Reviewerly/exHarmony huggingface dataset:
A study of applications of Block-chain in the MANET

MANET-Mobile Ad-hoc Network are infrastructure less networks created in an emergency situation. Nodes in this network self-organize and can operate as routers. They are self-organizing and run on batteries. Block chain is a new technology that emerged in 2008, and many researchers are exploring its potential applications in a variety of fields, including MANETs. This paper reviews existing research on the use of block chain in a MANET setting. Some researchers have developed security models based on Blockchain. Some have proposed blockchain-based routing systems. This paper investigates these studies.
Usability Evaluation of Augmented Reality as Immersive Product Guidelines

Augmented reality is a technology that has recently experienced rapid development and is supported in its application on mobile devices by Google through ARCore technology, thus made it accessible and can be used for various purposes in a practical and affordable manner. The nature of augmented reality that adds digital information to data obtained from the real world, such as images taken through cameras, can be used to three dimensionally showcase products owned by a company to potential customers. This allows consumers to have a complete understanding of a product before purchasing it by allowing them to examine precise specifications, view internal components, and even mimic its functionality, which reduces buying anxiety and increases consumer trust. The case study method is utilized in this research to create a prototype application that is used on mobile devices to display the features, specifications, and appearance of three-dimensional objects based on the recognition of markers and the electronic product's appearance as captured by a live camera. Evaluation using cognitive walkthrough of the AR prototype revealed that it serves as immersive user guidelines for an electronic product, but there are still usability concerns that hinder users from completing tasks flawlessly. Participant comments highlight the reasons behind task failures and can be utilized to enhance application usability. Future research aims to analyze specific UI/UX components that contribute to failures and re-evaluate them with a larger sample size.
Light Field Compression Based on Graph Sample and Aggregaet Algorithm

The application of light field technology has gained increasing attention owing to its remarkable ability to capture highdimensional scene information. However, the storage and transmission of the substantial amount of data generated by this technology pose significant challenges. To address this issue, we present a novel approach that utilizes the Graph sample and aggregate algorithm (GraphSAGE), a potent graph neural network model that learns node embeddings on graphs. Our method represents each view of the light field as a node in a graph and uses GraphSAGE to acquire a compressed set of node embeddings that effectively capture the light field. To evaluate our approach, we compare it against the state-of-the-art light field compression methods, including HEVC, Graph learning methods, and our previous work. Our experimental results demonstrate that our proposed approach achieves highly competitive compression performance when compared to these state-of-the-art methods.
Multi-Head Attention of Optical Character Recognition on Relay Protection Drawings

Recognizing engineering drawings becomes an urgent problem to be solved for digital application in power system relay protection field. The optical character recognition is an automatic technology to recognize characters in images, which is vital for drawing digitalization. However, existing studies on the optical character recognition can neither recognize the irregular shape of texts on the power grid engineering drawings nor accurately identify characters with concentrated distribution. In this paper, we present a new multi-head attention based method leveraging differentiable binarization, which is called EDOCR. The core is the seq2seq model with the multi-head attention strategy to capture the most associated information of the input image for text recognition. Experimental results on the real-world datasets demonstrate the advantages and competitiveness of our method.
Blockchain in Banking: A Bibliometric Review

This study is a bibliometric review of the blockchain in the banking sector. The use of blockchain in banking has been growing for the past few years. Scopus database was used for the study. Scopus database had 69 research papers relevant to the study out of which, 54 papers relevant to study were examined using VOS viewer. The time period of study was 2017 to 2022. Three major research themes emerged out of bibliographic coupling: compatibility, feasibility and risk management which influence blockchain technology in banking.
GSEK: Graph Structured Neural Networks with Energy Graph Kernel

Graph neural networks (GNNs) have become an increasingly popular framework for describing graph-structured data using deep learning. However, traditional GNNs may not accurately map features for graphs that contain noise or missing information. In order to address this issue, we propose a new method called Graph Structured Neural Networks with Energy Graph Kernel (GSEK) for classification. GSEK defines energy properties for nodes and extracts node subgraphs using maximum energy random wandering to ensure the centrality of the subgraphs' structure. It then uses Wasserstein Weisfeiler-Lehman (WWL) graph kernel to calculate isomorphism coefficients for subgraphs of different node pairs, which are used to define weight matrices and achieve graph reconstruction. Unlike traditional methods that solely focus on node features, GSEK places greater emphasis on the local structure of the graph. Our experiments on six publicly available graph classification datasets demonstrate that our proposed model outperforms other benchmark models.
Improved YOLOv5 Model Based on the Pyramid Structure of Dilated Convolutions

With the rapid development of deep learning, there have been numerous object-detection algorithms based on deep learning. However, during the study of the YOLOv5 model, it was found that the SPP-based feature extraction structure may lose some details during the max-pooling process. Additionally, the LeakyReLU activation function lacks sufficient smoothness, and the multi-scale feature fusion using <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{FPN+PAN}$</tex> structure in its Neck part is not sufficient. To address these issues, this paper proposes an improved YOLOv5 model based on the pyramid structure of dilated convolutions, which introduces dilated convolutions with channel attention mechanism based on the SPP structure, and makes some adjustments in activation function and Neck part. Comparative experiments on the PASCAL VOC2007 and VOC2012 benchmark datasets show that the proposed model achieves higher detection accuracy.
Big Data Analytics for Literary Translation: A Case Study on Female Images in ‘Moment in Peking’

This research aims to propose a data-driven analysis of literary translation in the portrayal of female characters in “Moment in Peking”. The study uses parallel corpus and natural language processing tools such as AntConc and Word Smith Tools 7.0 to extract various textual features, including word frequency and stylistic differences. By analyzing the translation strategies employed by the translator and exploring cultural differences and gender norms between the source and target languages, this research offers insights into the challenges and opportunities of literary translation. The expected results will highlight the importance of big data analytics in literary translation and contribute to the ongoing discourse on gender representation in literature.
SSOBS: Online buying and selling platform selection system based on store characteristics

A buying and selling platform influences the human mindset, especially in economics. Human behaviour that usually conventionally makes buying and selling transactions by meeting face to face in the market can change only with application-based transactions. The field of economics is one field that many people are involved in. By mastering the economic field, humans can seek profits to meet their needs. Not only do the consumers benefit from the emergence of various buying and selling platforms, but the sellers also benefit because they can minimize marketing operational costs. The emergence of various buying and selling platforms has prompted the need to conduct a study to determine the ideal buying and selling platform for users with the dominance of Generation Z. The technique used to determine and choose a buying and selling platform is Multi-Criteria Decision Making with data processing using a Decision Support System. In addition to analyzing the output results, this study also analyzes the effectiveness of the 2 Multi-Criteria Decision Making methods, including the Weighted Sum Model (WSM) method and the Smart Multi-Attribute Rating Technique (SMART) method. The WSM method produces the ideal Shopee platform output with a value of 4.45. In contrast, the SMART method produces the ideal Tokopedia platform output with a value of 0.65. The two methods produce inverse outputs for ranks 1 and 2 so that the two methods are effective enough to provide recommendations for selecting the ideal online buying and selling platform.
Application for Airport Tenant Selection Using Weighted Product-based Decision Support System

This research aims to develop an application for selecting the best new tenant at PT Angkasa Pura II Sultan Thaha Jambi Airport Branch by utilizing Weighted Product (WP) method as the decision support system. Our approach is descriptive research with data analysis technique using four prospective tenants coded A_1 to A_4. The data was collected through observation, interviews, documentation, and literature study. The steps for selecting the best new tenant using the WP method include determining the criteria, sorting the suitability of alternatives and criteria, determining the weight of each criterion and its normalization, calculating the score, S value as an alternative preference, and calculating the ranking, V value as the final alternative preference. Based on the final calculation results, the new tenant coded A_4 is the best alternative based on the data collected from the Financial Management (FHFM) Unit and the Commercial Unit, with a value of V_4 is 0.294.
Advancements in Microcontroller Technology for Wind Speed Measurement in Wind Tunnels

The wind speed gauge is essential in wind tunnel airflow simulations to accurately replicate real-world conditions. Traditional methods for measuring wind speed involve using a manometer with red fluid, which generates analog data. However, due to the expense and unavailability of the red manometer fluid in Indonesia, wind tunnels that still rely on manometers face a challenge. To overcome this, a new wind speed measuring instrument has been developed using easily obtainable materials and microcontroller technology. The instrument's design involved hardware, software, and system design stages, utilizing an Arduino Uno R3 microcontroller, a differential pressure sensor MPXV7002DP, and an LCD. This wind speed gauge is based on Bernoulli's principle, which uses a pitot tube to measure wind speed up to 25m/s, and displays the results on the LCD screen. To ensure its accuracy, the instrument was compared to a calibrated anemometer, confirming its suitability for measuring wind speed in low subsonic wind tunnels.
Survey on Data Privacy Preserving Techniques in Blockchain Applications

The use of blockchain in various fields is increasing rapidly because of its transparency and decentralisation. However, the transparency and immutability of data on the blockchain have prompted questions about data privacy. The intention of this study is to investigate the many theories and solutions made to deal with data privacy in blockchain applications. This paper covers an overview of privacy-preserving techniques like secure multi-party computation, ring signatures, homomorphic encryption, and zero-knowledge proofs, as well as the concept of local differential privacy in data privacy on blockchain. This paper also covers the data privacy challenges in blockchain applications. Overall, the aim of this article is to furnish the latest research and future trends regarding data privacy in blockchain applications and its importance.
Comparative Analysis of Cybersecurity Frameworks Utilized by Industries in the Philippines

Cybersecurity framework implementation is essential for risk minimization and business continuity. To provide organizations with a guide in adopting the best-fit framework for cyber threat prevention and mitigation, the authors consolidated existing literature and documents and interviewed cybersecurity experts to analyze cybersecurity frameworks utilized by industries in the Philippines. This paper aims to (1) provide a general discussion and definition of cybersecurity frameworks; (2) understand Philippine industries in the cyber context; and (3) compare cybersecurity frameworks utilized in selected industries. The study adopted Halverson and Conradi’s Taxonomy of software process improvement to assess the components and application of frameworks utilized in Business Process Outsourcing, Finance and Banking, and Security industries. Analysis and discussion of obtained data and information revealed that cybersecurity frameworks provide structure and serve as a common language for risk mitigation in a landscape where industry operations and assets face cyber risks and threats. The identification of frameworks utilized by the selected industries revealed that more than one framework could be implemented within an organization. Comparative analysis of framework features further revealed that the companies adopted ISO27001, NIST, and GDPR based on their current technical capabilities, security status, legal regulations, and security objectives.
Sign Language Identification using Skeletal Point-based Spatio-Temporal Recurrent Neural Network

Sign language is a visual language that serves as the primary means of communication in the non-hearing community. It is essential to research and develop sign language translation methods to enable smooth communication between the non-hearing and the hearing communities. The non-hearing community faces several challenges that hinder their interaction with the general public, such as the lack of knowledge of sign language and the availability of interpreters. A Sign Language Converter would thus be an important tool in breaking the communication barrier between the non-hearing and hearing communities. The paper aims to build a model that converts Indian Sign Language into corresponding words. We plan on using a skeletal-point feature extraction framework to identify hand landmarks from sequences containing distinct signs and use these landmarks to build a model for recognizing hand gestures using various Long Short-Term Memory (LSTM) Networks. [1] This approach can produce an accurate result compared to the traditional approach. The dataset is made by the co-authors of this paper due to the insufficiency of pre-existing ISL datasets. The user will be monitored and using the machine learning techniques discussed above, which will perform the real-time translation to display the final result.
Comparative Analysis of BERT Models for Sentiment Analysis on Twitter Data

Sentiment analysis is a technique in natural language processing that entails the recognition and categorization of viewpoints articulated in written or spoken language. The utilization of Twitter as a prevalent medium for the expression of opinions and emotions has resulted in a significant focus on the analysis of sentiments from Twitter’s Tweet data. Bidirectional Encoder Representations from Transformers (BERT) is a preexisting deep learning model that has demonstrated exceptional proficiency in Natural Language Processing (NLP) assignments, as in sentiment analysis. The present investigation entails a comparison of three different BERT models, namely, fine-tuned bert-base-multilingual-uncased, RoBERTuito, and RuBERT, with respect to their efficacy in performing Twitter data sentiment analysis. The models were graded according to their F1-score, recall, accuracy, and precision, utilizing a manually annotated dataset of 1,578,627 tweets. The experimental findings indicate that the RoBERTuito model exhibits superior in comparison to the other two models, attaining an accuracy score of 83.23%. The results offer valuable perspectives on the efficacy of BERT models in the context of analyzing Twitter data based on how people feel, thereby aiding scholars and professionals in selecting the most appropriate BERT model for their sentiment analysis task. The study emphasizes that the selection of the BERT model for Twitter data sentiment analysis should be contingent on the evaluation metric of interest. This study contributes to an understanding of the effectiveness of BERT models for sentiment analysis on Twitter data, which can be applied to social media monitoring and analysis.
Blockchain-Based Paper Review System

Paper review and publishing is an important part of a researcher’s life. It is where a researcher’s time and resources that he has spent bears fruit. And this is enabled by getting his work reviewed by different peers. In our current state of things, researchers face a lot of issues while they try to publish their research manuscript. These issues include quality of peer-reviews, rise of predatory journals, bias between reviewers, and other issues regarding academic integrity. These issues in the traditional system are what make the system inefficient. Therefore, we propose a more efficient blockchain-based system that will try to tackle these issues. Hyperledger Fabric is one of the most popular Blockchain platforms which provides a permissioned blockchain. Our proposed system would leverage the permissioned blockchain. This is to ensure that not everyone from outside the system has access to the data present in the system and only the members of the blockchain are able to access the data. The workflow of the peer-review system would be stored as transactions. The blockchain would be used to keep track of these transactions and order them properly in order to maintain provenance. This system would prove to be a lot more efficient than the traditional existing systems.
Descriptive Analysis of Web Security Vulnerabilities at Airport Servers Using The Open Web Application Security Project Security Standard

Web-based application security issues are still very interesting to study. This is because professional or nonprofessional web developers when finished creating a web and putting the web on a web server intentionally or unintentionally ignore the security issues of the web. Web security issues can be identified by taking legal action or illegally by performing penetration tests. This study examines the airport servers using the 2021 Open Web Application Security Project (OWASP) security standard. By collecting data from the website using tools available in Kali Linux, the research is continued by using web-based applications, namely Acunetix and ZAP for scanning and vulnerability assessment. The data from the scanning process and vulnerability assessment were analyzed descriptively, the amount of data obtained was 10, with a size value (range) of 20, the smallest value (minimum) of 0, the highest value (maximum) of 20, the total value (sum) 66, the average value (mean) ) 6.60, the standard error (std error) is 2.31, the standard deviation (std deviation) is 7.31 and the variance is 53.436. There is a security vulnerability with a medium level of risk on the web.
Performance Comparison of Time Scheduling Dual Axis Cartesian-Active Solar Panel and Static Solar Panel

Active position solar panel which performs sun tracking is one way to maximize the power output of a PV. By aligning the PV direction perpendicular to the sun position, the achieved output can be maximized. Previous research on sun tracking were done by using a photo sensor (LDR) to track the sun. However, this method demonstrated less accurate in cloudy conditions. To overcome this issue, this research proposed to develop an active tracking based on time scheduling. While active tracking based on scheduling has also been investigated widely, the mechanical designs were mostly based on polar / spin coordinate system. This research implemented and investigated different approach of mechanical design, which is using 2 axis cartesian model. The results of this study demonstrated that the proposed method produced higher total energy compared to static PV systems. Higher total energy is achieved especially due to the ability to track the sun in both sunny and cloudy conditions.
Capacity Configuration Method of Energy Storage System in Data Center Based on Improved Quantum Gravity Search Algorithm

In order to guarantee the power supply reliability(PSR) and reduce the electricity cost of data center, a capacity configuration method of energy storage system(ESS) in data center based on improved quantum gravity search algorithm(QGSA) is proposed. Firstly, the availability is used as an index to evaluate the PSR, and an availability model is formulated to analyze the PSR of data center; Secondly, considering the investment and revenue of ESS, the capacity configuration model of ESS considering the PSR is formulated to maximize the revenue; Then, the levy flight and adaptive adjustment strategy of contraction expansion(CE) coefficient is proposed to improve the QGSA, and the improved QGSA is used to solve the capacity configuration model of ESS; Finally, simulation analysis is conducted with typical daily loads of a certain data center, and the results show that the proposed capacity configuration method of ESS can ensure the PSR of data center and obtain considerable revenue.
Watershed Segmentation for Printed Source Classification

It’s acknowledgeable that different printers would produce different light and dark patterns that are not consistent with the direction in which the paper moves through the printer. Hence, the purpose of this study is to ascertain the differences in the printed characteristics of each printer. Moreover, the experimental procedure includes Watershed segmentation, image processing, classification using a neural network method, and performance evaluation using the Area Under the Curve (AUC) metric. The study uses an Arabic letter collection, with a particular emphasis on the letter "alif." The findings show the efficiency of various segmentation strategies and emphasize the need of selecting appropriate techniques for different printer types. The Dams segmentation approach consistently produces high AUC values ranging from 0.96 to 0.98 across all printer models, demonstrating better effectiveness in detecting counterfeit papers. This research has contributed to advancing the area of document authentication by directing the development of strong systems to prevent document forgery and improve security and trust in various applications.