This article is a preliminary draft for initiating and commencing a new pioneer dimension of expression. To deal with higher-dimensional data or information flowing in this modern era of information technology and artificial intelligence, some innovative super algebraic structures are essential to be formulated. In this paper, we have introduced such matrices that have multiple layers and clusters of layers to portray multi-dimensional data or massively dispersed information of the plithogenic universe made up of numerous subjects their attributes, (...) and sub-attributes. For grasping that field of parallel information, events, and realities flowing from the micro to the macro level of universes, we have constructed hypersoft and hyper-super-soft matrices in a Plithogenic Fuzzy environment. These Matrices classify the non-physical attributes by accumulating the physical subjects and further sort the physical subjects by accumulating their non-physical attributes. We presented themasPlithogenicAttributiveSubjectivelyWholeHyper-Super-Soft-Matrix(PASWHSS-Matrix)andPlithogenic Subjective Attributively Whole-Hyper-Super-Soft-Matrix (PSAWHSS-Matrix). Several types of views and level-layers of these matrices are described. In addition, some local aggregation operators for Plithogenic Fuzzy Hypersoft Set (PPFHS-Set) are developed. Finally, few applications of these matrices and operators are used as numerical examples of COVID-19 data structures. (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)

Big Data has the potential to enable unprecedentedly rigorous quantitative modeling of complex human social relationships and social structures. When such models are extended to nonhuman domains, they can undermine anthropocentric assumptions about the extent to which these relationships and structures are specifically human. Discoveries of relevant commonalities with nonhumans may not make us less human, but they promise to challenge fundamental views of what it is to be human.

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience (...) for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas. (shrink)

Linguistic structures have recently started to attract attention from economists as determinants of economic phenomena. This paper provides the first comprehensive review of this nascent literature and its achievements so far. First, we explore the complex connections between language, culture, thought and behaviour. Then, we summarize the empirical evidence on the relationship between linguistic structures and economic and social outcomes. We follow up with a discussion of data, empirical design and identification. The paper concludes by discussing implications for future (...) research and policy. (shrink)

Since Circular 34 from the Ministry of Science and Technology of Vietnam required the head of the national project to have project results published in ISI/Scopus journals in 2014, the field of economics has been dominating the number of nationally-funded projects in social sciences and humanities. However, there has been no scientometric study that focuses on the difference in productivity among fields in Vietnam. Thus, harnessing the power of the SSHPA database, a comprehensive dataset of 1,564 Vietnamese authors (854 males, (...) 705 females) with 2,410 publications in the 2008 – 2018 period was extracted and analyzed. Various factors were considered including age, gender, new authors, leading authors, co-authorship, and Impact Factor. The findings suggest a high level of contribution from authors at the age of 40 – 44 in economics (858 publications) in a 12-years period, which is equivalent to the social medicine total output, and two times more than the total output of the education. Moreover, the presence and reinforcement of male researchers are still dominating in Economics and other fields, with the only exception of education. Despite the rapid rise in the number of Vietnamese lead authors, gender disparity among disciplines is an issue. Contrary to the strong international collaboration-oriented tendency in social medicine, economics, and other fields, educational authors are not open to international collaborating. Finally, most of the publications in economics belong to the group with JIF from 0 to 2, in contrast with the high number of social medicine publications with JIF from 2 to 5, which suggesting the field of economics is fulfilling the quantity, but still, need more quality publications. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate (...) the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

In this paper we investigate if sentences presented as the result of the application of statistical models and artificial intelligence to large volumes of data – the so-called ‘Big Data’ – can be characterized as semantically true, or as quasi-true, or even if such sentences can only be characterized as probably quasi-false and, in a certain way, post-true; that is, if, in the context of Big Data, the representation of a data domain can be configured as (...) a total structure, or as a partial structure provided with a set of sentences assumed to be true, or if such representation cannot be configured as a partial structure provided with a set of sentences assumed to be true. (shrink)

House price prediction is a critical task in real estate analytics, influenced by various factors such as location, economic conditions, and property features. Traditional machine learning models rely heavily on structured data, while recent advancements in deep learning enable the integration of unstructured data such as images. This paper presents a novel hybrid approach that combines structured numerical data with image-based features using Regionbased Convolutional Neural Networks (R-CNN). The proposed model improves predictive accuracy by leveraging both property (...) characteristics and visual information. Experimental results demonstrate that the hybrid approach outperforms conventional methods in terms of mean absolute error and root mean squared error. (shrink)

Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in (...) biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from ‘the sequence tells the structure tells the function’ fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader. (shrink)

Big data ethics involves adherence to the concepts of right and wrong behavior regarding data, especially personal data. Big Data ethics focuses on structured or unstructured data collectors and disseminators. Big data ethics is supported, at EU level, by extensive documentation, which seeks to find concrete solutions to maximize the value of big data without sacrificing fundamental human rights. The European Data Protection Supervisor (EDPS) supports the right to privacy and the right (...) to the protection of personal data in the respect of human dignity. (shrink)

Was there a concept of data before the so-called ‘data revolution’? This paper contributes to the history of the concept of data by investigating uses of the term ‘data’ in texts of the Royal Society's Philosophical Transactions for the period 1665–1886. It surveys how the notion enters the journal as a technical term in mathematics, and charts how over time it expands into various other scientific fields, including Earth sciences, physics and chemistry. The paper argues that (...) in these texts the notion of data is not used merely as a rhetorical category, and also cannot strictly be identified with the category of evidence. Instead, the notion comes with an associated epistemic structure, one that is in line with its development from an early mathematical use. (shrink)

Qualitative data of a 'failing' junior school are used to highlight the ways in which a particular Local Education Authority (LEA) responded to 'serious weaknesses' outlined by a team of Office for Standards in Education inspectors and how staff mediated such LEA intervention. Such mediation will be theorised via the employment of analytical dualism, whereby structure and agency are held to be irreducible emergent strata of social reality. The purpose of this paper is not to complement and buttress (...) the ideological nature of school effectiveness research, but to provide a micro insight into current mediation of macro-level policy. (shrink)

This article explores how readers recognize their personal identities represented through data visualizations. The recognition is investigated starting from three definitions captured by the philosopher Paul Ricoeur: the identification with the visualization, the recognition of someone in the visualization, and the mutual recognition that happens between readers. Whereas these notions were initially applied to study the role of the book reader, two further concepts complete the shift to data visualization: the digital identity stays for the present-day passport of (...) human actions and the promise is the intimate reflection that projects readers towards their own future. This article reflects on the delicate meaning of digital identity and the way of representing it according to this structure: From Personal Identity to Media is a historical introduction to self-recognition, Data Visualization for Representing Identities moves the focus to visual representation, and The Course of Recognition breaks the self-recognition in through the five concepts above just before the Conclusion. (shrink)

Big Data ethics involves adherence to the concepts of right and wrong behavior regarding data, especially personal data. Big Data ethics focuses on structured or unstructured data collectors and disseminators. Big Data ethics is supported, at EU level, by extensive documentation, which seeks to find concrete solutions to maximize the value of Big Data without sacrificing fundamental human rights. The European Data Protection Supervisor (EDPS) supports the right to privacy and the right (...) to the protection of personal data in the respect of human dignity. DOI: 10.13140/RG.2.2.30867.43041. (shrink)

Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against vulnerable groups have been reported. The growing research field known as 'algorithmic fairness' aims to mitigate these harmful biases. Its primary methodology consists in proposing mathematical metrics to address the social harms resulting from an algorithm's biased outputs. The metrics are typically motivated by -- or substantively rooted in -- (...) ideals of distributive justice, as formulated by political and legal philosophers. The perspectives of feminist political philosophers on social justice, by contrast, have been largely neglected. Some feminist philosophers have criticized the local scope of the paradigm of distributive justice and have proposed corrective amendments to surmount its limitations. The present paper brings some key insights of feminist political philosophy to algorithmic fairness. The paper has three goals. First, I show that algorithmic fairness does not accommodate structural injustices in its current scope. Second, I defend the relevance of structural injustices -- as pioneered in the contemporary philosophical literature by Iris Marion Young -- to algorithmic fairness. Third, I take some steps in developing the paradigm of 'responsible algorithmic fairness' to correct for errors in the current scope and implementation of algorithmic fairness. I close by some reflections of directions for future research. (shrink)

The objective of the study was to develop a structural model that explores the relationship between Mathematics Performance and students’ self-regulated learning skills, grit, and expectancy-value towards science, technology, engineering and mathematics (STEM). The research collected survey data from 664 senior high school students from 17 STEM high schools, and conducted a covariance-based structural equation modeling (SEM) analysis. The results of the SEM analysis indicate that the Re-specified Self-Regulated Learning Skill – Expectancy-Value towards STEM – Grit – Mathematics Performance (...) (Re-specified SRL-EV-GR-MP) model is the most parsimonious fit, offering the best empirical support for the theoretical model of the study. The research findings suggest that the mathematics performance of senior high school students in STEM curriculum is attributed to their high expectancies for success and perceived values of the STEM tasks, high grit, and high self-regulated learning skills. Moreover, the research also observed evidence of mediating and moderating grit effects in the concurrent effects of expectancy-values towards STEM and self-regulated learning skills towards students’ mathematics performance. (shrink)

The re-use of publicly available (personal) data for originally unanticipated purposes has become common practice. Without such secondary uses, the development of many AI systems like large language models (LLMs) and ChatGPT would not even have been possible. This chapter addresses the ethical implications of such secondary processing, with a particular focus on data protection and privacy issues. Legal and ethical evaluations of secondary processing of publicly available personal data diverge considerably both among scholars and the general (...) public. While some of these uses are met with opposition and criticism, others are quite unanimously viewed as unproblematic. Often, proponents and opponents of such practices invoke the same ethical and legal standards for their opposite conclusions. This state of affairs shows that other considerations besides the public availability of data must play a role. It calls for a theoretical clarification of the additional criteria that should guide decisions about the (legally informed) ethical acceptability of re-processing practices. In order to make a contribution towards this goal, the present chapter maps the ongoing debate and systematises the existing contributions around three lines of argument: a consent-centred position, an approach that focuses on the distinction between data and information, and finally a line of argument that focuses on the contextual norms that govern the flows of information. The chapter further relates these arguments to three underlying conceptions of privacy and data protection: rights-based, structural and contextual, and discusses the advantages and disadvantages of each position in the light of concrete examples. It concludes by arguing for a mixed approach that combines core elements of the structural and contextual approaches. The chapter aims to contribute to existing research in the fields of data, AI and research ethics, and to reconnect the debate with ethical and legal scholarship on privacy and data protection. In doing so, it aims to make a theoretical contribution towards refining existing conceptions of privacy and data protection in order to make them more fit to ‘drive our digital world’ as far as the use of publicly available data is concerned. (shrink)

The concept of an individual, liberal data subject, who was traditionally at the centre of data protection efforts has recently come under scrutiny. At the same time, the particularly destructive effect of digital technology on Black people establishes the need for an analysis that not only considers but brings racial dimensions to the forefront. I argue that because Afrofuturism situates the Black struggle in persistent, yet continuously changing structural disparities and power relations, it offers a powerful departure point (...) for re-imagining data protection. Sketching an Afrofuturist data subject then centres on radical subjectivity, collectivity, and contextuality. (shrink)

This article probes the visual spatial représentations underlying the creative conceptual design of complex objects.

Integrative structure modeling is a method for using information from multiple sources to compute structural models of biomolecular systems. It proceeds via four steps: (i) defining the model representation, which determines the variables whose values will be computed; (ii) constructing a function for scoring alternative models according to how well they accommodate input information; (iii) searching a space of candidate models for acceptable models; and (iv) analyzing acceptable models to evaluate their fit with input information. These steps are iterated (...) until a model adequate for addressing biological questions is found. In this paper, we draw lessons from integrative modeling about effective integration and about modeling. We describe what it means to integrate information from multiple sources: Integration amounts to distributing information among the four steps of integrative modeling. Theory and data alike can be sources of information; this process thus generates models of information, rather than models of theory or models of data. We then propose heuristics for distributing information and designing multiple iterations of modeling. Effective iteration requires prioritizing the most reliable information and minimizing the time required to obtain an adequate model. Rather than being constructed from theory and assessed using data, models are constructed from any available information and assessed in a coherentist manner. (shrink)

Some Anglophone legal theorists look to analytic philosophy for core presuppositions. For example, the epistemological theories of Ludwig Wittgenstein and Willard Quine shape the theories of Dennis Patterson and Brian Leiter, respectively. These epistemologies are anti-foundational since they reject the kind of certain grounding that is exemplified in Cartesian philosophy. And, they are coherentist in that they seek to legitimate truth-claims by reference to entire linguistic systems. While these theories are insightful, the current context of information and communication technologies (ICT) (...) has created new informational concepts and issues. As a result, the analytic epistemologies are increasingly challenged by alternative perspectives. One such alternative is Structural Realism (SR), which is influential among the natural sciences, and especially physics. “Informational Structural Realism,” (ISR) is a variant of SR that was introduced by Luciano Floridi. Unlike the coherentist theories, ISR promotes examination of the connections among types of information and informational structures. It is an important shift for legal theory today, since the challenges that the ICT presents have to do with pattern recognition across vast domains of diverse data. An informational jurisprudence is now required to understand the issues emerging from the ICT. (shrink)

The study's general objective is to determine the students' stance on the most appropriate model of writing skills, using Structural Equation Modeling (SEM) as a basic design in the relationship of self-regulated learning strategies, communicative learning strategies, learning grammatical strategies, and writing skills. This study used a mixed-method sequential explanatory design, in which quantitative design is more widely used than qualitative Creswell, J., & Creswell, D. (2017). The researcher used the stratified random sampling technique for selecting respondents and, by using (...) the Raosoft sample size calculator, identified 390 respondents using a four-part questionnaire to collect data. Purposive sampling is the method for determining participants in a qualitative design using a guided questionnaire to obtain data from In-Depth Interviews and Focus Group Discussions. Model 5 was the best-fitting model for writing skills, showing significant correlations with the exogenous variables: self-regulated learning strategies, communicative learning strategies, learning grammatical strategies, and writing skills as endogenous variables. Both the exogenous and endogenous variables obtained a high level of overall description, meaning that students often practiced strategies to cultivate writing skills. The participants' stance on the most appropriate model is essential for tertiary students to have different strategies for developing writing skills. It is necessary to reach the full description of the highest level by using different strategies to build writing skills. (shrink)

It is exhibited that mature scientific economical theory is a set of propositions that describe the relationship between theoretical objects of two types - basic objects and derivative ones. The set of basic objects makes up the aggregate of initial idealizations (the Fundamental Theoretical Scheme or FTS) with no direct reference to experimental data. The derivative theoretical objects are formed from the basic ones according to certain rules. The sets of derivative objects form partial theoretical schemes or PTS. Any (...) mature economics scientific theory grows due to transitions, in order to describe each new experimental situation, from FTS to PTS. Each PTS construction from the FTS represents a problem that cannot be reduced to a strict algorithm. (shrink)

Recent studies tend to explain the importance of communication in the organisation as well as prescribing the most commonly practised techniques adopted by school managers. Studies on financial management are quite limited with the available ones suggesting that poor financial management is a source of conflict between school leaders and host communities. Little seems to be known on the connection between principals’ communication patterns and funds’ management as predictors of school-community relationship. This study builds on existing studies and appears to (...) be the first to assess the linkages between principals’ communication patterns, fund management practices and school- community relationship based on empirical rather than subjective data. A structural modelling approach was adopted to examine the nexus using quantitative primary data obtained from a random sample of 2108 respondents. A questionnaire which was designed and validated by the researchers served as the data collection device. Collected data were subjected to Exploratory and Confirmatory Factor Analyses, as well as Multiple Regression Analysis. Findings revealed various significant communication, funds management and school-community relationship practices that are available for adoption. However, it was found that the extent to which principals utilised such practices were below expected minimum standards. It was also found that there were no significant partial and composite effects of principals’ communication and funds management practices on school-community relationship (SCR). Based on these results, relevant theoretical, policy and practical implications are discussed. (shrink)

The Galilean explanatory style is characterized by the search for the underlying structure of phenomena, the positing of "deep" explanatory principles, and a view of the relation between theory and data, on which the search for "crucial data" is of primary importance. In this paper, I trace the dynamics of adopting the Galilean style, focusing on the science of episodic memory. I argue that memory systems, such as episodic and semantic memory, were posited as underlying competences producing (...) the observable phenomena of memory. Considered in idealized isolation from other systems, episodic memory was taken to underlay the ability of individuals to remember events from their personal past. Yet, in reality, memory systems regularly interact, standing in many-to-many relations to actual memory tasks and experiences. Upon this backdrop, I explore a puzzle about the increasing prominence of the notion of autonoetic consciousness in Tulving's theory of episodic memory. I argue that, contrary to widespread belief, the prominence is not best explained by the purported essential link between autonoetic consciousness and episodic memory. Rather, it is explained by the fact that autonoetic consciousness, hypothesized to uniquely accompany episodic retrieval, was considered a source of crucial data, predictable only from theories positing a functionally distinct episodic memory system. However, with the emergence of a new generation of theories, positing wider memory systems for remembering and imagination, the question of the relation between episodic memory and autonoetic consciousness has been reopened. This creates a pressing need for de-idealization, triggering a new search for crucial data. (shrink)

We describe a strategy for integration of data that is based on the idea of semantic enhancement. The strategy promises a number of benefits: it can be applied incrementally; it creates minimal barriers to the incorporation of new data into the semantically enhanced system; it preserves the existing data (including any existing data-semantics) in their original form (thus all provenance information is retained, and no heavy preprocessing is required); and it embraces the full spectrum of (...) class='Hi'>data sources, types, models, and modalities (including text, images, audio, and signals). The result of applying this strategy to a given body of data is an evolving Dataspace that allows the application of a variety of integration and analytic processes to diverse data contents. We conceive semantic enhancement (SE) as a lightweight and flexible process that leverages the richness of the structured contents of the Dataspace without adding storage and processing burdens to what, in the intelligence domain, will be an already storage- and processing-heavy starting point. SE works not by changing the data to which it is applied, but rather by adding an extra semantic layer to this data. We sketch how the semantic enhancement approach can be applied consistently and in cumulative fashion to new data and data-models that enter the Dataspace. (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)

Research into ancient physical structures, some having been known as the seven wonders of the ancient world, inspired new developments in the early history of mathematics. At the other end of this spectrum of inquiry the research is concerned with the minimum of observations from physical data as exemplified by Eddington's Principle. Current discussions of the interplay between physics and mathematics revive some of this early history of mathematics and offer insight into the fine-structure constant. Arthur Eddington's work (...) leads to a new calculation of the inverse fine-structure constant giving the same approximate value as ancient geometry combined with the golden ratio structure of the hydrogen atom. The hyperbolic function suggested by Alfred Landé leads to another result, involving the Laplace limit of Kepler's equation, with the same approximate value and related to the aforementioned results. The accuracy of these results are consistent with the standard reference. Relationships between the four fundamental coupling constants are also found. (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, law, medicine, social science, and some more. -/- This book explores the emerging field of Neutrosophic Algebraic Structures, focusing on both their theoretical foundations and practical applications. We apply innovative algorithmic methods to investigate the complex interactions of neutrosophic elements, such as neutrosophic numbers, sets, and functions, within algebraic systems. Our goal is to show how neutrosophic structures challenge and expand traditional algebraic approaches, offering solutions to problems across diverse fields like computer science, engineering, artificial intelligence, and decision-making. (shrink)

It is exhibited that mature scientific economical theory is a set of propositions that describe the relationship between theoretical objects of two types - basic objects and derivative ones. The set of basic objects makes up the aggregate of initial idealizations (the Fundamental Theoretical Scheme or FTS) with no direct reference to experimental data. The derivative theoretical objects are formed from the basic ones according to certain rules. The sets of derivative objects form partial theoretical schemes or PTS. Any (...) mature economics scientific theory grows due to transitions, in order to describe each new experimental situation, from FTS to PTS. Each PTS construction from the FTS represents a problem that cannot be reduced to a strict algorithm. (shrink)

The fields of Data Science and Artificial Intelligence (AI) have evolved rapidly over the past few decades, often intersecting in ways that have transformed industries, enhanced decision-making processes, and introduced new challenges. Data Science focuses on extracting knowledge and insights from structured and unstructured data, while AI aims to simulate human intelligence processes, including learning, reasoning, and problemsolving. This paper explores the synergies between these two domains, highlighting the key advancements, real-world applications, and challenges that arise from (...) their integration. It also examines how their intersection has led to innovations in machine learning, big data analytics, and automation. (shrink)

The Normative Structure of Responsibility deals with responsibility in legal, moral, and linguistic contexts. The book builds on conceptual analysis and data from everyday language, ethics, and the law in order to defend the thesis that responsibility is fundamentally normative, that is, it cannot be reduced to purely descriptive factors. The book is divided in three parts: the first part draws a conceptual map of various responsibility concepts, conceptions and conditions and their interaction with different kinds of rules; (...) the second part engages with arguments in favour of a descriptive understanding of responsibility based on ethics, neuroscience, and metaphysics and argues against it; the third part investigates the language of responsibility and its use in formal and informal contexts. (shrink)

A framework is developed for understanding what is “taken for granted” both in philosophy and in life generally, which may serve to orient philosophical inquiry and make it more effective. The framework takes in language and its development, as well as mathematics, logic, and the empirical sphere with particular reference to the exigencies of life. It is evaluated through consideration of seven philosophical issues concerned with such topics as solipsism, sense data as the route to knowledge, the possible reduction (...) of geometry to logic, and the existence and status of human rights. Various dichotomies and the notion of continuity are evidently highly strategic. (shrink)

The integration of information resources in the life sciences is one of the most challenging problems facing bioinformatics today. We describe how Language and Computing nv, originally a developer of ontology-based natural language understanding systems for the healthcare domain, is developing a framework for the integration of structured data with unstructured information contained in natural language texts. L&C’s LinkSuite™ combines the flexibility of a modular software architecture with an ontology based on rigorous philosophical and logical principles that is designed (...) to comprehend the basic formal relationships that structure both reality and the ways humans perceive and communicate about reality. (shrink)

The research discussed in this paper centers around the convergence of extended reality (XR) platforms, computational design, digital fabrication, and critical urban study practices. Its aim is to cultivate interdisciplinary and multiscalar approaches within these domains. The research endeavor represents a collaborative effort between two primary disciplines: critical urban studies, which prioritize socio-environmental justice, and integrated digital design to production, which emphasize the realization of volumetric or voxel-based structural systems. Moreover, the exploration encompasses augmented reality to assess its utilization in (...) both the assembly process of the structures and the integration of phygital (physical and digital) data with the physical environment. Within the context of these research scopes, this paper introduces FabriCity-XR as an interactive phygital installation. In addition to presenting an overview of the integrated research driven and performative design to production methodologies, the project showcases the practical implementation of web-based augmented reality trails, eliminating the requirement for external applications for interaction. This approach allows users to seamlessly navigate and engage with phygital content overlaid on physical objects using their personal smart devices. The result is a captivating and immersive user experience that effectively merges the physical and digital realms. (shrink)

Child labour remains widespread in the urban slums of Bangladesh. Empirical studies indicate that various local-level factors drive poor families and children to engage in child labour. However, the role of structural factors and environmental realities is underrepresented in the current scholarship. This investigation examined the role of these factors in normalizing child labour in the slum communities of Dhaka. The researcher adapted a socio-ecological model to develop a conceptual framework for collecting qualitative data from the slum communities of (...) two recently urbanized areas of the city: Hazaribagh and Matuail. A five-month fieldwork project was carried out to conduct 40 semi-structured interviews and two focus group discussions with parents and community members. The data analysis resulted in the following findings. A lack of access to schooling pushed many children into labour. The opportunities for children’s employment in the informal sector remained abundant, and the absence of documents complicated law enforcement. Due to inconsistent adult income, many families are involved in child labour to increase their household income. Local employers chose children over adults for certain occupations due to their low wages and easy-to-control nature. Natural disasters, climate change, and family difficulties drove a large number of families to these slums, and informal employment opportunities for both adults and children drew them there. The shortage of playgrounds and specialized schooling shaped children’s tendency to work more. All together, child labour has become a cultural reality. Policies and interventions aiming to reduce child labour must influence these structural factors and provide support to families and children. (shrink)

All societies have to balance privacy claims with other moral concerns. However, while some concern for privacy appears to be a common feature of social life, the definition, extent and moral justifications for privacy differ widely. Are there better and worse ways of conceptualising, justifying, and managing privacy? These are the questions that lie in the background of this thesis. -/- My particular concern is with the ethical issues around privacy that are tied to the rise of new information and (...) communication technologies (henceforth “ICT”). The focus here is on technologies involved in processing personal data in its broadest sense, including “any operation or set of operations which is performed on personal data or on sets of personal data, whether or not by automated means, such as collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction.” The issue of moral concern is commonly designated as that of 'data privacy'. -/- The first step is to consider why privacy matters, or ought to matter, in the first place. And here we run into our first difficulty, since both the conceptualisation of privacy and the justification for the right to privacy are matters for contention. On the one hand, we are animated by a moral concern for privacy in some sense; on the other, we lack the conceptual and moral resources for making sense of this concern or for promoting particular social policies and criticising others. This often-cited dichotomy and its ensuing confusion have been the central motivation for most of the philosophical literature on privacy – including this thesis. -/- In what follows, I will suggest a way out of the confusion that pervades the current debate on data privacy. I will essentially argue two claims: one, that the concept of (data) privacy is uniquely characterised by a particular context within which we exchange information; two, that because (data) privacy constitutes an intrinsic value, its moral concerns and justifications ought to be deliberated as such. -/- The first chapter sketches the current data privacy landscape by introducing a number of concepts and arguing the unique nature of the landscape. I argue that the traditional distinction between four kinds of privacy (physical, mental, decisional and informational) is no longer accurate, given the technological context that reduces, unifies and processes all aspects of people’s lives by means of digital data. In fact, to hold on to the distinction leads to a fundamental misconception of data privacy. I further argue that today’s distinction between data protection and data privacy runs the risk of ignoring the fact that the former is an inherent aspect of the latter. These arguments allow me to settle on a (provisional) definition of data privacy: controlling how data about identifiable people is processed by ICT. -/- Next, I draw a line between descriptive and normative accounts of data privacy, and explain why this book belongs to the latter category. After a brief discussion of the coherence and distinctiveness theses, and of the difference between instrumental and intrinsic theories of privacy, I introduce the problem of data privacy as a distinct, coherent, and intrinsic moral concern. Three arguments support this position: one, data privacy is a coherent moral concern because it is motivated by a singular moral concept; two, data privacy is a distinct moral concern because the privacy challenges we face today are of a fundamentally new nature – distinct from any moral concerns we’ve faced in the past; and three, data privacy is an intrinsic moral concern that is predominantly justified, not by what it instrumentally protects us from, but by what it intrinsically constitutes. -/- Chapter Two makes the case against a harm-based, consequentialist approach to data privacy. After a brief introduction to the basic tenets of consequentialism I discuss harm-based arguments in favour as well as against privacy. I explain how privacy may protect us against psychological distress, but at the same time constitute the source of that distress; how privacy may be necessary for the proper functioning of democratic societies, but at the same time provide a cloak for anti-social and immoral behaviour; and how privacy protects us against the improper use of our personal data by others, but at the same enables us to conceal information or spread false information about ourselves in ways that are harmful to others. Next, I argue that consequentialism essentially relies on two flawed presumptions – that we know what harm is, and that we know how to predict it – and discuss the controversies surrounding definitions and predictions of harm. This argument becomes especially pertinent in today’s rapidly changing technological context: we may guess, and make assumptions, perhaps even find correlations, but don’t really know or understand (yet) with any degree of certainty the harmful consequences today’s data privacy violations are causing or will eventually cause – if any. -/- I illustrate this with examples of recent data privacy violations, and with a brief overview of consequentialist arguments for and against data privacy. Those who invoke consequentialist arguments against data privacy claim that personal data is nothing more than a raw material that can be turned into something useful and valuable without harming anyone. Choosing to move fast and break things – a popular mantra that promotes the benefits of technological “permissionless innovation” over and above its risks and costs – they draw our attention to the many benefits free data flows generate. Those who rely on consequentialist arguments in favour of data privacy, on the other hand, insist on actual and potential misuse of personal data. Rather careful than sorry, they draw our attention to the urgent need to take back control and protect ourselves against data privacy violations – precisely because we don’t know which harm they may cause in the future. Others go one step further and argue that these violations are part of a broader ideology of power. -/- What Chapter Two demonstrates is that vague definitions, unreliable predictions and inconclusive allegations of harm from both sides of the debate perpetuate a stalemate that fails to produce morally defensible answers to our growing data privacy concerns. Deliberating about data privacy from a consequentialist perspective is a dead end. -/- Chapter Three proposes an alternative to the harm-based approach, namely that privacy is an intrinsic value that deserves moral protection for its own sake. After a brief account of what I take values to be, and why we have a need to protect them, I introduce the foundational value of respect for persons, and lay out my main argument in two steps. The conceptual component of my argument returns to the definition of data privacy I had temporarily settled on in Chapter One, but now with the added consideration of the particular context, and particular circumstances and attitudes, within which information is exchanged. This addition is crucial, as it reveals the fundamental value that is at stake in our data privacy concerns, namely the inherent value of each and every person, which we protect by upholding an ethical principle commonly known as the principle of respect for persons. It is for this reason that the violation of privacy cannot (only) be expressed in terms of harm. I further argue that the principle of respect for persons is consistent with, and in fact unthinkable without, some degree of privacy. -/- I reply to potential challenges to a value-based approach to data privacy by showing that (i) values and value trade-offs are fundamentally different from consequential benefits and cost-benefit analyses; (ii) the suspension of one value in favour of another is not inherently contradictory; what matters is morally defensible deliberation about values and valid individual consent; (iii) data privacy concerns cannot be reduced to other moral concerns and therefore deserve a specific, distinct protection. -/- Finally, because this is a thesis in applied ethics, I attempt an answer to the question as to how we ought to apply a value-based approach to practical data privacy concerns. I argue that a revised, three-pronged version of John Rawls’ reflective equilibrium, founded on the non-negotiable principle of respect for persons, is the best possible procedure to deliberate data privacy concerns in relation to other moral values. (shrink)

We describe a prototype ontology-driven information system (ODIS) that exploits what we call Portion of Reality (POR) representations. The system takes both sensor data and natural language text as inputs and composes on this basis logically structured POR assertions. The goal of our prototype is to represent both natural language and sensor data within a single framework that is able to support both axiomatic reasoning and computation. In addition, the framework should be capable of discovering and representing new (...) kinds of situations and thematic roles, (e.g., roles such as agent, patient and instrument), based on new compositions of existing representations. We applied our prototype in an intelligence analysis use case to test the hypothesis that a framework of this sort can produce usefully structured information from combined natural language and sensor data inputs. We further tested our hypothesis by adding an enhanced US Air Force ontology framework to our ODIS in order to (1) process a collection of sensor data, intel reports, and mission plans; (2) build composite POR representations from these data; and (3) machine analyze the fused results to infer mission threats. (shrink)

We reconstruct Kuhn’s philosophy of measurement and data paying special attention to what he calls the “fifth law of thermodynamics”. According to this "law," there will always be discrepancies between experimental results and scientists’ prior expectations. The history of experiments to determine the values of the fundamental constants offers a striking illustration of Kuhn’s fifth law of thermodynamics, with no experiment giving quite the expected result. We highlight the synergy between Kuhn’s view and the systematic project of iteratively determining (...) the value of physical constants, initiated by spectroscopist Raymond Birge, that was ongoing when Kuhn joined Berkeley in 1956. Our analysis sheds light on various underappreciated aspects of Kuhn’s thought, especially his notion of progress as improvement in measurement accuracy. (shrink)

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others’, (...) in order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. (shrink)

This paper investigates how large language models (LLMs) can be applied to publicly available financial data to generate automated financial summaries and provide actionable recommendations for investors. We demonstrate how LLMs can process both structured financial data (balance sheets, income statements, stock prices) and unstructured text (earnings calls, management commentary) to derive insights, predict trends, and automate financial reporting. By focusing on a specific publicly traded company, this research outlines the methodology for leveraging LLMs to analyze company performance (...) and generate investor-focused summaries and recommendations. (shrink)

Organizational learning can be described as a transfer of individuals’ cognitive mental models to shared mental models. Employees, seeking the same colleagues for advice, are structurally equivalent, and the aim of the paper is to study if the concept can act as a conduit for organizational learning. It is argued that the mimicking of colleagues’ advice seeking structures will induce structural equivalence and transfer the accuracy of individuals’ cognitive mental models to shared mental models. Taking a dyadic level of analysis (...) authors revisit a classical case and present novel data analyses.The empirical results indicate that the mimicking of advice seeking structures can alter cognitive accuracy. It is discussed the findings’ implications for organization learning theory and practice, addressed the study’s limitations, and suggested avenues for future research. (shrink)

In the era of big data and distributed ecosystems, understanding the origin, flow, and transformation of data across complex infrastructures is critical for ensuring transparency, accountability, and informed decision-making. As data-driven enterprises increasingly rely on hybrid cloud architectures, data lakes, and real-time pipelines, the complexity of tracking data movement and transformations grows exponentially. Traditional data lineage solutions, often based on static metadata extraction or rule-based approaches, are insufficient in dynamically evolving environments and fail to (...) provide granular, context-aware insights. This research introduces an AI-augmented, cognitive graph-based framework for autonomous data lineage, designed to enhance data traceability in large-scale and heterogeneous data ecosystems. The framework leverages cutting-edge machine learning (ML), natural language processing (NLP), and graph-based reasoning techniques to enable intelligent discovery, semantic interpretation, and continuous monitoring of data assets throughout their lifecycle. The core of the solution lies in the construction of a dynamic cognitive graph that represents relationships between datasets, processes, systems, and users, enriched with contextual annotations and temporal dimensions. Our architecture incorporates self-learning mechanisms, enabling adaptive lineage discovery and automated anomaly detection. By applying reinforcement learning and stream analytics, the framework not only maps data flows in real time but also evolves with system changes, schema variations, and business logic updates. It provides both forward and backward traceability, supports impact analysis, and enhances compliance auditing capabilities. Furthermore, our system is capable of processing both structured and unstructured metadata, employing advanced NLP models to extract implicit lineage information from data dictionaries, SQL queries, and documentation. The result is a holistic, intelligent, and scalable data lineage solution that reduces manual intervention, mitigates operational risks, and supports regulatory compliance frameworks such as GDPR, HIPAA, and SOX. Experimental evaluations conducted in hybrid cloud environments using tools such as Apache Kafka, Neo4j, Spark, and Python-based ML libraries demonstrate a significant improvement in lineage coverage, anomaly detection accuracy, and system scalability. Compared to conventional lineage tools, our AI-augmented framework delivers a 30% increase in traceability precision and a 40% reduction in manual effort required for lineage tracking and governance. This research lays the foundation for a new paradigm in data governance, where AI not only enhances observability but enables autonomous cognition within data infrastructure. The framework is poised to play a critical role in enabling data democratization, operational agility, and enterprise-wide data literacy. (shrink)

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to (...) be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes. (shrink)

Humans think and communicate in very flexible and schematic ways, and a Spatial Data Infrastructure (SDI) for the Amazon and associated information system ontologies should reflect this flexibility and the adaptive nature of human cognition in order to achieve semantic interoperability. In this paper I offer a conceptual investigation of SDI and explore the nature of cultural schemas as expressions of indigenous ontologies and the challenges of semantic interoperability across cultures. Cultural schemas are, in essence, our ontologies, but they (...) are markedly different than classical formal ontologies. They shape our ontological commitments to what exists in the world as well as the ways in which we approach and engage the world. And while they help structure our understanding of the world in which we are embedded, they are associative and flexible. They help to focus our attention to particular details of our experiences and give them salience, yet they cannot be simply reduced to a series of extracted features. They allow us to make meaning of the contextualized, cultural experience in which we are always immersed. An SDI is a shared social-technological-informational structure that, if it is to be useful and successful for sustainability in the Amazon, must incorporate and use indigenous cultural schemas. Indigenous communities must have the ability to contribute to the collection of geospatial data and their contributions recognized as legitimate forms of knowledge. In order for the SDI to work, it must recognize the larger cultural landscape to which cultural schemas can connect to the ready-to-hand elements of salient cultural experiences. (shrink)

Psychopathy refers to a range of complex behaviors and personality traits, including callousness and antisocial behavior, typically studied in criminal populations. Recent studies have used self-reports to examine psychopathic traits among noncriminal samples. The goal of the current study was to examine the underlying factor structure of the Self-Report of Psychopathy Scale–Short Form (SRP-SF) across complementary samples and examine the impact of gender on factor structure. We examined the structure of the SRP-SF among 2,554 young adults from (...) three undergraduate samples and a high-risk young adult sample. Using confirmatory factor analysis, a four-correlated factor model and a four-bifactor model showed good fit to the data. Evidence of weak invariance was found for both models across gender. These findings highlight that the SRP-SF is a useful measure of low-level psychopathic traits in noncriminal samples, although the underlying factor structure may not fully translate across men and women. (shrink)

The topics approached in the 52 papers included in this book are: neutrosophic sets; neutrosophic logic; generalized neutrosophic set; neutrosophic rough set; multigranulation neutrosophic rough set (MNRS); neutrosophic cubic sets; triangular fuzzy neutrosophic sets (TFNSs); probabilistic single-valued (interval) neutrosophic hesitant fuzzy set; neutro-homomorphism; neutrosophic computation; quantum computation; neutrosophic association rule; data mining; big data; oracle Turing machines; recursive enumerability; oracle computation; interval number; dependent degree; possibility degree; power aggregation operators; multi-criteria group decision-making (MCGDM); expert set; soft sets; LA-semihypergroups; (...) single valued trapezoidal neutrosophic number; inclusion relation; Q-linguistic neutrosophic variable set; vector similarity measure; fundamental neutro-homomorphism theorem; neutro-isomorphism theorem; quasi neutrosophic triplet loop; quasi neutrosophic triplet group; BE-algebra; cloud model; Maclaurin symmetric mean; pseudo-BCI algebra; hesitant fuzzy set; photovoltaic plan; decision-making trial and evaluation laboratory (DEMATEL); Choquet integral; fuzzy measure; clustering algorithm; and many more. (shrink)

The topics approached in this collection of papers are: neutrosophic sets; neutrosophic logic; generalized neutrosophic set; neutrosophic rough set; multigranulation neutrosophic rough set (MNRS); neutrosophic cubic sets; triangular fuzzy neutrosophic sets (TFNSs); probabilistic single-valued (interval) neutrosophic hesitant fuzzy set; neutro-homomorphism; neutrosophic computation; quantum computation; neutrosophic association rule; data mining; big data; oracle Turing machines; recursive enumerability; oracle computation; interval number; dependent degree; possibility degree; power aggregation operators; multi-criteria group decision-making (MCGDM); expert set; soft sets; LA-semihypergroups; single valued trapezoidal (...) neutrosophic number; inclusion relation; Q-linguistic neutrosophic variable set; vector similarity measure; fundamental neutro-homomorphism theorem; neutro-isomorphism theorem; quasi neutrosophic triplet loop; quasi neutrosophic triplet group; BE-algebra; cloud model; fuzzy measure; clustering algorithm; and many more. (shrink)

Etica Big Data presupune aderarea la conceptele de comportament corect și greșit în ceea ce privește datele, în special datele cu caracter personal. Etica Big Data pune accentul pe colectorii și diseminatorii de date structurate sau nestructurate. Etica Big Data este susținută, la nivelul UE, de o amplă documentație, prin care se încearcă să se găsească soluții concrete pentru maximizarea valorii Big Data fără a sacrifica drepturile fundamentale ale omului. Autoritatea Europeană pentru Protecția Datelor (AEPD) sprijină (...) dreptul la viață privată și dreptul la protecția datelor cu caracter personal în respectul demnității umane. DOI: 10.13140/RG.2.2.34749.82405. (shrink)