Reviewerly/exHarmony huggingface dataset:
‘Am I being unreasonable to vaccinate my kids against my ex’s wishes?’ – A corpus linguistic exploration of conflict in vaccination discussions on Mumsnet Talk’s AIBU forum

Online parenting forums are popular sources of information about childhood vaccinations, but vaccination discussions, especially online, are often polarised and polarising (Jenkins & Moreno 2020). This can have very real implications for ultimate vaccination decisions (Al-Hasan et al., 2021). Among the most visited parenting forums in the UK is Mumsnet Talk, hosted on the parenting website Mumsnet, which has a reputation for being straight-talking and combative. This supposedly applies particularly to its most popular Talk Topic, Am I being unreasonable?' (AIBU), which also includes numerous threads related to vaccinations. In this paper we combine corpus-based methods with qualitative discourse analysis to examine over 6-million words of vaccination-related discussions on 895 threads on AIBU from the inception of Mumsnet in 2000 to May 2021. We provide evidence of a greater presence of confrontation-related language in these threads when compared with similar threads not on AIBU, zooming in on nine keywords that can be used as insults (e.g. 'idiot' and 'bitch'). The use of these keywords reveals the multiple types of conflict at play, including between people with opposing and similar vaccine stances. While some insults are directed at each other within the forum, the majority are directed at external third parties, often the protagonists at the heart of offline conflicts detailed in the original post of an AIBU thread. We show that, although these insults perform impoliteness towards external third parties potentially perpetuating conflict that predates any posting on the forum, they simultaneously have a supportive and community enhancing function within the threads. We reflect on the implications of our findings for the role of AIBU as a point of reference and site of interaction for parents facing vaccination-related issues and decisions.
Defining Explanation and Explanatory Depth in XAI

Abstract Explainable artificial intelligence (XAI) aims to help people understand black box algorithms, particularly of their outputs. But what are these explanations and when is one explanation better than another? The manipulationist definition of explanation from the philosophy of science offers good answers to these questions, holding that an explanation consists of a generalization that shows what happens in counterfactual cases. Furthermore, when it comes to explanatory depth this account holds that a generalization that has more abstract variables, is broader in scope and/or more accurate is better. By applying these definitions and contrasting them with alternative definitions in the XAI literature I hope to help clarify what a good explanation is for AI.
A Comprehensive Survey on Video Saliency Detection With Auditory Information: The Audio-Visual Consistency Perceptual is the Key!

Video saliency detection (VSD) aims at fast locating the most attractive objects/things/patterns in a given video clip. Existing VSD-related works have mainly relied on the visual system but paid less attention to the audio aspect. In contrast, our audio system is the most vital complementary part of our visual system. Also, audio-visual saliency detection (AVSD), one of the most representative research topics for mimicking human perceptual mechanisms, is currently in its infancy, and none of the existing survey papers have touched on it, especially from the perspective of saliency detection. Thus, the ultimate goal of this paper is to provide an extensive review to bridge the gap between audio-visual fusion and saliency detection. In addition, as another highlight of this review, we have provided a deep insight into key factors that could directly determine AVSD deep models’ performances. We claim that the audio-visual consistency degree (AVC) — a long-overlooked issue, can directly influence the effectiveness of using audio to benefit its visual counterpart when performing saliency detection. Moreover, to make the AVC issue more practical and valuable for future followers, we have newly equipped almost all existing publicly available AVSD datasets with additional frame-wise AVC labels. Based on these upgraded datasets, we have conducted extensive quantitative evaluations to ground our claim on the importance of AVC in the AVSD task. In a word, our ideas and new sets serve as a convenient platform with preliminaries and guidelines, all of which can potentially facilitate future works in further promoting state-of-the-art (SOTA) performance.
Key-frame Guided Network for Thyroid Nodule Recognition Using Ultrasound Videos

Ultrasound examination is widely used in the clinical diagnosis of thyroid nodules (benign/malignant). However, the accuracy relies heavily on radiologist experience. Although deep learning techniques have been investigated for thyroid nodules recognition. Current solutions are mainly based on static ultrasound images, with limited temporal information used and inconsistent with clinical diagnosis. This paper proposes a novel method for the automated recognition of thyroid nodules through an exhaustive exploration of ultrasound videos and key-frames. We first propose a detection-localization framework to automatically identify the clinical key-frame with a typical nodule in each ultrasound video. Based on the localized key-frame, we develop a key-frame guided video classification model for thyroid nodule recognition. Besides, we introduce a motion attention module to help the network focus on significant frames in an ultrasound video, which is consistent with clinical diagnosis. The proposed thyroid nodule recognition framework is validated on clinically collected ultrasound videos, demonstrating superior performance compared with other state-of-the-art methods.
Fuzzy clustering for multiview data by combining latent information

Multiview data has become very important because it is often possible to obtain multiple representations for the same set of objects. From the perspective of soft partition, this paper proposes a novel fuzzy clustering method for multiview data by combining the latent information or the membership matrices from the classical Fuzzy C-means (FCM) in each view. Considering that multiview data are generated from the same latent subspace, to assist the membership matrix in one view to explore more complementary information from other views, the proposed approach first aligns the set of membership matrices from FCMs in different views to a consensus matrix. To this end, a new objective function of fuzzy clustering is formulated and the optimization method of membership matrices is provided. Then, the optimized latent information or the membership matrix for each view is concatenated into a tensor and the final clustering is derived with the help of tensor decomposition to further exploit high order correlations between different views. To balance the importance of each view, the weighted-view version of the proposed method is also developed. In addition, we analyze the convergence of proposed methods and their computational complexity. Three experimental indices NMI, F-measure and ACC demonstrate that the proposed approach is superior to the latest multiview fuzzy clustering algorithms.
Online action proposal generation using spatio-temporal attention network

Temporal action proposal generation aims to generate temporal boundaries containing action instances. In real-time applications such as surveillance cameras, autonomous driving, and traffic monitoring, the online localization and recognition of human activities occurring in short temporal intervals are important areas of research. Existing approaches of temporal action proposal generation consider only the offline and frame-level feature aggregation along the temporal dimension. Those offline methods also generate many redundant irrelevant proposal regions in the frames as temporal boundaries. This leads to higher computational cost along with slow processing speed which is not suitable for online tasks. In this study, we propose a novel spatio-temporal attention network for online action proposal generation as opposed to existing offline proposal generation methods. Our novel proposed approach incorporates the inter-dependency between the spatial and temporal context information of each incoming video clip to generate more relevant online temporal action proposals. First, we propose a windowed spatial attention module to capture the inter-spatial relationship between the features of incoming frames. The windowed spatial network produces more robust clip-level feature representation and efficiently deals with noisy features such as occlusion or background scenes. Second, we introduce a temporal attention module to capture relevant temporal dynamic information mutually to the localized spatial information to model the long inter-frame temporal relationship since most online real life videos are untrimmed in nature. By applying these two attention modules sequentially, the novel proposed spatio-temporal network model is able to generate precise action boundaries at a particular instant of time. In addition, the model generates fewer discriminative temporal action proposals while maintaining a low computational cost and high processing speed suitable for online settings.
GraformerDIR: Graph convolution transformer for deformable image registration

Deformable image registration (DIR) plays an important role in assisting disease diagnosis. The emergence of the Transformer enables the DIR framework to extract long-range dependencies, which relieves the limitations of intrinsic locality caused by convolution operation. However, suffering from the interference of missing or spurious connections, it is a challenging task for Transformer-based methods to capture the high-quality long-range dependencies.In this paper, by staking the graph convolution Transformer (Graformer) layer at the bottom of the feature extraction network, we propose a Graformer-based DIR framework, named GraformerDIR. The Graformer layer is consist of the Graformer module and the Cheby-shev graph convolution module. Among them, the Graformer module is designed to capture high-quality long-range dependencies. Cheby-shev graph convolution module is employed to further enlarge the receptive field.The performance and generalizability of GraformerDIR have been evaluated on publicly available brain datasets including the OASIS, LPBA40, and MGH10 datasets. Compared with VoxelMorph, the GraformerDIR has obtained performance improvements of 4.6% in Dice similarity coefficient (DSC) and 0.055 mm in the average symmetric surface distance (ASD) while reducing the non-positive rate of Jacobin determinant (Npr.Jac) index about 60 times on publicly available OASIS dataset. On unseen dataset MGH10, the GraformerDIR has obtained the performance improvements of 4.1% in DSC and 0.084 mm in ASD compared with VoxelMorph, which demonstrates the GraformerDIR with better generalizability. The promising performance on the clinical cardiac dataset ACDC indicates the GraformerDIR is practicable.With the advantage of Transformer and graph convolution, the GraformerDIR has obtained comparable performance with the state-of-the-art method VoxelMorph.
Multiple deep neural networks with multiple labels for cross-modal hashing retrieval

Most deep hashing methods for cross-modal retrieval use semantic labels to judge simply whether a pair of data are similar or dissimilar. However, they do not make full use of the different labels between the two instances. In addition, they do not generate more discriminative hash codes that scatter around the class centre. To this end, we propose multiple deep neural networks with multiple labels for cross-modal hashing retrieval (MDMCH). MDMCH constructs a multiple deep hash learning framework that contains three-stream deep neural networks for images, texts, and labels. In terms of the objective function, the semantic weight and class centre similarity are calculated according to the multi-labels. Then, the semantic weight is embedded into three cross-entropy loss terms for image, text, and label networks to better preserve inter-modal and intra-modal similarities. Meanwhile, MDMCH fuses the deep features of semantic labels and texts to further improve the text-to-image retrieval accuracy. Additionally, the class centre similarity is applied to the image and text networks, which forces the similar instance pairs to scatter around the class centre. Experiments on three popular benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art methods.
A Comparative Evaluation use Bagging and Boosting Ensemble Classifiers

In recent years, ensemble learning has sparked a lot of interest in the fields of machine learning. In a variety of issue areas domains and real-world applications, recently ensemble learning approach get a lot attention to provide results. Ensemble learning reduces overall variance by combining the output of numerous classifiers or a group of base learners. When compared to a single classifier or single basis learner, combining numerous Classifiers or a collection of base learners improves accuracy significantly. This research is aimed at comparison of two sort of ensemble learning approaches used in machine learning. The Extra Trees classifier had been the most accurate, with a score of accuracy of 90 %
Superdense-scale network for semantic segmentation

Great progress has been made in semantic segmentation based on deep convolutional neural networks. However, semantic segmentation in complex scenes remains challenging due to the large-scale variation problem. To handle this problem, the existing methods usually employ multiple receptive fields to capture multiscale features. Some works have verified that the denser the different receptive fields (scales), the easier it is to address the large-scale variation problem. To make denser scales, we propose a superdense-scale network (SDSNet). Specifically, we design a simple yet effective structure named the parallel-serial structure of atrous convolutions (PSSAC) in which superdense-scale high-level features are captured by explicitly adjusting the neuron’s receptive field. The PSSAC is an improvement over ASPP and DenseASPP by employing exponentially increasing scales with a serially connected multiple parallel structure. To extract more accurate features, we construct an SDSNet consisting of a modified aligned Xcepiton71 backbone followed by a PSSAC. Extensive experiments of semantic segmentation are conducted to evaluate our SDSNet on three datasets, namely, Cityscapes, PASCAL VOC 2012, and ADE20K. Experimental results show that our SDSNet achieves state-of-the-art performance.
TWAP Oracle Attacks: Easier Done than Said?

Blockchain "on-chain" oracles are critical to the functioning of many Decentralized Finance (DeFi) protocols. We analyze these oracles for manipulation resistance. Specifically, we analyze the cost of manipulating on-chain time-weighted average price (TWAP) oracles that use the arithmetic mean. It has been assumed that manipulating a TWAP oracle with the well-known multi-block attack is expensive and scales linearly with the length of the TWAP. We question this assumption with two novel results. First, we describe a single-block attack that works under the same setting as the multi-block attack but costs less to execute. Second, we describe a multi-block MEV (MMEV) style attack where the attacker colludes with a miner/proposer who can mine/propose two blocks in a row. This MMEV style attack makes oracle manipulation orders of magnitude cheaper than previously known attacks. In the proof-of-work setting, MMEV can be done by selfish mining even with very low shares of hashpower.
Performance variability and implications for yield prediction of rooftop PV systems – Analysis of 246 identical systems

While the performance of PV systems and its associated uncertainties are well understood for standard test conditions in the laboratory, there is still limited knowledge about the magnitude and mechanism of the PV performance variability under actual operating conditions. This paper aims to identify the performance variability between identical rooftop PV systems in the field and formulate risk mitigation strategies to reduce the error of annual yield prediction. To achieve the aim, long-term monitoring data of 246 identical rooftop PV systems in 19 sub-urban residential communities is analyzed. Through structured side-by-side comparisons, the mechanism of PV performance variations linked to location, module orientation, season, sky clearness, and system age is investigated. It is found that PV performance variability increases in the real built environment compared with the nameplate bandwidth declared by the manufacturers. Significant variation of PV operating performance is observed not only between different locations, but also between peer systems in the same neighborhood. Even in low-rise sub-urban settings, local shading and masking effects play a prominent role and can introduce great uncertainties. Due to the site-to-site and peer-to-peer uniqueness of PV performance, it is inappropriate to employ an identical empirical derate value for all cases. Commissioning and monitoring of PV systems in the field for at least one month with the largest range of solar elevation can significantly reduce PV yield prediction error and mitigate financial risks.
Active deep image clustering

Deep clustering has attracted increasingly more attention in recent years. However, due to the absence of labels, deep clustering sometimes still provides unreliable clustering results. Although semi-supervised deep clustering can alleviate this problem to some extent by involving few human annotations, we observe that the performance of semi-supervised clustering highly depends on the selection of data for human labeling, but unfortunately, the supervised information selection is still a tough problem as traditional semi-supervised methods pay no attention to it. To tackle this problem, in this paper, we propose a novel deep active clustering method, which can actively select the key data for human labeling and apply the human annotations to improve the deep clustering. Different from conventional semi-supervised deep clustering methods which use fixed pre-given supervised information, we design a simple yet effective strategy to select the informative and uncertain data for querying annotation, which is beneficial to the clustering task. Furthermore, we integrate deep representation learning, clustering, and data selection into a unified framework, so that each task can be boosted by each other. Finally, we conduct extensive experiments on benchmark data sets by comparing it with some state-of-the-art deep clustering methods and semi-supervised clustering methods. The experimental results show that our active clustering methods can outperform both the unsupervised and semi-supervised clustering methods, demonstrating the effectiveness of the proposed method. The codes of this paper are released in https://doctor-nobody.github.io/codes/ADC_codes.zip.
Touchless Head-Control (THC): Head Gesture Recognition for Cursor and Orientation Control

The touchless techniques in human-computer interaction (HCI) can effectively expand computer access capabilities for disabled people. This paper presents Touchless Head-Control (THC), an assistive system method for computer cursor control based on head pose captured with an RGB camera. Our work aimed to replace the standard cursor control using a device on the user's head. The convolutional neural networks with predicted fine-grained feature maps and binned classification were applied to estimate the head pose angles. The mouse pointer or cursor is moved to actual locations on the screen based on head movement (yaw and pitch) and the center position of the face. Head tilt to the right or left (roll) to control the mouse button. In addition, the proposed method can be used to simulate the movement of the robot or joystick using the head to control objects within three degrees of freedom (DOF). Various participants were involved in the interaction design evaluation, in which target selection accuracy, travel time, and path efficiency were measured. This technology allows people with limited motor skills to easily control a PC cursor and 3D object orientation without the use of additional equipment or sensors.
A deep learning approach for the development of an Early Earthquake Warning system

In the recent period, machine learning approaches have been widely used in many different fields. For example, in such applications where high immunities to noisy conditions are required. This is the case of an Early Earthquake Warning (EEW) system, a common technology used today to issue an alert in case of incoming seismic events. However, since most seismometers are installed in different locations of the Earth's surface, and different mechanical properties characterize them, each interpretation of a seismic earthquake could result in a highly complex task to be done in real-time using traditional approaches. Therefore, the proposed research has investigated the development of an innovative EEW system based on a novel deep learning system using both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The novel approach has been trained on about 5000 events retrieved from the IRIS University consortium. The achieved results have shown the excellent architecture capability in fully discovering the arrival of seismic events and good performance in the scoring of event intensity.
Skeleton-based Action Recognition via Adaptive Cross-Form Learning

Skeleton-based action recognition aims to project skeleton sequences to action categories, where skeleton sequences are derived from multiple forms of pre-detected points. Compared with earlier methods that focus on exploring single-form skeletons via Graph Convolutional Networks (GCNs), existing methods tend to improve GCNs by leveraging multi-form skeletons due to their complementary cues. However, these methods (either adapting structure of GCNs or model ensemble) require the co-existence of all skeleton forms during both training and inference stages, while a typical situation in real life is the existence of only partial forms for inference. To tackle this, we present Adaptive Cross-Form Learning (ACFL), which empowers well-designed GCNs to generate complementary representation from single-form skeletons without changing model capacity. Specifically, each GCN model in ACFL not only learns action representation from the single-form skeletons, but also adaptively mimics useful representations derived from other forms of skeletons. In this way, each GCN can learn how to strengthen what has been learned, thus exploiting model potential and facilitating action recognition as well. Extensive experiments conducted on three challenging benchmarks, i.e., NTU-RGB+D 120, NTU-RGB+D 60 and UAV-Human, demonstrate the effectiveness and generalizability of our method. Specifically, the ACFL significantly improves various GCN models (i.e., CTR-GCN, MS-G3D, and Shift-GCN), achieving a new record for skeleton-based action recognition.