Reviewerly/exHarmony huggingface dataset:
The Minimus digitizer platform: a user-friendly ecosystem for efficient network management and seismic station configuration

The Guralp Minimus broadband digitizer has led the way with introducing a number of innovative features to broadband digitizers including easy network configuration, compact form-factor, extensive State of Health (SOH) monitoring and low latency digitization. Since its introduction, there have been major technological advances in processing chips resulting in the power consumption of seismic digitizers decreasing drastically in the last few years. The next iteration of Minimus, Minimus2, takes advantage of modern chip power consumption to reduce overall nominal power consumption by over 50% whilst maintaining high functionality. This significant decrease in power consumption will facilitate far more simplified field deployments for offline deployments.The Minimus platform also provides a high level of functionality for online stations, including the industry unique option of sending State of Health (SOH) data via the SEEDlink protocol. This makes SOH monitoring far simpler for larger networks as SOH data be managed using similar methods as waveform data. This also allows for time-series analysis of SOH data to be able to proactively maintain stations and advance diagnose any issues before they result in any loss of data. The Minimus platform seamlessly interfaces the Discovery software to seamlessly integrate new stations into existing networks. The management of large numbers of real-time seismic stations is further enhanced with Guralp Data Centre (&amp;#8220;GDC&amp;#8221;) a cloud-based software package to build on the Discovery tool set.The Minimus platform was built from the ground up to provide one of the lowest latency digitizers available with digitization latencies down to 40ms, making it well suited to Earthquake Early Warning applications. This is achieved with the use of causal decimation filters, high sample rates and Guralp&amp;#8217;s proprietary GDI protocol. The Minimus platform is built as a modular digitizer platform that is available within a number of different packages to suit a range of applications, including as a standalone digitizer or built within Broadband seismometer and Force Balance Accelerometer systems.&amp;#160;
Data Provenance and Administrative Information in Library Linked Data: Reviewing RDA in RDF, BIBFRAME, and Wikidata

We examined how data provenance and additional information of element values including nomens, and administrative information on the metadata should be modeled and represented in the Resource Description Framework (RDF) for linked data of library catalogs. First, we classified such information types into categories and organized the combination with recording-units, i.e., a description statement or description set. Next, we listed the appropriate RDF representation patterns for each recording-unit. Then, we reviewed the methods to examine such information in Resource Description and Access (RDA) in RDF, BIBFRAME, and Wikidata, and pointed out the issues involved in them.
Aplikasi Augmented Reality (AR) untuk Pengenalan Varietas Anggur Hasil Balitbangtan

Augmented reality is a combination of two worlds: virtual and real world, which mean objects in the virtual world can be displayed in the real world. The use of augmented reality in the introduction media, in the form of applications on android smartphones designed as the introduction of varieties of grape from Balitbangtan. Balitbangtan (Badan Penelitian dan Pengembangan Pertanian) is the highest structural office in the Ministry of Agriculture with the task of researching, developing and innovating in agriculture. The purpose of this application is to introduce the grape varieties from Balitbangtan in the ebook, “Budidaya Tanaman Anggur” in a more interactive and easy to understand way by displaying objects in 3D virtual and can make sounds containing explanations about the variety of grape from Balitbangtan. This application will show three types of grape varieties of Balitbangtan with markers according to the image of the grape variety in the ebook. Apps are built using Unity, Vuforia, and Blender. The method used in this study is the method of collection of data questionnaires (wags) which is a data collection technique performed by asking questions or written statements to respondents to answer them. The questionnaire was conducted by researchers by spreading a list of questions to students and millennial farmers who read the e-book. Results from questionnaires found that 80% of respondents agreed with the implementation of augmented reality in the introduction of Balitbangtan grape varieties with category “Totally Agree”.Method that used in this research is MDLC method (multimedia development life cycle). There are six steps in this method such as concepting, designing material collection, creating, testing, and distribution. We are testing applications and user satisfaction. Testing user satisfaction done through survey or questionnaire. survey done by researcher with spread question list to college student and millennial farmer that read ebook Budidaya Tanaman Anggur and they have installed and using application introduction to grape variety from Balitbangtan. The result of the survey found that 80% respondent agree with augmented reality applied to the introduction to grape variety from Balitbangtan.
Perancangan UI/UX pada Aplikasi Taspen Otentikasi Berbasis Mobile Dengan Menggunakan Metode Design Thinking

Along with the development of today's technology, the role of information system services becomes very important in overcoming various kinds of community problems effectively and efficiently. Taspen Authentication is an example of an information system service aimed at retired civil servants (PNS). However, there are still problems with the taspen authentication application that make it difficult for some users to run it. The majority of users of this application are the elderly group aged ≥ 60 years and have limited physical function and cognitive function. Based on the results of initial observations and interviews of researchers involving Taspen and several users in the elderly category, it was found that there were problems that caused the lack of efficiency in using the taspen authentication application in terms of UI/UX. The purpose of this research is to design a UI/UX that suits the needs of users, especially the elderly, using the design thinking method. In addition to using the design thinking method, researchers also use User Experience Questionnaire (UEQ) at the testing stage to determine the feasibility of the application prototype that has been designed and get feedback from the target user. The results of the study show that the new interface design provides a very good user experience as evidenced by the results of the 6 UEQ scales which are of excellent value. So the researcher concludes that the UI/UX design on the taspen authentication application that uses the design thinking method is able to provide a description of user needs, especially for the elderly.
Approaches to Modeling Geothermal Heat Flow from Various Datasets

Geothermal heat flow models are currently developed for remote areas like Antarctica and parts of Africa. Due to the sparsity of actual geothermal heat flow measurements, indirect data based on various quantities related, e.g., to gravitational and magnetic information are used for predicting heat flow in such regions and quantifying its uncertainty. Here we present and compare two common approaches, a data driven random forest approach that is trained with several covariates (magnetic and gravitational anomalies, lithospheric thickness, topography, seismic velocities) and a physics based approach relating magnetic anomalies to Curie depth and subsequently to geothermal heat flow (requiring various simplifications and a priori assumptions on the underlying physics).&amp;#160;
Creating School Seismology Labs For the Development of Students’

Educational seismology projects all over the world allow schools to detect signals from large earthquakes happening anywhere in the world. It starts from the natural interest and fascination for the destructive power of earthquakes. Still, the experiment quickly became much more, from simple information to exposing fundamental concepts tested by practical experiments. In the framework of the Romanian Educational Seismic Network, classroom activities that enable students to learn about some basic science concepts using seismology and earthquakes as a unifying theme have been developed in the last decade. Students are guided to use concepts such as energy transfer, wave properties, and resonance to understand elementary models of earthquakes' causes and effects. From 2022, a new Erasmus Project adds one more piece to the framework, together with a truly regional approach - SEISMOLAB - Creating School Seismology Labs For the Development of Students&amp;#8217;. One of the aims of the project is to bring together seismologists, pedagogues, curriculum developers, local authorities, advanced educational developers, and schools to join forces during the implementation and co-design, develop and validate an innovative professional development program for supporting the in-service training of teachers on topics related, but not limited to seismology education. Thus, apart from Seismology, participants will be familiarised with the open schooling approach that provides a powerful framework to engage, discuss and explore how schools can facilitate open, more effective, and efficient co-design, co-creation, and use of educational content tools and services for personalised science learning and teaching and citizens science.In Romania, the project will be piloted in ten secondary and high schools where SEISMO-labs will be developed based on a framework for organising School Development Labs of Students&amp;#8217; Competences and a set of educational scenarios (SEISMO-Labs demonstrators). A SEISMO-Lab Toolkit and a teacher training program will prepare teachers to implement SEISMO-Labs in their schools, receiving training on the approach and the pedagogical methodologies. An evaluation methodology will be designed for mapping and monitoring the impact and effectiveness, both quantitatively and qualitatively, at the student, teacher, and school/institution level of the SEISMO-Labs.ACKNOWLEDGEMENTSThis work was done in the framework of the project &amp;#8220;Seismo-Lab&amp;#8221; which has been funded with support from the European Commission under the Erasmus+ programme (Grant agreement number 2021-1-EL01-KA220-SCH-000032578).
Non-destructive characterization of out-of-plane fiber waviness in carbon fiber reinforced polymers by X-ray dark-field radiography

Fibre waviness is a frequently encountered problem in composite design and manufacturing as it can severely influence mechanical properties of components. In this work we propose a new method for the detection and quantification of out-of-plane fibre waviness in carbon fibre composites using Talbot-Lau grating interferometry. The sensitivity of X-ray dark-field imaging to the orientation of carbon fibres is exploited to visualize fibre waviness by radiographic imaging and reduce measurement times in comparison to full computed tomography scans. We show that fibre waviness can be qualitatively detected by single radiographic images providing a valid option, e.g. for in-line monitoring of similar specimens. Furthermore, quantitative evaluation of fibre waviness angles can be performed by repeated radiographic imaging over an angular range of roughly 10°. Therefore, the number of required projection images can be reduced significantly. With this method, we evaluated wave angles at an error of less than ±1.5° compared to results achieved by full computed tomography scans.
Zero Imputation Methods for Log-transformation of Independent Variables

In studies in medical science or public health, it is common to apply log-transformation to independent variables that have severely right-skewed distribution. Among such variables, lab values have skewed distribution but have zeros due to limit of detection, which makes it impossible to apply log-transformation directly. There have been various methods proposed to deal with such situation, but most of these methods are rarely used in practice because they usually require additional step in calculation. In this study, five methods that removes zeros (shift by 1, shift by half of the smallest nonzero, shift by square root of the smallest nonzero, replace zeros with half of the smallest nonzero, replace zeros with square root of the smallest nonzero are presented. To compare performances of these methods we performed a simulation study based on randomly generated data with independent variable with log-normal distribution and response variable which has linear relationship with the independent variable, Shift by 1 method has the worst performance, and overall shift by half of the smallest nonzero method and replace zeros with half of the smallest nonzero method showed most stable performances.
A Study on Computing Sample Quantiles of Continuous Distributions

When sample data is given, we estimate the population quantiles by computing the quantiles of the data. In statistics textbooks there are two methods of computing sample quantiles. Method 1 uses a single order statistic or a simple mean of two adjacent order statistics as a sample quantile, and method 2 uses a weighted mean of two adjacent order statistics as a sample quantile. In this paper, we compared these two methods for the case of several continuous distributions by simulation. We considered some cases of uniform, normal, exponential, chi-square, and beta distributions, and for each distribution we performed 10,000 times of simulation of drawing a random sample of sizes 20, 30 and 50. In each time of simulation, we computed the difference between the population quantile and the sample quantile obtained by each method. We compared the two methods by two criteria: one is the mean square error over 10,000 times of simulation, and the other is the frequency of giving smaller difference from the population quantile than the competing method. As a result of simulation, method 1 turned out to be much more frequently superior to method 2 by criterion 2.
Assessing and reducing stratigraphic uncertainty in the subsurface: where are we standing?

Sedimentary strata are essential archives of the past conditions of the earth, and host significant natural resources in the subsurface. However, inferring the features of strata at depth (e.g., geometry, connectivity, physical or geological properties), remains a challenge prone to many uncertainties. Classically, the layers and their geometry are first interpreted from boreholes, geological outcrops and geophysical images, then layer properties can be addressed with geostatistical techniques and inverse methods. Theoretical models considering horizon depth uncertainty have been proposed decades ago, and geostatistical simulation can sample petrophysical uncertainties, but these approaches leave the number of layers fixed and are rely on conformable layering assumptions which are seldom met. We review some recent developments in well correlation in the frame of relative chronostratigraphy, which addresses the problem of locating potential gaps in the stratigraphic record. We also present some first results of the integration of the number of layers in inverse problems using a reversible jump Monte Carlo method. These two elements open interesting perspectives to jointly address topological, geometrical and petrophysical uncertainties at multiple scales in sedimentary basins. Although such uncertainties can have significant impact on quantitative geological and geophysical model forecasts, many computational challenges still lie ahead to appropriately sample uncertainties. Harnessing these challenges should open the way to finding, on a case-by-case basis, the suitable level of detail between detailed stratigraphic architectures and effective medium representations.
The forecasts for advances in predictive and prescriptive analytics and related technologies for the year 2022 and beyond

Descriptive analytics presents data about what happened in the past, either in textual or graphical form. Predictive analytics builds mathematical models based on past data to predict what might happen in the future and associated probabilities of occurrence (risk). Prescriptive analytics goes beyond predicting what will happen with associated risks of occurrence, it provides some guidance for deciding what can be done about it. More information on the various kinds of analytics is presented in Chapter 25. This chapter focuses on prescriptive analytics in healthcare. This activity is analogous to specification of medical treatments following diagnosis.
A Keyframe Extraction for Edited Video Summarization using Spatio -Temporal Analysis of GLCM based Texture Measures

Due to advancement of camera devices, huge amounts of digital video data are generated. To browse and analyze this video content, the user takes more time. In order to reduce the time to analyze and browse the video content, a summary is to be created. It can be achieved by extracting keyframes of the original video. This paper presents a summarization technique for edited video by using the GLCM representation. The unique content is determined for evaluating inter-frame difference by the following texture features such as Energy, Homogeneity, Mean, and Contrast. The pre-defined threshold values are set for each feature to select the appropriate keyframes. The summarization model is designed to capture the important properties of each frame. Specifically, this model is applicable to edited videos. This model was evaluated by two benchmark datasetsviz.,TVsum50 and SumMe. Finally, experimental results were compared with the benchmark existing algorithms to evaluate the efficiency of the proposed work.
Credit Card Fraud Detection with Data Sampling

Credit card fraud, although not directly impacting banks, does have repercussions for the financial sector as a whole. Criminals pose a significant threat to safety since they are continually thinking up new methods to commit these types of fraud. Early detection of fraudulent behavior is therefore vital to retain customer trust and defend the firm. Since lawful transactions far outnumber fraudulent transactions, which typically make up less than 1% of all transactions, addressing the class imbalance issue in the data presents a significant challenge for developing fraud detection algorithms. It is challenging to identify a positive example (fraudulent case), and this challenge increases as more data is gathered, leading to a lower proportion of positive examples. Because of this, research is very important. Models for making predictions were trained in this study utilizing a variety of sampling strategies, including the ANN, GBM, and the RF. Models used SMOTE, RUS, DBSMOTE, and SMOTE plus ENS were all examples of Synthetic Minority Over-Sampling Technique (SMOTEENN). This research suggests that SMOTE-based sampling techniques will provide positive results. The highest recall (0.81) was achieved by the SMOTE sampling method when a DRF classifier was used. It was determined that this classifier has an accuracy score of 0.87. The Stacked Ensembling algorithm was accomplished using altogether of collected data, and its average performance was 0.78, making it the clear winner. As a fraud detection model, the Stacked Ensemble has performed well in most sampling operations.
Changing library education in the context of developing human capital of Russia

The author addresses the problems of changing library education in the context of developing human capital of Russia. She substantiates the need for educating library professionals with changed competences in accordance with the new approved professional standard of specialists in library and information activities and updating Federal State Educational Standard for Higher Education. The author suggests to revive the library and information department of Moscow State Institute of Culture as a promising initiative aimed at improving the quality of library education.
Intellectual, developmental and educational activities of scientific libraries in the context of society digitalization

The author delineates between the two concepts, namely between intellectual developmental and science education (IDSE) activities of academic libraries and cultural and recreational work of public libraries. The background for these activities in scientific libraries is discussed. Based on the analysis of Russian and foreign experience, the prospective vectors are defined; the conclusions are made on the aims to be achieved through IDSE and building IDSE environment.
Testing Stationarity Concepts for ReLU Networks: Hardness, Regularity, and Robust Algorithms

We study the computational problem of the stationarity test for the empirical loss of neural networks with ReLU activation functions. Our contributions are: Hardness: We show that checking a certain first-order approximate stationarity concept for a piecewise linear function is co-NP-hard. This implies that testing a certain stationarity concept for a modern nonsmooth neural network is in general computationally intractable. As a corollary, we prove that testing so-called first-order minimality for functions in abs-normal form is co-NP-complete, which was conjectured by Griewank and Walther (2019, SIAM J. Optim., vol. 29, p284). Regularity: We establish a necessary and sufficient condition for the validity of an equality-type subdifferential chain rule in terms of Clarke, Fr\'echet, and limiting subdifferentials of the empirical loss of two-layer ReLU networks. This new condition is simple and efficiently checkable. Robust algorithms: We introduce an algorithmic scheme to test near-approximate stationarity in terms of both Clarke and Fr\'echet subdifferentials. Our scheme makes no false positive or false negative error when the tested point is sufficiently close to a stationary one and a certain qualification is satisfied. This is the first practical and robust stationarity test approach for two-layer ReLU networks.
The basics of deep learning algorithms and their effect on driving behavior and vehicle communications

Deep learning (DL) is now considered one of the most important fields in computer science for its capability of delivering the best results from enormous quantities of data and really complex information. When it comes to intelligent vehicle networks, deep learning can play an important factor in improving the communication between vehicles and improving road safety conditions. The analysis of vehicle communication is the analysis of all available data and features from the vehicles in order to decide on the best outcome and traffic route for a road-based scenario. This operation can seem complicated and challenging, especially when dealing with the unpredictability and the inescapable human error factor in the decision-making process that still interferes with the roads and traffic structures of our days. However, the evolution of deep learning algorithms is undeniably an exciting prospect to deal with these challenges, from the basic principles of artificial neural networks, convolutional neural networks, and recurrent neural networks to the newest state-of-the-art solutions of variational auto-encoders, generative adversarial networks, and transformers. These principles alongside the variant nuance architectures combined with the powerful computing powers available in our time are on the edge of delivering the futuristic vision of totally autonomous vehicles and road networks that we thought is still too far to achieve. In this chapter, we are going to discuss the basics of deep learning algorithms, the pros and cons of every method and the pre-processing steps and hyperparameters refinement. Then we are going to describe and elaborate more with some examples the effect of these principles on driving behavior and vehicle communication.