Reviewerly/exHarmony huggingface dataset:
Speciális dokumentumok és tartalmak – speciális metaadatszerkezetek Muzeális tartalmak és kéziratok repozitálása a Szegedi Tudományegyetem Klebelsberg Kuno Könyvtárában

At the beginning of the 2020s, the SZTE Klebelsberg Library will need to store the digitised museum documents and manuscript legacies that have been and are still being generated in a repository. The content was placed in the library’s thematic EPrints software-based repository Miscellanea. Different types of documents required different solutions, and their metadata required the development of a different metadata form. A solution was needed to create a structured structure in the repository to house the digital versions of the paper documents in the collection and the content transferred for digital preservation. The task was to define the different levels of access, the ongoing cleaning of the data and the establishment of the catalogue-repository link. The report presents possible solutions to these problems.
Growth, financial development and nascent entrepreneurship

This chapter investigates the relationship between growth and financial development while also accounting for nascent entrepreneurship. The chapter controls for other factors affecting growth, such as the rate of inflation and the ease of doing business, and accounts for the level of development of countries. The chapter uses merged data on entrepreneurship and country-level macroeconomic data. The results indicate that financial development matters for economic growth even when controlling for nascent entrepreneurship. The findings show that there is no significant positive effect of nascent entrepreneurship on economic growth. However, there is a need to account for the mediating effects of finance on entrepreneurship.
Η ελληνική ως ξένη γλώσσα: οι απόψεις των φοιτητών για τη διδασκαλία της γραμματικής και τη διόρθωση των λαθών

The aim of this study is to present relevant scientific studies and students’ beliefs related to error correction and grammar instruction. For the purpose of the study students of the Department of Modern Greek Studies, Faculty of Philology, University of Belgrade responded to a questionnaire about the controversial role of grammar teaching and error correction. As a result of the analysis, it can be said that they believe that error correction can be beneficial for the process of grammar learning and that grammar has a significant role in language pedagogy.
Small sample confidence intervals of the stress-strength reliability of inverse exponential distribution

이 논문에서는 역지수분포 (inverse exponential distribution)의 부하-강도 신뢰도 (stress-strength reliability)에 대한 신뢰구간 추정문제를 다룬다. 관심모수 (parameter of interest)가 부하-강도 신뢰도인 경우에 추정량의 표본분포를 이용한 신뢰구간과 우도함수 (likelihood function)추론에 기초한 근사신뢰구간을 비교한다. 우도함수추론에 기초한 근사신뢰구간을 구하기 위하여 부호화 로그우도비 (signed log-likelihood ratio) 통계량과 수정된 부호화 로그 우도비 (modified signed log-likelihood ratio) 통계량을 계산한다. 모의실험을 통하여 제안된 통계량에 기초한 신뢰구간을 추정된 포함확률과 포함확률의 대칭성, 신뢰구간이 길이측면에서 비교한다. 실제의 데이터를 이용하여 제안된 방법에 대한 분석사례를 제시한다.
Relationship Between Teaching Attitudes And Professional Values Of Secondary School Teachers Of Jaipur District

The present research paper is based on primary data in which the survey method has been used. The aim of this paper is to study the teaching attitudes and professional values of secondary school teachers of Jaipur district and to find out the relation between teaching attitudes and professional values of secondary school teachers. By using the simple random sampling technique 200 secondary school teachers of Jaipur district are selected as a sample for this study. Teacher Attitude Inventory (TAI)” by Dr. S.P. Ahluwalia and self-constructed Teacher Professional Value Scale was used to collect the data. The t-test and correlation was used for the analysis of the data. The result shows that there exists a significant relation betweenteaching attitudes and professional values of secondary school teachers.
An Application of Malay Short-Form Word Conversion Using Levenshtein Distance

Formerly, short-form word was widely used in the field of journalism. However, nowadays, short-form word has been widely used by many people, especially in online communication. These short-form words trigger problems in the field of data mining, especially those involving online text processing. It leads to inaccurate result of text mining activities. On the other hand, only few works have investigated on Malay short-form word identification and conversion. Therefore, this work aims to develop an application that can identify and convert Malay short-form words into its’ full word. In order to develop this application, the short-form rules need to be carefully examined. The formal rules from Dewan Bahasa &amp; Pustaka (DBP) are used as the primary reference for generating the short form word identification algorithm. While for the conversion algorithm, Levenshtein Distance (LD) is used to measure the similarity. The rule-based technique is also used as a complement to LD technique. As a result, 70.27% of the Malay short-form words have been correctly converted into their full words. The conversion rate is quite promising, and this work can be further strengthened by incorporating more rules into the algorithm.
Animation and Symbol editing with Macromedia Flash

This paper presents a didactic scenario for the creation of a multimedia application combining image and movement, which is intended for the course Informatics Applications of the Vocational High School. This is a two-hour scenario that introduces the student to the creation of a multimedia application, mobilizing the interest for the specific activity, since with simple and fast steps it leads him to the creation of his first animation. The first hour is dedicated to the general mobilization of the student and the discovery of the basic concepts and techniques, while the second leads him to the acquaintance with the environment of Macromedia Flash 8.0 and the creation of the first animation. The development and structure of the teaching scenario is based on the standard of level B training for IT teachers.
MULL'21

With the advent of deep neural networks, quite a lot of multimedia tasks have been significantly improved. While, however, deep neural networks still lack the ability of learning from less labeling, e.g., with limited exemplars or fast generalizing to new tasks. In order to address the current inefficiency of multimedia, there is pressing need to research methods to drastically reduce requirements for labeled training data. This workshop aims to provide a platform for discussing the challenges and corresponding innovative approaches in multimedia with less labeling. We hope more advanced technologies can be proposed or inspired, and also we invite several domain-specific experts for sharing their insights and research progress on the topic of MULL.
Domain Conditional Predictors for Domain Adaptation

Learning guarantees often rely on assumptions of i.i.d. data, which will likely be violated in practice once predictors are deployed to perform real-world tasks. Domain adaptation approaches thus appeared as a useful framework yielding extra flexibility in that distinct train and test data distributions are supported, provided that other assumptions are satisfied such as covariate shift, which expects the conditional distributions over labels to be independent of the underlying data distribution. Several approaches were introduced in order to induce generalization across varying train and test data sources, and those often rely on the general idea of domain-invariance, in such a way that the data-generating distributions are to be disregarded by the prediction model. In this contribution, we tackle the problem of generalizing across data sources by approaching it from the opposite direction: we consider a conditional modeling approach in which predictions, in addition to being dependent on the input data, use information relative to the underlying data-generating distribution. For instance, the model has an explicit mechanism to adapt to changing environments and/or new data sources. We argue that such an approach is more generally applicable than current domain adaptation methods since it does not require extra assumptions such as covariate shift and further yields simpler training algorithms that avoid a common source of training instabilities caused by minimax formulations, often employed in domain-invariant methods.
A Neuro-Fuzzy Technique for the Modeling of β-Glucosidase Activity from Agaricus bisporus

This paper proposes a neuro-fuzzy system to model β-glucosidase activity based on the reaction’s pH level and temperature. The developed fuzzy inference system includes two input variables (pH level and temperature) and one output (enzyme activity). The multi-input fuzzy inference system was developed in two stages: first, developing a single input-single output fuzzy inference system for each input variable (pH, temperature) separately, using the robust adaptive network-based fuzzy inference system (ANFIS) approach. The neural network learning techniques were used to tune the membership functions based on previously published experimental data for β-glucosidase. Second, each input’s optimized membership functions from the ANFIS technique were embedded in a new fuzzy inference system to simultaneously encompass the impact of temperature and pH level on the activity of β-glucosidase. The required base rules for the developed fuzzy inference system were created to describe the antecedent (pH and temperature) implication to the consequent (enzyme activity), using the singleton Sugeno fuzzy inference technique. The simulation results from the developed models achieved high accuracy. The neuro-fuzzy approach performed very well in predicting β-glucosidase activity through comparative analysis. The proposed approach may be used to predict enzyme kinetics for several nonlinear biosynthetic processes.
Effective Smart Contracts for Supply Chain Contracts

Blockchain is in its way of revolutionizing different sectors with its decentralized peer-to-peer networking. Smart contracts are the piece of software that have written rules to be executed automatically to update the state of the block chain in a systematic way. One of the main use of Smart contract is in Supply Chain management. Supply Chain management deals with lot of legal contracts at a time. Contracts are agreements between two or more parties that define the duties and obligations for execution of any kind of activities. In this research, we are trying to automate the supply chain related contracts by identifying the important entities such as contract type, start date, end date etc., by using Natural Language Processing methods, then convert the contract to smart contract. This provides an efficient template for creation of smart contracts from natural language contracts and thereby offer best smart contract template for a given type of contract in Supply Chain.
Nested Constructions in Restricted Move no-code Smart Contracts Descriptions

This paper presents the development of the Restricted Move language, which makes it possible to form complex structures as a sequence of calls of other contracts and the nesting of calls into each other. Unlike other programming languages where you can access different functions, Move syntax rules prohibit direct calling of users' scripts, which requires additional constructs and services to address this limitation.
Correction to: Context-Aware Evaluation of HCI Systems for Transportation in Urban Areas

A correction to this paper has been published: 10.1007/s13369-021-05986-w
Data-Driven Market Segmentation in Hospitality Using Unsupervised Machine Learning

Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering, based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.
Minimal linear codes with six-weights based on weakly regular plateaued balanced functions

Constructing minimal linear codes has a great interest in coding theory since they have an important role in describing access structures in secret sharing schemes and they are employed to design secure two-party computation protocols. Many methods of constructing linear codes have been proposed in the literature, and the most famous one is based on functions over finite fields. Linear codes derived from cryptographic functions have desirable algebraic structures that are significant from the application point of view. We in this paper study the construction of linear codes from some cryptographic functions over finite fields. We aim to construct new minimal codes by using a new type of function in the known construction method. To do this, we propose to use new subsets of the pre-images of weakly regular plateaued balanced functions. We then obtain five infinite classes of six-weight minimal codes from five different subsets of the pre-images of these functions.
Physical Transferable Attack against Black-box Face Recognition Systems

Recent studies have shown that machine learning models in general and deep neural networks like CNN, in particular, are vulnerable to adversarial attacks. Specifically, in terms of face recognition, one can easily deceive deep learning networks by adding a visually imperceptible adversarial perturbation to the input images. However, most of these works assume the ideal scenario where the attackers have perfect information about the victim model and the attack is performed in the digital domain, which is not a realistic assumption. As a result, these methods often poorly (or even impossible to) transfer to the real world. To address this issue, we propose a novel physical transferable attack method on deep face recognition systems that can work in real-world settings without any knowledge about the victim model. Our experiments on various state-of-the-art models with various architectures and training losses show non-trivial attack success rates. With the observed results, we believe that our method can enable further studies on improving adversarial robustness as well as security of deep face recognition systems.
The Application of Digital Teaching Resources in the Construction of Engineering Training Teaching Materials

The paper analyzes the common problems of engineering training teaching materials, and puts forward to solve the existing problems from four aspects: building a three-dimensional teaching material system, updating the content of teaching materials with the times, providing digital teaching resources and using AR technology to extend teaching materials.The construction of engineering training materials that meet the requirements can lay a solid foundation for engineering training.
A Blockchain-Enabled Digital Document Locker and Verification Model for Ethiopia

Blockchain technology has evolved as one of the emerging and revolutionary technologies in the world. In this applied technology realm, scientists and researchers have been trying to explore the possible convergence and exploration of salient emerging technologies for resolving the issues and challenges in the existing systems. As a matter of observation and fact, the advent of digitalization and printing technologies has created a new face of forgery and falsification of the important documents in developing countries like Ethiopia. Today, the exponential increase in tampering of documents has become a critical challenge countrywide in government and private organizations and requires serious attention. In the case of Ethiopia, the study discovered that there is no foolproof system practices that can be used to avoid such document forgery and fraud and maintain the integrity of the documents throughout the country. In order to resolve such issues and challenges, the existing systems of document locker/repositories need transformation towards strong immutability (tamper-resistant) and auditability. The prime objective of this study is to design a Blockchain-enabled Digital Document Locker and Verification (DDLV) Model for Ethiopia. This research study has used an exploratory and constructive research design and applied a Systematic Literature Review (SLR) for justifying the research gaps. In order to design and develop the proposed DDLV model, different Blockchain categories and platforms are assessed for suitability features.
Validating Readability and Complexity Metrics: A New Dataset of Before-and-After Laws

If algorithms are to be the policy analysts of the future, the policy metrics they produce will require careful validation. This paper introduces a new dataset that assists in the creation and validation of automated policy metrics. It presents a corpus of laws that have been redrafted to improve readability without changing content. The dataset has a number of use cases. First, it provides a benchmark of how expert legislative drafters render texts more readable. It thereby helps test whether off-the-shelf readability metrics such as Flesch-Kincaid pick up readability improvements in legal texts. It can also spur the development of new readability metrics tailored to the legal domain. Second, the dataset helps train policy metrics that can distinguish policy form from policy substance. A policy text can be complex because it is poorly drafted or because it deals with a complicated substance. Separating form and substance creates more reliable algorithmic descriptors of both.
Significance of NoSQL Databases with the Internet of Things

The significance of the Internet of Things is not that more and more devices, people and systems are ‘connected’ with one another. It is that the data generated from these ‘things’ is shared, processed, analysed and acted upon through new and innovative applications, applying completely new analysis methods and within significantly altered timeframes. The Internet of Things will drive Big Data, providing more information, from many different sources, in real-time, and allow us to gain completely new perspectives on the environments around us.The difference between machine-to-machine (M2M) and the Internet of Things (IoT) is driven by significant changes in the handling of data. In M2M, machine-generated data generally reflects well-defined data sets, communicated within established protocols and formats, and delivers well-defined alerts and notifications when values exceed their parameters. Applications in M2M make efficient use of this data as these applications have been developed hand-in-hand with what the characteristics of the data. In effect, application and data are intrinsically designed as one to meet the specific purposes of the application in fairly robust yet static model.
Development of Star Book Media to influence writing skills and carrying attitude in grade IV elementary school students

The purpose of carrying out this research and development is to produce Star Book Media that can streamline story writing skills and caring characters for fourth-grade elementary school students who are declared worthy of media expertise and material expertise. This research and development refers to Borg &amp; Gall's research and development steps. The material expert gave a final average score of 5 in the excellent category. The media expert gave a final average score of 4.69 in the excellent category. The results of operational trials show that Star Book media is effective in improving story writing learning. Based on the average score of the experiment, 1 class is 82, experiment class 2 has an average of 77, and the control class is 70. This shows that star book media is useful for improving the writing skill of fourth-grade elementary school students.
Integrated STEAM Approach in Outdoor Trails with Elementary School Pre-service Teachers

Due to the COVID-19 pandemic it was impossible to carry out on-campus teaching and examinations as planned for the first-year elementary school Bachelor’s degree teacher training courses during the summer term of 2019/2020. Therefore, we moved our on-campus STEAM (Science, Technology, Engineering, Arts and Mathematics) related courses to schooling at home. For their course examination, students designed outdoor trails in groups with the educational technology MathCityMap based on an integrated STEAM approach. Hence, they combined STEAM with real-world situations (e.g., monuments, marketplaces, playgrounds). The tasks within the trails required the use of technologies such as augmented reality (AR), digital modelling (e.g., GeoGebra 3D Graphing Calculator), and GPS. Analogue measuring tools (e.g., triangle ruler) were also used in the task designs. We collected data from 21 trails with 259 tasks from 49 pre-service teachers to analyse the effects on professional growth in STEAM education. Through hierarchical cluster analysis we identified three different clusters with patterns regarding STEAM in outdoor trails. This paper will describe a pedagogical framework for the integrated STEAM approach to designing and evaluating outdoor trails. Furthermore, we will explain patterns pre-service teachers developed during this professional development.
Learning Curvelet-based Directional Dictionaries for Single Image Super Resolution

Learning and reconstruction-based methods are the two main approaches to the solve single image super resolution (SISR) problem. In this paper, to exploit the advantages of both learning based and reconstruction based approaches, we propose a new SISR framework by combining them, which can effectively utilize their benefits. The external directional dictionaries (EDD) are learned from external high quality images. Additionally, we embeded the nonlocal means (NLM) filter and an isotropic total variation (TV) scheme in the reconstruction based method. We suggest a new supervised clustering scheme via curvelet based direction extraction method (CCDE) to learn the external directional dictionaries from candidate patches with sharp edges. Each input patch is coded by all the EDD. Each of the reconstructed patches under different EDD is applied with a weighted penalty to characterize the given input patch. To disclose new details, the local smoothness and nonlocal self-similarity priors are added on the recovered patch by TV scheme and NLM filter. Extensive experimental results validate the effectiveness and robustness of the proposed method comparing with the state-of- the-art algorithms in SISR methods. Our proposed schemes can retrieve more fine structures and obtain superior results than the competing methods with the scaling factors of 2 and 3.