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)

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)

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)

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)

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.

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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. (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.

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)

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)

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)

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)

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)

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)

We consider an extended notion of reinforcement learning in which the environment can simulate the agent and base its outputs on the agent's hypothetical behavior. Since good performance usually requires paying attention to whatever things the environment's outputs are based on, we argue that for an agent to achieve on-average good performance across many such extended environments, it is necessary for the agent to self-reflect. Thus weighted-average performance over the space of all suitably well-behaved extended environments could be considered (...) a way of measuring how self-reflective an agent is. We give examples of extended environments and introduce a simple transformation which experimentally seems to increase some standard RL agents' performance in a certain type of extended environment. (shrink)

The objective of this case study concentrated on examining the learning gap, going through some components of the transformation process, and coming up with some ways for aiding students who were experiencing lost learning. A qualitative research design was utilized by the researchers to understand and solve the cases related to Mathematics learning difficulties. Creswell (2008) asserts that qualitative research can be used to discover and comprehend the significance that certain people or groups assign to social or (...) human issues. The researchers used purposive sampling to determine 15 student participants to understand the cases of learning loss and prescribe better interventions to the students. To solve the problems associated with loss of learning experienced by the learners after remote learning, a case study will be utilized as the strategy of inquiry since it fits the situation that is to focus the said key cases, discuss some transforming process, and develop interventions. After analyzing the interviews, researchers identified three common themes across the three cases which serve to clarify the learners' experiences after the post-remote learning: the learners' learning experiences during remote learning, factors that caused learning loss in Mathematics, and learners' coping strategies. In a deeper analysis, the impact of distance learning on students during the pandemic goes beyond just the difficulties in learning and understanding lessons which resulted in difficulty in learning math during in-person classes. The lack of in-person interaction with classmates and teachers also contributes to a sense of isolation and boredom, negatively affecting students' overall well-being and mental health. (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. (shrink)

Today, artificial intelligence, especially machine learning, is structurally dependent on human participation. Technologies such as Deep Learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical (...) critique, I differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI”. This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control and discrimination of users. (shrink)

The human mind is central to comprehend and manoeuvre the natural world. Though it is the crown expression of Nature in the mortal world, it is inherently inconsistent. It is often captivated by many animalistic habits. One of the animalistic habits of mind is its fickleness. Sikh theology emphasizes that one must learn how to control and transform one’s mind to come out of this condition. An individual can do so by diverting his/her mind from its feral inclinations and turning (...) it to the mind’s innate longing for spiritual enlightenment. Sikh doctrines assert that the contemplation on God (imbibing love of God in one’s life) helps restrain the mind and attain self-realization and everlasting equipoise. (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)

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)

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)

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)

Palawan is a land of promise, and of paradox. On maps, it appears on the edge of the Philippines, isolated. Indeed, it is a kind of last frontier. Its population remained tiny for centuries, the government offering homestead land in the 1950s practically for free to attract migrants from outside. The Palawan State University was established by law in 1965, but did not become operational until 1972. A commercial airport did not exist until the 1980s, and for many years, flights (...) were limited. Yet Palawan is one of the oldest sites of human habitation in the Philippines with the famous Tabon Cave human fossils. The oldest bone fragment here has been dated to be about 47,000 years. We know, too, that trade with China goes back several centuries. Today, Palawan seems to be making up for lost time with new commercial investments pouring in at breakneck speed. In particular, outsiders have rediscovered its potentials around logging, mining, fisheries, and tourism. This has caused concern among individuals and civil society organizations who want sustainable development, and see the commercial developments mainly as extractive, not just of natural resources but of the human. There’s very cheap labor available. And when potential investors marvel about cheap land, they’re actually talking about displacing earlier settlers, including indigenous people, from their lands. A subtle but still insidious aspect of the exploitation of human resources is a transformation of the very concept of human development. Using the rhetoric of modernity, residents in Palawan are reorienting the way they view themselves as well as their families and friends. The value of a human being now hinges on how they look, and the desired appearance is defined from the outside, as we see in this anthology of research reports coming from the Chemical Youth project of the University of Amsterdam and the University of the Philippines Diliman. We read about the importance of fair skin as a projection of cleanliness, of high social status (meaning someone not engaged in manual labor and therefore not exposed to the sun). We read of how “femininity” is defined around body contours, and cosmetics, and how hormones are used by male-to-female transgenders. We go beyond the visual, reading about the importance of controlling or enhancing body odors among tour guides, who interestingly are especially concerned about the bad odor management of their foreign customers, using car perfumes to keep their work manageable and we learn how difficult it is for security guards to stay alert during their long shifts. Energy drinks and cigarettes help them perform their duties. All these transformations through what the French philosopher and historian Michel Foucault has called “technologies of the self” are as paradoxical as Palawan. On the surface, the products—which are technologies—seem to be mainly in the realm of the self but are, in reality, pushed, through marketing, from the outside, in contexts of inequality and exploitative labour relations. Personal aspirations are not personal but are for predefined standards of modernity, related to work-related demands and expectations. The self must be made presentable to the tourist, to the customers in malls, and to those who may threaten the properties that young people protect. It is not surprising that these transformations become problematic for the “self.” The skin whiteners, the hormones, the body deodorants, and the energy drinks are expensive and can distort budgetary priorities. The money for tonic drinks, for example, could well go into more nutritious food. The tragedy, too, many of the products used are of doubtful safety and efficacy. Even the energy drinks have much too high levels of caffeine that can cause cardiac palpitations. Cosmetics and the skin whiteners imported from China and unregistered with the Food and Drug Administration may contain toxic chemicals like mercury. But even registered skin whiteners can be problematic, their so-called “skin-whitening effect” coming about because they take away the upper layers of the skin, leaving behind a red glow (seen as “whitening”) which is actually inflammation. The whitened skin fails to protect against the sun, leading to adverse effects such as black spots. Ultimately though, the problems come with the very definition of the self. As the reports show, young people use the chemicals with some ambivalence, knowing how expensive they are and experiencing some of the undesirable side effects. There is, too, doubt about whether what they’re doing is indeed “good,” captured by how IP women will put on cosmetics only when they’re away from home and about to go to work. The cosmetics have to be removed before they return home because they are not socially acceptable. The research reports are not for Palawan alone. It must make us more critical and discerning as we revisit concepts of development and exploitation, modernity and tradition, self and community. The chemicals, in many ways, are like the products used in precolonial barter trade. For the Chinese, the beeswax and the sea cucumbers, for the inhabitants of Palawan the ceramics, represented faraway lands. To have those products gave prestige. Today, the skin whiteners and tonic drinks and other chemicals described in this anthology represent modernity with promises of not just of a more attractive self, but of better jobs, a better life. We are proud to have worked with the Palawan State University, and the people of Palawan, to gather powerful narratives that will now challenge the outside, the purveyors of modernity, to be more critical and discerning, the chemicals now to be seen not just as stuff applied to the biological body, but as powerful shapers of social bodies. (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. T. (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. Key words: Cyberbullying Detection, Social Media, Supervised Machine Learning, Natural Language Processing (NLP), Optimization Techniques. (shrink)

The approach to the study the role of strategic alliances in the conditions of digital business transformations has been further developed in the article. In the course of a critical analysis of the content of scientific publications of modern scientists who studied the behavior, peculiarities of activity and the meaning of strategic alliances, it was established that the role of strategic alliances in the conditions of digital transformations does not disappear, but brings new advantages, requiring the maximization of returns from (...) more dynamic partnership relations, which requires more in-depth study of such benefits. Dynamics analysis of the number of concluded agreements in the field of strategic partnership in the period of 2014-2023 demonstrates their increase starting from 2017, which led to the activation of digital business transformations. In the course of research, it was determined that the role of strategic alliances in the conditions of digital business transformations is to ensure strategic flexibility under the conditions of continuity of digital transformations, improve competitive positions on the market, increase their innovativeness and efficiency, improve organizational stability and the ability of companies to learn, create opportunities for obtaining synergistic effect. It develops the ability of companies to proactively manage changes, contributes to the joint solution of strategic tasks and goals of partner companies, provides significantly greater opportunities for the development of innovative processes through the development and implementation of new methods and forms of work, new solutions in economic sectors, innovations in management, contributes to survival in market competitive environment and is an important competitive imperative to ensure overall long-term digital transformation. (shrink)

In recent decades, Information and Communication Technology (ICT) has been advanced and widely spread around the globe in addition to ICT revolution and technological advances are considered the major role in the evolution of modern age, which is called "Digital Transformation Age". Therefore, Electronic Technology (E-Technology) has become one of the most prominent approaches such as Electronic Learning (E-Learning), Electronic Training (E-Training), Mobile Learning (M-Learning), Virtual Lab (V-Lab), Virtual University, etc. E-Technology includes some features, for instance (...) anyone, anywhere, anytime and reducing of geographical barriers. ETechnology is a great trend and influences many fields and sectors such as Learning, Training, Military, Navy, Aviation, Medicine, and Digital space. According to nature of usage, E-Technology can be used in positive or negative trends. E-Technology is considered as a valuable tool in providing several opportunities for learning and training processes for individuals and organizations, especially in critical issues. Finally, we give a quick overview of neutrosophic data and some recent applications. (shrink)

Several state-of-the-art machine and deep learning models in the mode of adversarial training, input transformation, self adaptive training, adversarial purification, zero-shot, one- shot, and few-shot meta learning had been proposed as a possible solution to an out-of-distribution problems by applying them to wide arrays of benchmark dataset across different research domains with varying degrees of performances, but investigating their performance on previously unseen out-of- distribution malware attack remains elusive. Having evaluated the poor performances of these state-of-the-art approaches in (...) our previous research on an out-of-distribution attack. In this research, we dived deeper to understand why they works better for other domain dataset but with poor performance on available benchmark malware dataset like Malimg, Malevis, Sorel, and Avast CTU malware dataset. We explored the both the embedding and vector spaces in datasets and compare them with that from other research domain, and find a surprising wide variation between the embedding and vector spaces in malware datasets. We assert that current state-of-the-art machine and deep learning models does not address the wide variation of embedding and vector spaces which are peculiar to malware dataset, hence their poor performance on out-of-distribution attack classification, and so concluded that addressing this variation in embedding and vector spaces will bring about substantial increase in detection of previously unseen out-of-distribution attack. (shrink)

The advent of digital games in language classrooms has ushered in a new era of interactive and dynamic learning experiences. Among these platforms, Kahoot stands out as an engaging and entertaining gaming platform, capturing the attention of both instructors and students. Despite the previous studies on the impact of Kahoot on language learning motivation, they have predominantly focused on assessing the effects of Kahoot before and after instruction overlooking direct comparisons with traditional instructional methods. Consequently, the present study (...) utilized a quasi-experimental research design, and participants were divided into two groups: an experimental group that received instruction using Kahoot, and a control group that followed traditional instructional methods. This research design allowed for a direct comparison of the impact of Kahoot on students’ motivation to learn the language. The results revealed a significant positive impact of Kahoot on students’ motivation to learn English. The students perceived Kahoot as engaging and enjoyable, resulting in heightened enthusiasm and interest inlanguage learning. These findings highlight the transformative potential of Kahoot as a catalyst for enhancing language learning motivation. Integrating Kahoot and other innovative online platforms into EFL instruction can create dynamic, interactive learning environments that foster student engagement and motivation. (shrink)

This study explored the integration of play-based learning strategies within teaching practices, examining the types of activities, their frequency, and alignment with the curriculum. A single public primary school in Gasabo District was purposefully selected for its unique approach to implementing play-based learning, which had not been examined in previous research. The participants included all 26 teachers at the school, consisting of 18 women and 8 men. Employing a qualitative approach, the study utilized semi-structured interviews to investigate into (...) the dynamics of play-based learning. Data analysis involved thematic coding to identify core themes, with participant responses triangulated across various sources and validated through member checking. The findings have shown a different array of play-based activities, with role-playing and storytelling standing out as the most frequently utilized. While some teachers readily incorporated these strategies into their daily lessons, others struggled due to time limitations and curriculum weights. Despite these challenges, the teachers acknowledged the significant benefits of play-based learning in enhancing student engagement, motivation, and socio-emotional development. These insights highlighted the potential of such strategies to transform classroom practices and raise a more interactive and supportive learning environment. To address the identified challenges, the study recommends sustained professional development programs and increased support for teachers to integrate play-based learning effectively. It emphasizes the need for collaborative efforts between policymakers and teachers to reduce barriers such as rigid curricula and time constraints. By aligning policy and practice, the study advises for the optimization of play-based approaches, ensuring their positive impact on Rwandan classrooms is maximized. (shrink)

The ‘community of inquiry’ as formulated by CS Peirce is grounded in the notion of communities of disciplinary-based inquiry engaged in the construction of knowledge. The phrase ‘converting the classroom into a community of inquiry’ is commonly understood as a pedagogical activity with a philosophical focus to guide classroom discussion. But it has a broader application, to transform the classroom into a community of inquiry. The literature is not clear on what this means for reconstructing education and how it translates (...) into schooling practices. Integral to the method of the community of inquiry is the ability of the classroom teacher to actively engage in the theories and practices of discipline-based communities of inquiry so as to become informed by the norms of the disciplines, not only to aspire to competence within the disciplines, but to develop habits of self-correction for reconstructing those same norms when faced with novel problems and solutions, including those in the classroom. (shrink)

Information communications and Technology were introduced into secondary schools with the goal of giving teachers access to information while encouraging their distinctive and innovative techniques for teaching and learning. Teachers become enthusiastic and committed to their work when given demanding tasks that result in positive educational outcomes. However, it is clear that instructors in Kenya continue to fall behind in this area. Without a grasp of leadership behaviors, ICT integration in Kenya remains a phantom. The study aims to evaluate (...) the principal's intellectual stimulation techniques and the empowering use of ICT in teaching and learning in Kenyan public secondary schools. The study used a sequential explanatory mixed method design and focused on 62 school principals chosen through census, 186 teachers, and 372 students selected using a simple random sample process. Data was collected using MLQ-structured questionnaire interviews, document analysis, and checklists. Descriptive analysis was used to explain the patterns in the data and the status of variables, while inferential analysis was used to evaluate the relationship between the variables using regression analysis. The study found a moderately substantial positive correlation between ICT use in school and intellectual stimulation r (108) = 0.534, p < 0.001). It was discovered that principals' actions were a significant predictor of ICT adoption in secondary schools; therefore, principals were recommended to foster a culture of trust among teachers by establishing a welcoming workplace atmosphere. This would encourage instructors to overcome challenges using creative and new approaches, ultimately boosting the usage of ICT in the classroom. (shrink)

This chapter touches upon the damaging impact of neoliberal reason on institutions of higher education, and my efforts as a teacher to help turn things around by re-vitalizing the classroom. After a critique of current neoliberal ‘borderline times’, the chapter takes the reader on a journey of diffractive re-imaginings in which I share some of my experiences of co-learning with undergraduates in an American feminist-philosophical classroom. My central argument is that the neoliberalism-induced crisis in education can be affirmatively counteracted (...) through experimentations with various posthuman and new materialist theories, and the Harawayan-Baradian methodology of diffraction in particular. Furthermore, informed by the impression that theory and pedagogical praxis go hand in hand in many contemporary feminist new materialisms, I zoom in on daily acts of resistance against the neoliberal corporatization of the American university, acts that actualized themselves as feminist new materialist pedagogies. Three examples of diffractive pedagogical strategies are then discussed in detail. (shrink)

Internet of Things (IoT) devices have transformed various industries, enabling advanced functionalities across domains such as healthcare, smart cities, and industrial automation. However, the increasing number of connected devices has raised significant concerns regarding their security. IoT networks are highly vulnerable to a wide range of cyber threats, making Intrusion Detection Systems (IDS) critical for identifying and mitigating malicious activities. This paper proposes a hybrid approach for intrusion detection in IoT networks by combining Machine Learning (ML) techniques with Signature-Based (...) Methods. The hybrid model leverages the strengths of both methodologies to achieve high detection accuracy, reduced false positives, and the ability to identify both known and unknown threats. We explore the integration of ML classifiers such as Random Forest, Support Vector Machines, and k-Nearest Neighbors with traditional signature-based techniques to create a robust IDS solution. The effectiveness of the proposed approach is evaluated using a publicly available IoT dataset, demonstrating its capability to detect a wide variety of attacks with high precision and recall. (shrink)

Concept maps are found to be useful in eliciting knowledge, meaningful learning, evaluation of understanding and in studying the nature of changes taking place during cognitive development, particularly in the classroom. Several experts have claimed the effectiveness of this tool for learning science. We agree with the claim, but the effectiveness will improve only if we gradually introduce a certain amount of discipline in constructing the maps. The discipline is warranted, we argue, because science thrives to be an (...) unambiguous and rigorously structured body of knowledge. Since learning science may be seen as a process where a novice is expected to be transformed into an expert, we use the context of learning science for making the proposal. Further, we identify certain anomalies in the evaluation of concept maps, and suggest that the evaluation should be based on semantics of the linking words (relation types) and not on graphical criteria alone. (shrink)