Reviewerly/exHarmony huggingface dataset:
THE USE OF LANDSAT ETM+ IN HYDROGEOLOGICAL INVESTIGATION IN BASEMENT TERRAIN, HAMISSANA AREA, N-E SUDAN

The objective of this study is to perform a hydrogeological survey using remote sensing techniques in order to delineate target zones for groundwater exploration within terrains underlain by crystalline rocks.The study area is situated in north western flanks of the Red Sea hills in the Hamissana area, Red Sea State within the Arabian Nubian shield (ANS) which belongs to the Pan-African Era.The region is characterized by desert climate, where acute shortages of water is experienced.The study area is covered by the basement complex rocks (Precambrian age) which is overlain by alluvial deposits.The remotely sensing data of Enhanced Thematic Mapper (ETM+) were used in this study in addition to Geographic Information System (GIS) to illustrate the geological features in the study area, mainly the Lineaments.The structural analysis had been applied to classify the open potential fractures that commonly found in NW-SE and in NE-SW directions, while the closed shear fractures are oriented in N-S direction.Based on the study of lineaments, target zones of ground water were indicated by the overlap of high-intensity opened lineaments and the lowdrainage intensity.
The concept of digital reality: meaning and significance

The article analyzes the processes of transformation of a person's attitude to the world, which arose because of the active introduction of computer technology into management and production systems, as well as the use of electronic and digital devices in everyday life. The author reconstructs the evolution of the attitude towards the reality that occurred as a result of the digitalization of social practices and of the philosophical and theoretical reflections on these problems. Special attention is paid to the concepts of “virtual reality “and” digital reality”, and the design features of each of them are shown. The article substantiates the thesis that digital reality is the result of digitalization of social practices, both individual and collective. The article considers the specifics of defining the concept of virtual reality and its definition in the context of identifying the concept of digital reality. The first attempts to conceptualize the phenomenon of digital reality are analyzed, the experiences of using this concept are reconstructed, the main meanings are highlighted. A separate case is considered with a report on the need for a system design of the digital reality segment, proposed for the field of an expert information support of Russian science.
Evaluation Model of Online and Offline Mixed Teaching Quality in Colleges and Universities Based on BP Neural Network

Hybrid online and offline teaching is becoming the mainstream teaching method in the postepidemic era. However, research on assessing its teaching quality is still limited. This article thus develops a teaching quality evaluation model based on the BP neural network. A three-dimensional indicator system involving 19 indicators is set in the model. The established model was demonstrated and validated by a case study. The results show that the developed model can accurately assess the teaching quality of hybrid online and offline teaching. Findings from this study can provide valuable references for improving the quality of hybrid online and offline teaching.
Eye-Tracker Study of Influence of Affective Disruptive Content on User’s Visual Attention and Emotional State

When reading interesting content or searching for information on a website, the appearance of a pop-up advertisement in the middle of the screen is perceived as irritating by a recipient. Interrupted cognitive processes are considered unwanted by the user but desired by advertising providers. Diverting visual attention away from the main content is intended to focus the user on the appeared disruptive content. Is the attempt to reach the user by any means justified? In this study, we examined the impact of pop-up emotional content on user reactions. For this purpose, a cognitive experiment was designed where a text-reading task was interrupted by two types of affective pictures: positive and negative ones. To measure the changes in user reactions, an eye-tracker (for analysis of eye movements and changes in gaze points) and an iMotion Platform (for analysis of face muscles' movements) were used. The results confirm the impact of the type of emotional content on users' reactions during cognitive process interruptions and indicate that the negative impact of cognitive process interruptions on the user can be reduced. The negative content evoked lower cognitive load, narrower visual attention, and lower irritation compared to positive content. These results offer insight on how to provide more efficient Internet advertising.
An In-Orbit Stereo Navigation Camera Self-Calibration Method for Planetary Rovers with Multiple Constraints

In order to complete the high-precision calibration of the planetary rover navigation camera using limited initial data in-orbit, we proposed a joint adjustment model with additional multiple constraints. Specifically, a base model was first established based on the bundle adjustment model, second-order radial and tangential distortion parameters. Then, combining the constraints of collinearity, coplanarity, known distance and relative pose invariance, a joint adjustment model was constructed to realize the in orbit self-calibration of the navigation camera. Given the problem of directionality in line extraction of the solar panel due to large differences in the gradient amplitude, an adaptive brightness-weighted line extraction method was proposed. Lastly, the Levenberg-Marquardt algorithm for nonlinear least squares was used to obtain the optimal results. To verify the proposed method, field experiments and in-orbit experiments were carried out. The results suggested that the proposed method was more accurate than the self-calibration bundle adjustment method, CAHVOR method (a camera model used in machine vision for three-dimensional measurements), and vanishing points method. The average error for the flag of China and the optical solar reflector was only 1 mm and 0.7 mm, respectively. In addition, the proposed method has been implemented in China’s deep space exploration missions.
The Application of an Improved Fuzzy Comprehensive Evaluation in Coal Quality Rating: The Case Study of China

Coal quality rating can help reduce greenhouse gas emissions, solving the global warming problem. It becomes more important as the carbon neutrality by the mid-21st century agreement is accepted by 195 countries, including China. In this paper, an improved fuzzy comprehensive evaluation method is introduced for coal quality rating. The data used in this work are of the Hostolgoi coalfield of the Xinjiang Province of China. Six industrial analysis indicators are determined as evaluation factors by taking the coal samples of different coal seam depths in the mining area. The super-standard multiple methods and the double-weight super-standard weighting method are combined to form a comprehensive weight. The results show that most of the coal samples of this coal mine are at grades I–II, and the overall coal is with good-quality stability. The evaluation results can improve the coal utilization efficiency and provide scientific guidance for evaluating and exploiting coal resources in coal geological exploration.
A Nonintrusive and Real-Time Classification Method for Driver’s Gaze Region Using an RGB Camera

Driver distraction has become a leading cause of traffic crashes. Visual distraction has the most direct impact on driving safety among various driver distractions. If the driver’s line of sight deviates from the road in front, there will be a high probability of visual distraction. A nonintrusive and real-time classification method for driver’s gaze region is proposed. A Multi-Task Convolutional Neural Network (MTCNN) face detector is used to collect the driver’s face image, and the driver’s gaze direction can be detected with a full-face appearance-based gaze estimation method. The driver’s gaze region is classified by the model trained through the machine learning algorithms such as Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN). The simulated experiment and the real vehicle experiment were conducted to test the method. The results show that it has good performance on gaze region classification and strong robustness to complex environments. The models in this paper are all lightweight networks, which can meet the accuracy and speed requirements for the tasks. The method can be a good help for further exploring the visual distraction state level and exert an influence on the research of driving behavior.
STABLE LIKELIHOOD COMPUTATION FOR MACHINE LEARNING OF LINEAR DIFFERENTIAL OPERATORS WITH GAUSSIAN PROCESSES

In many applied sciences, the main aim is to learn the parameters in the operational equations which best fit the observed data. A framework for solving such problems is to employ Gaussian process (GP) emulators which are well-known as nonparametric Bayesian machine learning techniques. GPs are among a class of methods known as kernel machines which can be used to approximate rather complex problems by tuning their hyperparameters. The maximum likelihood estimation (MLE) has widely been used to estimate the parameters of the operators and kernels. However, the MLE-based and Bayesian inference in the standard form are usually involved in setting up a covariance matrix which is generally ill-conditioned. As a result, constructing and inverting the covariance matrix using the standard form will become unstable to learn the parameters in the operational equations. In this paper, we propose a novel approach to tackle these computational complexities and also resolve the ill-conditioning problem by forming the covariance matrix using alternative bases via the Hilbert−Schmidt SVD (HS-SVD) approach. Applying this approach yields a novel matrix factorization of the block-structured covariance matrix which can be implemented stably by isolating the main source of the ill-conditioning. In contrast to standard matrix decompositions which start with a matrix and produce the resulting factors, the HS-SVD is constructed from the Hilbert−Schmidt eigenvalues and eigenvectors without the need to ever form the potentially ill-conditioned matrix. We also provide stable MLE and Bayesian inference to adaptively estimate hyperparameters, and the corresponding operators can then be efficiently predicted at some new points using the proposed HS-SVD bases. The efficiency and stability of the proposed HS-SVD method will be compared with the existing methods by several illustrations of the parametric linear equations, such as ordinary and partial differential equations, and integro-differential and fractional order operators.
ANALISIS BIBLIOMETRIK: PERKEMBANGAN PENELITIAN DAN PUBLIKASI MENGENAI KOORDINASI PROGRAM MENGGUNAKAN VOSVIEWER

Bibliometric analysis is a popular method for exploring and analyzing large amounts of scientific data. This article is a bibliometric analysis using Scopus indexed journals on program coordination using VOSviewer application. The purpose of bibliometric analysis in this research is to identify the development of studies related to program coordination. Specifically, this article discusses citation developments, publications trends, authors’ collaboration, trending term titles, trend term author keywords, trend term abstracts, and country statistics with the topic of program coordination 1943-2021. The data was collected from Scopus database using keyword “coordination program.” Furthermore, the author uses VOSviewer software to analyze and visualize the database obtained. The results showed that the results of the citation analysis showed the number of citations per year from 1943-2021 was 4.304 citations. Irvine, M.K is a researcher who has the largest productivity, which is ten publications. In the analysis of the trend term author keywords, it can be seen that there are 676 author keys used in selected articles using a minimum of five events, resulting nine author keywords that have a strong connection. The term “care coordination” is the term most frequently used by the author with 23 entries. Moreover, the country that published the most program coordination articles was the United States with 246 articles.
RetGen: A Joint Framework for Retrieval and Grounded Text Generation Modeling

Recent advances in large-scale pre-training such as GPT-3 allow seemingly high quality text to be generated from a given prompt. However, such generation systems often suffer from problems of hallucinated facts, and are not inherently designed to incorporate useful external information. Grounded generation models appear to offer remedies, but their training typically relies on rarely-available parallel data where information-relevant documents are provided for context. We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal. The model learns to reward retrieval of the documents with the highest utility in generation, and attentively combines them using a Mixture-of-Experts (MoE) ensemble to generate follow-on text. We demonstrate that both generator and retriever can take advantage of this joint training and work synergistically to produce more informative and relevant text in both prose and dialogue generation.
The Construction of Accurate Recommendation Model of Learning Resources of Knowledge Graph under Deep Learning

With the rapid development of science and technology and the continuous progress of teaching, it is now flooded with rich learning resources. Massive learning resources provide learners with a good learning foundation. At the same time, learners want to be precise from many learning resources. Second, it becomes more and more difficult to quickly obtain the learning resources you want. Therefore, it is very important to accurately and quickly recommend learning resources to learners. During the last two decades, a large number of different types of recommendation systems were adopted that present the users with contents of their choice, such as videos, products, and educational content recommendation systems. The knowledge graph has been fully applied in this process. The application of deep learning in the recommendation systems has further enhanced their performance. This article proposes a learning resource accurate recommendation model based on the knowledge graph under deep learning. We build a recommendation system based on deep learning that is comprised of a learner knowledge representation (KR) model and a learning resource KR model. Information such as learner’s basic information, learning resource information, and other data is used by the recommendation engine to calculate the target learner’s score based on the learner KR and the learning resource KR and generate a recommendation list for the target learner. We use mean absolute error (MAE) as the evaluation indicator. The experimental results show that the proposed recommendation system achieves better results as compared to the traditional systems.
An Improved User Identification based on Keystroke-Dynamics and Transfer Learning

Transfer learning is one of the most appreciated classification techniques, when a small size of training dataset and limited computer capabilities exist along with the presence of noisy data. Such a scenario is encountered in keystroke dynamics whereby the typing gesticulation composed to form ‘keystroke dynamics’, is used to distinguish a human. Keystroke dynamics is persistently gaining reputation as one of the most promising and cost-effective behavioral biometrics. AlexNet and ResNet are two separate types of pre-trained convolutional neural network models, normally used to apply deep transfer learning concepts within image-based systems. Conversion of keystroke data into image data and then the formation of the artificial image data by extending available data are crucial attraction of this paper. In this paper, the pre-trained models are utilized by applying fine-tuning (of parameters in some layers) and used as an end-to-end learning and classification system by us. On the keystroke dataset, the feature extraction method (support vector machine for classification) is also applied here with pre-trained models. A relative analysis of both approaches is provided and lastly, the better one is employed in the proposed (recognition based) system. Finally, 98.57% accuracy of successful recognition is recorded.
Exhaustive Search of Correspondences between Multimodal Remote Sensing Images Using Convolutional Neural Network

Finding putative correspondences between a pair of images is an important prerequisite for image registration. In complex cases such as multimodal registration, a true match could be less plausible than a false match within a search zone. Under these conditions, it is important to detect all plausible matches. This could be achieved by an exhaustive search using a handcrafted similarity measure (SM, e.g., mutual information). It is promising to replace handcrafted SMs with deep learning ones that offer better performance. However, the latter are not designed for an exhaustive search of all matches but for finding the most plausible one. In this paper, we propose a deep-learning-based solution for exhaustive multiple match search between two images within a predefined search area. We design a computationally efficient convolutional neural network (CNN) that takes as input a template fragment from one image, a search fragment from another image and produces an SM map covering the entire search area in spatial dimensions. This SM map finds multiple plausible matches, locates each match with subpixel accuracy and provides a covariance matrix of localization errors for each match. The proposed CNN is trained with a specially designed loss function that enforces the translation and rotation invariance of the SM map and enables the detection of matches that have no associated ground truth data (e.g., multiple matches for repetitive textures). We validate the approach on multimodal remote sensing images and show that the proposed "area" SM performs better than "point" SM.
FMEA in Smartphones: A Fuzzy Approach

Smartphones are attracting increasing interest due to how they are revolutionizing our lives. On the other hand, hardware and software failures that occur in them are continually present. This work aims to investigate these failures in a typical smartphone by collecting data from a class of people. Concerns have been raised that call into question the efficiency of applied methods for identifying and prioritizing the potential defects. The widely used hybridized engineering method, Fuzzy Failure Mode and Effect Analysis (F-FMEA), is an excellent approach to solving these problems. The F-FMEA method was applied to prioritize the potential failures based on their Severity (S), expected Occurrence (O), and the likelihood of Detectability (D). After collecting failure data from different users on a selected smartphone, two well-known defuzzification methods facing the Risk Priority Number (RPN) in F-FMEA were applied. Despite this interest, to the best of our knowledge, no one has studied smartphone failures with a technique that combines the results of different fuzzy applications. Thus, to combine the results of the derived fuzzy subsystems for the average value, we suggest a summative defuzzification method. Our findings indicate that F-FMEA with a summative defuzzification procedure is a clear improvement on the F-FMEA method. Even though the summation method modifies close results of the defuzzification one, it was shown that it provides more accurate results.
The Influence of Mobile Learning on the Optimization of Teaching Mode in Higher Education

The purpose of the study is to optimize the teaching mode of higher education. The teaching mode of colleges and the learning situation of college students are studied based on mobile learning. Artificial neural network (ANN) algorithm is implemented to train and test the student responses. The classification results show that compared with the Traditional Teaching Mode (TTM), the teaching mode of mobile learning can significantly improve students’ learning effectiveness, skill mastery, and learning enthusiasm. Compared with the TTM, the teaching mode of mobile learning can help college students get a better learning experience. The number of students participated or attended the classes through mobile applications is higher when compared with the other traditional methods.
Non-smooth interpolation of graph signals

We consider the problem of signal interpolation on graphs, i.e. recovering one or multiple graph signal values from incomplete measurements. We propose a review of the graph signal interpolation methods, which enlightens the restrictive underlying hypothesis of signal smoothness over the graph. We formulate a new interpolation framework based on a locality criterion designed to handle non-smooth signals, and provide corresponding specific solutions. We validate the proposed methods on synthetic and real-world interpolation problems, and analyse the stability of our methods regarding the interpolation problem main parameters.
Inter-Locking Dependency Evaluation Schema based on Block-chain Enabled Federated Transfer Learning for Autonomous Vehicular Systems

Federated Transfer Learning is a new approach to optimizing information and data training frameworks to achieve a higher order of distributed learning. Currently, the Internet of Vehicles (IoV) model is trained through online learning by exchanging the local and global updates. Context-based distributed learning with minimum communication becomes challenging due to the dynamic nature of autonomous systems. This paper aims to understand the past and future challenges, trends, and applications of new learning adopted in autonomous systems with a proposed schema of FTL-enabled autonomous models training. The goal behind minimizing the latency through the proposed FTL Interlocking Dependency Evaluation schema in exchanging the local and global updates is to match with the real-time application scenario by ensuring security and privacy features. The evaluation parameters used in estimating the performance of new learning are window size, back-off time, recovery policy (retransmission), scalability, peak data rate (Gbps), user-experienced data rate (Mbps), mobility (Kmph), latency (ms), end-to-end delay analysis, connection density (devices/km<sup>2</sup>), and area traffic capacity (Mb/s/m<sup>2</sup>). The evaluation results have validated the reliability of the proposed model for computing autonomous systems using a novel FTL Interlocking Dependency Evaluation schema for higher order of inter-dependency on vehicular environment processing.