Luce and Narens (Journal of Mathematical Psychology, 29:1–72, 1985) showed that rank-dependent utility (RDU) is the most general interval scale utility model for binary lotteries. It can be easily established that this result cannot be generalized to lotteries with more than two outcomes. This article suggests several additional conditions to ensure RDU as the only utility model with the desired property of interval scalability in the general case. The related axiomatizations of some special cases of RDU of independent interest (...) (the quantile utility, expected utility, and Yaari’s dual expected utility) are also given. (shrink)

Legal decision-support systems have the potential to improve access to justice, administrative efficiency, and judicial consistency, but broad adoption of such systems is contingent on development of technologies with low knowledge-engineering, validation, and maintenance costs. This paper describes two approaches to an important form of legal decision support—explainable outcome prediction—that obviate both annotation of an entire decision corpus and manual processing of new cases. The first approach, which uses an attention network for prediction and attention weights to highlight salient case (...) text, was shown to be capable of predicting decisions, but attention-weight-based text highlighting did not demonstrably improve human decision speed or accuracy in an evaluation with 61 human subjects. The second approach, termed semi-supervised case annotation for legal explanations, exploits structural and semantic regularities in case corpora to identify textual patterns that have both predictable relationships to case decisions and explanatory value. (shrink)

Cybernetics, or self-governance of animal and machine, requires the ability to sense the world and to act on it in an appropriate manner. Likewise, self-governance of a human society requires groups of people to collectively sense and act on their environment. I argue that the evolution of political systems is characterized by a series of innovations that attempt to solve two ‘scalability’ problems: scaling up a group’s ability to make sense of an increasingly complex world, and to cooperate in (...) increasingly larger groups. I then explore some recent efforts toward using the Internet and social media to provide alternative means for addressing these scalability challenges, under the banners of crowdsourcing and computer-supported argumentation. I present some lessons from those efforts about the limits of technology, and the research directions more likely to bear fruit. (shrink)

Data Transfer Service (DTS) is an open-source project that is developing a document-centric message model for describing a bulk data transfer activity, with an accompanying set of loosely coupled and platform-independent components for brokering the transfer of data between a wide range of (potentially incompatible) storage resources as scheduled, fault-tolerant batch jobs. The architecture scales from small embedded deployments on a single computer to large distributed deployments through an expandable ‘worker-node pool’ controlled through message-orientated middleware. Data access and transfer efficiency (...) are maximized through the strategic placement of worker nodes at or between particular data sources/sinks. The design is inherently asynchronous, and, when third-party transfer is not available, it side-steps the bandwidth, concurrency and scalability limitations associated with buffering bytes directly through intermediary client applications. It aims to address geographical–topological deployment concerns by allowing service hosting to be either centralized (as part of a shared service) or confined to a single institution or domain. Established design patterns and open-source components are coupled with a proposal for a document-centric and open-standards-based messaging protocol. As part of the development of the message protocol, a bulk data copy activity document is proposed for the first time. (shrink)

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

J. B. Predd S. R. Kulkarni H. V. Poor D. N. Osherson Department of Electrical Engineering Department of Psychology Princeton University Princeton University Princeton, NJ 08544 Princeton, NJ 08544 {jpredd,kulkarni,poor}@princeton.edu [email protected]..

The potential for scalable quantum computing depends on the viability of fault tolerance and quantum error correction, by which the entropy of environmental noise is removed during a quantum computation to maintain the physical reversibility of the computer’s logical qubits. However, the theory underlying quantum error correction applies a linguistic double standard to the words “noise” and “measurement” by treating environmental interactions during a quantum computation as inherently reversible, and environmental interactions at the end of a quantum computation as irreversible (...) measurements. Specifically, quantum error correction theory models noise as interactions that are uncorrelated or that result in correlations that decay in space and/or time, thus embedding no permanent information to the environment. I challenge this assumption both on logical grounds and by discussing a hypothetical quantum computer based on “position qubits.” The technological difficulties of producing a useful scalable position-qubit quantum computer parallel the overwhelming difficulties in performing a double-slit interference experiment on an object comprising a million to a billion fermions. (shrink)

With the increasing volume of web services in the cloud environment, Collaborative Filtering- based service recommendation has become one of the most effective techniques to alleviate the heavy burden on the service selection decisions of a target user. However, the service recommendation bases, that is, historical service usage data, are often distributed in different cloud platforms. Two challenges are present in such a cross-cloud service recommendation scenario. First, a cloud platform is often not willing to share its data to other (...) cloud platforms due to privacy concerns, which decreases the feasibility of cross-cloud service recommendation severely. Second, the historical service usage data recorded in each cloud platform may update over time, which reduces the recommendation scalability significantly. In view of these two challenges, a novel privacy-preserving and scalable service recommendation approach based on SimHash, named SerRecSimHash, is proposed in this paper. Finally, through a set of experiments deployed on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation. (shrink)

Effective climate change resilience in local communities must center each community’s unique challenges and essential role in developing climate resilience strategies. This article will discuss recent developments by the federal government that align with a community-centered approach, and how Community Health Workers can influence the outcomes.

Increased use of scales in data-driven consumer digital platforms and the management of organisations has led to greater interest in understanding social and psychological measurement expertise and techniques as historically constituted ‘technologies of power’ in the making of what Stark has labelled the ‘scalable subject’. Taking a genealogical approach, and drawing on published and archival data, this article focuses on self-rated health, a scale widely used in population censuses, national health surveys, patient-reported outcome measurement tools, and a variety of digital (...) apps. The article suggests that the first methodological articulation of self-rated health by the investigators of the Cornell Study of Occupational Retirement (1951–58) provides a window into the key epistemic, institutional, and cultural uncertainties about psychological and social measurement, processes of adjustment to ‘old age’, and the capacity of individuals to value their own health. I propose that these uncertainties have become incorporated into extant and operational measurements of health. (shrink)

The primary resource for quantum computation is Hilbert-space dimension. Whereas Hilbert space itself is an abstract construction, the number of dimensions available to a system is a physical quantity that requires physical resources. Avoiding a demand for an exponential amount of these resources places a fundamental constraint on the systems that are suitable for scalable quantum computation. To be scalable, the effective number of degrees of freedom in the computer must grow nearly linearly with the number of qubits in an (...) equivalent qubit-based quantum computer. (shrink)

MKM has been defined as the quest for technologies to manage mathematical knowledge. MKM “in the small” is well-studied, so the real problem is to scale up to large, highly interconnected corpora: “MKM in the large”. We contend that advances in two areas are needed to reach this goal. We need representation languages that support incremental processing of all primitive MKM operations, and we need software architectures and implementations that implement these operations scalably on large knowledge bases. We present instances (...) of both in this paper: the MMT framework for modular theory-graphs that integrates meta-logical foundations, which forms the base of the next OMDOC version; and TNTBase, a versioned storage system for XML- based document formats. TNTBase becomes an MMT database by instantiating it with special MKM operations for MMT. (shrink)

La scalabilità è una proprietà desiderabile di sistemi informatici associata a metriche di performance. Più precisamente, un sistema è definito scalabile quando riesce a gestire, senza calo di prestazioni, un numero crescente di elementi, processi, quantità di lavoro e/o quando può essere espanso a piacimento. Progettare un sistema scalabile garantisce un’ottimizzazione dei costi e delle prestazioni, e della produttività di un’azienda. Questi scopi sono stati perseguiti, dagli anni Ottanta, attraverso numerosi studi sulla scalabilità, che sono stati sviluppati in un ambito (...) esclusivamente tecnologico. Tuttavia, negli ultimi anni è sorto il bisogno di estendere la tradizionale prospettiva tecnologica della scalabilità perché troppo limitata considerata al di fuori delle organizzazioni e applicata alle situazioni di uso quotidiano delle nuove tecnologie. Per esempio, le rubriche dei telefonini sono tecnicamente scalabili: grazie alla funzione di ricerca, essi offrono un accesso immediato alle informazioni indipendentemente dal numero dei contatti memorizzati. Inoltre, il supporto di memorie digitali esterne molto capienti permette di inserire un numero pressoché illimitato di contatti. Le versioni più avanzate delle rubriche degli smartphones sono integrate anche con i social network come Facebook. Dal punto di vista cognitivo, negli utenti si crea un’illusione di essere in grado di gestire efficacemente reti sociali sempre più estese e ricche di contenuti. In realtà, vari studi hanno dimostrato che esistono dei limiti alla gestione di relazioni sociali rilevanti, quali il famoso numero di Dunbar. Pertanto, la prospettiva tecnologica sulla scalabilità è limitata nel senso che essa riesce a descrivere solo la performance di un sistema informatico, ma non ne spiega l’impatto su performance cognitiva e sociale. Un approccio alternativo potrebbe essere basato sul presupposto che la scalabilità è una proprietà importante non solo per computer e reti informatiche, ma anche per esseri umani e reti sociali. La comprensione della correlazione fra scalabilità tecnologica e umana, renderebbe possibile migliorare il design delle tecnologie per uso quotidiano. Per esempio, le rubriche dei telefonini, come anche i siti di social network, potrebbero ottimizzare l’accesso ai contenuti in base al significato delle relazioni sociali e della situazione nella quale sono considerate. Si può ipotizzare che la comprensione della scalabilità, sia umana sia tecnologica, possa permettere di migliorare la performance dell’azione individuale e/o collettiva. Socionics, un programma interdisciplinare di ricerca che integra sociologica e informatica, offre la possibilità di estendere la definizione di scalabilità anche ai sistemi sociali. Al contrario della prospettiva tradizionale della scalabilità, che è incentrata sull’aspetto quantitativo, Socionics pone al centro dell’analisi il problema da risolvere, che è analizzato sia dal punto di vista quantitativo sia da quello qualitativo . Grazie a Socionics, nell’articolo sono introdotti i concetti di scalabilità cognitiva e scalabilità sociale, che descrivono la capacità di gestire elegantemente a livello individuale o collettivo quantità crescenti di lavoro, e/o adattarsi alla loro complessità crescente. Il processo di valutazione della scalabilità cognitiva e sociale di un problema si compone di tre fasi: la prima riguarda la definizione del contesto dell’attività a cui il problema è associato. Questo richiede di specificare gli attori coinvolti, le risorse disponibili e i compiti che essi stanno svolgendo, individualmente o in gruppo. La seconda fase si propone di identificare i bisogni legati al contesto, che servono a capire in che misura il sistema è riuscito a soddisfare il bisogno, ovvero a misurare la performance. Nella terza e ultima fase si considera la funzione delle ICT nella risoluzione del problema, al fine di capire se le ICT abbiano alleviato, accentuato o rimosso eventuali limitazioni cognitive e/o sociali. Lo stesso problema è scalabile da un punto di vista cognitivo e/o sociale se esso è risolto in modo efficace al variare dei suoi elementi quantitativi e qualitativi. Per una prima valutazione dell’approccio, è discusso il problema della coordinazione sociale tramite telefonini, che non è ridotto semplicemente al numero di persone che i telefonini riescono a mobilizzare, ma anche alla qualità e impatto della loro performance. In conclusione, i concetti di scalabilità cognitiva e sociale offrono l’opportunità di informare la progettazione e favorire l’adozione di ICT grazie alla comprensione di come gli aspetti di scalabilità tecnologica influiscano sulla performance individuale e collettiva, e viceversa.Scalability has been for long time presented as a desirable property of a networked computer system associated to measure of its performance. As information and communication technologies become ubiquitous, a need of describing scalability also from a human perspective has emerged. Indeed, technical scalability only describes the performance of a computer system, but it does not explain anything about its impact on cognitive and social performance. Without acknowledging that technical scalability does not imply human scalability, there is the risk of not realizing the full potential of ICT in the everyday life. By adopting the Socionics approach, we extend the technical view of scalability with two additional concepts, namely cognitive and social scalability. These concepts describe the capacity to process individually or collectively growing volumes of work gracefully, and/or to adjust to their increasing complexity. The notion of social scalability is then evaluated in the activity-context of social coordination through mobile devices. We conclude that by improving our understanding of scalability issues, we may positively inform the design and use of ICT not only from the viewpoint of technical performance, but also for that of individual and collective performance. (shrink)

Abstract‘[T]he “mainstream” of global governance has changed course’ and in so doing, might well have ‘outrun the standard tools of critical, progressive, and reform-minded international lawyers’, Fleur Johns wrote in 2019. It is especially the critical tools of ‘appeals to history, context, language [and] the grassroots’ in response to universalist planning that Johns sees absorbed in the turn to prototyping as a new ‘style’ of governance. In this article, we take on this observation and explore how the ‘lean start-up mentality’ (...) that Johns described has taken hold of tech-based climate governance. We base our reflections on the ‘Tech for Our Planet’ challenge that took place over 2021 and was showcased at the UNFCCC COP26 in Glasgow. While a turn ‘from planning to prototypes’ is observable, we question how exactly this ‘change of course’ affects the high modernist style of global governance and its critique by international lawyers. The ‘digital solutions for climate challenges’ that were showcased in Glasgow are indeed based on localized experiments with data science, thereby seemingly overcoming high modernist impulses towards universalist ideals. Yet, these experimental prototypes are developed with the ambition of being replicable and scaled up, to become a stack of tools deployable in any given scenario. This form of scaling up neither breaks with modernist aspirations based on technologically-mediated replicability—of moving the same logic inscribed in code to different sites and contexts—nor with a modernist understanding of knowledge as universal in its application. In our analysis, the determining feature is then not so much a matter of planning or prototyping in ideal type forms, but of replicability of knowledge production and scalability of technological know-how that underpin both planning and prototyping. Prototyping in the start-up space does not depart from, but rather reinscribes, a modernist representation of the human subject that forces its epistemological lens onto a world of nonhuman objects amenable to governance. Critical international lawyers’ toolkits must therefore be reconfigured with a focus on a governance style of disembodied knowledge production that runs through both planning and prototyping. (shrink)

Most of the IoT-based smart systems require low latency and crisp response time for their applications. Achieving the demand of this high Quality of Service becomes quite challenging when computationally intensive tasks are offloaded to the cloud for execution. Edge computing therein plays an important role by introducing low network latency, quick response, and high bandwidth. However, offloading computations at a large scale overwhelms the edge server with many requests and the scalability issue originates. To address the above issues, (...) an efficient resource management technique is required to maintain the workload over the edge and ensure the reduction of response time for IoT applications. Therefore, in this paper, we introduce a metaheuristic and nature-inspired Artificial Bee Colony optimization technique that effectively manages the workload over the edge server under the strict constraints of low network latency and quick response time. The numerical results show that the proposed ABC algorithm has outperformed Particle Swarm Optimization, Ant Colony Optimization, and Round-Robin Scheduling algorithms by producing low response time and effectively managing the workload over the edge server. Furthermore, the proposed technique scales the edge server to meet the demand of high QoS for IoT applications. (shrink)

The gene regulation function (GRF) provides an operational description of a promoter behavior as a function of the concentration of one of its transcriptional regulators. Behind this apparently trivial definition lies a central concept in biological control: the GRF provides the input/output relationship of each edge in a transcriptional network, independently from the molecular interactions involved. Here we discuss how existing methods allow direct measurement of the GRF, and how several trade‐offs between scalability and accuracy have hindered its application (...) to relatively large networks. We discuss the theoretical and technical requirements for obtaining the GRF. Based on these requirements, we introduce a simplified and easily scalable method that is able to capture the significant parameters of the GRF. The GRF is able to predict the behavior of a simple genetic circuit, illustrating how addressing the quantitative nature of gene regulation substantially increases our comprehension on the mechanisms of gene control. (shrink)

There are an increasing number of cell therapy approaches being studied and employed world‐wide. An emerging area in this field is the use of human pluripotent stem cell (hPSC) products for the treatment of injuries/diseases that cannot be effectively managed through current approaches. However, as with any cell therapy, vast numbers of functional and safe cells are required. Bioreactors provide an attractive avenue to generate clinically relevant cell numbers with decreased labour and decreased batch to batch variation. Yet, current methods (...) of performing quality control are not readily scalable to the cell densities produced during bioreactor scale‐up. One potential solution is the application of inducible/controllable suicide genes that can trigger cell death in unwanted cell types. These types of approaches have been demonstrated to increase the quality and safety of the resultant cell products. In this review, we will provide background on these approaches and how they could be used together with bioreactor technology to create effective bioprocesses for the generation of high quality and safe hPSCs for use in regenerative medicine approaches. (shrink)

Although the bicoded map constitutes an interesting candidate representation, proposing it as the predominant representation for three-dimensional space is too restrictive. We present and argue for scalable spatial representation structures as a more comprehensive alternative account that includes the bicoded map as a special case.

The aim of this article is to analyze the underlying economic structures which have led to the rise of the tech giants. In their book Capitalism without Capital: The Rise of the Intangible Economy, economists Jonathan Haskel and Stian Westlake outline the four fundamental features of the technocapitalist (or what they consider the intangible) economy. Key to the emerging intangible economy, they suggest, are the four “S’s” of this new form of capitalism, viz., scalability, sunkenness, synergies, and spillovers. In (...) the following, I explicate the ways in which, if taken seriously, these four features of the technocapitalist economy tend towards heightened levels of economic centralization and economic inequality as compared to those experienced under industrial capitalism. (shrink)

Automatized scalable healthcare support solutions allow real-time 24/7 health monitoring of patients, prioritizing medical treatment according to health conditions, reducing medical appointments in clinics and hospitals, and enabling easy exchange of information among healthcare professionals. With recent health safety guidelines due to the COVID-19 pandemic, protecting the elderly has become imperative. However, state-of-the-art health wearable device platforms present limitations in hardware, parameter estimation algorithms, and software architecture. This paper proposes a complete framework for health systems composed of multi-sensor wearable health (...) devices, high-resolution parameter estimation, and real-time monitoring applications. The framework is appropriate for real-time monitoring of elderly patients' health without physical contact with healthcare professionals, maintaining safety standards. The hardware includes sensors for monitoring steps, pulse oximetry, heart rate, and temperature using low-power wireless communication. In terms of parameter estimation, the embedded circuit uses high-resolution signal processing algorithms that result in an improved measure of the HR. The proposed high-resolution signal processing-based approach outperforms state-of-the-art HR estimation measurements using the photoplethysmography sensor. (shrink)

Here, a streamlined, scalable, laboratory approach is discussed that enables medium‐to‐large dataset analysis. The presented approach combines data management, artificial intelligence, containerization, cluster orchestration, and quality control in a unified analytic pipeline. The unique combination of these individual building blocks creates a new and powerful analysis approach that can readily be applied to medium‐to‐large datasets by researchers to accelerate the pace of research. The proposed framework is applied to a project that counts the number of plasmonic nanoparticles bound to peripheral (...) blood mononuclear cells in dark‐field microscopy images. By using the techniques presented in this article, the images are automatically processed overnight, without user interaction, streamlining the path from experiment to conclusions. (shrink)

Where does the impetus towards ethical theory come from? What drives humans to make values explicit, consistent, and discursively justifiable? This paper situates the demand for ethical theory in human life by identifying the practical needs that give rise to it. Such a practical derivation puts the demand in its place: while finding a home for it in the public decision-making of modern societies, it also imposes limitations on the demand by presenting it as scalable and context-sensitive. This differentiates strong (...) forms of the demand calling for theory from weaker forms calling for less, and contexts where it has a place from contexts where it is out of place. In light of this, subjecting personal deliberation to the demand turns out to involve a trade-off. (shrink)

There are growing discontinuities between the research practices of data science and established tools of research ethics regulation. Some of the core commitments of existing research ethics regulations, such as the distinction between research and practice, cannot be cleanly exported from biomedical research to data science research. Such discontinuities have led some data science practitioners and researchers to move toward rejecting ethics regulations outright. These shifts occur at the same time as a proposal for major revisions to the Common Rule—the (...) primary regulation governing human-subjects research in the USA—is under consideration for the first time in decades. We contextualize these revisions in long-running complaints about regulation of social science research and argue data science should be understood as continuous with social sciences in this regard. The proposed regulations are more flexible and scalable to the methods of non-biomedical research, yet problematically largely exclude data science methods from human-subjects regulation, particularly uses of public datasets. The ethical frameworks for Big Data research are highly contested and in flux, and the potential harms of data science research are unpredictable. We examine several contentious cases of research harms in data science, including the 2014 Facebook emotional contagion study and the 2016 use of geographical data techniques to identify the pseudonymous artist Banksy. To address disputes about application of human-subjects research ethics in data science, critical data studies should offer a historically nuanced theory of “data subjectivity” responsive to the epistemic methods, harms and benefits of data science and commerce. (shrink)

The pace of technological change continues to outstrip the evolution of democratic institutions, creating an urgent need for innovative approaches to democratic reform. However, the experimentation bottleneck - characterized by slow speed, high costs, limited scalability, and ethical risks - has long hindered progress in democracy research. This paper proposes a novel solution: employing generative artificial intelligence (GenAI) to create synthetic data through the simulation of digital homunculi, GenAI-powered entities designed to mimic human behavior in social contexts. By enabling (...) rapid, low-risk experimentation with alternative institutional designs, this approach could significantly accelerate democratic innovation. I examine the potential of GenAI-assisted research to mitigate current limitations in democratic experimentation, including the ability to simulate large-scale societal interactions and test complex institutional mechanisms. While acknowledging potential risks such as algorithmic bias, reproducibility challenges, and AI alignment issues, I argue that the benefits of synthetic data are likely to outweigh their drawbacks if implemented with proper caution. To address existing challenges, I propose a range of technical, methodological, and institutional adaptations. The paper concludes with a call for interdisciplinary collaboration in the development and implementation of GenAI-assisted methods in democracy research, highlighting their potential to bridge the gap between democratic theory and practice in an era of rapid technological change. (shrink)

Let’s make three assumptions. First, we shouldn’t support factory farms. Second, if animal-friendly agriculture lives up to its name—that is, if animals live good lives (largely free of pain, able to engage in species-specific behaviors, etc.) and are slaughtered in a way that minimizes suffering—then there is nothing intrinsically wrong with killing them for food. Third, animal-friendly agriculture does, in fact, live up to its name. Given these assumptions, it might seem difficult to criticize individuals who source their animal products (...) from “animal-friendly agricultural operations. However, I argue that they should drastically reduce their support for animal-friendly agriculture because it isn’t scalable—i.e., if we were to switch to that form of agriculture, most people would be priced out of its products. I say that it’s wrong to support a solution to a moral problem without sharing its costs. (shrink)

Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.

Public participation is increasingly an aspect of policy development in many areas, and the governance of biomedical research is no exception. There are good reasons for this: biomedical research relies on public funding; it relies on biological samples and information from large numbers of patients and healthy individuals; and the outcomes of biomedical research are dramatically and irrevocably changing our society. There is thus arguably a democratic imperative for including public values in strategic decisions about the governance of biomedical research. (...) However, it is not immediately clear how this might best be achieved. While different approaches have been proposed and trialled, we focus here on the use of public deliberation as a mechanism to develop input for policy on biomedical research. We begin by explaining the rationale for conducting public deliberation in biomedical research. We focus, in particular, on the ELS (ethical, legal, social) aspects of human tissue biobanking. The last few years have seen the development of methods for conducting public deliberation on these issues in several jurisdictions, for the purpose of incorporating lay public voices in biobanking policy. We explain the theoretical foundation underlying the notion of deliberation, and outline the main lessons and capacities that have been developed in the area of conducting public deliberation on biobanks. We next provide an analysis of the theoretical and practical challenges that we feel still need to be addressed for the use of public deliberation to guide ethical norms and governance of biomedical research. We examine the issues of: (i) linking the outcomes of deliberation to tangible action; (ii) the mandate under which a deliberation is conducted; (iii) the relative weight that should be accorded to a public deliberative forum vs other relevant voices; (iv) evaluating the quality of deliberation; and (5) the problem of scalability of minipublics. (shrink)

Big Data has been variously defined in the literature. In the main, definitions suggest that Big Data possess a suite of key traits: volume, velocity and variety, but also exhaustivity, resolution, indexicality, relationality, extensionality and scalability. However, these definitions lack ontological clarity, with the term acting as an amorphous, catch-all label for a wide selection of data. In this paper, we consider the question ‘what makes Big Data, Big Data?’, applying Kitchin’s taxonomy of seven Big Data traits to 26 (...) datasets drawn from seven domains, each of which is considered in the literature to constitute Big Data. The results demonstrate that only a handful of datasets possess all seven traits, and some do not possess either volume and/or variety. Instead, there are multiple forms of Big Data. Our analysis reveals that the key definitional boundary markers are the traits of velocity and exhaustivity. We contend that Big Data as an analytical category needs to be unpacked, with the genus of Big Data further delineated and its various species identified. It is only through such ontological work that we will gain conceptual clarity about what constitutes Big Data, formulate how best to make sense of it, and identify how it might be best used to make sense of the world. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Hopeful and Just Futures Across ScaleIsabelle Boucher, Alex Custodio, Hanine El Mir, Janna Frenzel, and Robert MarinovSituated Solar Relations: Rethinking Scale for the Renewable Energy Age/ Solar Media Collective, Concordia University, Tio'tia:Ke (Montréal), Canada, 05 11, 2023In the face of global climate destruction and ecological collapse, many have witnessed—and perhaps grown numb to—the repeated failures of governments and industries to organize a meaningful transition toward more sustainable social and (...) economic formations. Against the troubling concern that the "novelty" and "impact" of techno-solutions have lost all meaning, the Solar Media Collective asks who or what dictates the scales at which change is made visible, meaningful, useful, and sustainable. In other words, we ask: where should we look for the hopeful gestures that define our future worlds, and how can we resourcefully resist and model alternative pathways to Big Tech's ever-grander "solutions" to the climate crisis? 1On May 11, 2023, the Solar Media Collective—a student-run research-creation group affiliated with Concordia University in Tio'tia:ke (Montréal)—ran a day-long symposium on the multiscalar dimensions of just and hopeful energy transitions. 2Taking inspiration from the transnational Solar Protocol network's exploration into the techniques and affordances of solar-powered computer networking, we asked how, when, and where social, political, [End Page 304]technical, and cultural protocols surrounding renewable energy and the production and disposal of digital technologies can be developed to encourage diverse designs and scales of deployment for sustainable innovations and practices. By exploring these themes through a series of roundtable discussions with invited guests, we attempted to imbue the word "sustainability" with new meaning and reclaim it from corporate optimization rhetoric.Situated Solar Relations: Rethinking Scale for the Renewable Energy Agechallenged the sitelessness of large-scale initiatives by asking what might happen if situatedsolar relations were to become the primary protagonist of research, production, and outreach. Across three roundtables, an interactive workshop, and a showcase of some of our functional prototypes, the symposium addressed creative practices as a mode of future building. Roundtable discussions approached the question of how to build a just solar future through multiple angles: art, literature, and aesthetics; ludic interactions; and social, political, and economic transformations. Rather than taking for granted the principle of "scaling up" that requires sustainability to be replicable and expandable into universal and precise sets of standardized models, we argue instead for the necessity of having (non)scalable, situated, and evolving relationships with the sun. 3In other words, we turn to process rather than product, hope rather than despair, and careful research rather than extractive data practices.Relations of scale are at the heart of these dichotomies. Max Liboiron and Josh Lepawsky have recently argued that power lies in the way things appear to be "true, natural and good," which is determined and reproduced on a homogeneous scale by specific interest groups. 4Intervening against extractive, energy-intensive, and polluting practices therefore requires multiple contextualized and situated knowledges of the processes at play, which in turn requires an understanding of the scales within which energy and waste-defining relationships occur. 5They write:Problems arise when one situated way of knowing disavows its context, becomes dominant to the exclusion of other ways of knowing, or thinks it accounts for all forms of discards. The effect of such monolithic approaches to knowledge can be termed "scalar mismatch," where one instance is taken to be the whole phenomenon, or where one perspective is assumed to work in all cases. 6Scale here is about the specific processes and relationships that matter in a given context. It is not about relative size, nor continuity, but the disjuncture [End Page 305]between different sets of relationalities, whether it be at the planetary, governmental, industrial, or molecular levels. 7Power, they explain, is the reproduction of order in one scale (planetary industrial production, for example), while it disrupts order at others (community or molecular processes). 8In order to disentangle these overlapping but incommensurable scales and to counteract the epistemic grip some have over the others, the authors suggest that it is important to find creative ways... (shrink)

Ontologies play a vital role in organizing and constructing knowledge across various domains, enabling effective knowledge management and sharing. The development of domain-specific ontologies, such as the ONTO-TDM ontology for teaching domain modeling, is essential for providing a comprehensive and standardized representation of knowledge within a given discipline. However, to maximize the usefulness and relevance of such ontologies, it is crucial to automate their population with domain-specific information, reducing manual work and ensuring scalability. This paper presents a novel method (...) for ontology population by extracting and integrating relevant information from diverse sources. The method combines the TextRank algorithm with Word2Vec to enhance keyword extraction, capturing both semantic meaning and textual importance. Keywords are then annotated and used to train a machine learning classifier, which aids in integrating new instances into the ontology. Experiments show that the proposed method achieves a precision of 63.33%, a recall of 61.29% and an F1-score of 62.28%, significantly improving keyword extraction and ontology population accuracy compared to existing methods. This validates the method’s effectiveness in semi-automatically extracting relevant instances from diverse data sources, enhancing the efficiency and accuracy of ontology population, and advancing automated knowledge management in domain-specific contexts. (shrink)

One important problem in the philosophy of science is whether there can be a normative theory of discovery, as opposed to a normative theory of justification. Although the possibility of developing a logic of scientific discovery has been often doubted by philosophers, it is particularly interesting to consider how the basic insights of a normative theory of discovery have been turned into an effective research program in computer science, namely the research field of machine learning. In this paper, I introduce (...) some current research on statistical models to a philosophical audience. In particular, I will stress those features of statistical models that make them plausible computational counterparts of scientific theories. After noticing how these models allow for the main kinds of inference that are a trademark of scientific theories, I will focus on the problem of learning statistical models from data. The analysis will show how machine learning is casting new light on traditional problems in the philosophy of science. More precisely, I will explore the implications of some results in statistical learning with respect to the role of simplicity in theory choice and to the role of scalability (as a formal property of induction) in making scientific discovery effective. (shrink)

To address the problems of both structured coordination of linked and modularised ontologies and to query a large dynamic ontology system, we propose a basic granularity framework and a set of functions to query such a granulated system. The granularity framework enforces a constrained and structured modularization. This facilitates automation of both dividing a large body of represented information as well as relinking the pieces. The functions enable basic cross-granular querying in a transparent and scalable way, as they rely on (...) the unambiguous management layer provided by the granularity framework, and are reusable for ontologies represented and stored in different formats. (shrink)

Using the flexibility of recently developed methods for causal discovery based on Boolean satisfiability solvers, we encode a variety of assumptions that weaken the Faithfulness assumption. The encoding results in a number of SAT-based algorithms whose asymptotic correctness relies on weaker conditions than are standardly assumed. This implementation of a whole set of assumptions in the same platform enables us to systematically explore the effect of weakening the Faithfulness assumption on causal discovery. An important effect, suggested by simulation results, is (...) that adopting weaker assumptions greatly alleviates the problem of conflicting constraints and substantially shortens solving time. As a result, SAT-based causal discovery is potentially more scalable under weaker assumptions. (shrink)

In this study, we use temporally aligned word embeddings and a large diachronic corpus of English to quantify language change in a data-driven, scalable way, which is grounded in language use. We show a unique and reliable relation between measures of language change and age of acquisition (AoA) while controlling for frequency, contextual diversity, concreteness, length, dominant part of speech, orthographic neighborhood density, and diachronic frequency variation. We analyze measures of language change tackling both the change in lexical representations and (...) the change in the relation between lexical representations and the words with the most similar usage patterns, showing that they capture different aspects of language change. Our results show a unique relation between language change and AoA, which is stronger when considering neighborhood-level measures of language change: Words with more coherent diachronic usage patterns tend to be acquired earlier. The results support theories positing a link between ontogenetic and ethnogenetic processes in language. (shrink)

Abstract“Scaling-up” is the next hurdle facing the local food movement. In order to effect broader systemic impacts, local food systems (LFS) will have to grow, and engage either more or larger consumers and producers. Encouraging the involvement of mid-sized farms looks to be an elegant solution, by broadening the accessibility of local food while providing alternative revenue streams for troubled family farms. Logistical, structural and regulatory barriers to increased scale in LFS are well known. Less is understood about the way (...) in which scale developments affect the perception and legitimacy of LFS. This value-added opportunity begs the question: Is the value that adheres to local food scalable? Many familiar with local food discourse might suggest that important pieces of added value within LFS are generated by the reconnection of producer and consumer, the direct exchange through which this occurs, and the shared goals and values that provide the basis for reconnection. However, these assertions are based on tenuous assumptions about how interactions within the direct exchange produce value, and how LFS are governed. Examination shows that existing assumptions do not properly acknowledge the hybridity, diversity, and flexibility inherent in LFS. A clear analysis of the potential of scale in LFS will depend on understanding both how value is determined within LFS, and the processes through which these systems are governed. Such an analysis shows that, while scaled-up LFS will be challenged to maintain legitimacy and an identity as “alternative”, the establishment of an open governance process—based on a “negotiation of accommodations”—is likely to enhance their viability. (shrink)