AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2024
Diaz, T. G.; Lee, X. Y.; Zhuge, H.; Vidyaratne, L.; Sin, G.; Watanabe, T.; Farahat, A.; Gupta, C.
AI+AR based Framework for Guided Visual Equipment Diagnosis Proceedings Article
In: C.S., Kulkarni; M.E., Orchard (Ed.): Proc. Annu. Conf. Progn. Health Manag. Soc., PHM, Prognostics and Health Management Society, 2024, ISBN: 23250178 (ISSN); 978-193626305-9 (ISBN).
Abstract | Links | BibTeX | Tags: Augmented Reality, Automated solutions, Customer loyalty, Customer satisfaction, Customers' satisfaction, Diagnosis, Equipment diagnosis, Failure Diagnosis, Failure repairs, High quality, Knowledge graphs, Language Model, Quality of Service, Query languages, Sales, Support services
@inproceedings{diaz_aiar_2024,
title = {AI+AR based Framework for Guided Visual Equipment Diagnosis},
author = {T. G. Diaz and X. Y. Lee and H. Zhuge and L. Vidyaratne and G. Sin and T. Watanabe and A. Farahat and C. Gupta},
editor = {Kulkarni C.S. and Orchard M.E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210227167&doi=10.36001%2fphmconf.2024.v16i1.3909&partnerID=40&md5=897ac8045a48e2e80aa7522870c2004f},
doi = {10.36001/phmconf.2024.v16i1.3909},
isbn = {23250178 (ISSN); 978-193626305-9 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. Annu. Conf. Progn. Health Manag. Soc., PHM},
volume = {16},
publisher = {Prognostics and Health Management Society},
abstract = {Automated solutions for effective support services, such as failure diagnosis and repair, are crucial to keep customer satisfaction and loyalty. However, providing consistent, high quality, and timely support is a difficult task. In practice, customer support usually requires technicians to perform onsite diagnosis, but service quality is often adversely affected by limited expert technicians, high turnover, and minimal automated tools. To address these challenges, we present a novel solution framework for aiding technicians in performing visual equipment diagnosis. We envision a workflow where the technician reports a failure and prompts the system to automatically generate a diagnostic plan that includes parts, areas of interest, and necessary tasks. The plan is used to guide the technician with augmented reality (AR), while a perception module analyzes and tracks the technician’s actions to recommend next steps. Our framework consists of three components: planning, tracking, and guiding. The planning component automates the creation of a diagnostic plan by querying a knowledge graph (KG). We propose to leverage Large Language Models (LLMs) for the construction of the KG to accelerate the extraction process of parts, tasks, and relations from manuals. The tracking component enhances 3D detections by using perception sensors with a 2D nested object detection model. Finally, the guiding component reduces process complexity for technicians by combining 2D models and AR interactions. To validate the framework, we performed multiple studies to:1) determine an effective prompt method for the LLM to construct the KG; 2) demonstrate benefits of our 2D nested object model combined with AR model. © 2024 Prognostics and Health Management Society. All rights reserved.},
keywords = {Augmented Reality, Automated solutions, Customer loyalty, Customer satisfaction, Customers' satisfaction, Diagnosis, Equipment diagnosis, Failure Diagnosis, Failure repairs, High quality, Knowledge graphs, Language Model, Quality of Service, Query languages, Sales, Support services},
pubstate = {published},
tppubtype = {inproceedings}
}
Automated solutions for effective support services, such as failure diagnosis and repair, are crucial to keep customer satisfaction and loyalty. However, providing consistent, high quality, and timely support is a difficult task. In practice, customer support usually requires technicians to perform onsite diagnosis, but service quality is often adversely affected by limited expert technicians, high turnover, and minimal automated tools. To address these challenges, we present a novel solution framework for aiding technicians in performing visual equipment diagnosis. We envision a workflow where the technician reports a failure and prompts the system to automatically generate a diagnostic plan that includes parts, areas of interest, and necessary tasks. The plan is used to guide the technician with augmented reality (AR), while a perception module analyzes and tracks the technician’s actions to recommend next steps. Our framework consists of three components: planning, tracking, and guiding. The planning component automates the creation of a diagnostic plan by querying a knowledge graph (KG). We propose to leverage Large Language Models (LLMs) for the construction of the KG to accelerate the extraction process of parts, tasks, and relations from manuals. The tracking component enhances 3D detections by using perception sensors with a 2D nested object detection model. Finally, the guiding component reduces process complexity for technicians by combining 2D models and AR interactions. To validate the framework, we performed multiple studies to:1) determine an effective prompt method for the LLM to construct the KG; 2) demonstrate benefits of our 2D nested object model combined with AR model. © 2024 Prognostics and Health Management Society. All rights reserved.
Na, M.; Lee, J.
Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing Journal Article
In: Applied Sciences (Switzerland), vol. 14, no. 18, 2024, ISSN: 20763417 (ISSN).
Abstract | Links | BibTeX | Tags: Artificial intelligence technologies, Augmented Reality, benchmarking, Computation offloading, Edge computing, Energy Efficient, Generative adversarial networks, Generative AI, Image enhancement, Mobile augmented reality, Mobile edge computing, Multi-access edge computing, Multiaccess, Quality of Service, Resolution process, super-resolution, Superresolution, Trade off
@article{na_generative_2024,
title = {Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing},
author = {M. Na and J. Lee},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205236316&doi=10.3390%2fapp14188419&partnerID=40&md5=0aa1c42cb7343cfb55a9dc1e66494dc6},
doi = {10.3390/app14188419},
issn = {20763417 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Applied Sciences (Switzerland)},
volume = {14},
number = {18},
abstract = {This paper proposes a novel offloading and super-resolution (SR) control scheme for energy-efficient mobile augmented reality (MAR) in multi-access edge computing (MEC) using SR as a promising generative artificial intelligence (GAI) technology. Specifically, SR can enhance low-resolution images into high-resolution versions using GAI technologies. This capability is particularly advantageous in MAR by lowering the bitrate required for network transmission. However, this SR process requires considerable computational resources and can introduce latency, potentially overloading the MEC server if there are numerous offload requests for MAR services. In this context, we conduct an empirical study to verify that the computational latency of SR increases with the upscaling level. Therefore, we demonstrate a trade-off between computational latency and improved service satisfaction when upscaling images for object detection, as it enhances the detection accuracy. From this perspective, determining whether to apply SR for MAR, while jointly controlling offloading decisions, is challenging. Consequently, to design energy-efficient MAR, we rigorously formulate analytical models for the energy consumption of a MAR device, the overall latency and the MAR satisfaction of service quality from the enforcement of the service accuracy, taking into account the SR process at the MEC server. Finally, we develop a theoretical framework that optimizes the computation offloading and SR control problem for MAR clients by jointly optimizing the offloading and SR decisions, considering their trade-off in MAR with MEC. Finally, the performance evaluation indicates that our proposed framework effectively supports MAR services by efficiently managing offloading and SR decisions, balancing trade-offs between energy consumption, latency, and service satisfaction compared to benchmarks. © 2024 by the authors.},
keywords = {Artificial intelligence technologies, Augmented Reality, benchmarking, Computation offloading, Edge computing, Energy Efficient, Generative adversarial networks, Generative AI, Image enhancement, Mobile augmented reality, Mobile edge computing, Multi-access edge computing, Multiaccess, Quality of Service, Resolution process, super-resolution, Superresolution, Trade off},
pubstate = {published},
tppubtype = {article}
}
This paper proposes a novel offloading and super-resolution (SR) control scheme for energy-efficient mobile augmented reality (MAR) in multi-access edge computing (MEC) using SR as a promising generative artificial intelligence (GAI) technology. Specifically, SR can enhance low-resolution images into high-resolution versions using GAI technologies. This capability is particularly advantageous in MAR by lowering the bitrate required for network transmission. However, this SR process requires considerable computational resources and can introduce latency, potentially overloading the MEC server if there are numerous offload requests for MAR services. In this context, we conduct an empirical study to verify that the computational latency of SR increases with the upscaling level. Therefore, we demonstrate a trade-off between computational latency and improved service satisfaction when upscaling images for object detection, as it enhances the detection accuracy. From this perspective, determining whether to apply SR for MAR, while jointly controlling offloading decisions, is challenging. Consequently, to design energy-efficient MAR, we rigorously formulate analytical models for the energy consumption of a MAR device, the overall latency and the MAR satisfaction of service quality from the enforcement of the service accuracy, taking into account the SR process at the MEC server. Finally, we develop a theoretical framework that optimizes the computation offloading and SR control problem for MAR clients by jointly optimizing the offloading and SR decisions, considering their trade-off in MAR with MEC. Finally, the performance evaluation indicates that our proposed framework effectively supports MAR services by efficiently managing offloading and SR decisions, balancing trade-offs between energy consumption, latency, and service satisfaction compared to benchmarks. © 2024 by the authors.
2018
Napoli, Claudia Di; Sabatucci, Luca; Cossentino, Massimo
Automatising Mashup of Cloud Services with QoS Requirements Proceedings Article
In: Complex, Intelligent, and Software Intensive Systems: Proceedings of the 11th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2017), pp. 896–905, Springer International Publishing, 2018.
Abstract | Links | BibTeX | Tags: AAL for the Elderly, Automatic service composition, Mashup applications, Quality of Service
@inproceedings{dinapoliAutomatisingMashupCloud2018,
title = {Automatising Mashup of Cloud Services with QoS Requirements},
author = { Claudia Di Napoli and Luca Sabatucci and Massimo Cossentino},
doi = {10.1007/978-3-319-61566-0_85},
year = {2018},
date = {2018-01-01},
booktitle = {Complex, Intelligent, and Software Intensive Systems: Proceedings of the 11th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2017)},
pages = {896--905},
publisher = {Springer International Publishing},
abstract = {Service mashups represent an appealing business opportunity for companies since value added applications can be provided to fulfill clients' needs by integrating their services with the ones available on the Internet accessible according to standard Web Services technologies. Clients' needs are usually expressed in terms of a required functionality that can be obtained as a mashup application, together with specified QoS requirements referring to non-functional characteristics of the application, such as price, time, reliability. In order to make this opportunity a reality, mechanisms allowing for automatic selection and composition of services are necessary to avoid human intervention in the composition process. Here, a framework for automatic mashup of Cloud services taking into account QoS users' preferences, is presented. It relies on both AI planning techniques for automatic service composition, and software agent negotiation to select a composition that meets the specified QoS preferences. It allows for a dynamic QoS-based mashup of services since the QoS values provided for the single services in the composition are not fixed, but they could vary according to the providers' strategy. The proposed approach can be applied when services are provided in the context of a competitive market of service providers},
keywords = {AAL for the Elderly, Automatic service composition, Mashup applications, Quality of Service},
pubstate = {published},
tppubtype = {inproceedings}
}
Service mashups represent an appealing business opportunity for companies since value added applications can be provided to fulfill clients' needs by integrating their services with the ones available on the Internet accessible according to standard Web Services technologies. Clients' needs are usually expressed in terms of a required functionality that can be obtained as a mashup application, together with specified QoS requirements referring to non-functional characteristics of the application, such as price, time, reliability. In order to make this opportunity a reality, mechanisms allowing for automatic selection and composition of services are necessary to avoid human intervention in the composition process. Here, a framework for automatic mashup of Cloud services taking into account QoS users' preferences, is presented. It relies on both AI planning techniques for automatic service composition, and software agent negotiation to select a composition that meets the specified QoS preferences. It allows for a dynamic QoS-based mashup of services since the QoS values provided for the single services in the composition are not fixed, but they could vary according to the providers' strategy. The proposed approach can be applied when services are provided in the context of a competitive market of service providers
Napoli, Claudia Di; Sabatucci, Luca; Cossentino, Massimo
Automatising Mashup of Cloud Services with QoS Requirements Proceedings Article
In: Complex, Intelligent, and Software Intensive Systems: Proceedings of the 11th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2017), pp. 896–905, Springer International Publishing, 2018.
Abstract | Links | BibTeX | Tags: AAL for the Elderly, Automatic service composition, Mashup applications, Quality of Service
@inproceedings{di_napoli_automatising_2018,
title = {Automatising Mashup of Cloud Services with QoS Requirements},
author = {Claudia Di Napoli and Luca Sabatucci and Massimo Cossentino},
doi = {10.1007/978-3-319-61566-0_85},
year = {2018},
date = {2018-01-01},
booktitle = {Complex, Intelligent, and Software Intensive Systems: Proceedings of the 11th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2017)},
pages = {896–905},
publisher = {Springer International Publishing},
abstract = {Service mashups represent an appealing business opportunity for companies since value added applications can be provided to fulfill clients’ needs by integrating their services with the ones available on the Internet accessible according to standard Web Services technologies. Clients’ needs are usually expressed in terms of a required functionality that can be obtained as a mashup application, together with specified QoS requirements referring to non-functional characteristics of the application, such as price, time, reliability. In order to make this opportunity a reality, mechanisms allowing for automatic selection and composition of services are necessary to avoid human intervention in the composition process. Here, a framework for automatic mashup of Cloud services taking into account QoS users’ preferences, is presented. It relies on both AI planning techniques for automatic service composition, and software agent negotiation to select a composition that meets the specified QoS preferences. It allows for a dynamic QoS-based mashup of services since the QoS values provided for the single services in the composition are not fixed, but they could vary according to the providers’ strategy. The proposed approach can be applied when services are provided in the context of a competitive market of service providers},
keywords = {AAL for the Elderly, Automatic service composition, Mashup applications, Quality of Service},
pubstate = {published},
tppubtype = {inproceedings}
}
Service mashups represent an appealing business opportunity for companies since value added applications can be provided to fulfill clients’ needs by integrating their services with the ones available on the Internet accessible according to standard Web Services technologies. Clients’ needs are usually expressed in terms of a required functionality that can be obtained as a mashup application, together with specified QoS requirements referring to non-functional characteristics of the application, such as price, time, reliability. In order to make this opportunity a reality, mechanisms allowing for automatic selection and composition of services are necessary to avoid human intervention in the composition process. Here, a framework for automatic mashup of Cloud services taking into account QoS users’ preferences, is presented. It relies on both AI planning techniques for automatic service composition, and software agent negotiation to select a composition that meets the specified QoS preferences. It allows for a dynamic QoS-based mashup of services since the QoS values provided for the single services in the composition are not fixed, but they could vary according to the providers’ strategy. The proposed approach can be applied when services are provided in the context of a competitive market of service providers
2017
Napoli, Claudia Di; Sabatucci, Luca; Cossentino, Massimo; Rossi, Silvia
Generating and Instantiating Abstract Workflows with QoS User Requirements. Proceedings Article
In: ICAART (1), pp. 276–283, 2017.
Abstract | Links | BibTeX | Tags: Automatic service composition, Distributed computer systems, Dynamic workflow, Multiagent negotiation, Quality of Service
@inproceedings{dinapoliGeneratingInstantiatingAbstract2017,
title = {Generating and Instantiating Abstract Workflows with QoS User Requirements.},
author = { Claudia Di Napoli and Luca Sabatucci and Massimo Cossentino and Silvia Rossi},
doi = {10.5220/0006203902760283},
year = {2017},
date = {2017-01-01},
booktitle = {ICAART (1)},
pages = {276--283},
abstract = {The growing availability of services accessible through the network makes it possible to build complex applications resulting from their composition that are usually characterized also by non-functional properties, known as Quality of Service (QoS). To exploit the full potential of service technology, automatic QoS-based composition of services is crucial. In this work a framework for automatic service composition is presented that relies on planning and service negotiation techniques for addressing both functional and non-functional requirements. The proposed approach allows for dynamic service composition and QoS attributes, and it can be applied when services are provided in the contest of a competitive market of service providers without knowledge disclosure.},
keywords = {Automatic service composition, Distributed computer systems, Dynamic workflow, Multiagent negotiation, Quality of Service},
pubstate = {published},
tppubtype = {inproceedings}
}
The growing availability of services accessible through the network makes it possible to build complex applications resulting from their composition that are usually characterized also by non-functional properties, known as Quality of Service (QoS). To exploit the full potential of service technology, automatic QoS-based composition of services is crucial. In this work a framework for automatic service composition is presented that relies on planning and service negotiation techniques for addressing both functional and non-functional requirements. The proposed approach allows for dynamic service composition and QoS attributes, and it can be applied when services are provided in the contest of a competitive market of service providers without knowledge disclosure.
Napoli, Claudia Di; Sabatucci, Luca; Cossentino, Massimo; Rossi, Silvia
Generating and Instantiating Abstract Workflows with QoS User Requirements. Proceedings Article
In: ICAART (1), pp. 276–283, 2017.
Abstract | Links | BibTeX | Tags: Automatic service composition, Distributed computer systems, Dynamic workflow, Multiagent negotiation, Quality of Service
@inproceedings{di_napoli_generating_2017,
title = {Generating and Instantiating Abstract Workflows with QoS User Requirements.},
author = {Claudia Di Napoli and Luca Sabatucci and Massimo Cossentino and Silvia Rossi},
doi = {10.5220/0006203902760283},
year = {2017},
date = {2017-01-01},
booktitle = {ICAART (1)},
pages = {276–283},
abstract = {The growing availability of services accessible through the network makes it possible to build complex applications resulting from their composition that are usually characterized also by non-functional properties, known as Quality of Service (QoS). To exploit the full potential of service technology, automatic QoS-based composition of services is crucial. In this work a framework for automatic service composition is presented that relies on planning and service negotiation techniques for addressing both functional and non-functional requirements. The proposed approach allows for dynamic service composition and QoS attributes, and it can be applied when services are provided in the contest of a competitive market of service providers without knowledge disclosure.},
keywords = {Automatic service composition, Distributed computer systems, Dynamic workflow, Multiagent negotiation, Quality of Service},
pubstate = {published},
tppubtype = {inproceedings}
}
The growing availability of services accessible through the network makes it possible to build complex applications resulting from their composition that are usually characterized also by non-functional properties, known as Quality of Service (QoS). To exploit the full potential of service technology, automatic QoS-based composition of services is crucial. In this work a framework for automatic service composition is presented that relies on planning and service negotiation techniques for addressing both functional and non-functional requirements. The proposed approach allows for dynamic service composition and QoS attributes, and it can be applied when services are provided in the contest of a competitive market of service providers without knowledge disclosure.