Reviewerly/exHarmony huggingface dataset:
INTOLERANCE CRIMES AND HATE CRIMES: CONCEPT ANALYSIS

A general theoretical analysis of the concepts of “crimes motivated by intolerance”, “hate crimes” and related concepts (such as “genocide”, “prejudice”, “xenophobia”) is provided in the article. The origin and meaning of the mentioned terms, their normative consolidation and practice of use in national legislation and international legal acts are analyzed. It has been proven that the concepts of “crimes motivated by intolerance” and “hate crimes” are close in meaning. As a legal category, crimes based on intolerance are a classification of a special type of crimes against a person caused by the feeling of intolerance felt by the subject of the crime towards a victim, a representative of another race, nationality, sex, religion, sexual orientation, gender identity, etc. It was found out that the basic structure of crimes motivated by impatience consists of two mandatory points: 1) the specified action is a crime according to the criminal law; 2) the motive for such an action is prejudice, that is, a false opinion formed about someone in advance, without familiarization, and the associated corresponding attitude. The signs of hate crimes have been substantiated and revealed: a) it is an action caused by prejudice, which is recognized as a crime according to international and national criminal legislation; b) such crimes have a significant public danger, are an extreme form of discrimination; c) these are crimes in which the victim, the object or the target of the crime were chosen based on their real or imagined belonging to a certain vulnerable group, connection with it; d) such imaginary or real grouping can be based on any socially relevant features or characteristics. It has been found out that the unifying feature of crimes motivated by intolerance is always a motive – a prejudiced or real feeling of hatred, hostility, intolerance towards the “other”, a representative of an imaginary or real “not their” social group. The need to bring the national legislation in line with international legal norms regarding the use of the term “hate crime” and the need to legislate the specified definition are substantiated. Key words: crimes motivated by intolerance, hate crimes; genocide, prejudice, xenophobia, national, racial, religious enmity, criminal law qualification.
CHARACTERISTICS OF MOTIVATION AND EMOTIONAL SPHERE OF THE PERSONALITY OF 1ST YEAR STUDENTS DURING THE PREPARATION FOR THE SESSION

The subject of the study are features of an emotional and personal sphere of 1st-year students of technical training profiles with different leading motives for learning during the preparation for the session. The authors present the results of an empirical study in which 81 first-year students of NSTU technical training profiles took part, 62 of them male, 19 female students; age from 17-20 years. The study was conducted in person 2-3 weeks before the start of the session using the STAI test, the Human Resilience Test by A.V. Makhnach, CERQ, and the test of Learning Motivation by T.I. Ilyina. As a result of the study, the predominance of students with a high level of situational anxiety (64%) was revealed. It means that the majority of students surveyed experience pronounced discomfort, tension, concern and physiological manifestations of anxiety caused by being in a stressful situation, which is also the first session at the university. The leading motivation for learning among the majority of first-year students was the motivation to gain knowledge (70% of respondents). A comparative analysis using the Mann-Whitney U-test showed that first-year students with a leading motivation for obtaining a diploma or mastering a profession, compared with students motivated by obtaining knowledge, are more characterized by self-confidence, more developed ability to overcome obstacles and demonstrate sufficient effectiveness in the event of difficulties, emotional and behavioral self-control, understanding of one’s own emotional experiences are more developed.
Examining the Influence of Technology Integration in Professional Education Curriculum

This research investigates the profound impact of technology integration within professional education curriculum, focusing on its implications for learning outcomes, technology utilization patterns, and variations across diverse professional fields. Leveraging a quantitative approach with a sample of 100 participants, the study unveils a significant positive correlation (p &lt; 0.001) between the extent of technology usage and academic performance. Those who embraced technology achieved higher average grades and assessment scores, emphasizing the pivotal role of technology in cultivating dynamic and immersive learning environments. Learning Management Systems (LMS) emerged as a linchpin of modern education, enhancing content access and communication. The prevalence of interactive simulations and virtual labs further underscores technology's ability to augment experiential learning. While variations exist across professional fields, the study reaffirms technology's universal potential to benefit learners. Nevertheless, the research highlights challenges concerning digital distractions and the need for enhanced digital literacy support. Ensuring equitable access to technology remains imperative. This study contributes to the discourse on technology integration in education and calls for collaborative efforts among educators, institutions, and policymakers to harness its benefits effectively
An Exploration of Python Libraries in Machine Learning Models for Data Science

Python libraries are used in this chapter to create data science models. Data science is the construction of models that can predict and act on data, which is a subset of machine learning. Data science is an essential component of a number of fields because of the exponential growth of data. Python is a popular programming language for implementing machine learning models. The chapter discusses machine learning's role in data science, Python's role in this field, as well as how Python can be utilized. A breast cancer dataset is used as a data source for building machine learning models using Python libraries. Pandas, numpy, matplotlib, seaborn, scikit-learn, and tensorflow are some Python libraries discussed in this chapter, in addition to data preprocessing methods. A number of machine learning models for breast cancer treatment are discussed using this dataset and Python libraries. A discussion of machine learning's future in data science is provided at the conclusion of the chapter. Python libraries for machine learning are very useful for data scientists and researchers in general.
Face recognition based on machine learning

Due to its widespread use, face recognition has emerged in the past 20 years as one of the most pervasive biometric identification technology disciplines. This paper briefly summarizes the history of face recognitions development, identifies the technologys present use cases, introduces the main methods of face recognition in detail from the perspective of machine learning and prospects for the future development of this technology. The result shows that this technology still faces many challenges, such as the problem of recognizing different expressions on the same face, the problem of recognizing twins and similar faces, the problem of using the color information of color face images efficiently and so on.
Research on communication efficiency and privacy security of federated learning

In recent years, to surmount the challenges of data sensitivity and data silos, federated learning (FL), a collaborative decentralized privacy preservation machine learning framework was proposed and rapidly became a current research hotspot. In this paper, we present an overview of FL workflow, as well as the communication overhead and privacy leakage problems associated with FL. In order to alleviate these two pain points, several corresponding solutions are presented in this paper along with their advantages and disadvantages. To reduce the communication overhead, this paper suggests three reliable approaches by reviewing related research including increasing the amount of computing on the edge side, model compression, and improving parallelism. To address the privacy concerns, this paper introduces three prevailing privacy preservation techniques: differential privacy, homomorphic encryption, and secure multiparty computation (SMC), and shows how to efficiently incorporate these privacy-preserving techniques into a federated learning framework by summarizing relevant literature. On the whole, this paper contributes to explaining the overall workflow of a FL framework and helps advance the understanding of FL in improving communication efficiency and enhancing privacy security.
Machine learning anti-cheating algorithm and a test against computer vision aimbot

In this article, we introduced a recurrent neural network (RNN) serves as a cheating detection method in CS: GO against traditional and AI cheating methods. Cheating methods today are growingly stronger, making traditional anti-cheating methods less and less helpful. The CV AimBot, which does not include any illegal operation but only reading the game footage, has made all traditional anti-cheating methods de facto useless. We developed a CV AimBot from scratch and used it to generate the sample data. We gathered datasets from traditional AimBot and newly invented CV AimBot and run it through an RNN. Our RNN shows high accuracy against both traditional and CV AimBot. It also shows that introducing data from CV AimBot with significantly lower the accuracy of the result from RNN.
BERT for sentiment analysis in the era of epidemic

With the continuous progress of Internet technology, the network platform has gradually entered everyone's life, providing a platform for ordinary people to express their ideas. Since the occurrence of COVID-19, monitoring and analyzing public opinion on the Internet platform has become more practical. Through timely monitoring and analysis, it is of great practical significance for the relevant departments to analyze and control sentiment information and stabilize and guide public sentiment. Therefore, it is essential and of practical significance to select a suitable model for classifying and analyzing public opinion on the Internet platform. This paper reviews the development of word vector technology from the perspective of technology development and then lead to the more advanced Bidirectional Encoder Representations from Transformers (BERT) model with great significance. On this basis, this paper fine-tunes the pre-trained Bert model. It applies the transfer learning strategy to analyzing the public sentiment of the occurrence of COVID-19 during the recent epidemic in Shanghai based on Sina Weibo data. In addition, tests are conducted to compare the model with the previous models. The experimental results show that the Bert model has significant advantages over the traditional model in character vector encoding and feature extraction.
PENERAPAN ALGORITMA APRIORI DALAM MENINGKATKAN PRODUKSI BUDIDAYA PERIKANAN MENGGUNAKAN ASSOCIATION RULE

Aquaculture production in North Sumatra needs to be increased in order to meet the increasing demand for aquaculture in the area from time to time. In order to optimize aquaculture production, recommendations are made for what types of aquaculture are most in demand by the community so that the North Sumatra Marine and Fisheries Service can prepare fishery supplies optimally. Recommendations for the provision of fish are carried out by utilizing data mining technology with an association rule algorithm. Processed data is fish sales history data. The result of this data processing is the combination of fish data that is most in demand by the community with a minimum amount of support of 40% to 3 itemset. Furthermore, the a priori algorithm is implemented at the marine and fisheries service to determine associations. By utilizing the results of this a priori algorithm analysis, the Department of Marine and Fisheries of North Sumatra can find out what types of aquaculture are the priorities for increasing production so that they can meet the needs of the community.
An Application of Fuzzy Techniques to Predict the Polymorphism of Selected Microsatellite Sequences

The complexity of genetic issues is unquestionable and it is therefore advisable to provide new techniques to support the analysis of genetic data. The paper presents an innovative use of data analysis methods based on the use of fuzzy techniques for genetic research. The proposed approach is based on the use of degrees of membership to fuzzy classes describing the degree of heterozygosity. A number of numerical experiments have shown that the proposed method is highly effective. Numerical experiments also checked the impact of the choice of explanatory variables on the effectiveness of forecasting. The proposed application of fuzzy techniques is completely unknown to specialists in the field of genetics.
Text Guided Facial Image Synthesis Using StyleGAN and Variational Autoencoder Trained CLIP

The average user may have little to no artistic skills but can describe what they envision in words. The user-provided text can be instantly transformed into a realistic image with the aid of generative neural architectures. This study intends to propose a novel approach to generate a facial image based on a user-given textual description. Prior works focus less on the manipulation aspects, hence the approach also emphasizes on manipulating and modifying the image generated, based on additional textual descriptions as required to further refine the expected face. It consists of a multi-level Vector-Quantized Variational Auto Encoder (VQVAE) that provides the image encodings, the Contrastive Language-Image Pre-Training (CLIP) module to interpret the texts and compute how close the final image encodings and the text are with each other within a common space, and a StyleGAN2 to decode and generate the required image output. The combination of such components within the architecture is unseen in previous studies and yields promising results, capturing the context of the text and generating realistic good quality images of human faces.
Vision-Based Mobile Robots Control Along a Given Trajectory

The paper presents the application of a computer vision approach to tracking the mobile robot’s state. As an exemplary environment, we use a feedback control system for the trajectory planning and control. The system in the feedback loop use images taken from a centrally placed camera and, based on this, calculates the robots states, i.e. position and angle of rotation. The solution is adopted for indoor experiments. The experimental part shows the application of trajectory planning for multiple robots to cover a given area. The robot state is calculated using the YOLO model. We show that current machine learning techniques are fast and accurate for such applications and do not require image preprocessing or camera calibration.
3D Reconstructions of Brain from MRI Scans Using Neural Radiance Fields

The advent of 3D Magnetic Resonance Imaging (MRI) has revolutionized medical imaging and diagnostic capabilities, allowing for more precise diagnosis, treatment planning, and improved patient outcomes. 3D MRI imaging enables the creation of detailed 3D reconstructions of anatomical structures that can be used for visualization, analysis, and surgical planning. However, these reconstructions often require many scan acquisitions, demanding a long session to use the machine and requiring the patient to remain still, with consequent possible motion artifacts. The development of neural radiance fields (NeRF) technology has shown promising results in generating highly accurate 3D reconstructions of MRI images with less user input. Our approach is based on neural radiance fields to reconstruct 3D projections from 2D slices of MRI scans. We do this by using 3D convolutional neural networks to address challenges posed by variable slice thickness; incorporating multiple MRI modalities to ensure robustness and extracting the shape and volumetric depth of both surface and internal anatomical structures with slice interpolation. This approach provides more comprehensive and robust 3D reconstructions of both surface and internal anatomical structures and has significant potential for clinical applications, allowing medical professionals to better visualize and analyze anatomical structures with less available data, potentially reducing times and motion-related issues.
Semantic Segmentation Neural Network in Automatic Weapon Detection

The goal of this paper is to introduce a semantic segmentation neural network designed for the detection of firearms. The proposed network applies a fully convolutional architecture, incorporating features such as skip connections and batch normalization to enhance its performance. The network was trained using a vast dataset of annotated images and its performance was evaluated using a separate dataset. The results show that the proposed network is highly effective, achieving top-notch results in the detection of firearms. The network’s high accuracy 99.1% and ability to perform pixel-wise classification make it a valuable solution for real-world gun detection applications.
Removing Ambiguity in Natural Language for Generating Self-Join Queries

Databases systems are used almost everywhere in modern life and a prior knowledge of query languages like SQL is required to interact with these systems. Generally, relational databases are very useful for storing a significant amount of the world’s data. But the user must know a query language to access this data efficiently. Nowadays, Natural Language Processing is used in almost every field of human-to-machine interaction. Mapping natural language into the form of structured query Language (SQL) has many benefits. An interface that uses natural language processing allows the user to interact with the relational database as if the communication is happening between human beings. There are different models available for mapping the natural language to SQL addressing various types of SQL queries. But some of the SQL queries like self join where the table needs to join with itself for generating the results, can not be handled by these existing state-of-the-art models as they require some common sense knowledge. And because of these ambiguities in natural language, converting natural language to SQL becomes a complex task. In this paper, a novel approach using word sense disambiguation, for understanding the context of the natural language question addressing self-join queries is being proposed. This proposed approach can be integrated with existing text to SQL models to understand the natural language questions which require common sense knowledge.
An Interpretable Two-Layered Neural Network Structure–Based on Component-Wise Reasoning

The success of deep neural networks have been compromised by their lack of interpretability. On the other hand, most interpretable models do not offer same accuracy as deep neural networks or the former depends on the latter. Inspired by Classification-by-Components networks, in this paper, we present a novel approach into designing a two-layered perceptron network, that offers a level of interpetability. Hence, we use the prediction power of a multi-layer perceptron, while a class of the adapted parameters make fair sense to human. We will visualize the weights, between input layer and the hidden layer, and show that Matching the right objective function with activation function of the output layer, is the key to interpreting the weights and their influence on component-wise classification.
Decentralized Federated Learning Loop with Constrained Trust Mechanism

Federated learning has made it possible to introduce parallel training of deep neural networks by multiple users. The use of model aggregation contributes to the generalization of it, although there is a possibility of attacks. An example of this is dataset poisoning. Hence, in this research paper, we propose the introduction of a constrained trust mechanism for individual clients. In addition, a decentralized approach makes it possible to increase the effectiveness of the training process by removing the server and reducing the risk of an attack on the transmitting model. The proposed modification of federated learning was subjected to performance tests and compared with other known solutions. The obtained results indicate an increase in safety and accuracy.
Bayesian Inference in Infinite Multivariate McDonald’s Beta Mixture Model

This paper presents a nonparametric Bayesian technique to overcome the challenge of selecting the appropriate number of mixture components for clustering. Our approach extends the finite McDonald’s Beta mixture model (FMcDBMM) to an infinite one, allowing us to effectively capture the underlying data distribution with an unknown number of mixture components. Specifically, we use a Dirichlet process prior that enables us to infer the number of mixture components and cluster assignments simultaneously. Additionally, our Bayesian learning approach leverages MCMC techniques for posterior estimation, making our proposed framework powerful and flexible for modelling and analyzing complex data. To demonstrate the effectiveness of our approach, we evaluated the proposed framework on two challenging real-world applications and compared its performance to Gaussian mixture model (GMM) and FMcDBMM. The results show the merits of our model.
Learning Representations by Crystallized Back-Propagating Errors

With larger artificial neural networks (ANN) and deeper neural architectures, common methods for training ANN, such as backpropagation, are key to learning success. Their role becomes particularly important when interpreting and controlling structures that evolve through machine learning. This work aims to extend previous research on backpropagation-based methods by presenting a modified, full-gradient version of the backpropagation learning algorithm that preserves (or rather crystallizes) selected neural weights while leaving other weights adaptable (or rather fluid). In a design-science-oriented manner, a prototype of a feedforward ANN is demonstrated and refined using the new learning method. The results show that the so-called crystallizing backpropagation increases the control possibilities of neural structures and interpretation chances, while learning can be carried out as usual. Since neural hierarchies are established because of the algorithm, ANN compartments start to function in terms of cognitive levels. This study shows the importance of dealing with ANN in hierarchies through backpropagation and brings in learning methods as novel ways of interacting with ANN. Practitioners will benefit from this interactive process because they can restrict neural learning to specific architectural components of ANN and can focus further development on specific areas of higher cognitive levels without the risk of destroying valuable ANN structures.