Reviewerly/exHarmony huggingface dataset:
Application of Factorial and Binomial identities in Cybersecurity

Abstract This paper focuses on the application of factorial and binomial identities in cybersecurity in connection with the symmetric and asymmetric encryptions. We can use the binomial identities and theorem in factorials as an effective security algorithm to protect the computing systems, programs, and networks.
Indonesian Language Learning Link with Environment

Environmental problems are currently the most common problems. Environmental problems are caused by human characteristics as economic creatures, starting from natural factors and humans themselves. This is in line with Lai (in Ramadhan, Sukma, and Indriani, 2019) stating that humans who live today are faced with environmental problems. Humanity has long been known that its existence depends on the status of its environment (Nazarenko, in Ramadhan, Sukma, and Indriani, 2019). #osf
Ensuring the security and balancing the load in the cloud computing by DCRI-RI hybrid method

In the cloud environment, the confidentiality of data is improved by protecting the cloud data from unauthorized user access. During data communication, the balancing of load across cloud servers helps to maintain the cloud service's reliability. Through intrusion detection, the performance of secured data communication is enhanced in a significant way. Some techniques were developed in the field of cloud computing to provide secure communication between the cloud server and cloud user. These developed techniques failed to improve the security and load balancing efficiency simultaneously during the data transmission on a cloud. There is no sufficient algorithm in the present situation to detect the intrusions and to provide improved results in the load balancing. In this research work we propose a novel "Dynamic Certificateless Random Identity (DCRI)" algorithm for ensuring cloud security and a "Rank-Indexing (RI)" algorithm based on weightage for load balancing. Our proposed DCRI-RI hybrid method will ensure a better rate of intrusion detection with higher load balancing efficiency. The DCRI algorithm is relied on a cryptographic concept based on advanced encryption and decryption signature authentication process performed on both cloud server and user end to detect and remove the unauthorized users in a significant way.
Adaptive Prompt Learning-based Few-Shot Sentiment Analysis

In the field of natural language processing, sentiment analysis via deep learning has a excellent performance by using large labeled datasets. Meanwhile, labeled data are insufficient in many sentiment analysis, and obtaining these data is time-consuming and laborious. Prompt learning devotes to resolving the data deficiency by reformulating downstream tasks with the help of prompt. In this way, the appropriate prompt is very important for the performance of the model. This paper proposes an adaptive prompting(AP) construction strategy using seq2seq-attention structure to acquire the semantic information of the input sequence. Then dynamically construct adaptive prompt which can not only improve the quality of the prompt, but also can effectively generalize to other fields by pre-trained prompt which is constructed by existing public labeled data. The experimental results on FewCLUE datasets demonstrate that the proposed method AP can effectively construct appropriate adaptive prompt regardless of the quality of hand-crafted prompt and outperform the state-of-the-art baselines.
ecpc: An R-package for generic co-data models for high-dimensional prediction

High-dimensional prediction considers data with more variables than samples. Generic research goals are to find the best predictor or to select variables. Results may be improved by exploiting prior information in the form of co-data, providing complementary data not on the samples, but on the variables. We consider adaptive ridge penalised generalised linear and Cox models, in which the variable specific ridge penalties are adapted to the co-data to give a priori more weight to more important variables. The R-package ecpc originally accommodated various and possibly multiple co-data sources, including categorical co-data, i.e. groups of variables, and continuous co-data. Continuous co-data, however, was handled by adaptive discretisation, potentially inefficiently modelling and losing information. Here, we present an extension to the method and software for generic co-data models, particularly for continuous co-data. At the basis lies a classical linear regression model, regressing prior variance weights on the co-data. Co-data variables are then estimated with empirical Bayes moment estimation. After placing the estimation procedure in the classical regression framework, extension to generalised additive and shape constrained co-data models is straightforward. Besides, we show how ridge penalties may be transformed to elastic net penalties with the R-package squeezy. In simulation studies we first compare various co-data models for continuous co-data from the extension to the original method. Secondly, we compare variable selection performance to other variable selection methods. Moreover, we demonstrate use of the package in several examples throughout the paper.
Design and Build Expert System Application for Diagnosing Facial Skin Disease based on Android

Today, skin health is the main focus for women and men who want healthy, well-groomed and clean facial skin. The use of chemicals and other external materials allows potential disease for facial skin health. Diseases caused on the face are quite diverse from small to medium scale. The problem that often occurs in the community is the lack of knowledge and limited sources of information about facial skin health causing people to tend to let the disease happen. Based on the problems that have been described previously, this expert system was created to help the public understand the symptoms of facial skin diseases experienced and solutions to overcome these diseases. In the development of this expert system using the forward chaining method as the inference engine and the certainty factor method to determine the diagnostic confidence value. In designing this expert system application the user can choose the symptoms of facial skin diseases, then the output produced is the level of confidence, the possibility of the disease experienced and an explanation of the solution for the initial treatment of the facial skin disease. From the test results based on the Black Box, the results obtained 100% functionality runs according to the list of system requirements. In the accuracy test, a very good accuracy value is obtained, which is 100% of the 10 existing sample data.
The electronic catalog creation in the library of the Znamensky Cathedral of Kemerovo diocese

The goal of the article is to present the experience of creating an electronic catalog of the library of the Znamensky Cathedral of Kemerovo diocese. The article examines the history of the library foundation, its role in the educational activities of Kemerovo diocese. The characteristic of documents of the library collections as objects of metadata forming when creating the electronic catalog is given, the availability of both religious and secular literature in different languages including reprint publications and electronic resources is emphasized. The technology for creating the electronic catalog using the “Authoritative Files” database, developed by the catalogers of the State Scientific Library of Kuzbass named after V. D. Fedorov, is described in detail. Regulatory documents including national standards are listed as well as the software in the form of the automated library and information system “OPAC-Global” and linguistic tools used in cataloging are presented. The classification table brief characteristic and its peculiarities used in the systematization of documents in the library under analysis is given. The experience of working together the Znamensky Cathedral, the State Scientific Library of Kuzbass named after V. D. Fedorov and Kemerovo State Institute of Culture in the project “The Electronic Catalog Creation in the library of the Znamensky Cathedral” is summarized. Participation of KemSIC Faculty of Information and Library Technologies students in this project within their work practice for to perfect their cataloging skills and abilities is emphasized. The quantitative results of the creation of bibliographic records in the electronic catalog for the period of 2021 are presented. In the conclusion, the outlook for the electronic catalog being created in the activities of the Znamensky Cathedral library is denoted: optimization of the information search, the opportunity to participate in the machine-readable cataloging projects, the creation of the quality system of library and information services for readers.
Identifying Fixation and Saccades in Virtual Reality

Gaze recognition can significantly reduce the amount of eye movement data for a better understanding of cognitive and visual processing. Gaze recognition is an essential precondition for eye-based interaction applications in virtual reality. However, the three-dimensional characteristics of virtual reality environments also pose new challenges to existing recognition algorithms. Based on seven evaluation metrics and the Overall score (the mean of the seven normalized metric values), we obtain optimal parameters of three existing recognition algorithms (Velocity-Threshold Identification, Dispersion-Threshold Identification, and Velocity & Dispersion-Threshold Identification) and our modified Velocity & Dispersion-Threshold Identification algorithm. We compare the performance of these four algorithms with optimal parameters. The results show that our modified Velocity & Dispersion-Threshold Identification performs the best. The impact of interface complexity on classification results is also preliminarily explored. The results show that the algorithms are not sensitive to interface complexity.
Device-based password management system (bulwark)

Password manager systems currently available for the users come with serious privacy and vulnerability issues of their own. This paper gives an introduction to Bulwark, a novel device-based password management system, which stores the login credentials on a mobile device, and can enter the credentials on any target device through an encrypted cross-platform system, only after being authenticated by the user biometrics to ensure that only the right user can fill the saved passwords. This technology aims to eliminate the need to trust and rely on third-party cloud-based services or computers with storing and managing our passwords, and has also resolved major privacy and vulnerability issues with the current options available for users.
IDENTIFICATION OF FACTORS IN SELECTING HIGH SCHOOL USING FACTOR ANALYSIS

Parents want the best education for their children. Before starting the academic year, parents focus on finding the most suitable schools for their children. This study aimed to examine the factors affecting parents’ decision-making when selecting schools. A sample of 150 parents whose children are incoming high school students in 2020/2021 is involved in this study and selected using a snowball sampling technique and confirmatory factor analysis. This study has shown that the quality of the teachers is the factor that parents consider the most in their decision-making process. It is followed by the tuition and fee costs, the school facilities, and the school achievements.
Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System

Decentralized learning is an efficient emerging paradigm for boosting the computing capability of multiple bounded computing agents. In the big data era, performing inference within the distributed and federated learning (DL and FL) frameworks, the central server needs to process a large amount of data while relying on various agents to perform multiple distributed training tasks. Considering the decentralized computing topology, privacy has become a first-class concern. Moreover, assuming limited information processing capability for the agents calls for a sophisticated \textit{privacy-preserving decentralization} that ensures efficient computation. Towards this end, we study the \textit{privacy-aware server to multi-agent assignment} problem subject to information processing constraints associated with each agent, while maintaining the privacy and assuring learning informative messages received by agents about a global terminal through the distributed private federated learning (DPFL) approach. To find a decentralized scheme for a two-agent system, we formulate an optimization problem that balances privacy and accuracy, taking into account the quality of compression constraints associated with each agent. We propose an iterative converging algorithm by alternating over self-consistent equations. We also numerically evaluate the proposed solution to show the privacy-prediction trade-off and demonstrate the efficacy of the novel approach in ensuring privacy in DL and FL.
Minimax and Expectimax Algorithm to Solve 2048

2048 is a puzzle game created by Gabriele Cirulli a few months ago. It was booming recently and played by millions of people over the internet. People keep searching for the optimal algorithm for solving the game. Here are few approaches: minimax and expectimax algorithm. The idea is to calculate all possible moves and then select the best move by some functions. Alpha-beta pruning is also used to speed up search time. The result depends on the limit of the depth of the search tree. The greater the limit, the better the result. At some point, expectimax algorithm reaches 80% winning rate.
Quantitative Discourse Cohesion Analysis of Scientific Scholarly Texts Using Multilayer Networks

Discourse cohesion facilitates text comprehension and helps the reader form a coherent narrative. In this study, we aim to computationally analyze the discourse cohesion in scientific scholarly texts using multilayer network representation and quantify the writing quality of the document. Exploiting the hierarchical structure of scientific scholarly texts, we design section-level and document-level metrics to assess the extent of lexical cohesion in text. We use a publicly available dataset along with a curated set of contrasting examples to validate the proposed metrics by comparing them against select indices computed using existing cohesion analysis tools. We observe that the proposed metrics correlate as expected with the existing cohesion indices. We also present an analytical framework, CHIAA (CHeck It Again, Author), to provide pointers to the author for potential improvements in the manuscript with the help of the section-level and document-level metrics. The proposed CHIAA framework furnishes a clear and precise prescription to the author for improving writing by localizing regions in text with cohesion gaps. We demonstrate the efficacy of CHIAA framework using succinct examples from cohesion-deficient text excerpts in the experimental dataset.
Use of technology during the Covid-19 pandemic in a program for re-enrolling high school dropouts

The International Journal of Integrated Care (IJIC) is an online, open-access, peer-reviewed scientific journal that publishes original articles in the field of integrated care on a continuous basis.IJIC has an Impact Factor of 5.120 (2020 JCR, received in June 2021)The IJIC 20th Anniversary Issue was published in 2021.
TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations

Bias is a common problem inherent in recommender systems, which is entangled with users' preferences and poses a great challenge to unbiased learning. For debiasing tasks, the doubly robust (DR) method and its variants show superior performance due to the double robustness property, that is, DR is unbiased when either imputed errors or learned propensities are accurate. However, our theoretical analysis reveals that DR usually has a large variance. Meanwhile, DR would suffer unexpectedly large bias and poor generalization caused by inaccurate imputed errors and learned propensities, which usually occur in practice. In this paper, we propose a principled approach that can effectively reduce bias and variance simultaneously for existing DR approaches when the error imputation model is misspecified. In addition, we further propose a novel semi-parametric collaborative learning approach that decomposes imputed errors into parametric and nonparametric parts and updates them collaboratively, resulting in more accurate predictions. Both theoretical analysis and experiments demonstrate the superiority of the proposed methods compared with existing debiasing methods.
Application of K-Medoids Clustering to Increase the 2020 Family Planning Program in Sleman Regency

Indonesia is a country with a large population. Based on the results of the 2020 census, Indonesia's population ranks fourth in the world. The Indonesian government has made a policy to reduce population growth, namely the Family Planning Program or Keluarga Berencana (KB). One of the areas that did not escape the target was the DIY. Based on BKKBN DIY data, there is a significant difference between the number of active KB participants and the number of couples of childbearing ages, the number of KB equipment and the number of KB health facilities that exist between sub-districts in Sleman Regency. Then the sub-district classification is carried out based on the 2020 KB data in Sleman Regency using the K-Medoids Clustering method. This study aims to see the sub-district grouping used as a reference by the government in increasing active KB participants in the community to overcome the population in Yogyakarta, primarily focusing on Sleman. The categories in each cluster, namely Cluster 1, which consists of 6 sub-districts, have a high level of KB active participants, couples of reproductive ages, KB equipment, and KB health facilities. Then Cluster 2, which consists of 6 sub-districts, has a medium level of KB active participants, couples of reproductive ages, KB equipment, and KB health facilities. While Cluster 3 consists of 5 sub-districts, where KB active participants, teams of reproductive age, KB equipment, and KB health facilities are low level.
OPPORTUNITIES OF THE IMPLEMENTATION OF BLENDED LEARNING

So far one of the 21st century biggest global events that brought about unforeseen changes in education was the pandemic caused by the SARS-CoV-19 virus. Measures to reduce the spread of the virus have made it necessary to replace traditional face-to-face education with alternative options, such as distance learning or remote learning. Blended learning, which combines face-to-face and online learning and is seen as a solution for future education, has also become relevant during the pandemic, providing an opportunity to promote the integration of digital technologies into learning process and to support equal access to education for all. The article deals with a case study to explore the principles of the implementation of blended learning and pilot them in a higher education institution. Based on the theory review, it has been concluded that blended learning is defined as a method and an approach, as well as a curriculum that combines face-to-face learning with online learning. The data have been collected by the content analysis of documents, questionnaires for students and lecturers and an interview with the director of a higher education institution. The obtained data allow to develop the recommendations for the management of higher education institution, lecturers and students for the implementation of blended learning in the study process.
Design of an Effective Evaluation System for English Fragmented Learning Based on Mobile Information Environment

In recent years, the fragmented learning mode of college English is the product of the integration of English teaching reform and information technology. Because of the mobile information environment, how knowledge is presented and distributed has altered significantly. As research on current college students continues to expand, the concept of fragmented learning is being utilized more and more frequently. Fragmented learning is defined as having features of fragmentation in time, space, content, and method of learning. Although fragmented learning makes it easier for college students to study, besides it also has several drawbacks, such as fragmented knowledge being scattered, no structure is developed, fragmented content being jumbled, and fragmented learning being flashy and merely a formality. To overcome these issues, this research work examines the evaluation system for the fragmented learning effect. In addition, it studies the effect evaluation of fragmented English learning in the mobile information environment and uses the integrated BPNN and genetic algorithm to evaluate the effect of fragmented English learning. The results show that this method has a strong effect on the evaluation of fragmented English learning in colleges and universities.
Tempting and Enforcing as Paradigms for the Design of Social Interactions in Social VR

***Note***: This paper was shared with the HCI community at the CHI 2021 Workshop "Social VR A New Medium for Remote Communication &amp;amp; Collaboration". Developers of Social VR applications have access to individual users' motion data and the resulting spatiotemporal relationship between them via VR hardware. This information opens up the possibility of identifying specific social events like handshakes and making them usable for concrete features that eventually shape the social experience within the virtual world. Following this line of thought, we put the paradigms of tempting and forcing specific social interactions in VR up for discussion to add a novel perspective to the growing body of literature on Social VR design strategies. In this position paper, we first give an illustrative example for linking body movement data and social features, then present a continuum of developer influence on social interactions in VR. In conclusion, we advocate the value of exploring social VR interaction techniques that extend beyond reality.
Calibration Spiking of MIR-DRIFTS Soil Spectra for Carbon Predictions Using PLSR Extensions and Log-Ratio Transformations

There is a need to minimize the usage of traditional laboratory reference methods in favor of spectroscopy for routine soil carbon monitoring, with potential cost savings existing especially for labile pools. Mid-infrared spectroscopy has been associated with accurate soil carbon predictions, but the method has not been researched extensively in connection to C lability. More studies are also needed on reducing the numbers of samples and on how to account for the compositional nature of C pools. This study compares performance of two classes of partial least squares regression models to predict soil carbon in a global (models trained to data from a spectral library), local (models trained to data from a target area), and calibration-spiking (spectral library augmented with target-area spectra) scheme. Topsoil samples were+ scanned with a Fourier-transform infrared spectrometer, total and hot-water extractable carbon determined, and isometric log-ratio coordinates derived from the latter measurements. The best RMSEP was estimated as 0.38 and 0.23 percentage points TC for the district and field scale, respectively—values sufficiently low to make only qualitative predictions according to the RPD and RPIQ criteria. Models estimating soil carbon lability performed unsatisfactorily, presumably due to low labile pool concentration. Traditional weighing of spiking samples by including multiple copies thereof in training data yielded better results than canonical partial least squares regression modeling with embedded weighing. Although local modeling was associated with the most accurate predictions, calibration spiking addressed better the trade-off between data acquisition costs and model quality. Calibration spiking with compositional data analysis is, therefore, recommended for routine monitoring.