Reviewerly/exHarmony huggingface dataset:
Bangla Handwritten Character and Words Recognition-based on the YOLOv5 Algorithm

Today, handwritten character recognition (HCR) is a significant research project issue in the Bangla language. One of the most well-known fundamental issues in Artificial Intelligence and it’s all sectors (ML, DL, IP) is considered preeminent difficult. Because of the wide range of different writing styles and organizational reflection amongst characters, it is a complicated motive. Bangla handwriting is characterized by many perplexing characters of handwritten and elaborate spontaneous writing style; despite progress in object identification technique, Handwritten Bangla Characters Recognition (HBCR) remains mostly unresolved. This paper has introduced a new approach based on Handwritten Bangla Character Recognition using Convolutional Neural Networks to enhance effectiveness. The real benefit of CNN methods is that they can isolate significant characteristics from given images, improving interpretation speed and efficiency and decreasing the model size. The proposed method was examined by employing a Convolutional Neural Network (CNN) and the You only look once (YOLO)v5 algorithm to detect Bangla handwritten continuous characters with higher recognition accuracy. For character recognition, the used dataset is comparably vast and robust. In this study, we have employed the Bangla-Lekha Isolated Dataset, which contains 50 primary characters, ten-character modifications, ten Bangla numerical digits, and 23 compound characters. Finally achieved 96% accuracy on Bangla character recognition and 91% on Bangla word recognition using the proposed approach, which is a substantially superior method.
Real-time Attendance Monitoring System using Machine Learning and Blockchain

In a vast majority of fields, the use of facial recognition for authentication is expanding. In this information age, authentication has become vital, and the need for faster and more secure methods of user authentication has been on the rise. The introduction of image processing technologies such as OpenCV has increased society’s reliance on face recognition. Using blockchain, information could be stored in blocks throughout the blockchain network. Blockchain is an extremely secure means for storing and protecting data from intruders. It is a highly disruptive technology that has the ability to alter every plane of society. This paper intends to implement open-source computer vision (OpenCV) to construct a facial detection model that will be employed in a blockchain-secured Attendance Monitoring System. It will not only automate the attendance procedure but also give the system unassailable security. This system will take a live video feed from a camera using OpenCV and identify the faces of students and record their attendance along with the entry time. The data will be kept in a distributed way over the blockchain network that will be accessible to everyone but data cannot be manipulated.
Game Balance

The game Rock, Paper, Scissors perfectly defines game balance. Rock can defeat Scissors, Scissors can defeat Paper, but Paper can defeat Rock as shown in Fig. 8.1. That means no one option has more advantages over any other option. Thus the goal in game balancing is to make every option exactly equal.
Video Cryptography with Chaos

Even though digital video encryption and decryption are less well known to the common people; they are an intense research area. Cryptography is the procedure of encrypting and decrypting information. It is the discipline of safeguarding information that has become a critical element of contemporary computer systems for secure data storage and transmission.Images and videos have extensive applications across various operations. Therefore, it is essential for users to ensure the protection of media data from unauthorized access. Arnold’s Cat Map is a cryptographic algorithm that safeguards digital media by performing n iterations on the pixel image, making the result indecipherable without knowledge of the iteration process value.Currently, image cryptography relies solely on chaos. Our proposed method is distinctive since video cryptography using chaotic functions has not been kept in light yet. We are developing this unique methodology of cryptography of digital media, video being our priority. The whole methodology of the cipher and decipher methodology is working on Chaos functions and maps.
Global Trends of IOT in Pharmaceutical Industry: A Bibliometric Analysis of Scopus Database

The Internet of Things (IoT) research in the pharma industry is reviewed in this report. To systematically and unbiasedly uncover the information growth in the domain of IOT research in context to background of the pharmaceutical industry, a complete study and bibliometric analysis were carried out. 1186 documents were chosen for study from the Scopus database. The publications were then scrutinized in light of bibliometric factors such the year of publication, sources, authors, and institutions. The relevant literature was grouped using a keyword co-occurrence network. The review’s findings and the results of the bibliometric analysis show that the pharmaceutical business has given IoT research a lot of consideration. Fifteen Pioneering sources and fifteen most productive authors are identified. The IoT literature in the pharmaceutical business is focused on AI, health care, medication, etc., according to the clustering of keywords. Researchers may now better comprehend IoT research in the pharmaceutical business and knowledge gaps that still need to be filled. The study may help practitioners stay up to date on the latest IoT topics and applications across various industrial sectors.
Effectiveness of E-Learning Cloud Computing and The Current Issues In Education

This paper aims to conclude the effectiveness of Limited Face-to-Face Meetings (PTM) by discussing the current problems in education associated with distance learning, e-learning cloud computing, as well as the solutions implemented in order to circumvent the negative risks that occur. This qualitative descriptive research is conducted by giving questionnaires to the respondents of college students, whose responses are then interpreted to the scale of 1,2, or 3 for negative, neutral, or positive opinion through collecting data using purposive sampling, then testing the validity and reliability. The results of the questionnaire values will be averaged to present the final results of this study. The average result for the preference of limited onsite courses is 2.38, indicating a positive opinion that proves the sample of college students respondents from Binus University feel a preference for Limited Face-to-Face Meetings (PTM). This concludes that the limited onsite courses provided by Binus University are sufficiently effective as a solution to the problems and risks associated with distance learning.
Nonlinear filters based parallel demosaicking and denoising of natural images infected with non Gaussian noise

In the process of image restoration from images corrupted with non-Gaussian noise, the nonlinear filters perform superior over linear filters, and over the conventional sequential demosaicking and denoising methods, the parallel demosaicking cum denoising is highly efficient and fast. These assumptions in this paper are analysed, implemented, evaluated and established objectively by the authors. Most the research is found to be non pragmatic as the restoration of the images from non-Gaussian noise infected images is rarely considered. Hence the authors, by implementing sixteen nonlinear filter techniques and one linear filter technique, reconstructed the color from the images infected with Gaussian and Non- Gaussian Noise.Objective performance metrics MMSE, CPSNR, and SCIELAB are obtained and compared for objective performance evaluation, and the reconstructed images are also presented for the viewers’ subjective evaluation. The standard image dataset is used for implementation of the proposed nonlinear filter based parallel demosaicking cum denoising of non-Gaussian noise infected images.
Modification of YOLOv4 for Human Detection in Elevated Thermal Images

Several deep learning networks have already been developed to detect objects given an input image. However, most of these networks are usually designed around detecting objects using a standard set of images or datasets such as MS COCO. Using these networks on a custom dataset already performs well but lacks context on the detected object. Furthermore, they can be improved upon by modifying the network depending on the simplicity or complexity of the dataset being used. With this, the paper presents two ways of modifying and improving a base network for detecting humans on the custom thermal elevated objects (TEO) human thermal dataset. The results show that TrimmedYOLO, the network created using the layer removal method, produced comparable results as the base YOLOv4 while being more lightweight regarding FPS and BFLOPS needed. On the other hand, TEO-EfficientYOLO-Mish and TEO-YOLO-ReT-Mish, the networks created using the backbone replacement method, can perform inference the fastest while still producing fairly high mAP results.
Hybridization of Discrete Cosine Transform and Principal Component Analysis to Achieve Digital Watermarking

This study presents an original method for the watermarking of grayscale images that are based on the discrete cosine transform in sun bands (SB-DCT) and principal component analysis (PCA). When applied to each non-overlapping image block, the SB-DCT is utilized to extract the image's features. These structures are used as a dataset for the PCA analysis that is carried out. PCA analysis inserts the binary watermark logo into image portions. that have a low level of variation. Because PCA has the best approximation function and the highest elaborating characteristic. capable of recovering the original watermark after demonstrating the effectiveness of several image processing methods According to the extensive testing results, the watermarked image has a High PSNR and a normalized correlation (NC). the retrieved watermark's high Bit Correct Ratio (BCR) value demonstrates the resilience and imperceptibility of the suggested approach When compared to cutting-edge approaches.
An Examination of the Feasibility of Various Deep Learning Object Detecting Techniques

The necessity for highly precise object detection has grown due to an increasing ubiquitousness of an automated driving, facial detection, and different fields involving the tracking of users. In these systems, each particular object in a picture must first be recognized, categorized, and finally localized by reflecting the proper coordinates around it, because of this, object detection is substantially more difficult to do than its typical computer vision predecessor, picture categorization. This study begin with concise overview of CNN, the tool that best exemplifies this methodology and then focuses on analyzing various deep learning-based object detection systems. This study compares three primary detection algorithms- Faster Region based CNN, Single Shot Detector and YOLO to choose fastest among the three, and compare associated performance. The performance of these three algorithms is analysed using MS COCO dataset. According to analysis's findings, it can be said that YOLO shows better performance than other two in a similar testing environment. Comparison to other two, YOLO could detect more objects in an image with higher F1 score.
Utilizing Hyperledger Fabric Based Private Blockchain and IPFS to Secure Educational Certificate Management

In recent decades, the quantity of fake educational certificates has increased, damaging testing centers’ reputations. The most common verification methods include email, phone, and proxy for background checking, which require additional costs, security, and time. As these processes are not transparent enough, it is possible to create counterfeit certificates easily. Blockchain- based solutions have basic features such as security, proof of disruption, transparency, and compliance, which give a secure network the ability to store only genuine certificates. Additionally, data history can be managed on a blockchain network in case of suspicious activity. In this project, we analyzed blockchain technology and established a blockchain-based education certification system for the issuance and certification of certificates using the hash algorithm technology for encryption and confidentiality of educational certificates to prevent disruption and copyright restrictions. The InterPlanetary File System, a peer- to-peer based decentralized storage, is used to store the soft copy of the certificates by generating a hash for each certificate. This study offers Hyperledger Fabric-based application development concepts, including the processing of assets and generating immutable transactions, to create a comprehensive private blockchain-based educational certificate management.
Mitigating Imbalanced Data in Online Social Networks using Stratified K-Means Sampling

The K-means clustering technique is widely used in many fields, such as anomaly detection, customer segmentation, cyber-physical system, medical diagnoses, sentiment analysis, fraud detection, and other similar tasks. We used this k-means technique in handling imbalanced datasets by preserving minority class structure using the stratified resampling technique. For this experimental study, we used a benchmark dataset from Kaggle. It is a labeled dataset collected from online social media regarding fake news. This proposed model, The Stratified k-means Sampling (SKMS), is compared with Synthetic Minority Oversampling Technique (SMOTE) by empirically experimenting using different machine learning algorithms. Random Forest (RF) algorithm gives significant accuracy, and Support Vector Classification (SVC) produces a better F1-score than other algorithms. The SMOTE technique was compared with the same dataset using these same algorithms. While SKMS seeks to preserve the structure of the minority class, SMOTE aims to diversify the minority class by interpolating between existing samples. Depending on the dataset, one might be more relevant than the other.
TMCIH: Perceptual Robust Image Hashing with Transformer-based Multi-layer Constraints

In recent decades, many perceptual image hashing schemes for content authentication have been proposed. However, existing algorithms cannot provide satisfactory robustness and discrimination in the face of complex manipulations in real scenarios. In this work, we propose a novel perceptual robust image hashing scheme with transformer-based multi-layer constraints. Specifically, we first exploit the Transformer structure into the field of perceptual image hashing, and an integrated loss function is designed to optimize the training of the model. In addition, to solve the issue of the simple content-preserving manipulations used in previous datasets, we construct a more challenging image dataset based on various manipulations, which can deal with complex image authentication scenarios. Experimental results demonstrate that our scheme achieves competitive results compared with existing schemes.
Steganography on Mobile Apps

Steganography is an ancient communication technique that hides a message inside a common object so that the message escapes scrutiny - today, we use digital files like photos and videos to hide and pass messages. Code to execute the message hiding inside digital photos was developed (on computers) in the 1990s. However, code to execute message hiding inside photos on an app for a smartphone was developed only in the last ten years. These mobile stego apps expanded steganography's reach from computer experts to the general public, where few technological skills are necessary.
User Interface Design to Improve User Experience for the Super Hana Applications

Technology development is very rapid in today's modern era. Almost every company uses technology to support business processes, including companies providing access network installation services. One of the companies in the area of access network installation services is PT Telkom Akses. Super Hana is a mandatory application owned by PT Telkom Akses employees. However, from several reviews or user experiences that are discussed every day, there are still several things that still need to be improved in the application. These deficiencies include the need for more location features when employees take attendance, a dashboard of how many employee absences, and no reminders if employees have not taken attendance. Therefore, to analyze the user experience value of the Super Hana application and improve its user interface, user experience testing using the User Experience Questionaire (UEQ) method and in-depth interviews were carried out. User Experience and User Experience Questionaire (UEQ) measurements are based on attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. In addition, the assessment was also carried out on employees using in-depth interviews to find out directly the obstacles experienced by employees when using the Super Hana application. Based on the results of our questionnaires and interviews, the Super Hana application has met the level of satisfaction with the user interface and menu features in the application. However, some features still need to be added and improved to support users in monitoring employee attendance and creating a better user interface in terms of the experience of using the Super Hana application.
UX Analysis of Legal Analytics Applications at Internal Telkom South Jakarta Using the TAM

Legal Analytics is a new application that was released on January 27, 2022, so this application can be said to be only a little over a year old. This app focuses on the process of drafting laws and regulations digitally. This research aims to measuring user interest in the Legal Analytics application to facilitate users in carrying out their daily work and implementing the Technology Acceptance Model (TAM) theory so that the Legal Analytics application can be accepted by users. The method used in this study is the Technology Acceptance Model (TAM) by explaining factors such as Perceived Usefulness, Attitude Toward Use, Actual System Usage, Behavioral Intention of Use and Perceived Ease of Use that influence the acceptance of the Legal Analytics application with the SmartPLS application to process test result. The results obtained from this study are Fornell Larcker which is valid, but there are two aspects of the HTMT that are invalid because they are above 0.9, namely Perceived Ease of Use on Attitude Toward Use which is worth 0.920 and Perceived Ease of Use on Behavioral Intention of Use which has a value of 0.915, so improvements are needed in the application. Another result is the R Square value which indicates that the majority of respondents have a positive attitude towards using the Legal Analytics application and have a high intention to use it, so it can be concluded that the results of this study indicate that the majority of users accept the Legal Analytics application and have a high interest in using the Legal application. Analytics in facilitating his daily work. The result of the implementation of the Technology Acceptance Model (TAM) method in the Legal Analytics application is an improvement in several views of the application that has been recommended, namely improving the appearance of the Document Recap and Document Setting & Drafting menus.
A Survey On Medical Health Record Management using Ethereum Blockchain Technology

A personal health record when stored on the internet is called Electronic Health Records (EHR). A patient’s personal EHR data is often shared with doctors, healthcare providers agencies, insurance companies and other entities in the medical network through the internet. But the internet is not a very Privacy and Security friendly place, it is prone to security breaches and hacks, which leaves the EHR data vulnerable. Blockchain technology can be an excellent solution to secure EHRs. Although blockchain gained its popularity due to cryptocurrencies and its use in financial and banking institutions, it is actually a sophisticated technology which has the potential to transform the entire internet. Blockchain makes use of the peer to peer network to store data in a distributed manner which is immutable and decentralised. The aim of this paper is to study the benefits of blockchain in creating an application where Patients have their medical records history securely stored, made highly available and can have the right to grant permission only to certain users to access the records, leading to an efficient and secure Role Based Access Control System.
Comparison between Ultra Lightweight Cryptographic Techniques on Microcontrollers for Smart Agriculture

The importance of security in the Internet of Things (IoT) has prompted numerous studies on scalable and modular encryption techniques. It is preferable to use lightweight and specialized cryptographic techniques for many IoT security applications due to the low budget and limited resources of high-volume consumer devices like RFID tags, smart cards, and wireless sensor nodes. This paper provides the detailed analysis of ultra-lightweight cryptographic algorithms (U-LWC) along with the hardware implementation. In this work, extensive classifications of algorithms are studied in terms of their performance metrics pertinent to an Internet of Things (IoT) based smart agriculture. Further, one of the most popular algorithms is implemented and analyzed for IoT application.
Scrum-based development model:Improve the engineering quality and testing method of blockchain projects

In order to improve software development efficiency, ensure software quality, and provide efficient project management O&M services, this paper explores the blockchain software engineering process based on Scrum agile development models and its various testing methods, such as CITA and DevOps, to improve the quality of software engineering in the blockchain domain. We outline the application of the Scrum-oriented development model in blockchain projects and introduce various methods and frameworks to improve blockchain software engineering, describing that the combination of Scrum, blockchain and DevOps can be used to create a robust and efficient software development process. By practicing agile development in development management, automating operations and maintenance with DevOps, and implementing continuous automated testing, the goal is to provide a solid, efficient, flexible, and future-proof operating platform for enterprise-level blockchain applications. By combining multiple approaches, organizations can create powerful and efficient software development processes that are being adopted by other industries and explored for potential applications in healthcare, government, and other sectors. That is, agile, secure and responsive to customer needs.