) We also incorporate an uncertainty margin concept that allows the network to measure its own confidence, and demonstrate that these reconstructed and virtually stained images can be used on previously-studied classification models of H-E images that have been computationally degraded and de-stained.The three proposed methods can be seamlessly incorporated on any existing architectures.We obtained balanced accuracies of 0.95 and negative predictive values of 1.00 over the reconstructed and virtually stained image-set on the detection of color-rectal tumoral tissue.This is of great importance as we reduce the need for extensive labeled datasets for training, which are normally not available on the early studies of a new imaging technology.
Cadmium isotopic constraints on metal sources in the Huize Zn–Pb deposit, SW China

The Sichuan–Yunnan–Guizhou (SYG) Zn–Pb metallogenic zone in SW China contains >400 carbonate-hosted hydrothermal Zn–Pb deposits. Some of these, such as the Huize, Tianbaoshan, and Daliangzi deposits, are super-large deposits with significant reserves of Cd, Ge, and Ag. However, the sources of these metals remain controversial. This study investigated the Cd isotopic geochemistry of the Huize deposit, the largest Zn–Pb deposit in the SYG area. Sphalerites formed at three stages in the deposit have different colors: black or dark brown (Stage I), red (Stage II), and light-yellow (Stage III). The δ114/110Cd values of the sphalerites are in the order Stage III < Stage I < Stage II. Kinetic isotopic fractionation is likely the key factor causing the lower δ114/110Cd values in the early formed Stage I sphalerites than in later-formed Stage II sphalerites, with cooling of ore-forming fluids being responsible for the still lower values of the Stage III sphalerites. In galena, the δ114/110Cd values are inversely correlated with Cd contents and tend to be higher in high-Zn galena. We speculate that Cd isotopic fractionation was significant during the precipitation of sphalerite and galena, with light Cd isotopes being enriched in galena rather than sphalerite. Comparison of the Cd isotopic signatures and Zn/Cd ratios of different endmembers suggests that the δ114/110Cd values and Zn/Cd ratios of sphalerite from the Huize deposit, as well as other large-scale deposits from the SYG area, are lie in those range of Emeishan basalts and sedimentary rocks and the mean δ114/110Cd values of these deposits show good negative correlation with 1/Cd, suggesting that the ore-forming materials of these deposits were derived from the mixing of Emeishan basalts and sedimentary rocks. This study demonstrates that Cd isotopes can be useful proxies in elucidating ore genesis in large Zn–Pb deposits.
Making the Relation Matters: Relation of Relation Learning Network for Sentence Semantic Matching

Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially BERT. Despite the effectiveness of these models, most of them treat output labels as meaningless one-hot vectors, underestimating the semantic information and guidance of relations that these labels reveal, especially for tasks with a small number of labels. To address this problem, we propose a Relation of Relation Learning Network (R2-Net) for sentence semantic matching. Specifically, we first employ BERT to encode the input sentences from a global perspective. Then a CNN-based encoder is designed to capture keywords and phrase information from a local perspective. To fully leverage labels for better relation information extraction, we introduce a self-supervised relation of relation classification task for guiding R2-Net to consider more about labels. Meanwhile, a triplet loss is employed to distinguish the intra-class and inter-class relations in a finer granularity. Empirical experiments on two sentence semantic matching tasks demonstrate the superiority of our proposed model. As a byproduct, we have released the codes to facilitate other researches.
SENT: Sentence-level Distant Relation Extraction via Negative Training

Distant supervision for relation extraction provides uniform bag labels for each sentence inside the bag, while accurate sentence labels are important for downstream applications that need the exact relation type.Directly using bag labels for sentence-level training will introduce much noise, thus severely degrading performance.In this work, we propose the use of negative training (NT), in which a model is trained using complementary labels regarding that "the instance does not belong to these complementary labels".Since the probability of selecting a true label as a complementary label is low, NT provides less noisy information.Furthermore, the model trained with NT is able to separate the noisy data from the training data.Based on NT, we propose a sentence-level framework, SENT, for distant relation extraction.SENT not only filters the noisy data to construct a cleaner dataset, but also performs a relabeling process to transform the noisy data into useful training data, thus further benefiting the model's performance.Experimental results show the significant improvement of the proposed method over previous methods on sentence-level evaluation and de-noise effect.
Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search

Internet search affects people's cognition of the world, so mitigating biases in search results and learning fair models is imperative for social good.We study a unique gender bias in image search in this work: the search images are often gender-imbalanced for genderneutral natural language queries.We diagnose two typical image search models, the specialized model trained on in-domain datasets and the generalized representation model pretrained on massive image and text data across the internet.Both models suffer from severe gender bias.Therefore, we introduce two novel debiasing approaches: an in-processing fair sampling method to address the gender imbalance issue for training models, and a postprocessing feature clipping method base on mutual information to debias multimodal representations of pre-trained models.Extensive experiments on MS-COCO (Lin et al., 2014) and Flickr30K (Young et al., 2014) benchmarks show that our methods significantly reduce the gender bias in image search models.
Interpretable Fuzzy Logic Control for Multirobot Coordination in a Cluttered Environment

Mobile robot navigation is an essential problem in robotics. We propose a method for constructing and training fuzzy logic controllers (FLCs) to coordinate a robotic team performing collision-free navigation and arriving simultaneously at a target location in an unknown environment. Our FLCs are organized in a multilayered architecture to reduce the number of tunable parameters and improve the scalability of the solution. In addition, in contrast to simple traditional switching mechanisms between target seeking and obstacle avoidance, we develop a novel rule-embedded FLC to improve the navigation performance. Moreover, we design a grouping and merging mechanism to obtain transparent fuzzy sets and integrate this mechanism into the training process for all FLCs, thus increasing the interpretability of the fuzzy models. We train the proposed FLCs using a novel multiobjective hybrid approach combining a genetic algorithm and particle swarm optimization. Simulation results demonstrate the effectiveness of our algorithms in reliably solving the proposed navigation problem.
Digital competence of higher education professors:

This article aims to analyse the differences associated with the variables of teaching and institutional nature in the level of proficiency of teachers in digital competences, using the DigCompEdu framework as theoretical reference. A quantitative approach was used and the data collection strategy was based on an online survey. The sample was composed of 846 Portuguese higher education professors linked to 37 universities and 76 polytechnic institutes. The results indicate that characteristics such as working at PhD program level, teaching online and being linked to polytechnic institutes are associated with higher levels of digital competence.
Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding

Automatic electrocardiogram (ECG) classification is a promising technology for the early screening and follow-up management of cardiovascular diseases. It is, by nature, a multi-label classification task owing to the coexistence of different kinds of diseases, and is challenging due to the large number of possible label combinations and the imbalance among categories. Furthermore, the task of multi-label ECG classification is cost-sensitive, a fact that has usually been ignored in previous studies on the development of the model. To address these problems, in this work, we propose a novel deep learning model–based learning framework and a thresholding method, namely category imbalance and cost-sensitive thresholding (CICST), to incorporate prior knowledge about classification costs and the characteristic of category imbalance in designing a multi-label ECG classifier. The learning framework combines a residual convolutional network with a class-wise attention mechanism. We evaluate our method with a cost-sensitive metric on multiple realistic datasets. The results show that CICST achieved a cost-sensitive metric score of 0.641 ± 0.009 in a 5-fold cross-validation, outperforming other commonly used thresholding methods, including rank-based thresholding, proportion-based thresholding, and fixed thresholding. This demonstrates that, by taking into account the category imbalance and predefined cost information, our approach is effective in improving the performance and practicability of multi-label ECG classification models.
Semantic Adversarial Network with Multi-Scale Pyramid Attention for Video Classification

Two-stream architecture have shown strong performance in video classification task. The key idea is to learn spatiotemporal features by fusing convolutional networks spatially and temporally. However, there are some problems within such architecture. First, it relies on optical flow to model temporal information, which are often expensive to compute and store. Second, it has limited ability to capture details and local context information for video data. Third, it lacks explicit semantic guidance that greatly decrease the classification performance. In this paper, we proposed a new two-stream based deep framework for video classification to discover spatial and temporal information only from RGB frames, moreover, the multi-scale pyramid attention (MPA) layer and the semantic adversarial learning (SAL) module is introduced and integrated in our framework. The MPA enables the network capturing global and local feature to generate a comprehensive representation for video, and the SAL can make this representation gradually approximate to the real video semantics in an adversarial manner. Experimental results on two public benchmarks demonstrate our proposed methods achieves state-of-the-art results on standard video datasets.
Pedestrian Attribute Recognition: A Survey

Recognizing pedestrian attributes is an important task in the computer vision community due to it plays an important role in video surveillance. Many algorithms have been proposed to handle this task. The goal of this paper is to review existing works using traditional methods or based on deep learning networks. Firstly, we introduce the background of pedestrian attribute recognition (PAR, for short), including the fundamental concepts of pedestrian attributes and corresponding challenges. Secondly, we introduce existing benchmarks, including popular datasets and evaluation criteria. Thirdly, we analyze the concept of multi-task learning and multi-label learning and also explain the relations between these two learning algorithms and pedestrian attribute recognition. We also review some popular network architectures which have been widely applied in the deep learning community. Fourthly, we analyze popular solutions for this task, such as attributes group, part-based, etc. Fifthly, we show some applications that take pedestrian attributes into consideration and achieve better performance. Finally, we summarize this paper and give several possible research directions for pedestrian attribute recognition. We continuously update the following GitHub to keep tracking the most cutting-edge related works on pedestrian attribute recognition~\url{https://github.com/wangxiao5791509/Pedestrian-Attribute-Recognition-Paper-List}
Discriminating among tectonic settings of spinel based on multiple machine learning algorithms

In geochemistry, researchers usually discriminate among tectonic settings by analyzing the chemistry elements of minerals. Previous studies have generally taken spinel and monoclinic pyroxene as subjects. Therefore, in this research, we took spinel as a breakthrough. Totally 1898 spinel samples with 14-dimension chemistry elements were collected from three different tectonic settings, including ocean island, convergent margin, and spreading center. In the experiment, 20 classification algorithms were conducted in the classification learner application of MATLAB. The validation accuracies, receiver operating characteristic curves (ROCs), and the areas under ROC curve (AUCs) show that the Bag Ensemble Classifier has the best performance in the problem. Its validation accuracy is 86.3%, and the average AUC is 0.957. For further analysis, we studied the importance of different major elements in discriminating. It has been found that TiO2 has the best impact on discrimination, and FeOT, Al2O3, Cr2O3, MgO, MnO, and ZnO are of less importance. Based on the Bag Ensemble Classifier, a MATLAB plug-in application named Discriminator of Spinel Tectonic Setting (DSTS) has been developed for promoting the usage of machine learning in geochemistry and facilitating other researchers to use our achievements.
Context-Aware Convolutional Neural Network over Distributed System in Collaborative Computing

As the computing power of end-point devices grows, there has been interest in developing distributed deep neural networks specifically for hierarchical inference deployments on multi-sensor systems. However, as the existing approaches rely on latent parameters trained by machine learning, it is difficult to preemptively select front-end deep features across sensors, or understand individual feature's relative importance for systematic global inference. In this paper, we propose multi-view convolutional neural networks exploiting likelihood estimation. Proof-of-concept experiments show that our likelihood-based context selection and weighted averaging collaboration scheme can decrease an endpoint's communication and energy costs by a factor of 3×, while achieving high accuracy comparable to the original aggregation approaches.
An Adaptive Local Descriptor Embedding Zernike Moments for Image Matching

Image matching is an important problem in computer vision and many technologies based on local descriptors have been developed. In this paper, we propose a novel local features descriptor based on an adaptive neighborhood and embedding Zernike moments. Instead of a fixed-size neighborhood, a size changeable neighborhood is introduced to detect the key-points and describe the features in the frame of Gaussian scale space. The radius is determined by the scale parameter of the key-point and the dominant direction is computed based on skew distribution fitting instead of the traditional eight-direction statistics. Then a 72-dimensional features vector based on a 3 × 3 grid is presented. A 19-dimensional vector consists of Zernike moments is applied to achieve better rotation invariance and finally contributes to a 91-dimensional descriptor. The accuracy and efficiency of proposed descriptor for image matching are verified by several numerical experiments.
Solar PV Generation Forecast Model Based on the Most Effective Weather Parameters

Solar energy is one of the major renewable energy sources with the potential to cope with the future energy challenges. But the penetration of solar PV generation in the electrical grid is a serious concern because of variable availability. Therefore, solar PV generation forecasting is essential for planning and efficient operation. The forecasting model is based on Artificial Neural Network (ANN) with forecasted and historical weather parameters i.e., temperature, dew point, relative humidity and wind speed as inputs. The aim of this study is to determine the most effective combination of weather variables to be used as input to the model. For this, all the possible combinations of the inputs are applied to ANN and the best one is obtained by analysis of the results. Mean Absolute Percentage Error (MAPE) is used as a measure to compare the results. To train the ANN model, one year's weather and generation data of 20.8 kW PV system with an hourly resolution is used. 24 hours ahead forecasting of the generation is done using forecasted weather data of 14 days selected from the dataset of 130 days. Combination of three parameters (temperature, relative humidity and dew point) results in an average MAPE of 14.86% while the use of all four parameters as inputs gives 14.33% of MAPE.
Personas for the translational workforce

Twelve evidence-based profiles of roles across the translational workforce and two patients were made available through clinical and translational science (CTS) Personas, a project of the Clinical and Translational Science Awards (CTSA) Program National Center for Data to Health (CD2H). The persona profiles were designed and researched to demonstrate the key responsibilities, motivators, goals, software use, pain points, and professional development needs of those working across the spectrum of translation, from basic science to clinical research to public health. The project's goal was to provide reliable documents that could be used to inform CTSA software development projects, educational resources, and communication initiatives. This paper presents the initiative to create personas for the translational workforce, including the methodology, engagement strategy, and lessons learned. Challenges faced and successes achieved by the project may serve as a roadmap for others searching for best practices in the creation of Persona profiles.