Andrea Calvagna

We face a decentralized renewable energy production scenario, where a large number of small energy producers, i.e., prosumers, contribute to a common distributor entity, who resells energy directly to end-users. A major challenge for the distributor is to ensure power stability, constantly balancing produced vs consumed energy flows. In this context, being able to provide quick restore actions in response to unpredictable unbalancing events is a must, as fluctuations are the norm for renewable energy sources. To this aim, the high scalability and diversity of sources are crucial requirements for the said balancing to be actually manageable. In this study, we explored the challenges and benefits of adopting a blockchain-based software architecture as a scalable, trustless interaction platform between prosumers’ smart energy meters and the distributor. Our developed prototype accomplishes the energy load balancing service via smart contracts deployed in a real blockchain network with an increasing number of simulated prosumers. We show that the blockchain-based application managed to react in a timely manner to energy unbalances for up to a few hundred prosumers.

Social media networks provide an aggregation of news and content, allowing users to share and discuss topics of greatest interest to them. Users can enrich the news by providing context and opinions that are useful to other users. Understanding topics of interest sheds light on the collective thinking of a group of individuals and offers important insights for exploring a given field. Among the fields of interest on social media networks, finance stands out. Automatically identifying and organizing the main issues that users discuss can be useful for multiple purposes, e.g., identifying the preferred types of loans could be useful for refining targeted advertising. Our work aims to identify and organize the topics discussed on a social media network that are related to the financial sector. For this, we propose an approach that consists of analyzing posts from Reddit communities oriented to finance. First, posts were gathered and cleaned to remove punctuation, links, and images. Then, textual similarity was computed to match posts with classes from dedicated ontologies designed for the financial sector. Finally, the populated ontology was analyzed to identify clusters of concepts. The results showed that the proposed approach and corresponding tool can summarize topics from a large number of Reddit posts using the identified classes. Over 70% of posts were linked to ontologies when considering both posts and comments, which shows that the automatic support given to posts related to financial concepts had a high degree of success.

The Electronic Health Record (EHR) is a system for collecting and storing patient medical records as data that can be mechanically accessed, hence facilitating and assisting the medical decision-making process. EHRs exist in several formats, and each format lists thousands of keywords to classify patients data. The keywords are specific and are medical jargon; hence, data classification is very accurate. As the keywords constituting the formats of medical records express concepts by means of specific jargon without definitions or references, their proper use is left to clinicians and could be affected by their background, hence the interpretation of data could become slow or less accurate than that desired. This article presents an approach that accurately relates data in EHRs to ontologies in the medical realm. Thanks to ontologies, clinicians can be assisted when writing or analysing health records, e.g., our solution promptly suggests rigorous definitions for scientific terms, and automatically connects data spread over several parts of EHRs. The first step of our approach consists of converting selected data and keywords from several EHR formats into a format easier to parse, then the second step is merging the extracted data with specialised medical ontologies. Finally, enriched versions of the medical data are made available to professionals. The proposed approach was validated by taking samples of medical records and ontologies in the real world. The results have shown both versatility on handling data, precision of query results, and appropriate suggestions for relations among medical records.

Social network systems are constantly fed with text messages. While this enables rapid communication and global awareness, some messages could be aptly made to hurt or mislead. Automatically identifying meaningful parts of a sentence, such as, e.g., positive or negative sentiments in a phrase, would give valuable support for automatically flagging hateful messages, propaganda, etc. Many existing approaches concerned with the study of people’s opinions, attitudes and emotions and based on machine learning require an extensive labelled dataset and provide results that are not very decisive in many circumstances due to the complexity of the language structure and the fuzziness inherent in most of the techniques adopted. This paper proposes a deterministic approach that automatically identifies people’s sentiments at the sentence level. The approach is based on text analysis rules that are manually derived from the way Italian grammar works. Such rules are embedded in finite-state automata and then expressed in a way that facilitates checking unstructured Italian text. A few grammar rules suffice to analyse an ample amount of correctly formed text. We have developed a tool that has validated the proposed approach by analysing several hundreds of sentences gathered from social media: hence, they are actual comments given by users. Such a tool exploits parallel execution to make it ready to process many thousands of sentences in a fraction of a second. Our approach outperforms a well-known previous approach in terms of precision.

We present a method, which incorporates knowledge awareness into the symbolic computation of discrete controllers for reactive cyber physical systems, to improve decision making about the unknown operating environment under uncertain/incomplete inputs. Assuming an abstract model of the system and the environment, we translate the knowledge awareness of the operating context into linear temporal logic formulas and incorporate them into the system specifications to synthesize a controller. The knowledge base is built upon an ontology model of the environment objects and behavioural rules, which includes also symbolic models of partial input features. The resulting symbolic controller support smoother, early reactions, which improves the security of the system over existing approaches based on incremental symbolic perception. A motion planning case study for an autonomous vehicle has been implemented to validate the approach, and presented results show significant improvements with respect to safety of state-of-the-art symbolic controllers for reactive systems.

We present a method, which incorporates knowledge awareness into the symbolic computation of discrete controllers for reactive cyber physical systems, to improve decision making about the unknown operating environment under uncertain/incomplete inputs. Assuming an abstract model of the system and the environment, we translate the knowledge awareness of the operating context into linear temporal logic formulas and incorporate them into the system specifications to synthesize a controller. The knowledge base is built upon an ontology model of the environment objects and behavioural rules, which includes also symbolic models of partial input features. The resulting symbolic controller support smoother, early reactions, which improves the security of the system over existing approaches based on incremental symbolic perception. A motion planning case study for an autonomous vehicle has been implemented to validate the approach, and presented results show significant improvements with respect to safety of state-of-the-art symbolic controllers for reactive systems.

Combinatorial interaction testing (CIT) is a testing technique that has proven to be effective in finding faults due to the interaction among inputs and in reducing the number of test cases, without losing effectiveness. Several tools have been proposed in the literature; however, generating tests remains a challenging task. In this paper, we present a technique for generating combinatorial test suites that uses a multi-thread architecture and exploits Satisfiability Modulo Theory (SMT) solvers to represent model parameters, constraints, and tuples, and it builds from SMT solver contexts the desired test suite. This technique is implemented by the tool KALI. The main advantage of using SMT solvers is that combinatorial models can contain all kinds of parameters and constraints. To evaluate our approach, we tested the impact of several optimizations and compared the performance of KALI with those of some existing tools for test generation. Our experiments confirm that the use of multi-threading is a promising technique but still requires some optimization for being more effective than the already available ones.

In this paper we present a novel parallel technique to compute t-wise covering arrays. The massive computational work, implied by the considered task when large configuration spaces are modeled, is distributed over a scalable set of parallel computing resources by means of an MPI-compliant algorithm. Due to NP-completeness of the covering array problem, existing research on combinatorial generation algorithms commonly assumes this computation task as strictly sequential. Conversely, basing on inherent combinatorial properties, we show that it is possible to scatter the overall workload into several and independent processing sub-tasks, and then collect all outcomes into a global solution whose size is still comparable to that of a sequentially computed solution. Reported results show that in this way significant speed-up is achieved on the computation times with respect to the sequential computation of the same task.