Reviewerly/exHarmony huggingface dataset:
SRI AUROBINDO’S THOUGHTS TOWARDS THE TEACHER AND EDUCATION

This research paper is presenting a famous educational philosopher’s views and thoughts regarding the teacher and education. Initially, the study described the struggle of an ideologist and his contribution toward national development. Thereafter, the study marked the importance of spiritual education, physical and mental education as well as recommended curriculum modification, including the practical aspects before assigning to a child, having control over body, and mind through yoga and meditation, etc. Further, the study reviewed the literary works of past researchers who put immense innovations in the school curriculum in alignment with Sri Aurobindo’s thoughts. As per his philosophy, a learner should have the choice of subjects, and languages and be provided proper guidance, counseling, and a free environment. Under the methodologies part, the study considered his thoughts, and ideas in the form of divine life, school, discipline, curriculum, and physical, spiritual, and mental education. Lastly, the study agreed on education which helps in every aspect of life to achieve any goals, tasks, consciousness, control, and success of an individual.
CLART: A cascaded lattice-and-radical transformer network for Chinese medical named entity recognition

Chinese medical named entity recognition (NER) is a fundamental task in Chinese medical natural language processing, aiming to recognize Chinese medical entities within unstructured medical texts. However, it poses significant challenges mainly due to the extensive usage of medical terms in Chinese medical texts. Although previous studies have made attempts to incorporate lexical or radical knowledge in order to improve the comprehension of medical texts, these studies either focus solely on one of these aspects or utilize a basic concatenation operation to combine these features, which fails to fully utilize the potential of lexical and radical knowledge. In this paper, we propose a novel Cascaded LAttice-and-Radical Transformer (CLART) network to exploit both lexical and radical information for Chinese medical NER. Specifically, given a sentence, a medical lexicon, and a radical dictionary, we first construct a flat lattice (i.e., character-word sequence) for the sentence and radical components of each Chinese character through word matching and radical parsing, respectively. We then employ a lattice Transformer module to capture the dense interactions between characters and matched words, facilitating the enhanced utilization of lexical knowledge. Subsequently, we design a radical Transformer module to model the dense interactions between the lattice and radical features, facilitating better fusion of the lexical and radical knowledge. Finally, we feed the updated lattice-and-radical-aware character representations into a Conditional Random Fields (CRF) decoder to obtain the predicted labels. Experimental results conducted on two publicly available Chinese medical NER datasets show the effectiveness of the proposed method.
The Impact of Social-media Celebrity on Buying Behavior of Retailer Customers in Riyadh, KSA

This study aimed to measure the impact of celebrity endorsement on the buying behavior of retailer customers in Riyadh. The study population consists of all buyers of hyper-stores in Riyadh. The unit of analysis was the Saudis and non-Saudis who are employed in the government and private sectors in Riyadh. The estimates showed that the size of the population reached 3.87 million people in 2019. The stratified-sample technique was used and the recommended sample size reached 387 buyers. The results of structural equation modeling (SEM) showed a statistically significant impact of celebrity on the purchasing behavior of retailer customers in Riyadh. Finally, the study presented several recommendations and marketing implications to enhance the role of celebrity in purchasing decisions.
Six Sigma methodology based on the PMBOK guide in the release management process in software developer enterprise

The objective of this work was to analyze the application of the Six Sigma methodology based on the PMBOK guide in the release management process in a software developer company. With the increasing complexity of the release management process in the software developer company, we saw the need to analyze the mapped process, and identify points of failure or improvement that could be fixed/improved. For the implementation of the Six Sigma methodology, it was defined that the PMBOK would be used as the guiding basis for the definition of which project management processes would be used. The methodology was used as a reference for quality methods and tools for application in the process carried out by the application infrastructure area. A qualitative approach was applied as a research methodology, with bibliographic research for the theoretical foundation and documentary research, making annotations about the observed process, as well as its results. By defining the project management processes, the project was executed in such a way as to provide the methodology to be applied assertively on the process, thus identifying the points of failure and improvement to be addressed, thus achieving the expected goal.
An innovative approach for dynamic key dependent S-Box to enhance security of IoT systems

The Internet of Things (IoT) foresees pervasive and connected smart nodes that interact independently while offering varied services. Today, IoT nodes are a treasure house of data and hence security has become a major concern in IoT networks. Conventional cryptography faces many challenges during implementation in IoT devices. These challenges can be tackled by lightweight cryptography which is highly secure with less resource requirements. The vital characteristic of security is fulfilled by one out of 6 inner structures such as Substitution-Permutation Network, Feistel Network, General Feistel Network, Add-Rotate-XOR, Non Linear-Feedback Shift Register and Hybrid. PRESENT, which is a NIST approved lightweight cryptographic algorithm with SPN structure uses fixed or static S-Box. This permits attackers to explore S-Box and identify weaknesses of the algorithm. Key dependent approaches are better because it impedes an attacker from any offline analyses of attacks on the S-Box. Additionally, this is susceptible to linear and differential attacks. The static S Box does not possess superior diffusion and does not have a good rate of avalanche effect. So, the solution is to create a robust dynamic key dependent algorithm with nonlinear S-Boxes to secure the communication between IoT devices and platform. Security analysis is done on Non-linearity, Differential and Linear Cryptanalysis and Avalanche characteristics, besides, encryption of IoT data to demonstrate its aptness for cold chain security applications. © 2017 Elsevier Inc. All rights reserved.
Semantic Annotation Based Mechanism for Web Service Discovery and Recommendation

Web Mining is regarded as one among the data mining techniques that aids in fetching and extraction of necessary data from the web. Conversely, Web usage mining discovers and extracts essential patterns usage over the webs which are being further utilized by various web applications. In order to discover and explore web services that are registered with documents of Web Services-Inspection, Discovery and Integration registry, Universal Description wants specific search circumstance similar to URL, category and service name. The document of Web Service Description Language (WSDL) offers a condition of the web services customers to take out operations, communications and the service format of right message. Therefore, WSDL is being utilized together with semantic explanation dependent substantiation for the extraction of different web services for related purpose, other supporting operations and attributes. The reason is that there subsist different web services having corresponding functionalities however altered or changeable attributes that are non–functional. Resultant, recognize the preeminent web service become tiresome for the user. A method is projected which caters the analysis of service resemblance with the aid of semantic annotation and machine learning (ML) algorithms depending on the analysis intended for enhancing the classification through capturing useful web services semantics related with real world. The emphasizes on the research technique of choosing preeminent web service for the user based on the semantic annotation. The research work in turn recommends a web mining technique that determines the best web service automatically thus ranking concepts in service textual documentation and classifies services on behalf of particular domains. Parallel computation is made easy with web services. The different management stages in the system of recommendation entail collection of dataset through WSDL on the semantic annotation basis, thereby recognizing the best service with the DOBT-Dynamic operation dependent discovering method, ranking through mechanisms MDBR - Multi-Dimensional based ranking, recommendation and classification. In this work, it has been employed a combination of fundamental ML estimators, namely Multinomial Naive Bayes (MNB) and Support Vector Machines (SVM), as well as ensemble techniques such as Bagging, Random Forests, and AdaBoost, to perform classification of Web services. It was observed from the investigate work that the adapted system of best web services recommendation defers high performance in contradiction of the existing recommendation technique regarding accuracy, efficiency in addition to processing time.
Impact of Workplace Conflict on Employees’ Performance in Commercial Banks of Nepal

Employee performance among the employees has been found as a fundamental element influencing the workplace conflict of the organisation. The purpose of this study was to examine the Impact of Workplace Conflict on Employees’ Performance in Commercial Banks of Nepal. The variables included in the study were the relationship conflict, task conflict, and process conflict were independent variable and employee performance was dependent variable. A survey research design using questionnaire method on 5-point Likert scale was used to collect. The study was conducted with the sample size of 170 and the data were analysed through the use of descriptive statistics, correlation, regression, t-test and multi-collinearity test using SPSS software. The findings of the study have a R square value of 0.626 meaning that 62.6% of the variation in the dependent variable is explained by the independent variables while 37.4% is explained by other variables outside the model and also showed that there is a strong positive correlation between the dependent variable and the set of independent variables. The result showed that there is significant impact of RC, TC, PC on EP.
Proposed Method for Estimating Parameters of Archimedean Spirals

Abstract Consider an Archimedean spiral in two-dimensional space series of points subject to random errors. In this paper, a methodology of a fitting procedure to spiral curve is studied. Three methods are proposed to give initial estimates of the spiral parameters. An Optimize algorithm proposed to updates the initial estimates. The approach is validated using simulated and real databases obtained from Parkinson patients handwriting. Finally, a comparison done between these three methods and Mishara’s method which shown that our methods give better results.
Functional hybridity in translation

Abstract The present study addresses a topic on the crossroads between contrastive linguistics and translation studies as we seek to investigate how the English verbal gerund is used in translated and non-translated English, as well as how it is translated into German and Dutch. Instead of merely analysing frequencies throughout a corpus, we aim to map out usage profiles and translation strategies through a multi-methodological and multifactorial approach, thereby offering a more contextualized approach to the English verbal gerund. On the basis of data from CroCo and DPC, two parallel and comparable corpora for the language pairs English-German and English-Dutch, a set of over 6,000 verbal gerunds was annotated. A hierarchical configural frequency analysis was conducted to uncover different usage profiles or ‘types’ of verbal gerunds. Second, a conditional inference tree and a random forest analysis were modelled to select the best predictors to help distinguish between a nominal or a clausal translation solution. In addition to providing more insight into the status of English verbal gerunds in translation, this paper offers new perspectives on two broader debates in contrastive linguistics and translation studies, viz. the ‘loose-fit’ or ‘tight-fit’ status of constructions and the treatment of unique items respectively.
Deep Learning Benchmark for First Break Detection from Hardrock Seismic Reflection Data

Deep learning techniques are used to tackle a variety of tasks related to seismic data processing and interpretation. Although many works have shown the benefits of deep learning, assessing the generalization capabilities of proposed methods for data acquired in different conditions and geologic environments remains challenging. This is especially true for applications in hardrock environments. The primary factors that impede the adoption of machine learning in geosciences include the lack of publicly available and labeled data sets and the use of inadequate evaluation methodologies. Because machine learning models are prone to overfit and underperform when the data used to train them are site specific, the applicability of these models on new survey data that could be considered “out-of-distribution” is rarely addressed. This is unfortunate, as evaluating predictive models in out-of-distribution settings can provide a good insight into their usefulness in real-world use cases. To tackle these issues, we develop a simple benchmarking methodology for first break picking to evaluate the transferability of deep learning models that are trained across different environments and acquisition conditions. For this, we consider a reflection seismic survey data set acquired at five distinct hardrock mining sites combined with annotations for first break picking. We train and evaluate a baseline deep learning solution based on a U-Net for future comparisons and discuss potential improvements to this approach.
Multi-CC: A New Baseline for Faster and Better Deep Clustering

The aim of our paper is to introduce a new deep clustering model called Multi-head Cross-Attention Contrastive Clustering (Multi-CC), which seeks to enhance the performance of the existing deep clustering model CC. Our approach involves first augmenting the data to form image pairs and then using the same backbone to extract the feature representation of these image pairs. We then undertake contrastive learning, separately in the row space and column space of the feature matrix, to jointly learn the instance and cluster representations. Our approach offers several key improvements over the existing model. Firstly, we use a mixed strategy of strong and weak augmentation to construct image pairs. Secondly, we get rid of the pooling layer of the backbone to prevent loss of information. Finally, we introduce a multi-head cross-attention module to improve the model’s performance. These improvements have allowed us to reduce the model training time by 80%. As a baseline, Multi-CC achieves the best results on CIFAR-10, ImageNet-10, and ImageNet-dogs. It is easily replaceable with CC, making models based on CC achieve better performance.
Topological insulator and quantum memory

Measurements performed on quantum systems are too specific. Unlike their classical counterparts, quantum measurements can be invasive and destroy the state of interest. Besides, quantumness limits the accuracy of measurements performed on quantum systems. Uncertainty relations define the universal accuracy limit of the quantum measurements. Relatively recently, it was discovered that quantum correlations and quantum memory might reduce the uncertainty of quantum measurements. In this paper, we study two different types of measurements performed on the topological system. Namely, we discuss measurements performed on the spin operators and measurements performed on the canonical pair of operators: momentum and coordinate. We quantify the spin operator's measurements through the entropic measures of uncertainty and exploit the concept of quantum memory. In contrast, for the momentum and coordinate operators, we exploit the improved uncertainty relations. We discover that quantum memory reduces the uncertainties of spin measurements. On the other hand, we prove that the uncertainties in the measurements of the coordinate and momentum operators depend on the value of the momentum and are substantially enhanced at small distances between itinerant and localized electrons (the large-momentum limit). We note that the topological nature of the system leads to the spin-momentum locking. The momentum of the electron depends on the spin, and vice versa. Therefore we suggest an indirect measurement scheme for the momentum and coordinate operators through the spin operator. Due to the factor of quantum memory, such indirect measurements in topological insulators have smaller uncertainties than direct measurements.
Innovation of History Teaching Mode Based on Digital Technology

With the continuous progress of science and technology, the trend of integrating science and technology into education has been unstoppable, and educators are constantly striving to find a perfect teaching method to serve education. In this article, the innovative research on the teaching mode of history course based on digital technology is carried out. This article puts forward the design of digital resources of history curriculum, and on this basis, designs and implements the algorithm of recommending history curriculum resources. Firstly, CF (collaborative filtering) algorithm based on users is implemented to find similar neighbors for users, and if there are no similar neighbors, it is calculated according to content recommendation. Secondly, the user's ability scoring model not only contains the basic attributes of users, but also involves the ability scoring of course training users, and the calculation of ability comes from the calculation of text mining.
INSTITUTIONAL DIMENSIONS AND ALLOCATION OF ENTREPRENEURIAL TALENTS TOWARD INNOVATIVE ACTIVITY IN NIGERIA

This study explores how certain institutional dimensions influence entrepreneurial allocations in Nigeria and its sectorial heterogeneity. We apply unconditional fixed-effect Tobit regression estimators on a combined dataset extracted from various sources over the period 2011-2015. Our findings suggest that property rights and oil rents strongly encourage young entrepreneurs to be innovative, while a number of business procedures and the rule of law discourage them from being innovative. The findings suggest that some of the institutions are complementary to one another in improving entrepreneurial innovativeness. Interactions between them are positive and significant. Sectorial results reveal that property rights, government effectiveness and oil rents significantly enhance entrepreneurial innovativeness, whereas government size reduces innovativeness among young entrepreneurs in the manufacturing sector. In the IT sector, property rights, control of corruption, regulatory quality and government effectiveness increase entrepreneurial innovativeness. Government size, business procedures and the rule of law reduce innovativeness. Overall, the institutions appear to have stronger and bigger effects in the IT sector than they do in the manufacturing sector. Policy implications include the need for institutional reforms targeting productive entrepreneurship to focus more on making business regulation and procedures more competition-friendly and less cumbersome and strengthening the quality of anti-graft and property right institutions.
Anomaly Detection in Renewable Energy Big Data Using Deep Learning

This work aims to review the literature on anomaly detection (AD) in renewable energy. Due to the significance of the RE data quality and sensor performance, it is crucial to ensure that the measurement device works correctly and maintains data accuracy. The review identifies the relevant studies on big data anomaly detection in the energy field and synthesizes the related techniques. Also, the study shows a need for segmentation annotations for solar system electroluminescence imagery complicating the domain development of anomaly segmentation approaches. Consequently, most processes create machine learning (ML) models using semi-supervised techniques. Still, these approaches need more generalization regarding variation in environmental or systematic conditions. Furthermore, the studies discussed here focus on existing algorithms that used big data and AD to propose an improved analysis framework. Finally, the work presents a framework to solve the problem of identifying sensors' issues that will appear in data anomalies.
Fringe projector with submillimeter fringe spacing at a meter-scale field of view

State-of-the-art fringe projection systems generate fringe patterns using digital light projectors (DLP). The axial uncertainty is limited by the smallest fringe period and is directly related to the pixel count. This results in limited accuracy of current DLP systems that affect applications such as in situ measurements for laser powder bed fusion systems, where a submillimeter fringe period is needed for field-of views larger than 500mm×500mm. This work presents a scalable fringe projection technique that enables the generation of stable fringe patterns over a large field of view spanning several meters while maintaining submillimeter fringe periods. This system uses geometric phase gratings to enable variable fringe spacing and fringe orientation capabilities. The system shears a coherent beam in the Fourier plane using a pair of geometric polarization gratings. The separation between the gratings directly affects the fringe spacing, and the orientation of the gratings affects the fringe orientation. The depth of focus is only limited by the coherence of the light source, enabling high fringe periods even on tilted planes. The system is designed with a single path configuration, making the system more robust to environmental noise. With a rotating linear polarizer, we demonstrate that phase-shifting methods could be employed to acquire phase information about the object. This paper employs a single-shot Fourier transform phase estimation technique to process the intensity maps acquired using projected fringe patterns. Further, we demonstrate the capabilities of the system to produce submillimeter fringe spacing and the ability to project fringes on larger scales for measurements.