1,720,960 research outputs found
Deep Learning Approaches to Goal Recognition
Full text linkRiconoscere il goal di un agente utilizzando una traccia di osservazioni è un compito importante con diverse applicazioni. In letteratura, molti approcci di goal recognition (GR) si basano sull'applicazione di tecniche di pianificazione automatica che richiedono un modello delle azioni del dominio e dello stato iniziale del dominio (scritto, ad esempio, in PDDL).
In questa tesi studiamo tre approcci alternativi (GRNet, Fast and Slow Goal Recognition e un approccio basato su BERT) in cui il goal recognition è formulato come un compito di classificazione affrontato utilizzando il machine learning.
Tutti questi approcci mirano principalmente a risolvere istanze di GR in un dato dominio, specificato da un insieme di proposizioni e da un insieme di nomi di azioni. In GRNet, le istanze di classificazione del dominio sono risolte da una rete LSTM. L'unica informazione richiesta come input della rete addestrata è una traccia di nomi di azioni, ognuno dei quali indica solo il nome di un'azione osservata. Un'esecuzione della LSTM elabora una traccia di azioni osservate per calcolare la probabilità che ogni proposizione del dominio faccia parte del goal dell'agente.
Fast and Slow Goal Recognition, ispirato al framework ``Thinking Fast and Slow'', è un modello a doppio processo che integra l'uso delle sopra-citate reti LSTM con le tecniche di pianificazione automatica.
Questa architettura può sfruttare sia il riconoscimento veloce dei goal, basato sull'esperienza, fornito dalla rete, sia l'analisi lenta e deliberata fornita dalle tecniche di pianificazione.
Infine, studiamo come un modello BERT addestrato sui piani sia in grado di comprendere il funzionamento di un dominio, le sue azioni e le loro relazioni reciproche. Questo modello viene poi sottoposto a fine-tuning per classificare le istanze di goal recognition.
Le analisi sperimentali confermano che le architetture presentate raggiungono buone prestazioni sia in termini di accuratezza della classificazione dei goal che di tempo di esecuzione, ottenendo spesso risultati migliori rispetto a un sistema di goal recognition allo stato dell'arte sui benchmark considerati.Recognising the goal of an agent from a trace of observations is an important task with many applications. In the literature, many approaches to goal recognition (GR) rely on the application of automated planning techniques which requires a model of the domain actions and of the initial domain state (written, e.g., in PDDL).
We study three alternative approaches (GRNet, Fast and Slow Goal Recognition and a BERT-based approach) where Goal Recognition is formulated as a classification task addressed by machine learning.
All these approaches are primarily aimed at solving GR instances in a given domain, which is specified by a set of propositions and a set of action names. In GRNet, the goal classification instances in the domain are solved by an LSTM network. The only information required as input of the trained network is a trace of action names, each one indicating just the name of an observed action. A run of the LSTM processes a trace of observed actions to compute how likely it is that each domain proposition is part of the agent's goal.
Fast and Slow Goal Recognition, inspired by the ``Thinking Fast and Slow'' framework, is a dual-process model which integrates the use of the aforementioned LSTM with the automated planning techniques.
This architecture can exploit both the fast, experience-based goal recognition provided by the network, and slow, deliberate analysis provided by the planning techniques.
Finally, we study how a BERT model trained on plans is able to understand how a domain works, its actions and how they are related to each other. This model is then fine-tuned in order to classify goal recognition instances.
Experimental analyses confirms that the presented architectures achieve good performance in terms of both goal classification accuracy and runtime, often obtaining better results w.r.t. a state-of-the-art GR system over the considered benchmarks
Machine Learning Models for Predicting Short-Long Length of Stay of COVID-19 Patients
No full textDuring 2020 and 2021, managing limited healthcare resources and hospital beds has been a fundamental aspect of the fight against the COVID-19 pandemic. Predicting in advance the length of stay, and in particular identifying whether a patient is going to stay in the hospital longer or less than a week, can provide important support in handling resources allocation. However, there have been significant changes in terms of containment measures, virus diffusion, new treatments, vaccines, and new variants of SARS-CoV-2 during the last period. These changes pose several conceptual drift issues that can limit the usefulness of machine learning in this context. In this work, we present a machine learning system trained and tested using data from more than 6000 hospitalised patients in northern Italy, distributed over almost two years of pandemic. We show how machine learning can be effective even by analysing data over this long period of time, also exploiting a model that predicts the patient's outcome in terms of discharge or death. Furthermore, learning from data that also consider deceased patients is a common issue in predicting the length of stay because they have severe conditions similar to patients with a long stay period, but may actually have a very short duration of hospitalisation. For this purpose, we present a method for handling data from alive and deceased patients, exploiting more patient records, increasing the robustness of the model and its performance in this task. Finally, we investigate the features that are most relevant to the prediction of the simplified length of stay
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Recurrent Neural Networks for Daily Estimation of COVID-19 Prognosis with Uncertainty Handling
Full text linkMost ML-based applications for COVID-19 assess the general conditions of a patient trained and tested on cohorts of patients collected over a short period of time and are capable of providing an alarm a few days in advance, helping clinicians in emergency situations, monitor hospitalised patients and identify potentially critical situations at an early stage. However, the pandemic continues to evolve due to new variants, treatments, and vaccines; considering datasets over short periods could not capture this aspect. In addition, these applications often avoid dealing with the uncertainty associated with the prediction provided by machine learning models, potentially causing costly mistakes. In this work, we present a system based on Recurrent Neural Networks (RNN) for the daily estimate of the prognosis of COVID-19 patients that is built and tested using data collected over a long period of time. Our system achieves high predictive performance and uses an algorithm to effectively determine and discard those patients for whom RNN cannot predict the prognosis with sufficient confidence
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