Reviewerly/exHarmony huggingface dataset:
Research Implementation of the Milkfish Feed Development from the Edible Mushrooms for a Digital-Based Module in Biotechnology Concept for High School Students

This study aimed to analyze the results of research on the content of edible mushrooms as milkfish feed to be implied as a digital module, a learning source for biotechnology concepts for high school students. The study was carried out through stages: identifying the problems in biotechnology concepts learning activities in four state senior high schools in Banten Province, identifying research methods and products through experiments, choosing and adapting research findings as the learning source content, and applying and developing research findings as learning. An interview grid and questionnaire were used to collect the data, and a qualitative analysis was used to interpret the questionnaires' results. The research results were in the form of a digital-based module in biotechnology concepts for high school students. The module contains activities that create student interest and motivation and help students master the concepts. In addition, the module's content was a research finding on the milkfish feed development from the edible mushrooms in the form of process and product. It can be concluded that a digital module on biotechnology concepts has been developed and can be used as a learning source for high school student.
PERBANDINGAN ALGORITMA EFFICIENTNETB0 DAN INCEPTIONV3 DALAM KLASIFIKASI CITRA JENIS ANJING

Dogs are pets that have hundreds of breeds. The various breeds of dogs can be distinguished by size, fur color, and type of fur. The identification of dog breeds can use images of dogs as one of the object recognition information. Image classification can be made using Deep Learning methods. Image classification is the process of grouping pixels in an image into a number of classes, so that each class can represent an entity with certain characteristics. Additionally, Deep Learning has many algorithms that can be used for image classification, including the InceptionV3 and EfficientNetB0 algorithms. The purpose of this research is to compare the accuracy results of the EfficientNetB0 and InceptionV3 algorithms in classifying dog breed images. The stages in making this program include collecting datasets, data preparation, model design, and interpreting the results. This image classification is made using the Python programming language and Visual Studio Code software. The program has an accuracy result with the best model being InceptionV3, with an accuracy of 97.30% on the training set and 97.07% on the validation set after 20 epochs.
MSR82 A Methodological Framework to Conduct Data Sources Landscaping for Real-World Studies (RWS)

Although randomized clinical trials (RCT) remain the gold standard for safety and efficacy evidence in healthcare research, real-world data (RWD) is increasingly used to capture larger and more representative patient populations, in a cost and time effective manner. However, the applicability of RWD to generate high-quality evidence should be carefully evaluated, especially in the context of product approval or reimbursement. Early identification of RWD sources is imperative to determine their suitability for evidence generation to support product strategy. We propose a methodological framework for conducting data source landscaping to identify fit-for-purpose real-world secondary data sources.
Segmentation and Classification of Pages for Digitized Documents of the Public Prosecutor’s Office

The digitization of physical documents is a common objective in the process of digital transformation of public organizations in Peru, however this process has a particular task that consumes a lot of time and resources: the segmentation and classification of pages in digitized documents, that is, determine where one document ends and the next begins, as well as naming each classified document. To this end, in this article we present a solution that uses machine learning and regular expressions for the automatic segmentation of pages in digitized documents of the Public Prosecutor’s Office and their classification. Our solution achieved an accuracy of 93% using Random Forest for page segmentation and high accuracy in the classification of document types above 90% accuracy using regular expressions based on rules.
Modern technologies and their role in the educational process

Educational institutions, both governmental and private, tend to find and provide effective means to help students learn more easily The modern teaching methods include computers, CD-ROMs, the Internet; and the audio-visual media. This paper is an attempt to highlight the importance of these techniques and their different means, and thus their impact on the development of the skills of the teacher and learner, To develop an educational system that follows the steps of the organization, using the potential provided by technology, in order to achieve educational and humanitarian goals.
Efficient Algorithms for Patterns Identification in Medical Data

Abstract Recently, medical databases have expanded rapidly, and the amount of information is huge. This abundance of data appears as a consequence of the new technologies that have been developed in the medical field and that allow easy data collection. The performance of the technique depends on the input data and available resources. Whereas, in Eclat the repeated scanning of the database is eliminated and consumes less time and we can conclude that Eclat is better than Apriori and Fpgrowth. If we refer to the execution time and memory usage, then the FP-Growth algorithm is more efficient than the Eclat algorithm or the Apriori algorithm. If we consider factor other than time, the result may vary from one factor to another.
Development of GeoGebra-Based Fraction Gap Learning Media to Improve Understanding of the Fraction Concept of Grade V Elementary School

Students need mathematics to solve everyday problems with fundamental technological developments. Technology-based media greatly supports success in mathematics learning, especially for elementary school students. This study aims to develop GeoGebra-based Fractional Gap learning media that is feasible in improving the understanding of the concept of fractions of grade V elementary school students. The implementation of this research uses the Research and Development (R&amp;D) method with the ADDIE development model (Analysis, Design, Development, Implementation, and Evaluation). Data collection techniques include interviews, observation, expert validation, and pretest-posttest tests. The study was conducted on V-A and V-C class students at SD IT Al-Falaah Sambi, totalling 40 students. The results of the media feasibility test amounted to 4.59, including the " Very Good " criteria; the material feasibility test amounted to 4.18, including the "Very Good" standards; and the feasibility test from practitioner validation amounted to 5, including the "Very Good" criteria. Product effectiveness tests were conducted using pretest-posttest test questions in experimental and control classes. The hypothesis test results obtained a probability of significance of 0.022 &lt; 0.05, meaning that students' understanding of the concept of fractions increased. From the study results, it was concluded that GeoGebra-based Fractional Gap learning media is feasible and practical to be applied to improve the understanding of the concept of fractions of grade V elementary school students.
Frequency-based aerial video recognition

Aerial video recognition is challenging due to various factors. Prior work on action recognition imposes constraints in terms of unavailability of object detection bounding box ground-truth inhibiting the application of localization models and computational constraints preventing the usage of expensive space-time self-attention. Optical flow and pretrained models for detecting human actor performing action do not work too well due to domain gap issues. Our contributions1, 2 are as follows: 1. We present a frequency-domain space-time attention method that encapsulates long-range space-time dependencies by emulating the weighted outer product in the frequency domain. 2. We present a frequency-based object background disentanglement method to inherently separate out the moving human actor from the background. 3. We present a mathematical model for static salient regions and an identity loss function to learn disentangled features in a differentiable manner.
Research on Ethnic Pattern Generation Based on Generative Adversarial Networks

In this paper, we take Mongolian ethnic patterns as the research object and conduct a technical study on ethnic pattern generation and super-resolution reconstruction based on deep learning generative adversarial networks to address the problems of low quality of ethnic pattern data collection and lack of innovation. In this paper, we use a database including 1621 Mongolian ethnic patterns, train StyleGAN2 on the pre-processed images, and generate images After that, we use ESRGAN to perform super-resolution reconstruction on the generated images to generate high-resolution patterns with Mongolian style. The model designed in this paper can generate high quality images with ethnic characteristics in the case of insufficient original data. Compared with the time-consuming and labor-intensive traditional ethnic pattern design methods, the model designed in this paper lowers the threshold of ethnic pattern innovation and contributes to the innovative design of ethnic patterns to a certain extent, which has some positive significance for the protection and inheritance of ethnic patterns.
Blockchain technologies in digital economy: advantages and challenges

Blockchain technology is known as one of the most transformative technologies of recent years, revolutionizing digital transactions and data storage. Originally developed as the underlying technology for Bitcoin, blockchain has since found numerous applications beyond cryptocurrencies, such as in supply chain management, identity verification, and smart contracts. In the digital economy, blockchain has the potential to significantly impact a wide range of industries and business models, from finance to healthcare and e-commerce. Blockchain issues have been developed by researchers who have focused on blockchain as the latest digital technology capable cause profound changes in society and are, to some extent, debatable. In particular, the work examines how blockchain and decentralized platforms have the potential to be powerful tools for managing large-scale social interactions and disrupting traditional power structures. However, it also cautions that the dominance of private interests within these distributed ecosystems could lead to the emergence of a stateless global society, posing significant risks. The purpose of the work is to explore the potential applications and impacts of blockchain technology in the digital economy, and to identify the opportunities and challenges that arise from the adoption of this technology in various sectors such as finance, healthcare, supply chain management, and intellectual property. Additionally, the work aims to provide insights into the role of blockchain technology in promoting digital transformation and innovation, and to assess the potential benefits and risks associated with the use of this technology in the context of the digital economy. It has been established that the use of blockchain in intellectual property can change the way creative works are protected, managed and monetized. It found that blockchain technology can be used to provide secure and transparent ownership records, automate licensing and royalty payments, and facilitate the identification of infringing uses. The main problems and limitations created by the use of blockchain technology are systematized: scalability, compatibility and regulatory regulation. As the technology continues to develop and evolve, it is imperative that these challenges are addressed in order to realize the full potential of blockchain. We believe that while blockchain technology is still in its early stages of development, it has the potential to transform various industries by providing secure, transparent and efficient data management solutions.
Investigating the Design of Just-in-Time Adaptive Interventions (JITAIs) Messages Targeting Eating Behaviors

Obesity and being overweight is an ongoing issue and a pervasive concern in the United States. There are many programs available that try to assist with weight loss in a variety of forms which are sometimes reactive in their philosophies and methods. This paper discusses insights on the design of SMS messages that seek to provide users just-in-time feedback regarding energy intake (J1) and eating rate (J2). This was accomplished by surveying participants' responses to SMS messages via a study to ascertain whether they would comply with the direction provided via SMS to further their weight loss journey. Results from this study suggest that participants overall felt that the SMS messages designed to aid them in their weight loss journey are helpful and would result in compliance. Results also suggest that participants had higher levels of comprehension with numeric messages.
Chatra Paryesana: A Student Enquiry System

In today’s technological era, chatbots not only mimic human conversation but can also perform sophisticated jobs like purchasing theatre tickets. RASA is a free and open-source programme that may be used to run various versions of the NLU and DIET models. It has the capacity for natural language processing, reinforcement learning, and API/database integration. nervous system. This project incorporates a number of rasa’s fundamental principles to allow for user interaction in its execution of tasks. Using the Rasa platform, we developed a chatbot specifically for universities. You can use this chatbot to obtain student information, view grades, and verify attendance. By integrating with Telegram, users no longer need to download and run a separate programme to use this bot. This method is far more interesting than the standard web page.
High-secure data collection in IoT sensor networks using homomorphic encryption

In wireless sensor networks, sensor nodes are deployed in open environments to gather and send collected data to a remote server. During the data transmission process, the collected data is vulnerable to security risks, making it crucial to encrypt the data to prevent it from falling into the wrong hands. This paper proposes a new approach that integrates homomorphic encryption (HE) algorithms into the sensor devices, allowing the data collected by the sensors to be encrypted before transmission. By utilizing HE in the sensor networks, the server can execute calculations on the encrypted data without requiring decryption. To implement the HE function, we use the Simple Encrypted Arithmetic Library (SEAL), which the Microsoft Cryptography Research Group developed. We evaluated the HE-based data collection scheme in IoT sensor networks with various network topologies to assess network lifetime, communication distance, and data collection latency.
The unsung hero: how synthetic data has helped computer vision, machine learning, and AI

This conference presentation was prepared for the Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications conference at SPIE Defense + Commercial Sensing 2023
Real-data performance evaluation of Unreal Engine synthetic IR data

In order to achieve state of the art classification and detection performance with modern deep learning approaches, large amounts of labeled data are required. In the infrared (IR) domain, the required quantity of data can be prohibitively expensive and time-consuming to acquire. This makes the generation of synthetic data an attractive alternative. The well-known Unreal Engine (UE) software enables multispectral simulation addon packages to obtain a degree of physical realism, providing a possible avenue for generating such data. However, significant technical challenges remain to design a synthetic IR dataset—varying class, position, object size, and many other factors is critical to achieving a training dataset useful for object detection and classification. In this work we explore these critical axes of variation using standard CNN architectures, evaluating a large UE training set on a real IR validation set, and provide guidelines for variation in many of these critical dimensions for multiple machine learning problems.
RELATIONSHIP BETWEEN FINANCIAL INCLUSION AND ECONOMIC GROWTH: RELATIONSHIP BETWEEN EVIDENCE FROM ARDL MODELLING

ABSTRACTThis study examines the impact of financial inclusion on economic growth in Nigeria from 1986 to 2020. The Zivot-Andrew unit root test was used to examine the statistical characteristics of the data. The Zivot Andrew unit root test shows that while Automated Teller Machines and foreign direct investment are stationary at level, the gross domestic product, commercial bank branches, and phone-based transactions are stationary at first difference. Bound test for long run shows that there is long run relationship among the variables of interest. According to the Auto Regressive Distributive Lag (ARDL) result, commercial bank branches in Nigeria have a short-term, positive, and significant impact on the country's gross domestic product. The Nigerian gross domestic product is negatively impacted by automated teller machines; however, this impact is negligible. The long run coefficient demonstrates positive and statistically significant influence of commercial bank branches on Nigeria's GDP. The Nigerian gross domestic product has positively and statistically significantly impacted by automated teller machines. Mobile phone-based transaction has positive and significant impact on gross domestic product in Nigeria. In Nigeria, foreign direct investment has a positive and statistically significant impact on GDP. Both in terms of sign and size, the error correction term (ECT) satisfies all theoretical and statistical requirements. The ECT coefficient is -0.522626 with a 5% level of significance. This shows that at 52.26 percent, the disequilibrium brought on by the shock in the years before is corrected to the long-term equilibrium in the present year. The Granger causality test demonstrates that while mobile phone-based transactions do not granger cause gross domestic product, commercial bank branches, automated teller machines, domestic depositors' money in banks, and foreign direct investment do. The studies recommend, the Central Bank of Nigeria should push commercial banks to increase the number of automated teller machines in each branch.Keywords: Financial inclusions, FDI, Economic growth ARDLJEL: P43.
[Heavy Metal Concentration, Source, and Pollution Assessment in Topsoil of Chuzhou City].

In order to understand the pollution level and ecological risk of heavy metals in topsoil of Chuzhou City, a total of 4360 soil samples in Chuzhou City were collected, and the concentrations of eight heavy metals including Cr, Zn, Pb, Cu, Ni, Cd, As, and Hg were measured. Correlation analysis, cluster analysis, and principal component analysis were used to analyze the sources of the heavy metals, and the enrichment factor index, single-factor pollution index, pollution load index, geo-accumulation index method, and potential ecological risk index were selected to assess the environmental risk of the eight heavy metals in the topsoil. The results showed that the average values of Cr, Zn, Pb, Cu, Ni, Cd, As, and Hg contents in the surface soil of Chuzhou City were higher than the background value of that in the soil of Yangtze-Huaihe River Basin of Anhui, and Cd, Ni, As, and Hg were significantly different in space and influenced by external disturbance. The eight types of heavy metals could be divided into four categories based on correlation analysis, cluster analysis, and principal component analysis. Cr, Zn, Cu, and Ni were from natural background sources; As and Hg mainly came from sources of industrial and agricultural pollution; Pb mainly came from the sources of transportation pollution and industrial and agricultural pollution; and Cd came from the sources of transportation pollution, natural background, and industrial and agricultural pollution. The overall pollution degree of Chuzhou City was low, and the ecological risk level was at a slight level based on the pollution load index and the potential ecological risk index; however, the ecological risk of Cd and Hg was generally serious, and these two heavy metals should be taken as the objects of priority control. The results provided a scientific basis for soil safety utilization and classification control in Chuzhou City.
[Source Analysis of Heavy Metals in Typical Farmland Soils Based on PCA-APCS-MLR and Geostatistics].

Human activities often increase the content of heavy metals in surface soils, thus affecting the precise quantification and evaluation of heavy metals in regional soils. In order to systematically study the spatial distribution characteristics and contribution rate of heavy metal pollution sources in typical farmland soil around stone coal mines in western Zhejiang Province, heavy metals such as Cd, Hg, As, Cu, Zn, and Ni in topsoil samples of arable land and agricultural products were collected and analyzed, with an emphasis on the geochemical characteristics of each element and ecological risk assessment of agricultural products. Correlation analysis, principal component analysis (PCA), and the absolute principal component score-multiple linear regression receptor model (APCS-MLR) were used to discuss the source and source contribution rate of soil heavy metal pollution in this area. Meanwhile, the spatial distribution characteristics of the contribution rate of Cd and As pollution sources of the soil in the study area were also expounded in detail by the geostatistical analysis method. The results showed that the contents of six heavy metal elements including Cd, Hg, As, Cu, Zn, and Ni in the study area all exceeded the risk screening value. Among them, two elements exceeded the risk control value, Cd and As, and the point-exceeding rates were 36.11% and 0.69%, respectively. The Cd in agricultural products was also seriously exceeded. According to the analysis, there were two main sources of heavy metal pollution in the soil in the study area. Source one (Cd, Cu, Zn, and Ni) was coming from mining activities and natural sources, and the contribution rates to Cd, Cu, Zn, and Ni were 78.53%, 84.41%, 87%, and 89.13%. Source two (Hg and As) was mainly an industrial source, and the contribution rates to As and Hg were 82.41% and 83.22%, respectively. Cd was the heavy metal with the greatest pollution risk in the study area, and measures should be taken to reduce the pollution risk. There was an abandoned stone coal mine rich in elements such as Cd, Cu, Zn, and Ni. Located in the northeastern part of the study area under the action of atmospheric deposition, the confluence of mine wastewater into irrigation water and farmland sediment was one of the important factors for forming the source of farmland pollution. The settled fly ash was the main pollution source of As and Hg, which was also closely related to agricultural production. The above research can provide technical support for the precise implementation of ecological and environmental management policies.