Human Thinking Process Research Articles

Good and Evil Ethical Concepts in the Linguistic Picture of the World of Kyrgyz

This article analyses actual problems of cognitive linguistics, intensively developing linguistic direction since the 80s of the XX century. The study concisely conducted a scientific and theoretical analysis of cognitivism, the object of the study of which becomes the human mind, thinking and mental processes. The formation of a concept as a basic concept of cognitive linguistics is connected with cognitive activity, world perception of a person. The concept as a result of national linguo culture reflects national, spiritual and cultural values in the linguistic picture of the world of any ethnic group. It is noted that language is a manifestation of cognitive processes, “activity of the mind” is studied on the material of language i.e. language means. The article is devoted to the analysis of ethical concepts “good” and “evil” in the language consciousness of the Kyrgyz people. It is noted that the key tokens are good and evil. The study is conducted on the material of analysis of lexical meanings of linguistic units with morphemes: zhakshy, zhaman, Kyrgyz proverb, a passage from the Epic of Manas, consisting of opposition “good” and “evil”. The article briefly describes the essence of the philosophy of good and evil of the Kyrgyz people, which reflects humanistic ideas, moral and ethical, national values, ethnic peculiarities of mentality of the Kyrgyz people. In them, good stands for love, good, success, faith, hope, honor and dignity, a sense of duty, and evil is manifested in destruction, greedy profligacy, slyness, anger, etc. The study notes that the conceptual part of the structure of concepts “good” and “evil” is “multiple and complex, as they use figurative expressions, which include purely personal perception, linguistic mentality and means of artistic expression. It has been determined that the moral and ethical concepts of “good” and “evil” are a neglected topic in Kyrgyz linguistics. They have a special place in the linguistic consciousness of the Kyrgyz people and require further in-depth study.

CHALLENGES OF USING ARTIFICIAL INTELLIGENCE AT THE UNIVERSITY LEVEL

The capacity of robots to mimic human thinking processes, especially in huge data systems for certain kinds of tasks, is known as artificial intelligence. The purpose of the study was to identify the barriers to artificial intelligence adoption at the university level. Research was quantitative in nature and a survey method was used to collect data. The population of this research is limited to all students enrolled in two public and two private universities located in the Lahore district. The 487 students were selected using a convenient sampling technique from two public and two private Universities in Lahore. Data was gathered by using a self-developed instrument. The data were analyzed through SPSS (Statistical Packages for Social Sciences). inferential statistics were used to calculate the data. Regarding the challenges posed by AI in higher education, male and female students' opinions did not significantly vary from one another. Universities should employ artificial intelligence, with explicit ethical principles for AI development that cover bias mitigation, student data protection, and responsible development. Keywords: Artificial Intelligence, Higher Education, Machine Learning

Critical thinking: An analytical study on the impact of Artificial Intelligence (AI) usage on students at Al Washliyah Darussalam University, Banda Aceh

Artificial Intelligence (AI) is a technology that can mimic human thinking and learning processes through algorithms and computer systems. AI brings significant changes to the education sector by providing various conveniences. On the other hand, the use of AI technology requires adjustments in the learning process at higher education institutions. This research aims to analyze the impact of Artificial Intelligence (AI) usage on the critical thinking skills of students at Al Washliyah Darussalam University (UNADA) Banda Aceh. This study employs a Mixed Methods approach, utilizing quantitative methods with a descriptive approach to analyze the frequency of AI usage, while qualitative methods are used to analyze changes in thinking patterns and students' analytical abilities in the learning process. Data collection was conducted through surveys. The sampling technique used was purposive sampling, involving 287 students who use AI in their learning. The research findings indicate that the distribution of questionnaire data shows that the usage of Artificial Intelligence (AI) includes 31.3% (ChatGPT), 6.3% (Perplexity), 12.5% (Gemini), and 5% using other AI technologies. The impact of AI usage reveals that AI can enhance students' critical thinking skills, particularly in data analysis, problem-solving, and decision-making. However, negative impacts faced by students include dependency on technology and a lack of active engagement in classroom discussions. The results and findings of this study highlight the importance for university lecturers and policymakers to design effective learning strategies that utilize AI to develop critical thinking skills among students.

Chain of Thought Utilization in Large Language Models and Application in Nephrology.

Chain-of-thought prompting enhances the abilities of large language models (LLMs) significantly. It not only makes these models more specific and context-aware but also impacts the wider field of artificial intelligence (AI). This approach broadens the usability of AI, increases its efficiency, and aligns it more closely with human thinking and decision-making processes. As we improve this method, it is set to become a key element in the future of AI, adding more purpose, precision, and ethical consideration to these technologies. In medicine, the chain-of-thought prompting is especially beneficial. Its capacity to handle complex information, its logical and sequential reasoning, and its suitability for ethically and context-sensitive situations make it an invaluable tool for healthcare professionals. Its role in enhancing medical care and research is expected to grow as we further develop and use this technique. Chain-of-thought prompting bridges the gap between AI's traditionally obscure decision-making process and the clear, accountable standards required in healthcare. It does this by emulating a reasoning style familiar to medical professionals, fitting well into their existing practices and ethical codes. While solving AI transparency is a complex challenge, the chain-of-thought approach is a significant step toward making AI more comprehensible and trustworthy in medicine. This review focuses on understanding the workings of LLMs, particularly how chain-of-thought prompting can be adapted for nephrology's unique requirements. It also aims to thoroughly examine the ethical aspects, clarity, and future possibilities, offering an in-depth view of the exciting convergence of these areas.

DEVELOPMENT OF A SYSTEM FOR DIAGNOSING PESTS AND DISEASES IN CORN PLANTS BASED ON THE INTENSITY OF DAMAGE USING THE CERTAINTY FACTOR METHOD

Corn plants have the potential to be attacked by pests or diseases that can appear at any time such as leaf blight, leaf rust and downy mildew. Obstacles facing farmers are sometimes the lack of information about the symptoms found so that sometimes the diagnosis is not correct and the treatment is wrong, resulting in reduced corn production. Along with technological advances, a technology has also been produced that can adopt the process of human thinking, namely artificial intelligence technology or AI or Artificial Intelligence. including an expert system, which is a computer-based system that adopts the knowledge and experience of an expert, and reasoning techniques to solve a problem, similar to how an expert in a particular field solves the problem. The development that will be made is "Development of a Diagnostic System for Pests and Diseases in Corn Plants Based on Damage Intensity Using the Certainty Factor Method".

Bioethics as a guarantor of the anthropocentricity of modern scientific and technological development

The relevance of the article is due to the acuteness of the public and scientific debate on the ethics of human cloning experiments: both from the point of view of violating the boundaries of human individuality, and from the standpoint of biological safety. Bioethics in this context becomes a reference point for any scientific research related to the human body, which is especially important in the modern «age of technological dehumanization». The article examines the disciplinary, philosophical, medical, anthropological aspects of bioethics in Russia and abroad, as well as the specifics of the legislative regulation of cloning issues. As a result of the analysis, the conclusion is postulated that ethical norms find themselves in a situation of opposition to the principles of bioeconomics, which implies the reproduction «to order» of a person with specified characteristics. A parallel is drawn with artificial intelligence, which also, in fact, imitates and reproduces individual processes of human thinking. Nevertheless, bioethics should not create barriers to the development of scientific knowledge and the improvement of medical technologies.

APPLICATION OF ONTOLOGICAL ENGINEERING METHODS IN OPTIMIZATION OF COMPLEX ML MODELS AND AI APPLICATIONS

In today's world, artificial intelligence (AI) and machine learning (ML) play a key role in many areas of human activity. However, the complexity and insufficient definition of the relevant models can create problems of their efficiency, interpretability and management when implementing technologies based on AI and ML. That is why it is important to use ontological engineering methods to improve these models. Ontological engineering is an approach that makes it possible to represent knowledge and dependencies between them in special information structures, so-called ontologies. Ontologies define concepts, relationships, and rules that describe a domain or problem area. At the same time, these information structures, in comparison with others, have extended functionality, which brings the manipulation of ontologies closer to the human thinking process.

Perspektif Filsafat Ilmu dalam Islamisasi Ilmu Pengetahuan Dikalangan Ilmuan Muslim

Very important in humans. Science is a way of thinking of humans and is used to separate Science helps humans to orient themselves in the world and solve various life problems. Problems that make them do not know how to develop themselves. Therefore, knowledge is humans from other creatures. process of human thinking and is used as a barrier between humans and other living things. In the beginning, everything that humans did was because human nature in their life has a noble purpose in life that is higher than mere existence. That's why humans always try to develop science. The philosophy of science can be used as a perspective in the idea of Islamization of science because at this time what has developed lately is one branch of philosophy that aims to realize the relationship between science and philosophy. By making the Philosophy of Science as a perspective, benchmark, or establishment in order to analyze and develop the framework of Islamic science, using the approaches and methods that exist in philosophy. Only used for the Islamization of knowledge.

Narrative as a Way of Understanding the World: Goodman's Epistemological View

브루너의 내러티브 이론은 내러티브를 생성하는 우리의 사고 과정이 어떠한 것인지를 탐구한 이론이다. 주로 이야기의 형태로 제시되는 내러티브는, ‘내러티브 사고 양식’이라는 우리의 특징적인 사고 작용의 결과물이다. 브루너는 이를 ‘패러다임 사고 양식’이라는 다른 방식의 사고 작용과 비교하여 설명하고 있다. ‘패러다임 사고 양식’은 기존의 심리학계에서 주로 연구되던 사고의 작용을 이르는 것으로, 논리적이고 과학적인 방식을 따라 전개되는 사고이다. 브루너는 이러한 사고 양식이 과학이나 수학의 이론과 같은 형태의 결과물을 생성하는 우리 정신의 작용을 설명하는 데에는 유효하지만, 문학의 분야를 설명하는 데에는 적절하지 못하다는 결론을 내린다. 그리고 이를 설명할 수 있는 개념을 굿맨의 인식론에서 발견하였다. 굿맨은 우리가 기호를 통해 세계를 알게된다고 본다. 기호는 우리가 어떤 것을 인식할 때 지각하는 형태·색조·이름 등의 특정한 요소들을 인지적으로 받아들이는 형식이다. 굿맨은 여러 학문 분야들이 기호화의 방식에서는 차이가 있으나, 그것이 기호화된 체계라는 점에서는 모두 동일하다고 말한다. 그리고 예술 분야의 대표적인 기호화의 방식으로 ‘예시’를 제시한다. ‘예시’는 대상이 가지는 어떤 속성의 견본을 하나의 예로서 보여주는 것으로, 여러 예시 중 하나를 제시한 것이기 때문에 다른 것과 비교하여 옳고 그름을 판단할 수는 없으며, 그것이 예로서 얼마나 적합한가만을 말할 수 있다. 브루너는 이러한 방식으로 작동하는 사고를 ‘내러티브 사고 양식’으로 이르고 있다. 그가 설명하는 ‘내러티브 사고 양식’의 특징들은 이러한 관점에서 일관되게 해석할 수 있다. 이는 또한 세계를 이해하는 방식으로서 문학과 예술이 가지는 의미를 이해할 수 있게 한다.J. S. Bruner's theory about narrative is an exploration of human thinking process that create narratives. Narrative, which is mainly produced in the form of stories, is the result of characteristic thinking process by humankind. Bruner called it “narrative mode of thought”. Bruner compares it to another way of thinking called “paradigmatic mode of thought”. The “paradigmatic mode of thought” indicates the kind of thinking that has traditionally been studied in psychology. It is a way of thinking that is logical and scientific. Bruner recognized that this mode of thought is valid for explaining the process of mind to produce theories in science and math, but is invalid for explaining the process of mind to create literature. He found an conception for narrative mode of thought in N. Goodman's epistemology. In Goodman's view, we come to know the world by symbols. When we perceive something, we identify it with certain elements, such as shape, color, name, and so on. These shapes, colors, names are the symbol which our cognition can take information about the world. Goodman proposes that although each field of studies is different in the way of symbolizing, they are all the same in that they are symbolized systems. He suggested “exemplification” as a typical form of symbolization in the arts. Exemplification is a sample of a property that is shown as an example. Because it is presented as one of many examples, it cannot be judged right or wrong in comparison to the others. We can say only how suitable it is as an example. Bruner refers to a process of thinking that works in this way as “narrative mode of thought”. The features of narrative mode of thought he describes can be explained with this perspective. Also it presents the meaning of literature and art as a way of understanding about the world.

Аналіз методів створення нових технічних систем

The creation of new technical systems takes place on the basis of design methods that take into account the psychological processes of human thinking (the main actions of division, union, comparison and permutation can be considered among them. Other actions are their derivatives), as well as the laws of the development of technical systems. The definition of these processes is given. The proposed classification of design (invention) methods, which may change depending on the selected criterion. According to the sign of generality, methods of invention can be divided into general, general and partial methods of invention. Universal methods of invention relate to the widest possible range of issues and are included among the strategic means of solving inventive tasks. General methods of invention are used to solve a wide range of inventive tasks in various fields of technology. Such methods include the methods of heuristic analogy, heuristic combination, heuristic inversion, etc. Partial methods of invention include methods designed to solve special inventive tasks or tasks in a defined, as a rule, narrow field of technology. They include, for example, the method of converting reciprocating motion into rotary motion, the remote hybridization method, the compounding method, etc. The process of creating new technical systems is generally divided into three phases (stages), which are called differently: 1) divergence, analysis, collection, accumulation of knowledge; 2) transformation, synthesis, maturation, incubation - ends with enlightenment; 3) convergence, assessment, control, verification. The stages of design are described from the point of view of the formation of the necessary direction of improvement of technical systems and the further formation of a rational structure using the mechanism of joint disjunction.

THE COGNITIVE ESSENCE OF THE METAPHOR

THE COGNITIVE ESSENCE OF THE METAPHOR

Toward an automatic detection of cardiac structures in short and long axis views

ObjectiveThis work aims to create an automatic detection process of cardiac structures in both short-axis and long-axis views. A workflow inspired by human thinking process, for better explainability. Methodswe began by separating the images into two classes: long axis and short axis, using a Residual Network model. Then, we used Particle Swarm Optimization for general segmentation. After segmentation, a characterization step based on shape descriptors calculated from bounding box and ANOVA for features selection were applied on the binary images to detect the location of each region of interest: lung, left and right ventricle in the short-axis view, the aorta, the left heart (left atrium and ventricle), and the right heart (right atrium and ventricle) in the long axis view. Resultswe achieved a 90% accuracy on view separation. We have selected: Elongation, Compactness, Circularity, Type Factor, for short axis identification; and:Area, Centre of Mass Y, Moment of Inertia XY, Moment of Inertia YY, for long axis identification. Conclusiona successful separation of long axis and short axis views allows for a better characterization and detection of segmented cardiac structures. After that, any method can be applied for segmentation, attribute selection, and classification. Significancean attempt to introduce explainability into cardiac image segmentation, we tried to mimic the human workflow while computerizing each step. The process seems to be valid and added clarity and interpretability to the detection.

KAT: A Knowledge Adversarial Training Method for Zero-Order Takagi-Sugeno-Kang Fuzzy Classifiers.

While input or output-perturbation-based adversarial training techniques have been exploited to enhance the generalization capability of a variety of nonfuzzy and fuzzy classifiers by means of dynamic regularization, their performance may perhaps be very sensitive to some inappropriate adversarial samples. In order to avoid this weakness and simultaneously ensure enhanced generalization capability, this work attempts to explore a novel knowledge adversarial attack model for the zero-order Tagaki-Sugeno-Kang (TSK) fuzzy classifiers. The proposed model is motivated by exploiting the existence of special knowledge adversarial attacks from the perspective of the human-like thinking process when training an interpretable zero-order TSK fuzzy classifier. Without any direct use of adversarial samples, which is different from input or output perturbation-based adversarial attacks, the proposed model considers adversarial perturbations of interpretable zero-order fuzzy rules in a knowledge-oblivion and/or knowledge-bias or their ensemble to mimic the robust use of knowledge in the human thinking process. Through dynamic regularization, the proposed model is theoretically justified for its strong generalization capability. Accordingly, a novel knowledge adversarial training method called KAT is devised to achieve promising generalization performance, interpretability, and fast training for zero-order TSK fuzzy classifiers. The effectiveness of KAT is manifested by the experimental results on 15 benchmarking UCI and KEEL datasets.

Towards Fine-Grained Reasoning for Fake News Detection

The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues. In this paper, we move towards fine-grained reasoning for fake news detection by better reflecting the logical processes of human thinking and enabling the modeling of subtle clues. In particular, we propose a fine-grained reasoning framework by following the human’s information-processing model, introduce a mutual-reinforcement-based method for incorporating human knowledge about which evidence is more important, and design a prior-aware bi-channel kernel graph network to model subtle differences between pieces of evidence. Extensive experiments show that our model outperforms the state-of-the-art methods and demonstrate the explainability of our approach.

NARRATIVE ESSENCE OF LANGUAGE CONTENT IN COMPOSERS’ EVALUATION OF THEIR CREATIVITY

The paper investigates the narratives of contemporary Ukrainian composers, in the context of their assessment of their own work and the actual musicological components of each work: its focus, conglomeration of musical expressive means, and their multidimensional and multifunctional context. The paper focuses on the problem of inconsistency of the author’s linguistic content in expressing his own ideas about his work with the actual work in terms of the logic of musicology, psychology, etc. A new narrative approach to the interpretation of musical expressive means has formed in modern compositional work. They are viewed as the combinations of different levels of perception: visual, sensory, linguistic, associative-ambiguous, abstract, etc. Thus, wherever there is an information and communicative environment, there function the complex cognitive formations allowing to reflect and evaluate the imagery of the musical work. Hence, the “conceptual sphere” of each work emerges that consists of the relevant ideas, concepts, images, and statements. The research methodology is based on the analysis of the sphere of expression of human thinking and communicative processes by R. Barthes, C. Bremond, F. Jameson, T. Titarenko, T. Todorov, E. Tshebinsky, and others. Also, the studies by H.-G. Gadamer, M. Heidegger, P. Ricoeur, R. Harré consider the narrative as a discursive structure formed on the basis of personal experience. In the field of musical art, the problem of narratology was addressed in the works of N. Gerasimova-Persidska, O. Zinkevych, Y. Chekan, and some other musicologists who expressed the essence of modern compositional thinking, in line with a new sense of time and space, discreteness and dialogic historical context of culture. To establish the artistic conceptuality of certain musical pieces, the comparative analysis of subjective and authorial understanding of composers of their own works in the musicological, cultural, and socio-political context was made. As the works have a significant number of allusions to the stylistics of different eras, the conflicting emotional reaction of the composers gives grounds to assert the idea of expressing a narrative of existential torment of a torn postmodern mind in the inter-genre web in their works. The paper concludes that assimilation and awareness of meaning are possible only through certain messages that can have many meanings, and complex decoding of them may take any form. Perception of oneself in the social environment occurs through the formation of narratives that express the author’s position. However, this stance voiced in interviews and other language content is not always consistent with the actual music series. Thus, an interpretive understanding of musical creativity emerges at the junction of the narrator’s and the listener’s meanings.

Cognitive prediction of obstacle's movement for reinforcement learning pedestrian interacting model

Abstract Recent studies in pedestrian simulation have been able to construct a highly realistic navigation behaviour in many circumstances. However, when replicating the close interactions between pedestrians, the replicated behaviour is often unnatural and lacks human likeness. One of the possible reasons is that the current models often ignore the cognitive factors in the human thinking process. Another reason is that many models try to approach the problem by optimising certain objectives. On the other hand, in real life, humans do not always take the most optimised decisions, particularly when interacting with other people. To improve the navigation behaviour in this circumstance, we proposed a pedestrian interacting model using reinforcement learning. Additionally, a novel cognitive prediction model, inspired by the predictive system of human cognition, is also incorporated. This helps the pedestrian agent in our model to learn to interact and predict the movement in a similar practice as humans. In our experimental results, when compared to other models, the path taken by our model’s agent is not the most optimised in certain aspects like path lengths, time taken and collisions. However, our model is able to demonstrate a more natural and human-like navigation behaviour, particularly in complex interaction settings.

A New Syllogism Closer to the Reality of Human Thinking -- On Lei Ma’s Substitution Logic

The follow-up research of Aristotle’s syllogism has different approaches. The traditional syllogism follows Aristotle’s conceptual system and hopes to make improvements within Aristotle’s theory. Mathematical logic proposes a new conceptual system to accurately interpret Aristotle’s syllogism. Lei Ma puts forward an extended syllogism whose conceptual system is different from Aristotelian logic and mathematical logic. He thinks that Aristotle’s syllogism and traditional syllogism have tedious figures, moods, and reasoning rules, which are difficult for us to memorize. It is a theoretical conclusion of the human reasoning process but does not conform to the actual human thinking process. Ma’s syllogism is called substitution logic, which mainly concerns the substitution characteristics of a human thinking process, and summarizes the substitution rules in the reasoning process. Substitution logic appropriately describes the actual human reasoning process, thus inspiring us to establish a unified scientific theory of thinking and carry out normative research on the unity of abstract thinking and imaginative thinking. Substitution logic may be applied to the field of artificial intelligence, making artificial intelligence closer to the reality of human thinking. It seems that the research direction of substitution logic will give birth to human-like AI systems and promote the revolutionary transformation of AI research.

Applications of Robotic Process Automation in Smart Governance to Empower COVID-19 Prevention

This paper explores how the robotic process automation (RPA) can benefit financial applications. To fully exploit RPA technologies potential will empower higher education and finance, which makes a better future together. The mechanism of RPA to mimic the process of human thinking in solving financial problems was discussed. Important technologies, challenges from cooperativeness, responsiveness and interconnectedness were explored. Exploration of automation technologies for COVID-19 prevention will reduce virus transmission, which empowers society governance, higher education and finance.

Research and Practice on the Integration and Development of Artificial Intelligence and Medical Image

The innovative development of medicine is synchronized with the innovative application of technology. Also, artificial intelligence technology brings new breakthroughs for medical research in the era of artificial intelligence. In particular, artificial intelligence technology has achieved conspicuous application expectations in medical image, realized the integration of intelligent technology and medical image research, and provided assistance for tumor diagnosis and cardiovascular disease diagnosis. As a computer technology operating in human thinking mode, artificial intelligence technology realized the imitation of human brain thinking process and overcame the subjective bias of human brain thinking, which is of great application value. The integration of artificial intelligence technology and medical image is an inevitable trend and the dawn of medical progress. In the new era, it is valuable to increase the research on the integration of artificial intelligence technology and medicine in order to realize the new development of medicine supported by artificial intelligence technology. In this context, this work mainly discussed the integration and development of artificial intelligence technology and medical image and analyzed the application of artificial intelligence technology in medical image on the basis of clarifying the current situation of medical application of artificial intelligence technology.

DUMA: Reading Comprehension With Transposition Thinking

Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question. Thus in addition to a powerful Pre-trained Language Model (PrLM) as encoder, multi-choice MRC especially relies on a matching network design which is supposed to effectively capture the relationships among the triplet of passage, question and answers. While the newer and more powerful PrLMs have shown their mightiness even without the support from a matching network, we propose a new DUal Multi-head Co-Attention (DUMA) model, which is inspired by human's transposition thinking process solving the multi-choice MRC problem: respectively considering each other's focus from the standpoint of passage and question. The proposed DUMA has been shown effective and is capable of generally promoting PrLMs. Our proposed method is evaluated on two benchmark multi-choice MRC tasks, DREAM and RACE, showing that in terms of powerful PrLMs, DUMA can still boost the model to reach new state-of-the-art performance.