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192 results on '"Kosta, Sokol"'

2. GAMAI, an AI-Powered Programming Exercise Gamifier Tool

Author

Montella, Raffaele, Giuseppe De Vita, Ciro, Mellone, Gennaro, Ciricillo, Tullio, Caramiello, Dario, Di Luccio, Diana, Kosta, Sokol, Damasevicius, Robertas, Maskeliunas, Rytis, Queiros, Ricardo, Swacha, Jakub, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Olney, Andrew M., editor, Chounta, Irene-Angelica, editor, Liu, Zitao, editor, Santos, Olga C., editor, and Bittencourt, Ig Ibert, editor

Published
2024
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3. Performance Analysis of the IOTA Chrysalis on Heterogeneous Devices

Author

Waleed, Muhammad, Skouby, Knud Erik, Kosta, Sokol, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Machado, José Manuel, editor, Prieto, Javier, editor, Vieira, Paulo, editor, Peixoto, Hugo, editor, Abelha, António, editor, Arroyo, David, editor, and Vigneri, Luigi, editor

Published
2023
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4. Decentralized Identifiers for IoT Systems: Current Status and Future Vision

Author

Waleed, Muhammad, Skouby, Knud Erik, Kosta, Sokol, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mehmood, Rashid, editor, Alves, Victor, editor, Praça, Isabel, editor, Wikarek, Jarosław, editor, Parra-Domínguez, Javier, editor, Loukanova, Roussanka, editor, de Miguel, Ignacio, editor, Pinto, Tiago, editor, Nunes, Ricardo, editor, and Ricca, Michela, editor

Published
2023
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5. ML for Attack and Defense of PUFs: Current Status and Future Directions

Author

Ferens, Mieszko, Dushku, Edlira, Kosta, Sokol, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mehmood, Rashid, editor, Alves, Victor, editor, Praça, Isabel, editor, Wikarek, Jarosław, editor, Parra-Domínguez, Javier, editor, Loukanova, Roussanka, editor, de Miguel, Ignacio, editor, Pinto, Tiago, editor, Nunes, Ricardo, editor, and Ricca, Michela, editor

Published
2023
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6. On the Feasibility of Real-Time 3D Hand Tracking using Edge GPGPU Acceleration

Author

Qammaz, Ammar, Kosta, Sokol, Kyriazis, Nikolaos, and Argyros, Antonis

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper presents the case study of a non-intrusive porting of a monolithic C++ library for real-time 3D hand tracking, to the domain of edge-based computation. Towards a proof of concept, the case study considers a pair of workstations, a computationally powerful and a computationally weak one. By wrapping the C++ library in Java container and by capitalizing on a Java-based offloading infrastructure that supports both CPU and GPGPU computations, we are able to establish automatically the required server-client workflow that best addresses the resource allocation problem in the effort to execute from the weak workstation. As a result, the weak workstation can perform well at the task, despite lacking the sufficient hardware to do the required computations locally. This is achieved by offloading computations which rely on GPGPU, to the powerful workstation, across the network that connects them. We show the edge-based computation challenges associated with the information flow of the ported algorithm, demonstrate how we cope with them, and identify what needs to be improved for achieving even better performance., Comment: 6 pages, 5 figures

Published
2018

8. FGPE Gamification Service: A GraphQL Service to Gamify Online Education

Author

Paiva, José Carlos, Haraszczuk, Alicja, Queirós, Ricardo, Leal, José Paulo, Swacha, Jakub, Kosta, Sokol, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Rocha, Álvaro, editor, Adeli, Hojjat, editor, Dzemyda, Gintautas, editor, Moreira, Fernando, editor, and Ramalho Correia, Ana Maria, editor

Published
2021
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9. The impact of decision tools during oncological consultation with lung cancer patients: A systematic review within the I3LUNG project

Author

Sebri, Valeria, primary, Marzorati, Chiara, additional, Dorangricchia, Patrizia, additional, Monzani, Dario, additional, Grasso, Roberto, additional, Prelaj, Arsela, additional, Provenzano, Leonardo, additional, Mazzeo, Laura, additional, Dumitrascu, Andra Diana, additional, Sonnek, Jana, additional, Szewczyk, Marlen, additional, Watermann, Iris, additional, Trovò, Francesco, additional, Dollis, Nina, additional, Sarris, Evangelos, additional, Garassino, Marina Chiara, additional, Bestvina, Christine M., additional, Pedrocchi, Alessandra, additional, Ambrosini, Emilia, additional, Kosta, Sokol, additional, Felip, Enriqueta, additional, Soleda, Mireia, additional, Roca, Aina Arbusà, additional, Rodríguez‐Morató, Jose, additional, Nuara, Alessandro, additional, Lourie, Yonah, additional, Fernandez‐Pinto, Melissa, additional, Aguaron, Alfonso, additional, and Pravettoni, Gabriella, additional

Published
2024
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11. Keep Your Nice Friends Close, but Your Rich Friends Closer -- Computation Offloading Using NFC

Author

Sucipto, Kathleen, Chatzopoulos, Dimitris, Kosta, Sokol, and Hui, Pan

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Cryptography and Security
Abstract

The increasing complexity of smartphone applications and services necessitate high battery consumption but the growth of smartphones' battery capacity is not keeping pace with these increasing power demands. To overcome this problem, researchers gave birth to the Mobile Cloud Computing (MCC) research area. In this paper we advance on previous ideas, by proposing and implementing the first known Near Field Communication (NFC)-based computation offloading framework. This research is motivated by the advantages of NFC's short distance communication, with its better security, and its low battery consumption. We design a new NFC communication protocol that overcomes the limitations of the default protocol; removing the need for constant user interaction, the one-way communication restraint, and the limit on low data size transfer. We present experimental results of the energy consumption and the time duration of two computationally intensive representative applications: (i) RSA key generation and encryption, and (ii) gaming/puzzles. We show that when the helper device is more powerful than the device offloading the computations, the execution time of the tasks is reduced. Finally, we show that devices that offload application parts considerably reduce their energy consumption due to the low-power NFC interface and the benefits of offloading., Comment: 9 pages, 4 tables, 13 figures

Published
2016

13. Security and Storage Issues in Internet of Floating Things Edge-Cloud Data Movement

Author

Montella, Raffaele, Di Luccio, Diana, Kosta, Sokol, Castiglione, Aniello, Maratea, Antonio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wyrzykowski, Roman, editor, Deelman, Ewa, editor, Dongarra, Jack, editor, and Karczewski, Konrad, editor

Published
2020
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14. Leveraging Large Language Models to Support Authoring Gamified Programming Exercises †.

Author

Montella, Raffaele, De Vita, Ciro Giuseppe, Mellone, Gennaro, Ciricillo, Tullio, Caramiello, Dario, Di Luccio, Diana, Kosta, Sokol, Damaševičius, Robertas, Maskeliūnas, Rytis, Queirós, Ricardo, and Swacha, Jakub

Subjects
LANGUAGE models, ARTIFICIAL intelligence, NATURAL languages, GAMIFICATION, SUPPLY & demand
Abstract

Featured Application: The presented solution can be applied to simplify and hasten the development of gamified programming exercises conforming to the Framework for Gamified Programming Education (FGPE) standard. Skilled programmers are in high demand, and a critical obstacle to satisfying this demand is the difficulty of acquiring programming skills. This issue can be addressed with automated assessment, which gives fast feedback to students trying to code, and gamification, which motivates them to intensify their learning efforts. Although some collections of gamified programming exercises are available, producing new ones is very demanding. This paper presents GAMAI, an AI-powered exercise gamifier, enriching the Framework for Gamified Programming Education (FGPE) ecosystem. Leveraging large language models, GAMAI enables teachers to effortlessly apply storytelling to describe a gamified scenario, as GAMAI decorates natural language text with the sentences needed by OpenAI APIs to contextualize the prompt. Once a gamified scenario has been generated, GAMAI automatically produces exercise files in a FGPE-compatible format. According to the presented evaluation results, most gamified exercises generated with AI support were ready to be used, with no or minimum human effort, and were positively assessed by students. The usability of the software was also assessed as high by the users. Our research paves the way for a more efficient and interactive approach to programming education, leveraging the capabilities of advanced language models in conjunction with gamification principles. [ABSTRACT FROM AUTHOR]

Published
2024
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17. CUDA Virtualization and Remoting for GPGPU Based Acceleration Offloading at the Edge

Author

Mentone, Antonio, Di Luccio, Diana, Landolfi, Luca, Kosta, Sokol, Montella, Raffaele, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Montella, Raffaele, editor, Ciaramella, Angelo, editor, Fortino, Giancarlo, editor, Guerrieri, Antonio, editor, and Liotta, Antonio, editor

Published
2019
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18. Load Balancing in Hybrid Clouds Through Process Mining Monitoring

Author

Azumah, Kenneth K., Kosta, Sokol, Sørensen, Lene T., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Montella, Raffaele, editor, Ciaramella, Angelo, editor, Fortino, Giancarlo, editor, Guerrieri, Antonio, editor, and Liotta, Antonio, editor

Published
2019
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20. Unleashing the Power of Mobile Cloud Computing using ThinkAir

Author

Kosta, Sokol, Aucinas, Andrius, Hui, Pan, Mortier, Richard, and Zhang, Xinwen

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Networking and Internet Architecture, Computer Science - Operating Systems
Abstract

Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method level computation offloading. Advancing on previous works, it focuses on the elasticity and scalability of the server side and enhances the power of mobile cloud computing by parallelizing method execution using multiple Virtual Machine (VM) images. We evaluate the system using a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for the N-queens puzzle and one order of magnitude for a face detection and a virus scan application, using cloud offloading. We then show that if a task is parallelizable, the user can request more than one VM to execute it, and these VMs will be provided dynamically. In fact, by exploiting parallelization, we achieve a greater reduction on the execution time and energy consumption for the previous applications. Finally, we use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements., Comment: 17 pages

Published
2011

21. Using GPGPU Accelerated Interpolation Algorithms for Marine Bathymetry Processing with On-Premises and Cloud Based Computational Resources

Author

Marcellino, Livia, Montella, Raffaele, Kosta, Sokol, Galletti, Ardelio, Di Luccio, Diana, Santopietro, Vincenzo, Ruggieri, Mario, Lapegna, Marco, D’Amore, Luisa, Laccetti, Giuliano, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Wyrzykowski, Roman, editor, Dongarra, Jack, editor, Deelman, Ewa, editor, and Karczewski, Konrad, editor

Published
2018
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22. Performance, Resilience, and Security in Moving Data from the Fog to the Cloud: The DYNAMO Transfer Framework Approach

Author

Montella, Raffaele, Di Luccio, Diana, Kosta, Sokol, Giunta, Giulio, Foster, Ian, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Xiang, Yang, editor, Sun, Jingtao, editor, Fortino, Giancarlo, editor, Guerrieri, Antonio, editor, and Jung, Jason J., editor

Published
2018
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25. Enabling Android-Based Devices to High-End GPGPUs

Author

Montella, Raffaele, Ferraro, Carmine, Kosta, Sokol, Pelliccia, Valentina, Giunta, Giulio, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Carretero, Jesus, editor, Garcia-Blas, Javier, editor, Ko, Ryan K.L., editor, Mueller, Peter, editor, and Nakano, Koji, editor

Published
2016
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28. The EU-funded I3LUNG Project: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care With Immunotherapy

Author

Prelaj, Arsela, primary, Ganzinelli, Monica, additional, Trovo’, Francesco, additional, Roisman, Laila C., additional, Pedrocchi, Alessandra Laura Giulia, additional, Kosta, Sokol, additional, Restelli, Marcello, additional, Ambrosini, Emilia, additional, Broggini, Massimo, additional, Pravettoni, Gabriella, additional, Monzani, Dario, additional, Nuara, Alessandro, additional, Amat, Ramon, additional, Spathas, Nikos, additional, Willis, Michael, additional, Pearson, Alexander, additional, Dolezal, James, additional, Mazzeo, Laura, additional, Sangaletti, Sabina, additional, Correa, Ana Maria, additional, Aguaron, Alfonso, additional, Watermann, Iris, additional, Popa, Crina, additional, Raimondi, Giulia, additional, Triulzi, Tiziana, additional, Steurer, Stefan, additional, Lo Russo, Giuseppe, additional, Linardou, Helena, additional, Peled, Nir, additional, Felip, Enriqueta, additional, Reck, Martin, additional, and Garassino, Marina Chiara, additional

Published
2023
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36. Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

Author

Prelaj, Arsela, Galli, Edoardo Gregorio, Miskovic, Vanja, Pesenti, Mattia, Viscardi, Giuseppe, Pedica, Benedetta, Mazzeo, Laura, Bottiglieri, Achille, Provenzano, Leonardo, Spagnoletti, Andrea, Marinacci, Roberto, De Toma, Alessandro, Proto, Claudia, Ferrara, Roberto, Brambilla, Marta, Occhipinti, Mario, Manglaviti, Sara, Galli, Giulia, Signorelli, Diego, Giani, Claudia, Beninato, Teresa, Pircher, Chiara Carlotta, Rametta, Alessandro, Kosta, Sokol, Zanitti, Michele, Di Mauro, Maria Rosa, Rinaldi, Arturo, Di Gregorio, Settimio, Antonia, Martinetti, Garassino, Marina Chiara, de Braud, Filippo G.M., Restelli, Marcello, Lo Russo, Giuseppe, Ganzinelli, Monica, Trovò, Francesco, and Pedrocchi, Alessandra Laura Giulia

Subjects
Cancer Research, machine learning, explainable artificial intelligence, treatment, Oncology, immunotherapy, non-small cell lung cancer
Abstract

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients.

Published
2023
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40. AIQUAM: Artificial Intelligence-based water QUAlity Model

Author

De Vita, Ciro Giuseppe, Mellone, Gennaro, Di Luccio, Diana, Kosta, Sokol, Ciaramella, Angelo, and Montella, Raffaele

Subjects
High-Performance Computing, Marine pollution forecast, Lagrangian model, Artificial Intelligence, poster-presentation
Abstract

Monitoring the impact of the pollutants on the sea is a crucial issue for coastal human activities, such as aquaculture. However, leveraging a continuous microbiological laboratory analysis is unfeasible for costs and practical reasons. Fish and mussel farms are critically sensitive to seawater quality and thus require continuous monitoring to enforce food security and prevent any possible disease affecting human health. Here we present a novel methodology finalized to predict water quality as categorized indexes leveraging an integrated approach between computational components and artificial intelligence techniques. As a paradigm demonstrator, we couple WaComM++ with AIQUAM. The use case presented is an application of AIQUAM in the Bay of Naples (Campania Region, Italy) for predicting bacteria contaminants in mussel farms. The results are encouraging as the model reached a correct prediction rate of 93%.

Published
2022
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49. Collaborative Automation and IoT Technologies for Coastal Ocean Observing Systems

Author

Mariani, Patrizio, Bachmayer, Ralf, Kosta, Sokol, Pietrosemoli, Ermanno, Ardelan, Murat V., Connelly, Douglas P., Delory, Eric, Pearlman, Jay S., Petihakis, George, Thompson, Fletcher, Crise, Alessandro, Mariani, Patrizio, Bachmayer, Ralf, Kosta, Sokol, Pietrosemoli, Ermanno, Ardelan, Murat V., Connelly, Douglas P., Delory, Eric, Pearlman, Jay S., Petihakis, George, Thompson, Fletcher, and Crise, Alessandro

Abstract

Coastal observing systems are typically nationally funded and built around national priorities. As a result, there are presently significant differences between countries in terms of sustainability, observing capacity and technologies, as well as methods and research priorities. Ocean observing systems in coastal areas must now move toward an integrated, multidisciplinary and multiscale system of systems, where heterogeneity should be exploited to deliver fit-for-purpose products that answer the diversity and complexity of the requirements from stakeholders and end-users. Essential elements of such distributed observation systems are the use of machine-to-machine communication, data fusion and processing applying recent technological developments for the Internet of Things (IoT) toward a common cyberinfrastructure. This perspective paper illustrates some of the challenges for sustained coastal observations and provides details on how to address present gaps. We discuss the role of collaborative robotics between unmanned platforms in coastal areas and the methods to benefit from IoT technologies. Given present trends in cost-effective solutions in ocean sensors and electronics, and methods for marine automation and communication, we consider that a distributed observation system can effectively provide timely information in coastal regions around the world, including those areas that are today poorly observed (e.g., developing countries). Adaptation in space and time of the sensing nodes, and the flexibility in handling different sensing platforms can provide to the system the ability to quickly respond to the rapid changes in oceanic and climatic processes, as well as to promptly respond to evolving stakeholder and end-user requirements.

Published
2021

50. Future Factories

Author

Vibild, Patrick, primary, Dimitrova, Demira, additional, Haraszczuk, Alicja, additional, Gabryel, Szymon, additional, Kosta, Sokol, additional, and Stefanović, Čedomir, additional

Published
2020
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