Reviewerly/exHarmony huggingface dataset:
BRICS Network University potential as an educational platform for innovative cooperation

The prospects for increasing the share of training and coverage of educational cooperation in the field of innovation in the BRICS Network University - one of the most important modern forms of cooperation in the field of education at the international level - are studied. The concepts of network structure and network university are considered. The effects of network interaction and the principles of functioning of network universities are revealed, and an overview of the approach to classifying network structures by the target feature of formation is given. The purpose is to search for opportunities to set up an innovative orientation of the BRICS Network University educational process as the general innovative cooperation stimulation platform of the participating countries. For this purpose, the experience of participation of Russian universities in other network universities (CIS and SCO) was explored, and possible formats of cooperation between innovation teams and interested students in the BRICS countries were proposed to stimulate both traditional mobility of students, teachers and employees, and general innovation exchange through the BRICS Network University.
Hidden Words Statistics for Large Patterns

We study here the so called subsequence pattern matching also known as hidden pattern matching in which one searches for a given pattern $w$ of length $m$ as a subsequence in a random text of length $n$. The quantity of interest is the number of occurrences of $w$ as a subsequence (i.e., occurring in not necessarily consecutive text locations). This problem finds many applications from intrusion detection, to trace reconstruction, to deletion channel, and to DNA-based storage systems. In all of these applications, the pattern $w$ is of variable length. To the best of our knowledge this problem was only tackled for a fixed length $m=O(1)$. In our main result we prove that for $m=o(n^{1/3})$ the number of subsequence occurrences is normally distributed. In addition, we show that under some constraints on the structure of $w$ the asymptotic normality can be extended to $m=o(\sqrt{n})$. For a special pattern $w$ consisting of the same symbol, we indicate that for $m=o(n)$ the distribution of number of subsequences is either asymptotically normal or asymptotically log normal. After studying some special patterns (e.g., alternating) we conjecture that this dichotomy is true for all patterns. We use Hoeffding's projection method for $U$-statistics to prove our findings.
Transformer-based Multi-Task Learning for Adverse Effect Mention Analysis in Tweets

While social media gains a broader traction, valuable insights and opinions on various topics representative for a wider audience can be automatically extracted using state-of-theart Natural Language Processing techniques.Of particular interest in the healthcare domain are adverse drug effects, which may be introduced in online posts, and can be effectively centralized and investigated.This paper presents our Multi-Task Learning architecture using pretrained Transformer-based language models employed for the Social Media Mining for Health Applications Shared Task 2021, where we tackle the three subtasks of Task 1, namely: classification of tweets containing adverse effects (subtask 1a), extraction of text spans containing adverse effects (subtask 1b), and adverse effects resolution (subtask 1c).Our best performing model ranked first on the test set at subtask 1b with an F 1score of 51% (P = 51%; R = 51%).Promising results were obtained on subtask 1a (F 1 -score = 44%; P = 45%; R = 43%), whereas subtask 1c was by far the most difficult task and an F 1score of only 17% (P = 17%; R = 18%) was obtained.
Monolingual and Cross-Lingual Intent Detection without Training Data in Target Languages

Due to recent DNN advancements, many NLP problems can be effectively solved using transformer-based models and supervised data. Unfortunately, such data is not available in some languages. This research is based on assumptions that (1) training data can be obtained by the machine translating it from another language; (2) there are cross-lingual solutions that work without the training data in the target language. Consequently, in this research, we use the English dataset and solve the intent detection problem for five target languages (German, French, Lithuanian, Latvian, and Portuguese). When seeking the most accurate solutions, we investigate BERT-based word and sentence transformers together with eager learning classifiers (CNN, BERT fine-tuning, FFNN) and lazy learning approach (Cosine similarity as the memory-based method). We offer and evaluate several strategies to overcome the data scarcity problem with machine translation, cross-lingual models, and a combination of the previous two. The experimental investigation revealed the robustness of sentence transformers under various cross-lingual conditions. The accuracy equal to ~0.842 is achieved with the English dataset with completely monolingual models is considered our top-line. However, cross-lingual approaches demonstrate similar accuracy levels reaching ~0.831, ~0.829, ~0.853, ~0.831, and ~0.813 on German, French, Lithuanian, Latvian, and Portuguese languages.
A universal quantum circuit design for periodical functions

We propose a universal quantum circuit design that can estimate any arbitrary one-dimensional periodic functions based on the corresponding Fourier expansion. The quantum circuit contains N-qubits to store the information on the different N-Fourier components and M + 2 auxiliary qubits with M = ⌈log2 N⌉ for control operations. The desired output will be measured in the last qubit qN with a time complexity of the computation of , which leads to polynomial speedup under certain circumstances. We illustrate the approach by constructing the quantum circuit for the square wave function with accurate results obtained by direct simulations using the IBM-QASM simulator. The approach is general and can be applied to any arbitrary periodic function.
Design of an Educational Virtual Assistant Software

Despite the great creators’ efforts of e-learning educational materials, it is not possible to define the content of these materials specifically for all students. Based on this, it is necessary to provide in e-learning education the possibility of a more detailed interpretation of specific parts of the educational material that may be unclear to specific students. Based on this fact, we decided to take the first step in the form of software design, which will represent a virtual assistant in teaching computer science. The role of this assistant will be the ability to answer technical questions related to the presented curriculum. From an architectural point of view, it will be a set of micro-services, each of which will serve a specific task. The prerequisite is the use of decision trees to determine a specific micro-service, which will be implemented in the form of a neural network. Main aim of this paper is to provide detail description of global software architecture for such a virtual assistant
Learning from Executions for Semantic Parsing

Semantic parsing aims at translating natural language (NL) utterances onto machineinterpretable programs, which can be executed against a real-world environment.The expensive annotation of utterance-program pairs has long been acknowledged as a major bottleneck for the deployment of contemporary neural models to real-life applications.In this work, we focus on the task of semi-supervised learning where a limited amount of annotated data is available together with many unlabeled NL utterances.Based on the observation that programs which correspond to NL utterances must be always executable, we propose to encourage a parser to generate executable programs for unlabeled utterances.Due to the large search space of executable programs, conventional methods that use approximations based on beam-search such as self-training and top-k marginal likelihood training, do not perform as well.Instead, we view the problem of learning from executions from the perspective of posterior regularization and propose a set of new training objectives.Experimental results on OVERNIGHT and GEOQUERY show that our new objectives outperform conventional methods, bridging the gap between semi-supervised and supervised learning.
Performance Analysis of Subtractive Clustering Algorithm in Determining the Number and Position of Cluster Centers

The basic concept of the subtractive clustering algorithm is to choose a data point that has the highest density (potential) in a space (variable) as the center of the cluster. The number and position of the cluster centers formed are influenced by the given radius (r) parameter value. If the radius value is very small, it will result in the neglect of potential data points around the center of the cluster. If the value of the radius parameter is too large, it increases the contribution of all potential data points, thereby canceling the effect of cluster density. The number of cluster centers in the subtractive clustering algorithm is determined based on the iteration process in finding data points with the highest number of neighbors. This study uses the clustering partition as a parameter value to determine a data point (candidate cluster center) will be selected to determine the effect of the radius (r) parameter value on the subtractive clustering algorithm in generating clustering. From the experiments that have been carried out on 4 datasets, the results have been obtained, for dataset 1 the highest average value of fuzzy silhouette with a parameter value of radius (r) 0.35 is 0.9088 and the number of clusters 2. While in dataset 2, the average value The highest fuzzy silhouette with a parameter value of radius (r) 0.40 is 0.6742 and the number of clusters 3. While in dataset 3, the average value of the highest fuzzy silhouette with a parameter value of radius (r) 0.50 is 0.7434 and the number of clusters 3. While in the dataset the last is the fourth dataset, the highest fuzzy silhouette average value with a radius (r) parameter value of 0.50 is 0.6630 and the number of clusters 2. This subscractive clustering algorithm is widely applied in the fields of transportation, GIS, big data, control of electric voltages, electrical energy needs, knowing the area of population density to health such as breast cancer diagnosis, which is related to the needs of human life.
Time Lag-Based Modelling for Software Vulnerability Exploitation Process

With the increase in the discovery of vulnerabilities, the expected exploits occurred in various software platform has shown an increased growth with respect to time. Only after being discovered, the potential vulnerabilities might be exploited. There exists a finite time lag in the exploitation process; from the moment the hackers get information about the discovery of a vulnerability and the time required in the final exploitation. By making use of the time lag approach, we have developed a framework for the vulnerability exploitation process that occurred in multiple stages. The time lag between the discovery and exploitation of a vulnerability has been bridged via the memory kernel function over a finite time interval. The applicability of the proposed model has been validated using various software exploit datasets.
Parallel Text Alignment and Monolingual Parallel Corpus Creation from Philosophical Texts for Text Simplification

Text simplification is a growing field with many potential useful applications.Training text simplification algorithms generally requires a lot of annotated data, however there are not many corpora suitable for this task.We propose a new unsupervised method for aligning text based on Doc2Vec embeddings and a new alignment algorithm, capable of aligning texts at different levels.Initial evaluation shows promising results for the new approach.We used the newly developed approach to create a new monolingual parallel corpus composed of the works of English early modern philosophers and their corresponding simplified versions.
CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation

Evaluation beyond aggregate performance metrics, e.g.F1-score, is crucial to both establish an appropriate level of trust in machine learning models and identify avenues for future model improvements.In this paper we demonstrate CrossCheck, an interactive capability for rapid cross-model comparison and reproducible error analysis.We describe the tool, discuss design and implementation details, and present three NLP use cases -named entity recognition, reading comprehension, and clickbait detection that show the benefits of using the tool for model evaluation.CrossCheck enables users to make informed decisions when choosing between multiple models, identify when the models are correct and for which examples, investigate whether the models are making the same mistakes as humans, evaluate models' generalizability and highlight models' limitations, strengths and weaknesses.Furthermore, CrossCheck is implemented as a Jupyter widget, which allows for rapid and convenient integration into existing model development workflows.
Time-resolved observation of a dynamical phase transition with atoms in a cavity

We present a dynamical, multilevel atom-cavity blockade effect and monitor its breakdown transition in time. As in the case of optical bistability, atoms initially impede transmission by detuning a cavity mode from the driving laser. The interacting system, however, eventually transitions into an uncoupled state via a critical runaway process, resulting in maximum transmission. These two extremes of transmission are macroscopic reflections of well-defined atomic states, and thus are interpreted as phases of a dynamical transition. By monitoring the output of the cavity, we make time-resolved measurements of the order parameter and that of the enhanced photon number fluctuations. Considering these results for different cavity driving intensities, we establish finite-size scaling relations that suggest such a runaway effect is in fact a genuine dynamical phase transition.
Pengujian Black Box pada Sistem Informasi Penjualan Buku Berbasis Web dengan Teknik Equivalent Partitions

With the development of information technology, then we should also follow these developments so that we will not miss any information. At this time there are many businesses who market their products and sell them by utilizing information technology as well as by using an information system based on the website. In the utilization of the information system, the quality of the software built or made becomes very important, because by having good software it will also have an impact with the convenience of the user. The higher the quality of the software created, the more convenient the user will use it. And to be able to see how qualified the software is made then we can do a software test. In software testing there are methods that can be used such as Whitebox Testing and Blackbox Testing that have been widely applied. In this study, the method that will be used is Blackbox Testing which will be implemented in the website-based book sales information system, with the aim that we can find out if the functions in the program will be in accordance with what is expected. In Blackbox testing method there are many techniques that can be used, one of which is Equvalent Partitions technique which is a testing technique based on input on the existing form, and here formya refers to the website-based book sales information system. This test will see if the function or form that is the test material will result in a valid or invalid value. If the result is valid then the software is in accordance with what we want and if it is not valid then there is an error and can be fixed, so that later the software to be used has good quality.
Software Defect Estimation using Machine Learning Algorithms

Software Engineering is a branch of computer science that enables tight communication between system software and training it as per the requirement of the user. We have selected seven distinct algorithms from machine learning techniques and are going to test them using the data sets acquired for NASA public promise repositories. The results of our project enable the users of this software to bag up the defects are selecting the most efficient of given algorithms in doing their further respective tasks, resulting in effective results.
DEVELOPMENT OF CONCENTRIC INFORMATION TECHNOLOGY OF DIGITAL TRANSFORMATION OF HIGHER EDUCATION INSTITUTIONS

Modern world trends in education are aimed at the digital transformation of educational institutions. Traditional approaches to higher education institutions informatization through their automation no longer meet the requirements of the time. Therefore, new approaches are needed, based on the digitalization of educational processes aimed at creating digital universities. For this purpose, the current state of information systems in higher education institutions use and the issue of higher education institutions digital transformation were studied. The problem of automation tools separation was revealed, which does not allow to effectively implement digitalization projects, does not allow to create a single system of building digital universities. It is proposed to solve the digital transformation of higher education institutions problem based on the use of concentric information technology developed in this study, which integrates all information bases, methods and means of solving functional problems into a single digital space to maximize information needs of higher education institutions. This technology is based on the following developed in the study: 1) concentric model of information technology of digital transformation of higher education institutions, which allows to integrate methods and tools of information management to create a single higher education institutions digital space; 2 functionally independent method of information management of higher education institutions, which allows to create universal tools of informatization, which are independent of the composition and specifics of the construction of the created tools of solving functional problems; 3) interlayer information interaction methods, providing reflection in the processes of each of the layers of changes in the information environment of neighboring layers. The results of the study were used in the construction of information and analytical system of the university educational activities support, which was developed and implemented at Cherkasy State Technological University.
Data Distribution for Multiple Receivers in a Connected Car Environment Using 5G Communication

The development of communication technology has brought changes to various environments. The evolution from 3G to 4G Long-Term Evolution (LTE) was mainly aimed at improving communication speed. However, the evolution from 4G LTE to 5G New Radio (NR) is not aimed at improving speed alone. In addition to the existing communication types, 5G aims to improve communication to support the Internet of Things (IoT), media, and complex content to which things are connected. In such environments, point-to-point communication has a very inefficient structure to allow content providers to transmit data to many content users. In the 5G era, content providers must distribute content to numerous users, and in this process, they need to protect the content. Multireceiver encryption (MRE) is an encryption technology developed for this purpose. MRE allows multiple recipients to decrypt data using their own private key with single encryption of a data provider. With this technology, even if the number of data recipients is 100,000 or 1,000,000, data can be distributed with single encryption. Therefore, while using the existing 1 : 1 encryption method, it is possible to solve the problem of inefficiency in performing encryption for each recipient. However, existing proposed MREs can cause key escrow problems and partial key verification problems. Furthermore, the privacy issues identifying the recipient may arise because anonymity is not available to the recipient. In addition, it is necessary to ensure a fair decryption process for all recipients which a legitimate user cannot decrypt. In this study, we attempted to address these problems, and through our model, it is possible to distribute the data more securely and efficiently in a 5G environment.
The Influence of the Culture of the Third Information Revolution on the Formation of Personality in the M. Serres Philosophical Discourse

The research is aimed at outlining aspects of the influence of the achievements of the culture of the third information revolution on the formation of the young generation in the philosophical discourse of the French philosopher M. Serres, revealing the features of the achievements of the third information revolution of modern culture, in particular in the spatial, temporal and, in fact, human dimensions, their influence on the formation of personality of the “hopthumb”. The theoretical basis of the study is based on culturological and anthropological aspects of the influence of information and computer technology on the formation of personality, human self-awareness in the modern world. The methodology of M. Serres is used in the work. The scientific novelty of the proposed article is the analysis of the philosophical and pedagogical achievements of the French philosopher M. Serres, in particular, argued the presence in the scientific heritage of a famous contemporary doctrine of special characteristics and features of the modern young man, which the author calls " hopthumb" against the background of the formation of a new space of culture. In addition, the study of the theoretical constants of M. Serres made it possible to outline the signs of the third information revolution, to generalize and actualize the philosophical reflection on the self-identification of the individual in the new conditions. Based on the analysis of the work of the modern philosopher, the connection between the rapid achievements of the information revolution and the culture of younger generations was revealed and understood.
Performance of Porous Asphalt Mixture Containing Seashell as Aggregate Replacement

Porous asphalt pavement is mainly used for parking lots which able to let water to drain through the pavement surface into a stone recharge bed and infiltrate into soils under the pavement. This study is to investigate the performance of seashell in porous asphalt and determine the image analysis. The seashell used in this study is cockle shell. The strength of seashell was determined through the Aggregate Impact Value Test (AIV) and Aggregate Crushing Value Test (ACV). The grade of bitumen used was grade 60/70. The porous asphalt volumetric properties was determined through Marshall Stablity Test. The permeability coefficient of sample that contain seashell as aggregate replacement was determined through Permeability Test. Generally, the results shows that the seashell’s percentage loss (AIV and ACV) is suitable use for porous asphalt mixture, where the percentage of loss for AIV and ACV was 27.84% and 7.65% respectively. Based on the Marshall Test, porous asphalt that containing seashell as aggregate replacement shows a different result, where it had lower stability value and it can increase the bulk density of porous asphalt mixture. Furthermore, the permeability coefficient also increase. The surface of seashell able to bond with bitumen. Lastly, the trend of VFA and VTM value is effected by the surface and position of seashell in the porous asphalt sample.