Reviewerly/exHarmony huggingface dataset:
Early identification of ICU patients at risk of complications: Regularization based on robustness and stability of explanations

The aim of this study is to build machine learning models to predict severe complications using administrative and clinical elements that are collected immediately after patient admission to the intensive care unit (ICU). Risk models are of increasing importance in the ICU setting. However, they generally present the black-box issue because they do not provide meaningful information about the logic involved in patient-specific predictions. Fortunately, effective algorithms exist for explaining black-box models, and in practice, they offer valuable explanations for model predictions. These explanations are becoming essential to engender trust and accreditation to the model. However, once the model is implemented, a major issue is whether it will continue to employ the same prediction logic as originally intended to. To build our models, features are obtained from patient administrative data, laboratory results and vital signs available within the first hour after ICU admission. This enables our models to provide great anticipation because complications can occur at any moment during ICU stay. To build models that continue to work as originally designed we first propose to measure (i) how the provided explanations vary for different inputs (that is, robustness), and (ii) how the provided explanations change with models built from different patient sub-populations (that is, stability). Second, we employ these measures as regularization terms that are coupled with a feature selection procedure such that the final model provides predictions with more robust and stable explanations. Experiments were conducted on a dataset containing 6000 ICU admissions of 5474 patients. Results obtained on an external validation cohort of 1069 patients with 1086 ICU admissions showed that selecting features based on robustness led to gains in terms of predictive power that varied from 6.8% to 9.4%, whereas selecting features based on stability led to gains that varied from 7.2% to 11.5%, depending on the target complication. Our results are of practical importance as our models predict complications with great anticipation, thus facilitating timely and protective interventions.
Effects of Shortcut-Level Amount in Lightweight ResNet of ResNet on Object Recognition with Distinct Number of Categories

This paper investigates effects of shortcut-level amount in ResNet of ResNet (RoR) based upon lightweight or small-size ResNet11 on object recognition using CIFAR-100 image dataset with different sizes of 10, 50, 100 object categories. Recognition performance comparison among the traditional ResNet11, 1–and 2–shortcut-level ResNet of ResNet (1L- and 2L-RoRs) is evaluated, relying on precision, recall, F1 and accuracy scores, averaged over 10-fold cross validation. Such cross validation is performed to ensure unbiased experimental results. Confusion matrix is also considered for more detail of results investigation. The comparison results indicate ResNet11, 1L- and 2L-RoRs provide best recognition accuracy of 96.38%, 98.70% and 98.55% respectively for 10-, 50- and 100–class CIFAR-100 datasets. It is also noticed the high competency of 2L-RoR on maintaining the recognition performance as increasing the number of data classes, relative to those 1L-RoR and ResNet11. In addition, 2L-RoR model employs only 0.780% more parameters than 1L-RoR; whilst 1L-RoR utilizes a few bits 0.392% higher parameters than ResNet11.
A new metric for feature selection on short text datasets

Abstract In recent years, short texts are everywhere, especially in social media networks. Short text classification is an essential task for various applications related to the operations on short text documents. In many cases, using the entire feature set causes the high dimensionality problem in short text data. This problem reason of time‐consuming and negatively impacts the performance of classifiers. This study presents an effective feature selection algorithm called XY method, which represents the features on XY line and calculates the distance of a feature to the XY line. Also, a value named λ is calculated. According to this value, the terms are divided into different regions such as negative, positive, and third to determine their discrimination capability. The novel XY method aims to select as few terms as possible in the negative region. The proposed method is evaluated using four different short text datasets with Macro‐F1 success measure. In comparisons with other existing feature selection algorithms such as chi‐square, information gain, deviation from Poisson distribution, recently proposed max‐min ratio, and distinguishing feature selector demonstrate that the XY method achieves either better or competitive performance in significantly reduced various feature sizes.
Advancing data literacy: Mapping business data literacy competencies to the ACRL framework

The relationship between data literacy and business librarianship continues to grow in relevance as the conversation intensifies in higher education and the business world. Establishing shared vocabularies and mappings to foundational library professional documents is essential to moving the discourse forward. This article presents a mapping between seven baseline business data literacy competencies and the ACRL Framework for Information Literacy for Higher Education.
Adaptive Capsule Network

A capsule is a group of neurons whose outputs represent different properties of the same entity. Typically, the capsule is produced by applying convolution layers called the primary capsule layer to group scalar neurons. However, randomly grouping the scalar neurons into capsule vectors can cause two problems: (i) The capsule vectors are difficult to obtain a better representation of the entities. (ii) The capsule vectors generated by the primary capsule layer lack spatial information and cannot effectively model the underlying spatial relationship among entities. In this paper, we present a flexible and efficient capsule network architecture called Adaptive Capsule CapsuleNet (AC-CapsNet). We replace the primary capsule layer of CapsNet with the adaptive capsule (AC) layer. In the AC-CapsNet, the adaptive capsule vector combines capsule vector and adaptive value generated by the AC layer. The adaptive value preserves spatial information of each capsule vector and local relationship among the scalar neurons contained in each capsule vector. Therefore, the adaptive capsule vector can not only dynamically adjust their state values according to the content information of scalar neurons inside capsule vector, but also model the spatial relationship between capsule vectors for low-level clusters. Extensive experiments in some public datasets such as CIFAR-100, CIFAR-10, SmallNORB, and SVHN show that the AC-CapsNet outperforms other variants of CapsNets with respect to classification accuracy and robustness to affine transformations and white-box adversarial attacks.
An automatic P-wave onset time picking method for mining-induced microseismic data based on long short-term memory deep neural network

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TARA: Training and Representation Alteration for AI Fairness and Domain Generalization

We propose a novel method for enforcing AI fairness with respect to protected or sensitive factors. This method uses a dual strategy performing training and representation alteration (TARA) for the mitigation of prominent causes of AI bias. It includes the use of representation learning alteration via adversarial independence to suppress the bias-inducing dependence of the data representation from protected factors and training set alteration via intelligent augmentation to address bias-causing data imbalance by using generative models that allow the fine control of sensitive factors related to underrepresented populations via domain adaptation and latent space manipulation. When testing our methods on image analytics, experiments demonstrate that TARA significantly or fully debiases baseline models while outperforming competing debiasing methods that have the same amount of information-for example, with (% overall accuracy, % accuracy gap) = (78.8, 0.5) versus the baseline method's score of (71.8, 10.5) for Eye-PACS, and (73.7, 11.8) versus (69.1, 21.7) for CelebA. Furthermore, recognizing certain limitations in current metrics used for assessing debiasing performance, we propose novel conjunctive debiasing metrics. Our experiments also demonstrate the ability of these novel metrics in assessing the Pareto efficiency of the proposed methods.
Performance evaluation of salient object detection techniques

Abstract Recently, the detection and segmentation of salient objects that attract the attention of human visual in images is determined by using salient object detection (SOD) techniques. As an essential computer vision problem, SOD has increasingly attracted the researchers’ interest over the years. While a lot of SOD models and applications have been proposed, there is still a lack of deep understanding of the issues and achievements. A comprehensive study on the recent techniques of SOD is provided in this paper. Precisely, this paper presents a review of SOD techniques from various perspectives. Various image segmentation techniques are presented such as segmentation based on machine learning or deep learning, the second perspective concentrates on classifying them into supervised and unsupervised learning techniques and the last one based on manual approach, semi-automatic approach, and fully automatic approach and so on. Then, the paper presents a summarization of datasets used for SOD. Finally, analyses of SOD models and comparison results are presented.
Supervised Discrete Multiple-Length Hashing for Image Retrieval

Hashing can facilitate efficient retrieval and storage for large-scale images due to the binary representation. In the real applications, the trade-off between retrieval accuracy and speed is essential for designing a hashing framework, which is reflected by variable hash code lengths. In light of this, the existing hashing methods need to train different models for different lengths of hash codes, leading to considerable training time cost and hashing flexibility reduction. Given that a sample can be represented by various hash codes with different lengths, there are some helpful relationships that can boost the performance of hashing methods. However, the existing hashing methods do not fully utilize these relationships. To address the aforementioned issues, we propose a new model, known as supervised discrete multiple-length hashing (SDMLH), to simultaneously learn hash codes with multiple lengths. In this proposed SDMLH method, three types of information are respectively derived, from the hash codes with different lengths. The original features of the samples, and the label, are applied for hash learning. Unlike the existing hashing methods, SDMLH can fully employ the assistance among hash codes with different lengths and learn them in one step. Furthermore, given a hash length meeting the demand of users, we propose a hash fusion strategy to obtain the hash code with this desirable length by fusing the multiple-length hash codes. This obtained hash code outperforms the one learned directly. In addition, SDMLH can generate the hash code of any length that is shorter than the sum length of given multiple hash codes with the fusion strategy. To the best of our knowledge, SDMLH is one of the first attempts for learning multiple-length hash codes simultaneously. We conduct extensive experiments based on three benchmark datasets, demonstrating the superiority of this proposed method.
Mixed Reality Training in Electrical Equipment Operating Procedures

The implementation of Industry 4.0 in the enterprise forces increased digitization, production flexibility, improvement of employees' competences and the integration of employees and IT systems. For this purpose, cutting-edge IT systems and solutions such as Virtual, Augmented and Mixed Reality (VR/AR/MR) are increasingly used. These technologies can be used, in training processes of ma-chines and equipment operating procedures. The paper is a case study on the possibility of using MR applications in training in the use of electrical devices (ARS 2 Pro switch disconnector). The aim of the work is to compare MR and standard training and evaluate their effectiveness in this type of training. The paper briefly describes developed applications and preliminary test-results. A group of 20 people in the age group 20 to 40. The participants were divided in-to 2 subgroups in order to properly analyze the effectiveness of individual training tools.
Feature adaptation-based multipeak-redetection spatial-aware correlation filter for object tracking

Visual tracking has always been an important research topic in the field of computer vision. During tracking, cluttered backgrounds, deformations, and occasional occlusion inevitably cause unpredictable appearance changes in the target, making the task very challenging. Traditional methods often tackle this problem using fixed central region constraints, simple combinations of features and independent classifiers. However, existing constraints are not very accurate; the simple mechanism of combining features lacks descriptive robustness, and the independent classifiers require complex additional training. Therefore, in this paper, we propose a correlation filter tracker called the feature adaptation-based multipeak-redetection spatial-aware correlation filter (FMCF) to accurately track targets that often change in appearance. In this tracker, the correlation filter has a dynamic spatial constraint that effectively reduces the response of the filter to the cluttered background area. To address target deformation, a new feature adaptation-based training method is designed, and the final response is a combination of the correlation filter and an independent statistical color model. Finally, a multipeak-redetection scheme, which can find the missing targets within multiple peaks of the final response map, is proposed to handle occlusion. In particular, the statistical color model is employed in the spatial-aware filter construction, the final response calculation, and the multipeak-redetection procedure to improve the robustness of the tracker, because of its ability to generate pixel-level color probabilities. Extensive experimental results on public tracking datasets prove that our proposed tracker can perform better than state-of-the-art trackers. Moreover, compared to the baseline tracker, the precision rate and success rate of FMCF are increased by 6.5% and 4.8% on the OTB100 benchmark and by 12.7% and 8.2% on the TC128 benchmark, respectively.
Dynamic stability analysis of power grid in high proportion new energy access scenario based on deep learning

The "carbon peaking and carbon neutralization" scheme formulated to solve the power energy shortage makes the new energy units incorporated into the power grid on a large scale. The randomness and volatility caused by the high proportion of new energy access have a certain impact on the dynamic stability of the power grid. Based on the dynamic stability theory, this paper applies the dual-stream CNN algorithm in the deep learning technology to model, takes the power of each node and line as the input and the key eigenvalue as the output, quickly identifies the main oscillation modes of the power system, makes a qualitative evaluation. Combined with the actual power grid analysis, the interval oscillation frequencies of the system are 0.57 Hz and 0.53 Hz respectively, which belong to strongly damped oscillation and can be quickly subsided after being excited. It can be concluded that the dual-stream CNN algorithm can provide reliable technical support for subsequent dispatching operation, and is conducive to improving the safe and stable operation level of power system.
Designing an E-Voting Framework Using Blockchain Technology

Blockchain technology provides a stable spot in electronic voting systems by providing a transparent platform for an election which prevents the failures faced by the traditional electronic voting systems and makes it a more accurate and secure platform for voting. However, little attention has been paid to the possible impacts of new technological disruptions on politics and the electoral systems. The main goal of this paper is to design a framework for electronic voting systems using Blockchain technology. To achieve this goal, the paper outlines these three objectives: (1) to explore and identify the challenges in existing voting systems, (2) to identify the methods that could enhance the security and privacy of the electoral voting systems using Blockchain technology, and (3) to design a framework for electronic voting systems that use Blockchain technology in order to address the flaws in current voting systems. A mixed approach is applied in this study to explore the opportunities and challenges of the Blockchain voting system, and design the framework accordingly.
Emoji use in China: popularity patterns and changes due to COVID-19

Emojis are small pictograms that are frequently embedded within micro-texts to more directly express emotional meanings. To understand the changes in the emoji usage of internet users during the COVID-19 outbreak, we analysed a large dataset collected from Weibo, the most popular Twitter-like social media platform in China, from December 1, 2019, to March 20, 2020. The data contained 38,183,194 microblog posts published by 2,239,472 unique users in Wuhan. We calculated the basic statistics of users' usage of emojis, topics, and sentiments and analysed the temporal patterns of emoji occurrence. After examining the emoji co-occurrence structure, we finally explored other factors that may affect individual emoji usage. We found that the COVID-19 outbreak greatly changed the pattern of emoji usage; i.e., both the proportion of posts containing emojis and the ratio of users using emojis declined substantially, while the number of posts remained the same. The daily proportion of Happy emojis significantly declined to approximately 32%, but the proportions of Sad- and Encouraging-related emojis rose to 24% and 34%, respectively. Despite a significant decrease in the number of nodes and edges in the emoji co-occurrence network, the average degree of the network increased from 34 to 39.8, indicating that the diversity of emoji usage increased. Most interestingly, we found that male users were more inclined towards using regular textual language with fewer emojis after the pandemic, suggesting that during public crises, male groups appeared to control their emotional display. In summary, the COVID-19 pandemic remarkably impacted individual sentiments, and the normal pattern of emoji usage tends to change significantly following a public emergency.The online version contains supplementary material available at 10.1007/s10489-022-03195-y.
Design and implementation of information extraction system for scientific literature using fine-tuned deep learning models

This paper presents an overview of a quality scoring system that utilizes pre-trained deep neural network models. Two types of DL models, a classification and extractive question answering (EQA) models are used to implement components of the system. The abstracts of the scientific literature are classified into two groups, in-vivo and in-vitro, and a question and answering model architecture is constructed for extracting the following types of information (animal type, the number of animals, exposure dose, and signal frequency). The Bidirectional Encoder Representations of Transformers (BERT) model pre-trained with a large text corpus is used as our baseline model for classification and EQA tasks. The models are fine-tuned with 455 EMF-related research papers. In our experiments, the fine-tuned model showed improved performance on EQA tasks for the four-categories of questions compared to the baseline, and it also showed improvements on similar questions that were not used in training. This suggests the importance of retraining of deep learning model specifically in some areas requiring domain expertise such as scientific papers. However, additional research is needed on some implementation issues, in such cases where there are still multiple answers, or where there is no answer given in a context.
A robust super-resolution reconstruction model of turbulent flow data based on deep learning

A new super-resolution model, namely the turbulence volumetric super-resolution (TVSR) model, is developed based on convolutional neural network (CNN) to reconstruct three-dimensional high-resolution turbulent flow field data from low-resolution data. Direct numerical simulation (DNS) and corresponding filtered DNS (FDNS) data of homogeneous isotropic turbulence at various Reynolds numbers are used to train the TVSR model. The proposed model is a modification of Liu et al. (2020), aiming to provide an improved generalization capability of the super-resolution model. For this purpose, we propose a patchwise training strategy in consideration of the property of turbulence that the velocity correlation between two points diminishes as the separation becomes sufficiently large. Furthermore, data at various Reynolds numbers are combined together to train the model. In comparison with existing models, the present TVSR model shows a better generalization capability in two aspects. First, the TVSR model trained using data at low Reynolds numbers is found robust and accurate in the super-resolution reconstructions of flow fields at higher Reynolds numbers. Second, although only DNS data are used for training, the TVSR model is also robust in reconstructing high-resolution flow fields from low-resolution data obtained from large-eddy simulation (LES). This feature of the TVSR model provides a new access to obtain turbulent motions at unresolved scales in LES studies of turbulent flows.
High-fidelity 3D real-time facial animation using infrared structured light sensing system

The acquisition of 3D facial models is crucial in the gaming and film industries. In this study, we developed a facial acquisition system based on infrared structured light sensors to obtain facial-expression models with high fidelity and accuracy. First, we employed time-multiplexing structured light to obtain an accurate and dense point cloud. The template model was then warped to the captured facial expression. A model-tracking method based on optical flow was applied to track the registered 3D model displacement. Finally, the live 3D mesh was textured using high-resolution images captured by three color cameras. Experiments were conducted on human faces to demonstrate the performance of the proposed system and methods.