Reviewerly/exHarmony huggingface dataset:
Financing and operating strategies for blockchain technology-driven accounts receivable chains

We focus on blockchain technology (BCT) adoption in a three-echelon supply chain in which upstream sellers offer trade credits to capital-constrained downstream buyers, forming an accounts receivable chain. According to different supply chain partners adopting BCT, we analyze the game equilibrium of the no, upper-stream, lower-stream, and entire BCT-driven accounts receivable chains (NBC, UBC, LBC, and EBC) to understand participants’ motivation and condition to adopt BCT and the different roles of participants in blockchain introduction. We find that trade credit management cost-saving and repayment risk-sharing effects, as facilitated by BCT, help increase the equilibrium order quantity, thereby increasing supply chain efficiency and participants’ profits, although BCT cannot fully coordinate the supply chain. Regarding supply chain efficiency, EBC is superior to LBC, which is better than UBC or NBC. As a supply chain leader and creditor, the supplier plays a passive role in achieving the positive effect of BCT. Given that the retailer and manufacturer are supply chain followers and debtors, their adoption of BCT is the key to enhancing supply chain efficiency and profits. The BCT platform can set appropriate technology-usage fees to guide participants to form the EBC structure through different paths. Meanwhile, the retailer’s initial working capital can evaluate the risk-sharing degree of the supply chain, and the stable EBC selection region defined by the risk-sharing degree can be ascertained. We further find that with the trade credit insurance premium increasing, supply chain participants have become more active in adopting BCT.
On the Physical Layer Security of Federated Learning Based IoMT Networks

Internet of Medical Things (IoMT) connects different medical devices, health sensors and hospital records to data platforms using wireless communications. Federated Learning (FL) is an emerging collaborative learning technique that can be beneficial for IoMT due to reduced communication overhead and enhanced security. This paper provides an overview of different architectures used in FL and potential approaches for FL based IoMT. We also discuss how Physical Layer Security (PLS) can be used for efficient privacy preservation of data in FL based IoMT. We highlight the recent work in this area and major research challenges related to PLS assisted FL in IoMT. We also provide a case study demonstrating that clustering of IoMT devices (such that a single device in each cluster acts as a cluster head) enhances the secrecy rate of the FL based IoMT network as compared to its non-clustered counterpart. Finally, we also discuss future opportunities and open research questions related to PLS assisted FL in IoMT.
Training of the feed forward artificial neural networks using dragonfly algorithm

One of the most important parts of an artificial neural network (ANN) which affects performance is training algorithms. Training algorithms optimize the weights and biases of the ANN according to the inputs–outputs pattern. Two types of training algorithms are widely used: Gradient methods and meta-heuristic methods. Gradient methods are effective in training the ANN. But they have some disadvantages. The main disadvantage of gradient methods is premature convergence. Secondly, the performance of gradient methods highly depends on the initial parameters and positions. Thirdly, they can easily get stuck in local optima. To overcome these disadvantages, this article presents a new hybrid algorithm (DA-MLP) to train the feed-forward multilayer neural networks (MLP) using the dragonfly algorithm. The dragonfly algorithm optimizes the weights and biases of the MLP. In the experiments, one real-world problem in the civil engineering area and eight classification datasets were used. To verify the success of the DA-MLP algorithm, the results of the DA-MLP algorithm were compared with the results of four algorithms (the BAT-MLP based on the bat optimization algorithm, the SMS-MLP based on the states of matter search optimization algorithm, the PSO-MLP based on the particle swarm optimization algorithm, and the backpropagation (BP) algorithm). The experimental study showed that the DA-MLP algorithm is more efficient than the other algorithms.
Automatic Speech Recognition Method Based on Deep Learning Approaches for Uzbek Language

Communication has been an important aspect of human life, civilization, and globalization for thousands of years. Biometric analysis, education, security, healthcare, and smart cities are only a few examples of speech recognition applications. Most studies have mainly concentrated on English, Spanish, Japanese, or Chinese, disregarding other low-resource languages, such as Uzbek, leaving their analysis open. In this paper, we propose an End-To-End Deep Neural Network-Hidden Markov Model speech recognition model and a hybrid Connectionist Temporal Classification (CTC)-attention network for the Uzbek language and its dialects. The proposed approach reduces training time and improves speech recognition accuracy by effectively using CTC objective function in attention model training. We evaluated the linguistic and lay-native speaker performances on the Uzbek language dataset, which was collected as a part of this study. Experimental results show that the proposed model achieved a word error rate of 14.3% using 207 h of recordings as an Uzbek language training dataset.
A federated calibration scheme for convolutional neural networks: Models, applications and challenges

Deep learning has been created as a practical artificial intelligence strategy that takes various layers of information and gives the best in the effects of different classes. The use of deep learning has indicated exceptional execution in other regions, especially in image clustering, division, and recognition. The ongoing advanced learning strategies implement image clustering, which expects to recognize subsequent-level of classifications . This paper gives a definite audit of different deep arrangements and models featuring attributes of a specific convolutional neural network model. Initially, we depicted the working of Convolutional neural networks and their segments, followed by a point-by-point display of various Convolutional Neural Network models beginning with the old-style LeNet model to AlexNet, GoogleNet, VGGNet, ResNet, DenseNet, Xception, PNAS/ENAS, and EfficientNet. We concluded the significant challenges associated with Spatial Exploitation based Convolutional neural network architecture, Depth Based Convolutional neural network architecture, Multi-Path based Convolutional neural network architectures, and width based Convolutional neural network architectures. A definite summary of the review, including the frameworks, information base, application, and precision for every model, is discussed for serving it as the future scope in the above areas.
A survey on firefly algorithms

Firefly algorithm (FA) is one of the popular algorithms of Swarm Intelligence domain. The literature has expanded significantly in the past few years. This paper provides a timely review of the FA and its new variants. The different variants of FA are classified and analyzed. The applications and case studies in the rapidly evolving domain are also reviewed and summarized in detail here. The problems to be further solved and future research directions of firefly algorithms will be given at the end of this paper.
Anti-Asian American Hate Crimes Spike During the Early Stages of the COVID-19 Pandemic

The recent high-profile cases of hate crimes in the U.S., especially those targeting Asian Americans, have raised concerns about their risk of victimization. Following the onset of the COVID-19 pandemic, intimations-and even accusations-that the novel coronavirus is an "Asian" or "Chinese" virus have been linked to anti-Asian American hate crime, potentially leaving members of this group not only fearful of being victimized but also at risk for victimization. According to the Stop AAPI Hate Center, nearly 1900 hate crimes against Asian Americans were reported by victims, and around 69% of cases were related to verbal harassment, including being called the "Chinese Coronavirus." Yet, most of the evidence martialed on spikes in anti-Asian American hate crime during the COVID-19 pandemic has been descriptive. Using data from four U.S. cities that have large Asian American populations (New York, San Francisco, Seattle, and Washington D.C.), this study finds that hate crime against Asian Americans increased considerably in 2020 compared with that of 2019. Specifically, hate crime against Asian Americans temporarily surged after March 16, 2020, when the blaming labels including "Kung flu" or "Chinese Virus" were used publicly. However, the significant spike after March 16, 2020, in anti-Asian American hate crime was not sustained over the follow-up time period available for analysis.
Multi-layer manifold learning for deep non-negative matrix factorization-based multi-view clustering

Multi-view data clustering based on Non-negative Matrix Factorization (NMF) has been commonly used for pattern recognition by grouping multi-view high-dimensional data by projecting it to a lower-order dimensional space. However, the NMF framework fails to learn the accurate lower-order representation of the input data if it exhibits complex and non-linear relationships. This paper proposes a deep non-negative matrix factorization-based framework for effective multi-view data clustering by uncovering both the non-linear relationships and the intrinsic components of the data. Both the consensus and complementary information present in multiple views are sufficiently learned in the proposed framework with the effective use of constraints such as normalized cut-type and orthogonal. The optimal manifold of multi-view data is effectively incorporated in all layers of the framework. Extensive experimental results show the proposed method outperforms state-of-the-art multi-view matrix factorization-based methods.
Deep learning for image colorization: Current and future prospects

Image colorization, as an essential problem in computer vision (CV), has attracted an increasing amount of researchers attention in recent years, especially deep learning-based image colorization techniques(DLIC). Generally, most recent image colorization methods can be regarded as knowledge-based systems because they are usually trained by big datasets. Unlike the existing reviews, this paper adopts a unique deep learning-based perspective to review the latest progress in image colorization techniques systematically and comprehensively. In this paper, a comprehensive review of recent DLIC approaches from algorithm classification to existing challenges is provided to facilitate researchers’ in-depth understanding of DLIC. In particular, we review DLIC algorithms from various perspectives, including color space, network structure, loss function, level of automation, and application fields. Furthermore, other important issues are discussed, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we discuss several open issues of image colorization and outline future research directions. This survey can serve as a reference for researchers in image colorization and related fields.
Rethinking U-Net from an Attention Perspective with Transformers for Osteosarcoma MRI Image Segmentation

Osteosarcoma is one of the most common primary malignancies of bone in the pediatric and adolescent populations. The morphology and size of osteosarcoma MRI images often show great variability and randomness with different patients. In developing countries, with large populations and lack of medical resources, it is difficult to effectively address the difficulties of early diagnosis of osteosarcoma with limited physician manpower alone. In addition, with the proposal of precision medicine, existing MRI image segmentation models for osteosarcoma face the challenges of insufficient segmentation accuracy and high resource consumption. Inspired by transformer's self-attention mechanism, this paper proposes a lightweight osteosarcoma image segmentation architecture, UATransNet, by adding a multilevel guided self-aware attention module (MGAM) to the encoder-decoder architecture of U-Net. We successively perform dataset classification optimization and remove MRI image irrelevant background. Then, UATransNet is designed with transformer self-attention component (TSAC) and global context aggregation component (GCAC) at the bottom of the encoder-decoder architecture to perform integration of local features and global dependencies and aggregation of contexts to learned features. In addition, we apply dense residual learning to the convolution module and combined with multiscale jump connections, to improve the feature extraction capability. In this paper, we experimentally evaluate more than 80,000 osteosarcoma MRI images and show that our UATransNet yields more accurate segmentation performance. The IOU and DSC values of osteosarcoma are 0.922 ± 0.03 and 0.921 ± 0.04, respectively, and provide intuitive and accurate efficient decision information support for physicians.
A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder

The improvement of photovoltaic (PV) power prediction precision plays a crucial role in the new energy consumption. This paper proposes a composite prediction framework (DC (DWT-DAE)-CNN) consisting of dual clustering and convolutional neural network to achieve day-ahead prediction of PV power. To avoid the temporal uncertainty of PV power and the high-dimensional complexity of numerical weather prediction, the raw data are processed by Discrete Wavelet Transform (DWT) and Deep Autoencoder respectively (DAE) to reduce the data redundancy. Secondly, a dual clustering pattern based on dynamic time warping distance clustering and Fuzzy C-Mean (FCM) clustering is proposed to progressively realize the dynamic characteristics of the power curve and numerical clustering of the weather information data. Finally, the data from PV plants in northeast China are used for validation. The results show that the annual average day-ahead prediction AR of the DC (DWT-DAE)-CNN model can reach 90.17%, which is better than other competing models. In addition, the dual clustering pattern performs better than other traditional clustering patterns with the same predictor. Using this method to predict the PV output power can provide better theoretical guidance for the stable and safe operation of grid-connected PV.
AI anthropomorphism and its effect on users' self-congruence and self–AI integration: A theoretical framework and research agenda

This paper examines how users of anthropomorphised artificially intelligent (AI) agents, which possess capabilities to mimic humanlike behaviour, relate psychologically to such agents in terms of their self-concept. The proposed conceptual framework specifies different levels of anthropomorphism of AI agents and, drawing on insights from psychology, marketing and human–computer interaction literature, establishes a conceptual link between AI anthropomorphism and self-congruence. The paper then explains how this can lead to self–AI integration, a novel concept that articulates the process of users integrating AI agents into their self-concept. However, these effects can depend on a range of moderating factors, such as consumer traits, situational factors, self-construal and social exclusion. Crucially, the conceptual framework specifies how these processes can lead to specific personal-, group- and societal-level consequences, such as emotional connection and digital dementia. The research agenda proposed on the basis of the conceptual framework identifies key areas of interest that should be tackled by future research concerning this important phenomenon.
Enhancing solar PV output forecast by integrating ground and satellite observations with deep learning

Accurate output forecasts are essential for photovoltaic projects to achieve stable power supply. Traditional forecasts based on ground observation time series are widely troubled by the phase lag issue due to the incomplete consideration of the impacts of cloud motion. With the consensus that this issue can be addressed by introducing satellite-derived cloud information, we propose an innovative framework that integrates ground and satellite observations through deep learning to enhance PV output forecasts. Cloud motion patterns are captured from satellite observations using convolutional neural networks, and the long-range spatio-temporal cloud impacts on subsequent PV outputs are established by long short-term memory network. The forecast accuracy of real-time PV output is significantly improved, with a minimum (maximum) relative root mean square error of 16% (29%). The ratio of phase lag is reduced to 15% on average. This work provides a potential for alleviating the power intermittency of solar PV system and making advance planning in solar energy utilization.
An opposition learning and spiral modelling based arithmetic optimization algorithm for global continuous optimization problems

In engineering applications, many real-world optimization problems are nonlinear with multiple local optimums. Traditional algorithms that require gradients are not suitable for these problems. Meta-heuristic algorithms are popularly employed to deal with these problems because they can promisingly jump out of local optima and do not need any gradient information. The arithmetic optimization algorithm (AOA), a recently developed meta-heuristic algorithm, uses arithmetic operators (multiplication, division, subtraction, and addition) to solve optimization problems including nonlinear ones. However, the exploration and exploitation of AOA are not effective to handle some complex optimization problems. In this paper, an opposition learning and spiral modelling based AOA, namely OSAOA, is proposed for enhancing the optimization performance. It improves AOA from two perspectives. In the first perspective, the opposition-based learning (OBL) is committed to taking both candidate solutions and their opposite solutions into consideration for improving the global search with a high probability of jumping out of local minima. Then, the spiral modelling is introduced as the second perspective, which is particularly useful in getting the solutions gathering faster and accelerating the convergence speed in the later stage. In addition, OSAOA is compared with other existing advanced meta-heuristic algorithms based on 23 benchmark functions and four engineering problems: the three-bar truss design, the cantilever beam design, the pressure vessel design, and the tubular column design. From our simulations and engineering applications, the proposed OSAOA can provide better optimization results in dealing with these real-world optimization problems.
Evaluating the Role of Robotics, Machine Learning and Artificial Intelligence in the Field of Performance Management

Although intelligent automation academic production has evolved fast (e.g., artificial intelligence, robots) we currently lack a full knowledge of the effects on organizational (companies) and individual (workforce) levels of the use of these innovations in performance management (PM). The purpose of this study is to systematize scholarly input on smart automation to date and to elucidate their primary contributions to the performance management difficulties. We found 45 publications on artificial intelligence, robotics as well as other technologically advanced within PM settings in the international business (IB) and general management (GM) and information management (IM) journals. The results demonstrate that smart automation technologies provide a new strategy to managing personnel and improving company performance, providing various performance management opportunities and also major technological and ethical problems. Its influence is based on performance management techniques such as job substitution, work collaboration between human robots and AI, policy-making and apprenticeship possibilities, and performance management activities, namely recruitment, education, and job performance. This paper addresses these changes in-depth and the main contribution to future studies theories and practices.
Optical Computing: Status and Perspectives

For many years, optics has been employed in computing, although the major focus has been and remains to be on connecting parts of computers, for communications, or more fundamentally in systems that have some optical function or element (optical pattern recognition, etc.). Optical digital computers are still evolving; however, a variety of components that can eventually lead to true optical computers, such as optical logic gates, optical switches, neural networks, and spatial light modulators have previously been developed and are discussed in this paper. High-performance off-the-shelf computers can accurately simulate and construct more complicated photonic devices and systems. These advancements have developed under unusual circumstances: photonics is an emerging tool for the next generation of computing hardware, while recent advances in digital computers have empowered the design, modeling, and creation of a new class of photonic devices and systems with unparalleled challenges. Thus, the review of the status and perspectives shows that optical technology offers incredible developments in computational efficiency; however, only separately implemented optical operations are known so far, and the launch of the world's first commercial optical processing system was only recently announced. Most likely, the optical computer has not been put into mass production because there are still no good solutions for optical transistors, optical memory, and much more that acceptance to break the huge inertia of many proven technologies in electronics.
Factors affecting cryptocurrency adoption in digital business transactions: The mediating role of customer satisfaction

This empirical study examines the factors influencing the adoption (AD) of cryptocurrencies in Malaysia's digital market. It is assumed that the adoption of cryptocurrencies would continue to increase. The role of the dependent variables of social influence (SI), transparency (TR), price value (PV), traceability (TRA), and attitude (AT) was examined to identify customer satisfaction as a mediator variable for cryptocurrency adoption. Random sampling was used to ensure that the research objectives were adequately examined. A total of 295 respondents answered the survey questions intended for cryptocurrency users in Malaysia. Data were analyzed using partial least squares structural equation modelling (PLS-LSM). The findings revealed that SI, PV, TRA, and AT were all impactful in terms of AD (dependent variable) through the mediation of customer satisfaction in Malaysia's digital market. However, TR negatively impacts Malaysia's digital market. Future researchers in other regions and industries may be able to reproduce these findings and use similar constructs to add to the present body of knowledge. This study adds to the small body of literature on Bitcoin and digital money. These findings can assist researchers in understanding the role of cryptocurrency and identifying its primary influences on the Malaysian cryptocurrency market.