Reviewerly/exHarmony huggingface dataset:
History of Mammography: Analysis of Breast Imaging Diagnostic Achievements over the Last Century

Breast cancer is the most common forms of cancer and a leading cause of mortality in women. Early and correct diagnosis is, therefore, essential to save lives. The development of diagnostic imaging applied to the breast has been impressive in recent years and the most used diagnostic test in the world is mammography, a low-dose X-ray technique used for imaging the breast. In the first half of the 20th century, the diagnosis was in practice only clinical, with consequent diagnostic delay and an unfavorable prognosis in the short term. The rise of organized mammography screening has led to a remarkable reduction in mortality through the early detection of breast malignancies. This historical review aims to offer a complete panorama of the development of mammography and breast imaging during the last century. Through this study, we want to understand the foundations of the pillar of radiology applied to the breast through to the most modern applications such as contrast-enhanced mammography (CEM), artificial intelligence, and radiomics. Understanding the history of the development of diagnostic imaging applied to the breast can help us understand how to better direct our efforts toward an increasingly personalized and effective diagnostic approach. The ultimate goal of imaging applied to the detection of breast malignancies should be to reduce mortality from this type of disease as much as possible. With this paper, we want to provide detailed documentation of the main steps in the evolution of breast imaging for the diagnosis of breast neoplasms; we also want to open up new scenarios where the possible current and future applications of imaging are aimed at being more precise and personalized.
An Innovative Development with Multidisciplinary Perspective in Metaverse Integrating with Blockchain Technology with Cloud Computing Techniques

The metaverse is a concept of creating an innovative virtual 3D environment immersed with social interconnections. This is the process of establishing the real world into a virtual environment through virtual and augmented reality. The metaverse is considered as the advanced recapitulation of the online platform. The virtual platform adopted through the digital environment helps in improvement with dilute in everyday lives. Thus the future revolution is done through the various advances and challenges in the virtual world. Hence the metaverse is the amalgamation of numerous technologies. The interconnections with the physical environment with the virtual world helps in the sharing of information in diverse field. The development of the digital environment through the exact replica of the real world helps in the overall development in social, economic and political fields. The important requirements of metaverse includes sensors, smart glasses with headsets. The necessary parameter needed to adopt is the privacy of the users in the metaverse environment. This is done through the blockchain technology with cloud computing techniques.
“Real Attackers Don't Compute Gradients”: Bridging the Gap Between Adversarial ML Research and Practice

Recent years have seen a proliferation of research on adversarial machine learning. Numerous papers demonstrate powerful algorithmic attacks against a wide variety of machine learning (ML) models, and numerous other papers propose defenses that can withstand most attacks. However, abundant real-world evidence suggests that actual attackers use simple tactics to subvert ML-driven systems, and as a result security practitioners have not prioritized adversarial ML defenses. Motivated by the apparent gap between researchers and practitioners, this position paper aims to bridge the two domains. We first present three real-world case studies from which we can glean practical insights unknown or neglected in research. Next we analyze all adversarial ML papers recently published in top security conferences, highlighting positive trends and blind spots. Finally, we state positions on precise and cost-driven threat modeling, collaboration between industry and academia, and reproducible research. We believe that our positions, if adopted, will increase the real-world impact of future endeavours in adver-sarial ML, bringing both researchers and practitioners closer to their shared goal of improving the security of ML systems.
Exploring the Hype of Blockchain Adoption in Agri-Food Supply Chain: A Systematic Literature Review

This study examines the effect of blockchain adoption on the agri-food supply chain. A systematic literature review approach was used to analyze and synthesize the findings from the existing literature, focusing on fundamental research themes, research gaps, and the direction of future research on the impact of blockchain adoption in the agri-food supply chain. Twenty-seven full-length articles were considered and thematically analyzed in this study. The authors identified eight themes from the literature, including factors responsible for blockchain adoption and new research areas such as digitalization and the impact after adoption. These themes shed light on the agri-food supply chain practices following the adoption of blockchain technology. Moreover, this study provides a foundation for strategic and policy initiatives in the agri-food industry involving blockchain technology. The findings indicate that critical factors driving blockchain technology adoption in the agri-food industry include ensuring food traceability and transparency, food safety and security, food supply and logistics, food integrity, environmental awareness, and reducing food waste. Additionally, this study highlights the importance of guidelines and policy-level involvement after adopting blockchain technology, particularly in facilitating accurate quantification and promoting digitalization to address challenges and streamline processes. The study concludes by suggesting future research avenues for blockchain technology in the supply chain domain.
Scalable and Flexible Classical Shadow Tomography with Tensor Networks

Classical shadow tomography is a powerful randomized measurement protocol for predicting many properties of a quantum state with few measurements. Two classical shadow protocols have been extensively studied in the literature: the single-qubit (local) Pauli measurement, which is well suited for predicting local operators but inefficient for large operators; and the global Clifford measurement, which is efficient for low-rank operators but infeasible on near-term quantum devices due to the extensive gate overhead. In this work, we demonstrate a scalable classical shadow tomography approach for generic randomized measurements implemented with finite-depth local Clifford random unitary circuits, which interpolates between the limits of Pauli and Clifford measurements. The method combines the recently proposed locally-scrambled classical shadow tomography framework with tensor network techniques to achieve scalability for computing the classical shadow reconstruction map and evaluating various physical properties. The method enables classical shadow tomography to be performed on shallow quantum circuits with superior sample efficiency and minimal gate overhead and is friendly to noisy intermediate-scale quantum (NISQ) devices. We show that the shallow-circuit measurement protocol provides immediate, exponential advantages over the Pauli measurement protocol for predicting quasi-local operators. It also enables a more efficient fidelity estimation compared to the Pauli measurement.
Evidential Random Forests

In machine learning, some models can make uncertain and imprecise predictions, they are called evidential models. These models may also be able to handle imperfect labeling and take into account labels that are richer than the commonly used hard labels, containing uncertainty and imprecision. This paper proposes an Evidential Decision Tree, and an Evidential Random Forest. These two models use a distance and a degree of inclusion to allow the model to group observations whose response elements are included in each other into a single node. Experimental results showed better performance for the presented methods compared to other evidential models and to recent Cautious Random Forests when the data is noisy. The models also offer a better robustness to the overfitting effect when using datasets that are effectively uncertainly and imprecisely labeled by the contributors. The proposed models are also able to predict rich labels, an information that can be used in other approaches, such as active learning.
Working with service robots? A systematic literature review of hospitality employees’ perspectives

Taking a systematic literature review approach, this study examines research on service robot adoption from hospitality employees' perspectives. The findings reveal the current status of service robot research on hospitality employees by summarizing publication channels, temporal distribution, classifications of publications, and countries of focus of existing studies. This study also identifies key theories and constructs applied in existing research. Building on these, a holistic conceptual model including key antecedents, mediators, outcomes, and moderators in terms of service robot adoption among hospitality employees is proposed. Furthermore, several key promising research directions have been identified and discussed in this study, which serve as important references for researchers interested in pursuing this topic.
Multi-view subspace clustering via adaptive graph learning and late fusion alignment

Multi-view subspace clustering has attracted great attention due to its ability to explore data structure by utilizing complementary information from different views. Most of existing methods learn a sample representation coefficient matrix or an affinity graph for each single view, then the final clustering result is obtained from the spectral embedding of a consensus graph using certain traditional clustering techniques, such as k-means. However, clustering performance will be degenerated if the early fusion of partitions cannot fully exploit relationships between all samples. Different from existing methods, we propose a multi-view subspace clustering method via adaptive graph learning and late fusion alignment (AGLLFA). For each view, AGLLFA learns an affinity graph adaptively to capture the similarity relationship among samples. Moreover, a spectral embedding learning term is designed to exploit the latent feature space of different views. Furthermore, we design a late fusion alignment mechanism to generate an optimal clustering partition by fusing view-specific partitions obtained from multiple views. An alternate updating algorithm with validated convergence is developed to solve the resultant optimization problem. Extensive experiments on several benchmark datasets are conducted to illustrate the effectiveness of the proposed method when compared with other state-of-the-art methods. The demo code of this work is publicly available at https://github.com/tangchuan2000/AGLLFA.
A systematic review on predicting PV system parameters using machine learning

Due to the growing demand, assessing performance has become obligatory for photovoltaic (PV) energy harvesting systems. Performance assessment involves estimating different PV system parameters. Traditional ways, such as calculating solar radiation using satellite data and the IV characteristics approach as assessment methods, are no longer reliable enough to provide a reasonable projection of PV system parameters. Estimating system parameters using machine learning (ML) approaches has become a reliable and popular method because of its speed and accuracy. This paper systematically reviewed ML-based PV parameter estimation studies published in the last three years (2020 - 2022). Studies were analyzed using several criteria, including ML algorithm, outcome, experimental setup, sample data size, and error metric. The analysis revealed several interesting factors. The neural network was the most popular ML method (32.55%), followed by random vector functional link (13.95%) and support vector machine (9.30%). Dataset was sourced from hardware tests and computer-based simulations: 66% of the studies used data from only computer simulation, 18% used data from only hardware setup, and the 16% experiments used data from both hardware and simulations to evaluate different system parameters. The top three most commonly used error metrics were root mean square error (29.1%), mean absolute error (17.5%), and coefficient of determination (15.9%). Our systematic review will help researchers assess ML algorithms' projection in PV system parameters estimation. Consequently, scopes shall be created to establish more robust governmental frameworks, expand private financing in the PV industry, and optimize PV system parameters.
A high-quality feature selection method based on frequent and correlated items for text classification

Abstract The feature selection problem is a significant challenge in pattern recognition, especially for classification tasks. The quality of the selected features plays a critical role in building effective models, and poor-quality data can make this process more difficult. This work explores the use of association analysis in data mining to select meaningful features, addressing the issue of duplicated information in the selected features. A novel feature selection technique for text classification is proposed, based on frequent and correlated items. This method considers both relevance and feature interactions, using association as a metric to evaluate the relationship between the target and features. The technique was tested using the SMS spam collecting dataset from the UCI machine learning repository and compared with well-known feature selection methods. The results showed that the proposed technique effectively reduced redundant information while achieving high accuracy (95.155%) using only 6% of the features.
Multi-view clustering for multiple manifold learning via concept factorization

Clustering of multi-view data has attracted great interest in machine learning. Multi-view data is based on complementary and consistent information due to the collection of numerous sources. However, the current multi-view clustering method does not focus on learning the manifold structure for consensus representation over the kernel space, the overfitting redundant features of diverse view data, and ignoring the correlation among these complementary multiple views. In this article, a novel multi-view clustering approach is introduced to tackle this issue, which deploys concept factorization to indulge the intrinsic data information. The intrinsic geometry of the data and consensus representation are preserved through the utilization of manifold learning. In addition, the correlation constraint is adopted for learning the common shared structure from diverse views, and the smooth regularization term is deployed to alleviate the overfitting of views. Finally, we fuse all individual terms to formulate the objective function and design a theoretically guaranteed optimization procedure to solve the model. Extensive experiments conducted on the benchmark datasets suggest that the proposed algorithm surpasses state-of-the-art approaches regarding clustering performance.
On the Robustness of Belief-Rule-Based Expert Systems

Belief rule base (BRB) expert system has been widely used in complex system modeling. Robustness is crucial to the modeling performance and safety of BRB. For a better understanding and utility of BRB, there is thereby an urgent need to know what kind of influence each part of BRB may have when the disturbance occurs. Aiming at this, a more comprehensive analysis of BRB robustness is conducted in this article. First, the Lipschitz condition for BRB is defined. With the definitions, a new robustness analysis method of BRB is proposed, which is conducted from four aspects: 1) the input transformation; 2) the matching degree calculation; 3) the matching degree normalization; and 4) the rule aggregation. Moreover, five guidelines for BRB construction are proposed by analyzing its robustness, which can offer a practical guide for users to establish, adjust, and improve the BRB model for specific applications. The robustness analysis of the BRB expert system for the relay health-state evaluation is conducted to verify the effectiveness of the proposed method.
Deep probabilistic graphical modeling for robust multivariate time series anomaly detection with missing data

Multivariate time series anomaly detection with missing data is one of the most pending issues for industrial monitoring. Due to scarcity of labeled anomalies, most advanced data-driven anomaly detection approaches fall in the unsupervised learning paradigm. As a premise in the presence of missing data, one needs to improve the data quality through data imputation with a separate model. Our concern lies in the consistency between data imputation and unsupervised learning for robust anomaly detection, regarding accurately discovering the spatiotemporal dependence among multiple variables over time. However, the existing practice tends to overlook this consistency and decouple the training process for these two closely linked tasks. This article novelly proposes a probabilistic multivariate time series anomaly detection framework that unifies data imputation and unsupervised learning. A deep probabilistic graphical model abbreviated SCNF is first devised for unsupervised density estimation. A tailored expectation maximization-based optimization scheme is then developed to achieve the joint training of data imputation and unsupervised learning with missing data. The efficacy is experimentally corroborated in several industrial applications, including chemical process, water treatment and network traffic. Briefly, the joint training framework enhances the AUROC of SCNF by averagely 6.34% for three applications under 50% data missing rate.
Quantum approximate optimization algorithm for qudit systems

A frequent starting point of quantum computation platforms is the two-state quantum system, i.e., the qubit. However, in the context of integer optimization problems, relevant to scheduling optimization and operations research, it is often more resource-efficient to employ quantum systems with more than two basis states, so-called qudits. Here, we discuss the quantum approximate optimization algorithm (QAOA) for qudit systems. We illustrate how the QAOA can be used to formulate a variety of integer optimization problems such as graph coloring problems or electric vehicle charging optimization. In addition, we comment on the implementation of constraints and describe three methods to include these in a quantum circuit of a QAOA by penalty contributions to the cost Hamiltonian, conditional gates using ancilla qubits, and a dynamical decoupling strategy. Finally, as a showcase of qudit-based QAOA, we present numerical results for a charging optimization problem mapped onto a maximum-$k$-graph-coloring problem. Our work illustrates the flexibility of qudit systems to solve integer optimization problems.
SAFF-SSD: Self-Attention Combined Feature Fusion-Based SSD for Small Object Detection in Remote Sensing

SSD is a classical single-stage object detection algorithm, which predicts by generating different scales of feature maps on different convolutional layers. However, due to the problems of its insufficient non-linearity and the lack of semantic information in the shallow feature maps, as well as the fact that small objects contain few pixels, the detection accuracy of small objects is significantly worse than that of large- and medium-scale objects. Considering the above problems, we propose a novel object detector, self-attention combined feature fusion-based SSD for small object detection (SAFF-SSD), to boost the precision of small object detection. In this work, a novel self-attention module called the Local Lighted Transformer block (2L-Transformer) is proposed and is coupled with EfficientNetV2-S as our backbone for improved feature extraction. CSP-PAN topology is adopted as the detection neck to equip feature maps with both low-level object detail features and high-level semantic features, improving the accuracy of object detection and having a clear, noticeable and definitive effect on the detection of small targets. Simultaneously, we substitute the normalized Wasserstein distance (NWD) for the commonly used Intersection over Union (IoU), which alleviates the problem wherein the extensions of IoU-based metrics are very sensitive to the positional deviation of the small objects. The experiments illustrate the promising performance of our detector on many datasets, such as Pascal VOC 2007, TGRS-HRRSD and AI-TOD.
A survey of graph neural network based recommendation in social networks

With the widespread popularization of social network platforms, user-generated content and other social network data are growing rapidly. It is difficult for social users to select interested contents from the numerous social data. To alleviate information overload problem and enhance overall user experience of social networks, recommendation systems relying on historical behavioural data and social friendship relations of users, are widely used in social networks. Although researches on social recommendations have been conducted in recent years, recommendation systems of social networks still suffer from several challenges, such as data sparsity and lower performance. Since graph neural network has huge advantages in graph data learning by aggregating neighbors representations of the central node, it has been gathering pace in recent years. In this survey, we review graph neural network based literature for solving recommendation problems in social networks. We first introduce backgrounds of graph neural network and recommendation systems in social networks. Then, for different types of recommendation problems in social networks, we review different graph neural network based recommendation methods briefly. In particular, we first review GNN-based methods for general social recommendation and then review GNN-based methods for different social recommendation scenarios (such as friend recommendation and point-of-interest recommendation). Finally, we briefly discuss promising future directions of the graph neural network based recommendation in social networks.
Towards a unified model of consumers’ intentions to use drone food delivery services

The aim of this study is to examine the factors influencing consumers' intentions to use drone food delivery services drawing on four theories, namely the theory of planned behaviour, norm activation theory, technology acceptance model, and perceived risk theory. 305 survey data are collected in Malaysia and analysed using partial least squares structural equation modelling. The findings show that attitudes, perceived behavioural control, and personal norms are significant predictors of intentions. Furthermore, ascribed responsibility and awareness of consequences significantly predict personal norms. Additionally, perceived ease of use, perceived usefulness, and psychological risk are also predictors of attitudes.