Reviewerly/exHarmony huggingface dataset:
A survey: Optimization and applications of evidence fusion algorithm based on Dempster–Shafer theory

Since Dempster–Shafer evidence theory was proposed, it has been widely and successfully used in many fields including risk analysis, fault diagnosis, wireless sensor networks, health prognosis, image processing, and target tracking, etc. However, many counter-intuitive results of data fusion will be obtained when evidence fused is highly conflicting. So far, this is still an open issue. To address this issue, many methods have been proposed, but they have not been comprehensively summarized in recent years. In this paper, a detailed survey is set forth about the optimization and application of evidence fusion algorithms based on Dempster–Shafer theory. Firstly, the principle of Dempster–Shafer evidence theory is introduced comprehensively. Then, the existing researches on modifying combination rule and pre-processed pieces of evidence to solve the counter-intuitive problem are reviewed in detail. Next, the performance of these studies is demonstrated, deeply analyzed, and discussed through experiments on general examples. And finally, the application of Dempster–Shafer evidence theory in different fields is critically summarized. What is more, analysis of the current status and the development trend of the research on evidence theory are concluded, which can provide a more comprehensive understanding of the development of the Dempster–Shafer evidence theory.
Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets

Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to support precision/personalized medicine initiatives such as computer-aided detection, diagnosis, prognosis, and treatment planning by using large-scale medical data. Bias and class imbalance represent two of the most pressing challenges for machine learning-based problems, particularly in medical (e.g., oncologic) data sets, due to the limitations in patient numbers, cost, privacy, and security of data sharing, and the complexity of generated data. Depending on the data set and the research question, the methods applied to address class imbalance problems can provide more effective, successful, and meaningful results. This review discusses the essential strategies for addressing and mitigating the class imbalance problems for different medical data types in the oncologic domain.
Real-Time One-Stream Semantic-Guided Refinement Network for RGB-Thermal Salient Object Detection

Salient Object Detection (SOD) has been widely used in practical applications such as multi-sensor image fusion, remote sensing, and defect detection. Recently, SOD from RGB and Thermal (T) has been rapidly developed due to its robustness to extreme situations like low illumination and occlusion. However, existing methods all utilize a dual-stream encoder, which significantly increases the computation burdens and hinders real-world deployment. To this end, we propose a real-time One-stream Semantic-guided Refinement Network (OSRNet) for RGB-T SOD. Specifically, we firstly fuse the RGB and T via concatenation, addition, and multiplication operations to dig the complementary information between each modality. The efficient early fusion not only facilitates the information exchange between each modality but also avoids the cumbersome dual-stream encoder structure. Then, the light-weight decoder is proposed, making the high-level semantic information filter the low-level noisy features and gradually refine the final prediction. Also, we apply deep supervision to make the training procedure more stable and fast. Due to the early fusion strategy, OSRNet can run at a real-time speed (53-60<i>fps</i>) on a single GPU. Extensive quantitative and qualitative experiments show our network outperforms eleven state-of-the-art methods in terms of seven evaluation metrics. Our codes have been released at: https://github.com/huofushuo/OSRNet.
GATrust: A Multi-Aspect Graph Attention Network Model for Trust Assessment in OSNs

Social trust assessment that characterizes a pairwise trustworthiness relationship can spur diversified applications. Extensive efforts have been put in exploration, but mainly focusing on applying graph convolutional network to establish a social trust evaluation model, overlooking user feature factors related to context-aware information on social trust prediction. In this article, we aim to design a new trust assessment framework GATrust which integrates multi-aspect properties of users, including user context-specific information, network topological structure information, and locally-generated social trust relationships. GATrust can assigns different attention coefficients to multi-aspect properties of users in online social networks, for improving the prediction accuracy of social trust evaluation. The framework can then learn multiple latent factors of each trustor-trustee pair to establish a social trust evaluation model, by fusing graph attention network and graph convolution network. We conduct extensive experiments on two popular real-world datasets and the results exhibit that our proposed framework can improve the precision of social trust prediction, outperforming the state-of-the-art in the literature by 4.3% and 5.5% on both two datasets, respectively.
Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast

Though image-level weakly supervised semantic seg-mentation (WSSS) has achieved great progress with Class Activation Maps (CAMs) as the cornerstone, the large su-pervision gap between classification and segmentation still hampers the model to generate more complete and precise pseudo masks for segmentation. In this study, we propose weakly-supervised pixel-to-prototype contrast that can provide pixel-level supervisory signals to narrow the gap. Guided by two intuitive priors, our method is executed across different views and within per single view of an image, aiming to impose cross-view feature semantic consistency regularization and facilitate intra(inter)-class compactness(dispersion) of the feature space. Our method can be seamlessly incorporated into existing WSSS models with-out any changes to the base networks and does not incur any extra inference burden. Extensive experiments manifest that our method consistently improves two strong baselines by large margins, demonstrating the effectiveness. Specifically, built on top of SEAM, we improve the initial seed mIoU on PASCAL VOC 2012 from 55.4% to 61.5%. Moreover, armed with our method, we increase the segmentation mIoU of EPS from 70.8% to 73.6%, achieving new state-of-the-art.
Mutual information based anomaly detection of monitoring data with attention mechanism and residual learning

Due to the damage of sensors or transmission equipment, abnormal monitoring data inevitably exists in the measured raw data, and it significantly impacts the condition assessment of measured structures. Detecting abnormal monitoring data is generally difficult and poses serious challenges to achieve high accuracy. Considering these critical challenges, this study proposes a residual attention network (RAN) that employs the attention mechanism and residual learning to improve classification efficiency and accuracy. With the help of mutual information correlation analysis, the hourly segmented measured data is transformed into the matrix form as the input of the deep learning model for training. The capability and effectiveness of the proposed method are validated with two datasets of acceleration data, one of which is from an arch bridge, and the other is from a cable-stayed bridge. The proposed RAN model is trained with the dataset of the arch bridge, and it identifies most anomaly data types in the test dataset with excellent classification performance. The good generalization performance of the proposed model is verified by another cable-stayed bridge dataset. Compared with the existing preprocessing and deep learning models, the proposed RAN also performs better in multi-classification and classification accuracy.
Cross-Modal Multitask Transformer for End-to-End Multimodal Aspect-Based Sentiment Analysis

As an emerging task in opinion mining, End-to-End Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to extract all the aspect-sentiment pairs mentioned in a pair of sentence and image. Most existing methods of MABSA do not explicitly incorporate aspect and sentiment information in their textual and visual representations and fail to consider the different contributions of visual representations to each word or aspect in the text. To tackle these limitations, we propose a multi-task learning framework named Cross-Modal Multitask Transformer (CMMT), which incorporates two auxiliary tasks to learn the aspect/sentiment-aware intra-modal representations and introduces a Text-Guided Cross-Modal Interaction Module to dynamically control the contributions of the visual information to the representation of each word in the inter-modal interaction. Experimental results demonstrate that CMMT consistently outperforms the state-of-the-art approach JML by 3.1, 3.3, and 4.1 absolute percentage points on three Twitter datasets for the End-to-End MABSA task, respectively. Moreover, further analysis shows that CMMT is superior to comparison systems in both aspect extraction (AE) and sentiment classification (SC), which would move the development of multimodal AE and SC algorithms forward with improved performance.
Complex-Valued Neural Networks: A Comprehensive Survey

Complex-valued neural networks (CVNNs) have shown their excellent efficiency compared to their real counter-parts in speech enhancement, image and signal processing. Researchers throughout the years have made many efforts to improve the learning algorithms and activation functions of CVNNs. Since CVNNs have proven to have better performance in handling the naturally complex-valued data and signals, this area of study will grow and expect the arrival of some effective improvements in the future. Therefore, there exists an obvious reason to provide a comprehensive survey paper that systematically collects and categorizes the advancement of CVNNs. In this paper, we discuss and summarize the recent advances based on their learning algorithms, activation functions, which is the most challenging part of building a CVNN, and applications. Besides, we outline the structure and applications of complex-valued convolutional, residual and recurrent neural networks. Finally, we also present some challenges and future research directions to facilitate the exploration of the ability of CVNNs.
Viewpoint-Based Kernel Fuzzy Clustering With Weight Information Granules

Domain knowledge can be introduced into fuzzy clustering with the aid of information granules, embodied by the concept of viewpoints. For such kind of fuzzy clustering methods, the strategy of acquisition of viewpoints has not been fully developed. Furthermore a way of determining the related information granules deserves more attention. Having these problems in mind, in this study, the density Viewpoint-based Weighted Kernel Fuzzy Clustering (VWKFC) algorithm is proposed. First, the kernel-based hypersphere density initialization (KHDI) algorithm is presented as a certain prerequisite, in which the kernel distance is utilized instead of the Euclidean distance. Besides, a novel density radius is put forward. Second, the concept of the weight information granule is established, which incorporates two parts. The feature weight matrix is provided, where different weights are assigned to different features to reduce the influence of unrelated features. Meanwhile a sample weight is assigned to each data point, thus the influence of noise and outliers on clustering can be reduced to a certain extent. Third, the data point with the highest local density obtained by KHDI is regarded as the density viewpoint. Then we combine kernel mechanism, density viewpoints, weight information granules and a maximum entropy regularization to design the VWKFC algorithm, and prove its convergence. Experimental results validate that VWKFC is superior over eight related clustering algorithms with regard to five evaluation indexes, especially when processing high-dimensional data. It has been shown that VWKFC makes the selection of initialized cluster centers and viewpoints more reasonable, and obtains better clustering results, and achieves higher convergence speed.
A Comprehensive Study on Metaverse and Its Impacts on Humans

Virtual Reality (VR) and Augmented Reality (AR) have revolutionized technology and taken the world by storm. They established the foundation for numerous future innovations. Virtual and augmented reality are now widely employed to improve user experiences in various areas. Over time, more and more companies and businesses have begun to use this cutting-edge technology to improve their products and services. Recently, the attention to VR and AR has exploded with the concept “Metaverse” surfacing in mainstream media. Many major companies have already set their goals in motion and are working on building the core of their metaverses. This review paper focuses on explaining the concept of the metaverse, its history, and its associated benefits. Through a survey, it helps understand people’s concerns with the metaverse and how it can impact and affect humans mentally, physically, and psychologically. The analysis of this paper can help humans prepare themselves for what the new technologies have to offer, in addition to assisting companies in building a flawless metaverse.
Classification of Breast Tumors Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods

Breast cancer is the most common cancer among women worldwide. Early-stage diagnosis of this disease can significantly improve the efficiency of treatment. Computer-Aided Diagnosis (CAD) Systems are adopted widely in this regard due to their reliability, accuracy and affordability. There are different imaging techniques for a breast cancer diagnosis; one of the most accurate ones is histopathology which is used in this paper. Deep feature transfer learning is used as the main idea of the proposed CAD system’s feature extractor. As such, the present paper works on sixteen different pre-trained networks with a focus on their classification phase , something that has not been studied enough. The Inception-ResNet-v2, which has both residual and inception networks profits together, has shown the best feature extraction capability in the case of breast cancer histopathology images among all tested Convolutional neural networks (CNNs). In the classification phase, the ensemble of Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost) and Light Gradient boosting Machine (LightGBM) has given the best average accuracy. The Breast Cancer Histopathological Image Classification (BreakHis) dataset helps evaluating the proposed method, i.e., IRv2-CXL, with the experimental results indicating that IRv2-CXL outperforms other state-of-the-art methods. • A novel deep feature transfer learning model, called IRv2-CXL, is proposed. • IRv2-CXL is implemented to improve prediction accuracy and lower variance. • IRv2-CXL is used for breast tumours classification in histopathological images. • Over 100 different structures are evaluated to identify the best model. • IRv2-CXL outperformes other state-of-the-art methods on the BreakHis dataset.
WOASCALF: A new hybrid whale optimization algorithm based on sine cosine algorithm and levy flight to solve global optimization problems

In recent years, researchers have been focused on solving optimization problems in order to determine the global optimum. Increasing the dimension of a problem increases its computational cost and complexity as well. In order to solve these types of problems, metaheuristic algorithms are used. The whale optimization algorithm (WOA) is one of the most well-known algorithms based on whale hunting behavior. In this paper, the WOA algorithm is combined with the Sine Cosine Algorithm (SCA), which is based on the principle of trigonometric sine-cosine. The WOA algorithm has superior performance in the exploration phase in contrast with the exploitation phase, whereas the SCA algorithm has weaknesses in the exploitation phase. The levy flight distribution has been used in the hybrid WOA and SCA algorithm to improve these deficiencies. This study introduced a novel hybrid algorithm named WOASCALF. In this algorithm, the search agents' position updates are based on a hybridization of the WOA, SCA, and levy flight. Each of these metaheuristic algorithms has reasonable performance, however, the Levy distribution caused small and large distance leaps in each phase of the algorithm. Thus, it is possible for the appropriate search agent to move in different directions of the search space. The performance of the WOASCALF has been evaluated by the 23 well-known benchmark functions and three real-world engineering problems. The result analysis demonstrates that the exploration ability of WOASCALF has strong superiority over other compared algorithms.
NISQ computing: where are we and where do we go?

Abstract In this short review article, we aim to provide physicists not working within the quantum computing community a hopefully easy-to-read introduction to the state of the art in the field, with minimal mathematics involved. In particular, we focus on what is termed the Noisy Intermediate Scale Quantum era of quantum computing. We describe how this is increasingly seen to be a distinct phase in the development of quantum computers, heralding an era where we have quantum computers that are capable of doing certain quantum computations in a limited fashion, and subject to certain constraints and noise. We further discuss the prominent algorithms that are believed to hold the most potential for this era, and also describe the competing physical platforms on which to build a quantum computer that have seen the most success so far. We then talk about the applications that are most feasible in the near-term, and finish off with a short discussion on the state of the field. We hope that as non-experts read this article, it will give context to the recent developments in quantum computers that have garnered much popular press, and help the community understand how to place such developments in the timeline of quantum computing.
The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study

Optimization algorithms are used to improve model accuracy. The optimization process undergoes multiple cycles until convergence. A variety of optimization strategies have been developed to overcome the obstacles involved in the learning process. Some of these strategies have been considered in this study to learn more about their complexities. It is crucial to analyse and summarise optimization techniques methodically from a machine learning standpoint since this can provide direction for future work in both machine learning and optimization. The approaches under consideration include the Stochastic Gradient Descent (SGD), Stochastic Optimization Descent with Momentum, Rung Kutta, Adaptive Learning Rate, Root Mean Square Propagation, Adaptive Moment Estimation, Deep Ensembles, Feedback Alignment, Direct Feedback Alignment, Adfactor, AMSGrad, and Gravity. prove the ability of each optimizer applied to machine learning models. Firstly, tests on a skin cancer using the ISIC standard dataset for skin cancer detection were applied using three common optimizers (Adaptive Moment, SGD, and Root Mean Square Propagation) to explore the effect of the algorithms on the skin images. The optimal training results from the analysis indicate that the performance values are enhanced using the Adam optimizer, which achieved 97.30% accuracy. The second dataset is COVIDx CT images, and the results achieved are 99.07% accuracy based on the Adam optimizer. The result indicated that the utilisation of optimizers such as SGD and Adam improved the accuracy in training, testing, and validation stages.
The promise of the metaverse in mental health: the new era of MEDverse

Since Mark Zuckerberg's announcement about the development of new three-dimensional virtual worlds for social communication, a great debate has been raised about the promise of such a technology. The metaverse, a term formed by combining meta and universe, could open a new era in mental health, mainly in psychological disorders, where the creation of a full-body illusion via digital avatar could promote healthcare and personal well-being. Patients affected by body dysmorphism symptoms (i.e., eating disorders), social deficits (i.e. autism) could greatly benefit from this kind of technology. However, it is not clear which advantage the metaverse would have in treating psychological disorders with respect to the well-known and effective virtual reality (VR) exposure therapy. Indeed, in the last twenty years, a plethora of studies have demonstrated the effectiveness of VR technology in reducing symptoms of pain, anxiety, stress, as well as, in improving cognitive and social skills. We hypothesize that the metaverse will offer more opportunities, such as a more complex, virtual realm where sensory inputs, and recurrent feedback, mediated by a "federation" of multiple technologies - e.g., artificial intelligence, tangible interfaces, Internet of Things and blockchain, can be reinterpreted for facilitating a new kind of communication overcoming self-body representation. However, nowadays a clear starting point does not exist. For this reason, it is worth defining a theoretical framework for applying this new kind of technology in a social neuroscience context for developing accurate solutions to mental health in the future.
Mastering the game of Stratego with model-free multiagent reinforcement learning

We introduce DeepNash, an autonomous agent that plays the imperfect information game Stratego at a human expert level. Stratego is one of the few iconic board games that artificial intelligence (AI) has not yet mastered. It is a game characterized by a twin challenge: It requires long-term strategic thinking as in chess, but it also requires dealing with imperfect information as in poker. The technique underpinning DeepNash uses a game-theoretic, model-free deep reinforcement learning method, without search, that learns to master Stratego through self-play from scratch. DeepNash beat existing state-of-the-art AI methods in Stratego and achieved a year-to-date (2022) and all-time top-three ranking on the Gravon games platform, competing with human expert players.
Kubric: A scalable dataset generator

Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details. But collecting, processing and annotating real data at scale is difficult, expensive, and frequently raises additional privacy, fairness and legal concerns. Synthetic data is a powerful tool with the potential to address these shortcomings: 1) it is cheap 2) supports rich ground-truth annotations 3) offers full control over data and 4) can circumvent or mitigate problems regarding bias, privacy and licensing. Unfortunately, software tools for effective data generation are less mature than those for architecture design and training, which leads to fragmented generation efforts. To address these problems we introduce Kubric, an open-source Python framework that interfaces with PyBullet and Blender to generate photo-realistic scenes, with rich annotations, and seamlessly scales to large jobs distributed over thousands of machines, and generating TBs of data. We demonstrate the effectiveness of Kubric by presenting a series of 13 different generated datasets for tasks ranging from studying 3D NeRF models to optical flow estimation. We release Kubric, the used assets, all of the generation code, as well as the rendered datasets for reuse and modification.