Reviewerly/exHarmony huggingface dataset:
“My personal forecast”: the digital transformation of the weather forecast communication using a fuzzy logic recommendation system

Abstract. Communicating the scientific data of the weather forecasts to the general public has always been a challenge. Using computer graphics' visual representations to convey the message to average people has certainly helped a lot to popularize the weather forecast consumption by the general public. However, these representations are not information rich since they are abstractions; moreover they are not very actionable on the receiver side to help one decide how s/he will “live” the forecast weather conditions and prepare appropriately. Therefore, there is a need to personalize the forecast based on past experience of the individuals and their personal needs. The forecast has to become more human- and needs-oriented and more focused to the particular requirements of each individual person. We, thus, propose a new co-creation process in which the audience is called to provide a daily feedback on how they lived the weather conditions personally on a daily basis, so that, “my personal forecast” can be produced making the forecast more actionable on the user side. Preliminary such attempts focused solely on the “feels like” temperature forecasts. To arrive at the “my personal forecast”, artificial intelligence based recommender systems need to be applied, using fuzzy logic as the appropriate method for the user to express the individually perceived weather conditions.
Entity Anchored ICD Coding

Medical coding is a complex task, requiring assignment of a subset of over 72,000 ICD codes to a patient's notes. Modern natural language processing approaches to these tasks have been challenged by the length of the input and size of the output space. We limit our model inputs to a small window around medical entities found in our documents. From those local contexts, we build contextualized representations of both ICD codes and entities, and aggregate over these representations to form document-level predictions. In contrast to existing methods which use a representation fixed either in size or by codes seen in training, we represent ICD codes by encoding the code description with local context. We discuss metrics appropriate to deploying coding systems in practice. We show that our approach is superior to existing methods in both standard and deployable measures, including performance on rare and unseen codes.
Features of Publicist Materials of Contemporary Ukrainian Online Publications

The article analyzes modern Ukrainian publicist, which is actively developing and functioning in the system of modern social communications, within the latest information technologies and actively uses the opportunities of new media to achieve their goals and objectives.The main means of realization of these tasks are publicist materials, which are designed not only to reflect the current state of literary-critical thought, but also to influence the formation of readers' preferences and quality literary content.
VML-MOC: Segmenting a multiply oriented and curved handwritten text lines dataset

This paper publishes a natural and very complicated dataset of handwritten documents with multiply oriented and curved text lines, namely VML-MOC dataset. These text lines were written as remarks on the page margins by different writers over the years. They appear at different locations within the orientations that range between 0 and 180 or as curvilinear forms. We evaluate a multi-oriented Gaussian based method to segment these handwritten text lines that are skewed or curved in any orientation. It achieves a mean pixel Intersection over Union score of 80.96% on the test documents. The results are compared with the results of a single-oriented Gaussian based text line segmentation method.
Construction of Web-GIS for integrating geophysical survey data with geotechnical information in the San’in region, southwest Japan

Abstract Constructing geotechnical information database to collect information such as geophysical survey results and borehole data and then sharing it among researchers and engineers will be useful for developing research on the subsurface structure and the prevention of disasters such as earthquakes and landslides. The geotechnical information includes analysis results based on geophysical surveys, seismic observations, and borehole data. This database can be visually displayed on a map using a Geographic Information System (GIS). The existing analysis results can be checked consecutively. This will allow us to consider new observation plans and to improve the efficiency and accuracy of the subsurface structure model analysis. Therefore, in this study, a database of geotechnical information was developed for the San’in, Japan region. The San’in, Japan region is the western part of Honshu on the Sea of Japan side and includes Tottori and Shimane prefectures. The database of geotechnical information includes the results of microtremor and gravity survey, estimation of the subsurface structure based on seismic records, and borehole data in Tottori and Shimane prefectures. We gather high dense survey points from multiple references in the San’in region, Japan, such that the average space intervals of single station microtremor, microtremor array, gravity survey, and borehole were around 0.1–1, 1–5, 1, and 0.1–1 km, respectively. Furthermore, the corresponding sites of each survey type were conducted over 6000, over 280, over 7500, and over 3700, respectively. In addition, we developed a system to display the database on a map in a web browser (Web-GIS). GIS, Leaflet, a JavaScript library, were used to display the database of geotechnical information using the Geospatial Information Authority of Japan (GSI) tiled map as the base map. The developed database on the GIS system will be a useful tool that can be used by researchers, engineers, and other stakeholders in the future. Graphical Abstract
Efficiency Evaluation of Segmentation Algorithms for AFM Images

The results of evaluating the efficiency of algorithms for segmentation of images of surfaces of materials with an absent or weakly expressed substrate and a convex shape of objects obtained using an atomic force microscope (AFM images), as well as synthesized in the Matlab and Gwyddion software are presented. For segmentation, algorithms were used based on wave growth of local maximum regions with their selection in decreasing order of values (without stopping and with stopping at a given level), marker watershed (with automatic placement of markers, under the control of the operator), watershed based on distances, growing areas (without selecting starting points, with the choice of starting points based on extrema), the Vincent – Sulli watershed (classical, with a preliminary calculation of the gradient in an eight-connected area, with the selection of the contours of the areas and their subsequent filling), a two-phase watershed. Segmentation algorithms realization in Matlab and in the specialized software package Gwyddion are considered. Algorithms efficiency was assessed using segments number, uniformity brightness within a segment, contrast at the border of adjacent segments, and a complex criterion that takes into account the uniformity of segments brightness, their number and size.
On the statistical distributions of substance moving through the nodes of a channel of network

We discuss a model of motion of substance through the nodes of a channel of a network. The channel can be modeled by a chain of urns where each urn can exchange substance with the neighboring urns. In addition the urns can exchange substance with the network nodes and the new point is that we include in the model the possibility for exchange of substance among the urns (nodes) and the environment of the network. We consider stationary regime of motion of substance through a finite channel (stationary regime of exchange of substance along the chain of urns) and obtain a class of statistical distributions of substance in the nodes of the channel. Our attention is focused on this class of distributions and we show that for the case of finite channel the obtained class of distributions contains as particular cases truncated versions of the families of distributions of Katz, Ord, Kemp, etc. The theory for the case of infinite chain of urns is presented in the Appendix.
Following Forrelation -- Quantum Algorithms in Exploring Boolean Functions' Spectra

Here we revisit the quantum algorithms for obtaining Forrelation [Aaronson et al, 2015] values to evaluate some of the well-known cryptographically significant spectra of Boolean functions, namely the Walsh spectrum, the cross-correlation spectrum and the autocorrelation spectrum. We introduce the existing 2-fold Forrelation formulation with bent duality based promise problems as desirable instantiations. Next we concentrate on the $3$-fold version through two approaches. First, we judiciously set-up some of the functions in $3$-fold Forrelation, so that given an oracle access, one can sample from the Walsh Spectrum of $f$. Using this, we obtain improved results than what we obtain from the Deutsch-Jozsa algorithm, and in turn it has implications in resiliency checking. Furthermore, we use similar idea to obtain a technique in estimating the cross-correlation (and thus autocorrelation) value at any point, improving upon the existing algorithms. Finally, we tweak the quantum algorithm with superposition of linear functions to obtain a cross-correlation sampling technique. To the best of our knowledge, this is the first cross-correlation sampling algorithm with constant query complexity. This also provides a strategy to check if two functions are uncorrelated of degree $m$. We further modify this using Dicke states so that the time complexity reduces, particularly for constant values of $m$.
Encoding High-level Quantum Programs as SZX-diagrams

The Scalable ZX-calculus is a compact graphical language used to reason about linear maps between quantum states.These diagrams have multiple applications, but they frequently have to be constructed in a case-by-case basis.In this work we present a method to encode quantum programs implemented in a fragment of the linear dependently typed Proto-Quipper-D language as families of SZX-diagrams.We define a subset of translatable Proto-Quipper-D programs and show that our procedure is able to encode non-trivial algorithms as diagrams that grow linearly on the size of the program.
Tuning nonequilibrium heat current and two-photon statistics via composite qubit-resonator interaction

Quantum thermal transport and two-photon statistics serve as two representative nonequilibrium features in circuit quantum electrodynamics systems. Here, we investigate quantum heat flow and two-photon correlation function at steady-state in a composite qubit-resonator model, where one qubit shows both transverse and longitudinal couplings to a single-mode optical resonator. With weak qubit-resonator interaction, we unravel two microscopic transport pictures, i.e., cotunneling and cyclic heat exchange processes, corresponding to transverse and longitudinal couplings respectively. At strong qubit-resonator coupling, the heat current exhibits nonmonotonic behavior by increasing qubit-resonator coupling strength, which tightly relies on the scattering processes between the qubit and corresponding thermal bath. Furthermore, the longitudinal coupling is preferred to enhance heat current in strong qubit-resonator coupling regime. For two-photon correlation function, it exhibits an antibunching-to-bunching transition, which is mainly dominated by the modulation of energy gap between the first and second excited eigenstates. Our results are expected to deepen the understanding of nonequilibrium thermal transport and nonclassical photon radiation based on the circuit quantum electrodynamics platform.
Analysis of modern web applications

Everyone is aware that web applications are getting bigger and more popular, but as they evolve, web applications need to get better and more modern. This thesis looks at various aspects of modern web applications. In the next chapters, analyses and evaluations of new technologies in web applications in terms of frontend and backend are presented, as well as common requirements and challenges, which also need to be taken into account. Furthermore, the thesis also contains an analysis of quality factors of web applications and deals with how quality can be ensured so that the finished product – the web application – is good, efficient, functional and that the users are satisfied. In addition, the design of the communication and other settings between the client and the web server will also be debated to ensure that the communication between them is secure, as well as the mathematical principles that are the basis of today’s security.
METHODOLOGICAL FOUNDATIONS OF THE FORMATION OF RESEARCH COMPETENCE AMONG HISTORY TEACHERS

The education system, which provides for the development of the personal potential of future specialists as a special goal, needs a teacher with a high level of high-quality professional training. This is primarily due to the fact that the spiritual and personal potential of the teacher cannot otherwise fulfill the competence of the personal development of the educational process. Thus, the result of the process of preparing teachers for research activities can be defined as the personal and professional development of the student, which is formed in the existing system of knowledge about research professional and pedagogical activities, personal and professional and professional pedagogical development. Pedagogical activity is reflected in practical preparation for research activities. The principle of humanization means that the system of postgraduate training of teachers should be aimed at meeting the needs of the individual in professional development. The idea of recognizing a person as a person in the education system, his right to freedom and equality; the idea of observing the principles of justice and goodness, the norms of human relations; the idea of creating conditions for the free development of creative abilities and human abilities.
CYBER BULLYING AS A PART OF MEDIA EDUCATION IN PRIMARY EDUCATION

The implementation of the topic of risky online behavior in the teaching process through the cross-cutting topic of Media education is one of the ways to inform the child user in time about the possible pitfalls of the online environment. The state educational program for primary education prefers an interdisciplinary approach. It thus creates the space for connecting individual educational areas and cross-cutting topics. The paper, therefore, deals with the issue of implementing the topic of risky online behavior into the educational process of primary education, through the cross-cutting topic of Media Education. It interprets the obtained data based on the research method – an electronic questionnaire addressed to primary school teachers.
PREFERRED VALUES OF INTELLECTUALLY GIFT STUDENTS

The publication analyzes research and studies on the preferred values of intellectually gifted students. Considering the obtained data, it defines four values, more significantly represented in gifted students than in children from the general population. The first value described concerns the aspect of justice. Gifted students show greater sensitivity in matters of justice and a higher level of moral judgment. The following one describes the value of the family and social relationships, whose preference stems from a quality parental relationship and supportive family background. The third area focuses on the ethical and virtuous values that we observe, such as prosocial behavior or positive environmental attitudes. The last value concerns the education obtained and the information obtained. The paper, in conclusion, states the relationship between above-average intellectual abilities and higher life values development.
EXPERIENCE IN PRESCHOOL SCIENCE TEACHING IN SERBIA AND GERMANY DURING PANDEMIC

During the pandemic of COVID-19, preschool teachers had to adapt to the novel teaching challenges of online communication with children and parents. In this study, the effect of pandemic to the preschool science teaching was evaluated from the perspective of preschool teachers in Serbia and Germany. The research was conducted from May to August 2021 and included 90 examinees. For the purpose of this survey, a questionnaire with 19 open- and closed-ended questions was constructed. The results showed that in the organization of educational practice in distant science activities there was no significant difference in kindergartens in Serbia and Germany. German preschool teachers had more postitive perception of their digital competencies compared to their colleagues from Serbia. The comparative analysis of parent engagement, as intermediaries in the realization of educational tasks, showed that in both countries the parents were equally willing to cooperate, but the tasks were more regularly completed with German parents. It was concluded that, although there were certain differences, preschool teachers in both countries shared similar experiences from the times of pandemic.
Smart Contract Repair

Smart contracts are automated or self-enforcing contracts that can be used to exchange assets without having to place trust in third parties. Many commercial transactions use smart contracts due to their potential benefits in terms of secure peer-to-peer transactions independent of external parties. Experience shows that many commonly used smart contracts are vulnerable to serious malicious attacks which may enable attackers to steal valuable assets of involving parties. There is therefore a need to apply analysis and automated repair techniques to detect and repair bugs in smart contracts before being deployed. In this work, we present the first general-purpose automated smart contract repair approach that is also gas-aware. Our repair method is search-based and searches among mutations of the buggy contract. Our method also considers the gas usage of the candidate patches by leveraging our novel notion of gas dominance relationship. We have made our smart contract repair tool SCRepair available open-source, for investigation by the wider community.
Neural Inverse Knitting: From Images to Manufacturing Instructions

Motivated by the recent potential of mass customization brought by whole-garment knitting machines, we introduce the new problem of automatic machine instruction generation using a single image of the desired physical product, which we apply to machine knitting. We propose to tackle this problem by directly learning to synthesize regular machine instructions from real images. We create a cured dataset of real samples with their instruction counterpart and propose to use synthetic images to augment it in a novel way. We theoretically motivate our data mixing framework and show empirical results suggesting that making real images look more synthetic is beneficial in our problem setup.
Implicit Deep Learning

Implicit deep learning prediction rules generalize the recursive rules of feedforward neural networks. Such rules are based on the solution of a fixed-point equation involving a single vector of hidden features, which is thus only implicitly defined. The implicit framework greatly simplifies the notation of deep learning, and opens up many new possibilities, in terms of novel architectures and algorithms, robustness analysis and design, interpretability, sparsity, and network architecture optimization.
Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity

Federated Learning is a novel paradigm that involves learning from data samples distributed across a large network of clients while the data remains local. It is, however, known that federated learning is prone to multiple system challenges including system heterogeneity where clients have different computation and communication capabilities. Such heterogeneity in clients' computation speeds has a negative effect on the scalability of federated learning algorithms and causes significant slow-down in their runtime due to the existence of stragglers. In this paper, we propose a novel straggler-resilient federated learning method that incorporates statistical characteristics of the clients' data to adaptively select the clients in order to speed up the learning procedure. The key idea of our algorithm is to start the training procedure with faster nodes and gradually involve the slower nodes in the model training once the statistical accuracy of the data corresponding to the current participating nodes is reached. The proposed approach reduces the overall runtime required to achieve the statistical accuracy of data of all nodes, as the solution for each stage is close to the solution of the subsequent stage with more samples and can be used as a warm-start. Our theoretical results characterize the speedup gain in comparison to standard federated benchmarks for strongly convex objectives, and our numerical experiments also demonstrate significant speedups in wall-clock time of our straggler-resilient method compared to federated learning benchmarks.
USE OF CLOUD COMPUTING IN EDUCATION

Education performs an necessary position in retaining the monetary increase of a country.Now a days the classroom educating is altering and college students are turning into greater technological know-how oriented and Therefore in his changing environment, it's essential that we suppose about the modern day applied sciences to include in the educating and learning process.One of the cutting-edge applied sciences prevailing now days is Cloud Computing.By sharing IT offerings in the cloud, educational group can outsource noncore offerings and higher listen on providing students, teachers, faculty, and workforce the imperative equipment to assist them succeed.This paper focuses on the influence of cloud computing on the education gadget and how we can supply the exceptional schooling by means of the use of the above technology.
Comparison of YOLO Versions for Object Detection from Aerial Images

Deep learning techniques are used across a wide range of fields for several applications. Deep learning-based object recognition from aerial or terrestrial photos has grown in popularity as a research topic in recent years. On this work, object detection was used by training the YOLOv2 and YOLOv3 algorithms in the Google Colaboratory cloud service using the DOTA dataset, which consists of aerial pictures, and the Python programming language. For assessment, 4 aerial pictures of 5 class items were used. Large vehicle, aeroplane, ship, basketball court, and swimming pool are some examples of these classifications. The outcomes of accuracy analyses of these two algorithms were compared in accordance with recall, precision, and F1-score for 5 classes. The top score with YOLOv2 was 99 percent F1 in the aeroplane class, whereas the best score with YOLOv3 was 83 percent in the pool class. While YOLOv2 can identify items in an average photo in 43 seconds, YOLOv3 has outperformed its predecessor in terms of speed, identifying objects on average in just 2.5 seconds.