Reviewerly/exHarmony huggingface dataset:
Searching for Copper in the age of Transition - the holistic exploration mindset

Summary The Energy Transition is driven by the need to mitigate climate change through progressive global switching to lower carbon energy sources. Critical minerals such as lithium, copper, cobalt and Rare Earth Elements will be needed in greater amounts for electrification, infrastructure upgrades as well as for providing the raw components for electric vehicles, renewable energy generation, battery storage and modern devices. However, it is becoming increasingly difficult to find large tonnage and high-grade mineral deposits at or very near the surface, especially of commodities that will increasingly be needed for the energy transition. Those prospecting to expand mineral portfolios increasingly need to look deeper and beyond known occurrences. We are taking applicable geological subsurface screening methods from the hydrocarbon industry, reliant upon an integrated tectono-stratigraphic framework compiled from public domain data, and applying them within a broader mineral system approach to exploration. The regional scale mineral systems screening methods presented here are innovative due to the integration of a high-temporal resolution and global sequence stratigraphic model, alongside the integration of plate tectonics. The results are promising and offer a consistent first pass to narrow down targets at the regional scale for frontier regions and buried undercover in the subsurface.
Communication Strategies in the Digital Environment as a Tool to Form the Communicator's Personal Brand

Based on the Russian educational standard, they are determined the vectors of digital transformation of the competence model of the future advertising and PR specialist as an effective communicator. The performing professional functions of a communicator in the digital sphere is based on specific communication strategies. They form the basis of a communications specialist's personal brand. Based on the method of system analysis, the authors represent the concept of professional digital competence from the point of view of the communication, not the technological aspect. It is also given its significance in the formation of a professional brand of a communications specialist. The main purpose of the research is to identify communicative strategies in the digital environment in the competence model and the stages of their formation as part of the personal brand of an advertising and public relations specialist. Based on the modeling method, it is described the nomenclature of such strategies. It is noted that they contribute to the formation of the personal brand of the future communicator in the digital environment, as well as the students' readiness to implement them in professional activities.
Combining Extreme Learning Machine Through Random Projections For Dimensional Information Taxonomy And Assembling

The Extreme Learning Machine (ELM) is a revolutionary feed-forward neural network learning technique that functions on a single hidden layer. It is based on the idea that the more information a neural network has, the better it can learn (SLFNs). It uses a random process to set the weights of the hidden neurons and uses the Moore-Penrose (MP) generalised inverse to analytically compute the weights of the output neurons. Both of these computations are done in parallel. ELM does not require iteration for the calculation of the SLFN parameters, in contrast to the sluggish gradient descent- based learning techniques that are typically used for SLFN. This is because the parameters for the hidden layer are initialised in a random manner and are maintained throughout the knowledge procedure. In addition, the output burdens are determined systematically. In most cases, this modification makes the input statistics more identifiable in the ELM topographies space, which makes it easier to solve the underlying or connected problems. In addition to its lightning-fast learning, the ELM algorithm also delivers outstanding results in terms of generalisation. This comes as a bonus to its lightning-fast learning. When classifying and clustering the data for this research article, we make use of ELM in conjunction with random projection (RP). It is conceivable that the choices could be cut in half. The first one is intended for use with enormous datasets, whereas the second one works much better with more limited ones. Building a small network with a strong generalization performance, while utilizing a limited neurons count in the secreted level is the goal of this research paper.
Accelerating heuristic convergence on the "Evolution of Mona Lisa" problem by including image-centric mutation operators

The "Evolution of Mona Lisa" problem aims to approximate a target image by overlapping many semi-transparent polygons. The problem has been tackled in the past using multiple Nature-Inspired heuristics, and our main contribution is adding image-centric mutation operators (scaling, rotating and translating polygons).We compare Genetic Algorithms, Hill-Climbing and Simulated Annealing. A candidate solution has variable length (of, at most, 300 decagons) and, due to the variable opacity of polygons, order matters – resulting, in practice, in a pseudo-Messy GA. We use the same representation and mutation operators for the trajectory methods which, due to the focus on wall-clock time, outperform our GA implementation.We find that these methods retain good image approximation at good run times: 98.9-99.2% (mean on 30 images), with a time limit of 30 minutes, on images 500-pixels tall.
Autonomous Human Following Robot Based on Follow the Gap Method

With the recent advances in robotics and artificial intelligence, human-robot interaction has become increasingly important for various applications. Autonomous robots are required to detect humans, provide safety, and follow them in some cases. Several approaches have been developed for detecting and tracking humans. In this paper, we propose a new approach that fuses two different human detection and tracking algorithms, namely the RWTH Upper Body Detector and Joint Leg Tracker, to provide a more robust human detection. We then use nearest-neighbor data association and Extended Kalman Filter to track the human. We also modify and integrate the Follow the Gap Method obstacle avoidance algorithm into the human tracking task to ensure a safe path and heading angle towards the person. Our proposed approach uses a similar approach to Adaptive Cruise Control for the robot to follow the person from a desired distance. We evaluate the performance of the proposed approach by considering the tracking distance error and heading angle difference between the human and robot in different scenarios.
Cross-Lingual Transfer with Language-Specific Subnetworks for Low-Resource Dependency Parsing

Abstract Large multilingual language models typically share their parameters across all languages, which enables cross-lingual task transfer, but learning can also be hindered when training updates from different languages are in conflict. In this article, we propose novel methods for using language-specific subnetworks, which control cross-lingual parameter sharing, to reduce conflicts and increase positive transfer during fine-tuning. We introduce dynamic subnetworks, which are jointly updated with the model, and we combine our methods with meta-learning, an established, but complementary, technique for improving cross-lingual transfer. Finally, we provide extensive analyses of how each of our methods affects the models.
Soldier identification based on improved YOLOv5 algorithm in battlefield environment

For soldier recognition in the battlefield environment, there are factors such as camouflage and object occlusion, thus leading to incomplete feature information and poor recognition effect. In this paper, we first construct a soldier target dataset conforming to the characteristics of the battlefield environment by analyzing the factors influencing the battlefield environment. Then this paper improves the yolov5 algorithm to detect soldier recognition quickly by adding a channel attention mechanism and improving the spatial pyramid pooling structure. The implementation results show that the predicted mAP value can reach 0.946 with a 3% improvement, the recall rate reaches 0.86, and the detection speed is improved by 5%. It achieves better recognition of soldiers in the battlefield environment.
Study on the application of UAV intelligent dehazing in landscape planning and design

In the field of landscape planning and design, Unmanned Aerial Vehicles (UAV) technology plays an increasingly important role. However, in hazing environment, the images acquired by drones affect not only visual appreciation, but also the efficiency the efficiency of landscape planning and design. Aiming at the above problems, this paper presents an intelligent method of UAV image based on adaptive convolution. Firstly, according to the inhomogeneity of image degradation in hazing environment, an adaptive convolution module is proposed to intelligently extract projection images. Secondly, the multi-scale processing module learns the feature information of the clear image from the degraded image; Finally, the unsupervised framework is combined with the imaging model, and the traditional data set-driven mode is cancelled, so as to better output clear images. The results show that the proposed intelligent dehazing method of UAV images can better deal with the real environment and improve the quality of degraded images in hazing environments. The subjective and objective evaluation indicators of this algorithm are better than other comparison algorithms.
Uncertainty prediction in human pose estimation based on Gaussian heatmap

As a fundamental task in computer vision, human pose estimation has undergone in-depth and thorough research, but we found that the key point labeling uncertainty is rarely paid attention to, but this uncertainty will affect the detection performance. Motivated by this finding, this paper first uses the patch-adaptive loss to address the label ambiguity problem inherent in keypoint annotation. In addition, this paper combines the advantages of convolution and Transformer architectures to inject long-range and short-range information into the network. Experimental results on publicly available datasets demonstrate that our method improves the performance of human poses outperforming state-of-the-art methods and improves the robustness of the network.
Design and implementation of satellite control unit fault simulation system based on virtual simulation technology

The fault simulation of satellite control subsystem is an important part of the satellite fault simulation verification platform. It adopts the international standard modeling and simulation universal interface FMI, and through the full digital simulation mode, uses advanced simulation technology and virtual instrument technology to build a high fidelity user monitoring interface to achieve visual simulation flight, equipment fault simulation, user operation, information monitoring and other functions. The system level key failure modes of the satellite are simulated and verified in the development stage, and the ground recurrence and analysis of the failure phenomena are assisted in the satellite on orbit operation stage. It supports the satellite test and stable operation in orbit through satellite fault verification, satellite electrical performance test and other work.
Image Patching with Convolutional Neural Networks

Image Patching with Convolutional Neural Networks
Design of intelligent dustbin based on machine learning

In recent years, with the development of economic society and the improvement of people's living standards, the number and types of household rubbish are increasing. However, on one hand, due to people's weak awareness of rubbish classification, rubbish in public places cannot be collected well. On the other hand, due to the lack of rubbish classification knowledge, people can not classify some rubbish correctly, which will result in environmental pollution and waste of resources. So we design a intelligent dustbin which can carry out rubbish classification management and delivery guidance, to ensure that rubbish can be correctly placed. For those who do not know the rubbish classification knowledge or cannot determine the category of rubbish, the rubbish characteristics can be identified by scanning the rubbish and compared with the information in the system database to identify the rubbish type and display it on the screen to promote rubbish classification and various rubbish types.
Modeling of educational space in an educational institution: the curriculum of the discipline for higher education applicants of the degree of Doctor of Philosophy in the specialty 011 Educational, pedagogical sciences

The working program of the educational discipline "Modeling of educational space in an educational institution" is intended for the training of students of the third (educational and scientific level) of higher education and was developed on the basis of competence and partnership principles of human-centeredness in accordance with the state policy in the field of education. It contains a list of general and professional competencies, which should be mastered by the holder of the degree of Doctor of Philosophy in Educational, Pedagogical Sciences, program learning outcomes, which should be achieved during the study of the discipline. The program is based on the European strategy for the development of lifelong learning education. Structurally, it consists of 2 content modules, the volume of which is 1 ECTS credit. The object of study is the educational space in the field of education and modeling of its innovative development. The theoretical content of the subject area includes the methodological and philosophical foundations of the general scientific phenomenon "educational space" in the perspective of time relativity; the specificity of the educational space in a binary system with the environment in the field of education, types of space and the scientific basis of their classification; features of a safe educational environment and conditions for its creation in educational institutions; an inclusive educational environment for joint learning, education and development of students taking into account their needs and capabilities; general scientific approaches to the modeling of space in the field of education and its integration into international educational and scientific spaces based on the integration of the achievements of various branches of science, criteria for evaluating the effectiveness of functioning and innovative development of educational space and environment; the role of man in general object, global educational, sociocultural and sociotechnological types of spaces as open systems.
Lightweight human pose estimation based on high-resolution network

Most of the existing methods of human pose estimation methods focus on improving the accuracy of prediction results, but due to network parameters and high computational complexity, a great deal of computing resources are needed. In this paper, a lightweight human pose estimation method based on high-resolution network is proposed. The bottleneck module and the basic module in the high-resolution network are redesigned by using the depth separable convolution instead of the ordinary convolution and integrating attention mechanism, which ensures the accuracy of the network and greatly reduces the number of parameters and computational complexity of the model. Experimental results on the COCO VAL2017 dataset show an 84.5% reduction in the number of model parameters, a 73.9%% reduction in computational complexity, and 0.2% increase in the accuracy of human keypoint detection compared to the high-resolution network.
The Health Industry's Achilles Heel: Cybersecurity Threats. The Health Industry's Achilles Heel: Cybersecurity Threats. The National cyber security centre Board Toolkit. (Preprint)

<sec> <title>UNSTRUCTURED</title> Hospitals have become increasingly reliant on digital technology to deliver healthcare services. This has led to significant improvements in patient care and operational efficiency. However, it has also made hospitals vulnerable to cyber attacks, which can have a significant impact on hospital operations. Cyber attacks can result in disruptions in patient care, loss of patient data, financial losses, and damage to hospital reputation. In view of the in March 2023, the National cyber security centre in United kingdom published a toolkit for the board members, which can be used by medical professional and leaders to tackle this ever-growing threat. </sec>
Influence of implicit iterative method on convergence rate of SIMPLE algorithm

In the application of SIMPLE algorithm, the generation and solving of linear equations with sparse coefficient matrix appear. Gauss-Seidel and ILU iteration are commonly used to solve the problem. Combined with the common characteristics of LU-SGS time advance method and ILU iterative method, the numerical iterative solutions of sparse matrix linear equations are studied. An iterative LU-SGS algorithm for solving general sparse matrix is proposed. Using C++ as programming language, a special equation solving library is constructed by using three-point data storage structure of multi-dimensional column vector and sparse matrix dot product algorithm. Combined with SIMPLE algorithm based on pressure, the transient NS equation in static grid is simulated numerically. Compared with Gauss-Seidel iterative method, this algorithm has the advantages of accurate calculation and fast convergence, so it has certain application value and reference value.
Conformer based hybrid ctc/attention end-to-end Tibetan speech recognition

Deep learning has led to remarkable success in many research fields,such as machine translation,speech recognition. Combined with the characteristics of the Tibetan and the current development of Tibetan, with the help of transformer, conformer and other frameworks, we studies how changes in modeling units and encoder types affect Tibetan speech recognition. The modeling unit in this paper generates subwords through bpe algorithm or automatically divides Tibetan characters, and the encoder types are transformer and conformer. Finally, we verify it on the open-source Tibetan language dataset xbmu-amdo31. The experimental results show that the Tibetan characters as the modeling unit and conformer as the encoder has the best performance. The CER of conformer encoder whose modeling unit is character is 11.89%, which is 44.51% lower than the transformer model. The WER of conformer encoder whose modeling unit is subword is 17.40%, which is relatively reduced by 32.27%.
An efficient recommender system based on improved KGPolicy module

As an appropriate method for mining negative signals from large-scale unobserved data, negative sampling plays a key role in recommender systems. Existing negative sampling methods, however, either static or adaptive ones, are insufficient to explicitly learn the structural representation information of user-item interactions. In this study, we hypothesize that indirect interactions between users and items, which enrich the structural representation information, could be useful to explicitly yield informative and high-quality negative samples. To fully exploit the indirect user-item interactions to generate negative samples, we construct a recommender system based on improved knowledge graph policy network (IKGPolicy), which works as a valid module to explore high-quality negatives. Specifically, by improving the graph learning module of the KGPolicy model, it is explicit to capture indirect and high-order interactive information, and yield a informative negative item to train the recommender. Experiments on the Amazon-Book, Last-FM, and Yelp2018 datasets show that our proposed method presents significant improvements over both negative sampling methods and knowledge graph-based models. Further analyses provide sufficient insights in capturing and learning the structural and interactive representation information.
Hybrid opposite-based learning marine predator algorithm

The marine predators algorithm (MPA) is a swarm-based metaheuristic algorithm which present good performance, but suffers from insufficient population diversity. To enhance the population diversity and prevent the algorithm searching for a partial optimal solution, in this paper, we suggest a hybrid opposite-based learning marine predator algorithm. In HOLMPA, the performance is improved by introducing two different opposite learning strategies. The effectiveness of HOLMPA is verified by applying a group of test functions.
A Critical Review of the Applications of Artificial Intelligence in Recommender Systems

Recommender systems are unsupervised machine learning algorithms that recommend goods, content, or services to people online based on their past actions and anticipated future preferences. With the help of advancing Artificial Intelligence, primarily via computational intelligence and machine learning, recommender systems have improved user experience and satisfaction. This paper thoroughly discusses the applications of Artificial Intelligence in recommender systems and the issues that arise with AI-based recommender systems.
Exploiting Deep Learning and Explanation Methods for Movie Tag Prediction

Indexing multimedia content with rich and accurate metadata allows for improving the quality of the search engines’ results and boosting the recommender systems performances, which can benefit from this information to yield more effective recommendation lists. Therefore, the adoption of tools able to automatically label multimedia content with informative tags represents an important task for all the companies offering streaming entertainment services. However, domain experts generally perform the tagging process manually, making it time-consuming and error-prone. In the last few years, Machine Learning techniques have been proposed as a promising solution to automate this type of task, but the lack of clean and labeled training data hinders the learning of robust classification models. To cope with the issues described above, in this work, we devised a Deep Learning based solution for semi-automatic multi-label classification integrating post-hoc explanation techniques. Specifically, model explanation methods are exploited to assist the operator in the labeling process by facilitating an understanding of the model predictions. The proposed approach has been validated on a real dataset, and the experimental results demonstrate its effectiveness.