Publications​

@inproceedings{pouriyeh_comprehensive_2017,

title = {A comprehensive investigation and comparison of machine learning techniques in the domain of heart disease},

author = {Seyedamin Pouriyeh and Sara Vahid and Giovanna Sannino and Giuseppe De Pietro and Hamid Arabnia and Juan Gutierrez},

url = {https://ieeexplore.ieee.org/abstract/document/8024530/},

year  = {2017},

date = {2017-01-01},

urldate = {2024-09-04},

booktitle = {2017 IEEE symposium on computers and communications (ISCC)},

pages = {204–207},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{brancati_dynamic_2017,

title = {Dynamic Colour Clustering for Skin Detection Under Different Lighting Conditions},

author = {Nadia Brancati and Giuseppe De Pietro and Maria Frucci and Luigi Gallo},

editor = {Viktor V. Krasnoproshin and Sergey V. Ablameyko},

doi = {10.1007/978-3-319-54220-1_3},

isbn = {978-3-319-54220-1},

year  = {2017},

date = {2017-01-01},

booktitle = {Pattern Recognition and Information Processing},

pages = {27–35},

publisher = {Springer International Publishing},

address = {Cham},

series = {Communications in Computer and Information Science},

abstract = {Skin detection is an important process in many applications like hand gesture recognition, face detection and ego-vision systems. This paper presents a new skin detection method based on a dynamic generation of the skin cluster range in the YCbCr color space, by taking into account the lighting conditions. The method is based on the identification of skin color clusters in the YCb and YCr subspaces. The experimental results, carried out on two publicly available databases, show that the proposed method is robust against illumination changes and achieves satisfactory results in terms of both qualitative and quantitative performance evaluation parameters.},

keywords = {Clustering, Computer Vision and Pattern Recognition, Image Processing, Skin detection, YCbCr color space},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{caggianese_rehabilitation_2017,

title = {A Rehabilitation System for Post-operative Heart Surgery},

author = {Giuseppe Caggianese and Mariaconsiglia Calabrese and Vincenzo De Maio and Giuseppe De Pietro and Armando Faggiano and Luigi Gallo and Giovanna Sannino and Carmine Vecchione},

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

doi = {10.1007/978-3-319-59480-4_55},

isbn = {978-3-319-59480-4},

year  = {2017},

date = {2017-01-01},

booktitle = {Intelligent Interactive Multimedia Systems and Services 2017},

pages = {554–564},

publisher = {Springer International Publishing},

series = {Smart Innovation, Systems and Technologies},

abstract = {Supervised exercise programs are an important aspect of the rehabilitation process of patients after heart surgery. A large number of factors must be taken into account before implementing a rehabilitation program. These mainly consist in the patient’s cognitive and physical capabilities after the operation and the expectations of recovery. A rehabilitation program should also be designed in relation to the stage of the healing process, with the therapist selecting the best sequence of exercises while taking into account the most appropriate effort level for the patient. This paper describes a customizable rehabilitation system for the early post-operative period, useful for the performance of an assessment of the patients, through an evaluation of their cognitive and motor abilities, and for a dynamic personalization of the therapy sessions focused on patient needs.},

note = {Place: Cham},

keywords = {Healthcare, Kinect, Rehabilitation, Serious game, Telemonitoring, Unity 3D},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cannavacciuololorellaComparativeQualitativeAnalysis19-20Ottobre,

title = {Comparative Qualitative Analysis to assess factors affecting Triage Decision-Making},

author = {Lorella Cannavacciuolo and Adelaide Ippolito and Cristina Ponsiglione and Simonetta Primario},

year  = {2017},

date = {2017-01-01},

booktitle = {The Digital Transformation of Business and Society XXVIII},

address = {Bari},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{galteriReadingTextWild2017,

title = {Reading Text in the Wild from Compressed Images},

author = {Leonardo Galteri and Dena Bazazian and Lorenzo Seidenari and Marco Bertini and Andrew D. Bagdanov and Anguelos Nicolau and Dimosthenis Karatzas and Alberto Del Bimbo},

url = {http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w34/degrees_leonardo.galteriunifi.it_dbazaziancvc.uab.es_lorenzo.seidenariunifi.it_ICCV_2017_paper.pdf},

doi = {10.1109/ICCVW.2017.283},

year  = {2017},

date = {2017-01-01},

booktitle = {2017 IEEE International Conference on Computer Vision Workshops (ICCVW)},

pages = {2399–2407},

publisher = {IEEE Computer Society, Los Alamitos, CA, USA},

abstract = {Reading text in the wild is gaining attention in the computer vision community. Images captured in the wild are almost always compressed to varying degrees, depending on application context, and this compression introduces artifacts that distort image content into the captured images. In this paper we investigate the impact these compression artifacts have on text localization and recognition in the wild. We also propose a deep Convolutional Neural Network (CNN) that can eliminate text-specific compression artifacts and which leads to an improvement in text recognition. Experimental results on the ICDAR-Challenge4 dataset demonstrate that compression artifacts have a significant impact on text localization and recognition and that our approach yields an improvement in both–especially at high compression rates.},

note = {tex.copyright: All rights reserved},

keywords = {Artificial Intelligence, Compression Artifact Removal, Computer Vision and Pattern Recognition, Image Compression, Image Processing, Image Restoration, Text Recognition},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{galteriDeepGenerativeAdversarial2017,

title = {Deep generative adversarial compression artifact removal},

author = {Leonardo Galteri and Lorenzo Seidenari and Marco Bertini and Alberto Del Bimbo},

url = {https://openaccess.thecvf.com/content_ICCV_2017/papers/Galteri_Deep_Generative_Adversarial_ICCV_2017_paper.pdf},

doi = {10.1109/ICCV.2017.517},

year  = {2017},

date = {2017-01-01},

booktitle = {Proceedings of the IEEE international conference on computer vision},

pages = {4826–4835},

abstract = {Compression artifacts arise in images whenever a lossy compression algorithm is applied. These artifacts eliminate details present in the original image, or add noise and small structures; because of these effects they make images less pleasant for the human eye, and may also lead to decreased performance of computer vision algorithms such as object detectors. To eliminate such artifacts, when decompressing an image, it is required to recover the original image from a disturbed version. To this end, we present a feed-forward fully convolutional residual network model trained using a generative adversarial framework. To provide a baseline, we show that our model can be also trained optimizing the Structural Similarity (SSIM), which is a better loss with respect to the simpler Mean Squared Error (MSE). Our GAN is able to produce images with more photorealistic details than MSE or SSIM based networks. Moreover we show that our approach can be used as a pre-processing step for object detection in case images are degraded by compression to a point that state-of-the art detectors fail. In this task, our GAN method obtains better performance than MSE or SSIM trained networks.},

note = {tex.copyright: All rights reserved},

keywords = {Artificial Intelligence, Compression Artifact Removal, Computer Vision and Pattern Recognition, Generative Adversarial Networks, Image Compression, Image Processing, Image Synthesis and Enhancement},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ceccacci_user-centered_2017,

title = {An user-centered approach to design smart systems for people with dementia},

author = {Silvia Ceccacci and Andrea Generosi and Luca Giraldi and Maura Mengoni},

url = {http://ieeexplore.ieee.org/document/8210650/},

doi = {10.1109/ICCE-Berlin.2017.8210650},

isbn = {978-1-5090-4014-8},

year  = {2017},

date = {2017-01-01},

urldate = {2024-12-28},

booktitle = {2017 IEEE 7th International Conference on Consumer Electronics - Berlin (ICCE-Berlin)},

pages = {273–278},

publisher = {IEEE},

address = {Berlin},

abstract = {This study describes an User-Centered approach to design an User Interface (UI) to support daily activities of people with dementia. Such interface is the main hub of a home automation system able to monitor the house and reminds to the users some information when they approach the door to leave the home. In order to involve end users in UI evaluation at the end of the first stage of the design process, a specific experimental protocol, based on task analysis, structural interview, and behavioral observation, is defined. It allows to evaluate user-machine interaction considering aspects related to both adequacy of product feature and user's subjective opinion and behavior. A disposable high fidelity prototype of the UI is realized by using a touch screen tablet. Two tests, respectively dedicated to verify the adequacy of the icons and the understandability of the interface, are performed. A total of 20 subjects with different MMSE score are involved. Results show that people with low and medium dementia are able to understand and use the touch interface and provide some suggestion about how the GUI can be improved. Finally, some approaches to support the future development activities and the next usability tests are discussed.},

keywords = {Healthcare, Human-Centered Design, Internet of Things},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{tomazzoli_brain_2017,

title = {The Brain Is a Social Network},

author = {Claudio Tomazzoli and Silvia Francesca Storti and Ilaria Boscolo Galazzo and Matteo Cristani and Gloria Menegaz},

url = {https://ceur-ws.org/Vol-1959/paper-10.pdf},

year  = {2017},

date = {2017-01-01},

booktitle = {CEUR Workshop Proceedings},

pages = {1–6},

abstract = {Social Network Analysis is employed widely as a means to compute the probability that a given message flows through a social net- work. This approach is mainly grounded upon the correct usage of three basic graph-theoretic measures: degree centrality, closeness centrality and betweeness centrality. We developed a model, using Semantic Social Net- work Analysis, that overcomes the drawbacks of general indices and we found that this model can be applied, after appropriate adaptations, to a very different domain such as brain connectivity.},

keywords = {brain connectomics, Machine Learning, semantic social network analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{tomazzoli_machine_2017,

title = {Machine Learning for Energy Efficiency - Automatic Detection of Electric Loads from Power Consumption},

author = {Claudio Tomazzoli and Simone Scannapieco},

doi = {10.23919/AEIT.2017.8240544},

year  = {2017},

date = {2017-01-01},

booktitle = {IEEE Xplore},

pages = {1–6},

abstract = {This work deals with the problem of energy efficiency and saving: we present a method to automatically extract behavioral rules from consumption data, so that these rules can be applied or fed to an automatic control system. To extract behavioral rules we shall be able to both (i) define power plants similarity techniques and (ii) analyze and gather rules from data, making the correct assumptions.},

keywords = {Edge computing, Energy management, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{scannapieco_ubiquitous_2017,

title = {Ubiquitous and Pervasive Computing for Real-Time Energy Management and Saving},

author = {Simone Scannapieco and Claudio Tomazzoli},

doi = {10.1007/978-3-319-61542-4_1},

year  = {2017},

date = {2017-01-01},

booktitle = {Advances in Intelligent Systems and Computing},

volume = {612},

pages = {3–15},

abstract = {In the present paper we investigate one of the emerging applicability fields of pervasive computing, that is, energy management and saving. We exploit innovative technologies to define a brand new system architecture for (i) centralized monitoring and (ii) real-time energy saving in distributed sub-networks of power consuming electric appliances. The architecture allows the definition of reactive and intelligent systems that take autonomous courses of action on electric devices. We also introduce a prototype which is an embodiment of such architecture, called .MyElettra},

keywords = {Edge computing, Energy Efficiency, Energy management, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cristani_viral_2017,

title = {Viral Experiments},

author = {Matteo Cristani and Francesco Olivieri and Claudio Tomazzoli},

url = {http://ceur-ws.org/Vol-1959/#paper-08},

year  = {2017},

date = {2017-01-01},

booktitle = {CEUR Workshop Proceedings},

pages = {1–6},

abstract = {When searching for confirming hypothesis on a social domain, scholars regu- larly form samples to be used for the research. Analogously, for instance, market researchers do. Social networks constitute a huge source of information for these investigations, and it is often used to collect these data. When the collection of the data is made rigorously, an important effort has to be put on the selection of the sample, that should respect criteria of correspondence to the ditribution of the population, balance, and minimality of sample classes, in order to guarantee good inferential statistics evaluation parameters. In this paper we investigate a systematic way of providing samples on social networks that takes in consdieration the above mentioned criteria shile forming the sample . We prove that an affordable and reliable method to construct the basic instrument for the above method (the random table) can de provided, show its computational properties and devise an experimental protocol (the viral experiment protocol) that makes direct usage of the method mentioned above},

keywords = {Semantic, semantic social network analysis},

pubstate = {published},

tppubtype = {inproceedings}

}