Reviewerly/exHarmony huggingface dataset:
A Survey on Secrete Communication through QR Code Steganography for Military Application

Abstract: The quick response code (QR) has become most popular barcode because of its larger data capacity and increased damage resistance. Barcode scanners can easily extract information hidden in the QR code when scanning data forms. However, some confidential data stored directly in QR codes are not secure in real world QR apps. To proposed approach to visual secret sharing scheme to encode a secret QR code into distinct shares. In assessment with other techniques, the shares in proposed scheme are valid QR codes that may be decoded with some unique that means of a trendy QR code reader, so that escaping increases suspicious attackers. An existing sharing technique is subjected to loss of security. On this premise, consider the strategy for (k, n) get to structures by using the (k, k) sharing occurrence on each k-member subset dependent on specific relationship. In addition, the secret message is recovered with the aid of XOR-ing the qualified shares. This operation which can effortlessly be achieved the use of smartphones or different QR scanning gadgets. Contribution work is, working on optimal partitioning methods and compare original message with shared message using hashing techniques. Keywords: Hashing, partitioning algorithm, error correction capacity, high security, Quick Response code, visual secret sharing scheme
Construction and Implementation of Embodied Mixed-Reality Learning Environments

As the research paradigm of cognitive science changes from disembodied to embodied, people realize that the body and its interaction with the environment have an important effect on learning activities more and more intensely. Focus on obtaining physical experience, embodied learning encourages the participation of the body’s sensor-motor system and the multimodal interaction between the body and the environment, and it puts forward some new requirements for the design and construction of learning environments. At the same time, the development of artificial intelligence, mixed reality, human-computer interaction and other technologies in recent years has provided a good support for the construction and application of embodied learning environments, especially the functions of virtual-real fusion, natural interaction and dynamic generation of the mixed reality. For this reason, we proposed to design embedded mixed -reality learning environments (EMRLE) for supporting and promoting embodied activities by using these technologies and the embodied cognitive theory. Through combing the origin and background of EMRLE, on the one hand, we can analyze the construction goals, basic characteristics, construction models and realization methods of EMRLE from two dimensions of theory and technology; on the other hand, combined with the existing relevant research and application status in the world, the main forms of EMRLE in education and teaching practice and its teaching activity design cases can be summarized to further demonstrate and analyze the necessity, rationality and technical feasibility of EMRLE construction. For this reason, embodied learning activities designed on the basis of building EMRLE also provide new ideas for promoting the innovation of teaching and learning forms.
Analisis Hidrologi untuk Perencanaan Sistem Polder di DKI Jakarta

The application of the polder system for flood control in DKI Jakarta has become a must for coastal areas, especially with the construction of sea dikes along the coast of Jakarta as a solution to anticipate tidal flooding. One thing to consider when using a polder system in flood control is how much pump capacity and reservoir are needed. To answer this quetions, a hydrological analysis has been carried out with several method approaches, starting from determining the rainfall design in the form of a depth duration frequency curve in 1 hour to 48 hours, and then applied area reduction factor (ARF) to corrected rainfall design. Natural Resources Conservation Service (NRCS) method are uses to calculated runoff or effective rainfall and then with the unit hydrograph by time area method to produce a runoff hydrograph. Based on this approach, the Sentiong Polder plan has been carried out with the result that the required pump capacity for a 25-year return period is 32 m3/s and 50 m3/s for a 100-year return period with a storage capacity used of 1,507,500 m3. If you want to reduce the pump capacity, it is necessary to increase the storage capacity.
Application of the Leslie Matrix to Predict the Number and Growth Rate of Women in West Java 2021

Data from the Central Statistics Agency 2019 collected that the total population of women in Indonesia is less than male, which is 131.48 million people if compared to the total male population, which is 132.68 million population. This matter is directly relation to the total female population in West Java which is also less than the male population around 24031252.0 female and 24652609.0 male population(Badan Pusat Statistika, 2019).This should be the focus of the government to balance the population growth of women and men in West Java because of the role of women being central in the population growth of the people of West Java. The targets of the development plan contained in the RPJM 2005-2025 is to improve the quality of human resources, including the role of women in development. The growth of the female population is an important thing that must be observed, considering the role of women is determining the development of the human population in the future, because without the role of women the population will not be able to develop. This encourages researchers to predict the number and rate of female population growth in West Java in 2021. The Leslie matrix is a matrix used to predict the number and growth rate of a population. By applying the Leslie matrix to predict the number and growth rate of women in West Java in 2021, it can be concluded that the number of female populations in West Java is around tends to increase.
Peran Mahasiswa Kampus Mengajar 2 Sebagai “Agent Of Change dan Social Control”

Students are the assets of a nation because students are a group of people trained in various fields of knowledge and skills. State Junior High School 2 Tanggulangin s one of the target schools selected by the author and Ministry of Education and Culture as the place for the Kampus Mengajar 2nd Generation as the student change agent of the education program created by us, among them the implementation of literacy learning activities and student numeration aimed at improving the understanding of literacy and student numeration. Social control is a tool to control himself or society. In this case, the society underneath is the entire State Junior High School 2 Tanggulangin and students as the main object of Kampus Mengajar 2nd Generation activities
Text Complexity Classification Data Mining Model Based on Dynamic Quantitative Relationship between Modality and English Context

With the rapid development of mobile internet technology, there are a large number of unstructured data in dynamic data, such as text data, multimedia data, etc., so it is essential to analyze and process these unstructured data to obtain potentially valuable information. This article first starts with the theoretical research of text complexity analysis and analyzes the source of text complexity and its five characteristics of dynamic, complexity, concealment, sentiment, and ambiguity, combined with the expression of user needs in the network environment. Secondly, based on the specific process of text mining, namely, data collection, data processing, and data visualization, it is proposed to subdivide the user demand analysis into three stages of text complexity acquisition, recognition, and expression, to obtain a text complexity analysis based on text mining technology. After that, based on computational linguistics and mathematical-statistical analysis, combined with machine learning and information retrieval technology, the text in any format is converted into a content format that can be used for machine learning, and patterns or knowledge are derived from this content format. Then, through the comparison and research of text mining technology, combined with the text complexity analysis hierarchical structure model, a quantitative relationship complexity analysis framework based on text mining technology is proposed, which is embodied in the use of web crawler technology. Experimental results show that the collected quantitative relationship information is identified and expressed in order to realize the conversion of quantitative relationship information into product features. The market data and text data can be integrated to help improve the model performance and the use of text data can further improve predictions for accuracy.
An Efficient System for Early Diagnosis of B reast Cancer using Support Vector Machine

There are many lives lost every year due to cancer and among them; among the women breast cancer causes the most deaths. For the better prediction of breast cancer risks, numerous studies have been undertaken incorporating data mining techniques. 1.1 million Cases of breast cancer were reported in 2004. It has been seen over the years that, that the numbers increase with the increasing industrialization and urbanization. It was earlier observed that mostly affected countries with breast cancer were high income countries such as America but now a days it is also very serious issue in middle and low income countries like Africa, Latin America and Asia. The main objective of this paper is to create a model which can more efficiently and accurately categorize a cancer as malignant or benevolent based on interpretation of the numerical values of attributes of ultrasound images of breast cancer. In this paper various data mining algorithm used like SVM(Support Vector Machine) for prediction and compared it with various other algorithms such as CART, Logistic Regression, KNN for the best training and test accuracy. SVM algorithm gives the most accurate results among the rest algorithm.
Biometric identity systems in law enforcement and the politics of (voice) recognition: The case of SiiP

Biometric identity systems are now a prominent feature of contemporary law enforcement, including in Europe. Often advanced on the premise of efficiency and accuracy, they have also been the subject of significant controversy. Much attention has focussed on longer-standing biometric data collection, such as finger-printing and facial recognition, foregrounding concerns with the impact such technologies can have on the nature of policing and fundamental human rights. Less researched is the growing use of voice recognition in law enforcement. This paper examines the case of the recent Speaker Identification Integrated Project, a European wide initiative to create the first international and interoperable database of voice biometrics, now the third largest biometric database at Interpol. Drawing on Freedom of Information requests, interviews and public documentation, we outline the emergence and features of SiiP and explore how voice is recognised and attributed meaning. We understand Speaker Identification Integrated Project as constituting a particular ‘regime of recognition’ premised on the use of soft biometrics (age, language, accent and gender) to disembed voice in order to optimise for difference. This, in turn, has implications for the nature and scope of law enforcement, people's position in society, and justice concerns more broadly.
A Road Truncation-Based Location Privacy-Preserving Method against Side-Weight Inference Attack

Taking advantage of precise positioning technology, location-based service (LBS) has brought a lot of convenience to people’s daily life and made the city smarter. However, the LBS applications also bring some challenges to personal location privacy protection. In order to obtain services from LBS providers, users have to upload their queries including sensitive information, such as identities and locations. This information may be leaked out by the LBS providers or even eavesdropped on by malicious adversaries, which may cause privacy leakage. To tackle this problem, many solutions have been investigated under the assumption that users are uniformly distributed. However, the users are not always uniformly distributed in real-world situations. For a side-weight inference attack, the adversary would infer that the target user is more likely to belong to the road section with more users, resulting in performance deterioration. In this paper, we investigate the issue of location privacy preservation against side-weight inference attack for non-uniform distributed road network. Meanwhile, we consider the cost function of LBS and formulate the object as a mixed integer programming problem. Then, we propose a road truncation-based scheme to protect location privacy. The road section with high user density is designed to be truncated. Finally, simulation results show that our scheme meets the demand for privacy protection at a low cost. As a result, our scheme is proven to protect users’ location privacy effectively and efficiently.
SMOTE: POTENSI DAN KEKURANGANNYA PADA SURVEI

Imbalanced data is a problem that is often found in real-world cases of classification. Imbalanced data causes misclassification will tend to occur in the minority class. This can lead to errors in decision-making if the minority class has important information and it’s the focus of attention in research. Generally, there are two approaches that can be taken to deal with the problem of imbalanced data, the data level approach and the algorithm level approach. The data level approach has proven to be very effective in dealing with imbalanced data and more flexible. The oversampling method is one of the data level approaches that generally gives better results than the undersampling method. SMOTE is the most popular oversampling method used in more applications. In this study, we will discuss in more detail the SMOTE method, potential, and disadvantages of this method. In general, this method is intended to avoid overfitting and improve classification performance in the minority class. However, this method also causes overgeneralization which tends to be overlapping.
Prediction of Bitcoin using Recurrent Neural Network

For the past couple of years, Machine learning and trading helped by artificial intelligence has drawn growing interest. Here, the approach is used to test the hypothesis that the inefficiency of cryptocurrency industry can be exploited in order to produce anomalous revenue. For the duration between Nov. 2015 and Apr. 2018, daily data for 1, 681 crypto currencies were analyzed. Simple trade techniques supported by state-of -the-art machine learning algorithms are seen to outperform the traditional benchmarks. The results obtained imply that non-trivial, but fundamentally simple, algorithmic processes will help to predict the short-term future of the cryptocurrency market. The popularity of cryptocurrencies had skyrocketed in 2017 due to several consecutive months of super-exponential growth of market capitalization. There are over 1,500 currently recorded cryptocurrencies actively trading today with the cryptocurrencies sitting on more than $300 billion [2], and a total market capitalization of over $800 billion in January 2018. According to a recent survey, between 2.9 and 5.8 million privates as well as institutional investors are in the numerous investment networks and access to markets has become easier over time. In a number of online markets, major crypto currencies can be purchased using fiat currency, and then used in order to purchase less known crypto currencies. The average trading amount is globally exceeding $15bn. About 170 money market funds had been invested in cryptocurrencies since 2017, and Bitcoin futures are launched in order to satisfy the Bitcoin trading and hedging demand for the market. The main objective of the work is to predict the Bitcoin prices, one of the most popular and widely used cryptocurrency which is a source of attraction for many investors as a source of profit or investment. But the market for the cryptocurrencies been volatile since the day it was first introduced. So, the approach towards the survey is to use LSTM RNN and use the available dataset and train the model to give the highest possible accuracy and to provide a real-time price of the Bitcoin for the following days.
How Will Information Services of Social Media Portals Change in the Future When Artificial Intelligence is implemented?

Innovation is surpassing our desires. Different AI-based instruments are created and help advertisers focus on perfect clients via web-based media at the ideal time.&#x0D; Purpose: the purpose of the study is to link the Information Services of Social Media Portals Change in the Future When Artificial Intelligence is implemented.&#x0D; Methodology: Surveys and qualitative methodologies were utilized to build up the theoretical perspective on A.I.'s effects on advancement across various foundations and social areas on the planet's economies. All the users of face book were population for the current study. Convenient sampling was the technique to access the sample. Focus groups and academic centres, and governmental institutions were likewise fused in acquiring valuable data on the effects of being knowledgeable about all the fields of advancement through A.I. innovations on social media.&#x0D; Findings: Most interviewees reacted emphatically about using technologically advanced tools and disseminating information in modern applications on social media. Advancement in A.I. innovations in the social media services expanded creation periods in the viral news than anticipated from a man if they needed to access vital information at every interval without an aspect of delay.&#x0D; Contribution: The future will be A.I. empowered and less miss-information. Tis paper do not generally know how soon or in what ways these dreams of the AI-empowered future will turn into a reality, yet even the most easygoing of eyewitnesses can see that these are unavoidable patterns. The one sure thing is that it is coming, and this paper now travelling toward that path.
Mixtures of Normalizing Flows

Normalizing flows fall into the category of deep generative models. They explicitly model a probability density function. As a result, such a model can learn probabilistic distributions beyond the Gaussian one. Clustering is one of the main unsupervised ma- chine learning tasks and the most common probabilistic approach to solve a clustering problem is via Gaussian mixture models. Although there are a few approaches for con- structing mixtures of normalizing flows in the literature, we propose a direct approach and use the masked autoregressive flow as the normalizing flow. We show the results obtained on 2D datasets and then on images. The results contain density plots or ta- bles with clustering metrics in order to quantify the quality of the obtained clusters. Although on images we usually obtain worse results than other classic models, the 2D results show that more expressive mixtures of distributions (than the Gaussian mixture models) can be learned indeed. The code which implements this method can be found at https://github.com/aciobanusebi/nf-mixture.
Exploring the Information Literacy Skills Among Minangkabau Community in Agam District, Indonesia

The purpose of this study was to examine the level of skill information literacy, of the Minangkabau community in Agam district, Indonesia.The trial was conducted to test the reliability and validity of the measuring instrument.The pilot study was conducted in 30 Minangkabau communities from the Agam district.In this way, the researchers distributed the instrument among 30 participants (male, n = 11; female, n = 19), and their responses were recorded.This study uses quantitative methods.The findings showed that first, the investigation of the Minangkabau Community information literacy level of skills was shown as follows (a) most of the respondents knew the required information 17 (56.7%),were able to define 17 (56.7%)and evaluated the required information 18 (60%) ; (b) most of the respondents do not understand the information search strategy, this can be seen from the respondents who disagreed with my statement in determining the source of the information I was looking for related to the topic sought 19 (63.3%);(c) most respondents do not understand the location and access to information, this can be seen from the statement that most respondents disagree with my statement in determining the steps used to find the information I need 14 (46%); (d) most respondents use information to solve problems 16 (53.3%);(e) related to organizing information, most respondents grouped information according to information subject 16 (53.3%)and could find information that had been stored easily and quickly 15 (50%), and (f) most respondents evaluated various information found 16 (53.3%).Second, the status of local wisdom of the Minangkabau community can be seen from (a) socialization, most respondents agree with the statement of social relations related to ancestral culture 20 (66.67%);(b) externalization, most respondents agree with the statement that the documentation of Minangkabau local wisdom is a cultural heritage that exists in the community which is carried out for generations by the community concerned; (c) the combination, most of the respondents disagree with the statement get used to learning from reading books, news, and documents indigenous knowledge 16 (53.3);and (d) internalization, most of the respondents disagree with the statement manage indigenous knowledge training program 16 (53.3%).
An Evaluation of Supervised Dimensionality Reduction For Large Scale Data

Experimenters today frequently quantify millions or even billions of characteristics (measurements) each sample to address critical biological issues, in the hopes that machine learning tools would be able to make correct data-driven judgments. An efficient analysis requires a low-dimensional representation that preserves the differentiating features in data whose size and complexity are orders of magnitude apart (e.g., if a certain ailment is present in the person's body). While there are several systems that can handle millions of variables and yet have strong empirical and conceptual guarantees, there are few that can be clearly understood. This research presents an evaluation of supervised dimensionality reduction for large scale data. We provide a methodology for expanding Principal Component Analysis (PCA) by including category moment estimations in low-dimensional projections. Linear Optimum Low-Rank (LOLR) projection, the cheapest variant, includes the class-conditional means. We show that LOLR projections and its extensions enhance representations of data for future classifications while retaining computing flexibility and reliability using both experimental and simulated data benchmark. When it comes to accuracy, LOLR prediction outperforms other modular linear dimension reduction methods that require much longer computation times on conventional computers. LOLR uses more than 150 million attributes in brain image processing datasets, and many genome sequencing datasets have more than half a million attributes.
СУЧАСНІ ТЕНДЕНЦІЇ ПРОЯВУ САМОВИРАЖЕННЯ У СТУДЕНТІВ В ОСВІТНЬОМУ ПРОСТОРІ ЗВО

Self-expression in today’s world has many opportunities, especially for young people. Digital technology allows you to express yourself anywhere in the world. Citizens’ self-expression and, most importantly, students are the basis for the development of a healthy and democratic society