Reviewerly/exHarmony huggingface dataset:
AusGeochem: An Open Platform for Geochemical Data Preservation, Dissemination and Synthesis

To promote a more efficient and transparent geochemistry data ecosystem, a consortium of Australian university research laboratories called the AuScope Geochemistry Network assembled to build a collaborative platform for the express purpose of preserving, disseminating and collating geochronology and isotopic data. In partnership with geoscience‐data‐solutions company Lithodat Pty Ltd, the open, cloud‐based AusGeochem platform ( https://ausgeochem.auscope.org.au ) was developed to simultaneously serve as a geosample registry, a geochemical data repository and a data analysis tool. Informed by method‐specific groups of geochemistry experts and established international data reporting practices, community‐agreed database schemas were developed for rock and mineral geosample metadata and secondary ion mass spectrometry U‐Pb analysis, with additional models for laser ablation‐inductively coupled‐mass spectrometry U‐Pb and Lu‐Hf, Ar‐Ar, fission‐track and (U‐Th‐Sm)/He under development. Collectively, the AusGeochem platform provides the geochemistry community with a new, dynamic resource to help facilitate FAIR (Findable, Accessible, Interoperable, Reusable) data management, streamline data dissemination and advanced quantitative investigations of Earth system processes. By systematically archiving detailed geochemical (meta‐)data in structured schemas, intractably large datasets comprising thousands of analyses produced by numerous laboratories can be readily interrogated in novel and powerful ways. These include rapid derivation of inter‐data relationships, facilitating on‐the‐fly data compilation, analysis and visualisation.
A systematic literature review of empirical research on quality requirements

Abstract Quality requirements deal with how well a product should perform the intended functionality, such as start-up time and learnability. Researchers argue they are important and at the same time studies indicate there are deficiencies in practice. Our goal is to review the state of evidence for quality requirements. We want to understand the empirical research on quality requirements topics as well as evaluations of quality requirements solutions. We used a hybrid method for our systematic literature review. We defined a start set based on two literature reviews combined with a keyword-based search from selected publication venues. We snowballed based on the start set. We screened 530 papers and included 84 papers in our review. Case study method is the most common (43), followed by surveys (15) and tests (13). We found no replication studies. The two most commonly studied themes are (1) differentiating characteristics of quality requirements compared to other types of requirements, (2) the importance and prevalence of quality requirements. Quality models, QUPER, and the NFR method are evaluated in several studies, with positive indications. Goal modeling is the only modeling approach evaluated. However, all studies are small scale and long-term costs and impact are not studied. We conclude that more research is needed as empirical research on quality requirements is not increasing at the same rate as software engineering research in general. We see a gap between research and practice. The solutions proposed are usually evaluated in an academic context and surveys on quality requirements in industry indicate unsystematic handling of quality requirements.
Contrastive Learning Assisted-Alignment for Partial Domain Adaptation

This work addresses unsupervised partial domain adaptation (PDA), in which classes in the target domain are a subset of the source domain. The key challenges of PDA are how to leverage source samples in the shared classes to promote positive transfer and filter out the irrelevant source samples to mitigate negative transfer. Existing PDA methods based on adversarial DA do not consider the loss of class discriminative representation. To this end, this article proposes a contrastive learning-assisted alignment (CLA) approach for PDA to jointly align distributions across domains for better adaptation and to reweight source instances to reduce the contribution of outlier instances. A contrastive learning-assisted conditional alignment (CLCA) strategy is presented for distribution alignment. CLCA first exploits contrastive losses to discover the class discriminative information in both domains. It then employs a contrastive loss to match the clusters across the two domains based on adversarial domain learning. In this respect, CLCA attempts to reduce the domain discrepancy by matching the class-conditional and marginal distributions. Moreover, a new reweighting scheme is developed to improve the quality of weights estimation, which explores information from both the source and the target domains. Empirical results on several benchmark datasets demonstrate that the proposed CLA outperforms the existing state-of-the-art PDA methods.
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Simultaneous multi-graph learning and clustering for multiview data

As many data in practical applications occur or can be arranged in multiview forms, multiview clustering utilizing certain complementary and heterogeneous information in various views to promote the clustering performance, has received much attention recently. Among varieties of methods, graph-based unsupervised learning methods are an essential approach for learning intrinsic structure relations of multiview data for clustering. Most of them firstly integrate information from each view into a consensus graph, which is then fed into the classic spectral clustering to achieve clustering. Such a two-step clustering paradigm is difficult to obtain the optimal clustering results even though every step performs individual optimization. This paper integrates multi-graph construction, consensus graph construction, and clustering in a unified learning framework, which can simultaneously consider the consistency and complementarity of multiview data to provide the clustering results directly. Moreover, we treat each view differently by automatic weight learning. Specifically, multi-graph learning, consensus graph learning, and weight learning are seamlessly integrated so that the related variables can be iteratively updated in the unified optimization framework–the clustering results towards an overall optimum. Comprehensive experiments on real multiview datasets verify the superiority of the proposed method over other state-of-the-art baselines in terms of three clustering evaluation metrics.
Student’s Digital Literacy Based on Students’ Interest in Digital Technology, Internet Costs, Gender, and Learning Outcomes

This study aims to analyze students' digital literacy in the context of learning biology material on the digestive system and identify students' digital literacy in terms of interest in digital technology, internet costs, gender, and learning outcomes. The research was conducted at one private high school in Medan, Indonesia. The instrument used is a questionnaire and a list of student grades from the teacher. The results showed that students interested in digital technology had a significantly higher digital literacy score than students who had less interest in digital technology. There was no significant difference in digital literacy between groups of students with internet fees &lt; US$ 14 per month and students with internet fees of US$ 14 per month, and between groups of male and female students. The result of the analysis also found a positive correlation between learning outcomes and digital literacy.
Technologies for GQM-Based Metrics Recommender Systems: A Systematic Literature Review

With this Systematic Literature Review (SLR), we aim to discover technologies to construct a Goal-Question-Metrics (GQM) based metrics recommender for software developers.Since such a system has not yet been described in the literature, we decided to analyse the technologies used in three main components of recommender systems -data sets, algorithms, and recommendations -independently.Methods: To achieve our goal we performed -following the best norms in our discipline -a systematic literature review (SLR).We first identified, through searches aptly performed, 422 potentially relevant papers, from which we selected -after applying inclusion and exclusion criteria -30 papers, which we eventually included in our final log.Results: Systems with textual data set preprocess information in nearly the same way and the majority use similarity scores to create recommendations.Systems with GQM-based algorithms consist of questionnaires and require users to explicitly answer questions to produce suggestions.With respect to the recommendations of reviewed systems, they range from application programming interfaces (APIs) to requirements, but no system presently recommends metrics.Conclusion: In our SLR we: (a) identified a sequence of the most popular steps for preprocessing in recommender systems; (b) proposed an optimisation strategy for such steps; (c) found out that the most promising approach includes both ranking and classification; and (d) established that there are no recommendation systems developed to date to process metrics.
A Neighborhood-Attention Fine-grained Entity Typing for Knowledge Graph Completion

Knowledge graph (KG) entity typing focuses on inferring possible entity type instances, which is a significant subtask of knowledge graph completion (KGC). Existing entity typing methods usually exploit the entity representation to model the transmission between entities and their types, which cannot fully explore the fine-grained entity typing on identifying the semantic type of an entity. To address these issues, we propose Neighborhood-Attention Neural Fine-Grained Entity Typing (AttEt), which considers the neighborhood information of the entities from KGs to bridge entities and their types together. In this paper, AttEt first develops a type-specific attention mechanism to aggregate the neighborhood knowledge of the given entity with type-specific weights. These weights are beneficial to capture various characteristics for different types of the entity, and further imply the complex correlation among these fine-grained types. Then, AttEt adaptively integrates the aggregated neighbor-level representation with entity inherent embedding to calculate the matching score between the entity and its candidate type. Besides, many entities are sparse in their relations with other entities in KGs, which makes the entity typing task more challenging. To solve this problem, we present a smooth strategy on relation-sparsity entities to improve the robustness of the model. Extensive experiments on two real-world datasets (Freebase and YAGO) show that AttEt significantly outperforms state-of-the-art baselines in the [email protected] by 2.11% on Freebase and by 8.42% on YAGO, respectively.
Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer

Breast cancer is widespread around the world and can be cured if diagnosed at an early stage. Digital mammograms are used as the most effective imaging modalities for the diagnosis of breast cancer. However, mammography images suffer from low contrast, background noise as well as contrast as non-coherency among the regions, and these factors makes breast cancer diagnosis challenging. These problems can be overcome by using a new image enhancement technique. The objective of this research work is to enhance mammography images to improve the overall process of segmentation and classification of breast cancer diagnosis. We proposed the image enhancement for mammogram images, as well as the ablation of the pectoral muscle. The image enhancement technique involves several steps. In the first step, we process the mammography images in three channels (red, green and blue), the second step is based on the uniformity of the background on morphological operations, and the third step is to obtain a well-contrasted image using principal component analysis (PCA). The fourth step is based on the removal of the pectoral muscle using a seed-based region growth technique, and the last step contains the coherence of the different regions of the image using a second order Gaussian Laplacian (LoG) and an oriented diffusion filter to obtain a much-improved contrast image. The proposed image enhancement technique is tested with our data collected from different hospitals in Qassim health cluster Qassim province Saudi Arabia, and it contains the five Breast Imaging and Reporting System (BI-RADS) categories and this database contained 11,194 images (the images contain carnio-caudal (CC) view and mediolateral oblique(MLO) view of mammography images), and we used approximately 700 images to validate our database. We have achieved improved performance in terms of peak signal-to-noise ratio, contrast, and effective measurement of enhancement (EME) as well as our proposed image enhancement technique outperforms existing image enhancement methods. This performance of our proposed method demonstrates the ability to improve the diagnostic performance of the computerized breast cancer detection method.
Self-supervised 3D human pose estimation from video

To accurately estimate 3D human pose from monocular camera images, a large amount of 3D annotated data is required. However, obtaining 3D annotated data outside the laboratory is not easy. In the absence of such data, weakly-supervised methods that rely on multi-view cameras during training and single-view cameras during inference have been proposed. These methods either use multi-view networks or classical triangulation to train the 3D human pose estimator. This study shows that these two paradigms can collaborate to further improve performance. The available unlabeled uncalibrated multi-view inputs are used to obtain pseudo-3D labels employing classical triangulation. A pose estimator is trained with these pseudo-3D labels and with multi-view re-projection loss. This loss enforces the 3D poses estimated from different views to be consistent and improves the performance. Therefore, our method relaxes the constraints (calibrated cameras, 2D/3D annotations), only requires multi-view videos for training, and is therefore convenient for in-the-wild settings. The proposed method outperforms previous works on two challenging datasets, Human3.6 M and MPI-INF-3DHP. Codes and pretrained models will be publicly available.
Public opinion of the Irish “COVID Tracker” digital contact tracing App: A national survey

Objective This study aims to gather public opinion on the Irish “COVID Tracker” digital contact tracing (DCT) App, with particular focus on App usage, usability, usefulness, technological issues encountered, and potential changes to the App. Methods A 35-item online questionnaire was deployed for 10 days in October 2020, 3 months after the launch of the Irish DCT App. Results A total of 2889 completed responses were recorded, with 2553 (88%) respondents currently using the App. Although four in five users felt the App is easy to download, is easy to use and looks professional, 615 users (22%) felt it had slowed down their phone, and 757 (28%) felt it had a negative effect on battery life. Seventy-nine percent of respondents reported the App's main function is to aid contact tracing. Inclusion of national COVID-19 trends is a useful ancillary function according to 87% of respondents, and there was an appetite for more granular local data. Overall, 1265 (44%) respondents believed the App is helping the national effort, while 1089 (38%) were unsure. Conclusions DCT Apps may potentially augment traditional contact tracing methods. Despite some reports of negative effects on phone performance, just 7% of users who have tried the App have deleted it. Ancillary functionality, such as up-to-date regional COVID-19, may encourage DCT App use. This study describes general positivity toward the Irish COVID Tracker App among users but also highlights the need for transparency on effectiveness of App-enabled contact tracing and for study of non-users to better establish barriers to use.
Blockchain-Enabled Vehicular Ad Hoc Networks: A Systematic Literature Review

This systematic literature review provides an extensive categorization of the blockchain-enabled applications across the domain of vehicular ad hoc networks (VANETs). Within the paradigm of distributed ledger technology (DLT), the communication models and practices for VANETs have been revolutionized. An analytical review and a survey were conducted to explore the advancements of blockchain and VANETs. The techniques, limitations, and advantages of blockchain deployment in VANETs are discussed for the effective implementation of a decentralized network. To this end, 68 studies were selected on the basis of the procedural steps to provide a comprehensive overview of blockchain and the smart contracts in VANETs. In particular, a decentralized communication model is also proposed for the advanced implementation of blockchain in VANETs. Researchers and practitioners are being attracted to these technologies for applications for various industrial sectors. Therefore, this study also emphasizes the identification of any blockchain-related open issues for future prospects. The comprehension of blockchain applications for the Internet of Vehicles (IoV) is also explored in order to fill the research gap on advanced communication networks across the Internet of Things.
Modified artificial bee colony algorithm with differential evolution to enhance precision and convergence performance

Artificial bee colony (ABC) and differential evolution (DE) are the most powerful and operative meta-heuristic algorithms inspired by the nature. Although both algorithms are successful, their successes vary from phase to phase, i.e. while ABC is better in the exploration ability, DE is well in the exploitation capability. Because the diversity of mutation and exponential crossover operators is prominently better than that of onlooker bee; in this study, the exploitation ability of ABC is enhanced by replacing the onlooker bee operator with those of mutation and the crossover phases of DE in order to increase the accuracy and speed up the convergence. We hereby introduce a novel modified algorithm denoted "modified ABC by DE" (mABC). The precision performance of mABC is verified through 20 classical benchmark functions and CEC 2014 test suit by a comprehensive comparison with recent ABC variants and hybrids for 30 and 50 dimensions. The results are interpreted using various statistical evaluations such as Wilcoxon, Friedman, and Nemenyi tests. Moreover, mABC is comparatively examined over convergence plots. In concise, the mean ranks of mABC are 1.4 and 2.3 for classical benchmark functions and CEC 2014, respectively. mABC outperforms the other variants averagely for 14 of 20 classical benchmark functions and 24 of 30 CEC 2014 functions. The results manifest that the proposed mABC is a robust and reliable algorithm as well as better than the existing ABC variants and hybrids with regard to high optimization performance like precision and convergence.
Efficient Virtual View Selection for 3D Hand Pose Estimation

3D hand pose estimation from single depth is a fundamental problem in computer vision, and has wide applications. However, the existing methods still can not achieve satisfactory hand pose estimation results due to view variation and occlusion of human hand. In this paper, we propose a new virtual view selection and fusion module for 3D hand pose estimation from single depth. We propose to automatically select multiple virtual viewpoints for pose estimation and fuse the results of all and find this empirically delivers accurate and robust pose estimation. In order to select most effective virtual views for pose fusion, we evaluate the virtual views based on the confidence of virtual views using a light-weight network via network distillation. Experiments on three main benchmark datasets including NYU, ICVL and Hands2019 demonstrate that our method outperforms the state-of-the-arts on NYU and ICVL, and achieves very competitive performance on Hands2019-Task1, and our proposed virtual view selection and fusion module is both effective for 3D hand pose estimation.
Implementation of Artificial Intelligence in Diagnostic Practice as a Next Step after Going Digital: The UMC Utrecht Perspective

Building on a growing number of pathology labs having a full digital infrastructure for pathology diagnostics, there is a growing interest in implementing artificial intelligence (AI) algorithms for diagnostic purposes. This article provides an overview of the current status of the digital pathology infrastructure at the University Medical Center Utrecht and our roadmap for implementing AI algorithms in the next few years.
Based on the Time-Spatial Power-Based Cryptocurrency Miner Driving Force Model, Establish a Global CO2 Emission Prediction Framework after China Bans Cryptocurrency

The energy consumption and carbon footprint of cryptocurrencies have always been a popular topic. However, most of the existing studies only focus on one cryptocurrency, Bitcoin, and there is a lack of long-term monitoring studies that summarize all cryptocurrencies. By constructing a time series hash rate/power model, this research obtained the 10-year time series data on energy consumption dataset of global top-25 cryptocurrencies for the first time. Both the temporal coverage and the spatiotemporal resolution of the data exceed previous studies. The results show that Bitcoin’s power consumption only accounts for 58% of the top-25 cryptocurrencies. After China bans cryptocurrencies, the conservative change in global CO2 emissions from 2020 will be between −0.4% and 4.4%, and Central Asian countries such as Kazakhstan are likely to become areas of rapid growth in carbon emissions from cryptocurrencies.
Clinical data classification with noisy intermediate scale quantum computers

Abstract Quantum machine learning has experienced significant progress in both software and hardware development in the recent years and has emerged as an applicable area of near-term quantum computers. In this work, we investigate the feasibility of utilizing quantum machine learning (QML) on real clinical datasets. We propose two QML algorithms for data classification on IBM quantum hardware: a quantum distance classifier (qDS) and a simplified quantum-kernel support vector machine (sqKSVM). We utilize these different methods using the linear time quantum data encoding technique ( $${\mathrm{log}}_{2}N$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>log</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> ) for embedding classical data into quantum states and estimating the inner product on the 15-qubit IBMQ Melbourne quantum computer. We match the predictive performance of our QML approaches with prior QML methods and with their classical counterpart algorithms for three open-access clinical datasets. Our results imply that the qDS in small sample and feature count datasets outperforms kernel-based methods. In contrast, quantum kernel approaches outperform qDS in high sample and feature count datasets. We demonstrate that the $${\mathrm{log}}_{2}N$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>log</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> encoding increases predictive performance with up to + 2% area under the receiver operator characteristics curve across all quantum machine learning approaches, thus, making it ideal for machine learning tasks executed in Noisy Intermediate Scale Quantum computers.