Reviewerly/exHarmony huggingface dataset:
Syntactic punctuation distraction. Comma: Over-usage Part 2

Coordinated noncore sentence constituents are likely to be disrupted by unnecessary comma punctuation.
The hitchhiker’s guide to the EMWA conference: Reflections and recommendations from first-time delegates

“The story so far: In the beginning, the Universe was created. This has made a lot of people very angry and been widely regarded as a bad move.” – Douglas Adams, The Ultimate Hitchhiker’s Guide to the Galaxy In 1992, EMWA was created. This has made a lot of people very happy and been widely regarded as a good move. In May 2023, two newbies, as yet unknown to one another, set out to join the five-day 55th EMWA conference in Prague, Czech Republic. Here, we share our impressions of this journey. This article is a personal reflection on the valuable insights gained, and the vibrant community spirit experienced, at our inaugural EMWA conference. It is also intended to serve as an informative guide for prospective, first-time delegates.
Knowledge-based HR Practices and Employee Retention: The Role of Social Capital, Knowledge Sharing and Innovative Work Behaviors

Knowledge-based HR practices (KHRP) were developed to enhance the organization's capacity to create, share and use new information (Caputo et al., 2019). So, knowledge-based HR practices could be crucial in attracting, selecting, positioning, maintaining, and transforming important human resources in developing novel processes and products (Manzaneque, et al., 2017). There has been a consensus among previous research that identifying KHRP is a determinant of innovation (Al-Tal and Emeagwali, 2019). Yet, it does not adequately describe how KHRP contributes to competitive advantage. Specifically, there is a gap in understanding how KSB can facilitate the translation of KHRP and SC (Singh et al., 2021) into improved ER outcomes, and whether this relationship varies across different organizational contexts or industries. Further research is needed to explore these relationships and identify effective strategies for promoting KSB to enhance employee retention in organizations. Keywords: Knowledge-based HR practices, Social Capital, Knowledge Sharing Behavior, Employee Retention, Innovative Work Behavior.
Bitcoin from Menger's perspective

Currently, fiat money not covered in ore (e.g. in gold or silver) plays a key role in the world. Cashless payments are also becoming more and more common. The inflationary nature of modern fiat money and the lack of a real possibility of returning to bullion money were some of the reasons for the search for market alternatives, which in 2009 led to the creation of a virtual currency called Bitcoin. From the very beginning, Bitcoin has generated a lot of controversy and interest - both on the part of its users and economists. This study is intended to discuss the issues related to the complex process of Bitcoin formation from the perspective of economic theory (Menger's achievements). It is a chapter devoted not so much to Bitcoin itself, but to Menger's theory of money, i.e. its universality in the context of Bitcoin. The study tries to answer the question whether this theory can help better understand the genesis of Bitcoin and its evolution as a medium of exchange/money.
An Online Course of Dynamics and Kinematics Physics Education Using eXtended Reality (XR) Technologies.

This chapter discusses the use of extended reality (XR) for the design and development of online learning applications using real-time physics simulations. The authors propose an instructional design based on the ARCS motivational model to improve aspects of the presentation, organization, and distribution of learning content for an online XR learning application, which presents an interactive and exploratory learning environment for physics education, through a real-time physics simulator of dynamics and kinematics. Depending on the characteristics of a XR-compliant device and platform, the authors can offer XR experiences that range from augmented reality (AR) to non-immersive and immersive virtual reality (VR) environments in a single application, being available to any device with internet capabilities. To evaluate the instructional design of the XR application, the authors present an assessment using the John Keller´s attention, relevance, confidence and satisfaction (ARCS) learning motivation model, which allows to analyze the correlation between student's motivation and the learning technique.
Efficient numerical approach for the simulations of high-power dispersive readout with time-dependent unitary transformation

We develop an efficient numerical approach for simulating the high-power dispersive readout in circuit quantum electrodynamics. In the numerical simulations of the high-power readout, a large-amplitude coherent state induced in a cavity is an obstacle because many Fock states are required to describe such a state. We remove the large-amplitude coherent state from the numerical simulations by simulating the dynamics in a frame where the amplitude of the coherent state is almost absent. Using the developed method, we numerically simulate the high-power dispersive readout of the two-level system and the transmon. Our proposed method succeeds in producing reasonable behaviors of the high-power dispersive readout which can be deduced from the photon-number dependence of the cavity frequency: The high-power dispersive readout works in the two-level-system case while it does not work in the transmon case.
Cascaded convolutional networks for unsupervised brain tissue segmentation and bias field estimation

Brain tissue segmentation from MR images is a critical step for quantifying the brain morphology in neuroimaging studies. While deep learning (DL) based brain tissue segmentation methods have achieved promising performance, most of them are built upon supervised learning and therefore their performance is bounded by the training data used and limited by the small size of datasets with manual segmentation labels. To leverage the large amount of unlabeled brain imaging data, we develop an unsupervised DL model for joint brain tissue segmentation and bias field estimation using cascaded convolutional networks. The proposed DL model consists of multiple cascaded bias field estimation modules and one segmentation module. The bias field estimation modules are applied to the input image for estimating the bias field and generating a bias-free image recursively, and the bias field corrected image is then fed into the segmentation module to obtain the brain tissue segmentation result. A Gaussian mixture model is adopted to characterize the bias-free image with tissue-specific intensity statistics and the model fitting error is adopted as the loss function to guide the optimization of the model parameters progressively in an unsupervised setting. We have evaluated the proposed method on the HCP-Aging and HCP-Development datasets. Quantitative results have demonstrated that our unsupervised DL model could obtain competitive bias field correction and segmentation performance, compared with state-of-the-art bias field correction methods and unsupervised segmentation methods.
Hyperparameters Optimization in XGBoost Model for Rainfall Estimation: A Case Study in Pontianak City

Estimating rainfall accurately is crucial for both the community and various institutions involved in managing water resources and preventing disasters. The XGBoost model has demonstrated its effectiveness in predicting rainfall, but it still requires fine-tuning of hyperparameters to enhance its performance. This study seeks to determine the optimal learning rate for rainfall prediction while keeping the max_depth and n_estimator parameters fixed. The hyperparameter optimization process was carried out using a two-step approach: an initial coarse search using RandomizedSearchCV followed by a more detailed fine-tuning using GridSearchCV. The model's foundation relied on historical rainfall data gathered over three months from the Automated Weather Observed System (AWOS) at the Pontianak Meteorological Station, recorded on an hourly basis. To assess the model's performance, several metrics were employed, including accuracy, precision, recall, F1 score, and ROC-AUC. The model demonstrated promising results, with accuracy, precision, recall, and F1 score all reaching 95%, indicating its ability to effectively predict rainfall. However, the ROC-AUC score was somewhat lower at 62%. After conducting the hyperparameter search, the optimal learning rate determined for the model, utilizing the 2040 dataset, was found to be 0.204.
Digital Learning: How the Process Enhances Students’ Digital Literacy

Information and Communication Technology (ICT) is now frequently applied in education. This makes the learning process one of the activities that can empower digital literacy. The research aims to explore information about students' digital literacy levels based on their self-assessment and how the learning process can train the indicators on digital literacy to students. This research was conducted using a qualitative descriptive approach. Using a purposive sampling technique, the sample for this research was 38 Tadris IPA students who had participated in lecture activities with digital learning substitution. The research instrument is a digital literacy level self-assessment questionnaire which refers to a global framework of reference on digital literacy skills which consists of 4 digital literacy sub-indices. The results showed that Tadris Physics students had a digital literacy index in the good category. This is supported by the implementation of lectures that provide opportunities for students to empower their digital literacy, such as the implementation of digital technology-based learning (use of LMS or blended learning), and an assignment system that requires students to think critically while searching for information independently through various digital platforms.
Semi-generative video steganography based on digital Carden Grille

Generative steganography is a research hotspot particularly with regard to information hiding, which involves the hiding of secret information by generating sufficiently “real” secret media. In recent years, generative steganography schemes have made significant progress in images, but the field of video steganography is still in the exploratory stage. Combined with deep convolutional generative adversarial nets (DCGAN), a semi-generative video steganography scheme based on a digital Carden Grille was proposed. A dual-stream video generation network based on DCGAN was designed to generate three parts of videos: foreground, background, and mask; generation network produced different videos with random noise. The digital Carden Grille is used as the key for embedding and extraction. The sender can generate a digital Carden Grille in the mask through two different methods, reasonably assign the embedding capacity among RGB channels, and use video pixels as the carrier to achieve the information embedding in a semi-generative way. The receiver can determine the embedding position of information through the Carden Grille and extract the secret information hidden in the pixels. Experimental results show that the stego video generated by this scheme has good visual quality, with a Frechet inception distance score of 92. The embedded capacity is better than the existing generative steganography schemes, up to 0.12 bpp. Using Syndrome Trellis Coding, the proposed scheme can transmit secret messages more efficiently and securely.
<i>Correction to:</i> Broad Geographic and Bi-Partisan Support of Industrial Biotechnology Indicates Timing May Be Right for Incentive Legislation, by Danielson and McKay <i>Ind Biotechnol</i> 2023;19(4):195–200; doi: 10.1089/ind.2023.29319.nda

Industrial BiotechnologyAhead of Print CorrectionOpen AccessCorrection to: Broad Geographic and Bi-Partisan Support of Industrial Biotechnology Indicates Timing May Be Right for Incentive Legislation, by Danielson and McKay Ind Biotechnol 2023;19(4):195–200; doi: 10.1089/ind.2023.29319.ndais erratum ofBroad Geographic and Bi-Partisan Support of Industrial Biotechnology Indicates Timing May Be Right for Incentive LegislationPublished Online:28 Sep 2023https://doi.org/10.1089/ind.2023.29319.nda.correx2AboutSectionsPDF/EPUB Permissions & CitationsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail In the August 2023 issue of Industrial Biotechnology (vol. 19, no. 4; 195–200), the article titled “Broad Geographic and Bi-Partisan Support of Industrial Biotechnology Indicates Timing May Be Right for Incentive Legislation,” by Danielson and McKay, requires correction.This article was originally published under the copyright of Mary Ann Liebert, Inc. publishers. It has now been updated to reflect Open Access, with copyright transferring to the author(s), and a Creative Commons License (CC-BY) added (http://creativecommons.org/licenses/by/4.0). The CC-BY license goes into effect September 22, 2023.The online version of this article has been corrected to reflect this.FiguresReferencesRelatedDetailsRelated articlesBroad Geographic and Bi-Partisan Support of Industrial Biotechnology Indicates Timing May Be Right for Incentive Legislation17 Aug 2023Industrial Biotechnology Volume 0Issue 0 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Correction to: Broad Geographic and Bi-Partisan Support of Industrial Biotechnology Indicates Timing May Be Right for Incentive Legislation, by Danielson and McKay Ind Biotechnol 2023;19(4):195–200; doi: 10.1089/ind.2023.29319.nda.Industrial Biotechnology.ahead of printhttp://doi.org/10.1089/ind.2023.29319.nda.correx2creative commons licenseOnline Ahead of Print:September 28, 2023PDF download
Blockchain-based Cybersecurity for Smart Manufacturing in Industry 4.0: Technology-Organization-Environment Approach

In the context of Industry 4.0, workplaces are becoming more digitally evolved, with more and more activities completed using technology. As technology advances, hackers continuously evolve their methods, and yet due to the traditional mindset, security methods have remained unattainable for organizations, leaving them highly vulnerable. This study explores the factors influencing organizations’ intent to implement blockchain for security practices. This study focused on the smart manufacturing industry, and a survey was rolled out to collect data from 312 respondents. The responses were tested on the conceptual model using SEM (structural equation modelling). The factors influencing blockchain adoption behaviour for organizations were modelled the Technology-Organization-Environment framework (TOE).
A hybrid approach for artwork recommendation

Museums usually exhibit thousands of artworks, and nowadays, they often have their collections online for visitors. In these collections, the curators are responsible for organizing the artworks seeking a delicate balance between emotion and reason. Given an initial artwork, however, a visitor is likely to select and admire a set of related artworks that match her interests. This setting can be seen as a recommendation problem in the art domain. Although image recommendation systems have been previously developed, considering the artwork nature is a fundamental aspect when designing a recommender system in this domain. Thus, we propose a hybrid recommendation approach that combines deep autoencoders with a social influence graph in order to capture the visual aspects and context of artworks (represented by images). These mechanisms inform the generation of rankings of related artworks. In this context, we report on a case-study with a group of art experts who assessed the rankings of artworks recommended by our approach. Although preliminary, the results showed a better precision than traditional strategies based solely on image features or metadata. Furthermore, the recommendations exhibited diversity properties, avoiding typical over-specialization problems of content-based techniques.
ANALISIS PELAKSANAAN PEMBELAJARAN MEMBACA PERMULAAN SISWA BERKEBUTUHAN KHUSUS DI KELAS RENDAH MINU ISLAMIYAH KECAMATAN ASEMBAGUS KABUPATEN SITUBONDO

This research aims to investigate learning activities in reading for students with special needs of lower class at MINU Islamiyah Kecamatan Asembagus Kabupaten Situbondo. This study engaged qualitative reserach approach, whreas researcher collected data using observation, interview, and documentation. analysis used was descriptive qualtative research, consisted of data collection, data reduction, and conlusion. The results of the study show that children with spesial needs require different tratment compared to normal students, on of wich requires a companion teacher to provide services for early reading learning activities. teachers must have a extraordinary educational background and must understand the characteristics of students wit spasicial needs. Students with spesial are children who in their development have disoders and obtacies. Thereby, their growt is different to normal children. Physical and psychological disordes, anmely mental, sensory motor, communication disorders, social behavior disorders, and learning difficulties
Weak measurement as a tool for characterizing coherence and quantum correlations in bipartite systems

Abstract In this article, we study quantum coherence of bipartite state from the perspective of weak measurement, which generalizes the notion of coherence relative to measurement. This is being illustrated by computing coherence for the well-known Bell diagonal and Werner states. Further, we have expanded the notion of coherence to encompass correlations, exploring its connections with other quantifications of nonlocality. We have determined that this coherence measure can be perceived as a generalized version of the Wigner-Yanese skew information measure and several other nonlocal measures. As a natural extension, we introduce uncertainty relations within the context of weak measurements. Additionally, we propose a novel form of variance by considering the weak value of an observable within a comprehensive framework.
Analysis of Student Errors in Solving SPLDV Problems based on Newman's theory

The purpose of this study was to describe the types of errors made by Class VIII students when solving SPLDV questions based on Newman's theory. This qualitative research aims to provide descriptive information about individuals or observed behavior in the form of written or spoken words. Written tests and interviews were used to collect data. The test was carried out on all 25 Class VIII students, and then 3 of the 25 students were selected as research subjects. Data were analyzed by reducing information, presenting information, and drawing conclusions. The results of the work of individual testers were analyzed for the types of errors using the Newman analysis method. The factors that led to student errors were then explained in the interviews. Based on the study results, it can be concluded that the errors made by students in the SPLDV questions are as follows: 1) Failure to understand the problem, including not recording what is known and what is not in the duration of the task. Ask when in trouble. 2) Conversion errors, including misunderstanding of the billing method used. 3) Commanding process failure involves failing to continue to complete the process. 4) Error in writing HP, including not writing down HP. Student errors occur when students are not careful when solving problems, are too busy, or do not practice narrative tasks. 3) Commanding process failure involves failing to continue to complete the process. 4) Error in writing HP, including not writing down HP. Student errors occur when students are not careful when solving problems, are too busy, or do not practice narrative tasks. 3) Commanding process failure involves failing to continue to complete the process. 4) Error in writing HP, including not writing down HP. Student errors occur when students are not careful when solving problems, are too busy, or do not practice narrative tasks
Using Pictorial Teaching Strategy in Resolving Reading Comprehension Challenges Among Primary School Pupils in Ikere Local Government of Ekiti State

The study examines pictorial teaching strategy to improve reading comprehension challenges among primary school pupils in Ikere Local Government Area of Ekiti State. A descriptive survey design is adopted for the study with the selection of one hundred female and male teachers randomly drawn from urban and rural areas of the study area. The instrument used is a self-structured questionnaire of twenty items tagged ' Pictorial Teaching Questionnaire' (PTQ). The instrument is validated by experts in Curriculum Studies. Data collected are analyzed using simple percentages and mean. The results show that picture reading strategy is utilized by primary school teachers which have impacted positively on the reading comprehension abilities of pupils. The study recommends that pictorial teaching should be more encouraged to aid reading comprehension and that government should strive to assist indigent pupils to procure relevant texts. Curriculum planners should try to integrate more pictures into English texts most especially at the elementary stage of reading for pupils.
Harnessing the Power of AI in Shaping Administrative Law Regulations

The rapid and transformative advancements in Artificial Intelligence (AI) have reached a pivotal juncture, necessitating a re-evaluation of the regulatory frameworks in various sectors, including administrative law. This paper delves into the potential of AI in shaping administrative law regulations. While the inherent computational abilities of AI allow for processing vast amounts of data, providing real-time insights, and predicting administrative needs, they also introduce challenges related to ethics, transparency, and accountability. By analyzing global best practices and case studies, this paper highlights the dual-edged nature of AI, where on one side, it can aid in making more informed regulatory decisions, reduce bureaucratic inefficiencies, and improve citizen engagement. On the other side, unchecked use can lead to biases, opaque decision-making, and potential encroachments on individual rights. The paper concludes by proposing a balanced approach that harnesses the potential of AI while ensuring ethical and transparent use in the realm of administrative law.