Reviewerly/exHarmony huggingface dataset:
The effect of business continuity management and technology acceptance model towards lecturers’ performance moderated by servant leadership

This research investigates the effect of business continuity management and technology acceptance models towards performance of lecturers moderated by servant leadership. The research respondents were 86 Halmahera University lecturers. The research method used was quantitative method by examining the effect of moderation and the direct effect by the method of interaction. The model was tested with structural equation modeling approach by using Smart PLS software. The results proved that servant leadership was able to influence lecturer performance. Servant leadership could also moderate the effect of business continuity management towards lecturer performance. However, business continuity management and technology acceptance models did not affect lecturer performance independently. Servant leadership also failed to moderate the effect of the technology acceptance model towards lecturer performance. This study recommends that servant leadership be applied at all levels of leadership because of the very important role in triggering lecturer performance. The application of servant leadership can also be combined with business continuity management since the interaction between of the two can improve lecturer performance.
The Role of E-marketing Tools in Constructing Online Consumer Behavior of Omani Youth in Muscat City

This study has concentrated on the impact of e-marketing on the marketing behavior of young Omani sellers in the city of Muscat. The study was conducted in Halban and some areas of Al Batinah. The main objective of this study is to identify the seller's behavior and deal with the buyer that helps the e-commerce organization to develop the effectiveness of online shopping. In addition to the buyer via obtaining sufficient information about the products. Moreover, the study was applied on 100 Omani. This research paper has used a quantitative technique to collect data from participants, afterwards, SPSS statistical techniques were used to obtain the findings of this research. The results showed that Omani youth mostly agreed that e-marketing generally save time and cost, more comfortable, product showing easy, the clarity of online instructions, and influence their selling behavior. Significantly, majority of the Omani youth 59% prefer to sell products through social media platforms such as Instagram.
Research on the Protection of Strong Electromagnetic Pulse to Reinforce the Computer Interface

In recent years, due to the continuous development of electromagnetic pulse technology, information electromagnetic equipment such as magnetrons and electronic rectifiers have also been widely used in electronic information systems. However, in order to prevent hardened computers from being affected by strong electromagnetic pulses, we must have a full understanding of the main types of strong electromagnetic pulses and their corresponding frequency and time domain characteristics, and accurately grasp the coupling paths of strong electromagnetic pulses. In addition, we also need to take effective protective measures for its interface according to the specific structural characteristics of the hardened computer and the main way the hardened computer is damaged by the strong electromagnetic pulse to prevent the strong electromagnetic pulse from causing serious damage to the electronic information system.
User Privacy Protection Technology of Tennis Match Live Broadcast from Media Cloud Platform Based on AES Encryption Algorithm

With the improvement of the current Internet software and hardware performance, cloud storage has become one of the most widely used applications. This paper proposes a user privacy protection algorithm suitable for tennis match live broadcast from media cloud platform. Through theoretical and experimental verification, this algorithm can better protect the privacy of users in the live cloud platform. This algorithm is a ciphertext calculation algorithm based on data blocking. Firstly, plaintext data are grouped, then AES ciphertext calculation is performed on each group of plaintext data simultaneously and respectively, and finally ciphertext data after grouping encryption is spliced to obtain final ciphertext data. Experimental results show that the algorithm has the characteristics of large key space, high execution efficiency, ciphertext statistics and good key sensitivity.
DPR-Geo: A POI Recommendation Model Using Deep Neural Network and Geographical Influence

In the wake of developments in artificial intelligence, deep learning technology has been used in location-based social networks (LBSNs) to provide web services that meet the needs of users. Point of interest (POI) recommendation, as one of the most important mobile services, aims to recommend new satisfactory POIs to users according to their historical records. However, existing models that uses original high-dimension user vector or location vector cannot capture useful information from historical records effectively. Meanwhile, most of them complete recommendation service only in terms of user’s perspective or location’s perspective. Hence, in this paper, we propose a novel deep learning framework for POI recommendation. Firstly, we use a multi-layer neural network to reduce the dimension of user vector and location vector. Then, we construct a union neural network by concatenating and multiplying vectors to obtain the preferences of users. Finally, considering the unique geographical characteristic of location, we model the distance probability to enhance recommendation. Experimental results on real-world dataset demonstrate our model outperforms some popular recommendation algorithms and achieves our expected goal.
DEVELOPMENT OF AR INFORMATION PLATFORM FOR ORGANIZING SCIENTIFIC AND METHODOLOGICAL EVENTS

It is very important to have a functional information platform, access to which will not be difficult for users. Therefore, the implementation of such a platform often in the web-space in the form of a separate informative site with many pages to help users navigate. The development of an information system in a web-space is divided into frontend development related to the design of the structure, design, page layout, and backend development related to the work of the database, server part, etc. Using a functional information platform as a university information system to automate the department’s research process will greatly simplify the paper and organizational component of the department’s work. This includes accepting applications for events and notifying participants about future events and their results, with the further possibility of finalizing the university’s information system and adding various functionalities. To solve this problem, the main aspects of a web development, various tools, automation tools for developing an information platform are considered.
Blockchain-mediated Licensing: Legal Engineering for Artist Empowerment

Licensing is one of the essential means of exploiting the monetary value of a musical work, and yet it is an area fraught with many issues and transactional costs which make it a difficult process for individuals and organizations. Many issues in music licensing arise from the legal complexity (e.g., national and international copyright law), business complexity (authentication, tracking, accounting, etc.), value web complexity (transparency of relationships among stakeholders), and technical complexity (e.g., establishing a global repertoire database for music, sufficient metadata standards) of working with music. Then, in addition to these issues, there are specific transactional costs (identification, negotiation, monitoring, and enforcement) associated with the licensing process. To mitigate the complexity and transactional costs associated with music and the licensing process, researchers and technologists have been investigating how new technologies and design models from the Web3 space, such as blockchain, linked data and Ricardian Contracts, can automate processes to reduce complexity, speed up payments, improve tracking, and provide other benefits in the music industry. In our report, we make our own attempt to reduce the complexity and transactional costs in the licensing process by developing an automated music license. In doing so, we first conducted a literature review synthesizing the intersection of music complexity and Web3 technologies to provide background and context to automating music licensing. Then we developed the Practical Tokenized Drafting (PTD) method, a set of core principles and practices for drafting Ricardian Contracts that interact with Web3 technologies (RC-Web3 Templates), and the Tokenized Music License (TML), an RC-Web3 Template standard form for music licensing on the OpenLaw platform. Both the PTD and TML can be adapted to meet the needs of music industry stakeholders and provide guidance to legal practitioners in drafting RC-Web3 Templates.
A two-stage urine sediment detection method

Urine sediment detection is of great significance as one of the routine testing items. The traditional urine sediment detection method is mainly manual microscopic examination. Thus, it incurs heavy human workload and complicated operation, while it is easy to miss the targets. To alleviate this problem, a two-stage urine sediment detection method is proposed in this paper. More specifically, the segmentation and classification tasks are transformed into object detection tasks, and the feature extraction is performed by Deep Convolutional Neural Networks (DCNN). In our method, HOG+SVM is used as region proposal, and Trimmed MobileNets is used for DCNN refining. The experimental results demonstrate that the proposed method achieves promising performance.
On the enumeration of Boolean functions with distinguished variables

Boolean functions have a fundamental role in neural networks and machine learning. Enumerating these functions and significant subclasses is a highly complex problem. Therefore, it is of interest to study subclasses that escape this limitation and can be enumerated by means of sequences depending on the number of variables. In this article, we obtain seven new formulas corresponding to enumerations of some subclasses of Boolean functions. The versatility of these functions does the problem interesting to several different fields as game theory, hypergraphs, reliability, cryptography or logic gates.
cqp4rdf: Towards a Suite for RDF-Based Corpus Linguistics

In this paper, we present cqp4rdf, a set of tools for creating and querying corpora with linguistic annotations. cqp4rdf builds on CQP, an established corpus query language widely used in the areas of computational lexicography and empirical linguistics, and allows to apply it to corpora represented in RDF. This is in line with the emerging trend of RDF-based corpus formats that provides several benefits over more traditional ways, such as support for virtually unlimited types of annotation, linking of corpus elements between multiple datasets, and simultaneously querying distributed language resources and corpora with different annotations. On the other hand, application support tailored for such corpora is virtually nonexistent, leaving corpus linguist with SPARQL as the query language. Being extremely powerful, it has a relatively steep learning curve, especially for people without computer science background. At the same time, using query languages designed for classic corpus management software limits the vast possibilities of RDF-based corpora. We present the middle ground aiming to bridge the gap: the interface that allows to query RDF corpora and explore the results in a linguist-friendly way.
CERN Analysis Preservation and Reuse Framework: FAIR research data services for LHC experiments

In this paper we present the CERN Analysis Preservation service as a FAIR (Findable, Accessible, Interoperable and Reusable) research data preservation repository platform for LHC experiments. The CERN Analysis Preservation repository allows LHC collaborations to deposit and share the structured information about analyses as well as to capture the individual data assets associated to the analysis. We describe the typical data ingestion pipelines, through which an individual physicist can preserve and share their final n-tuples, ROOT macros, Jupyter notebooks, or even their full analysis workflow code and any intermediate datasets of interest for preservation within the restricted context of experimental collaboration. We discuss the importance of annotating the deposited content with high-level structured information about physics concepts in order to promote information discovery and knowledge sharing inside the collaboration. Finally, we describe techniques used to facilitate the reusability of preserved data assets by capturing and re-executing reproducible recipes and computational workflows using the REANA Reusable Analysis platform.
PENINGKATAN KETERAMPILAN MEMBACA PUISI SISWA KELAS IV DENGAN MODEL DEMONSTRASI DIDUKUNG MEDIA VIDEO PEMBELAJARAN DI SDN 1 SUMBERSARI KOTA MALANG

The purpose of this study is to improve poetry reading skill of 4th grade students at SDN Sumbersari 1 in Malang City, to know the activities of the teacher and students. This type of this research is classroom action research. This classroom action research model uses the Kemmis And Mc Taggart model which consist of four steps or stages in conducting classroom action research, namely: planning, action, observation, and reflection. The result of the study show an increasing activity of the teacher and students. The result of this study are flows. (1) Pre-action, from 25 students, only 5 students or 20% of the total students are able to read poetry well and correctly, while 20 students or 80% of students experience difficulty in reading poetry with an average poetry reading skill of 54.56%. (2) Cycle I, the percentage of teacher activity reached 83% with very good predicate, the percentage of teacher activity reached 83% with very good predicate, the percentage of student activity was 83% with very good predicate, and classical learning completeness was 78.8% (3) Cycle II, the percentage of teacher activity reached 86% with very good predicate, the percentage of students is 87% with
A Taxonomy of Training Data: Disentangling the Mismatched Rights, Remedies, and Rationales for Restricting Machine Learning

This chapter addresses a crucial problem in artificial intelligence: many applications of machine learning depend on unauthorized uses of copyrighted data. Scholars and lawmakers often articulate this problem as a deficiency in copyright’s exceptions and limitations, reasoning that legal uncertainties surrounding today’s AI stem from the lack of a clear exception or limitation, and that such an exception or limitation could resolve the current predicament. In fact, the current predicament is a product of two systemic features of the copyright regime — the absence of formalities and the low threshold of copyright-able originality — combined with a technological environment that turns routine activities into acts of authorship. Equilibrating the economy for human expression in the AI age requires a solution that focuses not only on exceptions to existing copyrights, but also on the aforementioned doctrinal features that determine the ownership and scope of copyright entitlements at their inception.The chapter taxonomizes different applications of machine learning according to the qualities of their training data. Four categories emerge: (1) public-domain training data, (2) licensed training data, (3) market-encroaching uses of copyrighted training data, and (4) non-market-encroaching uses of copyrighted training data. Copyright can only regulate market-encroaching uses of data, but these uses represent a narrow subset of AI applications and exclude many of the most socially harmful uses of copyrighted materials. Moreover, paradoxically, copyright’s property-style remedies are ill-suited to addressing market-encroaching uses, and are in fact much more appropriate remedies for the categories of worrisome AI that fall outside copyright’s normative mandate.Finally, this chapter discusses a variety of remedies to the “AI problems” it identifies, with an emphasis on facilitating market-encroaching uses while affording human creators due compensation. It concludes that the exception for Text and Data Mining in the European Union’s Directive on Copyright in the Digital Single Market represents a positive development precisely because the exception addresses some structural causes of the training data problem that this chapter identifies. The TDM provision styles itself as an exception, but it may in fact be better understood as a formality: it requires rights holders to take positive action to exercise a right to exclude their materials from training datasets. Thus, the TDM exception addresses a root cause of the AI dilemma rather than trying to patch up the copyright regime post hoc. The chapter concludes that the next step for an equitable AI framework will be to transition towards rules that not only clarify that non-market-encroaching uses do not infringe copyright, but also facilitate remunerated uses of copyrighted works for market-encroaching purposes.
A Data-Driven Deep Learning Model to Pattern Recognition for Medical Diagnosis, by using Model Aggregation and Model Selection

In recent years, pattern recognition becomes a research area with increasing importance using several techniques. One of the most common techniques used is Deep Learning. This paper presents a new Deep Learning model to pattern recognition for medical diagnosis. The uncovering of hidden structures is performed by feature selection, model aggregation, and model selection. Our Deep Learning model has the ability to reach the optimal solution and create complex decision boundaries when used to look for and diagnose breast cancer. The evaluation, based on 10-fold cross-validation, showed that the proposed model, which is named BaggingSMF, yielded good results and performed better than Radial Basis Function, Bidirectional Associative Memory, and ELMAN Neural Networks. Experimental studies demonstrate the multidisciplinary applications of our model.
Quantum phase transition of two-level atoms interacting with a finite radiation field

We introduce a group-theoretical extension of the Dicke model, which describes an ensemble of two-level atoms interacting with a finite radiation field. The latter is described by a spin model whose main feature is that it possesses a maximum number of excitations. The approach adopted here leads to a nonlinear extension of the Dicke model that takes into account both the intensity dependent coupling between the atoms and the radiation field and an additional nonlinear Kerr-like or Pösch–Teller-like oscillator term, depending on the degree of nonlinearity. We use the energy surface minimization method to demonstrate that the extended Dicke model exhibits a quantum phase transition, and we analyze its dependence upon the maximum number of excitations of the model. Our analysis is carried out via three methods: through mean-field analysis (i.e., by using the tensor product of coherent states), by using parity-preserving symmetry-adapted states (using the critical values obtained in the mean-field analysis and numerically minimizing the energy surface), and by means of the exact quantum solution (i.e., by numerically diagonalizing the Hamiltonian). Possible connections with the qp-deformed algebras are also discussed.
Encoding technology of an asymmetric combined structured light for 3D measurement

Sinusoidal phase-shifting symmetrically combined with cyclic code is one of the most important encoding methods in the field of 3D measurement. Due to the modulation of the object surface and the influence of the noise of the image acquisition system, the periods of the cyclic code and the sinusoidal phase-shifting in the intensity image do not coincide completely, and they lead to large absolute phase decoding errors near the cycle boundaries, which are called cycle dislocation errors. In order to eliminate these errors in principle, the concept and method of region encoding for four-step sinusoidal phase-shifting are proposed, and the sinusoidal phase-shifting is combined with cyclic code asymmetrically. Under the premise that the cyclic code and the region code change at different times, the cycle dislocation error is reduced from one cycle of cyclic code to one pixel by the dual constraint of cyclic code and region code. The simulation measurement results of 3 ds max and the physical measurement results show that the asymmetric combination encoding method effectively eliminates the cycle dislocation errors; the maximum measurement error is reduced by an order of magnitude, and the root mean square measurement error is reduced by 70%.
A Deep learning model for image retargetting level detection

Detection of image retargeting based on seam carving which is one of the widely used content aware image-resizing algorithm is of importance in image forensics. In addition to machine learning based methods, researchers have developed many other deep learning based methods which involve a binary classifier to determine whether the image is seam carved or not. In this study, we developed a deep learning model that performs multiclass classification for the detection image retargetting level. The proposed model is a multiclass classifier that has four outputs, no (0%) seam carving, 10% seam carved, 20% seam carved and 40% seam carved classes. Training and validation image sets were obtained from ImageNet database. Seam carved images that were reduced by 10%, 20%, and 40% in the vertical dimensions were obtained from these sets. Accuracy analysis of the model was performed by testing model on the images and showing the results of confusion, precision, recall matrices in detail as well as the accuracy value.
Duct-Taping Databases, or How to Use Fragmentary Online Data for Researching “Japanese” Videogames

The internet provides exciting opportunities for scholars of Japanese videogames. Fans from all over the world passionately collect, order, and share information about their hobby online. In this paper, we will show how we can exploit these potentials for research on Japanese videogames. We call this approach “duct-taping” databases: the integration of information from various heterogeneous, fragmentary online resources with the goal of creating a robust research dataset. We will discuss the potentials and challenges of this approach and show how it allows us to better understand the historical development of Japan’s videogame production.
Spike and slab variational Bayes for high dimensional logistic regression

Variational Bayes (VB) is a popular scalable alternative to Markov chain Monte Carlo for Bayesian inference. We study a mean-field spike and slab VB approximation of widely used Bayesian model selection priors in sparse high-dimensional logistic regression. We provide non-asymptotic theoretical guarantees for the VB posterior in both $\ell_2$ and prediction loss for a sparse truth, giving optimal (minimax) convergence rates. Since the VB algorithm does not depend on the unknown truth to achieve optimality, our results shed light on effective prior choices. We confirm the improved performance of our VB algorithm over common sparse VB approaches in a numerical study.