MediaEval 2016: LAPI at Predicting Media Interestingness Task

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The document describes the LAPI team's approach for the MediaEval 2016 predicting media interestingness task. They used a classical machine learning approach where descriptors were generated for videos and images, and support vector machines (SVMs) were trained on the development set. The best SVM-descriptor combinations were identified on the development set and used to predict the test set. Descriptors included HSV histograms, HOG, SIFT, LBP, GIST, and AlexNet layers. SVMs with linear, polynomial, and RBF kernels were tested. The best results on the development set had a MAP of 0.214 for images and 0.179 for videos. On the test set, their best run was above the estimated MAP on

Science

LAPI AT MEDIAEVAL 2016 PREDICTING
MEDIA INTERESTINGNESS TASK
Mihai Gabriel Constantin, Bogdan Boteanu, Bogdan Ionescu
University Politehnica of Bucharest
LAPI - The Image Processing and Analysis Laboratory
contact: mgconstantin@alpha.imag.pub.ro
Faculty of Electronics, Telecommunications
and Information Technology
University Politehnica of Bucharest

 Classical machine learning approach
 Descriptors for videos and images are generated
 Support Vector Machines (SVM) are trained on devset
 Best SVM-descriptor combinations are identified
 Best combinations are used for predicting testset
OUR APPROACH
MediaEval 2016, Hilversum, Netherlands

MediaEval 2016, Hilversum, Netherlands
 For the video subtask we get the descriptors by
calculating the average of the individual frame
descriptors

 Hue-Saturation-Value Histogram
 Histogram of Oriented Gradients (HoG)
 Scale-Invariant Feature Transform (SIFT)
 Local Binary Patterns (LBP)
 GIST
 fc7 and fcprob layers of AlexNet
 Color Naming Histogram
DESCRIPTORS
MediaEval 2016, Hilversum, Netherlands

SVM LEARNING SYSTEMS
 Linear Kernel
 Polynomial Kernel
 Radial Basis Function (RBF) Kernel
 Weights (1/10) were used to balance the devset data
MediaEval 2016, Hilversum, Netherlands

EXPERIMENTS ON DEVSET
 Best results were chosen using 10-fold cross validation
 Best MAP
 Image subtask – 0.214
 Video subtask – 0.179
MediaEval 2016, Hilversum, Netherlands

 Only one run (run5) was above the estimated MAP on devset
 Best MAP
 Image subtask – 0.1714
 Video subtask – 0.1629
OFFICIAL RESULTS ON TESTSET
MediaEval 2016, Hilversum, Netherlands
above devset

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IEEE MultiMedia 2016 Title and Abstract

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MediaEval 2017 - Satellite Task: Visual and textual analysis of social media ...

multimediaeval

Presenter: Konstantinos Avgerinakis, Centre for Research & Technology Hellas - Information Technologies Institute, Greece Paper: http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_31.pdf Video: https://youtu.be/IRUxoWsCP2c Authors: Konstantinos Avgerinakis, Anastasia Moumtzidou, Stelios Andreadis, Emmanouil Michail, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris Abstract: This paper presents the algorithms that CERTH team deployed in order to tackle disaster recognition tasks and more specifically Disaster Image Retrieval from Social Media (DIRSM) and Flood-Detection in Satellite images (FDSI). Visual and textual analysis, as well as late fusion of their similarity scores, were deployed in social media images, while color analysis in the RGB and near-infrared channel of satellite images was performed in order to discriminate flooded from non-flooded images. Deep Convolutional Neural Network (DCNN), DBpedia Spotlight and combMAX was implemented to tackle DIRSM, while Mahalanobis Distance-based classification and morphological post-processing were applied to deal with FDSI.

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Generative AI Using HPC in Text Summarization and Energy Plants

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Quality assessment of deep-learning-based image compression

Marco Cagnazzo

The poster of our paper presented in MMSP2018 Very recently, deep generative models have been used to optimize or replace some image coding, with very promising results. However, so far no systematic and independent study of the coding performance of these algorithms has been carried out. In this paper, we conduct a subjective evaluation of two recent deeplearning- based image compression algorithms, comparing them to JPEG 2000 and to the recent BPG image codec based on HEVC Intra. We found that compression approaches based on deep auto-encoders can achieve coding performance higher than JPEG 2000, and sometimes as good as BPG. We also show experimentally that the PSNR metric is to be avoided when evaluating the visual quality of deep-learning-based methods, as their artifacts have different characteristics from those of DCT or wavelet-based codecs. In particular, images compressed at low bitrate appear more natural than JPEG 2000 coded pictures, according to a no-reference naturalness measure.

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Media Genre Inference for Predicting Media Interestingness

Benoit HUET

In this presentation, we present EURECOM's approach to address the MediaEval 2017 Predicting Media Interestingness Task. We developed models for both the image and video subtasks. In particular, we investigate the usage of media genre information (i.e., drama, horror, etc.) to predict interestingness. Our approach is related to the affective impact of media content and is shown to be effective in predicting interestingness for both video shots and key-frames. This approach obtained the best performance on the video task (in terms of MAP and MAP@10).

MediaEval 2017 - Interestingness Task: EURECOM @MediaEval 2017: Media Genre I...

multimediaeval

Presenter: Benoit Huet, Eurecom, France Paper: http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_29.pdf Video: https://youtu.be/bNhBXX-xOp0 Authors: Olfa Ben Ahmed, Jonas Wacker, Alessandro Gaballo, Benoit Huet Abstract: In this paper, we present EURECOM’s approach to address the MediaEval 2017 Predicting Media Interestingness Task. We developed models for both the image and video subtasks. In particular, we investigate the usage of media genre information (i.e., drama, horror, etc.) to predict interestingness. Our approach is related to the affective impact of media content and is shown to be effective in predicting interestingness for both video shots and key-frames.

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Media Genre Inference for Predicting Media Interestingness

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More from multimediaeval

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 Task

multimediaeval

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 Estimator

multimediaeval

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 Images

multimediaeval

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-Matching

multimediaeval

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 Segmentation

multimediaeval

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 Detection

multimediaeval

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 Attention

multimediaeval

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.

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.

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.

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Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal...

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The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed. In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine. Bio Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.

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Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.

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Cancer cell metabolism: special Reference to Lactate Pathway

AADYARAJPANDEY1

Normal Cell Metabolism: Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function. Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released. Cell utilize energy in the form of ATP. The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process. Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP. The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos). It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation. If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use. IN CANCER CELL: Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful. Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation. This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive. Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful. Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation. This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive. introduction to WARBERG PHENOMENA: WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside. Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme. WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.

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MediaEval 2016: LAPI at Predicting Media Interestingness Task

1. LAPI AT MEDIAEVAL 2016 PREDICTING MEDIA INTERESTINGNESS TASK Mihai Gabriel Constantin, Bogdan Boteanu, Bogdan Ionescu University Politehnica of Bucharest LAPI - The Image Processing and Analysis Laboratory contact: mgconstantin@alpha.imag.pub.ro Faculty of Electronics, Telecommunications and Information Technology University Politehnica of Bucharest

2.  Classical machine learning approach  Descriptors for videos and images are generated  Support Vector Machines (SVM) are trained on devset  Best SVM-descriptor combinations are identified  Best combinations are used for predicting testset OUR APPROACH MediaEval 2016, Hilversum, Netherlands

3. MediaEval 2016, Hilversum, Netherlands  For the video subtask we get the descriptors by calculating the average of the individual frame descriptors

4.  Hue-Saturation-Value Histogram  Histogram of Oriented Gradients (HoG)  Scale-Invariant Feature Transform (SIFT)  Local Binary Patterns (LBP)  GIST  fc7 and fcprob layers of AlexNet  Color Naming Histogram DESCRIPTORS MediaEval 2016, Hilversum, Netherlands

5. SVM LEARNING SYSTEMS  Linear Kernel  Polynomial Kernel  Radial Basis Function (RBF) Kernel  Weights (1/10) were used to balance the devset data MediaEval 2016, Hilversum, Netherlands

6. EXPERIMENTS ON DEVSET  Best results were chosen using 10-fold cross validation  Best MAP  Image subtask – 0.214  Video subtask – 0.179 MediaEval 2016, Hilversum, Netherlands

7.  Only one run (run5) was above the estimated MAP on devset  Best MAP  Image subtask – 0.1714  Video subtask – 0.1629 OFFICIAL RESULTS ON TESTSET MediaEval 2016, Hilversum, Netherlands above devset

8. THANK YOU

MediaEval 2016: LAPI at Predicting Media Interestingness Task

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