A theory of learning for the future advocates the teaching of a preparedness to respond in a creative way to difference and otherness. This includes an ability to act imaginatively in situations of uncertainties. John Dewey’s pragmatism holds the key to such a learning theory his view of the continuous meetings of individuals and environments as experimental and playful. That pragmatism has not yet been acknowledged as a relevant learning theory for the future may be (...) due to the immediate connotation and the many interpretations associated with the term ‘experience’, which is at the heart of Dewey’s educational thinking. Dewey defi ned experience in a way that is not well understood within educational research, and in a way that is easily confused with the term ‘experiential learning’. The latter refers to the importance of participants’ ‘experiences’ derived from bodily actions and stored in memory as more or less tacit knowledge. (shrink)

I propose that metaphysical study is initially indifferent to the truth of Metaphysical Realism about Metaphysics (MRM) and Metaphysical Realism and does not presuppose them. Metaphysical Realism is a metaphysical doctrine the truth of which cannot be settled logically prior to metaphysical investigation. MRM presupposes Metaphysical Realism and therefore, one should not hold MRM uncritically. An epistemological consequence of this is that arguments against the possibility of cognition about metaphysically real entities (by e.g., Hume) are not arguments against the epistemic (...) legitimacy of metaphysics without further arguments for MRM. This paper is also a case study of the philosophical significance of having rich enough acquaintance with the history of philosophy, the recent history included. (shrink)

The present online volume contains the papers prepared for the 11th Budapest Visual Learning Conference – ENVISIONING AN ELECTRIFYING FUTURE – held in a physical-online blended form on Nov. 13, 2024, organized by the University of Pécs (represented by Prof. Gábor Szécsi, Dean, Faculty of Cultural Sciences, Education and Regional Development), and the Hungarian Academy of Sciences (represented by Prof. Kristóf Nyíri, Member of the Hungarian Academy of Sciences). Nyíri and Szécsi were responsible for sending out the call (...) for abstracts and inviting plenary speakers, Szécsi organized the physical surroundings. Nyíri’s task was to build up and continuosly update the conference website, as well as to edit the accepted submissions. As we indicated in the call for submissions, the conference was planned as an interdisciplinary encounter of communication and media theory, cultural sciences, sociology, psychology, philosophy, pedagogy, history, political science, picture theory, and other disciplines. We insisted on achieving new scholarly results. Especially with AI now complementing, or intruding into, the world of the internet, what image of the future can we conceive of, what new patterns of life and in particular forms of education should we strive to create? (shrink)

The model is trained on a large dataset containing images of various food items along with their nutritional information. After preprocessing the input image, the model classifies the food and estimates the calorie count by leveraging its learned features. The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track (...) their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. (shrink)

The project titled "Efficient Machine Learning Algorithm for Future Gold Price Prediction" focuses on the development of a machine learning model that can accurately predict future gold prices using historical data and various economic indicators. Gold has long been regarded as a safe-haven asset, and its price is influenced by multiple factors, including global economic conditions, inflation rates, interest rates, and geopolitical events. This research aims to design and implement a robust machine learning model that (...) can analyze complex patterns and trends in gold prices to generate reliable predictions. The project employs a range of machine learning techniques such as Linear Regression, Random Forest, and Support Vector Machines (SVM) to build the prediction model. It utilizes key features like historical price data, currency exchange rates, commodity prices, and stock market trends as input variables. The performance of the model is evaluated using standard metrics such as accuracy, precision, recall, and RMSE (Root Mean Squared Error). The final model is optimized for accuracy and efficiency, making it a useful tool for investors and financial analysts who seek to forecast gold prices and make informed investment decisions. (shrink)

Integrating medical imaging with computing technologies, such as Artificial Intelligence (AI) and its subsets: Machine learning (ML) and Deep Learning (DL) has advanced into an essential facet of present-day medicine, signaling a pivotal role in diagnostic decision-making and treatment plans (Huang et al., 2023). The significance of medical imaging is escalated by its sustained growth within the realm of modern healthcare (Varoquaux and Cheplygina, 2022). Nevertheless, the ever-increasing volume of medical images compared to the availability of imaging experts. (...) Biomedical experts and radiologists have resulted in a widening disparity, causing an excess and overwhelming workload on these healthcare professionals (Chen et al., 2021). Several studies indicate that the present-day biomedical radiologist is now saddled with the daunting task of interpreting an image almost every 10 seconds to keep pace with the burgeoning clinical demands (McDonald et al., 2015; Hosny et al., 2018; Lantsman et al., 2022). This cognitive drain has invariably led to inevitable consequences such as delays in diagnosis and an amplified risk of diagnostic errors – thus, the biomedical imaging aspect is in dire need of methods that would aid accurate diagnostics and analytics for improved decision making. In this review, the importance of AI-related technologies such as ML and/or DL methods are reviewed in relation to the processing of medical or biomedical images along with their potentials, challenges, and possible suggestions for future studies in the health landscape. The focus will be on machine learning methods associated with the medical field of image classification. (shrink)

The study aimed to determine if differentiated instruction effectively addresses learning gaps in mathematics. In particular, it explored how it can improve the student’s learning gaps concerning mathematical performance and confidence. The study employed a quasi-experimental design with 30 purposively-selected Grade 10 participants divided into differentiated (n = 15) and control groups (n = 15), ensuring the utmost ethical measures. The mean and standard deviation were used to describe the participants’ performance and confidence. Independent samples t-tests were used (...) to determine the significant differences in the performance and confidence between the two groups. In contrast, dependent samples t-tests were used to determine the significant differences in each group’s pre and posttest performance and confidence. Findings bared that the differentiated instruction successfully addressed students’ performance in mathematics even in a short period. It also increased the participants’ confidence when answering fundamental problems. Continuing differentiated instruction activities are recommended since it benefits students who struggle in mathematics, particularly in answering fundamental operations. Differentiated teaching activities in mathematics can boost academic achievement and engagement and prepare students for future success while fostering a positive and inclusive classroom culture that values individual learning needs and preferences. (shrink)

Senior high school is known to be part of the newly implemented K-12 program in the Philippines' educational system. Hence, this program added two years to the academic learning program of students, which mainly focuses on different theoretical and vocational strands that aim to prepare and fully furnish the students for education and employment in the future. Due to adjustments to new online learning amidst the pandemic, students begin to experience various challenges, primarily social support and mental (...) well-being. Hence, this study investigates the relationship between social support and anxiety with 160 respondents from private schools. The findings revealed that social support and anxiety have a significant relationship (r=0.126). (shrink)

Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short- Term Memory (LSTM) and Convolutional Neural (...) Networks (CNNs), the model detects abnormal data access patterns that may indicate leakage. The system operates in real-time, continuously monitoring data interactions between users and the cloud. A large dataset containing normal and abnormal access logs is used to train and validate the model, ensuring it can effectively differentiate between legitimate and malicious activity. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with the system achieving over 96% accuracy in identifying potential data leaks. Furthermore, the proposed solution is designed to be scalable and adaptable, making it suitable for dynamic cloud environments with evolving threats. Future enhancements to the system include integrating multi- cloud support and refining the model’s ability to detect sophisticated insider threats. This research highlights the importance of leveraging deep learning for real-time, proactive cloud security. (shrink)

The Future of Humanity with the Full Implementation of the Universal Formula -/- Humanity has long grappled with fundamental questions about free will, decision-making, and the nature of societal progress. Over centuries, countless philosophical, scientific, and religious perspectives have sought to explain the forces driving human behavior and the challenges we face as a global society. The development of a universal formula that solves the problem of free will, grounded in natural laws like the law of balance and the (...) law of karma, presents a transformative opportunity for human civilization. If fully implemented, this universal formula has the potential to reshape humanity’s future, fostering a world marked by balance, sustainability, and reduced suffering. -/- 1. A Holistic Educational System for Critical Thinking -/- The first and most immediate change from the universal formula’s implementation would be seen in education. Current educational systems often prioritize rote memorization and narrow specializations, neglecting the foundational principles of natural law that govern human behavior. By introducing the universal law of balance and the principles of cause and effect (karma) into basic education, students would learn to view decision-making through the lens of natural, universal laws. -/- A holistic educational system, as envisioned by the formula, would emphasize critical thinking, systemic thinking, and the understanding of interdependence among individuals, societies, and the environment. This educational approach would equip future generations to make decisions that prioritize societal well-being, environmental sustainability, and global cooperation. As a result, the widespread ignorance and misinformation that often fuel societal conflict and poor decision-making would diminish. -/- 2. Ethical Leadership and Governance Based on Natural Laws -/- Leaders who understand and apply the universal formula’s principles would adopt a more balanced and ethical approach to governance. The formula asserts that decision-making processes, whether individual or collective, must align with natural laws to avoid imbalance and societal dysfunction. If implemented, policies across nations would shift from short-term gains toward long-term stability, fairness, and sustainability. -/- The law of karma, whichh highlights the necessity for defect-free systems, would guide governance structures to prioritize integrity, transparency, and accountability. Corruption, misinformation, and ideologically driven policies would decline as societies embrace natural laws as their guiding principles. With this shift, government policies on resource management, social welfare, and economic growth would aim for balance rather than unchecked expansion, minimizing societal inequalities and systemic failures. -/- 3. Global Cooperation and Environmental Sustainability -/- The universal law of balance stresses that human systems, like natural ecosystems, must remain in equilibrium to function properly. In an era of climate change, resource depletion, and geopolitical tensions, applying this law globally could unify countries in their efforts to address shared challenges. -/- Nations would move beyond competition and exploitation, understanding that the Earth’s resources are finite and that environmental degradation affects all societies. Policies on population control, economic growth, and environmental conservation would be guided by scientific data and natural principles, ensuring that human activity does not disrupt the delicate balance necessary for life’s continuity. The concept of continuous economic growth, currently dominant in global economics, would be replaced by a sustainable, balanced model that prioritizes ecological and societal well-being over profit. -/- 4. Technological Advancement Aligned with Human Well-Being -/- The development of artificial general intelligence (AGI) presents both opportunities and risks for humanity. If programmed with the universal formula, AGI could become a powerful tool for societal improvement. The law of balance would serve as a guiding framework for AGI, ensuring that its decisions promote human welfare, ethical practices, and environmental harmony. -/- For AGI to function effectively, as suggested by the formula, it must integrate a complete sensor system capable of perceiving and responding to the complexities of human and environmental feedback mechanisms. By doing so, AGI could assist in real-time governance, economic planning, and social development while minimizing the risks associated with unregulated AI development. -/- 5. Reduction of Human Suffering and Societal Conflicts -/- Human suffering, in many cases, stems from ignorance, misinformation, and decisions that disrupt natural balance. The universal formula posits that decisions violating natural laws—whether influenced by false beliefs, propaganda, or systemic defects—create internal and external imbalances that manifest as societal problems. -/- By embedding the principles of natural balance into education, governance, and societal structures, the formula offers a pathway to reduce suffering. Conflicts arising from ideological, religious, or political differences could diminish as people collectively recognize the universal principles governing human behavior. Religious beliefs, rather than being sources of division, would coexist harmoniously with scientific understanding, fostering mutual respect and shared values centered around balance and well-being. -/- 6. Balanced Population Growth and Resource Management -/- One of the most critical challenges facing humanity is the unchecked growth of populations and the overconsumption of resources. The universal formula emphasizes the need for societies to understand and respect the natural carrying capacities of their environments. -/- Policies promoting balanced population growth, informed by scientific research and natural laws, would help ensure resource availability for future generations. Societies would transition from pursuing limitless economic expansion to developing sustainable, self-regulating systems that maintain equilibrium with the natural world. -/- Conclusion: A New Era of Balanced, Sustainable Humanity -/- The full implementation of the universal formula to solve the problem of free will could herald a transformative era for humanity. Through a holistic educational system, ethical governance, environmental sustainability, responsible technological development, and a shared commitment to natural laws, societies could achieve unprecedented levels of harmony, stability, and well-being. -/- While the formula’s success would depend on global cooperation and genuine societal commitment, its principles—rooted in the natural laws governing decision-making—offer a clear, practical pathway to a more balanced and prosperous future. If embraced and applied worldwide, humanity could finally overcome many of its longstanding challenges and move toward a future defined by wisdom, balance, and sustainable progress. -/- . (shrink)

Abstract: The incorporation of Artificial Intelligence (AI) into educational technology has dramatically transformed learning through Intelligent Tutoring Systems (ITS). These systems utilize AI to offer personalized, adaptive instruction tailored to each student's needs, thereby improving learning outcomes and engagement. This paper examines the development and impact of ITS, focusing on AI technologies such as machine learning, natural language processing, and adaptive algorithms that drive their functionality. Through various case studies and applications, it illustrates how ITS have revolutionized (...) traditional educational methods and addresses the challenges and limitations, including data privacy and system biases. The discussion also covers future directions in ITS development, highlighting emerging trends and potential advancements. The goal is to provide a thorough understanding of how ITS use AI to enhance educational experiences and suggest avenues for future research in this evolving field. (shrink)

The environmental costs and energy constraints have become emerging issues for the future development of Machine Learning (ML) and Artificial Intelligence (AI). So far, the discussion on environmental impacts of ML/AI lacks a perspective reaching beyond quantitative measurements of the energy-related research costs. Building on the foundations laid down by Schwartz et al., 2019 in the GreenAI initiative, our argument considers two interlinked phenomena, the gratuitous generalisation capability and the future where ML/AI performs the majority of quantifiable (...) inductive inferences. The gratuitous generalisation capability refers to a discrepancy between the cognitive demands of a task to be accomplished and the performance (accuracy) of a used ML/AI model. If the latter exceeds the former because the model was optimised to achieve the best possible accuracy, it becomes inefficient and its operation harmful to the environment. The future dominated by the non-anthropic induction describes a use of ML/AI so all-pervasive that most of the inductive inferences become furnished by ML/AI generalisations. The paper argues that the present debate deserves an expansion connecting the environmental costs of research and ineffective ML/AI uses (the issue of gratuitous generalisation capability) with the (near) future marked by the all-pervasive Human-Artificial Intelligence Nexus. (shrink)

Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this (...) elusive feat. Through rigorous experimentation and data-driven approaches, we seek to determine the viability of AI in the realm of lottery predictions. Our findings reveal both the promise and limitations of AI in this context, shedding light on the complexities of lottery data and the potential need for quantum computing as a last resort. (shrink)

Learning from Arguments advances accessible versions of key philosophical arguments, in a form that students can emulate in their own writing, and with the primary aim of cultivating an understanding of the dynamics of philosophical argumentation. -/- The book contains ten core chapters, covering the problem of evil, Pascal’s wager, personal identity, the irrationality of fearing death, free will and determinism, Cartesian skepticism, the problem of induction, the problem of political authority, the violinist argument, the future-like-ours argument, the (...) ethics of eating meat, utilitarianism (both act and rule), and the trolley problem. Additionally, there is an introductory chapter explaining what arguments are and surveying some common argumentative strategies, an appendix on logic explaining the mechanics and varieties of valid arguments, and an appendix providing detailed advice for writing philosophy papers. -/- Each of the ten core chapters offers a sustained argument for some controversial thesis, specifically written for an audience of beginners. The aim is to introduce newcomers to the dynamics of philosophical argumentation, using some of the arguments standardly covered in an introductory philosophy course, but without the additional hurdles one encounters when reading the primary sources of the arguments: challenging writing, specialized jargon, and references to unfamiliar books, philosophers, or schools of thought. (shrink)

Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (...) (CNNs), the model detects abnormal data access patterns that may indicate leakage. The system operates in real-time, continuously monitoring data interactions between users and the cloud. A large dataset containing normal and abnormal access logs is used to train and validate the model, ensuring it can effectively differentiate between legitimate and malicious activity. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with the system achieving over 96% accuracy in identifying potential data leaks. Furthermore, the proposed solution is designed to be scalable and adaptable, making it suitable for dynamic cloud environments with evolving threats. Future enhancements to the system include integrating multi-cloud support and refining the model’s ability to detect sophisticated insider threats. This research highlights the importance of leveraging deep learning for real-time, proactive cloud security. (shrink)

We information-theoretically reformulate two measures of capacity from statistical learning theory: empirical VC-entropy and empirical Rademacher complexity. We show these capacity measures count the number of hypotheses about a dataset that a learning algorithm falsifies when it finds the classifier in its repertoire minimizing empirical risk. It then follows from that the future performance of predictors on unseen data is controlled in part by how many hypotheses the learner falsifies. As a corollary we show that empirical VC-entropy (...) quantifies the message length of the true hypothesis in the optimal code of a particular probability distribution, the so-called actual repertoire. (shrink)

Getting a graduate education is one of the most important parts of a professional in a field. It allows them to climb higher in the professional rankings or even get higher pay for their academic work. All graduate students are adults and self-directed due to their past experiences in work or practice. However, when the pandemic hit the world, these self-directed learners were not spared from shutting of schools. In the Philippines, most graduate schools deliver their lessons through the traditional (...) mode or face-to-face until the pandemic that they shifted in the emerging online learning modality. Using the mixed method of quantitative and qualitative type of study the researchers were able to gather 419 graduate students from various regions of the archipelago to take part in the study. The researchers found out that upon experiencing the benefits of online learning, graduate students want to continue this modality even in the future. More than a third of the respondents also claimed that they want the blended or hybrid type of graduate studies as opposed to going back to the more traditional face-to-face classes. The reasons for these can vary on each learner’s situation. (shrink)

I (mostly) accept Ancell’s argument that my proposal for dealing with the radical behavioral challenge entails what he calls ‘the excessive recusal problem’. I argue that this is no reason to reject my proposal, but rather an opportunity for further reflection on what behavioral and normative ethicists can learn from each other. I make some suggestions for future lines of inquiry for both fields.

The Future of Human Reproduction and Family Structure -/- Introduction -/- The future of human reproduction and family structure is set to undergo profound transformations due to advancements in science, technology, and shifting societal values. Breakthroughs in artificial reproduction, gene editing, AI-assisted parenting, and new family models are poised to redefine what it means to conceive, raise children, and form families. As these changes unfold, they will challenge traditional concepts of marriage, parenthood, and biological reproduction. This essay explores (...) the potential future of human reproduction and family structure, examining the scientific, ethical, and social implications. -/- 1. The Future of Human Reproduction -/- A. Assisted Reproductive Technologies (ART) and Genetic Engineering -/- Scientific advancements are making reproduction more accessible and customizable, allowing individuals to control fertility and genetic traits like never before. -/- 1. Artificial Wombs (Ectogenesis) -/- Artificial wombs, or ectogenesis, could enable embryos to develop outside the human body. This technology would: -/- Help individuals unable to carry pregnancies. -/- Reduce health risks associated with childbirth. -/- Offer reproductive freedom to same-sex couples and single individuals. -/- However, artificial wombs may raise ethical concerns about the emotional and psychological impact on maternal bonding and child development. Additionally, they may lead to commercialized pregnancy, where companies provide “womb services” for those who can afford them. -/- 2. Gene Editing & Designer Babies -/- Gene-editing tools like CRISPR allow parents to modify embryos to: -/- Eliminate genetic diseases. -/- Enhance intelligence, physical abilities, and even personality traits. -/- While this technology offers great potential for eradicating inherited disorders, it could also create genetic inequality between those who can afford enhancements and those who cannot, leading to the emergence of a genetic upper class. Governments may implement strict regulations, but underground markets for genetic enhancements could emerge. -/- 3. Lab-Grown Gametes -/- Scientists are developing methods to create sperm and eggs from stem cells, which could enable: -/- Infertile individuals to have biological children. -/- Single individuals to conceive without a partner. -/- While this could redefine reproduction by making parenthood accessible to all, it also raises ethical questions about whether parenthood should be solely based on biological connection or expanded to include other forms of caregiving. -/- B. Cloning and Synthetic Biology -/- 1. Human Cloning -/- While human cloning is currently illegal in most countries, advances in genetic engineering may eventually make it possible. Cloning could: -/- Allow individuals to reproduce without a partner. -/- Be used for medical purposes, such as replacing lost children. -/- Raise existential concerns about individuality and identity. -/- However, human cloning raises profound ethical concerns, including human rights, consent, and the potential exploitation of cloned individuals for labor or military purposes. -/- 2. Synthetic Embryos & Artificial Life -/- Scientists are developing synthetic embryos without using sperm or eggs, which could: -/- Enable reproduction without biological parents. -/- Help infertile individuals conceive. -/- Lead to new ethical dilemmas regarding the moral status of artificially created life forms. -/- If synthetic embryos become a reality, society may need to redefine what it means to be “human” and whether artificial life forms deserve legal rights. -/- C. Population & Environmental Considerations -/- 1. Controlled Population Growth -/- As overpopulation threatens global resources, some governments may: -/- Regulate reproduction using artificial reproductive technologies. -/- Implement birth quotas to maintain ecological balance. -/- Encourage reproduction in aging societies facing workforce decline. -/- These policies would raise ethical questions about personal freedom versus societal needs. -/- 2. Reproduction in Space -/- If humans colonize planets like Mars, reproduction in space will require: -/- Artificial wombs to protect embryos from cosmic radiation. -/- Genetic modifications to help humans adapt to low gravity. -/- New family structures, as space-born humans may develop unique physical traits. -/- This could lead to divergent human evolution, with space-born individuals differing from Earth-born humans over generations. -/- 3. The Future of Family Structure -/- A. Non-Traditional Family Models -/- 1. Multi-Parent Families -/- Technologies like three-parent IVF already exist, and in the future, more than two people may be able to contribute genetic material to a child. This could: -/- Redefine legal parenthood. -/- Enable new forms of family structures. -/- Challenge traditional parenting norms. -/- Laws will need to adapt to recognize multi-parent families and their rights. -/- 2. Single-Parent by Choice -/- With advanced reproductive technologies, more individuals will choose to have children without a partner. This will: -/- Normalize single parenthood. -/- Reduce societal pressure to marry for reproductive purposes. -/- Create new legal structures for co-parenting agreements. -/- B. Changes in Marriage & Parenting Norms -/- 1. Declining Marriage Rates -/- With reproduction becoming independent of relationships, marriage may decline, as people prioritize: -/- Career advancement. -/- Personal fulfillment. -/- Alternative forms of companionship. -/- 2. Co-Parenting Arrangements -/- People may legally agree to co-parent a child without being romantically involved. This would: -/- Offer a stable upbringing without traditional marriage. -/- Encourage partnership-based parenting. -/- Reduce legal disputes over custody. -/- 3. Extended Family Networks -/- Due to economic pressures, multi-generational households may become more common, with grandparents playing a bigger role in raising children. -/- C. AI & Robotics in Parenting -/- 1. AI Parenting Assistants -/- AI-driven robots may assist in: -/- Childcare and education. -/- Emotional development and mentorship. -/- Reducing parental stress. -/- However, heavy reliance on AI could impact traditional parent-child bonding. -/- 2. AI-Generated Children (Digital Offspring) -/- In the future, some people may choose to raise AI-based virtual children in digital environments instead of having biological kids. These “children” could: -/- Simulate real human relationships. -/- Reduce the need for biological reproduction. -/- Raise ethical questions: If AI children learn and grow, are they “alive”? -/- 3. Key Ethical and Social Questions -/- As these technologies reshape reproduction and family life, several ethical concerns arise: -/- 1. Genetic Inequality: Will access to genetic enhancements create a class divide? -/- 2. State vs. Individual Rights: Should governments regulate reproduction? -/- 3. Redefining Parenthood: Who qualifies as a parent in multi-parent or AI-assisted families? -/- 4. Traditional vs. Technological Families: Will humans still value biological reproduction, or will artificial and digital life forms become part of the family? -/- Conclusion -/- The future of human reproduction and family structure will be radically transformed by scientific advancements, shifting social norms, and ethical dilemmas. While artificial wombs, gene editing, and AI parenting promise greater reproductive freedom and inclusivity, they also pose existential challenges about human identity, societal structures, and the ethics of life creation. As these technologies advance, societies must carefully navigate their implications to ensure that progress benefits all of humanity while safeguarding fundamental rights and values. -/- . (shrink)

The Future of Individuality in a Universally Connected Intelligence System -/- Introduction -/- The concept of individuality has long been central to human existence, shaping our identities, intelligence, and decision-making. However, if information were universally accessible to every biological brain via quantum computers, the nature of individuality would fundamentally change. While thermodynamics suggests that individuality may be an illusion, the emergence of a universally shared knowledge system would challenge our understanding of intelligence, creativity, and free will. This essay explores (...) how such a system would affect human cognition, decision-making, creativity, and identity while considering its alignment with natural laws and homeostasis. -/- Individuality and the Laws of Thermodynamics -/- From a thermodynamic perspective, individuality is not an independent phenomenon but an emergent property of energy, matter, and information processing. The second law of thermodynamics states that systems naturally move toward increased entropy, yet biological brains counteract this by organizing energy and information. Each person’s intelligence develops uniquely due to different experiences, environments, and biological structures, following the laws of complexity and adaptation. -/- Despite these differences, individuality may be an illusion when viewed through the lens of thermodynamics. Human brains function as open systems, constantly exchanging energy and information with their environment. If all individuals had direct access to the same universal information via quantum computing, the differentiation in intelligence caused by personal learning and memory would be minimized. The uniqueness of individual cognition, shaped by life experiences, would be transformed into a more standardized, universally connected intelligence. -/- The Effect on Cognitive Differences and Decision-Making -/- Currently, human intelligence is shaped by a combination of genetics, environmental influences, and personal experiences. However, if quantum computing enabled instant and equal access to all knowledge, the factors that create cognitive diversity would be significantly reduced. In such a scenario, decision-making would shift from being influenced by personal biases and incomplete knowledge to being guided by a universally shared understanding of facts, consequences, and natural laws. -/- This raises the question: would free will still exist? If every person had access to the same knowledge base and predictive models, decisions might become more deterministic, driven by logical outcomes rather than personal reasoning. While this could reduce errors and societal conflicts, it might also challenge the perception of autonomy, making personal choice seem more like an illusion than a reality. -/- Impact on Creativity and Innovation -/- Creativity has traditionally been seen as a product of individual thought processes, shaped by unique experiences and perspectives. If knowledge were universally accessible, would creativity still exist? One possibility is that creativity would shift from being an individual phenomenon to an emergent property of collective intelligence. Instead of drawing from personal experiences, new ideas could arise from collaborative interactions with the universal knowledge system. -/- On the other hand, if individuality persists at a neural level—perhaps influenced by variations in neural architecture, emotional states, or subconscious processes—then creativity might evolve into a new form. Rather than being limited by personal knowledge, individuals could use their unique cognitive structures to interpret and apply universal knowledge in novel ways. -/- The Transformation of Personal Identity -/- Identity is often defined by personal achievements, expertise, and knowledge. If quantum computing provided instant access to all human knowledge, traditional concepts of learning, expertise, and mastery might become obsolete. Instead of being defined by what one knows, identity might be shaped by how one chooses to interact with the collective intelligence system. -/- This could lead to two possible outcomes: -/- 1. A more unified human society where knowledge gaps no longer create social or economic disparities. -/- 2. A psychological challenge, as individuals struggle with a loss of personal uniqueness and purpose. -/- Would human beings still feel a sense of individuality if everyone had the same knowledge and understanding? Or would personal identity be redefined by new forms of expression, emotional depth, and subjective experiences? -/- Potential Resistance and Psychological Conflicts -/- Not all individuals would easily accept a universally connected intelligence system. Some might resist integration, fearing the loss of personal autonomy or questioning the ethical implications of such a system. The psychological impact of merging individual cognition with a collective intelligence could lead to existential dilemmas, as humans redefine their roles in society. -/- Moreover, there could be unforeseen consequences, such as the manipulation of universal knowledge by those who control the system. If the flow of information were regulated, the promise of a universally connected intelligence could turn into a form of centralized control, limiting true intellectual freedom. -/- Homeostasis and the Balance of Universal Knowledge -/- From the perspective of natural balance, a universally connected intelligence system could be seen as an extension of the homeostatic principle. Just as the human body regulates internal stability, a universal intelligence network could regulate societal balance, reducing ignorance, misinformation, and destructive decision-making. -/- However, if homeostasis is disrupted—if individuals lose the ability to process knowledge uniquely or if the system becomes too rigid—then humanity might face unintended consequences. True balance requires adaptability, diversity, and self-regulation. The challenge would be ensuring that universal intelligence enhances, rather than diminishes, the dynamic equilibrium that allows individuality to thrive within a collective framework. -/- Conclusion -/- The integration of biological brains with a universally accessible quantum computing system would redefine individuality, intelligence, and decision-making. While it could eliminate knowledge-based disparities and enhance collective intelligence, it might also challenge free will, creativity, and personal identity. Whether such a transformation aligns with natural laws depends on how well it maintains homeostasis—balancing collective knowledge with individual autonomy. -/- Would this system create a more enlightened humanity, or would it erase the very essence of what makes us human? The answer lies in how this universal intelligence is designed, regulated, and integrated into the fabric of human society. -/- . (shrink)

Future-biased individuals systematically prefer positively valenced events to be in the future (positive future-bias) and negatively valenced events to be in the past (negative future-bias). The most extreme form of future-bias is absolute future-bias, whereby we completely discount the value of past events when forming our preferences. Various authors have thought that we are absolutely future-biased (Sullivan (2018:58); Parfit (1984:173) and that future-bias (absolute or otherwise) is at least rationally permissible (Prior (1959), (...) Hare (2007; 2008), Kauppinen (2018), Heathwood (2008)). The permissibility of future-bias is often held to be grounded in the structure of the temporal dimension. In this paper I consider several proposals for grounding the permissibility of such preferences and evaluate these in the light of the preferences we would have, and judge that we should have, in various time-travel scenarios. I argue that what we learn by considering these scenarios is that these preferences really have nothing to do with temporal structure. So if something grounds their permissibility, it is not temporal structure. (shrink)

Customer churn prediction is a critical challenge for businesses in retaining their customer base and optimizing their marketing strategies. Machine learning (ML) techniques offer a powerful approach to predict customer churn by analyzing historical customer behavior, demographic information, and usage patterns. This paper provides an overview of machine learning-based models used for predicting customer churn, including classification algorithms such as logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks. We explore how businesses can leverage (...) these models to identify customers who are likely to churn and take proactive measures to retain them. The paper also highlights the challenges in churn prediction, such as imbalanced data, overfitting, and the interpretability of models, and provides insights into the future of churn prediction models. Case studies in telecommunications, retail, and banking sectors are discussed to demonstrate practical applications. (shrink)

Lake et al. propose three criteria which, they argue, will bring artificial intelligence (AI) systems closer to human cognitive abilities. In this paper, we explore the application of these criteria to a particular domain of human cognition: our capacity for moral reasoning. In doing so, we explore a set of considerations relevant to the development of AI moral decision-making. Our main focus is on the relation between dual-process accounts of moral reasoning and model-free/model-based forms of machine learning. We also (...) discuss how work in embodied and situated cognition could provide a valu- able perspective on future research. (shrink)

In this study, we consider that enabling young people to experience direct contact with nearby natural environments can positively influence their knowledge and feelings about the biodiversity that occurs there, contributing to its protection and conservation for current and future generations. In this study, we explore how teenagers (n = 17) aged between 13 and 17 years old describe and perceive the nearby natural environment before and after an interpretive trail in Una, Bahia, Brazil. Participants were asked to draw (...) the Atlantic Forest with colored pencils on white paper and, based on the drawing, they answered the following questions: “What is in your drawing? and “What is happening in your drawing?,” in addition to other information such as the title of the drawing, difficulty of the activity, and sociodemographic aspects. Content analysis was used to analyze the information collected. From the drawings and responses of the participants, categories related to knowledge, experiences, and types of relationships with the visited place emerged. (shrink)

This quantitative study explores the acceptability of Online Learning Action Cell (LAC) practice as a school-based professional development strategy for Tagumpay National High School (TNHS) teachers. The research was motivated by the Department of Education (DepEd) Order No. 35 s. 2016 which prompts public schools to comply with the implementation of LAC sessions because it has a positive impact on teachers’ beliefs and practices resulting in education reforms for learners’ benefit. However, in compliance with DepEd’s policy on maximizing Time-On-Task (...) (DepEd Order No. 9 s. 2005) and the teacher’s conflict of schedule and other ancillary and coordination functions, absences or non-participation in classroom LAC was noted. The viability of offering Online LAC sessions using appropriate media and technologies to provide more options and flexibility for the study is suggested and tested utilizing a conceptual framework drawn from DepEd Order No. 35 s. 2016, Simonson’s Equivalency Theory and Anderson’s Community of Inquiry (COI). A descriptive survey method was used in this study with purposive sampling identifying ten Technology and Livelihood Education (TLE) teachers to pilot the Online LAC based on the memorandum issued by the school head. The subjects went through a week of experience in a LAC session using Facebook (FB) as Learning Management System (LMS) while a researcher-designed survey tool in the form of a Likert-type scale questionnaire was administered via Google forms before and after the online session to measure teacher’s acceptability of Online LAC Session practice. In the analysis, a descriptive statistic shows that pre-post survey results on Online LAC session planning is from ‘moderately acceptable’ to ‘very highly acceptable’; implementation is from ‘acceptable’ to ‘very highly acceptable’, and evaluation is from ‘moderately acceptable’ to ‘very highly acceptable’. TNHS teachers' acceptability of Online LAC session practice increases after their participation in a fully-online learning environment. Limitations, recommendations, future research directions, and conclusions are also included in the study. -/- . (shrink)

The study investigates the Bachelor of Science in Exercise and Sports Science (BSESS) program curriculum within Region III, specifically studying its alignment with the Commission on Higher Education Memorandum Order (CMO) No. 81, series of 2017, to distinguish potential curriculum and policy developments that backing life-wide learning and student employability. The research identifies existing gaps in career alignment, stakeholder engagement, graduate employability preparation, and policies supporting lifelong learning within the curriculum. Through multiple case study design, it explores curricular (...) practices, instructional strategies, student engagement, and assessment methods at two state universities, emphasizing their unique approaches to practical learning, community involvement, and stakeholder collaboration. Findings reveal significant areas such as institutional support, curricular planning, and stakeholder engagement, underscoring variations in each institution’s strategies to address community needs, facilitate student-centered learning, and prepare graduates for the sports and fitness industry. The two institutions demonstrate strong commitment to curriculum implementation through initiatives like experiential learning and community engagement, although with distinct approaches influenced by local demands. Challenges highlighted include infrastructure limitations, stakeholder involvement irregularities, and the need for enhanced policy support. Future directions for the BSESS program involve expanding student engagement through global internships, certification programs, and partnerships with local government units (LGUs) to foster community-based health initiatives, while ongoing curriculum reviews ensure alignment with industry demands and societal needs. (shrink)

Abstract: Early detection of breast cancer plays a pivotal role in improving patient prognosis and reducing mortality rates. Recent technological advancements have significantly enhanced the accuracy and effectiveness of breast cancer screening methods. This paper explores the latest innovations in early detection, including the evolution of digital mammography, the impact of 3D mammography (tomosynthesis), and the use of advanced imaging techniques such as molecular imaging and MRI. Furthermore, the integration of artificial intelligence (AI) in diagnostic tools is discussed, highlighting how (...) machine learning algorithms are refining imaging analyses and reducing diagnostic errors. Advances in genetic screening, liquid biopsies, and biomarkers are also examined, showcasing their potential in identifying high-risk individuals and enabling personalized treatment plans. This paper provides insights into the future of breast cancer detection, outlining both the opportunities and challenges that lie ahead in adopting these innovative technologies to improve patient outcomes. (shrink)

Speech Emotion Recognition (SER) is an essential component in human-computer interaction, enabling systems to understand and respond to human emotions. Traditional emotion recognition methods often rely on handcrafted features, which can be limited in capturing the full complexity of emotional cues. In contrast, deep learning approaches, particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, offer more robust solutions by automatically learning hierarchical features from raw audio data. This paper reviews recent advancements (...) in deep learning-based speech emotion recognition, discusses the various architectures used, and evaluates the challenges in real-world applications. We focus on the application of deep learning models to enhance the accuracy and robustness of SER, particularly in noisy environments. The study also discusses future directions for research, including multimodal emotion recognition and transfer learning to address challenges such as small datasets and cross-domain applications. (shrink)

Mindset plays a vital role in tackling the barriers to improving the preservice mathematics teachers’ (PMTs) conceptual understanding of problem-solving. As the COVID-19 pandemic has continued to pose a challenge, online learning has been adopted. This led this study to determining the PMTs’ mindset and level of conceptual understanding in problem-solving in an online learning environment utilising Google Classroom and the Khan Academy. A quantitative research design was employed specifically utilising a descriptive, comparative, and correlational design. Forty-five PMTs (...) were chosen through simple random sampling and willingly took part in this study. The data was gathered using validated and reliable questionnaires and problem-solving tests. The data gathered was analysed using descriptive statistics, analysis of variance, and simple linear regression. The results revealed that the college admission test, specifically numerical proficiency, influences a strong mindset and a higher level of conceptual understanding in problem-solving. Additionally, this study shows that mindset predicts the levels of conceptual understanding in problem-solving in an online environment where PMTs with a growth mindset have the potential to solve math problems. The use of Google Classroom and the Khan Academy to aid online instruction is useful in the preparation of PMTs as future mathematics teachers and problem-solvers. Further studies may be conducted to validate these reports and to address the limitations of this study. (shrink)

Traffic sign detection and recognition (TSDR) is a critical aspect of autonomous driving and intelligent transportation systems. Traditional methods of traffic sign detection rely on handcrafted features and classical machine learning algorithms, which often struggle to achieve high accuracy in complex real-world environments. In contrast, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown remarkable performance in both detecting and recognizing traffic signs in diverse conditions. This paper reviews the application of deep learning methods for TSDR, (...) focusing on recent advancements in the use of CNNs, Region-based CNNs (R-CNNs), and YOLO (You Only Look Once) for real-time detection and recognition of traffic signs. We discuss the challenges, including the variability of traffic signs, environmental factors, and computational requirements, and present solutions proposed in the literature. The paper also highlights future directions for improving the robustness, accuracy, and speed of traffic sign detection and recognition systems, with an emphasis on real-time applications. (shrink)

With the rapid evolution of information technology, malware has become an advanced cybersecurity threat, targeting computer systems, smart devices, and large-scale networks in real time. Traditional detection methods often fail to recognize emerging malware variants due to limitations in accuracy, adaptability, and response time. This paper presents a comprehensive review of machine learning algorithms for real-time malware detection, categorizing existing approaches based on their methodologies and effectiveness. The study examines recent advancements and evaluates the performance of various machine (...) class='Hi'>learning techniques in detecting malware with minimal false positives and improved scalability. Additionally, key challenges, such as adversarial attacks, computational overhead, and real-time processing constraints, are discussed, along with potential solutions to enhance detection capabilities. An empirical evaluation is conducted to assess the effectiveness of different machine learning models, providing insights for future research in real-time malware detection. Keywords: Real-t. (shrink)

Crime prediction has emerged as a critical application of machine learning (ML) and deep learning (DL) techniques, aimed at assisting law enforcement agencies in reducing criminal activities and improving public safety. This project focuses on developing a robust crime prediction system that leverages the power of both ML and DL algorithms to analyze historical crime data and predict potential future incidents. By integrating a combination of classification and clustering techniques, our system identifies crime-prone areas, trends, and patterns. (...) Key parameters such as time, location, type of crime, and socio-economic factors are considered to build a comprehensive predictive model. Machine learning algorithms like Decision Trees, Random Forest, and Support Vector Machines are used for efficient data preprocessing and pattern recognition. Deep learning models, including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), enable advanced feature extraction and temporal analysis for enhanced prediction accuracy. The system's performance is evaluated using metrics such as precision, recall, F1-score, and accuracy, demonstrating its reliability and scalability for real-world applications. Furthermore, the use of visualization tools allows stakeholders to comprehend crime patterns effectively, supporting proactive policing strategies. This data-driven approach not only improves crime deterrence but also aids policymakers in resource allocation and urban planning. The proposed system has the potential to revolutionize crime management by transforming traditional reactive measures into predictive, preventive, and intelligent solutions. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify (...) cyberbullying behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. (shrink)

There are (at least) three approaches to quantifying information. The first, algorithmic information or Kolmogorov complexity, takes events as strings and, given a universal Turing machine, quantifies the information content of a string as the length of the shortest program producing it [1]. The second, Shannon information, takes events as belonging to ensembles and quantifies the information resulting from observing the given event in terms of the number of alternate events that have been ruled out [2]. The third, statistical (...) class='Hi'>learning theory, has introduced measures of capacity that control (in part) the expected risk of classifiers [3]. These capacities quantify the expectations regarding future data that learning algorithms embed into classifiers. Solomonoff and Hutter have applied algorithmic information to prove remarkable results on universal induction. Shannon information provides the mathematical foundation for communication and coding theory. However, both approaches have shortcomings. Algorithmic information is not computable, severely limiting its practical usefulness. Shannon information refers to ensembles rather than actual events: it makes no sense to compute the Shannon information of a single string – or rather, there are many answers to this question depending on how a related ensemble is constructed. Although there are asymptotic results linking algorithmic and Shannon information, it is unsatisfying that there is such a large gap – a difference in kind – between the two measures. This note describes a new method of quantifying information, effective information, that links algorithmic information to Shannon information, and also links both to capacities arising in statistical learning theory [4, 5]. After introducing the measure, we show that it provides a non-universal analog of Kolmogorov complexity. We then apply it to derive basic capacities in statistical learning theory: empirical VC-entropy and empirical Rademacher complexity. A nice byproduct of our approach is an interpretation of the explanatory power of a learning algorithm in terms of the number of hypotheses it falsifies [6], counted in two different ways for the two capacities. We also discuss how effective information relates to information gain, Shannon and mutual information. (shrink)

Teachers in vocational education face two problems. (1) Learning involves the ability to transcend and modify learned knowledge to new circumstances. How should vocational education prepare students for future, unknown tasks? (2) Students should strive to produce work of good quality. How does vocational education help them develop their faculty of judgment to differentiate between better and worse quality? These two ques- tions are tightly interwoven. The paper compares the didactics of philosophy and sloyd. Both developed independently, but (...) their solutions to how one advances the capacity for reflective judgment are similar. Central to this capacity is not merely devising efficient means to work towards pre-existing aims, but to reflect in practice on what one’s aim are and should be. This implies reflection on what future problems, as well as solutions to them, demand of us cognitively, socially and morally. (shrink)

This paper argues that processes in the sensible realm can be in accord with reason in the Timaeus, since rationality is understood here as being based on regularity, which is conferred onto processes by time. Plato uses two different temporal structures in the Timaeus, associated with the contrast there drawn between Greek and Egyptian approaches to history. The linear order of before and after marks natural processes as rational and underlies the Greek treatment of history. By contrast, a bidirectional temporal (...) structure is the basis for the Egyptian approach to historical processes: present actions are not only determined by preceding ones in the past, but can also be influenced by the future (plans, aims). This latter temporal structure is shown to be necessary for learning from history, which makes human actions more regular. (shrink)

Coronavirus disease has a crisis with high spread throughout the world during the COVID19 pandemic period. This disease can be easily spread to a group of people and increase the spread. Since it is a worldly disease and not plenty of vaccines available, social distancing is the only best approach to defend against the pandemic situation. All the affected countries' governments declared locked-down to implement social distancing. This social separation and persons not being in a mass group can slow down (...) the spread of COVID19. It reduces the physical contact between infected persons and normal healthy persons. Almost every health organization tells that to follow social distancing people should maintain at least 6 feet of distance from each other. This research proposes a deep learning approach for social distancing which is developed for tracking and detecting people who are in indoor as well as outdoor scenarios using YOLO V3 video analytic technique. This approach focuses to inspect whether the people are maintaining social distancing in many areas, using surveillance video with measuring the distance in real-time performance. Most of the early studies of detecting social distance monitoring were based on GPS for tracking the movements of people where the signals could be lost. On the other hand, some countries use drones to detect large gatherings of people who cannot have a clear view at night times [10]. In the future, the proposed system can be used fully for detecting threats in the public crowded or it can detect any person affected by critical situations (ie fainting, Cordia arrest) or planting the crops in the forms evenly with a uniform measurement. This proposal could be used in many fields like crowd analysis, autonomous vehicles, and human action recognition and could help the government authorities to redesign the public place layout and take precautionary action in the risk zones. This system analyses the social distancing of people by calculating the distance between people to slow downing the spread of the COVID 19 virus. (shrink)

Machine learning (ML) has emerged as a powerful tool for transforming data-driven decision-making across various industries. By leveraging large volumes of data and advanced algorithms, machine learning models can uncover insights, make predictions, and enable businesses to make more informed decisions. This paper explores how machine learning is revolutionizing decision-making processes, enhancing efficiency, accuracy, and predictive capabilities. It also examines the key challenges, opportunities, and future directions for the integration of machine learning into decision-making frameworks.

By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. Additionally, (...) the system is designed to provide real-time feedback to farmers, helping them take immediate corrective action. This automated approach eliminates the need for expert human intervention and can be deployed on mobile devices for ease of use in rural areas. The potential future applications of this system include integration with precision agriculture techniques and expansion to a wider range of crops. (shrink)

Can fictional literature help us lead better lives? This essay argues that some works of literature can help us both change our personal narratives and develop new narratives that will guide our actions, enabling us to better achieve our goals. Works of literature can lead us to consider the hypothesis that we might beneficially change our future-oriented, personal narratives. As a case study, this essay considers Ben Lerner’s novel, 10:04, which focuses on humans’ ability to develop new narratives, and (...) which articulates a narrative that takes into account both everyday life and large-scale issues like the global, environmental crisis. (shrink)

Results indicate significant accuracy improvements over traditional machine learning approaches, demonstrating the potential of deep learning in species identification. This project holds promise for applications in wildlife monitoring, ecological research, and educational tools, promoting awareness and conservation efforts. Future work may include integrating the system into mobile applications or deploying it for real-time bird species identification in field conditions.

The purpose of this study was to evaluate the differentiated learning training program at the mathematics subject teachers' meeting (MGMP). A descriptive quantitative approach was used to identify the successes of the program and areas that require improvement. The sample included 21 mathematics teachers in Sub Rayon 2 of Lebak District. The instruments used were questionnaires in which data on participants' responses to resource persons, materials, and suggestions for future activities were collected, and the results of direct observations. (...) Data analysis was carried out using descriptive statistical methods to describe the overall evaluation results. Based on the findings, it is expected to provide an in-depth view of the differentiated learning program, contribute to the development of the program in the future, and become a guideline for improving the quality of mathematics learning in the Lebak district. The results of this training program evaluation show that the differentiated learning training activities have been well implemented in MGMP Mathematics Sub Rayon 2 Lebak District. This is evidenced by the level of teacher satisfaction in participating in the training, the majority of which were at scores 4 and 5, namely agreeing and strongly agreeing. This was also the case with the resource persons, materials, and training facilities, and organization. It is hoped that in the future continue to carry out ongoing training related to differentiated learning by current needs and to improve the competence of mathematics teachers in particular, as well as teachers of other subjects in general. (shrink)

As technology rapidly advances, the landscape of cybersecurity faces increasingly complex and evolving threats. This paper explores the future of cybersecurity, focusing on emerging threats and the corresponding mitigation strategies. Key threats include AI-driven attacks, ransomware evolution, quantum computing vulnerabilities, IoT security risks, cloud security challenges, and supply chain attacks. These threats have the potential to disrupt organizations and compromise sensitive data on an unprecedented scale. To address these challenges, the paper outlines effective mitigation strategies such as leveraging AI (...) and machine learning for defense, implementing Zero Trust architecture, adopting quantum-resistant cryptography, strengthening IoT security, improving cloud security practices, and managing third-party risks. A proactive, multi-layered approach to cybersecurity is essential to safeguard against the growing sophistication of cyber threats. This paper highlights the importance of continuous innovation and collaboration across industries to ensure a secure digital future. (shrink)

Preparing students for the real challenges in life is one of the most important goals in education. Constructivism is an approach that uses real-life experiences to construct knowledge. Problem-Based Learning (PBL), for almost five decades now, has been the most innovative constructivist pedagogy used worldwide. However, with the rising popularity, there is a need to revisit empirical studies regarding PBL to serve as a guide and basis for designing new studies, making institutional policies, and evaluating educational curricula. This need (...) has led the researchers to do a meta-analysis to analyse the effectiveness of PBL on secondary students’ achievement in different scientific disciplines. Following the set of inclusion and exclusion criteria, 11 studies in Eurasia, Africa, and America conducted from 2016 to 2020 have qualified for this study. Six of which focused on JHS (n = 1047) and five on SHS (n = 375). Studies were obtained from various meta-search engines including Google, ERIC, and JSTOR. Further, the researchers used Harzing’s Publish and Perish software to exhaust the search process. Sample size, mean, and standard deviation were analysed using the Comprehensive MetaAnalysis version 3 to determine the effect sizes (Hedge’s g) and the results of moderator analysis, forest plot, funnel plot, and Begg-Mazumdar test. Findings have shown that PBL, as an approach to teaching science, had a large and positive effect (ES = .871) on the achievement of secondary students. However, grade levels and various scientific disciplines did not influence students’ learning achievement. The conduct of more studies on the different factors affecting PBL implementation and specific effects of PBL on various student domains is recommended to facilitate comparative educational research in the future. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, (...) precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might be made, like adding wearable device data in real-time or investigating deep learning techniques. (shrink)

: COVID-19 has proliferated over the earth, exposing mankind at risk. The assets of the world's most powerful economies are at stake due to the disease's high infectivity and contagiousness. The capacity of machine learning algorithms can estimate the amount of future COVID-19 cases, which is now considered a possible threat to civilization. Five conventional measuring models, notably LR, LASSO, SVM, ES, and LSTM, were utilised in this work to examine COVID-19's undermining variables. Each model contains three sorts (...) of expectations: the number of newly contaminated cases, the number of passings, and the number of recoveries. However, it is hard to anticipate the patients' real outcomes. To address the issue, a suggested approach based on long transient memory (LSTM) forecasts the number of COVID-19 cases in the next 10 days as well as the impact of preventative measures such as social isolation and lockdown on COVID-19 spread. (shrink)

In “Process Reliabilism and the Value Problem” I argue that Erik Olsson and Alvin Goldman's conditional probability solution to the value problem in epistemology is unsuccessful and that it makes significant internalist concessions. In “Kinds of Learning and the Likelihood of Future True Beliefs” Olsson and Martin Jönsson try to show that my argument does “not in the end reduce the plausibility” of Olsson and Goldman's account. Here I argue that, while Olsson and Jönsson clarify and amend the (...) conditional probability approach in a number of helpful ways, my case against it remains intact. I conclude with a constructive proposal as to how their account may be steered in a more promising direction. (shrink)

Causal understanding is a defining characteristic of human cognition. Like many animals, human children learn to control their bodily movements and act effectively in the environment. Like a smaller subset of animals, children intervene: they learn to change the environment in targeted ways. Unlike other animals, children grow into adults with the causal reasoning skills to develop abstract theories, invent sophisticated technologies and imagine alternate pasts, distant futures and fictional worlds. In this Review, we explore the development of human-unique causal (...) learning and reasoning from evolutionary and ontogenetic perspectives. We frame our discussion using an ‘interventionist’ approach. First, we situate causal understanding in relation to cognitive abilities shared with non-human animals. We argue that human causal understanding is distinguished by its depersonalized (objective) and decontextualized (general) representations. Using this framework, we next review empirical findings on early human causal learning and reasoning and consider the naturalistic contexts that support its development. Then we explore connections to related abilities. We conclude with suggestions for ongoing collaboration between developmental, cross-cultural, computational, neural and evolutionary approaches to causal understanding. (shrink)

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into the research landscape has transforming almost every extending field, including pharmaceutical research. The idea of drug discovery itself is very conventional and has long been criticized for being overly lengthy and expensive, which sometimes may take more than 10 years and billions of dollars to develop a certain drug. AI and ML formulate the future of the drug discovery process by using big data to provide preliminary drug (...) candidates more effectively. This paper overviews the innovations defined by AI and ML in the field of drug discovery, major achievements, techniques, and use cases. Additionally, we explore how AI algorithms can enter biological data, inspect drug-target relations, determine optimal drug design, and potentially recompose famous drugs. This means through using big data with the help of AI in the process of research, previously undisclosed patterns that help in developing effective treatments for patients are found. The paper also addresses the related issues and limitations in applying AI in this domain, namely, data quality issues, the interpretability of AI solutions, and some ethical concerns. Herein, to provide a concrete foundation to the concepts mentioned so far, we discuss the different AI applications and case studies in drug discovery from a survey of the available literature. The article also provides information regarding the methodologies used in AI-enabled drug discovery like deep learning, reinforcement learning, and natural language processing. Moreover, we compare the use of conventional and artificial intelligence methods, while demonstrating what is good and what maybe in both. The results section offers a review and an integration of the most recent objectives and recommendations for subsequent research instruction. Therefore, despite these prohibitive AI and ML forecasts for drug discovery improvement, continuous interaction between computational scientists, biologists, and regulatory authorities is functional to fully unlock this potential. (shrink)

Abstract: Early detection of breast cancer is vital for improving patient outcomes and reducing mortality rates. Technological advancements have significantly enhanced the accuracy and efficiency of screening methods. This paper explores recent innovations in early detection, focusing on the evolution of digital mammography, the benefits of 3D mammography (tomosynthesis), and the application of advanced imaging techniques such as molecular imaging and MRI. It also examines the role of artificial intelligence (AI) in diagnostic tools, showing how machine learning algorithms are (...) improving imaging analysis and reducing diagnostic errors. Furthermore, the paper discusses advancements in genetic screening, liquid biopsies, and biomarkers, emphasizing their potential in identifying high-risk individuals and supporting personalized treatment plans. The paper provides a forward-looking perspective on the future of breast cancer detection, addressing both the opportunities and challenges associated with the adoption of these cutting-edge technologies to improve patient outcomes. (shrink)