Reviewerly/exHarmony huggingface dataset:
Improving Dutch Vaccine Hesitancy Monitoring via Multi-Label Data Augmentation with GPT-3.5

In this paper, we leverage the GPT-3.5 language model both using the Chat-GPT API interface and the GPT-3.5 API interface to generate realistic examples of anti-vaccination tweets in Dutch with the aim of augmenting an imbalanced multi-label vaccine hesitancy argumentation classification dataset.In line with previous research, we devise a prompt that, on the one hand, instructs the model to generate realistic examples based on the human dataset (gold standard) and, on the other hand, to assign one or multiple labels to the generated instances.We then augment our gold standard data with the generated examples and evaluate the impact thereof in a cross-validation setting with several state-of-the-art Dutch BERT models.This augmentation technique predominantly shows improvements in F1 for classifying underrepresented classes while increasing the overall recall, paired with a slight decrease in precision for more common classes.Furthermore, we examine how well the synthetic data generalises to human data in the classification task.To our knowledge, we are the first to utilise Chat-GPT and GPT-3.5 for augmenting a Dutch multilabel dataset classification task.
Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis

Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task which involves four elements from user-generated texts: aspect term, aspect category, opinion term, and sentiment polarity.Most computational approaches focus on some of the ABSA sub-tasks such as tuple (aspect term, sentiment polarity) or triplet (aspect term, opinion term, sentiment polarity) extraction using either pipeline or joint modeling approaches.Recently, generative approaches have been proposed to extract all four elements as (one or more) quadruplets from text as a single task.In this work, we take a step further and propose a unified framework for solving ABSA, and the associated sub-tasks to improve the performance in few-shot scenarios.To this end, we fine-tune a T5 model with instructional prompts in a multi-task learning fashion covering all the sub-tasks, as well as the entire quadruple prediction task.In experiments with multiple benchmark datasets, we show that the proposed multi-task prompting approach brings performance boost (by absolute 8.29 F1) in the few-shot learning setting.
A comparative study of the characteristics of hate speech propagators and their behaviours over Twitter social media platform

The Internet and social media have facilitated diverse communication genres, enabling widespread and rapid opinions-sharing. However, hate speech imposes a contemporary challenge on individuals and communities, given the user anonymity, freedom, and inadequate regulation. Therefore, It is imperative to identify the perpetrators responsible for spreading hate content and examine their behaviour to prevent and mitigate the negative impact. This study aimed to compare the characteristics of hate speech propagators and their behaviour with non-hate users on Twitter for the first time in Sri Lanka. The intrinsic and extrinsic profile features were extensively analyzed, employing Sinhala and English text analysis techniques. A corpus of 102882 posts from 530 hate and non-hate Twitter user profiles was selected for the study. This study investigates the unique characteristics of hate speech propagators and non-hate users by examining their profile self-presentation, conducting social network analysis, and analyzing sentiment and emotion through linguistic analysis. Hate users often refrained from expression, with infrequent account verification and geotagging. They tend to have a higher follower and following counts and more favourites, group memberships, and statuses than non-hate users. However, general Twitter user engagement with hate users was significantly low, with fewer likes, retweets, and replies. The limited involvement of normal users with hate content indicates that audiences can be effectively utilized to combat hate speech. The sentiment analysis between languages showed polarisation of negative tweets towards Sinhala, with the synergistic effect of English language users using positive sentiment to spread hate content. The novel findings shed light on the characteristics of hate users, facilitating their early detection and moderation of hate speech and aiding in developing algorithms to rank and categorize hate users using artificial intelligence. Moreover, it can be used for policy reforms, awareness programmes, and building social cohesion while combating hate speech.
TG-VQA: Ternary Game of Video Question Answering

Video question answering aims at answering a question about the video content by reasoning the alignment semantics within them. However, since relying heavily on human instructions, i.e., annotations or priors, current contrastive learning-based VideoQA methods remains challenging to perform fine-grained visual-linguistic alignments. In this work, we innovatively resort to game theory, which can simulate complicated relationships among multiple players with specific interaction strategies, e.g., video, question, and answer as ternary players, to achieve fine-grained alignment for VideoQA task. Specifically, we carefully design a VideoQA-specific interaction strategy to tailor the characteristics of VideoQA, which can mathematically generate the fine-grained visual-linguistic alignment label without label-intensive efforts. Our TG-VQA outperforms existing state-of-the-art by a large margin (more than 5%) on long-term and short-term VideoQA datasets, verifying its effectiveness and generalization ability. Thanks to the guidance of game-theoretic interaction, our model impressively convergences well on limited data (10^4 videos), surpassing most of those pre-trained on large-scale data (10^7 videos).
A new index to evaluate renewable energy potential: A case study on solar, wind and hybrid generation in Northeast Brazil

Human activities imply a high and increasing consumption of electricity. In this context, renewable sources, such as solar and wind energy, stand out due to the ecological and economic advantages associated with their use, but bring with them the need for monitoring and analyzing climatic variables such as solar irradiation and wind speed, in order to determine the potential of solar, wind or hybrid (solar and wind) generation of a region. The main objective of the present study was to develop a method to rank the top locations or regions for wind, solar and hybrid electricity generation potential. In addition, two databases were created using spatial interpolation techniques to define values related to solar and wind energy sources in a case study for the state of Paraíba, Northeast Brazil. Thus, using public data from ground stations and satellites, two interpolation procedures were evaluated, Inverse Distance Weighted (IDW) and Kriging. Next, the best evaluated method (Kriging) was used to develop a database with exclusive resolution of 0.05° (latitude and longitude) for analysis of the wind, solar and hybrid potential of the Paraíba state, Brazil, used as case study. Finally, an index was created to determine the municipalities in the state that stand out with the best indicators for hybrid generation. From the index created, it was observed that the municipalities belonging to the intermediate geographical regions of Sousa-Cajazeiras and Patos have the best indicators for solar generation, while the regions of Campina Grande and João Pessoa stand out with greatest potential for wind energy production. The region of Campina Grande also had the best results for hybrid generation potential. The IP-PB index was the main contribution of this work, since the method can be applied to different regions of the world for the same purpose and other indexes for estimating renewable production potential were not found in the literature.
CrisisHateMM: Multimodal Analysis of Directed and Undirected Hate Speech in Text-Embedded Images from Russia-Ukraine Conflict

Text-embedded images are frequently used on social media to convey opinions and emotions, but they can also be a medium for disseminating hate speech, propaganda, and extremist ideologies. During the Russia-Ukraine war, both sides used text-embedded images extensively to spread propaganda and hate speech. To aid in moderating such content, this paper introduces CrisisHateMM, a novel multimodal dataset of over 4,700 text-embedded images from the Russia-Ukraine conflict, annotated for hate and non-hate speech. The hate speech is annotated for directed and undirected hate speech, with directed hate speech further annotated for individual, community, and organizational targets. We benchmark the dataset using unimodal and multimodal algorithms, providing insights into the effectiveness of different approaches for detecting hate speech in text-embedded images. Our results show that multimodal approaches outperform unimodal approaches in detecting hate speech, highlighting the importance of combining visual and textual features. This work provides a valuable resource for researchers and practitioners in automated content moderation and social media analysis. The CrisisHateMM dataset and codes are made publicly available at https://github.com/aabhandari/CrisisHateMM.
OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution

360° omnidirectional images have gained research attention due to their immersive and interactive experience, particularly in AR/VR applications. However, they suffer from lower angular resolution due to being captured by fisheye lenses with the same sensor size for capturing planar images. To solve the above issues, we propose a two-stage framework for 360° omnidirectional image super-resolution. The first stage employs two branches: model A, which incorporates omnidirectional position-aware deformable blocks (OPDB) and Fourier upsampling, and model B, which adds a spatial frequency fusion module (SFF) to model A. Model A aims to enhance the feature extraction ability of 360° image positional information, while Model B further focuses on the high-frequency information of 360° images. The second stage performs same-resolution enhancement based on the structure of model A with a pixel unshuffle operation. In addition, we collected data from YouTube to improve the fitting ability of the transformer, and created pseudo low-resolution images using a degradation network. Our proposed method achieves superior performance and wins the NTIRE 2023 challenge of 360° omnidirectional image super-resolution.
U2RLE: Uncertainty-Guided 2-Stage Room Layout Estimation

While the existing deep learning-based room layout estimation techniques demonstrate good overall accuracy [17], they are less effective for distant floor-wall boundary. To tackle this problem, we propose a novel uncertainty-guided approach for layout boundary estimation introducing new two-stage CNN architecture termed U2RLE. The initial stage predicts both floor-wall boundary and its uncertainty and is followed by the refinement of boundaries with high positional uncertainty using a different, distance-aware loss. Finally, outputs from the two stages are merged to produce the room layout. Experiments using ZInD [4] and Structure3D [25] datasets show that U2RLE improves over current state-of-the-art, being able to handle both near and far walls better. In particular, U2RLE outperforms current state-of-the-art techniques for the most distant walls.
RGSB-UNet: Hybrid Deep Learning Framework for Tumour Segmentation in Digital Pathology Images

Colorectal cancer (CRC) is a prevalent gastrointestinal tumour with high incidence and mortality rates. Early screening for CRC can improve cure rates and reduce mortality. Recently, deep convolution neural network (CNN)-based pathological image diagnosis has been intensively studied to meet the challenge of time-consuming and labour-intense manual analysis of high-resolution whole slide images (WSIs). Despite the achievements made, deep CNN-based methods still suffer from some limitations, and the fundamental problem is that they cannot capture global features. To address this issue, we propose a hybrid deep learning framework (RGSB-UNet) for automatic tumour segmentation in WSIs. The framework adopts a UNet architecture that consists of the newly-designed residual ghost block with switchable normalization (RGS) and the bottleneck transformer (BoT) for downsampling to extract refined features, and the transposed convolution and 1 × 1 convolution with ReLU for upsampling to restore the feature map resolution to that of the original image. The proposed framework combines the advantages of the spatial-local correlation of CNNs and the long-distance feature dependencies of BoT, ensuring its capacity of extracting more refined features and robustness to varying batch sizes. Additionally, we consider a class-wise dice loss (CDL) function to train the segmentation network. The proposed network achieves state-of-the-art segmentation performance under small batch sizes. Experimental results on DigestPath2019 and GlaS datasets demonstrate that our proposed model produces superior evaluation scores and state-of-the-art segmentation results.
Cloud Computing for Decision Making within the Clinical Sector

Cities produce a significant quantity of land-use, ecological, economical, energy, and transportation data. An interdisciplinary approach to gathering and evaluating such massive amounts of data may provide a solution of the wide range educational, political, and regulatory, administration, and corporate decisions, as well as aid judgement in order to create a smarter ecosystem. This article proposes a cloud-based analytic service to provide an experimental and theoretical viewpoint on smart cities centered on large data gathering and evaluation. In order to showcase the effectiveness of an analytical service for large data mining, a prototype was created and constructed. Hadoop was used to develop the prototype, and the data were consistent. The tool examines the Bristol Open metadata for connections between a number of different urban ecological factors. The outcomes of experiments utilizing Hadoop are given in this article. The data catalogued was analyzed to evaluate the indicators distributed over decades to determine good and detrimental developments in standard of living, namely crimes, safety, economics, and unemployment.
Antecedents of intention to use electronic auctions in Jordan: Empirical study on the mediating role of users' attitudes

The present study attempted to determine the elements affecting users' opinions about using electronic auctions in Jordan. The study's target audience was Jordanian university students. We randomly selected 600 students from three public universities in Jordan after 600 users from each of the three universities responded to the surveys. The primary data for this study were gathered using a specially created questionnaire that was based on earlier research. SEM software (smart PLS 4.0.8.3) was employed in the evaluation of data. This study looked into the factors that led Jordanians to use electronic auctions and found four indirect significant relationships and ten direct significant relationships. First, user attitudes and independent variables (usefulness, awareness, ease of use, and techno trust) are directly significant antecedents of utilizing e-auctions in Jordan. The use of e-auctions is related to the independent variable in five direct and significant ways: usefulness, awareness, ease of use, digital divide, and techno trust. Thirdly, there are direct and statistically significant correlations between user attitudes and the independent variables (usefulness, awareness, ease of use, and techno trust). Fourthly, there appears to be a clear and significant link between user attitudes and the intention to use e-auctions. Fifth, there are four mediated meaningful indirect correlations between the intention to use e-auctions and the independent variable. The findings of the current study are based on a number of factors that affect the demand for e-commerce in Jordan. They also include some recommendations for academics and decision-makers in e-marketing affairs. The current study also contributes in identifying challenges and suggestions that would overcome obstacles related to shopping operations through e-auctions. The factors examined in this study have been the subject of prior research. The data gathered in this study are valuable to Jordanian decision-makers and web developers, as the data can be used to create efficient policies and strategies to launch work on electronic auction platforms.
Towards a unified terminology for sonification and visualization

Abstract Both sonification and visualization convey information about data by effectively using our human perceptual system, but their ways to transform the data differ. Over the past 30 years, the sonification community has demanded a holistic perspective on data representation, including audio-visual analysis, several times. A design theory of audio-visual analysis would be a relevant step in this direction. An indispensable foundation for this endeavor is a terminology describing the combined design space. To build a bridge between the domains, we adopt three of the established theoretical constructs from visualization theory for the field of sonification. The three constructs are the spatial substrate , the visual mark , and the visual channel . In our model, we choose time to be the temporal substrate of sonification. Auditory marks are then positioned in time, such as visual marks are positioned in space. Auditory channels are encoded into auditory marks to convey information. The proposed definitions allow discussing visualization and sonification designs as well as multi-modal designs based on a common terminology. While the identified terminology can support audio-visual analytics research, it also provides a new perspective on sonification theory itself.
Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment

Pre-trained vision-language models have inspired much research on few-shot learning. However, with only a few training images, there exist two crucial problems: (1) the visual feature distributions are easily distracted by class-irrelevant information in images, and (2) the alignment between the visual and language feature distributions is difficult. To deal with the distraction problem, we propose a Selective Attack module, which consists of trainable adapters that generate spatial attention maps of images to guide the attacks on class-irrelevant image areas. By messing up these areas, the critical features are captured and the visual distributions of image features are calibrated. To better align the visual and language feature distributions that describe the same object class, we propose a cross-modal distribution alignment module, in which we introduce a vision-language prototype for each class to align the distributions, and adopt the Earth Mover's Distance (EMD) to optimize the prototypes. For efficient computation, the upper bound of EMD is derived. In addition, we propose an augmentation strategy to increase the diversity of the images and the text prompts, which can reduce overfitting to the few-shot training images. Extensive experiments on 11 datasets demonstrate that our method consistently outperforms prior arts in few-shot learning. The implementation code will be available at https://gitee.com/mindspore/models/tree/master/research/cv/SADA.
ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation

Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction tasks, e.g., unsupervised semantic segmentation (USS). The extracted relationship among pixel-level representations typically contains rich class-aware information that semantically identical pixel embeddings in the representation space gather together to form sophisticated concepts. However, leveraging the learned models to ascertain semantically consistent pixel groups or regions in the image is non-trivial since over/ under-clustering overwhelms the conceptualization procedure under various semantic distributions of different images. In this work, we investigate the pixel-level semantic aggregation in self-supervised ViT pre-trained models as image Segmentation and propose the Adaptive Conceptualization approach for USS, termed ACSeg. Concretely, we explicitly encode concepts into learnable prototypes and design the Adaptive Concept Generator (ACG), which adaptively maps these prototypes to informative concepts for each image. Meanwhile, considering the scene complexity of different images, we propose the modularity loss to optimize ACG independent of the concept number based on estimating the intensity of pixel pairs belonging to the same concept. Finally, we turn the USS task into classifying the discovered concepts in an unsupervised manner. Extensive experiments with state-of-the-art results demonstrate the effectiveness of the proposed ACSeg.
UV Volumes for Real-time Rendering of Editable Free-view Human Performance

Neural volume rendering enables photo-realistic renderings of a human performer in free-view, a critical task in immersive VR/AR applications. But the practice is severely limited by high computational costs in the rendering process. To solve this problem, we propose the UV Volumes, a new approach that can render an editable free-view video of a human performer in real-time. It separates the high-frequency (i.e., non-smooth) human appearance from the 3D volume, and encodes them into 2D neural texture stacks (NTS). The smooth UV volumes allow much smaller and shallower neural networks to obtain densities and texture coordinates in 3D while capturing detailed appearance in 2D NTS. For editability, the mapping between the parameterized human model and the smooth texture coordinates allows us a better generalization on novel poses and shapes. Furthermore, the use of NTS enables interesting applications, e.g., retexturing. Extensive experiments on CMU Panoptic, ZJU Mocap, and H36M datasets show that our model can render <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$960\times 540$</tex> images in 30FPS on average with comparable photo-realism to state-of-the-art methods. The project and supplementary materials are available at https://fanegg.github.io/UV-Volumes.
Correspondence Transformers with Asymmetric Feature Learning and Matching Flow Super-Resolution

This paper solves the problem of learning dense visual correspondences between different object instances of the same category with only sparse annotations. We decompose this pixel-level semantic matching problem into two easier ones: (i) First, local feature descriptors of source and target images need to be mapped into shared semantic spaces to get coarse matching flows. (ii) Second, matching flows in low resolution should be refined to generate accurate point-to-point matching results. We propose asymmetric feature learning and matching flow super-resolution based on vision transformers to solve the above problems. The asymmetric feature learning module exploits a biased cross-attention mechanism to encode token features of source images with their target counterparts. Then matching flow in low resolutions is enhanced by a super-resolution network to get accurate correspondences. Our pipeline is built upon vision transformers and can be trained in an end-to-end manner. Extensive experimental results on several popular benchmarks, such as PF-PASCAL, PF-WILLOW, and SPair-71 K, demonstrate that the proposed method can catch subtle semantic differences in pixels efficiently. Code is available on https://github.com/YXSUNMADMAX/ACTR.