Reviewerly/exHarmony huggingface dataset:
Deployment of Slow power Hybrid Electric Vehicle based on Combustion Engine

In the current climate, global warming is a threat to society. As a result of burning fossil fuels in automobiles, carbon dioxide is released into the atmosphere, which adds to environmental pollution. One of the more constructive responses to this dilemma is the introduction of hybrid vehicles. Electric, solar, and IC engines all work together in a hybrid vehicle's transmission system. Solar power is used with a normal internal combustion engine in this method. Consequently, it is known as a "Hybrid Solar Car" (HSV). Both internal combustion engines and solar energy with an electric motor may be used to move it. Solar-powered automobiles do not produce any pollution in the real world. Development and introduction of hybrid electric vehicles are under underway. In order to travel long distances, these cars need to recharge their batteries often. This leads in an increase in the cost of electricity. Hybrid solar cars, which also contribute to a greener environment, will help to tackle these concerns.
Geochemical fingerprinting of continental and oceanic basalts: A machine learning approach

Basalts are ubiquitous, mantle-derived mafic rocks found within all tectonic settings. Studying the chemical composition of basalts has proven to be an effective way to understand tectonics-related mantle processes for more recent periods of Earth history when other geological and tectonic records are still well preserved. However, using basalt geochemistry to decipher ancient tectonic environments and mantle processes on Earth remains a significant challenge; interpretations are often non-unique, and weathering, erosion, and orogenic processes can modify the chemical composition of basalts, and eliminate or obscure other complementary geotectonic records. This is most apparent in the oceanic realm, where subduction-related processes have erased much of the geological record for times older than ∼200 Ma, only leaving small and rare dismembered blocks of oceanic lithosphere as ophiolite mélanges along tectonic sutures. As a result, workers must rely heavily on analyzing the chemical composition of preserved basalts to identify the tectonic settings of their original melt extraction from the mantle using various tectonic discrimination diagrams from the literature (e.g., Pearce and Cann, 1973). In this paper, we demonstrate that such widely used classic discrimination diagram approaches often suffer from the intrinsic shortfall of large ambiguity. Instead, we apply here a big-data approach to characterize basalts generated in some typical tectonic settings. We start with a bespoke data correction and machine learning workflow. Our results show that mid-ocean ridge basalts, ocean island basalts, continental flood basalts, arc basalts (from both the oceanic and continental realm), and oceanic flood basalts are statistically chemically different, thus presenting a novel and unique chemical ‘fingerprinting’ approach to more accurately discriminate basalts. The method successfully predicts the tectonic settings of basalt emplacement at a prediction accuracy of >96%.
Creating the ultimate virtual reality display

Scientists are exploring new material designs to make smaller and denser pixel displays.
Single nighttime image dehazing based on unified variational decomposition model and multi-scale contrast enhancement

Most of existing dehazing methods are unable to deal with nighttime hazy scenarios well due to complex degraded factors such as non-uniform illumination, low light, glows and hazes. To obtain high-quality image under nighttime haze imaging conditions, we propose a single nighttime image dehazing framework based on a unified variational decomposition model and multi-scale contrast enhancement to simultaneously address these undesirable issues. First, a unified variational decomposition model consisting of three regularization terms is proposed to simultaneously decompose a nighttime hazy image into a structure layer, a detail layer and a noise layer. Concretely, we employ ℓ1 norm to constrain the structure component, adopt ℓ0 sparsity term to enforce the piece-wise continuous of the residual of the gradients between the detail layer and the modified glow-free image, and use the Frobenius norm to estimate the noise layer. Next, the hazes in the structure layer are removed by inversing the physical model and the effective details in the texture layers are enhanced while the amplified noises are suppressed in a multi-scale fashion. Finally, the dehazed structure layer and the enhanced detail layers are integrated into a haze-free image. Experiments demonstrate that the proposed framework achieves superior performance on nighttime haze removal and noise suppression compared with state-of-the-art dehazing techniques.
TECM: Transfer learning-based evidential c-means clustering

As a representative evidential clustering algorithm, evidential c-means (ECM) provides a deeper insight into the data by allowing an object to belong not only to a single class, but also to any subset of a collection of classes, which generalizes the hard, fuzzy, possibilistic, and rough partitions. However, compared with other partition-based algorithms, ECM must estimate numerous additional parameters, and thus insufficient or contaminated data will have a greater influence on its clustering performance. To solve this problem, in this study, a transfer learning-based ECM (TECM) algorithm is proposed by introducing the strategy of transfer learning into the process of evidential clustering. The TECM objective function is constructed by integrating the knowledge learned from the source domain with the data in the target domain to cluster the target data. Subsequently, an alternate optimization scheme is developed to solve the constraint objective function of the TECM algorithm. The proposed TECM algorithm is applicable to cases where the source and target domains have the same or different numbers of clusters. A series of experiments were conducted on both synthetic and real datasets, and the experimental results demonstrated the effectiveness of the proposed TECM algorithm compared to ECM and other representative multitask or transfer-clustering algorithms.
Does digitalization contribute to lesser income inequality? Evidence from G20 countries

This study examines both the direct and moderating effects of digitalization on income inequality by using the panel data of Group of Twenty countries for 2002–2018. We find that digitalization alleviated income inequality and the interaction of digitalization with trade openness, and foreign direct investment helped narrow the income gap in the full sample, but the impact is heterogeneous by income level. The impact of digitalization on narrowing income gap has a greater effect on middle-income countries than on high-income countries. The interaction between digitalization and trade openness tends to widen the income inequality in high-income countries but has the opposite effect in middle-income countries. The interaction of digitalization with foreign direct investment has helped narrow income inequality in both high-income and middle-income countries. It implies that digitalization contributes to narrowing income inequality; there is a difference in the intensity of the digitalization effect between high-income and middle-income countries.
Measuring urban digitalization using cognitive mapping and the best worst method (BWM)

Rapid population growth and urbanization have a strong impact on individuals' well-being. These trends are currently important topics in city management. As a significant driver of digitalization, technological development has been found to have great potential as a mitigator of a wide range of issues that cities face. To ensure the available technologies are being correctly used, municipalities need to measure their degree of digitalization. This assessment is also important to improve city officials' understanding of areas that need interventions so that cities can be managed better and provide their residents with a better quality of life. This study applies the baseline principles of the multiple-criteria decision analysis (MCDA) approach to develop a decision-support system that facilitates urban digitalization evaluations. The results rely on insights provided by a panel of experts in this area, who used cognitive mapping and the best worst method (BWM) to identify important evaluation criteria within urban digitalization and to analyze their cause-and-effect relationships. The final outcomes were validated by the panel members and representatives of the Associação Nacional de Municípios Portugueses (i.e., National Association of Portuguese Municipalities) and the Agência National de Inovação (i.e., National Innovation Agency). These specialists confirmed that the evaluation system developed facilitates city differentiation in key areas of urban digitalization. The advantages and limitations of the proposed procedures are also discussed.
A very lightweight and efficient image super-resolution network

Deep convolutional neural networks significantly improve the performance of single image super-resolution (SISR). Generally, larger networks (i.e., deeper and wider) have better performance. However, larger networks require higher computing and storage costs, which limit their application on resource-constrained devices. Lightweight SISR networks with fewer parameters and smaller computational workloads are highly desirable. The key challenge is to obtain a better balance of model complexity and performance. In this paper, we propose a very lightweight and efficient SISR network. Our main contributions include: (1) Propose a frequency grouping fusion block (FGFB), which can better fuse high-/low-frequency feature information; (2) Propose a multi-way attention block (MWAB), which can exploit the multiple different clues of the feature information; (3) Propose a lightweight residual concatenation block (LRCB), which can combine the advantages of the residual connection and the channel concatenation; (4) Propose a lightweight convolutional block (LConv) for image super-resolution, which can significantly reduce the number of parameters; (5) Propose a progressive interactive group convolution (PIGC), which is more effective than the conventional group convolution. Extensive experimental results demonstrate that our method is significantly superior to other state-of-the-art methods currently available, with a better balance between model complexity and performance.
Hospitality Feedback System 4.0: Digitalization of Feedback System with Integration of Industry 4.0 Enabling Technologies

Digitalization enables the realization of the resilient infrastructure in every application for achieving sustainability. In the context of the hospitality business, resilient infrastructure based on digital technologies is critical for gaining the best customer feedback on providing quality service. Digital technology has already proved to enhance hospitality services with intelligent decisions through real-time data. In the previous studies, the significance of digital technologies in the hotel sector has been extended in numerous theoretical and empirical studies, yet there is a lack of research that provides a discussion on feedback systems in hospitality with digital technologies applications. With the motivation from the above aspects, this study intends to present the importance and application of the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and big data implementation in customer quality and satisfaction. Moreover, we have discussed each technology´s significance and application for realizing digital-based customer quality and satisfaction. It has been identified that the AI-based system collects the input data from different common websites and compares it with a different algorithm using a neural network. According to the findings of this study, AI and personnel quality of service have an impact on customer pleasure and loyalty. The study also concludes with the following recommendations, such as the design and development of dedicated hardware to gain the actual feedback from the customer on a large scale for improving the accuracy in the future.
A survey on blockchain consensus mechanism: research overview, current advances and future directions

Purpose As the core technology of blockchain, various consensus mechanisms have emerged to satisfy the demands of different application scenarios. Since determining the security, scalability and other related performance of the blockchain, how to reach consensus efficiently of consensus mechanism is a critical issue in the blockchain. Design/methodology/approach The paper opted for a research overview on the blockchain consensus mechanism, including the consensus mechanisms' consensus progress, classification and comparison, which are complemented by documentary analysis. Findings This survey analyzes solutions for the improvement of consensus mechanisms in blockchain that have been proposed during the last few years and suggests future research directions around consensus mechanisms. First, the authors outline the consensus processes, the advantages and disadvantages of the mainstream consensus mechanisms. Additionally, the consensus mechanisms are subdivided into four types according to their characteristics. Then, the consensus mechanisms are compared and analyzed based on four evaluation criteria. Finally, the authors summarize the representative progress of consensus mechanisms and provide some suggestions on the design of consensus mechanisms to make further advances in this field. Originality/value This paper summarizes the future research development of the consensus mechanisms.
Building and exploiting spatial–temporal knowledge graph for next POI recommendation

Next Point-of-Interest (POI) recommendation has shown great value for both users and businesses in the field of location-based services. Many spatial–temporal inferring methods have been developed to perform this task, but the data sparsity of POI trajectories greatly hinders the recommendation performance. Knowledge graphs (KGs) have been demonstrated as an effective way to tackle data sparsity in the general recommendation field by leveraging the valuable information of entities and relations. Yet, few studies have explored applying KGs for the next POI recommendation task because of the following challenges: (1) how to represent the dynamic mobility behaviors of users with the static entities and relations in KGs; and (2) how to utilize the different types of entities and relations in KGs to capture long- and short-term preferences of users. In this work, we investigate building a spatial–temporal KG (STKG) from check-in sequences of users to promote the next POI recommendation, without introducing any external attributes of users and POIs. In STKG, we design a novel spatial–temporal transfer relation to intuitively capture users’ transition patterns between neighboring POIs. Then, based on the STKG, we propose an innovative model, named STKGRec, for the next POI recommendation, which explicitly models long- and short-term preferences of users in an end-to-end manner. In particular, STKGRec learns both the spatial–temporal correlation of consecutive and nonconsecutive visits in the current check-in sequence to comprehensively capture the short-term preferences of users. Extensive experiments on four real-world datasets demonstrate the superiority of STKGRec against the state-of-the-art baseline methods. The code of our proposed model is available at https://github.com/WeiChen3690/STKGRec .
Energy consumption intelligent modeling and prediction for additive manufacturing via multisource fusion and inter-layer consistency

A novel deep network termed 3DPECP-Net is proposed to address an important, interesting, yet challenging problem in intelligent manufacturing: Accurately predicting energy consumption (EC) in the additive manufacturing (AM) process, commonly known as 3D printing (3DP). This task is of vital importance in fulfilling the low energy criteria under the policies to tackle energy shortage and climate change. According to the intelligent EC modeling in 3DP, the multisource data consists in three parts including pixel-source, motion-source, and processing-source. Compared with existing solutions, which either only harness the geometric characteristics of computer-aided design (CAD) models or simply integrate the processing parameters into the prediction, the proposed 3DPECP-Net is able to seamlessly fuse relevant data in 3DP, taking full advantage of the complementary information of diverse datasources for more accurate prediction. Specifically, the multisource fusion frame (MSFF) stacks multiple transformer blocks to catch global spatial information, and convolutional blocks to catch local feature details. Furthermore, we propose a continual attention memory network (CAMN) to ably extract and store the inter-layer consistency, enforcing to efficiently learn an exhaustive representation for 3DP EC prediction. Extensive experiments are conducted on an in-house dataset and the experimental results demonstrate that the proposed 3DPECP-Net achieves a superior prediction performance than state-of-the-art methods.
Blockchain-based Intrusion Detection System of IoT urban data with device authentication against DDoS attacks

• To model the arbiter PUF that secures the keys pairs using lightweight technology. • Authenticate the IoT devices using blockchain system. • Propose a collaborative anomaly detection system against DDOS Attacks on IoT devices with blockchain. The Internet of Things (IoT) is a technology enabler for enhancing the urban physical infrastructure and providing public services. However, public access to collected heterogeneous IoT urban data is more vulnerable to hackers invading intellectual property as all devices are connected to the internet. IoT security plays a vital role in the smart city. This IoT security is a real challenge as multiple goals and major inconveniences slow the rapid adaption of these smart devices. This paper presents a permissioned-based blockchain system that uses the arbiter PUF model to secure the key pairs of IoT devices using lightweight technology. A collaborative detection system is initially used to detect the DDoS on IoT devices using the machine learning-based ensemble technique, which provides a lower false-positive rate and better detection rate than the other classification technique. Next, we incorporated the blockchain system that securely shares the alarm alerts to all the nodes of the IoT network with adequately secured authentication.
Breast Cancer Screening Using Machine Learning Models

The main objective of this paper is the development of an Algorithm for Boolean Breast Cancer Classification based on 116 patients with results by testing blood along with physical information (life, BMI, Gluco-level, diabetes, etc.). The following machine learning models were compared for performance in this study: choice tree, arbitrary forest, K-closest neighbors, ANN, AVM, and replace analysis. With the following methodologies, the information was gathered: Data is separated among 80 percent training and 20 percent testing using k-fold cross-validation (k 10). Initially, the average of accuracy, as well as sensitivity, are examined, while the counts of accuracy, sensitivity, specificity, also region subjected to a number of tests were analyzed in the second. Later on, mass measures of mammography are likely to be conducted on terrible cancers. As a result, other techniques could be used in such evaluations to enhance choice, also ML could give a lot of value and an excellent cost-effective ratio in the breast cancer diagnosis strategy. In previous times, various research publications on breast-cancer indicators are been published. The applied strategy would look at two analytical parameters: the linear calibration curve and maturity. The q-parameter, Pearson-correlation analysis, also intake parameters dependency, the experiment only along the parameters having a criterion of the importance of 6% is also calculated with the evaluation of the standard deviation (evaluated with the KS experiment), these could be as Gluco-level, diabetic, resisting, and prototype for assessing equilibrium. The arbitrary forest is utilized for the learning procedure along with 10 factors as the best final classifier, with 84 percent accuracy, 100%_sensitivity, 65%_specificity, also 0.90 curved surface region.
Linear-depth quantum circuits for multiqubit controlled gates

Quantum circuit depth minimization is critical for practical applications of circuit-based quantum computation. In this work, we present a systematic procedure to decompose multiqubit controlled unitary gates, which is essential in many quantum algorithms, to controlled-not and single-qubit gates with which the quantum circuit depth only increases linearly with the number of control qubits. Our algorithm does not require any ancillary qubits and achieves a quadratic reduction of the circuit depth against known methods. We show the advantage of our algorithm with proof-of-principle demonstrations on the IBM quantum cloud platform.
Impact of blockchain and distributed ledger technology for the management of the intellectual property life cycle: A multiple case study analysis

The purpose of this paper is to examine how blockchain and in general distributed. ledger technology can help to solve intellectual property challenges of today´s era of digitization. We investigate business ventures leveraging blockchain technology to manage the intellectual property life cycle, in other words how intellectual properties are created, protected, managed and monetized. Our research method is a multiple case study examining a total of 120 use cases. across all primary types of intellectual properties including copyrights, trademarks, patents, trade secrets, industrial design and geographical indications. The empirical validation is based on an in-depth within-case analysis of 26 applications which were selected from the initial 120 cases based on business maturity and popularity criteria. Our goal is to understand which intellectual property challenges distributed ledger technology is trying to solve and how. We also investigate which blockchain benefits, in terms of technological security, immutability, decentralization, tokenization or disintermediation are promoted by these applications. We triangulate our case study results using a PESTEL coding framework to better understand how the blockchain ecosystem manages the intellectual property life cycle and to provide scholarly researchers and practitioners with insights on how enterprises can use distributed ledger technology to enhance the management of the intellectual property value chain. Moreover, we validate theoretical findings through the evidence collected from our multiple case study analysis, and finally we provide strategic guidance in investigating the current and anticipated challenges of blockchain entrepreneurs to drive further distributed ledger innovation.