Reviewerly/exHarmony huggingface dataset:
Supplemental Material: Controls on metal fertility of dioritic intrusions in the Laiwu district, North China Craton: Insights from whole-rock geochemistry and mineral compositions

&lt;p&gt;Table S1: Major- and trace-element geochemistry of whole-rock samples from the Fe-mineralized and barren dioritic intrusions in the Laiwu district. Table S2: Sr-Nd isotopic compositions of whole-rock samples from the Fe-mineralized and barren plutons in the Laiwu district. Table S3: Major- and trace-element compositions of amphibole from the Fe-mineralized and barren plutons in the Laiwu district. Table S4: U-Pb age data and trace elements of zircon from the Fe-mineralized and barren plutons in the Laiwu district. Table S5: Major and trace elements of apatite from the Fe-mineralized and barren plutons in the Laiwu district. Figure S1: Hand specimens of diorites collected from the Kuangshan, Jiaoyu, Jinniushan, and Tietonggou plutons. Figure S2: Cathodoluminescence (CL) images of representative zircons from the Kuangshan, Jiaoyu, Jinniushan, and Tietonggou diorites. Figure S3: U-Pb concordia diagrams of zircons from the Kuangshan, Jiaoyu, Jinniushan, and Tietonggou plutons. Figure S4: Selected oxide element Harker’s plots for the Fe-mineralized and barren plutons in the Laiwu district. &lt;/p&gt;
Polyarthrite et maladie de Huntington

One major barrier to advancing aerial autonomy has been collecting large-scale aerial datasets for training machine learning models. Due to costly and time-consuming real-world data collection through deploying drones, there has been an increasing shift towards using synthetic data for training models in drone applications. However, to increase widespread generalization and transferring models to real-world, increasing the diversity of simulation environments to train a model over all the varieties and augmenting the training data, has been proved to be essential. Current synthetic aerial data generation tools either lack data augmentation or rely heavily on manual workload or real samples for configuring and generating diverse realistic simulation scenes for data collection. These dependencies limit scalability of the data generation workflow. Accordingly, there is a major challenge in balancing generalizability and scalability in synthetic data generation. To address these gaps, we introduce a scalable Aerial Synthetic Data Augmentation (ASDA) framework tailored to aerial autonomy applications. ASDA extends a central data collection engine with two scriptable pipelines that automatically perform scene and data augmentations to generate diverse aerial datasets for different training tasks. ASDA improves data generation workflow efficiency by providing a unified prompt-based interface over integrated pipelines for flexible control. The procedural generative approach of our data augmentation is performant and adaptable to different simulation environments, training tasks and data collection needs. We demonstrate the effectiveness of our method in automatically generating diverse datasets and show its potential for downstream performance optimization. Our work contributes to generating enhanced benchmark datasets for training models that can generalize better to real-world situations.Video: youtube.com/watch?v=eKpOh-K-NfQ
Image-to-Text and Speech-based Learning Aid for Children

It is well known that kids are more curious and interested to refer to picture-based books and real examples of physical objects for their study rather than traditional books that are merely textual. This is an advantage for teachers and parents and is important for developing their linguistic skills. An application of image-to-text and speech-based learning aid for young children using a deep learning approach is presented in this paper. Using an android application, the image of an object is captured and uploaded on the Firebase, which acts as the primary Cloud Service. The image is then processed and recognized using Convolutional Neural Network (CNN) which yields an accuracy of 93.29% on the test set. The text and speech corresponding to the recognized image are then outputted to the user. The application serves as a learning aid for young children, especially in pandemic-like scenarios.
Object Detection Algorithm based on Structural Modification in the Convolutional Neural Network for Remote Sensing Satellite İmages

for machine vision to make sense of an image, object recognition techniques must first locate all of the items of interest within the frame of reference. Several strategies have been proposed to address this issue, many of which draw inspiration from computer vision and deep learning techniques. Existing methods, however, consistently underperform when trying to recognise small, dense objects and often fail altogether when trying to detect objects that have undergone random geometric alterations. In this research, we examine and compare state-of-the-art object detection techniques, then suggest a tweaked convolutional object detection network to address the problems with the status quo.Our investigation shows superior performance than current best practices. For this purpose, we employ deep convolutional networks to collect multi-scaled features, and we tweak the convolutional structures to account for geo-metric transformations..
Processed Food Traceability using Blockchain Technology

The system design for the supply chain of processed foods that makes use of the advantages of blockchain technology. This research emphasises on critical supply chain management for the success of many sectors and importance of protecting the visibility of the overall process. The suggested system design creates a multimode storage management that offers increased visibility, safety, and productivity in the supply chain for processed foods by combining the strengths of both traditional storage and blockchain storage. There are various benefits to using blockchain-based in the supply chain for packaged foods. It ensures the transparency of the entire process and removes the need of middlemen, lowering the danger of fraud or manipulation. Additionally, blockchain technology's ability to track food goods accurately from the farm to the customer makes it simpler to identify the source of food products in the event of a food safety concern. In addition to shielding consumers from potential health risks, this also preserves the industry's good name. Blockchain transactions' automatic and secure replaces with traditional human procedures, speed up transaction processing, and shorten the time it takes for food products to get to consumers. As a result, consumers may be more satisfied and food suppliers and manufacturers may incur cheaper expenses. In order to enhance the supply chain for processed foods, this system suggests a system architecture that combines the advantages of blockchain technology and machine learning models. Using linear regression with Lasso and a 0.2 test split, the machine learning algorithm achieved an Accuracy Rate of 0.79 and a Testing Accuracy of 0.84. Onion, sesame, peanut, mustard, and corn are the top 5 most in-demand crops, and the model gives the farmer entity recommendations for crop output based on anticipated demand. The supply chain for processed foods may become more effective and transparent thanks to the proposed approach.
Virtual Assistant for Appointment Booking

In the current day and age, systems that aid in carrying out tasks traditionally performed by human assistants are becoming more popular as human operators need more time for executing queries such as booking a ticket, purchasing items, or obtaining services. A single request may comprise many inquiries for information available on the Internet. Since business performance places a premium on time efficiency, an alternate method of accepting requests must be considered. Chatbot provides support 7 days a week, round-the-clock, and is not constrained by limited working hours. We studied various papers which approached this topic and presented their approach on creating a chatbot. Majority of the studies indicated that a retrieval-based chatbot is the best way to classify the user's context in a discussion using Utilizing Natural Language Processing (NLP) to assess the query and get some relevant keywords. Intent recognition, name entity recognition (NER) tagging, and information retrieval are essential processes in NLP. part of speech (POS) tagging helps to parse the interpretation of chat text. The rule base is intended to gather all keywords based on conversation content and is language-specific. The terms used in the booking dialogue include the date, time, city, scheduling, rescheduling, or cancellation of an appointment. In this paper, we have presented a review of the current systems, their advantages, and drawbacks.
Probabilistic Logic Networks for Temporal and Procedural Reasoning

Probabilistic Logic Networks (PLN) offers an excellent theory to frame learning and planning as a form of reasoning. This paper offers a complement to the seminal PLN book [3], in particular to its Chapter 14 on temporal and procedural reasoning, by providing formal definitions of temporal constructs, as well as inference rules necessary to carry temporal and procedural reasoning.
Secure Biomedical Document Protection Framework to Ensure Privacy Through Blockchain

In the recent health care era, biomedical documents play a crucial role, and they contain much evidence-based documentation associated with many stakeholders data. Protecting those confidential research documents is more difficult and effective, and a significant process in the medical-based research domain. Those bio-documentation related to health care and other relevant community-valued data are suggested by medical professionals and processed. Many traditional security mechanisms such as akteonline and Health Insurance Portability and Accountability Act (HIPAA) are used to protect the biomedical documents as they consider the problem of non-repudiation and data integrity related to the retrieval and storage of documents. Thus, there is a need for a comprehensive framework that improves protection in terms of cost and response time related to biomedical documents. In this research work, blockchain-based biomedical document protection framework (BBDPF) is proposed, which includes blockchain-based biomedical data protection (BBDP) and blockchain-based biomedical data retrieval (BBDR) algorithms. BBDP and BBDR algorithms provide consistency on the data to prevent data modification and interception of confidential data with proper data validation. Both the algorithms have strong cryptographic mechanisms to withstand post-quantum security risks, ensuring the integrity of biomedical document retrieval and non-deny of data retrieval transactions. In the performance analysis, Ethereum blockchain infrastructure is deployed BBDPF and smart contracts using Solidity language. In the performance analysis, request time and searching time are determined based on the number of request to ensure data integrity, non-repudiation, and smart contracts for the proposed hybrid model as it gets increased gradually. A modified prototype is built with a web-based interface to prove the concept and evaluate the proposed framework. The experimental results revealed that the proposed framework renders data integrity, non-repudiation, and support for smart contracts with Query Notary Service, MedRec, MedShare, and Medlock.
An Artificial Intelligence-equipped System for the Diagnosis of Cervical and Breast Cancer

Diagnosing the tumor at an early stage is important because the patient can be treated in a timely and effective manner. Currently, the most common methods of diagnosis are magnetic resonance and computer tomography. Diagnosing a tumor with routine tests is a novelty, and it is important to have a more serious and accurate analysis of the results of blood tests in order to find evidence of specific tumor growth in the body. Today, scientists are increasingly using artificial intelligence methods to solve this type of problem. The computer system that we created for the diagnosis of cervical cancer, which is based on artificial intelligence, namely, machine learning, is discussed. The system diagnoses using routine tests and is distinguished by fairly high accuracy. We think that this model will help clinicians to identify cancers and diagnose the disease in a timely manner.
LightGyro: A Batteryless Orientation Measuring Scheme Based on Light Reflection

In industrial production, the orientation of facility components can indicate whether the facility is on a regular operating track. For example, when a component get loose, the orientation variation of the component would exceed the normal range. A common approach for orientation measurement is to attach an inertial measurement unit (IMU) to the target device. However, the IMU requires additional power maintenance. This paper presents LightGyro, a cheap and efficient batteryless scheme to measure the orientation, in which we attach a reflective film to the target device and use a camera to capture the light spot on the reflective film. The basic idea of LightGyro is to extract the light spots in the captured frame and use their pixel coordinates to infer the orientation. It is difficult to recognize a single light spot because the spot lacks distinctive features. To solve the problem, we switch light sources on and off to regulate the appearance of light spots and utilize frame subtraction to extract light spots. The depth of field of light spot is lost in the process of camera projection, which is necessary for the orientation measurement. To address the issue, we propose a light array-based reflection model to extract the depth of field from the relative positions of multiple light spots. To the best of our knowledge, this is the first work to utilize reflection to measure orientation. Experiment results show that the orientation error of LightGyro decreases with the increasing length of the reflection route and the orientation error can achieve less than 1°.
A Kernel Fractional Low-Power Chaotic Time Series Prediction Algorithm Based on Lncosh Function

In this paper, a kernel fraction low-power adaptive filtering algorithm based on lncosh function (KFLP-LHCF) is proposed for chaotic time series prediction. The algorithm combines the lncosh function with the kernel fractional low-power algorithm. The nonlinear saturation characteristic of the lncosh function is used to achieve resistance to impulse noise, and the fractional low power is used to suppress the influence of error mutation on the performance of the algorithm. In addition, by adjusting the scale factor, a balance can be achieved between convergence speed as well as steady-state performance. In the alpha noise environment, the proposed algorithm is used to predict and simulate two typical chaotic time series of Lorenz and Mackey–Glass. Experimental results show that the algorithm has better steady-state performance and lower steady-state error than other kernel adaptive filtering algorithms, provided that the convergence speed is similar.
Detection of influencers in social networks: A Survey

&#x0D; Social media influencers have the power to influence others. Identifying influencers in online social networks is essential for various applications in many domains such as advertisement, community health campaigns, administrative science and politics. Detecting influencers on online social networks is achieved in accordance with specific criteria such as the number of subscribers, the number of interactions with them, the extent of people’s trust in them, etc. the present study encompasses differentmeasures such as application, techniques, dataset, factors, and dataset. Besides, a table summarising and illustrating the main ideas and approaches is given.&#x0D;
Graphic User Interface

For customers, providing interactive user interfaces is very important because customers can only manipulate on the intuitive screen. There are many types of interfaces provided by different programming languages and dependent on the case that programmer used. In addition, with the development of programming languages, it is possible to use interfaces between different programming languages. This chapter presents graphic user interface, guides learners on how to write classes that inherit interfaces as well as how to drag and drop controls on the interface to get the desired interface. In this chapter, readers will discover layout, basic controls label, button, etc. and list, table, tree, etc. For each type of control, detailed instructions are provided, which makes it easy for readers to grasp the theory and implement the practical parts. The chapter also presents how to create executables for Kotlin. At the end of the chapter, there are exercises to help readers practice user interface handling skills.
Conditional Execution

Almost every programming language has to use conditional constructs to handle the business of the software. Basically, the operating mechanism of these conditional structures is the same, but with the Kotlin programming language, it has added a lot of extended and very convenient features, it helps programmers more optimal options in the process of processing business. This chapter will present conditional constructs about simple if statement, if/else statement, nested conditionals, expression and block form {}, and when structure. The when structure has been greatly improved in Kotlin, it provides a lot of use cases so that programmers can easily handle business tasks more optimally, such as when with if/else statement, group multiple conditions. For each conditional structure, this chapter provides simple examples and detailed explanations to help readers understand the working mechanism of the conditional structure and apply them to specific business handling. At the end of the chapter, there is a list of exercises to help readers improve programming skills on conditional structures.
A SEQUENCE OF DISTRIBUTIONS WITH A FINITE SET OF VALUES ASYMPTOTICALLY EQUAL TO THE POISSON DISTRIBUTION

A sequence of random variables X(λ,n) with parameters 0 &lt; λ≤1, n∈N is investigated. For n→∞ X(λ,n) converges in distribution to a random variable Pois(λ) obeying Poisson's law with the parameter λ. At a fixed value n all moments of the distribution of a random variable X(λ,n) of order k≤n coincide with the moments of the distribution of Pois(λ). For the value of the parameter λ=1, the random variable X(1,n) coincides with the random variable equal to the number of «fixed» points in case of random permutations of the set of elements of power n.
Representing Scenes as Compositional Generative Neural Feature Fields Based on GIRAFFE for 3D Reconstruction of Classroom Scenes

Traditional online classroom learning through two-dimensional video cannot meet the interactivity and immersive class experience. Providing immersive teaching scenes through 3D reconstruction technology is the future trend of education. We use the GIRAFFE method to reconstruct classroom scenes that can be synthesized as a combination of multiple “feature fields” for the task of synthesizing controlled images. The reconstruction of complete and realistic 3D scenes is a prerequisite and basis for the realization of new forms of online courses. This experiment aims to restore a real classroom scene consisting of multiple objects, elements and movable elements such as desks and chairs, teacher images and classroom scenes to build a new model of online course.
Multiple-Threshold Erasable Mining Under the Tightest Constraint

Erasable data mining has become increasingly important in recent years because it can provide helpful suggestions for material procurement in factory manufacturing. In traditional erasable data mining, only a single threshold is set to judge whether an itemset is erasable. In this paper, we extend the problem to multiple thresholds and propose an algorithm to solve it by considering the tightest constraint. We prove the problem under the tightest constraint has the downward-closure property, and thus, we can search the solution space efficiently. Experimental results also show the effectiveness and efficiency of the proposed approach.
Pengaruh Belajar Dari Rumah (BDR) Pada Masa Pandemim Terhadap Kreativitas Siswa Tema II Subtema II Di Kelas IV MI Nurul Hikmah Dlanggu

Through the observations of the fourth grade researcher at MI Nurul Hikmah Dlanggu and interviews with the principal and principal, the researcher saw that the students were still less creative. The lack of student creativity is manifested in several aspects, namely students tend to be passive (rarely ask questions and express opinions), students only get information from what the teacher teaches, students lack the courage to develop their potential, and students are often asked to find problems. Difficulty when using alternative solutions. Learning From Home (BDR) is one of the lessons used by teachers during the pandemic. By doing learning through this BDR, teachers can deliver learning materials online (online). This study aims to determine the effect of learning from home (BDR) during the pandemic on the creativity of students in theme II sub-theme II in class IV MI Nurul Hikmah Dlanggu. The research design used is One Group Postes. The population of this study consisted of 55 students in class I, II, III, IV and 10 students in class IV as samples. The results of data analysis using the paired sample t-test of 0.000398, which means 0.000398 is smaller than 0.05, which means that there is an effect of learning from home on the creativity of fourth grade students of MI Nurul Hikmah Dlanggu.
Hubungan Minat Belajar Warga Belajar dengan Hasil Belajarnya dalam Mata Pelajaran Bahasa Inggris Program Paket B PKBM Generasi Muda Lubuk Basung

The background of this research is the low learning outcomes of the students in the English Subject of Package B Program PKBM Young Generation of Lubuk Basung. This study aims to: 1) describe the learning interests of the learning community; 2) describe the learning outcomes of learning residents; and 3) look at the relationship between the learning interest of the learning community and their learning outcomes in the English Subject of the Package B Program PKBM for the Young Generation of Lubuk Basung. This research uses a quantitative approach and the type of research is descriptive correlational. The population of this study were all 30 students learning English in the PKBM Young Generation package B program. The sampling technique that the researchers used was cluster random sampling of 20 people. The data collection technique used in this study was a questionnaire, with a data collection tool in the form of a questionnaire guide. The data analysis technique in this study uses the proportion formula and rank correlation order. The results showed that: (1) the learning interest of the focused learners was still low, (2) the learning outcomes of the focused learners were still low, (3) there was a significant relationship between the learning interests of the learning residents and their learning outcomes in the Package B Program English Subject. PKBM Lubuk Basung Young Generation. It is suggested to tutors to be able to vary the learning methods used to attract students' interest in learning so that students get maximum learning outcomes.