14,847 research outputs found
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence
Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an educational context such information may be deemed insufficient, it would be beneficial for both the student and the instructor to receive additional feedback about the overall development of the task. This work aims to tackle this limitation by considering the further exploitation of the information gathered by the OJ and automatically inferring feedback for both the student and the instructor. More precisely, we consider the use of learning-based schemes—particularly, Multi-Instance Learning and classical Machine Learning formulations—to model student behaviour. Besides, Explainable Artificial Intelligence is contemplated to provide human-understandable feedback. The proposal has been evaluated considering a case of study comprising 2,500 submissions from roughly 90 different students from a programming-related course in a Computer Science degree. The results obtained validate the proposal: the model is capable of significantly predicting the user outcome (either passing or failing the assignment) solely based on the behavioural pattern inferred by the submissions provided to the OJ. Moreover, the proposal is able to identify prone-to-fail student groups and profiles as well as other relevant information, which eventually serves as feedback to both the student and the instructor.This work has been partially funded by the “Programa Redes-I3CE de investigacion en docencia universitaria del Instituto de Ciencias de la Educacion (REDES-I3CE-2020-5069)” of the University of Alicante. The third author is supported by grant APOSTD/2020/256 from “Programa I+D+I de la Generalitat Valenciana”
Examples of works to practice staccato technique in clarinet instrument
Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato
geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı
sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de
durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt
çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham
verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her
aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır.
Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine
yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini
içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin
kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür
taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de
kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt
çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve
güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının
girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken
doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir
kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına
bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği
vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan
çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur.
Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir.
Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır.
Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların
yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve
sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır
Perfect is the enemy of test oracle
Automation of test oracles is one of the most challenging facets of software
testing, but remains comparatively less addressed compared to automated test
input generation. Test oracles rely on a ground-truth that can distinguish
between the correct and buggy behavior to determine whether a test fails
(detects a bug) or passes. What makes the oracle problem challenging and
undecidable is the assumption that the ground-truth should know the exact
expected, correct, or buggy behavior. However, we argue that one can still
build an accurate oracle without knowing the exact correct or buggy behavior,
but how these two might differ. This paper presents SEER, a learning-based
approach that in the absence of test assertions or other types of oracle, can
determine whether a unit test passes or fails on a given method under test
(MUT). To build the ground-truth, SEER jointly embeds unit tests and the
implementation of MUTs into a unified vector space, in such a way that the
neural representation of tests are similar to that of MUTs they pass on them,
but dissimilar to MUTs they fail on them. The classifier built on top of this
vector representation serves as the oracle to generate "fail" labels, when test
inputs detect a bug in MUT or "pass" labels, otherwise. Our extensive
experiments on applying SEER to more than 5K unit tests from a diverse set of
open-source Java projects show that the produced oracle is (1) effective in
predicting the fail or pass labels, achieving an overall accuracy, precision,
recall, and F1 measure of 93%, 86%, 94%, and 90%, (2) generalizable, predicting
the labels for the unit test of projects that were not in training or
validation set with negligible performance drop, and (3) efficient, detecting
the existence of bugs in only 6.5 milliseconds on average.Comment: Published in ESEC/FSE 202
L’Asie du Sud-Est 2023 : bilan, enjeux et perspectives
Chaque année, l’Institut de recherche sur l’Asie du Sud-Est contemporaine (IRASEC), basé à Bangkok, mobilise une vingtaine de chercheurs et d’experts pour mieux comprendre l’actualité régionale de ce carrefour économique, culturel et religieux, au cœur de l’Indo-Pacifique. Cette collection permet de suivre au fil des ans l’évolution des grands enjeux contemporains de cette région continentale et archipélagique de plus de 680 millions d’habitants, et d’en comprendre les dynamiques d’intégration régionale et de connectivités avec le reste du monde. L’Asie du Sud-Est 2023 propose une analyse synthétique et détaillée des principaux événements politiques et diplomatiques, ainsi que des évolutions économiques, sociales et environnementales de l’année 2022 dans chacun des onze pays de la région. Ce décryptage est complété pour chaque pays par un focus sur deux personnalités de l’année et une actualité marquante en image. L’ouvrage propose également cinq dossiers thématiques qui abordent des sujets traités à l’échelle régionale sud-est asiatique : les ressorts institutionnels de l’approche de santé intégrée One Health, le vieillissement de la population et sa prise en compte par les politiques publiques, les câbles sous-marins au cœur de la connectivité sud-est asiatique, l’aménagement du bassin du Mékong et ses multiples acteurs, et les enjeux politiques et linguistiques des langues transnationales. Des outils pratiques sont également disponibles : une fiche et une chronologie par pays et un cahier des principaux indicateurs démographiques, sociaux, économiques et environnementaux
Sparks of GPTs in Edge Intelligence for Metaverse: Caching and Inference for Mobile AIGC Services
Aiming at achieving artificial general intelligence (AGI) for Metaverse,
pretrained foundation models (PFMs), e.g., generative pretrained transformers
(GPTs), can effectively provide various AI services, such as autonomous
driving, digital twins, and AI-generated content (AIGC) for extended reality.
With the advantages of low latency and privacy-preserving, serving PFMs of
mobile AI services in edge intelligence is a viable solution for caching and
executing PFMs on edge servers with limited computing resources and GPU memory.
However, PFMs typically consist of billions of parameters that are computation
and memory-intensive for edge servers during loading and execution. In this
article, we investigate edge PFM serving problems for mobile AIGC services of
Metaverse. First, we introduce the fundamentals of PFMs and discuss their
characteristic fine-tuning and inference methods in edge intelligence. Then, we
propose a novel framework of joint model caching and inference for managing
models and allocating resources to satisfy users' requests efficiently.
Furthermore, considering the in-context learning ability of PFMs, we propose a
new metric to evaluate the freshness and relevance between examples in
demonstrations and executing tasks, namely the Age of Context (AoC). Finally,
we propose a least context algorithm for managing cached models at edge servers
by balancing the tradeoff among latency, energy consumption, and accuracy
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Antecedents of business intelligence system use
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Organisational reliance on information has become vital for organisational competitiveness. With increasing data volumes, Business Intelligence (BI) becomes a cornerstone of the decision-support system. However, employee resistance to use Business Intelligence Systems (BIS) is evident. This creates a problem to organisations in realising the benefits of BIS. It is thus important to study the enablers of sustained use of BIS amongst employees.
This thesis identifies existing theories that can be used to study BI system use. It integrates and extends technology use theories through a framework focusing on Business Intelligence System Use (BISU). Empirical research is then conducted in Kuwait’s telecom and banking industries through a close-ended, self-administered questionnaire using a five-point Likert scale. Responses were received from 211 BI users. The data was analysed using SmartPLS to study the convergent and discriminant validity and reliability. Partial least squares structural equation modelling (PLS-SEM) was used to study the direct and indirect relationships between constructs and answer the hypotheses. In addition to SmartPLS, SPSS was used for descriptive analysis.
The results indicated that UTAUT factors consisting of performance expectancy, effort expectancy and social influence positively impact BI system use. Voluntariness of use was found to positively moderate the relationship between social influence and BI system use. Furthermore, BI system quality positively impacts both performance expectancy and effort expectancy. The BI user’s self-efficacy also positively impacts effort expectancy. In addition, social influence was found to be positively influenced by organisational factors, namely top management support and information culture.
The findings of this research contribute to literature by determining and quantifying the factors that influence BISU through the lens of employee perspectives. This thesis also explains how employees’ object-based beliefs about BI affect their behavioural beliefs, which in turn impact BISU. Limitations of this research include the omission of UTAUT’s facilitating conditions and the limited variance of respondent demographics
Entities and their genera: Slicing up the world the medieval way--and does it matter to formal ontology?
Genera, typically hand-in-hand with their branching species, are essential elements of vocabulary-based information constructs, in particular scientific taxonomies. Should they also feature in formal ontologies, the highest of such constructs? I argue in this article that the answer is “Yes” and that the question posed in its title also has a Yes-answer: The way medieval ontologists sliced up the world into genera does matter to formal ontology. More specifically, the way Dietrich of Freiberg, a Latin scholastic, conceived and applied strictly generic criteria to slice up the world into its entities can provide some guidelines to the field of formal ontology with respect to not only its contents, but also its scope. In particular, Dietrich's information criterion plays here a central role
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