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Evidence of distrust and disorientation towards immunization on online social media after contrasting political communication on vaccines. Results from an analysis of Twitter data in Italy

Feb 18, 2020
Samantha Ajovalasit, Veronica Dorgali, Angelo Mazza, Alberto d' Onofrio, Piero Manfredi

Background. Recently, In Italy the vaccination coverage for key immunizations, as MMR, has been declining, with measles outbreaks. In 2017, the Italian Government expanded the number of mandatory immunizations establishing penalties for families of unvaccinated children. During the 2018 elections campaign, immunization policy entered the political debate, with the government accusing oppositions of fuelling vaccine scepticism. A new government established in 2018 temporarily relaxed penalties and announced the introduction of flexibility. Objectives and Methods. By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public. Results. The population appeared to be mostly composed by "serial twitterers" tweeting about everything including vaccines. Tweets favourable to vaccination accounted for 75% of retained tweets, undecided for 14% and unfavourable for 11%. Twitter activity of the Italian public health institutions was negligible. After smoothing the temporal pattern, an up-and-down trend in the favourable proportion emerged, synchronized with the switch between governments, providing clear evidence of disorientation. Conclusion. The reported evidence of disorientation documents that critical health topics, as immunization, should never be used for political consensus. This is especially true given the increasing role of online social media as information source, which might yield to social pressures eventually harmful for vaccine uptake, and is worsened by the lack of institutional presence on Twitter. This calls for efforts to contrast misinformation and the ensuing spread of hesitancy.

* 15 pages, 3 of appendix, 3 figures, 2 tables 

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DerainCycleGAN: An Attention-guided Unsupervised Benchmark for Single Image Deraining and Rainmaking

Jan 15, 2020
Yanyan Wei, Zhao Zhang, Jicong Fan, Yang Wang, Shuicheng Yan, Meng Wang

Single image deraining (SID) is an important and challenging topic in emerging vision applications, and most of emerged deraining methods are supervised relying on the ground truth (i.e., paired images) in recent years. However, in practice it is rather common to have no un-paired images in real deraining task, in such cases how to remove the rain streaks in an unsupervised way will be a very challenging task due to lack of constraints between images and hence suffering from low-quality recovery results. In this paper, we explore the unsupervised SID task using unpaired data and propose a novel net called Attention-guided Deraining by Constrained CycleGAN (or shortly, DerainCycleGAN), which can fully utilize the constrained transfer learning abilitiy and circulatory structure of CycleGAN. Specifically, we design an unsu-pervised attention guided rain streak extractor (U-ARSE) that utilizes a memory to extract the rain streak masks with two constrained cycle-consistency branches jointly by paying attention to both the rainy and rain-free image domains. As a by-product, we also contribute a new paired rain image dataset called Rain200A, which is constructed by our network automatically. Compared with existing synthesis datasets, the rainy streaks in Rain200A contains more obvious and diverse shapes and directions. As a result, existing supervised methods trained on Rain200A can perform much better for processing real rainy images. Extensive experiments on synthesis and real datasets show that our net is superior to existing unsupervised deraining networks, and is also very competitive to other related supervised networks.


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Image inpainting: A review

Sep 13, 2019
Omar Elharrouss, Noor Almaadeed, Somaya Al-Maadeed, Younes Akbari

Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has gained even more popularity because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper is a brief review of the existing image inpainting approaches we first present a global vision on the existing methods for image inpainting. We attempt to collect most of the existing approaches and classify them into three categories, namely, sequential-based, CNN-based and GAN-based methods. In addition, for each category, a list of methods for the different types of distortion on the images is presented. Furthermore, collect a list of the available datasets and discuss these in our paper. This is a contribution for digital image inpainting researchers trying to look for the available datasets because there is a lack of datasets available for image inpainting. As the final step in this overview, we present the results of real evaluations of the three categories of image inpainting methods performed on the datasets used, for the different types of image distortion. In the end, we also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against.


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ELKPPNet: An Edge-aware Neural Network with Large Kernel Pyramid Pooling for Learning Discriminative Features in Semantic Segmentation

Jun 27, 2019
Xianwei Zheng, Linxi Huan, Hanjiang Xiong, Jianya Gong

Semantic segmentation has been a hot topic across diverse research fields. Along with the success of deep convolutional neural networks, semantic segmentation has made great achievements and improvements, in terms of both urban scene parsing and indoor semantic segmentation. However, most of the state-of-the-art models are still faced with a challenge in discriminative feature learning, which limits the ability of a model to detect multi-scale objects and to guarantee semantic consistency inside one object or distinguish different adjacent objects with similar appearance. In this paper, a practical and efficient edge-aware neural network is presented for semantic segmentation. This end-to-end trainable engine consists of a new encoder-decoder network, a large kernel spatial pyramid pooling (LKPP) block, and an edge-aware loss function. The encoder-decoder network was designed as a balanced structure to narrow the semantic and resolution gaps in multi-level feature aggregation, while the LKPP block was constructed with a densely expanding receptive field for multi-scale feature extraction and fusion. Furthermore, the new powerful edge-aware loss function is proposed to refine the boundaries directly from the semantic segmentation prediction for more robust and discriminative features. The effectiveness of the proposed model was demonstrated using Cityscapes, CamVid, and NYUDv2 benchmark datasets. The performance of the two structures and the edge-aware loss function in ELKPPNet was validated on the Cityscapes dataset, while the complete ELKPPNet was evaluated on the CamVid and NYUDv2 datasets. A comparative analysis with the state-of-the-art methods under the same conditions confirmed the superiority of the proposed algorithm.


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Knowledge-rich Image Gist Understanding Beyond Literal Meaning

Apr 18, 2019
Lydia Weiland, Ioana Hulpus, Simone Paolo Ponzetto, Wolfgang Effelsberg, Laura Dietz

We investigate the problem of understanding the message (gist) conveyed by images and their captions as found, for instance, on websites or news articles. To this end, we propose a methodology to capture the meaning of image-caption pairs on the basis of large amounts of machine-readable knowledge that has previously been shown to be highly effective for text understanding. Our method identifies the connotation of objects beyond their denotation: where most approaches to image understanding focus on the denotation of objects, i.e., their literal meaning, our work addresses the identification of connotations, i.e., iconic meanings of objects, to understand the message of images. We view image understanding as the task of representing an image-caption pair on the basis of a wide-coverage vocabulary of concepts such as the one provided by Wikipedia, and cast gist detection as a concept-ranking problem with image-caption pairs as queries. To enable a thorough investigation of the problem of gist understanding, we produce a gold standard of over 300 image-caption pairs and over 8,000 gist annotations covering a wide variety of topics at different levels of abstraction. We use this dataset to experimentally benchmark the contribution of signals from heterogeneous sources, namely image and text. The best result with a Mean Average Precision (MAP) of 0.69 indicate that by combining both dimensions we are able to better understand the meaning of our image-caption pairs than when using language or vision information alone. We test the robustness of our gist detection approach when receiving automatically generated input, i.e., using automatically generated image tags or generated captions, and prove the feasibility of an end-to-end automated process.

* Data & Knowledge Engineering, Volume 117, September 2018, Pages 114-132 

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EM-like Learning Chaotic Dynamics from Noisy and Partial Observations

Mar 25, 2019
Duong Nguyen, Said Ouala, Lucas Drumetz, Ronan Fablet

The identification of the governing equations of chaotic dynamical systems from data has recently emerged as a hot topic. While the seminal work by Brunton et al. reported proof-of-concepts for idealized observation setting for fully-observed systems, {\em i.e.} large signal-to-noise ratios and high-frequency sampling of all system variables, we here address the learning of data-driven representations of chaotic dynamics for partially-observed systems, including significant noise patterns and possibly lower and irregular sampling setting. Instead of considering training losses based on short-term prediction error like state-of-the-art learning-based schemes, we adopt a Bayesian formulation and state this issue as a data assimilation problem with unknown model parameters. To solve for the joint inference of the hidden dynamics and of model parameters, we combine neural-network representations and state-of-the-art assimilation schemes. Using iterative Expectation-Maximization (EM)-like procedures, the key feature of the proposed inference schemes is the derivation of the posterior of the hidden dynamics. Using a neural-network-based Ordinary Differential Equation (ODE) representation of these dynamics, we investigate two strategies: their combination to Ensemble Kalman Smoothers and Long Short-Term Memory (LSTM)-based variational approximations of the posterior. Through numerical experiments on the Lorenz-63 system with different noise and time sampling settings, we demonstrate the ability of the proposed schemes to recover and reproduce the hidden chaotic dynamics, including their Lyapunov characteristic exponents, when classic machine learning approaches fail.


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Joint Embedding Learning and Low-Rank Approximation: A Framework for Incomplete Multi-view Learning

Dec 25, 2018
Hong Tao, Chenping Hou, Dongyun Yi, Jubo Zhu

In real-world applications, not all instances in multi-view data are fully represented. To deal with incomplete multi-view data, traditional multi-view algorithms usually throw away the incomplete instances, resulting in loss of available information. To overcome this loss, Incomplete Multi-view Learning (IML) has become a hot research topic. In this paper, we propose a general IML framework for unifying existing IML methods and gaining insight into IML. The proposed framework jointly performs embedding learning and low-rank approximation. Concretely, it approximates the incomplete data by a set of low-rank matrices and learns a full and common embedding by linear transformation. Several existing IML methods can be unified as special cases of the framework. More interestingly, some linear transformation based full-view methods can be adapted to IML directly with the guidance of the framework. This bridges the gap between full multi-view learning and IML. Moreover, the framework can provide guidance for developing new algorithms. For illustration, within the framework, we propose a specific method, termed as Incomplete Multi-view Learning with Block Diagonal Representation (IML-BDR). Based on the assumption that the sampled examples have approximate linear subspace structure, IML-BDR uses the block diagonal structure prior to learn the full embedding, which would lead to more correct clustering. A convergent alternating iterative algorithm with the Successive Over-Relaxation (SOR) optimization technique is devised for optimization. Experimental results on various datasets demonstrate the effectiveness of IML-BDR.


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