This book focuses on binomials (word pairs such as heart and soul, rich and poor, or if and when), and in particular on the degree of reversibility that English binomials demonstrate. Detailed and innovative corpus linguistic analyses investigate the correlates of the degree of reversibility, linguistic constraints that influence the ordering and reversibility of binomials and the diachronic development of reversibility. In addition, judgment data are analyzed for their convergence and divergence with corpus data regarding degrees of reversibility. (...) The book thus establishes reversibility as a complex characteristic of the binomial construction, at the same time throwing light on general questions in phraseology, lexicalization, language structure and language processing. (shrink)

Human brain is a highly evolved coordinating mechanism in the species Homo sapiens. It is only in the last 100 years that extensive knowledge of the intricate structure and complex functioning of the human brain has been acquired, though a lot is yet to be known. However, from the beginning of civilisation, people have been conscious of a 'mind' which has been considered the origin of all scientific and cultural development. Philosophers have discussed at length the various attributes of consciousness. (...) At the same time, most of the philosophical or scientific frameworks have directly or indirectly implied mind-body duality. It is now imperative that we develop an integrated approach to understand the interconnection between mind and consciousness on one hand and brain on the other. This paper begins with the proposition that the structure of the brain is analogous, at least to certain extent, to that of the computer system. Of course, it is much more sophisticated and complex. The second proposition is that the Chomskyean concept of 'mental organs' is a good working hypothesis that tries to characterise this complexity in terms of an innate cognitive framework. By following this dual approach, brain as a data processing system and brain as a superstructure of intricately linked mental organs, we can move toward a better understanding of 'mind' within the framework of empirical science. The one 'mental organ' studied extensively in Chomskyean terms is 'language faculty' which is unique in its relation to brain, mind and consciousness. (shrink)

Social Networking Services provide services in return for rights to commercialize users’ personal data. We argue that what makes this transaction permissible is not users’ autonomous consent but the provision of sufficiently valuable opportunities to exchange data for services. We argue that the value of these opportunities should be assessed for both (a) a range of users with different decision-making abilities and (b) third parties. We conclude that regulation should shift from aiming to ensure autonomous consent towards ensuring (...) that users face options that they will use to advance individual and common interests. (shrink)

The efficient processing of large-scale data has very important practical value. In this study, a data mining platform based on Hadoop distributed file system was designed, and then K-means algorithm was improved with the idea of max-min distance. On Hadoop distributed file system platform, the parallelization was realized by MapReduce. Finally, the data processing effect of the algorithm was analyzed with Iris data set. The results showed that the parallel algorithm divided more correct samples (...) than the traditional algorithm; in the single-machine environment, the parallel algorithm ran longer; in the face of large data sets, the traditional algorithm had insufficient memory, but the parallel algorithm completed the calculation task; the acceleration ratio of the parallel algorithm was raised with the expansion of cluster size and data set size, showing a good parallel effect. The experimental results verifies the reliability of parallel algorithm in big data processing, which makes some contributions to further improve the efficiency of data mining. (shrink)

Frameworks for responsible data stewardship, such as that proposed by McCoy et al. (2023), are intended to encourage and provide guidelines for data processors to engage in responsible data process...

To shorten the travel time and improve comfort, the automatic train driving system is considered to replace manual driving. In this article, an automatic control method of computer application data-processing system based on artificial intelligence is proposed. An automatic train operation (ATO) introduced the structure and function of an autopilot system (train), optimized the train running on the target curve, introduced the basic principle of fuzzy generalized predictive control (PC) algorithm, and combined with the characteristics of ATO system (...) design the speed controller based on optimization algorithm, the target curve to make use of the designed controller to track and simulation validation. The experimental outcomes demonstrate that when the train runs to 90 s, the displacement difference reaches about 40 m, which proves that the fuzzy PC has better displacement tracking, punctual arrival, and higher stopping accuracy. (shrink)

This article is a longitudinal analysis of the relation between gendered labour divisions and new data processing technologies at the Dutch Central Bureau of Statistics. Following social-constructivist and evolutionary economic approaches, the authors hold that the relation between technology and work organization is a two-way process. This means that technology does not only affect the relations between men and women at work, but that these relations also influence technological choices. The proportional numbers of men and women on the (...) labour market and changing conceptions of which work is deemed appropriate for women have been important factors in the strongly varying extent to which women participated in office work. They have also been important factors in determining the course of technological development. Shortages of women on the labour market have even determined search heuristics for new technological solutions in specific directions. (shrink)

Amongst other methods, political campaigns employ microtargeting, a specific technique used to address the individual voter. In the US, microtargeting relies on a broad set of collected data about the individual. However, due to the unavailability of comparable data in Germany, the practice of microtargeting is far more challenging. Citizens in Germany widely treat social media platforms as a means for political debate. The digital traces they leave through their interactions provide a rich information pool, which can create (...) the necessary conditions for political microtargeting following appropriate algorithmic processing. More specifically, data mining techniques enable information gathering about a people's general opinion, party preferences and other non-political characteristics. Through the application of data-intensive algorithms, it is possible to cluster users in respect of common attributes, and through profiling identify whom and how to influence. Applying machine learning algorithms, this paper explores the possibility to identify micro groups of users, which can potentially be targeted with special campaign messages, and how this approach can be expanded to large parts of the electorate. Lastly, based on these technical capabilities, we discuss the ethical and political implications for the German political system. (shrink)

In May 2021, the UK National Health Service (NHS) proposed a scheme—called General Practice Data for Planning Research (GPDPR)—for sharing patients’ data. Under that system, a patient who does not wish to participate must actively opt out of their data being shared with third parties for research and other purposes. In this paper, we analyse the lessons that can be learned for the responsible and ethical governance of health data from the NHS’ new scheme. More specifically, (...) we explore the extent to which the opt-out within the planned scheme complies with the requirements stemming from the General Data Protection Regulation (GDPR), particularly in relation to the principles of lawfulness and transparency. We then evaluate, from an ethical perspective, this opt-out ‘nudge’ and whether it is sufficiently resistible, reversible, and has appropriate goals. In light of the above, we then propose improvements for the scheme’s legal and ethical acceptability. (shrink)

With the rapid development of big data, artificial intelligence teaching systems have gradually been developed extensively. The powerful artificial intelligence teaching systems have become a tool for teachers and students to learn independently in various universities. The characteristic of artificial intelligence teaching system is to get rid of the constraints of traditional teaching time and space and build a brand-new learning environment, which is the mainstream trend of future learning. As the carrier of students’ autonomous learning, the artificial intelligence (...) teaching system provides a wealth of learning resources and learning tools on the one hand, and on the other hand, it gradually accumulates more and more learning behaviors, learning status, and other large amounts of data, which is an in-depth study of online learning and provides valuable and generative dynamic resources. Based on relevant researches on domestic and foreign related learning analysis and common big data analysis methods, combined with actual learning evaluation goals, this paper proposes an artificial intelligence teaching system using big data analysis methods and a modeling process framework for online learning evaluation and uses student data to carry out predictive evaluation modeling to evaluate student learning outcomes. The evaluation results can enable teachers to predict whether students can successfully complete the course of learning after a period of teaching. Through the final evaluation, students’ learning problems can be discovered in time based on the evaluation results, and targeted interventions can be made for students who are at risk. The scientific and objective learning evaluation obtained in this study through data analysis can not only provide teachers with relevant information and provide personalized guidance to students, but also improve the adaptive and personalized service functions of the learning platform of the artificial intelligence teaching system, greatly reducing teachers teaching burden. Artificial intelligence teaching evaluation can help educators understand the problems in teaching, adjust teaching strategies in time, and improve teaching results. (shrink)

Die Publikation enthält Beiträge zu theoretischen, lexikographischen und didaktischen Fragen von Kollokationen. Im Fokus stehen Kriterien der Delimitation der Mehrworteinheiten und die Fragen der didaktischen Relevanz von Kollokationen, ihrer Ermittlung und Vermittlung für die FS-Lerner. Die Beiträge zeigen die Notwendigkeit einer korpuslinguistischen methodischen und praktischen Grundkompetenz in der fremdsprachenphilologischen Ausbildung. In den didaktisch ausgerichteten Beiträgen wird analysiert, ob und in welcher Weise Kollokationen in den Lehrplänen behandelt werden und welche Bedeutung Kollokationen in nationalen und europäischen Dokumenten haben.

Wagenknecht a récemment introduit une distinction conceptuelle entre dépendance épistémique translucide et dépendance épistémique opaque, dans le but de mieux rendre compte de la diversité des relations de dépendance épistémique au sein des pratiques collaboratives de recherche. Dans la continuité de son travail, mon but est d’expliciter les différents types d’expertise requis lorsque sont employés instruments et ordinateurs dans la production de connaissance, et d’identifier des sources potentielles d’opacité. Mon analyse s’appuie sur un cas contemporain de création de connaissance scientifique, (...) à savoir le traitement de données astrophysiques. (shrink)

Data protection law has a linear logic, in that it purports to trace the lifecycle of personal data from creation to collection, processing, transfer, and ultimately its demise, and to regulate each step so as to promote the data subject’s control thereof. Big data defies this linear logic, in that it decontextualizes data from its original environment and conducts an algorithmic nonlinear mix, match, and mine analysis. Applying data protection law to the (...) class='Hi'>processing of big data does not work well, to say the least. This Article examines the case of big medical data. A survey of emerging research practices indicates that studies either ignore data protection law altogether or assume an ex post position, namely that because they are conducted after the data has already been created in the course of providing medical care, and they use de-identified data, they go under the radar of data protection law. These studies focus on the end-point of the lifecycle of big data: if sufficiently anonymous at publication, the previous steps are overlooked, on the claim that they enjoy immunity. I argue that this answer is too crude. To portray data protection law in its best light, we should view it as a process-based attempt to equip data subjects with some power to control personal data about them, in all phases of data processing. Such control reflects the underlying justification of data protection law as an implementation of human dignity. The process-based approach fits current legal practices and is justified by reflecting dignitarian conceptions of informational privacy. (shrink)

This paper examines classification practices in the domain of plant data semantics, and particularly methods used to label plant traits to foster the collection, management, linkage and analysis of data about crops across locations—which crucially inform research and interventions on plants and agriculture. The efforts required to share data place in sharp relief the forms of diversity characterizing the systems used to capture the biological and environmental characteristics of plant variants: particularly the biological, cultural, scientific and semantic (...) diversity affecting the identification and description of plant traits, the methods used to generate and process data, and the goals and skills of those with relevant expertise—including farmers and breeders. Through a study of the Crop Ontology (which explicitly recognizes and negotiates diversity) and its application to cassava breeding, I argue for a process-sensitive approach to the naming of plant traits that focuses on documenting environmental processes instead of biological products. I claim that this approach can foster reliable linkage and robust re-use of plant data, while at the same time facilitating dialogue between data scientists, plant researchers, breeders, and other relevant experts in ways that crucially inform agricultural interventions. I conclude that the study of data semantics and related descriptors constitutes a productive and underexplored way to think about the epistemic import of naming traits within plant science. The effort to articulate semantic differences among plant varieties and methods of data processing can generate newly inclusive ways to develop and communicate biological knowledge. In turn, such practices have the potential to defy existing understandings of systematisation and hierarchies of expertise in biology, thus bolstering the extent to which plant science can support biodiversity and sustainable agriculture. (shrink)

Emotions change over time. A comprehensive understanding of emotions will require that their temporal nature be observed and analysed. By observing emotion over time, one can disentangle and simultaneously analyse temporal variability within individuals and between-individual variability using a two-step process approach. First, within-person temporal patterns (e.g., covariation, lead–lag relation, periodicity, etc.) are assessed for each subject. Second, between-person analyses are conducted on the within-person patterns. These two steps can be done simultaneously with hierarchical linear models (HLM) or in two (...) actual steps with the process approach. HLM is limited to the intra-individual analysis of linear patterns (e.g., slope, intercept), whereas the process approach can be used to examine non-linear aspects of intra-individual change, such as multivariate patterns, within-subject skew, or phase relations between oscillating processes. In this paper we provide a description of the process approach and present several examples of, as well as suggestions for, intra-individual analysis applied to emotion and individual differences. (shrink)

The article “Ethical Responsibilities for Companies that Process Personal Data” (McCoy et al. 2023) provides a principled and pragmatic ethical framework for companies collecting, sharing, and usin...

The large-scale and intensive collection and analysis of digital data (commonly called “Big Data”) has become a common, popular, and consensual research method for the social sciences, as the automation of data collection, mathematization of analysis, and digital objectification reinforce both its efficiency and truth-value. This article opens with a critical review of the literature on data collection and analysis, and summarizes current ethical discussions focusing on these technologies. A semiotic model of data production and (...) circulation is then introduced to problematize the view that digital data has ceased to stand for a formalization method (a possible kind of representation among others), and effectively “becomes the world itself” (a direct presentation of the world outperforming all other modes of representation). Following Charles Sanders Peirce’s semiotics and pragmaticist philosophy, we characterize digitalization as a hypersymbolic semiotic process, and we highlight the naturalization of meaning, the illusion of iconicity, and rhetorical efficiency on which data’s truth value relies within the context of its large-scale, profit-driven, and results-oriented research uses. This outlines some epistemological and ethical implications of data’s visualization, use, and authority, and indicates avenues for critical semiotics of contemporary data science and analysis. (shrink)

Although Stanovich & West (S&W) are likely to be criticized for not proposing a process model, results of such a model (fuzzy-trace theory) support many of their conclusions. However, arguments concerning evolution and Gricean intelligence are weak. Finally, developmental data are relevant to rationality, but contradictory results suggest a dual-processes approach that differs from S&W's based on fuzzy-trace theory.

A novel data-driven model-free adaptive control approach is first proposed by combining the advantages of model-free adaptive control and data-driven optimal iterative learning control, and then its stability and convergence analysis is given to prove algorithm stability and asymptotical convergence of tracking error. Besides, the parameters of presented approach are adaptively adjusted with fuzzy logic to determine the occupied proportions of MFAC and DDOILC according to their different control performances in different control stages. Lastly, the proposed fuzzy DDMFAC (...) approach is applied to the control of particle quality in drug development phase of spray fluidized-bed granulation process, and its control effect is compared with MFAC and DDOILC and their fuzzy forms, in which the parameters of MFAC and DDOILC are adaptively adjusted with fuzzy logic. The effectiveness of the presented FDDMFAC approach is verified by a series of simulations. (shrink)

Over the past decade, support for reexamining and reconsidering the U.S. model of ethics review for protocols involving research with humans has grown, particularly for studies involving participants from multiple locales and organizations. The HMO Research Network received an infrastructure-building contract in 2004 that enabled us to evaluate issues in multi-institutional IRB review, examine possible changes, and propose a new model. We conducted key informant interviews and held meetings with IRB personnel, administrators, and researchers, eventually resulting in networkwide agreement to (...) pilot an alternative model of IRB review for minimal-risk, data-only studies that only entailed use of computerized data from our network sites. This article describes the organizational and interpersonal processes that led to the successful establishment of this alternative model, to inform other research consortia considering new multisite review models. (shrink)

In this paper, a data-driven superheating control strategy is developed for organic Rankine cycle processes. Due to non-Gaussian stochastic disturbances imposed on heat sources, the quantized minimum error entropy is adopted to construct the performance index of superheating control systems. Furthermore, particle swarm optimization algorithm is applied to obtain optimal control law by minimizing the performance index. The implementation procedures of the presented superheating control system in an ORC-based waste heat recovery process are presented. The simulation results testify the (...) effectiveness of the presented control algorithm. (shrink)

Background Sharing anonymized/de-identified clinical trial data and publishing research outcomes in scientific journals, or presenting them at conferences, is key to data-driven scientific exchange. However, when data from scientific publications are linked to other publicly available personal information, the risk of reidentification of trial participants increases, raising privacy concerns. Therefore, we defined a set of criteria allowing us to determine and minimize the risk of data reidentification. We also implemented a review process at Takeda for clinical (...) publications prior to submission for publication in journals or presentation at medical conferences. Methods Abstracts, manuscripts, posters, and oral presentations containing study participant information were reviewed and the potential impact on study participant privacy was assessed. Our focus was on direct and indirect identifiers, such as sex, age or geographical indicators in rare disease studies or studies with small sample size treatment groups. Risk minimization was sought using a generalized presentation of identifier-relevant information and decision-making on data sharing for further research. Additional risk identification was performed based on study participant/personnel parameters present in materials destined for the public domain. The potential for participant/personnel identification was then calculated to facilitate presentation of meaningful but de-identified information. Results The potential for reidentification was calculated using a risk ratio of the exposed versus available individuals, with a value above the threshold of 0.09 deemed an unacceptable level of reidentification risk. We found that in 13% of Takeda clinical trial publications reviewed, either individuals could potentially be reidentified or inappropriate data sharing plans could pose a data privacy risk to study participants. In 1/110 abstracts, 58/275 manuscripts, 5/87 posters and 3/58 presentations, changes were necessary due to data privacy concerns/rules. Despite the implementation of risk-minimization measures prior to release, direct and indirect identifiers were found in 11% and 34% of the analysed documents, respectively. Conclusions Risk minimization using de-identification of clinical trial data presented in scientific publications and controlled data sharing conditions improved privacy protection for study participants. Our results also suggest that additional safeguards should be implemented to ensure that higher data privacy standards are met. (shrink)

In this article, we extend the Bayesian nonparametric regression method Gaussian Process Regression to the analysis of longitudinal panel data. We call this new approach Gaussian Process Panel Modeling (GPPM). GPPM provides great flexibility because of the large number of models it can represent. It allows classical statistical inference as well as machine learning inspired predictive modeling. GPPM offers frequentist and Bayesian inference without the need to resort to Markov chain Monte Carlo-based approximations, which makes the approach exact and (...) fast. GPPMs are defined using the kernel-language, which can express many traditional modeling approaches for longitudinal data, such as linear structural equation models, multilevel models, or state-space models but also various commonly used machine learning approaches. As a result, GPPM is uniquely able to represent hybrid models combining traditional parametric longitudinal models and nonparametric machine learning models. In the present paper, we introduce GPPM and illustrate its utility through theoretical arguments as well as simulated and empirical data. (shrink)