Reviewerly/exHarmony huggingface dataset:
A multichannel optical computing architecture for advanced machine vision

Endowed with the superior computing speed and energy efficiency, optical neural networks (ONNs) have attracted ever-growing attention in recent years. Existing optical computing architectures are mainly single-channel due to the lack of advanced optical connection and interaction operators, solving simple tasks such as hand-written digit classification, saliency detection, etc. The limited computing capacity and scalability of single-channel ONNs restrict the optical implementation of advanced machine vision. Herein, we develop Monet: a multichannel optical neural network architecture for a universal multiple-input multiple-channel optical computing based on a novel projection-interference-prediction framework where the inter- and intra- channel connections are mapped to optical interference and diffraction. In our Monet, optical interference patterns are generated by projecting and interfering the multichannel inputs in a shared domain. These patterns encoding the correspondences together with feature embeddings are iteratively produced through the projection-interference process to predict the final output optically. For the first time, Monet validates that multichannel processing properties can be optically implemented with high-efficiency, enabling real-world intelligent multichannel-processing tasks solved via optical computing, including 3D/motion detections. Extensive experiments on different scenarios demonstrate the effectiveness of Monet in handling advanced machine vision tasks with comparative accuracy as the electronic counterparts yet achieving a ten-fold improvement in computing efficiency. For intelligent computing, the trends of dealing with real-world advanced tasks are irreversible. Breaking the capacity and scalability limitations of single-channel ONN and further exploring the multichannel processing potential of wave optics, we anticipate that the proposed technique will accelerate the development of more powerful optical AI as critical support for modern advanced machine vision.
Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors

The main question this work aims at answering is: "can morphing attack detection (MAD) solutions be successfully developed based on synthetic data?". Towards that, this work introduces the first synthetic-based MAD development dataset, namely the Synthetic Morphing Attack Detection Development dataset (SMDD). This dataset is utilized successfully to train three MAD backbones where it proved to lead to high MAD performance, even on completely unknown attack types. Additionally, an essential aspect of this work is the detailed legal analyses of the challenges of using and sharing real biometric data, rendering our proposed SMDD dataset extremely essential. The SMDD dataset, consisting of 30,000 attack and 50,000 bona fide samples, is publicly available for research purposes <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Diversity embedding deep matrix factorization for multi-view clustering

Multi-view clustering has attracted increasing attention by reason of its ability to leverage the complementarity of multi-view data. Existing multi-view clustering methods have explored nonnegative matrix factorization to decompose a matrix into multiple matrices for feature representations from multi-view data, which are not discriminative enough to deal with the natural data containing complex information. Moreover, most of multi-view clustering methods prioritize the consensus information among multi-view data, leaving a large amount of information redundant and the clustering performance deteriorated. To address these issues, this paper proposes a multi-view clustering framework that adopts a diversity loss for deep matrix factorization and reduces feature redundancy while obtaining more discriminative features. We then bridge the relation between deep auto-encoder and deep matrix factorization to optimize the objective function. This method avoids the challenges in the optimization process. Extensive experiments demonstrate that the proposed method is superior to state-of-the-art methods.
Dynamics of Open Quantum Systems—Markovian Semigroups and Beyond

The idea of an open quantum system was introduced in the 1950s as a response to the problems encountered in areas such as nuclear magnetic resonance and the decay of unstable atoms. Nowadays, dynamical models of open quantum systems have become essential components in many applications of quantum mechanics. This paper provides an overview of the fundamental concepts of open quantum systems. All underlying definitions, algebraic methods and crucial theorems are presented. In particular, dynamical semigroups with corresponding time-independent generators are characterized. Furthermore, evolution models that induce memory effects are discussed. Finally, measures of non-Markovianity are recapped and interpreted from a perspective of physical relevance.
Genetic Clustered Federated Learning for COVID-19 Detection

Coronavirus (COVID-19) has caused a global disaster with adverse effects on global health and the economy. Early detection of COVID-19 symptoms will help to reduce the severity of the disease. As a result, establishing a method for the initial recognition of COVID-19 is much needed. Artificial Intelligence (AI) plays a vital role in detection of COVID-19 cases. In the process of COVID-19 detection, AI requires access to patient personal records which are sensitive. The data shared can pose a threat to the privacy of patients. This necessitates a technique that can accurately detect the COVID-19 patients in a privacy preserving manner. Federated Learning (FL) is a promising solution, which can detect the COVID-19 disease at early stages without compromising the sensitive information of the patients. In this paper, we propose a novel hybrid algorithm named genetic clustered FL (Genetic CFL), that groups edge devices based on the hypertuned parameters and modifies the parameters cluster wise genetically. The experimental results proved that the proposed Genetic CFL approach performed better than conventional AI approaches.
FC-Kmeans: Fixed-centered K-means algorithm

Clustering is one of the data mining methods that partition large-sized data into subgroups according to their similarities. K-means clustering algorithm works well in spherical or convex data distribution of large-sized data sets. Most of the algorithms based on K-means have generally been interested in an initial cluster centers selection or cluster distribution. However, these algorithms may not meet satisfy some requirements in practice. This paper presents the FC-Kmeans algorithm, which enables clustering by fixing some cluster centers considering real conditions. Thus, while some of the cluster centers are fixed, it is tried to obtain the most appropriate cluster centers for the others and the best distribution of the data to the clusters. The K-means clustering algorithm is compared with two different fixed-centered clustering algorithms which are FC-Kmeans and FC-Kmeans 2. The experimental results show that although the FC-Kmeans algorithm has more limitations than K-means, it converges the performance of K-means algorithm according to some performance indicators such as SSE, DB Index and Silhouette Index.
Evolving ensembles using multi-objective genetic programming for imbalanced classification

Multi-objective Genetic Programming (MGP) plays a prominent role in generating Pareto optimal classifier sets and making trade-offs among multiple classes adaptively. However, the existing MGP algorithms show poor performance and are difficult to implement when dealing with imbalanced classification problems. This work proposes a new MGP-based algorithm designed for imbalanced classification. Firstly, an efficient evolutionary strategy with nondominated sorting, environmental selection, and an archiving mechanism is designed to optimize the false positive rate, the false negative rate and reduce the size of the resulting tree. Then, a weighted ensemble decision is made according to each classifier’s performance in the majority and minority classes to obtain final classification results. Experimental results on 21 binary-class datasets and 17 multi-class datasets show that the proposed method outperforms existing ones in several commonly used imbalanced classification metrics.
An integrated latent Dirichlet allocation and Word2vec method for generating the topic evolution of mental models from global to local

Mental models play a crucial role in explaining and driving human innovation activities. To help researchers clarify the changes of mental models in various innovation situations, an exploration of its topic dynamic evolution changes is urgently needed. However, most existing works have discussed the topic-semantic distributions of collected documents along the overall timeline, which ignores the semantic details of fusion and evolution between topics in continuous time. This paper discovers and reveals the multi-level information evolving of topics, by integrating latent Dirichlet allocation (LDA) and Word2vec harmoniously to generate the topic evolution maps of the corpus from global to local perspectives. Which include topic distribution trends and their dynamic evolution under the overall time series, as well as the merging and splitting of semantic information between topics in the adjacent time span. These reveal the correlation between topics and the full life cycle of a topic emerging, developing, maturing, and fading. Then, the integrated method was used to perform an analysis of topic evolution with 3984 abstracts of mental model-related papers published between 1980 and 2020. Finally, the performance of the proposed method was compared to that of three traditional topic evolution generated methods based on the standard evaluation metrics. The experimental results demonstrated that our method outperforms other methods both in terms of the content and strength of topic evolution. The proposed method could mine the latent evolution information more clearly and comprehensively from a vast number of papers and is also suited to the various applications of expert systems related to information mining works.
Wind power forecasting based on variational mode decomposition and high-order fuzzy cognitive maps

Accurate wind power forecasting can effectively reduce the adverse effects of wind power forecasting errors on wind power grid integration and power dispatch. However, current wind power forecasting technology, such as the method based on machine learning, belongs to the black box model and is not solvable. Variational mode decomposition (VMD) is a decomposition technique based on the time–frequency characteristics of the original time series, which has a mathematical theoretical foundation. Besides, fuzzy cognitive map (FCMs) is a kind of soft computing method with strong knowledge representation and reasoning ability. Therefore, to enhance the forecasting accuracy of wind power, in this paper, a novel time series forecasting method based on improved VMD (IVMD) and high-order FCM (HFCM), namely IVMDHFCM is proposed. IVMD can effectively extract the features in the raw time series depending on the time–frequency characteristics of the time series. Then, the subseries obtained by IVMD are modeled and forecasted by HFCM, and the Bayesian ridge regression method is adopted to learn the weight of HFCM. Finally, the differential evolution (DE) algorithm is used to get the optimal hyperparameters of IVMDHFCM. The performance of IVMDHFCM is verified by comparing it with that of state-of-the-art methods on ten publicly available datasets. Moreover, the proposed IVMDHFCM is compared with the existing HFCM based method on ten actual wind power datasets. The results show that the IVMDHFCM can effectively improve the accuracy of wind power forecasting and reduce the forecasting error. Besides, the IVMDHFCM can also effectively explore the fluctuations of wind power.
Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic

In this paper, we examine the impact of investor sentiment on Bitcoin returns. Using a large dataset of messages discussed on social media and several financial indicators, we create a sentiment indicator based on computational text analysis and driven by the principal component analysis (PCA) method. We utilize a vector autoregressive analysis and other analytical methods to examine the sentiment index–bitcoin return nexus. Our findings reveal that the sentiment index is a strong predictor of cryptocurrency market returns in the short term. Furthermore, we confirm that during the COVID-19 pandemic, investors' sentiments significantly impacted Bitcoin returns. Our results show that the proposed sentiment index can generate excess returns for investors who utilize it as a return predictor. Our empirical findings suggest important policy implications. • We develop a broad-based sentiment indicator for cryptocurrency using textual data. • The crypto sentiment index can help predict Bitcoin returns. • During COVID-19 pandemic, investors' sentiments have a significant impact on Bitcoin returns. • The proposed sentiment index can generate excess returns when used as a return predictor.
CasA: A Cascade Attention Network for 3-D Object Detection From LiDAR Point Clouds

3D object detection from LiDAR point clouds has gained great attention in recent years due to its wide applications in smart cities and autonomous driving. Cascade framework shows its advancement in 2D object detection but is less investigated in 3D space. Conventional cascade structures use multiple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">separate</i> sub-networks to sequentially refine region proposals. Such methods, however, have limited ability to measure proposal quality in all stages, and hard to achieve a desirable performance improvement in 3D space. This paper proposes a new cascade framework, termed CasA, for 3D object detection from LiDAR point clouds. CasA consists of a Region Proposal Network (RPN) and a Cascade Refinement Network (CRN). In CRN, we designed a new Cascade Attention Module that uses multiple sub-networks and attention modules to aggregate the object features from different stages and progressively refine region proposals. CasA can be integrated into various two-stage 3D detectors and improve their performance. Extensive experiments on KITTI and Waymo datasets with various baseline detectors demonstrate the universality and superiority of our CasA. In particular, based on one variant of Voxel-RCNN, we achieve state-of-the-art results on the KITTI dataset. On the KITTI online 3D object detection leaderboard, we achieve a high detection performance of 83.06%, 47.09%, and 73.47% Average Precision (AP) in the moderate Car, Pedestrian, and Cyclist classes, respectively. Code is available at https://github.com/hailanyi/CasA.
Smart Vehicle Path Planning Based on Modified PRM Algorithm

Path planning is a very important step for mobile smart vehicles in complex environments. Sampling based planners such as the Probabilistic Roadmap Method (PRM) have been widely used for smart vehicle applications. However, there exist some shortcomings, such as low efficiency, low reuse rate of the roadmap, and a lack of guidance in the selection of sampling points. To solve the above problems, we designed a pseudo-random sampling strategy with the main spatial axis as the reference axis. We optimized the generation of sampling points, removed redundant sampling points, set the distance threshold between road points, adopted a two-way incremental method for collision detections, and optimized the number of collision detection calls to improve the construction efficiency of the roadmap. The key road points of the planned path were extracted as discrete control points of the Bessel curve, and the paths were smoothed to make the generated paths more consistent with the driving conditions of vehicles. The correctness of the modified PRM was verified and analyzed using MATLAB and ROS to build a test platform. Compared with the basic PRM algorithm, the modified PRM algorithm has advantages related to speed in constructing the roadmap, path planning, and path length.
Cryptocurrency and stock market: bibliometric and content analysis

This study conducted a systematic review regarding the association between cryptocurrency and the stock market. This study used bibliometric and content analysis covering 151 articles from 2008 to November 2021. Using VOSviewer software, we explored the influential aspects of the literature, such as the prominent institutions, authors, countries, and journals. Additionally, we performed co-authorship, bibliographic coupling, and co-occurrence of keywords to understand the network. Furthermore, in the content analysis, we discussed key findings of four major research streams that we identified. Finally, we present seven research questions that can be explored in the future. The findings have a number of implications for the present state of the literature on cryptocurrency and the stock market, including study gaps and potential future research initiatives.
Link prediction approach combined graph neural network with capsule network

Graph Neural Networks (GNNs, in short) are a powerful computational tool to jointly learn graph structure and node/edge features. They achieved an unprecedented accuracy in the link prediction problem, namely the task of predicting if two nodes are likely to be tied by an edge in the near future. However, GNNs capture node attributes as scalars and such a representation can be non-optimal. In this paper we propose a link prediction approach which combines GNN with Capsule Networks (CapsNet), a powerful deep learning framework that obtained high quality representations when applied to image processing. Our approach first applies a GNN to generate node embeddings. Then, it makes use of a conversion block with the purpose of transforming node embeddings into a node pair feature map (called edge feature map ). Because of such a transformation, the link prediction problem is equivalent to a graph classification problem. Finally, our approach applies CapsNets to learn the feature representation of the edge feature map, so that the attributes of the node pairs are captured from different aspects. We evaluate the feasibility and effectiveness of our approach (called Graph Conversion Capsule Link, GCCL in short) on six networks without node attributes and three networks with node attribute. Experimental results show that in both conditions (with and without node attributes) our approach is significantly more accurate than the competitor methods, with an average accuracy improvement of about 20%.
Blockchain-Empowered Service Management for the Decentralized Metaverse of Things

The future of networking will be driven by the current emerging trends of combining the physical and virtual realities in cyberspace. Considering the ambient pandemic challenges, the role of virtual and augmented reality will definitely grow over time by transforming into the paradigm of the Metaverse of Things, where each person, thing or other entity will simultaneously exist within multiple synchronized realities. In this paper, we propose a novel framework for future metaverse applications composed of multiple synchronized data flows from multiple operators through multiple wearable devices and with different quality requirements. A new service quality model is proposed based on a customizable utility function for each individual data flow. The proposed approach is based on dynamic fine-grained data flow allocation and service selection using non-fungible tokens, which can be traded over the blockchain among users and operators in a decentralized mobile network environment.
A novel hybrid model for multi-step ahead photovoltaic power prediction based on conditional time series generative adversarial networks

The accuracy of photovoltaic power forecasting is crucial to the revenue of new energy generation projects in electricity market trading. However, due to the highly stochastic volatility and intermittent characteristics of photovoltaic power, it is difficult to construct high-performance photovoltaic power forecasting models. In this study, a multi-step short-term hybrid prediction model of photovoltaic power is proposed, which combines an improved sparrow search algorithm, Fuzzy c-means algorithm (FCM), improved complete ensemble empirical mode decomposition with adaptive noise (ICCEMDAN), and conditional time series generative adversarial networks (CTGAN). First, a new data clustering method based on FCM and data envelope theory is proposed to divide the dataset based on similar power patterns, and the parameters are optimized by an improved sparrow search algorithm. Second, the grey relational analysis and feature creation are combined to determine the optimal similar day for the forecasting day. Furthermore, the original photovoltaic power time series is decomposed using ICCEMDAN, and the components are reconstructed by sample entropy to reduce the computational cost of forecasting models. Finally, Wasserstein distance, gradient penalty, and Metropolis-Hastings are used to ensure CTGAN training stability. According to the experimental results, it can be concluded that the data envelope clustering method divides datasets more reasonably than meteorological factors. The optimization for the sparrow search algorithm increases its global and local optimization ability to further enhance the performance of FCM. Using CTGAN to generate realistic data that approximates real data distributions can train predictive models with better performance, which increases their adaptability to PV power fluctuations. The proposed hybrid model is validated based on different seasons, different weather conditions, and datasets from different locations, and the results demonstrate the advantage of the proposed 38-step predictive model in accuracy, application, and generalization capabilities over other models involved in this study.
A systematic review of healthcare recommender systems: Open issues, challenges, and techniques

The maintenance of health through prevention, diagnosis, and treatment of illnesses, injuries, and other mental and physical impairments in people is called healthcare that is performed by professionals in health fields. All activities performed in providing primary, secondary, and tertiary care, as well as in public health, are components of healthcare systems that are required to analyze massive amounts of patients’ data to gain insights and aid in disease prediction. The healthcare recommender system (HRS) is a growing and robust platform for healthcare services. The purpose of this paper is to identify, taxonomically classify, and compare current HRS researches in a systematic way. This study presents a systematic literature review (SLR) method for HRSs regarding 41 papers published between 2010 and 2021; the selected articles fall into five classes: collaborative-based, content-based, knowledge-based, context-based, and hybrid. Seventeen essential factors are identified to examine the selected HRSs, and each of the papers were studied to find advantages, disadvantages, evaluation types, and tools. In addition, we address a discussion of challenges, future directions, and open issues. This paper demonstrates that HRSs are still in their early stages of maturity, but they are developing. It identifies the need for a scalable, confidential, and reliable framework to aid in the improvement of both patients’ and health professionals’ trust in HRSs. This research reveals a lack of standardization of medical codes across various healthcare systems. In addition, this review identifies the need for novel patterns to address cold start problems.