776 research outputs found
Automated extraction of fragments of Bayesian networks from textual sources
Get PDFMining large amounts of unstructured data for extracting meaningful, accurate, and actionable information, is at the core of a variety of research disciplines including computer science, mathematical and statistical modelling, as well as knowledge engineering. In particular, the ability to model complex scenarios based on unstructured datasets is an important step towards an integrated and accurate knowledge extraction approach. This would provide a significant insight in any decision making process driven by Big Data analysis activities. However, there are multiple challenges that need to be fully addressed in order to achieve this, especially when large and unstructured data sets are considered. In this article we propose and analyse a novel method to extract and build fragments of Bayesian networks (BNs) from unstructured large data sources. The results of our analysis show the potential of our approach, and highlight its accuracy and efficiency. More specifically, when compared with existing approaches, our method addresses specific challenges posed by the automated extraction of BNs with extensive applications to unstructured and highly dynamic data sources. The aim of this work is to advance the current state-of-the-art approaches to the automated extraction of BNs from unstructured datasets, which provide a versatile and powerful modelling framework to facilitate knowledge discovery in complex decision scenarios
DLCD-CCE: A Local Community Detection Algorithm for Complex IoT Networks
Get PDFInternet of Things (IoT) refers to the complex systems generated by the interconnections among widely available objects. Such interactions generate large networks, whose complexity needs to be addressed to provide suitable computationally efficient approaches. In this article, we propose a distributed local community detection algorithm based on specific properties of community center expansions (DLCD-CCE) for large-scale complex networks. The algorithm is evaluated via a prototype system, based on Spark, to verify its accuracy and scalability. The results demonstrate that compared to the typical local community detection algorithms, DLCD-CCE has better accuracy, stability, and scalability, and effectively overcomes the problem that existing algorithms are sensitive to the location of initial seeds.</p
Feature dimensionality reduction via homological properties of observability
Get PDFFeature selection and its subsequent dimensionality reduction are significant problems in machine learning and it is at the core of several data science techniques. The ‘shape’ of data, or in other words its related topological properties, can provide crucial insights into the corresponding data types and sources and it enables the identification of general properties that facilitate its analysis and assessment. In this article, we discuss an information theoretic approach combined with data homological properties to assess dimensionality reduction, which can be applied to semantic feature selection
Post COVID-19 Remote Medicine and Telemedicine Evaluation via Natural Language Processing Techniques
Get PDFThe COVID-19 pandemic greatly increased the workload of hospitals and various restrictions were put in place to restrict the spread of the virus. As a result, clinics were shut down and patient interaction with the clinics and hospitals shifted to different forms of applications of telemedicine. Although telemedicine was in use in different parts of the world before the pandemic, it was mainly being used by people who were in remote locations and unable to access the healthcare facilities due to travel or certain medical disability. This also gave rise to opportunities to carry out improvement in telemedicine platforms and the ways by which healthcare professionals can measure and deliver more patient-centric healthcare services. One way is to use text mining techniques to extract the right words and their corresponding relations upon which a deep learning model healthcare application can be built to aid in the decision making of clinicians. As the patient data collected by the healthcare professional is mostly in the form of free text and the medical language being highly specialised, finding the relevant concepts and combination of words is quite challenging. In this chapter, we have extracted combination of words from PubMed database and tweets from Twitter related to telemedicine and remote consultations by using text mining techniques and Improved Sentiment Urgent Emotion Detection (ISUED) Model from a previous experiment to find the right combination of words. By using such techniques, we were able to determine the right semantic relation between the specified words to help detect the polarity from the unstructured data
La Vestale 'incesta'
No full textMarcello Salvadore: La Vestale incesta.
Dionysius of Halicarnassus, Pliny the Younger and Plutarch are the sources
of a detailed account of Vestalis incesta’s punishment: they say that she was
sentenced to death. Dionysius adds that there was no after death ritual.
Modern scholars generally accept what the three authors assert. In this article
the author surmises that the Vestalis incesta, together with the parricida, was
not condemned to death: both of them were sentenced to a particular kind
of banishment from the Society
Distributed temporal link prediction algorithm based on label propagation
Get PDFLink prediction has steadily become an important research topic in the area of complex networks. However, the current link prediction algorithms typically neglect the evolution process and they tend to exhibit low accuracy and scalability when applied to large-scale networks. In this article, we propose a novel distributed temporal link prediction algorithm based on label propagation (DTLPLP), governed by the dynamical properties of the interactions between nodes. In particular, nodes are associated with labels, which include details of their sources, and the corresponding similarity value. When such labels are propagated across neighbouring nodes, they are updated based on the weights of the incident links, and the values from same source nodes are aggregated to evaluate the scores of links in the predicted network. Furthermore, DTLPLP has been designed to be distributed and parallelised, and thus suitable for large-scale network analysis. As part of the validation process, we have designed a prototype system developed in Pregel, which is a distributed network analysis framework. Experiments are conducted on the Enron e-mails and the General Relativity and Quantum Cosmology Scientific Collaboration networks. The experimental results show that compared to the most of link prediction algorithms, DTLPLP offers enhanced accuracy, stability and scalability
Sentiment urgency emotion conversion over time for business intelligence
Get PDFPurpose: Social media has become a vital part of any institute’s marketing plan. Social networks benefit businesses by allowing them to interact with their clients, grow brand exposure through offers and promotions and find new leads. It also offers vital information concerning the general emotions and sentiments directly connected to the welfare and security of the online community involved with the brand. Big organizations can make use of their social media data to generate planned and operational decisions. This paper aims to look into the conversion of sentiments and emotions over time. Design/methodology/approach: In this work, a model called sentiment urgency emotion detection (SUED) from previous work will be applied on tweets from two different periods of time, one before the start of the COVID-19 pandemic and the other after it started to monitor the conversion of sentiments and emotions over time. The model has been trained to improve its accuracy and F1 score so that the precision and percentage of correctly predicted texts is high. This model will be tuned to improve results (Soussan and Trovati, 2020a; Soussan and Trovati, 2020b) and will be applied on a general business Twitter account of one of the largest chains of supermarkets in the UK to be able to see what sentiments and emotions can be detected and how urgent they are. Findings: This will show the effect of COVID-19 pandemic on the conversions of the sentiments, emotions and urgencies of the tweets. Originality/value: Sentiments will be compared between the two periods to evaluate how sentiments and emotions vary over time taking into consideration the COVID-19 as an affective factor. In addition, SUED will be tuned to enhance results and the knowledge that is mined when turning data into decisions is crucial because it will aid stakeholders handling the institute to evaluate the topics and issues that were mostly emphasized.</p
De Lope a Celano: la adaptación italiana de "Los tres diamantes"
Get PDFAbstract
This paper explores an Italian adaptation of Lope de Vega’s play Los tres diamantes, written in the
second part of the seventeenth century. Its author, Carlo Celano, was a famous writer of opere regie,
i.e., adaptations of Spanish comedies of situation. The analysis focuses on the way in which the
adaptation of the Aristotelian units of space and time leads to a reduction of the characters and a
simplification of the situation, although this is compensated by enriching its ludic component. This
last trait can be also observed in a previous re-elaboration of Lope’s comedy, the scenario of the
Commedia dell’arte titled Il cavaliere dai tre gigli d’oro
Due note critiche
Get PDFMarcello Garzaniti
Answers to Criticism
The author answers to the critics of M. Capaldo and A.Giambelluca Kossova with the aim to bring the different proposed questions back into the sphere of scientifi c dialogue
Le misure a supporto di lavoratori e imprese durante la pandemia di COVID-19 in Italia
No full textQuesto documento di lavoro fornisce un’analisi retrospettiva degli effetti della pandemia sul mercato del lavoro italiano e delle misure attuate dalle istituzioni pubbliche per attenuarne gli effetti negativi
sulle imprese e sul lavoro. La prima parte del documento (capitoli 2 e 3) si focalizza sugli impatti indotti
dalla crisi del COVID-19 sui diversi gruppi di lavoratrici e lavoratori. Il quadro che emerge evidenzia
che le lavoratrici e i lavoratori che, già prima della crisi, erano in una situazione di svantaggio si sono
trovati in uno stato di maggiore vulnerabilità a seguito del diffondersi della pandemia
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