The energy sector is facing a major paradigm shift from centralised production and management to distributed energy generation and management. Digital technologies play a crucial role in enabling such scenario; emphasis and attention has been given to Smart Grids and new energy management systems both for final users and companies. Energy, its consumption, and its production are at the centre of our everyday lives and are connected to everyday practices and habits. However, while this scenario can be seen as mundane, new spaces can be created for citizens and communities to participate and be empowered. This thesis presents the work done by the author within a three-years European Project used as his main research field. The focal points were: (i) the participatory design process of a community energy digital platform; and (ii) the advantages and disadvantages of a commons based approach to renewable energy management on the development and empowerment of local communities. First will be presented how a participatory design process opens a new space for citizen participation to design as an alternative energy management model. Then will be presented the energy budgeting framework designed within this process, discussing how social acceptance of technology affected the design and how energy has been translated to a new kind of value within this framework. Afterwards, it will be discussed how the participatory process and the framework contributed to the construction-in-practice of energy justice, and how this process reconfigured the relationships among civil society, the energy sector, and politics. Finally, the whole three years project experience will be analysed retrospectively using the interaction spaces framework, highlighting how participatory configurations evolved over time and how cross-participation is crucial for the boundary-spanning of design issues. Therefore, concluding reflections will be drawn based on this content, they will consider lessons learned, limitations of the experience and possible future work to continue explore the relationship between energy, digital technologies and participatory design.

The Web has evolved into a huge mine of knowledge carved in different forms, the predominant one still being the free-text document. This motivates the need for Intelligent Web-reading Agents: hypothetically, they would skim through disparate Web sources corpora and generate meaningful structured assertions to fuel Knowledge Bases (KBs). Ultimately, comprehensive KBs, like Wikidata and DBpedia, play a fundamental role to cope with the issue of information overload. On account of such vision, this thesis depicts a set of systems based on Natural Language Processing (NLP), which take as input unstructured or semi-structured information sources and produce machine-readable statements for a target KB. We implement four main research contributions: (1) a one-step methodology for crowdsourcing the Frame Semantics annotation; (2) a NLP technique implementing the above contribution to perform N-ary Relation Extraction from Wikipedia, thus enriching the target KB with properties; (3) a taxonomy learning strategy to produce an intuitive and exhaustive class hierarchy from the Wikipedia category graph, thus augmenting the target KB with classes; (4) a recommender system that leverages a KB network to yield atypical suggestions with detailed explanations, serving as a proof of work for real-world end users. The outcomes are incorporated into the Italian DBpedia chapter, can be queried through its public endpoint, and/or downloaded as standalone data dumps.

Laws and regulations are increasingly impacting the design and development of software systems, as legislations around the world attempt to control the impact of software on social and private life. Software systems need to be designed from the beginning in a law-aware fashion to ensure compliance with applicable laws. Moreover, they need to evolve over time as new laws pass and existing ones are amended. In this interdisciplinary field many challenges remain open. For any given norm, there are alternative ways to comply with it for a system-to-be. Moreover, revising some requirements or adding new ones can have an important impact on what norms apply. To complicate matters, there is a sizeable knowledge gap between technical and legal experts, and this hampers requirements analysts in dealing with the problem on their own. This thesis proposes to use conceptual models of law and requirements to help requirements engineers address these problems by answering questions such as ``Given this set of requirements, which norms are applicable?'', ``Which norms are complied with?'', ``What are the alternative ways I use to comply with a norm?''. The thesis proposes the Nomos 3 framework that includes a modeling language for law and requirements, reasoning support for Nomos 3 models, as well as a systematic process for establishing compliance. The proposed framework is evaluated by means of illustrative case studies, a scalability study for the reasoning mechanism, as well as other specific studies intended to assess the effectiveness of the proposed concepts, tools, and process.

The requirements elicited from stakeholders suffer from various afflictions, including informality, incompleteness, ambiguity, vagueness, inconsistencies, and more. It is the task of requirements engineering (RE) processes to derive from these an eligible (formal, complete enough, unambiguous, consistent, measurable, satisfiable, modifiable and traceable) requirements specification that truly captures stakeholder needs. We propose Desiree, a refinement calculus for systematically transforming stakeholder requirements into an eligible specification. The core of the calculus is a rich set of requirements operators that iteratively transform stakeholder requirements by strengthening or weakening them, thereby reducing incompleteness, removing ambiguities and vagueness, eliminating unattainability and conflicts, turning them into an eligible specification. The framework also includes an ontology for modeling and classifying requirements, a description-based language for representing requirements, as well as a systematic method for applying the concepts and operators in order to engineer an eligible specification from stakeholder requirements. In addition, we define the semantics of the requirements concepts and operators, and develop a graphical modeling tool in support of the entire framework. To evaluate our proposal, we have conducted a series of empirical evaluations, including an ontology evaluation by classifying a large public requirements set, a language evaluation by rewriting the large set of requirements using our description-based syntax, a method evaluation through a realistic case study, and an evaluation of the entire framework through three controlled experiments. The results of our evaluations show that our ontology, language, and method are adequate in capturing requirements in practice, and offer strong evidence that with sufficient training, our framework indeed helps people conduct more effective requirements engineering.

Users are often found in situations where they need to make selections from very large collections of items. These items may be digital artifacts e.g., web pages or forum posts, or digital representations of real world objects, e.g., products or people. There is a great deal of techniques for assisting users in making such selections. However, the plethora of systems and the size of the item collections makes the ability to provide the users with the items that really meet their standards in terms of interestingness and usefulness, a challenging task. We are dealing with the problem of providing items of interest to the users as response to explicit user requests or in the form of recommendations by exploiting a factor that has been poorly investigated so far in information systems: the goals for which items are intended, i.e., the goals for which items have been generated or produced; and the goals that may lead the user to “consume” them, i.e., the goals that s/he is willing to fulfill. The items may not be just items but interactions with items or actions that the user may be interested in performing. In this dissertation, we provide the required background and framework for exploiting goals in building better data managements systems. Within this context, we study three different problems. First, we are dealing with the problem of finding posts of interest (related posts) given a post-query in forums within user communities. Forum posts consist of segments each one serving a different goal that the author had in mind to communicate to the reader through the text. Therefore, plain content comparisons often fail to retrieve posts of interest, or they retrieve posts that despite the similar content are not related to the post-query. Instead, we have developed a goal-aware matching approach that uses content similarity over intention-based segmentations, i.e., over segments that are intended for different communication goals to perform more effective comparisons. Second, we are dealing with the goal-aware recommendation problem. This problem, opposed to the post matching mechanism to which we have referred earlier does not consider domain specific characteristics; thus it can be applied to any domain. The goal-aware mechanisms we have developed handle the diverse goals that the user can fulfill by first recognizing the intended user goals, deciding the priorities among them, and by quantifying the benefit of each item. Last but not least, we are dealing with the problem of building a goal implementation set from texts where users describe how they managed to fulfill certain goals in their real life. We have applied our technique on textual descriptions from a goal-setting site. For each solution we have designed, implemented and extensively evaluated models, algorithms and techniques that deal with all the individual tasks that are required for a goal-aware approach: the identification and extraction of goal-related information in the examined data sources, the modeling of the derived information, the matching of the user's request or previous activity to the goal model elements, and finally the exploitation of this matching into the forming of the system's response. The goal-aware techniques have been found to retrieve items that would not have been considered by the traditional techniques giving to the user a different and more complete view of the item collection. Moreover, the scalability of the techniques and the efficient structures and indexes that we use to store and retrieve the items alongside the goal-related data allows us to meet the requirements of modern online systems.

Loneliness increases mortality risk by 50% and is one of the main causes of depression. Several factors like living far away from the family, not being able to move much due to physical problems, or being unable to use communication technologies favor the likeliness of feeling lonely, especially in later life. We propose Lifehsare, a system for intergenerational communications that facilitates connecting people, enabling them to participate in the life of each other either in an active (synchronous interactions) or passive (asynchronous interactions) way. Current proposals for intergenerational communication do not address the problems related to the lack of time to share and lack of topic to talk that young usually have when interacting with their older relatives. Our proposal addresses these problems by implementing a method that requires no effort to share on the side of the young and by automatically enhancing the shared information. Furthermore, our experience with the evaluation of our proposal was translated into design recommendations that extend the current literature on design guidelines for applications for older adults.

EEnglish was the main focus of attention of the Natural Language Processing (NLP) community for years. As a result, there are significantly more annotated linguistic resources in English than in any other language. Consequently, data-driven tools for automatic text or speech processing are developed mainly for English. Developing similar corpora and tools for other languages is an important issue. However, this requires significant amount of effort. Recently, Statistical Machine Translation (SMT) techniques and parallel corpora were used to transfer annotations from a linguistic resource rich languages to a resource-poor languages for a variety of Natural Language Processing (NLP) tasks, including Part-of-Speech tagging, Noun Phrase chunking, dependency parsing, textual entailment, etc. This cross-language NLP paradigm relies on the solution of the following sub-problems: - Data-driven NLP techniques are very sensitive to the differences in training and testing conditions. Different domains, such as financial news-wire and biomedical publications, have different distributions of NLP task-specific properties; thus, the domain adaptation of the source language tools -- either the development of models with good cross-domain performance or tuned to the target domain -- is critical. - Another difference in training and testing conditions arises with cross-genre applications such as written text (monologues) and spontaneous dialog data. Properties of written text such as punctuation and the notion of sentence are not present in spoken conversation transcriptions. Thus, style-adaptation techniques to cover a wider range of genres is critical as well. - The basis of cross-language porting is parallel corpora. Unfortunately, parallel corpora are scarce. Thus, generation or retrieval of parallel corpora between the languages of interest is important. Additionally, these parallel corpora most often are not in the domains of interest; consequently, the cross-language porting should be augmented with SMT domain adaptation techniques. - The language distance play an important role within the paradigm, since for close family language pairs (e.g. Romance languages Italian and Spanish) the range of linguistic phenomena to consider is significantly less compared to the distant family language pairs (e.g. Italian and Turkish). The developed cross-language techniques should be applicable to both conditions. In this thesis we address these sub-problems on complex Natural Language Processing tasks of Discourse Parsing and Spoken Language Understanding. Both tasks are cast as token-level shallow parsing. Penn Discourse Treebank (PDTB) style discourse parsing is applied cross-domain and we contribute feature-level domain adaptation techniques for the task. Additionally, we explore PDTB-style discourse parsing on dialog data in Italian are report on challenges. The problems of parallel corpora creation, language style adaptation, SMT domain-adaptation and language distance are addressed on the task of cross-language porting of Spoken Language Understanding. This thesis contributes to the task with the language-style and domain adaptation techniques for machine translation of spoken conversations using off-the-shelf systems like Google Translate, SMT systems trained on both out-of-domain and in-domain parallel data. We demonstrate that the techniques are beneficial for both close and distant language pairs. We propose the methodologies for the creation of parallel spoken conversation corpora via professional translation services that considers speech phenomena such as disfluencies. Additionally, we explore the semantic annotation transfer using automatic SMT methods and crowdsourcing. For the later, we propose the computational methodology to obtain acceptable quality corpus without the target language references and the low worker agreement.

Big data, specifically Telecom Metadata, opens new opportunities for human behavior understanding, applying machine learning and big data processing computational methods combined with interdisciplinary knowledge of human behavior. In this thesis new methods are developed for human behavior predictive modeling based on anonymized telecom metadata on individual level and on large scale group level, which were studied during research projects held in 2012-2016 in collaboration with Telecom Italia, Telefonica Research, MIT Media Lab and University of Trento. It is shown that human dynamics patterns could be reliably recognized based on human behavior metrics derived from the mobile phone and cellular network activity (call log, sms log, bluetooth interactions, internet consumption). On individual level the results are validated on use cases of detecting daily stress and estimating subjective happiness. An original approach is introduced for feature extraction, selection, recognition model training and validation. Experimental results based on ensemble stochastic classification and regression tree models are discussed. On large group level, following big data for social good challenges, the problem of crime hotspot prediction is formulated and solved. In the proposed approach we use demographic information along with human mobility characteristics as derived from anonymized and aggregated mobile network data. The models, built on and evaluated against real crime data from London, obtain accuracy of almost 70% when classifying whether a specific area in the city will be a crime hotspot or not in the following month. Electric energy consumption patterns are correlated with human behavior patterns in highly nonlinear way. Second large scale group behavior prediction result is formulated as predicting next week energy consumption based on human dynamics analysis derived out of the anonymized and aggregated telecom data, processed from GSM network call detail records (CDRs). The proposed solution could act on energy producers/distributors as an essential aid to smart meters data for making better decisions in reducing total primary energy consumption by limiting energy production when the demand is not predicted, reducing energy distribution costs by efficient buy-side planning in time and providing insights for peak load planning in geographic space. All the studied experimental results combine the introduced methodology, which is efficient to implement for most of multimedia and real-time applications due to highly reduced low-dimensional feature space and reduced machine learning pipelines. Also the indicators which have strong predictive power are discussed opening new horizons for computational social science studies.

Peer-to-Peer (P2P) and Cloud Computing are two of the latest trends in the Internet arena. They both could be labelled as large-scale distributed systems, yet their approach is completely different: based on completely decentralized protocols exploiting edge resources the former, focusing on huge data centres the latter. Several Internet startups have quickly reached stardom by exploiting cloud resources. Instead, P2P applications still lack a well-defined business model. Recently, companies like Spotify and Wuala have started to explore how the two worlds could be merged by exploiting (free) user resources whenever possible, aiming at reducing the cost of renting cloud resource. However, although very promising, this model presents challenging issues, in particular about the autonomous regulation of the usage of P2P and cloud resources. Next-generation services need the possibility to guarantee a minimum level of service when peer resources are not sufficient, and to exploit as much P2P resources as possible when they are abundant. In this thesis, we answer the above research questions in the form of new algorithms and systems. We designed a family of mechanisms to self-regulate the amount of cloud resources when peer resources are not enough. We applied and adapted these mechanisms to support different Internet applications, including storage, video streaming and online gaming. To support a replication service, we designed an algorithm that self-regulates the cloud resources used for storing replicas by orchestrating their provisioning. We presented CLive, a video streaming P2P framework that meet the real-time constraints on video delay by autonomously regulating the amount of cloud helpers upon need. We proposed an architecture to support large scale on-line games, where the load coming from the interaction of players is strategically migrated between P2P and cloud resources in an autonomous way. Finally, we proposed a solution to the NAT problem that employs cloud resources to allow a node behind it to be seen from outside. Using extensive simulations, we showed that hybrid infrastructures can reduce the economical effort on the service providers, while offering a level of service comparable with centralized architectures. The results of this thesis proved that the combination of Cloud Computing and P2P is one of the milestones for next generation distributed P2P-based architectures.

Dealing with security is a central activity for todays organizations. Security breaches impact on the activities executed in organizations, preventing them to execute their business processes and, therefore, causing millions of dollars of losses. Security by design principles underline the importance of considering security as early as during the design of organizations to avoid expensive fixes during later phases of their lifecycle. However, the design of secure business processes cannot take into account only security aspects on the sequences of activities. Security reports in the last years demonstrate that security breaches are more and more caused by attacks that take advantage of social vulnerabilities. Therefore, those aspects should be analyzed in order to design a business process robust to technical and social attacks. Still, the mere design of business processes does not guarantee that their correct execution, such business processes have to be correctly implemented and performed. We propose SEcure Business process Engineering (SEBE), a method that considers social and organizational aspects for designing and implementing secure business processes. SEBE provides an iterative and incremental process and a set of verification of transformation rules, supported by a software tool, that integrate different modeling languages used to specify social security aspects, business processes and the implementation code. In particular, SEBE provides a new modeling language which permits to specify business processes with security concepts and complex security constraints. We evaluated the effectiveness of SEBE for engineering secure business processes with two empirical evaluations and applications of the method to three real scenarios.