KORE PlatformLaboratory for Speech Technology research and Machine Learning

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The laboratory

The Speech Technology and Machine Learning Laboratory of the Kore University of Enna (UKE) deal with several research topics, e.g. Speech Processing, Machine Learning, Cybersecurity, Predictive Maintenance based on IoT-AI and Fault Detection, AI for Health and Industry.

The Laboratory has been involved in several research projects:

    SCRIBE: Machine transcription of Norwegian conversational speech– 2021-2026.
    Id: 322964.
    Total budget: NOK 27 million (€ 2.65 million).
    Partners: Norwegian University of Science and Technology – NTNU (Norvegia), Telenor Research (Noway), Nasjonalbiblioteket (Norway)
  • MIUR PRIN – Projects of Significant National Interest – 2019-2022.
    Phonetic analysis of dysarthric speech by speakers of different varieties of Italian to develop clinical tools: objective, quantitative assessment for severity measurement, early diagnosis and rehabilitation planning, taking into account sociophonetic variation.
    ID: 2017JNKCYZ.
    Partners: University of Sannio, University of Bari, University of Enna Kore
    Aulus – Atomic units for universal language representation of speech – 2015-2019.
    Id: 240282/O70.
    Total budget: NOK 12 million (€ 1.34 million).

and is actively linked to numerous industrial research partnerships.


The Laboratory was established by Decree of the President of the University No.174 of 2016.

Prof. SINISCALCHI Saturday Marco is the director.



  • SINISCALCHI Sabato Marco (Full Professor)
  • CONTI Vincenzo (Associate Professor)
  • SALERNO Valerio Mario (Associate Professor)
  • SORCE Salvatore (Associate Professor)
  • CILIA Nicole Dalia (Researcher RTD-A)
  • GARRAFFA Giovanni (Researcher RTD-A)

Research Assistants and Past Students

  • MONTELEONE Salvatore
  • MNASRI Zied
  • SHAHREBABAKI Abdolreza Sabzi

Research activities

Communication and interaction between humans through voice is one of the main topics for research and teaching in the workshop.

Our mission is to understand the fundamental processes involved in human communication to understand better how they might be shaped and supported.

Research topics covered by STMLab members include: (i) Acoustic Modelling, (ii) Machine Learning, (ii) Extensive Vocabulary Recognition, (iv) Robust Speech Recognition, (v) Speech and Language Recognition.

We have several ongoing activities concerning health applications.

Neurodegenerative disorders (ND) encompass a wide range of conditions resulting from progressive damage to the cells and connections of the nervous system that are essential for mobility, coordination, strength, sensation and cognition.

We aim to address the early detection of ND using speech and writing.

In particular, we aim to build an automatic ND detection tool based on the speech signal, which can achieve high performance in noisy conditions by exploiting speech attributes, i.e. articulatory features, and which can work in interlingual scenarios.

In addition, we aim to develop tools to support early diagnosis and progression of NDs through handwriting analysis from online coordinates, offline images and RGCs.
The last instrument is in the patent phase.

Cancer is the second most common cause of death globally and involves highly variable clinical and biological scenarios.

Our goal is to build an automated or semi-automated and interactive approach based on artificial intelligence techniques to segment masses on dynamic contrast-enhanced breast MRI (DCE) and compare it with existing approaches based on classical image processing.
The proposed computerised approach could be implemented in clinical research environments by providing a reliable volumetric and radiomic analyses tool.

Due to the rapid progression of the disease worldwide, many have been working on solutions for the rapid and reliable detection of COVID-19.
We investigated and developed an artificial intelligence-based system for the early diagnosis of COVID-19 and certain types of pneumonia from chest X-ray images of people with suspected SARS-CoV-2 infection.

Despite their lower information content, X-ray imaging offers several advantages such as a lower radiation dose, lower costs for both the patient and the healthcare system, higher general availability and greater accessibility of equipment.

The solution achieves good accuracy, is cost-effective and reduces workload and patient-side impact.

We have two main activities concerning security applications.

  • Biometric Identity Management: Analysis and development of software and hardware prototypes of unimodal and multimodal biometric recognition systems.
    The subject under investigation is the management of biometric information and its processing through AI and ML techniques, through the development of software and hardware prototypes, to increase the robustness of authentication systems for access to centralised and/or distributed environments.
  • Bio-Inspired Methodology Application: Analysis and development of software prototypes based on survival techniques and cell mutation methodologies.
    Some metabolic network analysis techniques have been used to build bio-inspired models for analysing the robustness of a network and identifying points of attack, also known as vulnerabilities.
    With the appropriate mapping of the security metrics on the quantities typically involved in biochemical reactions, the formalism developed makes it possible to identify critical paths and highlight the points that present the highest level of vulnerability for the system.
  • Reachability Matrix Ontology (RMO) for the European project PANOSPETEC: RMO aims to describe networks and the cybersecurity domain to calculate reachability information (reachability matrix).
    As far as we know, RMO, which uses a combination of OWL, descriptive logic rules and SPARQL queries, represents an innovative approach to calculating the reachability matrix.
    The reachability matrix determines whether a node can reach another node (via the ISO/OSI layer protocol).
    RMO describes network elements, network connectivity information, and access control policies to achieve this.
    RMO also provides some SWRL rules that can calculate the Achievability Matrix.
    In addition to the RMO and SWRL rules, there is also a set of SPARQL queries to refine the calculation of the Reachability Matrix.

Patent analysis can provide information on engineering design and identify potential infringements to promote innovation.

However, designers do not regularly deal with patents because of the intricate structure and legal terminologies used, especially in the early stages of design.
We developed an artificial intelligence-based solution to automatically and quickly capture patent knowledge from various patent sections and produce visualisations to facilitate understanding, including citation analysis and comparison of claims.

The solution is based on NLP techniques and allows the automatic extraction of Subject-Action-Object triplets representing functional and invention characteristics. In addition, a visual semantic diagram is constructed for a quick view of the patent content.

Identification of the writer in ancient documents:

One of the most important research topics in palaeography is identifying the different scribes who participated in writing a medieval book.
Using traditional palaeographic tools, a palaeographer spends a lot of time reading, measuring and comparing thousands of letters or graphic signs.
Given the growing scientific interest that has been observed in recent years in the use of computer techniques applied to palaeographic research, the line of research aims to develop automatic analysis tools and a set of functionalities, which allow reliable identification of the scribes who contributed to the production of a given manuscript.

We have three main activities regarding control applications

  • Analysis and design of hybrid estimation algorithms address sensor fusion problems, especially indoor/outdoor localisation applications.
    Innovative algorithms, whose performance and stability are rigorously tested and analysed, are inspected to solve IMU/GPS/UWB and multilateration problems.
    The hybrid approach is used to work with asynchronous and sporadic measurement systems by providing intuitive design rules and deterministic limits on estimation errors.
  • Determination of robust motion control laws for autonomous flying mobile robots (quadcopters) focuses on online estimation of aerodynamic forces and moments, unknown disturbances and generally unmodelled dynamics.
  • Develop hardware and software prototypes to create IoT frameworks and firmware/driver development to implement acquisition systems based on network sensors.
    Using the acquired data to build and maintain a database of measurements for use in ML and AI applications for fault prediction, predictive maintenance and medical diagnostic systems.


The workshop covers an area of 225.00 square metres and has the following equipment:

  • DELL PowerEdge R750XA, NVIDIA Ampere A100, PCIe, 250W, 40GB Passive, Double Wide, Full Height GPU with Bracket

Selected Publications

  • A. S. Shahrebabaki, G. Salvi, T. Svendsen and S. M. SINISCALCHI, “Acoustic-to-Articulatory Mapping With Joint Optimization of Deep Speech Enhancement and Articulatory Inversion Models,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 30, pp. 135-147, 2022, doi: 10.1109/TASLP.2021.3133218. S.M. SINISCALCHI (2021).
  • Vector-to-Vector Regression via Distributional Loss for Speech Enhancement.
    IEEE Signal Processing Letters, 28, pp. 254-258, 0.1109/LSP.2021.3050386 J Qi, J Du, S. M. SINISCALCHI, X Ma, C.-H. Lee (2020).
  • On mean absolute error for deep neural network-based vector-to-vector regression.
    IEEE Signal Processing Letters, 27, pp. 1485-1489, doi: 10.1109/LSP.2020.3016837.
    J. Qi, J. Du, S. M. SINISCALCHI, X. Ma, and C.-H. Lee (2020).
  • Analysing Upper Bounds on Mean Absolute Errors for Deep Neural Network Based Vector-to-Vector Regression.
    68, pp.
    3411-3422, doi:
    I. Kukanov, S.M. SINISCALSHI, and et al.
  • Maximal Figure-of-Merit Framework to Detect Multi-label Phonetic Features for Spoken Language Recognition, IEEE/ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, vol.28, pp.682-695, doi:10.1109/TASLP.2020.2964953
    J. Qi, J. Du, S.M. SINISCALCHI, and C.-H. Lee, (2019).
  • A Theory on Deep Neural Network based Vector-to-Vector Regression with an Illustration of Its Expressive Power in Speech Enhancement.
    IEEE/ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, vol.27 (12), p. 1932-1943, doi:10.1109/TASLP.2019.2935891
    R. Hussain, S.M. SINISCALCHI, and et al (2020).
  • Ensemble Hierarchical Extreme Learning Machine for Speech Dereverberation.
    IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, vol.12(4), pp.744-758, 10.1109/TCDS.2019.2953620
    W. Li, N.F. Chen, S.M. SINISCALCHI, and C.-H. Lee, (2019).
  • Improving Mispronunciation Detection of Mandarin Tones for Non-native Learners with Soft-target Tone Labels and BLSTM-based Deep Tone models.
    IEEE/ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, vol.27 (12), pp.2012-2024, doi:10.1109/TASLP.2019.2936755
    J. Lin, W. Li, Y Gao, Y. Xie, N.F. Chen, SINISCALCHI S.M., J. Zhang, C.H. Lee (2018).
  • Improving Mandarin Tone Recognition Based on DNN by Combining Acoustic and Articulatory Features Using Extended Recognition Networks.
    Journal of Signal Processing Systems 90 (7), 1077-1087
    Wu, B., Li, K, Ge, F., Huang, Z., Yang, M., SINISCALCHI S. M., and Lee, C.-H.(2017).
  • An End-to-End Deep Learning Approach to Simultaneous Speech Dereverberation and Acoustic Modelling for Robust Speech Recognition.
    IEEE J-STSP, 2017, doi:[1}[2}10.1109/JSTSP.2017.2756439{3]{4]https://doi.org/10.1109/JSTSP.2017.2756439
    Huang Z., SINISCALCHI S. M., and Lee C.-H.(2017).
  • Hierarchical Bayesian Combination of Plug-in Maximum A Posteriori Decoders in Deep Neural Networks-based Speech Recognition and Speaker Adaptation.
    PATTERN RECOGNITION LETTERS, vol.98, p. 1-7, ISSN:0167-8655, doi:10.1016/j.patrec.2017.08.001
    Huang Z., SINISCALCHI S. M., and Lee C.-H.(2017).
  • Bayesian Unsupervised Batch and Online Speaker Adaptation of Activation Function Parameters in Deep Models for Automatic Speech Recognition.
    ON AUDIO SPEECH AND LANGUAGE PROCESSING, vol.25 (1); p. 64-75, doi:10.1109/TASLP.2016.2621669
    Wu, B., Yang, M., Li, K., SINISCALCHI S.M. et al.(2017).
  • A reverberation-time-aware DNN approach leveraging spatial information for microphone array dereverberation.
    EURASIP J. Adv.
    Signal Process.2017, 81 (2017). [1}[2}https://doi.org/10.1186/s13634-017-0516-6{3]{4]
    SINISCALCHI S. M., and V. M. Salerno (2017).
  • Adaptation to New Microphones using Artificial Neural Networks with Trainable Activation Functions.
    Z. Huang, SINISCALCHI S. M., and C.-H. Lee (2016).
  • A Unified Approach to Transfer Learning of Deep Neural Networks with Applications to Speaker Adaptation in Automatic Speech Recognition.
    218; p. 448-459, doi:10.1016/j.neucom.2016.09.018
    Behravan H., Hautamaki V., SINISCALCHI S. M., Kinnunen T., and Lee C.-H.(2016)
  • i-Vector Modelling of Speech Attributes for Automatic Foreign Accent Recognition.
    IEEE/ACM Transactions on Audio Speech and Language Processing, vol.
    24 (1); p. 29-41, ISSN:
    2329-9290, doi:
    SINISCALCHI S. M., Svendsen T., and C.-H. Lee (2014).
  • An Artificial Neural Network Approach to Automatic Speech Processing.
    140; p. 326-338, ISSN:
    0925-2312, doi:
    R. E. Barone, T. Giuffrè, SINISCALCHI S. M., Morgana M. A., and Tesoriere G.
  • Architecture for parking management in smart cities.
    8; p. 445-452, ISSN:
    1751-956X, doi:
    Lee C.-H., and SINISCALCHI S. M.
  • An Information-Extraction Approach to Speech Processing:
    Analysis, Detection, Verification, and Recognition.
    101; p. 1089-1115, ISSN:
    0018-9219, doi:
    SINISCALCHI S. M., Svendsen T., and Lee C.-H.
    A bottom-up modular search approach to large vocabulary continuous speech recognition.
    21; p. 786-797, ISSN:
    1558-7916, doi:
    SINISCALCHI S. M., Yu D., Deng L., and Lee C.-H.
    Speech recognition using long-span temporal patterns in a deep network model.
    20; p. 201-204, ISSN:
    1070-9908, doi:
    SINISCALCHI S. M., Li J., and Lee C.-H.
    Hermitian Polynomial for Speaker Adaptation of Connectionist Speech Recognition Systems.
    21; p. 2152-2161, ISSN:
    1558-7916, doi:
    SINISCALCHI S. M., Li J., and Lee C-H.
    Model-based margin estimation for hidden Markov model learning and generalisation.
    7; p. 704-709, ISSN:
    1751-9675, doi:
    SINISCALCHI S. M., Reed J., Svendsen T., and Lee C.-H.
    Universal attribute characterisation of spoken languages for automatic spoken language recognition.
    27; p. 209-227, ISSN:
    0885-2308, doi:
    SINISCALCHI S. M., Yu D., Deng L., and Lee C.-H.
    Exploiting Deep Neural Networks for Detection-Based Speech Recognition.
    106; p. 148-157, ISSN:
    0925-2312, doi:
    SINISCALCHI S. M., Lyu D.-C., Svendsen T., Lee C.-H.
    Experiments on cross-language attribute detection and phone recognition with minimal target-specific training data.
    20; p. 875-887, ISSN:
    1558-7916, doi:
    Combining Speech Attribute Detection and Penalised Logistic Regression for Phoneme Recognition.
    93; p. 10-18, ISSN:
    0925-2312, doi:
    Birkenes O., Matsui T., Tanabe K., SINISCALCHI S. M., Myrvoll T.A., Johnsen M.H.
    Penalised logistic regression with HMM log-likelihood regressors for speech recognition.
    18; p. 1440-1454, ISSN:
    1558-7916, doi:
    SINISCALCHI S. M., Lee C.-H.
    A study on integrating acoustic-phonetic information into lattice rescoring for automatic speech recognition.
    51; p. 1139-1153, ISSN:
    0167-6393, doi:
    V. Conti, S.S. Ruffo, A. Merlo, M. Migliardi, S. Vitabile (2018). “A bio-inspired approach to attack graphs analysis”, proc. of 10th International Symposium on Cyberspace Safety and Security, CSS 2018, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.
    11161 LNCS, 2018, Pages 63-76.
    V. Conti, A. Ziggiotto, M. Migliardi, S. Vitabile, “Bio-inspired Security Analysis for IoT Scenarios”, International Journal of Embedded Systems, Vol.
    13, No.
    2, pp.
    221-235, 2020.
    Militello, C., Rundo, L., Toia, P., Conti, V., Russo, G., Filorizzo, C., Maffei, E., Cademartiri, F., La Grutta, L., Midiri, M., Vitabile, S., “A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans”, Computers in Biology and Medicine, Vol.
    114, November 2019, Article number 103424
    Orazio Gambino, Vincenzo Conti, Sergio Galdino, Cesare Fabio Valenti, and Wellington Pinheiro dos Santos, Editorial:
    Image Segmentation Techniques for Healthcare Systems, Journal of Healthcare Engineering, Volume 2019, Article ID 2723419, 2 pages, DOI:
    V. Conti, S. S. Ruffo, S. Vitabile, L. Barolli, ‘BIAM:
    A New Bio-Inspired Analysis Methodology for Digital Ecosystems based on a Scale-free Architecture”, Soft Computing, Vol.
    23, issue 4, 2019 (First Online:
    14 September 2017), pp.
    1133-1150 ISSN:
    1432-7643 (Print) 1433-7479 (Online), Springer Editor, DOI:
    Vincenzo Conti, Leonardo Rundo, Giuseppe Dario Billeci, Carmelo Militello and Salvatore Vitabile, “Energy Efficiency Evaluation of Dynamic Partial Reconfiguration in Field Programmable Gate Arrays:
    An Experimental Case Study”, Energies, 2018, 11(4), 739, doi:10.3390/en11040739
    A. Alongi, V. Conti, G. Vitello, S. Vitabile, “An Empirical Set of Metrics for Embedded Systems Testing”, IEEE Design & Test, Volume 35, Issue 5, October 2018, Article number 8240747, pp.
    45-53, ISSN:
    2168-2356, DOI:
    C. Militello, L. Rundo, V. Conti, L. Minafra, F. P. Cammarata, S. Vitabile, G. Mauri, M. C. Gilardi, N. Porcino, “Area-Based Cell Colony Surviving Fraction Evaluation:
    A Novel Fully Automatic Approach Using General-Purpose Acquisition Hardware”, Computers in Biology and Medicine, 2017, Vol.
    89, pp.
    454-465, ISSN:
    0010-4825, Elsevier Editor, DOI: https://doi.org/10.1016/j.compbiomed.2017.08.005
    G. Vitello, A. Alongi, V. Conti, S. Vitabile, “A Bio-Inspired Cognitive Agent for Autonomous Urban Vehicles Routing Optimization”, IEEE Transactions on Cognitive and Developmental Systems, (first online publication on 12 September 2016), Vol.
    9, Issue 1, March 2017, pp.
    5-15, ISSN 2379-8920, DOI:
    G. Vitello, V. Conti, S. Vitabile, and F. Sorbello, “Fingerprint Quality Evaluation in a Novel Embedded Authentication System for Mobile Users”, Mobile Information Systems, Vol.
    2015 (2015), 13 pages, Hindawi Publishing Corporation, Article ID 401975, DOI: http://dx.doi.org/10.1155/2015/401975
    V. Conti, C. Militello, F. Sorbello and S. Vitabile, “Biometric Sensors Rapid Prototyping on FPGA”, The Knowledge Engineering Review, 2015, Vol.
    30, N° 2, pp.
    201-219, ISSN:
    0269-8889, EISSN:
    1469-8005, DOI:
    C. Militello, V. Conti, F. Sorbello, and S. Vitabile, “A Fast Fusion Technique for Fingerprint and Iris Spatial Descriptors in Multimodal Biometric Systems”, International Journal of Computer Systems Science and Engineering (IJCSSE), Vol.
    29, n° 3, pp.
    205-217, ISSN 0267-6192, © 2014 CRL Publishing Ltd.
    S. Vitabile, V. Conti, M. Collotta, G. Scatà, S. Andolina, A. Gentile, F. Sorbello, “A Real-Time Network Architecture for Biometric Data Delivery in Ambient Intelligent”, Journal of Ambient Intelligence and Humanized Computing (AIHC), © Springer-Verlag Editor, June 2013, Vol.
    4, Issue 3, pp.
    303-321, Print ISSN 1868-5137, Online ISSN 1868-5145, DOI:
    C. Militello, V. Conti, S. Vitabile and F. Sorbello, “Embedded Access Points for Trusted Data and Resources Access in HPC Systems”, The Journal of Supercomputing – An international journal of High-Performance Computer Design, Analysis and Use, Springer Netherlands Publisher, 2011, ISSN 0920-8542, Vol.
    55, N° 1, pp.
    4 – 27, (ISSN Online 1573-0484), DOI:10.1007/s11227-009-0379-1
    S. Vitabile, V. Conti, B. Lanza, D. Cusumano, and F. Sorbello, “Topological Information, Flux Balance Analysis, and Extreme Pathways Extraction for Metabolic Networks Behaviour Investigation”, Frontiers in Artificial Intelligence and Applications, IOS Press Editor, Vol.
    Neural Nets WIRN11, pp.
    66-73, 2011, ISSN 0922-6389, ISBN 978-1-60750-971-4, DOI:
    V. Conti, C. Militello, F. Sorbello, S. Vitabile. “A Frequency-based Approach for Features Fusion in Fingerprint and Iris Multimodal Biometric Identification Systems”, IEEE Transactions on Systems, Man, and Cybernetics (SMC) Part C:
    Applications & Reviews, Vol, 40 Issue 4, pp.
    384-395. 2010, ISSN 1094-6977, DOI:10.1109/TSMCC.2010.2045374
    C. Militello, V. Conti, S. Vitabile, F. Sorbello, “An Embedded Iris Recognizer for Portable and Mobile Devices”, Special Issue on “Frontiers in Complex, Intelligent and Software Intensive Systems” of International Journal of Computer Systems Science & Engineering, Vol.
    25, n° 2, pp.
    119-131, ISSN 0267-6192, © 2010 CRL Publishing Ltd{1]., ISSN:
    V. Conti, C. Militello, S. Vitabile and F. Sorbello, “A Multimodal Technique for an Embedded Fingerprint Recognizer in Mobile Payment Systems”, International Journal on Mobile Information Systems – Vol.
    5, No.
    2, 2009, pp.
    105-124, IOS Press Ed., ISSN:
    1574-017X, DOI:10.3233/MIS-2009-0076
    S. Vitabile, V. Conti, C. Militello, F. Sorbello, “An Extended JADE-S Based Framework for Developing Secure Multi-Agent Systems”, Computer Standard and Interfaces Journal, Elsevier Editor, Vol.
    31, issue 5, 2009, pp.
    913-930, ISSN:
    0920-5489, DOI:10.1016/j.csi.2008.03.017
    S. Vitabile, V. Conti, G. Lentini, F., Sorbello, “An Intelligent Sensor for Fingerprint Recognition”, Proc. of International Conference on Embedded and Ubiquitous Computing (EUC-05), Lecture Note in Computer Science (LNCS), Springer-Verlag, vol.
    3824, pp.
    27-36, ISBN 3-540-30807-5, 2005{1], DOI:
    10.1007/11596356_6 (conference on scopus)
    G. Pilato S. Vitabile, V. Conti, G. Vassallo, F. Sorbello (2003).
    “A Concurrent Neural Classifier for HTML Documents Retrieval”, B. Apolloni, M. Marinaro, R. Tagliaferri (Eds.):
    Neural Nets – WIRN 2003, Lecture Notes in Computer Science, vol.
    2859, pp.
    210-217, Springer-Verlag{1]{2], DOI:
    Vincenzo Conti, Leonardo Rundo, Carmelo Militello, Giancarlo Mauri, and Salvatore Vitabile, “Resource-Efficient Hardware Implementation of a Neural-based Node for Automatic Fingerprint Classification”, Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), Vol.
    8, N. 4, pp.
    19-36, December 2017, DOI:
    V. Conti, S. Vitabile, “Design Exploration of AES Accelerators on FPGAs and GPUs,” Journal on Telecommunications and Information Technology, 2017, issue 1, pp.
    28-38, ISSN 1509-4553, National Institute of Telecommunications Publisher
    G. Vitello, V. Conti, S. Vitabile, and F. Sorbello, “An Heuristic Approach for the Training Dataset Selection in Fingerprint Classification Tasks”, in Advances in Neural Networks:
    Computational and Theoretical Issues, proc. of WIRN 2014, Editors:
    S. Bassis, A. Esposito, F.C. Morabito, pp.
    217-227, Series:
    Smart Innovation, Systems and Technologies, Springer International Publishing, Vol.
    37, 2015, ISSN:
    2190-3018, DOI:
    V. Conti, M. Collotta, G. Pau, and S. Vitabile, “Usability Analysis of a Novel Biometric Authentication Approach for Android-based Mobile Devices,” Journal on Telecommunications and Information Technology, 2014, vol.
    2014, issue 4, pp.
    34-43, ISSN 1509-4553, Publisher:
    National Institute of Telecommunications
    M. Collotta, V. Conti, G. Scatà, G. Pau, S. Vitabile, “Smart Wireless Sensor Networks and Biometric Authentication for Real Time Traffic Light Junctions Management”, International Journal of Intelligent Information and Database Systems, Copyright © 2013 Inderscience Enterprises Ltd., ISSN online:
    1751-5866, ISSN print:
    1751-5858, Vol. 7, No. 5, 2013, pp. 454-478, DOI 10.1504/IJIIDS.2013.056392
    V. Conti, S. Vitabile, L. Agnello, F. Sorbello, “Fingerprint and Iris based Authentication in Inter-cooperative Emerging e-Infrastructures”, Studies in Computational Intelligence (Internet of things and inter-cooperative computational technologies for collective intelligence), ISBN 978-3-642-34951-5, ISSN 1860-949X, Vol. 460, 2013, pp. 433-462, Springer Berlin Heidelberg Editor
    M. Collotta, V. Conti, G. Pau, G. Scatà, S. Vitabile, “Fuzzy Techniques for Access and Data Management in Home Automation Environments”, Journal of Mobile Multimedia, Vol. 8, No. 3 (2012), pp.
    181-203, [1}© {2]Rinton Press (Princeton, New Jersey) Publisher, ISSN: 1550-4646
    S. Vitabile, V. Conti, B. Lanza, D. Cusumano, and F. Sorbello, “Metabolic Networks Robustness:
    Theory, Simulations and Results”, Journal of Interconnection Networks (JOIN), Vol. 12, Issue No.
    3, pp 221-240, [1] World Scientific Publishing Company, 2011, ISSN: 0219-2659 (print), 1793-6713 (online), DOI: 10.1142/S0219265911002964
    G. Pilato, S. Vitabile, G. Vassallo, V. Conti, F. Sorbello, “Web Directories as a Knowledge Based to Build a Multi-Agent System for Information Sharing”, An International Journal on Web Intelligence and Agent Systems, 2004, Vol. 2, No. 4, pp.
    265-277, ISSN: 1570-1263 (print) – 1875-9289 (Online), IOS Press{1] (IF 0.865)
    Pingfei Jiang, Mark Atherton, Salvatore Sorce, David Harrison & Alessio Malizia (2018) “Design for invention: a framework for identifying emerging design–prior art conflict”, Journal of Engineering Design, 29:10, 596-615, DOI: 10.1080/09544828.2018.1520204
    F. Alonge, F.D’Ippolito, G. Garraffa.
    A. Sferlazza, “A hybrid observer for localisation of mobile vehicles with asynchronous measurements”, Asian Journal of Control, 2019, Vol.21, No.4, pp.
    1506-1521, pub. Wiley.
    G. Garraffa, A. Sferlazza, F. D’Ipplito, F. Alonge, “Localisation based on Parallel Robots Kinematics As an alternative to trilateration”, IEEE Transactions on Industrial Electronics, 2021, Vol. 69, No. 1, pp. 999-1010, IEEE.
    F. Alonge, F. D’Ippolito, A. Fagiolini, G. Garraffa, A. Sferlazza, “Trajectory robust control of autonomous quadcopters based on model decoupling and disturbances estimation”, International Journal of Advanced Robotic Systems, 2021, Vol.
    18, No.
    2, SAGE Publications UK, London, England.
    Cilia N.D., D’Alessandro T., De Stefano C., Fontanella F., Molinara M. (2021).
    From online handwriting to synthetic images for Alzheimer’s disease detection using a deep transfer learning approach, IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2021.3101982.
    Cilia N.D., De Stefano C., Fontanella F, Scotto di Freca A. (2021).
    Feature selection as a tool to support the diagnosis of cognitive impairments through handwriting analysis, IEEE Access 9: 78226-78240.
    Cilia N.D., Tiziana D’Alessandro, De Stefano C., Fontanella F. (2021).
    Deep transfer learning algorithms applied to synthetic drawing images as a tool for supporting Alzheimer’s disease prediction, Machine Vision and Application, in press.
    Cilia N.D., De Stefano C., Fontanella F., Marrocco C., Molinara M., Scotto di Freca A (2021) Deep Transfer Learningfor Alzheimer’s disease detection.
    IEEE Xplore, 25th International Conference on Pattern Recognition (ICPR), pp. 9904-9911, doi: 10.1109/ICPR48806.2021.9412603.
    Cilia N.D., De Stefano C., Fontanella F., Marrocco C., Molinara M., Scotto di Freca A. (2020).
  • An Experimental Comparison between Deep Learning and Classical Machine Learning Approaches for Writer Identification in Medieval Documents.
    J. Imaging 6(9): 89.
    Cilia N.D., De Stefano C., Fontanella F., Molinara M., Scotto di Freca A. (2020)
  • What is the minimum training data size to reliably identify writers in medieval manuscripts?
    Pattern Recognition Letters, 129, pp. 198-204.
    Cilia N.D., De Stefano C., Fontanella F., Marrocco M., Molinara M., Scotto di Freca A. (2020).
  • An end-to-end deep learning system for medieval writer identification, Pattern Recognition Letters, 129, pp. 137-143.
    Cilia N.D., De Stefano C., Fontanella F., Scotto di Freca A. (2019).
  • An Experimental Comparison of Feature Selection and Classification Methods for Microarray Datasets, Information, 10(3): 109.
    Scarpato N. Cilia N .D, Romano M. (2019).
  • Reachability Matrix Ontology:
    A Cybersecurity Ontology, Applied Artificial Intelligence, 33(7), pp. 643-655.
    Cilia N.D., De Stefano C., Fontanella F., Scotto di Freca A. (2019).
  • A ranking-based feature selection approach for handwritten character recognition.
    Pattern Recognition Letters, 121, pp. 77-86.


We provide support to public and private research sectors in artificial intelligence, machine learning, machine control, speech processing, etc. Recently we have collaborated with:

Boehringer Ingelheim Pharmaceuticals Inc. Boehringer Ingelheim Pharmaceuticals Inc.

(2021) – Metodi di apprendimento approfondito per il rilevamento automatico delle particelle e il prelievo da immagini di microscopia elettronica criogenica

(2021) – Deep learning methods for automatic particle detection and sampling from cryogenic electron microscopy images

Boehringer Ingelheim Pharmaceuticals Inc. Boehringer Ingelheim Pharmaceuticals Inc.

(2020) – Valutazione della complessità dei polimorfi in silico basata sull’apprendimento automatico

(2020) – In silico polymorph complexity assessment based on machine learning


(2017) – Machine Learning per l’analisi della scena acustica

(2017) – Machine Learning for acoustic scene analysis


STMLab is located on the second floor of the Kore Platform Building, Polo scientifico e tecnologico di Santa Panasia, 94100 Enna (EN), Italy