Reviewerly/exHarmony huggingface dataset:
METHODOLOGY OF FORMING A STOCHASTIC WORLDVIEW WHILE STUDYING THE SECTION "RANDOM EVENTS. VEROBILITIES"

The article contains general provisions, characterizing the methodology of forming stochastic worldview of high school students. The methodology is carried out in accordance with the developed worldview significant educational material, accompanied by the support of specially selected digital technology, and in accordance with the main stages of the formation of worldview while teaching mathematics. The main stages of the implementation of the holistic process of learning stochasticity are: preparatory, problem-research, implementation and correctional. It is supposed that the methodology of forming stochastic outlook will be most effective if during the learning process students of 10–11 grades have sustainable learning and cognitive motives and motives for self-education, the system of solid subject knowledge of stochastics, the mastery of probabilistic-statistical methods; the change of activities is observed: reproductive to learning and research activity, productive to problem-creative activity. The article argues that the methodology of forming stochastic worldview of high school students, including the support and accompaniment of digital educational technologies, has a positive impact on motivation and cognitive interest to learning, changing attitudes towards stochastics, expanding scientific horizons. Through consistent work with the established stages of the formation of stochastic worldview in teaching mathematics, the realization of the integrity of motivational, value and cognitive constructs is achieved. In this article this process will be considered by studying the section “Random events. Probabilities”. At the first (preparatory) stage the basic concepts of the section are introduced, the skills of their understanding and application in learning activities are developed. At the second (problem-searching) stage the theorems and formulas of the section are examined, tools for problem solving are formed, the understanding of methods of action with stochastic situations is improved. At the third (realization) stage purposeful cognitive-research and problem-creative activity of senior pupils is organized, including development and consolidation of knowledge, abilities and skills of work with stochastic situations. At the fourth (correctional) stage reflexive activity of students is organized, the teacher determines the degree of expression of outlook qualities and reference points of high school students.
Design of Cybersecurity Threat Warning Model Based on Ant Colony Algorithm

In this paper, a cybersecurity threat warning model based on ant colony algorithm is designed to strengthen the accuracy of the cybersecurity threat warning model in the warning process and optimize its algorithm structure. Through the ant colony algorithm structure, the local global optimal solution is obtained; and the cybersecurity threat warning index system is established. Next, the above two steps are integrated to build the cybersecurity threat warning model based on ant colony algorithm, and comparative experiment is also designed. The experimental results show that, compared with the traditional qualitative differential game-based cybersecurity threat warning model, the cybersecurity threat warning model based on ant colony algorithm has a higher correct rate in the warning process, and the algorithm program is simpler with higher use value.
Using the Remote Sensing in Studying Ecosystems of Deposits of Nonferrous Metal Ores, Both Opencast and Abandoned, in the Northern Regions of Russia

The paper presents study results of the disturbed lands ecological state due to the opencast mining of deposits of non-ferrous metal ores in the Murmansk, Arkhangelsk and Krasnoyarsk regions. According to the OMS data on quarries, rock-disposal dumps, processed ore storages' surface there have been detected some self-restoring vegetation ecosystems. The necessity in rehabilitation of rock-disposal dumps piled during mining operations in areas with a short vegetative cycle has been substantiated.
Detection of Web Service Anti-patterns Using Various Combinations of WSDL Metrics.

Many IT enterprises today use Service Oriented Architecture(SOA) as the effective architectural approach for building their systems. Service-Based Systems(SBS) like other complex frameworks are liable to change to fit in the new user requirements. These may lead to the deterioration of the quality and design of the software systems and may cause the materialization of poor solutions called Anti-patterns. Similar to object-oriented systems, web services also suffer from anti-patterns due to bad programming practices, design, and implementation. An anti-pattern is defined as a commonly used process, structure, or pattern of action that, despite initially appearing to be an effective and appropriate response to a problem, has more bad consequences than good ones. Anti- pattern detection using Web Service Description Language(WSDL) metrics can be used as a part of the software development life cycle to reduce the maintenance of the software system and also to improve the quality of the software. The work is motivated by the need to develop an automatic predictive model for the prediction of web services anti- patterns using static analysis of the WSDL metrics. The core ideology of this work is to empirically investigate the effectiveness of classifier techniques i.e, ensemble and deep learning techniques in the prediction of web service anti-patterns. In this paper, we present an empirical analysis on the application of seven feature selection techniques, six data sampling techniques, and ten classifier techniques for the prediction of four different types of anti-patterns. The results confirm the predictive ability of WSDL metrics in the prediction of SOA anti-patterns.
Modeling Human Tendencies for Password Guessing

Passwords form a predominant part of all authentication mechanisms and likely will not be replaced soon, primarily because of their ease of use and straightforwardness. Human-generated passwords are particularly susceptible to guessing attacks as a consequence of the limitation of precise recall and hence are not random. In this paper, we make use of the excellent expressive power of sequence modeling neural networks such as LSTMs and GRUs to effectively guess passwords as compared to other cutting-edge password guessing techniques like Markov models, JtR, and PCFGs. LSTM and GRU models were able to match about 55% of the testing set when evaluated on the Rockyou dataset. This proves that sequence modeling neural networks can effectively learn the distribution of real passwords from previously leaked datasets.
The ProSiberia Platform: The First Year of Operation and Development Prospects

The article aims to review the experience of creating and launching the ProSiberia platform, and to discuss the prospects for its further development. Based on the aggregation of digital sources of Siberian origin, the ProSiberia project aims to create a research platform that will bring together scholars, content curators (libraries, museums, archives) and society in order to study Siberia and position the region in the global information space. Work on the platform began in 2020 within the framework of the Program for Increasing the Competitiveness of National Research Tomsk State Unversity by the staff of the Tomsk State University Research Library. Now the platform has implemented opportunities both for humanitarian researchers and for civic science representatives (volunteers). For researchers, access to digital copies of texts about Siberia with detailed description and full-text search is organized; it is possible to create one’s own collections of texts; there are some text collaboration tools. It is also possible to find researchers with similar interests and exchange views. A volunteer can edit texts, operate a blog, participate in challenges. The platform is also being filled with new texts. Prospects for the development of the project are considered; they were formulated based on the results of surveys and focus groups. The main directions of development include: work on improving the quality of texts (attracting volunteers for their correct recognition); aggregation of digital tools for the humanities in one place for effective work with texts; attraction of partners; integration into the educational process. The most difficult issue is the aggregation of digital tools. The article lists their most interesting groups: tools for creating and working with digital facsimiles, textometric tools, and a number of tools for working with digitized manuscripts. Thus, at the moment, the initial version of the ProSiberia platform has been created. It allows implementing remotely the basic functions characteristic for any research in the field of humanities. However, most probably, the most important and difficult will be the next stage of the work, which includes, among other things, the aggregation of digital tools for working with the text of interest for the researcher. Therefore, it is extremely important to have a broad discussion in the professional community of both the work already done within the ProSiberia project and the further prospects for its development.
Morphological Analysis on Affixes in Tombulu Language

This study aimed to reveal affixes' form and meaning in Tombulu language tenses, especially on past, present, and future tenses. Descriptive qualitative method used with applying Arikunto's procedure of collecting data which were interview and documentation. While, in analyzing data used Miles &amp; Huberman which consisted of data reduction, data display, and conclusion drawing. This study showed that there were different functions of Affixes in the tenses of Tombulu language and English. The simple past tense was Infix -im- and -in-, which were placed after vocal or consonant phonemes. Simple present tense with Prefix mah- which was located to the beginning of the word. Simple future tense with Infix -um-, which was placed after vocal or consonant phonemes.
Channel splitting attention network for low‐light image enhancement

Low-light enhancement is a crucial task in computer vision because of the limited dynamic range of digital imaging devices in poor lighting conditions. Images taken under low-light conditions often suffer from insufficient brightness and severe noise. At present, many models based on convolutional neural networks have been proposed to enhance low-light images. However, most models treat the features on different channels equally, which is not conducive to models learning hierarchical features. Consequently, the method proposed a channel splitting attention network (CSAN) that divides the shallow features into two branches, the residual and dense branches, transmitting different information. Residual branching facilitates feature reuse, while dense branching promotes the exploration of new features. In addition, CSAN uses merge-and-run mappings to assist information integration between different branches and distinguishes the information contained in different branch features through an attention module designed in this paper. Multiple experiment results show that the method proposed is superior to state-of-the-art methods in qualitative and quantitative evaluation. Furthermore, CSAN can better suppress chromaticity aberration while enhancing low-light images.
Puer bonus communicandi peritus. Esercizi e strumenti per dibattere tra pari

The peer learning project RApP (Ragazze e Ragazzi apprendono tra Pari) took place at the ISIS Gobetti-Volta high school in Bagno a Ripoli, Florence. With the aim to develop debate and public speaking activities, the project applies the IDEAM method, known as the five canons of ancient rhetoric (Inventio, Dispositio, Elocutio, Actio and Memoria). Thanks to this framework, it is possible to visualize and apply changes to the organization and writing of an effective speech, as well as to its memorization and delivery in public. A platform of applied training combined with a set of propaedeutic exercises based on the approach of ancient progymnasmata outlines a gradual pathway aimed at strengthening the uses of language as an individual skill, with a view to acquiring the skills to use language in the social environment of debate.
Effective Ways to Pass Exams in Programming Disciplines and Optimize the Process of Analyzing Results. Case Study: Suleyman Demirel University

The article identifies effective ways to pass exams in programming disciplines and how to optimize the process of analyzing results. Also we discussed a list of the problems that we may faced during the exam and their solutions(proposed method). The main part of our project was divided into 4 stages: Analysis of exam results using Google Sheets, Writing a special script(program) that automatically loads images, Detection of plagiarism manifestations using the principles of computer vision, Facial recognition using machine learning algorithms.
STUDI KOMPARASI MANAJEMEN SUMBER DAYA MANUSIA PERPUSTAKAAN LASA Hs DENGAN RICHARD E RUBIN

One of the factors that influence the progress of the library lies in its human resources. Reviewing library human resources can be done by analyzing several library figures who are currently concerned with reviewing human resources (HR). The figures include Lasa, Hs with his work entitled "Library Human Resource Management" and Richard. E Rubin with his work entitled "Human Resource Management in Libraries: Theory and Practice. The purpose of this study was to compare the concept of human resource management of the Lasa Hs library with Richard E Rubin. Through a literature study, the research results obtained the relevance of the two figures in determining the stages of library human resource management which include; planning, recruitment, introduction, empowerment, termination, career development, compensation, maintaining safety, comfort, measuring the performance of human resources. According to Lasa, Hs performance measurement stage is a basic need in reviewing human resources in the library itself so that its existence is very important to be applied, in contrast to Richar E Rubin who assesses all stages in library human resource management have the same value to be fulfilled without distinguishing one from the other. others, because in its application they are interconnected.
CONTROL SYSTEM FOR STRUCTURURALLY AND PARAMETRICALLY UNDEFINED NON-AFFINE PLANT WITH NEUTRAL TYPE DELAY AND CONTROL DELAY

The article deals with the synthesis of a controller for non-affine in input plant with neutral type delay and control delay, whose state variables are not available for direct measurement. The controller constructed on the basis of the hyperstability criterion compensates for the effects of delays, interference, parametric and structural uncertainties in the system.
Measurement angular error in continuous-variable quantum key distribution

We propose a theoretical model of the measurement angular error in the CV-QKD using homodyne detection, and corresponding simulation results are depicted to show its impacts on system performance.
THE MEDICAL INFORMATION SYSTEM CONCEPT FOR ACUTE LYMPHOBLASTIC LEUKEMIA DIAGNOSING

Continuous improvement of software and hardware platforms makes it possible to significant-ly expand the development capabilities of modern medical information systems (MIS) to reach a qualitatively new level associated with the transition to the creation of universal cross-platform web-applications. The article discusses the concept of the implementation of MIS for the diagnosis of acute leu-kemia (AL) and minimal residual disease (MRD) through the complex integration of systems based on laser flow cytometry (an integrated method for diagnosing hemoblastosis), intelli-gent computer microscopy based on expert neural network analyzer for recognition of blast cell images based on formed reference knowledge base. When making a diagnosis, this MIS will allow doctors to work with information about patients directly through the system inter-face by comparing data about new patients with images of an expert database and then sub-sequent generate the diagnostic conclusion based on the results of the studies performed. Data security in the system is ensured by the web server administration regulations and the division of access rights for various categories of users. MIS provides an opportunity for a comprehensive diagnostic study of patients making decisions about the tactics of treatment.
Blind image blurring by Gaussian filtering extreme channels prior

Previous extreme channels prior deblurring methods still cannot solve noise and avoid ringing artifacts in restored image problems well. In order to solve these caused, we propose a Gaussian filtering extreme channels prior (GFECP) to plug the Gaussian filtering into extreme channels prior work architecture for effective blind image deblurring. First, we computed image structure based on the Gaussian filtering model and extreme channels prior. Second, the half quadratic splitting technique is used to solve the non-convex problem of the model and estimate the clear image. Finally, we use an iterative multi-scale blind deconvolution method as regularization to restore the blur kernel. Experimental results clearly demonstrate that the proposed method outperforms state-of-the-art methods in terms of robustness, subjective visual effects and objective evaluation indexes.
History of ConnectNY: 2001–2021

ConnectNY, a 501(c)3 nonprofit organization, is a consortium of independent academic institutions in New York State. The mission of ConnectNY is to share collections, leverage resources, and enhance services through cooperative initiatives and coordinated activities. This paper describes the founding and growth of the consortium, 2001–2021. The files of former Executive Director, Bart Harloe, and the current Executive Director, Pamela Jones, were consulted to write the narrative.
Privacy-Preserving and Auditable Federated Deep Reinforcement Learning for Robotic Manipulation

DRL (Deep Reinforcement Learning) has been widely used in the field of robotic manipulation. The accuracy of DRL depends on large amounts of data for training. However, training data is distributed among different organizations and is difficult to share due to information security and privacy concerns. In order to solve the security problem of robotic manipulation and improve the accuracy of robotic manipulation, a new privacy-protecting federated reinforcement learning scheme, called PFRL, was proposed. Through PFRL, the robot learns the global reinforcement model safely through the local reinforcement model with the help of the cloud, and well protects the sensitive data of each robot. Specifically, with a secure multi-party vector aggregation algorithm, all local models are encrypted by their owners before being sent to the cloud, and can be directly operated without decryption. And in order to prevent malicious cloud servers or robots to damage the correctness of training, we have integrated non-interactive zero-knowledge proof tools to provide the auditability of collaborative training processes. Detailed security analysis shows that PFRL can resist various known security threats. Moreover, PFRL has been tested in Mujoco simulation environment, and a large number of simulations show that PFRL is feasible.
Modelling and visualising emotions in Twitter feeds

Predictive analytics on Twitter feeds is becoming a popular field for research. A tweet holds wealth of information on how an individual express and communicates their feelings and emotions within their social network. Large-scale mining of tweets will not only help in capturing an individual's emotion but also the emotions of a larger group. In this study, an emotion-based classification scheme has been proposed. By training the naïve Bayes multinomial and the random forest classifiers on different training datasets, emotion classification was performed on the test dataset containing tweets related to the 2016 US presidential election. Upon classifying the tweets in the test dataset to one of the four basic emotion types: anger, happy, sadness and surprise, and by determining the sentiments of the people, we have tried to portray the flux in the emotional landscape of the people towards the presidential candidates in the 2016 US election.
Personalized Intelligent Recommendation System for Electronic Commerce Based on Multi-dimensional Commodity Attributes

E-commerce recommendation department is faced with some common problems, such as user cold start problem and data sparsity problem. Solving user cold start problem and data sparsity problem is to improve the accuracy of recommendation results. An effective way is to improve the user experience. Driven by big data collected by e-commerce system and supported by natural language processing technology, a new model of e-commerce recommendation system is designed. This paper puts forward the personalized intelligent recommendation system for electronic commerce, which can effectively alleviate the problems of cold start and sparsity, and provide users with accurate and personalized recommendation results.
Products Liability in the Internet of Things Age

The IoT raises several questions germane to traditional products liability law and the UCC’s warranty provisions. These include how best to evaluate and remedy consumer harms related to insecure devices, malfunctioning devices, and the termination of services and software integral to a device’s operations. Consider that the modern IoT vehicle with an infotainment system generates massive quantities of data about drivers, and that mobile applications can be used to impact the operations of these vehicles.
The Challenges that English Teachers Face in the Employment of the Evaluation Matrix in Classes for Students with Learning Difficulties

The current study aimed at identifying the challenges that English teachers face when they employ the evaluation Matrix in classes for students with learning difficulties. The study followed the descriptive-analytical method. A questionnaire was designed to meet the study objectives. the study sample consisted of 136 English teachers of the primary education level in public schools in the city of Irbid. The findings revealed that the challenges that English teachers face in employing the evaluation Matrix in classes for students with learning difficulties came with a very high degree, they also revealed the absence of differences with statistical significance between the responses of the sample attributable to the variables of gender and experience years. Furthermore, the findings showed the presence of statistically significant differences between the responses of the sample attributable to the variable of academic qualification.