Reviewerly/exHarmony huggingface dataset:
Hybrid Model for Topic Classification of English Learning Accounts on Instagram Using LDA and SVM

English is an important international language to master, especially when in places with different language speakers. However, according to the EF English Proficiency Index, English proficiency among Indonesians is still very low. Indonesia ranks 74 out of 100 countries. Nowadays, many people use social media platforms, like Instagram, to share information and learn. There are numerous English learning accounts on Instagram with thousands of posts, making it difficult to group the topics found in these posts. Therefore, topic modeling and classification need to be performed to determine which topics account owners should improve to attract learners' interest. In this paper, the authors used the Latent Dirichlet Allocation (LDA) method for topic modeling and Support Vector Machine (SVM) for classification. The authors also compared the results when the labeling was done by experts. The expert labeling resulted in 9 topics, while using LDA produced 6 topics. This indicates that the topics generated by the experts are more specific compared to LDA. However, the SVM classification accuracy results from the LDA model are higher compared to the manual labeling. Specifically, the SVM classification accuracy using the polynomial kernel is 79.33%, using the sigmoid kernel is 79.33%, and using the RBF kernel is 80.00% for the LDA model. On the other hand, the SVM classification accuracy using the polynomial kernel is 35.33%, using the sigmoid kernel is 33.67%, and using the RBF kernel is 33.67% for the manual labeling.
Current Biases and Future Research Avenues for Archive Image Colourisation

Recent advances in deep learning (DL) have resulted in a proliferation of techniques able to colourise achromatic or monotone images. This was a previously dormant area of image processing due to the under-constrained nature of the problem. Many of these novel methods have been documented and reviewed. Despite this, many colourisation problems persist that include chromatic aberrations, lack of standard datasets and the absence of a quality no-reference assessment measure. This paper presents a brief review of the research field and highlights these salient problems re-framing them as possible avenues for further research; particularly in the archive image domain.
Development of Application for Conformance Measurement of Control Affordance Components with Functional Requirements based on Text Analysis on TESA Documentation

In application development, achieving synergy between evolving software and elicitation process outcomes emerges as a foundational objective. This endeavor is empowered by the role of text analysis, a tool that adeptly uncovers intricate data relationships and contextual intricacies. This is especially pertinent given the textual attributes of System Requirements Specification (SRS) information. Optical Character Recognition (OCR) adds depth to comprehension by converting images into text. However, challenges surface due to variances between functional requirements and the design's control and affordance components, arising from divergent interpretations of functional requirements during elicitation. Our focal point resides in crafting an application to evaluate the alignment between UI design and the textual core of functional requirements. This study reveals 13 Functional Requirements (d1-d13) and categorizes two control/affordance component documents (d14-d15). The integration of text analysis, steered by word2vec, drives an alignment process, culminating in a notable 0.397 score. Recommendations pivot on the Cohen's Kappa index; values below 0.21 prompt adjustments for alignment at a "sufficient" level (0.21 or higher). The culmination of research materializes in an advanced application adept at processing txt files, skillfully extracting inputs via text analysis, and semantically evaluating alignment through Word2Vec. It offers insights for data instances where standards fall short, firmly rooted in Cohen's Kappa index principles. This application functions as a comprehensive repository, methodically outlining enhancement suggestions, assessed against the backdrop of Cohen's Kappa index.
Improving Tamper-Resistance Exploiting Clock Phase Shifter Embedded in FPGAs

Power Analysis Attacks (PAA) are becoming realistic threats to cryptographic communications, since the countermeasure that addresses the threats completely has not been found. Therefore, many countermeasures to PAA had been proposed. However, conventional countermeasures focus on improving tamper-resistance instead of considering the area overhead. In terms of development cost for IoT edge devices, for small lot development, FPGAs can be adopted for targeted devices. A tamper-resistant design for FPGAs which can be applied to cost-sensitive devices such as IoT edge devices is required. This paper proposes an area saving design for tamper-resistance which exploits Clock Phase Shifter already embedded in FPGAs. Our proposed design can disturb the bit transition to attack targeted logic, and mask the characteristics on power consumption traces which can be used to PAA by attackers. In evaluation of this paper, comparable tamper-resistance to WDDL was confirmed with only 34.16% of area overhead.
Developing an Automatic Speech-to-Speech Translator Mobile Application using AWS

This paper presents the development of an automatic speech-to-speech translator application for an Android device that aims to enable real-time conversation between people speaking different languages, without the need for high-speed internet connection or storage capabilities on the mobile device. The application has been developed based on a hybrid approach, combining Google Speech services and Amazon Translation services to provide higher quality translations and free speech functionality for Android users. The system is based on machine learning methodologies for speech recognition and translation and supports two-way voice translation for 44 languages.
Proof of Equation: A Novel Consensus Algorithm for Dynamic Spectrum Access

The future of communication technology is moving from 5G to 6G with new innovations. Blockchain (BC) is a such immersive technology that significantly impacts the betterment of communication technology. BC-based spectrum-sharing solutions can be used in Dynamic Spectrum Access (DSA) systems to fulfill the need for secure and efficient communication. With the invention of cognitive radio networks, DSA became a popular topic for the scientific community. Spectrum misuse/violations can occur due to the rapid growth of spectrum sharing. As the system is open to malicious attacks, licensed spectrum owners must be identified and verified. However, the existing BC-based DSA solutions are more expensive, non-optimized, and lack spectrum misuse detection. This paper proposes a novel consensus algorithm called “Proof of Equation” for spectrum misuse detection. The core of the proposed algorithm is a consensus score calculation based on a numerical equation with three parameters rather than using cryptographic calculations. The performance of the proposed algorithm is studied using Python simulations, and simulation results show that the proposed algorithm outperforms the Proof of Work (PoW) and Proof of Stake (PoS) consensus algorithms in terms of block production time.
A Block Mining Based Machine Learning Scheme For Data Integrity in Blockchain Based IoT Solutions

The development of many blockchain-based applications has been done by integrating loT devices for information collection and sharing. However, faced with the challenges of the heterogeneity of loT devices and their resource constraints, the security of their use in the majority of blockchain solutions relies heavily on the authentication of these devices. In this paper, we propose a fully decentralized security protocol for blockchain-based loT solutions that focuses on the integrity of the data provided by the collector nodes before it is written to the blockchain. The protocol introduces the detection of possible anomalies in the nodes using machine learning algorithms fed by the nodes' operation traces in the mining process itself. Anomaly detection thus becomes an integral and mandatory part of the mining process. The protocol has extremely low coupling and is therefore completely independent of any machine learning model. The performance of the designed and developed system was tested using three well-known machine learning models: Decision tree (DTs), support vector machine (SVM) and multilayer perceptron (MLP). The simulation results show that SVM offers the best compromise between precision and recall, followed closely by the DTs. Regarding the processing time, SVM takes more time followed by the MLP and finally the DTs. In the end, results obtained allow us to conclude that the protocol is realistic and feasible and shows its ability to improve the mining process.
Denoising Autoencoder - Extreme Learning Machine for Improving the Classification Performance Integrated with IoT Water Quality Systems

Extreme learning machines (ELM) are fast, accurate, and require less user interference. However, this method cannot handle missing values in the data. By using the autoencoder (AE), missing value imputation can be handled. Combining these two methods provides a better evaluation of accuracy using some open-source datasets. This paper focuses on combining machine learning (ML) methods, just like combining denoising autoencoders and extreme learning machines (DAE-ELM), to provide better results. The proposed method is implemented in real-world problems, i.e., water quality monitoring in Tirtawening, which is one of the Indonesian regional water utility companies located in Bandung, West Java, Indonesia. Using DAE for missing value imputation and ELM classification can increase the performance improvement rate (PIR%) by 2.01%. The DAE-ELM combination improves the accuracy of drinking water quality datasets, with the accuracy increasing by 2.71% from 95.98% to 98.69%.
File Access Characteristics of Deep Learning Workloads and Cache-Friendly Data Management

Recently, the data size of deep learning grows rapidly, and accessing file data can significantly degrade deep learning performance. To quantify this, we analyze the file access characteristics of deep learning workloads and find out that they are quite different from those of traditional workloads. Specifically, in the deep learning’s training process, all file data are randomly accessed, and due to this feature, it is difficult to improve the performance of file access through caching. To cope with this situation, we present a cache-friendly file data management policy for accelerating data access in deep learning. Unlike conventional deep learning training processes that shuffle all datasets every epoch, our policy defines a shuffling unit called bundle, and improves the spatial locality of file access without compromising the model’s training efficiency. We also improve the temporal locality of data access by arranging bundles in an alternating order for each epoch. Experimental results show that our data management policy improves the miss ratio of file cache by 17.0%, and the execution time of training by 24.7% when accessing file data in deep learning.
Comparative Phishing Attack Simulations: A Case Study of Critical Information Infrastructure Organization Using Two Different Contents

Nowadays, cybersecurity is an important issue at the personal and organizational levels. Therefore, cybersecurity simulation should be conducted in organizations that are considered critical information infrastructure. According to the simulated phishing attack, during the initial attempt, an email offering a fake promotion from a well-known IT equipment store was sent to employees of a railway company in Thailand. The results indicated that out of around 700 employees who received these phishing emails, 9.5% fell for the trap. This demonstrated a higher level of awareness about cyber threats compared to the average rate of 12%. However, the training was performed for the employees who fell into the trap of the first attack. Then, a simulated phishing email attack was executed for the second time by sending an email, notifying the employees to change their passwords, from a fake IT administrator. The results revealed that 8.0% of the employees who do not fall in the first attack were deceived, while there was 1.4% of the employees who fell into both attacks with different contents. Therefore, this study shows that different contents can impact different awareness of users or employees. Thus, it suggests that the process of knowledge transfer based on cybersecurity awareness is very important still.
A Machine Learning Approach to Resource Management in Cloud Computing Environments

As businesses look to boost flexibility and cut costs, cloud computing is becoming more and more popular. There is always room for improvement, despite the fact that the major cloud service providers use a pay-as-you-go pricing model and provide consumers rapid scaling up and down. CPU and memory utilisation, which represents workload, frequently varies, which causes businesses to incur extra costs and has an adverse effect on the environment. To lessen the impact of this, a Random Forest algorithm which is a supervised machine learning model is proposed for resource scheduling in cloud computing environment. This proposed model is an ensemble algorithm for regression, classification and task decisions that deal with developing decision trees. The proposed strategy is compared to existing machine learning algorithms including XGBoost, Ridge and Lasso. Experimental results show that the proposed algorithm performs better than the compared algorithms in terms of the prediction accuracy of the CPU and memory. Experimental results conducted on go ogle colaboratory using Materna Dataset shows that the proposed algorithm achieved a prediction higher accuracy of 0.1 – 45.1 % higher than the compared algorithms in terms of the R-squared (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), the Root Mean Square Error (RMSE) and lower percentage errors for Mean Absolute Percentage Error (MAPE).
Potential Privacy Violation on Manual Contact Tracing: A Rwanda Case Study

A vast amount of personal information is available on the internet, making it easy to extract a significant amount of information from a single piece of data. This study examines the potential risk of privacy violations in the manual collection of personal data during the COVID-19 pandemic in Rwanda. The research involved eight security firms to assess their data collection and disposal practices. The findings reveal a potential danger of privacy breaches due to employee ignorance and the lack of an internal policy on data handling. Moreover, storing personal data on cloud platforms not hosted in Rwanda violates Rwandan Data Protection Law. The study recommends large-scale research and government support to improve data protection practices, providing employee training, developing internal policies, securing data storage, regular data disposal, and promoting citizen awareness of data protection laws and privacy violations.
Unlocking the Potential of Blockchain in Sub-Saharan Africa: Opportunities and Challenges

Blockchain technologies hold significant potential for sub-Saharan Africa, where two in five households have internet access - a figure rapidly increasing due to the affordability and ubiquity of smartphones. Blockchain technologies provide a more significant value proposition for sub-Saharan Africa compared to the developed world due to their potential to compensate for the lack of adequate formal enforcement of these emerging technologies, such as rules, laws, and regulations. This article examines key challenges regarding digital technology in Africa and assess the potential opportunities for blockchain to address them, particularly in cross-country remittances and access to capital, identity verification, property rights, and humanitarian aid disbursement.
Violation Detection on the Roadside of the Toll Roads using Cascade R-CNN Algorithm

The shoulder of the toll road is the left part of the toll road that is useful for vehicles experiencing emergencies. However, there are still many vehicles that use the shoulder unwisely, such as using it to pass other vehicles or stopping or resting for a long time. This can cause accidents and commotion. Based on the problem, the Cascade R-CNN algorithm is applied which can be used as an algorithm to detect violations on the toll road shoulder. Cascade R-CNN has multistage detection which can reduce overfitting due to lack of dataset. Cascade R-CNN has multistage detection consisting of three stages. The detection train results in the first stage will be trained again in the second stage, and so on until the third stage results. Cascade R-CNN is a well-regarded technique for object detection because of its multistage detection process. Three hyperparameters—epoch, batch size, and learning rate —are used to assess the performance of the Cascade R-CNN algorithm. The goal of hyperparameter testing is to find the most accurate prediction model. The optimal model was created using hyperparameters at epoch 12, batch size 16, learning rate 0.02, Average Precision=97.1%, Average Recall=79.1%, mAP@.5=97.1%, and mAP@.5:.95=74.8%.
Exploiting Content-Based Filtering for Publication Venue Recommendations

Academic activities in a wide variety of subjects have increased significantly, leading to the publication of thousands of scholarly articles worldwide. This amount of published data presents researchers with a common difficulty when selecting the right publication venues to submit their works. The enormous diversity of topics, research areas, and academic disciplines makes finding a suitable journal or conference even more complex. For researchers, it is inevitably essential to publish in the most suitable journal, but manual selection would be inefficient and time-consuming. To help the researcher decide where they should submit their paper manuscript, we present a publication venue recommendation model using GARUDA-indexed journals. This recommender model can recommend suitable journals as publication venue candidates. Our evaluation results show that the recommender model can achieve a precision of 95% and can suggest the best relevant journals for the researchers.
The Effect of Augmentation on Classification Algorithm to Determine Photo Angle

A technique for recognizing the angle in the suitable photo is required to ensure that the information is sent correctly. The image classification method is one approach for determining the angle of a photograph. Support Vector Machine (SVM) and K-Nearest Neighbour (K-NN) classification algorithms were employed in this study. The first total dataset collected from photographic websites contained 2,700 records and was split into three classes: high angle, eye level, and low angle. In addition, there is a second dataset with 679 datasets that was also directly downloaded from the same website. This dataset is separated into 5 classes: bird's-eye view, high angle, eye level, low angle, and frog's-eye view. An augmentation method is used with three strategies, including random rotation, random noise, and grayscale, to increase the number of datasets that have been collected. The entire dataset that will be classified contains 5700 data for the first dataset and 3179 data for the second dataset after the augmentation method adds data from the first and second datasets, totaling 3000 and 2500 data, respectively. The dataset is then divided into training and testing data using a ratio of 80:20. We used 2 scenarios in developing the model: the model with additional augmented data and the model with original data. The Support Vector Machine algorithm and the K-Nearest Neighbor technique in the 3-class and 5-class classification models provide low accuracy results, particularly in the range of 50%, for testing the first scenario. In the second scenario test, the KNN technique with K = 1 achieved an accuracy of 95.10% and an execution time of 3 seconds with a number of classes of 3, compared to 91.70% and 7 seconds with a number of classes of 5. In the SVM method, the RBF kernel was able to achieve an accuracy of 94.10% with an execution time of 6 minutes 32 seconds for the number of classes 3, while 90.60% with an execution time of 26 minutes was achieved for the number of classes 5 using the same parameters (C = 10, gamma = 0.001, and RBF kernel).
DC Motor Control using Type-2 Fuzzy Logic Controller

Position control of a DC motor is required for robot control, steering control, and other industrial purposes. This study contributed to the presentation of conventional control performance compared to Fuzzy Logic Control for controlling the position of dc motors. DC motors were widely used in everyday applications. The purpose of this study is to get the best and most suitable control for dc motors. To obtain the required parameters for conventional control, the Ziegler Nichols open loop method was used. The Fuzzy Logic Control used was type-1 and type-2 Fuzzy Logic Control with the Karnik Mendel algorithm. The input of this control was error and change error with the form of triangular and trapezoidal membership function. The inference engine used consists of nine rules. The best conventional control was PI control with rise time value of 242.140 ms, overshoot 32.570%, and settling time at 5.237 s. For type-1 Fuzzy Logic Control, the value of rise time was 267,381 ms, overshoot 18,432% and settling time at 4,200 s. Rise time of 271,234, overshoot 15,695%, and settling time of 4,043 s were generated when type-2 Fuzzy Logic Control was used. The robustness test also showed good performance for this type of control. The best and most suitable control for this dc motor was the type-2 Fuzzy Logic Control.
Software Development for Object Formation for Sequence Diagrams Based on Use Case Description Extraction: Case Study SRS Scenery

In designing software, developers need an SRS document containing system provisions or requirements. The SRS file should have related contents between artifacts based on the elicitation process. Use Case Description is the result of the elicitation process and used to calculate Similarity value with the Sequence Diagram. However, testing reveals an incompatibility issue between Use Case Description and Sequence Diagram. This can happen because there are differences in determining objects based on the Step Performed in the Use Case Description. The research focuses on Scenery, a mobile-based tourism information app in Indonesia. The purpose of this research is to develop software that performs the formation of all objects for Sequence Diagrams through the extraction of Text Mining in Use Case Descriptions. The result of this research activity is application development that starts with analyzing the Step Perform artifacts contained in the Use Case Description and Sequence Diagram. Then create an artifact model by labeling Step Perform dataset from d1-d15 and d16-d30 for the Sequence Diagram. Text preprocessing using Python and implementing Cosine Similarity and Cohen's Kappa for validity testing yield the highest object forming values: 0.820368, 0.789612, 0.778272, 0.778272, and 0.77754. These value categories are "Almost Perfect agreement" and "Substance agreement". Based on the results of the five highest similarity value data, the objects suitable for the Sequence Diagram are as follows: user, content creator, scenery app, home page, content page, profile page, notifications page, register page, authentication server, login page.