664 research outputs found

    Architecture for Collaborative Learning Activities in Hybrid Learning Environments

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    3D virtual worlds are recognized as collaborative learning environments. However, the underlying technology is not sufficiently mature and the virtual worlds look cartoonish, unlinked to reality. Thus, it is important to enrich them with elements from the real world to enhance student engagement in learning activities. Our approach is to build learning environments where participants can either be in the real world or in its mirror world while sharing the same hybrid space in a collaborative learning experience. This paper focuses on the system architecture and a usability study of a proof-of-concept for these hybrid learning environments. The architecture allows the integration of the real world and its 3D virtual mirror; the exchange and geolocalization of multimodal information, and also the orchestration of learning activities. The results of the usability evaluation show positive engagement effects on participants in the mirror world and, to a lesser extent, on those in the real world.This research has been partially supported by the following projects: “España Virtual” within the Ingenio 2010 program, subcontracted by Elecnor Deimos, "EEE" (TIN2011-28308-C03-01) funded by the Spanish National Plan of Research, Development and Innovation, and "eMadrid", S2009/TIC-1650 “Investigación y Desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” funded by the Region of Madrid.Publicad

    Object selection and scaling using multimodal interaction in mixed reality

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    Mixed Reality (MR) is the next evolution of human interacting with the computer as MR has the ability to combine the physical environment and digital environment and making them coexist with each other. Interaction is still a huge research area in Augmented Reality (AR) but very less in MR, this is due to current advanced MR display techniques still not robust and intuitive enough to let the user to naturally interact with 3D content. New techniques on user interaction have been widely studied, the advanced technique in interaction when the system able to invoke more than one input modalities. Multimodal interaction undertakes to deliver intuitive multiple objects manipulation with gestures. This paper discusses the multimodal interaction technique using gesture and speech which the proposed experimental setup to implement multimodal in the MR interface. The real hand gesture is combined with speech inputs in MR to perform spatial object manipulations. The paper explains the implementation stage that involves interaction using gesture and speech inputs to enhance user experience in MR workspace. After acquiring gesture input and speech commands, spatial manipulation for selection and scaling using multimodal interaction has been invoked, and this paper ends with a discussion

    Intelligent Agents and Their Potential for Future Design and Synthesis Environment

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    This document contains the proceedings of the Workshop on Intelligent Agents and Their Potential for Future Design and Synthesis Environment, held at NASA Langley Research Center, Hampton, VA, September 16-17, 1998. The workshop was jointly sponsored by the University of Virginia's Center for Advanced Computational Technology and NASA. Workshop attendees came from NASA, industry and universities. The objectives of the workshop were to assess the status of intelligent agents technology and to identify the potential of software agents for use in future design and synthesis environment. The presentations covered the current status of agent technology and several applications of intelligent software agents. Certain materials and products are identified in this publication in order to specify adequately the materials and products that were investigated in the research effort. In no case does such identification imply recommendation or endorsement of products by NASA, nor does it imply that the materials and products are the only ones or the best ones available for this purpose. In many cases equivalent materials and products are available and would probably produce equivalent results

    An artificial intelligence-based collaboration approach in industrial IoT manufacturing : key concepts, architectural extensions and potential applications

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    The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented

    The application of intelligent agents in libraries: a survey

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    Purpose - The purpose of this article is to provide a comprehensive literature review on the utilisation of intelligent agent technology in the library environment. Design/methodology/approach - Research papers since 1990 on the use of various intelligent agent technologies in libraries are divided into two main application areas: digital library (DL), including agent-based DL projects, multi-agent architecture for DLs, intelligent agents for distributed heterogeneous information retrieval and agent support to information search process in DLs; and services in traditional libraries, including user interface for library information systems, automatic reference services and multi-agent architecture for library services. For each paper on the topic, its new ideas or models, referred work, analyses, experiments, findings and conclusions are addressed. Findings - The majority of the literature covers DLs and there have been fewer studies about services in traditional libraries. A variety of architecture, framework and models integrating agent technology in library systems or services are proposed, but only a few have been implemented in the practical environment. The application of agent technology is still at the research and experimentation stage. Agent technology has great potential in many areas in the library context; however it presents challenges to libraries that want to be involved in its adoption. Practical implications - The survey has practical implications for libraries, librarians and computer professionals in developing projects that employ intelligent agent technology to meet end-users\u27 expectations as well as to improve information services within limited resources in library settings. Originality/value - The paper provides a comprehensive survey on the development and research of intelligent agents in libraries in literature

    Software architectural support for tangible user interfaces in distributed, heterogeneous computing environments

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    This research focuses on tools that support the development of tangible interaction-based applications for distributed computing environments. Applications built with these tools are capable of utilizing heterogeneous resources for tangible interaction and can be reconfigured for different contexts with minimal code changes. Current trends in computing, especially in areas such as computational science, scientific visualization and computer supported collaborative work, foreshadow increasing complexity, distribution and remoteness of computation and data. These trends imply that tangible interface developers must address concerns of both tangible interaction design and networked distributed computing. In this dissertation, we present a software architecture that supports separation of these concerns. Additionally, a tangibles-based software development toolkit based on this architecture is presented that enables the logic of elements within a tangible user interface to be mapped to configurations that vary in the number, type and location of resources within a given tangibles-based system

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    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

    Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

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    Machine Learning models are being deployed as parts of real-world systems with the upsurge of interest in artificial intelligence. The design, implementation, and maintenance of such systems are challenged by real-world environments that produce larger amounts of heterogeneous data and users requiring increasingly faster responses with efficient resource consumption. These requirements push prevalent software architectures to the limit when deploying ML-based systems. Data-oriented Architecture (DOA) is an emerging concept that equips systems better for integrating ML models. DOA extends current architectures to create data-driven, loosely coupled, decentralised, open systems. Even though papers on deployed ML-based systems do not mention DOA, their authors made design decisions that implicitly follow DOA. The reasons why, how, and the extent to which DOA is adopted in these systems are unclear. Implicit design decisions limit the practitioners' knowledge of DOA to design ML-based systems in the real world. This paper answers these questions by surveying real-world deployments of ML-based systems. The survey shows the design decisions of the systems and the requirements these satisfy. Based on the survey findings, we also formulate practical advice to facilitate the deployment of ML-based systems. Finally, we outline open challenges to deploying DOA-based systems that integrate ML models.Comment: Under revie

    Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

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    The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.publishedVersio

    BC4LLM: Trusted Artificial Intelligence When Blockchain Meets Large Language Models

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    In recent years, artificial intelligence (AI) and machine learning (ML) are reshaping society's production methods and productivity, and also changing the paradigm of scientific research. Among them, the AI language model represented by ChatGPT has made great progress. Such large language models (LLMs) serve people in the form of AI-generated content (AIGC) and are widely used in consulting, healthcare, and education. However, it is difficult to guarantee the authenticity and reliability of AIGC learning data. In addition, there are also hidden dangers of privacy disclosure in distributed AI training. Moreover, the content generated by LLMs is difficult to identify and trace, and it is difficult to cross-platform mutual recognition. The above information security issues in the coming era of AI powered by LLMs will be infinitely amplified and affect everyone's life. Therefore, we consider empowering LLMs using blockchain technology with superior security features to propose a vision for trusted AI. This paper mainly introduces the motivation and technical route of blockchain for LLM (BC4LLM), including reliable learning corpus, secure training process, and identifiable generated content. Meanwhile, this paper also reviews the potential applications and future challenges, especially in the frontier communication networks field, including network resource allocation, dynamic spectrum sharing, and semantic communication. Based on the above work combined and the prospect of blockchain and LLMs, it is expected to help the early realization of trusted AI and provide guidance for the academic community
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