COVID-19 and military actions on the territory of Ukraine fundamentally changed the organization of the educational process, forcing educational institutions to transfer classroom learning to virtual space. To organize the educational process remotely, we need to organize an educational environment in which we can ensure the implementation of all types of educational activities, as well as creation and selection of e-resources depending on their types. Having guidelines in the form of modes, forms and learning outcomes, it is necessary (...) to structure the content of educational disciplines, corresponding curricula, to ensure the activity of students during lectures and the implementation of practical tasks with the necessary tools, to determine and carry out the selection of electronic educational resources for the effective organization of the educational process in conditions of open education. First of all, it is important to take into account all types of student activities, namely: those ones, which are usually implemented in class and determine the possibility of their implementation in a remote format using cloud services. The monograph considers the problem of effective use of Microsoft 365 services for the organization of e-learning through the prism of the competency approach. In particular, the study established a correspondence between training activities and M365 services, which can provide tools support for their implementation. Chapter 1 analyzes the components of digital competence and the abilities necessary for the organization of the e-learning process. Based on the analysis, the correspondence between the components of digital competence and M365 services, the possession of which indicates the existing level of digital competence, was established. Chapter 2 of the monograph describes the digital competences necessary for the organization of various types of training using Microsoft 365 services. It studies features of using digital identity cloud services, Microsoft 365 services for digital data management and collaboration, e-learning management, design and creation of digital content, problem solving and innovation, digital communication, cooperation and interaction. Chapter 3 offers a developed model of hybrid educational environment architecture based on Microsoft Teams platform and analyzes digital competences for the organization of asynchronous interaction between students and teachers using MS Teams tools. Chapter 4 is devoted to the results of an experimental study of the effectiveness of the use of Microsoft 365 services for the organization of e-learning and the readiness of teachers and students to use them. (shrink)

At the beginning of 2020, the world was experiencing the Covid-19 pandemic, and Indonesia was no exception. The occurrence of this affects the learning system in Indonesia, the learning system that was originally face-to-face was forced to online form, in this case the teachers are required to provide a creative, efficient and optimal learning system for students. So the purpose of this study is to find out the difference in the average learning result of elementary school (...) students during the pandemic. The method used in this study is quantitative with a posttest-only control group design. The population in this study were grade 4 elementary school students in Majalengka district, Indonesia. There were 64 samples and was taken by purposive sampling. The results of this study are that there are differences in the average student learning results where students who study with the hybrid learning system are higher than the face-to-face learning system. The hybrid learning system is very reliable in the 4.0 era as well as learning during the Covid-19 pandemic. However, for the record, it is necessary to look at the facilities and infrastructure considering that this system relies on technology, it is necessary to understand and be able to control the learning media for both teachers and students so that learning outcomes can be optimal and minimize the occurrence of obstacles. The present study revealed the implementation of 21st century learning. (shrink)

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

The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge (...) data set, the cybersecurity warning systems index system is first constructed, then the index factors are picked and measured, and finally, the situation evaluation is done.Numerous bio-inspired techniques were used to enhance the productivity of an IDS by lowering the data dimensionality and deleting unnecessary and noisy input. The Grey Wolf Optimization algorithm (GWO) is a developed bio-inspired algorithm that improves the efficacy of the IDS in detecting both regular and abnormal congestion in the network. The smart initialization step integrates the different pre-processing strategies to make sure that informative features are incorporated in the early development stages, has been improved. Researchers pick multi-source material in a big data environment for the identification and verification of index components and present a parallel reduction approach based on the classification significance matrix to decrease data underlying data characteristics. For the simulation of this situation, grey wolf optimization and whale optimization were combined to detect the attack prevention and the deep learning approach was presented. Utilizing system software plagiarism, the TensorFlow deep neural network is intended to classify stolen software. To reduce the noise from the signal and to zoom the significance of each word in the perspective of open-source plagiarism, the tokenization and weighting feature approaches are utilized. Malware specimens have been collected from the Mailing database for testing purposes. The experimental findings show that the suggested technique for measuring cyber security hazards in IoT has superior classification results to existing methods. Hence to detect the attack prevention in IoT process Whale with Grey wolf optimization (WGWO) and deep convolution network is used. (shrink)

With the increasing degrees A.I.s and machines are applied across different social contexts, the need for implementing ethics in A.I.s is pressing. In this paper, we argue for a pluralist hybrid model for the implementation of moral A.I.s. We first survey current approaches to moral A.I.s and their inherent limitations. Then we propose the pluralist hybrid approach and show how these limitations of moral A.I.s can be partly alleviated by the pluralist hybrid approach. The core ethical decision-making (...) capacity of an A.I. based on the pluralist hybrid approach consists of two systems. The first is a deterministic algorithm system that incorporates different moral rules for making explicit moral decisions. The second is a machine learning system that calculates the value of the variables required by the application of moral principles. The pluralist hybrid system is better than the existing proposals as (i) it better addresses the moral disagreement problem of the top-down approach by including different moral principles, while (ii) it reduces the opacity of ethical decision-making compared with a bottom-up system by implementing explicit moral principles for moral decision making. (shrink)

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

Die aktuellen Erfolge von Künstlicher Intelligenz beruhen nicht nur auf technologischen Fortschritten, sondern auch auf einem grundlegenden soziotechnischen Strukturwandel. Denn maschinelle Lernverfahren wie Deep Learning benötigen eine große Menge Trainingsdaten, die nur über menschliche Mitarbeit gewonnen werden können. In einer Konvergenz von Methoden der Human-Computer-Interaction und der KI ist in den letzten zehn Jahren eine Fülle von Mensch-Maschine-Interfaces und medialen Infrastrukturen entstanden, durch die menschliche kognitive Ressourcen in hybride Mensch-Maschine-Apparate eingespannt werden. Diese Apparate vollbringen im Ganzen jene Leistung, die (...) als künstliche Intelligenz bezeichnet wird. KI in der damit vorliegenden Form ist somit ein distribuiertes, netzwerkförmiges Phänomen. (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 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.

Schools worldwide have started opening doors to welcome back students who, for almost two years, have been stuck studying at home. This study looks at the standpoint of Filipino students on going back to regular face-to-face schooling. There were 2,274 students of different tiers of education (high school, collegiate, graduate) from different major island groups of the Philippines (Luzon, Visayas, Mindanao) who participated in the study. The study used a mixed-method of descriptive statistics to present the quantitative data gathered and (...) thematic analysis of qualitative responses from the subjects. The majority of all the respondents favored going back to the physical classroom, and little only favored staying using the distance mode. In the qualitative analysis, the recurring reasons of the students varied from personal, economic, and fear of getting the virus. It was concluded that Filipino students want to go back to schooling. Moreover, a sizeable amount preferred hybrid while a small number preferred to stay in online or distance mode. Educational institutions should always observe the covid 19 protocol when students go back. (shrink)

The COVID-19 pandemic has had a significant impact on the education sector globally, with over one billion students being held out of school as a result of quarantine measures. In response, education systems had to quickly shift to online learning to ensure that students could continue their education. The sudden shift to online learning has resulted in educators having to adapt to the use of technology in education rapidly. The COVID-19 pandemic has highlighted the importance of digital literacy (...) for educators and the need for ongoing training in digital skills. The pandemic has served as an example of emerging, more creative methods of teaching methods, and technology use in education will continue to evolve. Hybrid learning will be critical to delivering realistic educational experiences, and teachers must adapt to become facilitators instead of teachers. The pandemic experience will affect educators’ attitudes towards integrating information and communication technology (ICT) in education, resulting in a rise in the proportion of people who have a favorable opinion of integrating ICT to education. Ongoing training in digital abilities is necessary to gain a viable education. This paper highlights the need for educators to embrace digital literacy and adapt to emerging technologies to provide a quality education in the post-vaccine world. (shrink)

This paper is concerned with learners who aim to learn patterns in infinite binary sequences: shown longer and longer initial segments of a binary sequence, they either attempt to predict whether the next bit will be a 0 or will be a 1 or they issue forecast probabilities for these events. Several variants of this problem are considered. In each case, a no-free-lunch result of the following form is established: the problem of learning is a formidably difficult one, in (...) that no matter what method is pursued, failure is incomparably more common that success; and difficult choices must be faced in choosing a method of learning, since no approach dominates all others in its range of success. In the simplest case, the comparison of the set of situations in which a method fails and the set of situations in which it succeeds is a matter of cardinality (countable vs. uncountable); in other cases, it is a topological matter (meagre vs. co-meagre) or a hybrid computational-topological matter (effectively meagre vs. effectively co-meagre). (shrink)

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports indicate that wearing facemasks while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A hybrid model (...) using deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images, we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our goal is to identify whether the person on image/video stream is wearing a face mask or not with the help of computer vision and deep learning. (shrink)

This research examined the lived experiences of public school teachers in the United States during the COVID-19 pandemic. A qualitative design was performed using interpretative phenomenological analysis. Twenty public school teachers in the United States formed the sample, with the inclusion criterion being a minimum of three years’ teaching experience, including the pandemic. To meet the safety measure protocols set by the U.S. government, the data gathering was conducted online using Microsoft Forms. The semi-structured interviews comprised two sections: the first (...) ascertained the respondents’ profiling and the second revealed their first-hand experiences. The thematic analysis approach was utilized to analyze the data. The findings disclosed five emerging themes: 1) low student engagement; 2) unstable internet connection; 3) professional development as school district support; 4) utilization of online engagement tools to leverage classroom engagement; and 5) evident parental support. School districts, teachers, parents, students, and potential researchers were provided with recommendations for improving online and hybrid instruction. In addition to implementing socio-emotional learning programs and addressing slow internet connections, it is advised that school districts train teachers on how to use the tools, software, and techniques for online and hybrid instruction. Teachers who are having difficulty in adjusting to online learning should think about developing their capabilities for helping students while finding a healthy balance in terms of stress management. The use of technology to support students should be adequately taught to parents. Future studies on the pandemic's effects on education should continue. (shrink)

Beck presents an outline of the procedure of bootstrapping of integer concepts, with the purpose of explicating the account of Carey. According to that theory, integer concepts are acquired through a process of inductive and analogous reasoning based on the object tracking system, which allows individuating objects in a parallel fashion. Discussing the bootstrapping theory, Beck dismisses what he calls the "deviant-interpretation challenge"—the possibility that the bootstrapped integer sequence does not follow a linear progression after some point—as being general to (...) any account of inductive learning. While the account of Carey and Beck focuses on the OTS, in this paper I want to reconsider the importance of another empirically well-established cognitive core system for treating numerosities, namely the approximate number system. Since the ANS-based account offers a potential alternative for integer concept acquisition, I show that it provides a good reason to revisit the deviant-interpretation challenge. Finally, I will present a hybrid OTS-ANS model as the foundation of integer concept acquisition and the framework of enculturation as a solution to the challenge. (shrink)

This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some (...) theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well. (shrink)

Abstract: Artificial Morality is a new, emerging interdisciplinary field that centres around the idea of creating artificial moral agents, or AMAs, by implementing moral competence in artificial systems. AMAs are ought to be autonomous agents capable of socially correct judgements and ethically functional behaviour. This request for moral machines comes from the changes in everyday practice, where artificial systems are being frequently used in a variety of situations from home help and elderly care purposes to banking and court algorithms. It (...) is therefore important to create reliable and responsible machines based on the same ethical principles that society demands from people. New challenges in creating such agents appear. There are philosophical questions about a machine’s potential to be an agent, or mora l agent, in the first place. Then comes the problem of social acceptance of such machines, regardless of their theoretic agency status. As a result of efforts to resolve this problem, there are insinuations of needed additional psychological (emotional and cogn itive) competence in cold moral machines. What makes this endeavour of developing AMAs even harder is the complexity of the technical, engineering aspect of their creation. Implementation approaches such as top- down, bottom-up and hybrid approach aim to find the best way of developing fully moral agents, but they encounter their own problems throughout this effort. (shrink)

The field of AI has undergone a series of transformations, each marking a new phase of development. The initial phase emphasized curation of symbolic models which excelled in capturing reasoning but were fragile and not scalable. The next phase was characterized by machine learning models—most recently large language models (LLMs)—which were more robust and easier to scale but struggled with reasoning. Now, we are witnessing a return to symbolic models as complementing machine learning. Successes of LLMs contrast with (...) their inscrutability, inaccuracy, and hallucinations, which underwrite concerns over the reliability and trustworthiness of these systems, motivating investigations into commonsense reasoning, AI explainability, and formal verification techniques. Moreover, proper assessments of hybrid machine learning/symbolic systems require novel strategies to facilitate comparisons of performance and guide future AI progress. The EDGeS AAAI 2023 Spring symposium brought together researchers focusing on novel assessments and benchmarks for evaluating hybrid and artificial general intelligence (AGI) systems. This symposium revealed what was already suspected: research concerning evaluation of machine learning/symbolic hybrids and AGI is, unfortunately, lacking. Even so, the discussion was fruitful. (shrink)

The Genetic Ranking Optimization Algorithm (GROA) is used to rank phishing content based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time.

The Genetic Ranking Optimization Algorithm (GROA) is used to rank phishing content based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time. The classification system was evaluated using benchmark phishing datasets, and the results demonstrated a significant improvement in detection accuracy and reduced (...) false positives. (shrink)

Neutrosophic theory and its applications have been expanding in all directions at an astonishing rate especially after of the introduction the journal entitled “Neutrosophic Sets and Systems”. New theories, techniques, algorithms have been rapidly developed. One of the most striking trends in the neutrosophic theory is the hybridization of neutrosophic set with other potential sets such as rough set, bipolar set, soft set, hesitant fuzzy set, etc. The different hybrid structures such as rough neutrosophic set, single valued neutrosophic rough (...) set, bipolar neutrosophic set, single valued neutrosophic hesitant fuzzy set, etc. are proposed in the literature in a short period of time. Neutrosophic set has been an important tool in the application of various areas such as data mining, decision making, e-learning, engineering, medicine, social science, and some more. (shrink)

The field of neutrosophic set theory and its applications has been rapidly expanding, particularly since the introduction of the journal "Neutrosophic Sets and Systems." New theories, techniques, and algorithms are being developed at a very high rate. One of the most notable trends in neutrosophic theory is its hybridization with other set theories such as rough set theory, bipolar set theory, soft set theory, hesitant fuzzy set theory, and more. Various hybrid structures like rough neutrosophic sets, neutrosophic soft set, (...) single valued neutrosophic hesitant fuzzy sets, among others, have been proposed in a short period. Neutrosophic sets have proven to be crucial tools across a wide array of fields including data mining, decision making, e-learning, engineering, medical diagnosis, social sciences, and beyond. -/- The third volume in the series “New Trends in Neutrosophic Theories and Applications” focuses on theories, methods, and algorithms for decision making, as well as applications involving neutrosophic information. Some topics introduce new sets such as the Pythagorean neutrosophic vague soft set, the triangular fuzzy penta-partitioned neutrosophic set, interval-valued neutrosophic b-open sets, and interval-valued neutrosophic b-closed sets. Other topics present applications in medical diagnosis, non-preemptive neutrosophic priority queues with uneven services (labeled as NM/NM/1), AHP in an interval neutrosophic set environment, MAGDM in a triangular fuzzy neutrosophic number environment, MAGDM in a pentapartitioned neutrosophic environment, the entropy-ARAS strategy in a single-valued neutrosophic number environment, and the MABAC strategy in a rough neutrosophic set environment. (shrink)

The rapid evolution of phishing techniques necessitates more sophisticated detection and classification methods. In this paper, we propose a novel approach to phishing content classification using a Genetic Ranking Optimization Algorithm (GROA), combined with dynamic weighting, to improve the accuracy and ranking of phishing versus legitimate content. Our method leverages features such as URL structure, email content analysis, and user behavior patterns to enhance the detection system's decision-making process. The Genetic Ranking Optimization Algorithm (GROA) is used to rank phishing content (...) based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time. (shrink)

The reasonable person standard is key to both Criminal Law and Torts. What does and does not count as reasonable behavior and decision-making is frequently deter- mined by lay jurors. Hence, laypeople’s understanding of the term must be considered, especially whether they use it predominately in an evaluative fashion. In this corpus study based on supervised machine learning models, we investigate whether laypeople use the expression ‘reasonable’ mainly as a descriptive, an evaluative, or merely a value-associated term. We find (...) that ‘reasonable’ is predicted to be an evaluative term in the majority of cases. This supports prescriptive accounts, and challenges descriptive and hybrid accounts of the term—at least given the way we operationalize the latter. Interestingly, other expressions often used interchangeably in jury instructions (e.g. ‘careful,’ ‘ordinary,’ ‘prudent,’ etc), however, are predicted to be descriptive. This indicates a discrepancy between the intended use of the term ‘reasonable’ and the understanding lay jurors might bring into the court room. (shrink)

The representations of deep convolutional neural networks (CNNs) are formed from generalizing similarities and abstracting from differences in the manner of the empiricist theory of abstraction (Buckner, Synthese 195:5339–5372, 2018). The empiricist theory of abstraction is well understood to entail infinite regress and circularity in content constitution (Husserl, Logical Investigations. Routledge, 2001). This paper argues these entailments hold a fortiori for deep CNNs. Two theses result: deep CNNs require supplementation by Quine’s “apparatus of identity and quantification” in order to (1) (...) achieve concepts, and (2) represent objects, as opposed to “half-entities” corresponding to similarity amalgams (Quine, Quintessence, Cambridge, 2004, p. 107). Similarity amalgams are also called “approximate meaning[s]” (Marcus & Davis, Rebooting AI, Pantheon, 2019, p. 132). Although Husserl inferred the “complete abandonment of the empiricist theory of abstraction” (a fortiori deep CNNs) due to the infinite regress and circularity arguments examined in this paper, I argue that the statistical learning of deep CNNs may be incorporated into a Fodorian hybrid account that supports Quine’s “sortal predicates, negation, plurals, identity, pronouns, and quantifiers” which are representationally necessary to overcome the regress/circularity in content constitution and achieve objective (as opposed to similarity-subjective) representation (Burge, Origins of Objectivity. Oxford, 2010, p. 238). I base myself initially on Yoshimi’s (New Frontiers in Psychology, 2011) attempt to explain Husserlian phenomenology with neural networks but depart from him due to the arguments and consequently propose a two-system view which converges with Weiskopf’s proposal (“Observational Concepts.” The Conceptual Mind. MIT, 2015. 223–248). (shrink)

A newborn baby is an infant within the first 28 days of birth. Diagnosis and treatment of infant diseases require specialized medical resources and expert knowledge. However, there is a shortage of such professionals globally, particularly in low-income countries. To address this challenge, a knowledge-based system was designed to aid in the diagnosis and treatment of neonatal diseases. The system utilizes both machine learning and health expert knowledge, and a hybrid data mining process model was used to extract (...) knowledge from a clinical dataset. The PART algorithm achieved the highest performance result with 98.06% accuracy under 10-fold cross-validation, and the generated rules were used to develop the knowledge-based system. The system achieved 90.9% accuracy in system performance testing and 89.2% in user acceptance testing, and is intended to serve as an assistant tool for healthcare experts. (shrink)

This paper explores some ways in which artificial intelligence (AI) could be used to improve human moral judgments in bioethics by avoiding some of the most common sources of error in moral judgment, including ignorance, confusion, and bias. It surveys three existing proposals for building human morality into AI: Top-down, bottom-up, and hybrid approaches. Then it proposes a multi-step, hybrid method, using the example of kidney allocations for transplants as a test case. The paper concludes with brief remarks (...) about how to handle several complications, respond to some objections, and extend this novel method to other important moral issues in bioethics and beyond. (shrink)

The theory of mind debate has reached a “hybrid consensus” concerning the status of theory-theory and simulation-theory. Extant hybrid models either specify co-dependency and implementation relations, or distribute mentalizing tasks according to folk-psychological categories. By relying on a non-developmental framework these models fail to capture the central connection between simulation and theory. I propose a “dynamic” hybrid that is informed by recent work on the nature of similarity cognition. I claim that Gentner’s model of structure-mapping allows us (...) to understand simulation as a process in which psychological representations are aligned, causing the spontaneous abstraction of theoretical generalizations about the psychological domain. (shrink)

The rapid advancements in artificial intelligence, particularly in natural language processing, have brought to light a critical challenge, i.e., the semantic grounding problem. This article explores the root causes of this issue, focusing on the limitations of connectionist models that dominate current AI research. By examining Noam Chomsky's theory of Universal Grammar and his critiques of connectionism, I highlight the fundamental differences between human language understanding and AI language generation. Introducing the concept of semantic grounding, I emphasise the need for (...) AI to connect language with real-world experiences and context to achieve true understanding. While multi-modal models represent a step forward, they fall short of addressing the core issue. The article concludes with a call for a course correction in AI development, advocating for the integration of embodied cognition, dynamic learning, hybrid models, and ethical practices. This shift is essential for creating AI systems that can meaningfully interact with the world, reducing the incidence of hallucinations and enhancing their reliability and utility. (shrink)

The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of nodes. This results (...) in Big Data congestion, data management, storage issues and various inefficiencies. Fog Computing aims at solving the issues with data management as it includes intelligent computational components and storage closer to the data sources. Often, an IoT-enabled infrastructure is shared among many users with various requirements. Sharing resources, sharing operational costs and collective decision making (consensus) among many stakeholders is frequently neglected. This research addresses an essential requirement for adaptive, autonomous and consensus-based Fog computational solutions which are able to support distributed and in-network schemes and policies. These network schemes and policies need to meet the requirements of many users. In this work, innovative consensus-based computational solutions are investigated. These proposed solutions aim to correlate and organise data for effective management and decision making in Fog. Instead of individual decision making, the algorithms aim to aggregate several decisions into a consensus decision representing a collective agreement, benefiting from the individuals variant knowledge and meeting multiple stakeholders requirements. In order to validate the proposed solutions, hybrid research methodology is involved that includes the design of a test-bed and the execution of several experiments. In order to investigate the effectiveness of the paradigm, three experiments were designed and validated. Real-life sensor data and synthetic statistical data was collected, processed and analysed. Bayesian Machine Learning models and Analytics were used to consolidate the design and evaluate the performance of the algorithms. In the Fog environment, the first scenario tests the Aggregation by Distribution algorithm. The solution contribute in achieving a notable efficiency of data delivery obtained with a minimal loss in precision. The second scenario validates the merits of the approach in predicting the activities of high mobility IoT applications. The third scenario tests the applications related to smart home IoT. All proposed Consensus algorithms use statistical analysis to support effective decision making in Fog and enable data aggregation for optimal storage, data transmission, processing and analytics. The final results of all experiments showed that all the implemented consensus approaches surpass the individual ones in different performance terms. Formal results also showed that the paradigm is a good fit in many IoT environments and can be suitable for different scenarios when applying data analysis to correlate data with the design. Finally, the design demonstrates that Fog Computing can compete with Cloud Computing in terms of accuracy with an added preference of locality. (shrink)

Neutrosophic theory and its applications have been expanding in all directions at an astonishing rate especially after of the introduction the journal entitled “Neutrosophic Sets and Systems”. New theories, techniques, algorithms have been rapidly developed. One of the most striking trends in the neutrosophic theory is the hybridization of neutrosophic set with other potential sets such as rough set, bipolar set, soft set, hesitant fuzzy set, etc. The different hybrid structures such as rough neutrosophic set, single valued neutrosophic rough (...) set, bipolar neutrosophic set, single valued neutrosophic hesitant fuzzy set, etc. are proposed in the literature in a short period of time. Neutrosophic set has been an important tool in the application of various areas such as data mining, decision making, e-learning, engineering, medicine, social science, and some more. (shrink)

We propose the concepts of philomatics and psychomatics as hybrid combinations of philosophy and psychology with mathematics. We explain four motivations for this combination which are fulfilling the desire of analytical philosophy, proposing science of philosophy, justifying mathematical algorithms by philosophy, and abstraction in both philosophy and mathematics. We enumerate various examples for philomatics and psychomatics, some of which are explained in more depth. The first example is the analysis of relation between the context principle, semantic holism, and the (...) usage theory of meaning with the attention mechanism in mathematics. The other example is on the relations of Plato's theory of forms in philosophy with the holographic principle in string theory, object-oriented programming, and machine learning. Finally, the relation between Wittgenstein's family resemblance and clustering in mathematics is explained. This paper opens the door of research for combining philosophy and psychology with mathematics. (shrink)

Now we live in the age of professionalism, and the public office in any nation is some reservoir of intelligent competition in their specific field. They are the leaders and paragon of community as a loyal and professional fiduciary. A hybrid nature of officers creates the rules and exercises their professional knowledge to serve a public good. The not unusual word,“scholar practitioner” may reflect the tendency of learning community within the business and government officers. They wish to overcome (...) a bum rap, say, most unproductive group or bureaucrats in maze, attributed to the bureaucrats generally. They now are required to be not only a manager but also professional researcher for his or her specific field of expertise. Beyond this duality, they may face with the challenge from a serious question about the tenet or theory within either a managerial responsibility or any research responsibility in the discipline of public policy. This paper begins with the classic loci for the theories and tenets, which have interacted with the research and professional circle. Then the author illustrates one hypothetical ego, who now works for the Korean government. In pursuit of his work role,he is situated to ramble encompassing the tenet, theories, and assumption of public policy discipline. The paper suggests three theories considered most foundational in the context of Korean politics and history at modern times, which would possibly be applicable to many other similar nations in the post colonial period. The author argues that it could be exemplary for the policy makers or administrators, who have to learn and research. (shrink)

Programming artificial intelligence to make fairness assessments of texts through top-down rules, bottom-up training, or hybrid approaches, has presented the challenge of defining cross-cultural fairness. In this paper a simple method is presented which uses vectors to discover if a verb is unfair or fair. It uses already existing relational social ontologies inherent in Word Embeddings and thus requires no training. The plausibility of the approach rests on two premises. That individuals consider fair acts those that they would be (...) willing to accept if done to themselves. Secondly, that such a construal is ontologically reflected in Word Embeddings, by virtue of their ability to reflect the dimensions of such a perception. These dimensions being: responsibility vs. irresponsibility, gain vs. loss, reward vs. sanction, joy vs. pain, all as a single vector. The paper finds it possible to quantify and qualify a verb as fair or unfair by calculating the cosine similarity of the said verb’s embedding vector against FairVec—which represents the above dimensions. We apply this to Glove and Word2Vec embeddings. Testing on a list of verbs produces an F1 score of 95.7, which is improved to 97.0. Lastly, a demonstration of the method’s applicability to sentence measurement is carried out. (shrink)

Text-guided image generation models can be prompted to generate images using nonce words adversarially designed to robustly evoke specific visual concepts. Two approaches for such generation are introduced: macaronic prompting, which involves designing cryptic hybrid words by concatenating subword units from different languages; and evocative prompting, which involves designing nonce words whose broad morphological features are similar enough to that of existing words to trigger robust visual associations. The two methods can also be combined to generate images associated with (...) more specific visual concepts. The implications of these techniques for the circumvention of existing approaches to content moderation, and particularly the generation of offensive or harmful images, are discussed. (shrink)

This chapter surveys hybrid theories of well-being. It also discusses some criticisms, and suggests some new directions that philosophical discussion of hybrid theories might take.

This is a draft of a chapter for the Routledge Handbook of Metaethics, edited by David Plunkett and Tristram McPherson. I offer an overview of hybrid views in metaethics, with main focus on hybrid cognitivist views such as those defended by Daniel Boisvert and David Copp, and on hybrid expressivist views such as those defended by Michael Ridge and myself.

This paper is a survey of recent ‘hybrid’ approaches to metaethics, according to which moral sentences, in some sense or other, express both beliefs and desires. I try to show what kinds of theoretical issues come up at the different choice points we encounter in developing such a view, to raise some problems and explain where they come from, and to begin to get a sense for what the payoff of such views can be, and what they will need (...) to do in order to earn that payoff. (shrink)

The transgressive ontological character of hybrids—entities crossing the ontological binarism of naturalness and artificiality, e.g., biomimetic projects—calls for pondering the question of their ethical status, since metaphysical and moral ideas are often inextricably linked. The example of it is the concept of “moral considerability” and related to it the idea of “intrinsic value” understood as a non-instrumentality of a being. Such an approach excludes hybrids from moral considerations due to their instrumental character. In the paper, we revisit the boundaries of (...) moral considerability by reexamining the legitimacy of identifying intrinsic value with a non-instrumental one. We offer the concept of “functional value,” which we define as a simultaneous contribution to the common good of the ecosystem and the possibility to disclose the full variety of aspects of a being’s identity. We argue that such a value of hybrids allows us to include them into the scope of moral considerability. (shrink)

The theory of collective agency and intentionality is a flourishing field of research, and our understanding of these phenomena has arguably increased greatly in recent years. Extant theories, however, are still ill-equipped to explain certain aspects of collective intentionality. In this article we draw attention to two such underappreciated aspects: the failure of the intentional states of collectives to supervene on the intentional states of their members, and the role of non-human factors in collective agency and intentionality. We propose a (...) theory of collective intentionality which builds on the ‘interpretationist’ tradition in metasemantics and the philosophy of mind as initiated by David Lewis and recently developed further by Robbie Williams. The collective-level analogue of interpretationism turns out to look different in some ways from the individual-level theory, but is well-suited to accommodating phenomena such as hybrid collective intentionality. Complemented with Kit Fine’s theory of variable embodiment, such a theory also provides a diachronic account of intentional collectives. (shrink)

Contextualist accounts of aesthetic predicates have difficulties explaining why we feel that speakers are disagreeing when they make true and compatible but superficially contradictory aesthetic judgments. One possible way to account for the disagreement is hybrid expressivism, which holds that the disagreement happens at the level of pragmatically conveyed, clashing contents about the speakers’ conative states. Marques defends such a strategy, combining dispositionalism about value, contextualism, and hybrid expressivism. This paper critically evaluates the plausibility of the suggested pragmatic (...) mechanisms in conveying the kind of contents Marques takes to explain disagreements. The positive part suggests an alternative account of how aesthetic judgments are sources of information about speakers’ conative aesthetic states. (shrink)

In virtue of what is something a reason for action? That is, what makes a consideration a reason to act? This is a metaphysical or meta-normative question about the grounding of reasons for action. The answer to the grounding question has been traditionally given in ‘pure’, univocal terms. This paper argues that there is good reason to understand the ground of practical normativity as a hybrid of traditional ‘pure’ views. The paper 1) surveys the three leading ‘pure’ answers to (...) the question of a normative ground, 2) examines one or two of the most difficult problems for each, proposing along the way a new objection to one, and 3) argues that a particular hybrid view about normative grounds –‘hybrid voluntarism’ – avoids each of the main problems faced by the three leading ‘pure’ views. (shrink)

This essay characterizes the perception of the visual hybrid as nonconceptual, introducing the terminology of nonconceptual content theory to aesthetics. The visual hybrid possesses a radical but nonetheless exemplary aesthetic composition and is well established in culture, art, and even design. The essay supplies a philosophical analysis of the results of cross-cultural experiments, showing that while categorization or conceptual hierarchization kicks in when the visual hybrids are juxtaposed with linguistic descriptions, no conceptual scheme takes effect when participants are (...) presented with mere visual hybrids. In isolation, the hybrids do not lend themselves to classification. I draw four conclusions from these experimental outcomes: 1. The perception of visual hybrids follows the structure of a nonconceptual mental content, because the original categories or concepts of the hybrids’ components are not combined into one, and their properties are not applied to one another, therefore none of the components reconstructs the other such that it is introduced to a new category. 2. Language freezes the hybridity of the visual hybrid into conceptuality. 3. Given that language has a freezing effect in the case of an extreme visual phenomenon such as the hybrid, it is all the more restraining in moderate artistic compositions, such as visual metaphors, in which properties of one component (the source) are applied to the other (the target). In those, nonconceptuality emerges from relatively organized compositions, forms, and relations, and from the dependence of objects and their properties on perceptual context. 4. Thus, the nonconceptualist terminology is suitable for the analysis of aesthetic perception in general and aesthetic perception’s relation to language. Keywords aesthetics, perception, nonconceptual content, hybrids, visual metaphors. (shrink)

Philosophers should consider a hybrid meta-ethical theory that includes elements of both moral expressivism and moral error theory. Proponents of such an expressivist-error theory hold that all moral utterances are either expressions of attitudes or expressions of false beliefs. Such a hybrid theory has two advantages over pure expressivism, because hybrid theorists can offer a more plausible account of the moral utterances that seem to be used to express beliefs, and hybrid theorists can provide a simpler (...) solution to the Frege-Geach problem. The hybrid theory has three advantages over pure error theory, because hybrid theorists can offer a more plausible account of the moral utterances that seem to be used to express attitudes, hybrid theorists can more easily explain moral motivation, and hybrid theorists can avoid the implausible claim that all moral discourse is radically mistaken. Accordingly, such a hybrid theory should be more attractive than pure expressivism or pure error theory to philosophers who are skeptical about moral facts and truth. (shrink)

This paper proposes a novel account of the contents of memory. By drawing on insights from the philosophy of perception, I propose a hybrid account of the contents of memory designed to preserve important aspects of representationalist and relationalist views. The hybrid view I propose also contributes to two ongoing debates in philosophy of memory. First, I argue that, in opposition to eternalist views, the hybrid view offers a less metaphysically-charged solution to the co-temporality problem. Second, I (...) show how the hybrid view conceives of the relationship between episodic memory and other forms of episodic thinking. I conclude by considering some disanalogies between perception and memory and by replying to objections. I argue that, despite there being important differences between memory and perception, those differences do not harm my project. (shrink)

In a very general sense, hybrid can be understood to be any organism that is the product of two (or more) organisms where each parent belongs to a different kind. For example; the offspring from two or more parent organisms, each belonging to a separate species (or genera), is called a “hybrid”. “Hybridity” refers to the phenomenal character of being a hybrid. And “hybridization ” refers to both natural and artificial processes of generating hybrids. These processes include (...) mechanisms of selective cross-breeding and cross-fertilization of parents of different species for the purpose of producing hybrid offspring. In addition to these processes, “hybridization ” also refers to natural and artificial processes of whole genome duplication that result in the doubling or trebling of the sets of chromosomes of the organism. This entry provides an overview of the impact of hybridity on agriculture. It begins with an historical sketch that traces the early horticulturalists’ and naturalists’ investigations of hybrids. This starts with the observations of Thomas Fairchild and Georges-Louis Leclerc, Comte de Buffon; and leads to the explanation of its mechanism by Gregor Mendel, James Watson and Francis Crick, and Ernst Mayr; and the eventual manipulation of hybrids and hybridization by Barbara McClintock. Following this, the reader is introduced to a number of key terms and concepts in use within current research as well as highlighting diverse ethical concerns that center on hybridization. Recent research that attempts to ascertain the role of hybridization in adaptive change will be introduced. This will include research on the evolution of crop species, increased biodiversity, and the use of hybrids to manipulate phenotypically desirable traits in agricultural crops. The focus of the discussion is on a particularly significant type of naturally occurring hybridization, polyploidy hybridization. Polyploids are organisms which have more than two complete genomes in each cell. This kind of hybridization is ubiquitous among crop plants. The role of polyploidy in plant evolution and the affects of polyploidy on plants and animals will be reviewed. A critical discussion of its agricultural value in the production of fertile polyploid hybrids highlights key epistemological, ontological, and ethical issues. These are illuminated with reference to the distinct processes of artificial and natural hybridization. A survey of these different kinds of hybridization includes the ethical and economic impacts of hybridity on global nutrition, the environment, and considerations of some practical implications for the agricultural industry. Tracking the role of hybrids, the process of hybridization, and the current impacts of it for agriculture requires knowledge of the history of its early conceptualization, understanding, and use. This is the topic of the following section. (shrink)

Disagreement is a pervasive feature of human discourse and a crucial force in shaping our social reality. From mundane squabbles about matters of taste to high-stakes disputes about law and public policy, the way we express and navigate disagreement plays a central role in both our personal and political lives. Legal discourse, in particular, is rife with disagreement - it is the very bread and butter of courtroom argument and legal scholarship alike. Consider a debate between two legal philosophers, Ronald (...) and Herbert, about the Eighth Amendment of the U.S. Constitution, which prohibits ‘cruel and unusual punishment’. Ronald asserts: ‘It is the law that capital punishment is prohibited’. In response, Herbert states: ‘It is not the law that capital punishment is prohibited’. We intuitively think Ronald and Herbert are disagreeing, which reveals in the fact that they are licensed to use ex¬pressions like no (it isn’t) and nuh-uh when responding to their opponent’s claim. But despite the ubiquity and significance of legal disagreement, its precise nature remains elusive. This chapter discusses what exactly is going on when two people disagree about what the law requires, and how can hybrid theory may answer this question. (shrink)

We claim that scientists working with deep learning (DL) models exhibit a form of pragmatic understanding that is not reducible to or dependent on explanation. This pragmatic understanding comprises a set of learned methodological principles that underlie DL model design-choices and secure their reliability. We illustrate this action-oriented pragmatic understanding with a case study of AlphaFold2, highlighting the interplay between background knowledge of a problem and methodological choices involving techniques for constraining how a model learns from data. Building successful (...) models requires pragmatic understanding to apply modelling strategies that encourage the model to learn data patterns that will facilitate reliable generalisation. (shrink)

A common starting point for ‘going hybrid’ is the thought that moral discourse somehow combines belief and desire-like aspects, or is both descriptive and expressive. Hybrid meta-ethical theories aim to give an account of moral discourse that is sufficiently sensitive to both its cognitive and its affective, or descriptive and expressive, dimensions. They hold at least one of the following: moral thought: moral judgements have belief and desire-like aspects or elements; moral language: moral utterances both ascribe properties and (...) express desire-like attitudes. This entry concerns hybrid theories of moral language. The main division within such theories is between those treating the expression of desire-like attitudes as semantic and those treating it as pragmatic. This entry exclusively focuses on pragmatic forms of and examines the prospects for treating moral attitude expression as working via certain standard pragmatic mechanisms. I explain these mechanisms, outline the properties that standardly define them, and test to see whether moral attitude expression matches them. At the end, I briefly explain a more minimal pragmatic alternative. The main conclusions are that we should disregard presupposition and conventional implicature views and that the most plausible options for a pragmatic hybrid view are a generalised conversational implicature view and a more minimal pragmatic view. (shrink)