73 research outputs found
High-Performance Modelling and Simulation for Big Data Applications
This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications
High-Performance Modelling and Simulation for Big Data Applications
This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications
Quantifying the impact of Twitter activity in political battlegrounds
It may be challenging to determine the reach of the information, how well it corresponds with
the domain design, and how to utilize it as a communication medium when utilizing social
media platforms, notably Twitter, to engage the public in advocating a parliament act, or
during a global health emergency. Chapter 3 offers a broad overview of how candidates running in the 2020 US Elections used Twitter as a communication tool to interact with voters.
More precisely, it seeks to identify components related to internal collaboration and public
participation (in terms of content and stance similarity among the candidates from the same
political front and to the official Twitter accounts of their political parties). The 2020 US
Presidential and Vice Presidential candidates from the two main political parties, the Republicans and Democrats, are our main subjects. Along with the content similarity, their tweets
were assessed for social reach and stance similarity on 22 topics. This study complements
previous research on efficiently using social media platforms for election campaigns. Chapter 4 empirically examines the online social associations of the top-10 COVID-19 resilient
nations’ leaders and healthcare institutions based on the Bloomberg COVID-19 Resilience
Ranking. In order to measure the strength of the online social association in terms of public
engagement, sentiment strength, inclusivity and diversity, we used the attributes provided
by Twitter Academic Research API, coupled with the tweets of leaders and healthcare organizations from these nations. Understanding how leaders and healthcare organizations may
utilize Twitter to establish digital connections with the public during health emergencies is
made more accessible by this study. The thesis has proposed methods for efficiently using
Twitter in various domains, utilizing the implementations of various Language Models and
several data mining and analytics techniques
Large Geographical Area Aerial Surveillance Systems Data Network Infrastructure Managed by Artificial Intelligence and Certified Over Blockchain: a Review
This paper proposes an aerial data network infrastructure for large geographical area surveillance systems. The work presents a review of previous works from the authors, existing technologies in the market and other scientific work, with the goal of creating a data network supported by Autonomous Tethered Aerostat Airships used for sensor fixing, drones deployment base and meshed data network nodes installation. The proposed approach for data network infrastructure supports several independent and heterogeneous services from independent, private and public companies. The presented solution employs Edge Artificial Intelligence (AI) systems for autonomous infrastructure management. The Edge AI used in the presented solution enables the AI management solution to work without the need of a permanent connection to cloud services and is constantly feed by the locally generated sensor data. These systems interact with other network AI services to accomplish coordinated tasks. Blockchain technology services are deployed to ensure secure and auditable decisions and operations, validated by the different involved ledgers
Artificial Intelligence Index Report 2023 - HAI
Le ‘Human-Centered Artificial Intelligence (HAI, Stanford University) annonce la publication de son rapport annuel « Artificial Intelligence Index Report 2023″ qui rassemble et visualise les tendances relatives à l’intelligence artificielle
Hierarchical Text Classification: a review of current research
t is often the case that collections of documents are annotated with hierarchically-structured concepts. However, the benefits of this structure are rarely taken into account by commonly-used classification techniques. Conversely, Hierarchical Text Classification methods are devisedto take advantage of the labels’ organization to boost classification performance. With this work,we aim to deliver an updated overview of current research in this domain. We begin by definingthe task and framing it within the broader text classification area, examining important shared concepts such as text representation. Then, we dive into details regarding the specific task,providing a high-level description of its traditional approaches. We then summarize recentlyproposed methods, highlighting their main contributions. We additionally provide statisticsfor the most adopted datasets and describe the benefits of using evaluation metrics tailored to hierarchical settings. Finally, a selection of recent proposals is benchmarked against
non-hierarchical baselines on five domain-specific datasets
Graph Learning and Its Applications: A Holistic Survey
Graph learning is a prevalent domain that endeavors to learn the intricate
relationships among nodes and the topological structure of graphs. These
relationships endow graphs with uniqueness compared to conventional tabular
data, as nodes rely on non-Euclidean space and encompass rich information to
exploit. Over the years, graph learning has transcended from graph theory to
graph data mining. With the advent of representation learning, it has attained
remarkable performance in diverse scenarios, including text, image, chemistry,
and biology. Owing to its extensive application prospects, graph learning
attracts copious attention from the academic community. Despite numerous works
proposed to tackle different problems in graph learning, there is a demand to
survey previous valuable works. While some researchers have perceived this
phenomenon and accomplished impressive surveys on graph learning, they failed
to connect related objectives, methods, and applications in a more coherent
way. As a result, they did not encompass current ample scenarios and
challenging problems due to the rapid expansion of graph learning. Different
from previous surveys on graph learning, we provide a holistic review that
analyzes current works from the perspective of graph structure, and discusses
the latest applications, trends, and challenges in graph learning.
Specifically, we commence by proposing a taxonomy from the perspective of the
composition of graph data and then summarize the methods employed in graph
learning. We then provide a detailed elucidation of mainstream applications.
Finally, based on the current trend of techniques, we propose future
directions.Comment: 20 pages, 7 figures, 3 table
Pathway to Future Symbiotic Creativity
This report presents a comprehensive view of our vision on the development
path of the human-machine symbiotic art creation. We propose a classification
of the creative system with a hierarchy of 5 classes, showing the pathway of
creativity evolving from a mimic-human artist (Turing Artists) to a Machine
artist in its own right. We begin with an overview of the limitations of the
Turing Artists then focus on the top two-level systems, Machine Artists,
emphasizing machine-human communication in art creation. In art creation, it is
necessary for machines to understand humans' mental states, including desires,
appreciation, and emotions, humans also need to understand machines' creative
capabilities and limitations. The rapid development of immersive environment
and further evolution into the new concept of metaverse enable symbiotic art
creation through unprecedented flexibility of bi-directional communication
between artists and art manifestation environments. By examining the latest
sensor and XR technologies, we illustrate the novel way for art data collection
to constitute the base of a new form of human-machine bidirectional
communication and understanding in art creation. Based on such communication
and understanding mechanisms, we propose a novel framework for building future
Machine artists, which comes with the philosophy that a human-compatible AI
system should be based on the "human-in-the-loop" principle rather than the
traditional "end-to-end" dogma. By proposing a new form of inverse
reinforcement learning model, we outline the platform design of machine
artists, demonstrate its functions and showcase some examples of technologies
we have developed. We also provide a systematic exposition of the ecosystem for
AI-based symbiotic art form and community with an economic model built on NFT
technology. Ethical issues for the development of machine artists are also
discussed
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