121 research outputs found
Airline service quality ranking based on TOPSIS-VIKOR-AISM
The service quality ranking of airlines is a crucial factor for their
sustainability in the intensely competitive airline market. This study intends
to offer further insights in this field to produce simpler and explanatory
results, however previous studies have been lacking in terms of sample size,
efficiency, and reliability. In order to develop an airline service quality
evaluation system that incorporates customer utilities, ideal points, and
regret values and performs a confrontation hierarchy topology analysis based on
the computation of compromise solutions, the TOPSIS-VIKOR-AISM model is
proposed in this work. In addition to supporting consumer choice and airline
development, this study offers fresh perspectives on how to assess the
effectiveness of airlines and other industries
Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection
Massive key performance indicators (KPIs) are monitored as multivariate time
series data (MTS) to ensure the reliability of the software applications and
service system. Accurately detecting the abnormality of MTS is very critical
for subsequent fault elimination. The scarcity of anomalies and manual labeling
has led to the development of various self-supervised MTS anomaly detection
(AD) methods, which optimize an overall objective/loss encompassing all
metrics' regression objectives/losses. However, our empirical study uncovers
the prevalence of conflicts among metrics' regression objectives, causing MTS
models to grapple with different losses. This critical aspect significantly
impacts detection performance but has been overlooked in existing approaches.
To address this problem, by mimicking the design of multi-gate
mixture-of-experts (MMoE), we introduce CAD, a Conflict-aware multivariate KPI
Anomaly Detection algorithm. CAD offers an exclusive structure for each metric
to mitigate potential conflicts while fostering inter-metric promotions. Upon
thorough investigation, we find that the poor performance of vanilla MMoE
mainly comes from the input-output misalignment settings of MTS formulation and
convergence issues arising from expansive tasks. To address these challenges,
we propose a straightforward yet effective task-oriented metric selection and
p&s (personalized and shared) gating mechanism, which establishes CAD as the
first practicable multi-task learning (MTL) based MTS AD model. Evaluations on
multiple public datasets reveal that CAD obtains an average F1-score of 0.943
across three public datasets, notably outperforming state-of-the-art methods.
Our code is accessible at https://github.com/dawnvince/MTS_CAD.Comment: 11 pages, ESEC/FSE industry track 202
Real-time Monitoring for the Next Core-Collapse Supernova in JUNO
Core-collapse supernova (CCSN) is one of the most energetic astrophysical
events in the Universe. The early and prompt detection of neutrinos before
(pre-SN) and during the SN burst is a unique opportunity to realize the
multi-messenger observation of the CCSN events. In this work, we describe the
monitoring concept and present the sensitivity of the system to the pre-SN and
SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is
a 20 kton liquid scintillator detector under construction in South China. The
real-time monitoring system is designed with both the prompt monitors on the
electronic board and online monitors at the data acquisition stage, in order to
ensure both the alert speed and alert coverage of progenitor stars. By assuming
a false alert rate of 1 per year, this monitoring system can be sensitive to
the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos
up to about 370 (360) kpc for a progenitor mass of 30 for the case
of normal (inverted) mass ordering. The pointing ability of the CCSN is
evaluated by using the accumulated event anisotropy of the inverse beta decay
interactions from pre-SN or SN neutrinos, which, along with the early alert,
can play important roles for the followup multi-messenger observations of the
next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure
Large expert-curated database for benchmarking document similarity detection in biomedical literature search
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe
APPLICATION OF REINFORCEMENT LEARNING ON TARGET-DRIVEN VISUAL INDOOR NAVIGATION
Master'sMASTER OF SCIENCE (RSH-FOS
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