Reviewerly/exHarmony huggingface dataset:
Multisource Data Analysis for Gold Potentiality Mapping of Atalla Area and Its Environs, Central Eastern Desert, Egypt

In this research, airborne geophysical and remote sensing datasets were integrated for gold potentiality mapping (GPM) over the Atalla area in Central Eastern Desert, Egypt. Utilizing aeromagnetic data, detailed structural complexity maps were constructed using the center for exploration targeting (CET) procedure. Then, spectrometric gamma-ray data primarily located hydrothermally altered tracts with discriminating various rock units. The latter are precisely outlined by implementing various techniques (false-color composite (FCC), band ratio (BR), relative absorption band depth (RBD), directed principal component analysis (DPCA), and constrained energy minimization (CEM)) to ASTER, Sentinel 2 and ALOS PRISM datasets, with reference to the geological maps. The study exhibits that gold mineralization is structurally controlled by NW-SE direction. The findings of structural complexity and hydrothermal alteration (argillic, advanced argillic, phyllic, and propylitic) were used as weighted inputs for contouring gold potentiality. The resultant GPM accentuated five gold-promising zones; two are confirmed via locations of ancient gold mines, while the remaining three zones are strongly recommended for their gold potentiality.
A hybrid quantum–classical neural network with deep residual learning

Inspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept. In this paper, a novel hybrid quantum–classical neural network with deep residual learning (Res-HQCNN) is proposed. We firstly analyse how to connect residual block structure with a quantum neural network, and give the corresponding training algorithm. At the same time, the advantages and disadvantages of transforming deep residual learning into quantum concept are provided. As a result, the model can be trained in an end-to-end fashion, analogue to the backpropagation in classical neural networks. To explore the effectiveness of Res-HQCNN , we perform extensive experiments for quantum data with or without noisy on classical computer. The experimental results show the Res-HQCNN performs better to learn an unknown unitary transformation and has stronger robustness for noisy data, when compared to state of the arts. Moreover, the possible methods of combining residual learning with quantum neural networks are also discussed.
A novel eye center localization method for multiview faces

Existing studies on eye center localization have mostly applied localization methods on faces viewed from frontal angles or faces with small yaw-rotation angles. This study proposed a novel eye center localization method that can effectively localize the eye centers under various situations on multiview faces with large yaw-rotation angles (from +67.5° to −67.5°). First, this study developed a multiview face detector that can be applied to large yaw angles and can flexibly detect and precisely capture the face region. The face detector can facilitate the generation of satisfactory results by a complete representation generative adversarial network (CR-GAN). Because a large yaw-rotation angle can substantially reduce the completeness of eye representation in an image or even cause the eyes to disappear within the face image, this study used a CR-GAN to produce frontal face images to solve the problem of incomplete representation in multiview face images. Furthermore, this study proposed a new iris-ripple filter to increase the accuracy and robustness of gradient localization. Finally, a new depth corresponding-points conversion method was proposed to automatically estimate the rotation variable between two faces and the conversion relationship between the eye centers. This method can effectively solve the problem of instability resulting from a CR-GAN during eye generation and can ensure localization accuracy for eyeballs with subtle changes. According to the experimental results, compared with other advanced methods, the proposed method exhibited higher accuracy and robustness in eye center localization when applied to images obtained from four databases that involved various challenging situations, including large yaw-rotation angle, illumination change, head pose change, gaze interaction, and complete occlusion of eyes.
SYMBALS: A Systematic Review Methodology Blending Active Learning and Snowballing

Research output has grown significantly in recent years, often making it difficult to see the forest for the trees. Systematic reviews are the natural scientific tool to provide clarity in these situations. However, they are protracted processes that require expertise to execute. These are problematic characteristics in a constantly changing environment. To solve these challenges, we introduce an innovative systematic review methodology: SYMBALS. SYMBALS blends the traditional method of backward snowballing with the machine learning method of active learning. We applied our methodology in a case study, demonstrating its ability to swiftly yield broad research coverage. We proved the validity of our method using a replication study, where SYMBALS was shown to accelerate title and abstract screening by a factor of 6. Additionally, four benchmarking experiments demonstrated the ability of our methodology to outperform the state-of-the-art systematic review methodology FAST 2 .
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks

To train robust deep neural networks (DNNs), we systematically study several target modification approaches, which include output regularisation, self and non-self label correction (LC). Two key issues are discovered: (1) Self LC is the most appealing as it exploits its own knowledge and requires no extra models. However, how to automatically decide the trust degree of a learner as training goes is not well answered in the literature? (2) Some methods penalise while the others reward low-entropy predictions, prompting us to ask which one is better?To resolve the first issue, taking two well-accepted propositions–deep neural networks learn meaningful patterns before fitting noise [3] and minimum entropy regularisation principle [10]–we propose a novel end-to-end method named ProSelfLC, which is designed according to learning time and entropy. Specifically, given a data point, we progressively increase trust in its predicted label distribution versus its annotated one if a model has been trained for enough time and the prediction is of low entropy (high confidence). For the second issue, according to ProSelfLC, we empirically prove that it is better to redefine a meaningful low-entropy status and optimise the learner toward it. This serves as a defence of entropy minimisation.We demonstrate the effectiveness of ProSelfLC through extensive experiments in both clean and noisy settings. The source code is available at https://github.com/XinshaoAmosWang/ProSelfLC-CVPR2021.
Optimizing S-box generation based on the Adaptive Agent Heroes and Cowards Algorithm

This paper introduces a new variant of a metaheuristic algorithm based on Agent Heroes and Cowards Algorithm (AHC), called Adaptive Agent Heroes and Cowards Algorithm (AAHC). The main feature of AAHC is the fact that the algorithm allows adaptive assignment of its population agents into cowards and heroes based on the exponential controlling functions. Furthermore, unlike its predecessor, AAHC also permits systematic manipulation of candidate solutions around the global best agent via the swap operator to boost its search intensification process. Meanwhile, to further enhance the diversification its solution, AHC also exploit the Tent map as the pseudo random generator replacement during its initial population initialization. Experimental results based on the generation of 8 × 8 substitution-box demonstrate that the proposed AAHC outperforms other competing metaheuristic algorithms in two main S-box criteria namely nonlinearity and strict avalanche criteria whilst maintaining commendable performances on bits independence criteria, differential approximation probability, linear approximation probability and transparency order.
Big data driven Internet of Things for credit evaluation and early warning in finance

The big data technology framework has been successfully used in the Internet of Things, and the financial industry also hopes to use the advanced technology of big data to integrate and improve internal and external data related to credit risks. Relying on more efficient machine learning algorithms to get a reasonable prediction of credit risk can reduce the self-generated losses of the Internet of Things finance and increase profits. This article uses distributed search engine technology to customize web crawlers to obtain the required bank card and transaction data from the multi-source heterogeneous data of the Internet of Things financial industry, design the corresponding Spark parallel algorithm to preprocess the data, and establish an inverted table and two Level index file provides data source for big data analysis platform. After the data source is determined, the Mutually Exclusive Collectively Exhaustive (MECE) analysis method is combined with the scores of many financial business experts in the industry to obtain a set of candidate indicators and quantification methods for the financial credit risk evaluation of the Internet of Things, and analyze the correlation of indicators and risk grading. The random forest algorithm in the big data machine learning library is used to select the feature of the candidate index set, and a multi-level spatial association rule algorithm based on the Hash structure is designed to mine the financial risk information of the Internet of Things, and build a credit risk assessment and intelligent early warning model. This paper selects 26 indicators of Internet of Things finance as the research objects, uses SPSS26.0 software to perform sample Kaiser–Meyer–Olkin (KMO) test and Bartlett sphere test on the original data, and describes the results of factor analysis in detail. The particle swarm algorithm is introduced into the parameter optimization of random forest, and the financial credit risk assessment model of the Internet of Things is established. The results show that this method can significantly reduce the probability of banks making the first and second error rates when evaluating the credit risk of financing the Internet of Things Finance. This is conducive to the smooth development of the Internet of Things financial business for banks, which enables banks to enhance their own profitability while effectively reducing losses due to incorrect credit provision.
The effectiveness of machine translation in foreign language education: a systematic review and meta-analysis

With a significant number of students using machine translation (MT) for academic purposes in recent years, language teachers can no longer ignore it in their classrooms. Although an increasing number of studies have reported its pedagogical benefits, studies have also revealed that language teachers are still sceptical about using MT for various reasons. Most of all, they have limited trust in MT quality and the effectiveness of using MT in foreign language (FL) learning. Hence, the current study examined these issues to provide an overview and a guide to language teachers regarding the use of MT in FL education. The study examined 87 MT studies related to FL education published between 2000 and 2019. It employed both systematic review (primary) and meta-analysis (secondary) as research methods for a robust data analysis. The results showed that the number of publications in this area has increased in recent years and that MT quality has been significantly enhanced. Most studies also reported the positive impacts of using MT in FL learning, particularly for writing. However, this study also found that students had mixed emotions about MT and that there are disparities in perceptions between instructors and students. Based on the results, the present study presents several pedagogical implications and suggestions for future research on MT.
Predicting Inpatient Flow at a Major Hospital Using Interpretable Analytics

Problem definition: Translate data from electronic health records (EHR) into accurate predictions on patient flows and inform daily decision making at a major hospital. Academic/practical relevance: In a constrained hospital environment, forecasts on patient demand patterns could help match capacity and demand and improve hospital operations. Methodology: We use data from 63,432 admissions at a large academic hospital (50% female, median age 64 years old, median length of stay 3.12 days). We construct an expertise-driven patient representation on top of their EHR data and apply a broad class of machine learning methods to predict several aspects of patient flows. Results: With a unique patient representation, we estimate short-term discharges, identify long-stay patients, predict discharge destination, and anticipate flows in and out of intensive care units with accuracy in the 80%+ range. More importantly, we implement this machine learning pipeline into the EHR system of the hospital and construct prediction-informed dashboards to support daily bed placement decisions. Managerial implications: Our study demonstrates that interpretable machine learning techniques combined with EHR data can be used to provide visibility on patient flows. Our approach provides an alternative to deep learning techniques that is equally accurate, interpretable, frugal in data and computational power, and production ready. History: This paper has been accepted for the Manufacturing &amp; Service Operations Management Special Section on Responsible Research in Operations Management. Funding: The research was funded by Beth Israel Deaconess Medical Center. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0971 .
Fake Currency Detection with Machine Learning Algorithm and Image Processing

This paper deals with the matter of identifying the currency that if the given sample of currency is fake. Different traditional strategies and methods are available for fake currency identification based on the colors, width, and serial numbers mentioned. In the advanced age of Computer science and high computational methods, various machine learning algorithms are proposed by image processing that gives 99.9% accuracy for the fake identity of the currency. Detection and recognition methods over the algorithms include entities like color, shape, paper width, image filtering on the note. This paper proposes a method for fake currency recognition using K-Nearest Neighbours followed by image processing. KNN has a high accuracy for small data sets making it desirable to be used for the computer vision task. In this, the banknote authentication dataset has been created with the high computational and mathematical strategies, which give the correct data and information regarding the entities and features related to the currency. Data processing and data Extraction is performed by implementing machine learning algorithms and image processing to acquire the final result and accuracy.
A hierarchical method based on improved deep forest and case-based reasoning for railway turnout fault diagnosis

• A hierarchical fault diagnosis method is proposed for railway turnout. • An improved deep forest which embedded priori knowledge is proposed. • Case-based Reasoning is utilized to achieve detailed and comprehensive diagnosis. Railway turnout system (RTS), which is widely laid along the railway tracks, is one of the most crucial devices in the whole railway infrastructures. Unpredictable outdoor weather, complex site conditions, and mechanical wear make the RTS the most vulnerable asset. Scientific and practical fault diagnosis is of great significance for reducing railway failures, improving operation efficiency, and ensuring transportation safety. In this paper, a hierarchical fault diagnosis method is proposed to realize accurate fault diagnosis of railway turnouts and improve the reliability and safety of railway systems. At the first level, an improved deep forest that embeds priori knowledge is adopted to distinguish the first-class fault sets. The embedded priori knowledge can help to outperform the traditional gcForest with fewer features produced at each level of the cascade forest. At the second level, a Case-based Reasoning component is utilized to classify the sub-class fault sets, which are difficult to be distinguished by the first level fault diagnosis due to their similar features. The hierarchical fault diagnosis method has been validated by using a real-world railway maintenance dataset. Extensive experiments show that the proposed method achieves superior performance compared to the state-of-the-art approaches in diagnostic precision under the constraint of limited data.
PURE

Recommender systems are powerful tools for information filtering with the ever-growing amount of online data. Despite its success and wide adoption in various web applications and personalized products, many existing recommender systems still suffer from multiple drawbacks such as large amount of unobserved feedback, poor model convergence, etc. These drawbacks of existing work are mainly due to the following two reasons: first, the widely used negative sampling strategy, which treats the unlabeled entries as negative samples, is invalid in real-world settings; second, all training samples are retrieved from the discrete observations, and the underlying true distribution of the users and items is not learned.
Time-Series Graph Network for Sea Surface Temperature Prediction

Sea surface temperature (SST) is an important indicator for balancing surface energy and measuring sea heat. Various effects caused by the sea temperature field significantly affect human activities to a large extent. It is important to predict the sea surface temperature efficiently and accurately. Existing SST prediction methods regard each sensor as an independent individual, without considering the structure or topology of the sensor network and ignoring the connectivity of the sensor network. To address this problem, this study investigates the SST prediction problem from the perspective of graph learning. We propose a time-series graph network (TSGN) that can jointly capture graph-based spatial correlation and temporal dynamics. TSGN uses a long short-term memory network to aggregate the features of time series data and establishes a graph neural network model to complete the SST prediction task. Experiments using SST data from the Pacific Northwest from 2001 to 2005 show that this method is more efficient and accurate when dealing with data containing time-series information and is superior to the existing SST prediction methods.
Discriminative group-sparsity constrained broad learning system for visual recognition

Broad Learning System (BLS) is an emerging network paradigm that has received considerable attention in the regression and classification fields. However, there are two deficiencies which seriously hinder its deployment in real applications. The first one is the internal correlations among samples are not fully considered in the modeling process. Second, the strict binary label matrix utilized in BLS provides little freedom for classification. In this paper, to address the above issues, we propose to impose group-sparsity constraints on the class-specific transformed features and label error terms, respectively. The effect is not only the more appropriate margins between data can be preserved, but also the learnt label space can be flexible for recognition. As a result, the obtained projection matrix can show more vital discriminative ability. Further, we employ the alternating direction method of multipliers to solve the resulting optimization problem. Extensive experiments and analysis on diverse benchmark databases are carried out to confirm our proposed model’s superiority in comparison with other competing classification methods.
ICT self‐efficacy mediates most effects of university ICT support on preservice teachers' TPACK: Evidence from three normal universities in China

Abstract A thorough understanding of how ICT support provided by teacher preparation programmes influences preservice teachers' technological pedagogical content knowledge (TPACK) has become increasingly essential. This study aimed at exploring the mediating role of ICT perception , perceived ICT competence and ICT self‐efficacy between university ICT support and preservice teachers' TPACK. Data were collected through an online self‐assessment questionnaire translated and adapted from existing studies and verified with confirmatory factor analysis (CFA). A total of 298 preservice teachers, who were senior students studying at three different normal universities in China, participated in this study. Structural equation modelling revealed the following: (a) university ICT support significantly predicted preservice teachers' ICT perception , perceived ICT competence and ICT self‐efficacy , but had no significant direct effects on their TPACK; (b) ICT self‐efficacy was a strong predictor of preservice teachers' TPACK and ICT perception was a weak one, while perceived ICT competence did not predict TPACK at all; and (c) among three potential mediators, ICT self‐efficacy mediated the most effects of university ICT support on preservice teachers' TPACK. These findings demonstrate strong positive effects of institutional support on preservice teachers' TPACK development, which was strongly underestimated in previous studies. Findings suggest normal universities should create practical opportunities for preservice teachers to perceive the power of technology, especially opportunities that could improve preservice teachers' self‐efficacy while applying technology into instruction. Practitioner notes What is already known about this topic ICT support from schools is an essential precondition while enhancing in‐service teachers' motivation to apply ICT into instruction. Research revealing how ICT support provided by teacher preparation programmes affects preservice teachers' TPACK is absent. What the paper adds This study adds to the previous literature by exploring the mediating role of ICT perception , perceived ICT competence and ICT self‐efficacy in the relationship between university ICT support and preservice teachers' TPACK. This study found that university ICT support significantly predicted preservice teachers' ICT perception , perceived ICT competence and ICT self‐efficacy , but had no significant direct effects on their TPACK. Among the three potential mediators, ICT self‐efficacy mediated the most effects of university ICT support on preservice teachers' TPACK. Implications for practice and/or policy Normal universities need to provide effective and professionally targeted ICT support to meet preservice teachers' needs. The mediating role of ICT self‐efficacy should be highlighted on the path from university ICT support to TPACK.