Reviewerly/exHarmony huggingface dataset:
Augmenting Stationary Covariance Functions with a Smoothness Hyperparameter and Improving Gaussian Process Regression Using a Structural Similarity Index

Abstract Gaussian process (GP) regression provides a probabilistic framework for modeling geochemistry in mineral resource estimation and environmental monitoring applications. An issue with this approach is that the kernel hyperparameters obtained by maximizing the log-marginal likelihood (LML) often produce GP posterior mean estimates that are overly smooth. This motivates the development of augmented kernels that are more capable of capturing the variability inherent in geological/geochemical processes than the existing stationary covariance functions like the Matérn kernels. This paper makes two contributions. First, it describes an extended class of stationary kernels that contain an extra smoothness hyperparameter ( $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> ) which can be learned from the input data. Valid intervals for $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> that lead to positive semi-definiteness are determined. Second, it uses a statistical measure called the structural similarity index (SSIM) to quantify smoothness and the spatial fidelity of GP solutions with respect to the input samples. This provides a new way for validating and optimizing GP models. Statistical and spectral analyses provide insights into the behavior of $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> in the augmented kernels which retain useful properties such as sparsity. Results on the Northern Great Basin geochemical dataset demonstrate that, all things being equal, (1) adjusting $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> increases spatial fidelity in GP regression; (2) SSIM is a more reliable spatial quality measure than LML; and (3) the optimal $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> value obtained is correlated with the Wiener entropy of the random process, which indicates the spectral flatness of the chemical signal in the Fourier domain. For GP regression, function smoothness may be defined using Sobolev space. The results show that $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> regulates over-smoothing by moderating the rate of decay in the power spectrum of the equivalent kernel. Graphic abstract
The Importance of Data Mining &amp; Predictive Analysis

Data mining is the process of analyzing enormous amounts of information and datasets, extracting (or “mining”) useful intelligence to help organizations solve problems, predict trends, mitigate risks, and find new opportunities. Data mining is like actual mining because, in both cases, the miners are sifting through mountains of material to find valuable resources and elements. Data mining also includes establishing relationships and finding patterns, anomalies, and correlations to tackle issues, creating actionable information in the process. Data mining is a wide-ranging and varied process that includes many different components, some of which are even confused for data mining itself.
DEEP FACE - On the Reconstruction of Face Images from Deep Face Templates

The paper on “Reconstruction of Face Images from Deep Face Templates" presents a novel approach for face image reconstruction using deep learning techniques. The proposed method utilizes a pre-trained deep face template, which is a convolutional neural network (CNN) trained on a large-scale face dataset, as a prior to guide the reconstruction process. Specifically, the method solves an optimization problem that balances the fidelity to the input image and the similarity to the deep face template. Its then evaluated with the method on two face image datasets, and demonstrate that their method outperforms several state-of-the-art methods in terms of reconstruction quality, especially for images with large occlusions or low resolutions. Moreover, they show that the deep face template can capture high-level face attributes, such as pose, identity, and expression, which can be used for various face-related tasks, such as face recognition, attribute manipulation, and generation. Overall, the paper presents a promising direction for face image reconstruction using deep learning techniques, and highlights the potential of deep face templates for capturing and utilizing high-level face attributes.
Er maskinoversetting fra nordsamisk nyttig for sørsamisk?

The article assesses the strengths and weaknesses of a rule-based translation program from North Saami to South Saami. As a tool for translators, the system can contribute to more text being produced in South Saami, both by using North Saami as an intermediary in the translation of texts from Norwegian and Swedish, and by translating texts that are originally written in North Saami. The system may also help teachers in academia to understand texts written by South Saami students. The system is still not good enough, in particular the number of word pairs in the lexicon must be increased considerably to get a better result. The translation is also not robust with respect to linguistic errors in the North Saami text. The linguistic difference between North Saami and South Saami syntax is big, and one could consider whether it would be appropriate to use a rule-based approach, which allows using information about the sentences' dependency trees, in order to more easily transform the sentences from North Saami syntax to acceptable South Saami.
Partiklar i sørsamisk

The article presents the most central particles in Southern Sami, and argues that they constitute a separate part of speech different from adverbs. Most particles usually occur in the second position of the sentence, but several particles may also occur in second position in complex verb phrases, i.e. in third position in the sentence. A group of words have in previous research been treated as both proclitic and enclitic particles, it is argued here that these should be treated as adverbs.
ResNet and PCA-Based Deep Learning Scheme for Efficient Face Recognition

Face recognition is an emerging field of research in recent days. With the rise of deep learning, face recognition has become efficient and precise, creating new milestones. The performance, accuracy, and computational time of the existing schemes can be enhanced by devising a new scheme. In this context, a multiclass classification framework for face recognition using residual network (ResNet) and principal component analysis (PCA) schemes of deep learning with Dlib library is proposed in this paper. The proposed framework produces face recognition accuracy of 99.6% and a reduction of computational time with 68.03% using principal component analysis.
Application of Artificial Intelligence Technology and Network Technology in Multimedia Courseware Making Course Education

With the continuous advancement of curriculum reform, how to improve the classroom efficiency and optimize the learning classroom model has become a topic of increasing concern for scholars and teachers. With the development and popularization of computer network, distance education has been widely used. It is in this case that the resource library came into being, which provides rich resources for Liu Xiye to make multimedia courseware. The learning of courses plays a fundamental role in education, which is reflected in modern distance education, that is, how to make courseware better. At the same time, online courses are also getting more and more attention from people. Online educators have higher and higher requirements for the design of each part of online courses. How to design feedback in the learning of online courses is also a must for every designer. Based on artificial intelligence education courseware, this paper introduces some audio and video technologies used in making multimedia CAI (computer-assisted instruction) courseware.
Teachers' Perceptions of Digital Language Learning Strategies

The last four decades witnessed a plethora of research on language learning strategies (LLS) in the English as a foreign language (EFL) classroom. However, there is a paucity of research on the newly developed digital language learning strategies (DLLS), particularly with reference to teachers' perceptions. For this study, the authors examined the perceptions of university teachers of the importance of DLLS for EFL learners. To this end, they collected data using Kim and Bae's 60-item survey from 52 teachers at a private Egyptian university. Using exploratory factor analysis (EFA), they removed 10 items from the survey because they revealed unsatisfactory loading numbers toward other variables. The EFA outcomes extracted six factors that described 54.13% of the total variance. In this context, the teachers perceived several cognitive, metacognitive, memory, compensation, affective, and social strategies as extremely important for students. This study calls for the systematic incorporation of the DLLS in the EFL classroom, particularly through explicit intervention. Additionally, it calls for further research on the DLLS for their great significance in our new digital world.
Analysis of Multimedia Feature Extraction Technology in College Vocal Performance Teaching Mode Based on Multimodal Multimedia Information

This article is based on the application research of multimedia feature extraction technology in the development of vocal performance teaching in universities. Combining with the core image and sound modules in feature extraction technology, this article proposes an application model of multimedia feature extraction technology based on image HOG algorithm and Mel spectrum for vocal audio recognition in vocal performance teaching in universities. Experiments have shown that the features extracted by this method can not only effectively identify the styles of different types of singing works, but also recognize the personality characteristics of singers. At the same time, it can effectively reduce the misclassification rate caused by noise interference, thereby improving the recognition rate.
Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

The blockchain network uses a Proof-of-Work (PoW) mechanism to validate transactions and keep the blockchain growth safe against tampering, but it is hugely energy-consuming with no benefit to the peer-to-peer network participants. In this paper, we proposed a blockchain network for distributing products to different locations based on the use of the Proof of Useful Work mechanism, in which miners use computing resources to optimize the traveling salesman problem (TSP) as an alternative to solving mathematical problems that represent the basis of the traditional PoW mechanism to get a new block. According to this proposed blockchain, it not only receives and securely stores the distribution locations but also improves the paths for salesmen when traveling between different locations during the transportation process. This strategy aims to take advantage of the miners' efforts to minimize the traveled distance by applying the clustering technique and computing the shortest path by Guided Local Search (GLS) for each cluster at the same time. According to the tested results on TSP-LIB instances, the used strategy works efficiently with an average of 0.08 compared to the rest of the meta-heuristics, and the proposed architecture reduced total distances with an average of 0.025%. In addition, the block generation time in the blockchain decreased by 11.11% compared to other works.
Pengaruh Model Pembelajaran Flipped Classroom Terhadap Kemandirian dan Hasil Belajar Siswa

This study aims to determine the effect of the flipped classroom learning model on the independence and learning outcomes of class X students of the accounting skills program at Vocasional High School 1 Lumajang with a case study on the basic competencies of basic accounting equations for the 2022/2023 school year. This research is an experimental research with the type of research Pre-Experimental Design and using the One Group Pretest-Posttest research design. Methods of data collection using questionnaires, tests, interviews and documentation. The data analysis method used is the paired sample t-test. The results showed that the flipped classroom learning model had an effect on student learning independence with a Sig. 0.00 &lt;0.05 and has an effect on learning outcomes with a Sig value. 0.03 &lt;0.05. The results of the Percent N-Gain test show that the flipped classroom learning model has an effect on learning independence by 57% and on learning outcomes by 22%.
MASTERING TECHNICAL ENGLISH AT A FOREIGN LANGUAGE LESSONS IN A PROFESSIONAL EDUCATIONAL ORGANIZATION (FROM WORK EXPERIENCE). LEARNING TECHNICAL ENGLISH

The article discusses fragments of teaching technical English in the&#x0D; vocational education system from the experience of a teacher, specifically by&#x0D; profession "Welder (manual and partially mechanized welding (surfacing))."&#x0D; Mastering technical vocabulary at the initial stage goes through reading instructions&#x0D; for the operation of household appliances in Russian, to hear and see the style of&#x0D; technical translation, and only then do students proceed to reading the instruction&#x0D; manual for professional tools and equipment in English, which makes it easier to&#x0D; understand the foreign language text.
A military reconnaissance network for small‐scale open‐scene camouflaged people detection

Abstract Although the work of identifying animal and plant objects with highly similar patterns (e.g., texture, intensity, colour, etc.) to the background has recently attracted more research interest but rarely involves the military complex environment. Design an efficient camouflage small‐scale object detection algorithm that is capable of quickly discriminating the objects in open scenes from a long distance, to pre‐empt the enemy. In this work, we first recognize the fact that existing open‐source training datasets are scarce, and we have created a specific disguised people benchmark covering multiple scenes and weather conditions. Second, because the severe corruption of camouflage capabilities and chaotic scenes in the open battlefield on detailed features and generalization intensifies the challenge of feature extraction from a long‐range perspective, we propose a novel end‐to‐end Small‐scale open scene Camouflage Object Detection Network, called SM‐CODN. Inspired by the characteristics of biological brain partition, a multi‐domain partition module (MPM) with domain‐decoupling is proposed to enable specific knowledge learning for samples with obvious discrepancies in camouflage domain distribution. Concurrent with our work, we have designed a multi‐scale fusion module (MFM) to strengthen the semantic features related to small‐scale disguised objects. Moreover, due to the convergence direction of the detector in reasoning being inconsistent, a feature separation enhancement module (FSEM) is also proposed. Experimental results show that SM‐CODN surpasses many classic object detection methods and shows strong competitiveness compared with state‐of‐the‐art ones.
Streets as experienced through the body, mind, and screen: The smartphone and the pedestrian's engagement with an urban public space

This study investigates the influence of smartphone use on the embodied experiences of pedestrians in urban public spaces. Participants in this study engaged in leisure walks in a bustling urban environment. The study employed a multi-sensory, multi-modal data collection approach, which incorporated mobile eye tracking, screen capture, think-aloud, and participant data review. Findings revealed dynamic relationships between the urban pedestrians’ embodied experiences (“streets as experienced through the body”), digital content (“streets on the screen”), and spatial knowledge (“streets in the mind”). The study explored the “unfolding” practice between multiple versions of the surrounding environment and sheds light on the complex interplay of cognitive, experiential, and digital inputs in shaping pedestrian actions. Moreover, the study uncovers the paradoxical effects of smartphone usage and introduces both serendipity and familiarity into the pedestrian's journey through public spaces. Further, the implications of this research highlight the need for mobile media studies to embrace “messy” and “noisy” data for a more comprehensive understanding of the interconnections between minds, bodies, places, and mobilities.
Evaluation of Implementation Effect of Reform and Practice of Vocational Literacy Curriculum for Higher Vocational Computer Science Majors Based on C4.5 Model

INTRODUCTION: The cultivation of vocational literacy of higher vocational computer students is related to the employment prospects of graduates and has a significant impact on the computer talent market. The success or failure of curriculum reform will directly impact the effect of cultivating professionalism in higher vocational computer majors.&#x0D; OBJECTIVES: This paper introduces the source and function of the C4.5 model, establishes model evaluation indexes to assess the application effect of the C4.5 model; the number of iterations and prediction accuracy are important indexes of the model's functioning and puts forward five measures suggestions for the reform of vocational literacy course of higher vocational computer majors.&#x0D; METHODS: C4.5 model is applied to reform vocational literacy courses for higher vocational computer majors, and the effect of practical implementation is evaluated using a questionnaire.&#x0D; RESULTS: By applying the C4.5 model to reform the vocational literacy course for senior computer majors, the graduates' vocational literacy has been significantly improved.&#x0D; CONCLUSION: The research contributes to the realization of the goal of cultivating higher vocational computer talents and provides the market with computer talents with outstanding professionalism.
THE MEDIA’S ETHICS

The media play a huge role in people's lives because they inform people about the happenings in the world. Sometimes one has to wonder how accurate that information really is and what role ethics play in the Universe. The media's ethics refers to moral principles and values that journalists and media houses should respect, including truthfulness, objectivity, precision, respect for privacy, etc. Fake news is a problem that occurs when the media presents incorrect or misleading information, if someone is malicious or wants to manipulate public opinion, they can easily exploit these falsehoods. That is why ethics in the media is very important in order to prevent the spread of fake news and preserve the integrity of journalism. One of the important issues related to ethics in the media is the question of the source of information, journalists should check their sources to make sure that the information they convey to the public is really true. Sometimes, journalists may be under pressure to publish information that is not fully verified in order to be first with the news or to please their editors and media houses.
The impact of robots and robotic systems on businesses in the workplace: systematic review

Purpose This study aims to identify articles examining human-robot interaction and the effects of robotic systems on employment. Design/methodology/approach In this research, electronic searches were performed for articles published between 2000 and 2022 in Emerald, Springer, PubMed, Science Direct, Wiley and Google Scholar. In the searches of robotic systems with keywords such as “motivation, job satisfaction, job loss, performance, job giving,” 5 quantitative and 5 qualitative studies were included in the systematic review. The selected research was conducted using the Johanna Briggs Analytical Cross-Sectional Studies Checklist from the Joanna Briggs Institute (JBI) critical evaluation lists and the JBI Critical Appraisal Checklist for Qualitative Research, depending on their type. The included studies are mostly on employee-robot collaboration. Findings Although the majority of the articles examined in this study are included in keywords or titles, it is determined that there is a gap in descriptive quantitative studies in the literature on the effects of employee-robot collaboration, robotic systems and robotic systems on variables such as motivation, job satisfaction, job loss, performance and employment, although they do not mention a framework that directly investigates human-robot interaction and the effects of robotic systems on employment. Research limitations/implications There are several limitations in this study. One of them is that, although the databases are comprehensively scanned, only studies published in English between 2000 and 2022 are included in the systematic review. Another limitation is the heterogeneity between studies. Practical implications As a result of the authors’ findings, the practical effects of the research are reflected as follows: It serves as a guide for future studies to fill the gap in the field, especially for academics and researchers working in the field of social sciences on robotic systems and intelligent automations. In addition to the qualitative studies on this subject, there is a need for the use of robotic systems in the field of human resources and management and quantitative studies with more sample sizes, especially at the corporate (firms) and individual (employees) level. Considering that the number of studies on this subject is very insufficient, this research is important in terms of shedding light on future studies. Originality/value The authors believe that the impact of robotic systems on employment is one of the few conceptual articles that systematically examines 6 dimensions (job satisfaction, performance, job loss, employment, motivation, employment).
Modelling and experimental testing of an optical synchronisation beacon designed for high‐loss satellite quantum communication

Abstract Long‐distance free space quantum key distribution based on CubeSats can be used to establish global quantum secure communication networks, with potential commercial applications benefitting from the low cost of its design and launch. Detecting single‐photon level optical pulses sent from space requires highly accurate and robust timing systems to pick out signals from the noise. For such high‐loss applications, we envisage a low‐repetition (sub‐MHz) beacon laser emitting short (ns) high‐peak‐power pulses from which interpolated quantum signal arrival windows can be derived. We firstly study theoretically the effects of jitter on the efficiency of gating quantum signals including all important jitter sources, and then experimentally investigated it by changing the clock jitter, and the result shows that the greater jitter will reduce the gating rate of the signal. The experimental interpolation error is tested against loss under laboratory conditions giving results close to our model. We also found that the jitter introduced by the Doppler effect can be ignored with a repetition rate larger than 1 kHz. This model can be directly used for the performance analysis and optimisation of all quantum and non‐quantum systems using similar synchronisation schemes over terrestrial free space or fibre.