Paper: http://ceur-ws.org/Vol-2882/paper11.pdf
YouTube: https://youtu.be/fBPuacAZkxs
Minh-Son Dao, Peijiang Zhao, Thanh Nguyen, Thanh Binh Nguyen, Duc Tien Dang Nguyen and Cathal Gurrin : Overview of MediaEval 2020 Insights for Wellbeing: Multimodal Personal Health Lifelog Data Analysis. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper provides a description of the MediaEval 2020 “Multimodal personal health lifelog data analysis". The purpose of this task is to develop approaches that process the environment data to obtain insights about personal wellbeing. Establishing the association between people’s wellbeing and properties of the surrounding environment which is vital for numerous research. Our task focuses on the internal associations of heterogeneous data. Participants create systems that derive insights from multimodal lifelog data that are important for health and wellbeing to tackle two challenging subtasks. The first task is to investigate whether we can use public/open data to predict personal air pollution data. The second task is to develop approaches to predict personal air quality index(AQI) using images captured by people (plus GAQD). This task targets (but is not limited to) researchers in the areas of multimedia information retrieval, machine learning, AI, data science, event-based processing and analysis, multimodal multimedia content analysis, lifelog data analysis, urban computing, environmental science, and atmospheric science.
Presented by: Peijiang Zhao
Use Visual Features From Surrounding Scenes to Improve Personal Air Quality ...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper40.pdf
YouTube: https://youtu.be/SL5Hvu1mARY
Trung-Quan Nguyen, Dang-Hieu Nguyen and Loc Tai Tan Nguyen : Use Visual Features From Surrounding Scenes to Improve Personal Air Quality Data Prediction Performance. Proc. of MediaEval 2020, 14-15 December 2020, Online.
In this paper, we propose a method to predict the personal air quality index in an area by using the combination of the levels of the following pollutants: PM2.5, NO2, and O3, measured from the nearby weather stations of that area, and the photos of surrounding scenes taken at that area. Our approach uses the Inverse Distance Weighted (IDW) technique to estimate the missing air pollutant levels and then use regression to integrate visual features from taken photos to optimize the predicted values. After that, we can use those values to calculate the Air Quality Index (AQI). The results show that the proposed method may not improve the performance of the prediction in some cases.
Personal Air Quality Index Prediction Using Inverse Distance Weighting Methodmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper39.pdf
YouTube: https://youtu.be/3r_oSguFPVM
Trung-Quan Nguyen, Dang-Hieu Nguyen and Loc Tai Tan Nguyen : Personal Air Quality Index Prediction Using Inverse Distance Weighting Method. Proc. of MediaEval 2020, 14-15 December 2020, Online.
In this paper, we propose a method to predict the personal air quality index in an area by only using the levels of the following pollutants: PM2.5, NO2, O3. All of them are measured from the nearby weather stations of that area. Our approach uses one of the most well-known interpolation methods in spatial analysis, the Inverse Distance Weighted (IDW) technique, to estimate the missing air pollutant levels. After that, we can use those levels to calculate the Air Quality Index (AQI). The results show that the proposed method is suitable for the prediction of those air pollutant levels.
Insights for wellbeing: Predicting Personal Air Quality Index using Regressio...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper51.pdf
Amel Ksibi, Amina Salhi, Ala Alluhaidan and Sahar A. El-Rahman : Insights for wellbeing: Predicting Personal Air Quality Index using Regression Approach. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Providing air pollution information to individuals enables them to understand the air quality of their living environments. Thus, the association between people’s wellbeing and the properties of the surrounding environment is an essential area of investigation. This paper proposes Air Quality Prediction through harvesting public/open data and leveraging them to get the Personal Air Quality index. These are usually incomplete. To cope with the problem of missing data, we applied the KNN imputation method. To predict Personal Air Quality Index, we apply a voting regression approach based on three base regressors which are Gradient Boosting regressor, Random Forest regressor, and linear regressor. Evaluating the experimental results using the RMSE metric, we got an average score of 35.39 for Walker and 51.16 for Car.
This research aims to predict the level of air pollution with a set of data used to make predictions through them and to obtain the best prediction using several models and compare them and find the appropriate solution
The two main challenges of predicting the wind speed depend on various atmospheric factors and random variables. This paper explores the possibility of developing a wind speed prediction model using different Artificial Neural Networks (ANNs) and Categorical Regression empirical model which could be used to estimate the wind speed in Coimbatore, Tamil Nadu, India using SPSS software. The proposed Neural Network models are tested on real time wind data and enhanced with statistical capabilities. The objective is to predict accurate wind speed and to perform better in terms of minimization of errors using Multi Layer Perception Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN) and Categorical Regression (CATREG). Results from the paper have shown good agreement between the estimated and measured values of wind speed.
Use Visual Features From Surrounding Scenes to Improve Personal Air Quality ...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper40.pdf
YouTube: https://youtu.be/SL5Hvu1mARY
Trung-Quan Nguyen, Dang-Hieu Nguyen and Loc Tai Tan Nguyen : Use Visual Features From Surrounding Scenes to Improve Personal Air Quality Data Prediction Performance. Proc. of MediaEval 2020, 14-15 December 2020, Online.
In this paper, we propose a method to predict the personal air quality index in an area by using the combination of the levels of the following pollutants: PM2.5, NO2, and O3, measured from the nearby weather stations of that area, and the photos of surrounding scenes taken at that area. Our approach uses the Inverse Distance Weighted (IDW) technique to estimate the missing air pollutant levels and then use regression to integrate visual features from taken photos to optimize the predicted values. After that, we can use those values to calculate the Air Quality Index (AQI). The results show that the proposed method may not improve the performance of the prediction in some cases.
Personal Air Quality Index Prediction Using Inverse Distance Weighting Methodmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper39.pdf
YouTube: https://youtu.be/3r_oSguFPVM
Trung-Quan Nguyen, Dang-Hieu Nguyen and Loc Tai Tan Nguyen : Personal Air Quality Index Prediction Using Inverse Distance Weighting Method. Proc. of MediaEval 2020, 14-15 December 2020, Online.
In this paper, we propose a method to predict the personal air quality index in an area by only using the levels of the following pollutants: PM2.5, NO2, O3. All of them are measured from the nearby weather stations of that area. Our approach uses one of the most well-known interpolation methods in spatial analysis, the Inverse Distance Weighted (IDW) technique, to estimate the missing air pollutant levels. After that, we can use those levels to calculate the Air Quality Index (AQI). The results show that the proposed method is suitable for the prediction of those air pollutant levels.
Insights for wellbeing: Predicting Personal Air Quality Index using Regressio...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper51.pdf
Amel Ksibi, Amina Salhi, Ala Alluhaidan and Sahar A. El-Rahman : Insights for wellbeing: Predicting Personal Air Quality Index using Regression Approach. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Providing air pollution information to individuals enables them to understand the air quality of their living environments. Thus, the association between people’s wellbeing and the properties of the surrounding environment is an essential area of investigation. This paper proposes Air Quality Prediction through harvesting public/open data and leveraging them to get the Personal Air Quality index. These are usually incomplete. To cope with the problem of missing data, we applied the KNN imputation method. To predict Personal Air Quality Index, we apply a voting regression approach based on three base regressors which are Gradient Boosting regressor, Random Forest regressor, and linear regressor. Evaluating the experimental results using the RMSE metric, we got an average score of 35.39 for Walker and 51.16 for Car.
This research aims to predict the level of air pollution with a set of data used to make predictions through them and to obtain the best prediction using several models and compare them and find the appropriate solution
The two main challenges of predicting the wind speed depend on various atmospheric factors and random variables. This paper explores the possibility of developing a wind speed prediction model using different Artificial Neural Networks (ANNs) and Categorical Regression empirical model which could be used to estimate the wind speed in Coimbatore, Tamil Nadu, India using SPSS software. The proposed Neural Network models are tested on real time wind data and enhanced with statistical capabilities. The objective is to predict accurate wind speed and to perform better in terms of minimization of errors using Multi Layer Perception Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN) and Categorical Regression (CATREG). Results from the paper have shown good agreement between the estimated and measured values of wind speed.
การนำเสนอบทความวิชาการในการประชุมวิชาการ 15th GMSARN International Conference 2020 on “Sustainable Energy, Environment and Climate Change Transitions in GMS” 21-22 December 2020, Krungsri River Hotel, Phra Nakhon Si Ayutthaya, Thailand. ในรูปแบบออนไลน์
หัวข้อ Integration of Future Meteorological Drought Hazard Assessment for Agriculture Area in Upper Ping River Basin, Thailand
From all comments received by the LTER Network Office, this year's meeting in Estes Park was a complete success! The 2015 LTER All Scientists Meeting was held from August 30 through the evening of September 2. The Conference was organized around the theme: " From Long-Term Data to Understanding: Toward a Predictive Ecology". Almost 600 people attended the meeting. There were over 300 poster presentations and more than 75 formal and ad-hoc working group meetings. Drs. James Olds, Diana Wall, Knute Nadelhoffer, Ned Gardener and Christine O'Connell provided excellent plenary presentations to highlight the meeting. Chloe Wardropper (NTL) won 1st place in the student poster competition with Alexandra Conway (BNZ), Shinjini Goswami (HBR), Hafsah Nahrawi (GCE) and Bonnie McGill (KBS) winning runner-up awards.
This research aim to forecast solar radiation,how much of electricity can be produced in next four months in two cities of India and performance evaluation of forecasting models. These models have been used for long-term forecasting of solar radiation using time series data.Forecasting models like ARIMA,TBATS have been used for this research.Forecasted solar radiation is further used for forecasting solar electricity generation.Performance evaluation of forecasting models has also been done.
In this deck from GTC 2019, Seongchan Kim, Ph.D. presents: How Deep Learning Could Predict Weather Events.
"How do meteorologists predict weather or weather events such as hurricanes, typhoons, and heavy rain? Predicting weather events were done based on supercomputer (HPC) simulations using numerical models such as WRF, UM, and MPAS. But recently, many deep learning-based researches have been showing various kinds of outstanding results. We'll introduce several case studies related to meteorological researches. We'll also describe how the meteorological tasks are different from general deep learning tasks, their detailed approaches, and their input data such as weather radar images and satellite images. We'll also cover typhoon detection and tracking, rainfall amount prediction, forecasting future cloud figure, and more."
Watch the video: https://wp.me/p3RLHQ-k2T
Learn more: http://en.kisti.re.kr/
and
https://www.nvidia.com/en-us/gtc/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
A Classification Urban Precinct Ventilation Zones using Key Indicators of Spa...Manat Srivanit
Session 6-Urban Planning and Development
2021 4th International Conference on Civil Engineering and Architecture (Virtual Conference): July 10-12, 2021; Seoul, South Korea
Presentation on the topic of sensing air-quality at city level based on Twitter data given at the IEEE Image, Video, and Multidimensional Signal Processing (IVMSP) 2018 workshop in Aristi, Greece.
Human thermal perception and outdoor thermal comfort under shaded conditions ...Manat Srivanit
The 6th International Conference on Sustainable Energy and Environment [Special Session: Urban Climate & Urban Air Pollution (UCUA)] 28-30 November 2016, Dusit Thani Bangkok Hotel, Thailand
15. Sächsisches GI/GIS/GDI Forum und Club of Ossiach Workshops
COPERNICUS PROGRAMME AND SENTINEL DATA FOR AGRICULTURE AND FORESTRY
Lenka Hladíková, CENIA, Czech Environmental Information Agency (CZ)
การนำเสนอบทความวิชาการในการประชุมวิชาการ 15th GMSARN International Conference 2020 on “Sustainable Energy, Environment and Climate Change Transitions in GMS” 21-22 December 2020, Krungsri River Hotel, Phra Nakhon Si Ayutthaya, Thailand. ในรูปแบบออนไลน์
หัวข้อ Integration of Future Meteorological Drought Hazard Assessment for Agriculture Area in Upper Ping River Basin, Thailand
From all comments received by the LTER Network Office, this year's meeting in Estes Park was a complete success! The 2015 LTER All Scientists Meeting was held from August 30 through the evening of September 2. The Conference was organized around the theme: " From Long-Term Data to Understanding: Toward a Predictive Ecology". Almost 600 people attended the meeting. There were over 300 poster presentations and more than 75 formal and ad-hoc working group meetings. Drs. James Olds, Diana Wall, Knute Nadelhoffer, Ned Gardener and Christine O'Connell provided excellent plenary presentations to highlight the meeting. Chloe Wardropper (NTL) won 1st place in the student poster competition with Alexandra Conway (BNZ), Shinjini Goswami (HBR), Hafsah Nahrawi (GCE) and Bonnie McGill (KBS) winning runner-up awards.
This research aim to forecast solar radiation,how much of electricity can be produced in next four months in two cities of India and performance evaluation of forecasting models. These models have been used for long-term forecasting of solar radiation using time series data.Forecasting models like ARIMA,TBATS have been used for this research.Forecasted solar radiation is further used for forecasting solar electricity generation.Performance evaluation of forecasting models has also been done.
In this deck from GTC 2019, Seongchan Kim, Ph.D. presents: How Deep Learning Could Predict Weather Events.
"How do meteorologists predict weather or weather events such as hurricanes, typhoons, and heavy rain? Predicting weather events were done based on supercomputer (HPC) simulations using numerical models such as WRF, UM, and MPAS. But recently, many deep learning-based researches have been showing various kinds of outstanding results. We'll introduce several case studies related to meteorological researches. We'll also describe how the meteorological tasks are different from general deep learning tasks, their detailed approaches, and their input data such as weather radar images and satellite images. We'll also cover typhoon detection and tracking, rainfall amount prediction, forecasting future cloud figure, and more."
Watch the video: https://wp.me/p3RLHQ-k2T
Learn more: http://en.kisti.re.kr/
and
https://www.nvidia.com/en-us/gtc/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
A Classification Urban Precinct Ventilation Zones using Key Indicators of Spa...Manat Srivanit
Session 6-Urban Planning and Development
2021 4th International Conference on Civil Engineering and Architecture (Virtual Conference): July 10-12, 2021; Seoul, South Korea
Presentation on the topic of sensing air-quality at city level based on Twitter data given at the IEEE Image, Video, and Multidimensional Signal Processing (IVMSP) 2018 workshop in Aristi, Greece.
Human thermal perception and outdoor thermal comfort under shaded conditions ...Manat Srivanit
The 6th International Conference on Sustainable Energy and Environment [Special Session: Urban Climate & Urban Air Pollution (UCUA)] 28-30 November 2016, Dusit Thani Bangkok Hotel, Thailand
15. Sächsisches GI/GIS/GDI Forum und Club of Ossiach Workshops
COPERNICUS PROGRAMME AND SENTINEL DATA FOR AGRICULTURE AND FORESTRY
Lenka Hladíková, CENIA, Czech Environmental Information Agency (CZ)
ONLINE SCALABLE SVM ENSEMBLE LEARNING METHOD (OSSELM) FOR SPATIO-TEMPORAL AIR...IJDKP
Environmental air pollution studies fail to consider the fact that air pollution is a spatio-temporal problem.
The volume and complexity of the data have created the need to explore various machine learning models,
however, those models have advantages and disadvantages when applied to regional air pollution analysis,
furthermore, most environmental problems are global distribution problems. This research addressed
spatio-temporal problem using decentralized computational technique named Online Scalable SVM
Ensemble Learning Method (OSSELM). Evaluation criteria for computational air pollution analysis
includes: accuracy, real time & prediction, spatio-temporal and decentralised analysis, we assert that these
criteria can be improved using the proposed OSSELM. Special consideration is given to distributed
ensemble to resolve spatio-temporal data collection problem (i.e. the data collected from multiple
monitoring stations dispersed over a geographical location). Moreover, the experimental results
demonstrated that the proposed OSSELM produced impressive results compare to SVM ensemble for air
pollution analysis in Auckland region.
ONLINE SCALABLE SVM ENSEMBLE LEARNING METHOD (OSSELM) FOR SPATIO-TEMPORAL AIR...IJDKP
Environmental air pollution studies fail to consider the fact that air pollution is a spatio-temporal problem. The volume and complexity of the data have created the need to explore various machine learning models, however, those models have advantages and disadvantages when applied to regional air pollution analysis, furthermore, most environmental problems are global distribution problems. This research addressed spatio-temporal problem using decentralized computational technique named Online Scalable SVM Ensemble Learning Method (OSSELM). Evaluation criteria for computational air pollution analysis includes: accuracy, real time & prediction, spatio-temporal and decentralised analysis, we assert that these criteria can be improved using the proposed OSSELM. Special consideration is given to distributed ensemble to resolve spatio-temporal data collection problem (i.e. the data collected from multiple monitoring stations dispersed over a geographical location). Moreover, the experimental results demonstrated that the proposed OSSELM produced impressive results compare to SVM ensemble for air pollution analysis in Auckland region.
ONLINE SCALABLE SVM ENSEMBLE LEARNING METHOD (OSSELM) FOR SPATIO-TEMPORAL AIR...IJDKP
Environmental air pollution studies fail to consider the fact that air pollution is a spatio-temporal problem.
The volume and complexity of the data have created the need to explore various machine learning models,
however, those models have advantages and disadvantages when applied to regional air pollution analysis,
furthermore, most environmental problems are global distribution problems. This research addressed
spatio-temporal problem using decentralized computational technique named Online Scalable SVM
Ensemble Learning Method (OSSELM). Evaluation criteria for computational air pollution analysis
includes: accuracy, real time & prediction, spatio-temporal and decentralised analysis, we assert that these
criteria can be improved using the proposed OSSELM. Special consideration is given to distributed
ensemble to resolve spatio-temporal data collection problem (i.e. the data collected from multiple
monitoring stations dispersed over a geographical location). Moreover, the experimental results
demonstrated that the proposed OSSELM produced impressive results compare to SVM ensemble for air
pollution analysis in Auckland region.
Air Quality Monitoring and Control System in IoTijtsrd
Air pollution that refers to the contamination of the air, irrespective of indoors or outside. A physical, biological or chemical alteration to the air in the atmosphere can be termed as pollution. It occurs when any harmful gases, dust, smoke enters into the atmosphere and makes it difficult for plants, animals, and humans to survive as the air becomes dirty. Proposed system considers pollution due to automobiles and provide a real time solution which is not just monitors pollution levels but also take into consideration control measures for reducing traffic and industrial zone in highly polluted areas. The solution is provided by a sensor based hardware module which can be placed along roads and plants. These modules can be placed on lamp posts and they transfer information about air quality wirelessly to cloud server. The proposed system also provides about air quality information through a mobile application which enables commuters to take up routes where air quality is good. Soe Soe Mon | Thida Soe | Khin Aye Thu "Air Quality Monitoring and Control System in IoT" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26554.pdfPaper URL: https://www.ijtsrd.com/computer-science/embedded-system/26554/air-quality-monitoring-and-control-system-in-iot/soe-soe-mon
CREATING A REGIONAL PM2.5 MAP BY FUSING SATELLITE AND KRIGING ESTIMATES Nabin Malakar
The presentation uses fusion of Spatial Kriging and Satellite remote sensing derived PM2.5 from MODIS AOD to produce regional PM2.5 estimation.
The methodology is discussed, and results are also presented showing a good spatial coverage over the northeast USA.
Background:
One of my student, Daniel Vidal from the City College of New York, came first in the final round of the technical paper competition in the Society of Hispanic Professional Engineers (SHPE) conference in Detroit, Michigan. 2014
Design and Implementation of Portable Outdoor Air Quality Measurement System ...IJECEIAES
Recently, there is increasing public awareness of the real time air quality due to air pollution can cause severe effects to human health and environments. The Air Pollutant Index (API) in Malaysia is measured by Department of Environment (DOE) using stationary and expensive monitoring station called Continuous Air Quality Monitoring stations (CAQMs) that are only placed in areas that have high population densities and high industrial activities. Moreover, Malaysia did not include particulate matter with the size of less than 2.5µm (PM2.5) in the API measurement system. In this paper, we present a cost effective and portable air quality measurement system using Arduino Uno microcontroller and four low cost sensors. This device allows people to measure API in any place they want. It is capable to measure the concentration of carbon monoxide (CO), ground level ozone (O3) and particulate matters (PM10 & PM2.5) in the air and convert the readings to API value. This system has been tested by comparing the API measured from this device to the current API measured by DOE at several locations. Based on the results from the experiment, this air quality measurement system is proved to be reliable and efficient.
Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper62.pdf
YouTube: https://youtu.be/gV-rvV3iFDA
Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri and Julien Morlier : Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal CNN for MediaEval 2020. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This work presents a method for classifying table tennis strokes using spatio-temporal convolutional neural networks. The fine-grained classification is performed on trimmed video segments recorded at 120 fps with different players performing in natural conditions. From those segments, the frames are extracted, their optical flow is computed and the pose of the player is estimated. From the optical flow amplitude, a region of interest is inferred. A three stream spatio-temporal convolutional neural network using combination of those modalities and 3D attention mechanisms is presented in order to perform classification.
Presented by: Pierre-Etienne Martin
HCMUS at MediaEval 2020: Ensembles of Temporal Deep Neural Networks for Table...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper50.pdf
Hai Nguyen-Truong, San Cao, N. A. Khoa Nguyen, Bang-Dang Pham, Hieu Dao, Minh-Quan Le, Hoang-Phuc Nguyen-Dinh, Hai-Dang Nguyen and Minh-Triet Tran : HCMUS at MediaEval 2020: Ensembles of Temporal Deep Neural Networks for Table Tennis Strokes Classification Task. Proc. of MediaEval 2020, 14-15 December 2020, Online.
The Sports Video Classification Tasks in the Multimedia Evaluation 2020 Challenge focuses on classifying different types of table tennis strokes in video segments. In this task, we - the HCMUS Team - perform multiple experiments, which includes a combination of models such as SlowFast, Optical Flow, DensePose, R2+1, Channel-Separated Convolutional Networks, to classify 21 types of table tennis strokes from video segments. In total, we submit eight runs corresponding to five different models with different sets of hyper-parameters in each of our models. In addition, we apply some pre-processing techniques on the dataset in order for our model to learn and classify more accurately. According to the evaluation results, one of our team's methods out-performs the other team's. In particular, our best run achieves 31.35\% global accuracy, and all of our methods show potential results in terms of local and global accuracy for action recognition tasks.
Sports Video Classification: Classification of Strokes in Table Tennis for Me...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper2.pdf
YouTube: https://youtu.be/-bRL868b8ys
Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri, Laurent Mascarilla, Jordan Calandre and Julien Morlier : Sports Video Classification: Classification of Strokes in Table Tennis for MediaEval 2020. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Fine-grained action classification has raised new challenges compared to classical action classification problems. Sport video analysis is a very popular research topic, due to the variety of application areas, ranging from multimedia intelligent devices with user-tailored digests, up to analysis of athletes' performances. Running since 2019 as a part of MediaEval, we offer a task which consists in classifying table tennis strokes from videos recorded in natural conditions at the University of Bordeaux. The aim is to build tools for teachers, coaches and players to analyse table tennis games. Such tools could lead to an automatic profiling of the player and adaptation of his training for improving his/her sport skills more efficiently.
Presented by: Pierre-Etienne Martin
Predicting Media Memorability from a Multimodal Late Fusion of Self-Attention...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper61.pdf
YouTube: https://youtu.be/brmI4g3jLS4
Ricardo Kleinlein, Cristina Luna-Jiménez, Fernando Fernández-Martínez and Zoraida Callejas : Predicting Media Memorability from a Multimodal Late Fusion of Self-Attention and LSTM Models. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper reports on the GTH-UPM team experience in the Predicting Media Memorability task at MediaEval 2020. Teams were requested to predict memorability scores at both short-term and long-term, understanding such score as a measure of whether a video was perdurable in a viewer's memory or not. Our proposed system relies on a late fusion of the scores predicted by three sequential models, each trained over a different modality: video captions, aural embeddings and visual optical flow-based vectors. Whereas single-modality models show a low or zero Spearman correlation coefficient value, their combination considerably boosts performance over development data up to 0.2 in the short-term memorability prediction subtask and 0.19 in the long-term subtask. However, performance over test data drops to 0.016 and -0.041, respectively.
Presented by: Ricardo Kleinlein
Essex-NLIP at MediaEval Predicting Media Memorability 2020 Taskmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper52.pdf
Janadhip Jacutprakart, Rukiye Savran Kiziltepe, John Q. Gan, Giorgos Papanastasiou and Alba G. Seco de Herrera : Essex-NLIP at MediaEval Predicting Media Memorability 2020 Task. Proc. of MediaEval 2020, 14-15 December 2020, Online.
In this paper, we present the methods of approach and the main results from the Essex NLIP Team’s participation in the MediEval 2020 Predicting Media Memorability task. The task requires participants to build systems that can predict short-term and long-term memorability scores on real-world video samples provided. The focus of our approach is on the use of colour-based visual features as well as the use of the video annotation meta-data. In addition, hyper-parameter tuning was explored. Besides the simplicity of the methodology, our approach achieves competitive results. We investigated the use of different visual features. We assessed the performance of memorability scores through various regression models where Random Forest regression is our final model, to predict the memorability of videos.
Overview of MediaEval 2020 Predicting Media Memorability task: What Makes a V...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper6.pdf
YouTube: https://youtu.be/ySGGu_4vaxs
Alba García Seco De Herrera, Rukiye Savran Kiziltepe, Jon Chamberlain, Mihai Gabriel Constantin, Claire-Hélène Demarty, Faiyaz Doctor, Bogdan Ionescu and Alan F. Smeaton : Overview of MediaEval 2020 Predicting Media Memorability task: What Makes a Video Memorable? Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper describes the MediaEval 2020 Predicting Media Memorability task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 3rd edition this year, as the prediction of short-term and long-term video memorability (VM) remains a challenging task. In 2020, the format remained the same as in previous editions. This year the videos are a subset of the TRECVid 2019 Video to Text dataset, containing more action rich video content as compare with the 2019 task. In this paper a description of some aspects of this task is provided, including its main characteristics, a description of the collection, the ground truth dataset, evaluation metrics and the requirements for the run submission.
Presented by: Rukiye Savran Kiziltepe
Fooling an Automatic Image Quality Estimatormultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper45.pdf
Benoit Bonnet, Teddy Furon and Patrick Bas : Fooling an Automatic Image Quality Estimator. Proc. of MediaEval 2020, 14-15 December 2020, Online.
In this paper we present our work on the 2020 MediaEval task: Pixel "Privacy: Quality Camouflage for Social Images". Blind Image Quality Assessment (BIQA) is a classifier that for any given image will return a quality score. Our task is to modify an image to decrease its BIQA score while maintaining a good perceived quality. Since BIQA is a deep neural network, we worked on an adversarial attack approach of the problem.
Fooling Blind Image Quality Assessment by Optimizing a Human-Understandable C...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper16.pdf
YouTube: https://youtu.be/ix_b9K7j72w
Zhengyu Zhao : Fooling Blind Image Quality Assessment by Optimizing a Human-Understandable Color Filter. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper presents the submission of our RU-DS team to the Pixel Privacy Task 2020. We propose to fool the blind image quality assessment model by transforming images based on optimizing a human-understandable color filter. In contrast to the common work that relies on small, $L_p$-bounded additive pixel perturbations, our approach yields large yet smooth perturbations. Experimental results demonstrate that in the specific context of this task, our approach is able to achieve strong adversarial effects, but has to sacrifice the image appeal.
Presented by: Zhengyu Zhao
Pixel Privacy: Quality Camouflage for Social Imagesmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper77.pdf
YouTube: https://youtu.be/8Rr4KknGSac
Zhuoran Liu, Zhengyu Zhao, Martha Larson and Laurent Amsaleg : Pixel Privacy: Quality Camouflage for Social Images. Proc. of MediaEval 2020, 14-15 December 2020, Online.
High-quality social images shared online can be misappropriated for unauthorized goals, where the quality filtering step is commonly carried out by automatic Blind Image Quality Assessment (BIQA) algorithms. Pixel Privacy benchmarks privacy-protective approaches that protect privacy-sensitive images against unethical computer vision algorithms. In the 2020 task, participants are encouraged to develop camouflage methods that can effectively decrease the BIQA quality score of high-quality images and maintain image appeal. The camouflaged images need to be either imperceptible to the human eye, or it can be a visible enhancement.
Presented by: Zhuoran Liu
HCMUS at MediaEval 2020:Image-Text Fusion for Automatic News-Images Re-Matchingmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper73.pdf
YouTube: https://youtu.be/TadJ6y7xZeA
Thuc Nguyen-Quang, Tuan-Duy Nguyen, Thang-Long Nguyen-Ho, Anh-Kiet Duong, Xuan-Nhat Hoang, Vinh-Thuyen Nguyen-Truong, Hai-Dang Nguyen and Minh-Triet Tran : HCMUS at MediaEval 2020:Image-Text Fusion for Automatic News-Images Re-Matching. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Matching text and images based on their semantics has an important role in cross-media retrieval. However, text and images in articles have a complex connection. In the context of MediaEval 2020 Challenge, we propose three multi-modal methods for mapping text and images of news articles to the shared space in order to perform efficient cross-retrieval. Our methods show systemic improvement and validate our hypotheses, while the best-performed method reaches a recall@100 score of 0.2064.
Presented by: Thuc Nguyen-Quang
Efficient Supervision Net: Polyp Segmentation using EfficientNet and Attentio...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper72.pdf
Sabarinathan D and Suganya Ramamoorthy : Efficient Supervision Net: Polyp Segmentation using EfficientNet and Attention Unit. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Colorectal cancer is the third most common cause of cancer worldwide. In the era of medical Industry, identifying colorectal cancer in its early stages has been a challenging problem. Inspired by these issues, the main objective of this paper is to develop a Multi supervision net algorithm for segmenting polys on a comprehensive dataset. The risk of colorectal cancer could be reduced by early diagnosis of poly during a colonoscopy. The disease and their symptoms are highly varying and always a need for a continuous update of knowledge for the doctors and medical analyst. The diseases fall into different categories and a small variation of symptoms may lead to higher rate of risk. We have taken Medico polyp challenge dataset, which consists of 1000 segmented polyp images from gastrointestinal track. We proposed an efficient Net B4 as a pre-trained architecture in multi-supervision net. The model is trained with multiple output layers. We present quantitative results on colorectal dataset to evaluate the performance and achieved good results in all the performance metrics. The experimental results proved that the proposed model is robust and provides a good level of accuracy in segmenting polyps on a comprehensive dataset for different metrics such as Dice coefficient, Recall, Precision and F2.
HCMUS at Medico Automatic Polyp Segmentation Task 2020: PraNet and ResUnet++ ...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper47.pdf
YouTube: https://youtu.be/vMsM4zg2-JY
Tien-Phat Nguyen, Tan-Cong Nguyen, Gia-Han Diep, Minh-Quan Le, Hoang-Phuc Nguyen-Dinh, Hai-Dang Nguyen and Minh-Triet Tran : HCMUS at Medico Automatic Polyp Segmentation Task 2020: PraNet and ResUnet++ for Polyps Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online.
The Medico task, MediaEval 2020, explores the challenge of building accurate and high-performance algorithms to detect all types of polyps in endoscopic images. We proposed different approaches leveraging the advantages of either ResUnet++ or PraNet model to efficiently segment polyps in colonoscopy images, with modifications on the network structure, parameters, and training strategies to tackle various observed characteristics of the given dataset. Our methods outperform the other teams' methods, for both accuracy and efficiency. After the evaluation, we are at top 2 for task 1 (with Jaccard index of 0.777, best Precision and Accuracy scores) and top 1 for task 2 (with 67.52 FPS and Jaccard index of 0.658).
Depth-wise Separable Atrous Convolution for Polyps Segmentation in Gastro-Int...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper31.pdf
Syed Muhammad Faraz Ali, Muhammad Taha Khan, Syed Unaiz Haider, Talha Ahmed, Zeshan Khan and Muhammad Atif Tahir : Depth-wise Separable Atrous Convolution for Polyps Segmentation in Gastro-Intestinal Tract. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Identification of polyps in endoscopic images is critical for the diagnosis of colon cancer. Finding the exact shape and size of polyps requires the segmentation of endoscopic images. This research explores the advantage of using depth-wise separable convolution in the atrous convolution of the ResUNet++ architecture. Deep atrous spatial pyramid pooling was also implemented on the ResUNet++ architecture. The results show that architecture with separable convolution has a smaller size and fewer GFLOPs without degrading the performance too much.
Deep Conditional Adversarial learning for polyp Segmentationmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper22.pdf
Debapriya Banik and Debotosh Bhattacharjee : Deep Conditional Adversarial learning for polyp Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This approach has addressed the Medico automatic polyp segmentation challenge which is a part of Mediaeval 2020. We have proposed a deep conditional adversarial learning based network for the automatic polyp segmentation task. The network comprises of two interdependent models namely a generator and a discriminator. The generator network is a FCN employed for the prediction of the polyp mask while the discriminator enforces the segmentation to be as similar as the real segmented mask (ground truth). Our proposed model achieved a comparative result on the test dataset provided by the organizers of the challenge.
A Temporal-Spatial Attention Model for Medical Image Detectionmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper21.pdf
Hwang Maxwell, Wu Cai, Hwang Kao-Shing, Xu Yong Si and Wu Chien-Hsing : A Temporal-Spatial Attention Model for Medical Image Detection. Proc. of MediaEval 2020, 14-15 December 2020, Online.
A local region model with attentive temporal-spatial pathways is proposed for automatically learning various target structures. The attentive spatial pathway highlights the salient region to generate bounding boxes and ignores irrelevant regions in an input image. The proposed attention mechanism allows efficient object localization and the overall predictive performance is increased because there are fewer false positives for the object detection task for medical images with manual annotations. The experimental results show that proposed models consistently increase the base architectures' predictive performance for different datasets and training sizes without undue computational efficiency.
HCMUS-Juniors 2020 at Medico Task in MediaEval 2020: Refined Deep Neural Netw...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper20.pdf
YouTube: https://youtu.be/CVelQl5Luf0
Quoc-Huy Trinh, Minh-Van Nguyen, Thiet-Gia Huynh and Minh-Triet Tran : HCMUS-Juniors 2020 at Medico Task in MediaEval 2020: Refined Deep Neural Network and UNet for Polyps Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online.
The Medico: Multimedia Task focuses on developing an efficient and accurate framework to computer-aided diagnosis systems for automatic polyp segmentation to detect all types of polyps in endoscopic images of the gastrointestinal (GI) tract. We are HCMUS-team approach a solution, which includes combination Residual module, Inception module, Adaptive Convolutional neural network with Unet model and PraNet to semantic segmentation all types of polyps in endoscopic images. We submit multiple runs with different architecture and parameters in our model. Our methods show potential results in accuracy and efficiency through multiple experiments.
Fine-tuning for Polyp Segmentation with Attentionmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper15.pdf
Rabindra Khadka : Transfer of Knowledge: Fine-tuning for Polyp Segmentation with Attention. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper describes how the transfer of prior knowledge can effectively take on segmentation tasks with the help of attention mechanisms. The UNet model pretrained on brain MRI dataset was fine-tuned with the polyp dataset. Attention mechanism was integrated to focus on relevant regions in the input images. The implemented architecture is evaluated on 200 validation images based on intersection over union and dice score between groundtruth and predicted region. The model demonstrates a promising result with computational efciency.
Bigger Networks are not Always Better: Deep Convolutional Neural Networks for...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper12.pdf
Adrian Krenzer and Frank Puppe : Bigger Networks are not Always Better: Deep Convolutional Neural Networks for Automated Polyp Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper presents our team's (AI-JMU) approach to the Medico automated polyp segmentation challenge. We consider deep convolutional neural networks to be well suited for this task. To determine the best architecture we test and compare state of the art backbones and two different heads. Finally we achieve a Jaccard index of 73.74\% on the challenge test set. We further demonstrate that bigger networks do not always perform better. However the growing network size always increases the computational complexity.
Ensemble based method for the classification of flooding event using social m...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper37.pdf
YouTube: https://youtu.be/4ROoOzdQzEI
Muhammad Hanif, Huzaifa Joozer, Muhammad Atif Tahir and Muhammad Rafi : Ensemble based method for the classification of flooding event using social media data. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper presents the method proposed and implemented by team FAST-NU-DS, in "The Flood-related Multimedia Task at MediaEval 2020". The task includes data of tweets in Italian language, extracted during floods between 2017 and 2019. The proposed method has utilized text of the tweet and its relevant image for the purpose of binary classification, which identifies whether or not the particular tweet is about flood incident. The proposed method has designed an ensemble based method for the classification of tweets, on the basis of textual data, visual data and combination of both. For visual data, the proposed method has utilized the technique of data augmentation for oversampling of the minority class and applied stratified random sampling for the selection of input. Moreover, Visual Geometry Group (VGG16) convolutional neural network, pretrained on ImageNet and Places365 is utilized by the proposed method. For classification of textual data, the technique of Term Frequency Inverse Document Frequency (TF-IDF) is utilized for feature representation and Multinomial Naive-Bayes classifier is used for the prediction of class. The prediction of image and text are combined for the prediction of each instance. The evaluation of method revealed 36.31%, 20.76% and 27.86% F1-score for text, image and combination of both text and image respectively.
Presented by: Muhammad Hanif
Flood Detection via Twitter Streams using Textual and Visual Featuresmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper35.pdf
Firoj Alam, Zohaib Hassan, Kashif Ahmad, Asma Gul, Michael Reiglar, Nicola Conci and Ala Al-Fuqaha : Flood Detection via Twitter Streams using Textual and Visual Features. Proc. of MediaEval 2020, 14-15 December 2020, Online.
The paper presents our proposed solutions for the MediaEval 2020 Flood-Related Multimedia Task, which aims to analyze and detect flooding events in multimedia content shared over Twitter. In total, we proposed four different solutions including a multi-modal solution combining textual and visual information for the mandatory run, and three single modal image and text-based solutions as optional runs. In the multi-modal method, we rely on a supervised multimodal bitransformer model that combines textual and visual features in an early fusion, achieving a micro F1-score of .859 on the development data set. For the text-based flood events detection, we use a transformer network (i.e., pretrained Italian BERT model) achieving an F1-score of .853. For image-based solutions, we employed multiple deep models, pre-trained on both, the Ima- geNet and places data sets, individually and combined in an early fusion achieving F1-scores of .816 and .805 on the development set, respectively.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Overview of MediaEval 2020 Insights for Wellbeing: Multimodal Personal Health Lifelog Data Analysis
1. Overview of MediaEval 2020:
Insights for Wellbeing -Multimodal
Personal Health Lifelog Data Analysis
Peijiang Zhao1, Minh-Son Dao1, Ngoc-Thanh Nguyen2,
Thanh-Binh Nguyen3, Duc-Tien Dang-Nguyen4, Cathal Gurrin5,
1National Institute of Information and Communications Technology, Japan
2University of Information Technology (VNUHCM-UIT), Vietnam
3 University of Science (VNUHCM-US), Vietnam
4University of Bergen, Norway
5Dublin City University, Ireland
2. NICT CONFIDENTIAL
2
Insights for Wellbeing -Multimodal
BACKGROUND
The association between people’s wellbeing and
the properties of the surrounding environment
is an essential area of investigation
In order to know about the surrounding
environment, A lot of IoT devices have been set
in many cities.
However, most of those research focused on the
general population but not personal scale.
On a personal scale, local information about air
pollution, weather, urban nature are very
important for personal health
It is not always possible to gather plentiful
amounts of personal scale environment data
3. BACKGROUND
Personal Environment Data
3
NICT CONFIDENTIAL
Decoder Transfer Learning for Predicting Personal Exposure to Air Pollution
• The impact of the environment on
the scale of individual people.
• Using wearable or moveable
environmental data collection
devices
• Can be used to customize personal
health plans
• Which road has better air and
good for health
• Cannot be obtained at all times
• Difficult to collect in large quantities
Example of personal air quality collection in Fukuoka,
Japan,
4. NICT CONFIDENTIAL
4
Insights for Wellbeing -Multimodal
BACKGROUND
In this workshop, we want to find the answer to this question:
“Does the personal air quality be predicted by using other data
that is easy to obtain?”
predict
public/open data
lifelog data
personal air quality
5. NICT CONFIDENTIAL
5
Insights for Wellbeing -Multimodal
TASK DESCRIPTION
“Does the personal air quality be predicted by using other data that is easy to obtain?”
Personal Air Quality Prediction
with public/open data
Personal Air Quality
Prediction with lifelog data.
TASK 1 TASK 2
Prediction Target
The value of personal air pollution
data (PM2.5, O3,and NO2)
Input Other Data
weather data (wind speed, wind
direction, temperature, humidity) and
air pollution data (PM2.5, O3, and NO2)
from public/open data sources
Prediction Target
The value of personal Air Quality
Input Other Data
Lifelog images data
Public/open data
Whether we can use public/open data to
predict personal air pollution data.
Whether we can use only lifelog plus
some data from open sources to predict
the personal air pollution data.
6. NICT CONFIDENTIAL
6
Insights for Wellbeing -Multimodal
DATA DESCRIPTION
Personal Air Quality Dataset along the Tokyo 2021 Olympics Marathon Course
(PAQD)
• Five data collection participants assigned to five
routes to collect the data via wearable sensors.
• Collected from March to April 2019.
• Routes 1–4 were along the marathon course for the
Tokyo 2020 Olympics. Route 5 was the running
course around the Imperial Palace. The length of
each route was 5 km.
• Each participant started data collection at 9 am
every weekday, and it took 1 hours to walk each
route.
• Include the temperature and humidity, O3, PM2.5,
and NO2, GPS , and lifelog image.
• For this dataset, the personal air quality is O3, PM2.5,
and NO2 on each route.
• 6 sensors collect the data by walkers
• 2 sensors collect the data by cars
7. NICT CONFIDENTIAL
7
Insights for Wellbeing -Multimodal
DATA DESCRIPTION
Global air pollution dataset in Tokyo (GAPD).
-26 monitoring stations
-11 Air pollutant
SO2,NO,NO2,Nox,CO,Ox,NMHC,
CH4,THC,PM10,PM2.5
-4 weather data
wind speed, wind direction,
temperature, humidity
8. NICT CONFIDENTIAL
8
Insights for Wellbeing -Multimodal
GROUND TRUTH AND EVALUATION
Evaluation Metrics
Symmetric Mean Absolute Percentage ErrorRoot Mean Squared Error Mean Absolute Error
Personal Air Quality Prediction
with public/open data
Personal Air Quality
Prediction with lifelog data.
TASK 1 TASK 2
The value of personal air pollution
data (PM2.5, O3,and NO2)
9. NICT CONFIDENTIAL
9
Insights for Wellbeing -Multimodal
REGISTERED TEAM
We have 11 registered teams
UEHB-ML nainasaid@uetpeshawar.edu.pk, (Pakistan)
NREI ngadtt@gmail.com, (Vietnam)
SCI-UTB P20190005@student.utb.edu.bn, (Brunei)
AISIA dat181197@gmail.com, (Vietnam)
BPGC f20180443@goa.bits-pilani.ac.in, (India)
QHL-UIT quannt.13@grad.uit.edu.vn, (Vietnam)
HCMUS tmtriet@fit.hcmus.edu.vn, (Vietnam)
CLCRO dumitru.cercel@upb.ro, (Romania)
MLRG jaisakthi.murugaiyan@vit.ac.in, (India)
MMSys 277945743@qq.com, (China)
pnu_ccis amel.ksibi@gmail.com (KSA)
At last, 4 submissions
NREI (University of Resources and
Environment, Vietnam)
AISIA (Vietnam National University in
HCM city, University of Science,
Vietnam)
QHL-UIT (Vietnam National University
in HCM city, University of
Information Technology, Vietnam)
PNU_CCIS (College of Computer and
Information systems, PNU , KSA)
10. NICT CONFIDENTIAL
10
Insights for Wellbeing -Multimodal
RESULTS
TASK 1
Personal Air Quality Prediction with
public/open data
Team
PM25
MAE
PM25
RMSE
PM25
SMAPE
NO2
MAE
NO2
RMSE
NO2
SMAPE
O3
MAE
O3
RMSE
O3
SMAPE
Score
AISIA 6.42 8.34 0.60 14.53 17.10 0.46 13.53 16.50 0.68 6
QHL UIT 3.70 5.45 0.41 15.29 18.04 0.54 16.58 20.75 0.69 3
NREI 13.20 16.75 0.80 15.09 18.28 0.47 14.09 17.02 0.67 0
pnu_ccis 30.24 36.74 1.59 21.60 25.74 0.86 14.33 18.06 0.81 0
BEST TEAM AISIA
11. NICT CONFIDENTIAL
11
Insights for Wellbeing -Multimodal
RESULTS
TASK 2
Personal Air Quality Prediction
with
lifelog data.
PM2.5 O3 NO2
AISIA-run1
MAE 3.491 7.186 15.692
RSME 3.756 8.684 17.173
SMAPE 0.149 0.567 0.567
AISIA-run2
MAE 4.574 7.698 15.817
RSME 5.425 8.555 17.994
SMAPE 0.202 0.595 0.575
QHL-run2
MAE 5.513 9.392 22.420
RSME 5.747 10.338 24.068
SMAPE 0.235 0.719 0.689
QHL-run1
MAE 5.759 16.059 28.629
RSME 5.986 17.263 31.155
SMAPE 0.275 1.987 1.995
BEST TEAM AISIA
12. NICT CONFIDENTIAL
12
Insights for Wellbeing -Multimodal
DISCUSSION
For Task 1
Personal Air Quality Prediction with public/open data
We also prepared a baseline mode Decode Transfer Learning
network
P. J Zhao and K Zettsu. 2019. Decoder Transfer Learning for Predicting Personal Exposure
to Air Pollution. In 2019 IEEE International Conference on Big Data. 5620–5629
Team
PM25
MAE
PM25
RMSE
PM25
SMAPE
NO2
MAE
NO2
RMSE
NO2
SMAPE
O3
MAE
O3
RMSE
O3
SMAPE
AISAI 6.42 8.34 0.60 14.53 17.10 0.46 13.53 16.50 0.68
QHL UIT 3.70 5.45 0.41 15.29 18.04 0.54 16.58 20.75 0.69
NREI 13.20 16.75 0.80 15.09 18.28 0.47 14.09 17.02 0.67
pnu_ccis 30.24 36.74 1.59 21.60 25.74 0.86 14.33 18.06 0.81
Baseline DTL 4.52 6.43 0.50 13.46 16.89 0.52 13.02 16.34 0.71
ladies and gentlemen thank you for coming today
My name is peijiang zhao, I’m a research of National Institute of Information and Communication Technology ,japan
