1,721,025 research outputs found
Mobility in Unsupervised Word Embeddings for Knowledge Extraction—The Scholars’ Trajectories across Research Topics
Full text linkIn the knowledge discovery field of the Big Data domain the analysis of geographic positioning and mobility information plays a key role. At the same time, in the Natural Language Processing (NLP) domain pre-trained models such as BERT and word embedding algorithms such as Word2Vec enabled a rich encoding of words that allows mapping textual data into points of an arbitrary multi-dimensional space, in which the notion of proximity reflects an association among terms or topics. The main contribution of this paper is to show how analytical tools, traditionally adopted to deal with geographic data to measure the mobility of an agent in a time interval, can also be effectively applied to extract knowledge in a semantic realm, such as a semantic space of words and topics, looking for latent trajectories that can benefit the properties of neural network latent representations. As a case study, the Scopus database was queried about works of highly cited researchers in recent years. On this basis, we performed a dynamic analysis, for measuring the Radius of Gyration as an index of the mobility of researchers across scientific topics. The semantic space is built from the automatic analysis of the paper abstracts of each author. In particular, we evaluated two different methodologies to build the semantic space and we found that Word2Vec embeddings perform better than the BERT ones for this task. Finally, The scholars’ trajectories show some latent properties of this model, which also represent new scientific contributions of this work. These properties include (i) the correlation between the scientific mobility and the achievement of scientific results, measured through the H-index; (ii) differences in the behavior of researchers working in different countries and subjects; and (iii) some interesting similarities between mobility patterns in this semantic realm and those typically observed in the case of human mobility
A peer-to-peer notification system for distributed online social networks
Full text linkCurrent social networking systems are almost always centralized systems. This architecture poses issues about privacy, censorship and control of personal data. On the other hand, peer-to-peer systems can overcome these issues, in exchange with additional architectural complexity. This paper describes a peer-to-peer system provided with a spanning tree for distributing online notifications inside a group of interested peers. These notifications may regard discussion messages for a chat system, or any kind of update messages for spreading social activities performed by users of a Distributed Online Social Network. In particular, we describe and compare different mechanisms for the creation and management of the spanning tree
Continual representation learning for node classification in power-law graphs
No full textThe recent advent of node embedding techniques enabled a more efficient application of machine learning techniques on graphs. These techniques allow each node of a network to be encoded into an arbitrary low-dimensional vector representation, which can be exploited by statistical learning models. However, the main limitation of these approaches is that the embedding task is solved as an optimization problem on a static snapshot of the graph. In a real scenario, temporal dynamics should be considered with some consequences: new nodes might join the network and get a representation of only these new ones. As a consequence, a new training step over the entire graph is required. Even more, training models with static approaches can have resource-intensive requirements, especially when dealing with large networks. In light of this, a continual feature learning that builds on top of previously already learned knowledge (previous partial embedding of the network) and well-known properties can be a solution to address both limitations efficiently in real scenarios. Our approach is suitable for graphs whose degree distribution is described by a power-law function that is a common property of real systems. This research work presents three main scientific contributions: (a) a continual feature learning meta-algorithm for node embedding, which exploits properties of power-law distribution and spaces alignment techniques; It is suitable with any traditional node embedding techniques that relies on embedding spaces (b) we demonstrate empirically, by performing node labeling tasks, that a lightweight solution to encode new nodes, based on limited knowledge of the embedding of the network hub-nodes, can provide comparable or better performances, with respect to static approaches. (c) Finally, we experimented our algorithm in the temporal graphs domain and we achieved better results in node classification compared with other state of the art techniques
HS-SPME/GC–MS AND CHEMOMETRIC APPROACH FOR THE STUDY OF VOLATILE PROFILE IN X-RAY IRRADIATED SURFACE-RIPENED CHEESES
No full textHS-SPME/GC–MS AND CHEMOMETRIC APPROACH FOR THE
STUDY OF VOLATILE PROFILE IN X-RAY IRRADIATED
SURFACE-RIPENED CHEESES
R. Zianni1
, M. Campaniello1
, A. Mentana1
, M. Tomaiuolo1
, M. Iammarino1
, D.
Centonze2
, C. Palermo3
1
Istituto Zooprofilattico Sperimentale della Puglia e della Basilicata, Laboratorio Nazionale di
Riferimento per il trattamento degli alimenti e dei loro ingredienti con radiazioni ionizzanti, Via
Manfredonia, 20 - 71121 Foggia, Italy
2Università di Foggia, Dipartimento di Scienze Mediche e Chirurgiche, Via Napoli 25 - 71122 Foggia,
Italy
3Università di Foggia, Dipartimento di Medicina Clinica e Sperimentale, Via Napoli 25 - 71122 Foggia,
Italy
Food irradiation is a preservation procedure that consists in exposing foodstuffs to doses
of ionising radiations, such as X-ray, γ-ray and electron beams1
. The irradiation of
surface mould-ripened soft cheese, such as Brie and Camembert, could be an alternative
to traditional thermal pasteurisation to guarantee the microbiological safety and prolong
shelf-life of such cheeses. To date, few analytical investigations have been carried out
on the effects of X-ray irradiation treatment on the volatile profile of soft cheeses1,2
.
Hence, volatolomic studies about the cheese fingerprint modified from the irradiation
and the related outcomes can be useful to identify potential markers of treatment. In this
study, X-ray irradiation was applied to Brie and Camembert-type cheeses produced with
cow milk and the modifications in the composition of volatile organic compounds have
been investigated. HS-SPME technique combined with GC-MS was used to extract and
analyse the volatile fraction from the dairy matrices. A Central Composite Design for
Response Surface Methodology was employed to optimise the HS-SPME parameters in
terms of extraction temperature, extraction time and sample amount. Hence, the best
conditions were applied to non-irradiated and X-ray irradiated samples at three dose
levels (2.0, 4.0 and 6.0 kGy) and the differences have been evaluated by means of a
chemometric discrimination. Principal Component Analysis and Partial Least
Square-Discriminant Analysis were used to discriminate the variation of volatile
profiles among non-irradiated and irradiated samples. The outcomes demonstrated that
the X-ray irradiation treatment differently affected the volatile classes of aroma Brie and
Camembert-type cheeses. Finally, the results could be useful to identify potential
markers of X-ray treatment for control purposes of irradiated soft cheeses.
References:
1. Zianni, R., Mentana, A., Tomaiuolo, M., Campaniello, M., Iammarino, M., Centonze, D., &
Palermo, C., Food Chemistry 423 (2023) 136239.
2. Zianni, R., Mentana, A., Campaniello, M., Chiappinelli, A., Tomaiuolo, M., Chiaravalle, A.E., &
Marchesani, G., LWT- Food Science and Technology 153 (2022) 1-7
Voice assistants in hospital triage operations
Full text linkThis paper analyzes the creation and usage of a voice assistant for the triage of emergency room patients. This human-centred intelligent system strongly relies on Mycroft, an extensible open source voice assistant. The patients are able to declare their symptoms to the agent, which recognizes the urgency and acts accordingly. The software can even provide useful medical informations to the users
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
Early identification in maternal blood of fetal sex in the presence of fetal DNA from previous pregnancies.
No full text
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