Reviewerly/exHarmony huggingface dataset:
On the Development of the Concept of Innovative Training of Future Teachers of Informatics in the Conditions of Digital Transformation of Society

The data of studies on the readiness of teachers for digital transformation are presented, a conclusion is made about the contradiction between the demands of the modern digital society for the quality of professional training of future teachers of informatics and the lack of approved innovative approaches for the implementation of the tasks. A mechanism for solving the problem is proposed - the development of a concept for innovative training of future teachers of informatics. For its implementation, the Innovation Center for the Training of Teachers of Informatics of the Tula Region was created.
School Research Activity as a Form of Early Mathematical Training

The aim of this study is to present the experience of engaging schoolchildren in research activities through project activities implementing similar principles to the concept of mathematization. Formation of interdisciplinary competencies through project activities in the field of applied mathematics allows students to realize the possibility of applying mathematical knowledge to problems formulated in a real context, which is an important motivator in the learning process. The paper deals with the peculiarities of organizing project activities for schoolchildren and gives examples of successfully implemented cases, which were presented at the Russian project program “Step into the Future”.
Improving the Efficiency and Effectiveness of Multilingual Classification Methods for Sentiment Analysis

The growing field of customer experience management relies heavily on natural language processing (NLP). An important current use of NLP in this industry is to efficiently build sentiment models in new languages. These new language models will allow access to a greater range of clients. In this work, we examine the practical effectiveness and training data requirements of transfer learning methods, specifically mBERT and XLM-RoBERTa, for developing sentiment analysis models in German. To provide a meaningful comparison that excludes transfer learning, we also utilize and train an LSTM classification model. The models are tested by studying the performance gains for different amounts of target language training data. The results enable efficient building of NLP models by allowing prediction of the data requirements for a desired accuracy.
Utilizing Contextualized Word Embeddings For Text Matching

Recent advances in information retrieval has shown a focus on developing neural ranking models based on pre-trained language models. Among them, the interaction-based neural retrieval models, despite the promising results, are mostly based on static word embedding techniques such as Word2Vec, without considering the dynamic nature of context. In this paper, we propose to utilize the recently emerged contextualized language models for improving the retrieval performance of interaction-based neural ranking models. Specifically, we incorporate the state-of-the-art DRMM and K-NRM models with the generalized autoregressive pre-trained language model based on Transformer-XL (XLNet) by taking individual query term occurrences within documents into account. Experimental results on two standard TREC collections demonstrate improved effectiveness brought by the contextualized word embeddings.
Windows Communication Foundation Penetration Testing Methodology

Windows Communication Foundation (WCF) is a communication framework for building connected, service-oriented applications, initially released by Microsoft as part of.NET Framework, but now open source. The WCF message-based communication is a very popular solution used for sending asynchronous messages from one service endpoint to another. Because WCF provides many functionalities it has a large-consuming development model and often the security measures implemented in applications are not proper. In this study we propose a methodology for offensive security analysis of an WCF endpoint or service, from red team perspective. A step by step approach, empirical information, and detailed analysis report of WCF vulnerabilities are presented. We conclude by proposing recommendations for mitigating attacks and securing endpoints.
Video Processing Method for Moving Objects Detection on Scenes with Complex Dynamic Background

The article is devoted to the problem of moving object detection on a video stream. One of the most important problems in this case is unstable background with moving elements that leads to increasing the level of misoperations while selecting moving objects on the scenes with background objects that change with the speed comparable with the speed of movement of object of interest through the image. Two types of dynamic background elements are considered: moving clouds and waving trees. Well known algorithms based on Gaussian mixture model, interest points detection approach (FAST, Harris, MinEigen and SURF) and optical flow (Farneback, Horne-Shunk and Lukas-Kanade) approaches are examined. Farneback based optical flow method is the most appropriate for this task. The new image series processing method is proposed in the paper and tested on the same examples with the background formed with moving clouds and waving trees. The results allow to conclude that it is good in case of moving clouds but its drawback is false detection in areas of waving trees. Future work requires a more advanced prediction model that can consider complicated trajectories of background fragments movement. #CSOC1120.
The Canny Blocks for Generation of Secured Epass in Pandemic COVID-19

The SARS COVID-19 has immensely affected human life across the world. The transport which completely blocked was released by issuing epass to the travelers with valid emergency. The purpose of issuing the epass is not only to make the restriction of frequent travel but also to control the traffic and to save the livelihood of people in local zones. There are distinct challenges in epass generation and issuance. The existing system uses the traditional cloud database for storing the user-sensitive data who applied for epass. It may result in data leakage and security threats due to centralized server. The unique technology which meets the current requirement of highly secured sensitive data available to all authorized person is Blockchain Technology. Blockchain is expounded as distributed decentralized ledger, maintains the non-modifiable ledger at all nodes in the network. In this paper, we proposed blockchain technology-based framework that can be deployed for generating and issuing the epass to improve the availability of sensitive data to the authorized person for effective controlled movement between the country. This decentralized distributed blockchain-based epass maintains the history of the user applied for the epass properly in the non-modifiable ledger properly.
New algebraic studies of pattern attributes in maximum-length shift-register sequences

In order to facilitate the ubiquitous heterogeneous and medium-independent communication networks, transmitter identification is crucial especially as the number of transmitters becomes tremendous in reality. Since the birth of spread-spectrum communications, pseudo-random identification sequences have been widely adopted due to their preferable nearly Dirac-Delta auto-correlation property, which would lead to the advantages in synchronization and multi-user interference mitigation. Maximum-length shift-register sequences (a.k.a. m-sequences) are pseudo-random sequences often adopted for multi-access communications, but categorization or grouping of the patterns (bit substrings) contained in m-sequences has not been investigated to the best of our knowledge. Thus, this paper is dedicated to the new study on the categorization of m-sequences, which would give rise to many applications involving assignment, addressing, and management of identification or spreading sequences. We define new essential parameters, namely single-pattern-searching parameters, and design a new parallel algorithm to spot such inherent parameters associated with each selected underlying pattern. Our proposed new approach will facilitate a highly computationally-efficient solution to find the pattern-attributed (pattern-contained) m-sequences without actually generating any m-sequence for subsequence-matching between an m-sequence and the underlying pattern(s). We further utilize these single-pattern-searching parameters to establish the new analysis of pattern capacity and quality by characterizing multiple pattern attributes within m-sequences. The first goal of this work is to find single-pattern-attributed m-sequences recursively until the final subset of m-sequences is acquired so that each m-sequence contains all underlying patterns. The second goal of this work is to find the shortest sequences containing specified patterns. To achieve this goal, we first construct the shortest binary sequences subject to the underlying patterns and it can be accomplished by solving the generalized traveling salesman problem (GTSP). Then, according to the constructed shortest binary sequences, the number of pattern-contained m-sequences can be determined thereby. Memory- and computational-complexity analyses are also presented to demonstrate that our proposed new scheme is much more computationally efficient than the conventional subsequence-matching method for searching and counting the pattern-attributed m-sequences. On the other hand, our proposed new scheme leads to the same memory-complexity as the conventional method for registering the spotted pattern-attributed m-sequences. Finally, three new metrics for assessing the underlying patterns are proposed. They are attributability, discriminability, and ambiguity. The associated numerical evaluation is also provided in this paper.
Doing Versus Observing: Virtual Reality and 360-Degree Video for Training Manufacturing Tasks

Virtual reality is increasingly used for training workers manufacturing processes in a low-risk, realistic practice environment. Elements of the manufacturing process may be time-consuming or costly to implement accurately in simulation. The use of 360-degree video on its own or combined with interactive elements can bridge the gap between affordable VR capabilities and requirements for effective training. This paper discusses use of 360-degree video to create “learning by observing” and “learning by doing” VR training modules. The “learning by doing” module addresses work task training entirely with 360-degree videos embedded in an interactive training environment. In the “learning by doing” module, a hybrid approach successfully leverages 360-degree video to show complex task processes while implementing fully interactive elements when it was cost-effective to do so. Participants suggest “learning by observing” is effective while “learning by doing” is more engaging.
A Conceptual Framework For Malay-English Mixed-language Question Answering System

Mixed language has turned into a current trend of language which refers to combining two or more languages either in spoken or written form. It has been widely used in social media forums to improve communication and for a greater range of expression. The current question answering (QA) system only supports monolingual queries, which restricts the capability of multilingual users to have a natural interaction with the system. In recent years, there has been a rise of interest in multilingual QA systems where translation models merged with machine learning algorithms in question classification are the commonly used solution. However, using words from other languages in a single sentence has led to the problem of the inability to identify code-switch from the monolingual sentence; this has also caused the problem of limited captured language context from machine translation processed mistranslated questions. The informal mixed-language representation that disobeys the natural linguistic rule in particular languages provides a challenge for automated QA systems, as the systems would need to translate and extract answers for the given questions. Additionally, lack of public resources such as Chunker, POS Tagger, and WordNet for mixed-language, especially for Malay-English, leads to low performance of the translation and classification model. Furthermore, the use of machine learning algorithms in question classification requires a large number of structured training data to ensure performance. This is impracticable in the Malay-English mixed-language domain since the availability of the mixed-language dataset is still an issue. To solve these problems, we aim to propose a framework consisting of the combination of enhanced translation models with deep learning; by using Convolutional Neural Networks (CNN) to address the Malay-English mixed-language question classification to generate the best answer. The first part will study the machine translation model, where word-level language identification and text normalization towards Malay-English mixed-language questions will be developed. The second part will focus on the deep learning algorithm, where we will explore CNN as the classification model to assist in the translated questions to provide the best answer. Thus, in this paper, a framework consisting of an enhanced translation model for Malay-English, and also an end-to-end mixed-language question answering system for the Malay-English Q&A system, is presented. This research will provide a significant contribution to a multilingual forum platform and also to intelligent Q&A systems (chatbots).
MOLE: Multiparty Open Ledger Experiment, Concept and Simulation Using BlockChain Technology

Key sectors like medicine, finance, education, IoT use BlockChain based applications to derive many benefits, this technology has to offer. The foundation of Bitcoin that is blockchain technology has received extensive attraction in research recently. The BlockChain Technology provides benefits in collaboration, trustability, identification, credibility and transparency. This technology acts as an untampered ledger which gives end user permission for various operations to be managed in decentralized manner. Various areas are centered around this, covering domains-like financial services, hospitality sector, healthcare management, E-Governance and so on. However, there are several technical hindrances of this emerging technology such as security and scalability issues, that need to be taken care of. This paper presents an insight into concept of blockchain, highlights the initiatives undertaken by Government of India and a real time simulation showcasing MOLE: Multiparty open ledger experiment, based on BlockChain Technology using MHRD’S Virtual lab.
Correction to: Flash Crowd Management in Beyond 5G Systems

The original version of this book was inadvertently published with the first author surname incorrect, which has now been corrected.
Heuristics-based Type Error Diagnosis for Haskell

Helium is a Haskell compiler designed to provide programmer friendly type error messages. It employs specially designed heuristics that work on a type graph representation of the type inference process.
Efficient parallel graph trimming by arc-consistency

Given a large data graph, trimming techniques can reduce the search space by removing vertices without outgoing edges. One application is to speed up the parallel decomposition of graphs into strongly connected components (SCC decomposition), which is a fundamental step for analyzing graphs. We observe that graph trimming is essentially a kind of arc-consistency problem, and AC-3, AC-4, and AC-6 are the most relevant arc-consistency algorithms for application to graph trimming. The existing parallel graph trimming methods require worst-case $$\mathcal O(nm)$$ time and worst-case $$\mathcal O(n)$$ space for graphs with n vertices and m edges. We call these parallel AC-3-based as they are much like the AC-3 algorithm. In this work, we propose AC-4-based and AC-6-based trimming methods. That is, AC-4-based trimming has an improved worst-case time of $$\mathcal O(n+m)$$ but requires worst-case space of $$\mathcal O(n+m)$$ ; compared with AC-4-based trimming, AC-6-based has the same worst-case time of $$\mathcal O(n+m)$$ but an improved worst-case space of $$\mathcal O(n)$$ . We parallelize the AC-4-based and AC-6-based algorithms to be suitable for shared-memory multi-core machines. The algorithms are designed to minimize synchronization overhead. For these algorithms, we also prove the correctness and analyze time complexities with the work-depth model. In experiments, we compare these three parallel trimming algorithms over a variety of real and synthetic graphs on a multi-core machine, where each core corresponds to a worker. Specifically, for the maximum number of traversed edges per worker by using 16 workers, AC-3-based traverses up to 58.3 and 36.5 times more edges than AC-6-based trimming and AC-4-based trimming, respectively. That is, AC-6-based trimming traverses much fewer edges than other methods, which is meaningful especially for implicit graphs. In particular, for the practical running time, AC-6-based trimming achieves high speedups over graphs with a large portion of trimmable vertices.
A critical analysis of bio-inspired classifiers and decision-making solutions

As society becomes more inter-dependent and inter-connected, the need of timely and efficient decision-making has become a necessity. Thus, the number of decision-making systems has steadily risen, allowing for an overview of their application relevant to their strengths and weaknesses. Concomitantly classifiers were developed, first to optimize performance, then as alternatives for decision-making tools. This paper presents a critical analysis of the bio-inspired classifiers and decision-making systems, showcasing their evolution in time, studying their application in several domains, emphasizing their advantages and shortcomings, while presenting performance data.
Computer simulation in informatics lessons as a factor of the continuity of school education in teaching mathematical modeling

The article actualizes the need to use elements of computer modeling from school to university when teaching mathematics to engineering students, analyzes the role of simulation models of applied mathematics. The experience of organizing classes using data visualization is presented, the use of simulators and games for studying mathematics in the context of mathematical education, in which students interact with interactive learning environments, is discussed, examples of using computer simulations in the classroom are discussed. The study lists and characterizes the types of didactic simulations, examines the process of transition of school education using simulators and games into the didactics of mathematics of technical universities using applied mathematics and mathematical modeling to elements of simulation. A pedagogical experiment of the continuity of school education (section “Information systems and programming” of the Donetsk Republican Small Academy of Sciences for Students) was carried out in teaching applied mathematics, informatics and mathematical modeling (the department “Transport technologies”) students of the engineering and transport direction of Automobile and Road Institute of Donetsk National Technical University.
The Register as a technical and legal means of implementing digital education in modern Russia

The article examines the emerging trends in the transition to the digital format of the domestic education system, shows the importance of the digital model of education for Russian society. The author identifies the role of the register as an information and digital tool, a technical and legal means of ensuring the process of creating and implementing a digital educational environment, and identifies positive opportunities for using distributed registry technology (blockchain) for the Russian education system.
The research of multi-target tracking based on improved YOLOv3

Abstract Multi-target tracking based on detection is a hot topic in the field of computer vision. Aiming at solving the problem of inconvenient application of large network model, this paper proposes a multi-target tracking algorithm based on improved YOLOv3. We use MobileNetV3 to replace the deep network as the backbone of YOLOv3. Compared with Faster R-CNN and YOLOv3, the model of MobileNetV3-YOLOv3 is greatly reduced and the detection speed is improved. Compared with the MobileNetV1-YOLOv3, the accuracy is improved and FPS is higher. The improved YOLOv3 model is used in DeepSORT algorithm for multi-target tracking. The experimental results show that the algorithm used in this paper has the best detecting and tracking effect.
Classification with Incoherent Kernel Dictionary Learning

In this paper we present a new classification method based on Dictionary Learning (DL). The main contribution consists of a kernel version of incoherent DL, derived from its standard linear counterpart. We also propose an improvement of the AK-SVD algorithm concerning the representation update. Our algorithms are tested on several popular databases of classification problems.
Rheumatism Information Extraction from Electronic Medical Records Using Deep Learning Approach

With the increasing adoption of Electronic Medical Records (EMRs) system, how to extract and use the medical data resources stored in the EMR is starting to attract the attention of researchers. Recently, Natural language processing (NLP) has been used as a common method to process text information in the EMR, which can also be used for Text categorization, Sentiment analysis, Word segmentation, Part-of-speech tagging, etc. Named Entity Recognition (NER) is the primary task of NLP, which can effectively identify valuable information in the text. For these reasons, this work aims to explore the NER model in the field of Rheumatism and the influence of text annotation methods on the performance of NER model. The results show that having compared with four different deep learning models, our proposed approach has achieved the higher level of accuracy. In addition, we found that the reduction of annotation types can improve the performance of the NER model applied to Rheumatism.
A controlled, reproducible, and extensible experiment for evaluating the impact of Tor latency

The Tor anonymity network is an essential protection for those living in oppressive regimes, who may be persecuted for their speech or other activities on the Internet. However, its usability is severely affected by high latency. Furthermore, because of the difficulty associated with conducting research evaluations on the Tor network, there has been insufficient research aimed at this problem. To address this, we develop a CloudLab profile that instantiates a private Tor testing network for easy, reproducible experimental evaluations on Tor. We demonstrate how to use this CloudLab profile to measure the page load time of selected web pages over a Tor network under different network conditions representative of typical Tor usage.
Application of Information Technology in Building Material Testing

With the rapid development of the construction industry, the construction industry has brought huge economic benefits and promoted the overall development of related fields. As we all know, the success of construction projects cannot be separated from the improvement of the quality of building materials. After scientific demonstration, the improvement and design response of the material testing information network monitoring platform, more integration of computer network technology, play a positive role in promoting the healthy development of construction projects. This article mainly elaborates the shortcomings in the quality inspection of building materials and the application of information technology in building materials, and puts forward some suggestions for the problems of material inspection.