Miracle-dz/UniFAR-FTUs huggingface dataset:
Building the Case for Paleontology

Roy Plotnick and Carrie Schweitzer have recently discussed paleontology in these pages, primarily from an academic, invertebrate paleontology perspective. I would like to contribute some thoughts on paleontology from a different point of view, primarily that of micropaleontology and industry biostratigraphy. My thoughts include the problem of paleontology without paleontologists, poor communication within the discipline, importance of new ways to apply paleontology to “outside” problems, and suggestions on the way forward.
The Standardization of Galician

In a volume that deals with processes of and contemporary challenges related to the standardization of minority languages (sometimes also called minoritized languages), the case of Galician as a Romance language somehow intermediate between Portuguese and Spanish presents itself as highly relevant1. Having been an almost strictly oral language from the late Middle Ages until the mid-nineteenth century, Galician was subject to a rather swift process of standardization in the second half of the 20th century. After a very brief sketch of the most relevant landmarks in the social history of Galician and a summary of key issues in the normative debate, this paper turns to a description of the intense process of standardization that took place since the 1970s. The main body of this contribution is devoted to a description of the main stages of the standardization process of Galician based on Haugen’s (1987) four-step model of language standardization. In addition to documenting the selection, codification, implementation and elaboration of the Galician standard variety, this contribution also addresses its acceptance by the community, as well as the importance of its new speakers for the future of the language, insofar as the members of this group typically use the standard variety in their daily practices.
Design and Development of Seven Stage Inverter for Driving AC Induction Motor

This research study proposes a novel three-speed AC motor drive that uses three phases. By connecting four regular 2-level VSIs in series, similar voltage and current analysis might provide more power to the open-end windings of a double 3-stage engine (lopsided six-stage machine). While implementing the proposed control model, the power supplied to the engine will be divided among the four DC sources at each exchanging step, resulting in voltage waveforms with several peaks, similar to those produced by a three-level inverter. The simulation data required to verify the validity of the proposed AC motor drive has also been provided.
User Satisfaction Evaluation for ASEAN Entrepreneurship Profiling Application Improvement

ASEAN Entrepreneurship Profiling is a survey tool in the form of an application to help entrepreneurs identify gaps in their profile by comparing it with the profile of an ideal entrepreneur. Through the application, entrepreneurs can increase their capacity so that they can become successful entrepreneurs. This study aims to evaluate the applications developed by assessing user satisfaction by determining user experience when using the website. Therefore users will feel comfortable and continue to use the application as a method of self-development as an entrepreneur. The evaluation method used in this research is the Human Center Design (HCD) method. In HCD, the user will be used as the human aspect in the evaluation, because the nature of this evaluation is about developing the system from the user's point of view. For this study, the evaluation method will use System Usability Scale (SUS) questions to assist the evaluation. The main reason for using SUS in this research is because SUS questions are implemented using a Likert Scale to collect data that will produce integer results that can help the analysis process more easily. The aim of this research is to produce results that can be used to improve the User Interface of the AEP website and in return will have a positive impact on the user experience which will directly increase user satisfaction when using the application.
Clarifact-AI: Detecting Fake News in Indonesian Language with Natural Language Processing Using BiLSTM and IndoBERT Models

The use of clickbait as a strategy to gain attention tends to make people disregard the importance of verifying the accuracy of the content, resulting in mass misinformation, otherwise known as fake news/hoaxes. Indonesia faces a significant challenge in dealing with the widespread exposure of its population to hoaxes in cyberspace, with only a small fraction capable of discerning between reliable and false information. The objective of this study is to compare the performance of two deep learning models, BiLSTM and IndoBERT, and determine the most effective one, which will be subsequently employed to build the Clarifact-AI platform. With these models, the project aims to develop a scenario for accurately predicting hoaxes in both input titles (headline) and narratives (body) of the articles. The datasets from reputable sources TurnBackHoax.ID, Tempo.co, and Kompas, were used to train the models. The BiLSTM model achieved outstanding performance with an average accuracy of 97.33% and F1 score of 97.46% on the narrative dataset, and average accuracy of 81.29% and an F1 score of 81.17% on the title dataset. In contrast, the IndoBERT model yielded promising results with 93.05 % accuracy and 93.51 % F1 score for narratives and 94.93 % accuracy and 94.81 % F1 score for titles. The study reveals that IndoBERT outperforms BiLSTM in title predictions, while BiLSTM marginally excels in predicting narratives.
Beam Space MIMO Radar at 24 GHz Using Butler Matrices for Transmit and Receive Beamforming

In this paper, we present a 24-GHz beam space (BS) multiple-input multiple-output (MIMO) radar. Unlike many other BS MIMO radars, the approach presented in this work employs BS processing in both the transmit as well as the receive direction. The BS function is implemented as hardware block by means of Butler matrices. Furthermore, a method which allows to transform the BS signals in order to apply conventional MIMO processing is derived. The presented approach is validated by means of radar measurements in an anechoic chamber.
Features Extraction Technique Analysis of EEG Signal for Motor Imagery Classification

For the daily life application of BCI, any assumptions could be violated during the practical test, especially with the small number of channels. Multiple electroencephalogram (EEG) channels could have high accuracy but are not practical for daily life usage compared to a single-channel EEG. However, the single-channel EEG signal has limited sources to extract informative features. Therefore, the aim of the study is to analyze the significant impact of different feature extraction techniques on single EEG channel classification performance. The BCI competition III (IVa) dataset is used for this purpose. Time-frequency domain (E1), and time-domain feature extraction techniques (E2) as well as the combination of both techniques (E3) have been considered to extract the relevant features set from the EEG signals for MI tasks classification. The classification accuracy is obtained with two classifiers: Support Vector Machine (SVM) and Logistic Regression (LR). The result shows that there is an enhancement in classification accuracy on certain channels and participants, with E3 being higher than E1 and E2. The C3 channel in E3 has the highest accuracy (87.1 %) when SVM is the classifier. The proposed approach improves the classification accuracy of AF4 by up to 70% and 70.4% by using SVM and LR, respectively, which is considered acceptable for usage in a BCI system. This implies that MI tasks can be detected with a single-channel EEG by using the proposed method even if the channel is located on the forehead.
Analysis of Power Consumption Efficiency of ESP Microcontroller Operation in the Implementation of IoT-Based Agricultural Monitoring Systems

ESP is an IoT based microcontroller. One of its uses for monitoring farming system. To produce good agricultural products, but agricultural land is far from a power source, the microcontroller's energy is limited. This difficulty is overcome by deep sleep mode to save energy. Tests were carried out on three types of ESP to determine the power efficiency level of the system's deep sleep mode. Testing is carried out by taking current and voltage data on the three ESPs during deep sleep and normal mode. The power efficiency results obtained were ESP 32 DEVKIT V1 WROOM 87.10883974%, ESP 32 DF Robot FireBeetle 99.87277596%, and ESP 8266 NODEMCU 80.2693633%. The time used for monitoring of the agricultural system produces, ESP 32 DEVKIT V1 WROOM 20 days 5 hours 38 minutes, ESP 32 DF Robot FireBeetle 186 days 15 hours 40 minutes, and ESP 8266 NODEMCU 24 days 1 hour 53 minutes.
Enhancing Lithology Classification Performance Through Random Forest, COPOD, and Bayesian Optimization

This study intended to enhance the performance of a machine learning model for lithology classification. We experimented with various scenarios, such as using or not using outlier removal using Copula Based Outlier Detection (COPOD) and Bayesian Optimization hyperparameter tuning. Four models, namely Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression, were compared for their performance. Our findings revealed that using outlier removal or Bayesian Optimization alone did not substantially improve the model's performance. The Random Forest model performed the best in every scenario. Combining outlier removal and Bayesian Optimization did not enhance the performance of machine learning models for multiclass problems.
Trust-based Blockchain Mechanism for V2X Networks in a Smart Grid Environment

The role of Vehicle-to-Everything (V2X) networks in smart grid applications has become increasingly important in recent years, largely due to integrating electric vehicles (EVs) with the grid, such as charging stations, other EVs, smart homes, and grid control centers. However, the decentralized and complex nature of V2X networks raises security concerns. Previous research suggests using blockchain technology to mitigate these concerns. Therefore, we examine a V2X network scenario employing a Trust-based Access control Blockchain mechanism for IoT networks (TABI). This mechanism, when applied to V2X networks, is referred to as the Trust-based Blockchain mechanism for V2X networks (TBVX) mechanism. We use the Hyperledger Fabric (HLF) blockchain network and Hyperledger Composer to analyze the V2X network scenario. Also, we use the Hyperledger caliper to evaluate the performance of TBVX in terms of throughput and latency. Our experimental analysis shows the effectiveness of the TBVX mechanism in the V2X network scenario.
Review on Big Data Analytics and its Impact on Marketing Strategy

In this competitive world, Big Data Analytics is an essential way to improve the marketing strategies of any business. Big Data Analytics are used to analyze production data and identify production bottlenecks. This can be done through predictive analytics, which can identify trends and correlations in production data. Big data analytics enables companies for collecting, storing, organizing, and analyzing data from a variety of sources. It provides the opportunity to uncover patterns and trends in the data that may have previously gone undetected. By leveraging these insights, companies are able to make better decisions, optimize operations, and increase profitability. This research study will provide insights on the big data analytics in marketing strategies, including how businesses can use big data to analyze customer behavior, develop targeted marketing campaigns, and optimize business processes. Additionally, the paper will explore the challenges that businesses may face when implementing big data analytics and provide best practices for successful adoption.
Opportunities and Challenges Brought by Digital Quality Management to Big Data Technology

As big data technology is widely used in the industrial field, the trend of digital transformation of quality management is becoming obvious. This paper analyzes three major trends in the application of big data technology to the digital quality management, and summarizes the challenges existing in the application of big data in quality management. Three feasible policy recommendations are put forward for the challenges.
The Semantic Overlay Architecture for Data Interoperability and Exchange

Today's digital landscape is characterized by diverse encodings, syntaxes, and semantics, which forces stakeholders to dedicate considerable resources to ensure smooth data exchange and interoperability. While the W3C-recommended Resource Description Framework (RDF) offers a solid solution to these challenges, many developers find i t d aunting t han conventional data structures like JSON or YML. There have been alternative solutions suggested for data interoperability, including DLite and Layered Schemas. However, these do not fully leverage existing open standards, which diminishes their overall effectiveness. To help address this issue, we propose the Semantic Overlay Architecture (SOyA), a lightweight RDF-centric approach for data interoperability and exchange. SOyA facilitates core functionalities for data interoperability and exchange while reducing the barrier of RDF adoption. Our preliminary evaluations, focusing on roles within Knowledge Graph engineering, show encouraging outcomes.
A Novel Area Efficient TIEO based Reversible Logic Gates in QCA Paradigm

One of the novel emerging nanotechnologies that appear to be excellent replacements for CMOS(Complementary Metal Oxide Semiconductor) technology is the QCA(Quantum dot Cellular Automata). There are many aspects of QCA technology that can be optimized, including the extremely low power consumption, quick switching times, and dense structures. In this work an area efficient QCA design of a reversible gate is introduced and to examine the usefulness of the suggested XOR gate. A revolutionary exclusive-OR (XOR) gate with eight cells introduced in this work. A novel plan of reversible gate is designed based on novel three input EXOR gate. Compared to foregoing QCA plans, the suggested layout design are applied with the least amount of QCA cells and occupies less with minimum delay. The submitted plan does not use of any wire crossing techniques. The proposed design accuracy and functionality is verified using QCADesigner Tool. The proposed design power analysis was done by QCADesigner E tool.
ReThink Your Processes! A Review of Process Mining for Sustainability

The transition towards more sustainable practices requires companies to assess their impact on the social and ecological environment and establish new processes in complex inter-organisational systems. Process mining is a collection of data-driven techniques to visualise, analyse and improve business processes. Its potential to increase sustainable business processes has been acknowledged by academia and industry but not systematically reviewed. This work analyses process mining's application for sustainability by conducting two consecutive literature studies - the first on the broad domain of sustainability, and the second on the circular economy, a widely accepted approach for pursuing sustainability. Results show the potential of process mining for assessing and analysing sustainability in business processes, allowing for data-driven decision support and targeted improvement. They also show that process mining has yet to reach the sustainability domain. To enable collaboration between both communities, we present PM4S, a framework for applying process mining for sustainability.
Determining the Main Symptoms of Lung Cancer with Machine Learning Methods

The ability to identify cancer at an early stage is critical to increase the likelihood of effective treatment or stopping the progression of the disease in the body. Lung cancer is one of the most common deadly diseases and quickly kills the patient. The number of deaths caused by lung cancer surpasses those of colon, rectal, breast, and prostate cancers combined. Unfortunately, only two percent of patients with advanced lung cancer survive for five years or more. However, the survival rates are better, with 49 % of the patients surviving for five years or more, if the disease is detected early. On the other hand, in this modern era, machine learning has become one of the most reliable tools in the world for healthcare, as machine learning can learn from the data obtained and process the data that will later be used to help complete certain tasks. Therefore, in this research, machine learning or especially classification algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest, Logistic Regression, and XGBoost are used to identify three main symptoms that serve as markers for early detection of lung cancer. Determining the three main symptoms is done by combining the results of the feature importance score on each model using the rank-averaging method. The result is an average ranking of each feature based on its combined importance with an accuracy of 93.5 %.
Evaluation of HTTP and MQTT Protocols in the Design of Telemetry Data for Embedded Systems on Smart Wheelchairs

Smart electric wheelchair is a standard wheelchair equipped with an embedded system and electric motor that can reduce the physical effort required by people with disabilities as a user to move the wheelchair. For the user riding profile analysis, data from the smart electric wheelchair are needed, such as handling data, speed, throttle, and battery level. To make real-time system monitoring, this research develops a telemetry system. This research aims to evaluate the architecture design that includes hardware, software, and communication protocol to provide the best performance for telemetry. HTTP and MQTT protocols are evaluated. Testing of HTTP and MQTT protocols is conducted based on latency, throughput, and packet loss percentage. Based on this research, it has been found that the HTTP protocol has a superior throughput compared to the MQTT protocol, but it has higher latency than the MQTT protocol. The packet loss percentage in the HTTP protocol tends to fluctuate more than the MQTT protocol. However, in this study, the HTTP protocol performs better than the MQTT protocol because the telemetry data generated from the embedded system in the smart wheelchair has relatively large packet sizes.
Toward Adaptive DDoS-Filtering Rule Generation

Despite various distributed denial-of-service (DDoS) filtering solutions proposed and deployed throughout the Internet, DDoS attacks continue to evolve and successfully overwhelm the victims with DDoS traffic. While current DDoS solutions in general employ a fixed filtering granularity (e.g., IP address, 4-tuple flow, or service requests) with a specific goal (e.g., maximum coverage of DDoS traffic), in this paper we investigate adaptive DDoS filtering. We design and experiment algorithms that can generate and deploy DDoS-filtering rules that not only adapt to the most suitable and effective filtering granularity (e.g., IP source address and a port number vs. an individual IP address vs. IP prefixes at different lengths), but also adapt to the first priorities of victims (e.g., maximum coverage of DDoS traffic vs. minimum collateral damage from dropping legitimate traffic vs. minimum number of rules). We evaluated our approach through both large-scale simulations based on real-world DDoS attack traces and pilot studies. Our evaluations confirm that our algorithms can generate rules that adapt to every distinct filtering objective and achieve optimal results.
Developing Smart Precision Farming Using Big Data and Cloud-Based Intelligent Decision Support System

This study discusses the development of smart precision farming systems using big data and cloud-based intelligent decision support systems. Big data plays an important role in collecting, storing, and analyzing large amounts of data from various sources related to agriculture, including data from weather stations, soil sensors, satellite imagery, crop yield records, pest and disease reports, and other sources. This study highlights the differences between smart farming and precision farming. This study describes key techniques and system architecture, including data collection, processing, analysis, and decision support components. Utilizing a cloud platform enables scalability and optimized performance, which lowers costs and makes it safer and easier to manage. The integration of big data and Alibaba cloud computing in smart precision farming can improve farming productivity by providing timely information and recommendations to farmers for better decision-making. Finally, the system produces smart precision farming, which provides cost-effective real-time monitoring and predictive analytics to increase agricultural production and sustainability.
Early-stage Conflict Detection in HLF-based Delay-critical IoT Networks

Hyperledger Fabric (HLF), a permissioned blockchain network recognized for high throughput and low transaction latency, has proven instrumental in augmenting security and privacy. However, with the surge in Internet of Things (IoT) devices, the volume of transactions relayed to the HLF blockchain has grown, leading to the problem of conflicting transactions. This problem occurs when two transactions attempt simultaneous read and write operations on the same ledger key. Given the time-sensitive nature of certain delay-critical IoT networks, these conflicts, resolved during the validation stage, can adversely affect key network performance metrics, including throughput and transaction latency. Current solutions, while addressing these conflicts, often fail to detect them at an early stage, resulting in performance degradation. To address this problem, we introduce a novel Early-stage Conflict Detection (ECD) mechanism. Designed specifically for HLF-based delay-critical IoT networks, the ECD mechanism employs a local cache, namely Sync.Map, to store and compare incoming transactions, enabling earlier conflict detection. Our results show that the ECD mechanism significantly improves valid throughput, transaction latency, and conflict detection time compared to existing approaches.