Reviewerly/exHarmony huggingface dataset:
Handwritten Multi-digit Recognition Using Convolutional Neural Networks with Par-Mod

Since the dawn of AI, the ability to convert handwritten text to digital form has been an active area of research. Various models were introduced to perform electronic conversion of various types of images containing texts into machine-encoded text, also known as the OCR problem in technical community. Multi-digit numbers recognition is a sub-problem of the same OCR problem. In this paper, the performance of various deep convolution neural network architectures is compared for the task of recognizing arbitrary multi-digit numbers and a final model is proposed with state-of-the-art accuracy. The model will be tested against multiple datasets that include SVHN dataset and self-made dataset from MNIST handwritten images dataset. A comparison is held among the results, and it is shown that the use of ZFNet architecture of convolutional neural networks with par-mod led to significant improvements among other CNN architecture for the same task.
The Pedagogical Aspect of Human-Computer Interaction in Designing: Pragmatic Examples

In the last two decades, behavioural science has influenced technology design and developed the models and methods of user-centred design, human-technology interaction, and user experience to increase user satisfaction. Currently, designers are utilizing the results of human science studies to develop the effectiveness of technology and increase acceptance, ease of use, desirability, and accessibility of technology for their users. Pedagogy is a purposeful and important aspect of human science that can guide designers to direct human mind and behaviour. Integrating pedagogy into technology is suggested in the recent Technological Pedagogical Content Design (TPCD) model of the authors. This article explains four pragmatic examples of using pedagogical theories and principles in designing technology: (1) utilizing learning dimensions for directing users’ minds (e.g., Bloom taxonomy); (2) utilizing pedagogy to understand users (e.g., VARK learning styles); (3) utilizing pedagogy to organize the content (e.g. concept map or Gestalt principles for organizing the content; and (4) utilizing pedagogy to provide user-interaction methods (e.g. Edgar Dale’s cone of experience).
IT Security Technical Controls

This chapter lists and explains the technical security controls that organizations must have to secure their assets. These controls implementation should be based on a zero trustZero Trust model where all users, whether inside or outside the organization, need to be authenticated and authorized, and continuously identified to have access to corporate resources. Although these controls are essential to secure the organization, they are not enough to accomplish it. With these technical controls, organizations must also implement effective management and operational processes and hire qualified resources with the proper skills to implement, administer and operate these controls.
An Investigation into the Application of Bidirectional Associative Memory and Pseudorandom Number for Steganography

Steganography is the craft of concealing the restricted information in the harmless information with the end goal that very presence of the mysterious message is hidden. We propose utilization of bidirectional associative memory alongside pseudorandom number in the field of steganography. Since a message is made out of limited number of ASCII type characters which is powerless against assaults. Hence, we reason to pick a bunch of non-straight codes that has great hamming distance between any two back-to-back codes to supplant the first ASCII type input. This plan will make our framework considerably more shortcoming lenient in correlation with ASCII code-based framework under direct assault. We additionally reason another strategy to shroud the tweaked code in the image utilizing pseudorandom number inclusion method. To recuperate an assaulted code, we utilize bidirectional associative memory to perform vague coordinating. It gives best trial brings about recuperating the information from the mutilated Images.
Markov Decision Processes with Discounted Costs: Improved Successive Over-Relaxation Method

In the mathematics field, discrete-time Markov chains are stochastic control processes. They give a particular plan of rules and ideas based on mathematics for describing and analyzing situations related to agent decision. This case is governed by randomness (chance) and is controlled by intervention of person or machine. To reduce complexity computational efforts as much as possible, action elimination procedures have been used with considerable success and have appeared in the literature of stochastic optimization to solve this problem. We focus our efforts on the computational complexity of some situations consisting to find optimal policy in Markov decision problems (MDPs) under the discounted cost criterion under infinite planning horizon. It is well known that those problems are solvable in polynomial time via the dynamic programming approach. In this regard, we provide an overview on the successive over-relaxation algorithm (SOR) which is a part of successive approximation methods. Next, we will be interesting to establish a new test for identifying non-optimal decisions and we will invest it in order to improve the SOR algorithm. Finally, we present an illustrative example for demonstrating our contribution and we compare our test to results already established in the same axis.
An Efficient Edge Localization Using Sobel and Prewitt Fuzzy Inference System (FIS)

This paper addresses the problem of edge detection to overcome the artifacts factors mainly by means of visual factors which appear on traditional edge detection methods using Sobel and Prewitt Fuzzy Inference System (FIS). In this research, we proposed a framework for Sobel and Prewitt FIS to enhance the edge detection process as compared to the traditional Sobel and Prewitt filter operations. Results show a considerable improvement on edge detection process, especially on the diversely varying intensity properties’ images.
Information Security Frameworks

This chapter covers industry-accepted information security frameworks, including NIST frameworks, COBIT, and CIS Controls, which help organizations understand current cyber risks, identify missing security controls that protect from cyber threats, and prioritize mitigative activities. This chapter also discusses select national-level security requirements and frameworks in Qatar, Singapore, Saudi Arabia, Turkey, and India, which organizations in those countries need to comply with.
Age Estimation of a Person by Compound Stratum Practice in ANN Using n-Sigma Limits

A face of a person reveals a lot of facts such as gender, identity as well as age. Human face plays a crucial part in the approximation of a person’s age. In this paper, a design is modelled to categorize the age of a person as per the topographies retrieved from facial images of humans using Artificial Neural Network (ANN). These years, ANN is widely implemented as a classification tool for resolving numerous decision sculpting anomalies. In this study, a feed frontward promulgation Artificial Neural Network is built for age cluster classification method for gray-scale frontal face images of human. Three age clusters namely Kids, Mid aged and Senior aged adults are considered in the clustering system. The facial images of the kids age group range from 1 to 16. The facial imageries of the Mid aged group range between 16 and 60. The Senior aged group is reflected as the age 60 and above. In this research paper, the compound stratum practice is proposed in which the outcome gotten from the ANN has been polished at the compound stratum for the purpose of improvising the precision of the rate of detection. The accurateness of the algorithm is investigated by the disparity on the boundary of the age clusters. The competence of the proposed system is validated and reflected in the experimental outcomes.
Breast Cancer Diagnosis Using Deep Learning

Breast cancer is one of the diseases that is gradually becoming more prevalent in today’s society. Machine learning is helping in early detection of mammographic lesions. In this work, the fully connected Neural Network (FCNN) deep learning architecture used to diagnose the breast cancer. In addition, we studied the effect of different techniques to ovoid overfitting and improve the performance of the designed deep neural network after that we select the best model. In this study, the breast cancer dataset called the Wisconsin Breast Cancer Dataset (WBCD) used. Dataset splatted into training set and test set, with percentage of 80% and 20%, respectively. The reduced network in terms of size model obtained the highest accuracy and least loss on training set and adding L2 weight regularization (FCNN + L2) model achieves the highest accuracy and the lowest loss on testing set.
A Comprehensive Analysis on Technological Approaches in Sign Language Recognition

Communication is one of the imperative sides of the twenty-first century. We as human being cannot even imagine this world without communicating with each other. For honest communication to happen two persons should agree on a common language that each of them understands. Keeping this context in mind, there will be a necessity for a translator in between a hearing/speech-impaired person and a normal person. But the translators or consultants who understand sign language do not seem to be simply and commonly offered all the days, if offered conjointly, they charge tons. To help the social interaction of hearing-impaired–speech debilitated individuals with the society economical interactive communication tools are made. As the importance of sign language interpretation is increasing day by day so several triple-crown applications for linguistic communication recognition comprise new forms of real-time interpretation with progressed artificial intelligence and image processing approaches. In this paper, we gave an entire outline of various methodologies and processes rooted in deep learning and discussed technological approaches for linguistic communication interpretation, also achieving 95.7% accuracy with two layers of CNN model classifier for the sign gestures of 26 English alphabets with readily available resources. This paper also provides current analysis and advances in this area and tends to distinguish openings and difficulties for future exploration.
Sentimental Analysis of Tweets During COVID-19 Pandemic: BERT Algorithm

As the coronavirus (COVID-19) grows its impact from China, expanding its catchment into surrounding regions and other countries, increased national and international measures are being taken to contain the outbreak. This perspective paper is written to capture and analyze the various mental state health issues being perceived via emotional analysis of Twitter data during the COVID-19 virus outbreak from a single nation further spread of to the whole world. A data-driven approach with higher accuracy as here can be very useful for a proactive response from the government and citizens. In the proposed work, tweets during the COVID situation have been collected and their sentiments are explored using BERT (Bidirectional Encoder Representation from Transformer) algorithm. BERT is the algorithm that takes text as input, and the trained basis on the epochs (number of passes performed). The performance parameters are computed such as accuracy, precision, recall, and F-measure. Further, the proposed approach is compared with other existing algorithms such as Naïve Bayes (NB), support vector machine (SVM), and logistic regression (LR). The performance measures indicate that the BERT algorithm outperforms all other existing algorithms with an accuracy of 86.7% as compared to 67.3%, 63.4%, and 61.2% with Naïve Bayes, support vector machine, and logistic regression, respectively. The government and other medical health agencies can use the outcomes of this paper for implementing and taking preventative measures to maintain the good mental and physical health of medical staff.
Factors Affecting Memory Retention in K-12 Students for the Development of AR-Based Learning Application

Retention is an important aspect not only for students but also for teachers. The main aim of the paper is to identify such principles that affect memory retention in students and then considering those factors while developing an educational application. For designing an application, some of the key aspects have been supported by literature such as usability and learnability. However, no studies were found in the literature which discuss the most suitable memory retention principles to follow while designing an augmented reality (AR)-based application for students. The study aims the development of the memory retention principles for designing AR-based learning application. The paper proposes 32 memory retention factors through the existing literature based on retention. The principles will then be checked for the degree of importance so that in the future it can help developers to build an easy to use, interactive, and effective learning application for students.
Classification for Diagnosis of Breast Cancer Using Machine Learning Techniques with Hyperparameter Tuning

Breast cancer mortality has risen dramatically in recent years. However, early diagnosis and treatment may greatly reduce the risk of death. Cancer cells may spread from the breast to other portions of the body over the bloodstream arrangement. They will move early in the process, when the tumour is minor stage, or later in the process, when the tumour will be in major stage. The aim is to propose a method that allows use of supervised machine learning (ML) classifiers such as linear regression, Naive Bayes (NB), support vector machine (SVM) and multilayer perceptron (MLP) that classify the mammogram images as benign or Malignant. The hyperparameters scheme used for the classifiers was manually allocated in order to increase the classifier’s accuracy and identify the cancer as benign or Malignant. The results show that by manually tuning the hyperparameters, all of the presented ML algorithms performed well on the classification task. In addition, the Wisconsin breast cancer (WBC) dataset was used in this analysis. The dataset was divided in the following way for the ML algorithms implement to classify the breast cancer: 60% for training and 40% for testing. The main purpose of this work is to compare multiple classifiers to discover the best classifier that provides better accuracy in breast cancer classification. The proposed model’s output is evaluated using various parametric values such as precision, recall, sensitivity, and F-measure.
Ontology-based Requirements Specification Process

Requirements specification activity in a human-readable and formal representation is a crucial task while developing a complex system. If the meaning of requirements description and their fragments can be handled, Stakeholders can produce more effective requirement specifications of a higher quality. To that end, an ontology is developed to control capturing requirements in a formal way. The proposed ontology is built using Protege tool. This paper focuses on developing a generic ontology that can be used to define different concepts of requirements description. The suggested ontology can be used in many ways in analyzing requirements specification in any requirement engineering process. It would help system engineers to design requirement models susceptible to detect incomplete and conflicted requirements and a variety of other problems. It could also ensure consistency between many requirements specifications and increase communication between involved protagonists due to the use of the same terminology during all system specification process.
Multi-objective Based Chan-Vese Method for Segmentation of Mass in a Mammogram Image

In the modern world, breast cancer is one of the most affecting health problems after skin cancer, and the solution to this is mainly early detection. Digital mammogram has arisen as the most mainstream screening method accepted for breast cancer detection. It gives them the freedom to create innovative algorithms for computer-aided detection (CAD). In the case of image segmentation for medical image-level sets, fuzzy set methods are mainly used. But these methods have limitations in managing local and global features. The active contour method is prominent for segmentation, but some improvement is needed because of the limitation of initial contour initialization. There are no solid strategies for detecting breast tumors; it motivates working to create an innovative method for identifying cancerous mass in the mammogram. A hybrid approach is introduced in this article, which is a multi-objective chan-vese method. The main objective is to reduce the computational complexity and accurate detection of mass in a mammogram. The findings are analyzed using the Mini Mammogram Image Analysis Society (MIAS) database and linked to other approaches. The success measurement indicates that the proposed approach is superior to different methods.
Design of Visual-Image Classifier for Web Application

In this paper, a self-designed model is put forward, in which a web application architecture interrelates with a self-designed classification-based image-retrieval search algorithm for electronic commerce grocery stores. This developed model for reference is named as “Search Images”. The architecture of this model is designed in two segments—primarily the designing of convolutional neural network (CNN) and secondarily combining the CNN model with web application segment. Dataset of vegetables and fruits is used to train the CNN model also pre-processing, data augmentation, plots of training and testing of the model are obtained before assessing the accuracy of the designed model. Precise accuracies of the CNN model are obtained. This developed model makes use of a forward-thinking technological method which automatically classifies and retrieves the picture of desired vegetable and fruit commodity on the web application with the assistance of this designed visual-image search engine. This advanced visual search engine eradicates the requirement of manual grocery search in the web application. This model can classify 131 classes of fruits and vegetables based on the selected dataset. This paper encompasses the methodology and description for designing this search engine applying visual-image classifier.
Social Cloud Computing: Architecture and Application

With the increase in the use of social networks and cloud computing, users have started a new way to interact with people, including their friends, colleagues, etc. Social networks reflect real-world relationships, which allows users to share information and establish connections with each other, thereby creating dynamic virtual organizations. Cloud applications need to provide a large number of heterogeneous, geographically distributed resources, which are managed and shared by many stakeholders who may or may not know earlier. Online relationships in social networks are usually based on real-world relationships, so they can be used to infer the degree of trust between users. Due to this, many security issues are arising, which, if not addressed carefully, may affect the adoption of this promising computing model. Apt response to these threats is of special significance in the social cloud environment. In the social cloud environment, computing resources are provided by users themselves. We believe that considering trust and reputation requirements can take advantage of security by incorporating the concepts of trust relationship and reputation into these schemes. In this paper, a survey is presented on the social cloud. The article includes its architecture, application on Facebook as a social networking site, and other usages. At last, the relation between trust and reputation is presented that is essential while sharing cloud resources in the social cloud environment.
A Study to Identify the Pharma 4.0 Influencing Factors within the Pharma Supply Chain

The main purpose of this research paper is to develop and validate a scale to measure the Pharma 4.0 deployment challenges within the pharma supply chain. This enables Pharmaceutical firms to incorporate scale-based considerations during their supply chain digital technology implementations. There are three steps within the scale development process 1) item generation through opinions of experts and literature review, 2) Scale validation and purification through pilot study and 3) Main study analysis using Non-response bias, common method bias, confirmatory factor analysis, face and convergent validity. The sample had 369 respondents with responses at 45%. The findings of the study resulted in a 36-item scale, which measures various factors like Leadership drive, digital capability, organizational culture, data availability, data security and regulatory acceptance along with one dependent factor Business benefits. The predictive validity assessment model revealed the importance and criticality of various Pharma 4.0 adoption constructs. The findings are aligned to the literature review. Further research studies should include other factors like cost, competition, and government regulations, currently not considered for analysis in the current study. This reliable and valid scale will help Pharma leaders and digital implementers to measure the factors affecting Pharma 4.0 adoption, which can enhance the smooth deployment of Pharma 4.0 technologies within the supply chain. The study explains a first-time attempt to develop a scale to measure the Pharma 4.0 adoption factors within the Pharma Supply chain.