Reviewerly/exHarmony huggingface dataset:
Accelerated Multi-View Stereo for 3D Reconstruction of Transmission Corridor with Fine-Scale Power Line

Fast reconstruction of power lines and corridors is a critical task in UAV (unmanned aerial vehicle)-based inspection of high-voltage transmission corridors. However, recent dense matching algorithms suffer the problem of low efficiency when processing large-scale high-resolution UAV images. This study proposes an efficient dense matching method for the 3D reconstruction of high-voltage transmission corridors with fine-scale power lines. First, an efficient random red-black checkerboard propagation is proposed, which utilizes the neighbor pixels with the most similar color to propagate plane parameters. To combine the pixel-wise view selection strategy adopted in Colmap with the efficient random red-black checkerboard propagation, the updating schedule for inferring visible probability is improved; second, strategies for decreasing the number of matching cost computations are proposed, which can reduce the unnecessary hypotheses for verification. The number of neighbor pixels necessary to propagate plane parameters is reduced with the increase of iterations, and the number of the combinations of depth and normal is reduced for the pixel with better matching cost in the plane refinement step; third, an efficient GPU (graphics processing unit)-based depth map fusion method is proposed, which employs a weight function based on the reprojection errors to fuse the depth map. Finally, experiments are conducted by using three UAV datasets, and the results indicate that the proposed method can maintain the completeness of power line reconstruction with high efficiency when compared to other PatchMatch-based methods. In addition, two benchmark datasets are used to verify that the proposed method can achieve a better F1 score, 4–7 times faster than Colmap.
Artificial Intelligence in Digital Pathology: What Is the Future? Part 2: An Investigation on the Insiders

Motivation: This study deals with the introduction of artificial intelligence (AI) in digital pathology (DP). The study starts from the highlights of a companion paper. Objective: The aim was to investigate the consensus and acceptance of the insiders on this issue. Procedure: An electronic survey based on the standardized package Microsoft Forms (Microsoft, Redmond, WA, USA) was proposed to a sample of biomedical laboratory technicians (149 admitted in the study, 76 males, 73 females, mean age 44.2 years). Results: The survey showed no criticality. It highlighted (a) the good perception of the basic training on both groups, and (b) a uniformly low perceived knowledge of AI (as arisen from the graded questions). Expectations, perceived general impact, perceived changes in the work-flow, and worries clearly emerged in the study. Conclusions: The of AI in DP is an unstoppable process, as well as the increase of the digitalization in the health domain. Stakeholders must not look with suspicion towards AI, which can represent an important resource, but should invest in monitoring and consensus training initiatives based also on electronic surveys.
Mobile Expert System: Exploring Context-Aware Machine Learning Rules for Personalized Decision-Making in Mobile Applications

Expert systems, a form of artificial intelligence (AI), are typically designed to solve many real-world problems by reasoning through knowledge, which is primarily represented as IF–THEN rules, with the information acquired from humans or domain experts. However, to assume such rules for personalized decision-making in an intelligent, context-aware mobile application is a challenging issue. The reason is that different mobile users may behave differently in various day-to-day situations, i.e., not identical, and thus the rules for personalized services must be reflected according to their symmetrical or asymmetrical behavioral activities. Therefore, our key focus is to solve this issue through adding personalized decision-making intelligence to develop powerful mobile applications to assist the end-users. To achieve our goal, in this paper, we explore on “Mobile Expert System”, where we take into account machine-learning rules as knowledge-base rather than traditional handcrafted static rules. Thus, the concept of a mobile expert system enables the computing and decision-making processes more actionable and intelligent than traditional ones in the domain of mobile analytics and applications. Our experiment section shows that the context-aware machine learning rules discovered from users’ mobile phone data can contribute in building a mobile expert system to solve a particular problem, through making personalized decisions in various context-aware test cases.
TACR-Net: Editing on Deep Video and Voice Portraits

Utilizing an arbitrary speech clip to edit the mouth of the portrait in the target video is a novel yet challenging task. Despite impressive results have been achieved, there are still three limitations in the existing methods: 1) since the acoustic features are not completely decoupled from person identity, there is no global speech to facial features (i.e., landmarks, expression blendshape) mapping method. 2) the audio-driven talking face sequences generated by simple cascade structure usually lack of temporal consistency and spatial correlation, which leads to defects in the consistency of changes in details. 3) the operation of forgery is always at the video level, without considering the forgery of the voice, especially the synchronization of the converted voice and the mouth. To address these distortion problems, we propose a novel deep learning framework, named Temporal-Refinement Autoregressive-Cascade Rendering Network (TACR-Net) for audio-driven dynamic talking face editing. The proposed TACR-Net encodes facial expression blendshape based on the given acoustic features without separately training for special video. Then TACR-Net also involves a novel autoregressive cascade structure generator for video re-rendering. Finally, we transform the in-the-wild speech to the target portrait and obtain a photo-realistic and audio-realistic video.
Automatic identification of triple negative breast cancer in ultrasonography using a deep convolutional neural network

Abstract Triple negative (TN) breast cancer is a subtype of breast cancer which is difficult for early detection and the prognosis is poor. In this paper, 910 benign and 934 malignant (110 TN and 824 NTN) B-mode breast ultrasound images were collected. A Resnet50 deep convolutional neural network was fine-tuned. The results showed that the averaged area under the receiver operating characteristic curve (AUC) of discriminating malignant from benign ones were 0.9789 (benign vs. TN), 0.9689 (benign vs. NTN). To discriminate TN from NTN breast cancer, the AUC was 0.9000, the accuracy was 88.89%, the sensitivity was 87.5%, and the specificity was 90.00%. It showed that the computer-aided system based on DCNN is expected to be a promising noninvasive clinical tool for ultrasound diagnosis of TN breast cancer.
Mapping Directional Mid-Air Unistroke Gestures to Interaction Commands: A User Elicitation and Evaluation Study

A stroke is the basic limb movement that both humans and animals naturally and repetitiously perform. Having been introduced into gestural interaction, mid-air stroke gestures saw a wide application range and quite intuitive use. In this paper, we present an approach for building command-to-gesture mapping that exploits the semantic association between interactive commands and the directions of mid-air unistroke gestures. Directional unistroke gestures make use of the symmetry of the semantics of commands, which makes a more systematic gesture set for users’ cognition and reduces the number of gestures users need to learn. However, the learnability of the directional unistroke gestures is varying with different commands. Through a user elicitation study, a gesture set containing eight directional mid-air unistroke gestures was selected by subjective ratings of the direction in respect to its association degree with the corresponding command. We evaluated this gesture set in a following study to investigate the learnability issue, and the directional mid-air unistroke gestures and user-preferred freehand gestures were compared. Our findings can offer preliminary evidence that “return”, “save”, “turn-off” and “mute” are the interaction commands more applicable to using directional mid-air unistrokes, which may have implication for the design of mid-air gestures in human–computer interaction.
Three dimensional path planning of UAV based on adaptive particle swarm optimization algorithm

Abstract Aiming at the problem of falling easily into local optimal solution of conventional particle swarm optimization algorithm, an adaptive particle swarm optimization algorithm is proposed, which adaptively adjusts the values of inertial weight and two learning factors in the iterated search process. The environment model of path planning is built for unmanned aerial vehicle (UAV) to perform reconnaissance task in mountain environment. The self-constraint conditions of UAV are analyzed. The fitness degree function of adaptive particle swarm optimization algorithm and flow chart of path planning algorithm are designed. The simulation experiments of three dimensional path planning of UAV are carried out by adopting respectively the adaptive particle swarm optimization algorithm and the conventional particle swarm optimization algorithm. The contrast of simulation result shows that the proposed adaptive particle swarm optimization algorithm has higher global search ability and search precision than the conventional particle swarm optimization algorithm.
Analysis of the relationship between income inequality and social variables: Evidence from Indonesia

This study tries to analyse the relationship between income inequality and social variables to provide additional empirical input on the related structural model.To achieve these objectives, this study uses the cointegration and long-run structural vector auto-regression (SVAR) for the panel data on 33 provinces in Indonesia over the 2005-2018 period.The study concludes that income inequality has a positive impact on population growth, unemployment, and poor health, whereas it also has a negative effect on education, human development, and urbanisation growth.Population growth, unemployment, poor health and urbanisation growth can increase income inequality, while education and human development index reduce income inequality.Furthermore, increased minimum wages can reduce income inequality, poor health, and increase education and per capita income.This study also found that per capita income has a long-term relationship with income inequality and population.
How is price explosivity triggered in the cryptocurrency markets?

Abstract We shed light on how the price explosivity characterising Bitcoin and other major cryptocurrencies is triggered, by employing the Quantile Self-Exciting Threshold Autoregressive (QSETAR) model. Our results for Bitcoin, Ripple, and Stellar reveal that the explosive behaviour originates from the extreme upper tails of the return distributions following a price increase in the preceding day. We do not find evidence of explositivity in the price of Litecoin.
SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records

Contrastive learning has demonstrated promising performance in image and text domains either in a self-supervised or a supervised manner. In this work, we extend the supervised contrastive learning framework to clinical risk prediction problems based on longitudinal electronic health records (EHR). We propose a general supervised contrastive loss ℒContrastiveCrossEntropy+λℒSupervisedContrastiveRegularizer for learning both binary classification (e.g. in-hospital mortality prediction) and multi-label classification (e.g. phenotyping) in a unified framework. Our supervised contrastive loss practices the key idea of contrastive learning, namely, pulling similar samples closer and pushing dissimilar ones apart from each other, simultaneously by its two components: ℒContrastiveCrossEntropy tries to contrast samples with learned anchors which represent positive and negative clusters, and ℒSupervisedContrastiveRegularizer tries to contrast samples with each other according to their supervised labels. We propose two versions of the above supervised contrastive loss and our experiments on real-world EHR data demonstrate that our proposed loss functions show benefits in improving the performance of strong baselines and even state-of-the-art models on benchmarking tasks for clinical risk predictions. Our loss functions work well with extremely imbalanced data which are common for clinical risk prediction problems. Our loss functions can be easily used to replace (binary or multi-label) cross-entropy loss adopted in existing clinical predictive models. The Pytorch code is released at https://github.com/calvin-zcx/SCEHR.
Joint Model and Data-Driven Receiver Design for Data-Dependent Superimposed Training Scheme With Imperfect Hardware

Data-dependent superimposed training (DDST) scheme has shown the potential to achieve high bandwidth efficiency, while encounters symbol misidentification caused by hardware imperfection. To tackle these challenges, a joint model and data driven receiver scheme is proposed in this paper. Specifically, based on the conventional linear receiver model, the least squares (LS) estimation and zero forcing (ZF) equalization are first employed to extract the initial features for channel estimation and data detection. Then, shallow neural networks, named CE-Net and SD-Net, are developed to refine the channel estimation and data detection, where the imperfect hardware is modeled as a nonlinear function and data is utilized to train these neural networks to approximate it. Simulation results show that compared with the conventional minimum mean square error (MMSE) equalization scheme, the proposed one effectively suppresses the symbol misidentification and achieves similar or better bit error rate (BER) performance without the second-order statistics about the channel and noise.
Semantic segmentation of vertebrate microfossils from computed tomography data using a deep learning approach

Abstract. Vertebrate microfossils have broad applications in evolutionary biology and stratigraphy research areas such as the evolution of hard tissues and stratigraphic correlation. Classification is one of the basic tasks of vertebrate microfossil studies. With the development of techniques for virtual paleontology, vertebrate microfossils can be classified efficiently based on 3D volumes. The semantic segmentation of different fossils and their classes from CT data is a crucial step in the reconstruction of their 3D volumes. Traditional segmentation methods adopt thresholding combined with manual labeling, which is a time-consuming process. Our study proposes a deep-learning-based (DL-based) semantic segmentation method for vertebrate microfossils from CT data. To assess the performance of the method, we conducted extensive experiments on nearly 500 fish microfossils. The results show that the intersection over union (IoU) performance metric arrived at least 94.39 %, meeting the semantic segmentation requirements of paleontologists. We expect that the DL-based method could also be applied to other fossils from CT data with good performance.
Data Protection by Design in the Context of Smart Cities: A Consent and Access Control Proposal

The growing availability of mobile devices has lead to an arising development of smart cities services that share a huge amount of (personal) information and data. Without accurate and verified management, they could become severe back-doors for security and privacy. In this paper, we propose a smart city infrastructure able to integrate a distributed privacy-preserving identity management solution based on attribute-based credentials (p-ABC), a user-centric Consent Manager, and a GDPR-based Access Control mechanism so as to guarantee the enforcement of the GDPR's provisions. Thus, the infrastructure supports the definition of specific purpose, collection of data, regulation of access to personal data, and users' consents, while ensuring selective and minimal disclosure of personal information as well as user's unlinkability across service and identity providers. The proposal has been implemented, integrated, and evaluated in a fully-fledged environment consisting of MiMurcia, the Smart City project for the city of Murcia, CaPe, an industrial consent management system, and GENERAL_D, an academic GDPR-based access control system, showing the feasibility.
relationship of online game addiction with learning motivation in school age children on COVID-19 pandemic

Playing online games during the Covid-19 pandemic has caused concern for children's learning motivation. The purpose of the study was to determine the relationship between addiction to playing online games with learning motivation in school-age children during the Covid-19 pandemic. This research method is analytic, with a cross sectional approach. The study population was all school-age children aged 6-18 years, totaling 57 children, the respondents were determined using total sampling. The study was conducted on July 13 to July 18, 2021. The analysis was carried out using the chi-square test. The results showed that addiction to playing online games was in the addiction category of 70.2%. Learning motivation for school-age children during the Covid-19 pandemic was in the 59.6% category. The p-value = 0.001 &lt; = 0.05 with an Odd Ratio (OR) value of 0.103, this proves that there is a relationship between addiction to playing online games and learning motivation in school-age children during the Covid-19 pandemic.
COLI: Collaborative clustering missing data imputation

Missing data imputation plays an important role in the data cleansing process. Clustering algorithms have been widely used for missing data imputation, yet, there is little research done on the use of clustering ensemble for missing data imputation, which aggregates multiple clustering results. This paper proposes a novel collaborative clustering-based imputation method, called COLI, which uses the imputation quality as a key criterion for the exchange of information between different clustering results. To the best of our knowledge, this is the first study on the impact of collaborative clustering on imputation performance. The main contributions of this paper are three-fold. A novel missing value imputation based on collaborative clustering is proposed, three amputation strategies are used to induce missingness on various complete and publicly available datasets with different mechanisms, distributions, and ratios, which allows evaluating the imputation quality of the proposed method in estimating missing values of various numerical datasets with different missingness mechanisms, distributions, and ratios. The proposed method is compared to several state-of-the-art imputation methods and attained results demonstrate that the proposed method is an effective method for handling missing data.
Towards a Digital Resource Mobilisation Approach for Digital Inclusion During COVID-19 and Beyond: A Case of a Township School in South Africa

COVID-19 is affecting the functioning of most countries globally, creating a situation now described as the “new normal”—a time of unexpected educational change. The national lockdown, accompanied by the closure of educational institutions, brought economic hardship and deepened the digital divide between the rich and the poor. Educational institutions capable of transitioning to an online mode of delivery made that shift, while the majority of South Africa’s schools remained excluded due to poverty and lack of technological infrastructure. The educational sector is at wits’ end to find strategies to curtail the growing digital divide. This paper offers a digital resource mobilisation approach as framework to keep schools on the path to achieving the National Development Plan’s aim of ICT capacitation. To consider developmental possibilities and respond to the digital exclusion of township schools, we asked the question: “What are the online teaching and learning experiences of school stakeholders?” Responses to this question assisted development of a digital resource mobilisation theory that is offered as a viable approach to digital inclusion and social change. Data were collected by telephonic interviews with three teachers, three learners, three school governing body parents, and one school principal. Based on the findings, recommendations for digital inclusion are suggested.
Digital entrepreneurship and its impact on digital supply chains: The mediating role of business intelligence applications

This study aimed to test and evaluate the impact of Digital Entrepreneurship and its impact on digital supply chains in Jordanian hotels, and the mediating role of digital supply chains in this relationship. The descriptive analytical method was used, and the study population consisted of (835) male and female employees, and a random sample was used with a simple random sample of (342) participants. To achieve the objectives of the study, a developed questionnaire was used to collect data from the sample members. The study adopted the Statistical Package for Social Sciences (SPSS.V.22) and Structural Equations Modeling (SEM) using the AMOS program for path analysis and to perform statistical analysis using: Descriptive and inferential statistics measures, including Multiple linear regression, Pearson correlation coefficient, skew coefficient, multiple linear correlation, variance inflation coefficient and permissible variance. The study reached results, the most important of which were: The most important results of the study were: the presence of an important impact of Digital Entrepreneurship in digital supply chains and the presence of a significant impact of Digital Entrepreneurship through business intelligence applications as an intermediate variable in digital supply chains. The study recommends the need to enhance Digital Entrepreneurship in Jordanian hotels by focusing on the holistic view of these hotels and their environment, whether the internal environment that focuses on strengths and weaknesses in the hotel’s capabilities or their external environment that brings opportunities and challenges.
Knowledge management: Preserving, managing and sharing indigenous knowledge through digital library

Indigenous peoples’ ways of preserving, managing and sharing knowledge are argued to have contributed to their knowledge being threatened with extinction. This article addresses the need for intergenerational transmission of culturally unique knowledge for future generations through digital libraries. The speedy use of digital media in the modern, dynamic world gives valuable opportunities to facilitate the process of preserving, managing and sharing knowledge that are unique to South African communities. The use of qualitative research ensured a deep inquiry and understanding of digitalising indigenous knowledge. The modernisation theory was grounded by African indigenous ways in designing the framework. This article proposes the use of digital libraries in knowledge management so as to reach a wider audience. More so, digitalisation should be premised to ensure that the knowledge is protected against intellectual theft and allow knowledge holders and communities to benefit from such initiatives. Contribution: The article makes a unique contribution by fostering the revival of indigenous knowledge management for the benefit of current and future generations through a digital library. Such a mammoth task establishes a bridge between the past and present to promote ways that are consistent with African realities.