Search (108 results, page 1 of 6)

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Abstract

Literature review articles are essential to summarize the related work in the selected field. However, covering all related studies takes too much time and effort. This study questions how Artificial Intelligence can be used in this process. We used ChatGPT to create a literature review article to show the stage of the OpenAI ChatGPT artificial intelligence application. As the subject, the applications of Digital Twin in the health field were chosen. Abstracts of the last three years (2020, 2021 and 2022) papers were obtained from the keyword "Digital twin in healthcare" search results on Google Scholar and paraphrased by ChatGPT. Later on, we asked ChatGPT questions. The results are promising; however, the paraphrased parts had significant matches when checked with the Ithenticate tool. This article is the first attempt to show the compilation and expression of knowledge will be accelerated with the help of artificial intelligence. We are still at the beginning of such advances. The future academic publishing process will require less human effort, which in turn will allow academics to focus on their studies. In future studies, we will monitor citations to this study to evaluate the academic validity of the content produced by the ChatGPT. 1. Introduction OpenAI ChatGPT (ChatGPT, 2022) is a chatbot based on the OpenAI GPT-3 language model. It is designed to generate human-like text responses to user input in a conversational context. OpenAI ChatGPT is trained on a large dataset of human conversations and can be used to create responses to a wide range of topics and prompts. The chatbot can be used for customer service, content creation, and language translation tasks, creating replies in multiple languages. OpenAI ChatGPT is available through the OpenAI API, which allows developers to access and integrate the chatbot into their applications and systems. OpenAI ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) language model developed by OpenAI. It is designed to generate human-like text, allowing it to engage in conversation with users naturally and intuitively. OpenAI ChatGPT is trained on a large dataset of human conversations, allowing it to understand and respond to a wide range of topics and contexts. It can be used in various applications, such as chatbots, customer service agents, and language translation systems. OpenAI ChatGPT is a state-of-the-art language model able to generate coherent and natural text that can be indistinguishable from text written by a human. As an artificial intelligence, ChatGPT may need help to change academic writing practices. However, it can provide information and guidance on ways to improve people's academic writing skills.

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Abstract

Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day. To enable generative agents, we describe an architecture that extends a large language model to store a complete record of the agent's experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors: for example, starting with only a single user-specified notion that one agent wants to throw a Valentine's Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time. We demonstrate through ablation that the components of our agent architecture--observation, planning, and reflection--each contribute critically to the believability of agent behavior. By fusing large language models with computational, interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior.

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Source

6th International Conference on Computer-Human Interaction Research and Applications, [https://www.researchgate.net/publication/364940444_A_Study_of_Search_User_Interface_Design_based_on_Hofstede's_Six_Cultural_Dimensions]

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Abstract

"I still don't know what to think about GPT-4, the new large language model (LLM) from OpenAI. On the one hand it is a remarkable product that easily passes the Turing test. If you ask it questions, via the ChatGPT interface, GPT-4 can easily produce fluid sentences largely indistinguishable from those a person might write. But on the other hand, amid the exceptional levels of hype and anticipation, it's hard to know where GPT-4 and other LLMs truly fit in the larger project of making machines intelligent.
They might appear intelligent, but LLMs are nothing of the sort. They don't understand the meanings of the words they are using, nor the concepts expressed within the sentences they create. When asked how to bring a cow back to life, earlier versions of ChatGPT, for example, which ran on a souped-up version of GPT-3, would confidently provide a list of instructions. So-called hallucinations like this happen because language models have no concept of what a "cow" is or that "death" is a non-reversible state of being. LLMs do not have minds that can think about objects in the world and how they relate to each other. All they "know" is how likely it is that some sets of words will follow other sets of words, having calculated those probabilities from their training data. To make sense of all this, I spoke with Gary Marcus, an emeritus professor of psychology and neural science at New York University, for "Babbage", our science and technology podcast. Last year, as the world was transfixed by the sudden appearance of ChatGPT, he made some fascinating predictions about GPT-4.
He doesn't dismiss the potential of LLMs to become useful assistants in all sorts of ways-Google and Microsoft have already announced that they will be integrating LLMs into their search and office productivity software. But he talked me through some of his criticisms of the technology's apparent capabilities. At the heart of Dr Marcus's thoughtful critique is an attempt to put LLMs into proper context. Deep learning, the underlying technology that makes LLMs work, is only one piece of the puzzle in the quest for machine intelligence. To reach the level of artificial general intelligence (AGI) that many tech companies strive for-i.e. machines that can plan, reason and solve problems in the way human brains can-they will need to deploy a suite of other AI techniques. These include, for example, the kind of "symbolic AI" that was popular before artificial neural networks and deep learning became all the rage.
People use symbols to think about the world: if I say the words "cat", "house" or "aeroplane", you know instantly what I mean. Symbols can also be used to describe the way things are behaving (running, falling, flying) or they can represent how things should behave in relation to each other (a "+" means add the numbers before and after). Symbolic AI is a way to embed this human knowledge and reasoning into computer systems. Though the idea has been around for decades, it fell by the wayside a few years ago as deep learning-buoyed by the sudden easy availability of lots of training data and cheap computing power-became more fashionable. In the near future at least, there's no doubt people will find LLMs useful. But whether they represent a critical step on the path towards AGI, or rather just an intriguing detour, remains to be seen."

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Abstract

The labor market is a system that is complex and difficult to manage. To overcome this challenge, the European Union has launched the ESCO project which is a language that aims to describe this labor market. In order to support the spread of this project, its dataset was presented as linked open data (LOD). Since LOD is usable and reusable, a set of conditions have to be met. First, LOD must be feasible and high quality. In addition, it must provide the user with the right answers, and it has to be built according to a clear and correct structure. This study investigates the LOD of ESCO, focusing on data quality and data structure. The former is evaluated through applying a set of SPARQL queries. This provides solutions to improve its quality via a set of rules built in first order logic. This process was conducted based on a new proposed ESCO ontology.

Source

International journal of advanced computer science and applications 11(2020) no.3

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Abstract

Within the knowledge organization systems (KOS) set, the term "ontology" is paradigmatic of the terminological ambiguity in different typologies. Contributing to this situation is the indiscriminate association of the term "ontology", both as a specific type of KOS and as a process of categorization, due to the interdisciplinary use of the term with different meanings. We present a systematization of the perspectives of different authors of ontologies, as representational artifacts, seeking to contribute to terminological clarification. Focusing the analysis on the intention, semantics and modulation of ontologies, it was possible to notice two broad perspectives regarding ontologies as artifacts that coexist in the knowledge organization systems spectrum. We have ontologies viewed, on the one hand, as an evolution in terms of complexity of traditional conceptual systems, and on the other hand, as a system that organizes ontological rather than epistemological knowledge. The focus of ontological analysis is the item to model and not the intentions that motivate the construction of the system.

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Abstract

Human brain size nearly quadrupled in the six million years since Homo last shared a common ancestor with chimpanzees, but human brains are thought to have decreased in volume since the end of the last Ice Age. The timing and reason for this decrease is enigmatic. Here we use change-point analysis to estimate the timing of changes in the rate of hominin brain evolution. We find that hominin brains experienced positive rate changes at 2.1 and 1.5 million years ago, coincident with the early evolution of Homo and technological innovations evident in the archeological record. But we also find that human brain size reduction was surprisingly recent, occurring in the last 3,000 years. Our dating does not support hypotheses concerning brain size reduction as a by-product of body size reduction, a result of a shift to an agricultural diet, or a consequence of self-domestication. We suggest our analysis supports the hypothesis that the recent decrease in brain size may instead result from the externalization of knowledge and advantages of group-level decision-making due in part to the advent of social systems of distributed cognition and the storage and sharing of information. Humans live in social groups in which multiple brains contribute to the emergence of collective intelligence. Although difficult to study in the deep history of Homo, the impacts of group size, social organization, collective intelligence and other potential selective forces on brain evolution can be elucidated using ants as models. The remarkable ecological diversity of ants and their species richness encompasses forms convergent in aspects of human sociality, including large group size, agrarian life histories, division of labor, and collective cognition. Ants provide a wide range of social systems to generate and test hypotheses concerning brain size enlargement or reduction and aid in interpreting patterns of brain evolution identified in humans. Although humans and ants represent very different routes in social and cognitive evolution, the insights ants offer can broadly inform us of the selective forces that influence brain size.

Source

Frontiers in ecology and evolution, 22 October 2021 [https://www.frontiersin.org/articles/10.3389/fevo.2021.742639/full]

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Abstract

Persistent identifiers (PID's) are essential for getting access and referring to library, archive and museum (LAM) collection objects in a sustainable and unambiguous way, both internally and externally. Heritage institutions need a universal policy for the use of PID's in order to have an efficient digital infrastructure at their disposal and to achieve optimal interoperability, leading to open data, open collections and efficient resource management. Here the discussion is limited to PID's that institutions can assign to objects they own or administer themselves. PID's for people, subjects etc. can be used by heritage institutions, but are generally managed by other parties. The first part of this article consists of a general theoretical description of persistent identifiers. First of all, I discuss the questions of what persistent identifiers are and what they are not, and what is needed to administer and use them. The most commonly used existing PID systems are briefly characterized. Then I discuss the types of objects PID's can be assigned to. This section concludes with an overview of the requirements that apply if PIDs should also be used for linked data. The second part examines current infrastructural practices, and existing PID systems and their advantages and shortcomings. Based on these practical issues and the pros and cons of existing PID systems a list of requirements for PID systems is presented which is used to address a number of practical considerations. This section concludes with a number of recommendations.

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Abstract

We consider use of ontological background knowledge in intelligent information systems and analyze directions of their reduction in compliance with specifics of particular user task. Such reduction is aimed at simplification of knowledge processing without loss of significant information. We propose methods of generation of task thesauri based on domain ontology that contain such subset of ontological concepts and relations that can be used in task solving. Combinatorial optimization is used for minimization of task thesaurus. In this approach, semantic similarity estimates are used for determination of concept significance for user task. Some practical examples of optimized thesauri application for semantic retrieval and competence analysis demonstrate efficiency of proposed approach.

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Abstract

The VOSviewer software provides easy access to bibliometric mapping using data from Dimensions, Scopus and Web of Science. The properly formatted and structured citation data, and the ease in which it can be exported open up new avenues for use during citation searches and eference interviews. This paper details specific techniques for using advanced searches in Dimensions, exporting the citation data, and drawing insights from the maps produced in VOS Viewer. These search techniques and data export practices are fast and accurate enough to build into reference interviews for graduate students, faculty, and post-PhD researchers. The search results derived from them are accurate and allow a more comprehensive view of citation networks embedded in ordinary complex boolean searches.

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Abstract

Several authors emphasize the need for a change in classification theory due to the influence of a dogmatic and monistic ontology supported by an outdated essentialism. These claims tend to focus on the fallibility of knowledge, the need for a pluralistic view, and the theoretical burden of observations. Regardless of the legitimacy of these concerns, there is the risk, when not moderate, to fall into the opposite relativistic extreme. Based on a narrative review of the literature, we aim to reflectively discuss the theoretical foundations that can serve as a basis for a realist position supporting pluralistic ontological classifications. The goal is to show that, against rather conventional solutions, objective scientific-based approaches to natural classifications are presented to be viable, allowing a proper distinction between ontological and taxonomic questions. Supported by critical scientific realism, we consider that such an approach is suitable for the development of ontological Knowledge Organization Systems (KOS). We believe that ontological perspectivism can provide the necessary adaptation to the different granularities of reality.

Source

Journal of information science. 54(2023) no.x, S.xx-xx

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Abstract

GPT-3 ist eine jener Entwicklungen, die binnen weniger Monate an Einfluss und Reichweite zulegen. Die Software wird sich massiv auf Ökonomie und Gesellschaft auswirken.

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Abstract

The initial dimensions extracted by latent semantic analysis (LSA) of a document-term matrixhave been shown to mainly display marginal effects, which are irrelevant for informationretrieval. To improve the performance of LSA, usually the elements of the raw document-term matrix are weighted and the weighting exponent of singular values can be adjusted.An alternative information retrieval technique that ignores the marginal effects is correspon-dence analysis (CA). In this paper, the information retrieval performance of LSA and CA isempirically compared. Moreover, it is explored whether the two weightings also improve theperformance of CA. The results for four empirical datasets show that CA always performsbetter than LSA. Weighting the elements of the raw data matrix can improve CA; however,it is data dependent and the improvement is small. Adjusting the singular value weightingexponent often improves the performance of CA; however, the extent of the improvementdepends on the dataset and the number of dimensions. (PDF) Improving information retrieval through correspondence analysis instead of latent semantic analysis.

Source

Journal of intelligent information systems [https://doi.org/10.1007/s10844-023-00815-y]

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Abstract

We investigate different systems for extracting mathematical entities from English texts in the mathematical field of category theory as a first step for constructing a mathematical knowledge graph. We consider four different term extractors and compare their results. This small experiment showcases some of the issues with the construction and evaluation of terms extracted from noisy domain text. We also make available two open corpora in research mathematics, in particular in category theory: a small corpus of 755 abstracts from the journal TAC (3188 sentences), and a larger corpus from the nLab community wiki (15,000 sentences).

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Abstract

Die ZBW - Leibniz-Informationszentrum Wirtschaft betreibt seit 2016 eigene angewandte Forschung im Bereich Machine Learning mit dem Zweck, praktikable Lösungen für eine automatisierte oder maschinell unterstützte Inhaltserschließung zu entwickeln. 2020 begann ein Team an der ZBW die Konzeption und Implementierung einer Softwarearchitektur, die es ermöglichte, diese prototypischen Lösungen in einen produktiven Dienst zu überführen und mit den bestehenden Nachweis- und Informationssystemen zu verzahnen. Sowohl die angewandte Forschung als auch die für dieses Vorhaben ("AutoSE") notwendige Softwareentwicklung sind direkt im Bibliotheksbereich der ZBW angesiedelt, werden kontinuierlich anhand des State of the Art vorangetrieben und profitieren von einem engen Austausch mit den Verantwortlichen für die intellektuelle Inhaltserschließung. Dieser Beitrag zeigt die Meilensteine auf, die das AutoSE-Team in zwei Jahren in Bezug auf den Aufbau und die Integration der Software erreicht hat, und skizziert, welche bis zum Ende der Pilotphase (2024) noch ausstehen. Die Architektur basiert auf Open-Source-Software und die eingesetzten Machine-Learning-Komponenten werden im Rahmen einer internationalen Zusammenarbeit im engen Austausch mit der Finnischen Nationalbibliothek (NLF) weiterentwickelt und zur Nachnutzung in dem von der NLF entwickelten Open-Source-Werkzeugkasten Annif aufbereitet. Das Betriebsmodell des AutoSE-Dienstes sieht regelmäßige Überprüfungen sowohl einzelner Komponenten als auch des Produktionsworkflows als Ganzes vor und erlaubt eine fortlaufende Weiterentwicklung der Architektur. Eines der Ergebnisse, das bis zum Ende der Pilotphase vorliegen soll, ist die Dokumentation der Anforderungen an einen dauerhaften produktiven Betrieb des Dienstes, damit die Ressourcen dafür im Rahmen eines tragfähigen Modells langfristig gesichert werden können. Aus diesem Praxisbeispiel lässt sich ableiten, welche Bedingungen gegeben sein müssen, um Machine-Learning-Lösungen wie die in Annif enthaltenen erfolgreich an einer Institution für die Inhaltserschließung einsetzen zu können.

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Abstract

In traditional classification, fictional works are indexed only by their form, genre, and language, while their subject content is believed to be irrelevant. However, recent research suggests that this may not be the best approach. We tested indexing of a small sample of selected fictional works by Integrative Levels Classification (ILC2), a freely faceted system based on phenomena instead of disciplines and considered the structure of the resulting classmarks. Issues in the process of subject analysis, such as selection of relevant vs. non-relevant themes and citation order of relevant ones, are identified and discussed. Some phenomena that are covered in scholarly literature can also be identified as relevant themes in fictional literature and expressed in classmarks. This can allow for hybrid search and retrieval systems covering both fiction and nonfiction, which will result in better leveraging of the knowledge contained in fictional works.

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Abstract

We present an overview of the NTCIR Math Tasks organized during NTCIR-10, 11, and 12. These tasks are primarily dedicated to techniques for searching mathematical content with formula expressions. In this chapter, we first summarize the task design and introduce test collections generated in the tasks. We also describe the features and main challenges of mathematical information retrieval systems and discuss future perspectives in the field.

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Abstract

The success of the use of ontology-based systems depends on efficient and user-friendly methods of formulating queries against the ontology. We propose a method to query a class of ontologies, called facet ontologies ( fac-ontologies ), using a faceted human-oriented approach. A fac-ontology has two important features: (a) a hierarchical view of it can be defined as a nested facet over this ontology and the view can be used as a faceted interface to create queries and to explore the ontology; (b) the ontology can be converted into an ontological database , the ABox of which is stored in a database, and the faceted queries are evaluated against this database. We show that the proposed faceted interface makes it possible to formulate queries that are semantically equivalent to $${\mathcal {SROIQ}}^{Fac}$$ SROIQ Fac , a limited version of the $${\mathcal {SROIQ}}$$ SROIQ description logic. The TBox of a fac-ontology is divided into a set of rules defining intensional predicates and a set of constraint rules to be satisfied by the database. We identify a class of so-called reflexive weak cycles in a set of constraint rules and propose a method to deal with them in the chase procedure. The considerations are illustrated with solutions implemented in the DAFO system ( data access based on faceted queries over ontologies ).

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Abstract

The UCLA Library reorganized its software developers into focused subteams with one, the Labs Team, dedicated to conducting experiments. In this article we describe our first attempt at conducting a software development experiment, in which we attempted to improve our digital library's search results with metadata from cloud-based image tagging services. We explore the findings and discuss the lessons learned from our first attempt at running an experiment.

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Abstract

COVID-19 is an unprecedented global health crisis that will have immeasurable consequences for our economic and social well-being. Tedros Adhanom Ghebreyesus, the director general of the World Health Organization, stated "We're not just fighting an epidemic; we're fighting an infodemic". Currently, there is no robust scientific basis to the existing definitions of false information used in the fight against the COVID-19infodemic. The purpose of this paper is to demonstrate how the use of a novel taxonomy and related model (based upon a conceptual framework that synthesizes insights from information science, philosophy, media studies and politics) can produce new scientific definitions of mis-, dis- and malinformation. We undertake our analysis from the viewpoint of information systems research. The conceptual approach to defining mis-,dis- and malinformation can be applied to a wide range of empirical examples and, if applied properly, may prove useful in fighting the COVID-19 infodemic. In sum, our research suggests that: (i) analyzing all types of information is important in the battle against the COVID-19 infodemic; (ii) a scientific approach is required so that different methods are not used by different studies; (iii) "misinformation", as an umbrella term, can be confusing and should be dropped from use; (iv) clear, scientific definitions of information types will be needed going forward; (v) malinformation is an overlooked phenomenon involving reconfigurations of the truth.