Reviewerly/exHarmony huggingface dataset:
A Comparison of Six Convolutional Neural Networks for Weapon Categorization

This research aimed to employ artificial intelligence to classify handheld weapons. Convolutional Neural Networks (CNN) was the favourable method in which architectures including Inception-ResNet-V2, InceptionV3, ResNet50, ResNet101, SqueezeNet and VGG-16 have been applied. The performance of the CNN models was enhanced via the manipulation of various hyperparameters such as dropout rate, learning rate, number of epochs, loss function and optimizer. The performance matrices of all models in weapon categorization were promising, in which their performance in terms of precision, recall, specificity, F1 score and accuracy were all above 95%, except for SqueezeNet which achieved performance ranging from 75% to 90%. InceptionV3 appeared to have an upper hand with an accuracy of 99%. The CNN model was also robust against the challenge of increment learning in which it was able to classify weapons despite the addition of non-weapon classes and the occurrence of catastrophic forgetting. Given its promising performance, the application of CNN in weapon classification is much anticipated.
Automating Public Complaint Classification Through JakLapor Channel: A Case Study of Jakarta, Indonesia

The DKI Jakarta provincial government is ready to support the digital transformation program with a series of digitally integrated policies. Residents of DKI Jakarta can now easily submit complaints about problems in their surrounding environment through the JakLapor service feature on the JAKI application. However, incoming reports are still manually classified. As a result, many citizens still report unsuitable complaints based on their category. This research aims to compare and find the best complaint classification model by applying multiple machine learning models to classify texts automatically. We also use feature extraction to see which model performs the best. This study employed Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) algorithms as the machine learning model. Meanwhile, we use Count Vectorizer, Terms Frequency-Inverse Document Frequency (TF-IDF), N-Gram, and Latent Semantic Analysis (LSA) as the feature extraction algorithms. The classification results show that the Random Forest method model with TFIDF feature extraction is the most accurate and optimal model among the others, with a 90% accuracy rate.
Self-sovereign, verifiable, ubiquitous and privacy preserving public entity documents through the use of blockchain technology

Nowadays, citizens tend to use digital services for their interaction with public entities and third party entities. In such cases, they are usually required to present personal public documents, to prove the validity of personal information. Each citizen shares all personal data included in such documents with multiple entities, in order to prove the validity of a subset of those. On top of that, such systems run the risk of documents being forged. In fact, citizens do not have complete control over their personal data management. In this work, a decentralized system for creating and handling documents is presented. We propose a blockchain-based system that enables citizens to actively take part into the creation and management of such documents. Through a granular access control mechanism for the fields of the document, privacy leakage for citizens is minimized. The system enables the seamless interaction between citizens and entities while ensuring privacy and integrity of citizen’s personal data.
Guidance and Navigation Controls for Drone Swarm Applications

This paper describes the research utilizing Quanser equipment to create GNC systems to maneuver a swarm comprised of QDrones, QBots, and QCars in different formations and specified movements [1]. Drones have become commonplace in many industries, taking place of human agents doing tedious or dangerous tasks such as infrastructure inspections, deliveries, or search and rescue missions. While Drones are utilized in many industries, research and innovative projects have been focused on swarm applications. Swarm applications refer to the use of a group or ‘swarm’ of agents which can be drones, ground vehicles, or other semi-autonomous equipment which can send and receive data or commands. This research has been applied to various issues across many fields, but mainly in industries which deal with data collection and exchange, where the swarm’s efficiency can be most readily observed. There are many challenges involved with swarm applications as controlling many agents can lead to issues with safety, legislation, and computational limitations specifically as it pertain to drone agents. In this study, a method was developed to control multiple drones and unmanned ground vehicles synchronously. The primary challenge to overcome was the creation of a control system that can command each agent individually, this will require an individual controller alongside a central server which collects data and calculates the desired maneuvers for each agent in real-time. The project yielded a variety of successful maneuvers that could be applied to indoor environments and eventually to outdoor platforms. Each maneuver had a different control system that took advantage of many different control types such as state flows, handheld controls, and many more. Future research will include developing other large swarm maneuvers that incorporate more agents, eventually allowing the swarm to gather data to train Graphing Neural Networks (GNNs) to the point where GNNs will plan and control swarm agents with minimal human input.
Optimization Of Infant Birth Predictions During The Covid-19 Pandemic Using The Particle Swarm Optimization Based K-Nn Algorithm Method

Every mother wants to give birth to a perfect and healthy child. many things cause newborns to die, some of which are malnutrition during the womb, fetuses that have abnormalities in the body, and factors of premature birth. Deaths due to exposure to the Covid-19 virus are certainly a serious problem. Several factors influence childbirth, such as placental and fetal factors, maternal factors, lifestyle factors, and what is happening now due to the covid-19 virus. Therefore, the author is interested and wants to review to find out the characteristics of mothers who give birth due to exposure to the covid virus and are normal. The results of tests carried out by optimizing the Particle Swarm Optimization-based K-NN Algorithm resulted in an accuracy value of 93%. The accuracy value can be said to be good enough to determine the characteristics of the mother who gave birth under normal or premature conditions.
Knn Algorithm For Foodstuff Classification Using Hsv Color Space And Feature Extraction

Indonesian nourishment is one of the wealthiest culinary conventions in the world, characterized by its solid taste. This sort of nourishment requires extra fixings in certain sorts of cooking. In any case, most of the foodstuff photographs were disseminated on social media as they contained few nourishment fixings that are unpredictable and troublesome to distinguish. In truth, several clients who need to discover approximately the fixing of nourishment in look engines still utilize inquiries within the shape of content. This thinks, proposes the KNN algorithm combined with HSV color space and includes extraction, such as histogram-based surface features, lacunarity, and GLCM to bunch the nourishment within the form of pictures. In testing utilizing the (k) esteem on KNN, the most excellent highlight comes about are gotten that deliver a tall level of precision, review, and exactness of 100% at values K=1 and K=3 comes about from the combination of Lacunarity highlights, histogram-based surface highlights with color space HSV, and from the esteem of K=1 and K=3 F1-Score execution esteem.
Comparison of Clustering Methods for Health Clinic Stock Goods

Clinics are health service facilities for the community that provides medical services, but sometimes some clinics do not have the stock of goods needed by the community. This is because the inventory management system does not always update the stock of goods that are empty or piled up. The problem is certainly detrimental to the clinic and the community. Because this is done optimization on the stock of goods. One way to group goods is to use the clustering method. In this study, the author will compare the X-Means Algorithm, K-Means Algorithm, and K-Medoids Algorithm, to compare which algorithm is more optimal in grouping stock items at Health Clinic. The results obtained using 3 clusters show that the X-Means Algorithm has the smallest DBI value of 0.075. So that we can conclude that the X-Means algorithm is more optimal than the K-Means algorithm and the K-Medoids algorithm.
Self-organized criticality in a neural network with the small-world topology

The phenomenon of self-organized criticality in a neural network with the “Small-world” topology has been studied. We studied the critical value of coupling strength as a function of the total number of connections in the network. The dependence of critical coupling strength on the number of connections obeys the power law.
The role of connections topology on synchronization in neural network

Complex networks describe a wide range of systems in nature and society. Every complex network has certain topological features which strongly influence the dynamics. We studied the dynamics of a complex network of excitable elements with different coupling topologies. The role of network topology, noise level and coupling strength on the resulting dynamical modes has been observed. The effect of hub removal on network dynamics has been studied.
20 years of the Grammar Matrix: cross-linguistic hypothesis testing of increasingly complex interactions

The Grammar Matrix project is a meta-grammar engineering framework expressed in Head-driven Phrase Structure Grammar (HPSG) and Minimal Recursion Semantics (MRS). It automates grammar implementation and is thus a tool and a resource for linguistic hypothesis testing at scale. In this paper, we summarize how the Grammar Matrix grew in the last decade and describe how new additions to the system have made it possible to study interactions between analyses, both monolingually and cross-linguistically, at new levels of complexity.
Design of Integrated Platform for Vehicle-road Collaborative Perception in Smart Transportation

In view of the problems existing in the current transportation industry, such as the isolated island of traffic business data information, the lack of fusion and collaboration of various traffic data and the insufficient collection of semantic information of traffic environment, this paper proposes to build an integrated platform of vehicle-road collaborative perception. Through the technology of device access, device management and device collaboration, the goal of unified access to multi-type, multi-protocol and multi-scene traffic environment sensing devices is realized. The digital map of all-factor traffic resources is constructed by using the technologies of ubiquitous communication network of vehicle networking, multi-sensor fusion, edge computing and semantic segmentation of computer vision images. It provides rich semantic data support for analyzing traffic problems, mastering traffic characteristics and predicting traffic development trends, and provides data interfaces and consistent analysis platforms for various existing traffic business platforms.
Blind Verification

Verification as a quality control step has a variety of meanings in forensic science processes, from a simple review of paperwork to a blind replication of an examiner’s key work steps to validate their work. Open verification can admit cognitive biases, which can have an effect on the outcome of the process. Blind verification, where the reviewer does not know the examiner’s decisions, is thought to provide better error detection but discussion continues on the effectiveness of verification in forensic science
Coronary Artery Centerline Tracking with the Morphological Skeleton Loss

Coronary computed tomography angiography (CCTA) provides a non-invasive imaging solution that reliably depicts the anatomy of coronary arteries. Diagnosing coronary artery diseases (CAD) entails a clinical evaluation of stenosis and plaques, which is in turn essential for obtaining a reliable coronary-artery centerline from CCTA 3D imaging. This work proposes a centerline extraction algorithm by combining local semantic segmentation and recursive tracking. To this end we propose a Morphological Skeleton Loss (MS_Loss) suited for 3D centerline segmentation based on an improved morphological skeleton algorithm coupled with a resource-efficient back-propagation scheme. This work employs 225 CCTA examinations paired with manually annotated coronary-artery centerlines. This method is compared against the deep-learning state of the art in the literature using a standardized evaluation method for coronary-artery tracking.
MINIMIZING IDLE CAPACITY AND EFFICIENCY COST BY MODIFYING FEEDING AND FINISHING PF-I TO PRODUCE NPK

With the policy of revoking the SP-36 fertilizer subsidy in 2022, an alternative is needed to minimize idle capacity and cost efficiency caused by stopping operations at the Phosphate I Fertilizer Unit. Based on national fertilizer stock data for NPK 15-10-12 fertilizer, there is a gap between the actual stock nationally and the minimum stock. The low actual stock nationally can lead to a scarcity of NPK fertilizer which can affect national food security. So as an alternative solution to solving the problem, the Phosphate I Fertilizer Factory is utilized to be able to produce NPK fertilizer. This problem is solved by using the PDCA Cycle (Plan-Do-Check-Action) method. Based on the data and facts in the field, it is known that the existing equipment condition of the Phosphate I Fertilizer Unit has not been able to produce NPK fertilizer. The results of the project found the best solution to the problem of equipment conditions at the Phosphate Fertilizer Unit I by modifying the feeding system facility which was originally manual to weigher based by installing 3 hoppers, dozometers, BC mixing, and DAP crusher in the Curing 250 Warehouse, then installing coating facilities and coloring NPK fertilizer on the existing cooler in the Phosphate I Fertilizer Unit, making key operating parameters, and conducting an On Job Training scheme for the Phosphate I Fertilizer Unit personnel at the NPK II/III/IV Plant. So with this modification, the Phosphate Fertilizer Factory I is expected to be able to produce NPK fertilizer.
Opinion mining indonesian presidential election on twitter data based on decision tree method

Indonesia is a country led by a president. The term of the leadership of a president will be democratically elected every five years. The current president will end his term of office in 2024. So that in that year, the people will hold a direct general election to determine the president between 2024 and 2029. Before the general election was held in Indonesia itself, it was thick related to the campaign for each presidential candidate carried out by his supporters. The campaign is carried out directly to village locations and on social media Twitter/Facebook/YouTube. His campaign writing on Twitter is exciting to analyze. Even now, many tweets related to the 2024 presidential election contain various opinions from the public. This study will examine the sentiment of someone's tweet to see the public's statement regarding the 2024 presidential election. The resulting sentiment categories are positive, negative, and neutral, and the word tweet related to the sentiment category will be visualized. The results of the sentiment category will then be classified using a tree-based method, namely a decision tree. The accuracy generated by applying the decision tree method is 99.3%. The decision tree method is also superior to the regression-based way by 2.5%.
Physical world and its system-scale levels

The author shows that it is more and more difficult to obtain new objective data about the physical world. After all, modern scientific practice has an increasingly indirect character. We are increasingly moving away from our familiar realm of reality. Studying the microcosm, the scientist has to overcome ever greater challenges. In addition to the "instrumental mediation" of scientific knowledge, there is also a "theoretical mediation". The latter can be understood as all the growing theoretical constructions that describe the object under study. The materials presented in the article may be useful in the study of the discipline "Concepts of Modern Natural Science".
Branding in the system of higher education (on the example of the V.I. Vernadsky Crimean Federal University)

The article identifies and analyzes the main elements of the brand platform of the V.I. Vernadsky Crimean Federal University. In addition, a general description of the brand positioning of the organization in the regional market of educational services is presented.
Practices of digital communication in the educational space of higher education

The article analyzes modern communicative practices in the educational space of higher education. The possibilities and limitations of using digital communication with direct, indirect and hybrid options for the interaction of participants in the educational process are considered. Specific technical, social, psychological and pedagogical problems of applying digital communication practices in the educational process are highlighted.
A Study on Stable Control of Quadcopter with Disturbance using Intelligent Controller

In the past decade, drones and artificial intelligence have been two of the objects that have captured the attention of researchers, modern technology lovers and enthusiasts. Drone operations are often unstable due to many external environmental influences, so altitude control and tracking trajectory control play an important role. Applying an intelligent controller is an effective solution for adjusting the stability of the quadcopter
Reality

What do we mean when we say that something is real? What ought we to mean? I offer the following definition, in the interest of making an operational notion of reality: an entity is real to the extent that there are operationally coherent activities that can be performed by relying significantly on its existence and its properties. Reality (in the sense of real-ness) conceived in this way is a matter of degrees, and it is domain-dependent. Real entities (or, realities) are mind-framed, and crafting better realities is an achievement of conceptual engineering. When we take ontology in the context of practices, ontological pluralism no longer appears absurd: there are different sets of realities operative in different systems of practice. I illustrate these points with a range of examples drawn from the history of the physical sciences. The traditional picture of physical objects constituted as mereological sums of immutable building-blocks is unwarranted and creates undue hindrance to practice-based ontology.
Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning

Earthquakes can inflict significant damage to structures and infrastructures. This paper presents a machine learning model to predict ground surface deformation (GDS) induced by earthquake events. The data on historical GSD is extracted from radar product of Synthetic Aperture Radar (SAR) data of one-year over five magnitude earthquakes that occurred within 200 kilometers of the Kota Padang Regency, West Sumatra. Building topology data of its footprint area, distance from shoreline, elevation, and coordinate were incorporated as the main features in the dataset. The earthquake parameters were taken from the USGS earthquake data catalog. Four machine learning algorithms of Neural Network (NN), Random Forest (RF), k-Nearest Neighbors (kNN), and Gradient Boosting (GB) are applied. The GSD from the trained models is predicted and compared with the measured GSD from the SAR’s product. The performances of proposed algorithms are evaluated in terms of the statistical index. A new dataset from the earthquake event in March 2022 is used to predict the GSD and further test the performance of the trained models. Overall, the four machine learning algorithms have outstanding performance, with a coefficient determinant of more than 0.9. The kNN algorithm outperforms compared to others in delineating the GSD. The trained models gave deficient prediction performance on the new dataset with a correlation coefficient of 0.228 predicted by the RF algorithm. Additional earthquake datasets and more unique features will improve the performance of the machine learning algorithms.
"Reform of History Lesson Using Learning of Thematic Unit History in Japan - Focusing on Practical Cases of Kobe University Secondary School -"

This study is aimed at deriving a reform plan for the history lesson by analyzing learning of thematic unit history, researched and developed by Kobe University Secondary School. The characteristic of the learning of thematic unit history is that it consists of units that organically connect each one-hour class based on a specific topic, and utilize various learning methods that can promote learners’ independent learning and historical thinking. Learning of thematic unit history provides clues that can fundamentally transform the conventional one-hour knowledge transmission and teacher-centered history lesson paradigm. The principle of the learning content composition and learning method of thematic unit history present many implications for improvement of Korean history lesson. First, history teachers may utilize the content composition that emphasizes time series while composing units around the subject, as a way to reorganize educational content. Second, if the entire unit is organized into theme-centered learning and a learning type consists of lessons with different characteristics each time, learners will be able to independently utilize various learning methods for further study. Third, learning of thematic unit history makes the learners acquire the basic conceptual knowledge of history, and develop theme-centered learning based on the knowledge, which results in an organic combination of the acquisition of knowledge and the cultivation of historical skills and abilities. Finally, the following issues were raised in the research and development process of learning of thematic unit history. First, the rationale for setting the theme as the axis of the curriculum is unclear. It is necessary to keep in mind the overall history formed through learning of unit and set the topics of each unit in an organic relations. Second, it is not clear at what standard the basic conceptual knowledge to be learned in each unit is established. It is necessary for teachers to analyze and extract the curriculum and textbooks, and to organically associate them with the subject of each unit, rather than arbitrarily selecting the basic conceptual knowledge to be learned through the learning of unit.