Coherence and correspondence are classical contenders as theories of truth. In this paper we examine them instead as interacting factors in the dynamics of belief across epistemic networks. We construct an agent-based model of network contact in which agents are characterized not in terms of single beliefs but in terms of internal belief suites. Individuals update elements of their belief suites on input from other agents in order both to maximize internal belief coherence and to incorporate ‘trickled in’ elements (...) of truth as correspondence. Results, though often intuitive, prove more complex than in simpler models (Hegselmann & Krause 2002, 2006; Grim, Singer, Reade & Fisher 2015). The optimistic finding is that pressures toward internal coherence can exploit and expand on small elements of truth as correspondence is introduced into epistemic networks. Less optimistic results show that pressures for coherence can also work directly against the incorporation of truth, particularly when coherence is established first and new data is introduced later. (shrink)

We critically engage two traditional views of scientific data and outline a novel philosophical view that we call the pragmatic-representational view of data. On the PR view, data are representations that are the product of a process of inquiry, and they should be evaluated in terms of their adequacy or fitness for particular purposes. Some important implications of the PR view for data assessment, related to misrepresentation, context-sensitivity, and complementary use, are highlighted. The PR view provides (...) insight into the common but little-discussed practices of iteratively reusing and repurposing data, which result in many datasets’ having a phylogeny—an origin and complex evolutionary history—that is relevant to their evaluation and future use. We relate these insights to the open-data and data-rescue movements, and highlight several future avenues of research that build on the PR view of data. (shrink)

Despite an enormous philosophical literature on models in science, surprisingly little has been written about data models and how they are constructed. In this paper, I examine the case of how paleodiversity data models are constructed from the fossil data. In particular, I show how paleontologists are using various model-based techniques to correct the data. Drawing on this research, I argue for the following related theses: first, the ‘purity’ of a data model is (...) not a measure of its epistemic reliability. Instead it is the fidelity of the data that matters. Second, the fidelity of a data model in capturing the signal of interest is a matter of degree. Third, the fidelity of a data model can be improved ‘vicariously’, such as through the use of post hoc model-based correction techniques. And, fourth, data models, like theoretical models, should be assessed as adequate for particular purposes. (shrink)

Attempts to introduce Gestalt theory into the realm of visual neuroscience are discussed on both theoretical and experimental grounds. To define the framework in which these proposals can be defended, this paper outlines the characteristics of a standard model, which qualifies as a received view in the visual neurosciences, and of the research into natural images statistics. The objections to the standard model and the main questions of the natural images research are presented. On these grounds, this paper (...) defends the view that Gestalt psychology and experimental phenomenology provide a contribution to the research into perception by the construction of phenomenological models for an ecologically meaningful interpretation of the empirical evidence and the hypothetical constructs of the natural image research within the visual neuroscience. A formal framework for the phenomenological models is proposed, wherein Gestalt theoretical principles and empirical evidence are represented in terms of topological properties and relationships, which account for the order and structures that make the environment accessible to observers at a relevant behavioural level. It is finally argued that these models allow us to evaluate the principles and the empirical evidence of various natures which are integrated from different fields into the research into perception, and in particular into visual neurosciences. (shrink)

Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are not only data-laden, but data are also model-laden or model filtered. In this paper I elaborate and defend the second, more controversial, component of the symbiosis view. In particular, I construct a preliminary taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. (...) These include data conversion, data correction, data interpolation, data scaling, data fusion, data assimilation, and synthetic data. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than the so-called raw data, and hence beneficially serve the epistemic aims of science. By illuminating the methods by which raw data are turned into scientifically useful data sets, this taxonomy provides a foundation for developing a more adequate philosophy of data. (shrink)

The COVID-19 pandemic has made it especially visible that mortality data are a key component of epidemiological models, being a single indicator that provides information about various health aspects, such as disease prevalence and effectiveness of interventions, and thus enabling predictions on many fronts. In this paper we illustrate the interrelation between facts and values in death statistics, by analyzing the rules for death certification issued by the World Health Organization. We show how the notion of the underlying cause (...) of death can change in view of public health goals. This brings us to a general point about how non-epistemic factors, such as values and goals, are reflected in the choice of different measures in epidemiological models. We finally argue that this analysis is not only relevant from a theoretical point of view but also has important practical consequences. (shrink)

Public health care interventions—regarding vaccination, obesity, and HIV, for example—standardly take the form of information dissemination across a community. But information networks can vary importantly between different ethnic communities, as can levels of trust in information from different sources. We use data from the Greater Pittsburgh Random Household Health Survey to construct models of information networks for White and Black communities--models which reflect the degree of information contact between individuals, with degrees of trust in information from various sources correlated (...) with positions in that social network. With simple assumptions regarding belief change and social reinforcement, we use those modeled networks to build dynamic agent-based models of how information can be expected to flow and how beliefs can be expected to change across each community. With contrasting information from governmental and religious sources, the results show importantly different dynamic patterns of belief polarization within the two communities. (shrink)

The present research used archaeological data, i.e., the data obtained from kamekan jar burials in the Mikuni Hills of the northern Kyushu area in the Mid- dle Yayoi period, to test the parochial altruism model. This model argued that out-group hate and in-group favor coevolved via prehistoric intergroup conflicts. If this model is accurate, such an out-group hate and in-group favor could be re- flected in the archaeological remains, such as pottery making; the more frequent (...) intergroup conflicts are and the more each group is opposed, the more independent and coherent each group will be and more evident cultural identity could be estab- lished within each group. We employed an elliptic Fourier analysis for the shapes of kamekan jar burials. We examined whether frequent intergroup conflicts in the period influenced kamekan jar pottery between subareas of the Mikuni Hills. The results suggested that the shapes of kamekan jar burials after the KIIIa type are slightly different between subareas, which is partially consistent with the model. However, the results do not support the model directly. (shrink)

This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. We (...) also reviewed 12 data ethics frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities. (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 argue that both zemblanity and self-fulfilling prophecy may emerge from the application of Big Data models in society due to the presence of feedback loops.

The following considerations concern modelling Belief Dynamics (BD) not just in the sense of a formalization, but rather in the sense of building a computational model and implementing the corresponding data structures and algorithms of recomputing beliefs. The purpose of such a project is to illustrate some ideas about belief changes in a Web of Beliefs (WoB) to explore and deepen one's understanding of belief changes by trying to implement or improve corresponding algorithms.

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.

This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. -/- Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author (...) shows how and why computers, data treatment devices and programming languages have occasioned a gradual but irresistible and massive shift from mathematical models to computer simulations. -/- . (shrink)

We argue that concerns about double-counting—using the same evidence both to calibrate or tune climate models and also to confirm or verify that the models are adequate—deserve more careful scrutiny in climate modelling circles. It is widely held that double-counting is bad and that separate data must be used for calibration and confirmation. We show that this is far from obviously true, and that climate scientists may be confusing their targets. Our analysis turns on a Bayesian/relative-likelihood approach to incremental (...) confirmation. According to this approach, double-counting is entirely proper. We go on to discuss plausible difficulties with calibrating climate models, and we distinguish more and less ambitious notions of confirmation. Strong claims of confirmation may not, in many cases, be warranted, but it would be a mistake to regard double-counting as the culprit. 1 Introduction2 Remarks about Models and Adequacy-for-Purpose3 Evidence for Calibration Can Also Yield Comparative Confirmation3.1 Double-counting I3.2 Double-counting II4 Climate Science Examples: Comparative Confirmation in Practice4.1 Confirmation due to better and worse best fits4.2 Confirmation due to more and less plausible forcings values5 Old Evidence6 Doubts about the Relevance of Past Data7 Non-comparative Confirmation and Catch-Alls8 Climate Science Example: Non-comparative Confirmation and Catch-Alls in Practice9 Concluding Remarks. (shrink)

In this book, we present a study of linear models and algorithms to find the optimal solution for them using the concepts of neuroscientific science. We know that the linear programming method is one of the important methods of operations research, the science that was the product of the great scientific development that our contemporary world is witnessing. The name operations research is given to the group of scientific methods used. In analyzing problems and searching for optimal solutions, it is (...) a science whose applications have achieved widespread success in various fields of life. What is meant by neutrosophic models are models in which the data are neutrosophic values, that is, variables such as in the objective function, which expresses profit if the model is a maximization model, and expresses a cost if the model is a minimization model, which in turn is affected by environmental conditions. (shrink)

The Korea National Health and Nutrition Examination Survey (2020) reported that the prevalence of obesity (≥19 years old) was 31.4% in 2011, but it increased to 33.8% in 2019 and 38.3% in 2020, which confirmed that it increased rapidly after the outbreak of COVID-19. Obesity increases not only the risk of infection with COVID-19 but also severity and fatality rate after being infected with COVID-19 compared to people with normal weight or underweight. Therefore, identifying the difference in potential factors for (...) obesity before and after the pandemic is an important issue in health science. This study identified the keywords and topics that were formed before and after the COVID-19 pandemic in the South Korean society and how they had been changing by conducting a web crawling of South Korea's news big data using “obesity” as a keyword. (shrink)

Data security is, protecting data from ill- conceived get to, utilize, introduction, intrusion, change, examination, recording or destruction. Cloud computing is a sort of Internet-based computing that grants conjoint PC handling resources and information to PCs what's more, different gadgets according to necessity. It is a model that empowers universal, on-request access to a mutual pool of configurable computing resources. At present, security has been viewed as one of the best issues in the improvement of Cloud Computing. (...) The key issue in effective execution of Cloud Computing is to adequately deal with the security in the cloud applications. This paper talks about the part of cryptography in cloud computing to improve the data security. The expectation here is to get bits of knowledge another security approach with the usage of cryptography to secure information at cloud data centers. (shrink)

We discuss potential biorisks from large language models (LLMs). AI assistants based on LLMs such as ChatGPT have been shown to significantly reduce barriers to entry for actors wishing to synthesize dangerous, potentially novel pathogens and chemical weapons. The harms from deploying such bioagents could be further magnified by AI-assisted misinformation. We endorse several policy responses to these dangers, including prerelease evaluations of biomedical AIs by subject-matter experts, enhanced surveillance and lab screening procedures, restrictions on AI training data, and (...) access controls on biomedical AI. We conclude with a suggestion about future research directions in bioethics. (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)

An important method for finding the optimal solution for linear and nonlinear models is the graphical method, which is used if the linear or nonlinear mathematical model contains one, two, or three variables. The models that contain only two variables are among the most models for which the optimal solution has been obtained graphically, whether these models are linear or non-linear in references and research that are concerned with the science of operations research, when the data of the (...) issue under study is classical data. In this research, we will present a study through, which we present the graphical method for solving Neutrosophical nonlinear models in the following case: A nonlinear programming issue, the objective function is a nonlinear function, and the constraints are linear functions. Note that we can use the same method if (i) the objective function follower is a linear follower and the constraints are nonlinear; (ii) the objective function is a non-linear follower and the constraints are non-linear. In the three cases, the nonlinear models are neutrosophic, and as we know, the mathematical model is a nonlinear model if any of the components of the objective function or the constraints are nonlinear expressions, and the nonlinear expressions may be in both. At the left end of the constraints are neutrosophic values, at least one or all of them. Then, the possible solutions to the neutrosophic nonlinear programming problem are the set of rays that fulfills all the constraints. As for the region of possible solutions, it is the region that contains all the rays that fulfill the constraints. The optimal solution is the beam that fulfills all constraints and at which the function reaches a maximum or minimum value, depending on the nature of the issue under study (noting that it is not necessary to be alone). (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)

In 1995, as a student of psychology inspired by natural science, I defined a logical model of personality explaining psychosis. I created (for my MA thesis, 1998 and grant research, 1999) new kind of tests assessing intelligence, creativity, prejudices, expectations to show more exact methods in psychology. During my Phd study in economics, I developed 'Maximization of Uniqueness (Originality)' model enhancing the classic utility to explain irrational motivations linking economics and psychology. Later I became computer programmer developing functional (...) programming. According to Personality model (and my experience in chess composition), I've built server and client logic of information system www.each.co.uk, and produced visual art. I presented the excerpts of 'Personality Model' in a few articles, and later as part of my art exhibitions: From Animation (Oct 2013, Holland Park, London), Parallax 12 (Feb 2015, Chelsea Town Hall, London), Fading Memory (Sep 2015, Weißenohe / Nuremberg, Germany). Another work: The Science is a subset of the Art extends Personality Model to art, society. At the end I add example and explanation of a special problem, when the higher intelligence leads to a wrong solution. It was published in Japanese Journal Problem Paradise in 1999. All presented results can be repeated. Computer testing would enable to acquire more data and more exact analysis. (shrink)

The ability to collect fine-grained energy data from smart meters has benefits for utilities and consumers. However, a proactive approach to data privacy is necessary to maximize the potential of these data to support low-carbon energy systems, and innovative business models.

We present a study of linear models using the concepts of neutrosophic science, the science that was built on the basis that there is no absolute truth, there is no confirmed data, issues cannot be limited to right and wrong only. There is a third state between error and right, an indeterminate, undetermined, uncertain state. It is indeterminacy. Neutrosophic science gave each issue three dimensions, namely (T, I, F), correctness in degrees, indeterminacy in degrees, and error in degrees. It (...) was founded by the American philosopher and mathematician Florentin Smarandache, in 1995 and came as a generalization of fuzzy logic that was founded by the scientist Lotfi. A. Zadeh, in 1965. (shrink)

As the world grapples with the urgent need for sustainable development, the transition towards green energy stands as a critical imperative. Financing this transition poses significant challenges, requiring innovative approaches that align financial objectives with environmental sustainability goals. This review presents a conceptual framework for leveraging data-driven techniques in sustainable finance to facilitate the transition towards green energy. The proposed framework integrates principles of sustainable finance with advanced data analytics to enhance decision-making processes across the financial ecosystem. At (...) its core, the framework emphasizes the importance of harnessing vast datasets related to energy production, consumption, environmental impact, and financial performance. By leveraging machine learning algorithms and predictive modeling techniques, financial stakeholders can gain deeper insights into the risks and opportunities associated with green energy investments. Key components of the framework include data collection and aggregation, risk assessment, impact measurement, and investment optimization. Data sources range from traditional financial indicators to environmental metrics, social impact assessments, and geopolitical factors. Through comprehensive data analysis, financial institutions can assess the long-term viability and sustainability of green energy projects, while also evaluating potential social and environmental impacts. Risk assessment methodologies within the framework consider both financial risks, such as market volatility and regulatory uncertainty, and non-financial risks, such as climate change impacts and community resilience. By integrating these factors into risk models, investors can make more informed decisions that mitigate potential losses and maximize returns. Furthermore, impact measurement tools enable stakeholders to quantify the environmental and social benefits of green energy investments. By tracking metrics such as carbon emissions reduction, energy efficiency improvements, and job creation, investors can assess the contribution of their portfolios towards broader sustainability objectives. Finally, the framework incorporates investment optimization strategies that align financial goals with environmental objectives. Through portfolio diversification, asset allocation, and innovative financial instruments such as green bonds and impact investing funds, financial institutions can allocate capital more efficiently towards green energy projects. The conceptual framework presented herein offers a systematic approach to integrating data-driven methodologies into sustainable finance practices. By leveraging advanced analytics and comprehensive datasets, financial stakeholders can drive the transition towards green energy while simultaneously achieving financial returns and positive environmental outcomes. (shrink)

The anti-exceptionalist debate brought into play the problem of what are the relevant data for logical theories and how such data affects the validities accepted by a logical theory. In the present paper, I depart from Laudan's reticulated model of science to analyze one aspect of this problem, namely of the role of logical data within the process of revision of logical theories. For this, I argue that the ubiquitous nature of logical data is responsible (...) for the proliferation of several distinct methodologies for logical theories. The resulting picture is coherent with the Laudanean view that agreement and disagreement between scientific theories take place at different levels. From this perspective, one is able to articulate other kinds of divergence that considers not only the inferential aspects of a given logical theory, but also the epistemic aims and the methodological choices that drive its development. (shrink)

In this paper, we will argue that, while Big Data enthusiasts imply that the analysis of massive data sets can produce serendipitous (that is, unexpected and fortunate) discoveries, the way those models are currently designed not only does not create serendipity so easily but also frequently generates zemblanitous (that is, expected and unfortunate) findings.

We describe a strategy that is being used for the horizontal integration of warfighter intelligence data within the framework of the US Army’s Distributed Common Ground System Standard Cloud (DSC) initiative. The strategy rests on the development of a set of ontologies that are being incrementally applied to bring about what we call the ‘semantic enhancement’ of data models used within each intelligence discipline. We show how the strategy can help to overcome familiar tendencies to stovepiping of intelligence (...) data, and describe how it can be applied in an agile fashion to new data resources in ways that address immediate needs of intelligence analysts. (shrink)

It has been argued that ethical frameworks for data science often fail to foster ethical behavior, and they can be difficult to implement due to their vague and ambiguous nature. In order to overcome these limitations of current ethical frameworks, we propose to integrate the analysis of the connections between technical choices and sociocultural factors into the data science process, and show how these connections have consequences for what data subjects can do, accomplish, and be. Using healthcare (...) as an example, attention to sociocultural conversion factors relevant to health can help in navigating technical choices that require broader considerations of the sociotechnical system, such as metric tradeoffs in model validation, resulting in better ethical and technical choices. This approach promotes awareness of the ethical dimension of technical choices by data scientists and others, and that can foster the cultivation of 'ethical skills' as integral to data science. (shrink)

Standard linguistic analysis of syntax uses the T-model. This model requires the ordering: D-structure > S-structure > LF, where D-structure is the sentences deep structure, S-structure is its surface structure, and LF is its logical form. Between each of these representations there is movement which alters the order of the constituent words; movement is achieved using the principles and parameters of syntactic theory. Psychological analysis of sentence production is usually either serial or connectionist. Psychological serial models do not (...) accommodate the T-model immediately so that here a new model called the P-model is introduced. The P-model is different from previous linguistic and psychological models. Here it is argued that the LF representation should be replaced by a variant of Frege'sA three qualities (sense, reference, and force), called the FregeA representation or F-representation. In the F-representation the order of elements is not necessarily the same as that in LF and it is suggested that the correct ordering is: F-representation > D-structure > S-structure. This ordering appears to lead to a more natural view of sentence production and processing. Within this framework movement originates as the outcome of emphasis applied to the sentence. The requirement that the F-representation precedes the D-structure needs a picture of the particular principles and parameters which pertain to movement of words between representations. In general this would imply that there is a preferred or optimal ordering of the symbolic string in the F-representation. The standard ordering is retained because the general way of producing such an optimal ordering is unclear. In this case it is possible to produce an analysis of movement between LF and D-structure similar to the usual analysis of movement between S-structure and LF. The necessity of analyzing corrupted data suggests that a maximal amount of information about a language's grammar and lexicon is stored. (shrink)

This article argues that common intuitions regarding (a) the specialness of ‘use-novel’ data for confirmation and (b) that this specialness implies the ‘no-double-counting rule’, which says that data used in ‘constructing’ (calibrating) a model cannot also play a role in confirming the model’s predictions, are too crude. The intuitions in question are pertinent in all the sciences, but we appeal to a climate science case study to illustrate what is at stake. Our strategy is to analyse (...) the intuitive claims in light of prominent accounts of confirmation of model predictions. We show that on the Bayesian account of confirmation, and also on the standard classical hypothesis-testing account, claims (a) and (b) are not generally true; but for some select cases, it is possible to distinguish data used for calibration from use-novel data, where only the latter confirm. The more specialized classical model-selection methods, on the other hand, uphold a nuanced version of claim (a), but this comes apart from (b), which must be rejected in favour of a more refined account of the relationship between calibration and confirmation. Thus, depending on the framework of confirmation, either the scope or the simplicity of the intuitive position must be revised. (shrink)

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by using different machine learning (...) algorithm, and finally using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

Big Data can generate, through inferences, new knowledge and perspectives. The paradigm that results from using Big Data creates new opportunities. Big Data has great influence at the governmental level, positively affecting society. These systems can be made more efficient by applying transparency and open governance policies, such as Open Data. After developing predictive models for target audience behavior, Big Data can be used to generate early warnings for various situations. There is thus a positive (...) feedback between research and practice, with rapid discoveries taken from practice. DOI: 10.13140/RG.2.2.14677.17120 . (shrink)

Human epistemic subjects cannot but employ imperfect and limited tools to gain knowledge. Even in the seemingly simple business of acquiring knowledge of the value of a physical quantity, what the instrument reads or perception tells more often that not does not correspond to real value. However, even though both our perceptual apparatus and measuring instruments are sensible to background noise, under certain conditions, collecting more information of the same quantity using the same tools leads to an improvement of the (...) subject's epistemic condition. The aim of this paper is to formalize this intuition employing a model which extends Williamson (2013) on the arising of Gettier cases in epistemic logic. From a model where each world is univocally represented by the pair r, a defined by the real value and the apparent one, I will be considering a scenario wherein the subject has collected n appearances of the real parameter, so that each world is identified by a n + 1-tuple. On the assumptions that the n data are all independent and regarded by the subject as equally reliable, a plausible model should be able to describe three phenomena. First of all, the more coherent and closer to represent the real value the set of n appearances is, the more knowledge of the real value is gained. Secondly, the more the subject goes on acquiring "good" data, the more she knows. Thirdly, the world where the n apparent values and the real one match is the best world for the subject. Following Williamson, I shall put forth a system for multiple appearances in epistemic logic that aims at modeling these phenomena by describing the interval within which the real value is known to lie. (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)

In engineering, as in other scientific fields, researchers seek to confirm their models with real-world data. It is common practice to assess models in terms of the distance between the model outputs and the corresponding experimental observations. An important question that arises is whether the model should then be ‘tuned’, in the sense of estimating the values of free parameters to get a better fit with the data, and furthermore whether the tuned model can be (...) confirmed with the same data used to tune it. This dual use of data is often disparagingly referred to as ‘double-counting’. Here, we analyse these issues, with reference to selected research articles in engineering. Our example studies illustrate more and less controversial practices of model tuning and double-counting, both of which, we argue, can be shown to be legitimate within a Bayesian framework. The question nonetheless remains as to whether the implied scientific assumptions in each case are apt from the engineering point of view. (shrink)

This paper describes an AFOSR-supported basic research program that focuses on developing a new framework for combining hard with soft data in order to improve space situational awareness. The goal is to provide, in an automatic and near real-time fashion, a ranking of possible threats to blue assets (assets trying to be protected) from red assets (assets with hostile intentions). The approach is based on Conceptual Spaces models, which combine features from traditional associative and symbolic cognitive models. While Conceptual (...) Spaces are revolutionary, they lack an underlying mathematical framework. Several such frameworks have attempted to represent Conceptual Spaces, but by far the most robust is the model developed by Holender. His model utilizes integer linear programming in order to obtain an overall similarity value between observations and concepts that support the formation of hypotheses. This paper will describe a method for building Conceptual Spaces models for threats that utilizes ontologies as a means to provide a clear semantic foundation for this inferencing process; in particular threat ontologies and space domain ontologies are developed and employed in this approach. A space situational awareness use-case is presented involving a kinetic kill scenario and results are shown to assess the performance of this fusion-based inferencing framework. (shrink)

Odată cu creșterea volumului de date pe Internet, în media socială, cloud computing, dispozitive mobile și date guvernamentale, Big Data devine în același timp o amenințare și o oportunitate în ceea ce privește gestionarea și utilizarea acestor date, menținând în același timp drepturile persoanelor implicate. În fiecare zi, folosim și generăm tone de date, alimentând bazele de date ale agențiilor guvernamentale, companiilor private și chiar cetățenilor privați. Beneficiem în multe feluri de existența și utilizarea Big Data, dar trebuie (...) să ne amintim de asemenea că „nu există nimic gratuit”. Există riscuri în utilizarea Big Data, un fel de parte întunecată. Schimbările de piață pot fi obținute prin utilizarea Big Data: în timp ce acestea pot fi pozitive pentru cineva, ele pot dăuna altcuiva. Riscurile potențiale provin și de la agențiile publice. Este clar că datele mari pot aduce beneficii cetățenilor și companiilor, dar trebuie să fie echilibrate de o conștientizare sporită a „părții întunecate” a Big Data în exploatarea noilor capacități și oportunități pe care le oferă tuturor . -/- CUPRINS -/- 1. Big Data - Definiție - Caracteristici - Clasificarea datelor mari - Arhitectura - Tehnologii - Oportunități - Provocări - Critici - - Criticile paradigmei Big Data - - Criticile modelului "V" - - Criticile de noutate - - Criticile execuției Big Data - 1.1 Viața datelor mari - - Nașterea - - Copilăria - - - Educație - - - Divertisment - - Adolescența - - - Servicii online - - - Comunicații mobile - - - Servicii de streaming - - Adulți tineri - - - Căutarea de locuri de muncă - - - Servicii bancare și asigurări - - - Utilități - - - Dating online și pornografie - - Vârsta medie - - - Servicii de sănătate - - - Aplicații pentru îngrijire și aplicații pentru sănătate și fitness - - - Căutarea de locuri de muncă - - - Dating online - - Bătrânețea - - - Îngrijirea sănătății - - Moartea - 1.2 Aplicații - - Guvern - - Dezvoltare internațională - - Fabrici - - Sănătate - - Educaţie - - Mass-media - - Asigurare - - Internetul obiectelor (IoT) - - Tehnologia informației - 1.3 Studii de caz - - Guvern - - - China - - - India - - - Israel - - - Regatul Unit - - - Statele Unite ale Americii - - Cu amănuntul - - Ştiinţă - - Sport - - Tehnologie - - Activități de cercetare - - - Prelevarea de date mari - 1.4 Agenda europeană privind societatea digitală - - Cadrul juridic - - Peisajul opiniei părților interesate - 1.5 GDPR - - Etapele procesării datelor personale - - Principiile procesării datelor - - Politica de confidențialitate și transparența - - Scopurile procesării datelor - - Confidențialitate prin design și implicită - - - Paradoxul (legal) al Big Data - - Domeniul teritorial al GDPR - - Definirea datelor mari, a datelor personale și a datelor anonime - - Etapele procesării datelor cu caracter personal - - Principii de prelucrare a datelor - - Politica de confidențialitate și Transparența - - Scopuri de prelucrare a datelor - - Confidențialitate prin design și confidențialitate în mod implicit - - Paradoxul (legal) al datelor mari 2. Analiza Big Data - 2.1 Analitica - - Analitica vs. analiză - - Aplicații ale analiticii - - - Optimizarea marketingului - - - Analiza oamenilor - - - Analiza portofoliului - - - Analitica riscurilor - - - Analitica digitală - - - Analitica securității - - - Analitica software-ului - - Provocări - - Riscuri - 2.2 Analitica predictivă - - Definiție - - Procesul de analitica predictivă - - Tipuri - - - Modele predictive - - - Modele descriptive - - - Modele de decizie - - Aplicații - - - Managementul relațiilor cu clienții analitic (CRM) - - - Protecția copilului - - - Sisteme de suport decizional clinic - - - Analiticile colecțiilor - - - Vânzări încrucișate - - - Retenția clienților - - - Marketing direct - - - Detectarea fraudelor - - - Prezicerea rezultatelor deciziilor legale - - - Previziuni privind portofoliul, produsele sau economia - - - Managementul riscului de proiect - - - Acceptanța (Underwriting) - - Tehnologia și influențele big data - - Tehnici analitice - - - Tehnici de regresie - - - - Model de regresie liniară - - - - Modele de alegere discretă - - - - Regresie logistică - - - - Regresie logistică multinomială - - - - Regresie probit - - - - Logit versus probit - - - - Modele pentru serii de timp - - - - Analiza supraviețuirii sau a duratei - - - - Arborele de clasificare și regresie (CART) - - - - Funcții spline adaptive de regresie multivariate - - - Tehnici de învățare automată - - - - Rețele neuronale - - - - Perceptron multistrat (MLP) - - - - Funcții radiale de bază - - - - Mașini cu vectori de suport - - - - Bayesiene naive - - - - Vecinul k-cel mai apropiat - - - - Modelarea predictivă geospațială - - Instrumente - - - PMML - - Critici - 2.3 Analitica prescriptivă - - Istorie - 2.4 Studiul critic al datelor - - Perspective critice - - Abordare: Pragmatismul și etica discursului 3. Etica Big Data - Datele personale ale copiilor - Rolul instituțiilor - - State naționale - - Bănci - Articole relevante de știri despre etica datelor - Legislație relevantă privind etica datelor - 3.1 Principii ale eticii Big Data - - Etica în cercetare - - Conștientizarea - - Consimţământul - - Controlul - - Transparența - - Încrederea - - Proprietatea - - Supravegherea și securitatea - - Identitatea digitală - - Realitatea ajustată - - De-anonimizarea - - Inegalitatea digitală - - Confidențialitatea - - Financiar - - - Cât de mult merită datele? - - Deschidere - 3.2 Probleme etice în cercetare - - Etica cercetării pentru cercetarea socială - - Principalele aspecte etice ale cercetării sociale - - - Intimitate - - - Consimțământ informat - - - De-identificarea - - - Inegalitate – Diviziune digitală - - Integritatea cercetării - - Probleme emergente - - Aviz legal - 3.3 Aspecte filosofice - 3.4 10 reguli de bază în lucrul cu Big Data - - Concluzie Referințe Despre autor - Nicolae Sfetcu - - De același autor - - Contact Editura - MultiMedia Publishing . 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Abstract—Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to perform (...) a data mining practical experiment using the Waikato Environment for Knowledge Analysis (WEKA) tool. The WEKA Java application represents a rich resource for conducting performance metrics during the execution of experiments. Pre-processing and feature extraction are used to optimize the data. The classification process used in this study was summarized through thirteen experiments. Additionally, 10 experiments using various different classification algorithms were conducted. The introduced algorithms were: Naïve Bayes, Logistic Regression, Lazy IBK (Instance-Bases learning with parameter K), Lazy Kstar, Lazy Locally Weighted Learner, Rules ZeroR, Decision Stump, Decision Trees J48, Random Forest and Random Trees. The process of producing a predictive model was automated with the use of classification accuracy. Further, several experiments on classification of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer, were conducted to compare the success rates of the different methods. Results conclude that Lazy IBK classifier k-NN can achieve 98% accuracy among other classifiers. The main advantages of the study were the compactness of using 13 different data mining models and 10 different performance measurements, and plotting figures of classifications errors. (shrink)

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest (...) over a restful Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model. (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)

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)

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)

The sunspot data seems to indicate that the Sun is likely to enter Maunder Minimum, then it will mean that low Sun activity may cause low temperature in Earth. If this happens then it will cause a phenomenon which is called by some climatology experts as “The Little Ice Age” for the next 20-30 years, starting from the next few years. Therefore, the Earth climate in the coming years tend to be cooler than before. This phenomenon then causes us (...) to ask: what can we do as human being in Earth to postpone or avoid the worsening situation in terms of Earth cooling temperature in the coming years? We think this is a more pressing problem for the real and present danger that we are facing in the Earth. What we are suggesting in this paper is that perhaps it is possible to model Sun-Earth interaction in terms of Shannon entropy. Since Shannon entropy can be expressed as bits of information, then it would mean that perhaps we can do something with Earth temperature by controlling the amount of information transfer and storage in the Earth. This proposal is somewhat in resemblance with message of a 2012 movie “A Thousand words” where we shall strive to love our neighbours and nature, instead of being absorbed in a culture of less-meaningful fast-talk (starred by Eddie Murphy). (shrink)

Purpose of the study: Not all languages have a universal concept of the same object, and this creates problems in translation. This paper aims to examine the semiotic model for equivalence or non-equivalence in translation which attempts to define the semiotic model, to use the model for translation, and to offer the benefits of this model to solving translation’s problem in equivalence and non-equivalence. Methodology: The data of this research are derived from the novel Lelaki (...) Harimau, as the source language and L'homme Tigre, as the target language. This model is used in the Indonesian novel which has been translated into 14 languages, one of which is in French. The authors use a semiotic approach to analyze the equivalence and non-equivalence in the translation. Main Findings: This study reveals that the concept of signified in the semiotic theory proposes two models: the first: translation using the same concept in the source text (ST) and target text (TT), which is broadly known as equivalence, the second: translation using different concept between ST and TT, this called non-equivalence. This article not only explores the issue of meaning contextually in translation, but also the use of the semiotic model in translation which shows that the language perspective depends on the relationship between the sign and the object. Applications of this study: The model for this study can be used not only in translation studies at universities but also in providing supporting data for applied linguistic studies. Novelty/Originality of this study: This study provides a novelty in translation research with a semiotic approach. The contribution of this study is that the semiotics perspective suggests that a sign in the concept level (signified) will not be universal due to different cultural backgrounds. (shrink)

Science is the basis for managing the affairs of life and human activities, and living without knowledge is a form of wandering and a kind of loss. Using scientific methods helps us understand the foundations of choice, decision-making, and adopting the right solutions when solutions abound and options are numerous. Operational research is considered the best that scientific development has provided because its methods depend on the application of scientific methods in solving complex issues and the optimal use of available (...) resources in various fields, private and governmental work in peace and war, in politics and economics, in planning and implementation, and in various aspects of life. Its basic essence is to use the data provided for the issue under study to build a mathematical model that is the optimal solution. It is the basis on which decision makers rely in managing institutions and companies, and when operations research methods meet with the neutrosophic teacher, we get ideal solutions that take into account all the circumstances and fluctuations that may occur in the work environment over time. One of the most important operations research methods is the linear programming method. Which prompted us to reformulate the linear models, the graphical method, and the simplex method, which are used to obtain the optimal solution for linear models using the concepts of neutrosophic science. In this research, and as a continuation of what we presented previously, we will reformulate the modified simplex algorithm that was presented to address the difficulty that we were facing when applying the direct simplex algorithm. It is the large number of calculations required to be performed in each step of the solution, which requires a lot of time and effort. (shrink)

In this paper, we present an expansion of one of the well-known classical inventory management models, which is the static model of inventory management without a deficit and for a single substance, based on the neutrosophic logic, where we provide through this study a basis for dealing with all data, whether specific or undefined in the field of inventory management, as it provides safe environment to manage inventory without running into deficit , and give us an approximate ideal (...) volume of inventory. Since the ideal size is affected by the rate of demand for inventory, we present in this paper a study of the rate of demand for inventory when it is not precisely refined, and we find that the indeterminate values of the demand rate cannot be ignored, because they actually affect determining the ideal size of inventory and calculating its costs, thus affecting on the efficiency of the facility and achieve great profits for it. (shrink)

The college hopes that every semester students are able to pay tuition properly and smoothly. The hope is that the institution will be able to maintain monthly cash flow so that its operational and maintenance costs can be met. Therefore, this study was conducted to predict and fulfill the institution's cash-in from the method of paying tuition fees either by cash, installments, or sometimes late payments every semester. In predicting the method of paying tuition fees, using student profile data (...) (name, name, study program) and achievement index every semester for 5 semesters passed and the method of payment (cash, installments, and late--cash or installments). Using the Naive Bayes (NB) method, Support Vector Machine (SVM), and Deep Learning, this study aims to forecast tuition costs. The Classification Prediction Model with Naive Bayes, SVM, and Deep Learning produces Confusion Matrix Performance NB with an Accuracy of 91.49%, Confusion Matrix Performance SVM with an Accuracy of 85.11%, and Confusion Matrix Performance Deep Learning with an Accuracy of 89.36%, according to the research findings. (shrink)

Learning mathematics through distance education can be challenging, with the “logical” and “rewarding” nature proving difficult to measure. This article aimed to articulate an argument explaining the “logical” and “rewarding” nature of online mathematics learning, elucidating their causal factors. Existing data from the literature that involving students at Visayas State University, Philippines, were utilized in this study. The study used statistical measures to capture descriptions from the data, and quantile regression analysis was employed to forecast the predictors of (...) the logicality and rewarding nature of learning mathematics at a distance. Results indicate that learning mathematics in the new normal is perceived as “logical” and moderately “rewarding”. The regression and correlation analyses revealed a significant positive association between the perceived “logical” nature and the rewarding experience of learning mathematics. The constructed statistical models depicted that the determinants of logicality in learning mathematics include family income, money spent on the internet, learning environment, household size, social status, and health. Moreover, causal factors such as family income, money spent on the internet, learning environment, leisure activities, social status, and health significantly determine the rewarding nature of learning mathematics online. Conclusively, institution must support college students with their online learning needs. Furthermore, mathematics instructors should create lively and exciting online discussions that boost their logical thinking. Providing problem-solving task that are intrinsically rewarding can contribute to a more fulfilling learning experience. (shrink)