In the current paper, we propose a latent interdependence approach to modeling psychometric data in social networks. The idea of latent interdependence is adopted from social relations models, which formulate a mutual-rating process by both dyad members’ characteristics. Under the framework of the latent interdependence approach, we introduce two psychometric models: The first model includes the main effects of both rating-sender and rating-receiver, and the second model includes a latent distance effect to assess the influence from the (...) dissimilarity between the latent characteristics of both sides. The latent distance effect is quantified by the Euclidean distance between both sides’ trait scores. Both models use Bayesian estimation via Markov chain Monte Carlo. How accurately model parameters were estimated was evaluated in a simulation study. Parameter recovery results showed that all parameters were accurately recovered under most of the conditions investigated. As expected, the accuracy of model estimation was significantly improved as network size grew. Also, through analyzing empirical data, we showed how to use the estimates of model parameters to predict the latent weight of connections among group members and rebuild either a univariate or multivariate network at a latent trait level. Finally, we discuss issues regarding model comparison and offer suggestions for future studies. (shrink)

While Bachman's role data model is often cited, it appears that its contribution, the introduction of role types and their conception as unions of entity types that occupy the places of relationship types, has mostly been ignored. This is unfortunate since it has led to countless reinventions of the wheel, and sometimes even to regress. With this homage, the author wishes to shed some light on the natural elegance of Bachman's role concept, and to make clear why he (...) believes that it is valid until this day. (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)

Although databases have been well-defined and thoroughly discussed in the computer science literature, the actual users of databases often have varying definitions and expectations of this essential computational infrastructure. Systems administrators and computer science textbooks may expect databases to be instantiated in a small number of technologies, but there are numerous examples of databases in non-conventional or unexpected technologies, such as spreadsheets or other assemblages of files linked through code. Consequently, we ask: How do the materialities of non-conventional databases differ (...) from or align with the materialities of conventional relational systems? What properties of the database do the creators of these artifacts invoke in their rhetoric describing these systems—or in the data models underlying these digital objects? To answer these questions, we conducted a close analysis of four non-conventional scientific databases. By examining the materialities of information representation in each case, we show how scholarly communication regimes shape database materialities—and how information organization paradigms shape scholarly communication. These cases show abandonment of certain constraints of relational database construction alongside maintenance of some key relational data organization strategies. We discuss the implications that these relational data paradigms have for data use, preservation, and sharing, and discuss the need to support a plurality of data practices and paradigms. (shrink)

A recurring problem in conceptual modelling and ontology development is the representation of part-whole relations, with a requirement to be able to distinguish between essential and mandatory parts. To solve this problem, we formally characterize the semantics of these shareability notions by resorting to the temporal conceptual model E RVT and its formalization in the description logic DLRUS.

Predictive risk modelling is a computational method used to generate probabilities correlating events. The output of such systems is typically represented by a statistical score derived from various related and often arbitrary datasets. In many cases, the information generated by such systems is treated as a form of evidence to justify further action. This paper examines the nature of the information generated by such systems and compares it with more orthodox notions of evidence found in epistemology. The paper focuses on (...) a specific example to illustrate the issues: The New Zealand Government has proposed implementing a predictive risk modelling system which purportedly identifies children at risk of a maltreatment event before the age of five. Timothy Williamson’s conception of epistemology places a requirement on knowledge that it be explanatory. Furthermore, Williamson argues that knowledge is equivalent to evidence. This approach is compared to the claim that the output of such computational systems constitutes evidence. While there may be some utility in using predictive risk modelling systems, I argue, since an explanatory account of the output of such algorithms that meets Williamson’s requirements cannot be given, doubt is cast upon the resulting statistical scores as constituting evidence on generally accepted epistemic grounds. The algorithms employed in such systems are geared towards identifying patterns which turn out to be good correlations. However, rather than providing information about specific individuals and their exposure to risk, a more valid explanation of a high probability score is that the particular variables related to incidents of maltreatment are just higher amongst certain subgroups in a population than they are amongst others. The paper concludes that any justification of the information generated by such systems is generalised and pragmatic at best and the application of this information to individual cases raises various ethical issues. (shrink)

Several key areas in modeling the cardiovascular and respiratory control systems are reviewed and examples are given which reflect the research state of the art in these areas. Attention is given to the interrelated issues of data collection, experimental design, and model application including model development and analysis. Examples are given of current clinical problems which can be examined via modeling, and important issues related to model adaptation to the clinical setting.

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)

The distinction between data and phenomena introduced by Bogen and Woodward (Philosophical Review 97(3):303–352, 1988) was meant to help accounting for scientific practice, especially in relation with scientific theory testing. Their article and the subsequent discussion is primarily viewed as internal to philosophy of science. We shall argue that the data/phenomena distinction can be used much more broadly in modelling processes in philosophy.

In this paper I review the Neo-Davidsonian semantics of prepositional phrases and secondary predication. I argue that certain types of examples pose challenge to this semantics. I present an alternative to the Neo-Davidsonian analysis which successfully deals with the problematic examples. The core idea lies in representing theta-roles not as functions from events to their participants, but rather as argument-labels encoding the role of each argument in a given verb. As a result, natural-language predicates can now be treated in the (...) manner in which relations are treated in the relational model of data, that is, as naming sets of tuples in which every object is given together with its role named by a corresponding attribute (a theta-role). Such a representation allows the employment of relational algebra operators to calculate the extensions of complex predicates (predicates built out of atomic predicates and/or atomic predicates and prepositions). I lay out the foundations of a relational FOL appropriate for the representation of natural-language predicates and present solutions to the problematic examples. From a practical perspective, the expressions of a relational FOL can be translated to a relational algebra or SQL, which makes it possible to operate with these three languages on the same relational model of data. From a philosophical perspective, a relational FOL permits a return to ‘property-based’ semantics, one in which properties named by predicates are those of individuals, and not of events. (shrink)

For Jari-Erik Nurmi, the practice of model-making in psychology is a complex process operating on different levels simultaneously. At first sight, his account seems to reflect Suppes' (1962) notion of a hierarchy of models: from low-level data models to high-level theoretical models, where at each level the model represents "structure" at a different degree of abstraction, and the levels are connected through structural isomorphism.1In this commentary, I want to complement and perhaps somewhat redirect Nurmi's analysis of his (...) own modeling efforts—away from the idea of an interconnected hierarchy of isomorphic structures, towards more autonomous roles of the models at different levels, each with its own .. (shrink)

The relation between models and the world is mediated by experimental procedures generating data that are used as evidence to evaluate the model. Data can serve as empirical evidence, for or against, only if they result from reliable experimental procedures. The aim of this article is to discuss the role of relevance judgments in the evaluation of reliability and to clarify the conditions under which reliability can be a strictly empirical matter. It is argued that reliability is (...) a strictly empirical issue only in the restricted case in which the claim under test/investigation is about a data-generating procedure. (shrink)

The description of how individual models in families of models are related to each other is crucial for the general philosophical understanding of model-based scientific practice. We focus on the Capital Asset Pricing Models (CAPM) family, a cornerstone in financial economics, to provide a descriptive analysis of model relations within a family. We introduce the concepts of theoretical and empirical complementarity to characterise model relations. Our complementarity analysis of model relations has two types of payoff. Specifically (...) regarding the CAPM, our analysis reveals why this model family, which has been empirically contested, has yet remained popular and important: the different models that have been added over time have made new empirically and theoretically complementary contributions to the model family. More generally, our analysis reveals the dynamic character of model-based scientific practice. Characterising relations between models as theoretically and/or empirically complementary yields three hitherto underappreciated lessons: (i) actual modelling purposes are not always primarily epistemic, (ii) a model's individual import is relative, not absolute, and (iii) there is an important interplay between theory and data models. Faithfully characterising scientific modelling in this way facilitates subsequent analysis of models' epistemic import. (shrink)

The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives, public data (...) trusts and personal data sovereignty. We propose a social science-informed conceptualisation of data governance. Drawing from the notion of data infrastructure we identify the models as a function of the stakeholders’ roles, their interrelationships, articulations of value, and governance principles. Addressing the politics of data, we considered the actors’ competitive struggles for governing data. This conceptualisation brings to the forefront the power relations and multifaceted economic and social interactions within data governance models emerging in an environment mainly dominated by corporate actors. These models highlight that civic society and public bodies are key actors for democratising data governance and redistributing value produced through data. Through the discussion of the models, their underpinning principles and limitations, the article wishes to inform future investigations of socio-technical imaginaries for the governance of data, particularly now that the policy debate around data governance is very active in Europe. (shrink)

This paper proposes an original definition of samples as a kind of data within the relational framework of data. The distinction between scientific objects (e.g., samples, data, models) often needs to be clarified in the philosophy of science to understand their role in the scientific inquiry. The relational framework places data at the forefront of knowledge construction. Their epistemic status depends on their evaluation as potential evidence in a research situation and their ability to (...) circulate among researchers. While samples are significant in data-generating science, their role has been underexplored in the philosophy of data literature. I draw on a case study from data-centric microbiology, viz. amplicon sequencing, to introduce specifications of the relational framework. These specifications capture the distinctive epistemic role of samples, allowing the discussion of their significance in the inquiry process. I argue that samples are necessarily transformed to be considered as evidence, portable in the limits of a situation, and they act as world anchors for claims about a phenomenon. I compare these specifications with other data and evidence frameworks and suggest they are compatible. The paper concludes by considering the extension of these criteria in the context of biobanking. The specifications proposed here help analyze other life sciences cases and deepen our understanding of samples and their epistemological role in scientific research. (shrink)

Recent frameworks, models, and reports highlight the critical need to address social determinants of health for achieving health equity in the United States and around the globe. In the United States, data play an important role in better understanding community‐level and population‐level disparities particularly for local health departments. However, data‐driven decision‐making—the use of data for public health activities such as program implementation, policy development, and resource allocation—is often presented theoretically or through case studies in the literature. We (...) sought to develop a preliminary model that identifies the factors that contribute to data‐driven decision‐making in US local health departments and describe relationships between them. Guided by implementation science literature, we examined organizational‐level capacity and individual‐level factors contributing to using data for decision‐making related to social determinants of health and the reduction of county‐level disparities. This model has the potential to improve implementation of public health interventions and programs aimed at upstream structural factors, by elucidating the factors critical to incorporating data in decision‐making. (shrink)

According to the hierarchy of models account of scientific experimentation developed by Patrick Suppes and elaborated by Deborah Mayo, theoretical considerations about the phenomena of interest are involved in an experiment through theoretical models that in turn relate to experimental data through data models, via the linkage of experimental models. In this paper, I dispute the HoM account in the context of present-day high-energy physics experiments. I argue that even though the HoM account aims to characterize experimentation as (...) a model-based activity, it does not involve a modeling concept for the process of data acquisition, and it thus fails to provide a model-based characterization of the theory-experiment relationship underlying this process. In order to characterize the foregoing relationship, I propose the concept of a model of data acquisition and illustrate it in the case of the ATLAS experiment at CERN’s Large Hadron Collider, where the Higgs boson was discovered in 2012. I show that the process of data acquisition in the ATLAS experiment is performed according to a model of data acquisition that specifies and organizes the experimental procedures necessary to select the data according to a predetermined set of selection criteria. I also point out that this data acquisition model is theory-laden, in the sense that the underlying data selection criteria are determined by considering the testable predictions of the theoretical models that the ATLAS experiment is aimed to test. I take this sense of theory-ladenness to indicate that the relationship between the procedures of the ATLAS experiment and the theoretical models of the phenomena of interest is first established, prior to the formation of data models, through the data acquisition model of the experiment, thus not requiring the intermediary of other types of models as suggested by the HoM account. I therefore conclude that in the context of present-day HEP experiments, the HoM account does not consistently extend to the process of data acquisition so as to include models of data acquisition. (shrink)

Distributional semantic models (DSMs) are a primary method for distilling semantic information from corpora. However, a key question remains: What types of semantic relations among words do DSMs detect? Prior work typically has addressed this question using limited human data that are restricted to semantic similarity and/or general semantic relatedness. We tested eight DSMs that are popular in current cognitive and psycholinguistic research (positive pointwise mutual information; global vectors; and three variations each of Skip-gram and continuous bag of words (...) (CBOW) using word, context, and mean embeddings) on a theoretically motivated, rich set of semantic relations involving words from multiple syntactic classes and spanning the abstract–concrete continuum (19 sets of ratings). We found that, overall, the DSMs are best at capturing overall semantic similarity and also can capture verb–noun thematic role relations and noun–noun event-based relations that play important roles in sentence comprehension. Interestingly, Skip-gram and CBOW performed the best in terms of capturing similarity, whereas GloVe dominated the thematic role and event-based relations. We discuss the theoretical and practical implications of our results, make recommendations for users of these models, and demonstrate significant differences in model performance on event-based relations. (shrink)

When relating genomic data to survival outcomes, there are three main challenges that are the censored survival outcomes, the high-dimensionality of the genomic data, and the non-normality of data. We propose a method to tackle these challenges simultaneously and obtain a robust estimation of detecting significant genes related to survival outcomes based on Accelerated Failure Time model. Specifically, we include a general loss function to the AFT model, adopt model regularization and shrinkage technique, cope (...) with parameters tuning and model selection, and develop an algorithm based on unified Expectation–Maximization approach for easy implementation. Simulation results demonstrate the advantages of the proposed method compared with existing methods when the data has heavy-tailed errors and correlated covariates. Two real case studies on patients are provided to illustrate the application of the proposed method. (shrink)

With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation (...) system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered. (shrink)

This paper takes laptops as an example to carry out research on quantitative model of brand recognition based on sentiment analysis of big data. The basic idea is to use web crawler technology to obtain the most authentic and direct information of different laptop brands from first-line consumers from public spaces such as buyer reviews of major e-commerce platforms, including review time, text reviews, satisfaction ratings and relevant user information, etc., and then analyzes consumers’ sentimental tendencies and recognition (...) status of the product brands. This study extracted a total of 437,815 user reviews of laptops from e-commerce platforms from January 1, 2019 to December 31, 2021, and performed data preprocessing on the obtained review data, followed by sentiment dictionary construction, attribute expansion, text quantification and algorithm evaluation. This paper analyzed the information receiving and processing hierarchy of the quantitative model of brand recognition, discussed the interactive relationship between brand recognition and consumer sentiment, discussed the brand recognition bias, style and demand in the context of big data, and performed the sentiment statistics and dimension analysis in the quantitative model of brand recognition. The study results show that the quantitative model of brand recognition based on sentiment analysis of big data can transform and map the keywords in text to word vectors in the high-dimensional semantic space by performing unsupervised machine learning on the text based on artificial neural network computer bionic metaphors; the model can accumulate each brand-related buyer review in the corresponding brand recognition dimension, so as to obtain the value of each product in each dimension of brand recognition; finally, the model will add the values of each dimension of brand recognition, that is, obtain the relevant value of the sum of each brand recognition. The results of this paper may provide a reference for further research on the quantitative model of brand recognition based on sentiment analysis of big data. (shrink)

The information in the working environment of industrial Internet is characterized by diversity, semantics, hierarchy, and relevance. However, the existing representation methods of environmental information mostly emphasize the concepts and relationships in the environment and have an insufficient understanding of the items and relationships at the instance level. There are also some problems such as low visualization of knowledge representation, poor human-machine interaction ability, insufficient knowledge reasoning ability, and slow knowledge search speed, which cannot meet the needs of intelligent and (...) personalized service. Based on this, this paper designs a cognitive information representation model based on a knowledge graph, which combines the perceptual information of industrial robot ontology with semantic description information such as functional attributes obtained from the Internet to form a structured and logically reasoned cognitive knowledge graph including perception layer and cognition layer. Aiming at the problem that the data sources of the knowledge base for constructing the cognitive knowledge graph are wide and heterogeneous, and there are entity semantic differences and knowledge system differences among different data sources, a multimodal entity semantic fusion model based on vector features and a system fusion framework based on HowNet are designed, and the environment description information such as object semantics, attributes, relations, spatial location, and context acquired by industrial robots and their own state information are unified and standardized. The automatic representation of robot perceived information is realized, and the universality, systematicness, and intuition of robot cognitive information representation are enhanced, so that the cognition reasoning ability and knowledge retrieval efficiency of robots in the industrial Internet environment can be effectively improved. (shrink)

Introduction: The present study aimed to investigate the effects of two high-intensity interval training shuttle-run-based models, over ten weeks on aerobic, anaerobic, and neuromuscular parameters, and the association of the training load and heart rate variability with the change in the measures in young futsal players. Methods: Eleven young male futsal players participated in this study. This pre-post study design was performed during a typical 10 weeks training period. HIIT sessions were conducted at 86% and 100% of peak speed of (...) the FIET. Additionally, friendly and official matches, technical-tactical and strength-power training sessions were performed. Before and after the training period, all players performed the FIET, treadmill incremental, repeated sprint ability, sprint 15-m, and vertical jump tests, and the HRV was measured. Training load was monitored using the session rating of perceived effort. Data analysis was carried out using Bayesian inference methods. Results: The HIIT86 model showed clear improvements for the peak oxygen uptake, peak speed in the treadmill incremental test, first and second ventilatory thresholds, RSA best and mean times, CMJ, and SJ. The HIIT100 model presented distinct advances in VO2peak, peak speed in the treadmill incremental test, RSA mean time, and CMJ. Between HIIT models comparisons showed more favorable probabilities of improvement for HIIT86 than HIIT100 model in all parameters. TL data and HIIT models strongly explained the changes in the RSA mean and best times, as well as HRV changes, and HIIT models explained positively VO2peak changes. All other changes in the parameters were low to moderately explained. Conclusion: The HIIT86 proved to be more effective for improving aerobic, RSA, and neuromuscular parameters than HIIT100 during a typical 10-week futsal training period. So, strength and conditioning specialists prescribing shuttle-run intermittent exercises at submaximal intensities can manage the individual acceleration load imposed on athlete increasing or decreasing either the set duration or the frequency of change of direction during HIIT programming. (shrink)

Many clinical and psychological constructs are conceptualized to have multivariate higher-order constructs that give rise to multidimensional lower-order traits. Although recent measurement models and computing algorithms can accommodate item response data with a higher-order structure, there are few measurement models and computing techniques that can be employed in the context of complex research synthesis, such as meta-analysis of individual participant data or integrative data analysis. The current study was aimed at modeling complex item responses that can arise (...) when underlying domain-specific, lower-order traits are hierarchically related to multiple higher-order traits for individual participant data from multiple studies. We formulated a multi-group, multivariate higher-order item response theory (HO-IRT) model from a Bayesian perspective and developed a new Markov chain Monte Carlo (MCMC) algorithm to simultaneously estimate the (a) structural parameters of the first- and second-order latent traits across multiple groups and (b) item parameters of the model. Results from a simulation study support the feasibility of the MCMC algorithm. From the analysis of real data, we found that a bivariate HO-IRT model with different correlation/covariance structures for different studies fit the data best, compared to a univariate HO-IRT model or other alternate models with unreasonable assumptions (i.e., the same means and covariances across studies). Although more work is needed to further develop the method and to disseminate it, the multi-group multivariate HO-IRT model holds promise to derive a common metric for individual participant data from multiple studies in research synthesis studies for robust inference and for new discoveries. (shrink)

Children’s cognitive control and knowledge at school entry predict growth rates in analogical reasoning skill over time; however, the mechanisms by which these factors interact and impact learning are unclear. We propose that inhibitory control (IC) is critical for developing both the relational representations necessary to reason and the ability to use these representations in complex problem solving. We evaluate this hypothesis using computational simulations in a model of analogical thinking, Discovery of Relations by Analogy/Learning and Inference with (...) Schemas and Analogy (DORA/LISA; Doumas et al., 2008 ). Longitudinal data from children who solved geometric analogy problems repeatedly over 6 months show three distinct learning trajectories though all gained somewhat: analogical reasoners throughout, non-analogical reasoners throughout, and transitional – those who start non-analogical and grew to be analogical. Varying the base level of top-down lateral inhibition in DORA affected the model’s ability to learn relational representations, which, in conjunction with inhibition levels used in LISA during reasoning, simulated accuracy rates and error types seen in the three different learning trajectories. These simulations suggest that IC may not only impact reasoning ability but may also shape the ability to acquire relational knowledge given reasoning opportunities. (shrink)

Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure which is physically related to rainfall on land and thus able to predict unseen (...) rainfall events. Daily rainfall of east coast of Peninsular Malaysia was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine, Bayesian regularized neural networks, Bayesian additive regression trees, extreme gradient boosting, and hybrid neural fuzzy inference system were used considering the complex relationship of rainfall with sea level pressure. Principle components of SLP domain correlated with daily rainfall were used as predictors. The results revealed that the efficacy of AI models is predicting daily rainfall one day before. The relative performance of the models revealed the higher performance of BRNN with normalized root mean square error of 0.678 compared with HNFIS, BART, xgBoost, and ELM. Visual inspection of predicted rainfall during model validation using density-scatter plot and other novel ways of visual comparison revealed the ability of BRNN to predict daily rainfall one day before reliably. (shrink)

Through their transcript products genes regulate the rates at which an immense variety of transcripts and subsequent proteins occur. Understanding the mechanisms that determine which genes are expressed, and when they are expressed, is one of the keys to genetic manipulation for many purposes, including the development of new treatments for disease. Viewing each gene in a genome as a distinct variable that is either on or off, or more realistically as a continuous variable, the values of some of these (...) variables influence the values of others through the regulatory proteins they express, including, of course, the possibility that the rate of expression of a gene at one time may, in various circumstances, influence the rate of expression of that same gene at a later time. If we imagine an arrow drawn from each gene expression variable at a given time to a gene variable whose expression it influences a short while after, the result is a network, technically a directed acyclic graph. For example, the DAG in Figure 1 is a representation of a system in which the expression level of gene G1 at time 1 ) causes the expression level of G2, which in turn causes the expression level of G3. The arrows in Figure 1 which do not have a variable at their tails are “error terms” which represent all of the causes of a variable other than the ones explicitly represented in the DAG. The DAG describes more than associations—it describes causal connections among gene expression rates. A shock to a cell—by mutation, heating, chemical treatment, etc. may alter the DAG describing the relations among gene expressions, for example by activating a gene that was otherwise not expressed, producing a cascade of new expression effects. Although “knockout” experiments can reveal some of the underlying causal network of gene expression levels, unless guided by information from other sources, such experiments are limited in how much of the network structure they can reveal, due to the sheer number of possible combinations of experimental manipulations of genes necessary to reveal the complete causal network. Recent developments have made it possible to compare quantitatively the expression of tens of thousands of genes in cells from different sources in a single experiment, and to trace gene expression over time in thousands of genes simultaneously. cDNA microarrays are already producing extensive data, much of it available on the web. Thus there are calls for analytic software that can be applied to microarray and other data to help infer regulatory networks. In this paper we will review current techniques that are available for searching for the causal relations between variables, describe algorithmic and data gathering obstacles to applying these techniques to gene expression levels, and describe the prospects for overcoming these obstacles. (shrink)

Past research suggests that spiritual leadership plays a pivotal role in enhancing employee job performance, yet we have little understanding of how and when spiritual leadership enhances employee job performance. The present study explores how and when spiritual leadership promotes job performance by examining relational energy as a mediator and leader integrity and relational energy differentiation as boundary conditions. We tested the theoretical model with data gathered across three phases over 12 months from 497 employees and (...) their supervisors in 108 groups. Results showed that the positive relationship between spiritual leadership and job performance was mediated by relational energy. Moreover, we found that leader integrity amplified the mediated relationship between spiritual leadership and employee job performance via relational energy. In contrast, relational energy differentiation weakened this mediated relationship. (shrink)

Modelling with Words is an emerging modelling methodology closely related to the paradigm of Computing with Words introduced by Lotfi Zadeh. This book is an authoritative collection of key contributions to the new concept of Modelling with Words. A wide range of issues in systems modelling and analysis is presented, extending from conceptual graphs and fuzzy quantifiers to humanist computing and self-organizing maps. Among the core issues investigated are - balancing predictive accuracy and high level transparency in learning - scaling (...) linguistic algorithms to high-dimensional data problems - integrating linguistic expert knowledge with knowledge derived from data - identifying sound and useful inference rules - integrating fuzzy and probabilistic uncertainty in data modelling. (shrink)

Recurrent neural network has been widely applied to many sequential tagging tasks such as natural language process and time series analysis, and it has been proved that RNN works well in those areas. In this paper, we propose using RNN with long short-term memory units for server load and performance prediction. Classical methods for performance prediction focus on building relation between performance and time domain, which makes a lot of unrealistic hypotheses. Our model is built based on events, which (...) is the root cause of server performance. We predict the performance of the servers using RNN-LSTM by analyzing the log of servers in data center which contains user’s access sequence. Previous work for workload prediction could not generate detailed simulated workload, which is useful in testing the working condition of servers. Our method provides a new way to reproduce user request sequence to solve this problem by using RNN-LSTM. Experiment result shows that our models get a good performance in generating load and predicting performance on the data set which has been logged in online service. We did experiments with nginx web server and mysql database server, and our methods can been easily applied to other servers in data center. (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 presents the results of the study of the impact and effects of communication models of public relations and organization–public relationships on company credibility and financial performance in companies in Serbia. The data were obtained by interviewing 415 respondents (PR managers, PR practitioners and marketing experts) working in 93 companies in Serbia. The dimensions of the organization–public relationships have stronger positive influences and effects on company credibility and financial performance than the dimensions of communication models of public relations. (...) The influence of the dimensions of trust, commitment, satisfaction and communal relationship is particularly emphasized. Of the four communication models, the strongest positive impact on company credibility and financial performance is achieved by the two-way symmetrical dimension, and then the two-way asymmetrical dimension. The dimensions of communication models of public relations and organization–public relationships have the greatest influence and predictive effect on the dimension of trustworthiness, followed by the dimension of expertise, and finally the dimension of financial performance. Therefore, a greater impact exists on company credibility than on financial performance. (shrink)

My model-theoretic realist account of science places linguistic systems and the corresponding non-linguistic structures at different stages of the scientific process. It is shown that science and its progress cannot be analysed in terms of only one of these strata. Philosophy of science literature offers mainly two approaches; to the structure of scientific knowledge analysed in terms of theories and their models, the "statement" and the "non-statement" approaches. In opposition to the statement approach's belief that scientific knowledge is embodied (...) in theories symbolic language) with direct interpretative links---via so-called "bridge principles"---to reality, the defenders of the non-statement approach believe in an analysis where the language in which the theory is formulated plays a much smaller role than the structures which satisfy that theory. ;The model-theoretic realism expounded here retains the notion of a scientific theory as a set of sentences, while simultaneously emphasising the interpretative role of the conceptual models of these theories. My criticism against the non-statement approach is based on the fact that merely "giving" the theory "in terms of" its mathematical structures leaves out any real interpretation of the nature and role of general terms in science. Against the statement approach's "direct" linking of general theoretical terms to reality, my approach interpolates models between theories and reality in the interpretative chain. ;The links between the general terms of scientific theories and their interpretations in the various models of the theory regulate the whole referential process. The terms of a theory are "general" in the sense that they are the result of certain abstractive conceptualisations of the object of scientific investigation and subsequent linguistic formulations of these conceptualisations. Their meanings can be "given back" only by interpreting them in the limited context, of the various conceptual models of their theory and, finally, by finding an isomorphic relation between some substructure of the conceptual model in question and some empirical conceptualisation of relevant experimental data. In this sense the notion of scientific "truth" becomes inextricably linked with that of articulated reference, as it---given its model-dependent nature---should be. (shrink)

Increasing numbers of decisions about everyday life are made using algorithms. By algorithms we mean predictive models (decision rules) captured from historical data using data mining. Such models often decide prices we pay, select ads we see and news we read online, match job descriptions and candidate CVs, decide who gets a loan, who goes through an extra airport security check, or who gets released on parole. Yet growing evidence suggests that decision making by algorithms may discriminate people, (...) even if the computing process is fair and well-intentioned. This happens due to biased or non-representative learning data in combination with inadvertent modeling procedures. From the regulatory perspective there are two tendencies in relation to this issue: (1) to ensure that data-driven decision making is not discriminatory, and (2) to restrict overall collecting and storing of private data to a necessary minimum. This paper shows that from the computing perspective these two goals are contradictory. We demonstrate empirically and theoretically with standard regression models that in order to make sure that decision models are non-discriminatory, for instance, with respect to race, the sensitive racial information needs to be used in the model building process. Of course, after the model is ready, race should not be required as an input variable for decision making. From the regulatory perspective this has an important implication: collecting sensitive personal data is necessary in order to guarantee fairness of algorithms, and law making needs to find sensible ways to allow using such data in the modeling process. (shrink)

climate scientists have been engaged in a decades-long debate over the standing of satellite measurements of the temperature trends of the atmosphere above the surface of the earth. This is especially significant because skeptics of global warming and the greenhouse effect have utilized this debate to spread doubt about global climate models used to predict future states of climate. I use this case from an under-studied science to illustrate two distinct philosophical approaches to the relation among data, scientists, measurement, (...) models, and theory. I argue that distinguishing between 'direct' empiricist and 'complex' empiricist approaches helps us understand and analyze this important scientific episode. I also introduce a complex empiricist account of testing and evaluation, and contrast it with the basic Hypothetico-Deductive approach to the climate models used by the direct empiricists. This more developed complex empiricist apporach will serve philosophy of science well, as computational models become more wide-spread in the sciences. (shrink)

This paper is based on ethnographic research of data practices in a public health project called Weather Health and Air Pollution. I examine two different kinds of practices that make air pollution data, focusing on how they relate to particular modes of sensing and articulating air pollution. I begin by describing the interstitial spaces involved in making measurements of air pollution at monitoring sites and in the running of a computer simulation. Specifically, I attend to a shared dimension (...) of these practices, the checking of a numerical reading for error. Checking a measurement for error is routine practice and a fundamental component of making data, yet these are also moments of interpretation, where the form and meaning of numbers are ambiguous. Through two case studies of modelling and monitoring data practices, I show that making a ‘good’ measurement requires developing a feeling for the instrument–air pollution interaction in terms of the intended functionality of the measurements made. These affective dimensions of practice are useful analytically, making explicit the interaction of standardised ways of knowing and embodied skill in stabilising data. I suggest that environmental data practices can be studied through researchers’ materialisation of error, which complicate normative accounts of Big Data and highlight the non-linear and entangled relations that are at work in the making of stable, accurate data. (shrink)

The present study examined the direct and indirect (via relational social capital) relationships between supervisors’ ethical leadership and knowledge hiding. It also tested the moderating role of instrumental thinking in the relationship between supervisors’ ethical leadership and knowledge hiding and the relationship between supervisors’ ethical leadership and relational social capital. Data were collected from 245 employees in different firms spanning different manufacturing and service sectors. The results showed that supervisors’ ethical leadership was negatively related to knowledge hiding, (...) both directly and via relational social capital. The results revealed that instrumental thinking moderated the positive relationship between supervisors’ ethical leadership and relational social capital, such that the relationship was weak when instrumental thinking was high. The results also showed that instrumental thinking moderated both direct and indirect relationships between supervisors’ ethical leadership and knowledge hiding, such that the relationships were weak when instrumental thinking was high. The study carries important practical implications for managers concerned about the destructive consequences of knowledge hiding. (shrink)

Relational reasoning is a complex form of human cognition involving the evaluation of relations between mental representations of information. Prior studies have modified stimulus properties of relational reasoning problems and examined differences in difficulty between different problem types. While subsets of these stimulus properties have been addressed in separate studies, there has not been a comprehensive study, to our knowledge, which investigates all of these properties in the same set of stimuli. This investigative gap has resulted in different (...) findings across studies which vary in task design, making it challenging to determine what stimulus properties make relational reasoning—and the putative formation of mental models underlying reasoning—difficult. In this article, we present the Multidimensional Relational Reasoning Task, a task which systematically varied an array of stimulus properties within a single set of relational reasoning problems. Using a mixed-effects framework, we demonstrate that reasoning problems containing a greater number of the premises as well as multidimensional relations led to greater task difficulty. The MRRT has been made publicly available for use in future research, along with normative data regarding the relative difficulty of each problem. (shrink)

Against the tradition, which has considered measurement able to produce pure data on physical systems, the unavoidable role played by the modeling activity in measurement is increasingly acknowledged, particularly with respect to the evaluation of measurement uncertainty. This paper characterizes measurement as a knowledge-based process and proposes a framework to understand the function of models in measurement and to systematically analyze their influence in the production of measurement results and their interpretation. To this aim, a general model of (...) measurement is sketched, which gives the context to highlight the unavoidable, although sometimes implicit, presence of models in measurement and, finally, to propose some remarks on the relations between models and measurement uncertainty, complementarily classified as due to the idealization implied in the models and their realization in the experimental setup. (shrink)

This article questions the effects on urban research dynamics of the Big Data and AI turn in urban management. Increasing access to large datasets collected in real time could make certain mathematical models developed in research fields related to the management of urban systems obsolete. These ongoing evolutions are the subject of numerous works whose main angle of reflection is the future of cities rather than the transformations at work in the academic field. Our article proposes grasp the scientific (...) dynamics in areas of research related to two urban systems: transportation and water. The article demonstrates the importance of grasping these dynamics if we want to be able to apprehend what the urban management of tomorrow's cities will be like. To analyse these research areas’ dynamics, we use two complementary materials: bibliometric data and interviews. The interviews conducted in 2018 with academics and higher education officials in Paris and Edinburgh suggest avenues for hybridization between traditional modelling approaches and research in machine learning, artificial intelligence and Big Data. The bibliometric analysis highlight the trends at work: it shows that traffic flow as well as transportation studies are focussing more and more on AI and Big Data and that traffic flow studies are arousing a growing interest among computer scientists, while, so far, this interest is less pronounced in the water research area, and more especially regarding water quality. The differences observed between research on transportation and that on water confirm the multifaceted nature of the developments at work and encourage us to reject overly hasty and simplistic generalisations about the transformations underway. (shrink)

Moral circles help define the boundaries of one’s moral consideration. One’s moral circle may provide insight into how one perceives or treats other entities. A data-driven model exploration was conducted to explore predictors and prediction models. Candidate predictors were built upon past research using moral foundations and political orientation. Moreover, we also employed additional moral psychological indicators, i.e., moral reasoning, moral identity, and empathy, based on prior research in moral development and education. We used model exploration methods, (...) i.e., Bayesian model exploration, Bayesian model averaging, and elastic-net regression. The study successfully replicated past research supporting the relationship between moral foundations, political orientation, and the moral circle. Additional moral psychological constructs, such as post-conventional moral reasoning and moral identity, significantly predicted the moral circle width. The identified components of the moral circle were conceptually related to phronesis, i.e., practical wisdom. We discussed the educational implications of the findings, particularly those related to moral education focusing on phronesis cultivation, multiculturalism and global citizenship, and climate justice. (shrink)

Biomedical deployments of data science capitalise on vast, heterogeneous data sources. This promotes a diversified understanding of what counts as evidence for health-related interventions, beyond the strictures associated with evidence-based medicine. Focusing on COVID-19 transmission and prevention research, I consider the epistemic implications of this diversification of evidence in relation to: (1) experimental design, especially the revival of natural experiments as sources of reliable epidemiological knowledge; and (2) modelling practices, particularly the recognition of transdisciplinary expertise as crucial to (...) developing and interpreting data models. Acknowledging such shifts in evidential, experimental and modelling practices helps avoid harmful applications of data-intensive methods. (shrink)

Tuberculosis (TB) is a potentially fatal infectious disease that, in Aotearoa New Zealand (NZ), inequitably affects Asian, Pacific, Middle Eastern, Latin American, and African (MELAA), and Māori people. Medical research involving genome sequencing of TB samples enables more nuanced understanding of disease strains and their transmission. This could inform highly specific health interventions. However, the collection and management of TB isolate samples for research are currently informed by monocultural biomedical models often lacking key ethical considerations. Drawing on a qualitative kaupapa (...) Māori–consistent study, this paper reports on preliminary discussions with groups of Māori, Pacific, and Afghan people in NZ, whose communities have been harmed by TB and TB stigma. Participants’ discussions highlight key concerns and meanings that ought to inform the development of guidelines and a more robust consultative process for the governance of how TB isolate samples are collected and used both retrospectively and in future research. We argue for ethical processes to be culturally nuanced and community-generated, flexible and meaningful, and situated in relation to the physical and symbolic effects of TB. We discuss the significance of Indigenous data sovereignty, rights, and kāwanatanga (governorship) in shaping a multicultural data sovereignty model. (shrink)

We develop and use mathematical models that describe changes in the South African population over the last decades, brought on by HIV and AIDS. We do not model all the phases in HIV progression but rather, we show that a relatively simple model is sufficient to represent the data and allows us to investigate important aspects of HIV infection: firstly, we are able to investigate the effect of awareness on the prevalence of HIV and secondly, it enables (...) us to make a comparison between South Africa and Botswana. A comparison is made between two models: a model that does not reflect awareness of the devastating impact of HIV and AIDS, and a model with an added psychological awareness factor. Both models are fitted to data that reflects the incidence of HIV and AIDS within South Africa. This allows us to examine the impact of psychological awareness. We show that inclusion of the effect of awareness is absolutely necessary to arrive at a model description that satisfactorily fits the available HIV and AIDS data for South Africa. We also show that a relatively simple modelling of awareness (as opposed to more complex mathematical techniques that have been used in past studies) is sufficient to accurately describe the observed patterns in the data. Even though awareness alone is not sufficient to eradicate any disease and other control strategies should be explored and implemented concurrently with educational campaigns, we are able to conclude (through thorough model analyses procedures) that the current level of awareness in South Africa is far below the level that is effectively required to eradicate HIV from the South African population. The awareness model is also fitted to HIV-related data for Botswana and we compare the results with the South African case. Though the effect of awareness is currently estimated at a much higher level in Botswana, other factors such as poorer health care and cultural differences may play a role in limiting the ability of awareness to combat HIV in Botswana. (shrink)

In fear differential conditioning, stimuli that resemble the conditioned stimulus (CS+) are more likely to trigger fear responses. Excessive fear responses on stimuli not like CS + are often associated with anxiety. However, the threat judgments process and how this process manifests itself differently in subgroups with different generalisation rule applications, is unclear. This study examines whether anxiety biases the threat decision process in fear generalisation paradigm and whether subgroups characterised by different generalisation gradients was interpreted differently by drift-diffusion (...) class='Hi'>model. We gathered behavioural data through a binary fear generalisation judgment task and clustered participants based on their responses. Reaction time distributions and individual scale scores were analyzed using the hierarchical drift-diffusion model. The model results suggested that similarity and state anxiety facilitated evidence-gathering processes that favoured “threat” judgments, but at the same time, state anxiety weakened the effect of stimulus similarity as evidence. Further cluster analyses revealed that this effect of anxiety on threat judgments only held true for specific subgroups of participants. This pioneering computational modelling effort in fear generalisation underscores the significant role of strategy preference and its complex interaction with anxiety in shaping stimulus processing. (shrink)

An empirically based family assessment can help family therapists understand how a family functions. In systemic therapy a family is seen as a dynamic system in which the family members form interdependent subsystems. The Social Relations Model is a useful tool to study such interdependence within a family. According to the SRM, each dyadic score is viewed as the sum of an unobserved family effect, an individual actor and partner effect, and a relation-specific effect. If dyadic data are (...) obtained for a specific family using a round robin design, these different SRM effects can be calculated using an ANOVA-approach. To gain insight into the functioning of a particular family, the family-specific SRM effects can be compared to those from a norm sample and it can be deduced whether that family has deviating scores on a particular SRM effect. Currently, such a family assessment relies on the mean and variance of the SRM ANOVA scores in the norm sample. However, family therapists may not always have access to these data, making the current approach of SRM family assessment not as useful in practice. In this article, we introduce a user-friendly web application that uses an alternative method for SRM family assessment. This alternative strategy requires as input the population parameter estimates of SRM means and variances more commonly described in SRM family literature. (shrink)

The replicability and importance of the correlation between cognitive style and religious belief have been debated. Moreover, the literature has not examined distinct psychological accounts of this relationship. We tested the replicability of the correlation (N = 5284; students and broader samples of Canadians, Americans, and Indians); while testing three accounts of how cognitive style comes to be related to belief in God, karma, witchcraft, and to the belief that religion is necessary for morality. The first, the dual process (...) class='Hi'>model, posits that analytical thinking is recruited in overriding intuitions related to supernatural beliefs. The second, the expressive rationality model, posits that analytical thinking is recruited in supporting already-held beliefs in an identity-protective manner. And the third, the counter-normativity rationality model, posits that analytical thinking is recruited to question beliefs supported by prevailing cultural norms. In Study 2, we tested the replicability of our results in a re-analysis of published data. The association between analytic thinking style and beliefs was replicated. We conclude that whereas the counter-normativity rationality model was contradicted by the data, both the dual process and expressive rationality models received varying degrees of empirical support, but neither model fully accounted for all the patterns in the data. (shrink)