Miracle-dz/UniFAR-FTUs huggingface dataset:
#Coronavirus or #Chinesevirus?!: Understanding the negative sentiment reflected in Tweets with racist hashtags across the development of COVID-19

Situated in the global outbreak of COVID-19, our study enriches the discussion concerning the emergent racism and xenophobia on social media. With big data extracted from Twitter, we focus on the analysis of negative sentiment reflected in tweets marked with racist hashtags, as racism and xenophobia are more likely to be delivered via the negative sentiment. Especially, we propose a stage-based approach to capture how the negative sentiment changes along with the three development stages of COVID-19, under which it transformed from a domestic epidemic into an international public health emergency and later, into the global pandemic. At each stage, sentiment analysis enables us to recognize the negative sentiment from tweets with racist hashtags, and keyword extraction allows for the discovery of themes in the expression of negative sentiment by these tweets. Under this public health crisis of human beings, this stage-based approach enables us to provide policy suggestions for the enactment of stage-specific intervention strategies to combat racism and xenophobia on social media in a more effective way.
SGDN: Segmentation-Based Grasp Detection Network For Unsymmetrical Three-Finger Gripper

In this paper, we present Segmentation-Based Grasp Detection Network (SGDN) to predict a feasible robotic grasping for a unsymmetrical three-finger robotic gripper using RGB images. The feasible grasping of a target should be a collection of grasp regions with the same grasp angle and width. In other words, a simplified planar grasp representation should be pixel-level rather than region-level such as five-dimensional grasp representation.Therefore, we propose a pixel-level grasp representation, oriented base-fixed triangle. It is also more suitable for unsymmetrical three-finger gripper which cannot grasp symmetrically when grasping some objects, the grasp angle is at [0, 2{\pi}) instead of [0, {\pi}) of parallel plate this http URL order to predict the appropriate grasp region and its corresponding grasp angle and width in the RGB image, SGDN uses DeepLabv3+ as a feature extractor, and uses a three-channel grasp predictor to predict feasible oriented base-fixed triangle grasp representation of each pixel.On the re-annotated Cornell Grasp Dataset, our model achieves an accuracy of 96.8% and 92.27% on image-wise split and object-wise split respectively, and obtains accurate predictions consistent with the state-of-the-art methods.
AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition

Modern Automatic Speech Recognition (ASR) technology has evolved to identify the speech spoken by native speakers of a language very well. However, identification of the speech spoken by non-native speakers continues to be a major challenge for it. In this work, we first spell out the key requirements for creating a well-curated database of speech samples in non-native accents for training and testing robust ASR systems. We then introduce AccentDB, one such database that contains samples of 4 Indian-English accents collected by us, and a compilation of samples from 4 native-English, and a metropolitan Indian-English accent. We also present an analysis on separability of the collected accent data. Further, we present several accent classification models and evaluate them thoroughly against human-labelled accent classes. We test the generalization of our classifier models in a variety of setups of seen and unseen data. Finally, we introduce accent neutralization of non-native accents to native accents using autoencoder models with task-specific architectures. Thus, our work aims to aid ASR systems at every stage of development with a database for training, classification models for feature augmentation, and neutralization systems for acoustic transformations of non-native accents of English.
Monaural Speech Enhancement Using a Multi-Branch Temporal Convolutional Network

Deep learning has achieved substantial improvement on single-channel speech enhancement tasks. However, the performance of multi-layer perceptions (MLPs)-based methods is limited by the ability to capture the long-term effective history information. The recurrent neural networks (RNNs), e.g., long short-term memory (LSTM) model, are able to capture the long-term temporal dependencies, but come with the issues of the high latency and the complexity of this http URL address these issues, the temporal convolutional network (TCN) was proposed to replace the RNNs in various sequence modeling tasks. In this paper we propose a novel TCN model that employs multi-branch structure, called multi-branch TCN (MB-TCN), for monaural speech enhancement.The MB-TCN exploits split-transform-aggregate design, which is expected to obtain strong representational power at a low computational complexity.Inspired by the TCN, the MB-TCN model incorporates one dimensional causal dilated CNN and residual learning to expand receptive fields for capturing long-term temporal contextual information.Our extensive experimental investigation suggests that the MB-TCNs outperform the residual long short-term memory networks (ResLSTMs), temporal convolutional networks (TCNs), and the CNN networks that employ dense aggregations in terms of speech intelligibility and quality, while providing superior parameter efficiency. Furthermore, our experimental results demonstrate that our proposed MB-TCN model is able to outperform multiple state-of-the-art deep learning-based speech enhancement methods in terms of five widely used objective metrics.
C3VQG: category consistent cyclic visual question generation

Visual Question Generation (VQG) is the task of generating natural questions based on an image. Popular methods in the past have explored image-to-sequence architectures trained with maximum likelihood which have demonstrated meaningful generated questions given an image and its associated ground-truth answer. VQG becomes more challenging if the image contains rich contextual information describing its different semantic categories. In this paper, we try to exploit the different visual cues and concepts in an image to generate questions using a variational autoencoder (VAE) without ground-truth answers. Our approach solves two major shortcomings of existing VQG systems: (i) minimize the level of supervision and (ii) replace generic questions with category relevant generations. Most importantly, by eliminating expensive answer annotations, the required supervision is weakened. Using different categories enables us to exploit different concepts as the inference requires only the image and the category. Mutual information is maximized between the image, question, and answer category in the latent space of our VAE. A novel category consistent cyclic loss is proposed to enable the model to generate consistent predictions with respect to the answer category, reducing redundancies and irregularities. Additionally, we also impose supplementary constraints on the latent space of our generative model to provide structure based on categories and enhance generalization by encapsulating decorrelated features within each dimension. Through extensive experiments, the proposed model, C3VQG outperforms state-of-the-art VQG methods with weak supervision.
A global method to identify trees inside and outside of forests with medium-resolution satellite imagery

Scattered trees outside of dense forests are very important for carbon sequestration, supporting livelihoods, maintaining ecosystem integrity, and climate change adaptation and mitigation. In contrast to trees inside of forests, not much is known about the spatial extent and distribution of scattered trees at a global scale. Due to the very high cost of high-resolution satellite imagery, global monitoring systems rely on medium resolution satellites to monitor land use and land use change. However, detecting and monitoring scattered trees with an open canopy using medium resolution satellites is difficult because individual trees often cover a smaller footprint than the satellites resolution. Here we present a globally consistent method to identify trees inside and outside of forests with medium resolution optical and radar imagery. Biweekly cloud-free, pansharpened 10 meter Sentinel-2 optical imagery and Sentinel-1 radar imagery are used to train a fully convolutional network, consisting of a convolutional gated recurrent unit layer and a feature pyramid attention layer. Tested across more than 215,000 Sentinel-1 and Sentinel-2 pixels distributed from -60 to +60 latitude, the proposed model exceeds 75 percent users and producers accuracy identifying trees in hectares with a low to medium density (less than 40 percent) of canopy cover, and 95 percent user's and producer's accuracy in hectares with dense (greater than 40 percent) canopy cover. When applied across large, heterogeneous landscapes, the results demonstrate potential to map trees in high detail and consistent accuracy over diverse landscapes across the globe. This information is important for understanding current land cover and can be used to detect changes in land cover such as agroforestry, buffer zones around biological hotspots, and expansion or encroachment of forests.
Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator

This letter proposes three-filters-to-normal (3F2N), an accurate and ultrafast surface normal estimator (SNE), which is designed for structured range sensor data, <italic>e.g.</italic>, depth/disparity images. 3F2N SNE computes surface normals by simply performing three filtering operations (two image gradient filters in horizontal and vertical directions, respectively, and a mean/median filter) on an inverse depth image or a disparity image. Despite the simplicity of 3F2N SNE, no similar method already exists in the literature. To evaluate the performance of our proposed SNE, we created three large-scale synthetic datasets (easy, medium and hard) using 24 3D mesh models, each of which is used to generate 1800–2500 pairs of depth images (resolution: 480 × 640 pixels) and the corresponding ground-truth surface normal maps from different views. 3F2N SNE demonstrates the state-of-the-art performance, outperforming all other existing geometry-based SNEs, where the average angular errors with respect to the easy, medium and hard datasets are 1.66<inline-formula><tex-math notation="LaTeX">$\boldsymbol{^\circ }$</tex-math></inline-formula>, 5.69<inline-formula><tex-math notation="LaTeX">$\boldsymbol{^\circ }$</tex-math></inline-formula> and 15.31<inline-formula><tex-math notation="LaTeX">$\boldsymbol{^\circ }$</tex-math></inline-formula>, respectively. Furthermore, our C++ and CUDA implementations achieve a processing speed of over 260 Hz and 21 kHz, respectively. Our datasets and source code are publicly available at <uri>sites.google. com/view/3f2n</uri>.
Weather-Dependent Pollinator Activity in an Apple Orchard, with Special Reference to Osmia cornuta and Apis mellifera (Hymenoptera: Megachilidae and Apidae)

Abstract The foraging activity of pollinator insects in relation to weather factors (ambient temperature, solar radiation, relative humidity, and wind speed) was studied in an apple orchard with special reference to two managed bee species, Osmia cornuta (Latreille) and Apis mellifera L. Over the range of observed weather values, A. mellifera activity was significantly dependent on temperature, solar radiation, and wind speed; O. cornuta activity was dependent on solar radiation and wind speed. These results were confirmed through video recordings at one O. cornuta nesting shelter and one A. mellifera hive. For both species, daily activity started at lower temperatures than it ceased, whereas solar radiation did not differ between these two events. In general, O. cornuta was active from 10 to 12°C and 200 w/m2, and A. mellifera from 12 to 14°C and 300 w/m2. O. cornuta was the only bee species seen visiting apple flowers under strong wind or light rain. Because of its greater tolerance to inclement weather, O. cornuta pollinated apple flowers for longer periods (both daily and seasonally) than other flower visitors.
Multi-modal Automated Speech Scoring using Attention Fusion

In this study, we propose a novel multi-modal end-to-end neural approach for automated assessment of non-native English speakers' spontaneous speech using attention fusion. The pipeline employs Bi-directional Recurrent Convolutional Neural Networks and Bi-directional Long Short-Term Memory Neural Networks to encode acoustic and lexical cues from spectrograms and transcriptions, respectively. Attention fusion is performed on these learned predictive features to learn complex interactions between different modalities before final scoring. We compare our model with strong baselines and find combined attention to both lexical and acoustic cues significantly improves the overall performance of the system. Further, we present a qualitative and quantitative analysis of our model.
Neutral Bots Reveal Political Bias on Social Media

Social media platforms attempting to curb abuse and misinformation have been accused of political bias. We deploy neutral social bots on Twitter to probe biases that may emerge from interactions between users, platform mechanisms, and manipulation by inauthentic actors. We find evidence of bias affecting the news and information to which U.S. Twitter users are likely to be exposed, depending on their own political alignment. Partisan accounts, especially conservative ones, tend to receive more followers, follow more automated accounts, and find themselves in echo chambers. Conservative accounts are exposed to more low-credibility content. Liberal accounts are exposed to moderate content shifting their experience toward the political center, while the interactions of conservative accounts are skewed toward the right. We find some evidence of central bias in the news feed ranking algorithm for partisan accounts. These findings help inform the public debate about how social media shape exposure to political information.
Technologies and Workflow of Creative Coding Projects: Examples from the Google DevArt Competition

Recently, many artists and creative technologists created computer programming as a goal to create something expressive instead of something functional. In this paper, I analyzed 18 creative coding projects from the Google DevArt competition and summarized the critical technologies, types of content, and key workflows of these creative coding projects. The paper also discusses the potential research opportunities for creative coding and art technologies.
Inference on the history of a randomly growing tree

The spread of infectious disease in a human community or the proliferation of fake news on social media can be modelled as a randomly growing tree‐shaped graph. The history of the random growth process is often unobserved but contains important information such as the source of the infection. We consider the problem of statistical inference on aspects of the latent history using only a single snapshot of the final tree. Our approach is to apply random labels to the observed unlabelled tree and analyse the resulting distribution of the growth process, conditional on the final outcome. We show that this conditional distribution is tractable under a shape exchangeability condition, which we introduce here, and that this condition is satisfied for many popular models for randomly growing trees such as uniform attachment, linear preferential attachment and uniform attachment on a D‐regular tree. For inference of the root under shape exchangeability, we propose O(n log n) time algorithms for constructing confidence sets with valid frequentist coverage as well as bounds on the expected size of the confidence sets. We also provide efficient sampling algorithms which extend our methods to a wide class of inference problems.
Morphological and Histochemical Characterization of the Mucosa of the Digestive Tract in Engraulis anchoita (Hubbs and Marini, 1935)

The histomorphological aspects as well as the histochemical content and distribution of glycoproteins (GPs) in the mucosa of the digestive tract of the anchovy Engraulis anchoita were studied. The buccopharyngeal cavity is lined by a squamous stratified epithelium with mucous superficial cells; the oesophagus shows two zones, cranial with a squamous stratified epithelium with mucous superficial cells and caudal with a columnar secretory epithelium. Finally, the stomach presents both the cranial and pyloric portion lined with a simple columnar epithelium. Tubular branched glands, formed by a single type of glandular cell, located along the stomach, are more numerous in the cranial portion. The GPs were identified with (1) oxidizable vicinal diols; (2) sialic acids and some of their chain variants, C7 or C9; (3) sialic acid residues with O‐acyl substitution at C7 or C8; (4) carboxyl groups and (5) sulphate groups. Histochemical tests showed that the buccopharyngeal cavity presented the largest amount of the different types of mucosubstances. Epithelial secretory cells were found in the oesophagus, which synthesized a large quantity of sialosulphoglycoproteins likely to be related to a protective role. The surface epithelium of the stomach synthesizes and secretes acid and neutral GPs, probably related to the movement of fluids and to the absorption of easily digested substrates, respectively. Although great differences exist between different species, in E. anchoita as in other fish species, the wall of the digestive tract is composed of the four layers classically described for vertebrates. The GPs secreted by the epithelial cells are suggested to be important for the protection and inhibition of microorganisms. In additon, they are involved in enzymatic digestion of food, absorptive functions and lubrication of the alimentary tract.
Carboxylic Acids Produced Through Oxidative Cleavage of Aromatic Rings During Degradation of Lignin in Spruce Wood by Phanerochaete Chrysosporium

Abstract Pre-extracted spruce wood chips were decayed by Phanerochaete chrysoaporium to a 20% loss in lignin (8% loss in total weight), and extracted successively with petroleum ether, chloroform, acetone, methanol, and aqueous dioxane. The low molecular weight fraction of the methanol extract was analyzed by gas chromatography/ high resolution mass spectrometry after acetylation and nethylation. Examination of the spectra resulted in structural assignments for 28 compounds, 10 of which were aromatic acids identified and reported previously (Holzforschung 36,3 (1982)). At least 13 of the remaining compounds were formed via aromatic ring cleavage. In addition to ring cleavages, the new structures revealed oxidation of α- and γ-hydroxyl groups, oxidative cleavage of Cα−Cβ and Cβ–Cγ bonds, and 3-O-demethylation. It is postulated that oxidative cleavage of aromatic rings in the lignin units with an ether linkage at C-4 of the guaiacyl group involves 3-O-demethylation, hydroxylation at C-2 and subsequent o-cle.
Visual Relationship Detection using Scene Graphs: A Survey

Understanding a scene by decoding the visual relationships depicted in an image has been a long studied problem. While the recent advances in deep learning and the usage of deep neural networks have achieved near human accuracy on many tasks, there still exists a pretty big gap between human and machine level performance when it comes to various visual relationship detection tasks. Developing on earlier tasks like object recognition, segmentation and captioning which focused on a relatively coarser image understanding, newer tasks have been introduced recently to deal with a finer level of image understanding. A Scene Graph is one such technique to better represent a scene and the various relationships present in it. With its wide number of applications in various tasks like Visual Question Answering, Semantic Image Retrieval, Image Generation, among many others, it has proved to be a useful tool for deeper and better visual relationship understanding. In this paper, we present a detailed survey on the various techniques for scene graph generation, their efficacy to represent visual relationships and how it has been used to solve various downstream tasks. We also attempt to analyze the various future directions in which the field might advance in the future. Being one of the first papers to give a detailed survey on this topic, we also hope to give a succinct introduction to scene graphs, and guide practitioners while developing approaches for their applications.
Universalization of Any Adversarial Attack using Very Few Test Examples

Deep learning models are known to be vulnerable not only to input-dependent adversarial attacks but also to input-agnostic or universal adversarial attacks. Dezfooli et al. [8, 9] construct universal adversarial attack on a given model by looking at a large number of training data points and the geometry of the decision boundary near them. Subsequent work [5] constructs universal attack by looking only at test examples and intermediate layers of the given model. In this paper, we propose a simple universalization technique to take any input-dependent adversarial attack and construct a universal attack by only looking at very few adversarial test examples. We do not require details of the given model and have negligible computational overhead for universalization. We theoretically justify our universalization technique by a spectral property common to many input-dependent adversarial perturbations, e.g., gradients, Fast Gradient Sign Method (FGSM) and DeepFool. Using matrix concentration inequalities and spectral perturbation bounds, we show that the top singular vector of input-dependent adversarial directions on a small test sample gives an effective and simple universal adversarial attack. For standard models on CIFAR10 and ImageNet, our simple universalization of Gradient, FGSM, and DeepFool perturbations using a test sample of 64 images gives fooling rates comparable to state-of-the-art universal attacks [5, 9] for reasonable norms of perturbation.
Pressure-Induced Topological and Structural Phase Transitions in an Antiferromagnetic Topological Insulator

Recently, natural van der Waals heterostructures of (MnBi2Te4)m(Bi2Te3)n have been theoretically predicted and experimentally shown to host tunable magnetic properties and topologically nontrivial surface states. We systematically investigate both the structural and electronic responses of MnBi2Te4 and MnBi4Te7 to external pressure. In addition to the suppression of antiferromagnetic order, MnBi2Te4 is found to undergo a metal–semiconductor–metal transition upon compression. The resistivity of MnBi4Te7 changes dramatically under high pressure and a non-monotonic evolution of ρ (T) is observed. The nontrivial topology is proved to persist before the structural phase transition observed in the high-pressure regime. We find that the bulk and surface states respond differently to pressure, which is consistent with the non-monotonic change of the resistivity. Interestingly, a pressure-induced amorphous state is observed in MnBi2Te4, while two high-pressure phase transitions are revealed in MnBi4Te7. Our combined theoretical and experimental research establishes MnBi2Te4 and MnBi4Te7 as highly tunable magnetic topological insulators, in which phase transitions and new ground states emerge upon compression.