The shift from conventional, face-to-face classroom teaching to distance education is a complex process that brings various challenges. To better understand the impact of this transition, the researchers examined the perceptions of secondary science teachers (n = 42) and students (n = 137). Specifically, the study focused on evaluating learner-centered, action-oriented, and transformative learning – referred to as LCAOT learning – in science distance education. The researchers developed a 26-item, 4-point Likert scale questionnaire that was distributed online (...) to the target respondents. Additionally, the researchers interviewed teachers and students and analyzed various documents, such as self-learning modules and learners’ activity sheets, to triangulate the survey data. The findings revealed that the principles of LCAOT learning were apparent in science distance education and exemplified through tools such as the Know, Want to Know, and Learned charts and personal journals. The study also revealed that teachers and students faced challenges during the transition to distance education, including inadequate equipment and poor internet connectivity. However, they responded to these challenges by using various means of communication, collaborating with peers, and exploring new roles and identities. The researchers recommend using the developed instrument and continuing to evaluate the effectiveness of teaching strategies employed in distance education in science, as well as further studies on the impact of LCAOT learning on students’ academic achievement. (shrink)

Through examples of embodied and learning-centered pedagogy, we discuss transformative learning of transgressive topics. We begin with a taxonomy of types of learning our students undergo as they resolve inconsistencies among their pre-existing beliefs and the material they confront in our course on feminist ethics and epistemology. We then discuss ways to help students maximize their learning while confronting internal inconsistencies. While we focus on feminist topics, our approach is broad enough to be relevant to (...) anyone teaching a transgressive or controversial topic. (shrink)

This study explores a liberatory epistemic virtue that is suitable for good learning as a form of liberating socially situated epistemic agents toward ideal virtuousness. First, I demonstrate that the weak neutralization of epistemically bad stereotypes is an end of good learning. Second, I argue that weak neutralization represents a liberatory epistemic virtue, the value of which derives from liberating us as socially situated learners from epistemic blindness to epistemic freedom. Third, I explicate two distinct forms of epistemic (...) transformation: constitutive and causal epistemic transformation. I argue that compared with the ideal conception of epistemic virtue, constitutive epistemic transformation that involves good learning has a transcendent value in light of agents constantly renewing their default epistemic status toward ideal virtuousness. (shrink)

Today e-commerce has had tremendous growth in the past years primarily due to changes in technology and customer’s buying behavior. One of the big shifts in the process has been the use of ML in advertising which has the capability to transform the marketing domain together with consumer interactions. This paper discusses the viability of using machine learning for designing realistic models of advertising to increase effectiveness of target and personalized advertising, as well as conversion rates in e-commerce. Several (...) techniques are explored in the study, such as supervised and unsupervised learning, recommender systems, and content optimization with natural language processing. By using case studies and experimental models, we discuss how and to what extent ML is beneficial in e-commerce advertising transformation. The results presented in this paper indicate that using machine learning in advertising has a potential of dramatically improving the customer experience while simultaneously increasing brand recognition and sales. (shrink)

The research outlines a workflow that incorporates data collection, preprocessing, model training, and optimization. Real-world datasets from retail and e-commerce sectors are utilized to validate the proposed methodology, showcasing substantial improvements in model performance. The results indicate that optimized models not only provide better predictions of consumer behaviour but also enhance customer segmentation and targeting strategies. The study concludes with recommendations for future research, including the exploration of hybrid optimization techniques and the application of these methods in real-time analytics.

Through examples of embodied and learning -centered pedagogy, we discuss transformative learning of transgressive topics. We begin with a taxonomy of types of learning our students undergo as they resolve inconsistencies among their pre-existing beliefs and the material they confront in our course on feminist ethics and epistemology. We then discuss ways to help students maximize their learning while confronting internal inconsistencies. While we focus on feminist topics, our approach is broad enough to be relevant (...) to anyone teaching a transgressive or controversial topic. (shrink)

Edge computing, combined with machine learning (ML), is emerging as a transformative paradigm for handling the data deluge generated by the Internet of Things (IoT) devices. Traditional cloud computing is often inadequate for the low-latency, high-throughput demands of IoT applications, especially in real-time analytics. By processing data locally at the edge of the network, edge computing reduces latency, enhances privacy, and alleviates the bandwidth burden on centralized cloud servers. The integration of ML algorithms into edge devices further augments (...) the decision-making process by enabling real-time data analytics, improving operational efficiency, and delivering immediate insights. This paper explores the synergy between edge computing, ML, and IoT, discussing how real-time data analytics at the edge can transform industries such as smart cities, healthcare, and industrial automation. We also present challenges, future directions, and practical applications of machine learning-based real-time analytics for edge computing in IoT ecosystems. (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.

This article does three things. First, it asks a new question about transformative education, namely ‘what is the role of power and trust in the decision of whether to transform one’s meaning scheme in the face of new information or whether to simply reject the new information?’ Secondly, it develops a five-stage model which elaborates on the role of this decision in transformative learning. Finally, it uses grounded-theory and the five-stage model to argue that power and trust (...) play an important role in facilitating transformative learning. (shrink)

This comprehensive article explores the convergence of cloud computing and Java technologies, highlighting their synergistic relationship in modern software development. It examines key concepts in cloud computing, including virtualization, microservices architecture, CI/CD practices, and serverless computing. The article also discusses various cloud service models (SaaS, PaaS, and IaaS), their applications, and essential tools and technologies for cloud development. A financial institution's infrastructure modernization case study illustrates the practical implementation of these concepts, demonstrating how cloud technologies and Java can enhance agility, (...) reliability, cost-efficiency, and innovation in traditional industries. (shrink)

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to (...) a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain. (shrink)

Why do public philosophy in prisons? When we think about the value and aims of public philosophy there is a well-entrenched tendency to think in transactional terms. The academy has something of value that it aims to pass on or transmit to its clients. Usually, this transaction takes place within the confines of the university, in the form of transmission of valuable skills or knowledge passed from faculty to students. Public philosophy, construed within this transactional mindset, then consists in passing (...) on something valuable from inside the academy to the outside. In this paper, we reflect on our experiences of taking philosophy into prisons and argue that making the case for public philosophy in general, and philosophy in prisons in particular in these transactional terms risks obscuring what we take to be a distinctive and valuable outcome of public philosophy. Importantly, it risks obscuring what those who participate in a particular kind of public philosophy – including the professional philosophers – experience as valuable about the activity: its transformational potential. (shrink)

This study made use of descriptive correlational method of research that utilized standard questionnaires as primary data gathering tools, substantiated by extensive documentary analysis. The respondents of the study were teachers and principals of public secondary schools in the City Division of Malolos. The following null hypotheses were subjected for testing at .05 level of significance. “The transformational leadership of principals in the City Division of Malolos does not significantly affect students’ academic achievement” “The quality of school learning environment (...) in the City Division of Malolos does not significantly affect students’ academic achievement”. School principals who are good transformational leaders are contributors of best possible leadership outputs that facilitate more students learning and academic achievement, likewise would help teachers to become more productive in practicing their teaching professions as committed member of school organization. The very good quality of school learning environment in the City Division of Malolos can be a springboard to the achievement of 21st century classroom experience. Principals’ transformational leadership significantly influences the academic achievement of students in English, Math, and Science. The quality of school learning environment significantly influences the academic achievement of students in English, Math, and Science. Implications drawn from the findings of the study are good insights that may be used in further improving the quality academic achievement of students in public secondary schools. (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)

Data normalization is a crucial step in database management systems (DBMS), ensuring consistency, minimizing redundancy, and enhancing query performance. Traditional methods of normalization in supermarket sales databases often demand significant manual effort and domain expertise, making the process time-consuming and prone to errors. This paper introduces an innovative machine learning (ML)-based framework to automate data normalization in supermarket sales databases. The proposed approach utilizes both supervised and unsupervised ML techniques to identify functional dependencies, detect anomalies, and suggest optimal schema (...) transformations. Experiments on supermarket sales datasets show substantial improvements in accuracy, scalability, and processing time compared to traditional approaches. The results emphasize the potential of incorporating ML into database management practices to boost operational efficiency and support better decision-making. (shrink)

Standard ways of conceptualizing moral development and measuring pedagogical interventions in ethics classes privilege the growth of moral judgment over moral sensitivity, moral motivation, and moral habits by too often conflating improvement in moral judgment with holistic moral development. I argue here that if we care about students’ construction and cultivation of their ethical selves, our assessment design principles ought to take seriously the transformative possibilities of philosophy as a way of life and be based on a more robust (...) and holistic account of moral development. I illustrate these principles of assessment design through an examination of the Character Project, which I created to help students engage in their own deliberate ethical transformation through self-directed, individualized, and concentrated practice. Finally, I conclude with a discussion about how to appropriately and fairly assess this kind of deeply personal learning. (shrink)

Deliberative Transformative Moments (DTM) is a new concept that serves as an amendment to the DQI. With this new concept it is easier to get at the quick give-and-take of discussions of small groups of ordinary citizens. As an illustration, we apply the concept to discussions about the peace process among Colombian ex-combatants, ex-guerrillas and ex-paramilitaries. Specifically, we show how personal stories can transform a discussion from a low to a high level of deliberation and how they can have (...) the opposite effect. To increase the level of deliberation in the general population, we recommend that good illustrations of DTM’s should be part of the school programs from an early age on, so that children learn how to discuss with others who have different opinions and values. (shrink)

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the (...) proposed model, it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

This research is motivated by the need to unravel the progression of learning models, which have been adapting to meet the demands of society in its constant dynamics of fluctuation and transformation. The aim of this work is to systematically examine the evolution of learning models, highlighting the paradigmatic changes that have favored the transition from traditional learning approaches to more innovative and transdisciplinary proposals. To achieve this, a bibliographic analysis is carried out, supported by the hermeneutic (...) method for the contextual interpretation of literature and discourses, in order to unravel the complexities inherent in the evolution of learning models. The results highlight the limiting influence of the simplicity paradigm in traditional educational formation and the need for a shift towards dialogicity and disciplinary collaboration and integration to advance towards complexity. It concludes by arguing in favor of adopting the complexity paradigm, advocating for a transdisciplinary epistemology and an interstructuring approach to learning, which allow for integral human development. Recognizing human complexity in teaching and learning demands a radical transformation of education towards more holistic and transformative practices, essential for building an equitable and just society. (shrink)

A central task of developmental psychology and philosophy of science is to show how humans learn radically new concepts. Famously, Fodor has argued that such learning is impossible if concepts have definitional structure and all learning is hypothesis testing. We present several learning processes that can generate novel concepts. They yield transformations of the fundamental feature space, generating new similarity structures which can underlie conceptual change. This framework provides a tractable, empiricist-friendly account that unifies and shores up (...) various strands of the neo-Quinean approach to conceptual development. (shrink)

The liberal democratic regimes rest on a well-developed public sphere accessible to all citizens that favors free discussions based on reason and critical debate and serves as a space where public opinion is formed through reasoned dialogue. The new digital technologies disrupted many parts of contemporary democratic societies and transformed their public sphere. Digital transformation alters industries and markets, changing the perceived subjective value, satisfaction, and usefulness of goods or services and displacing established companies and products. Within the political realm, (...) digital transformation creates a cleavage between the vulnerable populations who are ill-informed and lack digital fluency and politicians who tend to learn about peoples‟ problems not to deal with them but to weaponize new technologies to engineer elections and win power. The rise of misinformation and disinformation undermines public trust in democratic institutions and discourages or incapacitates citizens from engaging in debates within the public sphere. On the other hand, the digital transformation of the public sphere empowers ordinary people to aggregate in various publics and counteract the domination of the mainstream parties. Our paper aims to answer whether new technologies provide citizens with ways to counter the undemocratic tendencies caused by digital transformation and engage actively in the public sphere. (shrink)

Abstract: As the frequency and sophistication of cyberattacks continue to rise, organizations face increasing challenges in safeguarding their digital infrastructures. Traditional cybersecurity measures often struggle to keep pace with rapidly evolving threats, creating a pressing need for more adaptive and proactive solutions. Artificial Intelligence (AI) has emerged as a transformative force in this domain, offering enhanced capabilities for detecting, analyzing, and preventing cyberattacks in real- time. This paper explores the pivotal role of AI in strengthening cybersecurity defenses by leveraging (...) machine learning algorithms, predictive analytics, and automation to anticipate and mitigate potential threats before they manifest. Furthermore, it examines AI's ability to evolve with emerging attack vectors, providing a dynamic response to an ever-changing threat landscape. The paper also addresses the limitations and ethical considerations surrounding AI-driven cybersecurity, advocating for a balanced approach to its deployment. Through this exploration, the research underscores how AI is redefining the future of cyber defense by shifting the focus from reactive to proactive strategies. (shrink)

. Thyroid diseases represent a significant global health concern, necessitating accurate and timely diagnostic methods for effective treatment. Traditional diagnostic approaches often rely on complex blood tests and imaging techniques that can be challenging to interpret. This paper explores the application of machine learning (ML) and deep learning (DL) techniques, particularly Vision Transformers (ViT), for thyroid disease detection. We conducted a comprehensive literature survey that highlights various studies employing ML and DL models, revealing high accuracy rates but also (...) significant limitations such as small sample sizes and dataset imbalances. Our research methodology involved creating a custom dataset, preprocessing images, and developing robust models using both ML algorithms and advanced DL architectures. Furthermore, we discuss the implications of our findings for clinical practice and propose future research directions to enhance diagnostic capabilities. This study underscores the potential of leveraging AI technologies to improve the accuracy of thyroid disease detection while addressing existing challenges in traditional diagnostic methods, ultimately contributing to better patient outcomes in thyroid health management. (shrink)

The proliferation of the Internet of Things (IoT) has transformed various industries by enabling smart environments and improving operational efficiencies. However, this expansion has introduced numerous security vulnerabilities, making IoT systems prime targets for cyberattacks. This paper proposes a machine learning-based intrusion detection framework tailored to the unique characteristics of IoT environments. The framework leverages feature engineering, advanced machine learning algorithms, and real-time anomaly detection to identify and mitigate security threats effectively. Experimental results demonstrate the efficacy of the (...) proposed approach in detecting diverse IoT-specific attacks, including denial-of-service (DoS), man-in-the-middle (MITM), and malware-based attacks. The Internet of Things (IoT) has revolutionized modern living by interconnecting devices and enabling seamless communication. However, the increasing reliance on IoT systems has exposed significant vulnerabilities, making them a prime target for security attacks. This paper proposes a machine learning-based intrusion detection framework to detect and mitigate security attacks in IoT environments. The framework integrates diverse machine learning algorithms to identify abnormal behavior and potential threats. Through comprehensive experiments and evaluations, this research demonstrates the efficacy of the proposed framework in terms of accuracy, scalability, and robustness. (shrink)

Quantum machine learning (QML) provides a transformative approach to data analysis by integrating the principles of quantum computing with classical machine learning methods. With the exponential growth of data and the increasing complexity of computational tasks, quantum algorithms offer tremendous advantages in terms of processing speed, memory efficiency, and the ability to resolve issues intractable for classical systems. In this work, the use of QML techniques for both supervised and unsupervised learning problems is explored. Quantum-enhanced models (...) such Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs) show outstanding performance in classification and regression tasks by using quantum kernels and entanglement in supervised learning. Moreover, hybrid quantum-classical solutions offer useful implementations on noisy intermediate-scale quantum (NISQ) devices, hence bridging the gap between present quantum technology and practical uses. By means of comparative analysis, this paper emphasizes the possible benefits and drawbacks of QML, thereby providing understanding of its future importance in sectors including material science, finance, and healthcare. In the end, QML opens the path for a new era of intelligent data processing and solves until unthinkable difficult challenges. (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)

Personalized learning experiences have undergone revolutionary transformations as a result of the use of artificial intelligence (AI) into education. The application of AI-driven adaptive learning methods designed especially for the Parakh assessment system's multiple-choice question (MCQ) selection is examined in this abstract. In order to dynamically evaluate a student's ability and modify the question complexity in real time, the suggested method makes use of machine learning models. The system carefully chooses questions that address each learner's unique (...) class='Hi'>learning gaps and offer suitable challenge levels based on past performance data and question information. Preprocessing assessment data, extracting features from student interaction logs, and creating a recommendation system to improve the accuracy of MCQ selection are all part of the methodology. Evaluation measures like knowledge retention rates, time to completion, and response accuracy are utilized to evaluate the effectiveness of the system. By optimizing learning outcomes, this AI-based strategy seeks to provide a more stimulating and productive assessment environment. The method might completely change the way formative evaluations are carried out and guarantee that every student gets a customized education that is in line with their particular academic requirements. (shrink)

How does philosophy learn to speak a new language? That is, how does some particular language come to serve as the means for the expression of philosophical ideas? In this paper, I present an answer grounded in four historical case studies and suggest that this answer has broad implications for contemporary philosophy. I begin with Jonathan Rée’s account of philosophical translation into English in the sixteenth century, and the debate between philosopher-translators who wanted to acquire – wholesale or with modifications (...) – foreign terms, and those who wished to take existing words and transform them from their ordinary to a philosophical use. I sketch how these twin processes of ‘acquisition’ and ‘transformation’ manifested themselves in philosophical translations from Greek to Latin, Greek to Arabic and both Greek and Arabic to Gə’əz and argue that comparative work in this vein could yield interesting and significant results. I suggest that not only is Rée’s approach useful for thinking about philosophical translation historically, but that philosophical translation between very different languages is important for contemporary philosophy insofar as it reveals the linguistic presuppositions of philosophical theories expressed in some particular language, and that this constitutes an argument against the prevailing monolingualism in philosophy. (shrink)

The educational process has been dramatically transformed by technology; this transformation has caused the emergence of central concepts, among which is distance learning. The latter has brought new opportunities to learn from; meanwhile, it has presented challenges that have impacted learning in many ways. Thus, the present study investigated the implementation of E-learning among EFL students at Moulay Ismail University to identify students' perceptions about this type of learning and consider the pros and cons to ensure (...) best practice solutions that endorse online learning quality. The study included 315 students at Moulay Ismail University in Morocco who completed an online questionnaire and used descriptive analysis because the data was presented in simple summaries that aimed to describe the basic features of the data and measures. The findings, both quantitative and qualitative, indicated that e-learning is still in its early stages. At the same time, students are not entirely prepared to supersede traditional learning with distance education due to some reasons, in particular, such as lack of university support in offering resources to learn online, lack of internet connection, malfunction of technological tools, etc. All in all, EFL students prefer blended learning because it allows them to combine a new technological experience with a traditional one. Meaning that e-learning should not be excluded entirely from the educational system, but it can be used only as a support to foster EFL students' academic achievements. (shrink)

.Terrain recognition is critical in various applications, including autonomous navigation, disaster response, and remote sensing. Traditional methods rely heavily on convolutional neural networks (CNNs), which require significant computational resources for high accuracy. Vision transformers (ViTs) have recently emerged as a novel approach to image processing, offering superior capability in processing long-range dependencies in visual data. This paper proposes a terrain recognition model based on Vision Transformers, aiming to improve classification accuracy and processing efficiency on complex terrain datasets. Key steps include (...) pre-processing satellite imagery, feature extraction through transformer architecture, and performance evaluation. Our results demonstrate that ViTs significantly enhance recognition accuracy, making them a promising alternative to CNNs in terrain analysis tasks. (shrink)

In computer science, Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Deep Learning is a new field of research. One of the branches of Artificial Intelligence Science deals with the creation of theories and algorithms (...) that allow the machine to learn by simulating neurons in the human body. Most in-depth learning research focuses on finding high-level methods. The strippers analyze a large data set using linear and nonlinear transformations. The method of deep learning is used in the detection of several diseases including blood cell diseases and their classification using the radiography of blood cells to help decision makers to know the type of blood cell and its associated diseases and the results will be presented in detail and discussed. This thesis is using python language and deep learning to detect blood cell diseases and their classifications. The proposed deep learning model was trained, validated and the tested. The accuracy of proposed model was 98.00%. (shrink)

Generative AI is revolutionizing the landscape of creative learning by empowering students, educators, and creators to generate content, enhance project-based learning, and facilitate art generation. This paper explores the integration of generative AI tools in content creation, where learners can produce text, audio, and visual content with ease, fostering creativity and personalized learning experiences. In project-based learning, AI-driven platforms provide innovative approaches for hands-on projects, allowing students to simulate real-world scenarios, problem-solving, and interdisciplinary collaboration. Furthermore, AI-generated (...) art is reshaping the art world by enabling new forms of expression and democratizing the creative process. This study highlights the potential of generative AI to transform education through enhanced creativity, engagement, and personalized learning paths. (shrink)

Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. (...) Additionally, it discusses challenges like data privacy, barriers to technology adoption, and the ethical implications of AI in farming. Integrating AI into agricultural practices holds the promise of greater efficiency and sustainability, paving the way for future innovations. (shrink)

Education can transform our cognitive world. Recent use of enactivist and enactivist-friendly work to propose understanding transformational learning in terms of affective reframing is a promising first step to understanding how we can have or inculcate transformational learning in different ways without relying on meta-cognition. Building on this work, I argue that to fully capture the kind of perspectival changes that occur in transformational learning we need to further distinguish between ways of reorienting one’s perspective, and I (...) specify why different ways are differently valuable. I propose that recent approaches to Confucian ritual provide a clue to what is missing in characterisations of perspective transformation and the resultant transformed perspective. I argue that focussing on ritualised interpersonal interactions provides a further clue as to what’s missing from a mere appeal to the ritual-based inculcation of new perspectives, namely the kind of lightness and flexibility that some ritualised interactions encourage participants to have, and the deepening of perspective associated with that lightness. I argue that a case study of a project implementing a highly ritualised philosophical practice with prisoners in Scotland shows how these constraints, seemingly paradoxically, function so as to actually deepen the perspectival spaces of those agents. This case study provides a proof of concept for the proposal that certain forms of ritual engagement can reliably bring about the kind of transformation of perspective that is the target phenomenon of transformative learning theory. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and diagnostic support. (...) Additionally, the paper addresses the challenges faced in AI-driven medical imaging, including data quality, model interpretability, and ethical considerations. By examining recent advancements and real-world case studies, this paper provides insights into the current state of AI in medical imaging and its potential future directions. The findings highlight the ongoing evolution of AI technologies and their crucial role in advancing medical diagnostics and treatment strategies. (shrink)

The purpose of this study is to investigate the leadership strategies and curriculum management practices of the Program Chair responsible for the Bachelor of Secondary Education (BSED) and Bachelor of Physical Education (BPED) programs at a higher education institution. It focuses on the Program Chair's methods for fostering professional development, supporting faculty growth, and conducting program evaluations. For the methodology, a qualitative case study approach was used, incorporating semi-structured email interviews to collect information about the Program Chair's strategies for curriculum (...) management, faculty development initiatives, and evaluation processes. The research findings indicate that effective leadership, which is rooted in ongoing professional development, collaborative decision-making, and faculty growth, is crucial for successful curriculum management. The study also emphasizes the need to incorporate 21st-century skills, digital literacy, and critical thinking into the curriculum. Furthermore, it shows that strong faculty development programs, including mentorship and professional development opportunities, enhance teaching practices and better equip graduates to meet the challenges of the 21st-century workforce. In conclusion, the study suggests that higher education institutions can greatly benefit from updated curricula, improved faculty development programs, data-driven evaluation systems, and modern learning environments to maintain the success and relevance of their teacher education programs. These recommendations offer practical insights for institutions aiming to enhance their teacher education offerings. (shrink)

In group discussions of any kind there tends to be an up and down in the level of deliberation. To capture this dynamic we coined the concept of Deliberative Transformative Moments (DTM). In deeply divided societies deliberation is particularly important in order to arrive at peace and stability, but deliberation is also very difficult to be attained. Therefore, we wanted to learn about the conditions that in group discussions across the deep divisions of such societies help deliberation. We organized (...) such group discussions between ex-guerrillas and ex-paramilitaries in Colombia, Serbs and Bosnjaks in Srebrenica, and poor residents and local police officers in the favelas (slums) of Brazil. We could identify factors that help to transform discussions from low to high deliberation and risk transformations in the opposite direction. We could also identify factors that help to keep a discussion at a high level of deliberation, and, in a next step, we could determine to what extent long sequences of deliberation had a positive impact on the outcomes of the discussions. Finally, we show how our research results can have a long term effect if it is used in schools of such deeply divided societies. (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)

Cloud computing has transformed data management and storage by providing unmatched scalability, flexibility, and cost-effectiveness. However, rising cloud storage use has raised data security and privacy issues. As sensitive data being outsourced to third-party cloud providers, security is crucial. Traditional encryption methods secure data but make data recovery difficult. Specifically, efficiently searching encrypted data without compromising security is difficult.

This book is an edited collection of original research papers on the digital revolution of the public and governance. It covers cyber governance in Finland, and the securitization of cyber security in Finland. It investigates the cases of Brexit, the 2016 US presidential election of Donald Trump, the 2017 presidential election of Volodymyr Zelensky, and Brexit. It examines the environmental concerns of climate change and greenwashing, and the impact of digital communication giving rise to the #MeToo and Incel movements. It (...) considers how digitilization can serve to emancipate women through ride-sharing, and how it leads to the question of robot rights. It considers fake news and algorithmic governance with respect to case studies of the Chinese social credit system, the US FICO credit score, along with Facebook, Twitter, Cambridge Analytica and the European effort to regulate and protect data usage. (shrink)

-/- Reprogramming Society: Aligning Human Learning, Education, and AI with the Universal Law of Balance -/- Introduction -/- Throughout history, human societies have struggled with misinformation, irrational decision-making, and social imbalance. The root cause of these issues lies in the way human minds are programmed from birth. Negative thinking and behavior are not inherent traits but the result of flawed learning systems that fail to align with the universal law of balance in nature. To correct this, a holistic (...) transformation of education, governance, media, and AI is necessary. This essay explores how all human learning follows natural laws, how misinformation disrupts balance, and how a properly designed educational system—supported by AI—can reprogram society toward rational and stable decision-making. -/- The Principle of Learning and the Universal Law of Balance -/- All humans are born with the same fundamental learning mechanisms, guided by feedback and adaptation. Learning is a continuous process of correcting imbalances through experience, much like how natural systems maintain equilibrium. However, when incorrect information, false beliefs, or societal conditioning interfere, they create negative programming that leads to irrational decisions and harmful behaviors. -/- Under the universal law of balance, every thought and action should ideally contribute to stability. When individuals or societies operate outside this balance—due to ignorance or misinformation—they generate problems such as corruption, social injustice, and environmental destruction. Therefore, for humanity to function harmoniously, its learning systems must be structured to filter out falsehoods and reinforce rational thinking based on natural laws. -/- The Role of Education in Correcting Negative Programming -/- Current educational systems fail because they do not integrate the principle of balance. Instead of fostering critical thinking and truth-based decision-making, they often reinforce biases, outdated ideologies, and economic or political agendas. This misalignment perpetuates misinformation, leading to poor governance and societal dysfunction. -/- To correct this, education must be redesigned to follow the universal law of balance. This means: -/- 1. Teaching critical thinking – Students must learn how to evaluate information and recognize misinformation. -/- 2. Eliminating dogma-based teaching – While religious and cultural beliefs can coexist with science, education should be rooted in objective reality. -/- 3. Promoting holistic understanding – Instead of fragmented knowledge, subjects should be interconnected to reflect the natural balance of life. -/- 4. Instilling ethical decision-making – Education should train individuals to make choices that maintain societal equilibrium rather than disrupt it. -/- Governance, Media, and the Spread of Misinformation -/- Education alone is not enough; governance and media must also align with the law of balance. Governments often create policies based on political or economic pressures rather than natural equilibrium, leading to resource mismanagement, social inequality, and environmental crises. Meanwhile, media—whether traditional or digital—plays a crucial role in shaping public perception but often spreads misinformation for profit or control. -/- To fix these issues: -/- Governance must be based on rational, balanced decision-making, using policies that prioritize sustainability and long-term stability over short-term gains. -/- Media should be held accountable for spreading misinformation, ensuring that public discourse is grounded in truth rather than manipulation. -/- The Role of AI In Reprogramming Society -/- Artificial intelligence offers a unique opportunity to accelerate the reprogramming of human minds and societal systems. Unlike human institutions, AI can process vast amounts of data without emotional or ideological bias. If AGI is programmed according to the universal law of balance, it can function as an analytical system that: -/- Filters misinformation from education, governance, and media. -/- Validates the accuracy of knowledge before it influences decision-making. -/- Guides leaders and citizens toward rational, balanced choices. -/- However, for AI to achieve this, it must be designed with a deep understanding of the universal formula. AI developers must either be educated on this principle or integrate it directly into AI training models to ensure that future AGI serves as a force for balance rather than chaos. -/- Conclusion -/- Human society is at a critical juncture where misinformation, flawed education, and unbalanced governance threaten global stability. Since all negative human thinking and behavior stem from negative programming, the solution lies in a complete reformation of education, governance, and media—guided by the universal law of balance. AI, if programmed correctly, can serve as the ultimate tool to reprogram society, ensuring that human learning, decision-making, and leadership align with the natural order. Only through this holistic transformation can humanity achieve true progress and harmony. -/- . (shrink)

Image captioning has seen remarkable advancements with the integration of deep learning techniques, notably Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, for generating descriptive captions for images. Despite these improvements, capturing intricate details and context remains a challenge. This project introduces an enhanced image captioning model that integrates transformers with an attention mechanism to address these limitations. By leveraging CNNs for feature extraction and LSTMs for sequence generation, while utilizing transformers to apply sophisticated attention to significant (...) image regions, the proposed model aims to generate more contextually rich and coherent captions. Experimental results indicate that incorporating transformers with attention mechanisms leads to a significant enhancement in caption accuracy and descriptiveness, surpassing traditional CNN-LSTM models. This advancement is particularly beneficial in various applications, including assistive technologies for the visually impaired, content-based image retrieval systems, automatic image annotation for digital asset management, and improved human-computer interaction. This approach represents a substantial step forward in achieving more precise and detailed image captioning, with potential impacts across numerous fields. (shrink)

Understanding the policymaking process in an emerging economy in the global south, such as Bangladesh, holds significant importance. The country's remarkable socio-economic development, once the most impoverished in the region, has been facilitated by post-globalization economic transformation. While the literature on institutional change has predominantly focused on states in industrialist countries, this dissertation presents an innovative theoretical approach. It deeply explores primary case materials to illustrate how the state engages in policy evolution in a developing country's gradual shift from the (...) import substitution model towards globalization and deregulation. The central argument posited is that Bangladesh's globalization, particularly through its private sector-led garment exports, stands as a significant success story. Bangladesh is now the world's second-largest garment exporter after China. This achievement is attributed to the entrepreneurial development it fosters and its role in driving socio-economic change. Moreover, it has enabled the mobilization of resources, reducing aid dependence, enhancing climate resilience, improving public health and wellbeing, empowering women, and addressing critical needs in physical infrastructure and human development. The argument presented here diverges from the conventional explanation found in the literature, which tends to emphasize the role of external forces, particularly institutions like the World Bank, the International Monetary Fund, and donors, in triggering policy reforms in Bangladesh. These reforms, commonly linked with structural adjustment packages implemented during the balance of payments crisis, are frequently perceived as predominantly influenced by external pressures. Additionally, the influence of the business class or Bangladeshi non-governmental organizations (NGOs) is often asserted. This dissertation proposes an alternative explanation by suggesting that ideational viewpoints within the state, specifically the ideas espoused by top technocrats and political executives, played a significant role. It argues that these internal ideational factors were more influential than materialistic ones, highlighting the causal role of the policy process driven by endogenous imperatives. The introduction of the dissertation addresses the research problem and research puzzle while suggesting conjectures after problematizing existing arguments. The significant economic transformation observed in Bangladesh today is portrayed as the outcome of a gradual process spanning several decades. The transition towards promoting the private sector and embracing globalization commenced in 1991, following a series of incremental changes dating back to the 1970s and 1980s. During these periods, the institutional path progressed towards empowering the private sector and embracing globalization, laying the groundwork for a trajectory led by the private sector in the context of globalization. The theory chapter serves to establish the conceptual and theoretical foundation for analyzing both economic change and transformation by examining the evolutionary nature of the process. The study adopts an institutional change framework, drawing on the theory of gradual change and focusing on concepts such as layering and tipping points. Within this framework, causal variables such as powering and puzzling, social learning, and ideas are identified. The methodological approach is grounded in historical institutionalism, employing the process-tracing "within case" method. The selection of cases deliberately targets an important yet under-researched area—the policy process in Bangladesh concerning the success of garment-led industrialization. This dissertation fills a noticeable gap in the literature through rigorous analysis, enabling an in-depth exploration of the institutional dynamics spanning four governments from 1972 to 1991. The study relies on substantial primary materials, including interviews with key officials. Each empirical chapter not only presents data but also connects the findings to conceptual and theoretical conjectures, underscoring the significant contributions of this research. The key causal variables identified include the concepts of ‘puzzling’ and ‘social learning’ within the state capacity framework, along with a narrowly defined and developed concept of a "policy committee." The learning process entails technocrats engaging in what Hugh Heclo and Peter Hall term as "social learning," where they play a crucial role in addressing policy challenges, particularly during times of uncertainty and political turmoil, based on their learning from past experiences. Additionally, the concept of ‘powering’ is highlighted, emphasizing that while technocrats provide policy recommendations, it is ultimately the political executive who endorses policies. The interplay between ‘puzzling and powering’ and ‘social learning’ shapes the outcomes of the policy process. The first empirical chapter focusing on the Sheikh Mujibur Rahman Government (1972-1975) illustrates the emergence of policy learning during this period. Initially, this government pursued import substitution under strict government controls, with a predominant public sector. The historical context of exploitation under Pakistani rule and the centralization of industry by West Pakistan fostered socialism as an ideal for East Pakistan's economic emancipation. Additionally, close ties with India and the Soviet Union reinforced this socialist tendency. However, due to severe economic conditions resulting from stringent control on private investment and emphasis on large-scale public investment, policy puzzling and learning ensued. As a result, incremental changes were initiated, allowing limited private sector participation. The second empirical chapter explores the General Ziaur Rahman government's tenure (1975-1981) and how deregulation was consolidated within a layering step, paving the way for a shift towards globalization. General Zia and his technocrats, operating under authoritarian conditions, acknowledged the inefficacy of existing policies. Furthermore, General Zia's inclination towards the West fostered closer collaboration with donors. The Multifiber Agreement (MFA) presented an opportunity for garments, coinciding with the decline in jute exports. This government opened up industrial sectors for private participation, and banks provided funding across various sectors, with special emphasis on the burgeoning garment industry. The chapter also explores the origins of the garment industry during this period. Notably, Noorul Quader Khan, upon retiring as cabinet secretary, collaborated with a Korean multinational, Daewoo, with the support of General Zia, leading to the establishment of Desh Garments—a pioneering venture that catalyzed the export orientation of the sector. The third empirical chapter delves into the economic policies of the General Hussain Muhammad Ershad Government (1982-1990), highlighting its emphasis on greater private sector involvement. Under this administration, there was a political inclination towards globalization and deregulation, leading to the appointment of technocrats aligned with this vision. The leadership believed that promoting the private sector would be more effective in job creation. Despite facing significant political opposition, efforts towards disinvestment were made, eventually resulting in compromises like public-private partnerships. The decentralization of administration and restructuring of ministries aimed at promoting industrialization and economic globalization driven by the private sector at the regional level. Initiatives such as the Industrial Promotion and Development Company, with support from organizations like the International Finance Corporation, aimed to boost the private sector. Efforts towards globalization were synchronized with the establishment of an export processing zone in Chittagong, focusing on promoting private and foreign investment. Foreign investment promotion was encouraged through diplomatic efforts and legislative measures to safeguard investors. Currency devaluation was utilized to stimulate exports, along with some degree of import liberalization. The chapter convincingly argues that the groundwork for globalization, initiated under General Zia, came to fruition under General Ershad. The final empirical chapter contends that the system shifted towards globalization and deregulation in 1991. Although a severe balance of payments crisis occurred in 1989, it wasn't perceived as the sole cause of the industrial policy of 1991. The democratic uprising in 1990 led to the downfall of the military-backed government and the rise of a democratic government. The chapter argues that while economic and political changes may have been intertwined, the economic shift in 1991 was primarily driven by ideas within the state and driven by endogenous imperatives. It examines preceding periods to illustrate how the tipping mechanism might have operated, detailing the roles of top technocrats and political executives. The chapter provides insights into key policymakers and their ideas, as well as their interactions with industrialists, entrepreneurs, and representatives of multilateral organizations like the World Bank. Significant reform measures, including tariff reduction, the introduction of value-added tax, banking sector reform, and the adoption of floating exchange rates, are outcomes of the policy change. Additionally, sectors such as power, banks, and telecommunications, formerly monopolized by the public sector, are now deregulated. (shrink)

Studies suggest college students/adult learners interacting with current search tools like Google display tendency to power-browse and adhere to page-ranking order in choosing sources to supplement writing. Such limitations may limit critical reflectivity. We present a tool, ThoughtShuffler which allows users to malleably alter neighborhoods of keywords and presents results as arrays of cards and collections that can be compared and contrasted. We conducted binary qualitative coding of essays produced by 39 users divided into experimental and control conditions and computed (...) a one-tailed t-test to understand if using ThoughtShuffler produced greater likelihood for higher-order reflectivity expressed in writing about efficacy of varied healthcare models in the US, compared to the use of Google to write about the same prompt. Our results suggest that updating search engine interfaces to afford capacity for lateral reading may present potential to augment reflective information search. (shrink)

.Terrain recognition is critical in various applications, including autonomous navigation, disaster response, and remote sensing. Traditional methods rely heavily on convolutional neural networks (CNNs), which require significant computational resources for high accuracy. Vision transformers (ViTs) have recently emerged as a novel approach to image processing, offering superior capability in processing long-range dependencies in visual data. This paper proposes a terrain recognition model based on Vision Transformers, aiming to improve classification accuracy and processing efficiency on complex terrain datasets. Key steps include (...) pre-processing satellite imagery, feature extraction through transformer architecture, and performance evaluation. Our results demonstrate that ViTs significantly enhance recognition accuracy, making them a promising alternative to CNNs in terrain analysis tasks. (shrink)

AI-powered records science is revolutionizing the way facts are analyzed and understood. It can significantly improve the exceptional of information evaluation and boost its speed. AI-powered facts technological know-how enables access to more extensive, extra complicated information sets, faster insights, faster trouble solving, and higher choice making. Using the use of AI-powered information technological know-how techniques and tools, organizations can provide more accurate outcomes with shorter times to choices. AI-powered facts technology also offers more correct predictions of activities and developments (...) and superior accuracy in sample reputation.Furthermore, AI-powered facts science techniques can help agencies discover anomalies in datasets, learn new patterns, and better understand the points. This paper discusses the advantages of AI-powered statistics science and how it may enhance data analysis. It additionally discusses a number of the challenges going through organizations in imposing AI-powered records science projects and gives excellent practices for deploying and coping with records technology projects. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

Generative AI is revolutionizing the landscape of creative learning by empowering students, educators, and creators to generate content, enhance project-based learning, and facilitate art generation. This paper explores the integration of generative AI tools in content creation, where learners can produce text, audio, and visual content with ease, fostering creativity and personalized learning experiences. In project-based learning, AI-driven platforms provide innovative approaches for hands-on projects, allowing students to simulate real-world scenarios, problem-solving, and interdisciplinary collaboration. Furthermore, AI-generated (...) art is reshaping the art world by enabling new forms of expression and democratizing the creative process. This study highlights the potential of generative AI to transform education through enhanced creativity, engagement, and personalized learning paths. (shrink)

Iris Murdoch's famous case of M and D illustrates the moral importance of learning to see others in a more favourable light through renewed attention. Yet if we do not read this case in the wider context of Murdoch's work, we are liable to overlook the attitudes and transformations involved in coming to change one's mind as M does. Stanley Cavell offers one such reading and denies that the case represents a change in M's sense of herself or the (...) possibilities for her world of the kind exemplified by Nora in Ibsen's A Doll's House. In this essay, I challenge Cavell's reading, suggesting that the case, while it may not be an exemplar of the perfectionist outlook as described by Cavell, can and should be interpreted in perfectionist terms. To see this, I reflect on Murdoch's views on the endless perfectibility of language, the importance of humility, and the role of love and attention in moral learning. I conclude that Murdoch's work uniquely sheds light on how we might cultivate a perfectionist outlook in ourselves and others, and describes the distinctive role that some novels can play in moral education. (shrink)