Reviewerly/exHarmony huggingface dataset:
A Study on CBDCS and e-Rupee with Special Reference to e-Yuan

The attributes of money are evolving, developing a new financial environment for the economy. The digitalization of money is a profound advancement in the history of currency as a result of the advent of cutting-edge technologies which has enabled the creation of a novel type of currency known as Central Bank Digital Currencies (CBDCs). Research into CBDCs has only proliferated globally in the recent past, owing to digital payment mechanisms and a decline in the use of fiat currency. Latest advancements in virtual or online payment methods have prompted central banks across the world to take cognizance of issuing a CBDC to be at par with the current innovation trend. Central banks across the globe are currently investigating their potential advantages, and nations have multifarious motives for exploring and launching CBDCs. CBDCs are an emerging paradigm with a lack of context and clarity. The predicament of uncertainty regarding the e-Rupee may be resolved through this research. Obliviousness and incomprehension are the core predicaments concerning CBDCs and e-Rupee that this research endeavors to address through inferences from e-Yuan/e-CNY. Therefore, the study aims to explore and acquire in-depth knowledge and insights on CBDCs by examining circulars, working papers, and reports issued by central banks and financial institutions globally. The research also intends to elucidate the implications of CBDCs in national and international economies. Qualitative and Conceptual Analysis remains the dominant paradigm of research, however, empirical analysis has also furnished additional perspective to the same. This research will be able to shed light on certain aspects of CBDCs and their impact on economies and trade. Furthermore, this research will provide assistance and additional understanding to individuals, institutions and regulatory authorities.
Monolingual, multilingual and cross-lingual code comment classification

Code comments are one of the most useful forms of documentation and metadata for understanding software implementation. Previous research on code comment classification has focused only on comments in English, typically extracted from a few programming languages. This paper addresses the problem of code comment classification not only in the monolingual setting, but also in the multilingual and cross-lingual one, in order to examine whether they can outperform the traditional monolingual approach. To tackle this task, we introduce a novel, publicly available code comment dataset, consisting of over 10,000 code comments collected from software projects written in eight programming languages (C, C++, C#, Java, JavaScript/TypeScript, PHP, Python, and SQL). About half of them are written in Serbian while the other half are written in English. This dataset was manually annotated according to a newly proposed taxonomy of code comment categories. We fine-tune and evaluate multiple monolingual and multilingual pre-trained neural language models on the code comment classification task and compare their performances to several baselines. The best results for Serbian comments are obtained using the monolingual neural model BERTić, trained on Serbian and closely related languages. On the other hand, the optimal choice for English is the multilingual neural model multilingual BERT, which successfully extracts useful patterns from data in both languages. Although the cross-lingual setting shows some promise for simple binary classification, it has yet to reach sufficiently high performance levels for practical use. We also analyze model performance across different programming languages.
Opposition-based Laplacian distribution with Prairie Dog Optimization method for industrial engineering design problems

Prairie Dog Optimization is a population-based optimization method that uses the behavior of prairie dogs to find the optimal solution. This paper proposes a novel optimization method, called the Opposition-based Laplacian Distribution with Prairie Dog Optimization (OPLD-PDO), for solving industrial engineering design problems. The OPLD-PDO method combines the concepts of opposition-based Laplacian distribution and Prairie Dog Optimization to find near-optimal solutions. This causes faster convergence to the optimal solution and reduces the chances of getting stuck in a local minimum. The OPLD-PDO method was tested on several benchmark problems to validate its performance. The results were compared with other methods, and the OPLD-PDO method was superior regarding solution quality. The results of this study demonstrate the potential of the OPLD-PDO method as a useful tool for solving industrial engineering design problems and photovoltaic (PV) solar problems.
An enhanced grey wolf optimizer boosted machine learning prediction model for patient-flow prediction

Large and medium-sized general hospitals have adopted artificial intelligence big data systems to optimize the management of medical resources to improve the quality of hospital outpatient services and decrease patient wait times in recent years as a result of the development of medical information technology and the rise of big medical data. However, owing to the impact of several elements, including the physical environment, patient, and physician behaviours, the real optimum treatment effect does not meet expectations. In order to promote orderly patient access, this work provides a patient-flow prediction model that takes into account shifting dynamics and objective rules of patient-flow to handle this issue and forecast patients' medical requirements. First, we propose a high-performance optimization method (SRXGWO) and integrate the Sobol sequence, Cauchy random replacement strategy, and directional mutation mechanism into the grey wolf optimization (GWO) algorithm. The patient-flow prediction model (SRXGWO-SVR) is then proposed using SRXGWO to optimize the parameters of support vector regression (SVR). Twelve high-performance algorithms are examined in the benchmark function experiments' ablation and peer algorithm comparison tests, which are intended to validate SRXGWO's optimization performance. In order to forecast independently in the patient-flow prediction trials, the data set is split into training and test sets. The findings demonstrated that SRXGWO-SVR outperformed the other seven peer models in terms of prediction accuracy and error. As a result, SRXGWO-SVR is anticipated to be a reliable and efficient patient-flow forecast system that may help hospitals manage medical resources as effectively as possible.
Design of blockchain-based applications using model-driven engineering and low-code/no-code platforms: a structured literature review

Abstract The creation of blockchain-based software applications requires today considerable technical knowledge, particularly in software design and programming. This is regarded as a major barrier in adopting this technology in business and making it accessible to a wider audience. As a solution, low-code and no-code approaches have been proposed that require only little or no programming knowledge for creating full-fledged software applications. In this paper we extend a review of academic approaches from the discipline of model-driven engineering as well as industrial low-code and no-code development platforms for blockchains. This includes a content-based, computational analysis of relevant academic papers and the derivation of major topics. In addition, the topics were manually evaluated and refined. Based on these analyses we discuss the spectrum of approaches in this field and derive opportunities for further research.
Assessing the efficacy of augmented reality in enhancing EFL vocabulary

Augmented reality (AR) has drawn interest in education because AR helps learners practice real-life experiences during their learning. AR describes the process of adding computer-generated perceived data to the physical surroundings to generate an interactive experience. Implementing AR in developing vocabulary impacts English as a foreign language (EFL) learners’ capacity to master a language. The present study aims to determine the effects of AR in developing vocabulary learning using a mixed-method design to collect the data. The participants of the study were 95 male EFL learners from Common First Year students of Prince Sattam Bin Abdulaziz University (PSAU). The instruments used in the study were pretest, post-test, delayed post-tests, and semi-structured interviews. T-Test was used to analyse the data from the pretest, post-test and delayed post-test. The results revealed that AR helped learners develop their vocabulary skills. The learners also demonstrated positive perceptions of the implementation of AR in vocabulary learning.
PRV-FCM: An extension of fuzzy cognitive maps for prescriptive modeling

In this paper, we present a methodology based on fuzzy cognitive maps (FCMs) and metaheuristic algorithms to generate prescriptive models, called PRescriptiVe FCM (PRV-FCM). FCMs are a set of concepts interrelated that describe the behavior of a system. This kind of modeling has been extensively used to build descriptive and predictive models. We propose an extension of FCMs to develop prescriptive models and support decision-making in different domains. This adaptation characterizes FCMs, using system and prescriptive concepts. After that, it uses a metaheuristic algorithm (in this case, we use a genetic algorithm) to optimize prescriptive concepts based on system concepts and the stability of the FCM. Our proposed prescriptive approach was implemented and tested in four scenarios where it demonstrated its capability to find solutions that lead to desired values for the variables of interest. Specifically, no significant differences were found between the values of the prescriptive variables in the datasets and those generated by PRV-FCM.
Active learning on a programmable photonic quantum processor

Abstract Training a quantum machine learning model generally requires a large labeled dataset, which incurs high labeling and computational costs. To reduce such costs, a selective training strategy, called active learning (AL), chooses only a subset of the original dataset to learn while maintaining the trained model’s performance. Here, we design and implement two AL-enpowered variational quantum classifiers to investigate the potential applications and effectiveness of AL in quantum machine learning. Firstly, we build a programmable free-space photonic quantum processor, which enables the programmed implementation of various hybrid quantum-classical computing algorithms. Then, we code the designed variational quantum classifier with AL into the quantum processor, and execute comparative tests for the classifiers with and without the AL strategy. The results validate the great advantage of AL in quantum machine learning, as it saves at most <?CDATA $85\%$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>85</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> labeling efforts and <?CDATA $91.6\%$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>91.6</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> percent computational efforts compared to the training without AL on a data classification task. Our results inspire AL’s further applications in large-scale quantum machine learning to drastically reduce training data and speed up training, underpinning the exploration of practical quantum advantages in quantum physics or real-world applications.
A Conceptual introduction of Machine Learning Algorithms

The study of statistical models and algorithms that computers use to complete a particular task without external guidance or being explicitly programmed is known as machine learning. Lots of applications that we are using on regularly use ML algorithms. When we use search engine like Google to search the internet, it uses a machine learning algorithm.Teaching computers to maximize a performance criterion based on prior knowledge or sample data is known as machine learning. We have a model with certain parameters stated, and learning is the process of utilizing training data or prior knowledge to run a computer programme to improve the parameters. The model might be pictorial to learn from the data, foretelling to make forecasting, or both.ML algorithms have figured out the way to rank websites. These are used for various type of things, including data collection, data preprocessing, visualization, prediction of data and take decision.The main aid of using machine learning algorithm is that formerly it learns how to handle data, it can finish work independently. This analysis provides a rapid review and viewpoint on the vast applications of machine learning techniques.
Human Activity Recognition using Deep Learning: Past, Present and Future

The study of human activities has always incite researchers. The human actions are acquired from raw time-series signals using smartphones and wearable devices' integrated sensors. Human Activity Recognition (HAR) has wide applications in rehabilitation centres, geriatric care houses, orphanages, and public places with large crowds. The essential stages of HAR architecture are feature detection, feature selection, and feature extraction. The majority of existing HAR algorithms implement conventional machine learning techniques for detection and classification. However, the desired computational results are not obtained for complex human activities. Thus, deep learning techniques have attracted attention of researchers. The deep learning techniques are widely used in various applications involving feature extraction, classification and detection. The deep learning techniques automatically extract features and reduces the computational complexity. This paper discusses various existing benchmark datasets to evaluate the performance of HAR models. A comprehensive review of sensor-based human activity recognition using convolution neural network-based models, long short-term memory-based models, and hybrid models is presented. Finally, the challenges and significance of human activity recognition is discussed.
Hedera: A Permissionless and Scalable Hybrid Blockchain Consensus Algorithm in Multiaccess Edge Computing for IoT

Multiaccess edge computing (MEC) network, as one of the key infrastructures of IoT, provides cloud computing capabilities at the edge of the radio access network (RAN) by integrating telecommunication and IT services. Integrating blockchain into the MEC network can provide users with secure, private, and traceable edge computing services at the near end, thereby improving IoT security, privacy, and automated use of resources. Due to some characteristics of the MEC network, there are still some challenges to integrate blockchain and edge computing into one system, especially the consensus algorithm of blockchain. The resources of edge computing nodes are limited, and the scale of the network is constantly expanding. Therefore, the blockchain consensus algorithm for the MEC network should occupy as little computing resources as possible, be green, and be permissionless. This article proposes a permissionless and scalable consensus algorithm “Hedera” for MEC network, which has the advantages of permissionless, security, decentralization, scalability, and greenness. The Hedera consensus algorithm is a hybrid blockchain consensus algorithm that combines the permissionless Proof-of-Capacity algorithm and the permissioned asynchronous Byzantine algorithm. This article tests the fairness, throughput, scalability, latency, and resource consumption of the algorithm by developing and deploying a prototype system. The experimental results show that the Hedera algorithm proposed in this article is fair, the throughput reaches 13986.3 TPS, and the resource consumption is much lower than the PoW consensus. By analyzing its security and liveness, it can resist the sybil attack, nothing-at-stake attack, selfish mining attack, and message hijacking attack, and has good liveness.
Car side impact design optimization problem using giant trevally optimizer

One of the evaluation criteria used to determine the effectiveness of newly developed algorithms is their application to solving real-world mechanical problems. Car side impact design optimization problem (CSDP) is one of the most complex problems in the industry field. According to international safety standards, a car is exposed to a side-impact. Here, the aim is to minimize the weight using eleven mixed design variables while maintaining safety performance according to the standards. In this paper, the recently developed Giant Trevally Optimizer (GTO) is used to be implemented on the aforementioned complex optimization problem. GTO mimics the hunting strategies of marine predator giant trevally while hunting birds. The effectiveness of GTO is tested by comparison with ten well-known optimizers by solving the car side impact problem. The results show that GTO generally outperforms the tested competitors and demonstrate the practicability of the GTO optimizer in solving challenging real-world problems. Moreover, new cost function has been found by GTO for CSDP.
Quantum Machine Learning for Join Order Optimization using Variational Quantum Circuits

The optimization of queries speeds up query processing in databases. One of the most time-consuming tasks in query processing is the join operation, where the order of the joins plays a crucial role in determining the number of tuples to be processed for intermediate results, and hence, the overall processing costs. In this paper, we use a variational quantum circuit (VQC) to create a hybrid classical-quantum machine learning algorithm to predict efficient join orders by learning from past join orders. We develop an encoding of the join order problem using a low number of qubits. We show that VQCs with filtering of cross joins outperform the classical dynamic programming optimizer of PostgreSQL with a 2.7% faster execution time.
Domain adaptation deep learning and its T-S diagnosis networks for the cross-control and cross-condition scenarios in data center HVAC systems

Deep learning has the inspiring potential for artificial intelligence (AI) control in data centers of smart city. However, deep learning-based method is limited when the application scenarios are cross-control or cross-conditions. This paper proposes a novel domain adaptation deep learning model and Teacher-Student (T-S) networks to diagnose the faults of Heating, Ventilation and Air Conditioning (HVAC) systems under cross-control modes and cross-conditions. Firstly, domain adaptation deep belief network (DBN) model is presented to enhance the cross-control generalization performance of extreme learning machine (ELM) and K-nearest neighbor (KNN) diagnosis model. Secondly, the Teacher-Student networks are constructed. With the weights learned from the DBN model in Teacher Network, the cross-conditions capacity is improved for DELM model in the Student Network. Moreover, both the single and multiple faults are experimented and validated, which includes refrigerant leakage, reduced condenser water flow and reduced evaporator flow. The experimental results show that the domain adaptation deep learning model and its T-S diagnosis networks can reach the satisfied generalization performance. For the cross-control tests, the Accuracy of proposed method can reach 91%, 89% and 80% for fault-free, REW and RCW faults. For the cross-temperature and cross-load tests, the proposed method also obtained the high Accuracy of 98% and 88%, respectively.
Open access books through open data sources: assessing prevalence, providers, and preservation

Purpose Science policy and practice for open access (OA) books is a rapidly evolving area in the scholarly domain. However, there is much that remains unknown, including how many OA books there are and to what degree they are included in preservation coverage. The purpose of this study is to contribute towards filling this knowledge gap in order to advance both research and practice in the domain of OA books. Design/methodology/approach This study utilized open bibliometric data sources to aggregate a harmonized dataset of metadata records for OA books (data sources: the Directory of Open Access Books, OpenAIRE, OpenAlex, Scielo Books, The Lens, and WorldCat). This dataset was then cross-matched based on unique identifiers and book titles to openly available content listings of trusted preservation services (data sources: Cariniana Network, CLOCKSS, Global LOCKSS Network, and Portico). The web domains of the OA books were determined by querying the web addresses or digital object identifiers provided in the metadata of the bibliometric database entries. Findings In total, 396,995 unique records were identified from the OA book bibliometric sources, of which 19% were found to be included in at least one of the preservation services. The results suggest reason for concern for the long tail of OA books distributed at thousands of different web domains as these include volatile cloud storage or sometimes no longer contained the files at all. Research limitations/implications Data quality issues, varying definitions of OA across services and inconsistent implementation of unique identifiers were discovered as key challenges. The study includes recommendations for publishers, libraries, data providers and preservation services for improving monitoring and practices for OA book preservation. Originality/value This study provides methodological and empirical findings for advancing the practices of OA book publishing, preservation and research.
CEHMR: Curriculum learning enhanced hierarchical multi-label classification for medication recommendation

The medication recommendation (MR) or medication combination prediction task aims to predict effective prescriptions given accurate patient representations derived from electronic health records (EHRs), which contributes to improving the quality of clinical decision-making, especially for patients with multi-morbidity. Although in recent years deep learning technology has achieved great success in MR, the performance of current multi-label based MR solutions is unsatisfactory. They mainly focus on improving the patient representation module and modeling the medication label dependencies such as drug-drug interaction (DDI) correlation and co-occurrence relationship. However, the hierarchical dependency among medication labels and diversity of difficulty among MR training examples lack sufficient consideration. In this paper, we propose a framework of Curriculum learning Enhanced Hierarchical multi-label classification for MR (CEHMR). Motivated by the category hierarchy of medications which organizes standard medication codes in a hierarchical structure, we utilize it to provide more trustworthy prior knowledge for modeling label dependency. Specifically, we design a hierarchical multi-label classifier with a learnable gate fusion layer, to simultaneously capture the level-independent (local) and level-dependent (global) hierarchical information in the medication hierarchy. In addition, to overcome the diversity of training example difficulties, and progressively achieve a smoother training process, we introduce a bootstrap-based curriculum learning strategy. Hence, the example difficulty can be measured based on the predictive performance of the MR model, and then all training examples would be retrained from easy to hard under the guidance of a predefined training scheduler. Experiments on the real-world medical MIMIC-III database demonstrate that the proposed framework can achieve state-of-the-art performance compared with seven representative baselines, and extensive ablation studies validate the effectiveness of each component of CEHMR.