Reviewerly/exHarmony huggingface dataset:
Virtual Reality Immersive Interactive Design Based on Big Data Technology

The current research on immersive interaction design is in its infancy. Most researches are limited to immersive displays or simple interactions using handles, which are difficult to meet the routine work analysis needs of domain experts. Therefore, there is still much space for research on the immersive interactive technology of virtual reality. Based on this, this article focuses on the design and technical issues of 3D immersive interaction. This article starts with virtual reality, and on the basis of fully investigating user interaction needs, proposes a three-dimensional immersive interaction model (GG model) based on Gaze and Gesture. Based on LeapMotion and HTC Vive Pro, a three-dimensional immersive visualization system that supports the entire analysis process is realized. Finally, the effectiveness of the GG interaction method is verified through the evaluation and comparison experiment of the interaction model. This paper conducts design research on virtual reality immersive interaction depend on big data technology, in order to further promote the application of immersive interaction technology in the field of virtual reality, provide users with efficient and natural interaction, improve the user's immersive experience, and serve follow-up research provides valuable reference.
A forgotten pioneer: Eugene Marteney and the American renaissance of hautboy-making, 1962–1969

Abstract Coming from a background in science and philosophy, the American electrical engineer Eugene Marteney (1934–2008) stumbled into hautboy-making in 1961–2, inspired by the playing of Josef Marx. Marteney learned the techniques of woodwind manufacture from Friedrich von Huene, who ultimately referred potential buyers to him. Selling hautboys after 1964, he did everything himself, from measuring antique specimens to designing and making the instruments and their reeds. He made connections with Marx, von Huene, Bruce Haynes, Paul Hailperin and, through him, with Michel Piguet and Bernhard Schermer, which would set off modern hautboy-making at original pitches. Marteney did this all half a decade before anyone else. In so doing he influenced all of the people we remember today as modern hautboy pioneers, laying the foundations for the Baroque revival of the 1970s and 80s. In 1969, as Schermer and Haynes started making quality hautboys at original pitches, Marteney stopped doing so. His work was then forgotten until 2010. This article explores the hautboy-making of this remarkable man and shows how he influenced more well-known figures.
Escala para medir la significación del pensamiento complejo en entornos virtuales de aprendizaje de instituciones de educación superior

The concept of complex thought in Higher Education Institutions has been little addressed and understood, since it is still in the intellectual imagination of educational actors that it is a theoretical construct with which problems of formal sciences can be solved, more not of the factual ones; If we add to this that during the Covid-19 pandemic Virtual Learning Environments had to be used with much greater intensity, the challenge of perceiving the concept and merging it with ICTs for the construction of meta-complex virtual environments turns out to be a challenge. The main objective of this document was to develop an instrument to measure the significance of complex thinking in Virtual Learning Environments in Higher Education Institutions; For this purpose, the instrumental methodology of Hernández (2011) was used, applying the validation of experts, using the focus group method, where a value of .77 was obtained, which is considered a good degree of validity. Reliability was also analyzed using Cronbach's Alpha, which showed a value of .780, which is why it is considered adequate. The questionnaire is made up of 63 items, 57 of which are integrated using a 4-level Likert scale; Therefore, it is considered to be a valid and reliable instrument. The instrument proposes to know precisely the relationship that exists between the concept of complex thought and virtuality and how these are merged by educational actors.
An Efficient Memory Utilization for Path Construction of a de Bruijn Graph

The large memory requirement becomes the challenge for the NGS technologies, as the memory is the bottleneck for every application. There is need to reduce the memory utilization for the de Bruijn graph, so as to it could fit in low memory space. This work provides the efficient memory utilization for the de Bruijn graph. The minimizer breaks the reads into numerous little partitions with the goal that every partition could be loaded into main memory one by one; they process separately then later on merge to form the simple path of de Bruijn graph. This reduces the space usage required during construction of de Bruijn graph.
Explanation of BMI data using Linear Regression Model in R

Abstract: This paper describes the regression analysis between different variable like Weight &amp; BMI, Weight &amp; Height, and Height &amp; BMI using Linear Regression Model &amp; data visualization techniques in R Programming from a sample data of 68 students of BCA. The collected data were analyzed for underweight, overweight, obese personalities by using conditional statements. The result of the model will give Residual Standard Error, Multiple R2 , Adjusted R2 , F-statistic and p-value. There is visualization of data using ggplot() and geom() in last steps. Keywords: BMI, Multiple R2 , Adjusted R2 , F-statistic, p-value, R, ggplot, geom.
Improving Accuracy Of Fluid Contact Determination Through The Use Of An Auxiliary Transform Method

Fluid contact(s) in reservoir sets the lower limit above which an accumulation of hydrocarbon (i.e. oil or gas) has the maximum level of mobility under a specific circumstance. This mobile hydrocarbon determines the reservoir’s production feasibility. In this light, an accurate knowledge over position of fluid contact contributes to accuracy in the estimation of initial hydrocarbon in place and its corresponding reserves. Determination of fluid contact in reservoir may utilize any available sources of information but well pressure survey data is usually regarded as the primary source of information. Despite the importance, the data is sometimes not in ideal condition usually marked by absence in clear change of pressure gradient and/or data scatter for various reasons. The use of Hough transform – as introduced by Kang and Xue (2009) – for supporting fluid contact detection can solve the problem. In this study, the method that is usually used for, among others, recognizing regular shapes in images has been successfully applied for fluid contact detection. The study uses three sets of data with different level of difficulty, and the technique proves to work well for the all cases. The study also shows that the Hough transform can be used reliably in a simple way without employing the full weight of it.
Ensemble based data assimilation of bio-geochemical profile data in the Baltic Sea with a Nemo-ERGOM model system

&amp;lt;p&amp;gt;The presentation focuses on two key points in bio-geochemical ensemble based data assimilation: the ensemble generation methods as well as the impact of the data assimilation on various bio-geochemical processes. We conduct the data assimilation experiments from October 2014. These are preparations for a future reanalysis product provided by the Baltic Monitoring and Forecasting Centre (BAL-MFC), which will cover the years 1993 &amp;amp;#8211; 2021. The Local Error Subspace Kalman Transform Filter (LESKTF) algorithm in the Parallel Data Assimilation Framework PDAF (http://pdaf.awi.de) is applied for data assimilation of profile data from the SHARK database on a daily basis. After the daily analysis is performed, the mean of the ensemble members is used in the Nemo-ERGOM model system. This method is used operationally by the BAL-MFC.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Effects of the number of ensembles, transformations, generation techniques and deflation of the ensemble are explored and verified in our ensemble generation studies. Rank histograms, skewness and kurtosis of the ensembles before and after assimilation are computed.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The influences of dissolved oxygen profile data assimilation on the nutrients in deep layers are studied and compared with the integral influences of univariate data assimilation of dissolved oxygen, nitrate, phosphate and ammonium on the same variables. Validation results of the univariate data assimilation scheme are presented and discussed in regards to quality enhancement for the future reanalysis product.&amp;lt;/p&amp;gt;
The Effect of Non-audible Low Frequency, Deep Micro Vibrotactile, DMV Sounds on Music

Much of the research on low frequencies is on biological effects or noise pollution.We have confirmed that adding low frequencies to music increases autocorrelation and enhances the higher frequency components.This paper ensures that the addition of low frequencies to music produces 1/f fluctuations.
Data Driven Approaches for Climate Predictability

&amp;lt;p&amp;gt;Reduced-order dynamical models play a central role in developing our understanding of predictability of climate. In this context, the Linear Inverse Modeling (LIM) approach (closely related to Dynamic Mode Decomposition DMD), by helping capture a few essential interactions between dynamical components of the full system, has proven valuable in being able to give insights into the dynamical behavior of the full system. While nonlinear extensions of the LIM approach have been attempted none have gained widespread acceptance. We demonstrate that Reservoir Computing (RC), a form of machine learning suited for learning in the context of chaotic dynamics, by exploiting the phenomenon of generalized synchronization, provides an alternative nonlinear approach that comprehensively outperforms the LIM approach. &amp;amp;#160;Additionally, the potential of the RC approach to capture the structure of the climatological attractor and to continue the evolution of the system on the attractor in a realistic fashion long after the ensemble average has stopped tracking the reference trajectory is highlighted. Finally, other dynamical systems based methods and probabilistic deep learning methods are considered and a broader perspective on the use of data-driven methods in understanding climate predictability is offered&amp;lt;/p&amp;gt;
The Effect of The Writing Process on Writing Skills and Writing Motivation for Students at Risk of Learning Disabilities in Distance Education

Due to the COVID-19 pandemic, the Ministry of Education in Korea introduced a distance education system to each level of school. Still the academic achievement gap between low and high achievers continues to increase. The aim of this study is to investigate the effect of writing programs based on the writing process and distance education for the writing skills and writing motivation for students at risk of writing learning disabilities. Firstly, the program was devised based on the curriculum of Korean language subjects within the common curriculum and validity of the program was secured through the content validity verification and pilot study. Subsequently, by applying a multiple-probe design across subjects, an intervention was conducted for a total of 26 sessions for four students at risk of writing learning disabilities in grades 3-4. Writing skill was analyzed through visual analysis and non-overlapping analysis PND, IRD, Tau-U), which are mainly used in single-subject research. Additionally, writing motivation was measured once each as a pre-post test by reconstructing the previously developed writing motivation test. As a result, in the visual analysis, most of the students showed an increasing trend in the intervention phase. The effect size of writing skills was also high, and composition skills was the highest among other variables of writing skills. As well, the pre-post average value for writing motivation was increased. Based on these results, we discussed the implications and significance of the study; we also presented the limitations of practice and made suggestions for further studies.
Fractal dimension of fault systems and its constranits on the formation of gold deposit in Kalamaili area, Eastern Junggar

&amp;lt;p&amp;gt;Kalamaili area is located in the northeastern margin of Junggar Basin, which recognized as the richest gold deposit area of China. The fault systems and the fractal dimension of the area are complex and play a significant control on the deposits of the gold mine. However, the fractal dimension of the study area has not been carried out, and the previous studies lack of the analysis on the relationship between the fractal dimension of fault systems and the distribution of the gold deposit. Therefore, further studies on the fractal dimension of fault systems and its constranits on the formation of gold deposit in Kalamaili area are needed. In our study, we used the box-counting method to analyze the fractal dimension of fault systems, and correlate the relationship between the fractal dimension of fault systems and the formation of gold deposit. The results show that: (1) The fractal dimension of all faults is 1.421, and faults with different strike have various fractal dimension. In detail, the fractal dimension of NW-striking faults is 1.382, fractal dimension of NWW-striking faults is 1.223, fractal dimension of the EW-striking faults is 0.998, and the fractal dimension of the NE-striking faults is 0.960. The fractal dimension values of NW-striking faults are greater than the standard value of fractal dimension (1.22-1.38). (2) Based on the fractal dimension analysis of the fault systems, the geological bodies distributed along the NW-striking faults should have good connectivity, which improve the migration and accumulation of the gold mineralization hydrothermal fluid. The supposed relationship between the NW-striking faults and the gold deposit distribution of our study is coincided with the nature examples of the study area. Based on the exploration of the gold deposit in the study area, most of the gold deposit is distributed along the NW-striking Kalamaili fault, Qingshui-Sujiquan fault, and the associated secondary faults of the two faults.&amp;lt;/p&amp;gt;
Emergency and crisis management

This chapter explores two different research lines. One is a series of projects carried out in close collaboration with firefighters that resulted in a technically innovative solution. It allows some of the conditions of practice-based research to be examined in a safety-critical domain. The other research line—built around the idea of developing a technology that supports the coordination of professionals and volunteers operating in critical settings—offers the opportunity to learn about the impact of technology choices on the sustainability of a design solution. It also highlights the challenges involved in moving in a highly regulated field characterized by heterogeneous interests.
The Correlation between Self-Efficacy, Grade, and Sex on the In-tention to Participate in Peer-Education Activities in Drug Abuse Prevention

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Layout Aware Semantic Element Extraction for Sustainable Science &amp; Technology Decision Support

New scientific and technological (S&amp;T) knowledge is being introduced rapidly, and hence, analysis efforts to understand and analyze new published S&amp;T documents are increasing daily. Automated text mining and vision recognition techniques alleviate the burden somewhat, but the various document layout formats and knowledge content granularities across the S&amp;T field make it challenging. Therefore, this paper proposes LA-SEE (LAME and Vi-SEE), a knowledge graph construction framework that simultaneously extracts meta-information and useful image objects from S&amp;T documents in various layout formats. We adopt Layout-aware Metadata Extraction (LAME), which can accurately extract metadata from various layout formats, and implement a transformer-based instance segmentation (i.e., Vision based Semantic Elements Extraction (Vi-SEE)) to maximize the vision-based semantic element recognition. Moreover, to constructing a scientific knowledge graph consisting of multiple S&amp;T documents, we newly defined an extensible Semantic Elements Knowledge Graph (SEKG) structure. For now, we succeeded in extracting about 6 million semantic elements from 49,649 PDFs. In addition, to illustrate the potential power of our SEKG, we provide two promising application scenarios, such as a scientific knowledge guide across multiple S&amp;T documents and questions and answering over scientific tables.
Our Design and Implementation of Multi-Factor Authentication Deployment for Microsoft 365 in Kyushu University

In Kyushu University, Information Infrastructure Initiative manages a Microsoft 365 tenant for our university members. We started offering Office 365 in 2016 and migrated our university-wide email service to Microsoft 365 Exchange Online in 2018. Due to the recent outbreak of COVID-19, off-campus uses of Microsoft 365 have increased, and concerns about account security arose. We discussed how to deploy Multi-Factor Authentication (MFA) to protect our users. Microsoft 365 comes with Azure Active Directory (Azure AD), and it includes built-in MFA functionality. With the basic Azure AD MFA, individual users can register MFA information anytime but have no control to enable or disable MFA. Tenant administrators need to enable MFA for each account. For a gradual deployment, we want to allow users to enroll in MFA and register information at their convenience. In addition to that, we want to prevent malicious attackers from registering their MFA information if an account should be already compromised. Such control was difficult with the basic Azure AD MFA. Since 2020 our tenant subscribes to Azure AD Premium P2 licenses, which provides Azure AD Conditional Access. Conditional Access enables fine controls of MFA and other user access behavior with security groups. We designed an MFA self-enrolling and configuration system, and implemented it with Microsoft Forms, Power Automate, Conditional Access, and in-house web applications. By design, this system prohibits MFA information registration until user’s self-enrollment in MFA, and requests the user to register MFA information upon the next sign-in after the self-enrollment. This is supposed to reduce the possible unauthorized registration of MFA information. We extensively discussed implementation of various measures and preparation of documents to counter users’ troubles and complaints. We started deploying MFA in April 2021, but we have not yet fully mandated MFA due to a push back from some executives expressing concern about the adverse effects of enforcing MFA too quickly.
Implementation of Real-time Face Mask Detection with Convolutional Neural Network (CNN) and OpenCV

In this global pandemic of COVID-19, there is a critical need for self-protective devices, and the most important of them is a face mask. Our project’s main aim is for identifying the presence of a face mask on person's face. A strategy should be formulated to make the people accept this essential safety measure. To check this, a face mask detector system should be used. To check the presence of a face mask on a human face, the primary step is the detection of human face. This can be divided into two parts: verifying faces on the images and detection of masks on their faces. Face masks are used to prevent cross-contamination as part of an infection control strategy. Using TensorFlow, Kera's library, and OpenCV, we created a very rudimentary convolutional neural network (CNN) model. Our experiment demonstrates that it operates effectively on test data, having a precision of 100% and a recall of 99%, respectively.
Publisher's Note: “Low-strain effective Young's modulus model and validation for multi-layer vocal fold-based silicone specimens with inclusions” [J. Appl. Phys. 131, 054701 (2022)]

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Design and implementation of hash function scheme targeted at short message processing

The Hash function targeted at the short message processing is proposed in this paper. Various randomized components are designed based on the introduced thoughts of randomization, and the chaotic encryption algorithm of the variable parameters is applied to improve the avalanche performance of the Hash function. At last, the LWE problem is introduced into the operation, so to accomplish the design of the lattice-based collision resistant Hash function. Meanwhile, relative experiments are carried out, which prove the satisfactory randomization, collision resistant, and diffusion performances of the Hash function proposed in this paper.
VideoAI

In this work, we describe a unique system and solution, VideoAI, that allows Comcast to rapidly create Artificial Intelligence (AI) and Machine Learning (ML) based media analytics experiences. The unprecedented growth of Artificial Intelligence, Machine Learning and Deep Learning (DL) has enabled a new level of insight into video content from detecting faces objects to understanding sentiment and emotions. However, many of the solutions today are highly customized to the desired solution and do not easily scale when applied to other solutions.
Testing seismic velocity models with quarry blast data

&amp;lt;p&amp;gt;Seismic event locations are usually performed by means of iterative, linearized arrival time inversion considering 1D velocity models with fixed data errors. Both the use of inaccurate velocity structure or data error estimates may however affect the quality of event location and uncertainty evaluation. Here, we test a 3D P- and S-wave velocity model built in a previous work for Metropolitan France (the part of France located in Europe) and we compare it with the &amp;amp;#8220;Auvergne&amp;amp;#8221; 1D velocity model used by BCSF-R&amp;amp;#233;NaSS (Bureau Central Sismologique Fran&amp;amp;#231;ais - R&amp;amp;#233;seau National de Surveillance Sismique) using quarry blast data. The reason for using quarry blast data is that, to some extent, their epicentral location and depth are known, which is not the case for earthquakes. We first identify potential active quarries over the territory of Metropolitan France by comparing catalog quarry blast locations with those from quarries visible from satellite images. Relocation is achieved by means of a Hierarchical Bayesian inversion procedure in which not only the hypocentral parameters (longitude, latitude, depth, origin time) are inverted for, but also P- and S-wave arrival time errors. The area of interest is a 1&amp;amp;#176; by 1&amp;amp;#176; zone located between 4&amp;amp;#176;E and 5&amp;amp;#176;E in longitude and 44&amp;amp;#176;N and 45&amp;amp;#176;N in latitude, the region where the Le Teil earthquake occurred (Mw 4.9, 2019/11/11). We first demonstrate the ability of the algorithm to properly determine hypocentral parameters and data noise using two simple synthetic experiments. Then we apply it to real data and relocate 147 quarry blasts that occurred in the region between 1980 and 2020 and that were located wit the &amp;amp;#8220;Auvergne&amp;amp;#8221; 1D velocity model. Relocations obtained with the 1D and 3D model are rather similar. Estimated data errors are larger, in both cases, than the amplitude of picking uncertainties, meaning that both models could be improved, by seismic arrival time tomography for instance. They are larger in the 3D case, suggesting that, from that point of view, the 1D model is in better agreement with the data. Distances between relocated hypocenters and the closest known quarry are comparable but relocations with the 3D model are characterized by shallower hypocenters than those obtained with the 1D model, so they appear more consistent with the fact that events are quarry blasts. In both cases, some events are quite far away from the closest quarry, suggesting that some of them might be natural events.&amp;lt;/p&amp;gt;