Reviewerly/exHarmony huggingface dataset:
The Brazilian Neodocumentalist Movement: an Historical Perspective

This article presents early studies on the repercussion of the neo-documentation movement in Brazilian Information Science, through a literary review on the history and evolution of Documentation in Brazil. Some currently approached questions by Brazilian researchers are presented here, with regard to the document and documentation under the neo-documentation perspective. Based on the work of these Brazilian researchers, by means of reconstituting the theoretical steps in the construction of these researches, it is traced to the pathways that indicate an original Brazilian Information Science neo-documentation movement. It is recommended that the subject be widely explored in the future, for being a rich source of Information Science historiography.
Machine Learning with Core ML

This chapter introduces the Core ML API and shows how it is now possible to use contemporary machine learning models to implement intelligent image analysis and computer vision solutions, such as object detection and recognition and scene classification. It introduces a few basic concepts in machine learning and deep learning, and shows how Core ML models can be used with the Vision Framework in an image analysis workflow.
Spacecraft in Orbit Fault Prediction Based on Deep Machine Learning

Abstract Spacecraft is playing a more and more important role in military reconnaissance, environmental disaster reduction and other fields in china. The research on fault prediction technology based deep machine learning will realize the transformation from traditional post fault processing to prior system maintenance. In this paper, combined with different in orbit fault modes of spacecraft and the complexity of the fault modes, multi kind of prediction methods for single parameter and multi parameter faults are proposed. Based on this, the in orbit fault prediction system is constructed, and for the results of a typical in orbit case show that the prediction results are good. In the future, it can be considered to further expand the fault prediction methods of the system, such as modeling the in orbit data directly by the graph neural network algorithm, so as to further improve the prediction accuracy.
Introduction to CSEM

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
JGraphs: A Toolset to Work with Monte-Carlo Tree Search-Based Algorithms

Monte-Carlo methods are the basis for solving many computational problems using repeated random sampling in scenarios that may have a deterministic but very complex solution from a computational point of view. In recent years, researchers are using the same idea to solve many problems through the so-called Monte-Carlo Tree Search family of algorithms, which provide the possibility of storing and reusing previously calculated results to improve precision in the calculation of future outcomes. However, developers and researchers working in this area tend to have to carry out software developments from scratch to use their designs or improve designs previously created by other researchers. This makes it difficult to see improvements in current algorithms as it takes a lot of hard work. This work presents JGraphs, a toolset implemented in the Java programming language that will allow researchers to avoid having to reinvent the wheel when working with Monte-Carlo Tree Search. In addition, it will allow testing experiments carried out by others in a simple way, reusing previous knowledge.
Proto-Japonic Verbal Morphology: Insights from the South Ryukyuan Negative Forms of Vowel Verbs

Abstract This paper provides a comparative analysis of Old Japanese negative forms of bigrade verbs and their cognates in Sakishima/South Ryukyuan languages. South Ryukyuan cognates of bigrade verbs in their negative forms, as well as a number of other forms traditionally associated with the irrealis ( mizenkei , 未然形) base, essentially present two suffixes: one, a straightforward cognate with the analogous suffixes elsewhere in Japonic, being traceable to Proto-Sakishima *-en-, the other being traceable to Proto-Sakishima *-on-, with no clear Japonic cognates. My argument is that despite the appearances, this *-en-/*-on- alternation is not a result of a Sakishima innovation, but instead is rooted in Proto-Japonic morphology of vowel verbs. By applying the rules of vowel crasis and contraction known for Proto-Japonic and Old Japanese, I postulate the proto-language coexistence of two forms of verbal negation: one derived from the Proto-Japonic ancestor of nominalized (one of the functions of the ren’yōkei 連用形) and the other from the finite ( shūshikei 終止形) base forms. I conclude that the former was broadly distributed throughout the Proto-Japonic area, while the latter was confined to the regions that eventually bore Proto-Sakishima speakers.
Privacy Amplification by Decentralization

Analyzing data owned by several parties while achieving a good trade-off between utility and privacy is a key challenge in federated learning and analytics. In this work, we introduce a novel relaxation of local differential privacy (LDP) that naturally arises in fully decentralized algorithms, i.e., when participants exchange information by communicating along the edges of a network graph without central coordinator. This relaxation, that we call network DP, captures the fact that users have only a local view of the system. To show the relevance of network DP, we study a decentralized model of computation where a token performs a walk on the network graph and is updated sequentially by the party who receives it. For tasks such as real summation, histogram computation and optimization with gradient descent, we propose simple algorithms on ring and complete topologies. We prove that the privacy-utility trade-offs of our algorithms under network DP significantly improve upon what is achievable under LDP (sometimes even matching the utility of the trusted curator model), showing for the first time that formal privacy gains can be obtained from full decentralization. Our experiments illustrate the improved utility of our approach for decentralized training with stochastic gradient descent.
RETRACTED: Strength test of grouting material for coastal construction engineering based on 5G network and Internet of Things

At high geothermal tunnel for practical purposes, cementations grout material inevitably leads to a decrease in mechanical properties, they are exposed to humidity and high temperature. Based on two typical conditions, hot, dry high geothermal tunnel characteristics in environments attenuate varying the high-temperature heat, much mechanical strength test, grout a cured product under different environmental conditions it conducted in. The future of communication, a network of standardization conference, depends on the heterogeneous structure; it will be taken into account to bring innovation trends consistent with ubiquitous. However, rather than to improve the exhibition of the existing organization, corresponding to progress, to adjust the completely at the same time its heterogeneous system, does not allow the classification to be used in a variety of areas. Repeatedly reuse of the pool from the different cells within the cell. Wirelessly will try cells (transmitter and collector) to program all 5G through channels remote gadget mobile phone, close to the receiving wires exhibited. The low power phone has sent a periodic dispersive wave.
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning.

Due to communication constraints and intermittent client availability in federated learning, only a subset of clients can participate in each training round. While most prior works assume uniform and unbiased client selection, recent work on biased client selection has shown that selecting clients with higher local losses can improve error convergence speed. However, previously proposed biased selection strategies either require additional communication cost for evaluating the exact local loss or utilize stale local loss, which can even make the model diverge. In this paper, we present a bandit-based communication-efficient client selection strategy UCB-CS that achieves faster convergence with lower communication overhead. We also demonstrate how client selection can be used to improve fairness.
Pedagogical conditions for forming the information competence of pedagogical students

The article is devoted to the problem of the formation of information competence of students of the pedagogical direction. An approach to the essence of the concept, structure and content of information competence is stated. The pedagogical conditions for the formation of information competence of future teachers are presented, and the results of experimental work on the introduction of pedagogical conditions into the educational process, aimed at the formation of information competence of students of the pedagogical direction.
A machine learning software to estimate morphological parameters of distant galaxies

We develop a machine learning (ML) software to estimate morphological parameters (e.g., the half-light radius r<sub>e</sub>) of high redshift galaxies in the Subaru/Hyper Suprime-Cam data. To make the ML software capture simultaneously galaxy morphological features and point spread function (PSF) broadening effects, we implement a two-stream convolutional neural network (CNN) for inputs of galaxy and PSF images. Thanks to large training samples of galaxy and PSF images, the two-stream CNN estimates re more accurately than a single-stream CNN with only galaxy images. Our ML software would be a useful tool to investigate galaxy morphological properties with PSF-unstable images obtained in future large-area ground-based surveys.
Process Modeling in Decentralized Organizations Utilizing Blockchain Consensus

Blockchain technologies allow for multiple organizations, individuals, and software to become part of a decentralized network where they can reach consensus on the state of the system including all data stored. Due to the ability of reaching consensus even among untrusted actors, the idea of decentralized organizations has been proposed, where both the system components as well as their coordination are distributed. Given such a system of distributed actors, the problem of decentralized coordination for following common goals and planning becomes apparent. This paper addresses the decentralized coordination problem by means of a blockchain-based approach that uses conceptual modeling to reach consensus in decentralized organizations. With a unified view on the processes and instances of distributed actors, the aim is a decentralized planning and execution through models. For this purpose, an existing approach for decentralized process modeling and instance tracking is applied and extended with the possibility for actors to form consensus on an organizational level through blockchain transactions.
Statistical Inference via Data Science: A Modern Dive into R and the Tidyverse

"Statistical Inference via Data Science: A Modern Dive into R and the Tidyverse." Journal of Quality Technology, ahead-of-print(ahead-of-print), pp. 1–3
Cyber–Physical Mobile Arm Gesture Recognition using Ultrasound and Motion Data

Alternative and more intuitive input methods for the control of consumer devices (e.g. tablets, smart-phones and home automation systems), vehicle entertainment systems and industrial machines and robots are on the rise. In this contribution, we propose a body worn setup, which supplements the omnipresent 3 DoF motion sensors with a set of ultrasound transceivers. This allows for an active time–of–flight based measurement of distances between independent nodes on upper limbs and torso without requiring calibration or remote reference sensors like video cameras. A body worn demonstrator is introduced, which consists of three independent but synchronized nodes, equipped with motion sensors and Capacitive Micromachined Ultrasonic Transceivers. Results of an initial test series using eight wide arm gestures show, that combining ultrasound distance measurements and motion data improves the classification accuracy.
Internal marketing strategies in United Arab Emirates higher education

Purpose This paper seeks to analyze the impact of internal marketing orientations (IMOs) on the competitiveness of higher education institutions (HEIs) in the United Arab Emirates (UAE). Design/methodology/approach A stratified random sampling was employed to collect data from a pool of 5,968 faculty members in 102 HIEs in the UAE. An online survey questionnaire was placed on Qualtrics online data collection platform and sent to selected participants. Out of a total of 638 surveys send through email, 349 surveys were returned, duly filled. The model was tested using SmartPLS structural equation modeling (PLS-SEM). Findings IMO dimensions of information generation (IG), information dissemination (ID) and information responsiveness (IR) are inexplicably linked to HEI's competitiveness and faculty performance. IR proved to be a more significant predictor of HEI's competitiveness compared to IG and ID. Practical implications The study proposes that HEI policymakers encourage the exchange of value among the internal stakeholders and formulate employee-focused policies as part of their corporate objectives. The findings also advocate in improving working conditions and enhancing work–life balance to strengthen the institutions' competitive position within the industry. Originality/value This study is the first to explore the interrelationship among the IMO dimensions of IG, ID and IR and their impact on HEI competitiveness, particularly in the UAE.
THE COMPETENCE AND KNOWLEDGE APPROACHES: PHILOSOPHICAL AND EDUCATIONAL PROBLEMS OF UNDERSTANDING AND APPLICATION

The philosophical and educational methodology of the educational process organization is discussed in the aspect of understanding the system, essential and practical-applied principles of the knowledge and competence approaches. Ontological, epistemological and instrumental methods of research of these approaches are considered as integrative qualities of the individual. Contradictions and interrelation of knowledge and competences in the aspect of their influence on the theoretical and practical component of the content of education are shown on the basis of the ratio of semantic and structural-content parameters.
Bitcoin Mining to Reduce the Renewable Curtailment: A Case Study of Caiso

The enormous energy demand from Bitcoin mining is a considerable burden to achieve the climate agenda and the energy cost is the major operation cost. On the other side, with high penetration of renewable resources, the grid makes curtailment for reliability reasons, which reduces both economic and environment benefits from renewable energy. Deploying the Bitcoin mining machines at renewable power plants can mitigate both problems. Here we present a case study on CAISO, analyzing the profitability of such solution. The simulation tells the system can earn 5.6–48.1 million dollars in 2018 and reduce 50.8%–79.9% of the curtailment. The probability to make profits, with volatile bitcoin price and mining difficulty, is positively correlated with the capacity factor of the mining machines. However, there is a tradeoff between curtailment usage rate and machines’ capacity factor. To reduce more curtailment, the system will deploy more machines and more sensitive to the Bitcoin market.
Sistem Monitoring Prestasi Akademik Siswa Pada Sekolah Menengah Atas Negeri 5 Pagar Alam

The process of monitoring academic activity of SMA N 5 Pagar Alam City is still manual, because the data storage is still scattered in each field of teachers, sometimes the data entered in Microsoft Excel is not very accurate. From this problem, then a system of monitoring the processing of academic achievement data to determine the academic achievement of each student, where achievement data in the form of scores and activities of extracurricular activities will be easier to process, and the creation of reporting will be easier. This research uses waterfall methods and UML design, the devices used to build these systems using PHP and MySQL programming as databases. The system can be applied as a monitoring of students' academic performance so that academic data can be stored properly. The result of this study is the system of monitoring the academic achievement of high school students N 5 Pagar Alam City, with this system the school and parents are more effective in controlling and assessing activities related to the academic achievement of students.
A Two-Stage Highly Robust Text Steganalysis Model

With the development of natural language processing, deep learning, and other technologies, text steganography is rapidly developing. However, adversarial attack methods have emerged that gives text steganography the ability to actively spoof steganalysis. If terrorists use the text steganography method to spread terrorist messages, it will greatly disturb social stability. Steganalysis methods, especially those for resisting adversarial attacks, need to be further improved. In this paper, we propose a two-stage highly robust model for text steganalysis. The proposed method analyzes and extracts anomalous features at both intra-sentential and inter-sentential levels. In the first phase, every sentence is first transformed into word vectors. To obtain a high dimensional sentence vector, we use Bi-LSTM to obtain feature information for all words in the sentence while retaining strong correlations. In the second phase, we input multiple sentences vectors into the GNN, from which we extract inter-sentential anomaly features and make a judgment as to whether the text contains secret messages. In addition, to improve the robustness of the model, we add adversarial examples to the training set to improve the robustness and generalization of the steganalysis model. Theoretically, our proposed method is more robust and more accurate in detection compared to existing methods.
SM algorithms-based encryption scheme for large genomic data files

Abstract With the rapid development of the genomic sequencing technology, the cost of obtaining personal genomic data and analyzing it effectively has been gradually reduced, and the analysis and utilization of genomic data has gradually entered the public view, while the leakage of genomic data privacy has aroused the attention of researchers. The security of genomic data is not only related to the protection of personal privacy, but also related to the biological information security of the country. However, there is still no effective genomic data privacy protection scheme using Shangyong Mima(SM) algorithms. In this paper, we analyze the widely used genomic data file formats and design a large genomic data files encryption scheme based on the SM algorithms. Firstly, we design a key agreement protocol based on the SM2 asymmetric cryptography and use the SM3 hash function to guarantee the correctness of the key. Secondly, we used the SM4 symmetric cryptography to encrypt the genomic data by optimizing the packet processing of files, and improve the usability by assisting the computing platform with key management. Software implementation demonstrates that the scheme can be applied to securely transmit the genomic data in the network environment and provide an encryption method based on SM algorithms for protecting the privacy of genomic data.
URGENSI LEARNING RESOURCES (SUMBER BELAJAR) DALAM MENINGKATKAN KUALITAS PEMBELAJARAN

Learning as a process of interaction between educators, students, and learning resources. In learning, educators must make themselves a source of learning and prepare and design other learning resources including media or media or multimedia as support in learning to improve the quality of learning. The quality of learning is determined by educators. Educators must have creativity and productivity in managing learning resources strting from planning learning resources, using learning resources and evaluating learning resources. In planning learning resources, educators must determine and choose learning resources that are appropriate to the learning objectives, characteristics of students, and the situation and conditions of the school. In terms of using learning resources, educators must be able and skilled in designing and utilizing learning resources in learning, and in terms of evaluating learning resources, educators must be able to assess appropriate learning resources, which provide more benefits and assess learning resources that have advantages and limitations in learning certain things. Keywords : learning resources , quality of learning
Analysis of Production and Procurement Plan of Shipbuilding based on Queueing Theory with Variability

In order to establish an accurate production plan, the net working time must be determined in consideration of unit working time and production volume, then the production time must be determined in consideration of the appropriate allowance time with the net working time. However, since the unit working time of shipbuilding is inaccurate and there are many difficulties in calculating the production volume, most shipyards are following the top-down procedure based on the past ship’s production records. In addition, shipyards have very large variability affecting production time, such as delay and waiting, because human factors dominate. Nevertheless, there has been little scientific analysis of this variability. Variability is a kind of statistical variable. When applied to a production system in combination with the queuing theory, it is possible to analyze the production process through scientific analysis and draw productivity improvement because it is possible to analyze the variability considering production time and process capability. Researches on variability have been applied to mass production industries such as automobile production. The target of most researches of variability in the mass production industry is to reduce the variability of production time to secure higher productivity with the given production resources. However, in the case of the shipbuilding industry, the variability in production time is very high and difficult to control compared to the mass production industry. So, queuing analysis with high variability never has been studied in shipbuilding. In this study, the quantitative variability level of the shipbuilding of Korea is investigated through the queuing analysis with variability with respect to the schedule and procurement plan of the shipyard, and the scientific analysis is conducted for the analysis of the relationship between the productivity and the composition of shipyard production resources.
Study on the Evaluation of Practical Teaching Quality in Applied Undergraduate Colleges

In order to improve the teaching quality of applied undergraduate colleges, the construction and optimization of practical teaching quality evaluation system is particularly important. In this paper, DEA model is used to build the practical teaching evaluation index of applied undergraduate colleges, analyze the input and output, and compare the comprehensive efficiency, pure technical efficiency and scale efficiency. In addition, this paper provides effective improvement methods for non DEA decision-making units from establishing evaluation indexes, optimizing teaching staff, realizing resource sharing and innovating management mode, so as to achieve DEA effectivity.