Reviewerly/exHarmony huggingface dataset:
A novel video noise reduction method based on PDE, adaptive grouping, and thresholding techniques

Undoubtedly, video block-matching and 3D filtering (VBM3D) has achieved a significant improvement in video denoising. Nevertheless, in practice, failure to distinguish between the different noise areas, ignoring noise variances and pixel intensity, false-similar patches, and poor matching are the challenges faced by the VBM3D filter. To avoid these drawbacks, a new video denoising algorithm is proposed. This algorithm based on the nature of the noise areas and the spatial distance between the reference block and its candidate blocks. In the algorithm, hard-thresholding in VBM3D is replaced by adaptive filtering. In this adaptive filter, soft-thresholding is applied to the heavily contaminated areas, whereas anisotropic diffusion filter is applied to the slight-noise areas. Applying adaptive filtering creates a balance between noise removal and edges conservation. To avert the occurrence of the poor choice of the threshold, noise variances, clean image coefficients, and pixel intensity are taken into consideration during computing the proposed adaptive threshold. Due to the strong possibility of similar correlative blocks happening in the vicinity, an adaptive grouping technique is proposed to compute the distance between a reference block and its candidate blocks. Applying this technique helps to reduce the occurrence of false-similar blocks and poor matching.
Fault Tolerance Directed by Service Level Agreement in Cloud Computing Environments

The Cloud Computing environments are susceptible to frequent failures as a result of their dynamic and unstable nature.Failures have a significant effect on cloud performance and on the benefits that customers and providers expect.Therefore, fault tolerance is necessary to mitigate the impact of failures and preventing revenue losses due to service level agreements (SLA) violation penalties.In this paper, we propose a fault tolerance directed by the SLA contracts established between cloud providers and customers.The proposed fault tolerance includes two phases, the first is based on the use of idles VMs according to selection strategies.The second phase is based on both advanced operation of degradation of quality of service and on VMs selection strategies.The advanced operation of degradation consists of beneficial combinations of VMs deallocation and allocation between customers leading to avoid SLA violation penalties.According to the experimental results, our proposed fault tolerance decreases by more than 10% the rate of considered SLA violations compared to existing approaches.
Deteksi Masker Wajah Menggunakan Metode Adjacent Evaluation Local Binary Patterns

The COVID-19 pandemic is still ongoing until 2021 and is likely to continue until an uncertain time. This arises because the spread of the SARS-CoV-2 virus also continued to occur in the community. Of the five points in 5M that has been initiated by the government, the focus of this study is the use of face masks. In this study, an image-based automatic mask detection method using a classification approach is proposed. This method can be used in automated systems to increase public discipline in wearing masks to suppress the spread of the SARS-CoV-2 virus. The classes used in the classification are "with mask" and "without mask". The adjacent evaluation local binary patterns (AELBP) method, which is an extension of the local binary patterns (LBP) method, is used to extract the texture features of each image. Tests were carried out on 2,172 facial images of various sizes, facial accessories, and facial expressions. The test results using the AELBP method show that the accuracy and F-measure are 98.39% and 98.08%, respectively. This result is better than other methods which are also evaluated. In addition, testing of the AELBP method execution time shows that this method is feasible to use on real systems.
A NEW TECHNIQUE FOR DETERMINING REGION OF INTEREST IN SELECTIVE VIDEO PROTECTION APPROACH

Today, the use of video communication in real-time applications is rising at a rapid rate and most of these videos require secure transmission like surveillance, video conferencing, video on demand, and medical and military imaging systems. The trade-off between data security and real-time performance is the main challenge in this field. This paper presents a protection approach for H.246 compression standard that provides a balancing between the security level and coding efficiency. A new technique of region selection for the motion information of the video is designed based on canny edge detection scaling which reduces the amount of encrypted data ,and a strong and fast stream encryption is presented using key generation model of chaos method and RC4 algorithm. The experimental results of the performance metrics confirmed the high perceptual and cryptographic security of the proposed approach against attacks and showed that all the requirements of compression efficiency are satisfied.
A Hybrid Approach for Recognition of Hand Written Devnagri Compound Characters

Being an effective tool in the world of communication, numerous techniques have been developed for documenting the handwritten text. Few of the exceptional techniques describe the environment of handwritten scripts and further convert it into electronic data by implementing various algorithms. Devnagri is one of the widely used scripts for most popular and commonly used languages like Marathi and Hindi. Recent development in the field of handwritten character recognition based on different methodologies like neural network, fuzzy logic, and deep neural networks has shown remarkable improvement in character recognition accuracy from 75% to 96%. We propose a fuzzy-Neural hybrid approach for recognition of hand written Devnagri compound character that uses a rotation invariant rule-based thinning algorithm as one of the major pre-processing activity. Thinning the characters to their central line, preserving the shape of the character are the distinctive features of thinning algorithm. Concurrent application of different rules to each pixel of the character image results into symmetrical thinning as well as improves the overall speed of the system. The system is trained using Neural Network where the weights are optimized using fuzzy rules improving the accuracy of the system.Results obtained for the fuzzy-neural based system with thinning helps in preserving the topology of the characters written in Devnagri and prove that accuracy of the system has stabilized in the band of 92-97% which was fluctuating in the band of 89-94% for the previously implemented systems. The system also shows a substantial improvement in accuracy for recognition of compound characters in comparison with our previously implemented system.
Adversarial and adaptive tone mapping operator: multi-scheme generation and multi-metric evaluation

Tone mapping is one of the main techniques to convert high-dynamic range (HDR) images into low-dynamic range (LDR) images. We propose to use a variant of generative adversarial networks to adaptively tone map images. We designed a conditional adversarial generative network composed of a U-Net generator and patchGAN discriminator to adaptively convert HDR images into LDR images. We extended previous work to include additional metrics such as tone-mapped image quality index (TMQI), structural similarity index measure, Fréchet inception distance, and perceptual path length. In addition, we applied face detection on the Kalantari dataset and showed that our proposed adversarial tone mapping operator generates the best LDR image for the detection of faces. One of our training schemes, trained via 256 × 256 resolution HDR–LDR image pairs, results in a model that can generate high TMQI low-resolution 256 × 256 and high-resolution 1024 × 2048 LDR images. Given 1024 × 2048 resolution HDR images, the TMQI of the generated LDR images reaches a value of 0.90, which outperforms all other contemporary tone mapping operators.
Collective Funding to Reclaim Scholarly Publishing

The reason is the article processing charge (APC), which typically runs $3,000 to $5,000.The APC model, with its tolled access to authorship, is the subscription model seen through a camera obscura: author paywalls in place of reading paywalls.Most scholars cannot afford the steep fees, a fact masked by the privileged segment who can: scientists in the rich industrialized world, and scholars in a handful of wealthy European countries and North American universities.The fees are often paid via so-called "read-and-publish" deals, which fold APCs into the subscription contracts
The Effect of Erp Effectiveness on the Accounting Information Quality with Personal Competence as Moderating Variabel on Pt. Aneka Gas Industri Tbk

This study aims to examine and analyze the effect of Production Planning and Sales &amp; Distribution variables on The Accounting Information Quality moderated by personal competence at PT Aneka Gas Industri Tbk Wil I North Sumatra. Type of this research using causal research. This study using 126 respondent as a sample with cencus method, questionnaire were given and filled out by employees of PT Aneka Gas Industri Tbk Wil I North Sumatra using ERP-SAP. The data were analyzed using the partial least squares (SEM-PLS) structural equation model. The results of this study indicate that the Production Planning variable has a significant and positive effect on the accounting information quality. Sales &amp; Distribution variables have a significant and positive effect on the accounting information quality. In addition, this study also found that personal competence did not moderate the effect of Production Planning and Sales &amp; Distribution variables on The Accounting Information Quality.
Data Stream Clustering Using Fuzzy-based Evolving Cauchy Algorithm

Many different applications in the real world can generate huge amount of data, that has unconventional features including massive size, fast access, besides the evolving in its nature; this is data stream.Data stream clustering algorithms began to grow at breakneck speed.evolving Cauchy (eCauchy) is a significant algorithm of density-based data stream clustering.The major limitation of eCauchy is the high number of clusters generated in dynamic environments.This paper presents an evolving model for data stream by optimizing e-Cauchy algorithm to decrease the number of clusters and reach to an ideal number by implementing evolving mechanisms (adding, merging, splitting clusters) based on a specific membership function.Model is tested by two real datasets NSL-KDD99 and keystroke.Proposed model outperforms two other algorithms, e-Cauchy and FEAC-Stream.Model constructs five and four clusters with less time to implement 1.30 and 2.30 minutes respectively for each dataset.
A Spectroscopy-Network-Based Method for Forgery Detection of Documents

This article introduces a novel approach for forgery detection of documents based on the spectroscopy of documents' matters.The proposed approach uses concepts from network science to generate a weighted network of spectrums for both the original and questioned documents together.The nodes of the network represent the spectrums and the edges are the correlations among them.The detection method is based on the number of clusters obtained from the tested network using a modified version of the Louvain algorithm.The spectrums of the inks and papers that were used in printing the documents were obtained using Laser-Induced Breakdown Spectroscopy (LIBS) technology.The proposed approach was tested under variety of cases such as inkjet prints, laser prints, and different kinds of printing papers.It was also examined under several clustering algorithms.The findings showed that the approach always successful in distinguishing different kinds of documents with an accuracy of 100%.Moreover, the results of the proposed approach can be visually interpreted, which is more comfortable to investigators.Finally, the proposed approach is considered simple and does not need complex computations compared to the approaches in the literature.
AYURVEDIC MANAGEMENT OF PAKSHAGHATA (ISCHAEMIC STROKE): CASE REPORT

Stroke occurs when there is interrupted blood supply to a part of brain or when the blood supply is reduced, leads to brain cell damage. Stroke is mainly of two types: ischemic and haemorrhagic stroke. Ischemic type of stroke is the most common type. The prevalence of stroke is approximately 250 per 1 lakh person and 9.95 percent of total death. The present case reported to the Sri Jayendra Saraswathi Ayurveda college and hospital, Nazarathpettai, Chennai, Tamilnadu with the complaints of inability to move left hand and left leg with the history of stroke 6 months back, also had slurred speech. This patient’s condition improved very well by internal medicines and panchakarma procedures. At the end of the treatment the patient’s upper and lower limbs range of movements and functions improved. There was also considerable recovery in the mobility, mode, energy and language ability. Recovery was promising and worth documenting.
Data Pricing and Data Asset Governance in the AI Era

Data is one of the most critical resources in the AI Era. While substantial research has been dedicated to training machine learning models using various types of data, much less efforts have been invested in the exploration of assessing and governing data assets in end-to-end processes of machine learning and data science, that is, the pipeline where data is collected and processed, and then machine learning models are produced, requested, deployed, shared and evolved. To provide a state-of-the-art overall picture of this important and novel area and advocate the related research and development, we present a tutorial addressing two essential problems. First, in the pipeline of machine learning, how can data and machine learning models be priced properly so that contributions from various parties can be assessed and recognized in a fair manner? Second, in the collaboration among many parties in building, distributing and sharing machine learning models, how can data as assets be managed? Accordingly, the first part of our proposal surveys data and model pricing in the pipeline of machine learning, while the second part discusses data asset governance for collaborative artificial intelligence. Each part is self-contained. At the same time, the two parts echo each other and connect a series of interesting and important problems into a dynamic big picture.
Study of challenges faced by Enterprises using Security Information and Event Management (SIEM)

The field of information security plays an important role in education, IT, health domain, etc. Much research has been carried out in order to secure data in hardware, on the cloud, and during transmission over the network. A secure data transmission and securing the stored data is still taken as one of the concerned areas. Cloud-based SIEM is used nowadays, which is the art and science to secure the information of the organization. SIEM is Security Information and Event Management, which means securing the organization containing network devices and devices holding critical and sensitive information. In this paper, a survey is carried out to determine the gap in current security providers and areas that need attention. We take logs as input and send them to SIEM for analysis. Whether a SIEM is capable enough to determine the unknown threats and user behavior to identify insider threats. Also, terms such as EPS, False positive Rate, Mean Time to Resolution are used as compassion and aim to keep False positive rate and mean time resolution value low and EPS no restriction.
Microblogging in Denmark and Poland — a contrastive analysis. Part I.

The anthology Microblogs global is an international study of Twitter. Fifteen researchers examined tweets in Chinese, German, English, French, Italian, Japanese, Dutch, Portuguese, Russian, and Spanish regarding the following linguistic phenomena: orthography, spoken language, vocabulary, reduction, syntax, graphostylistics, interaction and the functional aspects. The book was an inspiration for the analysis of tweets in Danish and Polish because the two languages were not included in the original study. Furthermore, a contrastive analysis of the Polish and Danish tweets is included to highlight the differences in the language of the tweets. The following article is the first part of this study. It deals with the social network and microblogging tool Twitter, including the more technical side of microblogging. The many types of tweets and the extensive terminology involved are thoroughly and conscientiously explained. The contrasts regarding orthography and spoken language are analyzed whereas the discrepancies in vocabulary, reduction, syntax, graphostylistics, interaction and the functional aspects will be described in the second part of the study. The basis for the description is a compilation of 640 tweets — 320 Polish and 320 Danish — from an inhomogeneous community that posts mainly in Polish / Danish. Profiles were chosen completely by chance and they belong to various politicians, journalists and individuals. The study covers the period from March 30 to April 6, 2019.
IMPLEMENTASI SISTEM PENGENALAN WAJAH UNTUK KEAMANAN AKSES BERBASIS UBUNTU MENGGUNAKAN PYTHON

Security is one of the most important needs for human beings in both the building and the house. For the development of security technology used face recognition. Face recognition is a system that identifies facial features that are capable of detecting familiar faces and unknown faces. In this research is implemented with computer vision where the computer can see and understand so that it is information from an image or video. This computer can also mimic the ability of human intelligence. To classify a face object, OpenCv uses the Haar Cascade classifier and uses Python programming language. Application used face Recognition program is PyCharm Comunity 2018 version 3 with Linux operating system Ubuntu 18.04.2 LTS version. The results showed that the accuracy of face reconition depends on the analysis of OpenCv and the classification of Cascade for computer vision process.
SUSTAINABLE DEVELOPMENT OF EDUCATIONAL INSTITUTIONS IN THE CONTEXT OF THE INTRODUCTION OF ELEMENTS OF DISTANCE EDUCATION IN THE LEARNING PROCESS

Distance education is essentially the basis for the development of educational institutions that provide innovative services, which cannot be obtained elsewhere. It is believed that distance education is a substitute for the traditional as part of simplifying and cheapening the learning process. At the same time, the leading global trends suggest the need for the development of elements of self-education and the expansion of the geographical nature of educational offers. The relevance of the study is that it is necessary to distinguish between the use of distance education methods in the traditional sphere and the sphere of training professionals. The paper presents the concept of the need to integrate elements of distance education in the process of preparing university teachers. The authors believe that teachers in drawing up courses and applying learning technologies use the same techniques as in distance learning. Therefore, the use of distance learning technologies can be shown as the basis for the development of advanced training programs and additional professional education. The authors proposed a study on the need and sufficiency of the use of distance education technologies and the possibilities for their integration into the process of postgraduate education. The practical significance of the work is determined by the fact that the possibilities of self-development of teachers are fully disclosed, not only as subjects of the educational process but also as subjects of training.
Problems of Introducing Information and Communication Technologies into the Educational Process during the COVID-19 Pandemic

Informatization of postmodern society is a promising path to economic, social and educational development. The informatization of education is aimed at the formation and development of the intellectual potential of the nation, the improvement of the forms and content of the educational process, the introduction of computer teaching and testing methods, allows solving problems at the highest level, taking into account world requirements. One of the important directions in the development of informatization of education in the context of a pandemic is new computer technologies. Interactivity, intensification of the learning process, feedback - are the real advantages of these technologies, which necessitated their application in various fields of human activity, primarily in those related to education and professional training. Now, in a pandemic, the number of studies has noticeably increased, the subject of which was the use of information and communication technologies in the educational process. The article deals with the problem of psychological and didactic aspects of the introduction of information communication technologies in the process of teaching students of higher educational institutions. An analysis of the problems and prospects of using information communication technologies in higher educational institutions was carried out, as a guarantee of building an intensive process of assimilating knowledge, skills, and abilities of students of higher educational institutions.
Rekomendasi Paket Menu Angkringan Waru Tanjung Bias Dengan Algoritma Frequent Pattern Growth Berbasis Web

Culinary business using carts selling various kinds of heavy food, light and drinks, is favored by many people to just fill their stomachs, gather with friends and even family. Culinary businesses or culinary destinations like this are known as Angkringan which are increasingly mushrooming in the millennial generation. Angkringan Waru, located in Tanjung Bias, is a gathering destination for all people to enjoy a relaxed atmosphere on the beach. Angkringan Waru provides 85 types of menus for its customers, the many menus often confuse customers in choosing snacks while enjoying the beachside atmosphere. Starting from these problems, data mining techniques are used with the Frequent Pattern Growth (Fp-Growth) algorithm to recommend items in producing a menu package consisting of 1 snack item and 1 drink item. The dataset used is transaction data from Angkringan Waru as many as 870 transactions, the resulting output is a menu package recommendation rule and implemented in a web for Angkringan Waru. The Fp-Growth Data Mining Application by providing a minimum support value of 20% and Confident 50% with a lift ratio &gt; 1 produces 57 rules or menu package recommendations that will be offered to Angkringan Waru customers. The results of the application in the form of 57 menu package recommendations are then used as recommendations for Angkringan Waru customers, where these menus are the favorite menus of customers at Angkringan Waru.
Importance of Cyber Security in Digital Era

Abstract: Cyber security acknowledges an enthusiastic job in the space of data innovation. Shielding the data has become a gigantic issue in the current day. The cyber security the primary concern that begins as a top priority is 'digital violations' which are increasing on large scale. Today, network protection is at a critical concern to many. At whatever point we contemplate the network protection the primary thing that rings a bell is 'digital wrongdoings' which are expanding colossally step by step. This paper generally points out on the network protection, cybersecurity and digital psychological warfare. The digital psychological oppression could cause relationship to lose billions of dollars in the area of associations. Keywords: Network, Cyber Security, Digital, Data Security