Reviewerly/exHarmony huggingface dataset:
Sounding Video Generator: A Unified Framework for Text-guided Sounding Video Generation

As a combination of visual and audio signals, video is inherently multi-modal. However, existing video generation methods are primarily intended for the synthesis of visual frames, whereas audio signals in realistic videos are disregarded. In this work, we concentrate on a rarely investigated problem of text guided sounding video generation and propose the Sounding Video Generator (SVG), a unified framework for generating realistic videos along with audio signals. Specifically, we present the SVG-VQGAN to transform visual frames and audio melspectrograms into discrete tokens. SVG-VQGAN applies a novel hybrid contrastive learning method to model inter-modal and intra-modal consistency and improve the quantized representations. A cross-modal attention module is employed to extract associated features of visual frames and audio signals for contrastive learning. Then, a Transformer-based decoder is used to model associations between texts, visual frames, and audio signals at token level for auto-regressive sounding video generation. AudioSetCap, a human annotated text-video-audio paired dataset, is produced for training SVG. Experimental results demonstrate the superiority of our method when compared with existing textto-video generation methods as well as audio generation methods on Kinetics and VAS datasets.
KGFlex: Efficient Recommendation with Sparse Feature Factorization and Knowledge Graphs

Collaborative filtering models have undoubtedly dominated the scene of recommender systems in recent years. However, due to the little use of content information, they narrowly focus on accuracy, disregarding a higher degree of personalization. Meanwhile, knowledge graphs are arousing considerable interest in recommendation models thanks to their ability to enrich the system with content features that captures subtle user-item relations. Nevertheless, with many high-quality features, the models become more complex and challenging to train. We extend KGFlex [ 16 ], a hybrid model that analyzes historical data to understand the semantic features the user decisions depend on. KGFlex represents item features as embeddings, and it models user-item interactions as a factorized entropy-driven combination of the item attributes relevant to the user, thus reducing the complexity and raising the degree of personalization. The method does not neglect long tail items, reducing the popularity bias and ensuring a high level of fairness. The user-item prediction is mediated by the user’s personal views of the embeddings that grant a high degree of expressiveness. This extension analyzes different entropy measurement strategies, an enhanced user negative decision modeling, and assesses the fairness of KGFlex and the impact of its hyperparameters. KGFlex is available at https://split.to/kgflex .
SURVEY ON SMART HAND GESTURE DETECTION SYSTEM

Words prediction using hand gesture recognition is a developing technology aimed at helping deaf individuals to communicate more efficiently.The idea behind this technology is to use a hand gesture recognition system that will help to translate gestures made by deaf people into spoken or written words.This literature review paper aims to examine the current state of the art in this area and provide an overview of the research done in this field.
Chapter 10: Data Mining

10.1. IntroductionData mining is a sprawling activity whose goal is the discovery of patterns in large data sets. The extraction of data from public or private repositories precedes data mining. The current chapter exposes readers to some of the most popular algorithms for clustering and classification. The methods surveyed are notable for their simplicity, speed, scalability, mathematical elegance, and historical precedence. New algorithms are being invented on a daily basis, so we make no pretense to completeness.
Preface

After successfully held for two consecutive years, 2020 3rd International Conference on Computer Information Science and Application Technology (CISAT 2020) was planned to be held on July 17, 2020. Due to the unexpected COVID 19 pandemic, that closed the world to national and international travel, the organization decided to hold the CISAT 2020 totally online. It was a challenge, not only to the organizers, but also to the participants who were delivering their speeches using videoconference tools, under different time zones. CISAT 2020 is to bring together innovative academics and industrial experts in the field of computer information science and application technology to a common forum. The conference will bring together about 180 leading researchers, engineers and scientists in the domain of interest from China, United States, Malaysia, India, Czech Republic. The primary goal of the conference is to promote research and developmental activities in computer information science and application technology and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. List of General Conference Chairmen, Local Co-chairmen, International Technical Committee, Technology Program Committee, and Conference secretary are available in this pdf.
Forewords from the Head of Committee

Assalamualaikum Warahmatullahi Wabarakatuh. We are delighted to introduce the proceedings of the first edition of 2018 1st Workshop on Engineering, Education, Applied Sciences, and Technology (WEAST 2018). The technical program has brought researchers and practitioners around the world to a good forum for discussing, leveraging and developing all scientific and technological aspects that are relevant to digital society. This workshop acquired 106 fullpapers with 4 categories paper with most paper are from Computer Science, Information Technology, Applied Science, Environmental Science, and Operation Research. We hope that the future Workshop on Engineering, Education, Applied Sciences, and Technology will be as successful and stimulating, as indicated with the contributions presented in this volume. Thank you very much for attention. Assalamu Alaikum Warahmatullahi Wabarakatuh. Regard, Dr. M. Ikhsan Setiawan Chairman
Synrift clayey rocks: bulk chemical composition and position on discriminant paleogeodynamic diagrams

The paper analyzes the bulk chemical composition and distribution of the fields of syn-rift clayey rocks on a number of discriminant paleogeodynamic diagrams. It is shown that, in general, there are significant variations in the bulk chemical composition for syn-rift clayey rocks. Thus, for example, the average SiO2 content varies from 44.74 to 66.42 wt. %, the average content of Al2O3 varies from 16.62 to 29.92 wt. %, and the K2Oaver are in the range 0.24 ... 5.77 wt. %. Based on the distribution of the figurative points of the syn-rift clayey rocks of various objects/riftogenous structures in the F1–F2 diagram, it can be assumed that the sources of fine aluminosilicoclastic were magmatic and sedimentary rocks of a wide range of compositions. The substantial overlap of the fields of various objects in the classification diagrams [(Na2O + K2O)/Al2O3]–[(Fe2O3tot + MgO)/SiO2] and K/Al–Mg/Al indicates, in general, the similarity of the compositions of the syn-rift fine-grained clastic rocks of various types of riftogenic structures. The localization of the composition fields of the clayey rocks of different riftogenous structures on such discriminant paleogeodynamic diagrams as K2O/Na2O–SiO2/Al2O3 and SiO2–K2O/Na2O suggests that they do not allow correctly distinguishing between syn-rift clayey rocks and fine-grained rosks of other geodynamic environments. The position of the syn-rift clayey rocks fields presented in our database on the diagram DF1–DF2 has its own characteristics. In most cases, they occupy a particular position in the areas characterizing collision and rifting environments, and a number of fields are located in all three classification areas of this diagram. A significant part of the midpoints of the syn-rift clayey rocks is localized in the DF1–DF2 diagram in the collision field. It seems that all of the above indicates that the DF1–DF2 diagram also does not allow us to obtain a correct information about the geodynamic nature of terrigenous associations.
An Efficient Secure and Flexible Data Sharing in Cloud Computing using Asymmetric Algorithm

Cloud Security is a technique of safeguarding applications, infrastructures and data involved in cloud computing. Cloud security uses a set of technologies, policies and controls to protect data and application from unauthorized access. RSA encryption and decryption algorithm is presented here for cloud security. The cloud can access the data only after receiving the user’s encrypted message using RSA algorithm. However, while transmitting the encrypted message more bandwidth is consumed as the message is transmitted over wireless channel. To minimize the bandwidth, the encrypted message is transmitted into a number of encoded bits and the cloud service provider can append the bits and recover the original message. The performance results for RSA algorithm and the RSA algorithm using Fountain Code are simulated. The results show that the energy, end-to-delay are minimized while using Fountain Code RSA algorithm. In addition, the packet loss ratio decrease and the packet delivery ratio increase.
More than 80 % of sequentially bilaterally implanted CI-patients are not treated sufficiently during the bimodal phase

Introduction In the bimodal phase between the two implantations, patients with sequential bilateral CI had shown a worse speech intelligibility than expected according to the literature. Therefore, the course of rehabilitation of this patient group was analyzed in more detail.
The Development of Interactive Learning Media Based on PBL Learning Models on Theme 9 Sub-theme 3 for Fifth Grade Elementary ASchool

The background that drives this research is that teachers are less than optimal in using instructional media in the learning process. Lack of use of learning media results in less active and passive student activities, so that students' understanding of the material is still lacking. This research aims to produce a product in the form of interactive learning media based on the PBL learning model that is valid and suitable for use in theme 9 sub-themes 3 for grade V SD. This type of research and development uses the ADDIE model. The data collection instrument in this study used a questionnaire. The method of data analysis in this research and development was carried out by using qualitative descriptive analysis techniques and quantitative descriptive analysis. Media experts and material experts validated that interactive learning media products developed were validated, showing the percentage of 84.89% and 82.67% were categorized as "very valid and very suitable for use". Meanwhile, the teacher responses from the three primary schools showed a percentage of 96%, 82%, 96% were categorized as "very valid and very suitable for use", and the responses of students from the three primary schools showed a percentage of 97.39%, 96.09%, 97.5 % in the category "very valid and very worthy of use". So it can be concluded that interactive learning media is very valid and very feasible to use.
Internet-of-Things IoT based Smart Farming Towards Making the Precision Farming in Agriculture

Internet of Things (IoT) technology, has been changed every area of human life by making everything. Well-Developed and bright IOT communicates that a network of things or people which make a self-building network. Smart farming food excellently and supportable for the public. High quality farming-based is one of the most important arena systems of increasing food excellently and supportable for the public. High quality farming is one of the well-known applications of IoT in the agriculture are and plentiful establishments are taking advantage of this procedure around the world. One more type of IoT invention in farming and one more element of high-quality farming is harvest management devices. And weather positions, they should be placed in the field to collect data designed only for happening and only with in crop farming, from temperature and rainfall to leaf water and complete crop health. The use of farming-based drones in smart farming. Also called as UAVs (unmanned air vehicles), drones are drones are well prepared than airplanes and satellites to collect farming-based data. With the increasing approval of the Internet of Things (IoT), connected devise have got into every trait of our life from health, home automation, self-pushed and planning required to move people and supplies to where they’re needed, smart cities and industrial IoT.
Review Paper on “Dog Breed Classification Using Convolutional Neural Network”

Dogs are one of the most common domestic animals. Due to a large number of dogs, there are several issues such as population control, decreased outbreak such as Rabies, vaccination control, and legal ownership. At present, there are over 180 dog breeds. Each dog breed has specific characteristics and health conditions. In order to provide appropriate treatments and training, it is essential to identify individuals and their breeds. Machine learning gives the strength on the thanks to train algorithms model which will handle the difficulties of information classification also prediction grounded on totally on arising information as of raw information. Convolutional Neural Networks ( CNNs ) gives single often used methods for image classification and detection. In this exertion , we define a CNN based approach for spotting dogs in perchance complex images and due to this fact reflect inconsideration on the identification of the one of kinds of dog breed. The experimental outcome analysis supported the standard metrics and thus the graphical representation confirms that the algorithm ( CNN ) gives good analysis accuracy for all the tested datasets.
Security Preservation in Blockchain IoT

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Efficient Ultra-Elastic Resource Provisioning through Hyper-Converged Cloud Infrastructure using Hybrid Machine Learning Techniques.

Ultra-flexibility is future asset provisioning method so as to deftly meet the clients' prerequisite in powerful way. Be that as it may, more components are required for execution improvement, for example, CPU and the capacity. It is trying to decide a reasonable edge to effectively scale the assets up or down. In this paper, we propose an efficient resource provisioning using hybrid machine learning techniques (ERP-HML) that emphasis on mutually advance the vitality utilization of servers and system. Here, the proposed asset provisioning is utilized for ultra-versatile cloud benefits in hyper-joined cloud framework. In a Hyper-converged Infrastructure the resources such as CPU, storage and Network will be virtualized and software-defined as pools to meet the current demand. The principal commitment is to present an artificial plant optimization algorithm to improve the administration inertness and lessening over-provisioning of flexible cloud administrations. The subsequent commitment is to delineate a deep Q neural network (DQNN) for anticipating the server's preparing load. At that point, an improved hunting search (IHS) calculation is use to register the quantity of assets that must be provisioned dependent on the anticipated burden. The principle target of proposed ERP-HML strategy is precisely foresee the handling heap of a conveyed server and gauge the proper number of assets that must be provisioned to decrease vitality utilization. At last, the presentation of the proposed ERP-HML strategy is contrast and the current condition of-craftsmanship strategies as far as energy consumption, infrastructure costs and QoS.
An end-to-end Novel Forecasting Model for Crime Prediction based on Big Data

Big data analytics is a field in which we analyse and process information from large or convoluted data sets to be managed by methods of data-processing. Big data analytics is used in analysing the data and helps in predicting the best outcome from the data sets. Big data analytics can be very useful in predicting crime and also gives the best possible solution to solve that crime. In this system we will be using the past crime data set to find out the pattern and through that pattern we will be predicting the range of the incident. The range of the incident will be determined by the decision model and according to the range the prediction will be made. The data sets will be non-linear and in the form of time series so in this system we will be using the prophet model algorithm which is used to analyse the non-linear time series data. The prophet model categories in three main category and i.e. trends, seasonality, and holidays. This system will help crime cell to predict the possible incident according to the pattern which will be developed by the algorithm and it also helps to deploy right number of resources to the highly marked area where there is a high chance of incidents to occur. The system will enhance the crime prediction system and will help the crime department to use their resources more efficiently.
PLANEJAMENTO DA ASSISTÊNCIA DE ENFERMAGEM À GESTANTE EM UMA UTI COVID

The World Health Organization (WHO) defined 2020 as the year of nursing, but the pandemic caused by the SARSCoV-2 virus brought a huge challenge of adaptation and holistic patient care among the chaos of the world health system.Even though there is a lack of further evidence about the severity of COVID-19 for pregnant women, the WHO has classified them as a risk group.The objective of this Experience Report (ER) is to demonstrate that the Systematization of Nursing Care (SNC) can be the differential for patients in hospital treatment due to complications related to COVID-19.The experience happened in the period from 11/14/2020 to 11/23/2020, during the hospitalization of a pregnant woman 1 Relato de experiência.
Conversation-based Learning Agents Framework

Student's performance and achievement is always a concern.Teaching and learning process with not only blended learning but with learning agents too is found benefited.In Malaysia, it was found that incorporating conversationbased agents in teaching and learning is very rarely.Thus, this project aims to propose a framework to develop a conversation-based agent that assists students' learning in blended learning and to examine the effects of incorporating conversation-based agent on students' achievement in mathematics.Results from phase I showed that all students had never used the conversation-based learning agents in their learning and they are looking forward to use the conversation-based learning agents which they believe that the agents will make the learning fun and interesting.It is hoped that the framework that will be designed and developed in this study is significant to the instructional designers and educators in helping students to reduce difficulties and anxiety level in mathematics and thus improve performance in mathematics.
Faculty Employment Conditions

Higher Education AbstractsVolume 55, Issue 3 p. 255-255 FACULTY EMPLOYMENT CONDITIONS Faculty Employment Conditions First published: 30 July 2020 https://doi.org/10.1111/hea.17_12154Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume55, Issue3July 2020Pages 255-255 RelatedInformation
Cyber Exploitation

When people generally speak of cyber-attacks or cyber warfighting, the onus typically seems to be on the end effect of the attack. This is understandable as the attack portion of cyber-attacks is usually a cyber-physical effect that even non-technical people can understand the impact of. When a cyber-attack can take control of a vehicle’s braking and steering, for instance, the cyber-physical effect of the attack is what makes the news. To the non-technical, losing control of their vehicle is highly relatable. Whereas the hackers among us are more interested in how the vehicle controlling code was delivered to the vehicle and how it was able to take over those computing functions. The term cyber-attack is commonly conveyed and interpreted as the entire process of bringing the attack end effect to bear on a system. In actuality the process involves cyber intelligence gathering or reconnaissance, cyber exploitation, and then ultimately a cyber-attack effect.
Improving time-dependent deformation measurement

&lt;p&gt;Phase-shifted digital speckle shearography (PSDSS) is a technique that uses the interference patterns of light waves to measure the slope of a deformed surface. It shows promise for non-destructive testing of aircraft structures, specifically for strain analysis and noncontact flaw detection. Yet because it requires at least three consecutive images, it is more applicable for measuring static shape changes. Recently, researchers have focused on expanding the technique to capture deformations occurring over time. One such approach, the phase of difference method, takes one image of an object after deformation and several beforehand. We adopted this method to PSDSS, but noise reduced the image quality. For this system to be effective, new algorithms are needed to process the images.A new method for characterizing shape changes over time may improve nondestructive testing.&lt;/p&gt;
Thyroid Nodules Classification in Medical Ultrasound Images using Deep Learning

Ultrasound scanning is most excellent significant diagnosis techniques utilized for thyroid nodules identification. A thyroid nodule is unnecessary cells that can develop in your base of neck which can be normal or cancerous. Many Computer added diagnosis systems (CAD) have been developed as a second opinion for radiologist. The thyroid nodules classification using machine learning and deep learning approach is latest trend which is using to improve accuracy for differentiation of thyroid nodules from benign and malignant type. In this paper we review the most recent work on CAD system which uses different feature extraction technique and classifier used for thyroid nodules classification with deep learning approach. This paper we illustrate the result obtained by these studies and highlight the limitation of each proposed methods. Moreover we summarize convolution neural network (CNN) architecture for classification of thyroid nodule. This literature review is meant at researcher but it also useful for radiologist who is interesting in CAD tool in ultrasound imaging for second opinion.