Reviewerly/exHarmony huggingface dataset:
RAW DATA SECURITY BY USING ELGAMAL AND SHA 256 PUBLIC KEY ALGORITHM

The development of information technology has grown exponentially and various of data collections and its method has been obtained. In the era of big data, data has now become an asset that held important values, while in the implementation of data delivery, it clearly is not always safe. One of the method to secure data delivery is data encryption using Cryptography. Cryptography provides an encryption service to secure data delivery by transforming it to random values so that it can no longer be read. The goal in this study was to produce an application that could be used to encrypt data, using ElGamal's cryptography method and hash checking using the SHA256 algorithm. After encryption, to ensure the encrypted data is still the original data without any changes or manipulation by unauthorized 3rd party then done by checking the hash generated using SHA256 algorithm. The data used in this study was a sample of raw data from the ATPWTP survey (ability to pay and willing to pay) conducted by the BPS Cirebon (Central Statistics) in 2019 and the data was in the form of Excel and txt files. The encryption process resulted in a cipher larger than the plaintext and takes longer for the data encryption process than during the data cipher decryption process.
Intervención educativa sobre la COVID-19 en trabajadores de la Filial de Ciencias Médicas de Baracoa

Introduction : The new coronavirus, SARS-CoV-2, causes the infectious disease known as COVID-19. Currently, this disease is a pandemic affecting many countries around the world and evolving rapidly in such a way that new data are emerging every day with respect to the number of sick and fatal cases. Objective : To determine the effectiveness of an educational intervention about COVID-19. Methods : A quasiexperimental study, of before-after type, without a control group, was carried out through an educational intervention, during the period from February to March 2020. The study universe was made up of 133 workers belonging to the Baracoa Medical Sciences Campus. A stratified random sample of 80 workers was selected. Data collection was carried out through a survey applied before and after the educational intervention. The results were presented in statistical tables, using absolute frequencies and percentages for the descriptive analysis of the data, while, for the comparative analysis of knowledge, the McNemar test was applied, with a significance level of P <0.05. Results : A significant increase in the level of adequate knowledge was observed after the educational intervention about the generalities of COVID-19 (95.0%), signs and symptoms of disease (95.0%), complications (96.3 %), prevention measures (97.5%), and general knowledge about COVID-19 (96.3%). Conclusions : The educational intervention was effective because an adequate level of knowledge was achieved in almost all the workers surveyed.
Meningkatkan Kosakata Bahasa Inggris Taruna Poltekbang Surabaya Melalui Strategi Ide Utama dan Strategi Meringkas

One of the main pillars in language education and as the core of a major language expertises. English in the world of aviation is vocabulary. This scrutiny works to influence the consequence of the implementation of the main inspiration strategy and the summarization strategy on the English vocabulary education of the Surabaya Poltekbang cadets. Comprehension deficiency of the vocabulary of the Surabaya Poltekbang cadets causes the cadets to experience innumerable difficulty in using English, which affects their language skills (reading, writing, observing and having dialogue). Taking non-random illustrations, 48 ​​cadets with the Surabaya aviation polytechnic. Entrants were stipulate to the experimental group (n = 24) and the control (n = 24). Vocabulary test as a pra uji and pasca uji given to entire entrants. Descriptive and inferential statistical analysis information using the SPSS application. The contrivances show that the enforcement of the main inspiration strategy and the summarization strategy as a strategy to improve knowledge of English vocabulary is not only useful in the conceptual process and description of cadets for creating, mastering and mastering the significance of vocabulary but also for putting and internalizing it in their reflections over effectively.
Algorithm for discovery of sequential patterns in big data and application on price of brent crude oil

Frequent sequence mining in large population is important for economics, engineering, genetics and many other databases analysis. Exact algorithms are designed to search for frequent sequence, they repeatedly re-selects the entire population. If the population is large, the search is slow or requires very large computer resources. The paper proposes a new probabilistic frequencies sequence finding algorithm which analyze a randomized sample of the primary population concluded in a specific way. Based on this analysis, statistical inferences are made about frequent sequence in the primary population. This algorithm is not accurate, but it runs much faster than the exact algorithms and is suitable for exploratory statistical analysis. Probabilities of errors are estimated by statistical methods. The probabilistic algorithm can be combined with the exact algorithms of frequent sequences search.
FORMIONG HIGHER-EDUTATION STUDENTS RESEARCH COMPETENCE

In this article, in the psychological literature, creativity is defined as “the creative abilities (abilities) of a person, thinking, emotions, communication, manifestation in certain types of competence, characteristics of the personality as a whole or its individual aspects
Performance Analysis of Cascaded Hybrid Symmetric Encryption Models

Over a few years, there is rapid increase of exchange of data over the net has brought data confidentiality and its privacy to the fore front. Data confidentiality can be achieved by implementing cryptography algorithms during transmission of data which confirms that data remains secure and protected over an insecure network channel. In order to ensure data confidentiality and privacy, cryptography service encryption is used which makes data in unreadable form while the reverse process rearranges data in readable form and known as decryption. All encryption algorithms are intended to provide confidentiality to data, but their performance varies depending on many variables such as key size, type, number of rounds, complexity and data size used. In addition, although some encryption algorithms outperform others, they have been found to be prone to particular attacks. This paper reviews and summarizes the various common hybrid cascaded n-tier encryption models. Additionally, this paper compares and analyzes the performance of common hybrid cascaded 2-tier and 3-tier encryption models obtained during simulation based on encryption/decryption time, avalanche effect and throughput. The models compared with AES are 2-tier models (AES-TWOFISH, AES-BLOWFISH, TWOFISH-AES, BLOWFISH-AES, AES-SERPENT and SERPENT-TWOFISH) and 3-tier models (DES-BLOWFISH-AES, AES-TWOFISH-SERPENT and SERPENT-TWOFISH-AES). The hybrid cascaded model like AES-TWOFISH, AES-BLOWFISH and SERPENT-TWOFISH-AES are better hybrid models with respect to throughput and avalanche effect.
Polynomial Substitution Based Image Encryption Using Shamir Scheme

An efficient (𝑡, 𝑛) secret sharing is proposed for encryption of images based on polynomial substitution with Shamir secret sharing. The proposed technique enciphers the secret image as two different images: one is made public which is the enciphered image and additional other one is secret that can be shared among the 𝑛 members. In the proposed method, 𝑡 − shares are implemented by 𝑛 −participants. The pixel values of the secret image are treated as the co-efficient in the polynomial equation. The reconstruction of the shares is done by the 𝑛 −participants and the probability of recovering the whole image is (1/256) 𝑚 without any errors. It reveals that the implemented scheme is highly secure from attacks. By solving the polynomial equations using recovered 𝑡 −values the secret image can be recovered. A security analysis is also performed for the proposed method to shows its efficiency.
A method for comparing text and graphic fragments in electronic documents using a hybrid criterion.

A method for comparing text and graphic fragments in electronic documents by a hybrid criterion is considered. This method allows you to determine the integrated similarity value between the request associated with the image in the request and the text-graphic image in the document database. The criterion used provides for the use of weights for images with annotations and for images without annotations. The prospects for using the proposed method are determined
Computer Vision Concepts and Applications

This chapter includes the most important aspects of computer vision like feature extraction, object detection, and image segmentation. The applications of computer vision have been growing exponentially, especially in terms of control systems, detecting events, modelling environments, target detection, automatic inspection, navigation, etc.
DEEP RECURRENT NEURAL NETWORK BASED AUDIO SPEECH RECOGNITION SYSTEM

Speech recognition system has become an integral part of how the computer technologies can be used to influence and improve the human work and activities. These are being used extensively, from personal assistants to self-driving car Human Computer Interfaces (HCIs) and other industries as well. While the most common approach to speech recognition system building is using the Hidden Markov Models (HMMs). The HMM models assume a specific structure of the data and unable to capture temporal dependencies. This paper however presents a unique approach for isolated word recognition based on deep learning models using Recurrent Neural Networks (RNNs) particularly, which can perform end to end speech recognition without any assumption of structure in data using Bidirectional LSTM (BiLSTM). The network proposed in the paper can learn both features in the data and capture temporal dependencies.
Virtual Machine Placement using Analytic Approach in Data Center

Cloud services are being used rapidly for various purposes and that leads to rapid growth of Data centers since Data Centers are serving as remote storage medium which is done by a group of servers. Virtual machines can also be considered as major reason for the success of cloud computing in such a large scale. Virtual machine requests are getting more day by day which directly implies that there is a need for allocation policy which can be efficient in dealing with the VM placement in data centers since placement of virtual machines plays a vital role in Data Centers. After a thorough consideration of the current scenario, it indirectly implies that a dynamic allocator which is aware of power needs to be studied. In this, every request for Virtual machine requests gets characterized based on four parameters. Those four parameters are disk, RAM, CPU and bandwidth. These allocators are designed in such a way that it accepts as many requests as possible. Here we tried various allocation strategies and these allocators are differing in terms of policies like different approaches. They are also differing in terms of policies like objective optimization or joint or disjoint selection of the resources. The behavior evaluation of this policy strategy is carried out in a way that varies the load in DC and also varies the quantity of virtual machines that are to be allocated. Furthermore, the results do clearly illustrate that disjoint approaches are outperformed by jointapproaches.
Abstract Syntax as Interlingua: Scaling Up the Grammatical Framework from Controlled Languages to Robust Pipelines

Abstract syntax is an interlingual representation used in compilers. Grammatical Framework (GF) applies the abstract syntax idea to natural languages. The development of GF started in 1998, first as a tool for controlled language implementations, where it has gained an established position in both academic and commercial projects. GF provides grammar resources for over 40 languages, enabling accurate generation and translation, as well as grammar engineering tools and components for mobile and Web applications. On the research side, the focus in the last ten years has been on scaling up GF to wide-coverage language processing. The concept of abstract syntax offers a unified view on many other approaches: Universal Dependencies, WordNets, FrameNets, Construction Grammars, and Abstract Meaning Representations. This makes it possible for GF to utilize data from the other approaches and to build robust pipelines. In return, GF can contribute to data-driven approaches by methods to transfer resources from one language to others, to augment data by rule-based generation, to check the consistency of hand-annotated corpora, and to pipe analyses into high-precision semantic back ends. This article gives an overview of the use of abstract syntax as interlingua through both established and emerging NLP applications involving GF.
중소기업 비즈니스 디지털라이제이션의 공급사슬 성과에 대한 실증연구 : 공급사슬 동적역량 영향성 중심으로

Business digitalization has had pervasive effects on organizations, industry structures and the economy as a whole. Business digitalization is being emphasized as an innovation strategy on the supply chain and the burden on the competitive advantage, which must adapt to business digitalization, is the cause of the company's difficulties in dealing with environmental changes. Therefore, the purpose of this study is to empirically analyze the effect of supply chain dynamic capabilities that enable smooth exchange of resources and information in the front and rear of the supply chain and interaction with partnership firms on business digitalization performance. To this end, data was collected through online surveys targeting companies participating in manufacturing and supply chain that are implementing digitalization. Specifically, we selected electronics, automobiles, pharmaceuticals, machinery, etc., which are digitalization applied to Seoul/Gyeonggi, Daejeon and Gyeonsang regions. In order to minimize common method bias, this survey was conducted by clarifying the concepts such as “supply chain dynamic capability” and “business digitalization” as measured variables through a preliminary survey. In addition, in order to minimize non-response convenience, it was attempted to increase the validity of the analysis results by excluding them from analysis. As a result, we found that supply chain dynamic capabilities had a positive and significant effect on business digitalization performance. Also, although the effect of business digitalization performance on supply chain performance has been shown to have a positive and significant effect, it prove that supply chain performance decreases as external opportunities increase among business digitalization performance. Therefore, by focusing on the dynamic capabilities of the supply chain, companies can increase their understanding of the introduction of new technologies and increase the operational efficiency of the supply chain environment. we identified that the network due to convergence with digital technology can be linked to the occurrence of security incidents for various information such as market demand, inventory, and quality in the supply chain.
site selection Police stations in the marginal settlements -Case Study: Bumehen city

Marginal areas are that have long-lasting characteristics in terms of physical, economic, environmental, social and cultural dimensions. These areas are automobile without planning and are generally of low security due to the presence of characteristics such as economic challenges, demographic groups, migrations, unplanned and organic tissues, and the challenges of providing security to the force. Police have created. The city of Bumehen has also been one of the areas encountered with the phenomenon of interstellar marginalization. Special features of the city of Bumehen , both from the environmental and economic and physical aspects, have caused the security of the city to be encountered by the police. Hence, the purpose of this article is to site selection Police station in the marginal settlements of the city of Bumehen , in order to locate the favorable locations for the establishment of security centers in the city of Bumehen, in view of the variables that affect the law enforcement. This is an applied-theoretical study and its method is descriptive-analytic. For this purpose, after extraction of variables by studying relevant documents and setting up a fuzzy questionnaire and using the Fuzzy Hierarchy (FAHP) method and GIS software, conceptual maps Production and integration were carried out. By laying the layers, the areas of the Bahonar Street on the north of Damavand road and the limits of Asgari Street in the south of Damavand Road were the appropriate areas for the establishment of security centers in the city of Bumehen .
CREATIVE STORY THROUGH 9GAG APPLICATION IN TEACHING SPEAKING

This study aims to improve students’ speaking abilities through creative stories using the 9GAG application. The study is a quantitative method with Quasi-Experimental design. Speaking tests are employed as research instruments in collecting the data. The researchers used two classes of the ninth grade of MTs Al-Islamiyah, Tangerang for the population. The sampling technique used was cluster sampling in which the 9.4 class was determined as the experimental class and the 9.5 class as the control class. The data was analyzed statistically by using a t-test to find out the significant difference. The result of this analysis indicated that the average value of the students was 70.33, the statistical calculation showed that the results of to (t-observation) was greater than tt (t-table) in which to obtain was 19.24 while the value of t-t was 2.015 with a significant level of 5% (0.05). Therefore, alternative hypothesis (Ha) was accepted while null hypothesis was rejected. This research summarized that 9GAG application has a positive effect to improve students’ speaking skills.
Effectiveness of a Hybrid Deep Learning Model Integrated with a Hybrid Parameterisation Model in Decision-Making Analysis

Deep learning has recently gained the attention of many researchers in various fields. A new and emerging machine learning technique, it is derived from a neural network algorithm capable of analysing unstructured datasets without supervision. This study compared the effectiveness of the deep learning (DL) model vs. a hybrid deep learning (HDL) model integrated with a hybrid parameterisation model in handling complex and missing medical datasets as well as their performance in increasing classification. The results showed that 1) the DL model performed better on its own, 2) DL was able to analyse complex medical datasets even with missing data values, and 3) HDL performed well as well and had faster processing times since it was integrated with a hybrid parameterisation model.
To Thine Own Self Be True? Incentive Problems in Personalized Law

Recent years have seen an explosion of scholarship on “personalized law.” Commentators foresee a world in which regulators armed with big data and machine learning techniques determine the optimal legal rule for every regulated party, then instantaneously disseminate their decisions via smartphones and other “smart” devices. They envision a legal utopia in which every fact pattern is assigned society’s preferred legal treatment in real time. But regulation is a dynamic process; regulated parties react to law. They change their behavior to pursue their preferred outcomes—which often diverge from society’s—and they will continue to do so under personalized law: They will provide regulators with incomplete or inaccurate information. They will attempt to manipulate the algorithms underlying personalized laws by taking actions intended to disguise their true characteristics. Personalized law can also (unintentionally) encourage regulated parties to act in socially undesirable ways, a phenomenon known as moral hazard. Moreover, regulators seeking to combat these dynamics will face significant constraints. Regulators will have imperfect information, both because of privacy concerns and because regulated parties and intermediaries will muddle regulators’ data. They may lack the authority or the political will to respond to regulated parties’ behavior. The transparency requirements of a democratic society may hinder their ability to thwart gamesmanship. Concerns about unintended consequences may further lower regulators’ willingness to personalize law. Taken together, these dynamics will limit personalized law’s ability to optimally match facts to legal outcomes. Personalized law may be a step forward, but it will not produce the utopian outcomes that some envision.
ADGraph: Accurate, Distributed Training on Large Graphs

Graph neural networks (GNNs) have been emerging as powerful learning tools for recommendation systems, social networks and knowledge graphs. In these domains, the scale of graph data is immense, so that distributed graph learning is required for efficient GNNs training. Graph partition-based methods are widely adopted to scale the graph training. However, most of the previous works focus on scalability other than the accuracy and are not thoroughly evaluated on large-scale graphs. In this paper, we introduce ADGraph (accurate and distributed training on large graphs), exploring how to improve accuracy while keeping large-scale graph training scalability. Firstly, to maintain complete neighbourhood information of the training nodes after graph partitioning, we assign l-hop neighbours of the training nodes to the same partition. We also analyse the accuracy and runtime performance of graph training, with different l-hop settings. Secondly, multi-layer neighbourhood sampling is performed on each partition, so that the mini-batch generated can accurately train target nodes. We study the relationship between convergence accuracy and the sampled layers. We also find that partial neighbourhood sampling can achieve better performance than full neighbourhood sampling. Thirdly, to further overcome the generalization error caused by large-batch training, we choose to reduce batchsize after graph partitioned and apply the linear scaling rule in distributed optimization. We evaluate ADGraph using GraphSage and GAT models with ogbn-products and Reddit datasets on 32 GPUs. Experimental results show that ADGraph achieves better performance than the benchmark accuracy of GraphSage and GAT, while getting 24-29 times speedup on 32 GPUs.
Reimagining heritage buildings as technological spaces

In the course of two immersive projects, Digital Ghost Hunt (UKRI/AHRC) and XR3 (UKRI/AHRC; in process), we have developed a framework for temporarily reconfiguring heritage buildings as technological performance spaces for roaming audiences, without the need for making any permanent changes to the fabric of the buildings. The performances produced within this framework are designed as participatory, ‘storified’ encounters with heritage buildings and their history, utilising a range of simple hand-held sensor devices (SEEK detectors, designed and built for the project), the heritage building itself, and a custom ‘ghost story’ that allows participants to uncover the history of the building. At the immediate level, our framework builds new young and young adult audiences to engage with heritage and enter the technological design process as collaborative makers and performers. Beyond this immediate level, our approach superimposes a technological space onto an architectural heritage space, emphasising its potential as an ‘experience machine’ that is animated by our movements, perceptions and actions. Reconfiguring and combining technological spaces and heritage spaces, or aim is to create a subject position that is located where they intersect, inviting participation as agents that connect and ‘caretake’.
LibGuides: Finding Resources & Using Interlibrary Loan: Home

A Step by Step Process to help students request books and articles through Interlibrary Loan.