Query routing in cooperative semi-structured peer-to-peer information retrieval networks

Conventional web search engines are centralised in that a single entity crawls and indexes the documents selected for future retrieval, and the relevance models used to determine which documents are relevant to a given user query. As a result, these search engines suffer from several technical drawbacks such as handling scale, timeliness and reliability, in addition to ethical concerns such as commercial manipulation and information censorship. Alleviating the need to rely entirely on a single entity, Peer-to-Peer (P2P) Information Retrieval (IR) has been proposed as a solution, as it distributes the functional components of a web search engine – from crawling and indexing documents, to query processing – across the network of users (or, peers) who use the search engine. This strategy for constructing an IR system poses several efficiency and effectiveness challenges which have been identified in past work. Accordingly, this thesis makes several contributions towards advancing the state of the art in P2P-IR effectiveness by improving the query processing and relevance scoring aspects of a P2P web search. Federated search systems are a form of distributed information retrieval model that route the user’s information need, formulated as a query, to distributed resources and merge the retrieved result lists into a final list. P2P-IR networks are one form of federated search in routing queries and merging result among participating peers. The query is propagated through disseminated nodes to hit the peers that are most likely to contain relevant documents, then the retrieved result lists are merged at different points along the path from the relevant peers to the query initializer (or namely, customer). However, query routing in P2P-IR networks is considered as one of the major challenges and critical part in P2P-IR networks; as the relevant peers might be lost in low-quality peer selection while executing the query routing, and inevitably lead to less effective retrieval results. This motivates this thesis to study and propose query routing techniques to improve retrieval quality in such networks. Cluster-based semi-structured P2P-IR networks exploit the cluster hypothesis to organise the peers into similar semantic clusters where each such semantic cluster is managed by super-peers. In this thesis, I construct three semi-structured P2P-IR models and examine their retrieval effectiveness. I also leverage the cluster centroids at the super-peer level as content representations gathered from cooperative peers to propose a query routing approach called Inverted PeerCluster Index (IPI) that simulates the conventional inverted index of the centralised corpus to organise the statistics of peers’ terms. The results show a competitive retrieval quality in comparison to baseline approaches. Furthermore, I study the applicability of using the conventional Information Retrieval models as peer selection approaches where each peer can be considered as a big document of documents. The experimental evaluation shows comparative and significant results and explains that document retrieval methods are very effective for peer selection that brings back the analogy between documents and peers. Additionally, Learning to Rank (LtR) algorithms are exploited to build a learned classifier for peer ranking at the super-peer level. The experiments show significant results with state-of-the-art resource selection methods and competitive results to corresponding classification-based approaches. Finally, I propose reputation-based query routing approaches that exploit the idea of providing feedback on a specific item in the social community networks and manage it for future decision-making. The system monitors users’ behaviours when they click or download documents from the final ranked list as implicit feedback and mines the given information to build a reputation-based data structure. The data structure is used to score peers and then rank them for query routing. I conduct a set of experiments to cover various scenarios including noisy feedback information (i.e, providing positive feedback on non-relevant documents) to examine the robustness of reputation-based approaches. The empirical evaluation shows significant results in almost all measurement metrics with approximate improvement more than 56% compared to baseline approaches. Thus, based on the results, if one were to choose one technique, reputation-based approaches are clearly the natural choices which also can be deployed on any P2P network.

Chest Breadths to Predict Individuals' Age - A Case Based View

It is well known that rib cage dimensions depend on the gender and vary with the age of the individual. Under this setting it is therefore possible to assume that a computational approach to the problem may be thought out and, consequently, this work will focus on the development of an Artificial Intelligence grounded decision support system to predict individual’s age, based on such measurements. On the one hand, using some basic image processing techniques it were extracted such descriptions from chest X-rays (i.e., its maximum width and height). On the other hand, the computational framework was built on top of a Logic Programming Case Base approach to knowledge representation and reasoning, which caters for the handling of incomplete, unknown, or even contradictory information. Furthermore, clustering methods based on similarity analysis among cases were used to distinguish and aggregate collections of historical data in order to reduce the search space, therefore enhancing the cases retrieval and the overall computational process. The accuracy of the proposed model is satisfactory, close to 90%.

Semi-automatic keyword based approach for FIRE 2016 Microblog Track

This paper describes our semi-automatic keyword based approach for the four topics of Information Extraction from Microblogs Posted during Disasters task at Forum for Information Retrieval Evaluation (FIRE) 2016. The approach consists three phases.

A concept for the viticulture of 'tropical wines'.

At worldwide level, the classical viticulture produces wines is almost all places in different climate types, where it is possible to obtain only one harvest per year. In these conditions, bud burst in vines occurs as a result of temperature raising at the end of the winter?beginning of spring. With the development of the vegetative cycle, grapes arrive to maturity/harvest period at the end of the summer?beginning of the fall season. After the fall of the leaves, vines undergo a dormant period with a vegetative repose (condition found also in some intertropical producer regions). A lot of grapes are produced for a long-time in the intertropical zone, but only a little part is used to winemaking. Considered a challenge in the past, to produce quality wines in the tropics became reality. The present industry of fine wines began about 30 years ago. Today, there are many commercial wineries in several countries in Occident and Orient in the tropical zone of the globe, like in Brazil, India, Thailand and Venezuela, producing some million liters of fine wines per year - called ?tropical wines?. This article analysis these climates, in relation to the particular viticulture adapted and developed, where it is possible to have more than one cycle per year, with one or more harvests per year. Based on the particularities of this viticulture, a characterization is proposed for the viticulture of ?tropical wines?. Some examples showing different tropical climates are presented.

Smantellamento di un reattore di potenza gas-grafite di I generazione: metodologie e tecnologie di rimozione della grafite e smantellamento del nocciolo

Il presente lavoro di tesi ha riguardato lo studio dello smantellamento di un reattore gas grafite di potenza di I Gen. L’indagine è stata focalizzata in particolare al recupero della grafite irraggiata che ne costituisce il core. Viene presentata una descrizione referenziata del reattore e dei suoi componenti per mettere in evidenza la particolare architettura e le specifiche problematiche ad essa correlate. A valle di un’indagine sulle esperienze internazionali in merito al decommissioning e allo smantellamento di questi tipi di reattori, si forniscono una possibile sequenza di accesso alla cavità del reattore e una procedura per il suo smantellamento; si descrivono sommariamente le tecnologie di taglio e di handling, attualmente allo stato dell’arte, considerate come più idonee a questo tipo di applicazione. Vengono descritte le principali criticità della grafite nuclear grade ed illustrati i fenomeni caratteristici che ne determinano l’evoluzione nel reattore. Sulla base dei dati resi disponibili dalla Sogin S.p:A. e ricorrendo ai dati di letteratura per quelli non disponibili, è stato effettuato un assessment della grafite irraggiata costituente il nocciolo del reattore, rivolto in particolare a determinarne le caratteristiche meccaniche e la resistenza residua post-irraggiamento. Per valutare la possibilità di prelevare la grafite dal nocciolo è stato ipotizzato un dispositivo di presa che agganci per attrito i blocchi di grafite del moderatore attraverso il canale assiale. Infine è stata valutata la fattibilità di tale metodo attraverso una serie di simulazioni agli elementi finiti dirette a verificare la resistenza del blocco in varie condizioni di carico e vincolo. Come risultato si è dimostrata la fattibilità, almeno in via preliminare, del metodo proposto, determinando l’inviluppo di utilizzo del dispositivo di presa nonché la compatibilità del metodo proposto con le tecnologie di handling precedentemente individuate.

Modeling of Electric Hybrid Vehicles based on Innovative Energy Storage Technologies for Super Sports Cars Applications

Today, the contribution of the transportation sector on greenhouse gases is evident. The fast consumption of fossil fuels and its impact on the environment has given a strong impetus to the development of vehicles with better fuel economy. Hybrid electric vehicles fit into this context with different targets, starting from the reduction of emissions and fuel consumption, but also for performance and comfort enhancement. Vehicles exist with various missions; super sport cars usually aim to reach peak performance and to guarantee a great driving experience to the driver, but great attention must also be paid to fuel consumption. According to the vehicle mission, hybrid vehicles can differ in the powertrain configuration and the choice of the energy storage system. Lamborghini has recently invested in the development of hybrid super sport cars, due to performance and comfort reasons, with the possibility to reduce fuel consumption. This research activity has been conducted as a joint collaboration between the University of Bologna and the sportscar manufacturer, to analyze the impact of innovative energy storage solutions on the hybrid vehicle performance. Capacitors have been studied and modeled to analyze the pros and cons of such solution with respect to batteries. To this aim, a full simulation environment has been developed and validated to provide a concept design tool capable of precise results and able to foresee the longitudinal performance on regulated emission cycles and real driving conditions, with a focus on fuel consumption. In addition, the target of the research activity is to deepen the study of hybrid electric super sports cars in the concept development phase, focusing on defining the control strategies and the energy storage system’s technology that best suits the needs of the vehicles. This dissertation covers the key steps that have been carried out in the research project.

Preparation and assessment of immobilized laccase-based micro- and nanocatalysts for the degradation of anthropogenic compounds

Since the dawn of its presence on earth, the human being has been able to exploit the enzymes for its subsistence. More recent is the meeting between the enzymatic processes and the urgent need for technologies that aim to preserve our planet. In this field nowadays enzymatic catalysis is tested either to depollution/remediation as well as waste disposal. The work presented in this thesis, regarding both these two topics, is tailored on two European projects (EU 2020), MADFORWATER and TERMINUS respectively. Firstly, production of micro- and nanocatalysts via immobilization of laccases (a lignin-degrader enzyme) is performed. In the second part of the thesis laccase is applied to a tertiary treatment of wastewater with the aim to degrade 9 pharmaceutical active compounds in batch reactors. Despite several optimizations, poor degradation is reached and we did not proceed with the study of different bioreactor setups. Therefore, the focus is moved to a project concerning the production of smart multi-layer plastic packaging containing enzymes to improve the possibilities of recycling. In this field shielded nanocatalysts produced via coating techniques able to interact with redox mediators are investigated. The target substrate in this second project is produced in laboratory (i.e. polyurethane like compounds), starting from monomers whose degradation had already been tested, as a proof of concept. The first enzyme studied is still the laccase.

Investigating interpreter-mediated interaction in the Chinese bilingual courtroom: Analysis based on a multimodal corpus

This research project is based on the Multimodal Corpus of Chinese Court Interpreting (MUCCCI [mutʃɪ]), a small-scale multimodal corpus on the basis of eight authentic court hearings with Chinese-English interpreting in Mainland China. The corpus has approximately 92,500 word tokens in total. Besides the transcription of linguistic and para-linguistic features, utilizing the facial expression classification rules suggested by Black and Yacoob (1995), MUCCCI also includes approximately 1,200 annotations of facial expressions linked to the six basic types of human emotions, namely, anger, disgust, happiness, surprise, sadness, and fear (Black & Yacoob, 1995). This thesis is an example of conducting qualitative analysis on interpreter-mediated courtroom interactions through a multimodal corpus. In particular, miscommunication events (MEs) and the reasons behind them were investigated in detail. During the analysis, although queries were conducted based on non-verbal annotations when searching for MEs, both verbal and non-verbal features were considered indispensable parts contributing to the entire context. This thesis also includes a detailed description of the compilation process of MUCCCI utilizing ELAN, from data collection to transcription, POS tagging and non-verbal annotation. The research aims at assessing the possibility and feasibility of conducting qualitative analysis through a multimodal corpus of court interpreting. The concept of integrating both verbal and non-verbal features to contribute to the entire context is emphasized. The qualitative analysis focusing on MEs can provide an inspiration for improving court interpreters’ performances. All the constraints and difficulties presented can be regarded as a reference for similar research in the future.

Synthesis and catalytic activity of new cu-based catalytic systems for the enhanced electrochemical CO2 reduction

This Ph.D. thesis concerns the synthesis of nanostructured Cu-containing materials to be used as electrode modifiers for the CO2 electroreduction in aqueous phase and the evaluation of their catalytic performances. Inspired by the fascinating concept of the artificial photosynthesis-oriented systems, several catalytic layers were electrochemically loaded on carbonaceous gas diffusion membranes, i.e., 3D structures that allow the design of eco-friendly materials for applications in green carbon recycling processes. In particular, early studies on Cu(I-II)-Cu(0) nanostructured materials were carried out to produce films on 4 cm2 sized supports by means of a fast and low-cost electrochemical procedure. Besides, through a screening of potentials, it was possible to find out a selective value for the CH3COOH production at -0.4 V vs RHE with a maximum productivity (1h reaction), ensured by the presence of the Cu+/Cu0 active redox couple (0.31 mmol gcat-1 h-1). On the basis of these results, further optimisations of the electrocatalyst chemical composition were carried out with the aim of (i) facilitating the interaction with CO2, (ii) increasing the dispersion of the catalytic active phase, and (iii) enhancing the CH3COOH productivity. To this aim, novel electrocatalysts based on layered double hydroxides (LDHs) were optimised, having as a final goal the formation of a new Cu2O-Cu0 based electrocatalyst derived from electrochemically achieved CuMgAl LDHs, subjected to calcination and reduction processes. The as-obtained electrocatalysts were tested for the selective production of CH3COOH and unprecedented results were obtained with the pristine CuMgAl LDH (2.0 mmol gcat-1 h-1). Additional characterisations of such an electrocatalyst have highlighted the possibility to achieve a ternary LDH in intimate contact with Cu2O-Cu0 species starting from the electrochemical deposition. The presence of these species, along with an alkaline environment on the electrode surface, were essential to preserve the selectivity towards the desired product, as confirmed by further operando studies.

Understanding 3D structures from sketches

Sketches are a unique way to communicate: drawing a simple sketch does not require any training, sketches convey information that is hard to describe with words, they are powerful enough to represent almost any concept, and nowadays, it is possible to draw directly from mobile devices. Motivated from the unique characteristics of sketches and fascinated by the human ability to imagine 3D objects from drawings, this thesis focuses on automatically associating geometric information to sketches. The main research directions of the thesis can be summarized as obtaining geometric information from freehand scene sketches to improve 2D sketch-based tasks and investigating Vision-Language models to overcome 3D sketch-based tasks limitations. The first part of the thesis concerns geometric information prediction from scene sketches improving scene sketch to image generation and unlocking new creativity effects. The thesis proceeds showing a study conducted on the Vision-Language models embedding space considering sketches, line renderings and RGB renderings of 3D shape to overcome the use of supervised datasets for 3D sketch-based tasks, that are limited and hard to acquire. Following the obtained observations and results, Vision-Language models are applied to Sketch Based Shape Retrieval without the need of training on supervised datasets. We then analyze the use of Vision-Language models for sketch based 3D reconstruction in an unsupervised manner. In the final chapter we report the results obtained in an additional project carried during the PhD, which has lead to the development of a framework to learn an embedding space of neural networks that can be navigated to get ready-to-use models with desired characteristics.

Mainstreaming of Nature-Based Solutions for the mitigation of hydro-meteorological hazard: governance analysis of a socio- technical change

The Nature-Based Solutions (NBS) concept and approach were developed to simultaneously face challenges such as risk mitigation and biodiversity conservation and restoration. NBSs have been endorsed by major International Organizations such as the EU, the FAO and World Bank that are pushing to enable a mainstreaming process. However, a shift from traditional engineering “grey” solutions to wider and standard adoption of NBS encounters technical, social, cultural, and normative barriers that have been identified with a qualitative content analysis of policy documents, reports and expert interviews. The case of the region Emilia-Romagna was studied by developing an analytical framework that brought together the social-ecological context, the governance system and the characteristics of specific NBSs.

Modelling and classification with quantile-based distributions

In this work, we explore and demonstrate the potential for modeling and classification using quantile-based distributions, which are random variables defined by their quantile function. In the first part we formalize a least squares estimation framework for the class of linear quantile functions, leading to unbiased and asymptotically normal estimators. Among the distributions with a linear quantile function, we focus on the flattened generalized logistic distribution (fgld), which offers a wide range of distributional shapes. A novel naïve-Bayes classifier is proposed that utilizes the fgld estimated via least squares, and through simulations and applications, we demonstrate its competitiveness against state-of-the-art alternatives. In the second part we consider the Bayesian estimation of quantile-based distributions. We introduce a factor model with independent latent variables, which are distributed according to the fgld. Similar to the independent factor analysis model, this approach accommodates flexible factor distributions while using fewer parameters. The model is presented within a Bayesian framework, an MCMC algorithm for its estimation is developed, and its effectiveness is illustrated with data coming from the European Social Survey. The third part focuses on depth functions, which extend the concept of quantiles to multivariate data by imposing a center-outward ordering in the multivariate space. We investigate the recently introduced integrated rank-weighted (IRW) depth function, which is based on the distribution of random spherical projections of the multivariate data. This depth function proves to be computationally efficient and to increase its flexibility we propose different methods to explicitly model the projected univariate distributions. Its usefulness is shown in classification tasks: the maximum depth classifier based on the IRW depth is proven to be asymptotically optimal under certain conditions, and classifiers based on the IRW depth are shown to perform well in simulated and real data experiments.

Massive MIMO-based system analysis and comparison with MIMO based-systems

The thesis is focused on introducing basic MIMO-based and Massive MIMO-based systems and their possible benefits. Then going through the implementation options that we have, according to 3GPP standards, for 5G systems and how the transition is done from a non-standalone 5G RAN to a completely standalone 5G RAN. Having introduced the above-mentioned subjects and providing some definition of telecommunications principles, we move forward to a more technical analysis of the Capacity, Throughput, Power consumption, and Costs. Comparing all the mentioned parameters between a Massive-MIMO-based system and a MIMO-based system. In the analysis of power consumption and costs, we also introduce the concept of virtualization and its benefits in terms of both power and costs. Finally, we try to justify a trade-off between having a more reliable system with a high capacity and throughput while keeping the costs as low as possible.

Knowledge-based Chord Embeddings

This thesis develops AI methods as a contribution to computational musicology, an interdisciplinary field that studies music with computers. In systematic musicology a composition is defined as the combination of harmony, melody and rhythm. According to de La Borde, harmony alone "merits the name of composition". This thesis focuses on analysing the harmony from a computational perspective. We concentrate on symbolic music representation and address the problem of formally representing chord progressions in western music compositions. Informally, chords are sets of pitches played simultaneously, and chord progressions constitute the harmony of a composition. Our approach combines ML techniques with knowledge-based techniques. We design and implement the Modal Harmony ontology (MHO), using OWL. It formalises one of the most important theories in western music: the Modal Harmony Theory. We propose and experiment with different types of embedding methods to encode chords, inspired by NLP and adapted to the music domain, using both statistical (extensional) knowledge by relying on a huge dataset of chord annotations (ChoCo), intensional knowledge by relying on MHO and a combination of the two. The methods are evaluated on two musicologically relevant tasks: chord classification and music structure segmentation. The former is verified by comparing the results of the Odd One Out algorithm to the classification obtained with MHO. Good performances (accuracy: 0.86) are achieved. We feed a RNN for the latter, using our embeddings. Results show that the best performance (F1: 0.6) is achieved with embeddings that combine both approaches. Our method outpeforms the state of the art (F1 = 0.42) for symbolic music structure segmentation. It is worth noticing that embeddings based only on MHO almost equal the best performance (F1 = 0.58). We remark that those embeddings only require the ontology as an input as opposed to other approaches that rely on large datasets.

Nature-based solutions for urban heat island effect mitigation: the case study of Isla, Malta

Global population growth reflects how humans increasingly exploited Earth's resources. Urbanization develops along with anthropization. It is estimated that nearly 60% of the world's population lives in urban areas, which symbolize the denaturalized dimension of current modernity. Cities are artificial ecosystems that suffer most from environmental issues and climate change. The Urban Heat Island (UHI) effect is a common microclimatic phenomenon affecting cities, which causes considerable differences between urban and rural areas temperatures. Among the driving factors, the lack of vegetation in urban settlements can damage both humans and the environment (health diseases, heat waves caused deaths, biodiversity loss, and so on). As the world continues to urbanize, sustainable development increasingly depends on successful management of urban areas. To enhance cities’ resilience, Nature-based Solutions (NbSs), are defined as an umbrella concept that encompasses a wide range of ecosystem-based approaches and actions to climate change adaptation (CCA) and disaster risk reduction (DRR). This paper analyzes a 15-days study on air temperature trends carried out in Isla, a small locality in the Maltese archipelago, and proposes Nature-based Solutions-characterized scenarios to mitigate the Urban Heat Island effect the Mediterranean city is affected by. The results demonstrates how in some areas where vegetation is present, lower temperatures are recorded than in areas where vegetation is absent or scarce. It also appeared that in one location, the specific type of vegetation does not contribute to high temperature mitigation, whereas in another one, different environmental parameters can influence the measurements. Among the case-specific Nature-based Solutions proposed there are vertical greening (green wall, façades, ground based greening, etc.), tree lines, green canopy, and green roofs.