Reviewerly/exHarmony huggingface dataset:
Integration aspects of child development and educators' role in practice

Summary: Preschool education is considered to be the basic foundation in the entire educational system, and preschool institutions and educators within them are key links in the early learning process. With the expansion of pedagogical knowledge, the circle of educators' jobs is expanding and requires multicompetence and adaptation to modern roles. Our goal is to present an example of good practice that is the result of the daily engagement of authors in striving to make children's lives in kindergarten better and more beautiful. The paper consists of two parts. The first part tried to point out the importance of early learning, the preschool period, the role of educators in preschool, and the importance of integration in practice, while the second part of the paper is a practical model or flow of scenarios of learning activities on Insects aimed at popularizing application models to help those who are already working in practice, beginners but also students of pedagogical orientation. The greatest value of the presented work is the unification of theory and practice, and the possibility of practical applicability.
Rainbow: reliable personally identifiable information retrieval across multi-cloud

Personally identifiable information (PII) refers to any information that links to an individual. Sharing PII is extremely useful in public affairs yet hard to implement due to the worries about privacy violations. Building a PII retrieval service over multi-cloud, which is a modern strategy to make services stable where multiple servers are deployed, seems to be a promising solution. However, three major technical challenges remain to be solved. The first is the privacy and access control of PII. In fact, each entry in PII can be shared to different users with different access rights. Hence, flexible and fine-grained access control is needed. Second, a reliable user revocation mechanism is required to ensure that users can be revoked efficiently, even if few cloud servers are compromised or collapse, to avoid data leakage. Third, verifying the correctness of received PII and locating a misbehaved server when wrong data are returned is crucial to guarantee user's privacy, but challenging to realize. In this paper, we propose Rainbow, a secure and practical PII retrieval scheme to solve the above issues. In particular, we design an important cryptographic tool, called Reliable Outsourced Attribute Based Encryption (ROABE) which provides data privacy, flexible and fine-grained access control, reliable immediate user revocation and verification for multiple servers simultaneously, to support Rainbow. Moreover, we present how to build Rainbow with ROABE and several necessary cloud techniques in real world. To evaluate the performance, we deploy Rainbow on multiple mainstream clouds, namely, AWS, GCP and Microsoft Azure, and experiment in browsers on mobile phones and computers. Both theoretical analysis and experimental results indicate that Rainbow is secure and practical.
Cooperative Channel Capacity Learning

In this paper, the problem of determining the capacity of a communication channel is formulated as a cooperative game, between a generator and a discriminator, that is solved via deep learning techniques. The task of the generator is to produce channel input samples for which the discriminator ideally distinguishes conditional from unconditional channel output samples. The learning approach, referred to as cooperative channel capacity learning (CORTICAL), provides both the optimal input signal distribution and the channel capacity estimate. Numerical results demonstrate that the proposed framework learns the capacity-achieving input distribution under challenging non-Shannon settings.
Review on Linear and Non - Linear Filter

Image denoising is the operation of the image data to produce a visually high quality image. The existing or current denoising algorithms or approaches are filtering approach, multifractal approach and wavelet based approach. Different noise models include noise as additive and multiplicative type. They include Gaussian noise, salt and pepper noise (impulsive noise), Brownian noise and speckle noise. Noise arises due to various factors like bit error rate, speed, dead pixels. Denoising algorithm is application dependent i.e. the application of a specific filter is beneficial against a specific kind of noise. The filtering approach has been proved to be the best when the image is corrupted with salt and pepper noise. In the filtering approach Median filter provides best result against impulsive noise i.e. salt and pepper noise. The wavelet based approach finds applications in denoising images corrupted with Gaussian noise. If the noise characteristics are complex, then multifractal approach can be used.
Optimal Decision Tree Algorithm in Sports Video Tracking Technology

For the current social development, human motion analysis is a very important direction in the field of virtual reality in recent years. The prospect of its future technological development will be directly proportional to the degree of social importance. It will have its presence in almost all important fields of life, including intelligent traffic monitoring, sports analysis, animation generation and so on. Sports video behavior sequence tracking, detection and recognition technology is one of the main research technologies in the research. One of the purposes is to automatically track and detect the behavior sequence from at least one arbitrary group of video image sequence information containing multi person behavior information, and accurately and quickly identify and analyze the behavior. Among them, motion detection and trajectory tracking belong to high-level vision problems. Based on the optimized decision tree algorithm, this paper makes relevant research on sports video tracking technology, analyzes the background significance, queries the relevant technology, constructs the experimental model, and finally obtains the feasibility of the results through the experiment.
Study on the application of multi-angle imaging related technology in the construction process

Abstract The mufti-angle imaging technique is proposed to address the wall position offset and the side flaps and gate flaps caused by the sparse array in the ultra-wide band through-wall radar building layout imaging, using the characteristic that the azimuth upward distance profile is similar around the main flap but different around the side flap/gate flap. Firstly, after the backward projection algorithm obtains the wall image of each viewpoint, the mufti-directional two-dimensional matching filter is used to enhance the image of each viewpoint and eliminate the effect of side flaps and raster flaps. Then the images of each viewpoint are decomposed by combining the multistage transform, i.e., the non-down sampling contour let transform, and the obtained low-frequency components and high-frequency components are fused using the corresponding fusion rules, respectively. Finally, the final fused building layout image is obtained by non-down sampling contour let inverse transform. The simulation experimental data show that the running time of the method in this paper is 10.99s. It shows that the proposed method enhances the outline and detail information of the building wall, and the final fused image obtained has prominent wall image, clearer edge contours, and good retention of overall detail information. The quality of image fusion is greatly improved than other methods, which paves the way for subsequent fusion methods including chatterers such as construction building corners and windows and doors.
A Function Area Division Approach for Autonomous Transportation System Based on Text Similarity

Along with emerging technologies and increasing demands, autonomation has become a significant trend in current transportation systems. Within this context, the autonomous transportation system (ATS) framework hinges on functions that serve as fundamental units to support its operation. Recognizing the divisions among these function areas can enhance our understanding of their meanings and interrelationships. This study introduces a method for dividing function areas within the ATS framework, grounded in text similarity, to mitigate reliance on subjective experience. Precisely, this method quantifies the similarity between functions based on their textual descriptions, and implements hierarchical clustering to delineate them into distinct function areas. To validate the effectiveness of this proposed method, a case study analyzing a vehicle automatic driving scenario was conducted. The results demonstrate that our approach can efficiently divide function areas, producing clustering outcomes that possess superior accuracy and purity when juxtaposed with reference classifications. Consequently, this method has the potential to facilitate the formulation of function areas within ATS, thereby supporting the autonomous operation and construction of ATS. Moreover, its applicability extends beyond ATS, showing promise for other clustering problems that involve multiple texts, such as in text classification.
Long-Tailed Recognition by Hierarchical Rebalancing Dual-Classifier

Image classification techniques have succeeded greatly on various large-scale visual datasets using deep convolution neural networks. However, previous deep models usually suffer severe performance degradation in highly skewed datasets, which restricts their practical application. In this paper, we propose a novel Hierarchical Rebalancing Dual-Classifier model for long-tailed recognition. To better identify the tail samples and maintain the performance of head classes, we propose a dual-classifier framework with a uniform sampler for performing their duties. For balancing the learning of feature representation and classifiers, a dynamic weight is introduced to adjust the model’s attention. To alleviate the feature deviation between training data and testing data, a hierarchical rebalancing loss is designed for the re-weighting branch, which adjusts the decision values in predicted logits to facilitate the model actively compensating for tail categories. Finally, we conduct extensive experiments on standard long-tailed benchmarks Cifar10-LT, Cifar100-LT, ImageNet-LT, and iNaturalist2018, demonstrating the effectiveness and superiority of our HRDC.
End-to-End 3D Liver CT Image Synthesis from Vasculature Using a Multi-Task Conditional Generative Adversarial Network

Acquiring relevant, high-quality, and heterogeneous medical images is essential in various types of automated analysis, used for a variety of downstream data augmentation tasks. However, a large number of real image samples are expensive to obtain, especially for 3D medical images. Therefore, there is an urgent need to synthesize realistic 3D medical images. However, the existing generator models have poor stability and lack the guidance of prior medical knowledge. To this end, we propose a multi-task (i.e., segmentation task and generation task) 3D generative adversarial network (GAN) for the synthesis of 3D liver CT images (3DMT-GAN). To the best of our knowledge, this is the first application for a 3D liver CT image synthesis task. Specifically, we utilize a mask of vascular segmentation as the input because it contains structural information about a variety of rich anatomical structures. We use the semantic mask of the liver as prior medical knowledge to guide the 3D CT image generation, reducing the calculation of a large number of backgrounds, thus making the model more focused on the generation of the region of the liver. In addition, we introduce a stable multiple gradient descent algorithm (MGDA) reconstruction method into our model to balance the weights of the multi-task framework. Experiments were conducted on a real dataset, and the experimental results show that the segmentation task achieves a Dice similarity coefficient (DSC) of 0.87, while the synthesis task outperforms existing state-of-the-art methods. This study demonstrates the feasibility of using vascular images to synthesize images of the liver.
Clip thinking as a consequence of virtual communication: Pedagogical methods of neutralisation in foreign language lessons

The development of modern information and communication technologies has led to the fact that online communication has become almost the only means of feedback between teachers and students, but the clip thinking that results from virtual communication reduces attention, performance and quality of knowledge. The purpose of this research is to consider the specific features of virtual communication and clip thinking, to identify the challenges caused by the interaction of the phenomena of online communication and clip thinking, and to suggest ways to improve the effectiveness of the pedagogical process, in particular in foreign language lessons. The main research methods - analysis of new literary sources on the chosen subject, generalisation, specification and description of the results obtained. The research proves that the active interaction of Internet communication and clip thinking determines the need to mitigate the adverse effects and successfully transform their interaction into a positive one, in particular, by pedagogical methods. For the first time, the synthesis of clip thinking and virtual communication is considered, and specific ideas are presented to neutralise its certain adverse effects. It is established that the mediation of communication by electronic means of communication leads to quick thinking. It was identified that anonymity, the voluntariness of contacts and uncertainty of linguistic and social provisions - are the causes of linguistic minimalism and linguistic poverty, while convenience, synchronicity and asynchrony lead to the fact that learners have to do several things at once, and polyphony can cause any sign of clip thinking. It has been established that the replacement of non-verbal means of communication with emoticons leads to linguistic minimalism and poverty due to the inability to correctly describe one’s own emotions or inaccuracies in the emoticon. It is proved that the global intercultural nature of electronic communication due to the lack of knowledge of a foreign language can cause linguistic minimalism and poverty, and Internet communication with all its features leads to students’ preference for particular thinking. The study allows considering all the features discussed in the organisation of the pedagogical process and more carefully selecting methods of developing logical-conceptual and systemic thinking of adolescents, in particular, in foreign language lessons.
vGameReviews: An R package for harnessing video game reviews for scientific research

Researchers can harness feedback on video games in the form of online reviews and apply these datasets to investigate a wide range of disciplines including urban and landscape planning and science education. However, accessing online datasets requires advanced coding knowledge. Here, we introduce the vGameReviews R package which has been designed to allow users to access video game reviews easily. We also developed a shiny application, which provides users a graphical user interface option for searching for and saving these valuable datasets. The vGameReviews package increases the accessibility of Steam store data to a wide range of researchers.
A SURVEY ON EFFICIENT AUDITING SCHEME FOR SECURE DATA STORAGE IN FOG-TO-CLOUD COMPUTING

With the rise of the Internet of Things, fog-to-cloud computing has emerged as a new cutting-edge approach (IoT). Except for the cloud service provider, fog-to-cloud computing necessitates the participation of additional entities, such as mobile sinks and fog nodes (CSP). As a result, fog-to-cloud storage integrity audits will differ from standard cloud storage auditing. Tian et al. has completed the first stage in developing a public auditing system for fog-to-cloud computing. However, because they employ complex public key cryptography techniques like bilinear mapping and proof of knowledge, their system becomes inefficient. We offer a more broad and efficient auditing system based on MAC and HMAC, two prominent private key cryptography algorithms in this study. By implementing MAC and HMAC, we give a real instantiation of our auditing system. Finally, theoretical analysis and actual findings show that our suggested system has lower communication and computing costs.
Öğretmenlerin Dijital Yeterliliklerinin Farklı Değişkenlere Göre İncelenmesi

This study examines the digital competence of teachers according to different variables. In this direction, 695 teachers working in primary, secondary, and high schools participated in the study. The Teacher Digital Competence Scale (2022), developed by Gümüş and Kukul, was used in the study analyzed using the scanning model. In line with the results obtained in the study, while digital competencies differ according to gender, and age range, no difference was observed according to school type, and foreign language levels. On the general evaluation of the analyses, it is thought that although the teachers have medium-level digital competences, considering the difficulties experienced during the pandemic period, the digital competences of the teachers observed at the medium level are eventually insufficient and the teachers should develop themselves in this regard.
Syntactic computation of Fagin-Halpern conditioning in possibility theory

Conditioning plays an important role in revising uncertain information in light of new evidence. This work focuses on the study of Fagin and Halpern (FH-)conditioning in the context where uncertain information is represented by weighted or possibilistic belief bases. Weighted belief bases are extensions of classical logic belief bases where a weight or degree of belief is associated with each propositional logic formula. This paper proposes a characterization of a syntactic computation of the revision of weighted belief bases (in the light of new information) which is in full agreement with the semantics of the FH- conditioning of possibilistic distributions. We show that the size of the revised belief base is linear with respect to the size of the initial base and that the computational complexity amounts to performing O(log2(n)) calls to the propositional logic satisfiability tests, where n is the number of different degrees of certainty used in the initial belief base.
Automated quantitative mineralogy analysis reveals characteristics of Co occurrence in the Jinchang porphyry deposit, NE China

A critical and strategic metal, demand for cobalt (Co) has increased in recent years given its widespread use in new energy batteries. This has led to the need to search for new Co sources. Porphyry deposits have provided large quantities of Cu, Mo, Au and other critical elements such as Re, Se and Te. However, there is a paucity of research on the occurrence, enrichment mechanisms, and metallogenic potential of Co in porphyry deposits. The Palaeo-Pacific slab subduction related Jinchang Au deposit (∼80 t @ 8 g/t Au; Heilongjiang province, NE China) has recently been recognized as a cobalt-rich porphyry Au deposit. We conducted MapsMin (automated mineralogy analysis) on two bulk samples (10-c and Z1-13) from the J0 orebody of the Jinchang deposit to accurately determine the elemental content and Co distribution, and to definitively identify the Co minerals. MapsMin analysis revealed Co contents of 0.27% and 0.07% for samples 10-c and Z1-13, respectively. In addition, we conducted whole-rock geochemistry on ten powdered samples with inductively coupled plasma-atomic emission spectroscopy/mass spectrometry. The results showed Co ranges from 0.04 wt% to 0.29 wt% (average 0.1 wt%). We compared the results of MapsMin on bulk samples with inductively coupled plasma-atomic emission spectroscopy/mass spectrometry on powders, and confirmed the accuracy of the MapsMin analysis. The relative difference in the content of the main ore-forming elements Cu, Fe, Ti, and Co in the samples is less than 30%. MapsMin identified two Co minerals (siegenite and cobaltite) and revealed that there is an isomorphic Co-rich zone in pyrite rims. The good negative correlation between Co, (Co + Cu + Ni) and Fe in pyrite indicates that Co can enter the pyrite lattice through the substitution Co2++ (Cu2++Ni2+)↔ Fe2+. Crystal size distribution data shows that siegenite in 10-c was formed under two different conditions and petrographic evidence indicates that siegenite was the first to precipitate with chalcopyrite and pyrite in the Jinchang deposit. The formation of pyrite spans the entire mineralization process. During late-formed pyrite precipitation, Co-rich hydrothermal fluids were reinjected, causing a second precipitation of siegenite. High levels of Co entered the rim of pyrite, forming a Co-rich zone. Finally, a decrease of fO2 led to the precipitation of cobaltite. Mapsmin can provide a fast, accurate and reliable mineralogy data which can be used to constrain the contents and occurrences of cobalt and other metals, such as nickel, copper, gold, rare earth elements, etc.
Modular zk-rollup on-demand

The rapid expansion of the use of blockchain-based systems often leads to a choice between customizable private blockchains and more secure, scalable and decentralized but expensive public blockchains. This choice represents the trade-off between privacy and customization at a low cost and security, scalability, and a large user base but at a high cost. In order to improve the scalability of secure public blockchains while enabling privacy and cost reduction, zk-rollups, a layer 2 solution, appear to be a promising avenue. This paper explores the benefits of zk-rollups, including improved privacy, as well as their potential to support transactions designed for specific applications. We propose an innovative design that allows multiple zk-rollups to co-exist on the same smart contracts, simplifying their creation and customization. We then evaluate the first implementation of our system highlighting a low overhead on existing transaction types and on proof generation while strongly decreasing the cost of new transaction types and drastically reducing zk-rollup creation costs.
Orthogonal parametric non-negative matrix tri-factorization with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si114.svg" display="inline" id="d1e4261"><mml:mi>α</mml:mi></mml:math>-divergence for co-clustering

Co-clustering algorithms can seek homogeneous sub-matrices into a dyadic data matrix, such as a document-word matrix. Algorithms for co-clustering can be expressed as a non-negative matrix tri-factorization problem such that X≈FSG⊤, which is associated with the non-negativity conditions on all matrices and the orthogonality of F (row-coefficient) and G (column-coefficient) matrices. Most algorithms are based on Euclidean distance and Kullback–Leibler divergence without parameters to control orthogonality. We propose to apply the orthogonality of parameters by adding two penalty terms based on the α-divergence objective function. Orthogonal parametric non-negative matrix tri-factorization uses orthogonal parameters for row and column space, separately. Finally, we compare the proposed algorithms with other algorithms on six real text datasets.