Human Understanding Research Articles

MENUJU TRANSFORMASI: PENDIDIKAN PASTORAL KONSELING SEBAGAI JEMBATAN KESEJAHTERAAN JIWA

In today's rapidly changing world, mental well-being is a critical aspect of human life. The complexities of modern living often lead to stress, anxiety, and disillusionment, compromising mental health. As holistic beings comprising body, soul, and spirit, achieving mental well-being cannot be separated from spiritual dimensions. This study explores the role of pastoral counseling education as a bridge to mental well-being, particularly within the Christian faith. By integrating spiritual and psychological perspectives, this research aims to provide a comprehensive understanding of mental well-being, addressing the intricate relationships between interpersonal relationships, work, and life's meaning. Ultimately, this study seeks to contribute to the development of a transformative pastoral counseling approach that fosters mental well-being, promoting a deeper sense of peace and wholeness that transcends human understanding

Swarm Intelligence in Multiplexed Robots Problems Identified

Although kindling with the very consciousness of the human mind may have been banned, this did not stop scientists from tinkering and innovating with all the technology they were surrounded by. Stumbled by mathematical problems, mathematicians developed certain tactics we call algorithms. Algorithms are the layman's logic language for any device to function according to the desired task.Well, Mother Nature has already built us an unimaginable and incomprehensible algorithm propelled by personal choices, emotions, feelings, and sometimes rationality. For people who still didn't get it, we call it brain in day-to-day language. Our mind subconsciously designs such algorithms for us to efficiently function in our every day to day lives. Our project is an effort to reconstruct such rationality of thinking by artificial means through the use of reinforcement learning and robots to conclude the following 1) Is it possible to recreate human understanding? 2) If yes, then how do we channel it into something practical and physical? 3) How do we utilize this so-formed machine as a result of the conclusion of the second question? Asking this to ourselves we pursued to make our project P3 Hexa which is an endeavor to tinker with swarm intelligence and physically experience the functioning of a self-designed algorithm

Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement Learning

Continuously learning new skills is essential for talents to gain a competitive advantage in the labor market. Despite extensive efforts on relevance- or preference-based skill recommendations, little attention has been given to the practical effects of job skills in the market. To bridge this gap, we propose an explainable personalized skill learning recommendation system that considers the long-term learning benefits and costs. Specifically, we model skill learning utilities based on salary and learning cost associated with job positions and propose a multi-objective deep reinforcement learning framework to model and maximize long-term utilities. Furthermore, we propose a Self-explaining Skill Recommendation Deep Q-network (SeSRDQN) that captures and prototypes prevalent skill sets in the market into representative exemplars for decision-making. SeSRDQN quantitatively decomposes the talent’s long-term learning utility into contributions from each exemplar, offering a comprehensive and multi-factorial explanation across various skill learning options. To tackle the combinatorial complexity of the skill space, we develop an MCTS-based optimization-decoding iterative training procedure for explanation fidelity and human understandability. In this way, talents will receive a tailored roadmap of essential skills, complemented by exemplar-based explanations, to effectively plan their careers. Extensive experiments on a real-world dataset validate the effectiveness and explainability of our approach.

Some (Wittgensteinian) Remarks on the Ethics of Artificial Intelligence

I argue in favor of a distinction between human understanding and machine “understanding”. Based on Wittgenstein’s view on machines and his considerations on understanding, I aim to demonstrate that no machine with artificial intelligence can reach functional equality with human beings. In particular, this also holds for ethical praxis because it consists of an extremely blurred net of language– games, guided by ethical rules. Therefore, a machine can never have the human ability (disposition) to act ethically and cannot be a moral agent.

İbn Tufeyl’de Din Dili ve İdrak Mertebeleri

Throughout human history, the concept of religion has been of great importance among all societies and each community has lived within its own set of beliefs. The concepts of God-Universe-Human and their relations with each other have been among the subjects of both religion and philosophy. For this reason, religion and philosophy have sometimes been brought into conflict with each other, and sometimes it has been stated that these two sciences are in harmony with each other and that they support each other. In our study, Ibn Tufail’s Hayy bin Yaqzān is taken as the center and the language of religion and the levels of understanding used here are addressed. In addition, the relationship between religion and philosophy has been evaluated in the historical process and the possibility of a new language of religion has been the main subject of this paper. In this research, we discussed the possibility of a new religious language in the light of Hayy bin Yaqzān and emphasized the importance of it. Because we think that the language of conflict should be abandoned as a prerequisite for the harmony and unity between religion and philosophy, and a new reconciliatory religious language is necessary. For this reason, apart from the studies in the literature, we looked at the relationship between philosophy and religion from the perspective of Ibn Tufail and Hayy bin Yaqzān and grounded in a new language of religion by taking into account the levels of understanding. By this way, we aimed to make a small contribution to both the field of Islamic philosophy and other related fields. In this paper, we emphasize why the unity of philosophy and religion is important and we have tried to reveal the negative and dangerous aspects of using a confrontational language of religion. As a solution, the concepts of ’language of religion’ and different human understanding used by our philosopher in his work are emphasized and it is stated that a new language of religion is important in terms of ensuring the harmony between philosophy and religion. In this study, first of all, Ibn Tufail’s Hayy bin Yaqzān is addressed in the context of the language of religion. Then, the basic sources, articles, doctoral and master’s theses published on the subject were reviewed. All these collected data were addressed and evaluated using qualitative research methods and technics. As a result; in the historical process, there have been those who talk about the compatibility of religion and philosophy as well as those who argue that both sciences can’t be side by side. Each person has a different level of understanding. For this reason, the development of a new language of religion will make a great contribution to the field of religion and philosophy, and will enable us to better address the world of meaning of every human being.

Introduction to the Special Issue on Deep Learning for Robust Human Body Language Understanding

This editorial introduces the Special Issue on Deep Learning for Robust Human Body Language Understanding , hosted by the ACM Transactions on Multimedia Computing, Communications, and Applications in 2024. Human body language understanding has emerged as a critical research area, addressing challenges in analyzing, recognizing, and synthesizing multimodal human behavioral data such as gestures, poses, facial expressions . This Special Issue highlights recent advancements in deep learning techniques that enhance the robustness, scalability, and applicability of human body language understanding in diverse scenarios, including healthcare, education, and barrier-free human-computer interaction systems. The issue features a total of eight research articles focusing on key aspects of human body language understanding. These contributions are categorized into major research areas: gesture and sign language understanding , pose and action recognition , and facial expression and emotion analysis . Each article provides novel insights into challenges such as data scarcity, multimodal integration, adversarial robustness, and cross-modal generative modeling . We summarize the main contributions of the included works and emphasize their role in advancing the field of human body language understanding. Finally, we discuss ongoing challenges and future opportunities in this rapidly evolving domain, particularly in the context of integrating human-centric AI systems into real-world applications.

Artificial Intelligence as a Metaphysical Event

The paper focuses on the questions of whether, to what extent, and in what ways the implications of the rapid development of artificial intelligence are changing the nature of one of the fundamental philosophical questions, “What does it (even) mean to understand?” It draws on two sources in particular: Hinton’s explanation of the technological development and functioning of deep neural networks and Nietzsche’s deconstruction of human understanding based on his key concept of “embodied errors.” In doing so, it reveals a series of unexpected parallels, relating in particular to the notion of micro- evolution and the function of error in the processes underlying “thinking” and “intelligence.” The paper therefore draws certain parallels and demarcation lines between human understanding and the “learning” procedures of digital neural networks. At the same time, it addresses the question of what it means for the interpretation of human understanding that, for the first time in history, understanding is faced with a real, existing antithesis, represented by intelligent systems which, although they do not understand, are capable of performing the tasks of understanding, and capable of replacing understanding.

Employing Some Statistical Models in Facial Recognition

Face recognition is a computer vision approach that analyzes facial features to identify or verify a person's identity. it may be required to combine different biometric identification methods to improve security and recognition accuracy. To identify an individual, facial recognition systems simulate human face understanding and recognition abilities management, there is difficulty in detecting data when using various patterns of facial image capture, especially when there is a large overlap between the patterns, it takes time to determine a person's membership, Many statistical methods have been proposed in recent years, and in this study, will be employing the Support Vector Machine (SVM), which is a statistical model for face recognition that has been used for classification and regression tasks, so this model will be compared with logistic regression, one of the significant statistical models used in prediction, classification, and facial recognition. Various methods will be evaluated with a group of datasets and varied samples (number of images); this group represented the real data, which consisted of 100 individuals. The results showed that SVM had the lowest mean square error (MSE) for the dataset, followed by LR, and all the methods reached the highest accuracy rate of 100% for facial image recognition on real data.

Probabilistic Explanations for Entropic Knowledge Extraction for Automated Satellite Component Detection

The escalating risk of collisions and the accumulation of space debris in Low Earth Orbit (LEO) has reached critical concern due to the ever-increasing number of spacecraft. Addressing this crisis, especially in dealing with noncooperative and unidentified space debris, is of paramount importance. This paper contributes to efforts in enabling autonomous swarms of small chaser satellites for target geometry determination and safe flight-trajectory planning for proximity operations in LEO. Our research explores on-orbit use of the You Only Look Once v5 object detection model trained to detect satellite components. Although this model has shown promise, its inherent lack of interpretability hinders human understanding, a critical aspect of validating algorithms for use in safety-critical missions. To analyze the decision processes, we introduce Probabilistic Explanations for Entropic Knowledge extraction (PEEK), a method that utilizes information theoretic analysis of the latent representations within the hidden layers of the model. Through both synthetic and hardware-in-the-loop experiments, PEEK illuminates the decision-making processes of the model, helping to identify its strengths, limitations, and biases.

Group Theory Explanation of Microscopic Superconductivity Theory

In 1911, Dutch physicist Onnes discovered that after liquefying helium gas. At around 15 K, the resistance of mercury drops sharply to zero, which is named superconductivity and marks the beginning of human research on superconductors. So far, over a hundred types of superconducting materials have been discovered. With the discovery of superconducting materials and phenomena, many theoretical models have emerged to explain superconducting phenomena, promoting human understanding of condensed matter micro magnetism and other fields. Group theory is a mathematical concept. In mathematics and abstract algebra, group theory studies the algebraic structure of groups. In the study of physics and chemistry, many different physical structures, such as crystal structures and hydrogen atomic structures, can be modeled using group theory methods. So group theory and related group representation theory have a lot of applications in physics and chemistry. This article will elaborate on the phonon model that explains superconductivity theory, Cooper’s model. And attempt to construct a model using molecular point group crystal dynamics knowledge to explore micro superconducting phenomena and theories.

Փոխաբերության ճանաչողական էությունը

Metaphors by their complex semantic structure never cease to attract the attention of linguists, philologists and literary scholars and are often found as central objects of examination in different studies. In the course of the development of science, metaphors having not only linguistic nature but also being phenomena expressing peculiarities of mental processes, have been studied in the fields of psychology, cognitive science, cognitive linguistics. The fact that metaphors are omnipresent serves as a reason for their study not only for the purpose of examining imaginative language and speech elements typical of the works of fiction but also for the exploration and description of human psychological portrait and understanding. In the present article an attempt is made to clarify and examine the essence of metaphors as well as the psychological and cognitive motives for their creation. From a psychological perspective, the object of examination consists in the explanation of the nature of metaphor emergence in human brain. The cognitive peculiarities of their mechanism of operation are presented in the article as well. With the objective of accomplishing a comprehensive and profound examination of metaphors, the consideration of the achievements in the fields of psychology and conginitive science, i.e. adherence to inter-disciplinary approach, which implies application of the well-defined principles of both linguistics and other sciences, is essentially important and topical.

The Urgency of Philosophy of Science in the Context of Human Civilization: An Epistemological Analysis

This research aims to find out how the urgency of philosophy of science in the context of human civilization with epistemological analysis. An important part in creating and advancing human civilization is played by the philosophy of science. A thorough understanding of epistemology, the field of philosophy that deals with theory, offers a critical perspective on how people learn and develop science. With a focus on the epistemological viewpoint that serves as the foundation of scientific thinking, this article examines the importance of philosophy of science in relation to the advancement of human civilization. To gain a comprehensive understanding, this research uses descriptive qualitative research with a literature review. This study shows how the philosophy of science can guide human civilization towards more sustainable, knowledge-based and equitable growth. Philosophy of science plays a very important role in shaping human understanding of knowledge and the development of scientific methodology. Through critical analysis of basic concepts and epistemological approaches, philosophy of science helps us evaluate the validity of knowledge and the methods used in research. With the growing complexity of challenges facing humanity, such as climate change and social injustice, the critical thinking taught by philosophy of science is becoming increasingly relevant. In addition, philosophy of science contributes to innovation by encouraging reflective and interdisciplinary approaches in research. Therefore, the urgency of philosophy of science in the context of human civilization lies in its ability to facilitate ethical dialogue, encourage sustainable innovation, and ensure that the knowledge produced is relevant and beneficial to the whole of society.

A Blended Teaching Approach to Linear Algebra

Linear algebra is an important fundamental course for university students in technology and science. However, most students think linear algebra is very abstract. To give students a much better learning effect, we chose a blended teaching method that uses both offline and online resources. By analyzing the characteristics of linear algebra, the aim of teaching, and the characteristics of human understanding. Concrete teaching methods and some linear algebra examples are displayed in this paper.

Empowering Nanostores for Competitiveness and Sustainable Communities in Emerging Countries: A Generative Artificial Intelligence Strategy Ideation Process

This exploratory study investigates Generative Artificial Intelligence’s (GenAI) use in strategy ideation for nanostores—i.e., small independent grocery retailers—to enhance their competitiveness while contributing to community sustainability. Nanostores, particularly in emerging countries, face intense competition and rapidly changing trends. These stores adopt various strategies by leveraging their proximity to consumers in neighbourhoods, resulting in different business configurations. While the existing literature highlights the broader nanostores’ functions, there is limited research on how they may develop comprehensive strategies to face their challenges. By employing a thing ethnography methodology, this work proposes GenAI thing interviewing—i.e., with ChatGPT 3.5 and Microsoft Copilot—through incremental prompting to explore potential strategy ideation and practices. Key findings suggest GenAI conversations can aid shopkeepers in strategy ideation through human-like written language, aligning with small business dynamics and structures. This proposition results in a GenAI ideation framework for strategy generation and definition. Moreover, this technology can enhance nanostore competitiveness and sustainability impact by enacting improved strategy practices in stakeholder engagements. Accordingly, this work’s main contribution underscores a GenAI-enabled conversational approach to facilitate nanostores’ strategy ideation and embedding in everyday business operations. Future work must address the limitations and further investigate GenAI’s influence on human understanding and technological creation, strategy ideation, adoption, and usability in nanostores.

Earliest modern human genomes constrain timing of Neanderthal admixture.

Modern humans arrived in Europe more than 45,000 years ago, overlapping at least 5,000 years with Neanderthals1-4. Limited genomic data from these early modern humans have shown that at least two genetically distinct groups inhabited Europe, represented by Zlatý kůň, Czechia3 and Bacho Kiro, Bulgaria2. Here we deepen our understanding of early modern humans by analyzing one high-coverage genome and five low-coverage genomes from ~45,000 year-old remains from Ilsenhöhle in Ranis, Germany4, and a further high-coverage genome from Zlatý kůň. We show that distant familial relationships link the Ranis and Zlatý kůň individuals and that they were part of the same small, isolated population that represents the deepest known split from the Out-of-Africa lineage. Ranis genomes harbor Neanderthal segments that originate from a single admixture event shared with all non-Africans that we date to ~45,000-49,000 years ago. This implies that ancestors of all non-Africans sequenced to-date resided in a common population at this time, and further suggests that modern human remains older than 50,000 years from outside Africa represent different non-African populations.

Story-weaving: homo narrans, popular culture and the role of stories in tourism

This article proposes to understand popular culture-inspired tourism as a process of ‘story-weaving’. Exploring the role of stories from the perspectives of tour guides and visitors, we analyse the collaborative process in which biographical stories, places and histories become entwined. Drawing on Walter Fisher's ‘narrative paradigm’ and his understanding of humans as ‘ homo narrans’ or storytelling animals, we argue that story-weaving goes beyond stories being packaged and sold to visitors by tour guides or shaping fan experiences of place. Through interviews with Scotland-based tour guides who run Outlander tours and with visitors as well as participant-based observations we unpack the dynamic nature of story-weaving and examine how visitors and tour guides participate in it as a way of engaging with and in tourism.

Integrating Biological Systems with Electronic Technologies in Bioelectronics Advancements

Bioelectronics is a multidisciplinary field that seamlessly integrates the biological systems with electronic techniques, driving the progress in areas such as modern medication, military management and environmental monitoring. This paper explores the fundamental underlying the bioelectronic devices, viewing some recent advancement in biometric sensors that are central to their operation. By examining the recent research on the different applications of the bioelectronic devices, including medical diagnosis, real-time health monitoring and environmental detection, this paper provides some future prospect on this technique to address contemporary issues. In addition, paper discusses some main challenges it faces such as the biocompatibility and the efficiency to process enormous information. The transformative impact that bioelectronics provide enhance people’s understanding of the way humans interact with their own bodies and the external environment. This integration of biology and electronics also offers people a new insight on the biological process and revolutionize the way human use to treat disease and the environment detection.

Old Testament Law, Pastoral Counseling, and Community Engagement in Lutheran Perspective

The paper explores Martin Luther's stance on Old Testament law, specifically his dismissal of the Mosaic law’s binding nature for Christians. It argues that Luther’s reliance on natural law as an inherent instinct for grasping the order of creation raises challenges due to the inherent ambiguity and fallen nature of human understanding. The analysis delves into biblical, philosophical, and historical perspectives on natural law, underscoring the intricacies surrounding its practical application. Additionally, the article criticizes Luther’s supersessionist theology, which posits the replacement of Mosaic law by Christ, asserting that it lacks exegetical rigor. The significance of comparative studies in comprehending Mosaic Law is demonstrated by drawing insights from ancient Near Eastern legal traditions. This approach presents a model that views Mosaic law as normative covenantal instruction adaptable to real-life situations. Building on these insights, the paper recommends considering Richard Averbeck’s and Nicolai Winther-Nielsen’s approaches to Mosaic law and its application in contemporary Christian life.

A theory for words in Georgian: traditional constructs versus corpus annotation

Abstract Part-of-speech annotation, as an exercise in categorisation, necessitates a category schema, based on some model or theory of the grammar of the language. Such a model may (sometimes, must) deviate from traditional approaches for human understanding, as exploration of theoretical issues arising from a Georgian POS schema illustrates. Consistency on classifying by form versus function is problematised by difficult pronoun/demonstrative and adjective/noun distinctions. Adverb subcategorisation illustrates exclusion of semantic/derivational distinctions that traditional approaches readily admit. Variation in plural inflection has implications for how diachronicity is handled, as does “zero case”. Postpositions make necessary a specific approach to cliticisation in which enclitic elements are handled as separate tokens bearing their own analysis. Suffixaufnahme provides a case study in inclusion versus exclusion of a rare but current phenomenon. Verb morphology illustrates how simplifying assumptions help favour abstraction of categories over descriptive exhaustiveness. Divergence between the resulting model and traditional characterisations do not invalidate either, but evidence how a model’s design is inseparable from its purpose. With regard to these select issues of Georgian grammar, this discussion aims both to demonstrate the overall argument regarding theorisation/schematisation for a specific, practical purpose (POS annotation) and to justify solutions proposed to problems at hand.

Terapan Aksiologi pada Artificial Intelligence Chatbot

The rapid advancement of technology has made artificial intelligence (AI), particularly generative chatbots, an integral part of everyday life. These chatbots, powered by Natural Language Processing (NLP) and deep learning technologies, are widely used in various fields such as customer service, education, and entertainment. However, the increasing prevalence of such technologies brings forth important philosophical concerns, particularly in the realm of axiology—the branch of philosophy that deals with the nature of values, including the practical and ethical implications of knowledge and technology. This study investigates the practical benefits and ethical responsibilities associated with generative chatbots, using ChatGPT as a case study. The research examines whether ChatGPT adheres to the axiological principles of science, specifically its usefulness in enhancing human life and its ethical responsibilities. Through a qualitative content analysis, this research evaluates the responses of ChatGPT to a series of questions based on the axiological framework outlined by Sumantri. The study focuses on two main aspects of science's axiological evaluation: the practical benefits of science and technology, and the ethical responsibilities tied to their application. The findings indicate that ChatGPT is capable of providing useful insights that contribute to human understanding, improve quality of life, simplify complex tasks, and offer solutions to various problems. However, the ethical considerations of AI technology, such as fairness, transparency, and accountability, remain a crucial area of concern. This research highlights the importance of balancing technological progress with ethical responsibility, emphasizing that AI systems like ChatGPT must be developed and applied in ways that align with human values to ensure their positive impact on society.