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ICPR 2020

Assessement of CNN-Based Methods for Poverty Estimation from Satellite images

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### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!<br><br><br>

To gain access to this event page you are required to register.<br> Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

technical paper

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 



This paper presents chewing data analysis based on the new approach of chewing detection for diet monitoring applications. The proposed approach is based on chewing detection using a proximity sensor in capturing the temporalis muscle movement during chewing. The aim is to support the development of non-contact-based chewing detection. A wearable device of eye-glass is used with the sensor mounted at the right side of the eyeglass temple using a 3D printed housing. The main activity involved in this study is resting and eating, three test food that represents different food hardness (carrot, banana, and apple) with a portion of one spoonful. Several upper cut-off frequencies (fc2) of bandpass filters were used during the analyses. The signals were evaluated using accuracy and F1-score for classification and the absolute mean of error for chewing count estimation. In the classification stage, using the setting of a 10-fold cross-validation method and a 3s segmented window, fc2 of 6Hz gives the highest accuracy with 97.6%, while, 2.5Hz gives the lowest accuracy of 92.6%. However, in the chewing count estimation stage, which is based on a 240s segmented window, 2.4Hz able to give a smaller percentage absolute error of 2.69%, compare to 6Hz with 12.11%. It can be concluded that the chewing frequency was under 2.5Hz, but, the self-reporting labeling approach used in this study reduced the accuracy of the system as fc2 equal to 2.5Hz is used. Further analysis of chewing count shows that might in relating the total chewing count with different food hardness.

Analysis of Chewing Signals Based on Chewing Detection Using Proximity Sensor for Diet Monitoring

King-Sun Fu Award: Ching Yee Suen

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https://d3smihljt9218e.cloudfront.net/transcript/9295/subtitle/c6c22873515666ed6268af247c930669.vtt

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Assessement of CNN-Based Methods for Poverty Estimation from Satellite images

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Analysis of Chewing Signals Based on Chewing Detection Using Proximity Sensor for Diet Monitoring

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