Prof. Nilufar Abdurakhmonova,
National University of Uzbekistan, Uzbekistan

PARATRANSLATOR-UZBEK PARALLEL CORPUS: APPROACHES, CHALLENGES AND SOLUTION

Currently digital linguistic available data in the internet is major means of implementation to solve problems involving natural language processing. Particularly intellectual text and speech technologies are binding with huge linguistic resources that is required all aspect of understanding human mind. Recent year’s machine translation system developed rapidly due to enhancing capabilities human language infrastructure regarding to corpus technologies. In this case parallel corpora for machine translation technologies are crucial base to mine data to align appropriate matching lexemes and sentence pairs. Obviously English has vast opportunities for all world languages using AI neural system in multilingual machine translation platform and dominated in this sphere for smart technologies. We can say one vivid example is huge contribution to develop translation technology creation Europarl corpus which consists of 30 million words of 11 official languages of the European Union. Hence it can’t be said that the quality of translation is not good enough for scientific and official texts from Uzbek into English. For example Google is one instance for Uzbek the fact that word sense disambiguation and terminological system is considered as a challenge.
Considering above mentioned core issues to enrich parallel corpora for machine translation we obtained scientific-practical project entitled “PRATRANSLATOR: Creation corpus based context logical electronic translation platform” (2024-2025) financed by Ministry of Higher Education, Science and Innovations of the Republic of Uzbekistan. This project focused to build parallel texts Uzbek, English, French, Russian, and Turkish. Our methodological approach is to compile the texts using translation memory and Crawler of Python to segment as the stage of betexts based web corpus and human factors for all styles translated texts into Uzbek or from Uzbek for other foreign languages. It helps to fill up the database of terminological and polisemantic words which translated in literary texts as metaphor and phraseological unites which Google mismatching segments semantically. We hope that this contribution encourages investigations on machine translation technologies and translation studies for lexicography and terminography.

Prof. Dr. Cathal Brugha,
University College Dublin, Ireland

A META-SYSTEM FOR CONFLICT RESOLUTION

The nomology, the science of the laws of the mind is used, providing frameworks based on four embedded layers that explain how people come into conflict. A decision science approach is used to structuring qualitative decisions, to put Drama Theory, and Confrontation and Collaboration Analysis, into a framework based on a succession of dichotomies, whether to use direct or indirect action, combine to form General Political Adjustment Activities, which have corresponding dilemmas: Collaboration (Harmony), Cooperation (Agreement), Confrontation (Persuasion) and Conflict (Escalation). The third dichotomy is based on whether to use a more personal approach generates eight Principal Political Adjustment Activities along with corresponding Dilemmas: Unilateralism (Backlash), Negotiation (Recognition), Credibility (Awareness), Trust (Renege), Inducement (Rejection), Deterrence (Incitement), Positioning (Vulnerabilty), and Threat (Weakness). Of these, Unilateralism and Negotiation are new to Drama Theory. Also, most of the dilemmas are named here for the first time. The person involved in a conflict resolution process should try to draw the conflicted person out ‚anger‘ to ‚avoid more violence‘ , from ‚grief‘ to ‚show respect‘, from ‚fear‘ to ‚improve conditions‘, from ‚shock‘ to ‚include all parties‘, from feeling the sense of ‚atrocity‘ to ‚encourage civil society‘, from ‚retaliation‘ to ‚consult/train/counsel‘, from ‚revenge‘ to ‚bridge-building‘, from ‚bitterness‘ to ‚truth and reconcilation‘.

Prof. Dr. Marat Akhmet,
Middle East Technical University, Turkey

ULTRA POINCARE CHAOS AND SOCIAL SCIENCES

We recently launched an innovative concept in advanced dynamics known as ultra Poincaré chaos. In contrast to conventional conservative methods, this form of chaos is based entirely on the dynamics of a single trajectory. The numerical validation of this behaviour is both reliable and straightforward, making it particularly relevant for effective application in the social sciences. This approach utilizes data gathered from observations and experiments in conjunction with solutions to differential and difference equations. Additionally, we will examine the advantages of this chaotic model in confirming synchronization.
Our discussion will underscore the significance of chaotic models in analysing various domains, including economics, history, politics, and education. We will focus particularly on neural network dynamics, given that brain activity serves as a fundamental basis for all social sciences.
Building on our mathematical findings, we propose new functions designed specifically for modelling time series that have emerged in contemporary research. During our presentation, we will showcase examples of these applications, illustrating how the study of social dynamics—one of the most complex areas—can effectively utilize our insights. Finally, we will present persuasive arguments underscoring the significance of studying chaos and the presence of thresholds in the history of dynamical systems, particularly in relation to complexity. We will contrast what we refer to as “chaotic” modelling with “industrial” modelling. The relevance of chaotic models for the social sciences is driven by the inherent complexity of the dynamics involved.

Prof. Madina Mansurova,
Al-Farabi Kazakh National University, Kazakhstan

THE ROLE OF ARTIFICIAL INTELLIGENCE AND VISUAL ANALYTICS IN DIGITAL SOCIETY

In the contemporary digital society, artificial intelligence (AI) and visual analytics play essential roles in transforming extensive datasets into actionable insights and enhancing decision-making processes. AI‘s advanced algorithms automate the analysis of complex data, identifying patterns and making predictions that are crucial for managing the enormous volumes of data generated by modern technologies. This capability enables organizations to handle and interpret data more effectively than traditional methods allow.
Visual analytics complements AI by providing interactive and intuitive visual representations of data, which facilitate easier understanding and exploration. By translating raw data into visual formats such as graphs and heat maps, visual analytics helps users quickly grasp complex information and identify trends and anomalies that may not be evident through numerical data alone. This approach enhances the interpretative process, making it more accessible and actionable.
The integration of AI and visual analytics results in a powerful synergy that enhances both data processing and visualization. This combined approach is beneficial across various sectors, including healthcare, urban planning, and business. It supports improved decision-making, fosters innovation, and addresses the multifaceted challenges of the digital era by offering deeper insights and more effective solutions.

Dr. Akylbek Zhamanshin,
Herriot-Watt University Aktobe Campus, Kazakhstan

UNPREDICTABILITY IN COHEN-GROSSBERG NEURAL NETWORKS

Cohen–Grossberg neural networks were firstly proposed by Cohen M. and Grossberg S. in 1983 [1]. The class of models has intensive applications within various engineering and scientific fields such as neuro-biology, population dynamics and computing technology. Moreover, Cohen–Grossberg neural networks include as sub-classes cellular and Hopfield neural networks. This is why, many researchers are focused on investigating the dynamics of Cohen–Grossberg neural networks, in particular, periodic and almost periodic oscillations. In turn, a few years ago, Professor Akhmet M. and his colleague extended the concept of recurrent motions to unpredictable ones [2]. The newly introduced movements have chaotic dynamics. In our study, we provide theoretical as well as numerical results for recurrent oscillations in Cohen-Grossberg neural networks with variable inputs and strengths of connectivity for cells, which are unpredictable functions. A special case, when the coefficients are compartmental with periodic and unpredictable ingredients is also carefully researched. By numerical and graphical analysis, it is shown how a constructive technical characteristic, the degree of periodicity, reflects contributions of the ingredients in final outputs of the neural networks. Sufficient conditions are obtained to guarantee the existence of exponentially stable unpredictable outputs of the models. They are specified for Poisson stability by utilizing the original method of included intervals. Examples with numerical simulations that support the theoretical results are provided.

References
1. M. Cohen, S. Grossberg, Absolute stability and global pattern formation and parallel memory storage by competitive neural networks, IEEE Trans Syst Man Cybernet 13 (1983) 815–826.
2. M. Akhmet, M. Fen, Poincare chaos and unpredictable functions, Communications in Nonlinear Science and Numerical Simulation 48 (2017), 85–94.

Dr. Zakhira Nugayeva,
Herriot-Watt University Aktobe Campus, Kazakhstan

THEORETICAL INVESTIGATION OF IMPULSIVE HOPFIELD NEURAL NETWORKS DYNAMICS IN SOCIAL SCIENCE RESEARCH

The advent of artificial intelligence and machine learning has brought transformative changes across various fields of study. Neural networks, a critical component of this technological revolution, have been at the forefront of these developments. The networks, inspired by the human brain's architecture, have demonstrated remarkable capabilities in tasks ranging from image recognition and natural language processing to predictive analytics.
While neural networks are widely recognized for their applications in processing and interpreting large datasets, their potential for advancing theoretical and applied research in the social sciences is still underexplored. Social scientists are increasingly faced with complex systems that are difficult to model using traditional statistical methods. This is particularly true in areas where the dynamics of human behavior, social interactions, and societal changes involve non-linear relationships, feedback loops, and unpredictable influences [1]. As such, there is a growing need to explore how advanced neural network models, particularly those that accommodate more inmtricate and less predictable dynamics, can be integrated into social science research.
This research aims to contribute to this field by studying the dynamics of Hopfield neural networks with impulsive effects [2], focusing on Poisson stable rates, synaptic connections, and unpredictable external inputs. The specific focus of this research is on the dynamic behaviors of Hopfield neural networks when subject to impulsive effects—sudden, discontinuous changes that can significantly alter the state of the network. The impulsive effects can be thought of as analogous to sudden shocks or disruptions in social systems, such as economic crises, social upheavals. By examining the effects of these impulses on the stability and behavior of the network, this study seeks to extend the theoretical understanding of how such networks can model complex social phenomena.

References
1. Garson, G.D. Neural Networks: An Introductory Guide for Social Scientists. Sage Publications. London 1998.
2. Hopfield, J.J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 1982, 79, 2554–2558.