Reviewerly/exHarmony huggingface dataset:
From Barter to Libra Do Money and Cryptocurrencies Lie Along the Same Evolutionary Line?

A spectre is haunting World: the idea that money is nothing but an illusion, a thing no more real than a fiction we tell each other and we have decided to believe in. But behind the scenes, someone is quetioning this story and something is happening. Every day a new cryptocurrency is born. Among those that have been recently created, the most important is undoubtedly Libra, sponsored by Facebook, which puts itself forward as a real international currency and has aroused both curiosity and concern in the banking, political and institutional world. The change seems unstoppable and is destined to significantly affect the daily life of each of us. The real question is: are cryptocurrencies money? Can they aspire to replace in all respects traditional national currencies? Is there a single evolutionary line that begins with bartering, in the mists of time, and ends with Libra, in the glittering digital age? Or are cryptocurrencies a different and temporary phenomenon, perhaps destined to carve out a small niche in the global digital ecosystem, but nothing more? In this paper, I will try to answer these questions by adopting a new perspective compared to the traditional technical and regulatory approach, observing the phenomenon from a historical, social and macroeconomic perspective. I will conclude by pointing out that money is nothing more than a register of public debt (coins and banknotes are a means of updating the register), which was at first centralised, then decentralised, and is now distributed thanks to blockchain technology. Cryptocurrencies are therefore money for all intents and purposes.
A Method for Attributing Non-Refitting Fragments to a Single Artifact: The Case of Bronze Age Flat Figurines

In recent decades, several new methods for studying archaeological artifacts, mostly based on digital technologies, have been developed. One of the most promising trends is 3D modeling, allowing researchers to deal with an exact virtual copy of the artifact, which can be manipulated in every way. We propose a new method for determining whether non-applicable fragments belong to one artifact, based on 3D modeling and mathematical statistics. After applying it to two (and possibly more) fragments, one gets an unambiguous answer as to whether the application is statistically reliable (i.e., falls within 95 % confidence limits). Precise computerized measurements on 3D models, following a single algorithm, allow us to verify the results. This method was tested on non-refitting fragments of figurines from the Bronze Age cemetery Tourist-2. Two anthropomorphic figurines from the same cemetery were used to verify the conclusions and elaborate the algorithm.
Development of geographic information systems for Small and Medium Enterprise centres

Abstract The small and medium enterprises (SME) actors support the economic development in Indonesia. The number of SMEs is increasing rapidly every year. One obstacle faced by SME is the difficulty of consumers to get business information and find the location of the place of business. The aim of this research is to design and to develop a geographic information system regarding the mapping of business centres. The system developed provides information in the form of a visual display of the business profile and business location of the SME. The method used is the Rational Unified Process, which consists of inception, elaboration, construction and transition. The results of the study consisted of the design and prototype of the geographic information system of the SME business centre that made it easy for users to find location information and SME business profiles. The business centre searches feature displays business locations in the form of a map that is connected with google map technology. In addition, the system built displays the latest and most accessible information as well as the SME contact information of search results. For system users who are interested in becoming members of the business centre, registration features are provided so that they get the facilities provided by the business centre. Future studies are expected to be able to show the route of travel from the user’s position to the business centre by considering the closest distance or based on criteria determined by the user.
Security System for Big Data Cloud Computing Using Secure Dynamic Bit Standard

Big data (BD) is a high-volume resource that requests savvy and imaginative types of data handling for improved knowledge revelation, dynamic, and procedure streamlining. CC gives a solid, deficiency open-minded, and adaptable condition to the enormous information appropriated the board frameworks. The Secure Dynamic Bit Standard (SDBS) calculation gives the security through two unique keys, for example, the ace key and meeting key created by cloud service providers (CSP). The SDBS calculation contains the three distinctive key lengths, for example, 128 bits, 256 bits, and 512 bits. The length of the ace key and meeting key is arbitrarily created during the encryption procedure. CSP scrambles the ace key with the meeting key and sends the encoded ace key and meeting key to the information suppliers on demand. The information supplier unscrambles the ace key with the meeting key and encodes the information record with the decoded ace key. This strategy will decrease the most extreme number of unauthenticated and unapproved clients in a huge information cloud.
Dimensionality Reduction by Consolidated Sparse Representation and Fisher Criterion with Initialization for Recognition

A sparse representation-based classifier has demonstrated potential results in face recognition but meets a small sample problem i.e. number of input images is less than an image dimension. To overcome this issue, dimensionality reduction methods can be employed in a sparse representation framework. Along this direction, sparse representation is often clubbed with the Fisher discriminant criterion. Most of these methods consider a random projection matrix to start with. The performance of dimensionality reduction procedure mostly depends on the projection matrix. In this paper, we show that a better-initialized projection matrix can perform much better than its random counterpart. Further, we are able to reduce the dimension of the projection matrix by half without losing much information. The experiments performed on the Extended Yale B, CMU-PIE and Coil-20 datasets demonstrate the efficacy of the proposed approach.
PENINGKATAN HASIL BELAJAR IPA SISWA MELALUI METODE DISCOVERY DI KELAS VI SEKOLAH DASAR NEGERI 16 PADANG

This research starts from the process of learning which is still centered on the teacher, the teacher is less engaging students for learning to take place, the students looked passive. The resulting state of the results of learning IPA students low and has not yet been as expected. One way is to develop the basic skills and to improve student learning outcomes is by using the discovery method. The purpose of the research was to explain the improvement of study IPA grade VI SDN 16 Surau Gadang Padang. This type of research is actions research class with II cycles, each cycle consisting of 3 times. The subject is a student of class VI SDN No. 16 Padang. Results observation sheet instrument learning aspects of the affective and psychomotor aspects of teacher observation sheets, and  test results about the study. The results showed that the average value of the results of learning cognitive aspects of students on IPA cycles I was finish with the learning outcome 6.3 30%; cycle II average of 8.4 result of student learning outcomes by 91%. Assessment of learning outcomes in the affective aspect of cycle I 62% and cycle II 89%. Learning outcomes assessment aspects of psychomotor on cycle I 75% and cycle II 86%. Overall it can be seen that by using the method of discovery can improve learning outcomes students IPA SDN 16 Padang. Key Words : results of learning, learning outcomes and, discovery method
The Performance Study of Database Full-Text Retrieval

In the paper, it implements the full-text retrieval in MYSQL Database by Lucene API and compares the performance and recall–precision of Lucene full-text retrieval with that of database full-text retrieval and like clauses retrieval. By comparing, we found Lucene is close to the database in full-text retrieval performance, and the time consuming of Lucene is 1/5 that of like clauses retrieval. What is more, the Lucene full-text retrieval can achieve the 77.2% recall ratio in Chinese retrieval aspects that is substantially higher than database full-text retrieval. Therefore, as for MYSQL database with larger quantity data, the Lucene full-text retrieval will be a good choice.
Assessing the Benefits of Model Ensembles in Neural Re-ranking for Passage Retrieval

Our work aimed at experimentally assessing the benefits of model ensembling within the context of neural methods for passage re-ranking. Starting from relatively standard neural models, we use a previous technique named Fast Geometric Ensembling to generate multiple model instances from particular training schedules, then focusing or attention on different types of approaches for combining the results from the multiple model instances (e.g., averaging the ranking scores, using fusion methods from the IR literature, or using supervised learning-to-rank). Tests with the MS-MARCO dataset show that model ensembling can indeed benefit the ranking quality, particularly with supervised learning-to-rank although also with unsupervised rank aggregation.
PENERAPAN METODE EXTREME PROGRAMMING PADA SISTEM PEMBAYARAN ELEKTRONIK SMK BAKTI IBU 3 PALEMBANG

Advances in information technology have been collaborated with a variety of fields including the financial sector in the form of payments. The Bakti Ibu 3 Vocational School is one of the developing schools in the city of Palembang, as a developing educational institution. The Bakti Ibu 3 Vocational School is computerized. The school and parents / guardians find it difficult to monitor the discipline of students paying tuition fees on time with the money given by parent/guardian. Based on these problems, the solution is by applying the Extream Programming Method to the Electronic Payment System of SMK Bakti Ibu 3. Extreme Programming (XP) is a software development methodology aimed at improving software quality and responsiveness to changing customer needs. The results showed that this system able to record payments automatically and efficiently.
Handwriting Recognition on Filled-in Forms Using CNN

Handwriting recognition is the most complex research area in computer vision. Automation of Form documents such as bank cheques, bank account opening forms, Patient admission forms, driving license forms, etc. is the necessity of most industries and government sectors. There has been a lot of work going on in the Optical character recognition (OCR) techniques for English text but it’s not giving promising results for recognition of ambiguous characters. We propose a recognition model, built on a nested convolutional neural network which improves the accuracy for ambiguous characters such as 2 and z, 5 and S, etc. Depending on the results of the experiments using the EMNIST dataset for training, validation, and testing, the new approach that uses CNN as a feature extractor and end classifier achieved a better recognition rate than previous models. The system integrates preprocessing, word extraction, classification whether it is printed or handwritten, segmentation, and character recognition together. The system’s output is an editable key-value paired text.
Transfer Learning for Domain-Specific Named Entity Recognition in German

Automated text analysis as named entity recognition (NER) heavily relies on large amounts of high-quality training data. Transfer learning approaches aim to overcome the problem of lacking domain-specific training data. In this paper, we investigate different transfer learning approaches to recognize unknown domain-specific entities, including the influence on varying training data size. The experiments are based on the revised German SmartData Corpus, and a baseline model, trained on this corpus.
LIBRARY: EXPERIENCE OF WORKING IN REMOTE MODE

The article discusses the experience of working with libraries in remote mode. The analysis of information posted on the websites of federal and central regional libraries showed that, having stopped accepting users in their premises, libraries continued to serve them remotely, through their official websites, social network accounts, and Internet platform channels
Digital Humanities and a New Research Culture

Research data are any information that has been collected, generated, or created to validate original research findings. This essay is focused on data produced by scholars rather than data that are collected and already accessible (e.g. open cultural heritage data). The hypothesis of the paper is that the open research data movement engages two cultures which are guided by different kinds of motives and goals: the administrative or managerial side of the institution that demands opening data and research service infrastructure and provides support in managing and opening data; and the academic researchers’ culture, in which academics collect, produce, and reuse data. One challenge is thus to reconcile institutional requirements with researchers’ motivations. The objectives of this article are to present two views on open research data from the perspective of institution and researchers, to introduce the role of a data agent as a bridge between administrative services and academics, to analyse the impact of the emerging research culture on the transformation of the humanities, and to highlight the role of the digital humanities in the establishment of this new approach. The analysis is based on a case study of Finnish open research data policy, actions, and digital humanities practices using interviews, open data sets, and reports. The open research culture in the humanities represents a move towards a processual approach, in which each research task and piece of data can be traced, validated, and reproduced. When considering the field of digital humanities, there are still a lot of challenges to be met, but, ultimately, the application of open science practices will essentially transform the entire ecosystem of humanities research.
Character Segmentation to the Case Study Image of Quadratic Equation Expression

The introduction of digital mathematical expressions can be said to be unusual because mathematical expressions consist of various symbols. The introduction of mathematical expressions can be divided into two main steps, namely the introduction of symbols and structural analysis. Segmentation of an image is an important part in the recognition of handwritten mathematical expressions, because segmentation is the first step of the recognition process. In this study, we will present the process of handwriting image segmentation for mathematical expressions in the form of quadratic equations using the Connected Component Labeling (CCL) method. From the results of the research conducted, it can be concluded that the segmentation process has succeeded in segmenting the handwritten images of mathematical expressions in the form of quadratic equations by producing the characters (compound characters) that make up mathematical expressions and grouping the ranks and basic numbers of quadratic equations.
Construction of STEM Interdisciplinary Integration Model Supported by Educational Artificial Intelligence

Under the background of the vigorous development of education, the STEM interdisciplinary integration model has gradually developed, and it has an extremely important role in promoting education. At the same time, with the support of current educational artificial intelligence, education work has also injected new vitality and development space. Therefore, the article first makes a detailed analysis and discussion on the connotation of the STEM interdisciplinary integration model, and then explores the role of educational artificial intelligence in promoting the STEM interdisciplinary integration model, and finally puts forward corresponding measures based on the key points of the work strategies, in order to attract others.
Aspect-Based Sentiment Analysis Based on Multi-Channel and Dynamic Weight

Abstract With the wide use of graph convolution network (GCN) in various NLP tasks, researchers introduce GCN to aspect-based sentiment analysis. They utilize GCN over dependency tree to capture the syntactic information and obtain the final representation by modeling the interaction between the semantic and syntactic information. However, due to the unreliability of the construction of dependency tree, these interaction-based models may suffer from the side effect of unreliable syntactic information. To tackle the problem, we propose a multi-channel aspect-specific attention mechanism to ease the possible side effect by retaining and exploiting the original semantic information, which is not affected by the syntactic information. We also propose a dynamic weighted concatenation mechanism to sufficiently utilize the multi-channel information by dynamically assigning weights to the multi-channel information. The extensive experiments on five benchmark datasets indicate the effectiveness of the proposed mechanisms.
PAUTAS DE CONDUCTA Y ÉXITO PROFESIONAL DE LOS INFLUENCERS. UN ESTUDIO DE PERCEPCIONES

The aim of this study is getting to know what are the keys that support the success of the influencers in social media. The purpose is to identify certain acting ways that arise in their posts, as well as studying any other aspects related to their personal style, the technology applied, the ammount of time employed on the Internet, etc. The sample used is based by 103 spanishspeaking influencers, and the data recolleted by a 20 items handmade questionnaire which has enabled a quantitative analysis with an overrated frecuencies and calculations. In addition to, a qualitative research composed by an open questions interview. The results throw the possibility to consider a method of working that reveals some patterns which cannot be calified as casual or arbritrary. The results remark that profesional influencers take care of the content they upload, as well as its format, the Schedule, the length and the strategies used in order to interact with their folllowers. Also, takin into consideration their profile target and the companies that support them. Related to this investigation’s implications, we consider that our study offers an exact relation between real and actual database, which would be useful for those who are interested in such activity in a profesional way or because they are looking for ways to link themselves with potential companies
Evaluating Models with Dynamic Sampling Holdout

Automated Machine Learning (Auto-ML) is a growing field where several techniques are being developed to address the question of how to automate the process of defining machine learning pipelines, using diverse types of approaches and with relative success, but still, being far from solved. Among these still unsolved questions, the computational cost is one of the major issues. In this context, evaluating a model takes a lot of time and resources, and yet that is still a step that has not received much attention in the Auto-ML literature. In this sense, this work revisits the Auto-CVE (Automated Coevolutionary Voting Ensemble) and proposes a new method for model evaluation: the dynamic sampling holdout. When compared to the regular Auto-CVE with cross-validation and the popular TPOT (Tree-based Pipeline Optimization Tool) algorithm, Auto-CVE with dynamic holdout shows competitive results in both predictive performance and computing time.
Analysis of ammonium and phosphates concentration time series in river water

Abstract The Ammonium prediction in river water and the Phosphate prediction in river water datasets (provided by the Ukrainian government) have been handled. The ARIMA (p, d, q) model of low order (p&lt;5 and q&lt;5) and high-order models (p&gt;4 or q&gt;4) have been constructed to describe behaviour of concentration time series at different river stations. Orders and coefficients in the first case are obtained via information criteria minimization. In the second case the ACF and PACF functions analysis is used. The ArDL(p, q) models are used to investigate correlation between concentrations of ammonium and phosphates at different river stations. Dependence of these parameters at the target water station and at other stations is confirmed with statistical tests. Conclusions about water pollution are made.
A Case Study on the Basic Planning of Elementary School Libraries Through the User Participatory Method

The purpose of this study was to provide information about elementary school library design and user participatory methods for children, focusing on the analysis and evaluation of user activities during the participation process. Burns(1979) posited four categories of user participation: awareness, perception, decision-making, and implementation. The results of the study were as follows. First, reviews of curriculum and the spatial relationships between the library and other spaces of the school, and investigation of students’ spatial experiences were necessary for the awareness process. However, it is necessary to develop effective insight opportunities for local students, who do not have many chances to encounter visual stimulation. Second, for the perception process, video clips of good designs were shown and free discussion about the library was encouraged. Third, in workshops for students, parents, and teachers involved in the decision-making process, participants requested activities such as taking a rest, playing, chatting with friends, and small meetings at the library, and suggested deck space for the outdoor spaces, stair seating, a duplex attic, etc. Modeling activities were so time consuming that the development of a modeling kit or easily handled 3D software was needed. Fourth, in the implementation process, a basic plan was suggested and shared with the participants. In conclusion, guidelines of research methodology for user participation need to be developed, especially for children.
A Hard Science Spoken Word List

A Hard Science Spoken Word List (HSWL) was developed and validated to help second language learners of hard sciences better comprehend academic speech at English-medium universities. It consists of the 1,595 most frequent and wide ranging word families in a 6.5-million running word hard science spoken corpus which represents 12 subjects across two equally-sized sub-corpora. Its coverage in different discourse types indicates that the HSWL truly reflects the language in hard science academic speech. The comparison between the HSWL with Dang, Coxhead, and Webb’s (2017) Academic Spoken Word List shows that the HSWL focuses more on specialized vocabulary in hard science speech. Depending on their vocabulary levels, learners may achieve 93%–96% coverage of hard science academic speech with knowledge of the HSWL words.