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India has produced several drafts of data policies. In this work, they are referred to [1] JBNSCR 2018, [2] DPDPR 2018, [3] NSAI 2018, [4] RAITF 2018, [5] PDPB 2019, [6] PRAI 2021, [7] JPCR 2021, [8] IDAUP 2022, [9] IDABNUP 2022. All of them consider Artificial Intelligence (AI) a social problem solver at the societal level, let alone an incentive for economic growth. However, these policy drafts warn of the social disruptions caused by algorithms and encourage the careful use of computational technologies in various social contexts. Hence, the emerging data society and its implications in India's social contexts demand immense social science attention, which needs to be improved in the policy drafts, primarily because they are creations of industry stakeholders, technocrats, bureaucrats, and experts from tech schools. In the larger social milieu of digital infrastructure emerging, the fundamental question is whether India's national philosophy envisioned in the Indian constitution is reflected in the policy papers. The paper enquires whether the national data policy upholds the core values dispersed through the philosophy of the Indian constitution, which, among other things, is not confined only to inclusion, diversity, rights, liberty, justice and equality. By focusing on constitutional values, the paper seeks to offer a broader and more critical understanding of India's approach to AI policy by bringing together analyses of a wide array of policy documents available in the public realm. [ABSTRACT FROM AUTHOR]

The Euler problem on the stability of a thin elastic rod under compressing forces is widely known. In a number of papers this problem is generalized to finding the shape of such a rod under the simultaneous action of a compressing force and a torsional moment. The shape is determined by solving a complex nonlinear boundary value problem by numerical methods. In this paper, an approach providing a full analytical solution of the problem for a broad range of external conditions is discussed. [ABSTRACT FROM AUTHOR]

Recent negotiations of 'data' in schools place focus on student assessment and NAPLAN. However, with the rise in artificial intelligence (AI) underpinning educational technology, there is a need to shift focus towards the value of teachers' digital data. By doing so, the broader debate surrounding the implications of these technologies and rights within the classroom as a workplace becomes more apparent to practitioners and educational researchers. Drawing on the Australian Human Rights Commission's Human Rights and Technology final report, this conceptual paper focusses on teachers' rights alongside emerging technologies that use or provide predictive analytics or artificial intelligence, also called 'personalisation'. The lens of Postdigital positionality guides the discussion. Three potential consequences are presented as provocations: (1) What might happen if emerging technology uses teachers' digital data that represent current societal inequality? (2) What might happen if insights provided by such technology are inaccurate, insufficient, or unrepresentative of our teachers? (3) What might happen if the design of the AI system itself is discriminatory? This conceptual paper argues for increased discourse about technologies that use or provide predictive analytics complemented by considering potential consequences associated with algorithmic bias. [ABSTRACT FROM AUTHOR]

Through an ethnographic study of Chinese IT professionals who integrate a form of data culture into the digital platforms they design, maintain, and operate daily within one of China's tech giants, this paper reveals numerous overlaps and interrelations between the data practices of Chinese IT professionals and the broader social implications that arise from them. The aim is to foster a more productive dialogue between the social studies of quantification and platform studies. This original research proposes the backstage as a potent methodology for inquiring into the role of Chinese IT professionals and domestic tech giants in advancing measuring systems and audit culture. This paper concludes by suggesting that such an approach can also be applied to wider studies of the paradox in quantification between its general claims and specific effects. [ABSTRACT FROM AUTHOR]

In the present paper, a finite-dimensional phenomenological model of unsteady interaction of a rigid plate with a flow is proposed. It is assumed that the plate performs translational motion across the flow. The internal dynamics of the flow is modeled by the attached second order dynamical system. It is shown that the model allows satisfactory agreement with experimental data. With the developed model an inverse problem of dynamics is examined for the situation where the plate performing uniform translational motion at some moment begins uniform deceleration and finally stops. It is shown that for sufficiently large values of the plate acceleration for some time range the flow does not resist the motion of the plate but “accelerates” it. It is shown also that the equations of motion in the context of the proposed model can be reduced to the integro-differential form, and comparison with the known model of S. M. Belotserkovsky is performed. The structural resemblance of the motion equations for a body in flow in both models is noted. The domain of applicability of the quasi-stationary model is examined. [ABSTRACT FROM AUTHOR]

In airborne gravimetry the data received from an airborne survey system is used to obtain the specific gravity along the aircraft path and to construct maps of the gravity anomaly. The quality of the maps depends largely on both the instrument noise and modeling errors. The paper discusses the errors in the estimated gravity caused by imperfect calibration of the gravimeter and how to calibrate the instrument using the survey flights data and the gravity maps. [ABSTRACT FROM AUTHOR]

Although data sharing is one of the primary measures proposed to improve the integrity and quality of published research, studies show it remains the exception not the rule. The current study examines the availability of data in papers reporting the results of analyses of empirical data from original research in high-impact addiction journals. Thirteen high-impact journals with data sharing policies were selected from those included in the substance abuse category of the 2018 Clarivate Analytics' Journal Citation Report. The first 10 full or short original research reports that included empirical data in the most recent complete issue of each journal were electronically searched and reviewed for reference to where their data can be obtained and for a formal data sharing statement. Only eight of the 130 papers contained a data sharing statement in their text or supplementary online materials, and just one contained a direct link to the data analyzed. Data sharing was rare in the 13 high-impact addiction journals reviewed. The nature of the data reported in addiction journals might partly explain this. Currently, data sharing is not a procedure likely to improve the quality and integrity of published addiction research. [ABSTRACT FROM AUTHOR]

Data not only plays an essential role in traditional shopfloor management, but it is also becoming even more important in Industry 4.0, particularly due to the increasing possibilities offered by new digital and data technologies and developments. In this context, the literature often refers to digital shopfloor management, the next generation shopfloor or other evolutionary synonyms. This raises the question of how to differentiate the content of data-oriented shopfloor management from digital shopfloor management. This paper discusses the state of the art — in terms of both data and digital perspectives — using a systematic literature review. Due to the complexity of the topic, three different levels of consideration — technology, organisation and people — are examined and discussed. Existing conceptual approaches are analysed in terms of conclusions and research gaps. It was found that the area of technology, including dedicated applications, is very well represented and researched in the literature. In comparison, there are larger research gaps in the other areas of organisation and people, which could be a possible reason for the lack of implementation of digital shopfloor management in practice. There is also a lack of holistic approaches that consider all three levels simultaneously and provide an overarching concept of maturity as a guideline, as well as taking into account the increasing trend towards value stream orientation. Apart from the research gaps, this paper could also define the term data-oriented shopfloor management and distinguish it from digital shopfloor management. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The proliferation of fake news in the digital age has spurred extensive research efforts toward developing effective detection techniques. This abstract delves into recent trends and challenges within the domain of fake news detection. The ubiquity of social media platforms and user-generated content has led to the rapid dissemination of misinformation, necessitating robust mechanisms for differentiating between authentic and fabricated news. This paper explores emerging approaches, such as advanced machine learning models, natural language processing techniques, and cross-modal analysis, which leverage textual, visual, and contextual cues to enhance detection accuracy. However, as fake news tactics become more sophisticated, challenges like adversarial attacks, data scarcity, and domain adaptation come to the forefront. This abstract highlights the ongoing efforts to address these challenges and emphasizes the importance of interdisciplinary collaboration to devise comprehensive solutions for combating the intricate landscape of fake news dissemination. [ABSTRACT FROM AUTHOR]

Concerns about research’s environmental impacts have been articulated in the research arena, but questions remain about what types of role responsibilities are appropriate to place on researchers, if any. The research question of this paper is: what are the views of UK health researchers who use data-intensive methods on their responsibilities to consider the environmental impacts of their research? Twenty-six interviews were conducted with UK health researchers using data-intensive methods. Participants expressed a desire to take responsibility for the environmental impacts of their research, however, they were unable to consolidate this because there were often obstacles that prevented them from taking such role responsibilities. They suggested strategies to address this, predominantly related to the need for regulation to monitor their own behaviour. This paper discusses the implications of adopting such a regulatory approach as a mechanism to promote researchers’ role responsibilities using a neo-liberal critique. [ABSTRACT FROM AUTHOR]

Grinding surface quality is a key indicator to determine the performance of parts and the reliability of products. During the grinding process, the pre-control technology makes appropriate adjustments in advance on its key aspects to improve the grinding surface quality. Therefore, this paper constructs a data-driven pre-control system for grinding surface quality including target layer, acquisition layer, analysis layer, and application layer. From the perspective of this system, the research progress of grinding surface quality by data-driven is reviewed. In the target layer, the connotation, formation mechanism, and main influencing indexes of grinding surface quality are described in detail. In the acquisition layer, the acquisition and processing methods of grinding data and their advantages and disadvantages are comprehensively analyzed. In the analysis layer, the methods of using result data, process data, and real-time signal data to characterize grinding surface quality are introduced. In the application layer, the research status of grinding surface quality prediction, optimization, and control are comprehensively and systematically summarized. Finally, the paper presented a summary and outlook on the improvement of grinding surface quality by data-driven. [ABSTRACT FROM AUTHOR]

The multidimensional nature of digital libraries evaluation domain poses several challenges to the research communities that intend to assess criteria, methods, products and tools, and also practice them. The amount of scientific production that is published in the domain hinders and disorientates the interested researchers. These researchers need guidance to exploit effectively the considerable amount of data and the diversity of methods, as well as to identify new research goals and develop their plans for future studies. This paper proposes a methodological pathway to investigate the core topics that structure the digital library evaluation domain and their impact. Further to the exploration of these topical entities, this study investigates also the researchers that contribute substantially to key topics, their communities and their relationships. The proposed methodology exploits topic modeling and network analysis in combination with citation and altmetrics analysis on a corpus consisting of the digital library evaluation papers presented in JCDL, ECDL/TDPL and ICADL conferences in the period 2001–2013. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has the idea of receiving data and sending them for each object within the communication network. One of the main issues in this type of networks is handling a growing volume of data with various data sources and data types to satisfy the performance requirements of applications. In this regard, data management in the IoT plays an important role in its efficient operations and has become a major research topic. However, the data management has a crucial role in the IoT, there is not any comprehensive and systematic work to analyze its approaches. Thus, main propose of this paper is to systematically review and study the existing data management approaches in the IoT. The data management approaches are classified into three main classes, including SQL database, NoSQL database, and graph database. In addition, the detailed comparison of the important mechanisms in each category brings a recommendation for further works. [ABSTRACT FROM AUTHOR]

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Textual formats to structure data, such as JSON, XML, and YAML, are widely used for structuring data in various domains, from configuration files to research data. However, manually editing data in these formats can be complex and time-consuming. Graphical user interfaces (GUIs) can significantly reduce manual efforts and assist the user in editing the files, but developing a file-format-specific GUI requires substantial development and maintenance efforts. To address this challenge, we introduce MetaConfigurator: an open-source web application that generates its GUI depending on a given schema. Our approach differs from other schema-to-UI approaches in three key ways: 1) It offers a unified view that combines the benefits of both GUIs and text editors, 2) it enables schema editing within the same tool, and 3) it supports advanced schema features, including conditions and constraints. In this paper, we discuss the design and implementation of MetaConfigurator, backed by insights from a small-scale qualitative user study. The results indicate the effectiveness of our approach in retrieving information from data and schemas and in editing them. [ABSTRACT FROM AUTHOR]

Information is a unique resource. Asymmetries that arise out of information access or processing capacities, therefore, enable a distinctive form of injustice. This paper builds a working conception of such injustice and explores it further. Let us call it informational injustice. Informational injustice is a consequence of informational asymmetries between at least two agents, which are deeply exacerbated due to modern information and communication technologies but do not necessarily originate with them. Informational injustice is the injustice of having information from an informational surplus being used to disadvantage the agent with less information. This paper argues that informational injustice exploits an agent as a knower, specifically exploiting the agent’s limitation in possessing or processing information—an agent is exploited because she is not informed or lacks in her ability to process information. In the case of lack of information, the agent simply does not know the information under consideration; a person is algorithmically manipulated or nudged to buy a product or vote for someone. In the case of a lack of capacity to process information, the agent simply cannot use the information, despite having access to it, to reach epistemically valuable states such as knowledge; a lawyer dupes you because he knows more about the inner workings of a courtroom and the law. Technically, you have access to the information your lawyer has, but you cannot make use of it due to constraints on time and cognitive effort. Informational injustice excludes the harmed agent from participating in knowledge practices. Thus, informational injustice is also a kind of epistemic exclusion. After fixing the concept of informational injustice, the paper distinguishes between two kinds of informational injustices: interactional informational injustice and structural informational injustice. The former concerns interactions between agents, while the latter concerns social structures that emerge out of interactions between agents. [ABSTRACT FROM AUTHOR]

The Modern Synthesis has received criticism for its purported gene-centrism. That criticism relies on a concept of the gene as a unit of instructional information. In this paper I discuss information concepts and endorse one, developed from Floridi, that sees information as a functional relationship between data and context. I use this concept to inspect developmental criticisms of the Modern Synthesis and argue that the instructional gene arose as an idealization practice when evolutionary biologists made comment on development. However, a closer inspection of key claims shows that at least some associated with the Modern Synthesis were in fact adopting the data led definition I favour and made clear arguments for the role of developmental processes beyond genetic input. There was no instructional gene. [ABSTRACT FROM AUTHOR]

Data is essential to governing those emerging matters of concern that confront the agrifood every day. But data is no neutral intermediary. It disrupts, exposes, and creates new social, economic, political, and environmental possibilities, whilst simultaneously hiding, excluding, and foreclosing others. Scholars have become attuned to both the constitutive role of data in creating everyday worlds, and the need to develop critical accounts of the materialities, spatialities and multiplicities of data relationships. Whereas this emerging work develops insight to the capacity for data topologies to reterritorialise the spatial performances of everyday life, it has largely reduced the associated temporal dimensions to matters of fact. The effect of these performances has been to naturalize the temporal quality of speed and elide the multiple temporalities required to enact contemporary data worlds. Applying the lenses of infrastructuring, performativity and ferality, this paper explores temporality and data in the everyday worlds produced through the New Zealand kiwifruit industry's focus on dry matter. The paper argues that temporalities are deeply embedded in the kiwifruit industry's data relations. We show that while temporal data relations are critical to the industry, we also highlight ways in which those relations introduce new, potentially destabilizing performances into kiwifruit relations. [ABSTRACT FROM AUTHOR]

The author of the article offers guidelines for submission of qualitative research papers to the journal "Sex Roles." The journal publishes scientific research that could be replicated by other researchers, so that information about the research's methodology and interpretation of data is required. The guidelines for submission involve the introduction to the paper, information about the methodology, including description of the sample population, and information about how interview studies were carried out as well as guidelines about how results are stated.

The article presents the mathematical model of ecologist Anthony Ives which shows the fragility of midges to both natural and human-induced change. It notes that the mathematical model is the result of Ives' collaboration with researchers Arni Einarsson and Arnthor Gardarsson at the University of Iceland in Reykjavik. It mentions that these researchers had been gathering data on the population density of the midges, Tanytarsus gracilentus, in Lake Myvatn. With this partnership, Ives cobbled the statistical tools for stock market activity and applied them to the field data gathered by the researchers.

Qualitative researchers sometimes talk about objectivity in relation to qualitative data sets. In this paper, I defend a reconstructed notion of objective qualitative data sets that may serve as a useful and reachable guiding ideal in qualitative data generation. In the first part of the paper, I develop the ideal. According to it, a qualitative data set is objective to the extent that it, in conjunction with true assumptions, possesses a combination of good-making features (epistemic values, epistemic virtues) in virtue of which the data set is suited to serve as evidence base for a satisfying answer to the research question under study. In the second part of the paper, I examine and reject two possible lines of objection to this ideal: One is that it picks out the wrong good-making features. The other is that the very focus on good-making features is misguided: the objectivity of a qualitative data set should instead be seen as a matter of how it was generated or evaluated. [ABSTRACT FROM AUTHOR]

Phenomenology presents itself not as an explanation or interpretation of phenomena but as a description of them. Describing experience means making its internal structure explicit, which, in phenomenology, is an eidetic structure. The method of phenomenological explication or clarification is, however, by no means univocal. This paper aims to isolate the two fundamental ways in which phenomenological description is achieved. The first refers to a phenomenology of manifestation, based on the concept of determination or datum, which is realized in the phenomenological-static approach and, in particular, on the concept of extensive quality. The second refers to a phenomenology of disposition, based on the concept of tension or force – which is realized in the genetic approach as well as in Merleau-Ponty's phenomenology of perception – and, in particular on the concept of intensive or forceful quality. The analysis of the difference between the two approaches allows us to introduce the crucial distinction between digital and analogue dimensions within phenomenology. [ABSTRACT FROM AUTHOR]

Empirical data can be an extremely powerful and influential tool in bioethical research. However, when researchers or policy makers look for answers to ethical questions by engaging with empirical research, there can be a tendency (conscious or unconscious) to shape, report, and use empirical research in a way that confirms their own preferred ethical conclusions. This skewing effect - what we call 'normative bias' - is often so subtle it falls short of clear misconduct and thus can be difficult to call out. However, we argue that this subtle influence of bias has the potential to significantly influence debate and policy around highly sensitive ethical issues and must be guarded against. In this paper we share the lessons we have learned through a journey of self-reflection around the effect that normative bias can have when reporting on and referring to empirical data relating to ethical issues. We use a variety of papers from our area of the ethics of routine prenatal screening to illustrate these subtle but often powerfully distorting effects of bias. Our aim in doing so is not to criticise the work of others, as we recognise our own normative bias, but to improve awareness of this issue, remind the need for reflexivity to guard against our own biases, and introduce a new criterion - the idea of a 'limitation prominence assessment' - that can work as a practical way to evaluate the seriousness of the limitations of an empirical study and thus, the risks of the study being misread or misinterpreted through superficial reading. [ABSTRACT FROM AUTHOR]

In accordance with the Sendai framework, we need to protect our global neighbours from the risk of future disasters. This short paper calls for the disaster risk reduction (DRR) community to make investments in standardising and collecting the muchneeded vulnerability-relevant exposure data to help quantify, and therefore reduce risk. [ABSTRACT FROM AUTHOR]

This paper argues that individuating practices are produced through iterative processes of community and agent-level interactions. This claim will be demonstrated by using three case studies from biology: The structuring of data categories for data collection tables and models; establishing spatial and temporal threshold markers or limits; and the comparative use of phenomenal characteristics as cues for object identification. By drawing from examples of data classification and comparative analysis in the biological sciences, I offer a view about ‘individuation’ as double-barreled according to the method of co-constitutive conceptual analysis. Specifically, the capacity—i.e., the ability to individuate—is co-constituted by community level choices and agent applications: Individuation’s evidential role is generated, revised, and refined by scientific communities and their members through an iterative process of community and agent-level interactions. [ABSTRACT FROM AUTHOR]

Although videos appear to be very high-dimensional in terms of duration × frame-rate × resolution, temporal smoothness constraints ensure that the intrinsic dimensionality for videos is much lower. In this paper, we use this idea for investigating Domain Adaptation (DA) in videos, an area that remains under-explored. An approach that has worked well for the image DA is based on the subspace modeling of the source and target domains, which works under the assumption that the two domains share a latent subspace where the domain shift can be reduced or eliminated. In this paper, first we extend three subspace based image DA techniques for human action recognition and then combine it with our proposed Eclectic Domain Mixing (EDM) approach to improve the effectiveness of the DA. Further, we use discrepancy measures such as Symmetrized KL Divergence and Target Density Around Source for empirical study of the proposed EDM approach. While, this work mainly focuses on Domain Adaptation in videos, for completeness of the study, we comprehensively evaluate our approach using both object and action datasets. In this paper, we have achieved consistent improvements over chosen baselines and obtained some state-of-the-art results for the datasets. [ABSTRACT FROM AUTHOR]

This article considers Ifenthaler and Schumacher's (in Educ Technol Res Dev 64(5):923–938, 2016) paper entitled "Student perceptions of privacy principles for learning analytics" from a policy perspective. The increasing shift to digital occurring across the education landscape has resulted in greater access to data that can be used to enhance the student learning experience. However, it is essential that appropriate policy be in place to ensure the ethical use of such data, and equally important that the student voice be included in the development of such policy. Ifenthaler and Schumacher's study investigates students' preferences for the types of learning analytics systems they would like to be able to access and willingness to share their data for use in these systems. They find that students would like access to learning analytics systems that provide a broad range of information that can promote metacognitive awareness and provide personalised feedback, but are not entirely comfortable with sharing the full range of data necessary to inform such systems. These findings are important in acknowledging the difficulties in building a shared understanding and vocabulary when speaking to students about the possibilities and ethical implications of learning analytics. Consequently, further consideration is needed to build students' understanding of the purposes and benefits of sharing data as well as awareness of the range of policies that inform a consultative process around systems implementation to ensure transparency and accountability so that learning analytics can play an effective, but safe, role in our increasingly digital education environments. [ABSTRACT FROM AUTHOR]

According to how AI has defined itself from its beginning, thinking in non-living matter, i.e., without life, is possible. The premise of symbolic AI is that operating on representations of reality machines can understand it. When this assumption did not work as expected, the mathematical model of the neuron became the engine of artificial "brains." Connectionism followed. Currently, in the context of Machine Learning success, attempts are made at integrating the symbolic and connectionist paths. There is hope that Artificial General Intelligence (AGI) performance can be achieved. As encouraging as neuro-symbolic AI seems to be, it remains unclear whether AGI is actually a moving target as long as AI itself remains ambiguously defined. This paper makes the argument that the intelligence of machines, expressed in their performance, reflects how adequate the means used for achieving it are. Therefore, energy use and the amount of data necessary qualify as a good metric for comparing natural and artificial performance. [ABSTRACT FROM AUTHOR]

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself—OME-Zarr—along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain—the file format that underlies so many personal, institutional, and global data management and analysis tasks. [ABSTRACT FROM AUTHOR]

Internet of Multimedia Things (IoMT) has gained popularity due to its immersive growth and real-time applications range from smart health to smart transportation systems. These systems always have various threats related to security breaches and privacy. With the passage of time, all types of cybercrimes and terrorism activities and its planning were initiated by using these networks and technologies. Terrorist attacks are unavoidable because of well-organized and well-planned attack planning. The all the data among these threat actors transmitted by using IoMT sensors and devices. Therefore, there is a need to analyze this type of data and predict the terrorist activities for in-time decision making. This paper presents a complete overview of existing models for such type of data and proposes a Terrorist Attacks Internet of Multimedia Things (TA-IoMT) model and a predictive model for public security in IoMT networks. The proposed model provides more effective data handling, cloud storage data management, and prediction to control and detect these kinds of activities. The results show an average of 89% accuracy, 0.73% sensitivity, and 0.92% specificity as compared to existing solutions. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses various topics within the issue including the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, microdata set transformation using data suppression and development of data and knowledge exchange.

The current "AI Summer" is marked by scientific breakthroughs and economic successes in the fields of research, development, and application of systems with artificial intelligence. But, aside from the great hopes and promises associated with artificial intelligence, there are a number of challenges, shortcomings and even limitations of the technology. For one, these challenges arise from methodological and epistemological misconceptions about the capabilities of artificial intelligence. Secondly, they result from restrictions of the social context in which the development of applications of machine learning is embedded. And third, they are a consequence of current technical limitations in the development and use of artificial intelligence. The paper intends to provide an overview of current challenges which the research and development of applications in the field of artificial intelligence and machine learning have to face, whereas all three mentioned areas are to be further explored in this paper. [ABSTRACT FROM AUTHOR]

This paper examines the Saudi Food and Drug Authority's (SFDA) Guidance on Artificial Intelligence (AI) and Machine Learning (ML) technologies based Medical Devices (the MDS-G010). The SFDA has pioneered binding requirements designed for manufacturers to obtain Medical Device Marketing Authorization. The regulation of AI in health is at an early stage worldwide. Therefore, it is critical to examine the scope and nature of the MDS-G010, its influences, and its future directions. It is argued that the guidance is a patchwork of existing international best practices concerning AI regulation, incorporates adapted forms of non-AI-based guidelines, and builds on existing legal requirements in the SFDA's existing regulatory architecture. There is particular congruence with the approaches of the US Food and Drug Administration (FDA) and the International Medical Device Regulators Forum (IMDRF), but the SFDA goes beyond those approaches to incorporate other best practices into its guidance. Additionally, the binding nature of the MDS-G010 is complex. There are binding 'components' within the guidance, but the incorporation of non-binding international best practices which are subordinate to national law results in a lack of clarity about how penalties for non-compliance will operate. [ABSTRACT FROM AUTHOR]

In recent decades, higher education institutions around the world have come to depend on complex digital infrastructures. In addition to registration, financial, and other operations platforms, digital classroom tools with built-in learning analytics capacities underpin many course delivery options. Taken together, these intersecting digital systems collect vast amounts of data from students, staff, and faculty. Educators' work environments—and knowledge about their work environments—have been shifted by this rise in pervasive datafication. In this paper, we overview the ways faculty in a variety of institutional status positions and geographic locales understand this shift and make sense of the datafied infrastructures of their institutions. We present findings from a comparative case study (CCS) of university educators in six countries, examining participants' knowledge, practices, experiences, and perspectives in relation to datafication, while tracing patterns across contexts. We draw on individual, systemic, and historical axes of comparison to demonstrate that in spite of structural barriers to educator data literacy, professionals teaching in higher education do have strong and informed ethical and pedagogical perspectives on datafication that warrant greater attention. Our study suggests a distinction between the understandings educators have of data processes, or technical specifics of datafication on campuses, and their understanding of big picture data paradigms and ethical implications. Educators were found to be far more knowledgeable and comfortable in paradigm discussions than they were in process ones, partly due to structural barriers that limit their involvement at the process level. [ABSTRACT FROM AUTHOR]

In recent years, machine learning has become a hot research method in various fields and has been applied to every aspect of our life, providing an intelligent solution to problems that could not be solved or difficult to be solved before. Machine learning is driven by data. It learns from a part of the input data and builds a model. The model is used to predict and analyze another part of the data to get the results people want. With the continuous advancement of ocean observation technology, the amount of ocean data and data dimensions are rising sharply. The use of traditional data analysis methods to analyze massive amounts of data has revealed many shortcomings. The development of machine learning has solved these shortcomings. Nowadays, the use of machine learning technology to analyze and apply ocean data becomes the focus of scientific research. This method has important practical and long-term significance for protecting the ocean environment, predicting ocean elements, exploring the unknown, and responding to extreme weather. This paper focuses on the analysis of the state of the art and specific practices of machine learning in ocean data, review the application examples of machine learning in various fields such as ocean sound source identification and positioning, ocean element prediction, ocean biodiversity monitoring, and deep-sea resource monitoring. We also point out some constraints that still exist in the research and put forward the future development direction and application prospects. [ABSTRACT FROM AUTHOR]

In the era of digital agriculture, Precision agriculture (PA) data sources are diverse in terms of the range of technology options and the type of data they generate. Government institutions, scientists, and the private sectors generate much of the PA data at the innovation, validation, and dissemination phases. At scale-up phases, farmers also generate tremendous amounts of data that might have privacy and ownership concerns. There remains the possibility of a better way to integrate PA data continent-wide in Africa. Data privacy and ownership issues must be addressed while still maintaining the integration of PA data at scale. The objective of this paper is to review the major challenges of PA data harmonization in Africa and discuss the existing opportunities in relation to technological advancements in PA data applications to address data sharing without compromising data privacy, ownership, and stewardship. Finally, a new PA data sharing and reward model—'PrecisioNexion' is proposed to rationalize data network systems by establishing a robust and self-sustaining business model. The model uses AI and blockchain technology to track and stamp PA data using unique dataset_IDs or PrecisionPrint (like a fingerprint), determining credit amounts using 'pVouchers' (like eVouchers) and distributing credits between PA data owners or 'PrecisionProprietor', data clients or 'PrecisionClient' and funders or 'PrecisionPatron'. The proposed system provides a foundation for win–win–win PA data sharing and self-sustaining business models for data owners, technology solutions providers and funders, while ensuring a strong partnership between farmers' cooperatives, private sector, scientists, governments, and financial institutions, and countries. [ABSTRACT FROM AUTHOR]

Citizen science (CS), as an enabler of open science (OS) practices, is a low-cost and accessible method for data collection in biodiversity monitoring, which can empower and educate the public both on scientific research priorities and on environmental change. Where OS increases research transparency and scientific democratisation; if properly implemented, CS should do the same. Here, we present the findings of a systematic review exploring "openness" of CS in biodiversity monitoring. CS projects were scored between − 1 (closed) and 1 (open) on their adherence to defined OS principles: accessible data, code, software, publication, data management plans, and preregistrations. Openness scores per principle were compared to see where OS is more frequently utilised across the research process. The relationship between interest in CS and openness within the practice was also tested. Overall, CS projects had an average open score of 0.14. There was a significant difference in open scores between OS principles (p = < 0.0001), where "open data" was the most adhered to practice compared to the lowest scores found in relation to preregistrations. The apparent level of interest in CS was not shown to correspond to a significant increase in openness within CS (p = 0.8464). These results reveal CS is not generally "open" despite being an OS approach, with implications for how the public can interact with the research that they play an active role in contributing to. The development of systematic recommendations on where and how OS can be implemented across the research process in citizen science projects is encouraged. [ABSTRACT FROM AUTHOR]

In many applications, top-k skyline query is an important operation to return k skyline tuples with the highest domination scores in a potentially huge data space. It is analyzed that the existing algorithms cannot process top-k skyline query on massive data efficiently. In this paper, we propose a novel table-scan-based algorithm RSTS to compute top-k skyline results on massive data efficiently. RSTS first builds the presorted table, whose tuples are arranged in the order of round-robin retrieval on sorted column lists. RSTS consists of two phases. In phase 1, the candidate tuples are acquired by the sequential scan on the presorted table. In phase 2, RSTS calculates the domination scores of the candidates and returns query results by another sequential scan. It is proved that RSTS has the characteristic of early termination, along with the theoretical analysis of scan depths. The pruning rule for candidate tuples is devised in this paper. The theoretical pruning effect shows that majority of the skyline results can be discarded directly. The extensive experimental results, conducted on synthetic and real-life data sets, show that RSTS outperforms the existing algorithms significantly. [ABSTRACT FROM AUTHOR]

In many applications, dynamic skyline query is an important operation to find the interesting tuples in a potentially huge data space. Given the query point, dynamic skyline query returns tuples which are not dynamically dominated by other tuples. It is found that the existing algorithms cannot process dynamic skyline query on massive data efficiently. This paper proposes a novel dynamic-sorted-list-based DDS algorithm to efficiently compute dynamic skyline results on massive data. Given the query point, the dynamic sorted list of each attribute is not materialized but generated dynamically by the sorted list of the attribute. DDS retrieves the tuples in the involved dynamic sorted lists in the round-robin fashion until the early termination condition is satisfied, and computes the dynamic skyline results by retrieving the candidates. The pruning operation is devised to reduce the number of the retrieved candidates. The extensive experimental results, conducted on synthetic and real-life data sets, show that DDS outperforms the existing algorithms significantly. [ABSTRACT FROM AUTHOR]

This paper underscores the need for detailed data on health and disaster risks for sub-Saharan African cities, particularly for their informal settlements. Systems that should contribute to the information base on health and health risks in each locality are rarely functional. In most cities, there is a lack of data on health risks, health outcomes, and health determinants; where data are available, they are usually too aggregated to be useful to urban governments. Such data shortfalls likely hide the scale of premature death, serious illness, and injury in informal settlements; limited data can also curtail the identification of particularly vulnerable urban residents. After outlining data shortfalls, this paper considers two sources of data that can help fill data gaps on health and health determinants. The first is from city case studies undertaken within a research programme called Urban Africa: Risk Knowledge (Urban-ARK). Urban-ARK's findings reveal the large spectrum of health risks in informal settlements, ranging from 'everyday' risks (e.g. infectious and parasitic diseases) to small- and larger-scale disasters. The second is from data collected by slum/shack dweller federations, which offer qualitative and quantitative findings on health, disasters, and other health determinants in informal settlements. Our conclusion reflects upon the need for additional data on multiple risks to advance urban health and well-being and support the 2030 Agenda for Sustainable Development. It also highlights the need to strengthen accountable urban governance in sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

The paper describes Dolores, a model designed to predict football match outcomes in one country by observing football matches in multiple other countries. The model is a mixture of two methods: (a) dynamic ratings and (b) Hybrid Bayesian Networks. It was developed as part of the international special issue competition Machine Learning for Soccer. Unlike past academic literature which tends to focus on a single league or tournament, Dolores is trained with a single dataset that incorporates match outcomes, with missing data (as part of the challenge), from 52 football leagues from all over the world. The challenge involved using a single model to predict 206 future match outcomes from 26 different leagues, played from March 31 to April 9 in 2017. Dolores ranked 2nd in the competition with a predictive error 0.94% higher than the top and 116.78% lower than the bottom participants. The paper extends the assessment of the model in terms of profitability against published market odds. Given that the training dataset incorporates a number of challenges as part of the competition, the results suggest that the model generalised well over multiple leagues, divisions, and seasons. Furthermore, while detailed historical performance for each team helps to maximise predictive accuracy, Dolores provides empirical proof that a model can make a good prediction for a match outcome between teams x and y even when the prediction is derived from historical match data that neither x nor y participated in. While this agrees with past studies in football and other sports, this paper extends the empirical evidence to historical training data that does not just include match results from a single competition but contains results spanning different leagues and divisions from 35 different countries. This implies that we can still predict, for example, the outcome of English Premier League matches, based on training data from Japan, New Zealand, Mexico, South Africa, Russia, and other countries in addition to data from the English Premier league. [ABSTRACT FROM AUTHOR]

Abstract: Prevention of data loss from digital media includes processes such as backup. It can be done manually by copying data to external media or automatically on schedule using special software. There are also remote backup systems, when data are saved over the network to some remote repository. Such systems are multi-user and process large amounts of data. A shared storage can have files containing the same fragments. The elimination of repeated data is based on the mechanism of deduplication. It is a method of information compression, when the search for copies is carried out in the entire dataset rather than within a single file. The main advantage of using this technology is significant saving of disk space. However, the mechanism of eliminating repetitive data can significantly reduce the rate of saving and restoring information. This paper is devoted to the problem of implementing such a mechanism in the backup system with information storage in a relational database. In this work we consider an example of implementation of such a system working in two modes: with and without data deduplication. This paper illustrates a class diagram for the development of the client part of the application as well as the description of tables and their relationships in a database that belongs to the backend. The author proposes an algorithm for saving data with deduplication, and also provides results of comparative tests on the speed of the algorithms for saving and recovering information when working with relational database management systems from various manufacturers. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic illustrates that we are never far away from situations that have a scale and impact, which are difficult to predict. Positioned at the intersection of crisis management and resilience, this insider case-study provides the opportunity for a more complete understanding of the organisation-adversity relationship (Williams et al., 2017), by focusing on the third Covid19 wave in Ireland (Dec 2020) and resulting response by an Intensive Care Unit crisis team. The study examines the evolution of seven data supply chains that were developed to support the ICU crisis team through the surge of cases which put the highest level of strain on the Irish health system since the pandemic began. The study focuses on 289 data reviews, which triggered 63 changes each requiring a new iteration of a data supply chain. Incorporating Organisational Mindfulness as the theoretical framework, the study provides an insight into the realities of data management during a crisis but also provides a rich awareness of the complexities of data management that often go unrecognised. In doing so, the study contributes the concept of 'mindful data', which aids managers to understand the key characteristics of resilient data supply chains. The study also provides a rare first-hand insight into how mindful data was constructed, presented, and evolved into an essential element within the critical care environment. [ABSTRACT FROM AUTHOR]

Numerous recommendation approaches are in use today. However, comparing their effectiveness is a challenging task because evaluation results are rarely reproducible. In this article, we examine the challenge of reproducibility in recommender-system research. We conduct experiments using Plista's news recommender system, and Docear's research-paper recommender system. The experiments show that there are large discrepancies in the effectiveness of identical recommendation approaches in only slightly different scenarios, as well as large discrepancies for slightly different approaches in identical scenarios. For example, in one news-recommendation scenario, the performance of a content-based filtering approach was twice as high as the second-best approach, while in another scenario the same content-based filtering approach was the worst performing approach. We found several determinants that may contribute to the large discrepancies observed in recommendation effectiveness. Determinants we examined include user characteristics (gender and age), datasets, weighting schemes, the time at which recommendations were shown, and user-model size. Some of the determinants have interdependencies. For instance, the optimal size of an algorithms' user model depended on users' age. Since minor variations in approaches and scenarios can lead to significant changes in a recommendation approach's performance, ensuring reproducibility of experimental results is difficult. We discuss these findings and conclude that to ensure reproducibility, the recommender-system community needs to (1) survey other research fields and learn from them, (2) find a common understanding of reproducibility, (3) identify and understand the determinants that affect reproducibility, (4) conduct more comprehensive experiments, (5) modernize publication practices, (6) foster the development and use of recommendation frameworks, and (7) establish best-practice guidelines for recommender-systems research. [ABSTRACT FROM AUTHOR]

Data has become one of the most valuable assets for governments and firms. Yet, we still have a limited understanding of how data reshapes international economic relations. This paper explores various aspects of data politics through the lens of China's digital rise and the country's global engagement. I start with the theoretical premise that data differs from traditional strategic assets (e.g., land, oil, and labor), in that it is nonrival and partially excludable. These characteristics have generated externality, commitment, and valuation problems, triggering three fundamental changes in China's external economic relations. First, data's externality problem makes it necessary for states to regulate data or even to pursue data sovereignty. However, clashes over data sovereignty can ignite conflicts between China and other countries. Second, the commitment problem in data use raises global concerns about foreign government surveillance. As data is easier to transfer across borders than physical commodities, Chinese tech companies' investments abroad are vulnerable to national security investigations by foreign regulators. Chinese tech companies, therefore, confront a "deep versus broad" dilemma: deep ties with the Chinese government help promote their domestic business but jeopardize their international expansion. Lastly, data's valuation problem makes traditional measures (e.g., GDP) ill-suited to measure the relative strengths of the world's economies, which may distort perceptions of China and other states. [ABSTRACT FROM AUTHOR]

This paper uses a multi-objective optimisation approach to support investigation of the trade-offs in various notions of fairness between multiple customers. Results are presented to validate the approach using two real-world data sets and also using data sets created specifically to stress test the approach. Simple graphical techniques are used to visualize the solution space. The paper also reports on experiments to determine the most suitable algorithm for this problem, comparing the results of the NSGA-II algorithms, a widely used multi objective evolutionary algorithm, and the Two-Archive evolutionary algorithm, a recently proposed alternative. [ABSTRACT FROM AUTHOR]

Platforms create value by enabling interactions between consumers and external producers through infrastructures and rules. We define platform data strategy to encompass all data-related rules undertaken by platforms to foster competitive advantage over the long term. Platform firms face growing pressure to increase accountability for how they use data; yet, an explicit treatment of platforms' data strategies and a systematic discussion of forces influencing such data-related choices are absent in the academic literature. We articulate how a platform's data strategy varies based on platform type and business circumstances. Given the interdependencies within a platform's ecosystem, its data strategy must balance incentives of all stakeholders. Besides discussing these topics, the paper identifies promising research opportunities in platform data strategy to better inform future academic research, strategic decision-making, and regulatory analysis. [ABSTRACT FROM AUTHOR]

High efficiency video coding (HEVC) standard is highly contributive in data hiding. Combining the HEVC standard with data hiding methods is a complex task, and because the highly efficient coding process is a powerful attack, eliminating some of the original video data would not have a significant effect on obtaining the best compression rates and transmission efficiency. In this paper, an efficient secret data hiding method is proposed through intra prediction modes integrated with HEVC, where the intra prediction modes of HEVC encoded videos are applied as a secret data carrier obtained from the N smallest prediction units. Every ⌊log2[N + 1]⌋ + 1 secret bit is embedded in a set of N intra prediction modes, named carrier vectors, where 1 is added to or subtracted from one of these modes, resulting in the accomplished data hiding. Due to the fact that, at the most, only one intra prediction mode is modified and the N smallest prediction units are chosen, this method is contributive in appropriate data hiding performance and steganalysis. The experimental results indicate that the embedding capacity increases up to 50%, while obtaining the best capacity, rate and distortion trade-offs among the compared methods. [ABSTRACT FROM AUTHOR]

Measuring head impacts in sports can further our understanding of brain injury biomechanics and, hopefully, advance concussion diagnostics and prevention. Although there are many head impact sensors available, skepticism on their utility exists over concerns related to measurement error. Previous studies report mixed reliability in head impact sensor measurements, but there is no uniform approach to assessing accuracy, making comparisons between sensors and studies difficult. The objective of this paper is to introduce a two-phased approach to evaluating head impact sensor accuracy. The first phase consists of in-lab impact testing on a dummy headform at varying impact severities under loading conditions representative of each sensor's intended use. We quantify in-lab accuracy by calculating the concordance correlation coefficient (CCC) between a sensor's kinematic measurements and headform reference measurements. For sensors that performed reasonably well in the lab (CCC ≥ 0.80), we completed a second phase of evaluation on-field. Through video validation of impacts measured by sensors on athletes, we classified each sensor measurement as either true-positive and false-positive impact events and computed positive predictive value (PPV) to summarize real-world accuracy. Eight sensors were tested in phase one, but only four sensors were assessed in phase two. Sensor accuracy varied greatly. CCC from phase one ranged from 0.13 to 0.97, with an average value of 0.72. Overall, the four devices that were implemented on-field had PPV that ranged from 16.3 to 91.2%, with an average value of 60.8%. Performance in-lab was not always indicative of the device's performance on-field. The methods proposed in this paper aim to establish a comprehensive approach to the evaluation of sensors so that users can better interpret data collected from athletes. [ABSTRACT FROM AUTHOR]