The anthropomorphization of human-robot interactions is a fundamental aspect of the design of social robotics applications. This article describes how an interaction model based on multimodal signs like visual, auditory, tactile, proxemic, and others can improve the communication between humans and robots. We have examined and appropriately filtered all the robot sensory data needed to realize our interaction model. We have also paid a lot of attention to communication on the backchannel, making it both bidirectional and evident through auditory and visual signals. Our model, based on a task-level architecture, was integrated into an application called W@ICAR, which proved efficient and intuitive with people not interacting with the robot. It has been validated both from a functional and user experience point of view, showing positive results. Both the pragmatic and the hedonic estimators have shown how many users particularly appreciated the application. The model component has been implemented through Python scripts in the robot operating system environment.

I database a grafo sono diventati tecnologie tradizionali e stanno diventando sempre più preziosi per le organizzazioni di qualsiasi settore. Sono più flessibili dei database relazionali tradizionali in quanto ci consentono di sfruttare le relazioni nei nostri dati in un modo che i database relazionali non possono fare. In un momento in cui le organizzazioni cercano di ottenere il massimo dai propri dati, questo crea opportunità per qualsiasi organizzazione. Tuttavia, lo sviluppo con database a grafo porta a molte sfide, in particolare, tra gli altri, quando si tratta di modellare i dati e mantenerne la coerenza. Nei capitoli seguenti, vedremo come TypeDB si confronta con i database a grafo, in particolare i labelled property graphs, e come TypeDB affronta queste sfide. Sebbene entrambe le tecnologie condividano alcune somiglianze, sono fondamentalmente diverse. Vedremo come leggere e scrivere dati, come modellare domini complessi e vedremo anche la capacità di TypeDB di eseguire operazioni di reasoning automatico. Le principali differenze tra TypeDB e i database a grafo possono essere riassunte come segue: 1. TypeDB fornisce uno schema a livello di concetto con un sistema di tipizzazione che implementa completamente il modello Entity- Relationship (ER). Invece, i database a grafo utilizzano vertici e archi senza vincoli di integrità imposti da uno schema. 2. TypeDB contiene un reasoner automatico integrato, mentre i database a grafo non forniscono capacità di reasoning native. 3. TypeDB è un livello di astrazione su un grafico. TypeDB sfrutta un database grafico dietro le quinte per creare un'astrazione di livello superiore, con il risultato che entrambe le tecnologie funzionano a diversi livelli di astrazione Sono disponibili diverse tecnologie per i grafi. Alcuni di questi sono basati su RDF e SPARQL, altri sono linguaggi di query imperativi basati sui path come Gremlin. Il più popolare, tuttavia, è Cypher, che è cresciuto fino a diventare il linguaggio di query del database a grafo più adottato per i property graph. Per questo motivo, in questo confronto ci concentreremo solo su Cypher e sui labelled property graph.

In questo lavoro, metteremo a confronto TypeDB con gli standard per il Semantic Web, concentrandoci in particolare su RDF, XML, RDFS, OWL, SPARQL e SHACL. Esistono alcune somiglianze chiave tra questi due insiemi di tecnologie, principalmente perché entrambi sono radicati nel campo dell'IA simbolica, della rappresentazione della conoscenza e del reasoning automatico. Queste somiglianze includono: 1. Entrambi consentono agli sviluppatori di rappresentare ed eseguire query su set di dati complessi ed eterogenei. 2. Entrambi danno la possibilità di aggiungere semantica a set di dati complessi. 3. Entrambi consentono all'utente di eseguire ragionamenti deduttivi automatizzati su grandi quantità di dati. Tuttavia, esistono differenze fondamentali tra queste tecnologie, poiché sono state progettate per diversi tipi di applicazioni. Nello specifico, il Semantic Web è stato ideato, appunto, per il Web, con dati incompleti provenienti da molte fonti, dove chiunque può contribuire alla definizione e mappatura tra le fonti di informazione. TypeDB, al contrario, non è stato creato per condividere dati sul web, ma per funzionare come database transazionale per organizzazioni "chiuse". Per questo motivo, confrontare le due tecnologie, a volte, potrebbe essere fuorviante. Queste differenze possono essere riassunte nel modo seguente: 1. Rispetto al Semantic Web, TypeDB riduce la complessità mantenendo un alto grado di espressività. Con TypeDB, evitiamo di dover apprendere diversi i standard del Semantic Web, ciascuno con alti livelli di complessità. Ciò consente di essere produttivi più velocemente. 2. TypeDB fornisce un'astrazione di livello superiore per lavorare con dati complessi rispetto agli standard Semantic Web. Con RDF modelliamo il mondo in triple, che è un modello di dati di livello inferiore rispetto allo schema a livello di concetto di relazione tra entità di TypeDB. Modellazione e query per relazioni di ordine superiore e dati complessi sono nativi in TypeDB. 3. Gli standard Semantic Web sono costruiti per il Web, TypeDB funziona per sistemi chiusi con dati privati. I primi sono stati progettati per funzionare con i dati collegati su un Web aperto con dati incompleti, mentre TypeDB funziona come un tradizionale sistema di gestione di database in un ambiente chiuso. In questo lavoro metteremo in evidenza che ci sono forti sovrapposizioni nel modo in cui entrambe le tecnologie offrono strumenti per la rappresentazione della conoscenza e il reasoning automatico e illustreremo i concetti più importanti ad alto livello senza entrare troppo nei dettagli. L'obiettivo è aiutare gli utenti con un background RDF/OWL a familiarizzare con TypeDB.

Sin dagli anni '70, SQL è stato lo standard di fatto in termini di linguaggio per interagire con i database. Essendo un linguaggio dichiarativo, è abbastanza semplice scrivere query e costruire potenti applicazioni. I database relazionali, però, entrano in difficoltà quando hanno a che fare con dati complessi ed interconnessi. Con questo tipo di dati, infatti, con SQL sorgono problemi soprattutto nella modellazione e nell'interrogazione dei dati. TypeQL è il linguaggio di query utilizzato in TypeDB. Proprio come SQL è il linguaggio di query standard nei database relazionali, TypeQL è il linguaggio di query di TypeDB. Sia SQL che TypeQL sono linguaggi di query dichiarativi che astraggono le operazioni di livello inferiore. Entrambi sono: o linguaggi che cercano di essere leggibili e comprensibili; o linguaggi che cercano di consentire di porre domande a un livello più alto; o linguaggi grazie ai quali il sistema capisce come eseguire operazioni di basso livello. In termini pratici, ciò significa che tali linguaggi diventano accessibili a gruppi di persone che altrimenti non avrebbero potuto accedervi. In questo documento vengono esaminati concetti comuni specifici e ci si concentrerà poi sul confronto e sull'esplorazione delle differenze tra i due linguaggi.

ASS4HR is both a design and a ROS (Robot Operating System) implementation of an artificial somatosensory system for a humanoid robot. The design takes inspiration from the biological model and has been adapted to the robot's characteristics. The implementation is based on the use of the soft sensors paradigm. ASS4HR allows the robot to experience sensations that we call "roboceptions", exploiting from the measurements of the basic sensors it is equipped with. This way, the robot is aware of its physical condition and can adapt its behavior. For each "roboception" that constitutes the artificial somatosensory system, both the ascending path and the descending path have been implemented. Therefore, ASS4HR allows you to experience, modulate or inhibit the "roboceptions". By doing so, each robot can have its own character.

This article deals with the problem of the interaction and engagement between humans and humanoid Robots in circumstances where it is essential to be sure that engagement has occurred and it persists for the right time. In particular, in Robot-aided medical care (but also in other critical situations), it is essential to be sure that the flow of information between the human and the humanoid Robot effectively occurs between the actual patient and the Robot. Many sensory data of the Robot will be involved in a data fusion algorithm to provide robustness and stability in the human-Robot interaction. The methodology described in this article has been realized on real Robots (Nao and Pepper) and implemented through Python scripts in ROS (Robot Operating System).

BioGrakn DN is a single knowledge graph of biomedical data describing a disease network, ingested from Uniprot, Reactome, DGIdb, DisGeNET, HPA-Tissue, EBI IntAct, Kaneko, Gene Expression Omnibus and TissueNet.

The field of systems biology is characterized by a huge amount of heterogeneous data, hard to integrate due to their complex nature and rich semantics. One of the key goals in this scope is understanding the complex relationships among these biological data and, certainly, we need solutions to speed up their integration and querying. Anyhow, analyzing large volumes of biological data through traditional database systems is troublesome and challenging. In this work, we demonstrate how using a semantic knowledge graph for complex biological relationships, such as BioGrakn Disease Network (BioGraknDN), would accelerate the knowledge discovery process.

Nell'era dell'Internet degli oggetti (IoT), dove ogni giorno si diffonde la disponibilità della connettività dati e dei dispositivi intelligenti, la capacità di acquisire i dati dei sensori attraverso una soluzione semplice, economica ed efficiente in grado di utilizzare lo standard affermato per l'osservazione dei sensori, è un fattore di promozione per la creazione collaborativa di una serie di dati secondo la filosofia Open Data. Questa impostazione è particolarmente adatta al monitoraggio ambientale attraverso la misurazione ripetitiva di parametri fisici e chimici. Questo rapporto tecnico presenta una nuova soluzione software per l'acquisizione dei dati basata su un terminale Raspberry PI dotato di un sensore termico e connettività dati a livello di sistema. Sfrutta le funzionalità standard del Sensor Observation Service (SOS)come il modello di dati, i formati di comunicazione e assume la disponibilità di un server SOS per archiviare i dati, e un implementazione open source da 52° North come servizio remoto.

When someone says a statement about a particular subject, we memorize the assertion and, implicitly, we can construct all the possible questions that have as a right answer to the assertion just heard. This means that, in this specific case, our learning process based on assertions subsists. When we read a book, we do nothing but learn through a succession of assertions. In this article, we present a system for automatically constructing a conversational agent, which uses only assertions to build the dialog engine. The whole architecture is based on the "Robot Operating System" (ROS), and the experiments were conducted using a humanoid robot.

In this paper, an extension of a MultiModel NoSQL database is proposed in order to transform it on an ebXML registry, a complete system for Business-to-Business (B2B) information exchange. It can be used as key component of the interoperability infrastructure developed within the OpenInFSE project. OrientDB has been chosen as MultiModel NoSQL database, having the characteristic of a graph database also including the document layer. Moreover, EXPO, an EbXml Plugin for OrientDB, is proposed as native extension for the OrientDB Server.

The era of "big data" arose the need to have computational tools in support of biological tasks. Many types of bioinformatics tools have been developed for different biological tasks as target, pathway and gene set analysis, but integrated resources able to incorporate a unique web interface, and to manage a biological scenario involving many different data sources are still lacking. In many bioinformatics approaches several data processing and evaluation steps are required to reach the final results. In this work, we face a biological case study by exploiting the capabilities of an integrated multi-component resources database that is able to deal with complex biological scenarios. As example of our problem-solving approach we provide a case study on the analysis of functional effect of miRNA single nucleotide polymorphisms (SNPs) in cancer disease.

Monolithic applications are the most common development paradigm but they have some drawback related to the maintenance, upgrading and scaling. Microservice architectures were recently proposed in order to solve some of these issues, because they are simpler to scale and more flexible. Both architectures use a database and this component can act as a component for micro service. In the paper we present a pattern for microservice architecture that uses a database as component, and this pattern is used in an health record application. We explain also the requirements of the database for this pattern and the advantages achieved.

Microservice architectures are used as alternative to monolithic applications because they are simpler to scale and more flexible. Microservices require a careful design because each service component should be simple and easy to develop. In this paper, a new microservice pattern is proposed: a database that can be considered a microservice by itself. We named this new pattern as The Database is the Service. The proposed simplified database pattern has been tested by adding ebXML registry capabilities to a noSQL database.

IT complexity in enterprise today continues to grow at a dizzying rate. Technology innovation, vendor heterogeneity, and business demands are major reasons why organizations are exposed to new risks, based on the gaps opened between the options and features of each IT element and product, and how they are implemented to support a well-defined policy and company strategy. Moreover, the impact of such risks is exacerbated exponentially by failing to identify the handshakes and correlations of interrelated elements. Products, vendors, and IT layers, must work in tandem to prevent potential "black holes": risks related to availability, resiliency and data loss. In this paper, we propose a possible approach to reduce complexity, and thus the related risks, and also to gain improvements in terms of simplicity, speed and scalability. In order to achieve those objectives, we can add new behaviors and a business logic at the database level, if easily obtainable. We did it with OrientDB, a Multi-Model NoSQL database, through the development of native extensions, such as the XML-over-SOAP service study case.

RNA sequencing (RNA-seq) is a New Generation Sequencing (NGS) method used for the analysis of transcripts and differential gene expression profiles. MicroRNA (miRNAs, 22--25 nt long) are, among small non coding RNAs (sncRNA) obtained through RNA-seq, key regulators in multiple cellular functions, as they play a crucial role in different physiological processes. miRNAs are in fact differentially expressed in several types of cancer, in specific tissues and during specific cell status. Clustering algorithms have been applied to microarray data in order to discover groups of genes (clusters) that are co-regulated with respect to certain experimental conditions. Because many regulation mechanisms involve only set of genes and limited set of experimental conditions, a new approach is needed. In this context, biclustering techniques represent suitable approaches because they allow to separate, in a data matrix, groups of rows and columns, standing for genes and samples , that exhibits similar values or similar characteristics. We present a biclustering approach in order to identify some patterns of miRNA expression deregulation in human breast cancer versus healthy controls. We applied the Iterative Signature Algorithm (ISA) tool, which has proved one of the most efficient when applied to gene expression datasets. Considering a real word breast cancer dataset, composed of 185 samples, we identified 12 miRNA biclusters, each of them involving different types of tumor samples and miRNA families. We showed the association between specific sub-class of tumor samples having the same immuno-histo-chemical (IHC) and/or histological features. Biclusters have been validated in the current scientific using the MetaMirClust and UCSC Genome Browser online tools, as well as another biclustering algorithm (SAMBA). The proposed biclustering led to the identification of different groups of miRNAs and patient conditions, that eventually have to be validated by in-vitro experiment.

MicroRNAs (miRNAs) are small non coding RNAs with regulatory functions to post-transcriptional level. They play an important role in molecular and cellular mechanisms, thanks to their ability to bind and interact with many RNA messengers (mRNAs) of coding product involved in a wide range of biological pathways, cellular status, and conditions. We present miRNA Target Interaction Predictor (miRNATIP), a Self Organizing Map (SOM) based method for the miRNA target prediction. miRNATIP is composed of four steps (see Fig. 1): in the first step, a set of miRNA seeds (8 nt) is used for the training of a SOM. The second step is the projection of a mRNA sequence over the trained SOM. For this reason, we extracted all the 8-length mRNA fragments through a 8-mer sliding window. The result of this step is, for each neural unit (cluster), a list of miRNA_seed-mRNA_fragment. Each cluster can be considered as a preliminary list of predicted miRNAs-mRNAs interaction. Then we computed a dissimilarity measure based on normalised euclidean distance between the remaining part of both miRNA and mRNA sequences, and we retained only the couples whose distance is below a certain threshold. Finally, in the fourth step we performed another filtering to the miRNA-mRNA interaction list, by computing the free-energy of the miRNA-target site duplex. We tested our method by predicting the miRNA target interactions of the C. elegans and human species. miRNA mature sequences were downloaded from miRBase, while verified 3'UTR mRNA sequences were extracted from Ensembl. Experimentally validated miRNA-target interaction were taken from mirTarBase and Tarbase. We compared our results with other target predictors: PITA, miRanda, TargetScan, Pictar, Diana-microT. Prediction results, in terms of sensitivity and specificity, demonstrated that outperforms or is comparable to the other six state-of-the-art methods, in terms of validated target and non-target interactions, respectively.

The traditional Java API for Registries (JAXR) pro- vides a useful way for Java developers to use a single simple abstraction API to access a variety of registries. The unified JAXR information model (Infomodel), which describes content and metadata within registries, provides a simple way to access registries information. However, when a JAXR registry provider implements the OASIS (Organization for the Advancement of Structured Information)/ebXML Registry Services Specification, it internally works with ebXML Registry Information Model (RIM) objects which should be exposed as JAXR Infomodel objects. Furthermore, any application using ebXML RIM objects, before the access to the registry via a JAXR provider, has to convert them to the Infomodel counterpart. To deal with this problem, in this paper we suggest a new tool for the ebXML software development, which focuses on an extension of the traditional JAXR layer, providing the native use of ebXML RIM objects in client side applications with a direct access to the ebXML RIM SOAP service, and avoiding any conversion to/from JAXR Infomodel objects. The proposed approach reduces the developers duty, allowing them to focus exclusively on the ebXML objects use and it considerably speeds up the interactions with every ebXML registries.