137 research outputs found
Leveraging full-text article exploration for citation analysis
No full textScientific articles often include in-text citations quoting from external sources. When the cited source is an article, the citation context can be analyzed by exploring the article full-text. To quickly access the key information, researchers are often interested in identifying the sections of the cited article that are most pertinent to the text surrounding the citation in the citing article. This paper first performs a data-driven analysis of the correlation between the textual content of the sections of the cited article and the text snippet where the citation is placed. The results of the correlation analysis show that the title and abstract of the cited article are likely to include content highly similar to the citing snippet. However, the subsequent sections of the paper often include cited text snippets as well. Hence, there is a need to understand the extent to which an exploration of the full-text of the cited article would be beneficial to gain insights into the citing snippet, considering also the fact that the full-text access could be restricted. To this end, we then propose a classification approach to automatically predicting whether the cited snippets in the full-text of the paper contain a significant amount of new content beyond abstract and title. The proposed approach could support researchers in leveraging full-text article exploration for citation analysis. The experiments conducted on real scientific articles show promising results: the classifier has a 90% chance to correctly distinguish between the full-text exploration and only title and abstract cases
rOGER: A method for determining the geothermal potential in urban areas
No full textShallow geothermal energy is increasingly adopted for heating and cooling purposes because of the short payback time of initial installation investments. As a result, a relevant concentration of Ground Heat Exchangers is being experienced in urban areas. Planning issues thus arise to manage interferences and optimize the use of underground heat resources without depletion, harm to the environment nor efficiency losses on heat pumps or plant oversizing. This study provides a rational approach to optimise geothermal resources based on the use of Geographic Information Systems and transient 3D Thermo-Hydro numerical models. An optimised semianalytical formula for the assessment of Borehole Heat Exchangers geothermal potential in hydrodynamic conditions is developed through a parametric numerical study. The long-term performances of BHE subjected to groundwater velocity in the range of 0 to 1 m/day were analysed with multiple aquifer thermal parameters. This analytical expression allows a fast and accurate assessment of the potential even in large areas without leading to excessively conservative evaluations. This may serve designers in the preliminary sizing of installations and city planners in the development of appropriate policies for the promotion and management of shallow geothermal resources. An example of the application to the central district of the city of Turin (Italy) is also shown
Exploiting pivot words to classify and summarize discourse facets of scientific papers
No full textThe ever-increasing number of published scientific articles has prompted the need for automated, data-driven approaches to summarizing the content of scientific articles. The Computational Linguistics Scientific Document Summarization Shared Task (CL-SciSumm 2019) has recently fostered the study and development of new text mining and machine learning solutions to the summarization problem customized to the academic domain. In CL-SciSumm, a Reference Paper (RP) is associated with a set of Citing Papers (CPs), all containing citations to the RP. In each CP, the text spans (i.e., citances) have been identified that pertain to a particular citation to the RP. The task of identifying the spans of text in the RP that most accurately reflect the citance is addressed using supervised approaches. This paper proposes a new, more effective solution to the CL-SciSumm discourse facet classification task, which entails identifying for each cited text span what facet of the paper it belongs to from a predefined set of facets. It proposes also to extend the set of traditional CL-SciSumm tasks with a new one, namely the discourse facet summarization task. The idea behind is to extract facet-specific descriptions of each RP consisting of a fixed-length collection of RP’s text spans. To tackle both the standard and the new tasks, we propose machine learning supported solutions based on the extraction of a selection of discriminating words, called pivot words. Predictive features based on pivot words are shown to be of great importance to rate the pertinence and relevance of a text span to a given facet. The newly proposed facet classification method performs significantly better than the best performing CL-SciSumm 2019 participant (i.e., the classification accuracy has increased by + 8%), whereas regression methods achieved promising results for the newly proposed summarization task
Experimental fitting of efficiency Hill chart for Kaplan hydraulic turbine
No full textThe development of hydroelectric technology and much of the “knowledge” on hydraulic phenomena derive from scale modeling and “bench” tests to improve machinery efficiency. The result of these experimental tests is mapping the so-called “hill chart”, representing the “DNA” of a turbine model. Identifying the efficiency values as a function of the specific parameters of the flow and energy coefficient (which both identify the operating point) allows us to represent the complete behavior of a turbine in hydraulic similarity with the original model developed in the laboratory. The present work carries out a “reverse engineering” operation that leads to the definition of “an innovative research model” that is relatively simple to use in every field. Thus, from the experimental survey of the degree of efficiency of several prototypes of machines deriving from the same starting model, the hill chart of the hydraulic profile used is reconstructed. The “mapping” of all the characteristic quantities of the machine, together with the physical parameters of the regulating organs of a four-blade Kaplan turbine model, also made it possible to complete the process, allowing to identify not only the iso-efficiency regions but also the curves relating to the trend of the angle of the impeller blades, the specific opening of the distributor, and the identification of critical areas of cavitation. The development of the hill chart was made possible by investigating the behavior of 33 actual prototypes and 46 characteristic curves derived from the same reference model based on practical experiments for finding the optimal blade distributor “setup curve”. To complete this, theoretical characteristic curves of “not physically realized” prototypes were also mapped, allowing us to complete the regions comprising the diagram. The study of the unified hill charts found in previous documentation of the most famous manufacturers was of great help. Finally, the validation of the “proposed procedure” was obtained through the experimental survey of the actual efficiency of the new prototype based on the theoretical values defined in the design phase on the chart obtained with the method described
Identifying collaborations among researchers: a pattern-based approach
No full textIn recent years a huge amount of publications and scientific reports has become available through digital libraries and online databases. Digital libraries commonly provide advanced search interfaces, through which researchers can find and explore the most related scientific studies. Even though the publications of a single author can be easily retrieved and explored, understanding how authors have collaborated with each other on specific research topics and to what extent their collaboration have been fruitful is, in general, a challenging task. This paper proposes a new pattern-based approach to analyzing the correlations among the authors of most influential research studies. To this purpose, it analyzes publication data retrieved from digital libraries and online databases by means of an itemset-based data mining algorithm. It automatically extracts patterns representing the most relevant collaborations among authors on specific research topics. Patterns are evaluated and ranked according to the number of citations received by the corresponding publications. The proposed approach was validated in a real case study, i.e., the analysis of scientific literature on genomics. Specifically, we first analyzed scientific studies on genomics acquired from the OMIM database to discover correlations between authors and genes or genetic disorders. Then, the reliability of the discovered patterns was assessed using the PubMed search engine. The results show that, for the majority of the mined patterns, the most influential (top ranked) studies retrieved by performing author-driven PubMed queries range over the same gene/genetic disorder indicated by the top ranked pattern
Discovering cross-topic collaborations among researchers by exploiting weighted association rules
No full textIdentifying the most relevant scientific publications on a given topic is a well-known research problem. The Author-Topic Model (ATM) is a generative model that represents the relationships between research topics and publication authors. It allows us to identify the most influential authors on a particular topic. However, since most research works are co-authored by many researchers the information provided by ATM can be complemented by the study of the most fruitful collaborations among multiple authors. This paper addresses the discovery of research collaborations among multiple authors on single or multiple topics. Specifically, it exploits an exploratory data mining technique, i.e., weighted association rule mining, to analyze publication data and to discover correlations between ATM topics and combinations of authors. The mined rules characterize groups of researchers with fairly high scientific productivity by indicating (1) the research topics covered by their most cited publications and the relevance of their scientific production separately for each topic, (2) the nature of the collaboration (topic-specific or cross-topic), (3) the name of the external authors who have (occasionally) collaborated with the group either on a specific topic or on multiple topics, and (4) the underlying correlations between the addressed topics. The applicability of the proposed approach was validated on real data acquired from the Online Mendelian Inheritance in Man catalog of genetic disorders and from the PubMed digital library. The results confirm the effectiveness of the proposed strategy
Self-Learning Classifier for Internet traffic
No full textNetwork visibility is a critical part of traffic engineering, network management, and security. Recently, unsupervised algorithms have been envisioned as a viable alternative to automatically identify classes of traffic. However, the accuracy achieved so far does not allow to use them for traffic classification in practical scenario. In this paper, we propose SeLeCT, a Self-Learning Classifier for Internet traffic. It uses unsupervised algorithms along with an adaptive learning approach to automatically let classes of traffic emerge, being identified and (easily) labeled. SeLeCT automatically groups flows into pure (or homogeneous) clusters using alternating simple clustering and filtering phases to remove outliers. SeLeCT uses an adaptive learning approach to boost its ability to spot new protocols and applications. Finally, SeLeCT also simplifies label assignment (which is still based on some manual intervention) so that proper class labels can be easily discovered. We evaluate the performance of SeLeCT using traffic traces collected in different years from various ISPs located in 3 different continents. Our experiments show that SeLeCT achieves overall accuracy close to 98%. Unlike state-of-art classifiers, the biggest advantage of SeLeCT is its ability to help discovering new protocols and applications in an almost automated fashio
Speech Analysis of Language Varieties in Italy
No full textItaly exhibits rich linguistic diversity across its territory due to the distinct regional languages spoken in different areas. Recent advances in self-supervised learning provide new opportunities to analyze Italy's linguistic varieties using speech data alone. This includes the potential to leverage representations learned from large amounts of data to better examine nuances between closely related linguistic varieties. In this study, we focus on automatically identifying the geographic region of origin of speech samples drawn from Italy's diverse language varieties. We leverage self-supervised learning models to tackle this task and analyze differences and similarities between Italy's regional languages. In doing so, we also seek to uncover new insights into the relationships among these diverse yet closely related varieties, which may help linguists understand their interconnected evolution and regional development over time and space. To improve the discriminative ability of learned representations, we evaluate several supervised contrastive learning objectives, both as pre-training steps and additional fine-tuning objectives. Experimental evidence shows that pre-trained self-supervised models can effectively identify regions from speech recording. Additionally, incorporating contrastive objectives during fine-tuning improves classification accuracy and yields embeddings that distinctly separate regional varieties, demonstrating the value of combining self-supervised pre-training and contrastive learning for this task
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