The blockchain framework has increasingly moved from purely finance and the digital world to acquire a greater role as an indispensable diaphragm between the physical and the information processing parts of Cyber-Physical Systems. A significant need is the requirement for trustworthy contact between the two domains, which today can be handled with the Oracle concept and its inherent limitations. This proposal calls on the holonic paradigm to mitigate the Oracle problem, employing a constructivistic and second-order cybernetic stance that allows the trust model of the oracle to evolve and follow the dynamics of complex reality.

Blockchain technologies and paradigms can provide new ground for the management of complex socio-technical problems like supply chains. The contribution of this work is a work-in-progress experimental setting to demonstrate the effectiveness of a novel distributed business process management scheme that integrates the RMAS (Relational-model Multi Agent System) and the Blockchain frameworks. The research work is conducted in a food supply chain case study aiming to sustainability and social good through a purposeful organisation of the autonomous behaviour of the actors in the system.

This paper gives a novel perspective about the role of the self-similarities in the modelling of system of systems with cyber-physical representation. With the adoption of virtual overlays of dynamically self-adapting holarchies, the emergent characteristics and behaviors of the information and automation systems are mapped into a recursive tree of autonomic components that can be typically realized through holonic multiagent systems. The nested computing structures that are used to achieve a productivity goal are kept simple by means of self-similarities. This simplification provides a viable methodology for the pervasive management and the automated programming of the holonic components. The technique described is suitable for a prompt adoption on a vast class of manufacturing, robotics, mechatronics, and facility management processes, whilst enabling a continuum between humans and machines. An advantage of the proposed method is its agnosticism with respect to most of the specific but heterogeneous technological means adopted in the already existent automation realizations.

Time is a useful dimension to explore in text databases especially when historical and factual information is concerned. As documents generally refer to different events and time periods, understanding the focus time of key sentences, defined as the time the content refers to, is a crucial task to temporally annotate a document. In this paper, we leverage a bag of linked entities representation of sentences and temporal information from Wikipedia and DBpedia to implement a novel approach to focus time estimation. We evaluate our approach on sample datasets and compare it with a state of the art method, measuring improvements in MRR.

This work reviews and recaps some lessons learnt in the industrial cyber-physical systems playground to indicate a viable path towards an effective use of artificial intelligence in distributed automation. The holon concept is used as a pivotal tool towards a simplification of the design and implementation of architectures that exploit recursive patterns and self-similarities to keep the industrial systems-of-systems problem under control, whilst addressing the consistent coupling between the physical environmental and the reasoning processes. The Beer’s viable system model is used as an inspiration towards new viable holistic visions of the production system-of-systems.

The complexity of cyber-physical systems (CPSs) poses new challenges in their design, model checking and maintenance. The hardware and software designers are in search, more than ever, for simple and interoperable approaches that render the complexity of CPSs a treatable matter. In this work, database language is suggested as an enabling technology and a lean technique to the purpose. An example with best available embedded database technology is conducted by means of a deployment test on tiny embedded electronics.

The paper introduces a formalisation of opportunities, as situations that can be exploited obtaining valuable outcomes, in the context of the social networks, and defines a methodology for discovering opportunities through the analysis of the relation among network actors. The proposed methodology is then applied to the research-oriented networks, whose members share paper coauthorship or potential research interests. Finally, its validity is tested by evaluating the research collaborations opportunities exploited in the context of two distinct research communities, modelled through the analysis of their publications over time.

In this paper we present a method for discovering collaboration opportunities in research-oriented networks based on opportunity networks, modelled as a directed attributed multigraph, an opportunity exploiter, a set of opportunity patterns and a set of opportunity ranking functions. We show how the discovery of joint-research collaboration opportunities in a research-oriented network can be formulated in terms of the proposed model, and we perform an evaluation on a real-world dataset.

Relationships among actors in traditional social network analysis are modelled as a function of the quantity of relations (co-authorships, business relations, friendship, etc.). In contrast, within a business, social or research community, network analysts are interested in the communicative content exchanged by the community members, not merely in the number of relationships. In order to meet this need, this paper presents a novel social network model, in which the actors are not simply represented through the intensity of their mutual relationships, but also through the analysis and evolution of their shared interests. Text mining and clustering techniques are used to capture the content of communication and to identify the most popular topics.

This paper presents a methodology and a software application to support the analysis of collaborations and collaboration content in scientific communities. High quality terminology extraction, semantic graphs and clustering techniques are used to identify the relevant research topics. Social analysis tools are then used to study the emergence of interests around certain topics, the evolution of collaborations around these themes, and to identify potential for better cooperation.

The NoE (Network of Excellence) INTEROP is an instrument for strengthening the excellence of European research in interoperability of enterprise applications, by bringing together the complementary competences required to develop interoperability in

While it is generally acknowledged that domain ontologies can significantly improve knowledge management systems (KMS) within organizations and among distributed web communities, we have little evidence of operational ontology-based KMS and their practical utility in real settings in the literature. We describe here the INTEROP KMap, a fully implemented, semantically indexed, competency management system, used to facilitate research collaboration and coordination of a Network of Excellence (NoE) on Enterprise Interoperability. Since the main highlighted advantages of ontologies are improved information access and interoperability, our aim in this paper is to give experimental support to these claims. We provide a summary description and usage data on the KMap, as well as experiments to quantify the added value of semantic search wrt traditional document ranking measures.

The need to extract and manage domain-specific taxonomies has become increasingly relevant in recent years. A taxonomy is a form of business intelligence used to integrate information, reduce semantic heterogeneity, describe emergent communities and interest groups, and facilitate communication between information systems. We present a semiautomated strategy to extract domain-specific taxonomies from Web documents and its application to model a network of excellence in the emerging research field of enterprise interoperability

We describe a possible approach to the problem of extracting knowledge from the analysis of questionnaires through machine learning. The idea guiding our research was to investigate the existence of association rules among the topics covered in a course. The data used came from the questionnaires administered to the freshmen in electronic engineering attending the course of foundation of computer science at our university. Each questionnaire was coded into feature vectors that were classified with respect to the grade obtained by the student and analysed with C4.5. Some statistical results and hints for further work are discussed.

Proper nouns form an open class, making the incompleteness of manually or automatically learned classification rules an obvious problem. The purpose of this paper is twofold: first, to suggest the use of a complementary “backup” method to increase the robustness of any hand-crafted or machine-learning-based NE tagger; and second, to explore the effectiveness of using more fine-grained evidence—namely, syntactic and semantic contextual knowledge—in classifying NEs.

The recognition of Proper Nouns (PNs) is considered an important task in the area of Information Retrieval and Extraction. However the high performance of most existing PN classifiers heavily depends upon the availability of large dictionaries of domain-specific Proper Nouns, and a certain amount of manual work for rule writing or manual tagging. Though it is not a heavy requirement to rely on some existing PN dictionary (often these resources are available on the web), its coverage of a domain corpus may be rather low, in absence of manual updating. In this paper we propose a technique for the automatic updating of an PN Dictionary through the cooperation of an inductive and a probabilistic classifier. In our experiments we show that, whenever an existing PN Dictionary allows the identification of 50% of the proper nouns within a corpus, our technique allows, without additional manual effort, the successful recognition of about 90% of the remaining 50%.

In this paper, we describe a context-based method to semantically tag unknown proper nouns (U-PNs) in corpora. Like many others, our system relies on a gazetteer and a set of context-dependent heuristics to classify proper nouns. However, proper nouns are an open-end class: when parsing new fragments of a corpus, even in the same language domain, we can expect that several proper nouns cannot be semantically tagged. The algorithm that we propose assigns to an unknown PN an entity type based on the analysis of syntactically and semantically similar contexts already seen in the application corpus. The performance of the algorithm is evaluated not only in terms of precision, following the tradition of MUC conferences, but also in terms of information gain, an information theoretic measure that takes into account the complexity of the classification task.

A critical phase in the task of software engineering is represented by the elicitation of requirements from the users. The paper presents a cognitive approach to the elicitation of functional requirements of software systems founded on the modelling of the application domain. The framework outlined is grounded on an incremental process aimed at building a description of the real world, as perceived by the user and expressed in an informal way, in terms of physical objects and activities on them. The domain model is developed by applying the analyst stereotypical knowledge to the real world as described by the user, and requires the identification of knowledge chunks that may consent the process of specification reuse in different application domains.>