Reviewerly/exHarmony huggingface dataset:
Contrastive transformer based domain adaptation for multi-source cross-domain sentiment classification

Cross-domain sentiment classification aims to predict the sentiment tendency in unlabeled target domain data using labeled source-domain data. The wide range of data sources has motivated research into multi-source cross-domain sentiment classification tasks. Conventional domain adaptation methods focus on reducing the domain difference between the source and target domains to realize sentiment migration, which ignores the selection of effective sources and fails to deal with negative transfer, leading to limited performance. To address these problems, we propose a contrastive transformer-based domain adaptation (CTDA) method, which not only develops a multi-source domain selection strategy, but also improves the problem of negative transfer from the perspective of data quality. Specifically, the proposed CTDA includes four stages: (1) designing a mixed selector to weight all related sources or pick out the Top-K sources according to the spatial similarity between both domains, (2) building an adaptor to extract domain-invariant information of features by minimizing the Wasserstein distance between both domains, (3) constructing a discriminator to capture the domain-private information of features by contrastive learning, and (4) performing a weighted classifier to predict the sentiment tendency of the target domain according to multiple trained source classifiers. Extensive experiments were performed on two public benchmarks, and the results demonstrated that our CTDA model significantly outperforms state-of-the-art approaches.
Privacy-Preserving Multi-Class Support Vector Machine Model on Medical Diagnosis

With the rapid development of machine learning in the medical cloud system, cloud-assisted medical computing provides a concrete platform for remote rapid medical diagnosis services. Support vector machine (SVM), as one of the important algorithms of machine learning, has been widely used in the field of medical diagnosis for its high classification accuracy and efficiency. In some existing schemes, healthcare providers train diagnostic models with SVM algorithms and provide online diagnostic services to doctors. Doctors send the patient's case report to the diagnostic models to obtain the results and assist in clinical diagnosis. However, case report involves patients' privacy, and patients do not want their sensitive information to be leaked. Therefore, the protection of patient's privacy has become an important research direction in the field of online medical diagnosis. In this paper, we propose a privacy-preserving medical diagnosis scheme based on multi-class SVMs. The scheme is based on the distributed two trapdoors public key cryptosystem (DT-PKC) and Boneh-Goh-Nissim (BGN) cryptosystem. We design a secure computing protocol to compute the core process of the SVM classification algorithm. Our scheme can deal with both linearly separable data and nonlinear data while protecting the privacy of user data and support vectors. The results show that our scheme is secure, reliable, scalable with high accuracy.
Long short-term memory model – A deep learning approach for medical data with irregularity in cancer predication with tumor markers

Machine learning (ML) has emerged as a superior method for the analysis of large datasets. Application of ML is often hindered by incompleteness of the data which is particularly evident when approaching disease screening data due to varied testing regimens across medical institutions. Here we explored the utility of multiple ML algorithms to predict cancer risk when trained using a large but incomplete real-world dataset of tumor marker (TM) values.TM screening data were collected from a large asymptomatic cohort (n = 163,174) at two independent medical centers. The cohort included 785 individuals who were subsequently diagnosed with cancer. Data included levels of up to eight TMs, but for most subjects, only a subset of the biomarkers were tested. In some instances, TM values were available at multiple time points, but intervals between tests varied widely. The data were used to train and test various machine learning models to evaluate their robustness for predicting cancer risk. Multiple methods for data imputation were explored and models were developed for both single time-point as well as time-series data.The ML algorithm, long short-term memory (LSTM), demonstrated superiority over other models for dealing with irregular medical data. A cancer risk prediction tool was trained and validated for a single time-point test of a TM panel including up to four biomarkers (AUROC = 0.831, 95% CI: 0.827-0.835) which outperformed a single threshold method using the same biomarkers. A second model relying on time series data of up to four time-points for 5 TMs had an AUROC of 0.931.A cancer risk prediction tool was developed by training a LSTM model using a large but incomplete real-world dataset of TM values. The LSTM model was best able to handle irregular data compared to other ML models. The use of time-series TM data can further improve the predictive performance of LSTM models even when the intervals between tests vary widely. These risk prediction tools are useful to direct subjects to further screening sooner, resulting in earlier detection of occult tumors.
A novel SMOTE-based resampling technique trough noise detection and the boosting procedure

Most of the classification methods assume that the numbers of class observations are balanced. In such cases, models are predicted by giving biased weight to the the class with more observations. Therefore, the classifiers ignore the class with smaller number of observations and the majority class makes biased predictions. There are some advised performance measures to be used in datasets, as well as recommended approaches to solve class imbalance problem. One of the most widely used methods is resampling method. In this study, the difficulties relevant to random oversampling (ROS) and synthetic minority oversampling technique (SMOTE), which are some of the oversampling methods, are discussed. This study aims to propose a combination of a new noise detection method and SMOTE to overcome those difficulties. Using the boosting procedure in ensemble algorithms, noise detection is possible with the proposed SMOTE with boosting (SMOTEWB) method, which makes use of this information to determine the appropriate number of neighbors for each observation within SMOTE algorithm.
Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text Summarization

Abstract The Query-Focused Text Summarization (QFTS) task aims at building systems that generate the summary of the text document(s) based on the given query. A key challenge in addressing this task is the lack of large labeled data for training the summarization model. In this article, we address this challenge by exploring a series of domain adaptation techniques. Given the recent success of pre-trained transformer models in a wide range of natural language processing tasks, we utilize such models to generate abstractive summaries for the QFTS task for both single-document and multi-document scenarios. For domain adaptation, we apply a variety of techniques using pre-trained transformer-based summarization models including transfer learning, weakly supervised learning, and distant supervision. Extensive experiments on six datasets show that our proposed approach is very effective in generating abstractive summaries for the QFTS task while setting a new state-of-the-art result in several datasets across a set of automatic and human evaluation metrics.
Online Teaching in COVID-19 Pandemic: Secondary School Teachers’ Beliefs on Teaching Presence and School Support

The COVID-19 pandemic, with the consecutive lockdowns, has led schools around the world to transition suddenly from face-to-face education to online teaching. The purpose of this paper was to investigate secondary school teachers’ beliefs on online teaching presence and school support for online learning during the pandemic. The sample was 238 teachers in Greece who are interested in e-learning and received some preparation to teach online courses. An online questionnaire was administered and three factors were revealed: instruction and organization, facilitation and cognitive activation, and school support. Teachers’ perceived online teaching presence was strong (over 63% expressed agreement), and the highest percentages of agreement were linked to clear instruction, communication of time frames and course topics, and facilitation and encouragement that could help students learn. On the other hand, perceived school support for online learning was weak (over 50% expressed disagreement and neutral views), and the highest percentages of disagreement regarded the existence of a professional development strategy and of clear objectives/vision towards online learning. Gender, years of teaching experience, and experience with online teaching had a small effect on teachers’ beliefs. Implications for teacher professional development, educational policy, and the design of educational technology applications are discussed.
Cerner real-world data (CRWD) - A de-identified multicenter electronic health records database

Cerner Real-World DataTM (CRWD) is a de-identified big data source of multicenter electronic health records. Cerner Corporation secured appropriate data use agreements and permissions from more than 100 health systems in the United States contributing to the database as of March 2022. A subset of the database was extracted to include data from only patients with SARS-CoV-2 infections and is referred to as the Cerner COVID-19 Dataset. The December 2021 version of CRWD consists of 100 million patients and 1.5 billion encounters across all care settings. There are 2.3 billion, 2.9 billion, 486 million, and 11.5 billion records in the condition, medication, procedure, and lab (laboratory test) tables respectively. The 2021 Q3 COVID-19 Dataset consists of 130.1 million encounters from 3.8 million patients. The size and longitudinal nature of CRWD can be leveraged for advanced analytics and artificial intelligence in medical research across all specialties and is a rich source of novel discoveries on a wide range of conditions including but not limited to COVID-19.
Blockchain Technology as a Game Changer for Green Innovation: Green Entrepreneurship as a Roadmap to Green Economic Sustainability in Peru

Blockchain technology has been heralded as a game changer for addressing severe environmental and economic sustainability challenges.In response to rising environmental concerns, blockchain technology (BCT) is transforming green innovation, culminating in green economic practices and well-established business models.Recognizing this, we investigated the role of blockchain technology in green innovation practices and its impact on green economic sustainability, which has an impact on green environmental sustainability.Moreover, 184 small-and medium-sized enterprises (SMEs) were surveyed in Lima, Peru.Data for this cross-sectional study were gathered using stratified random sampling.The positivist approach was implemented using a statistical induction method.Prior studies' research constructs were measured using validated measurement scales.For quantitative data analysis, using the partial least squares structural equation modeling (PLS-SEM) framework, this study provided two key findings.First, sustainability orientation and sustainability attitude have a positive and significant effect on the adoption of green innovation that employs green energy (solar) technology towards a sustainable green economy.Second, the intention to use blockchain technology mediates the relationship between sustainability orientation and social perception with the adoption of green innovation that employs green energy (solar) technology towards a sustainable green economy.We recommend that small-and medium-sized enterprises embrace green innovation and blockchain technology to protect the environment and boost community cohesiveness.
How does (im)balanced acceptance of robots between customers and frontline employees affect hotels’ service quality?

With the increasingly prevalent of service robots in tourism and hospitality sectors, service success relies heavily on robot acceptance. However, most of tourism practitioners are challenged by a dilemma of focusing on customer or employee acceptance of robots. Borrowing the lenses of resource-based view and information processing theory, this study explores the differential effectiveness of imbalanced robotic strategy (i.e., higher/lower customer acceptance of robots than employee acceptance) implemented by hotels in driving service quality under customer-side (i.e., customer demandingness) and employee-side (i.e., frontline task ambidexterity) demand uncertainties. Polynomial regression analysis on a dataset of 1066 matched customer-employee dyads reveals that, an imbalanced robotic strategy is superior than a balanced one for service quality. In addition, when customer demandingness is high, a customer-focused robotic strategy (i.e., higher customer acceptance of robots than employee acceptance) is the optimal choice to improve service quality. However, when frontline task ambidexterity is high, the positive effects of imbalanced robotic strategy on service quality diminish. This study contributes to the literature on and practices of robot acceptance from a customer-robot-employee triadic perspective.
A graph neural network-based node classification model on class-imbalanced graph data

Node classification for highly imbalanced graph data is challenging, with existing graph neural networks (GNNs) typically utilizing a balanced class distribution to learn node embeddings on graph data. However, when dealing with an imbalanced class distribution, they tend to bias the nodes of the majority classes while the nodes of the minority classes are under-represented. To overcome this challenge, this work introduces a novel GNN-based Imbalanced Node Classification Model (GNN-INCM) that is appropriate for class-imbalanced graph data, comprising two cooperative modules: Embedding Clustering-based Optimization (ECO) and Graph Reconstruction-based Optimization (GRO). ECO first employs a two-layer graph convolutional network (GCN) to obtain node embeddings and then performs clustering analysis to enhance the representative nature of the node embeddings and ease classification. Moreover, GRO employs an inner product decoder to reconstruct graph structure and minimize information loss. In particular, we design a hard sample strategy and integrate it into ECO and GRO to ensure that the embeddings of the hard nodes are correctly represented. Furthermore, we propose a Hard Sample-based Knowledge Distillation Method (HSKDM) to train multiple GNN-INCM models simultaneously and improve the overall classification performance. Experiments on three well-known class-imbalanced graph datasets demonstrate that GNN-INCM outperforms current state-of-the-art methods in node classification tasks and that HSKDM can substantially improve the overall classification performance.
Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus

Mammographic breast density (BD) is commonly visually assessed using the Breast Imaging Reporting and Data System (BI-RADS) four-category scale. To overcome inter- and intraobserver variability of visual assessment, the authors retrospectively developed and externally validated a software for BD classification based on convolutional neural networks from mammograms obtained between 2017 and 2020. The tool was trained using the majority BD category determined by seven board-certified radiologists who independently visually assessed 760 mediolateral oblique (MLO) images in 380 women (mean age, 57 years ± 6 [SD]) from center 1; this process mimicked training from a consensus of several human readers. External validation of the model was performed by the three radiologists whose BD assessment was closest to the majority (consensus) of the initial seven on a dataset of 384 MLO images in 197 women (mean age, 56 years ± 13) obtained from center 2. The model achieved an accuracy of 89.3% in distinguishing BI-RADS a or b (nondense breasts) versus c or d (dense breasts) categories, with an agreement of 90.4% (178 of 197 mammograms) and a reliability of 0.807 (Cohen κ) compared with the mode of the three readers. This study demonstrates accuracy and reliability of a fully automated software for BD classification. Keywords: Mammography, Breast, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2022.
Smartphone-Based Human Activities Recognition System using Random Forest Algorithm

The advancements of smartphone technology, bring together doctors and patients for monitoring clinical activities, remote assistants, and preemptive measures, specifically for those who are under medical control. As smartphones become an integral part of human life, they are employed to monitor health conditions within the human activity recognition (HAR) system. This paper presents a Smartphone-Based Human Activities Recognition System using Random Forest Algorithm(RFA). The RFA algorithm is a classifier that has several decision trees on subclasses of the dataset. The RFA takes the average to enhance the projecting accuracy of the given dataset. Four evaluation parameters such as F1 Score, accuracy, precision, and sensitivity are considered to estimate the proposed system performance. Further, comparing the performance of the proposed RFA with existing classifiers, the accuracy of the proposed system achieves 98.34&#x0025; as compared with other classifiers.
Generalized Extreme Gradient Boosting model for predicting daily global solar radiation for locations without historical data

Information on global solar radiation (Rs) is indispensable in many fields. However, reliable measurements of Rs are challenging worldwide because of high costs and technical complexities. Here, temperature- and sunshine-based generalized Extreme Gradient Boosting (XGBoost) models were proposed to estimate daily Rs for locations where historical Rs data are unknown. Four combinations of input variables were assessed. The first two included: (1) maximum, minimum, mean, and diurnal temperature, and extra-terrestrial radiation (Ra); and (2) sunshine duration, maximum possible sunshine duration, and Ra. In the first two inputs, the latter two further included geographical variables, i.e., latitude, longitude, and altitude. The developed models were also compared with temperature- and sunshine-based generalized empirical models. Daily data of Rs, maximum and minimum temperature, and actual sunshine duration during the period of 2007–2016 from 96 radiation stations of China were collected to develop and evaluate the models. The results showed that accuracy of the generalized XGBoost models was improved when geographical variables were further included in various climate zones. The generalized XGBoost model using temperature and geographical data as inputs slightly reduced accuracy compared to the temperature-based local-trained XGBoost model but is still superior to the temperature-based generalized empirical model. Somewhat surprisingly, there was comparable performance between the generalized XGBoost model using sunshine and geographical data as inputs and the local-trained sunshine-based XGBoost model. Therefore, the generalized XGBoost model was highly recommended to estimate daily Rs incorporating sunshine/temperature data and routinely available geographical information for locations where historical data are prior unknown.
Unrestricted multi-hop reasoning network for interpretable question answering over knowledge graph

Knowledge graphs significantly boost the answer retrieval quality for natural language questions. The knowledge graph based question answering (KGQA) task returns accurate answer entities instead of keyword matches. For the more challenging task of multi-hop KGQA, existing methods either address fixed-length multi-hop reasoning, or perform a delayed detection of termination that requires an extra hop of reasoning. In addition, they suffer from two mapping problems between the question and relations: (1) one-to-many mapping when an individual question word corresponds to multiple hops of reasoning; (2) many-to-one mapping when a single hop of reasoning corresponds to multiple question words. Therefore, in this paper, we address these two issues of delayed determination and mapping problems by proposing an Unrestricted Multi-Hop Reasoning Network for Interpretable KGQA named UMRNet. Specifically, the proposed dynamic update strategy of question embeddings based on our attention redistribution mechanism is capable of handling the mapping problems. Furthermore, to avoid the need for an extra hop of reasoning, we propose a non-delayed termination detection mechanism that performs effective evaluation of the remaining reasoning information based on history attention. Extensive ablation studies and comparative experiments have been conducted on four KGQA benchmark datasets. The results demonstrate that the major modules of UMRNet are effective, and UMRNet outperforms the state-of-the-art methods regarding both accuracy and efficiency.
Novel target attention convolutional neural network for relation classification

Relation classification (RC) is an essential task in natural language processing (NLP), which extracts relationships of entity pairs in sentences of text. In the paper, a novel target attention convolutional neural network (TACNN) is proposed for the RC by fully utilizing word embedding information and position embedding information. Simultaneously, a target attention mechanism (TAM) is applied into a context layer of the convolutional neural network (CNN) model, which increases the effect of the relationship matrix weights of two entities in the sentence, while ignoring the calculation of irrelevant terms. And the TACNN is essentially to modify the weight of the relationship matrix of entities in the sentence at the context layer and connect the relationship feature composed of the lexical layer feature with the target attention layer feature. Therefore, the TACNN simplifies the structure of the CNN and improves the computational efficiency. On SemEval-2010 Task 8 dataset and Conll04 dataset, the TACNN obtains 85.3% and 71.4% of the F1-score, respectively. In contrast to previously available public models, the TACNN achieves a state-of-the-art level in the F1-score of the RC.