Publications

@inproceedings{trifiroHowAreYou2020,

title = {How Are You? How a Robot Can Learn to Express Its Own Roboceptions},

author = { Irene Trifir{`o} and Agnese Augello and Umberto Maniscalco and Giovanni Pilato and Filippo Vella and Rosa Meo},

editor = { Matteo Cristiani and Carlos Toro and Cecilia {Zanni-Merk} and Robert J. Howlett and Lakhmi C. Jain},

doi = {10.1016/j.procs.2020.08.050},

year  = {2020},

date = {2020-01-01},

booktitle = {Procedia Computer Science},

volume = {176},

pages = {480--489},

publisher = {Elsevier B.V.},

abstract = {This work is framed on investigating how a robot can learn associations between linguistic elements, such as words or sentences, and its bodily perceptions, that we named ``roboceptions''. We discuss the possibility of defining such a process of an association through the interaction with human beings. By interacting with a user, the robot can learn to ascribe a meaning to its roboceptions to express them in natural language. Such a process could then be used by the robot in a verbal interaction to detect some words recalling the previously experimented roboceptions. In this paper, we discuss a Dual-NMT approach to realize such an association. However, it requires adequate training corpus. For this reason, we consider two different phases towards the realization of the system, and we show the results of the first phase, comparing two approaches: one based on the Latent Semantic Analysis paradigm and one based on the Random Indexing methodology.},

keywords = {Human computer interaction, Knowledge Representation, Latent Semantic Analysis, Natural Language Processing, Robotics, Semantic Computing, Social Robots},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{trifiro_how_2020,

title = {How are you? How a robot can learn to express its own roboceptions},

author = {Irene Trifirò and Agnese Augello and Umberto Maniscalco and Giovanni Pilato and Filippo Vella and Rosa Meo},

editor = {Matteo Cristiani and Carlos Toro and Cecilia Zanni-Merk and Robert J. Howlett and Lakhmi C. Jain},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093358258&doi=10.1016%2fj.procs.2020.08.050&partnerID=40&md5=d262d3c7852f492f6a871ed2c4b7e941},

doi = {10.1016/j.procs.2020.08.050},

year  = {2020},

date = {2020-01-01},

booktitle = {Procedia Computer Science},

volume = {176},

pages = {480–489},

publisher = {Elsevier B.V.},

abstract = {This work is framed on investigating how a robot can learn associations between linguistic elements, such as words or sentences, and its bodily perceptions, that we named “roboceptions”. We discuss the possibility of defining such a process of an association through the interaction with human beings. By interacting with a user, the robot can learn to ascribe a meaning to its roboceptions to express them in natural language. Such a process could then be used by the robot in a verbal interaction to detect some words recalling the previously experimented roboceptions. In this paper, we discuss a Dual-NMT approach to realize such an association. However, it requires adequate training corpus. For this reason, we consider two different phases towards the realization of the system, and we show the results of the first phase, comparing two approaches: one based on the Latent Semantic Analysis paradigm and one based on the Random Indexing methodology.},

keywords = {Human computer interaction, Knowledge Representation, Latent Semantic Analysis, Natural Language Processing, Robotics, Semantic Computing, Social Robots},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{caggianeseSituatedVisualizationAugmented2019,

title = {Situated Visualization in Augmented Reality: Exploring Information Seeking Strategies},

author = { Giuseppe Caggianese and Valerio Colonnese and Luigi Gallo},

doi = {10.1109/SITIS.2019.00069},

year  = {2019},

date = {2019-11-01},

booktitle = {2019 15th International Conference on Signal-Image Technology Internet-Based Systems (SITIS)},

pages = {390--395},

abstract = {In recent years augmented reality applications have been increasingly demonstrating the requirement for an interaction with information related to and directly shown in the surrounding environment. Situated information is visualized in its semantic and spatial context, building up an environment enhanced by an information level that dynamically adapts to the production of the information and to the actions of the user. The exploration and manipulation of this type of data through see-through augmented reality devices still represents a challenging task. The development of specific interaction strategies capable to mitigating the current limitations of augmented reality devices is essential. In this context, our contribution has been to design possible solutions to address some of these challenges allowing a dynamic interaction with situated information. Following the visual "information-seeking mantra" proposed by Shneiderman and introducing some "superpowers" for the users, in this work we present different strategies aimed at obtaining an overview and filtering, and acquiring details of a collection of situated data.},

keywords = {Augmented Reality, Human computer interaction, Task analysis, Visualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{caggianese_situated_2019,

title = {Situated Visualization in Augmented Reality: Exploring Information Seeking Strategies},

author = {Giuseppe Caggianese and Valerio Colonnese and Luigi Gallo},

doi = {10.1109/SITIS.2019.00069},

year  = {2019},

date = {2019-11-01},

booktitle = {2019 15th International Conference on Signal-Image Technology Internet-Based Systems (SITIS)},

pages = {390–395},

abstract = {In recent years augmented reality applications have been increasingly demonstrating the requirement for an interaction with information related to and directly shown in the surrounding environment. Situated information is visualized in its semantic and spatial context, building up an environment enhanced by an information level that dynamically adapts to the production of the information and to the actions of the user. The exploration and manipulation of this type of data through see-through augmented reality devices still represents a challenging task. The development of specific interaction strategies capable to mitigating the current limitations of augmented reality devices is essential. In this context, our contribution has been to design possible solutions to address some of these challenges allowing a dynamic interaction with situated information. Following the visual "information-seeking mantra" proposed by Shneiderman and introducing some "superpowers" for the users, in this work we present different strategies aimed at obtaining an overview and filtering, and acquiring details of a collection of situated data.},

keywords = {Augmented Reality, Human computer interaction, Task analysis, Visualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{caggianeseViveControllersVs2019,

title = {The Vive Controllers vs. Leap Motion for Interactions in Virtual Environments: A Comparative Evaluation},

author = { Giuseppe Caggianese and Luigi Gallo and Pietro Neroni},

editor = { Giuseppe De Pietro and Luigi Gallo and Robert J. Howlett and Lakhmi C. Jain and Ljubo Vlacic},

doi = {10.1007/978-3-319-92231-7_3},

isbn = {978-3-319-92231-7},

year  = {2019},

date = {2019-01-01},

booktitle = {Intelligent Interactive Multimedia Systems and Services},

pages = {24--33},

publisher = {Springer International Publishing},

address = {Cham},

series = {Smart Innovation, Systems and Technologies},

abstract = {In recent years, virtual reality technologies have been improving in terms of resolution, convenience and portability, fostering their adoption in real life applications. The Vive Controllers and Leap Motion are two of the most commonly used low-cost input devices for interactions in virtual environments. This paper discusses their differences in terms of interaction design, and presents the results of a user study focusing on manipulation tasks, namely Walking box and blocks, Block tower and Numbered cubes tasks, taking into account both quantitative and qualitative observations. The experimental findings show a general preference for the Vive Controllers, but also highlight that further work is needed to simplify complex tasks.},

keywords = {Head-mounted displays, Human computer interaction, Input devices, User study, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{caggianese_vive_2019,

title = {The Vive Controllers vs. Leap Motion for Interactions in Virtual Environments: A Comparative Evaluation},

author = {Giuseppe Caggianese and Luigi Gallo and Pietro Neroni},

editor = {Giuseppe De Pietro and Luigi Gallo and Robert J. Howlett and Lakhmi C. Jain and Ljubo Vlacic},

doi = {10.1007/978-3-319-92231-7_3},

isbn = {978-3-319-92231-7},

year  = {2019},

date = {2019-01-01},

booktitle = {Intelligent Interactive Multimedia Systems and Services},

pages = {24–33},

publisher = {Springer International Publishing},

address = {Cham},

series = {Smart Innovation, Systems and Technologies},

abstract = {In recent years, virtual reality technologies have been improving in terms of resolution, convenience and portability, fostering their adoption in real life applications. The Vive Controllers and Leap Motion are two of the most commonly used low-cost input devices for interactions in virtual environments. This paper discusses their differences in terms of interaction design, and presents the results of a user study focusing on manipulation tasks, namely Walking box and blocks, Block tower and Numbered cubes tasks, taking into account both quantitative and qualitative observations. The experimental findings show a general preference for the Vive Controllers, but also highlight that further work is needed to simplify complex tasks.},

keywords = {Head-mounted displays, Human computer interaction, Input devices, User study, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{messinaBioGraphBotConversationalAssistant2018,

title = {BioGraphBot: A Conversational Assistant for Bioinformatics Graph Databases},

author = { Antonio Messina and Agnese Augello and Giovanni Pilato and Riccardo Rizzo},

editor = { L Barolli and T Enokido},

doi = {10.1007/978-3-319-61542-4_12},

isbn = {978-3-319-61542-4 978-3-319-61541-7},

year  = {2018},

date = {2018-01-01},

booktitle = {INNOVATIVE MOBILE AND INTERNET SERVICES IN UBIQUITOUS COMPUTING, IMIS-2017},

volume = {612},

pages = {135--146},

publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},

address = {GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND},

series = {Advances in Intelligent Systems and Computing},

abstract = {Chatbots technology allows to easily add a conversational interface to a large set of applications. In this paper, we show a conversational agent based on ALICE framework aimed at playing the role of interface between human users and a bioinformatics graph database. The system has been embedded into the web frontend of BioGraphDB, a publicly available resource that uses Gremlin as the primary query language. To be successfully exploited, domain experts, such as biologists and bioinformaticians, should also have familiarity with that query language. The use of a chatbot allows translating queries expressed in natural language to queries expressed in Gremlin, simplifying the interaction with BioGraphDB.},

keywords = {Biomedical, Chatbots, Gremlin, Human computer interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{messina_biographbot_2018,

title = {BioGraphBot: A Conversational Assistant for Bioinformatics Graph Databases},

author = {Antonio Messina and Agnese Augello and Giovanni Pilato and Riccardo Rizzo},

editor = {L Barolli and T Enokido},

doi = {10.1007/978-3-319-61542-4_12},

isbn = {978-3-319-61542-4 978-3-319-61541-7},

year  = {2018},

date = {2018-01-01},

booktitle = {INNOVATIVE MOBILE AND INTERNET SERVICES IN UBIQUITOUS COMPUTING, IMIS-2017},

volume = {612},

pages = {135–146},

publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},

address = {GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND},

series = {Advances in Intelligent Systems and Computing},

abstract = {Chatbots technology allows to easily add a conversational interface to a large set of applications. In this paper, we show a conversational agent based on ALICE framework aimed at playing the role of interface between human users and a bioinformatics graph database. The system has been embedded into the web frontend of BioGraphDB, a publicly available resource that uses Gremlin as the primary query language. To be successfully exploited, domain experts, such as biologists and bioinformaticians, should also have familiarity with that query language. The use of a chatbot allows translating queries expressed in natural language to queries expressed in Gremlin, simplifying the interaction with BioGraphDB.},

keywords = {Biomedical, Chatbots, Gremlin, Human computer interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gentileBodyGesturesSpoken2017,

title = {Body Gestures and Spoken Sentences: A Novel Approach for Revealing User's Emotions},

author = { Vito Gentile and Fabrizio Milazzo and Salvatore Sorce and Antonio Gentile and Agnese Augello and Giovanni Pilato},

doi = {10.1109/ICSC.2017.14},

isbn = {978-1-5090-4896-0},

year  = {2017},

date = {2017-01-01},

booktitle = {Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017},

pages = {69--72},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In the last decade, there has been a growing interest in emotion analysis research, which has been applied in several areas of computer science. Many authors have contributed to the development of emotion recognition algorithms, considering textual or non verbal data as input, such as facial expressions, gestures or, in the case of multi-modal emotion recognition, a combination of them. In this paper, we describe a method to detect emotions from gestures using the skeletal data obtained from Kinect-like devices as input, as well as a textual description of their meaning. The experimental results show that the correlation existing between body movements and spoken user sentence(s) can be used to reveal user's emotions from gestures. textcopyright 2017 IEEE.},

keywords = {Emotion Analysis, Emotion Recognition, Facial Expressions, Gestural user interfaces, Human computer interaction, Semantic Computing, Sentiment Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{milazzoExploitingCorrelationBody2017,

title = {Exploiting Correlation between Body Gestures and Spoken Sentences for Real-Time Emotion Recognition},

author = { Fabrizio Milazzo and Agnese Augello and Giovanni Pilato and Vito Gentile and Antonio Gentile and Salvatore Sorce},

doi = {10.1145/3125571.3125590},

isbn = {978-1-4503-5237-6},

year  = {2017},

date = {2017-01-01},

booktitle = {ACM International Conference Proceeding Series},

volume = {Part F131371},

publisher = {Association for Computing Machinery},

abstract = {Humans communicate their affective states through different media, both verbal and non-verbal, often used at the same time. The knowledge of the emotional state plays a key role to provide personalized and context-related information and services. This is the main reason why several algorithms have been proposed in the last few years for the automatic emotion recognition. In this work we exploit the correlation between one's affective state and the simultaneous body expressions in terms of speech and gestures. Herewe propose a system for real-Time emotion recognition from gestures. In a first step, the system builds a trusted dataset of association pairs (motion datatextrightarrow emotion pattern), also based on textual information. Such dataset is the ground truth for a further step, where emotion patterns can be extracted from new unclassified gestures. Experimental results demonstrate a good recognition accuracy and real-Time capabilities of the proposed system. textcopyright 2017 Copyright held by the owner/author(s).},

keywords = {Emotion Recognition, Gestural user interfaces, Human computer interaction, Sentiment Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gentile_body_2017,

title = {Body Gestures and Spoken Sentences: A Novel Approach for Revealing User's Emotions},

author = {Vito Gentile and Fabrizio Milazzo and Salvatore Sorce and Antonio Gentile and Agnese Augello and Giovanni Pilato},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018255013&doi=10.1109%2fICSC.2017.14&partnerID=40&md5=23d8bb016146afe5e384b12d84f3fb85},

doi = {10.1109/ICSC.2017.14},

isbn = {978-1-5090-4896-0},

year  = {2017},

date = {2017-01-01},

booktitle = {Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017},

pages = {69–72},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In the last decade, there has been a growing interest in emotion analysis research, which has been applied in several areas of computer science. Many authors have contributed to the development of emotion recognition algorithms, considering textual or non verbal data as input, such as facial expressions, gestures or, in the case of multi-modal emotion recognition, a combination of them. In this paper, we describe a method to detect emotions from gestures using the skeletal data obtained from Kinect-like devices as input, as well as a textual description of their meaning. The experimental results show that the correlation existing between body movements and spoken user sentence(s) can be used to reveal user's emotions from gestures. © 2017 IEEE.},

keywords = {Emotion Analysis, Emotion Recognition, Facial Expressions, Gestural user interfaces, Human computer interaction, Semantic Computing, Sentiment Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{milazzo_exploiting_2017,

title = {Exploiting correlation between body gestures and spoken sentences for real-Time emotion recognition},

author = {Fabrizio Milazzo and Agnese Augello and Giovanni Pilato and Vito Gentile and Antonio Gentile and Salvatore Sorce},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034630360&doi=10.1145%2f3125571.3125590&partnerID=40&md5=b3d73715f756aded80e6b0a330ace70a},

doi = {10.1145/3125571.3125590},

isbn = {978-1-4503-5237-6},

year  = {2017},

date = {2017-01-01},

booktitle = {ACM International Conference Proceeding Series},

volume = {Part F131371},

publisher = {Association for Computing Machinery},

abstract = {Humans communicate their affective states through different media, both verbal and non-verbal, often used at the same time. The knowledge of the emotional state plays a key role to provide personalized and context-related information and services. This is the main reason why several algorithms have been proposed in the last few years for the automatic emotion recognition. In this work we exploit the correlation between one's affective state and the simultaneous body expressions in terms of speech and gestures. Herewe propose a system for real-Time emotion recognition from gestures. In a first step, the system builds a trusted dataset of association pairs (motion data→emotion pattern), also based on textual information. Such dataset is the ground truth for a further step, where emotion patterns can be extracted from new unclassified gestures. Experimental results demonstrate a good recognition accuracy and real-Time capabilities of the proposed system. © 2017 Copyright held by the owner/author(s).},

keywords = {Emotion Recognition, Gestural user interfaces, Human computer interaction, Sentiment Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}