Reviewerly/exHarmony huggingface dataset:
Investigating quantum cause-effect graphs

Cause-effect graphs have shown promising results in identifying relations among causes and effects of classical software systems, followed by designing effective test cases from them. Towards this end, we investigate the use of cause-effect graphs for quantum programs. Classical cause-effect graphs apply classical logic (e.g., AND, OR) to express these relations, which might not be practical for describing similar relations in quantum programs due to quantum superposition and entanglement. Thus, we propose an extension of cause-effect graphs, where quantum logic inspired functions (e.g., Hadamard) and their generalizations are defined and applied. Moreover, we present a metamodel describing various forms of cause-effect graphs. Finally, we demonstrate a possible method for generating test cases from a quantum cause-effect graph applied to a Bell state quantum program. Lastly, the design and utility of the resulting testing method is discussed, along with future prospects for general quantum cause-effect graphs.
Bone Chapels: who might be interested in visiting and why?

The purpose of this research is to better understand the various segments of tourists that want to attend dark exhibitions such as the Capuchin Crypt in Rome (a chapel of bones). The findings supported four motivational variables based on a sample of 383 people and quantitative analysis: ‘reflecting on death and mortality’, ‘learning and curiosity’, ‘dark fascination’, and ‘prestige’. Additionally, five tourists categories were discovered: ‘dark contemplative tourist’, ‘dark history tourist’, ‘dark passive tourist’, ‘dark enthusiast tourist’, and ‘dark occasion tourist’. The most motivated visitors are ‘dark enthusiast tourists’. This is the first study on this type of dark attraction, and the findings allow for a better understanding of tourist motivations and segmentation, as well as the enhancement and management of appealing dark tourist locations.
PA3DNet: 3-D Vehicle Detection With Pseudo Shape Segmentation and Adaptive Camera-LiDAR Fusion

3-D vehicle detection is a key perception technique in autonomous driving.In this article, a novel 3-D vehicle detection framework that fuses camera images and LiDAR point clouds is proposed, named PA3DNet.The key novelties of PA3DNet is the proposing of a Pseudo Shape Segmentation (PSS) model and an Adaptive Camera-LiDAR Fusion (ACLF) module.The PSS model leverages selfassembled vehicle prototypes to learn shape-aware vehicle features.In order to achieve the adaptive fusion between visual semantics and LiDAR point features, learnable weight parameters are developed in the ACLF module to formulate an implicit complementarity between the two modalities.Extensive experiments on the widely used autonomous driving KITTI dataset demonstrate that PA3DNet achieves competitive accuracy when compared to advanced methods.It achieves 5.37% higher AP on Easy difficulty of 30-50m and 9.67% higher AP on Moderate difficulty of >50m.
Dual-Aligned Feature Confusion Alleviation for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) aims to recognize both seen and unseen samples by leveraging the connections between semantic and visual representations. Recently, a majority of GZSL methods focus on generating visual features for unseen categories conditioned on category-level semantic attributes. However, there is a considerable gap between generated features and real unseen features since the generator is trained with only seen samples. The final classifier may get confused by the unfaithful generated features and make misclassification. To alleviate this issue, we propose a dual-aligned feature confusion alleviation (DFCA) framework that simultaneously generates faithful and discriminative features for unseen categories. Specifically, our DFCA attains the faithfulness via a conditional invertible neural network (cINN) and aligns the generated visual features and reconstructed semantic conditions with their real counterparts, respectively. To further encourage distinguishable synthetic features, we learn discriminative category-level semantic conditions for cINN with an attributes mapping layer. To verify the proposed method, we conduct extensive experiments on five widely used benchmarks. Experimental results show that our method outperforms previous state-of-the-arts and successfully alleviates features confusion problem in GZSL. For instance, our method achieves the best performance in terms of seen accuracy, unseen accuracy and harmonic mean accuracy on FLO.
Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

In the last decades, deep learning (DL) has emerged as a powerful and dominant technique for solving challenging problems in various fields. Likewise, in the field of digital image forensics, a large and growing body of literature investigates DL-based techniques for detecting and classifying tampered regions in images. This article aims to provides a comprehensive survey of state-of-the-art DL-based methods for image-forgery detection. Copy-move images and spliced images, two of the most popular types of forged images, were considered. Recently, owing to advances in DL, DL-based approaches have yielded much better results as compared to traditional non-DL-based ones. The surveyed techniques were proposed by developing or fusing various efficient DL methods, such as CNN, RCNN, or LSTM to adapt to detecting tampered traces.
Multiple Relational Learning Network for Joint Referring Expression Comprehension and Segmentation

Multi-task learning is a successful learning framework which improves the performance of prediction models by leveraging knowledge among related tasks. Referring expression comprehension (REC) and segmentation (RES) are highly relevant tasks, which both are language-guided visual recognition tasks. However, their relations have not yet been fully exploited in previous works. In this paper, a Multiple Relational Learning Network (MRLN) is proposed for multi-task learning of REC and RES. First, a feature-feature interaction learning module is introduced to handle the complicated interactions among features. Moreover, we propose a feature-task dependence learning module, which associates the related features with target tasks. Furthermore, a task-task relationship learning module is designed, which captures the relationships among tasks automatically and guides the REC and RES fine-tuning adaptively. To verify our proposed approach, experiments are conducted on three benchmark datasets, i.e., RefCOCO, RefCOCO+, and RefCOCOg. Extensive experiments demonstrate that the multiple relationships are more appealing since it alleviates the prediction inconsistency issue in multi-task setup. In addition, the experimental results report the significant performance gains of MRLN over most existing methods, i.e., up to 83.46 % for REC and 63.62 % for RES over state-of-the-art methods, which demonstrate the validity and superiority of MRLN.
How do Plastic Surgeons use the Metaverse: A Systematic Review

In 2021, metaverse became a buzzword. The metaverse is a digital virtual world in which people can live as a digital virtual identity. In the metaverse, people can participate in making rules and create their own worlds. As plastic surgeons in the new era, we have been thinking about the application of the metaverse in plastic surgery. Therefore, we carried out this study to systematically review the current published articles on the application of metaverse in medicine, so as to provide a reference for the rational and effective application of metaverse by plastic surgeons in the future.The researchers searched the Wanfang, Weipu, China National Knowledge Infrastructure (CNKI), PubMed, the Cochrane Library, and the Embase database. The retrieval time was set from the database establishment to April 2022. All studies on the use of the metaverse in medicine were included in our study.A total of 4 studies were included in this study. According to our study, the metaverse is mostly used for skill training for residents and medical students. The metaverse was used for the first time to train doctors in lung cancer surgery at Seoul National University Bundang Hospital, Korea. Augmented reality in the metaverse has also been used to aid surgery and to address a variety of medical and mental health problems for patients.At present, the application of metaverse is still in the preliminary stage of exploration, and the practical effect of metaverse on plastic surgery needs to be observed and evaluated. Although there are still many problems in the metaverse, the new medical model of plastic surgery supported by the metaverse is worth looking forward to.
Aspects of modelling requirements in very-large agile systems engineering

Using models for requirements engineering (RE) is uncommon in systems engineering, despite the widespread use of model-based engineering in general. One reason for this lack of use is that formal models do not match well the trend to move towards agile developing methods. While there exists work that investigates challenges in the adoption of requirements modelling and agile methods in systems engineering, there is a lack of work studying successful approaches of using requirements modelling in agile systems engineering. To address this gap, we conducted a case study investigating the application of requirements models at Ericsson AB, a Swedish telecommunications company. We studied a department using requirements models to bridge agile development and plan-driven development aspects. We find that models are used to understand how requirements relate to each other, and to keep track with the product's evolution. To cope with the effort to maintain models over time, study participants suggest to rely on text-based notations that bring the models closer to developers and allow integration into existing software development workflows. This results in tool trade-offs, e.g., losing the possibility to control diagram layout.
A linear Bayesian filter for input and state estimation of structural systems

Abstract This paper proposes a linear recursive Bayesian filter for minimum variance unbiased joint input and state estimation of structural systems. Unlike the augmented Kalman filter (AKF), the proposed filter falls within the category of Bayesian filters in which unknown inputs are estimated without attributing any fictitious input model or statistics. Also, in contrast with the existing algorithms in the latter category, such as the Gillijns and De Moor Filters (GDFs), the developed filter applies to systems with and without direct feedthrough, in particular, systems with a rank‐deficient feedforward matrix. Because of the latter features, the filter is referred to as universal filter (UF) for convenience. The numerical examples show that the UF performs better than the AKF. Due to its structure, the UF does not require the tuning of the hyperparameters for inputs, and therefore the problematic instability of the AKF is not encountered in the case of a large modeling error variance of the input. For systems with direct feedthrough, the error and covariance propagation terms differ due to the distinct state space. Consequently, the UF can enhance estimations due to the well‐conditionedness of the relevant inversion problem. Moreover, the UF can deal with systems with rank‐deficient feedforward matrix where these systems are not covered by GDFs.
An extensive study on Internet of Behavior (IoB) enabled Healthcare-Systems: Features, facilitators, and challenges

The Internet of Behaviour (IoB) is an effort to dissect behavioural patterns as explained by data collection. IoB is an extension of the Internet of Things (IoT). Therefore, both are anticipated to experience exponential growth in the upcoming years. Healthcare firms have many opportunities to employ IoB to provide individualised services and anticipate patients’ behaviour. As behaviour and analysis are closely related to psychology, many techniques exist to collect relevant data. The IoB improves the doctor’s and patient’s experience. As IoT and IoB are interconnected, IoB technology collects and analyses data depending on user activity. These offer a practical technique for developing real-time remote health monitoring systems. This technology aids in the optimisation of auto insurance premiums in the healthcare sector. It tries to alter patient behaviour in order to improve the treatment process. IoB has applications in various areas, including retail and entertainment, and has the potential to change the marketing sector significantly. This technology is helpful for the appropriate analysis and comprehension of behavioural data used for creating valuable services for treatment. The primary purpose of this paper is to study IoB and its need for healthcare. The working process structure and features of IoB for the healthcare domain are studied. This paper further identifies and analyses the significant applications of IoB for healthcare. In the future, IoB technologies will give us a higher quality of life and well-being. IoB is the ideal fusion of technology, data analytics, and behavioural science. This will help healthcare professionals collect data and analyse the patient’s behaviours for an efficient treatment process. The IoB will be the digital ecosystem’s intelligence in a few years.
Multi-Task Learning Model Based on BERT and Knowledge Graph for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to identify the sentiment of an aspect in a given sentence and thus can provide people with comprehensive information. However, many conventional methods need help to discover the linguistic knowledge implicit in sentences. Additionally, they are susceptible to unrelated words. To improve the performance of the model in the ABSA task, a multi-task sentiment analysis model based on Bidirectional Encoder Representation from Transformers (BERT) and a Knowledge Graph (SABKG) is proposed in this paper. Expressly, part-of-speech information is incorporated into the output representation of BERT, thereby obtaining textual semantic information through linguistic knowledge. It also enhances the textual representation to identify the aspect terms. Moreover, this paper constructs a knowledge graph of aspect and sentiment words. It uses a graph neural network to learn the embeddings in the triplet of “aspect word, sentiment polarity, sentiment word”. The constructed graph improves the contextual relationship between the text’s aspect and sentiment words. The experimental results on three open datasets show that the proposed model can achieve the most advanced performance compared with previous models.
Dual parallel net: A novel deep learning model for rectal tumor segmentation via CNN and transformer with Gaussian Mixture prior

Segmentation of rectal cancerous regions from Magnetic Resonance (MR) images can help doctor define the extent of the rectal cancer and judge the severity of rectal cancer, so rectal tumor segmentation is crucial to improve the accuracy of rectal cancer diagnosis. However, accurate segmentation of rectal cancerous regions remains a challenging task due to the shape of rectal tumor has significant variations and the tumor and surrounding tissue are indistinguishable. In addition, in the early research on rectal tumor segmentation, most deep learning methods were based on convolutional neural networks (CNNs), and traditional CNN have small receptive field, which can only capture local information while ignoring the global information of the image. Nevertheless, the global information plays a crucial role in rectal tumor segmentation, so traditional CNN-based methods usually cannot achieve satisfactory segmentation results. In this paper, we propose an encoder-decoder network named Dual Parallel Net (DuPNet), which fuses transformer and classical CNN for capturing both global and local information. Meanwhile, as for capture features at different scales as well as to avoid accuracy loss and parameters reduction, we design a feature adaptive block (FAB) in skip connection between encoder and decoder. Furthermore, in order to utilize the apriori information of rectal tumor shape effectively, we design a Gaussian Mixture prior and embed it in self-attention mechanism of the transformer, leading to robust feature representation and accurate segmentation results. We have performed extensive ablation experiments to verify the effectiveness of our proposed dual parallel encoder, FAB and Gaussian Mixture prior on the dataset from the Shanxi Cancer Hospital. In the experimental comparison with the state-of-the-art methods, our method achieved a Mean Intersection over Union (MIoU) of 89.34% on the test set. In addition to that, we evaluated the generalizability of our method on the dataset from Xinhua Hospital, the promising results verify the superiority of our method.
Contextual Embeddings-Based Web Page Categorization Using the Fine-Tune BERT Model

The World Wide Web has revolutionized the way we live, causing the number of web pages to increase exponentially. The web provides access to a tremendous amount of information, so it is difficult for internet users to locate accurate and useful information on the web. In order to categorize pages accurately based on the queries of users, methods of categorizing web pages need to be developed. The text content of web pages plays a significant role in the categorization of web pages. If a word’s position is altered within a sentence, causing a change in the interpretation of that sentence, this phenomenon is called polysemy. In web page categorization, the polysemy property causes ambiguity and is referred to as the polysemy problem. This paper proposes a fine-tuned model to solve the polysemy problem, using contextual embeddings created by the symmetry multi-head encoder layer of the Bidirectional Encoder Representations from Transformers (BERT). The effectiveness of the proposed model was evaluated by using the benchmark datasets for web page categorization, i.e., WebKB and DMOZ. Furthermore, the experiment series also fine-tuned the proposed model’s hyperparameters to achieve 96.00% and 84.00% F1-Scores, respectively, demonstrating the proposed model’s importance compared to baseline approaches based on machine learning and deep learning.
Taxing bitcoin: Incentivizing the difficulty adjustment mechanism to reduce electricity usage

This paper develops a model of the bitcoin market that views the bitcoin as a tradeable commodity whose supply is managed by the Bitcoin protocol. Miners utilize equipment and electricity to solve complex computational problems and the first miner to solve a problem is rewarded with bitcoins. The protocol adjusts the difficulty of the problem to target a constant growth rate in the supply of bitcoins over time. The model demonstrates that an increase (decrease) in the difficulty works in effect like a government's placing an ad valorem tax (subsidy) on the price of a commodity. The rents that would have arisen from limiting supply, however, are wasted as electricity costs. It is shown that an actual tax on the price of the bitcoin can be used to displace the electricity costs. Using data from March 2014 to January 2019, it is estimated that the difficulty adjustment mechanism resulted in net welfare losses to the miners and buyers of bitcoins of 373.8 million USD. Average initial tax rates of 35% and 347.5% would have fully displaced the electricity costs and maximized their reduction, respectively.
Contrastive graph clustering with adaptive filter

Graph clustering has received significant attention in recent years due to the breakthrough of graph neural networks (GNNs). However, GNNs frequently assume strong data homophily, which is not true in many real-world applications. Furthermore, practical graphs are typically noisy and sparse, which inevitably degrades the clustering performance. To this end, we propose a novel Contrastive Graph Clustering (CGC) method with adaptive filter framework. We first design an adaptive filter that can automatically learn a suitable filter for different data, mining holistic information beyond low-frequency components and encoding topology structure information into features. Afterward, we learn a refined graph based on a graph-level contrastive mechanism, which further boosts graph discriminability. Extensive experiments show that the proposed CGC method achieves significant improvement over state-of-the-art methods on several benchmark datasets. In particular, our simple method, which does not employ neural networks, outperforms many deep learning approaches.
Robust Dimensionality Reduction via Low-rank Laplacian Graph Learning

Manifold learning is a widely used technique for dimensionality reduction as it can reveal the intrinsic geometric structure of data. However, its performance decreases drastically when data samples are contaminated by heavy noise or occlusions, which leads to unsatisfying data processing performance. We propose a novel robust dimensionality reduction method via low-rank Laplacian graph learning for classification and clustering tasks to solve the above problem. First, we construct a low-rank Laplacian graph by combining manifold learning and subspace learning. This graph can capture both global and local structural information of the data. And we introduce rank constraints for the Laplacian graph to make it more discriminative. Second, we put the learning of projection matrix and sample affinity graph into a unified framework. The projection matrix is embedded into a robust low-rank Laplacian graph so that the low-dimensional mapping of data can maintain the structural information in the graph well. Finally, we add a regularization term to the projection matrix to make it have the ability of both feature extraction and feature selection. Therefore, the proposed model can resist the interference of noise or data damage to learn the optimal projection to achieve better performance in dimensionality reduction through such a data dimensionality reduction joint framework. Comprehensive experiments on various benchmark datasets with varying degrees of occlusions or corruptions are carried out to evaluate the performance of the proposed method. Compared with the state-of-the-art dimensionality reduction methods in the literature, the experimental results are inspiring, showing our method’s effectiveness and robustness in classification and clustering, especially in object recognition scenarios with noise or occlusions.
Saliency Attack: Towards Imperceptible Black-box Adversarial Attack

Deep neural networks are vulnerable to adversarial examples, even in the black-box setting where the attacker is only accessible to the model output. Recent studies have devised effective black-box attacks with high query efficiency. However, such performance is often accompanied by compromises in attack imperceptibility, hindering the practical use of these approaches. In this article, we propose to restrict the perturbations to a small salient region to generate adversarial examples that can hardly be perceived. This approach is readily compatible with many existing black-box attacks and can significantly improve their imperceptibility with little degradation in attack success rates. Furthermore, we propose the Saliency Attack, a new black-box attack aiming to refine the perturbations in the salient region to achieve even better imperceptibility. Extensive experiments show that compared to the state-of-the-art black-box attacks, our approach achieves much better imperceptibility scores, including most apparent distortion (MAD), L 0 and L 2 distances, and also obtains significantly better true success rate and effective query number judged by a human-like threshold on MAD. Importantly, the perturbations generated by our approach are interpretable to some extent. Finally, it is also demonstrated to be robust to different detection-based defenses.