Nowadays, protecting trust in social sciences also means engaging in open community dialogue, which helps to safeguard robustness and improve efficiency of research methods. The combination of open data, open review and open dialogue may sound simple but implementation in the real world will not be straightforward. However, in view of Begley and Ellis’s (2012) statement that, “the scientific process demands the highest standards of quality, ethics and rigour,” they are worth implementing. More importantly, they are feasible to (...) work on and likely will help to restore plausibility to social sciences research. Therefore, I feel it likely that the triplet of open data, open review and open dialogue will gradually emerge to become policy requirements regardless of the research funding source. (shrink)

The Health Level 7 Reference Information Model (HL7 RIM) is lauded by its authors as ‘the foundation of healthcare interoperability’. Yet even after some 10 years of development work, the RIM is still subject to a variety of logical and ontological flaws which have placed severe obstacles in the way of those who are called upon to develop implementations. We offer evidence that these obstacles are insurmountable and that the time has come to abandon an unworkable paradigm.

The aim of the research is to identify the relationship between the performance criteria and the achievement of the objectives of supervision which is represented in the performance of the job at the Islamic University in Gaza Strip. To achieve the objectives of the research, the researchers used the descriptive analytical approach to collect information. The questionnaire consisted of (22) paragraphs distributed to three categories of employees of the Islamic University (senior management, faculty members, their assistants and members of the (...) administrative board). A random sample of 314 employees was selected, 276 responses were retrieved with a return rate of 88.1%. The SPSS program was used to enter, process, and analyze the data. The results of the study showed a positive relationship between the performance criteria and the achievement of the control objectives represented by the job performance in the Islamic University from the point of view of the members (senior management, faculty and their assistants and the administrative board). The researchers also recommended a number of recommendations, including the provision of an appropriate level of control system components today through the continuous updating and development of performance standards and the need to provide the necessary physical and financial resources to continue the development and achievement within the university. Expand the development of technology in the various activities of the university through the construction of a complete and integrated system to support the control systems in the university to suit its size. The researchers also recommended the follow-up, review of the performance standards and work to modify them in line with the mission of the university and the goals that the university seeks to reach. (shrink)

The setting of evidential standards is a core practice of scientific assessment for policy. Persuaded by considerations of inductive risk, philosophers generally agree that the justification of evidential standards must appeal to non-epistemic values but debate whether the balance of non-epistemic reasons favours varying evidential standards versus maintaining fixed high evidential standards in assessment, as both sets of standards promote different and important political virtues of advisory institutions. In this paper, I adjudicate the evidential (...) class='Hi'>standards debate by developing a novel argument from justice, drawing on the IPCC’s assessment of climate impacts as a case study. I argue that in assessments marked by background evidential inequality, maintaining fixed high evidential standards results in an unequal distribution of ‘epistemic power’ among stakeholders, producing a ‘powerful assessment’ for the data-rich (a high rate of findings) and a ‘weak assessment’ for the data-poor (a low rate of findings). Where such inequalities of epistemic power disadvantage those in data-poor regions with respect to fundamental interests, such as basic human rights, we have decisive reasons of justice to vary evidential standards. (shrink)

The rapid evolution of the financial sector, particularly in banking and fintech, necessitates continuous innovation in financial product development and testing. However, challenges such as data privacy, regulatory compliance, and the limited availability of diverse datasets often hinder the effective development and deployment of new products. This research investigates the transformative potential of AI-driven synthetic data generation as a solution for accelerating innovation in financial product development. Synthetic data, generated through advanced AI techniques such as Generative Adversarial (...) Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models, can simulate real-world financial scenarios with a high degree of fidelity while preserving privacy and compliance standards. The use of synthetic data enables financial institutions and fintech companies to conduct rigorous testing, modeling, and validation of new products and services without relying on sensitive customer data. By generating realistic yet artificial datasets, organizations can explore a broader range of scenarios, including rare or extreme market conditions, thus enhancing the robustness and reliability of their financial models. This paper provides a comprehensive analysis of the underlying methodologies for synthetic data generation, focusing on their application to financial product development. It delves into the specific architectures and frameworks used in generating synthetic data, including GANs, VAEs, and synthetic minority over-sampling techniques (SMOTE), and examines their respective advantages and limitations. The paper also addresses the critical issue of ensuring the quality and utility of synthetic data, emphasizing metrics such as statistical similarity, privacy preservation, and applicability to real-world use cases. The discussion extends to the ethical and regulatory implications of deploying AI-driven synthetic data in finance, highlighting the need for transparent and explainable AI models to ensure trust and compliance. Moreover, the research explores practical case studies where financial institutions and fintech firms have successfully implemented synthetic data to develop and test new products, demonstrating significant reductions in time-to-market and development costs. One of the key contributions of this research is the exploration of how AI-driven synthetic data generation can facilitate the development of innovative financial products such as algorithmic trading strategies, risk management tools, credit scoring models, and fraud detection systems. By simulating diverse market behaviors and customer interactions, synthetic data enables the fine-tuning of algorithms and models to achieve higher accuracy and performance. Additionally, the paper discusses the integration of synthetic data generation into existing financial data ecosystems, proposing a framework for leveraging hybrid datasets that combine synthetic and real data to optimize model training and validation. The potential for synthetic data to drive collaborative innovation in finance is also considered, as it allows multiple stakeholders, including banks, fintech startups, and regulators, to share and analyze data without compromising confidentiality or privacy. The research also addresses the limitations and challenges associated with synthetic data generation in the financial domain, including issues related to data representativeness, overfitting, and the potential misuse of synthetic datasets. It emphasizes the need for ongoing research to develop more sophisticated algorithms that can generate highly realistic and diverse financial data. Furthermore, it identifies areas for future exploration, such as the use of federated learning and differential privacy techniques to enhance the security and privacy of synthetic data generation processes. The findings of this paper underscore the importance of AI-driven synthetic data generation as a catalyst for innovation in banking and fintech, providing a secure, scalable, and cost-effective means to develop, test, and validate new financial products and services. As the financial industry continues to evolve, the role of synthetic data in shaping the future of financial product development will become increasingly critical, paving the way for more efficient and innovative financial solutions. (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)

News about personal data breaches or data abusive practices, such as Cambridge Analytica, has questioned the trustworthiness of certain actors in the control of personal data. Innovations in the field of personal information management systems to address this issue have regained traction in recent years, also coinciding with the emergence of new decentralized technologies. However, only with ethically and legally responsible developments will the mistakes of the past be avoided. This contribution explores how current data management (...) schemes are insufficient to adequately safeguard data subjects, and in particular, it focuses on making these data flows transparent to provide an adequate level of accountability. To showcase this, and with the goal of enhancing transparency to foster trust, this paper investigates solutions for standardizing machine-readable policies to express personal data processing activities and their application to decentralized personal data stores as an example of ethical, legal, and technical responsible innovation in this field. (shrink)

Training artificial intelligence (AI) systems requires vast quantities of data, and AI developers face a variety of barriers to accessing the information they need. Synthetic data has captured researchers’ and industry’s imagination as a potential solution to this problem. While some of the enthusiasm for synthetic data may be warranted, in this short paper we offer critical counterweight to simplistic narratives that position synthetic data as a cost-free solution to every data-access challenge—provocations highlighting ethical, political, (...) and governance issues the use of synthetic data can create. We question the idea that synthetic data, by its nature, is exempt from privacy and related ethical concerns. We caution that framing synthetic data in binary opposition to “real” measurement data could subtly shift the normative standards to which data collectors and processors are held. And we argue that by promising to divorce data from its constituents—the people it represents and impacts—synthetic data could create new obstacles to democratic data governance. (shrink)

There is no consensus yet on the definition of semantic information. This paper contributes to the current debate by criticising and revising the Standard Definition of semantic Information (SDI) as meaningful data, in favour of the Dretske-Grice approach: meaningful and well-formed data constitute semantic information only if they also qualify as contingently truthful. After a brief introduction, SDI is criticised for providing necessary but insufficient conditions for the definition of semantic information. SDI is incorrect because truth-values do not (...) supervene on semantic information, and misinformation (that is, false semantic information) is not a type of semantic information, but pseudo-information, that is not semantic information at all. This is shown by arguing that none of the reasons for interpreting misinformation as a type of semantic information is convincing, whilst there are compelling reasons to treat it as pseudo-information. As a consequence, SDI is revised to include a necessary truth-condition. The last section summarises the main results of the paper and indicates some interesting areas of application of the revised definition. (shrink)

There is no consensus yet on the definition of semantic information. This paper contributes to the current debate by criticising and revising the Standard Definition of semantic Information as meaningful data, in favour of the Dretske-Grice approach: meaningful and well-formed data constitute semantic information only if they also qualify as contingently truthful. After a brief introduction, SDI is criticised for providing necessary but insufficient conditions for the definition of semantic information. SDI is incorrect because truth-values do not supervene (...) on semantic information, and misinformation is not a tyoe of semantic information, but pseudo-information, that is not semantic information at all. This is shown by arguing that none of the reasons for interpreting misinformation as a type of semantic information is convincing, whilst there are compelling reasons to treat it as pseudo-information. As a consequence, SDI is revised to include a necessary truth-condition. The last section summarises the main results of the paper and indicates the important implications of the revised definition for the analysis of the deflationary theories of truth, the standard definition of knowledge and the classic, quantitative theory of semantic information. (shrink)

The contribution of the data paper publishing paradigm to the knowledge generation and validation processes is becoming substantial and pivotal. In this paper, through the information-processing perspective of Mindsponge Theory, we discuss how the data article publishing system serves as a filtering mechanism for quality control of the increasingly chaotic datasphere. The overemphasis on machine-actionality and technical standards presents some shortcomings and limitations of the data article publishing system, such as the lack of consideration of humanistic (...) values, radical race for big data, and inadequate use of expertise in data evaluation. Without addressing the shortcomings and limitations, the reusability of data will be hindered, and scientific investment to facilitate data sharing will be wasted. Thus, we suggest that the current data paper publishing paradigm needs to be updated with a new philosophy of data. (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)

Ontologies, as the term is used in informatics, are structured vocabularies comprised of human- and computer-interpretable terms and relations that represent entities and relationships. Within informatics fields, ontologies play an important role in knowledge and data standardization, representation, integra- tion, sharing and analysis. They have also become a foundation of artificial intelligence (AI) research. In what follows, we outline the Coronavirus Infectious Disease Ontology (CIDO), which covers multiple areas in the domain of coronavirus diseases, including etiology, transmission, epidemiology, pathogenesis, (...) diagnosis, prevention, and treatment. We emphasize CIDO development relevant to COVID-19. (shrink)

Background: Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is a comprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic health data. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but these efforts have been hampered by the size and complexity of SCT. -/- Method: Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the terms in (...) SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks of definitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-level SCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS). -/- Results: The SCTO is implemented in OWL 2, to support automatic inference and consistency checking. The approach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundry ontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-level ontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555 annotations. It is publicly available through the bioportal. -/- Conclusion: The resulting ontology can enhance the semantics of clinical decision support systems and semantic interoperability among distributed electronic health records. In addition, the populated ontology can be used for the automation of mobile health applications. (shrink)

The study aimed to identify the intermediate role of the standard of operations in the relationship between the focus on students and beneficiaries in achieving satisfaction of students in Palestinian universities. The study used the analytical descriptive method. The study was conducted on university leadership in Al-Azhar, Islamic and Al-Aqsa Universities. The study sample consisted of (200) individuals, 182 of whom responded, and the questionnaire was used in collecting the data. The results of the study were as follows: - (...) There is a statistically significant effect on the adoption of the criterion of focus on the students benefiting to achieve the satisfaction of students in the Palestinian universities, where the value of R Square (35.3%), there is a statistically significant effect on the adoption of the criterion of focus on the students benefiting on the use of the standard of operations in Palestinian universities. The value of the R Square is (62.8%), there is a statistically significant impact of the criterion of operations on the satisfaction of students in Palestinian universities. Where the value of the R is (36.2%), there is a significant effect on the adoption of the criterion of emphasis on students benefiting from the satisfaction of students in Palestinian universities with the existence of the standard of operations as an intermediary variable. The role of the mediator in the relationship between the adoptions of the criterion of focus on the students benefiting to achieve the satisfaction of students in Palestinian universities was revealed through the use of Path Analysis. The study presented a number of recommendations, the most important of which is: to increase the focus of the universities on students and beneficiaries in a manner appropriate to achieve the objectives of the university on the one hand, and students and beneficiaries on the other, and in line with the approach of quality and excellence, the universities more attention to the management of operations through the design and development of programs Academic and internal services according to the needs of students, and improving processes in innovative ways to generate added value for students and beneficiaries, the universities have made more efforts to create satisfaction among students and beneficiaries by developing internal performance indicators to measure student satisfaction, Students and other university beneficiaries periodically to improve their satisfaction. (shrink)

This research paper presents an in-depth analysis of advanced artificial intelligence (AI) algorithms designed to automate data preprocessing in the healthcare sector. The automation of data preprocessing is crucial due to the overwhelming volume, diversity, and complexity of healthcare data, which includes medical records, diagnostic imaging, sensor data from medical devices, genomic data, and other heterogeneous sources. These datasets often exhibit various inconsistencies such as missing values, noise, outliers, and redundant or irrelevant information that necessitate (...) extensive preprocessing before being analyzed by machine learning or statistical models. Traditional data preprocessing methods, which are largely manual and time-consuming, can result in errors that affect the quality of the data and, subsequently, the performance of predictive and diagnostic models. Thus, there is a growing need for intelligent, automated systems that can enhance data quality, streamline the preprocessing pipeline, and reduce the time and effort required by healthcare professionals and data scientists. The study begins by outlining the specific challenges associated with healthcare data, including its high dimensionality, incompleteness, and variability across different data sources and formats. These issues not only complicate the preprocessing stage but also hinder the ability to develop robust models capable of making accurate predictions or diagnoses. The paper then explores how AI algorithms—particularly those based on machine learning (ML), deep learning (DL), and reinforcement learning (RL)—can automate key data preprocessing tasks such as data cleaning, feature selection, normalization, and transformation. These algorithms are designed to identify patterns in data, detect anomalies, and automatically apply corrections or transformations based on predefined rules or learned behaviors, thereby minimizing human intervention. The paper also delves into specific AI techniques that have been successfully applied to healthcare data preprocessing. For instance, supervised learning models, such as decision trees and support vector machines (SVMs), have been utilized to perform imputation of missing data by predicting the most likely values based on the available information. Similarly, unsupervised learning methods, such as clustering algorithms, have been employed to group similar data points and remove outliers that could distort the performance of analytical models. Moreover, deep learning techniques, particularly autoencoders and generative adversarial networks (GANs), have demonstrated remarkable effectiveness in transforming high-dimensional medical data into lower-dimensional representations, enabling more efficient and accurate model training. In addition to the discussion of these algorithms, the paper emphasizes the role of natural language processing (NLP) in automating the preprocessing of unstructured healthcare data, such as clinical notes and diagnostic reports. NLP techniques, including named entity recognition (NER) and word embeddings, are instrumental in extracting relevant information from unstructured text, standardizing terminologies, and converting textual data into structured formats suitable for downstream analysis. Furthermore, AI-based feature selection algorithms are explored, which aim to identify the most relevant features in the dataset, thereby reducing its dimensionality and improving the computational efficiency of predictive models. The study goes on to highlight the significant reduction in processing time achieved by AIdriven automation of preprocessing tasks. In conventional settings, data preprocessing accounts for a substantial portion of the time spent on building healthcare models, often requiring expert intervention to manually inspect and clean the data. By employing AI algorithms, not only can this process be expedited, but the accuracy of the resulting data is also enhanced, which translates into better model performance. The paper provides a detailed comparative analysis of manual preprocessing methods versus automated AI-driven approaches, demonstrating the substantial time savings and improvements in data quality brought about by automation. (shrink)

Throughout the debate on psi, skeptics have almost universally insisted on different standards for evaluating the evidence, claiming that psi represents a radical departure from our current scientific understanding. Thus, there is considerable ambiguity about what standard of evaluation psi must meet. Little attention has been paid to the possible harm to the integrity of scientific investigation from this resulting inconsistency in testing standards. Some have proposed using a Bayesian framework as an improvement on this dilemma in order (...) to more explicitly model beliefs, assumptions, and background scientific knowledge, especially when evaluating a controversial hypothesis. Recently, Kuhn’s notion of paradigms, which constrains scientific research within bound- aries believed to be most productive, has been incorporated into a Bayesian framework. Within this framework, I explore a likely paradigm or meta-theory used by skeptics that typically constrains research and makes it difficult for psi evidence to be accepted. It appears that such a paradigm would in many respects have difficulty accounting for consciousness, which is fundamental to an understanding of psi. I discuss why psi data are likely to play a key role in making progress in solving the problem of consciousness. Thus, applying different standards of evaluation to psi data is likely counterproductive. (shrink)

A study was conducted regarding ways in which higher education institutions can improve ethics. Theoretical frameworks used included: Virtue ethics, deontological and environmental ethics theories. The total sampled written texts were 94. Non-probability sampling was used. The type that was used was online convenience sampling through web scraping. Philosophical assumption that guided this study was interpretivism and the approach was Qualitative. Case study was used as a design and content analysis as a method of data analysis. Some of the (...) findings of the study included but were not limited to the following: Higher education's ethical and professional standards are critical for retaining the trust of students, staff, and the larger society. Institutions that prioritise ethics and standards will be better positioned to carry out their goal and positively impact society. The study recommended the following to Higher education stakeholders: Create faculty-led ethics committees to evaluate and advise on ethical issues such as teaching, research, and academic freedom. Make certain that these committees are composed of a diverse spectrum of faculty members with ethics expertise; Create explicit rules and protocols for using AI, machine learning, and big data ethically in academic research and administrative processes. Address the issues of discrimination, privacy, and accountability in AI applications; and Involve alumni in ethics programs and projects to encourage them to support and contribute to ethical education and research. Create opportunities for alumni to advise students on how to develop ethical professions. (shrink)

"Data mining is not an invasion of privacy because access to data is only by machines, not by people": this is the argument that is investigated here. The current importance of this problem is developed in a case study of data mining in the USA for counterterrorism and other surveillance purposes. After a clarification of the relevant nature of privacy, it is argued that access by machines cannot warrant the access to further information, since the analysis will (...) have to be made either by humans or by machines that understand. It concludes that the current data mining violates the right to privacy and should be subject to the standard legal constraints for access to private information by people. (shrink)

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)

In today's digital era, large enterprises face the daunting task of managing and analyzing vast volumes of financial data to inform strategic decision-making and maintain a competitive edge. Traditional data warehousing solutions often fall short in addressing the scale, complexity, and performance demands of modern financial analytics. This paper explores the architectural principles, technological strategies, and best practices essential for building scalable data warehouses tailored to the needs of financial analytics in large organizations. It delves into (...) class='Hi'>data integration techniques, performance optimization methods, security measures, and compliance with regulatory standards. Through in-depth analysis and real-world case studies, the paper provides a comprehensive roadmap for practitioners aiming to design and implement robust, scalable, and secure data warehousing solutions. (shrink)

Implementation of Bhutan Professional Standards for Teachers in Bhutan is a key impetus for teachers across the country to enhance impeccable teacher competency. Studies on teachers’ attitudes towards professional standards show a positive as there was a significant correlation between teachers’ competencies, learners’ academic achievement, and the quality of education. However, Bhutan Professional Standards for Teachers is relatively new and there is little study exists relative to Bhutan. The study on Teachers’ Attitudes to the Implementation of Bhutan (...) Professional Standards for Teachers in Bhutan aimed to explore and get a snapshot view of teachers on Bhutan Professional Standards for Teachers. Through a cross-sectional survey design, data were collected from 155 participants using the Five-Pointed Likert Scale and two open-ended questionnaires. The descriptive analysis revealed that teachers have fairly positive attitudes toward the implementation of Bhutan Professional Standards for Teachers. Associated challenges, the implication of the findings, and recommendations are also discussed. (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 paper is concerned with the semantics of knowledge attributions(K-claims, for short) and proposes a position holding that K-claims are contextsensitive that differs from extant views on the market. First I lay down the data a semantic theory for K-claims needs to explain. Next I present and assess three views purporting to give the semantics for K-claims: contextualism, subject-sensitive invariantism and relativism. All three views are found wanting with respect to their accounting for the data. I then propose (...) a hybrid view according to which the relevant epistemic standards for evaluating K-claims are neither those at the context of the subject (subject-sensitive invariantism), nor those at the context of the assessor (relativism), but it is itself an open matter. However, given that we need a principled way of deciding which epistemic standards are the relevant ones, I provide a principle according to which the relevant standards are those that are the highest between those at the context of the subject and those at the context of the assessor/attributor. In the end I consider some objections to the view and offer some answers. (shrink)

The study aimed to identify the availability of the resource and partnership standard as one of the possibilities of excellence in Palestinian universities according to the European model. The study used the analytical descriptive method. The study was conducted on the university leadership at Al - Azhar and Islamic Universities, where the study population consisted of (282) individuals. The study sample consisted of (135) individuals, (119) of them responded, and the questionnaire was used in collecting the data. The study (...) has reached a number of results, the most important of which is the existence of a high standard of resources and partnership in the Palestinian public universities operating in the southern governorates. The fields of the resource and partnership criteria were as follows: (79.8%) management of internal and external partnerships; management of technical resources (technology) (76.4%), Buildings, equipment and resources (76%), Finance Management (72.8%). The study presented a number of recommendations, the most important of which is: Increasing the interest of universities in applying the criteria of resources and partnership as one of the criteria of excellence, increasing the interest of universities in managing finance and obtaining funding for their various activities, periodic maintenance of buildings and equipment and ensuring their suitability to the requirements of the educational process. (shrink)

We present a statistical analysis of the realization of definiteness in Tunisian Arabic (TA) texts written in Arabizi, a hybrid system reflecting some features of TA phonetics (assimilation), but also showing orthographic features, as the use of arithmographs. In §1, we give an overview of definiteness in TA from a semantic and syntactic point of view. In §2 we outline a typology of definite articles and show that TA normally marks definiteness with articles or similar devices, but also presents zero-markings (...) or weak definites. In §3 we discuss TA and how definiteness is instantiated in TA. In §4, we present data from the Tunisian Arabizi Corpus (TAC), a multidisciplinary work with a hybrid approach based on dialectological questions, corpus linguistics standards, and deep learning techniques. In §5 we define the behavior of TA with respect to what we observed in §1, §2 and §3, describing our TAC-based analysis. (shrink)

Wearable devices are increasingly present in the health context, as tools for biomedical research and clinical care. In this context, wearables are considered key tools for a more digital, personalised, preventive medicine. At the same time, wearables have also been associated with issues and risks, such as those connected to privacy and data sharing. Yet, discussions in the literature have mostly focused on either technical or ethical considerations, framing these as largely separate areas of discussion, and the contribution of (...) wearables to the collection, development, application of biomedical knowledge has only partially been discussed. To fill in these gaps, in this article we provide an epistemic (knowledgerelated) overview of the main functions of wearable technology for health: monitoring, screening, detection, and prediction. On this basis, we identify 4 areas of concern in the application of wearables for these functions: data quality, balanced estimations, health equity, and fairness. To move the field forward in an effective and beneficial direction, we present recommendations for the 4 areas: local standards of quality, interoperability, access, and representativity. (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)

What follows is a contribution to the horizontal integration of warfighter intelligence data as defined in Chairman of the Joint Chiefs of Staff Instruction J2 CJCSI 3340.02AL: -/- Horizontally integrating warfighter intelligence data improves the consumers’ production, analysis and dissemination capabilities. HI requires access (including discovery, search, retrieval, and display) to intelligence data among the warfighters and other producers and consumers via standardized services and architectures. These consumers include, but are not limited to, the combatant commands, Services, (...) Defense agencies, and the Intelligence Community. (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)

The main foundations of the standard \CDM model of cosmology are that: the redshifts of the galaxies are due to the expansion of the Universe plus peculiar motions; the cosmic microwave background radiation and its anisotropies derive from the high energy primordial Universe when matter and radiation became decoupled; the abundance pattern of the light elements is explained in terms of primordial nucleosynthesis; and the formation and evolution of galaxies can be explained only in terms of gravitation within a inflation (...) + dark matter + dark energy scenario. Numerous tests have been carried out on these ideas and, although the standard model works pretty well in fitting many observations, there are also many data that present apparent caveats to be understood with it. In this paper, I offer a review of these tests and problems, as well as some examples of alternative models. (shrink)

Generative Adversarial Networks (GANs) have demonstrated significant potential in generating synthetic data for various applications, including those involving sensitive information like healthcare and finance. However, two major issues arise when GANs are applied to sensitive datasets: (i) the model may memorize training samples, compromising the privacy of individuals, especially when the data includes personally identifiable information (PII), and (ii) there is a lack of control over the specificity of the generated samples, which limits their utility for tailored use-cases. (...) To address these challenges, we propose a novel framework that integrates differential privacy with latent representation learning to ensure privacy while providing control over the specificity of generated data. Our approach ensures that the synthetic data does not reveal individual data points, and by learning effective latent codes, it allows for the generation of specific and meaningful data. We evaluate our method using the MNIST dataset, showing that it preserves privacy and demonstrates a privacy-utility trade-off, where increased privacy leads to decreased classification accuracy. Additionally, we highlight the computational challenges, as the training process incurs a tenfold increase in time compared to standard GAN models. Finally, we extend our approach to the CelebA dataset, demonstrating how privacy and specificity can be controlled to generate high-quality, private synthetic data. (shrink)

One of the basic principles of the general definition of information is its rejection of dataless information, which is reflected in its endorsement of an ontological neutrality. In general, this principles states that “there can be no information without physical implementation” (Floridi (2005)). Though this is standardly considered a commonsensical assumption, many questions arise with regard to its generalised application. In this paper a combined logic for data and information is elaborated, and specifically used to investigate the consequences of (...) restricted and unrestricted data-implementation-principles. (shrink)

In the management of biomedical data, vocabularies such as ontologies and terminologies (O/Ts) are used for (i) domain knowledge representation and (ii) interoperability. The knowledge representation role supports the automated reasoning on, and analysis of, data annotated with O/Ts. At an interoperability level, the use of a communal vocabulary standard for a particular domain is essential for large data repositories and information management systems to communicate consistently with one other. Consequently, the interoperability benefit of selecting a particular (...) O/T as a standard for data exchange purposes is often seen by the end-user as a function of the number of applications using that vocabulary (and, by extension, the size of the user base). Furthermore, the adoption of an O/T as an interoperability standard requires confidence in its stability and guaranteed continuity as a resource. (shrink)

This chapter addresses the morality of two types of national security electronic surveillance (SIGINT) programs: the analysis of communication “metadata” and dragnet searches for keywords in electronic communication. The chapter develops a standard for assessing coercive government action based on respect for the autonomy of inhabitants of liberal states and argues that both types of SIGINT can potentially meet this standard. That said, the collection of metadata creates opportunities for abuse of power, and so judgments about the trustworthiness and competence (...) of the government engaging in the collection must be made in order to decide whether metadata collection is justified in a particular state. Further, the moral standard proposed has a reflexive element justifying adversary states’ intelligence collection against one another. Therefore, high-tech forms of SIGINT can only be justified at the cost of justifying cruder versions of signals intelligence collection practiced by a state’s less-advanced adversaries. (shrink)

The paper takes as its starting point the observation that people can be led to retract knowledge claims when presented with previously ignored error possibilities, but offers a noncontextualist explanation of the data. Fallibilist epistemologies are committed to the existence of two kinds of Kp -falsifying contingencies: (i) Non-Ignorable contingencies [NI-contingencies] and (ii) Properly-Ignorable contingencies [PI-contingencies]. For S to know that p, S must be in an epistemic position to rule out all NI-contingencies, but she need not be able (...) to rule out the PI-contingencies. What is required vis-à-vis PI-contingencies is that they all be false . In mentioning PI-contingencies, an interlocutor can lead S mistakenly to think that these contingencies are NI-contingencies, when in fact they are not. Since S cannot rule out these newly mentioned contingencies and since she mistakenly takes them to be NI-contingencies , it is quite natural that she retract her earlier knowledge claim. In short, mentioning NI-contingencies creates a distortion effect. It makes S think that the standards for knowledge are higher than they actually are, which in turn explains why she mistakenly thinks she lacks knowledge. Conclusion: The primary linguistic data offered in support of contextualism can be explained without resorting to contextualism. (shrink)

Statistics play a critical role in biological and clinical research. However, most reports of scientific results in the published literature make it difficult for the reader to reproduce the statistical analyses performed in achieving those results because they provide inadequate documentation of the statistical tests and algorithms applied. The Ontology of Biological and Clinical Statistics (OBCS) is put forward here as a step towards solving this problem. Terms in OBCS, including ‘data collection’, ‘data transformation in statistics’, ‘data (...) visualization’, ‘statistical data analysis’, and ‘drawing a conclusion based on data’, cover the major types of statistical processes used in basic biological research and clinical outcome studies. OBCS is aligned with the Basic Formal Ontology (BFO) and extends the Ontology of Biomedical Investigations (OBI), an OBO (Open Biological and Biomedical Ontologies) Foundry ontology supported by over 20 research communities. We discuss two examples illustrating how the ontology is being applied. In the first (biological) use case, we describe how OBCS was applied to represent the high throughput microarray data analysis of immunological transcriptional profiles in human subjects vaccinated with an influenza vaccine. In the second (clinical outcomes) use case, we applied OBCS to represent the processing of electronic health care data to determine the associations between hospital staffing levels and patient mortality. Our case studies were designed to show how OBCS can be used for the consistent representation of statistical analysis pipelines under two different research paradigms. By representing statistics-related terms and their relations in a rigorous fashion, OBCS facilitates standard data analysis and integration, and supports reproducible biological and clinical research. (shrink)

Assertions are speech acts by means of which we express beliefs. As such they are at the heart of our linguistic and social practices. Recent research has focused extensively on the question whether the speech act of assertion is governed by norms, and if so, under what conditions it is acceptable to make an assertion. Standard theories propose, for instance, that one should only assert that p if one knows that p (the knowledge account), or that one should only assert (...) that p if p is true (the truth account). In a series of four experiments, this question is addressed empirically. Contrary to previous findings, knowledge turns out to be a poor predictor of assertability, and the norm of assertion is not factive either. The studies here presented provide empirical evidence in favour of the view that a speaker is warranted to assert that p only if her belief that p is justified. (shrink)

It is impossible to study philosophical methodology without being struck by the state of absolute chaos of the field’s methodological practices, methodological norms, and metaphilosophical beliefs. Not only are the methods of formal epistemology nothing like the methods of aesthetics, but even within specific debates and subfields, there are often significant disagreements about standards of proof, to say nothing about disagreements about the ultimate nature of the debate. The question facing metaphilosophers is whether this chaos is a feature or (...) a bug. Is the chaos a part of philosophy’s value as an incubator for new ideas or a sign that many philosophers have lost their way and are holding back philosophical progress? (shrink)

The rapid adoption of digital technologies in healthcare has revolutionized patient care, enabling seamless data sharing, remote consultations, and enhanced medical record management. However, this digital transformation has also introduced significant challenges to network security and data protection. Healthcare institutions face a dual challenge: ensuring uninterrupted connectivity for critical operations and safeguarding sensitive patient information from cyber threats. These challenges are exacerbated by the increased use of interconnected devices, electronic health records (EHRs), and cloud-based solutions, which, while enhancing (...) efficiency, expand the attack surface for malicious actors. This research focuses on addressing the pressing need for robust network security in healthcare institutions. It examines the unique vulnerabilities of healthcare networks, including the risks posed by outdated infrastructure, insufficient encryption protocols, and the lack of standardized security practices across systems. The paper highlights the critical role of advanced cybersecurity frameworks, such as zero-trust architectures and realtime threat detection systems, in mitigating risks. Additionally, it explores the integration of artificial intelligence (AI) and machine learning (ML) in enhancing the predictive capabilities of security tools, enabling institutions to proactively identify and neutralize potential threats. Connectivity remains a cornerstone of modern healthcare operations, facilitating collaboration among healthcare providers and ensuring timely access to patient data. However, maintaining connectivity without compromising security is a complex task. This research delves into strategies for achieving this balance, such as implementing secure Virtual Private Networks (VPNs), multi-factor authentication (MFA), and end-to-end encryption. It also emphasizes the importance of regular network assessments, employee training programs, and the adoption of compliance standards like HIPAA to ensure a secure and resilient network environment. The study includes case analyses of healthcare institutions that successfully navigated cybersecurity challenges, providing actionable insights into effective practices and lessons learned. These examples underscore the importance of integrating technological solutions with a culture of security awareness, emphasizing collaboration between IT professionals and medical staff. As cyber threats continue to evolve, healthcare institutions must remain vigilant and adaptive, embracing innovative solutions to safeguard their networks and protect patient data. This paper contributes to the broader discourse on cybersecurity in healthcare by proposing a comprehensive approach that addresses connectivity and data protection challenges while fostering operational efficiency. By prioritizing network security, healthcare institutions can build trust with patients, comply with regulatory requirements, and ensure the uninterrupted delivery of quality care. This research provides a roadmap for healthcare organizations seeking to strengthen their cybersecurity posture, highlighting the necessity of adopting a proactive, multi-layered approach to combat emerging threats. It calls for continuous investment in advanced technologies and emphasizes the role of collaboration among stakeholders to create a secure and connected healthcare ecosystem. (shrink)

In the context of mid-level professional writing jobs, we examine the productivity effects of a generative artificial intelligence technology, namely the assistive chatbot ChatGPT. We used ChatGPT to randomly expose half of the 444 college-educated professionals to occupation-specific, incentive-based writing tasks in a preregistered online experiment. Our results show that ChatGPT considerably increases average productivity: output quality improves by 0.4 standard deviations and task completion time drops by 0.8 standard deviations. By compressing the production distribution, ChatGPT also lessens worker inequality, (...) mainly helping individuals with lesser starting abilities. ChatGPT shifts the focus of activities from rough-drafting to idea generation and editing, thereby replacing worker effort rather than enhancing worker skills. Furthermore, exposure to ChatGPT increases job satisfaction and self-efficacy while also heightening both concerns and excitement about automation technologies. (shrink)

AI-powered technology can both promote accuracy and hide the standards of measurement and circulation of information. It can also produce models that are opaque and hard to access. As such, the new paradigm of AI asks to pounder about societal values and sets of priorities we want to promote, especially as these technologies are further deployed in times of warfare. The systemic tracking of people’s life and the opaqueness of the models designate a new paradigm in the formation of (...) truth, as censorship is enabled on a new scale. (shrink)

Generative Adversarial Networks (GANs) have demonstrated significant potential in generating synthetic data for various applications, including those involving sensitive information like healthcare and finance. However, two major issues arise when GANs are applied to sensitive datasets: (i) the model may memorize training samples, compromising the privacy of individuals, especially when the data includes personally identifiable information (PII), and (ii) there is a lack of control over the specificity of the generated samples, which limits their utility for tailored usecases. (...) To address these challenges, we propose a novel framework that integrates differential privacy with latent representation learning to ensure privacy while providing control over the specificity of generated data. Our approach ensures that the synthetic data does not reveal individual data points, and by learning effective latent codes, it allows for the generation of specific and meaningful data. We evaluate our method using the MNIST dataset, showing that it preserves privacy and demonstrates a privacy-utility trade-off, where increased privacy leads to decreased classification accuracy. Additionally, we highlight the computational challenges, as the training process incurs a tenfold increase in time compared to standard GAN models. Finally, we extend our approach to the CelebA dataset, demonstrating how privacy and specificity can be controlled to generate high-quality, private synthetic data. (shrink)

Peer review process helps in evaluating and validating of research that is published in the journals. U.S. Office of Research Integrity reported that data fraudulence was found to be involved in 94% cases of misconduct from 228 identified articles between 1994–2012. If fraud in published article are significantly as high as reported, the question arise in mind, were these articles peer reviewed? Another report said that the reviewers failed to detect 16 cases of fabricated article of Jan Hendrick Schon. (...) Superficial peer reviewing process does not reveals suspicion of misconduct. Lack of knowledge of systemic review process not only demolish the academic integrity in publication but also loss the trust of the people of the institution, the nation, and the world. The aim of this review article is to aware stakeholders specially novice reviewers about the peer review system. Beginners will understand how to review an article and they can justify better action choices in dealing with reviewing an article. (shrink)

The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing objective (...) class='Hi'>data in other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors’ rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs. (shrink)

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient (...) class='Hi'>data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use. (shrink)

Increasingly, in data-intensive areas of the life sciences, experimental results are being described in algorithmically useful ways with the help of ontologies. Such ontologies are authored and maintained by scientists to support the retrieval, integration and analysis of their data. The proposition to be defended here is that ontologies of this type – the Gene Ontology (GO) being the most conspicuous example – are a part of science. Initial evidence for the truth of this proposition (which some will (...) find self-evident) is the increasing recognition of the importance of empirically-based methods of evaluation to the ontology development work being undertaken in support of scientific research. Ontologies created by scientists must, of course, be associated with implementations satisfying the requirements of software engineering. But the ontologies are not themselves engineering artifacts, and to conceive them as such brings grievous consequences. Rather, ontologies such as the GO are in different respects comparable to scientific theories, to scientific databases, and to scientific journal publications. Such a view implies a new conception of what is involved in the authoring, maintenance and application of ontologies in scientific contexts, and therewith also a new approach to the evaluation of ontologies and to the training of ontologists. (shrink)

We begin at the beginning, with an outline of Aristotle’s views on ontology and with a discussion of the influence of these views on Linnaeus. We move from there to consider the data standardization initiatives launched in the 19th century, and then turn to investigate how the idea of computational ontologies developed in the AI and knowledge representation communities in the closing decades of the 20th century. We show how aspects of this idea, particularly those relating to the use (...) of the term 'concept' in ontology development, influenced SNOMED CT and other medical terminologies. Against this background we then show how the Foundational Model of Anatomy, the Gene Ontology, Basic Formal Ontology and other OBO Foundry ontologies came into existence and discuss their role in the development of contemporary biomedical informatics. (shrink)

Monoclonal antibodies are essential biomedical research and clinical reagents that are produced by companies and research laboratories. The NIAID ImmPort (Immunology Database and Analysis Portal) resource provides a long-term, sustainable data warehouse for immunological data generated by NIAID, DAIT and DMID funded investigators for data archiving and re-use. A variety of immunological data is generated using techniques that rely upon monoclonal antibody reagents, including flow cytometry, immunofluorescence, and ELISA. In order to facilitate querying, integration, and reuse (...) of data, standardized terminology for describing monoclonal antibody reagents and their targets needs to be used for annotating data submitted to ImmPort. (shrink)

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved (...) in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed. (shrink)