Reviewerly/exHarmony huggingface dataset:
Using Books in Early Childhood to Prevent the Assumption of Traditional Gender Roles

Recently, the question of gender has become a popular topic of discussion. Gender issues are also found in early childhood education, where male students are considered to be more active, and to dominate the class, and female students are considered to be more passive and to tend to be shy. Teachers and administrators are mostly female, and non-text materials – children's activity books – that are used in early childhood education contain material that reflects gender bias. As a result, this study aimed to produce a textbook that reflects gender equality. The study aimed to 1) develop and design early childhood education textbooks that promote the notion of gender equality, and 2) examine the suitability of early childhood education book designs that promote gender equality. The ADDIE model was used in this study, and the output was a children's activity book comprising eight themes that emphasize gender equality, and which was subjected to a feasibility test. The research participants were children of 5–6 years old who attended kindergarten in Indonesia, media experts, content experts, and other stakeholders. We collected data through interview, observations, and surveys. We analyzed data both qualitatively and quantitatively. The research instrument was an observation sheet. The results of data analysis show that textbooks with content that promotes gender equality are suitable for early childhood education learning processes. This finding is based on material and learning tool validation, as well as on implementing the textbook with kindergarten students. Thus, a children's activity book that has a clear, direct, and explicit theme of gender equality was developed for use in early childhood education. The developed activity book is expected to become an early childhood education reference book that does not emphasize gender roles, and promotes gender equality.
Encrypt Password with Using New Key Generation Processed Random Key

Random password-based encryption key may be a preferred algorithm for block ciphers for imparting protection services. In this paper, we've proven the motives of the loopholes in random password-based encryption keys and additionally have supplied an answer by means of the utilization of our Symmetric Random Function Generator (SRFG). It additionally contrasts consequences with the unique random password-based encryption key-based totally upon some parameters like nonlinearity, resiliency, balancedness, and propagation characteristics. The results exhibit that our proposed model of random password-based encryption key is best in withstanding attacks.
Introduction to Cloud Storage Services

Cloud computing has become one of the hottest technologies of the emerging 21st century technology, Cloud computing is related to a variety of themes that take on a larger dimension of mainstream media, there are various cloud computing books have also been available on shelves in recent years. The Internet makes the world more accessible. Cloud storage is an important infrastructure for achieving seamless data sharing and experience of service communications from different users, different applications, different devices around the world. It will be a new public infrastructure service, such as water, electricity, available anytime, anywhere. Cloud is not a specific product, but a way to deliver IT services that can be used when needed, expand up and down as needed, and follow a usage model. Cloud computing is a computer style in which computer resources are easily accessible and accessible, easy to use, cheap, and just working. Cloud is not a point product or a single technology, but a way to deliver IT services in a way that provides self-sufficiency, demand and use for each payment. By making data available in the cloud, easily accessible and ubiquitous, usually at much lower costs, increase their value by enabling opportunities for improved collaboration, integration, and analysis on a common shared platform.
How AI-based biometrics can guarantee e-signature security

E-signatures are increasingly being used in online business, but they still present security risks. Mo Sahib of Borderless Security and FilesDNA explains how the latest intelligent biometric systems can solve this.
IoT and Blockchain Rostrum 2021: Implementation, Challenges, and Future Phenomena

IoT and blockchain are the area that focuses on the security of the data. So many researches are already addressed in this relevant domain. Still there are many opportunities in IoT that need to be addressed [1]. For example, as the number of devices are increasing, the memory space is decreasing, while using the network layer communications protocol for relaying datagram. Its routing function enables internet working, and essentially establishes the internet (IPv4 and IPv6), as well as security issues, such as vulnerable access control mechanisms [5]. This paper’s main objective is to focus on the advantages of blockchain and implement it on IoT. In this research paper, how the combination of blockchain and IoT can create the wonders in the field of data security of smart devices is also discussed. Moreover, based on the past data, some challenges of IoT are also addressed.
Perceived Effects of Tutors’ Pedagogical skills on Academic Performance of Pre-service Deaf teachers at the Presbyterian College of Education, Akropong Akuapem

This study explored the perceived effect of tutors’ pedagogical skills on the academic performance of pre-service Deaf teachers at the Presbyterian College of Education, Akropong Akuapem. The research design used was phenomenology where eight pre-service Deaf teachers were purposively sampled for the study. Data was collected by using face-to-face semi-structured interviews. Thematic analysis was employed to analyse the data. The results of the study showed that tutors’ way of presentation makes it difficult for pre-service teachers who are deaf at the Presbyterian College of Education, Akropong Akuapem to understand what is taught. Again, it was found out that most tutors in the College could not use the Sign Language effectively; therefore, they were not able to effectively communicate with pre-service teachers who are deaf. Furthermore, it was revealed that pre-service teachers who are deaf preferred practically oriented and demonstration lessons to the theoretical style of learning to understand concepts taught. The paper concludes that some tutors’ way of teaching negatively affect the academic performance of pre-service Deaf teachers at the Presbyterian College of Education.
Non-intrusive load event detection algorithm based on DWT

In order to achieve high-efficiency power management of residential electricity, household load working status needs to be accurately monitored to further improve the utilization rate of power resources.This paper studies the non-intrusive household load event detection method, and improves the optimization detection algorithm to improve the accuracy of load recognition in the complex power system environment of the family.A method based on the second-order differential modulus ratio of wavelet transform is proposed to accurately locate the location of the switching point and its properties. The event detection algorithm is verified by experiments with typical household electrical appliances data. The accuracy of this method is higher than the traditional wavelet modulus maximum method, and also has good accuracy under the background of multiple electrical appliances.
Predictors of Academic Buoyancy: A Study of Demographic Correlates

Academic buoyancy refers to to learners’ potential to face challenges and difficulties that are regular in nature under educational settings or environment. Demographic correlates were found and its effect on academic buoyancy was obtained through regression analysis. The sample consists of 1200 high school students belonging to three different boards of Lucknow City. Multiple regression analysis was done in variables where independent variables which are non-metric and a single dependent variable viz. academic buoyancy is metric in nature. Regression analysis is done through dummy coding where predictors of academic buoyancy are found by keeping one benchmark sub-category in each category.
Server Load Balancing Analysis for Client Task Assignment in Distributed Systems

The client-server model is widely used in many distributed systems in which proper assignment of client is an important factor for the better performance of system. A large number of clients may exist in a distributed system that establishes communication with one another through a variety of intermediary servers. The primary objective of the overall system is to allocate clients to servers to maintain the load balancing as well as communication cost. There are two criteria for server load evaluation. The first one is complete load, followed by task scheduling, with complete load and task scheduling being diametrically opposed measurements. It seems to be NP-hard to figure out the appropriate client-server allotment for a distributed system's total load and load balancing. In this research, we examine distinct server load balancing strategies for the assignment of clients in distributed systems.
Demystifying Data Justice: Legal Response to India’s Privacy and Security Standards: Challenges in Cloud Computing

Data is the new oil of this economy. Cloud computing acts in the capacity of storing databases, in operational analytics, networking, and intelligence. Indian cloud computing market is valued at 2.2 billion dollars, which is said to scale by 30 percent in 2022. It’s therefore pertinent to understand Indian’s data protection landscape in the light of the Personal Data Protection Bill, 2018 to answer the questions of ownership, controlling, and processing of data in order to reflect upon the liability, obligations, and compliances by intermediaries, dispute resolution forums, data portability, and indemnification. The authors will explore by means of doctrinal method and the challenges posed on the content regulatory mechanism for the internet architecture, which paves responsibility of data classification into lawful and unlawful, with the exception of section 79 of the Information Technology Act. The authors will further examine the encryption standard tools exhibiting data security and the obstacles created by the 40-bit limit encryption standard as part of the DoT’s telecom licensing conditions and section 84A IT Act, 2008, to provide suggestions towards pragmatic delimitation. Cloud computing, being the next growth frontier of the IT industry, makes it more evident to enable cloud forensics in entrusting with investigations and establishing confidence within end-users.
Study and Analysis of Various Authentication and Authorization for IoT Devices: A Challenging Overview

Nowadays, Internet of Things (IoT) is being achieved significant improvement in the scientific community. Both industry and academia are concentrated on the concepts of improving security, maintainability and utility through the improvement and standardization of optimal practices. There are various existing approaches are arisen in the security of IoT, ranging from cryptography to network security for identifying management. Thus, this paper focused on the security due to its impacts of limiting factors to adoption of wider IoT. This paper discusses the survey of various existing approaches suitable for IoT environment in the domain of authentication and authorization. Hence, this survey analyzes various techniques corresponding to authentication and authorization for IoT devices. This study is to utilize 25 research papers concentrated on various techniques and the review of researches technique-wise is to be provided. Finally, the survey will encourage the analysis based on the publication year, research methodology, performance metrics, and achievement of the research techniques toward authentication and authorization for IoT devices, as well as the journals. Finally, the research gaps and difficulties with the methodologies will be highlighted. Furthermore, the motive for establishing an effective approach for authentication and authorisation in IoT device techniques will be disclosed.
Watermarked Hashing As a Video Content Authentication Technique

The Authentication is the primary problem of cyber security as a dimension. It is just a method of approving a digital proof of identification. Different levels of access or completion of tasks are granted to different individuals, depending on who has signed up for the task at hand. There are many different types of authentication, ranging from passwords to fingerprints to bio metrics. These technologies add a layer of protection and prevent security lapses like data infringements. Digital video authentication is a method of evaluating whether the recorded video is genuine and has not been tampered with. Video hashing is commonly deployed in closer detection, video authentication and video tamper detection and a look for anti-piracy. Perceptual hashing has emerged as a powerful tool for verifying the validity of multimedia content and it continues to pique the interest of experts working in analyzing multimedia domains and spotting illegal or counterfeit videos on the internet.
Improvised Average Gradient for Predicting Multi-Traffic Scene Night Images Using Linear Regression Algorithm Compared with K Means Clustering Algorithm

To improve the average gradient for predicting multi-traffic scene night images using linear regression algorithm. Materials and methods: Novel linear regression algorithm analyzes the correlation and extract texture features that characterize the samples. The novel linear regression algorithm and K Means clustering algorithm with samples N=10 is iterated 20 times for evaluating each group. Result: The fog images are recovered to clear image using linear regression algorithm with an accuracy of 95.02% and K means clustering with an accuracy of 92.82%. Conclusion: Within the limits of this study, the result proved that the novel linear regression algorithm has better accuracy than the novel K means algorithm. The statistical significance of accuracy value for the equal variance assumed is 0.05.
Evolving generalizable multigrid-based helmholtz preconditioners with grammar-guided genetic programming

Solving the indefinite Helmholtz equation is not only crucial for the understanding of many physical phenomena but also represents an outstandingly-difficult benchmark problem for the successful application of numerical methods. Here we introduce a new approach for evolving eficient preconditioned iterative solvers for Helmholtz problems with multi-objective grammar-guided genetic programming. Our approach is based on a novel context-free grammar, which enables the construction of multigrid preconditioners that employ a tailored sequence of operations on each discretization level. To find solvers that generalize well over the given domain, we propose a custom method of successive problem difficulty adaption, in which we evaluate a preconditioner's efficiency on increasingly ill-conditioned problem instances. We demonstrate our approach's effectiveness by evolving multigrid-based preconditioners for a two-dimensional indefinite Helmholtz problem that outperform several human-designed methods for different wavenumbers up to systems of linear equations with more than a million unknowns.
Accuracy Analysis of Data Fraud Detection for Company Transactions Using Two-Layered Feed Forward Neural Network Approach Compared with Random Forest

Aim: To find accuracy of data fraud detection for company transactions using two-layered feed forward Neural Network approaches compared with Random Forest. Materials and methods: Number of groups is 2. Group 1 - Two-layered feed forward Neural Network, and Group 2 - Random Forest. Results: The accuracy of the innovative feed forward Neural Network method is 83.4% and Random Forest is 85.5%. The accuracy of the Random Forest algorithm appears to be a slight increase. Conclusion: Random Forest algorithm finds the fraud detection appearance to be better accuracy than the feed forward Neural Network.
Design of 32x32 Reversible Unsigned Multiplier Using Dadda Tree Algorithm

Multipliers are essential parts of every processor or computer. Microcontrollers and digital signal processors typically measure their performance on how many multiplications they can execute in a given amount of time. As a result, better multiplier designs are sure to increase system efficiency. A reversible Dadda multiplier is one such possible approach. The Dadda tree technique is used to construct two 32x32 reversible unsigned multipliers in this paper. The TG and FG gates are accustomed to create the partial product circuit in the first design. The PG and TG are utilized to create a partial product circuit in the second design. For adding partial products, the PG and reversible full adder gates are used. The design is implemented using Xilinx ISE 14.7 design suite.
Design of Automated Health Monitoring System Using IoT

This project is designing of Automated Health Monitoring System Using IoT. Internet of Things (IoT) describes the network of physical objects, “things” that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet. By this system, people can monitor their day-to-day activities of health constraints and take necessary action in timely events. Further, the vital parameters of body should monitor always such as body temperature, room temperature, pulse counting, and heartbeat every day. As compared to existing one, the proposed design added with GSM and sound sensor. GSM sends the information to the family members with family doctor and notifies the consultant doctor for the medication in the individual risk factor. Any user can use this design. It is not limited to only patients and also no need to rush to hospital at regular intervals, at every time for the check-up, and do check-up safely at home. If any risk factor arises, one can visit the hospital.
A Hybrid Graphical Password Technique for Mobile Data Security Based on Direction

Mobile Security has exponentially increased data sensitivitydue to the users upsurge in mobile devices. People are utilizing smartphones as the substitute for desktop or laptop systems, from which they access, download, upload, modify, create, and share their files. Every data has become crucial as and when exposed to other platforms via smartphone. Therefore, all smartphones require security from malicious users. Forthcoming mobile devices will have hybrid graphical passwords as prospects for customer authentication. Users can easily interact with their devices through mixed authentication techniques on mobiles by safely entering the graphical passwords. This paper focuses on Character Set and Direction (CSD) technique developed on the android application for mobiles, which takes a character set and a unique direction as a graphical password for its input method. The significant advantage of the proposed technique is that; it is user-friendly, lightweight, has an extraordinary input/output modality, and has a custom-built mobile application prototype for the Registration and Login Phase. Developed a mobile application for Character set and Direction with four standard directions to construct a unique password scheme and investigate its security, memorability, and graphical user interface. The results are highly encouraging as this technique can be a highly secured authentication technique and high user memorability, and increased usability.
A Study to Examine the Behavioral Intentions of Practitioners to Use Forensic Accounting Technique as Fraud Detection and Prevention Mechanism in India

The rising number of frauds in the recent years and the authorities' inability to combat fraud has brought the concept of Forensic Accounting (FA) in limelight. FA is the branch of accounting that investigates fraud. The primary objective of this study is to look into practitioners' intentions to employ various FA approaches for fraud detection and prevention in India. The study has been conducted in the North region, and the population for this study include the practitioners in accounting fields, Certified Public Accountants (CPAs), Chartered Accountants (CAs), Association of Certified Chartered Accountants (ACCAs),Certified Management Accountants (CMAs), and Lawyers. Snowball sampling technique was used for data collection. For this study, structured questionnaire was used to collect the data and further SMART PLS was used to analyze the data.
Design and Development of Decorrelation Stretch Technique for Enhancing the Quality of Satellite Images with Improved MSE and UIQI in Comparison with Wiener Filter

Image enhancement is the process of increasing the quality of an image by enhancing certain features of an image and the resultant image is more suitable than the input image. This research is aimed at developing more efficient image enhancement techniques in the spatial domain. Materials and methods: Image enhancement using decorrelation stretch technique (N=30) is proposed in this work, and this method is compared with Wiener filter (N=30). The sample size used for both groups is 30. Results: The quality enhanced image is measured using Mean Square Error (MSE) and Universal Image Quality Index measure (UIQI). High UIQI and low MSE indicates the better enhanced image. The parameters of MSE (db) and UIQI are having higher values than the parameters of Wiener filter. The mean MSE value of decorrelation is 4.2715 db. Mean MSE of wiener filter is 32.0476 db. The mean UIQI value of decorrelation is 0.5640 db and that of Wiener filter is 0.5766 db. Conclusion: Image enhancement using decorrelation stretch technique is significantly performed better than Wiener filtering based on the experimental results and independent sample T test results using IBM-SPSS software.
Real-time feedback engine for online jawi character recognition

Jawi is a type of cursive writing derived from the Arabic alphabets and adopted for writing the Malay language. The Jawi alphabet has 36 basic characters, in which 28 characters are similar to Arabic characters. Studies on online Jawi characters recognition are still minimal; most studies focus more on offline. Therefore, the online Jawi characters recognition engine has developed, involving two stages; the template modelling process and the recognition process using matching templates. The recognition engine developed can provide real-time feedback on the accuracy of Jawi characters writing activities by users. The recognition engine's feedback accuracy assessed using a comparative analysis method by looking at the agreement score between recognition engine feedback and experts' feedback. The Krippendorff's Alpha Reliability Coefficient Index (Krippendorff's ) agreement score was used to measure the agreement between the recognition engine feedback and experts' feedback. Krippendorff's  agreement score assessment results found that the accuracy of recognition engine feedback was almost the same as the experts' feedback. Therefore, it can conclude that the recognition engine developed has high accuracy and can be used to recognise Jawi characters online.
Influence of Diverse Feature Selection Methods in Sentiment Analysis

With the enormous comments on social media and ecommerce, government, companies, and individuals eagerly want to know about people opinion about their policies, services, or any other. Sentiment analysis plays a major role to understand people emotions. Sentiment analysis alias opinion mining uses machine learning techniques to classify the given review as positive, negative, or neutral. This paper provides implementation of diverse feature selection techniques with different classifiers in sentiment analysis. The method is evaluated on various domains. Bag of Model with SVM provides satisfactory result on the datasets.