Reviewerly/exHarmony huggingface dataset:
Innovative Trends in the Formation of the Content of Master's Training in Speciality 029 “Information, Library and Archival Affairs”

The relevance of the study is due to the need of modernisation of the information sphere of activity in accordance with European standards and the proper level of training of specialists in this field for further professional development. The aim of the work is to create an innovative model of the content of the training of masters majoring in 029 “Information, Library and Archival Affairs” based on a comparison of the current state of the Ukrainian, American, and European educational systems. To achieve this goal, the author used the following general scientific research methods during three stages of scientific research: structural and comparative analysis, synthesis, generalisation, and systematisation, as well as modelling. The theoretical basis in the form of scientific and journalistic literature on the training of Master’s in library and archival affairs in Ukraine and abroad has been developed. The experience of Ukrainian higher educational institutions, American and European institutions of higher education is compared in order to identify the focus of training specialists in speciality 029 “Information, Library and Archival Affairs”. The theoretical orientation of the Ukrainian educational system for the formation of masters of this speciality and at the same time the practical orientation of universities in the USA, France, Australia, Spain and Sweden are determined. An innovative structural and functional model of forming the content of training masters of information and library science based on comparative analysis and theoretical and practical components is developed. The practical value of scientific work lies in the creation of the author's model of promising functional training of masters of speciality 029 “Information, Library and Archival Affairs” in accordance with the requirements of modernity, with its subsequent possible implementation in the activities of higher educational institutions
Implementation of Internal Control in the Accounting Information System of Credit and Cash Sales at PT Wings Surya in Surabaya

In the current industrial era, there is a lot of emerging competition in technology and information. The increase in information causes the growth of technology to be faster. This study aims to determine whether the application of internal control accounting information systems for credit and cash sales at PT Wings Surya in Surabaya is in accordance with the theory. This research method uses descriptive method. The data collection used were interviews, observations, documentation, and literature studies. The credit and cash sales accounting information system implemented at PT Wings Surya has used a computerized system well. Namely by using the SmartOpr system and SAP System. The results of the study show that the internal control of the credit and cash sales accounting information system at PT Wings Surya in Surabaya is in practice in accordance with existing theory.
The Study of Development and Implementation of Adaptive Learning Design Model in Chinese Language Education

Facing the pandemic, not only in Korea but also in many countries the platform of edcation moved into online. Since the interaction was limited in webina class, there were many difficulties in foreign language learning classes, where interaction plays a key role in acquiring knowledge. Normarly, though it’s the same class, there are many different language level learners gather together in Korea. If it’s a offline class, teachers don’t have trouble to catch our the each learners’ language ability by glance. Unlike the offline class, it’s hard for teachers to know there language ability in first day of online class. Moreover, learners have the the different learning tendency,<BR> However, in most of the online Chinese language classes conducted in 2020, most of the class format were limited webina such as Zoom. This paper proposes a TEA-ISD model and class operation idea for adaptive learning. First, we review the previous studies of adaptive learning, and present a circular instructional design model and TEA learning and development strategy for Chinese as a foreign language classes in an e-learning environment. In accordance with this TEA-ISD model and development strategy, We developed educational content to be used in the class that will conduct adaptive classes, We conducted internal validation of the TEA-ISD and classroom implementation process. As a result of expert verification, this instructional model was evaluated as appropriate for realizing adaptive learning in Chinese language education. However, it was discussed that providing a differentiated learning path and learning support service at the course implementation stage is the key to realizing adaptive learning successfully.
Union Filesystem Source Directory Protect Architecture

Union filesystem has the advantage of providing a single integrated view of files on the same path by eliminating duplication of files on the same path at the mount point. A directory mounted by union filesystem should not be able to have access to that source directory in accordance with the security principle. However, the current operating system does not provide a separate security feature for the directory mounted by the union filesystem. In such an environment, there is a security threat in which unauthorized personnel access the source directory for mounting with union filesystem to create arbitrary files or change critical files. This study proposes an approach control architecture that can protect the directory to mount with union filesystem. The proposed architecture can work at the Kernel Level to block users from bypassing access. In order to verify the effectiveness of the proposed mechanism, the Positive and Negative function tests were conducted in this study. As a result, the proposed union filesystem source directivity architecture has been verified to be effective.
Hybrid Fruit Fly Optimization for EHR Applications in Cloud Environments for Load Balancing Optimization

Cloud computing is considered as the most inventive type of innovation as it has acquired its consideration in pretty much every area name in manufacturing, IT, training, automotive industry, and so forth. It is progressing as a lively innovation for clear reasons-customer well-being security and protection. It has stated its position in the medical market by storing electronic patient information and cloud-based analytic frameworks. The expanding utility of cloud-based innovation as a rule makes vulnerability in asset the executives and creates high force utilization, lackluster showing, and working expenses. The suggested method is utilized to achieve optimal resource use while lowering energy consumption and costs in a suitable simulation environment. The outcome of the current proposal provides a better path of action. The preliminary results demonstrate that the suggested estimation outperforms the present weight-changing calculations profitably.
Design Recommendations for Using Textual Aids in Data-Science Programming Courses

Despite a recent shift towards online learning, recommendations for multimedia design principles in programming-based instruction remain unclear. Specifically, how can we teach people to code, a text-heavy medium, properly in online instruction? This question is especially important since the text-based format of screencasts may interact with psychological mechanisms known to affect cognitive processing and learning. We investigate this question, and find that previous results from other domains do not necessarily hold in the programming education. We also explore how design changes in textual aids affect learners' performance in programming-based multimedia learning. Our results suggest that the redundancy effect does not significantly hinder learning, which conflicts with previous findings, and that the spatial contiguity effect occurs even between textual components. This work contributes to an evidence-based understanding of how to design more effective multimedia learning environments for programming-based instruction.
Swarm Plot: Data Redistribution in Non-Random Technique

Data visualization is a process of converting data into visual forms with the purpose to extract hidden knowledge behind the data. One of the commonly used visualization is swarm plot which is suitable for small dataset. This is due to the fact that as data getting bigger, the data points will tend to overlap and be congested on the plot. Thus, it might lead to misrepresentation of data patterns and could result in false knowledge extraction. Thus, there is a need to have an effective swarm plot technique that can manage bigger data with no overlapping of data points. To achieve the objective, this research has formulated and enhances the process of identifying the right coordinate for each point on the plots. Overall, the system has successfully improved the redistribution of data points as such it can manage bigger data and at the same time avoid points from overlapping.
No More Leaky PageRank

We have surveyed multiple PageRank implementations available with popular graph processing frameworks, and discovered that they treat sink vertices (i.e., vertices without outgoing edges) incorrectly. This leads to two issues: (i) incorrect PageRank scores, and (ii) flawed performance evaluations (as costly scatter operations are avoided). For synchronous PageRank implementations, a strategy to fix these issues exists (accumu-lating all values from sinks during an algorithmic superstep of a PageRank iteration), albeit with sizeable overhead. This solution, however, is not applicable in the context of asynchronous frameworks. We present and evaluate a novel, low-cost algorithmic solution to address this issue. For asynchronous PageRank, our key target, our solution simply requires an inexpensive O(Vertex) computation performed alongside the final normalization step. We also show that this strategy has advantages over prior work for synchronous PageRank, as it both avoids graph restructuring and reduces inline computation costs by performing a final score reassignment to vertices once at the end of processing.
Research on the Technology of Generating Single-Table sql Query Sentences in Chinese Natural Language

With the rapid development of the Internet nowadays, the amount of data has become larger and larger. It is easier to save data in the database, but finding the information people want from the database has become an urgent problem. The main task of the natural language generating SQL query statement task is to convert the natural language into an executable SQL query statement. However, how to convert natural language into SQL queries has become a difficult task for this task. This research mainly uses the template-filled SQLNet model as the benchmark model to design a model from Chinese natural language to SQL query statements. At the same time, two modules, select num and where relation, are added to the Select clause and the where clause, respectively. At the same time, modify the condition value module in the where clause and then predict the condition value after adding the column type information. This Chinese NL2SQL deep learning model was experimentally verified on an open Chinese data set TableQA. The experimental results show that the model can effectively convert Chinese natural language into SQL query sentences.
Malignancy Detection in Breast Histo-Images Using Multi-layer Perceptron

Breast cancer is the most prevalent cancer among women, and it is a major cause of high death rates. Manual identification of this condition takes several hours and the assistance of professionals. As a result, an automated breast cancer diagnostic is compelling necessity to minimize the time it takes to diagnose the disease and to slow its progression. In this paper, we employed both primary and secondary data sets of breast cancer histo-images, selected 48 top breast cancer histo-image features, and trained the proposed Multilayer perceptron (MLP) model for breast cancer diagnosis. In chronological order, image processing algorithms for histo-images taken from the microscope are discussed. After testing the competing approaches, the best in class image processing methods are identified. The suggested model’s efficiency is measured using eight machine learning metrics, with a high accuracy of 96.77% and a F1 score of 97.61. Future researchers can use the optimum parameter values for the MLP model that we have reported.
Prompt and Secure Data Storage and Recovery System

Cloud computing has included an essential part of its industry and statistics garage is the main service provided, where a huge amount of data can be stored in a virtual server. Storing data in public platforms may be vulnerable to threats. Consequently, the obligation of secure usage and holistic backup of statistics falls upon the corporation providers. Subsequently, an affordable and compliant mechanism of records auditing that permits groups to audit the facts stored in shared clouds whilst acting quick and trouble- unfastened healing might be a fairly sought-after cloud computing task concept. There is a lot of advantage in growing this domain and there is considerable precedence to follow from the examples of dropbox, google power among others.
The diachrony of word-final obstruent devoicing in Maltese

The present paper is an attempt at reconstructing the diachrony of word-final obstruent devoicing in Maltese. The period covered ranges from the 15th century to the beginning of the 19th century. The sources considered include: texts such as Caxaro’s Cantilena, Buonamico’s Sonnet, Mifsud’s sermons, Soldanis’ grammar, Wzzino’s Christian Catechism, Soldanis’ dialogues; lexicographical works such as Megiser’s wordlist, Thezan’s dictionary, Skippon’s wordlist, Maius’ wordlist, de Sentmenat’s vocabulary, Soldanis’ dictionary; records of personal names and place-names. It is shown that the first clear instances suggestive of the occurrence of this phenomenon date from the end of the 16th century. For the next approximately two centuries, there is evidence, on the one hand, of word-by-word extension of word-final obstruent devoicing as well as, on the other hand, of considerable variation in its occurrence. Word-final obstruent devoicing appears to have become a categorical rule of standard Maltese phonology only towards the end of the 18th century, i.e. it is a rather late development in the phonological component of the language. The rule has been operative ever since. In a diachronic perspective, word-final obstruent devoicing in Maltese is a case of rule addition, an illustration of lexical diffusion, and an instance of a persistent rule.
Abnormal Perception of Railway Perimeter Based on Trackside Surveillance Video

Aiming at the characteristics of large amount of railway surveillance data and high timeliness of foreign intrusion detection, this paper proposed a method of abnormal perception of railway perimeter based on trackside surveillance video, by the pixel value change rate and gray projection. It consists of four steps: the color type of image is converted to HSV from original RGB in preprocessing; background modelling; abnormal pixel is identified according to the pixel value change rate; the location and size of abnormal area are extracted by gray projection, realizing abnormal perception of the railway perimeter. The small calculation of change rate can speed up the method which is suitable for large-scale camera numbers in the railway scene. Meanwhile, gray projection reflects relative position of abnormal area in the horizontal and vertical directions, obtaining effective information in the video data and getting rid of most useless information. The proposed method in this paper can achieve 0.964 accuracy and 35 FPS on CPU platform.
A Study on Location Based Services and TTP based Privacy Preserving Techniques

The motivation for every location based information system is: ”To assist with the exact details, with customized configuration and location sensitivity at the right place in real time”. The progressions in area location aware gadgets, for example, cell phones, RF-ID chips, GPS gadgets, and diverse situating advancements have prompted the development. Location-based Services provides the client with numerous administrations, such as locating a course, disclosing nearby buddies, locating crisis focuses, and so on. LBS (Location Based Service) has become increasingly more famous as of late. LBS permits Users to investigate the general condition. Location based services (LBS) use geospatial information of cell phones to give data progressively. A key worry in utilizing these administrations is the need to uncover the client’s accurate area, which may permit an enemy to construe private data about the client. Right now, play out an exploratory study about the different methods that have been proposed by numerous specialists dependent on brought together methodology, to protect location security of the client. In this paper subtleties and survey the different existing methods for protecting area security of the taking part client in LBS..
Adding abductive reasoning to a propositional logic

Abstract We present a technique for obtaining a logic with abductive reasoning extending a given propositional logic. Abduction, along with deduction and induction, is recognized as important for machine learning, namely in identifying possible causes that may lead to the occurrence of an event and in providing new ways for a computational device to achieve a certain objective. Each rule in the original calculus induces a set of multiple-conclusion abductive rules. Moreover, rules stating generic properties of abduction have to be added. In the induced logic, the deductive mechanism of the base logic coexists with this abductive component. A new notion of a multiple-conclusion derivation had to be developed. Due to the canonical nature of obtaining such a logic, we prove the preservation of soundness, completeness, decidability and computational complexity. These concepts and results are illustrated in a robot navigation problem using a multimodal logic.
Emergency Resource Scheduling Model of Rail Transit Based on Transportation Capacity

Availability of emergency resources is the basis and key to emergency management. In order to ensure the availability of emergency resources, it is necessary to have immediate and efficient emergency resource scheduling. In view of the situation that most subway stations are located in underground emergency resource dispatching speed and efficiency are limited by many factors, firstly, the dispatching process and influencing factors of subway emergency emergency resources are analyzed, and the relationship between emergency event types, impact levels and emergency rescue is sorted out. Secondly, the common knowledge meta-model is used as the basis of unified structured knowledge representation for emergency case components. Then, the transport capacity is considered comprehensively to construct the emergency resource scheduling model based on the transport capacity and subject to the time window, and the solving algorithm is designed. Finally, an example of Beijing subway network is taken to verify the effectiveness of the emergency resource scheduling model.
New Event Detection for Web Recommendation Using Web Mining

In this study, specifically, we describe the methods that can be used to aid in the analysis of web uncertainty in this study. An important component of this strategy is the development of a new set of eb pages for the purpose of monitoring, summarizing, and tracking events. We are calculating the response time for this collection of web-based applications. For the purpose of improving web service performance, the Multilayer Perception model was applied to the problem. This review of the literature discusses non-content-based approaches for web pages as well as content-based strategies for web pages, which are two different classifications of web pages. These assumptions are then applied to website recommendations in this article, which discusses various strategies for determining the number of linguistic uncertainties that underpin various online events. Our goal is to create a web event in which various keywords are mingled and then applied to the event’s uncertainties and dangers, in order to achieve this. A pyramid-like structure is commonly used on the internet to organize uncertainty hierarchies, and these levels are denoted by a pyramid-like structure used to denote the levels of linguistic uncertainty.
Self-Supervised Planning for Robots using Video Prediction

Video forecast innovation offers modern benefits for robots in completing a arrangement of assignments based on self-supervised arranging. In a video expectation errand in a scene from the physical world, we must continuously go to to the genuineness of the expectation. Be that as it may, the expectation demonstrate can demonstrate as it were the particular scene; when the scene changes, we got to prepare a modern demonstrate from scratch to fit the space dissemination of the information, which regularly requires a expansive sum of specific data and long preparing time. Subsequently, ways to play down the limitations on demonstrate learning for unused faculties is an critical issue. Pointing at its realness and generalization, this paper analyzes video expectation based on antagonistic preparing and unsupervised transfer learning.For the intuitively operation of a controller, a strategy of video forecast based on a generative adversarial Network (GAN) utilizing the transfer learning procedure is proposed. Utilizing the Sawyer controller solid dataset as a direct and a UR5 controller dataset as a target, the comes about of tests confirmed that the forecast generator prepared by the ill-disposed strategy can create reasonable comes about.
Coarse-to-Fine Loss Based On Viterbi Algorithm for Weakly Supervised Action Segmentation

Weakly supervised action segmentation has been extensively studied to get the category and start time of actions that occur in videos, but it remains an unsolved issue because of lacking great annotation data in video analysis. To handle this issue, weakly supervised action segmentation only uses the action annotation on the whole sequence in a long video instead of specific labeling of each frame, which greatly reduces the difficulty of obtaining video datasets. However, the task remains challenging for the complex temporal length partition of actions in the videos. In this paper, we make use of the Viterbi algorithm to generate an initial action segmentation as the baseline and then design a new coarse-to-fine loss function to refine the length partition and learn the scores of valid and invalid segmentation routes respectively. The new coarse-to-fine loss is learned in the pipeline to reduce the weight of invalid segmentation routes and obtain the best video segmentation. Comparing with the state-of-the-art (SOTA) methods, the experiments on the breakfast and 50 salads datasets show that our fine partition model and coarse-to-fine loss function can be used to obtain higher frame accuracy and significantly reduce the time spent for action segmentation.