Reviewerly/exHarmony huggingface dataset:
The Effect of Learners’ Characteristics on Learning Performance in Non-Face-To-Face Liberal Arts Coding Class

Purpose: In this study, we investigate the characteristics of learners in the liberal arts coding class and study the relationship between learners’ characteristics and learning outcomes to find methods to enhance learning performance. Through this study, we intend to provide the basic data to be used by the schools and educators operating coding education in the real world to establish the teaching strategy for coding education according to the characteristics of the learners. Method: The collected data was analyzed using SPSS 23, as follows. The reliability of the measurement tools on self-efficacy, learning interest, learning motivation, digital literacy, and learning satisfaction were calculated and verified. Through a survey, data on self-efficacy, learning motivation, digital device efficacy, learning satis-faction, and academic achievement were collected and descriptive statistical analysis was performed to identify trends. We performed multiple regression analyses with self-efficacy, learning motivation, and digital device ef-ficacy as independent variables, whereas learning satisfaction and academic achievement as dependent varia-bles. Before the regression analysis, a Pearson correlation analysis was performed between self-efficacy, learning motivation, digital device efficacy, learning satisfaction, and academic achievement to confirm the significance of the relationship between the measurement variables. Results: The significance test results of the regression model showed that self-efficacy and digital device effi-cacy had a positive effect on learning satisfaction and learning motivation and digital device efficacy also had a positive effect on academic achievement. In other words, we evaluated that higher self-efficacy and digital device efficacy lead to higher learning satisfaction whereas higher learning motivation and digital device efficacy lead to higher academic achievement. Conclusion: Through this study, we confirmed that learners’ characteristics have a high influence on both learning satisfaction and academic achievement. In the future study, we plan to add multiple factors of learners’ characteristics that affect learning performance and studies on educators’ characteristics to obtain various and meaningful results.
Analysis of Functional Relationship Recognition and Expression of Middle School Students through Eye-Tracking: Focusing on the Stacking Tree Task

This research was conducted to analyze the students' thinking processes and the causes of errors in the process of recognizing and expressing the relationship between changing quantities. This could be analyzed by tracking students' eye movements in the process of solving the stacking tree three-dimensional function task. As a result of the analysis, students' functional thinking processes could be categorized into recursive patterning, covariational thinking, and correspondence relationship. In addition, the students made errors such as not being able to correctly connect the relationship between the values ​​of quantities, and the causes could be analyzed in various ways.
Effective Teaching of Literature-in-English in Nigerian Secondary Schools: The Persistent Problems

Literature-in-English is a major school subject that equips students with language and other soft skills needed for entry into the workforce. For students to acquire the skills, effective teaching, which is dependent on the availability of basic teaching–learning resources, is essential. Education is underfunded in Nigeria. This affects the quality of teaching and impedes learning. The rate at which students fail Literature-in-English, which is getting worse each year, indicates that there is a problem in the teaching–learning process. Using a mixed-method research design, this study investigated the problems of teaching Literature-in-English in 51 public senior secondary school in Ibadan metropolis, Oyo State. Random and purposive sampling techniques were used to select three local government areas (LGAs), 20 teachers, and 7,090 students. Data were gathered through Availability of Literature Teachers Checklist (ALTC), Interview Guide for Literature-in-English Teachers (IGLT) and Students’ Text Possession Checklist (STPC). The result shows that teachers are faced with a lot of challenges that prevents them from delivering quality instruction. These include poor learning environment, acute shortage of textbooks, low language proficiency misconceptions about the value of literature, heavy workload, and lack of parental support. Some schools do not have literature teachers. The biggest obstacle, however, is lack of textbooks. The situation is critical and points to a crisis of teaching and learning in Literature-in-English classrooms. Urgent interventions are needed if the objectives of this school subject are to be achieved. Some recommendations were made.
Research on image segmentation algorithm based on fuzzy clustering

Image segmentation is the basis of image processing and an important technology in computer vision and other fields. With the continuous development of computer technology, image segmentation also presents a new look. Therefore, the research on image segmentation methods has far-reaching significance to make it better serve people's lives. The quality of image segmentation results will directly affect the quality of subsequent applications such as image processing. The Fuzzy C-means Clustering (FCM) algorithm also has certain defects or deficiencies. The traditional FCM algorithm is divided based on the grayscale of the image pixels, which is unreasonable to some extent. In order to speed up the convergence speed of the algorithm and improve the noise resistance of the algorithm, this paper selects an FCM based on spatial information for improvement, and proposes an improved FCM segmentation algorithm. The experimental results show that the improved algorithm has high segmentation efficiency, high segmentation accuracy and strong anti-noise.
Is it practicable for consultants to work well without sharing their knowledge? Employee resiliency and its importance

Knowledge sharing is frequently discussed in dynamic company situations and as employees become more aware with virtual needs. This study looks at how consultants can improve knowledge workers perform better by sharing their knowledge. This study is interesting in that it looks at how knowledge sharing affects knowledge worker performance via employee resilience. This survey included 203 consultants from various consulting service sectors as respondents. SEM was used in this study, and it was processed using Lisrel 8.50 software with a descriptive design. Surprisingly, the findings of this study show that knowledge sharing has no impact on knowledge worker performance. Employee resilience, on the other hand, has a favorable impact on the impact of knowledge sharing to the performance of knowledge worker.
Comparison of Data 2Text Generation models using Sequence-to-Sequence

In this study, we compare the modified encoding method and attention method using a sequence-to-sequence model in the Data2Text task. In addition, we propose a model that complements the shortcomings. Data2Text refers to the task of generating description from tabular data. The data consists of a set of values and fields, and there are significant performance differences depending on how the model learns these structures. Therefore, we compare two representative models utilizing sequence-to-sequence under the same conditions to find a more effective methodology for learning the structure of the data. In addition, it adds a copy mechanism to improve performance by allowing the output of words that are not in the word vocabulary, such as proper nouns. As a result of the experiment, encoding methods using values, fields, position information and dual attention, which combines values and field attention, showed the best performance. In addition, performance improvement in the sequence-to-sequence model was confirmed when the copy mechanism was utilized.
Video Usefulness Detection in Big Surveillance Systems

In the past few decades, the safety of human beings and their properties have been issued for survival in real life. The different smart connected communities have taken several steps using prominent components of video surveillance systems for public safety. Sometimes video surveillance system is not working properly to identify the activities/gestures of humans. Thus, the operation and maintenance teams can take the responsibility for significant quantity of time and recognizing the breakdown in a huge video surveillance system. But both useful as well as useless video data consumes data rates through the network and stock up in the cloud. It needs appearance of edge computing for video pre-processing with an edge camera. Thus, it considered the video usefulness (VU) system using special computing system such as edge computing to generate several visual data in huge video surveillance systems. The identification of failure with good bandwidth is considered to find the location of failure and forward to corresponding users. This difficulty is solved by three categories such as (a) calculation of VU values, (b) online failure finding techniques, and (c) good network bandwidth. The 290above categories lessen overload video data in network based on unused VU values.
Extraction of f-Waves in Electrocardiograms Using Echo State Network Based Nonlinear Adaptive Filters

Atrial fibrillation (AF) is the most common arrhythmia in clinical practice. The paper introduces a new method for ventricular activity cancellation in AF from surface electrocardiogram (ECG) signals. The proposed method is based on AF signal extraction using adaptive echo state neural network (ESN). Adaptive ESN estimates a time-varying, nonlinear transfer function between two ECG leads and separates ventricular activity from atrial activity. The method was compared with conventional pre-whitened recursive least squares (RLS) based linear adaptive filter. Both algorithms were applied to surrogate ECG data with known component of AF signal. The results revealed that the ESN based nonlinear filter extracts f-waves more accurately than the conventional pre-whitened RLS based linear algorithm, especially in lower amplitude (&lt; 0.05 mV) AF signals.
Optimal information fusion in stochastic unknown input observers network

The problem of optimal information fusion in stochastic unknown-input observer network, with ensure the state and input signal estimation for dynamic systems under the presence of stochastic disturbance and random measurement noise is considered. Stochastic unknown-input observer and observer-based inverse model optimal design method is proposed and the conditions of steady-state observer existence are established. The solution of optimal state estimates fusion problem is presented for distributed stochastic observers and consensus unknown-input observer network.
Overview of Metal Pollution

Heavy metal is commonly defined as any metal and metalloid element that has a relative density ranging from 5.3 to 7 g/cm3, atomic number above 20 and is toxic or poisonous at low concentrations (Abbas et al., 2014; Gautam et al., 2014). The main elements considered as heavy metals are As, Cr, Mn, Co, Cu, Zn, Mo, Hg, Ni, Sn, Pb, Cd, Sb, Tl, etc. There are three groups of heavy metals that are of concern, and they are the following: toxic metals (such as Hg, Cr, Pb, Zn, Cu, Ni, Cd, As, Co, Sn, etc.), precious metals (such as Pd, Pt, Ag, Au, Ru, etc.), and radionuclides such as U, Th, Ra, Am, etc. Four of them, Pb, Hg, Cd and Cr4+, are known to have the highest toxicity among various metal ions, and the first three are in the limelight due to their major impact in the environment (Abbas et al., 2014).
Malaria Classification on Thick Blood Film

This chapter describes an automated device for the detection and classification of Plasmodium species on thick blood films. An overview of Plasmodium parasites that cause malaria, and current diagnosis methods and their limitations are discussed. The chapter concludes with the detailed introduction of an automated device, consisting of an image acquisition unit and its image analysis software module, which could be mounted on a conventional light microscope and operated by a health practitioner/technician with minimal training in the detection and diagnosis of malaria parasites.
Digitalization and Management of Innovation: The Role of Technology, Environment, and Governance

The focus of this chapter is on the organization of innovation in an increasingly digitalized and networked world. Products, services, business processes, workplaces, and supply chains become connected, enabled by digital technologies and platforms, and leading to new ways of value creation based on new types of business models. Digitalization also affects the public sector, e.g., in domains such as health care, energy, education and transport, and urban life in the “smart city.” Digitalization reshapes the process of innovation through facilitating new organizational forms such as collaborative networks and platform-based ecosystems. This chapter discusses examples of the new forms of managing innovation management in industry, the public sector, and also addresses the relatively new form of blockchain innovation. The aim is to understand the forces at work affecting how digital innovation in collaborative settings is organized to clarify the implications for managing innovation and to provide future-oriented recommendations to innovation stakeholders.
Building Secure Data Centers for Cloud Based Services – A Case Study

The invention of the steam engine dates back to the middle of the 17th century and it marked the early beginning of the first wave of the an industrialized world. This was followed by the second wave in the end of the 18th century with mass production and the wide spread use of electricity. In particular, the invention of the light bulb by Thomas Edison in 1879 was a particularly ground breaking innovation of that time.
QR Codes Cryptography: A Lightweight Paradigm

A QR Code is a two-dimensional barcode scanned by a digital device or smartphone that holds data as a sequence of pixels in a square-shaped pattern. QR codes are widely employed in commercial tracking systems, encoding URLs, contact information, map coordinates, and physical and digital documents. Nowadays, several smartphones have built-in QR readers; they are often employed in marketing and advertising campaigns. More recently, QR codes have recreated a critical role in tracing COVID-19 pandemic exposure and slowing the spread of the virus. Web attackers can encode malicious URLs of custom malware or phishing site into a QR code, which could violate or disclose personal or financial information on a smartphone’s data when scanned. This study investigates several symmetrical lightweight cryptography (LWC) algorithms to enhance QR code protection. Modern well-defined LWC features (performance and security) are compared and evaluated. The results adopt reliable and safe mechanisms for QR codes’ security issues.
Fast Learning from Label Proportions with Small Bags

In learning from label proportions (LLP), the instances are grouped into bags, and the task is to learn an instance classifier given relative class proportions in training bags. LLP is useful when obtaining individual instance labels is impossible or costly.In this work, we focus on the case of small bags, which allows to design an algorithm that explicitly considers all consistent instance label combinations. In particular, we propose an EM algorithm alternating between optimizing a general neural network instance classifier and incorporating bag-level annotations. Using two different image datasets, we experimentally compare this method with an approach based on normal approximation and two existing LLP methods. The results show that our approach converges faster to a comparable or better solution.
Reconstruction Optimization Algorithm of 3D Temperature Distribution Based on Tucker Decomposition

For the purpose of solving the large temperature field reconstruction error caused by different measuring point arrangements and the problem that the prior dataset cannot be built due to data loss or distortion in actual measurement, a three-dimensional temperature profile reconstruction optimization algorithm is proposed to repair the empirical dataset and optimize the arrangement of temperature measuring points based on Tucker decomposition, the minimum condition number method, the greedy algorithm, and the hill climbing algorithm. We used the Tucker decomposition algorithm to repair the missing data and obtain the complete prior dataset and the core tensor. By optimizing the dimension of the core tensor and the number and position of the measuring points calculated by the minimum condition number method, the greedy algorithm, and the mountain climbing algorithm, the real-time three-dimensional distribution of the temperature field is reconstructed. The results show that the Tucker decomposition optimization algorithm could accurately complete the prior dataset, and compared with the original algorithm, the proposed optimal placement algorithm improves the reconstruction accuracy by more than 20%. At the same time, the algorithm has strong robustness and anti-noise, and the relative error is less than 4.0% and 6.0% with different signal-to-noise ratios. It indicates that the proposed method can solve the problem of building an empirical dataset and 3D temperature distribution reconstruction more accurately and stably in industry.
Quantitative analysis of the authorship problem of “The Tale of Genji”

This study involves quantitative research on the authorship problem of “The Tale of Genji,” which is the most famous Japanese classical literary work, using statistical analysis methods. “The Tale of Genji” was written by Murasaki Shikibu (around 930-1014), who is one of the most famous female novelists from the Heian period (794-1185). It is a full-length tale set at the Heian Imperial Court that depicts the love story of Hikaru Genji, the main character. The tale has been widely read across generations. There is a theory that the final 13 volumes were penned by different authors because of differences in the stage and the main character. Japanese literary research has been unable to resolve this authorship problem, and it is still unclear whether there had been one or more authors for this literary work. This study applies quantitative methods to solve the authorship problem of this tale. In this study, a word n-gram and word length n-gram were used for analysis in order to resolve the authorship problem, but the result showed no difference in measurement characteristics between the final 13 and the other volumes of “The Tale of Genji.” The authorship problem of the literary work has been discussed for a long time, but the quantitative analysis performed by this study shows that there is no evidence supporting the theory that there might have been multiple authors of “The Tale of Genji.” It can, therefore, be concluded that “The Tale of Genji” was likely written by a single author.
Dating and geolocation of medieval and modern Spanish notarial documents using distributed representation

This paper proposes a method to probabilistically date and geolocate medieval and modern Spanish notarial documents. Our model is inspired by distributed representation of words. We construct a neural network for learning spatio-temporal similarity among words so that the words that appear in documents written within a chrono-geographically close area have similar embedding vectors. The spatio-temporal similarity is learned in a framework of multi-task learning to acquire more suitable representation than when learned independently. One of the advantages of our model is its ability to detect the most contributing words to estimation. The degree of contribution corresponds to vector norm. We observed that most of the words with largest norm turn out to be those closely related with certain chrono-geographical area, well-known to Hispanic Philology. Our proposed model constitutes the first step toward developing a quantitative method of dating and geolocation accompanied by empirical evidence.
33 Text production, text reception, and emotion

This chapter takes the position that text production and text reception in the form of narrative stories are useful in understanding how multilinguals use emotion vocabulary in constructing identities since narratives shed light on how individuals use language to shape their lived, emotional experiences. The chapter examines how Puerto Rican return migrant students use emotional language as they construct Hip Hop identities through narrative stories in digitally mediated communication such as emails and websites. The chapter also focuses on how the electronic and interactive features of digitally mediated communication facilitate the use of emotional language in text production and text reception as Puerto Rican return migrant students engage in translanguaging in narratives to index Hip Hop identities. Results indicate that Puerto Rican return migrant students use emotional language in Standard English and African American English but not in Spanish when translanguaging in text production and text reception. Factors such as topic, language proficiency, and sociocultural competence contribute to this outcome. The electronic and interactive characteristics of emails and websites in digitally mediated communication seemed to influence interaction among text production, text reception and emotion vocabulary. Additionally, these characteristics spurred Puerto Rican return migrant students’ engagement in the acquisition of lexico-grammatical features of academic language in text production and text reception.
Role of artificial intelligence and machine learning in diagnosis and treatment of women centric cancer

Cancer is a multifactorial disease, associated with several alterations at various molecular levels, which provides a favorable and selective advantage for aberrant cell growth. Enormous genetic alterations in cancer can be captured through next-generation sequencing. The analysis of high-dimensional data from sequencing experiments requires a huge amount of computational power, which can be addressed using algorithms in artificial intelligence (AI)/machine learning (ML). Early diagnosis and prognosis of a cancer type have been instrumental in clinical research due to its ability to assist clinicians to make a more informed treatment decision. The visualization of tumor growth and its genetic cause is an essential part of cancer treatment. Here, AI/ML can address this by analyzing and linking the image-derived data to genomics data from cancer patients. In this work, we present a review of recent advances in image analysis using ML/AI for cancer diagnosis and treatment. In addition to that, we have discussed the recent methods of genomic data analysis for cancer classification and biomarker discovery.