A method to generate context information sets from analysis results with a unified abstraction model based on an extension of data enrichment scheme

2020 ◽  
Author(s):  
Yoosang Park ◽  
Jonghyeok Mun ◽  
Jongsun Choi ◽  
Jaeyoung Choi ◽  
Hoda Kim
Keyword(s):  
Context Information ◽  
Data Enrichment ◽  
Model Based ◽  
Information Sets

scholarly journals An Extensible Data Enrichment Scheme for Providing Intelligent Services in Internet of Things Environments

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yoosang Park ◽  
Jongsun Choi ◽  
Jaeyoung Choi
Keyword(s):  
Internet Of Things ◽  
Semantic Web ◽  
Real World ◽  
The Internet ◽  
Context Information ◽  
Data Abstraction ◽  
The Real ◽  
Data Enrichment ◽  
Information Sets ◽  
The Internet Of Things

Recent technologies in the Internet of Things (IoT) environment aim to provide intelligent services to users. Intelligent services can be managed and executed by systems that handle context information sets. Handling intelligent services leads to three major considerations: objects in the real world that should be described as metadata, a data enrichment procedure from sensing values for representing states, and controlling functionalities to manage services. In this study, an extensible data-enrichment scheme is proposed. The proposed scheme provides a way to describe profiles, data abstraction procedures, and functionalities that support the building of context information sets derived from raw datasets in the manner of a semantic web stack. Finally, data enrichment will help any system that uses context information by providing improved, understandable, and readable datasets to the service developers or the systems themselves.

Model-based Head Orientation Estimation for Smart Devices

2021 ◽  
Vol 5 (3) ◽  
pp. 1-24
Author(s):  
Qiang Yang ◽  
Yuanqing Zheng
Keyword(s):  
Radiation Pattern ◽  
Microphone Array ◽  
Estimation Error ◽  
Microphone Arrays ◽  
Smart Devices ◽  
Head Orientation ◽  
Context Information ◽  
Orientation Estimation ◽  
Model Based ◽  
The Voice

Voice interaction is friendly and convenient for users. Smart devices such as Amazon Echo allow users to interact with them by voice commands and become increasingly popular in our daily life. In recent years, research works focus on using the microphone array built in smart devices to localize the user's position, which adds additional context information to voice commands. In contrast, few works explore the user's head orientation, which also contains useful context information. For example, when a user says, "turn on the light", the head orientation could infer which light the user is referring to. Existing model-based works require a large number of microphone arrays to form an array network, while machine learning-based approaches need laborious data collection and training workload. The high deployment/usage cost of these methods is unfriendly to users. In this paper, we propose HOE, a model-based system that enables Head Orientation Estimation for smart devices with only two microphone arrays, which requires a lower training overhead than previous approaches. HOE first estimates the user's head orientation candidates by measuring the voice energy radiation pattern. Then, the voice frequency radiation pattern is leveraged to obtain the final result. Real-world experiments are conducted, and the results show that HOE can achieve a median estimation error of 23 degrees. To the best of our knowledge, HOE is the first model-based attempt to estimate the head orientation by only two microphone arrays without the arduous data training overhead.

Fund Rating Model Based on Finite Normal Mixture Distribution

2011 ◽  
Vol 14 (01) ◽  
pp. 1-33
Author(s):  
Zhangpeng Gao ◽  
Shahidur Rahman ◽  
Shafiqur Rahman
Keyword(s):  
Mixture Distribution ◽  
Parametric Bootstrap ◽  
Fund Performance ◽  
Normal Mixture ◽  
Cross Sectional ◽  
Model Based ◽  
Information Sets ◽  
Rating Method ◽  
The Difference ◽  
Rating Model

This paper proposes a new method of fund rating based on the cross-sectional distribution of fund performance measured by alpha. This distribution-based fund rating model is more flexible and provides more interesting results than current commercial fund rating method used by Morningstar. Unlike Morningstar's rating, this method does not use preset percentiles to rate funds. It is the distribution of alpha that dictates the number of performance groups in a given fund category and time period. The framework is based on the crucial assumption that the expected fund performance may be different, and the difference of the expected fund performance arises from the segmented market information and/or the differentiated ability of mangers to acquire and analyze information. The multimodal shape and formal normality tests prompt us to model the distribution of alpha by finite normal mixture model. We introduce the parametric bootstrap procedure to determine the number of performance groups in the model. We then employ expectation and maximization (EM) algorithm to estimate the model. Based on the estimated posterior probabilities of the fund, we assign the rating to funds. Our empirical results show that the number of performance groups is not fixed and varies across time and fund categories. We observe a clear tendency of the merging of information sets, which implies that the fund market has become gradually more efficient over time as information was well transmitted and analyzed.

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