Reviewerly/exHarmony huggingface dataset:
Dark Triad Personality Traits Prediction through Twitter Analysis using Support Vector Machine Method

Personality is a combination of a person's characteristics and behaviors in any particular condition. Several personality models are used to predict personality tendencies, one of them is a personality with a dark side, or it is called the Dark Triad Personality, which is divided into three traits, namely Machiavellianism (manipulative), Narcissism (narcissistic), and Psychopath (antisocial). This study uses Dirty Dozen questionnaire data distributed publicly using Google Form as the initial label of Dark Triad personality traits. It compares with the result data from Twitter which is crawled with Twitter API using Rstudio. Twitter users can interact with each other and express themselves through tweets, so their personalities can be known through the words or characters represented in a tweet. This study uses the Support Vector Machine method to predict Dark Triad Personality tendencies through Twitter analysis. From the results of this study, the classification with the highest accuracy is 73.3%, obtained from scenario 2 with a comparison of training data and testing data is 80:20.
Training Medical-Diagnosis Neural Networks on the Cloud with Privacy-Sensitive Patient Data from Multiple Clients

Artificial neural networks (ANNs) are changing the paradigm in medical diagnosis. However, it remains an open problem how to outsource the model training operations to the cloud while protecting the privacy of distributed patient data. Homomorphic encryption suffers from high overhead over data independently encrypted from numerous sources, differential privacy introduces a high level of noise which drastically increases the number of patient records needed to train a model, while federated learning requires all participants to perform synchronized local training that counters our goal of outsourcing all training operations to the cloud. This paper proposes to use matrix masking for outsourcing all model training operations to the cloud with privacy protection. After outsourcing their masked data to the cloud, the clients do not need to coordinate and perform any local training operations. The accuracy of the models trained by the cloud from the masked data is comparable to the accuracy of the optimal benchmark models that are trained directly from the original raw data. Our results are confirmed by experimental studies on privacy-preserving cloud training of medical-diagnosis neural network models based on real-world Alzheimer’s disease data and Parkinson’s disease data.
Design and implementation of a dual-feature fusion target tracking detection system based on Python Productivity for ZYNQ

To address the drawbacks of high power consumption and slow running speed of image processing algorithms on traditional hardware platforms, we design and implement a target tracking detection system based on PYNQ (Python productivity for Zynq) heterogeneous processor platform through a hardware-software co-design approach. In the PS (Process System) part of the processor system of PYNQ, the Haar and Edgelet dual-feature fusion training AdaBoost classifier is completed. In the Programmable Logic (PL) part of PYNQ, the acceleration design of the inter-frame difference method IP core is completed, and the motion target tracking detection is completed. The system was tested in terms of real-time performance and power consumption, and the results showed that the performance was better than that of traditional hardware platforms.
Progressive calligraphy stroke extraction

Calligraphy is a valuable traditional culture in China. The study of calligraphy can be used in many aspects. At present, there is little research on calligraphy stroke extraction, and there is a lack of practical and effective automatic calligraphy stroke extraction algorithm. In this paper, a stroke extraction algorithm for calligraphy image is proposed, which uses multiple rounds of iterative and progressive method to extract strokes. It is mainly divided into four steps: segment skeleton acquisition, stroke skeleton generation, stroke acquisition with width information and improved stroke skeleton acquisition. Firstly, the algorithm obtains the skeleton of each pen segment of calligraphy, judges the pen segment type, and combines the pen segment skeleton to generate the stroke skeleton. Determine the stroke intersection type, determine the inflection point combined with the contour characteristics and the center point of the intersection area, and complete the stroke contour with Bezier curve. Then the connected part of the strokes is processed to obtain the strokes with width information. Finally, the extracted strokes are refined to obtain the improved stroke skeleton. The experiments show that the proposed algorithm has achieved good results in the stroke extraction of calligraphy image, and the improved stroke skeleton can better reflect the real trend of strokes, which can lay a foundation for the research of calligraphy based on stroke level.
Research on Detection Method of Environmental Homologous Complaint Reports Based on Decomposition Attention Network

In order to solve the problem that it is difficult to efficiently identify homologous complaints and reports in dealing with complaints and reports, this paper proposes to detect homologous complaint reports in the decomposed attention network. Firstly, TextCNN is used to extract local features of complaint reports text, and then the matching task between features is carried out. Due to the high noise of the complaint reports text, the matching method of a few key features is better than capturing the global features of the text when judging whether the two complaint reporting tasks are the same origin events. The resolvable attention network decomposed the same-origin complaint reporting task into the matching task between recognition channels, and then judged the relationship between complaint reporting according to the judgment results of each sub-task. This paper compares the accuracy of multiple neural network models on the complaint reporting data set, and the experimental results of these models show that the decomposition of the attention network model in this paper has a better effect and stronger stability.
Bridge between logic and algebra

The algebraic counterpart of classical sentential logic LS is the variety BA of Boolean algebras. Why is this so important? The answer lies in the general experience that it is usually much easier to solve a problem concerning LS by translating it to BA, solving the algebraic problem, and then translating the result back to LS (than solving it directly in LS).
PETRO-GEOCHEMISTRY OF SUBVOLCANIC AND EXTRUSIVE FORMATIONS OF THE KEDROVO-KORGONCHIKOVSKY DISTRICT OF THE GORNY ALTAI

The paper provides data on petro-geochemistry, petrology and ore content of subvolcanic and extrusive formations of the Korgon Devonian complex of the Kedrovo-Korgonchikovsky district of the Gorny Altai. Low-alkali rhyodacites, sodium-potassium rhyodacites, trachyrhyodacites, trachyrhyolites and rhyolites are characterized. They are attributed to the aluminous series and mainly to magnesian varieties. Increased gold contents and decreased silver contents are observed in rocks. Ratios of light to heavy lanthanoids show a large range of values – from 2.1 to 26.2 and light ones to medium ones – from 1.94 to 11.8. The tetrad effect of W-type REE fractionation, varying from 0.29 to 0.68 and indicating an abundance of volatile components, including H2O, CO2, S or H2S, was revealed in rocks, that served as the formation of quartzites and argillizites. Volcanites contain signs of mantle formations and contamination by crustal material due to melting of greywackes and amphibolites of the lower crust. The ore content of the district is realized in the formation of promising epithermal gold-silver and copper-molybdenum-gold-porphyry occurrences. The district is thought to be the object of the process manifestation of the fluid mantle-crustal ore genesis during formation of largevolume epithermal gold-silver, copper-molybdenum-gold-porphyry mineralization.
Participatory Learning and Action as Tool for Facilitating Teachers’ Education during the Pandemic: The TEN-G Project in Perspective

This paper examines the Participatory Learning and Action (PLA) tools used in facilitating teachers’ education during the Covid-19 pandemic. It uses the Teachers Network for Girls Education (TEN-G) project, a Commonwealth of Learning (COL) initiative that trained teachers on open educational resources and Mobile Learning with Multimedia, as a premise to argue for inclusive space where knowledge is shared between facilitators and learners. In this study, we use Methodological Conversation as a functional tool that opens up spaces for active participation across diverse methodological processes so much so that the participants are empowered through living out the lessons learnt. We draw our population from the teachers of Government primary schools in Sabon Gari and Zaria LGAs. We use Focus Group Discussion (FGD), matrix and pair wise ranking as research instrumentations to get data on choice issues around note-making, chunking and module-to-module contents in podcasting. Our study found out that the TEN-G project offers teachers diverse opportunities to explore different educational options in reaching rural girls in the time of global health crisis as in the pandemic. It concludes that the PLA methodology deployed in the implementation of the TEN-G project offers an enduring sustainability plan that is anchored on sharing and change forecast.
AR Based Product Visualisation for Online Shopping

The augmented reality (AR), is nothing but combining the both real world and the artificial environment using computer vision technology. The most recent handheld devices are able to providing an artificial world around us. The most developments in AR algorithms have also play major role for its expansion. This project aid us to get a clear aware of that how a new product will fit in our room or a place, we don’t need to close our eyes to imagine it. With using our Android mobile, we can plainly open AR application to saw the products, and after that we need to select one product that we need, and we need to wait until the product 3D model gets downloaded after the download finished, the camera will open on your smart phone and now you can fix the product anywhere you need to fix. It will decrease the return rate and also it will save the time to make decision. So finally it will improve the shopping experience on the user
Statistical grouping of the observation results of the fall number into the interval variation series

Statistical accounting of observations as results of experience is subjected to grouping of the collected data in order to achieve a representative sample. As a rule, statistical grouping of observation results is carried out by presenting distribution series based on a ranking operation. When defining the members of the empirical population in the variational series of distribution, the goal is to reveal the pattern of variation of the trait under study. In the technology of bakery production, a special role is assigned to monitoring the quality of the processed grain and obtained from it flour, including when defects are detected, most of which are violations of the quality standards of washed gluten and the falling number. The purpose of this work was to statistically group the results of observations of the falling number of wheat flour into an interval series for a sample with a reduced enzymatic activity that does not correspond to the upper limit of the quality standard. For the investigated indicator – the falling number, when compiling a discrete and intervalless series, you can only study the characteristics of a dimensional feature, its coefficient of variation and compliance with the standard distribution of a random variable, and when compiling an interval series, also an empirical feature distribution function. The representativeness of the obtained sample with a given probability of 0.95 provides the result when the average value for a larger sample does not go beyond the found confidence interval for the general average. In a practical sense, the obtained results of the analysis of variance are important for the statistical accounting of observations and the compilation of an empirical population of the falling number experimental data with the subsequent presentation of data in distribution series and obtaining an empirical distribution function of the characteristic measured in the experiment.
PENGARUH SOFTWARE SAP DALAM MENINGKATKAN KINERJA KARYAWAN BAGIAN KEUANGAN DAN AKUNTANSI PADA PT. PERKEBUNAN NUSANTARA VI

A Employee performance is of course one of the important components for the company because it affects the company's operations. This study aims to analyze the effect of SAP software in improving employee performance in finance and accounting at PT. Perkebunan Nusantara VI, used a questionnaire as an instrument to take a sample of 30 respondents. The analytical method used in this research is multiple linear regression analysis. The independent variables in this study are software quality, information quality, software user satisfaction and the impact of using accounting software, while the dependent variable is employee performance of 65.3%, the remaining 34.7% is influenced by other variables. The results of this study indicate that the variables of software user satisfaction and the impact of using accounting software have a significant effect on employee performance with a significant value &lt;0.05 with a value of Tcount &lt; Ttable (-2.182 &lt; 2.060) and (6.064 &gt; 2.060). Meanwhile, the variables of software quality and information quality have no effect on employee performance because they have a significant value &gt; 0.05 with a value of Tcount &lt; Ttable (-0.293 &lt; 2.060) and (-1.347 &lt; 2.060). For the overall effect, the value of Fcount &gt; Ftable is (11.870 &gt; 2.76) with a significant value of 0.000 &lt;0.05.
SISTEM INFORMASI PERSEDIAAN PERLENGKAPAN ALAT ALAT ISI ULANG AIR MINUM PADA TOKO GUN CS

Gun cs shop is a company engaged in the sale of refill equipment. The process of data collection of incoming goods and goods out of stock inventory when searching for goods data is still using the manual method. With this, in order to make it easier, it is recommended to use an inventory information system. The purpose of this inventory system is to overcome the system that is currently running at the gun cs shop as well as a solution so that the store can manage inventory data more quickly. The result of this research is to produce a web-based inventory information system with the LIFO FIFO and Avarage method which provides convenience in providing information on inventory data through incoming and outgoing goods using data flow diagrams, DFD, ERD and MySQL for data processing.
Estimation of Precision in Fake News Detection Using Novel Bert Algorithm and Comparison with Random Forest

This study aims to improve the prediction rate with a novel model of bidirectional encoder representation for transformers (BERT) compared with random forest algorithm. A dataset of size 1100 is used to compare Novel BERT's performance with Random Forests. With Random Forest, a framework for identifying fake news in electronic media networks is proposed. clinical calculates a sample size of 20 according to the framework. Regarding to Precision rate, the Novel Bert algorithm beats the Random Forest algorithm by 8.33%. In comparison to the random forest algorithm, BERT achieves a rate of 0.002 that is significantly better than it. It is concluded that the novel BERT algorithm outperforms Random Forest predicting of fake information in this study.
Blocking and Blocked Processes

SQL Server ensures that your data is protected even as the data is changed. It does this through a process called locking. However, even as SQL Server is locking within the I/O system in support of your data changes, this is causing what is called blocking, through contention for those resources. As the number of users on your systems increases, performance degrades as they contend for those resources. In order to understand how to deal with blocking, you need to understand how, and why, locking occurs.
Optimization of Autonomous UAV Control Technology based on Computer Algorithms

In today’s world of rapid economic development, UAV technology is an advanced, efficient and intelligent high-tech means, which has been widely used in military combat. With the continuous progress and innovation of science and technology and the improvement of people’s requirements for security, UAV technology as a new type of military science and technology will have more space for development in the future. Therefore, it is important to apply computer algorithm optimization to autonomous aerial vehicles. In this paper, we first analyze the current status of UAS and propose some control technology solutions based on computer algorithm optimization of autonomous aerial vehicles, and then program the implementation through MATLAB simulation software.
CHALLENGES OLD AND NEW: MAPPING A POTENTIAL RESOURCE IN THE SOUTHERN APPALACHIAN BASIN

Assessment of critical mineral and rare-earth element resources in coal, coal related sediments, and coal mining and processing wastes in the Southern Appalachian Basin (SAB) of northern Alabama, northwestern Georgia, and southern Tennessee is a goal of the Manufacturing Coal Derived Products in Southern Appalachia project. In addition to geochemical data, outcrop maps, structure contour maps, and isopach/isocore maps are needed, presenting various challenges for the project. The SAB includes the Black Warrior Basin, Cahaba Coal Field, Coosa Coal Field, the southern Tennessee coal field, and several coal fields/areas within the Appalachian fold and thrust belt in Alabama and Georgia. Each of the coal fields has unique stratigraphy; and stratigraphy for some coal fields can be correlated to neighboring areas. However, this is not always possible. Various maps and data sources for coal and coal related sediments exist in the literature and in the records of the Tennessee Geological Survey (TGS) and the Geological Survey of Alabama (GSA). Coal seam data (depth and thickness) are more common than data for coal-related sediments (e.g., underclays, shale interbeds, roof rock), as coals have long been a resource of interest. Location and depth of related sediments can be estimated from the coal seam data. Thicknesses of coal-related sediments are harder to estimate. Additionally, all data become sparser away from major production areas, particularly subsurface data. In the past, maps of coal seams were often more localized, and broader correlations were not necessary, particularly in areas with little coal production; however, for this effort, the broader correlations are needed. Owing to time-constraints, new field mapping for thicknesses of underclays is not possible. Existing public geochemical data for the coal is limited, but there is even less for related sediments or mining and processing waste. Sampling for this project is focusing on the underclays and coal mine/processing plant waste. Underclay sampling has begun from cores in the GSA core repository. However, gaining access to coal mine and processing waste is proving a challenge, making it difficult to obtain needed samples for analysis.
Improving Performance of Electronic Healthcare Record Management Systems (EHRMS) using Low Complexity Blockchain

Electronic health record (EHR) security is a multidomain job that includes authentication, privacy protection, document storage security, etc. Trusted access control requires authentication, safe data transfer needs privacy, and preserved records security needs security to fend off data-level assaults. Researchers have suggested a broad range of techniques, such as biometric authentication, smartphone authentication, role-based access control, data encryption, hashing, etc., to carry out these responsibilities. These models are implemented utilizing centralized or decentralized strategies, which regulate the system’s overall data flow. Security issues with centralized methods include distributed denial of service (DDoS), Sybil, trojan horse attacks, etc. Decentralized systems’ slower performance restricts their ability to deploy in real time. This essay suggests a minimal complexity blockchain-based EHR management solution to lessen the likelihood of these problems. The suggested model describes the architecture of an unique semi-centralized technique for delivering EHR security and makes use of the benefits of centralized & decentralized approaches. When the suggested system’s security performance is compared to several previously produced models, it is found that the proposed model has superior security and a quicker reaction time. Additionally, this book suggests a few machine learning models that may be combined with the suggested model to enhance its performance in terms of security and quality of service.
IMPACT OF ACADEMIC MOBILITY ON THE GLOBAL COMPETENCE OF MODERN STUDENTS

Introduction. The paper considers the issue of building global competence of modern students through their participation in academic mobility programs. Methods and materials. The paper presents the results of the research carried out at the National Research Tomsk State University. The research aimed to identify the specific features of students’ participation in academic mobility programs designed to build global competence. The literature review focused on the intercultural interaction in the course of professional and personal activities of students, the clarification of the basic concepts, and establishment of the research conceptual framework. The research outcomes. The specific features of building global competence of modern university students were updated. On this basis, certain organizational opportunities of the academic mobility for building global competence were justified for modern educational practices. Conclusion. The views of Russian and foreign scientists on the issue of building the global competence which contributes to the organization of productive intercultural joint activities are considered and analyzed. The paper also presents the findings of an empirical study.
Accessing the Suffix Array via $$\phi ^{-1}$$-Forest

Kärkkainen et al. (CPM, 2009) defined the concept of $$\phi $$ that later became key to the construction of the r-index. Given a string S[1..n], its suffix array $$\textrm{SA}$$ and its inverse suffix array $$\textrm{ISA}$$ , we define $$\phi $$ as the permutation of $$\{1,\ldots ,n\}$$ such that $$\phi (i) = \textrm{SA}[\textrm{ISA}[i]-1]$$ if $$\textrm{ISA}[i] > 1$$ , and $$\phi (i) = \textrm{SA}[n]$$ otherwise. Gagie et al. (JACM, 2020) showed that it is possible to store $$\mathcal {O}(r)$$ words such that the permutations $$\phi $$ and $$\phi ^{-1}$$ are evaluated in $$\mathcal {O}(\log \log _w(n/r))$$ -time, which was improved to $$\mathcal {O}(1)$$ -time by Nishimoto and Tabei (ICALP, 2021). In this paper, we introduce the concept of $$\phi ^{-1}$$ -forest, which is a data structure using sampled $$\textrm{SA}$$ values to speed up random access to $$\textrm{SA}$$ . We implemented our approach and compared its performance with respect to the r-index.