Reviewerly/exHarmony huggingface dataset:
The data sharing security system of cloud storage

Because of the openness of the cloud storage architecture and the sharing of resources, data owners have lost control of the stored data, leading to frequent leakage of user privacy data, and security issues have been a significant element restricting the development of cloud storage. In this paper, three key algorithms for key distribution, hybrid encryption and dynamic key update are proposed for the key issues of data sharing security in cloud storage environment. Based on the above algorithms, a data security sharing model in cloud storage environment is proposed to solve cloud storage Trust dependence, user collusion attacks, and data dynamic security issues in the environment to protect private data during storage and sharing security. The analysis of the experimental results shows that the data security sharing technology can resist selective plaintext and collusion attacks. Therefore, the system of cloud storage encrypts shared data by using data security sharing technology, which can effectively protect the data confidentiality under context of the cloud storage.
Analysis of Clustering Algorithms for Mall

Clustering is a technique that use to finding similar information within the cluster. The data has same things in the dataset cluster use to together base on the most and the minimum of the data. Clustering is procedures in which matter that clustered and divided group are together, based the rule to maximize the in the group resemblance and minimizing the inter-group resemblance. In other words, it is a combination of links, associations and whole patterns contained in massive databases however hidden or unknown. So as to perform the analysis, we'd like software system and tools. Set of tool, that are permit to user analyze information for various perspectives and angles, in order to find meaningful relationships. Cluster if similar information in the information set is the data is separate in the file. Clustering if similar data in the dataset is the data is separate in the file. In this paper, we study and compare the varying algorithms and technique used the group analysis that is used for RAPIDMINER. The best working on datasets for these type of cluster. Different clustering algorithms have been developed different results. In the paper we analysis two type of clustering for Algorithm: x-Mean &k-Mean cluster algorithm that compute the work in two type of cluster algorithm that work on correct classes. In the test of one field of Mall Customers data set working on RAPID MINER tools to find correct cluster.
ANALISIS PENERAPAN SISTEM INFORMASI AKUNTANSI PENERIMAAN DAN PENGELUARAN KAS PADA ORGANISASI NIRLABA DI KABUPATEN BARRU ( Studi Kasus Lembaga Masjid Modern Kurir Langit)

Fatimah.2020.Thesis. Analysis of The Application of Accounting Information System of Receipt and Cash Expenditure to Non-Profit Organizations in Barru Regency (Case Study of Modern Mosque Institute of The Sky Courier) guided by Firman Menne, S.E., M.Si., Ak. CA and Adil Setiawan, A.Md.Kom., S.E., M.Si.,Ak.,CAThe purpose of this research is in accordance with the formulation of the above problems, namely to know the application of accounting information system of receipt and cash expenditure at modern mosque institutions courier langit.The results showed that: The organizational structure of the Yayasan masjid kurir langit has not involved financial fields and separates the areas among them: Sharia Supervisory Board, Board of Trustees of the Foundation, President, Executive Director, Baitul Maal Field, Markaz Tijarah Bumm Field as well as ziswaf distribution agency, and Tijarah Headquarters Business Unit. Related functions in the cash receipt information system include: Baitul maal field and executive director. The new documents used in the Cash Receipt Accounting Information System at the Modern Mosque Institute Kurir Langit are still: deposit slips, receipts, savings books.Sharia Supervisory Board, Board of Trustees of the Foundation, President, Executive Director, Baitul Maal Field, Markaz Tijarah Bumm Field as well as ziswaf distribution agency, and Tijarah Headquarters Business Unit. Related functions in the cash receipt information system include: ZISWAF Distribution Agency and executive director. The documents used in the Cash Expenditure Accounting Information System at Yayasan Masjid Modern Kurir Langit are: Notes and Receipts.
Open Access Digital Thesaurus on Ethnic Groups in the Mekong River Basin

The ethnic group domain, in particular, is characterized by rich and diverse data sets in the Mekong River Basin (MRB). Ethnic groups’ vocabulary and relevant data come from various sources that cross history, language, and geography. As a result, distinct language is used by specialized groups to characterize their artifacts. Data interoperability among multiple catalogs is highly challenging as a result of this. The usage of controlled vocabularies and thesauri is generally considered a major practice for making preparations for standardization, which is essential for data reuse and sharing. In contrast, when used together, thesauri eliminate ambiguity in natural language, making it easier to identify and integrate data from different sources and allow scholars and computer programs to understand data more efficiently. This paper describes the modeling process of the EGMRB Thesaurus, its integration and role in the infrastructure, its publication as Linked Open Data, and the results of this work after six months of development. This paper presents the rationale behind the realization of this thesaurus. Thesaurus EGMRB (http://thesaurus.asiana.net/vocab/) provides a semantic resource on ethnic groups in the Mekong river basin. EGMRB is the outcome of interdisciplinary cooperation of specialists from the domains of ethnic groups and information science, who collaborated in the context of collaborative research. The thesaurus was developed in Simple Knowledge Organization System (SKOS), a standard data format based on the Resource Description Framework (RDF), using semantic web standard technologies. EGMRB is freely available online, with a SPARQL endpoint (http://thesaurus.asiana.net/vocab/sparql.php) for querying and an API (http://thesaurus.asiana.net/vocab/services.php) for system integration. Digital collections, digital exhibits, and a virtual study environment are being built as part of a digital platform that will give scholars and the general users search and content curation services. EGMRB, which provides unified ideas with related unique and resolvable URIs, can profoundly reduce the barriers to data discovery, integration, and sharing if adopted as a standard and carefully implemented and expanded by the academic community.
Pricing Educational Service in Iraqi private Universities and Colleges

The research aims to study the foundations adopted by Iraqi private universities and colleges when they are pricing educational service and to explain the reasons for the disparity in fees for the tuitions and to determine the appropriate methods that are supposed to be applied for pricing educational service.The data was collected through the electronic form reviewed by specialists, which were distributed to 4 of the Iraqi private universities and 18 colleges to achieve the research goals.The research reach a set of conclusions, the most important of which: private universities and colleges focus on determining tuitions according to a single pricing basis without studying all factors affecting pricing decisions.This lead to an increase in their return in certain academic specializations, but it will be reflected in a decrease in its return in other academic specializations.Therefore, the research suggests a set of recommendations, the most important of which are: Iraqi universities and private colleges should study all factors that affect the pricing decisions, and to adopt a pricing basis that is appropriate to achieve the goals for which they were established, which is reflected in determining the tuitions to ensures continuity in the provision and development of educational service.
FORMATION OF PRACTICAL SKILLS IN STUDENTS WITH THE HELP OF EXPERIMENTAL PROBLEMS IN PHYSICS

The article introduces one of the ways to form students' practical skills by solving experimental problems focused on a specific practical method that can be successfully used to solve life's cognitive problems. The introduction of the proposed approach was preceded by the study of the opinions of physics teachers of secondary schools in the Volyn region of Ukraine. The attitude and wishes of students from the same region to solving experimental problems are clarified. The definition of experimental problems is offered as follows: these are the problems with the data taken from the experiment to obtain a solution. The solution of experimental problems is considered as a part of educational physical experiment, which is an essential component of the methodical system of teaching physics, as it provides the formation of the necessary practical and research skills. A series of experimental problems that provide the formation of practical skills of students on the example of educational material devoted to the study of the concept of "density of matter". A solution is proposed for each problem. The views of scientists on the problem of using experimental problems in the practice of physics lessons are reflected; the difficulties that teachers face when using the mentioned type of tasks are outlined. A study of corresponding textbooks in recent years on the problem of using experimental problems in the practice of teaching students is conducted. Methodical recommendations on the choice of time for solving the given series of experimental problems, forms of work of students in a modern physics lesson are offered. Attention is paid to the selection and solution of experimental problems of other educational material related to the concept of "density of matter", the place and forms of their solution in the practice of the lesson are substantiated. The effectiveness of the proposed approach to solving experimental problems on the basis of expert evaluation is established.
PEMBUATAN SISTEM INFORMASI KOIN BOND DI PT SAGARA ASIA TECHNOLOGY MENGGUNAKAN SWIFT 4

ABSTRACTPT Sagara Asia Technology or better known as Sagara Technology is a company engaged in technology consulting and digital products. Sagara Technology manufactures and provides premium software development, training and talent products. The work done at Sagara Technology focuses on developing website technology and mobile applications. Investment is an option for many groups of people to invest in a company because investment promises long-term benefits. Therefore, PT Lunaria Annua Teknologi decided to create a Bond Coin application to reach all groups of people who want to invest and can take advantage of investment returns according to their wishes.The methodology used during the creation of this application is Scrum. Scrum is used as a framework because this methodology is suitable for small teams, clear application specifications and Scrum allows iteration and incremental of a product requirement that will be found during product development. Bond coins make it easier for investors to conduct checks on Government Securities purchased, including transparency, security and efficiency in transactions.
Education Quality Management in the Modern School: Approaches in the Context of Continuing Education

The article is devoted to the study of the quality management system of education in a modern school. In researching this direction, the author relies on the main characteristics of the quality that education must correspond to. Attention is focused on what should be the criteria in solving the problem of education quality. It is concluded that the teacher today has lost the role of a person transferring knowledge, who acts by authoritarian or totalitarian methods, trying to give as much factual information as possible. A teacher today is a mentor who guides and organises educational and upbringing work with students at school. Consequently, the quality of education is aimed at meeting the public demand for the formation of a personality that is ready to actively respond to permanent changes in the social environment and the challenges that education faces today.
Decentralized Multi-Target Cross-Domain Recommendation for Multi-Organization Collaborations

Recommender Systems (RSs) are operated locally by different organizations in many realistic scenarios. If various organizations can fully share their data and perform computation in a centralized manner, they may significantly improve the accuracy of recommendations. However, collaborations among multiple organizations in enhancing the performance of recommendations are primarily limited due to the difficulty of sharing data and models. To address this challenge, we propose Decentralized Multi-Target Cross-Domain Recommendation (DMTCDR) with Multi-Target Assisted Learning (MTAL) and Assisted AutoEncoder (AAE). Our method can help multiple organizations collaboratively improve their recommendation performance in a decentralized manner without sharing sensitive assets. Consequently, it allows decentralized organizations to collaborate and form a community of shared interest. We conduct extensive experiments to demonstrate that the new method can significantly outperform locally trained RSs and mitigate the cold start problem.
Content Analysis of Farmers’ KVK WhatsApp Group of Villupuram District in Tamil Nadu

The penetration and use of smart mobile phones with internet connectivity paved way to the increased use of social media tools and among them, WhatsApp is widely used by farmers for personal and group use. The efforts made by public institutions, development departments in reaching farmers and general public through WhatsApp has led to a creation of number of farmers WhatsApp groups for sharing of agricultural knowledge and information. The present study was carried out to analyse the content shared through KVK WhatsApp group. Viluppuram KVK WhatsApp group was purposively selected and the content shared was analyzed by non-participant observation method for 30 days. Data analyzed using frequency and percentage analysis. It was found that majority (83.97%) of the content shared by the farmers were related to agricultural activities. Farmers preferred to use text communication (37.18%) over other mode of messages. Farmers were actively engaged during morning hours in WhatsApp group due to their free time rather than later hours. Farmers opined that WhatsApp positively impacted them in obtaining timely information as well as personalized information.
Generating Scenery Images with Larger Variety According to User Descriptions

In this paper, a framework based on generative adversarial networks is proposed to perform nature-scenery generation according to descriptions from the users. The desired place, time and seasons of the generated scenes can be specified with the help of text-to-image generation techniques. The framework improves and modifies the architecture of a generative adversarial network with attention models by adding the imagination models. The proposed attentional and imaginative generative network uses the hidden layer information to initialize the memory cell of the recurrent neural network to produce the desired photos. A data set containing different categories of scenery images is established to train the proposed system. The experiments validate that the proposed method is able to increase the quality and diversity of the generated images compared to the existing method. A possible application of road image generation for data augmentation is also demonstrated in the experimental results.
519 - PET@home: Research protocol for a toolkit to improve care for non-residential long term care clients owning pets

Background Over half of the households in The Netherlands have one or more pets. In elderly people, owning a pet is associated with a better quality of life and less loneliness, anxiety, depression and agitation. Many non-residential long term care (LTC) clients rely on support of others to take care of their pets. However, that may place a significant burden on the social support network of the LTC client. Issues relevant to keeping pets are not explicitly incorporated in the Dutch Long-term Care Act. Many LTC organizations have no instruments for care workers, clients and their family (1) to consolidate the positive role of pets for clients’ quality of life and (2) to address whether it is possible to keep the pets and to organize care accordingly. Research Objectives To help care workers, clients and their family to gain insight into the role of the pets in the clients’ life and their social support network; to develop practical instruments that help making decisions about owning and caring for pets. Method PLAN: In months 0-16, a narrative systematic review will be conducted (STUDY 1.1) on the meaning of pets for elderly people in general. A qualitative STUDY 1.2 with LTC clients, their informal carers and care professionals will validate and further explore the topic. STUDY 1.3 and 1.4 develop and (cognitively) validate work cards for interviews of clients and relatives by care providers. In months 17-29, an Experience based co-design method (STUDIES 2.1-2.3) will be used to develop the PET@home toolkit. The method includes (1) discovery interviews (10 clients and their family), (2) focus groups with healthcare providers (N = 2x6); (3) focus groups with 6-8 clients and informal and professional carers. In STUDY 3.1, potential users will pre-test the Toolkit. In months 30-34, a process evaluation (STUDY 3.2) is performed in 10-15 clients. A dissemination and an implementation plan will be developed. Conclusions The project will result in an innovative PET@home toolkit that will help to assess the pets role in the clients’ quality of life and support network, and will help making decisions about owning and caring for pets.
Bitcoin Vision: Using Machine Learning and Data Mining to Predict the Short-Term and Long-Term Price of Bitcoin

Cryptocurrencies are non-physical currency that solely exist as represented by 0s and 1s within the world of computers. One of the most popular cryptocurrencies in the market right now being Bitcoin, was first invented to solve the inherent problem with using traditional currency when purchasing online. However, unexpectedly Bitcoin soon found itself to be one of the most profitable investment opportunities to be hedged on with its yearly growth unrivaled by any traditional investment product such as stocks, bonds, or real-estate. However, unlike the stock market which has been the subject of multitude of research papers, the cryptocurrency market has not been treated the same way and as such there is still a huge opportunity open in this industry. Thus, Bitcoin Vision wants to utilize this opportunity and propose the use of machine learning and deep learning architecture to predict the price movement trend of bitcoin (up or down) for the short-term prediction and predict the price of bitcoin for the long-term prediction. Being able to predict the future of the market prove to be useful in the stock market and as such this paper decide to replicate that opportunity to be presented in the cryptocurrency market as well. In this literature review paper, we have proposed the comparison of state-of-the-art deep learning model such as Long Short-Term Memory (LSTM) with traditionally successful machine learning model such as Random Forest and ARIMA to find out which model provide the best result.
Search stopped: Police have called off a major search in our southeast, launched after swimmers reported seeing a dead baby floating in water at a popular beach.

Police have called off a major search in our southeast, launched after swimmers reported seeing a dead baby floating in water at a popular beach. But it's not the end of the investigation. Officers...
Outcomes of distance education in Mathematics at secondary technical school: a case study

Education at secondary vocational schools has an important role in preparing graduates to perform professional activities in practice. These schools provide a complete secondary education, and Mathematics is also a part of the study plan. In this paper, we focused on the success of students in solving selected types of mathematical problems during the school year 2020/2021, when the study process at secondary schools was carried out in a distance form. The main goal was to evaluate the mathematical knowledge of the secondary school students through a set of selected tasks. The research sample consisted of 21 students of the 3rd year of the secondary technical school with a focus on civil engineering study program, for which we analyzed tasks of online tests in the first and second half of the school year. Using methods of mathematical statistics, we compared and evaluated the success of students in solving problems that were part of the tests from the mathematics curriculum. The results show that students have mastered the necessary electronic tools and methods of distance learning in acquiring mathematical knowledge.
Robust Attack with Adaptive Compress Adversarial Perturbations

Adversarial examples expose the vulnerability of deep neural networks that perform well in various fields. However, adversarial perturbations crafted by the existing attack methods are often aimed at the whole image. They are usually random, and the human eye can even easily perceive some of them. This paper proposes an adaptive method to compress the adversarial perturbation. Under the premise of ensuring the success of attacks, generating perturbations as small as possible to change the decision of classifiers. First, the authors find the minimum point of loss function by the optimization method, to expand the spanning space of adversarial examples. Calculating and selecting the smaller perturbation between this point and the original input. Then, in order to retain the useful perturbation and remove redundancy, the authors look for important regions in the input data that determine the network predict results, and construct an importance mask for the smaller perturbation of the previous stage. Extensive experiments on the ImageNet dataset and multiple network classifiers show that our method is effective. Compared with advanced attack methods, the $\mathbf{L}_{2}$ distance of adversarial perturbation obtained by our method is smaller and more practical, and the generated adversarial examples have strong transferability.
Performance Analysis of Various Classifiers for Social Intimidating Activities Detection

The emergence of social networks is at a great boom today. Every big news before telecasting on television comes to these forums, therefore raises many dilemmas due to misinterpretation regarding the freedom of speaking. One of this trouble is social intimidation that is very disturbing misbehavior that can cause troubling consequences for the victim. Existing works of social intimidation focuses on only one or two topics of harassment. The main aim of this study is to analyze the hub of social intimidation i.e. twitter, consisting of 25,000 tweets covering five topics of harassment i.e. sexism, racism, appearance related, political and intellectual. Moreover, five machine learning and four deep learning techniques were used namely sequential minimal optimization (SMO), random forest, multinomial naïve bayes, logistic regression (LR), decision tree J48, CNN-CB, CNN-GRU, CNN-LRCN and CNN-Bi-LSTM. Each of the classifiers are evaluated using accuracy, precision, recall and f-measure as a performance metric on the dataset. Results indicate the dominance of CNN-Bi-LSTM and logistic regression among all classifiers used.