Reviewerly/exHarmony huggingface dataset:
A Study on Determinants of Customer Satisfaction in E-enabled bus booking platforms

E-enabled ventures (especially the online bus booking services) have witnessed impressive growth in India during last decade. Especially after COVID-19, customer shift towards online services is expected to augment. This paper is an attempt to develop a scale for determinants of customer satisfaction amongst the existing users of online bus booking (e-enabled platform).Using e-SERVQUAL model by Parasuraman et al. (2005), a draft instrument was designed. Since online bus services also includes physical experience of services offered, Tangibility factor was added in the construct. The questionnaire was tested on a sample of existing users of online bus booking services (n=105). After checking for the adequate reliability score, Exploratory factor Analysis (EFA) was performed. It resulted into extraction of four factors such as Contact, Fulfilment, Privacy and Responsiveness, accounting for 50.5% of total variance explained. Validating this scale on a larger sample for generalisation of the results would be the future agenda of research. Development of India specific scale for e-enabled business models in public transports can be considered as one of the major contribution of this study.
Security as a Service by Verification using Information System (Info-Sys) in Cloud Network

Security in Cloud Network is essential and data security is required when data is transmitted from the sender device to receiver end and vice versa. Sometimes, the eavesdroppers might steal sensitive data which is dangerous for the users of the cloud network. The goal of the paper is to propose a newer dimension to Cloud Computing - ‘Security as a Service by verification using Information System in Cloud Network’ in which an authentication procedure is used from the user side to ensure the privacy of the network. AES algorithm is used to encrypt the Unique Identification Number (UID) provided by the user to hide from the eavesdroppers and decrypt the same at the receiver end. In this algorithm, the same key is used for encryption and decryption process. Therefore, the sender and the receiver must know and use the same secret key. We have introduced an Information System that keeps track of all users and helps during the verification. Decryption of the encrypted UID will also be done in this system. It also helps to manage session of all users who have logged into the system and tries to access the information with the UID. If the user shares one’s UID with other personnel and if it causes any problem of insecurity in organizational network, then it will also be caught by the Information System. This helps to protect the cloud network from the trespassers and increases security of the network.
A Performance Perspective Analysis: A Detailed Vision on Denial of Service and Distributed Denial of Service on Cloud Computing

In recent days cloud computing and cloud-based service, provisions play a vital and significant role in Internet-based information computing. It interrelates various applications like sales, purchase, banking, customer service, etc. and it behaves entirely as a service-oriented platform or environment. The primary objective of the cloud computing is sharing the resources within increased efficiency regarding time and cost for all kind of customers who needs a cloud service badly and immediately. Though the energy is high, it cannot assure that the cloud computing, service providing, and customer maintenance are highly secured. Service providers in the cloud are not strictly public; it may be private, community and hybrid. Malicious activities can be created or occurred in the middle of the communication and it is difficult to predict a particular person in the middle becomes a malicious user, from where and how. Secured data transmission and discussion in cloud computing considered as the main problem, and various earlier research works focused on tightening the security. The primary objective of this paper is to discuss different security mechanisms applied to multiple malicious threats in the cloud to understand the various issues and challenges faced in earlier research works. It provides a summary of the risks, appropriate method and the limitations and it helps to understand the primary and main problems related to security.
ARID Platform after Four Years of Its Establishment (2016-2020): A bibliometric Study

After four years of the official opening of ARID (Arabic Researcher ID) platform, the number of its users exceeded 44 thousand who can speak Arabic from all over the world. ARID platform is the first Arabic platform to serve scientific research in a way that keeps pace with technical development, which made the access to the scientific research available to all users. The study aims to introduce ARID platform in the language of numbers to transmit a statistical picture for the scientists, experts and researchers on the first digital platform for Arabic speakers and how it can be benefit from this experience and data analysis in digital platforms that serve the Arabic language. The study concluded several results, however, the most important of which is that the number of users on ARID platform is increasing dramatically, and their numbers vary in different countries and universities. The platform included a wide range of various disciplines, most notably the specializations of educational sciences, sciences, arts, and political sciences, with a large number of holders of higher degrees (masters and doctorate), as they exceeded eight thousand users. Participation in conferences tops the list of academic activities, followed by the participation in scientific courses. Meanwhile, the number of scientific publications on the platform exceeded 12 thousand, and on top of them were the articles that published in scientific journals, where the number exceeded nine thousand research papers. In addition, 36 types of academic activities have been keyed in, including publishing books and participating in conferences and seminars. In conclusion, the researchers recommended that the awareness of joining the platform should be spread among Arabic-speaking researchers in all countries of the world to preserve the Arab identity, share scientific interests, and enrich Arabic content with scientific researches.
Interacting with Human Simulations: a Prototype Application

The process of interacting and executing agent-based simulations of human phenomena is complex and difficult to follow for lay people. This is due to 1) limited access to computing power, 2) difficulty in writing analytics scripts and 3) the size and complexity of the simulation space. Consequently, agent-based simulations are normally used once to answer a limited set of questions instead of exploring all the potential paths that the model offers. Here, we describe an approach for developing user experiences that automate the analytic process and rely on interactive visualizations and life histories of societies and agents. This approach helps the user visualize simulation results, pin-point consequences of different initial conditions on the societies' evolution and agents' behavior, and ultimately allow the user to build a narrative or story-telling to present complex information. We present a prototype application that we implemented in the R language.
Optimization of Pattern Recognition of Hijaiyah Letters using Normalized Cross Correlation Tchniques

The development of analysis in digital image increasingly developed with various methods, one of which is in recognition of letter patterns. Each letter written using handwriting must have different writing patterns, such as the thickness and shape of the letter pattern. This research will be doing on the pattern recognition of hijaiyah letters of handwriting by applying the Normalized Cross Correlation (NCC) technique. NCC is a technique used to match two images. Before the NCC process, it should be done with the preprocessing using convolution and without convolution using the binary image. The convolution technique used was the Sobel and Prewitt edge detection with the aimed to get the edge of an object and compared the number of matching letters between using edge detection and without edge detection. The tests were done by using the different sized image of 32x32 pixels, 64x64 pixels and then match it against a similar sample data, a different sample data, a different objects font sample data and a different sample data of original image size. The results show that the matching of the letter pattern depends on the size of the image that is more matches to the image of 32x32 pixels. The binary image had better matching numbers than the convolution techniques. While in convolution techniques, Prewitt edge detection had the higher accuracy and matching results compared to the image using Sobel edge detection.
Improving the effectiveness of teaching chemistry in accordance with the updated program

In order to increase the effectiveness of teaching chemistry on the basis of the updated program, it is envisaged that students need to improve the quality of education and training, ensure a high level of scientific education, ensure deep mastery of the basics of natural science, it is necessary to build the learning process not only as a process of mastering the system of knowledge, skills and competencies that make up the instrumental basis of the student's educational activity, but also as a process of personality development, the adoption of spiritual, moral, social, family and other values. The introduction of modern teaching technologies and their systematic use contributes to improving the quality of learning, motivation, the formation of functional literacy of students and key competencies, the development of potential abilities of students .. New technologies provide new opportunities for the formation of personal potential and ensuring the success of the graduate Today, the use of modern educational technologies that provide x personal development of students by reducing the share of reproductive activity (reproduction of what is left in memory) in the educational process can be considered as a key condition for improving the quality of education, reducing the workload of students, more efficient use of study time. Development of cognitive and creative interests among students is facilitated by various types of technologies.
The File System Recommendations to Reduce the Space and Time Parameters in Hadoop File Storage and Map Reduce Processing of Big Data Applications

The study of Hadoop Distributed File System (HDFS) and Map Reduce (MR) are the key aspects of the Hadoop framework. The big data scenarios like Face Book (FB) data processing or the twitter analytics such as storing the tweets and processing the tweets is other scenario of big data which can depends on Hadoop framework to perform the storage and processing through which further analytics can be done. The point here is the usage of space and time in the processing of the above-mentioned huge amounts of the data definitely leads to higher amounts of space and time consumption of the Hadoop framework. The problem here is usage of huge amounts of the space and at the same time the processing time is also high which need to be reduced so as to get the fastest response from the framework. The attempt is important as all the other eco system tools also depends on HDFS and MR so as to perform the data storage and processing of the data and alternative architecture so as to improve the usage of the space and effective utilization of the resources so as to reduce the time requirements of the framework. The outcome of the work is faster data processing and less space utilization of the framework in the processing of MR along with other eco system tools like Hive, Flume, Sqoop and Pig Latin. The work is proposing an alternative framework of the HDFS and MR and the name we are assigning is Unified Space Allocation and Data Processing with Metadata based Distributed File System (USAMDFS).
Evasion Attack on Text Classified Training Datasets

Machine learning algorithms are widespread used in real world training data classification and detection malware. The learning algorithms to detect malware adversarial manipulated training datasets in evasion. The evasion attacker has certain knowledge on training datasets either internal in deploying time attack or external attack do based on adversarial knowledge. Evasion attack targeted document properties features malware. To present this paper, to do an evasion attack on collected text documents using extraction keyword and find mean words using Naive Bayes models . Also to analyses different machine learning algorithms classification on evasion attacked training datasets and discussed defense methods to prevent training dataset from evasion attack.
Applying Preliminary Risk Assessment Method on Research Websites

Research websites represents the objectives of the research entities such as preparing a community among researchers to improve the developing process for new researches activities, the engagement of different target users according to their interests, getting sources of funds for new projects, and other activities.From this perspective, applying assessment the websites to identify the risks that have bad impact on achieving its objectives are playing a vital role in maintaining and improving the performance of the website.Nowadays, the risk management usage has been increasing globally, and increasingly requires organizations to develop a policy to deal with high risk level in their plans, projects, and processes.The development of efficient processes for managing and analyzing risks that allow identifying a solution or plan to prevent risks is essential, thus, risk analysis aims at protecting the organization's human, material, and financial resources.A Prelimianary Risk Assessment Technique is used to evaluate the capabilities of the currently used Agricultural Research Center (ARC) website as an example for the research websites to identify, and analyze the risks and set the required suggesting recommendations on how to solve or minimize these risks.Results have shown, the main risks found through the ARC website were: power outage (PO), network subsystem failure (NSF), Attacking System Security (ASS) and Saftey System Failure (SSF), Failure in Usability of the ARC web site (FUWS), Failure in Supportive Entities, Failure in Supportive Processes, and Failure in Work Processes.The main recommendations suggesting having a maintenance contract and standrad operation procedure for power subsystem including UPS and gernerator, high availability solution for network equipment and internet connectivity, build procedures to handle emergency cases inside the ARC website such as ensure the good performance for accessing the ARC website as a daily action.Employees training and awareness considers as another solution to alleviate the failure in work process.
2907 Published By: Blue Eyes Intelligence Engineering &amp; Sciences Publication Retrieval Number B2463078219/19©BEIESP DOI: 10.35940/ijrteB2463.078219 Journal Website: www.ijrte.org Classification of Gene Expression Data Set using Support Vectors Machine with RBF Kernel

The huge amount of data being generated by different organizations and its underlying advantages in multiple fields like decision making, data security, research purposes have made data classification a very important and mandatory process now-a-days. Data Classification is the process of grouping data of similar characteristics into categories. Classification can be done based on the output we are looking forward to. Hence it is considered very useful. Classifying data allows us to predict the nature of future data-sets and discover useful patterns among them. This project aims at classifying gene data sets. Gene data sets are the information collected from a set of genes put to a specific test. It can be used for medical research purposes; by studying the pattern in the datasets allows us to predict the kind of genes that are more vulnerable to a particular disease there by allowing us to prevent the manifestation of the disease right at its beginning, just as they say, prevention is better than cure. In this paper, such classification is effort using a supervised machine learning algorithm – SVM (Support Vector Machine). There are many algorithms in existence to perform classification but this algorithm has its own lead over the others. It is capable of both classification and regression. It works well with structured, semi-structured and unstructured data too. It contains a kernel function which when used appropriately can solve any complex problem. The summary of this project is, taking gene data sets as input and obtaining classified clusters as output.
Workshop on the Embodiment of Things (EoT 2021)

The goal of this workshop is to bring researchers and practitioners from national and international cultural heritage (CH) organisations together with those from across HCI and related fields.We aim to establish how an alertness to tertiary embodiment (mind-body-artefact) can provide the ground for bringing artefacts to life, catalyse collective knowledge production, facilitate collaboration, and encourage the creative integration of computational and archival thinking.Key examples of engaging with this theory in practice can be seen in the projects of the Arts and Humanities Research Council's Towards a National Collection research programme.In this workshop we will explore how embodiment can be and has been applied to digital objects and practice in CH collections.We will do this via three interwoven critical interventions: digital objects (digital collections, interfaces, tangible computing), situated interaction (how context shapes meaning) and virtual connections (collaboration and communication in digital rather than physical space (Computer-supported Cooperative Work (CSCW), crowd-sourcing and citizen research, knowledge-sharing).Embodiment.AI.Machine Learning.Crowd-sourcing.Situated and
Telugu Character Recognition using Adaptive Fuzzy Membership Functions With Adaptive Genetic Algorithm Based Techniques

A novel Telugu character recognition technique is proposed in this paper where the given Telugu handwritten document is processed by normalizing the document and removing the noise. Then slant detection followed by correction process is conceded using the bilinear interpolation method to get more accurate result. Thus the de-skewed documents text lines and characters are separated by making use of Adaptive Histogram Equalization (AHE). In the next stage, the characteristics of the segmented characters are mined with the help of the zoning method. In zoning method, an adaptive fuzzy membership function will be developed by the Adaptive Genetic Algorithm (AGA). By using AGA in zoning method the characteristics are mined from the separated characters. The mined structures are applied to the Feed Forward Back Propagation Neural Network (FFBNN) for accomplishing the learning process. During testing, more number of handwritten segmented Telugu characters will be set to the FFBNN to verify whether the input character is recognized or not. Thus, the proposed method has given more accurate recognition results by using our proposed adaptive fuzzy membership function with AGA method. The proposed method performance is evaluated by getting more number of handwritten Telugu documents and compared with the GA-FFBNN and FFBNN.
Effects of Emotions in Cognitive Based Game

Recognition of emotion has been done through different perspectives and is useful in many domains of Human Computer Interaction (HCI), especially in intelligent systems, interactive robots, web applications interaction, and to elaborate social media. Our aim is to design a system which encapsulates the behavior's set into roles which helps other developers in wrap behaviors into segments that can diverse dynamically, based on player moves. Games utilize contextual data about players and their environment to explore new means of interactions and enhance game experience. Cognitive science plays an important role in the performance of cognitive ASs. Recent research claims that the cognitive based games must incorporate in adaptive mechanisms especially in real- time adaption. Our research focuses on the domain of computer games based on emotions of player. In this research paper, we will layout a framework for adaptive agent game at which player's emotions are shown at each move in emotion avatar. With the help of cognition adaptive nature, system can change its level of difficulty as any change occur in performance of player. After achieving a real cognitive improvement, system will be able to provide appropriate challenges. Emotions in games enhance the motivation of player's by dynamically adapt the procedure of agent systems.
Detection and Localization of Image and Video Tampering

Digital images and videos are widely used in various fields like courtrooms ,military ,medicine ,research, social media etc. So, maintaining the authenticity and integrity of these digital contents is a major concern. For the past few years, researches were going on to find out tampering in the digital contents. Active and passive approaches are the major classification for digital tampering detection. Here, we discuss some of the active and passive approaches and their significance in the current scenario.
What does it mean to trust AI systems?

The High-Level Expert Group on Artificial Intelligence (AI HLEG) appointed by the EU Commission is tasked with elaborating recommendations on ethical, legal and societal issues related to AI and on policy and investments.The group has produced a document released in April 2019 entitled "Ethics Guidelines for Trustworthy Artificial Intelligence".Computerized socio-technical systems, based on algorithmic computations and decisions that impact human individuals and society in a way or another, must indeed be trustworthy, and AI systems are no exception.The academic and industrial communities developing softwarebased systems have produced several techniques to achieve their "dependability" or "resilience".Software validation and verification techniques, such as error detection and recovery mechanisms, model checking, detection of incorrect or incomplete system knowledge, and resilience to unexpected changes due to environment or system dynamics have been developed for several critical applications such as aviation.However, as decisions usually devoted to humans are being more and more delegated to machines, sometimes running computational algorithms based on learning techniques, ethical considerations become central.New issues have to be considered technically, in the design process and in the governance of these systems, and properties such as transparency, accountability and explainability take more importance.
Credit Card Fraudulent Detection Using Machine Learning Algorithm

The fraudulent transactions that occur in credit cards end in huge financial crisis. Since the web transactions has grown rapidly, the results of digitalized process hold an enormous sharing of such transactions. So, the financial institutions including banks offers much value to the applications of fraud detection. The Fraudulent transactions can occur in different ways and in various categories. Our work mainly focuses on detecting the illegal transactions effectively. Those transactions are addressed by employing some machine learning models and therefore the efficient method is chosen through an evaluation using some performance metrics. This work also helps to select an optimal algorithm with reference to the machine learning algorithms. We illustrate the evaluation with suitable performance measures. We use those performance metrics to evaluate the algorithm chosen. Within the existing system the algorithms provide less efficiency and makes the training model slow. Hence within the proposed system we used Multilayer Perceptron and Random Forest to supply high efficiency. From these algorithms efficient one is chosen through evaluation.