29,285 research outputs found
A Bayesian-Based Approach for Public Sentiment Modeling
Public sentiment is a direct public-centric indicator for the success of
effective action planning. Despite its importance, systematic modeling of
public sentiment remains untapped in previous studies. This research aims to
develop a Bayesian-based approach for quantitative public sentiment modeling,
which is capable of incorporating uncertainty and guiding the selection of
public sentiment measures. This study comprises three steps: (1) quantifying
prior sentiment information and new sentiment observations with Dirichlet
distribution and multinomial distribution respectively; (2) deriving the
posterior distribution of sentiment probabilities through incorporating the
Dirichlet distribution and multinomial distribution via Bayesian inference; and
(3) measuring public sentiment through aggregating sampled sets of sentiment
probabilities with an application-based measure. A case study on Hurricane
Harvey is provided to demonstrate the feasibility and applicability of the
proposed approach. The developed approach also has the potential to be
generalized to model various types of probability-based measures
Beyond subjective and objective in statistics
We argue that the words "objectivity" and "subjectivity" in statistics
discourse are used in a mostly unhelpful way, and we propose to replace each of
them with broader collections of attributes, with objectivity replaced by
transparency, consensus, impartiality, and correspondence to observable
reality, and subjectivity replaced by awareness of multiple perspectives and
context dependence. The advantage of these reformulations is that the
replacement terms do not oppose each other. Instead of debating over whether a
given statistical method is subjective or objective (or normatively debating
the relative merits of subjectivity and objectivity in statistical practice),
we can recognize desirable attributes such as transparency and acknowledgment
of multiple perspectives as complementary goals. We demonstrate the
implications of our proposal with recent applied examples from pharmacology,
election polling, and socioeconomic stratification.Comment: 35 page
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
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What Turkey expects from logistics outsourcing ?
Copyright @ 2011 Yasar UniversityThe economies of the world have become increasingly interdependent, and organizations have come under tremendous pressure to maximize productivity and profitability. Creating value through outsourcing has emerged as a popular competitive strategy for firms of all sizes in all types of industries. The aim of this research is to investigate the use of third party logistics in Turkish companies from the users’ perspective to identify the types of logistics services outsourced, problems encountered in outsourcing these services, logistics costs, decision makers in outsourcing logistics activities, and information sources used in the decision-making process. A structured survey was selected as the tool for data collection. The field study involved face-to-face interviews with 204 companies out of top 500 companies ranked in terms of turnover that are registered with industrial associations and chambers of commerce in Turkey. Moreover, a decision support system based on Bayesian Causal Map is proposed for 3PLs in order to assist them in their service proposals for different sectors. This study is a first attempt to reveal and compare the outsourcing perception of the companies in different sectors, to expose the firms’ underlying motives as well as the respective importance of these motives for outsourcing logistics activities in Turkey. The use of Bayesian Causal Map based on the survey results provides an important guide to the 3PL providers to pick a suitable strategy and to prioritize their operational activities in different sectors in such a way to achieve a competitive advantage
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