Reviewerly/exHarmony huggingface dataset:
Critical discourse analysis of hate speech from a linguistic perspective

The present study is concerned with the analysis of hate speech – an offensive discourse, which is harmful to democracy and to the members of targeted groups and/or individuals, whose inherent characteristics or public image is threatened. It seems reasonable to assume that the democratic and peace-loving world has been astounded by the offensive language often used by Hungarian Prime Minister Viktor Orbán, a NATO leader and a representative of a European Union (EU) member state. Therefore, a speech by Hungarian Prime Minister V. Orbán (2022) delivered at the Bálványos Free Summer University and Student Camp on 23 July 2022, with an approximate research corpus of 9 000 words, has been selected as an object of the linguistic study. This speech has been referred to as a “racist tirade” (Coakley 2022), and European Parliament political group leaders have condemned his “openly racist declarations”, breaching the EU values (EU Parliament Press Room 2022), to mention but a few reactions to the speech. This is even more alarming at present in the context of Russia’s war against Ukraine, when Prime Minister V. Orbán’s pro-Russian stance subverts the EU’s fundamental values and hinders the EU decision-making process. The study employed InfraNodus and Wmatrix methodology for the thematic analysis, the analysis of keyness at the word, part of speech and semantic domain level focusing on the three aspects of discursive proximization that provide for the discussion on the ideological schism in the discourse space. The results of a micro-linguistic level study of the speech indicate that from the perspective of its form, the hate speech under analysis demonstrates Prime Minister V. Orbán’s rather uncompromising stance towards the events being referred to, as well as his attitude towards the prospective action, which might be taken by the target audience. As a result, the qualitative research findings demonstrate that the hate speech under analysis exhibits the features of offensive discourse, created by means of context-dependent linguistic realizations.
Research on Image Deformation Monitoring Algorithm Based on Binocular Vision

Ensuring the safety of critical structures like tunnels, buildings, and bridges is paramount. This paper introduces a cost-effective and efficient non-contact deformation monitoring method based on digital imagery. The focus is on developing an automated methodology using binocular vision. It involves preprocessing digital images through filtering and enhancement, utilizing algorithms like SIFT and gauss-newton for precise planar displacement calculation. Enhanced Semi-Global Matching (SGM) enables stereo matching, producing a disparity map for depth computation. This allows accurate measurement of vertical displacement, facilitating precise three-dimensional displacement assessment. The methodology undergoes a comparative evaluation against traditional methods and existing algorithms to validate its accuracy. Results demonstrate that the proposed binocular vision-based deformation measurement technique effectively captures structural deformations with superior accuracy and efficiency compared to existing methods.
Translating Latvian diminutive nouns into Estonian

The study focuses on the translation of Latvian diminutives into Estonian. In both Latvian and Estonian, the main goal of diminutives is to express smallness, tenderness or affection, but in some cases also pejorative meaning (Erelt et al. 1995; Vulāne 2015; Kasik 2015; Kalnača 2015). Estonian translators of contemporary Latvian literature have stated that diminutive formations are widespread in the Latvian language, and that they do not translate all Latvian diminutives into corresponding Estonian equivalents. The purpose of this study is to analyse diminutives in three Latvian works of fiction: how many diminutives occur, how many of them are translated into Estonian, and how they have been translated. In Latvian, only diminutive nouns with suffixes -(t)iņ-, and -īt- were selected from the material. The research results show that Latvian diminutives are not very often translated into Estonian, and the main equivalent of the diminutive is frequently a base word. However, in the cases when the diminutive is translated, a variety of approaches can be observed.
Survey on Blockchain-Enhanced Machine Learning

The convergence of blockchain and Machine Learning (ML) promises to reshape technological innovation by enhancing security, efficiency, and transparency in ML systems. This survey explores the transformative potential of integrating these two technologies. We outline the foundational principles of blockchain and ML, clarifying their capabilities and synergies. We examine how blockchain strengthens ML as a secure, immutable platform for data sharing, model validation, and executing tasks. We emphasize the opportunities for heightened data security, improved model validation, and decentralized, privacy-preserving systems. However, challenges exist like scalability, energy-wise, and the need for new tailored consensus mechanisms. We provide insights based on recent research at this intersection. Additionally, we explore emerging trends and future directions, like blockchain’s application in federated learning for secure, transparent data sharing and model validation. We also investigate privacy-preserving systems such as Proof of Learning, where blockchain enables secure execution while maintaining data privacy. Moreover, we examine the potential for decentralized AI systems leveraging blockchain to deploy and execute models. This survey offers a comprehensive overview of the evolving landscape at the intersection of blockchain and ML, highlighting opportunities and challenges while suggesting future research directions.
Sparse Representation with Gaussian Atoms and its use in Anomaly Detection

We propose sparse representation and dictionary learning algorithms for dictionaries whose atoms are characterized by Gaussian probability distributions around some central atoms. This extension of the space covered by the atoms permits a better characterization of localized features of the signals. The representations are computed by trading-off representation error and the probability of the actual atoms used in the representation. We propose two types of algorithms: a greedy one, similar in spirit with Orthogonal Matching Pursuit, and one based on L1-regularization. We apply our new algorithms to unsupervised anomaly detection, where the representation error is used as anomaly score. The flexibility of our approach appears to improve more the representations of the many normal signals than those of the few outliers, at least for an anomaly type called dependency, thus improving the detection quality. Comparison with both standard dictionary learning algorithms and established anomaly detection methods is favorable.
TaP2-CSS: A Trustworthy and Privacy-Preserving Cooperative Spectrum Sensing Solution Based On Blockchain

In cognitive radio networks, cooperative spectrum sensing (CSS) is a key approach to effectively discover spectrum opportunities for secondary users. However, due to the presence of malicious nodes, CSS faces significant challenges in the trust issue of sensing results caused by spectrum sensing data falsification and the privacy leakage of sensing nodes. In this paper, we develop a trustworthy and privacy-preserving CSS solution based on blockchain, TaP2-CSS. It achieves the transparency and trustworthiness in exchanging and fusing sensing reports, and preserves privacy of sensing nodes. More specifically, a fusion scheme is proposed to realize the high defense capability against the spectrum sensing falsification attack launched by lurking and persistent malicious nodes. Furthermore, to address privacy threats of sensing nodes, we propose a privacy-preserving sensing scheme based on dynamic sensing time for resource-constrained sensing nodes. It effectively limits the location information leaked by sensing reports without the need for complex cryptographic computation and protocol interaction. Comprehensive evaluation and comparison show that the proposed solution achieves high sensing accuracy in the presence of malicious nodes while preserving the privacy of sensing nodes.
VoicePAT: An Efficient Open-Source Evaluation Toolkit for Voice Privacy Research

Speaker anonymization is the task of modifying a speech recording such that the original speaker cannot be identified anymore. Since the first Voice Privacy Challenge in 2020, along with the release of a framework, the popularity of this research topic is continually increasing. However, the comparison and combination of different anonymization approaches remains challenging due to the complexity of evaluation and the absence of user-friendly research frameworks. We therefore propose an efficient speaker anonymization and evaluation framework based on a modular and easily extendable structure, almost fully in Python. The framework facilitates the orchestration of several anonymization approaches in parallel and allows for interfacing between different techniques. Furthermore, we propose modifications to common evaluation methods which improves the quality of the evaluation and reduces their computation time by 65 to 95%, depending on the metric. Our code is fully open source.
The Differential Equations Linear Homogeneous System Solutions Investigation

The paper shows that the fundamental zero-normalized solution of a linear homogeneous differ-ential equations system can be represented as an exponential matrices products formal series. If the system satisfies the equations system triangulation Perron theorem conditions, then the system solution can be represented as an exponential matrices finite product. In addition, an exponential matrix function differentiating formula is derived. Also, the transformation constructing problem is considered. Such, a homogeneous differential equations system allows to reduce to a triangular form.
Non-pooling Network for Medical Image Segmentation

Existing studies tend to focus on model modifications and integration with higher accuracy, which improve performance but also carry huge computational costs, resulting in longer detection times. In medical imaging, the use of time is extremely sensitive. And at present most of the semantic segmentation models have encoder-decoder structure or double branch structure. Their several times of the pooling use with high-level semantic information extraction operation cause information loss although there is a reverse pooling or other similar action to restore information loss of pooling operation. In addition, we notice that visual attention mechanism has superior performance on a variety of tasks. Given this, this paper proposes non-pooling network (NPNet), non-pooling commendably reduces the loss of information and attention enhancement module (AEM) effectively increases the weight of useful information. The method greatly reduces the number of parameters and computation costs by the shallow neural network structure. We evaluate the semantic segmentation model of our NPNet on three benchmark datasets comparing with multiple current state-of-the-art (SOTA) models, and the implementation results show that our NPNet achieves SOTA performance, with an excellent balance between accuracy and speed.
Sweep Formulas Applying to Encrypt Text Data

In this paper we consider for the first time the possibility of applying the tridiagonal runthrough formulas for text data encryption. The encryption algorithm consists in computing the right part of a linear algebraic equations system with a tridiagonal matrix. In the problem, all the equations coefficients, the right-hand side and the solution take the values of the residues modulo a prime number p. The decryption algorithm consists in solving the SLAE on the class of prime modulo p deductions. The decryption algorithm uses the tridiagonal run method. Two theorems are proved for the algorithm correctness. Theorem 2 is a sufficient condition for correctness. Theorem 3 is the necessary conditions for correctness. Three encryptions of the text of 65, 67 symbols examples are given for illustrate the theorems applicability conditions. The keys spatial power is estimated.
Proposal of a Person-Tracking Audio Input-Output System Using Directional Microphone and Directional Speaker

Care robots are becoming more and more important. There are various doors and doorways in homes, but it is difficult for a robot to move through them. Therefore, as a preliminary step in developing an autonomous mobile robot that can open and close any door, this paper aims to identify doorknobs and handles in images using machine learning. Houses have various types of doors and doorknobs. In this study, they are classified into 6 patterns. We use a Convolutional Neural Network (CNN) to classify them from images. We trained six typical doorknob patterns, 800 images were prepared for each pattern. As the result, the recognition accuracy improved to 79.9% compared to a conventional method. In future, in order for a robot to open and close any door, it will be necessary to detect 3D position of door.
DIGITAL TRANSFORMATION OF EDUCATION: THE KEY TO INNOVATING FUTURE LEARNING METHODS

Digital transformation of education has become a hot topic in the education field today. With the rapid development of science and technology, the application of digital technology in education is becoming increasingly popular. This article explores the importance of digital transformation in education and explains how it is key to innovating the future of learning.
The Philosophical Code in the Performances Staged by Anatoly Efros of the 1960s

The poetics of Anatoly Efros’s performances and his metaplot in the directing process in the context of the theatre aesthetics of the Soviet era was often analyzed from the point of view of hermeneutics. Many reviews of his contemporaries are written from this perspective. The approach to the analysis of Efros's work by codes, described in detail by the French theatre critics Anne Ubersfeld and Patrice Pavis, seems to be the most accurate for understanding the deep semantic structures of his individual productions and his work as a united whole. On the example of the performances On the Wedding Day (1964), 104 Pages About Love (1964), My Poor Marat (1965), A Movie Is Made (1965), The Seagull (1966), Molière (1966) there is considered the transformation of the social theme, which initially dominated in the artistic semiotics of Efros, into a metaphysical one. The philosophical code of Anatoly Efros’s performances at the Lenin Komsomol Theatre is connected with the director’s comprehension of spiritual life, the problem of the characters’ internal choice, their transcendence, the rapidly changing time, the ideas of the existentialists and European philosophy of the 20th century that were penetrating social mind. This is verified by the fact of Efros’s performances in the genre of tragedy at the Theatre on Malaya Bronnaya. They are Romeo and Juliet (1970), The Man from the Outside (1971), Don Juan (1973), The Marriage (1975) and Othello (1976). Efros tried to make this genre up-to-date, not via laboratory testing of book concepts, but, first of all, overcoming the boundaries of the theatre, immersion in the spiritual life of the hero and artist, and lyricism.
Enhancing Marketing Strategies for Office Stationery Stores Using Equivalence Class Transformation Algorithms

Office stationery stores facilitate operations for offices, schools, and businesses. However, the store owner must overcome the challenge of selecting the right products for promotion. This research aims to tackle this issue by comparing the performance of the Equivalence Class Transformation algorithms in discovering association rules using Support, confidence, and lift ratios. The findings reveal that the algorithm generates association rules based on Support, confidence, and lift values. Ten critical rules are identified, shedding light on algorithm effectiveness. Ultimately, this study underscores the significance of refining marketing approaches for brick-and-mortar stationery businesses and the value of data-driven methods in aiding decision-making. In the early promotion catalog phase, priority is assigned to rules with solid agreement by the Shop Owner, guiding the selection of featured items based on robust product associations.
Blockchain Integration in Artificial Intelligence: Benefits, Applications, Research Challenges

Now-a-days block chain and artificial intelligence are the two emerging technologies. Both strategies bring change to the market, but the level of creativity and complexity varies. Block chain is a decentralized and distributed ledger technology which is used for storing information in multiple locations without any centralized monitoring system. Artificial intelligence imitates the problem-solving capability of human mind and decision making abilities by using computers, data, and sometimes machines. When both technologies are merged, their formed new opportunities and possibilities from the advantages of both, like productivity gain and the security and transparency. The convergence of AI with blockchain technology can bring a wide change in businesses by securing data , transparent data, and making it overall efficient. One of the most useful concepts of the two technologies is in cybersecurity. The technologies, which combine the capabilities of AI with the block chain's dependable, scattered nature, may be utilized to enable management of recourses and decision making in areas such as education, impact on society, healthcare, agricultural production, urban planning, and others. Artificial intelligence (AI) is capable of helping detect and react to risks, whereas the integrity and security of information may be ensured using blockchain technology. A comprehensive survey of various applications of blockchain for AI has been completed. Evaluated various literature based on the technologies, and the new blockchain-based applications, mediums, and technological advances that target the AI sector were collected and summarized. In addition, Open research issues concerning the use of blockchain technology for AI are addressed.
Personalization plus self-supervised learning is the key to precision digital therapeutics: a perspective (Preprint)

<sec> <title>UNSTRUCTURED</title> Therapeutic and interventional digital health solutions often use machine learning (ML) models to make predictions of repeated adverse health outcomes. For example, models may be used to analyze patient data and identify patterns that can anticipate the likelihood of disease exacerbations, enabling timely interventions and personalized treatment plans to improve overall health outcomes. However, many digital health applications require the prediction of highly heterogeneous health events. The cross-subject variability of these events makes traditional ML approaches, where a single generalized model is trained to classify a particular condition, infeasible without overfitting to the training set. A natural solution is to train a separate model for each individual or subgroup, essentially overfitting the model to the unique characteristics of the individual without negatively overfitting in terms of the desired prediction task. Unfortunately, such an approach requires extensive data labels from each individual, a reality that has rendered personalized ML infeasible for precision healthcare. The recent popularization of self-supervised learning, however, provides a solution to this issue: by pre-training deep learning models on the vast array of unlabeled data streams arising from patient generated health data, personalized models can be fine-tuned to predict the health outcome of interest with much fewer labels, making personalization of deep learning models much more achievable from a practical perspective. This perspective describes the current state-of-the-art in both self-supervised learning and ML personalization for healthcare as well as growing efforts to combine these two ideas. Personalized self-supervised learning is likely to enable a wide array of digital therapeutics in the coming years. </sec>
Design and Implementation of a Software Result Processing and Transcript Generation

Result processing can be seen as a continuous process of converting data (scores, grade points, credit units etc.) into a definite and meaningful information (statement of result, transcripts etc.). These results are used to check the performance of each student in various courses. These results when processed manually leads to many problems such as error during computation, insecurity of results, untidy results after changes must have been affected and work load on the exam officer(s) etc. However, designing and implementing, as well as the computerization of result processing and transcript generation system will reduce these problems to the barest minimum. Providing password can do this, which grants access to only authorized user(s). Corrections or changes are affected without making the work untidy. Also stress on exam officer(s) is or are reduced. The design and implementation will be developed using the structured system analysis and design methodology (SSADM). The new system was designed using Visual Basic and MS Access. This language was chosen because of its easy syntax and features for developing web applications.
Verification of the effectiveness of digital therapeutics principle textbooks for elementary and secondary school teachers

&lt;p class="abstract"&gt;As the digital transformation of medical systems accelerates, digital therapeutics based on digital technology is attracting attention. Research on digital therapeutics has just begun, and the digital therapeutic market is growing internationally. For students to prepare for an intelligent information society, teachers must be prepared to lead and teach the principles of promising convergence technologies in the future. In this paper, we developed textbooks that can teach the principle of digital therapeutics (DTx) to elementary and secondary school teachers. Textbooks for elementary school teachers were designed with the principle of DTx for attention-deficit/hyperactivity disorder (ADHD), and textbooks for middle school teachers were developed with DTx corresponding to digital dramas as the central theme. The textbooks were developed based on the analyze learners, state objectives, select methods, media, and materials, utilize media and materials, require learner participation, evaluate and revise (ASSURE) model and included teaching and learning plans, worksheets, and reading materials for immediate use in the school field. In addition, textbooks can be used in non-face-to-face classes by using customized online teaching tools. In order to confirm the effectiveness of the developed textbooks, we applied the textbooks to 33 Korean teachers. As a result, teachers’ teaching and learning competency and information teaching efficacy were improved in preparation for the intelligent information society of elementary and secondary school teachers. It was a statistically significant result.&lt;/p&gt;