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.

Using a Dialogue Manager to Improve Semantic Web Search

Question Answering systems that resort to the Semantic Web as a knowledge base can go well beyond the usual matching words in documents and, preferably, find a precise answer, without requiring user help to interpret the documents returned. In this paper, the authors introduce a Dialogue Manager that, through the analysis of the question and the type of expected answer, provides accurate answers to the questions posed in Natural Language. The Dialogue Manager not only represents the semantics of the questions, but also represents the structure of the discourse, including the user intentions and the questions context, adding the ability to deal with multiple answers and providing justified answers. The authors’ system performance is evaluated by comparing with similar question answering systems. Although the test suite is slight dimension, the results obtained are very promising.

Ad summum bonum iter: revisione testuale e commento di Seneca, Epistulae morales ad Lucilium XLVIII, CXI.

Questa tesi di dottorato “Ad summum bonum iter. Revisione testuale e commento di Seneca, Epistulae morales ad Lucilium XLVIII, CXI” (relatore Prof. F. Citti), si propone di fornire un commento scientifico al testo latino di alcune Epistulae morales ad Lucilium (48 e 111), corredato da traduzione, note esegetiche di carattere lessicale, stilistico-retorico e storico-letterario e discussione delle principali problematiche testuali (testo di riferimento l'edizione critica di Reynolds, Oxonii 1965). Scopo della ricerca è approfondire e completare lo studio delle lettere 48 e 111 del carteggio, esaminato solo episodicamente e non sistematicamente, soffermandosi sugli aspetti filosofici salienti e sui motivi conduttori più significativi, come la critica senecana alla nimia subtilitas (tema presente soprattutto nelle cosiddette epistole dialettiche, connesse alle diverse forme di sillogismi e alle modalità di contestazione degli stessi), con particolare riferimento alle ambiguità derivanti dall'uso di termini in senso proprio e metalinguistico riflessivo e non riflessivo; la ricerca si sofferma inoltre sul linguaggio colloquiale, vicino a quello della commedia e della satira, utilizzato da Seneca per ridicolizzare i sillogismi stoici; sul tema dell'amicizia; sull'uso di citazioni e/o allusioni poetiche (virgiliana e non virgiliana), decontestualizzate e risemantizzate in funzione educativa e psicagogica, piegate ad esprimere il personale messaggio etico; sull'uso del lessico specialistico, proveniente da diversi ambiti semantici (giuridico, economico, militare, medico), applicato da Seneca alla parenesi filosofica e, soprattutto, sul rapporto tra lessico filosofico latino e modelli greci. Infine, oltre ad approfondire il motivo delle egestas temporis e i riferimenti diatribici contenuti nelle epistole, sono stati individuati e approfonditi alcuni topoi presenti.

Abstractive Long Document Summarization: Studio e Sperimentazione di Modelli Generativi Retrieval-Augmented

In questa tesi si trattano lo studio e la sperimentazione di un modello generativo retrieval-augmented, basato su Transformers, per il task di Abstractive Summarization su lunghe sentenze legali. La sintesi automatica del testo (Automatic Text Summarization) è diventata un task di Natural Language Processing (NLP) molto importante oggigiorno, visto il grandissimo numero di dati provenienti dal web e banche dati. Inoltre, essa permette di automatizzare un processo molto oneroso per gli esperti, specialmente nel settore legale, in cui i documenti sono lunghi e complicati, per cui difficili e dispendiosi da riassumere. I modelli allo stato dell’arte dell’Automatic Text Summarization sono basati su soluzioni di Deep Learning, in particolare sui Transformers, che rappresentano l’architettura più consolidata per task di NLP. Il modello proposto in questa tesi rappresenta una soluzione per la Long Document Summarization, ossia per generare riassunti di lunghe sequenze testuali. In particolare, l’architettura si basa sul modello RAG (Retrieval-Augmented Generation), recentemente introdotto dal team di ricerca Facebook AI per il task di Question Answering. L’obiettivo consiste nel modificare l’architettura RAG al fine di renderla adatta al task di Abstractive Long Document Summarization. In dettaglio, si vuole sfruttare e testare la memoria non parametrica del modello, con lo scopo di arricchire la rappresentazione del testo di input da riassumere. A tal fine, sono state sperimentate diverse configurazioni del modello su diverse tipologie di esperimenti e sono stati valutati i riassunti generati con diverse metriche automatiche.

Sperimentazione di Deep Metric Loss per Self-Supervised Information Retrieval Systems su CORD19.

Dopo lo sviluppo dei primi casi di Covid-19 in Cina nell’autunno del 2019, ad inizio 2020 l’intero pianeta è precipitato in una pandemia globale che ha stravolto le nostre vite con conseguenze che non si vivevano dall’influenza spagnola. La grandissima quantità di paper scientifici in continua pubblicazione sul coronavirus e virus ad esso affini ha portato alla creazione di un unico dataset dinamico chiamato CORD19 e distribuito gratuitamente. Poter reperire informazioni utili in questa mole di dati ha ulteriormente acceso i riflettori sugli information retrieval systems, capaci di recuperare in maniera rapida ed efficace informazioni preziose rispetto a una domanda dell'utente detta query. Di particolare rilievo è stata la TREC-COVID Challenge, competizione per lo sviluppo di un sistema di IR addestrato e testato sul dataset CORD19. Il problema principale è dato dal fatto che la grande mole di documenti è totalmente non etichettata e risulta dunque impossibile addestrare modelli di reti neurali direttamente su di essi. Per aggirare il problema abbiamo messo a punto nuove soluzioni self-supervised, a cui abbiamo applicato lo stato dell'arte del deep metric learning e dell'NLP. Il deep metric learning, che sta avendo un enorme successo soprattuto nella computer vision, addestra il modello ad "avvicinare" tra loro immagini simili e "allontanare" immagini differenti. Dato che sia le immagini che il testo vengono rappresentati attraverso vettori di numeri reali (embeddings) si possano utilizzare le stesse tecniche per "avvicinare" tra loro elementi testuali pertinenti (e.g. una query e un paragrafo) e "allontanare" elementi non pertinenti. Abbiamo dunque addestrato un modello SciBERT con varie loss, che ad oggi rappresentano lo stato dell'arte del deep metric learning, in maniera completamente self-supervised direttamente e unicamente sul dataset CORD19, valutandolo poi sul set formale TREC-COVID attraverso un sistema di IR e ottenendo risultati interessanti.

Developing a large scale FrameNet for Italian - The IFrameNet experience

In this thesis we present the development and the current status of the IFrameNet project, aimed at the construction of a large-scale lexical semantic resource for the Italian language based on Frame Semantics theories. We will begin by contextualizing our work in the wider context of Frame Semantics and of the FrameNet project, which, since 1997, has attempted to apply these theories to lexicography. We will then analyse and discuss the applicability of the structure of the American resource to Italian and more specifically we will focus on the domain of fear, worry, and anxiety. We will finally propose some modifications aimed at improving this domain of the resource in relation to its coherence, its ability to accurately represent the linguistic reality and in particular in order to make it possible to apply it to Italian.

RNA interference technology as a potential control method for fruit and horticultural crops pathogens Botrytis cinerea and Plasmopara viticola

Chapter 1, a general introduction on Botrytis cinerea and its threat to crop production is presented. What Botrytis looks like, its life cycle, why it is a threat to agricultural production, its worldwide pest status, and its current state of management is further elaborated on. Chapter 2, a general introduction on Plasmopara viticola, its threat to grape production and management strategies presented. Chapter 3, titled " RNA Interference Strategies for Future Management of Plant Pathogenic Fungi: Prospects and Challenges ", presents the rapid improvement and extensive implementation of RNA interference (RNAi) technology for the management of fungal pathogens. In this chapter, we describe the application of exogenous RNAi involved in plant pathogenic fungi and discuss dsRNA production, formulation, and RNAi delivery methods. Chapter 4, titled " Exogenous dsRNAs against chitin synthase and glucan synthase genes suppress the growth of the pathogenic fungus Botrytis cinerea " addresses two important questions: Is RNAi technology functional for B. cinerea control ? And which target genes can be exploited for RNAi-based B.cinerea disease control ? Upon target genes selections, an exogenous RNAi protocol was set up and we could effectively deliver a known dose of bacterially produced double stranded RNA (dsRNA) to induce RNAi in B. cinerea. Chapter 5, titled " Double-Stranded RNA Targeting Dicer-Like Genes Compromises the Pathogenicity of Plasmopara viticola on Grapevine “, which deals mainly on RNAi induction against Plasmopara viticola. This chapter addresses two main questions: Is RNAi technology functional in contrasting Plasmopara viticola? And which target genes can be exploited for RNAi-based disease control in Plasmopara viticola?. In the last Chapter (Chapter 6) titled “General discussions and perspectives for future research”, the major research findings from this thesis are discussed together with perspectives for future research.

Mocku for change: an explorative study on the use of “mockumentary” as a new language for climate change education

In the ‘society of acceleration and uncertainty’ (Rosa, 2013), the young are struggling to interpret our complex and fast-changing world. The entity and the velocity of changes are so enormous that we need new narratives or languages to conceptualise them. Among those changes “Climate Change” is placed in a particular difficult position. As the writer A. Ghosh said: “The current climate crisis is also a crisis of culture, and thus of the imagination”. In fact, in today’s literature and cinema a strong dichotomy exists between fictional and non-fictional works, but none of those extremities seems suitable to picture an “adequate representation” of climate change issues, particularly those that are related to future. The main goal of my study, carried out within FEDORA EU project, was to understand to what extent the hybrid film form called “mockumentary” (a language that adopts the aesthetics of factual production to give an illusion of truth to invented stories) could inspire and help students in overcoming the mentioned dichotomy, working as a tool to foster the development of argumentative and imaginative skills needed to picture “immaginary yet realistic” climate change scenarios.

Bio-QA-GNN: Reasoning with Language Models and Knowledge Graphs for Interpretable Biomedical Question Answering

Nowadays the idea of injecting world or domain-specific structured knowledge into pre-trained language models (PLMs) is becoming an increasingly popular approach for solving problems such as biases, hallucinations, huge architectural sizes, and explainability lack—critical for real-world natural language processing applications in sensitive fields like bioinformatics. One recent work that has garnered much attention in Neuro-symbolic AI is QA-GNN, an end-to-end model for multiple-choice open-domain question answering (MCOQA) tasks via interpretable text-graph reasoning. Unlike previous publications, QA-GNN mutually informs PLMs and graph neural networks (GNNs) on top of relevant facts retrieved from knowledge graphs (KGs). However, taking a more holistic view, existing PLM+KG contributions mainly consider commonsense benchmarks and ignore or shallowly analyze performances on biomedical datasets. This thesis start from a propose of a deep investigation of QA-GNN for biomedicine, comparing existing or brand-new PLMs, KGs, edge-aware GNNs, preprocessing techniques, and initialization strategies. By combining the insights emerged in DISI's research, we introduce Bio-QA-GNN that include a KG. Working with this part has led to an improvement in state-of-the-art of MCOQA model on biomedical/clinical text, largely outperforming the original one (+3.63\% accuracy on MedQA). Our findings also contribute to a better understanding of the explanation degree allowed by joint text-graph reasoning architectures and their effectiveness on different medical subjects and reasoning types. Codes, models, datasets, and demos to reproduce the results are freely available at: \url{https://github.com/disi-unibo-nlp/bio-qagnn}.

An Asp49 Phospholipase A2 From Snake Venom Induces Cyclooxygenase-2 Expression And Prostaglandin E2 Production Via Activation Of Nf-κb, P38mapk, And Pkc In Macrophages.

Phospholipases A2 (PLA2) are key enzymes for production of lipid mediators. We previously demonstrated that a snake venom sPLA2 named MT-III leads to prostaglandin (PG)E2 biosynthesis in macrophages by inducing the expression of cyclooxygenase-2 (COX-2). Herein, we explored the molecular mechanisms and signaling pathways leading to these MT-III-induced effects. Results demonstrated that MT-III induced activation of the transcription factor NF-κB in isolated macrophages. By using NF-κB selective inhibitors, the involvement of this factor in MT-III-induced COX-2 expression and PGE2 production was demonstrated. Moreover, MT-III-induced COX-2 protein expression and PGE2 release were attenuated by pretreatment of macrophages with SB202190, and Ly294002, and H-7-dihydro compounds, indicating the involvement of p38MAPK, PI3K, and PKC pathways, respectively. Consistent with this, MT-III triggered early phosphorylation of p38MAPK, PI3K, and PKC. Furthermore, SB202190, H-7-dihydro, but not Ly294002 treatment, abrogated activation of NF-κB induced by MT-III. Altogether, these results show for the first time that the induction of COX-2 protein expression and PGE2 release, which occur via NF-κB activation induced by the sPLA2-MT-III in macrophages, are modulated by p38MAPK and PKC, but not by PI3K signaling proteins.

Microbial Lipid Production: Screening With Yeasts Grown On Brazilian Molasses.

Rhodotorula glutinis CCT 2182, Rhodosporidium toruloides CCT 0783, Rhodotorula minuta CCT 1751 and Lipomyces starkeyi DSM 70296 were evaluated for the conversion of sugars from Brazilian molasses into single-cell oil (SCO) feedstock for biodiesel. Pulsed fed-batch fermentations were performed in 1.65 l working volume bioreactors. The maximum specific growth rate (µmax), lipid productivity (Pr) and cellular lipid content were, respectively, 0.23 h(-1), 0.41 g l(-1) h(-1), and 41% for Rsp. toruloides; 0.20 h(-1), 0.27 g l(-1) h(-1), and 36% for Rta. glutinis; 0.115 h(-1), 0.135 g l(-1) h(-1), and 27 % for Rta. minuta; and 0.11 h(-1), 0.13 g l(-1) h(-1), and 32% for L. starkeyi. Based on their microbial lipid productivity, content, and profile, Rsp. toruloides and Rta. glutinis are promising candidates for biodiesel production from Brazilian molasses. All the oils from the yeasts were similar to the composition of plant oils (rapeseed and soybean) and could be used as raw material for biofuels, as well as in food and nutraceutical products.

Islet Neogenesis-associated Protein (ingap): The Role Of Its Endogenous Production As A Positive Modulator Of Insulin Secretion.

Islet neogenesis-associated protein (INGAP) is a peptide found in pancreatic exocrine-, duct- and islet- non-β-cells from normal hamsters. Its increase induced by either its exogenous administration or by the overexpression of its gene enhances β-cell secretory function and increases β-cell mass by a combination of stimulation of cell replication and islet neogenesis and reduction of β-cell apoptosis. We studied the potential modulatory role of endogenous INGAP in insulin secretion using two different experimental approaches. Hamster islets transfected with INGAP-small interfering RNA (INGAP-siRNA) were used to study glucose-stimulated insulin secretion (GSIS). In parallel, freshly isolated islets were incubated with high glucose and the same concentration of either a specific anti-INGAP rabbit serum or normal rabbit serum. INGAP-siRNA transfected islets reduced their INGAP mRNA and protein content by 35.1% and 47.2%, respectively whereas GSIS decreased by 25.8%. GSIS by transfected islets attained levels comparable to those recorded in control islets when INGAP pentadecapeptide (INGAP-PP) was added to the culture medium. INGAP antibody in the medium decreased significantly GSIS in a dose-dependent manner. These results indicate that endogenous INGAP plays a physiological positive modulatory role in insulin secretion, supporting its possible use in the treatment of prediabetes and Type 2 diabetes.

Measurement Of Longitudinal Spin Asymmetries For Weak Boson Production In Polarized Proton-proton Collisions At Rhic.

We report measurements of single- and double-spin asymmetries for W^{±} and Z/γ^{*} boson production in longitudinally polarized p+p collisions at sqrt[s]=510  GeV by the STAR experiment at RHIC. The asymmetries for W^{±} were measured as a function of the decay lepton pseudorapidity, which provides a theoretically clean probe of the proton's polarized quark distributions at the scale of the W mass. The results are compared to theoretical predictions, constrained by polarized deep inelastic scattering measurements, and show a preference for a sizable, positive up antiquark polarization in the range 0.05

Macrophage Cell Activation With Acute Apical Abscess Contents Determined By Interleukin-1 Beta And Tumor Necrosis Factor Alpha Production.

This clinical study has investigated the antigenic activity of bacterial contents from exudates of acute apical abscesses (AAAs) and their paired root canal contents regarding the stimulation capacity by levels of interleukin (IL)-1 beta and tumor necrosis factor alpha (TNF-α) throughout the root canal treatment against macrophage cells. Paired samples of infected root canals and exudates of AAAs were collected from 10 subjects. Endodontic contents were sampled before (root canal sample [RCS] 1) and after chemomechanical preparation (RCS2) and after 30 days of intracanal medication with calcium hydroxide + chlorhexidine gel (Ca[OH]2 + CHX gel) (RCS3). Polymerase chain reaction (16S rDNA) was used for detection of the target bacteria, whereas limulus amebocyte lysate was used to measure endotoxin levels. Raw 264.7 macrophages were stimulated with AAA exudates from endodontic contents sampled in different moments of root canal treatment. Enzyme-linked immunosorbent assays were used to measure the levels of TNF-α and IL-1 beta. Parvimonas micra, Porphyromonas endodontalis, Dialister pneumosintes, and Prevotella nigrescens were the most frequently detected species. Higher levels of endotoxins were found in samples from periapical exudates at RCS1 (P < .005). In fact, samples collected from periapical exudates showed a higher stimulation capacity at RCS1 (P < .05). A positive correlation was found between endotoxins from exudates with IL-1 beta (r = 0.97) and TNF-α (r = 0.88) production (P < .01). The significant reduction of endotoxins and bacterial species achieved by chemomechanical procedures (RCS2) resulted in a lower capacity of root canal contents to stimulate the cells compared with that at RCS1 (P < .05). The use of Ca(OH)2 + CHX gel as an intracanal medication (RCS3) improved the removal of endotoxins and bacteria from infected root canals (P < .05) whose contents induced a lower stimulation capacity against macrophages cells at RCS1, RCS2, and RCS3 (P < .05). AAA exudates showed higher levels of endotoxins and showed a greater capacity of macrophage stimulation than the paired root canal samples. Moreover, the use of intracanal medication improved the removal of bacteria and endotoxins from infected root canals, which may have resulted in the reduction of the inflammatory potential of the root canal content.

[lean Production And Psychosocial Risks: The Case Of A Multinational Merger In A Metallurgical Company In Brazil].

This study focused on the method known as lean production as a work-related psychosocial risk factor in a Brazilian multinational auto parts company after its merger with other multinational companies. The authors conducted a qualitative analysis of two time points: the first using on-site observation and key interviews with managers and workers during implementation of lean production in 1996; the second, 16 years later, comparing data from a document search in labor inspection records from the Ministry of Labor and Employment and legal proceedings initiated by the Office of the Public Prosecutor for Labor Affairs. The merger led to layoffs, replacements, and an increase in the workday. A class action suit was filed on grounds of aggravated working conditions. The new production model led to psychosocial risks that increased the need for workers' health precautions when changes in the production process introduced new and increased risks of physical and mental illnesses.