Reviewerly/exHarmony huggingface dataset:
About Electronic Textbook “Mathematical Tasks Programming. First Steps”

The goal of knowledge quality improving of secondary school students in the field of exact sciences determines the relevance of creating interactive electronic educational resources for tasks solving of mathematical models programming. The paper presents the electronic textbook description, its content and structure, model, as well as design and software development technology. It is intended to improve qualitatively the preparation of high school students and junior university students in mathematics and programming. In particular, it can be used as a textbook at elective classes and young programmers’ clubs, as well as in the process of individual work to train schoolchildren and students for programming Olympiads. The content of the electronic textbook is based on a set of mathematical tasks. Each task contains a statement of the task, instructions for its solution and an algorithm for solving it, implemented in the form of program code in Pascal, C/C++ and Python. The model of learning system using an electronic training manual is described. The e-tutorial software is a Web application and is built on client-server architecture. The basis of the Web-application is the “Textbook” software module, which contains a complex of mathematical tasks with the author’s algorithms of solving. The web application contains a software module for expanding the electronic textbook content in the form of a built-in editor for mathematical tasks. A qualified user of the system has the opportunity to develop new tasks within the existing textbook format with a description of the algorithm for their solution. The automatic system of checking the proposed algorithm for task solving provides the basis for an expert opinion on placing a new task in the library for further use. The approbation of the Web-application in the educational process was carried out in secondary educational institutions of Kherson, and it received a positive assessment from students and teachers.
Intelligent Traffic Monitoring Systems Using Deep Learning Algorithms

We are proposing an intelligent traffic monitoring system with deep learning (DL) algorithms. Monitoring videos contribute a great deal to unstructured data. Surveillance cameras are deployed everywhere where safety is important. Manual monitoring seems long and tedious. Security can be defined in various contexts such as theft identification, detection of violence, explosion chances. This paper includes an extensive learning method, starting with detecting vehicles and recognizing objects in crowds. The main focus is about image analysis, detecting the number of vehicles involved and the happens are the main focus of this use of deep learning model. Effective time processing is also considered as an important topic to be further examined in this field. The suggested ITM system is implemented, and challenges problems such as parallel training and model sync are addressed. Task and model-level parallel training methods are proposed to speed up the analysis even more. We offer low latency and precise solutions for image frame analysis. Traffic monitoring and analysis can be handled efficiently by the proposed DL model. We implement deep learning model using a neural network algorithms in deep learning.
Checkable FPGA-Based Components of Safety-Related Systems

The paper is devoted to the problem of checkability of circuits in FPGA components of safety-related systems, which are designed to operate in two modes: normal and emergency for providing their own functional safety and the safety of control facilities in order to prevent accidents and reduce losses in case of their occurrence. Functional safety is ensured through the use of fault-tolerant solutions that are sensitive to sources of multiple failures, including hidden faults. They can accumulate during a prolonged normal mode with limited checkability of the circuits and simultaneously manifest themselves with the beginning of the emergency mode. A fault-tolerant structure becomes fail-safe if it is checkable. The problem of hidden faults manifests itself in the memory of the LUT units of FPGA components with LUT-oriented architecture. The program code written in the memory of the LUT units is checked with a checksum, but it can be corrupted when reading its bits on the outputs of the LUT units. Bits observed only in emergency mode reduce the checkability of FPGA components and are potentially hazardous. Checkability can be increased by the operation of circuits on successively replaced versions of the program code that can be obtained for the same hardware implementation. Versions move potentially hazardous bits to checkable positions observed in normal mode. However, the set of these versions are significantly limited by connecting the inputs of the LUT units to the inputs of the FPGA component. The proposed method overcomes this limitation by introducing an additional scheme. Experimental studies of library FPGA designs show a low level of their checkability and efficiency of the proposed method, which provides totally checkable circuits.
Relation Extraction from Videos Based on IoT Intelligent Collaboration Framework

In the Internet of Things (IoT) era, various devices generate massive videos containing rich human relations. However, the long-distance transmission of huge videos may cause congestion and delays, and the large gap between the visual and relation spaces brings about difficulties for relation analysis. Hence, this study explores an edge-cloud intelligence framework and two algorithms for cooperative relation extraction and analysis from videos based on an IoT system. First, we exploit a cooperative mechanism on the edges and cloud, which can schedule the relation recognition and analysis subtasks from massive video streams. Second, we propose a Multi-Granularity relation recognition Model (MGM) based on coarse and fined granularity features. This means that better mapping is established for identifying relations more accurately. Specifically, we propose an entity graph based on Graph Convolutional Networks (GCN) with an attention mechanism, which can support comprehensive relationship reasoning. Third, we develop a Community Detection based on the Ensemble Learning model (CDEL), which leverages a heterogeneous skip-gram model to perform node embedding and detect communities. Experiments on SRIV datasets and four movie videos validate that our solution outperforms several competitive baselines.
A Computational Lexicon of Ukrainian Discourse Connectives

We introduce a new lexicon of discourse connectives for the Ukrainian language. Discourse connectives like ‘because’, ‘therefore’ are grammatical elements which link clauses and sentences semantically and play a crucial role in discourse structure. They have shown to be useful for many tasks in natural language processing from argumentation mining to authorship analysis. We introduce a semi-automatic method for inventorizing discourse connectives in underresourced languages, by leveraging existing lexicons from other languages. As a result, we provide the first computer-readable lexicon of 129 Ukrainian discourse connectives. We provide syntactic as well as semantic information for these items. Finally, we carry out a small pilot study using the lexicon for discourse level corpus annotation, and report on the distribution of connectives in Ukrainian in two different types of media.
Nonparametric Methods of Authorship Attribution in Ukrainian Literature

The paper presents the results of the comparison of two nonparametric methods of authorship identification of the Ukrainian literature texts. The paper describes the implementation of the corresponding methods based on the Klyushin–Petunin tests and its simplified version. The method of n-gram selection is applied. For testing a collection of texts up to 200,000 characters from 10 authors was used. As a result of carrying out the test, it was found out that the simplified test appears to be more sensitive and specific, and monograms and bigrams in opposite to trigrams provide clear detection of authorship.
Targeted Sentiment Analysis for Ukrainian and Russian News Articles

One of the most challenging problems in sentiment analysis is an analysis of multiple targets in the same text without clear boundaries between target contexts. This problem is even harder for Ukrainian and Russian languages because of the lack of datasets and established approaches. Responding to the business needs of our company, we created the bilingual dataset, manually annotated for targeted sentiment according to strict guidelines. This dataset allowed us to fine-tune a pre-trained multilingual BERT model and improve key metrics (macro F1, F1 for negative and positive classes) over the baseline models. As a by-product, we have trained new NER models for both languages. NER and targeted sentiment models were successfully introduced into the production environment at Semantrum.
A Vehicle Comparison and Re-Identification System Based on Residual Network

In the highway intelligent monitoring system, it is difficult to find the target vehicle through millions of pictures because of the presence of fake-licensed vehicles. In order to solve this problem, a vehicle comparison and re-identification (Re-ID) system is built in this paper. By introducing Circle loss and Generalized-Mean(GeM) pooling, vehicle feature extraction and storage, vehicle comparison and vehicle search can be realized. Experimental results show that the proposed algorithm reaches 95.79% of the mean Average Precision (mAP) on the vehicle search task, which meets the requirements of practical applications.
Privacy Ensured Transmission of Healthcare Records Using IoT-Enabled Systems

As an end result of the huge development of the Web of Points within the hospital treatment place, the safety and safety, and also the honesty of the medical statistics became huge annoying situations for healthcare answers programs. In this paper a hybrid safety model for shielding the analysis message records is proposed in patient medical scan reports or images. The endorsed version is advanced with integrating either 2-D discrete wavelet exchange 1 diploma (2D-DWT-1L) or 2-D discrete wavelet trade 2 diploma (2D-DWT-2L) steganography approach with a proposed cross breed document encryption plan. The proposed crossbreed encryption schema is built utilizing an aggregate of Advanced File encryption Requirement, and moreover Rivets, Shamir, and also Adelman algorithms. The proposed version begins by us in encrypting the secret statistics; then it hides the bring about a cover photo the use of 2-D discrete wavelet exchange 1 diploma or 2-D discrete wavelet trade 2 diploma. Both color as well as gray-scale photographs are utilized as cover snap shots to hide one in every of a type message dimensions. This machine performance end up tested mainly based on six parameters are the pinnacle signal-to-noise percent (PSNR), imply square mistake (MSE), little bit mistake price (BER), structural resemblance (SSIM), structural Internet content material (SC), and also connection. The PSNR worth had been quite differed from (50.59 to 57.44) in case of color pix in addition to from (50.5 to 56.09) with the gray-scale pix. The MSE (0.12 to 0.57) for the shade images and from (0.14 to 0.57) for the gray-scale picas. The little bit mistake worth was 0 for both photographs, while SSIM, SC, and connection worth have been ones for each photo. Compared with the latest approaches, the advocated version showed its functionality to cover the private patient's facts right into a transmitted cover picture with immoderate imperceptibility, capability, and additionally minimum condition inside the acquired steno-image.
Automated Skin Disease Detection Using Machine Learning Techniques

Dermatological problems are among the world’s most prevalent illnesses. Although its frequent diagnosing, its intricacies of physical appearance, existence of hair are very challenging. Identification and observation of skin diseases is a significant issue for the medical sector. Because medical services are not accessible in rural locations, people often overlook initial signs which might deteriorate over time. This is thus an increasing demand for a high-precision automated skin disorder classification method. To detect skin diseases, it is first necessary to remove the skin from the body. Five different machine learning methods were selected and performed on skin infection information to forecast the precise skin illness classification. From a few methods for machine learning, Naïve Bayes, uninformed mountain pastures, logistical regression, SVM and KNN have been developed. A comparable analysis was conducted using graphs based on cross validation values and training accuracy. It has been shown that KNN is best trained to meet the appropriate expectations of all chosen skin disorders.
Double MAC Supported CNN Accelerator

Convolutional neural network (CNN) is a type of deep learning and a major workload that requires powerful hardware expansion. The difference between deep learning workloads is that they are highly flexible for smaller numerical errors and perform well on low-fidelity hardware. We propose a dual MAC process, which combines two multiplication and accumulation (MAC) operations into a single DSPFPGA chip and potentially doubles the computational speed of the CNN accelerator. When using CNN throttle mode, some technical issues need to be resolved. We tested our method using FPGA synthesis and Verilog simulation and tested it on a modern CNN accelerator. Our dual MAC is unique in two ways: It has an equal operand and an unsigned operand. In the context of the CNN accelerator, we have shown how to overcome these limitations and accuracy issues. We have shown that using the dual MAC method can double the computational throughput of a CNN spreadsheet. Performance improvements at the network layer (all convolution layers together) depend on the CNN program and FPGA scale, from 14% to over 80% in a fully configured next generation acceleration solution, without significantly shortening production efficiency.
ASIC Implementation of Division Circuit Using Reversible Logic Gates Applicable in ALUs

Reversibility has extended applications in designing complex circuits. It also addresses power optimization, complexity effectively. This paper has demonstrated the division circuit applicable in ALU's using reversible logic gates. An Application-specific integrated circuit (ASIC) is used to implement a division circuit. These are integrated circuits that are not used for any general-purpose applications, but they have particular use. ASIC's functionality is described by the hardware descriptive languages like VHDL or Verilog. The main intention to use reversible logic gates is to optimize the power consumption, avoid loss of information, and increase the system's performance. Cost parameters like quantum cost, transistor cost, the total number of unused outputs, the total number of gates are calculated. The software used to execute the proposed design is Xilinx ISE 9.2. Verilog coding is used to code the proposed model.
Multi-layer Hopfield Like Neural Network

In this research paper, Hopfield type neural network with multiple layers of neurons is proposed. It is showed that such a network acts as associative memory.
Operation of Asymmetric Hopfield Associative Memory: Orientation Selectivity

In this research paper, the dynamics of a Asymmetric Hopfield Neural Network (AHNN) is studied. The concepts like left stable states, left anti-stable states are introduced. Based on these concepts it is reasoned that for some initial conditions the associated memory states are orientation selective (row vector/column vector). Also, synthesis of AHNN is discussed.
Applications of Artificial Intelligence for Autonomous Landing and Multicopter Unmanned Aerial Vehicles Design by Space Exploration Machines

Artificial intelligence plays a major role in space exploration. In this new era of space exploration, artificial intelligence is widely used for landing rover and crew and cargo, on mars, moon, and beyond. As a part of space exploration, landing the rover safely beyond earth without any damage and safety landing. So that rover can conduct the exploration on that planet. This paper will discuss about the landing system by using artificial intelligence, improvements for operability for future launch vehicles.
Adorno’s Ideas on Stravinsky’s Neoclassicism Meet the Pianist’s Work: Reflecting Playing Experience with Adorno’s Key Concepts

Theodor W. Adorno’s ideas on jazz and Igor Stravinsky’s neoclassicism (1936; 1949; 1963) are notorious for their strident criticism, pathologizing metaphors and prejudicial language. What has been overlooked, however, is the value of Adorno’s ideas from the music performer’s perspective, his cultural background and the genre of post-war French neoclassicism. In this article, I discuss Adorno’s commentaries side-by-side with my own observations as a pianist. For this purpose, I use two methods: conceptual analysis and embodied intertextuality, in which research premises are based not on the notated score but primarily on analyzing and re-conceptualizing the playing experience. Despite the embodied aspect, praxis in this context is not treated as an object but as a tool for conducting research. Characteristic of Adorno in Philosophie der neuen Musik (1949) is his distinctive conceptual thinking. The latter main chapter of the work, “Stravinsky and restauration” proceeds through thematic subheadings, each representing different aspects of Stravinsky’s intentions, aesthetics and psychology. My article follows a similar conceptual-based procedure with the exception that I reflect on my playing experiences through the lens of Adorno’s key concepts in such neoclassical works as Piano-Rag-Music (1919). Finally, Adorno’s critical ideas on Stravinsky’s neoclassicism and its main manifestation, hybrid intertextuality, encounter the pianist’s playing experience on a conceptual level. The most significant difference between Adorno’s and the performer’s view is condensed in Adorno’s interpretation of Stravinsky’s neoclassicism as alienated from its own material. For the performer, Stravinsky’s neoclassicism represents a historically rich intertextual network in which the previous and contemporary styles perform side by side.
Optimal unambiguous discrimination of Bell-like states with linear optics

Quantum information processing using linear optics is challenging due to the limited set of deterministic operations achievable without using complicated resource-intensive methods. While techniques such as the use of ancillary photons can enhance the information processing capabilities of linear optical systems they are technologically demanding. Therefore, determining the constraints posed by linear optics and optimizing linear optical operations for specific tasks under those constraints, without the use of ancillas, can facilitate their potential implementation. Here, we consider the task of unambiguously discriminating between Bell-like states without the use of ancillary photons. This is a basic problem relevant in diverse settings, for example, in the measurement of the output of an entangling quantum circuit or for entanglement swapping at a quantum repeater station. While it is known that exact Bell states of two qubits can be discriminated with an optimal success probability of 50% we find, surprisingly, that for Bell-like states the optimal probability can be only 25%. We analyze a set of Bell-like states in terms of their distinguishability, entanglement as measured by concurrence, and parameters of the beam-splitter network used for unambiguous discrimination. Further, we provide the linear optical configuration comprised of single photon detectors and beam splitters with input state-dependent parameters that achieves optimal discrimination in the Bell-like case.
Solving old problems or making new ones? Blockchain technology for the protection of refugees and migrants

Blockchain technology has applications that can revolutionize political and economic governance. Although most of the academic literature on blockchain has focused on Bitcoin, there is a need to look at the feasibility of new humanitarian applications. This study will proceed in two steps. First, it surveys current theoretical and practical work on how blockchain can be used to help protect the human rights of migrants and refugees, primarily through creation of digital identities. Then it conducts a critical examination of two major cases: the Building Blocks initiative by the World Food Programme in Jordan and the Rohingya Project. We find that blockchain can be useful in empowering vulnerable individuals, but the empowerment of organizations creates potential human rights risks, such as the infringement of privacy and discrimination. Therefore, adequate safeguards should be in place to ensure that blockchain initiatives meet their true purposes of protecting the most vulnerable groups.
Comparing the effectiveness of the genetic algorithm and ant colony optimization algorithms for the traveling salesman problem

In this paper, we apply the genetic algorithm and ant colony optimization algorithms, which is a kind of meta-heuristics search algorithm, for the traveling salesman problem. We perform experiments to evaluate which one among these algorithms solves the problem more efficiently by means of the solution quality and the execution time. The experimental results show that the ant colony optimization algorithms are efficient in terms of the solution quality, while the genetic algorithm is efficient in terms of the execution time for large traveling salesman problems
The Relationship between Belief in a Just World and Embitterment: The Mediating Effects of Self -Compassion for Adults in Their 20s and 30s-

본 연구는 2030세대의 정당한 세상에 대한 믿음이 울분에 미치는 관계에서 자기자비의 매개효과를 살펴보았다. 본 연구에서는 수도권(서울, 경기도, 인천)에 거주하는 만 20-39세 남녀(303명)를 대상으로하였다. 연구대상에게 정당한 세상에 대한 믿음, 울분, 자기자비에 관한 질문지를 실시하였고, 분석 자료는 SPSS 21.0을 통하여 빈도분석, 일원변량분석, Pearson의 상관관계, 단순회귀분석, 중다회귀분석, 단계적 회귀분석을 실시하였고, SPSS Macro 3.5를 통해 매개효과를 검증하였다. 본 연구의 주요 결과는 다음과 같다. 첫째, 2030세대가 ‘지속적인 울분 상태(1.6≤PTEDS)’임을 확인하였다. 둘째, 2030세대의 정당한 세상에 대한 믿음, 울분, 자기자비의 유의미한 상관관계를 보였다. 셋째, 2030세대의 정당한 세상에 대한 믿음과 울분의 관계에서 자기자비의 부분매개 역할을 하는 것으로 확인되었다. 결과적으로, 본 연구에서는 2030세대의 울분이 개인적, 사회적 측면에서 다루어져야 할 필요가 있는 수준임을 보여주었으며, 2030세대의 울분에 자기자비의 매개효과를 검증하여 심리사회적 개입의 근거를 제시하였다는 의의를 갖는다.In this study, we investigated the mediating effect of the self-compassion parameter on the relationship between the belief in a just world and embitterment among adults in their 20s and 30s. This study targeted men and women aged 20-39 (n=303) living in metropolitan areas of South Korea. We conducted a survey of belief in a just world, embitterment, and self-compassion on the subjects of our research. Frequency analysis, ANOVA, Pearson correlation, simple regression analysis, multiple linear regression analysis, and stepwise regression analysis were carried out via SPSS 21.0. According to the results, mediation effect was verified via SPSS Macro 3.5. The main results of this study are as follows. First, we confirmed that adults in their 20s and 30s were in the “continuous state of embitterment.” Second, it showed a meaningful correlation between belief in a just world, embitterment, and self-compassion for the adults in their 20s and 30s. Third, it was confirmed that self-compassion acts as a partial mediator in the relationship between belief in a just world and embitterment among adults in their 20s and 30s.