Research, Development and Technology Transfer Centre
Social Thingum is a technology transfer centre (CTT) with the task of carrying out training and technological consultancy activities, as well as providing technology transfer services to companies employed in certain economic and industrial sectors, identified by the Ministry of Economic Development.
These are companies and entities that prove to carry out technological projects in sectors such as additive manufacturing, augmented reality, the internet of things, the cloud, cybersecurity and big data analysis.
The certificate is released by Unioncamere, the Italian institution that plays an important role in the process of digitization of companies and, more generally, of modernization of the country through the implementation of projects and the provision of services.
Social Thingum is one of the 28 technology transfer centers in Italy, whose specialization has been recognized in the fields of Industrial Internet, Internet of Things and/or Internet of Machines, Big Data and Analytics, related to the activities of:
- Technological training
- Consulting training on organizational and/or business, technical and - - - industrial models
- Design and planning of technology implementation interventions
- Industry 4.0
- Industrial research and experimental development including prototyping, including virtual prototyping
- Production of technical or documentary material on technologies 4.0
Social Thingum is also a Research, Development and Technology Transfer Centre accredited by the Lombardy Region through Questio.
In this quality, we can help companies to obtain non-reimbursable funding or financial facilities for the implementation of Research and Development (R&D) projects and/or product and/or process innovation.
We aim to assist you in the drafting of the documentation necessary to participate in the call and, above all, in the implementation of the projects themselves. In fact, being Social Thingum also an accredited Research Centre, our costs are expensable within the project.
In particular, we also have seven Innovation Managers accredited by MISE, able to offer a wide variety of services to support innovation projects for companies.
Autori: Epifania F, Marconi L, Mauri G, Manzoni S, Dondi R, Zoppis I
Tag: Recommender Systems
Recommender Systems have became extremely appealing for all technology enhanced learning researches aimed to design, develop and test technical innovations which support and enhance learning and teaching practices of both individuals and organizations. In this scenario a new emerging paradigm of explainable Recommander Systems leverages social friend information to provide (social) explanations in order to supply users with his/her friends’ public interests as explained recommendation. In this paper we introduce our educational platform called “WhoTeach”, an innovative and original system to integrate knowledge discovery, social networks analysis, and educational services. In particular, we report here our work in progress for providing “WhoTeach” environment with optimized Social Explainable Recommandations oriented to design new teachers’ programmes and courses.
Autori: Andri PERL, Sebastiaan BONGERS, Simone Bassis, Bruno Apolloni, Swiss Reinsurance Co Ltd
An electronic, real-time system performs maneuver recognition of vehicles based on dynamically measured telematics data, particularly the sensory data of smartphone sensors, and more particularly data from the accelerometer sensor and the global positioning system (GPS) sensor and/or the gyroscope sensor of a smartphone. The axes of the smartphone may be moving independently relative to the axes of the vehicle and thus do not need to be aligned with the axes of the vehicle. Driver behaviors and operational parameters are automatically measured and discriminated, based on automatically individuated and measured driver maneuvers within various measured to vehicle trajectories, and an output signal is generated based upon derived risk measure parameters and/or crash attitude measure parameters. The system can use score-driven, especially risk-score driven, operations associated with motor vehicles or transportation modes for passengers or goods, and reliant on a dynamic, telematics-based data aggregation and dynamically measured driving maneuvers, respectively.
Autori: Bruno Apolloni, Simone Bassis
Tag: Algorithmic inference machine learning parameter distribution confidence intervals learning Boolean functions
We start from the very operational perspective – having data, organize them in a suitable way to be used in the future – to enter the long standing fray on the nature of inferred parameters within a machine learning thread. Still in an operational perspective, we introduce a parametric inference approach that unprecedentedly gets rid of most drawbacks incurred by current methods to compute confidence intervals. The key idea is to consider the parameters of the distribution underlying a sample to be random, where randomness is expressed in terms of a probability measure of the compatibility of the parameter values with the actually observed data. The probability is understood, in a frequentist acceptation, in terms of the asymptotic frequency of those parameter values matching the observed sample in a story of infinite observations. The aim of this paper is to recap and complete theoretical results obtained through our approach as presented in preceding papers. In particular, here we focus on statistical tools both for computing confidence regions, at the basis of appraising the learnability of a function, and for checking their efficacy. We basically support our theory with a series of well-known benchmarks where, as for both volume and coverage of the confidence regions, our method proves superior – with very few ties – to those of competitors. Then we mention some results in computational learning theory that have been achieved recently exactly by adopting our approach, with a special focus on a new data_ accuracy - sample_complexity trade off.
Autori: Bruno Apolloni, Simone Bassis, Marco Mesiti, Stefano Valtolina, Francesco Epifania
Tag: Recommender system, Decision trees, Genomic features
We introduce a new recommending paradigm based on the genomic features of the candidate objects. The system is based on the tree structure of the object metadata which we convert in acceptance rules, leaving the user the discretion of selecting the most convincing rules for her/his scope. We framed the deriving recommendation system on a content management platform within the scope of the European Project NETT and tested it on the Entree UCI benchmark.
Autori: Francesco Epifania
Tag: Social Network, LMS, RS, entrepreneurship
With the ambition of providing teachers with a novel concrete tool called “Social Intelligent Learning Management System (SILMS)” for worldwide exploiting didactic contents to feature their courses, I faced the problem of creating a social platform with adequate functionalities to satisfy the teacher expectations. This goal involved many disciplines and practices ranging from DB management, content management, social networking, till the exploitation of new cognitive systems in the thread of WEB4. 0 services. At the same time my approach was much oriented to realize a real tool of concrete usage, still with distinguishably advanced features. Thus, starting with a well designed architecture I endowed it with key functionalities that become the stakeholders of the emerging social networks: 1) a quality system ensuring the value of the materials the users put in the platform repository as their contribution to the social business, 2) a recommender system based on either ontology assisted navigator or computational intelligence techniques constituting the principal tool to guide teachers along the assembling of materials into courses.
Autori: Francesco Epifania, Riccardo Porrini
Tag: Recommender System, Learning Resources, Social Network, e-Learning, User-centric Evaluation.
The NETT Recommender System (NETT-RS) is a constraint-based recommender system that recommends learning resources to teachers who want to design courses. As for many state-of-the-art constraint-based recommender systems, the NETT-RS bases its recommendation process on the collection of requirements to which items must adhere in order to be recommended. In this paper we study the effects of two different requirement collection strategies on the perceived overall recommendation quality of the NETT-RS. In the first strategy users are not allowed to refine and change the requirements once chosen, while in the second strategy the system allows the users to modify the requirements (we refer to this strategy as backtracking). We run the study following the well established ResQue methodology for user-centric evaluation of RS. Our experimental results indicate that backtracking has a strong positive impact on the perceived recommendation quality of the NETT-RS.
Autori: B Apolloni, F Epifania, M Mesiti, M Mesenzani, S Valtolina
E’un dato singolare che nonostante le ripetute note degli organi di governo e delle imprese che rilevano la mancanza di spirito imprenditoriale e d’altro canto le benemerenze rispetto al paese di chi fa impresa, a tutt’oggi l’Educazione di Imprenditorialità non fa parte dei programmi ministeriali delle scuole di qualsiasi ordine e grado, ad eccezione di alcune Università. Non è così nel resto di Europa, come mostra la mappa in Fig. 1 (Eurydice, 2012). Benché datata di 5 anni questa mappa illustra appunto l’assenza di strategie nel settore in Italia. Questa situazione peraltro non è isolata come sottolineato dalla Comunità Europea che ha promosso varie iniziative per migliorarla (Europe, 2020). Tra queste è il progetto NETT (Networked Entrepreneurship Training of Teachers--http://www. nett-project. eu/), finanziato a un consorzio di 4 partner (due Italiani, uno Bulgaro e uno Turco) per creare una social network di educatori di impresa attraverso la realizzazione di una piattaforma ad hoc. L’idea di base è che, trattandosi di una disciplina ancora acerba, per la sua maturazione occorra il contributo di coloro che già la insegnano o prevedano di farlo nell’immediato futuro. Per questo motivo occorrono strumenti che permettano agli operatori del settore di scambiarsi materiali e di favorire la discussione e la valutazione dei materiali al fine di identificare quelli utili e di qualità per l’insegnamento di questa disciplina.
Autori: BRUNO APOLLONI, LUCA MARCONI, FRANCESCO EPIFANIA, ALESSIO ANGHILERI, MARCO MESITI, STEFANO VALTOLINA, SERENA DI GAETANO, ALBERTO SCHIAFFINO, MATTEO REINA, ROBERTO PELLEGRINI
Tag: Social appliances; green social network; machine learning; collective intelligence
We discuss a Cloud-based Collective Intelligence model and its in-progress implementation to direct users toward an optimal usage of their home appliances as a way of getting both personal advantage and an overall reduction of pollution and energy consumption. In this model sustainability is considered with respec t to two types of resources: natural ones, to be mostly preserved, as indicated above, and brain resources, in te rms of intention and knowledge, to be convoyed to a common target. Having the first aspect for a given, in this paper we focus on the secondby examining three distinct factors: user experience, knowledge achievement and business model. Our service paradigm is rooted on a Social Networks of Facts that requires experts’ know, like that owned by the appliance manufacturer, but exploits it in an autonomous way so as to comply with the specific intentions of the individual users. While cloud architectural and communication aspects are solved in a standard, though advanced, way, the interplay between user and expertsisconsidered variously within a range of business models. As the success of these models is related to the network population, here we discuss some preliminary simulations based on an effectively implemented infrastru cture and on the ex trapolation of early collected data
Other publications by our co-founders
Prof. Francesco Epifania Ph.D: https://scholar.google.it/citations?hl=en&user=pxgD96gAAAAJ&view_op=list_works&sortby=pubdate
Prof. Bruno Apolloni: https://dblp.uni-trier.de/pers/hy/a/Apolloni:Bruno
Prof. Nikolov Roumen Ph.D: https://dblp.uni-trier.de/pers/hd/n/Nikolov:Roumen
Prof.ssa Eugenia Kovatcheva Ph.D: https://dblp.uni-trier.de/pers/hd/k/Kovatcheva:Eugenia
Simone Bassis Ph.D: https://dblp.org/pers/hd/b/Bassis:Simone