Reviewerly/exHarmony huggingface dataset:
Application of CAD in the diagnosis of breast cancer

Breast cancer is an extremely dangerous disease, which if diagnosed in time has a high survival rate. The incidence and mortality rate from breast malignancies are increasing, so in order to reduce these numbers, new technological solutions are being looked into, that should enable the earliest possible detection of breast carcinoma. Although the original solution was seen in traditional computer-assisted detection systems, CAD, applied to various radiological breast imaging methods, the results of various studies discussed in this paper found that they did not meet their original expectations in breast cancer diagnosis. The use of conventional CAD systems still had too many limitations such as a decrease in particularity and positive predictive value with an increase in incorrect results and an increase in revocation rates. However, the development of artificial intelligence, AI-based algorithms has improved the quality and accuracy of conventional CAD systems. Unlike conventional CAD systems based on hand-crafted features, deep learning, as a subfield of AI, is based on representational learning. In representational learning, the algorithm itself during training determines features in the image that indicate the presence of lesions. Recently, such deep learning algorithms have been applied to various radiological methods. In this paper, the analysis of various studies discusses the possibilities, but also the limitations of new AI-based applications for different modalities of breast imaging. Due to the small number of studies conducted on the topic of AI systems and the need for an extremely large set of data for training and validation of the algorithm, many scientists still doubt this new method. Despite these limitations, the AI approach has the ability to detect useful features in an image that are still invisible to the human eye. Future advances in technology will significantly improve AI systems and their implementation in health systems will be inevitable.
Face Detection by Measuring Thermal Value to Avoid Covid-19

In this study, custom Single Shot Detection (SSD) was used to detect infected people faces because of Covid-19.It has been suggested to determine around the eyes area where the body temperature of the person most accurate by determining the facial landmarks with Ensemble of Regression Trees (ERT) model on these detected faces.Finally, the thermal value was measured from around the eyes area in a non-contact way using sensor fusion.As a result of the analyzes made, it was observed that the proposed system gave results close to the different measurement methods.
“A Large Playground”: Examining the Current State and Implications of Conversational Agent Adoption in Organizations

This study explores the short- and long-term expectations about adoption of conversational agents in the organizational frontline. Drawing from in-depth interviews with managers and developers in organizations that have implemented these agents, it sheds light on how the deployment of and collaboration with technology-based autonomous agents influences service activities, and expectations of changes in organizational processes. The interviews revealed that implementations are done on a rather iterative and experimental basis, often balancing the current limits of technology while aiming at improved efficiency, augmenting the work of frontline employees, and meeting the ever-growing demands from consumers.
Design and Fabrication of Water Tank Cleaning Machine

This paper proposes to design and develop a machinery for cleaning domestic rectangular water tanks. It is known fact that cleaning of water tank is a meticulous and labor intensive task. It is very important to regularly clean and maintain the water tank to keep the water borne diseases from spreading. The water in storage units are used for both domestic and industrial purposes. The presence of toxic substances in water can also cause skin diseases. This can be avoided with the use of an efficient tank cleaning system. The system consists of a machine designed with a movable body and an extendable shaft mechanism supporting a rotating brush. The movement of shaft and brush scrubs the walls of the tank. A sprinkler mechanism rinses the sediment deposits from walls of the tank and additionally a vacuum device ingests the sludge from the floor of the tank. The object of this machine is to reduce the overall time and effort invested in the cleaning of water tanks.
Use of social media by selected Indian private sector banks

We are living in a digital world where everything is planned to be digitised leading to an increased use of social media and information technology in our daily lives. Businesses today need to reach out to their customer in a cost efficient manner, while improving efficiency. This is where social media comes into play by helping them gather information and reach out to wide spread customers effectively and efficiently. Presently, banks have also joined the bandwagon of social media simply because of the presence of huge number of users of social media. The present study explores different social media platforms used by the Indian private sector banks with an objective to stay connected with their customers. Ten top private sector banks constitute the sample of the study and Facebook, Twitter, LinkedIn, YouTube are the social media platforms which are being studied. Findings show that Facebook has more extensive usage by banks followed by Twitter, whereas YouTube and LinkedIn have lesser usage.
Comparison between RSA and ECC

Cryptography plays a significant role in the society with today’s advanced development of technology and the nature of it is data exchange. There are two important factors that should be considered in the progress: speed and security. Different algorithms are used to protect secure data between original sender and receiver by a system which uses public key and private key, and this is so-called as public key cryptosystem. In this paper, RSA algorithm and ECC algorithm are shown about their encryption and decryption and their advantages and disadvantages are compared. Elliptic curve cryptography has recently attracted much attention. The benefit of ECC compared to RSA is that it provides the same security but with smaller bit sizes, thus less processing overhead. The encryption and decryption algorithm which is utilized to encrypt and decrypt the video gives the video files more and more security and the most common one is elliptic curve algorithm, which are used to develop schemes such as key exchange and digital signature. However, since the mathematical background of ECC is not as easy as other cryptosystems, as in geometry, abstract algebraic, number theory, it is hard to choose a good curve. Therefore, finally, there are some suggestions to the drawbacks in RSA algorithm in its mechanism to help to reduce the attacks so that people won’t overwhelm reply to much on ECC. Suggestions are given according to the parameters involved in the algorithm and both RSA and ECC are preferred methods until the appearance of quantum computers.
An efficient Task Scheduling Algorithm using Modified Whale Optimization Algorithm in Cloud Computing

Cloud computing brings computing resources such as software and hardware, it serve service to the users through a network. Major concept of cloud computing is to share the marvellous storage section. In cloud computing, the user jobs are prepared and executed with appropriate resources to successfully deliver the services. There are large amount of task allocation techniques that are used to accomplish task planning. In order to improve the task scheduling technique, so we proposed method of efficient task scheduling algorithm. Optimization techniques are solving NP-hard problems is very famous. In this proposed technique, user tasks are stored in the order of queue methods. The priority is designed and allocated suitable resources for the task. New tasks are investigated and kept in the on-demand priority of queue. The output of the on-demand queue is given to the MWOA. It has been proved that this algorithm is capable to eliminate optimization problems and outperform the current algorithms. The method is proposed to the required more number of iterations is reduced. The proposed algorithm is compared with various scheduling algorithms such as, genetic algorithm, ant colony, standard grey wolf optimization and particle swarm optimization. The outcomes of tests indicate the better efficiency of the MWOA in expressions of makespan and energy consumption.
Knowledge and Education in Digital Era

The article describes the crisis experienced by the education system during the development of the digital economy. The main presupposition of the crisis rests upon the following paradox: although the modern economy is based on the accumulation, processing and dissemination of knowledge, a fragmented perception of knowledge as such is being formed nowadays in the whole of society. The urgency of this crisis is especially noticeable against the background of the unfolding pandemic, which exacerbated many serious problems in the academic structures both in Russia and in foreign countries. This crisis of education is supplemented (and enhanced by) the crisis of science as a source of authority in postmodern era. In particular, expansion of social networks within the digital economy leads to the crisis of rational discourse in the society, because of the tendency of individuals to form closed interest communities, based not only on free discussion, but also rather on common misunderstandings, conspiracy theories and esoteric, contra-scientific forms of knowledge. The purpose of the article is to highlight the most important features of this crisis, as well as to outline its specificity within the Russian context. The main conclusion of the article is, although the education crisis in Russia is in many ways more severe than in developed countries, there are still opportunities to overcome it, not only within the framework of an academic system as a whole, but also through a wide range of grassroots initiatives related to with the promotion of scientific knowledge to a mass audience, covering wide sectors of society.
Secure and Access Control Data Monitoring in Vehicular Ad Hoc Network

Present days innovation identifies with internet of Vehicles (IOV) has been expanded to break down traffic in the board frameworks. It is utilized to portray traffic examination and improve proficiency of the vehicle traffic. The stage can take care of the issue of capacity, investigation and multi terminal dispersion of mass information, give traffic data administrations to traffic the board offices and people in general, it is a helpful endeavor to apply propelled data innovation to the transportation business. Causing the broad worries in the exploration network. To empower credible and classified correspondences among a gathering of haze hubs, in this paper, we propose a productive key exchange protocol based on cipher text policy attribute-based encryption (CP-ABE) to set up secure interchanges among the members. To accomplish classification, verification, certainty, and access control, we consolidate CP-ABE and computerized signature strategies. We investigate the productivity of our convention regarding security and execution. We likewise execute our convention and contrast it and the endorsement based plan to delineate its practicality.
A Research Travelogue on Classification Algorithms using R Programming

Classification is a machine learning task which consists in predicting the set association of unclassified examples, whose label is not known, by the properties of examples in a representation learned earlier as of training examples, that label was known. Classification tasks contain a huge assortment of domains and real world purpose: disciplines such as medical diagnosis, bioinformatics, financial engineering and image recognition between others, where domain experts can use the model erudite to sustain their decisions. All the Classification Approaches proposed in this paper were evaluate in an appropriate experimental framework in R Programming Language and the major emphasis is on k-nearest neighbor method which supports vector machines and decision trees over large number of data sets with varied dimensionality and by comparing their performance against other state-of-the-art methods. In this process the experimental results obtained have been verified by statistical tests which support the better performance of the methods. In this paper we have survey various classification techniques of Data Mining and then compared them by using diverse datasets from “University of California: Irvine (UCI) Machine Learning Repository” for acquiring the accurate calculations on Iris Data set.
Research on the Application of Artificial Intelligence in Computer Network Technology

With the continuous development of science and technology, computer technology, information technology have gradually been widely used in today's society. The actual use needs of users are also constantly improving. With the continuous update of Internet technology and information technology, for computer networks, realizing humanized and intelligent services has become an inevitable trend. Intelligent services exist not only in life, but also in all aspects of the work. While introducing the concept of artificial intelligence, it combines the main advantages of AI and discusses the application of artificial intelligence in computer network technology.
Ai and Ml Based Google Assistant for an Organization using Google Cloud Platform and Dialogflow

Many people adopting Smart Assistant Devices such as Google Home. Now a days of solely engaging with a service through a keyboard are over. The new modes of user interaction are aided in part by this research will investigate how advancements in Artificial Intelligence and Machine Learning technology are being used to improve many services. In particular, it will look at the development of google assistants as a channel for information distribution. This project is aimed to implement an android-based chatbot to assist with Organization basic processes, using google tools such as Dialogflow that uses Natural language processing NLP, Actions on Google and Google Cloud Platform that expose artificial intelligence and Machine Learning methods such as natural language understanding. Allowing users to interact with the google assistant using natural language as input and to train the chatbot i.e. google assistant using Dialogflow Machine learning tool and some appropriate methods so it will be able to generate a dynamic response. The chatbot will allow users to view all their personal academic information, schedule meetings with higher officials, automating the organization process and organization resources information all from within the chatbot i.e. Google Assistant. This project uses the OAuth authentication for security purpose. The Dialogflow helps to understand the users query by using machine learning algorithms. By using this google assistant we are going to use the Cloud Vision API for advancement. We will use Dialogflow as key part to develop Google assistant.
Comparative study of dynamic programming and Pontryagin's minimum principle for autonomous multi-wheeled combat vehicle path planning

This paper presents a comparative study of two path planning algorithms using optimal control theory for the autonomous multi-wheeled combat vehicle. The developed optimal path planning algorithms use Pontryagin's minimum principle (PMP) and dynamic programming (DP) approaches. PMP and DP are two major branches of the optimal control theory. A simplified two degrees of freedom (DOF) vehicle model is used to derive the differential equations of the vehicle. The cost function associated with the path generation is to be minimised with the vehicle dynamics equations. A comparative study and performance analysis of generated optimal paths using the proposed algorithms was carried out for various scenarios. The simulation results demonstrate that the generated optimal solution using PMP is very close to the DP solution, which is the guaranteed global optimum. In addition, the initial and final condition parameters and the vehicle dynamics are satisfied. However, the PMP computation time is significantly less than the DP.
An Impact of Employee Commitment and Organizational Climate on Employee Job Satisfaction and Retention in Star Hotels in Coimbatore District

In the competitive world, the hotel has become a rapidly growing industry. It has created more job opportunities and developed infrastructure. The inspiring growth of Foreign Tourist Arrivals (FTAs) shows that India still has a huge untapped potential in tourism. In this study we look into the impact of organizational climate and employee commitment on job satisfaction and employee retention. The structural equation model (SEM) is carried out to identify the importance of climate and its effects on the commitment level of employees, and assess the employee’s job satisfaction and employee retention in the hotel industry. It explores how employee commitment and organization climate help them get job satisfaction and us predict the employee turnover.
Duplicate Bug Report Detection by Using Sentence Embedding and Fine-tuning

The International Conference on Software Maintenance and Evolution is the premier international forum for researchers and practitioners from academia, industry, and government to present, discuss, and debate the most recent ideas, experiences, and challenges in software maintenance and evolution.
Recurrent Neural Networks

The field of natural language processing (NLP) has witnessed phenomenal growth with the advent of deep learning. A lot of this movement can be credited to recurrent neural networks (RNNs) and their variants. Voice-based AI assistants, auto-completion of text in smartphone keyboards, and text-based reviews classified based on sentiments are all problems effectively solved by RNNs.
E-Learning Methods in Students’ Education

The rhythm of modern life and the widespread dissemination of digital technology leads us to the need to review traditional education systems. Opportunities for realizing the potential inherent in electronic training systems are revealed only if they are used. Expanding the range of educational materials used due to the possibility of multimedia tools allows improving the quality and speed of assimilation of the material. The possibilities of e-learning using multimedia technologies increase the visibility and attractiveness of educational content. Also, these technologies make it possible to demonstrate in the educational process such material that previously due to its nature (complexity, high cost, inaccessibility) could not be widely used Effective use of the opportunities provided by the Internet allows you to implement incredibly flexible and adaptive training programs. The student’s access to educational material at any convenient time from any convenient point allows the formation of the most effective curriculum program, which increases the digestibility of educational material.
Password Authentication System using Coordinates &amp; Location

Password authentication system is a very important factor for every system which needs to be secure. Every password is easy to crack and people are looking for a strong password to their systems. Here we use a password authentication system that is designed for high security and could be easily put into old system. In our frame work we are using cryptographic representation for converting location point into coordinates. Our primary aim is to prevent hacking through all kinds of brute force algorithms. It is concerned with including client’s geographical location as an important authentication factor to enhance security. Techniques to integrate location as an authentication factor as well as techniques to generate location based cryptographic keys are reviewed and discussed .Most importantly our system combine graphical user authentication and location coordinates .Existing system was vulnerable to dictionary attack algorithm and salt data algorithm ,so efforts are been taken to generate non repeatable graphical user interface system using coordinates .
Ontology-based modelling of extended web service secure conversation pattern

Securing an application based on Service Oriented Architecture provides defences against a number of security threats arising from exposing applications and data to the internet. Various security guidelines are available to apply security in web applications. But these guidelines are sometimes difficult to understand and generate inconsistencies. In this study, an extended web service secure conversation pattern is presented in the presence of a man-in-the-middle attack. An ontology-based modelling and refinement framework is presented for semantically analysing an extended web service secure conversation pattern. A metamodel is introduced to provide rigorous modelling of security services in terms of concepts, properties, and relationships. At the end of this study, an evaluation of the proposed approach has been made by performing experiments for security requirements against security policies in presence of proposed description logic rules.
Learning prototypes for multiple instance learning

Multiple instance learning (MIL) is a weakly supervised learning method that works on the labeled bag of instances data.A prototypical network is a popular embedding approach in MIL.They overcome the common problems that other MIL approaches may have to deal with including dimensionality, loss of instance-level information, and complexity.They demonstrate competitive performance in classification.This work proposes a simple model that provides a permutation invariant prototype generator from a given MIL data set.We aim to find out prototypes in the feature space to map the collection of instances (i.e.bags) to a distance feature space and simultaneously learn a linear classifier for MIL.Another advantage of prototypical networks is that they are commonly used in the machine learning domain to facilitate interpretability.Our experiments on classical MIL benchmark data sets demonstrate that the proposed framework is an accurate and efficient classifier compared to the existing approaches.