Institution
University of Sannio
Education•Benevento, Italy•
About: University of Sannio is a education organization based out in Benevento, Italy. It is known for research contribution in the topics: Gravitational wave & LIGO. The organization has 1278 authors who have published 6125 publications receiving 167577 citations. The organization is also known as: Università degli Studi del Sannio & Universita degli Studi del Sannio.
Papers published on a yearly basis
Papers
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TL;DR: The findings mostly contradict common wisdom stating that smells are being introduced during evolutionary tasks, and call for the need to develop a new generation of recommendation systems aimed at properly planning smell refactoring activities.
Abstract: Technical debt is a metaphor introduced by Cunningham to indicate “not quite right code which we postpone making it right”. One noticeable symptom of technical debt is represented by code smells, defined as symptoms of poor design and implementation choices. Previous studies showed the negative impact of code smells on the comprehensibility and maintainability of code. While the repercussions of smells on code quality have been empirically assessed, there is still only anecdotal evidence on when and why bad smells are introduced, what is their survivability , and how they are removed by developers. To empirically corroborate such anecdotal evidence, we conducted a large empirical study over the change history of 200 open source projects. This study required the development of a strategy to identify smell-introducing commits, the mining of over half a million of commits, and the manual analysis and classification of over 10K of them. Our findings mostly contradict common wisdom, showing that most of the smell instances are introduced when an artifact is created and not as a result of its evolution. At the same time, 80 percent of smells survive in the system. Also, among the 20 percent of removed instances, only 9 percent are removed as a direct consequence of refactoring operations.
309 citations
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TL;DR: The transient noise backgrounds used to determine the significance of the event (designated GW150914) are described and the results of investigations into potential correlated or uncorrelated sources of transient noise in the detectors around the time of theevent are presented.
Abstract: On 14 September 2015, a gravitational wave signal from a coalescing black hole binary system was observed by the Advanced LIGO detectors. This paper describes the transient noise backgrounds used to determine the significance of the event (designated GW150914) and presents the results of investigations into potential correlated or uncorrelated sources of transient noise in the detectors around the time of the event. The detectors were operating nominally at the time of GW150914. We have ruled out environmental influences and non-Gaussian instrument noise at either LIGO detector as the cause of the observed gravitational wave signal.
308 citations
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TL;DR: A parallel selection model of metastatic progression, where branched evolution could be traced back to immune-escaping clones is proposed, which could inform the understanding of cancer dissemination and the development of immunotherapeutics.
306 citations
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TL;DR: This research trains Hidden Markov Models (HMMs) on both static and dynamic feature sets and compares the resulting detection rates over a substantial number of malware families, finding a fully dynamic approach generally yields the best detection rates.
Abstract: In this research, we compare malware detection techniques based on static, dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs) on both static and dynamic feature sets and compare the resulting detection rates over a substantial number of malware families. We also consider hybrid cases, where dynamic analysis is used in the training phase, with static techniques used in the detection phase, and vice versa. In our experiments, a fully dynamic approach generally yields the best detection rates. We discuss the implications of this research for malware detection based on hybrid techniques.
306 citations
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TL;DR: To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—the authors estimated additional population equivalents with UHC effective coverage from 2018 to 2023, and quantified frontiers of U HC effective coverage performance on the basis of pooled health spending per capita.
304 citations
Authors
Showing all 1300 results
Name | H-index | Papers | Citations |
---|---|---|---|
Alberto Vecchio | 115 | 572 | 79416 |
Andrea Alù | 109 | 1138 | 47717 |
Vijay P. Singh | 106 | 1699 | 55831 |
Kenneth A. Strain | 105 | 485 | 70966 |
N. A. Robertson | 105 | 384 | 69504 |
G. D. Hammond | 100 | 352 | 67549 |
B. Sorazu | 98 | 347 | 65989 |
I. W. Martin | 97 | 352 | 64772 |
Maria Ilaria Del Principe | 93 | 398 | 62000 |
Innocenzo M. Pinto | 89 | 378 | 56567 |
Karl Henrik Johansson | 88 | 1089 | 33751 |
Vincenzo Pierro | 83 | 263 | 42535 |
R. DeSalvo | 83 | 225 | 51227 |
Paolo Addesso | 71 | 202 | 45552 |
Francesco Borrelli | 66 | 327 | 17254 |