Reviewerly/exHarmony huggingface dataset:
Soft Skills Development Issues in Young IT Specialists

The development of information technologies and mass computerization have largely contributed to the transition of society from an industrial to a post-industrial society, in which employers seek to hire employees who are not only professionally developed, but also personally capable of thinking outside the box and creatively approaching tasks. However, the transition to post-industrialism that has not fully ended and the education system that has not had time to adapt are not yet able to fully educate graduates that employers need. Today there is an urgent need for promising young specialists with higher education, but this need is not being met at the proper level, since the education system does not have enough resources necessary for the development of soft skills among students and graduates. Employers are looking for employees who have not only applied knowledge, but also know how to negotiate, communicate with customers, members of the production team, employees who are able to analyze the situation and take risks in cases that justify it. All these qualities are an integral part of soft skills. The essence of the term reflects the personality qualities that greatly contribute to the improvement of a person's career growth. The modern education system is adapted to produce employees whose advantages are highly developed hard skills, which are more in demand in an industrial society. However, modern society is a post—industrial society, which is characterized mainly by other aspects of economic life. In this regard, the education system is in urgent need of transformation, which could provide employers with qualified specialists, which would ultimately have a positive impact on the level of economic development and society as a whole.
Underwater image restoration based on channel compensation and dark channel prior

Due to the absorption and scattering of light by water medium and suspended particles, underwater images come with color deviation and blurred details. For different light absorption in different underwater environments, an adaptive compensation method for local color channels is proposed to enhance the compensation for severe attenuation areas, which is suitable for underwater scenes with multiple color deviations. The dark channel prior is used to remove the detail blurring caused by scattering, the multi-scale Gaussian convolution and gamma correction are adopted to estimate the local light, and the tolerance parameter is used to increase the transmittance of bright areas to compensate for the dark channel prior conditions. The enhanced image quality is evaluated by Underwater Image Quality Measurement (UIQM), Average Gradient (AG), and entropy. Experimental data show that the method improves the color deviation of underwater images in different environments, enhances color information, and improves the contrast of images.
Mine image enhancement algorithm based on bilateral filtering and gamma correction

Aiming at the problems of uneven illumination, detail loss and color distortion of video images caused by low illumination and contrast of mine environment, a mine image enhancement algorithm based on bilateral filtering function is proposed. The algorithm first converts the image from RGB space to HSV space to avoid destroying the color space, extracts the illuminance layer and reflection layer of the image according to the classical Retinex theory combined with bilateral filtering with edge retention, adaptively adjusts the lighting by gamma function, and uses the CLAHE algorithm to enhance the overall contrast. Experimental results show that the proposed algorithm is superior to other commonly used algorithms in terms of visual effects, information entropy, average gradient, standard deviation, etc., which effectively improves the overall brightness and contrast of the image and improves the problems of detail loss and color distortion and realizes the effective enhancement of mine images.
The design of a method for recovering degraded images of atmospheric turbulence based on deep learning

When imaging long-range targets using ground-based optical systems, the presence of atmospheric turbulence can cause blurring, dithering, and other degradations in the observed images. In previous research work, the research objects are mostly point targets, and the research work on the recovery of extended targets is yet to be perfected. With the rapid development of deep learning, neural networks driven by data can be used to obtain the recovered images directly by establishing a nonlinear mapping relationship between the degraded and original images. Therefore, the use of wavefront phase detection devices can be avoided by deep learning algorithms. The neural network directly reconstructs the original target of the turbulent degraded image that solves the problem of image blurring due to dynamic turbulence. In this paper, we propose DeblurNet, which employs the global self-attentive module. It improves channel and spatial information extraction, reduces information loss between network layers and global interaction representation to improve the performance of deep neural networks. DeblurNet is used to minimize the effect of turbulence on images and is validated on the NWPU-RESISC45 dataset. We refer to two image evaluation criteria, PSNR and SSIM. From the results, the direct reconstruction of the original target image by deep learning has a good recovery effect.
Study of Issues with Cloud Security

Cloud computing is now widely recognized as the most cost-effective infrastructure for the delivery of online information technology services to both businesses and individual customers. On the other hand, the fact that functions are delegated to third parties is generally recognized as the key barrier to broad adoption, and it is generally agreed upon that security is the primary concern in this regard. Keeping this information in mind, we will now discuss the many security problems that are associated with the three distinct service kinds of cloud computing technologies, which are referred to as software as a service, platform as a service, and infrastructure as a service. This chapter provides a succinct summary of all of the individual faults that are related to the service models as well as the dangers that are linked with those defects. In the preceding paragraph, we spoke about several possible solutions that may make the already excellent degree of security offered by the cloud even better.
Overview of Technology Research in Quantum Communication System

As the established communication networks no longer meet people’s need in the speed and the security in transferring information, quantum communication draws people’s attention. Taking advantage of the basic quantum properties, quantum communication guarantees the message being delivered promptly and safely. This paper first introduces a general background quantum communication. After that, we introduced the history of the development of quantum communication and Quantum Key Distribution (QKD) as well as the general flow of quantum communication. Based on that, we evaluated a scheme of quantum communication network based on QKD, proposing different methods to improve the security and efficiency of the transmission of information. We encapsulated the history of the development of QKD. We conclude that the improvement of quantum communication depends on the algorithms. While various algorithms are proposed, we discussed a potentially possible way to combine the advantages of the algorithms. However, the actual effectiveness of our method still needs to be testified in future researches.
Data Expansion Approach with Attention Mechanism for Learning with Noisy Labels

In recent years, the development of deep learning has contributed to various areas of machine learning. However, deep learning requires a huge amount of data to train the model, and data collection techniques such as web crawling can easily generate incorrect labels. If a training dataset has noisy labels, the generalization performance of deep learning significantly decreases. Some recent works have successfully divided the dataset into samples with clean labels and ones with noisy labels. In light of these studies, we propose a novel data expansion framework to robustly train the models on noisy labels with the attention mechanisms. First, our method trains a deep learning model with the sample selection approach and saves the samples selected as clean at the end of training. The original noisy dataset is then extended with the selected samples and the model is trained on the dataset again. To prevent over-fitting and allow the model to learn different patterns of the selected samples, we leverage the attention mechanism of deep learning to modify the representation of the selected samples. We evaluated our method with synthetic noisy labels on CIFAR-10 and CUB-200-2011 and real-world dataset Clothing1M. Our method obtained comparable results to baseline CNNs and state-of-the-art methods.
THE EFFECT OF THINK-TALK-WRITE STRATEGY ON STUDENTS’ ABILITY IN WRITING DESCRIPTIVE TEXT

The purpose of this researchwas to find out how the Think Talk Write (TTW) Strategy affects students' descriptive text writing skills, particularly with regard to generic structure. The research design used for this quantitative study was a quasi-experimental one. 397 students from SMA Negeri 3 Pematangsiantar's class X make up the sample population. The sample was chosen using purposeful sampling. The sample was divided into the experimental and control groups. A 34-student experimental class called Class X IPA 6 was taught using the Think Talk Write (TTW) Strategy. 34 students in Class X IPA 2 make up the control group, who receive traditional instruction. The instrument for gathering data was a writing test. The results of data analysis using the t-test formula showed that the t-test value was greater than the t-table value (t-test t-table = 2,5 1.668). The results show that while Ho is rejected, Ha is accepted. It is therefore possible to draw the conclusion that the Think Talk Write (TTW) strategy significantly affects students' capacity to write descriptive text.
THE EFFECT OF SCHOOL FACILITIES ON STUDENTS’ LEARNING MOTIVATION ON ENGLISH

School facilities are a very important tool in supporting learning in schools. Facilities will be needed by teachers and students in teaching and learning because the facilities will increase students' learning motivation, with complete facilities will also support the performance of teachers in delivering material to students. The purpose of this study is to observe school facilities, especially MTS Ali Imron and see the effect of students' learning if the school facilities are inadequate, especially in English subjects. The case study in this study was conducted at MTS Ali Imron Medan. The method in this study uses a qualitative method by observing schools and conducting interviews with English teachers at MTS Ali Imron Medan.
IMPLEMENTATION OF THE ADIWIYATA-BASED CURRICULUM IN ENGLISH LEARNING ACTIVITY AT MTsN 1 SIDOARJO

This research is focused on the implementation of the Adiwiyata-based curriculum in English learning at MTsN 1 Sidoarjo. The researcher applies a qualitative descriptive method which aims to determine the implementation of the Adiwiyata school program and its implementation in learning English. Data collection technique that is used in this study is qualitative data which are gained from observation,interview,and field note. Furthermore, the qualitative data are analyzed by: assembling the data, coding the data, comparing the data, building interpretations, and reporting the outcomes. The result of the research shows that (a) the Adiwiyata-based curriculum adds or inserts indicators that are integrated with the environment; (b) The procedure for developing adiwiyata-based curriculum is the same as in the general curriculum; (c) Implementation of the adiwiyata-based curriculum, starting from the preparation of lesson plans to learning activities and other activities related to the environment; (d) The purpose of implementing the Adiwiyata-based curriculum at MTsN 1 Sidoarjo. Hence, the findings reveal that implementation of the Adiwiyata-based curriculum in English learning at MTsN 1 Sidoarjo was successfully viewed from a certain perspective.
THE RELATIONSHIP BETWEEN READING ATTITUDE AND READING COMPREHENSION OF VOCATIONAL STUDENTS

The purpose of this research is to find out whether there is a significant correlation between students’ reading attitude and students’ reading comprehension of eleventh grade students of SMKS Hidayatullah Batam in the academic years 2020/2021. Students who have low reading attitude have low reading comprehension. The data of the research were taken from a reading attitude scale to obtain data about students’ reading attitude and the TOEIC reading test to obtain data about students’ reading comprehension. The population were 50 students of eleventh grade students of SMKS Hidayatullah Batam in the academic years 2020/2021. The sample were taken from 24 samples of research data selected by using cluster random sampling. Before testing the hypothesis, the researcher conducted a normality test by using Shapiro-Wilk test. The data of students’ reading attitude has a significance value of 0.080 and a significance value of students’ reading comprehension was 0.112 is bigger than 0.05. After the normality test was carried out, the researcher conducted the hypothesis testing by using Pearson Product Moment. The research data showed that the rcount (-0.160) is lower tha rtable (0.404). based on the result of the research data, it can be concluded that there was no significance correlation between students’ reading attitude and students’ reading comprehension of eleventh grade students of SMKS Hidayatullah Batam in the academic years 2020/2021.
Planning to Celebrate an Anniversary?

Nonprofit Communications ReportVolume 21, Issue 3 p. 8-8 Event Planning Planning to Celebrate an Anniversary? First published: 13 February 2023 https://doi.org/10.1002/npcr.32144Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume21, Issue3March 2023Pages 8-8 RelatedInformation
Sorption of Phosphorus on Leucoxene: Experimental Studies

We carried out experimental studies of the dependence of the sorption of phosphate ion with ilmenite-leucoxene ore (non-magnetic fraction of the gravitational concentrate, Pizhemskoe deposit, Middle Timan, Russia) on the concentration of sodium phosphate solution and pH of the medium. Phosphorus sorbed mainly in the form of HPO42– and H2PO4– ions on the surface of leucoxene mineral phases causing a change in pH of the medium. The result was a change in the magnitude and sign of the surface charge. The experiments have confirmed the idea that leucoxene is most likely the main geochemical barrier for phosphorus under hypergenesis conditions.
Gold-Bismuth Mineralization of Pasechnoe Deposit (South Sikhote-Alin)

This paper shows the results of detailed mineralogical research of the Pasechnoe deposit ores. Ore zones are steeply dipping and made of quartz-sulfide, quartz, carbonate-quartz, rare of tourmaline-quartz ores with gold, gold-bismuth, gold-silver, and sometimes tungsten-bearing mineralization. Mineral samples were obtained from ore zones located in different parts of the deposit. Based on conducted mineralogical and mineralogical-geochemical investigations, various rare sulfosalts of Ag-Pb-Bi elemental composition (lilianite homologs − vikingite, eskimoite; pavonite homologs − makovickyite, and a sulfosalt of Ag10PbBi30S51), gold intermetallic (maldonite), bismuth sulfo-telluride (joseiteA), bismuthinite, native bismuth and gold, and also secondary bismuth minerals − bismite, spherobismite, and rooseveltite were identified for the first time in the studied samples. It has been established that presence of bismuth mineralization closely associated with native gold serves as typomorphic indicator of mineralization formation conditions. The character of relations between minerals and their chemical composition allowed distinguishing several paragenetic associations: (1) bismuth-sulfide-sulfo-telluride association with native bismuth, maldonite, gold, joseite A, and bismuthinite; (2) and bismuth-sulfosalt association with sulfosalts of bismuth, lead, and silver.
AN ANALYSIS OF FEATURE SELECTION TECHNIQUES FOR AN EVOLUTIONARY APPROACH TO GENETIC ALGORITHMS

Data magnitude is growing expeditiously, which pretends the challenges to extensive majority of current mining and learning algorithms, such as the bane of dimensionality, huge storage requirement, and enormous computational cost. Feature selection has been proven to be an effective and efcient way to prepare high-dimensional data for data mining and machine learning. The recent evolution of novel techniques and new types of data and aspects not only advances existing feature selection research but also emerges the feature selection constantly, becoming applicable to a expansive range of applications. In this entry, we aim to provide a basic introduction to feature selection including basic concepts, classications of existing systems, recent development, and applications.
Acoustopolariscopy is the Method for Determining Mineral Samples and Rocks Quality

Investigations of some rock forming minerals and rocks from German (KTB-HB) borehole by means of the acoustopolariscopy method were carried out. As a whole, acoustopolariscopy of minerals of different syngony provides extensive information about their elastic-anisotropic properties, inhomogeneities, including oriented ones. Analysis of some rock samples from KTB-HB showed that the use of the acoustopolariscopy method can reveal the level of heterogeneities and orientation of the elastic symmetry elements.
Cognitive Misperception and Chronic Disease Awareness: Evidence from Blood Biomarker Data

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Method for Multiple Analysis of Indicator Mineral Compositions of Kimblites to Estimate the Presence of Type IIa Large Diamonds

In the last decade, researchers are especially interested in giant type IIa diamonds, which differ strongly in composition and physical characteristics from diamonds of “P” and “E” generations. Especially large diamonds of this type of high quality are mined at several deposits in Africa and Russia. The cost of some crystals of such diamonds reaches tens of millions of dollars (Fig. 1). Their genesis has not yet been fully elucidated, and it represents a large field for the study of this amazing phenomenon of kimberlite nature. One of the ways to decrypt the genesis of these diamonds is to simulate the mineralogical and geochemical conditions of the diamond generation environment by the chemical compositions of the KIM that characterize this environment - pyropes, chromites, ilmenites and pyroxenes. 5E diagrams of the KIM compositions for five elements - Cr, Al, Fe, Mg, Mn were constructed for Karove and Grib industrial deposits, mainly containing large and giant type of diamonds. On the basis of such diagrams, a method for assessing the probability of the presence of large and giant type IIa diamonds in kimberlites of explored deposits is proposed based on the method of comparison with the standard (analogy method).
Truth-Table Net: A New Convolutional Architecture Encodable by Design into SAT Formulas

With the expanding role of neural networks, the need for complete and sound verification of their property has become critical. In the recent years, it was established that Binary Neural Networks (BNNs) have an equivalent representation in Boolean logic and can be formally analyzed using logical reasoning tools such as SAT solvers. However, to date, only BNNs can be transformed into a SAT formula. In this work, we introduce Truth Table Deep Convolutional Neural Networks (TTnets), a new family of SAT-encodable models featuring for the first time real-valued weights. Furthermore, it admits, by construction, some valuable conversion features including post-tuning and tractability in the robustness verification setting. The latter property leads to a more compact SAT symbolic encoding than BNNs. This enables the use of general SAT solvers, making property verification easier. We demonstrate the value of TTnets regarding the formal robustness property: TTnets outperform the verified accuracy of all BNNs with a comparable computation time. More generally, they represent a relevant trade-off between all known complete verification methods: TTnets achieve high verified accuracy with fast verification time, being complete with no timeouts. We are exploring here a proof of concept of TTnets for a very important application (complete verification of robustness) and we believe this novel real-valued network constitutes a practical response to the rising need for functional formal verification. We postulate that TTnets can apply to various CNN-based architectures and be extended to other properties such as fairness, fault attack and exact rule extraction.
Adversarially Robust Panoptic Segmentation (ARPaS) Benchmark

We propose the Adversarially Robust Panoptic Segmentation (ARPaS) benchmark to assess the general robustness of panoptic segmentation techniques. To account for the differences between instance and semantic segmentation, we propose to treat each segment as an independent target to optimise pixel-level adversaries. Additionally, we include common corruptions to quantify the effect of naturally occurring image perturbations in this task. We deploy the ARPaS benchmark to evaluate the robustness of state-of-the-art representatives from families of panoptic segmentation methods on standard datasets, showing their fragility in the face of attacks. To gain further insights into the effects of attacking the models, we introduce a diagnostic tool to decompose the error analysis. Finally, we empirically demonstrate that a baseline adversarial training strategy can significantly improve the robustness of these methods.
SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning

Person tracking using computer vision techniques has wide ranging applications such as autonomous driving, home security and sports analytics. However, the growing threat of adversarial attacks raises serious concerns regarding the security and reliability of such techniques. In this work, we study the impact of multi-task learning (MTL) on the adversarial robustness of the widely used SiamRPN tracker, in the context of person tracking. Specifically, we investigate the effect of jointly learning with semantically analogous tasks of person tracking and human keypoint detection. We conduct extensive experiments with more powerful adversarial attacks that can be physically realizable, demonstrating the practical value of our approach. Our empirical study with simulated as well as real-world datasets reveals that training with MTL consistently makes it harder to attack the SiamRPN tracker, compared to typically training only on the single task of person tracking.