Questionnaire analysis to define the most suitable survey for port-noise
investigation
- URL: http://arxiv.org/abs/2007.06915v1
- Date: Tue, 14 Jul 2020 08:52:55 GMT
- Title: Questionnaire analysis to define the most suitable survey for port-noise
investigation
- Authors: Andrea Cerniglia, Davide Chiarella, Paola Cutugno, Lucia Marconi, Anna
Magrini, Gelsomina Di Feo, Melissa Ferretti
- Abstract summary: The paper analyses a sample of questions suitable for the specific research, chosen as part of the wide database of questionnaires internationally proposed for subjective investigations.
The questionnaire will be optimized to be distributed in the TRIPLO project (TRansports and Innovative sustainable connections between Ports and LOgistic platforms)
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The high level of noise pollution affecting the areas between ports and
logistic platforms represents a problem that can be faced from different points
of view. Acoustic monitoring, mapping, short-term measurements, port and road
traffic flows analyses can give useful indications on the strategies to be
proposed for a better management of the problem. A survey campaign through the
preparation of questionnaires to be submitted to the population exposed to
noise in the back-port areas will help to better understand the subjective
point of view. The paper analyses a sample of questions suitable for the
specific research, chosen as part of the wide database of questionnaires
internationally proposed for subjective investigations. The preliminary results
of a first data collection campaign are considered to verify the adequacy of
the number, the type of questions, and the type of sample noise used for the
survey. The questionnaire will be optimized to be distributed in the TRIPLO
project (TRansports and Innovative sustainable connections between Ports and
LOgistic platforms). The results of this survey will be the starting point for
the linguistic investigation carried out in combination with the acoustic
monitoring, to improve understanding the connections between personal feeling
and technical aspects.
Related papers
- Downstream-Pretext Domain Knowledge Traceback for Active Learning [138.02530777915362]
We propose a downstream-pretext domain knowledge traceback (DOKT) method that traces the data interactions of downstream knowledge and pre-training guidance.
DOKT consists of a traceback diversity indicator and a domain-based uncertainty estimator.
Experiments conducted on ten datasets show that our model outperforms other state-of-the-art methods.
arXiv Detail & Related papers (2024-07-20T01:34:13Z) - Qsnail: A Questionnaire Dataset for Sequential Question Generation [76.616068047362]
We present the first dataset specifically constructed for the questionnaire generation task, which comprises 13,168 human-written questionnaires.
We conduct experiments on Qsnail, and the results reveal that retrieval models and traditional generative models do not fully align with the given research topic and intents.
Despite enhancements through the chain-of-thought prompt and finetuning, questionnaires generated by language models still fall short of human-written questionnaires.
arXiv Detail & Related papers (2024-02-22T04:14:10Z) - Crowdsourced Adaptive Surveys [0.0]
This paper introduces a crowdsourced adaptive survey methodology (CSAS)
The method converts open-ended text provided by participants into Likert-style items.
It applies a multi-armed bandit algorithm to determine user-provided questions that should be prioritized in the survey.
arXiv Detail & Related papers (2024-01-16T04:05:25Z) - Bayes-enhanced Multi-view Attention Networks for Robust POI
Recommendation [81.4999547454189]
Existing works assume the available POI check-ins reported by users are the ground-truth depiction of user behaviors.
In real application scenarios, the check-in data can be rather unreliable due to both subjective and objective causes.
We propose a Bayes-enhanced Multi-view Attention Network to address the uncertainty factors of the user check-ins.
arXiv Detail & Related papers (2023-11-01T12:47:38Z) - A Survey on Interpretable Cross-modal Reasoning [64.37362731950843]
Cross-modal reasoning (CMR) has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics.
This survey delves into the realm of interpretable cross-modal reasoning (I-CMR)
This survey presents a comprehensive overview of the typical methods with a three-level taxonomy for I-CMR.
arXiv Detail & Related papers (2023-09-05T05:06:48Z) - A Demand-Driven Perspective on Generative Audio AI [1.0639605996067534]
In this paper, we present the results of a survey conducted with professional audio engineers.
We summarize the current challenges in audio quality and controllability based on the survey.
arXiv Detail & Related papers (2023-07-10T00:58:28Z) - FeedbackMap: a tool for making sense of open-ended survey responses [1.0660480034605242]
This demo introduces FeedbackMap, a web-based tool that uses natural language processing techniques to facilitate the analysis of open-ended survey responses.
We discuss the importance of examining survey results from multiple perspectives and the potential biases introduced by summarization methods.
arXiv Detail & Related papers (2023-06-26T23:38:24Z) - Predicting Survey Response with Quotation-based Modeling: A Case Study
on Favorability towards the United States [0.0]
We propose a pioneering approach for predicting survey responses by examining quotations using machine learning.
We leverage a vast corpus of quotations from individuals across different nationalities to extract their level of favorability.
We employ a combination of natural language processing techniques and machine learning algorithms to construct a predictive model for survey responses.
arXiv Detail & Related papers (2023-05-23T14:11:01Z) - Open vs Closed-ended questions in attitudinal surveys -- comparing,
combining, and interpreting using natural language processing [3.867363075280544]
Topic Modeling could significantly reduce the time to extract information from open-ended responses.
Our research uses Topic Modeling to extract information from open-ended questions and compare its performance with closed-ended responses.
arXiv Detail & Related papers (2022-05-03T06:01:03Z) - A Revised Generative Evaluation of Visual Dialogue [80.17353102854405]
We propose a revised evaluation scheme for the VisDial dataset.
We measure consensus between answers generated by the model and a set of relevant answers.
We release these sets and code for the revised evaluation scheme as DenseVisDial.
arXiv Detail & Related papers (2020-04-20T13:26:45Z) - Improving Multi-Turn Response Selection Models with Complementary
Last-Utterance Selection by Instance Weighting [84.9716460244444]
We consider utilizing the underlying correlation in the data resource itself to derive different kinds of supervision signals.
We conduct extensive experiments in two public datasets and obtain significant improvement in both datasets.
arXiv Detail & Related papers (2020-02-18T06:29:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.