Centro di Ricerca, Sviluppo e Trasferimento tecnologico

Social Thingum è un anche Centro di Ricerca, Sviluppo e Trasferimento tecnologico accreditato dalla Regione Lombardia mediante Questio.

In questa veste, possiamo aiutare le aziende ad ottenere finanziamenti a fondo perduto o agevolazioni finanziarie per la realizzazione di progetti di Ricerca e Sviluppo (R&D) e/o di innovazione di prodotto e/o processo. Queste opportunità di finanziamento sono concrete e si realizzeranno anche a breve. Infatti, sono stati pubblicati due bandi di finanziamento per iniziative di Ricerca ed Innovazione: il bando per Innovation Manager e il bando Innodriver.

Ci proponiamo di assistervi nella stesura della documentazione necessaria alla partecipazione al bando e, soprattutto, nella realizzazione dei progetti stessi. Infatti, essendo Social Thingum anche Centro di Ricerca accreditato, i nostri costi sono spesabili all'interno del progetto.

In particolare, abbiamo anche ben sette Innovation Manager accreditati dal MISE, in grado di offrire un'ampia varietà di servizi a supporto di progetti di innovazione​ per le aziende.


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

Tag: /

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.


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