Reviewerly/exHarmony huggingface dataset:
IMPROVING CAMERA POSE ESTIMATION USING SWARM PARTICLE ALGORITHMS

Abstract. Most computer vision and photogrammetry applications rely on accurately estimating the camera pose, such as visual navigation, motion tracking, stereo photogrammetry, and structure from motion. The Essential matrix is a well-known model in computer vision that provides information about the relative orientation between two images, including the rotation and translation, for calibrated cameras with a known camera matrix. To estimate the Essential matrix, the camera calibration matrices, which include focal length and principal point location must be known, and the estimation process typically requires at least five matching points and the use of robust algorithms, such as RANSAC to fit a model to the data as a robust estimator. From the usually large number of matched points, choosing five points, the Essential matrix can be determined based on a simple solution, which could be good or bad. Obtaining a globally optimal and accurate camera pose estimation, however, requires additional steps, such as using evolutionary algorithms (EA) or swarm algorithms (SA), to prevent getting trapped in local optima by searching for solutions within a potentially huge solution space.This paper aims to introduce an improved method for estimating the Essential matrix using swarm particle algorithms that are known to efficiently solve complex problems. Various optimization techniques, including EAs and SAs, such as Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), Improved Gray Wolf Optimization (IGWO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA) and Whale Optimization Algorithm (WOA), are explored to obtain the global minimum of the reprojection error for the five-point Essential matrix estimation based on using symmetric geometric error cost function. The experimental results on a dataset with known camera orientation demonstrate that the IGWO method has achieved the best score compared to other techniques and significantly speeds up the camera pose estimation for larger number of point pairs in contrast to traditional methods that use the collinearity equations in an iterative adjustment.
EFFECT OF HYPERPARAMETERS ON DEEPLABV3+ PERFORMANCE TO SEGMENT WATER BODIES IN RGB IMAGES

Abstract. Deep Learning (DL) networks used in image segmentation tasks must be trained with input images and corresponding masks that identify target features in them. DL networks learn by iteratively adjusting the weights of interconnected layers using backpropagation, a process that involves calculating gradients and minimizing a loss function. This allows the network to learn patterns and relationships in the data, enabling it to make predictions or classifications on new, unseen data. Training any DL network requires specifying values of the hyperparameters such as input image size, batch size, and number of epochs among others. Failure to specify optimal values for the parameters will increase the training time or result in incomplete learning. The rationale of this study was to evaluate the effect of input image and batch sizes on the performance of DeepLabV3+ using Sentinel 2 A/B RGB images and labels obtained from Kaggle. We trained DeepLabV3+ network six times with two sets of input images of 128 × 128-pixel, and 256 × 256-pixel dimensions with 4, 8 and 16 batch sizes. The model is trained for 100 epochs to ensure that the loss plot reaches saturation and the model converged to a stable solution. Predicted masks generated by each model were compared to their corresponding test mask images based on accuracy, precision, recall and F1 scores. Results from this study demonstrated that image size of 256 × 256 and batch size 4 achieved highest performance. It can also be inferred that larger input image size improved DeepLabV3+ model performance.
Peningkatan Prestasi Belajar Matematika Siswa Sekolah Dasar dengan Media Tangga Pintar

Mathematics in everyday life has a significant contribution. Some students still think that mathematics is difficult. The results of observations of mathematics learning achievement in class III are low by 73% of students who have not finished yet. One of the materials considered difficult by students is the unit length and weight material. The purpose of this study is to improve student achievement in the subject matter of units of length and units of weight in mathematics in grade 3 SD NU Insan Cendekia using smart ladder media. This type of research is classroom action research with a cyclical flow of planning, action, observation and reflection. The research subjects were 12 male students and 9 female students in grade 3 of SD NU Insan Cendekia. Instruments in the form of tests, observations, documentation and field notes. Data were analyzed using descriptive analysis. The study used 2 cycles with the result that in the first cycle the average value was 78 with a percentage of 81% of students completing. The average score in cycle II reached 89. 100% of students achieved the KKM score. Based on these results, smart ladder media can improve mathematics learning achievement.
Students’ Mastery of English Grammar towards Effective Writing and Speaking Competence

English language proficiency is one of the most pressing issues, particularly in the field of education. Speaking and writing are two of the most important macroskills to master, especially in today's increasingly connected world. The current study seeks to ascertain students' comprehension and mastery of English grammar and explores whether grammar explains the students' proficiency in speaking and writing in Grade 10 Junior High School. The respondents were chosen for the study using a simple random sampling design. Additionally, the researcher's questionnaire, with three qualified validators tested for validity and a pilot tested for reliability, produced a satisfactory Cronbach's alpha result and served as the basis for gathering the study's data. Findings show that students' competence in English grammar, writing, and speaking is satisfactory. The outcome demonstrates that mastery of English grammar accounts for developing an acceptable level of speaking and writing abilities. The study concluded that students' command of English grammar explains their writing and speaking abilities.&#x0D;
Permainan Edukasi sebagai Media Pembelajaran Bahasa Inggris bagi Siswa Madrasah Ibtidaiyah

English is one of the important subjects that students must learn so that teachers need to be creative in presenting the material and learning media. This study aims to develop educational games based on power point as a learning media for grade 1 students of MI Al-Hikmah Kajen on English alphabet material. The type of research used is R &amp; D (Research and Development) model Borg and Gall. The population of this study were all grade 1 students of MI Al-Hikmah Kajen, totaling 46 students. The results of the research on the development of educational games based on power point are able to make all students enthusiastic in learning, and make students able to understand better the alphabet material delivered with learning media. The results of the feasibility test for the validation stage and the teacher questionnaire obtained an average score of 4 in the Good category. While the questionnaire distributed to students obtained an average score of 5 with a very good category. The results of this validation indicate that the learning media made are very feasible to use as teaching materials at MI Al-Hikmah Kajen.
Minangkabau Language as Ethnic Identity in Digital Era

This study aims to research Minangkabau language as an ethnic identity in this digital era. The researcher did the study in five areas in West Sumatra. It is a qualitative study in which all language phenomena contributing to the validity of findings are considered. Data are collected through participative and nonparticipative observations. The data for this study are in the form of written and spoken language observed in the domain of families, public and cyberspaces. The researcher had discussions and interviews through participative observation. Taking notes and photos of written and printed Minangkabau language are done through nonparticipative observation. The researcher also observed Minangkabau language attitude through nonparticipative observation. The result of the study indicates that the productivity and creativity of using the Minangkabau language in family domains, public and cyberspaces strengthen the role of the Minangkabau language as an ethnic identity. Cultural setting and the needs to advertise local products are among the factors which trigger the use of the Minangkabau language as the symbol of Minangkabau identity.
Pollutant Burden on Pollution Sources in the Wai Batu Merah Watershed, Sirimau District, Ambon City

This study aims to identify sources of pollution and determine the magnitude of the potential pollution load from pollutant sources in the Wai Batu Merah watershed, Sirimau District, Ambon City. The method for identifying sources of pollution is carried out by using saturated or census sampling methods, calculating pollutant loads using direct methods for point source pollutant sources and indirect methods for non-point sources . The direct calculation method uses wastewater discharge data and the results of wastewater quality analysis results from measurements directly at the point source pollutant wastewater treatment plant (WWTP). The wastewater sampling technique uses the grab sampling method. While the indirect method uses effluent factors and supporting data from non-point source pollution sources that have been identified in the Wai Batu Merah watershed. The results showed that the pollutant sources found in the Wai Batu Merah watershed were point sources originating from hotel and health facility activities and non-point sources originating from domestic activities from residential areas, agricultural and livestock businesses, non-point source land (forest and open land), micro, small and medium enterprises (MSMEs), small scale industries and waste. The dominant pollutant source in the Wai Batu Merah watershed comes from domestic non-point sources. The highest point source pollution loads for BOD, COD and TSS parameters in the Wai Batu Merah watershed came from hotel activities in segment 6 with the contribution of each parameter of 17.82 kg/day, 23.82 kg/day and 0.029 kg/day. Meanwhile, the highest non-point source pollution load for BOD, COD and TSS parameters came from non-point source waste in segment 6 with the contribution of each parameter of 2825.3 kg/day, 3884.8 kg/day and 2684.03 kg/day.
Development and Validation of an Interactive E-Book in Physics 9

The study developed and validated an interactive e-book in Physics in Grade 9 Science to help augment the learning gaps and the least mastered competencies based on the regional diagnostic assessment. The content of the lessons in the interactive e-book was based on the six (6) least learned, most essential learning competencies that were provided by the Department of Education. The material was conceptualized based on objectives, content or parts, and evaluation tools. The developed interactive e-book was validated in terms of content quality, instructional quality, technical quality, and other findings. Based on the results of the validation of the six (6) faculty experts, it revealed that the crafted material was very satisfactory in all the criteria, which means that it was valid and ready for use by the students. The learners evaluated the developed material in terms of content, adequacy of scope, and appropriateness of exercises. It inferred that the interactive e-book was acceptable and reliable for its intended users. The result of this study has a significant implication to teaching Physics to Grade 9 students. It means that the material can serve as supplementary material since the students were engaged in learning and had shown interest when the lesson involved educational technology, and since there are insufficient modern instructional materials in Physics.
Channel Estimation Based on An Improved Conditional GAN for MIMO-OFDM Systems

Convolutional neural network (CNN) based channel estimation approaches have achieved remarkable success in massive multiple-input multiple-output (MIMO) systems. However, the channel estimation performance even needs to be improved. Meanwhile, the loss functions are not well designed in traditional deep learning (DL) based schemes for the problem of channel estimation. To improve the channel estimation accuracy, we propose an improved conditional generative adversarial network (GAN) based channel estimation method in this paper. Specifically, we first design two novel loss functions to better train the proposed conditional GAN model. The training stability of GANs is improved by coupling the mapping from the received signals to the channel matrices with its inverse mapping. Then, by jointly training these two loss functions and the proposed conditional GAN, we develop an improved conditional GAN to estimate channels more accurately. Simulation results demonstrate that the proposed improved conditional GAN based method improves the estimation performance and shows great robustness in MIMO systems.
Digital Twin-enabled Secure Charging Reservation for Electric Vehicle

Nowadays, electric vehicles (EVs) are increasingly being embraced as a primary mode of transportation. However, the issue of efficient and secure charging remains a major barrier to the widespread adoption of EVs. Drivers may have to spend a lot of time and effort finding suitable charging stations. As a virtual agent for EVs in cyberspace, a digital twin can monitor and analyze the EV’s operation and battery status from physical raw data, contact the charging management center to reserve charging stations in advance, and guide the EV there using decision feedback. While the digital twin can improve the charging experience and convenience for drivers, adversaries can launch attacks on the vulnerable channel resulting in receiving incorrect decisions for EV from the digital twin. In this paper, we will utilize an extended Chebyshev chaotic map to design a digital twin-assisted secure charging reservation scheme for EV that ensures secure data transmission and defends against potential attacks. Furthermore, we also provide informal security analysis and performance evaluation to demonstrate the security and efficiency of our proposed scheme.
Artificial Neural Networks: An Overview

Neural networks, also known as artificial neural networks (ANNs) or artificially generated neural networks (SNNs) are a subset of machine learning that provide the foundation of deep learning techniques. Their name and form are inspired by the human brain, and they replicate the way real neurons communicate with one another. Artificial neural networks (ANNs) are massively parallel systems comprised of a huge number of interconnected basic processors. This paper discuss about the artificial neural network and its basic types. This article explains the ANN and its basic outlines the fundamental neuron and the artificial computer model. It describes network structures and learning methods, as well as some of the most popular ANNs.
Digital Forensic Analysis of WhatsApp Business Applications on Android-Based Smartphones Using NIST

WhatsApp Business is an Android application that can be downloaded on Playstore to serve small business owners. This provides an opportunity for criminals to take advantage of the app’s features. These crimes can take the form of fraud, misdirection, and misuse of applications, so digital forensics is necessary because there has never been any research that has done this. This study aims to obtain digital evidence and is carried out on Android smartphones with the WhatsApp Business application installed with four scenarios tested. This study uses the NIST SP 800-101 Rev 1 guidelines with four stages: preservation, acquisition, inspection &amp; analysis, and reporting. The forensic method used is static forensics using the MOBILedit forensic express forensic tools and SysTools SQLite Viewer. The results of this study in scenario 1, by not deleting, get a 100% percentage. Then, scenario 2, namely direct write-off, gets a percentage of 71%. Furthermore, scenario 3, namely uninstalling the application, does not get digital evidence, and scenario 4, namely deleting data through the application manager, also does not get any evidence. The contribution of this research is expected to be a reference in uncovering cases in the WhatsApp Business application with digital forensics.
E-Mortality using Agile Scrum Method to Improve Information Services Effectiveness

The advancement of information system technology is presently used extensively in many disciplines, including the field of health care or hospitals. This research aims to create an information system to handle hospital data to facilitate the processing patient data that has been declared dead with quality. In this study, it was found that there was no information system in the form of a program or application to handle death data. The management process still relies on Microsoft Excel, considered less efficient. In addition, the development of this information system is assisted by choosing the suitable software development methodology and considering existing needs. This research uses the Agile Development Method with the Scrum framework for software development. This research is qualitative descriptive and uses the observation method in data collection. C# programming language and MySQL database are also used in this system. This research produces an information system to handle death data by the product backlog. It is intended to meet user needs to assist in processing death data more effectively and efficiently and reduce the error rate associated with recording death data manually.
Research on the AGV Global Path Planning Algorithm Based on FI-RRT*

To address the issue of slow speed for AGV during global planning tasks, we propose the FI-RRT* algorithm based on the Informed-RRT* algorithm. In the sampling phase of the algorithm, we adjust the distribution probability of randomly sampled points using a Gaussian distribution function, which saves memory and increases node utilization. In the node reselection phase, we adopt an improved elliptical convergence domain strategy to further reduce the size of the convergence domain, thereby enhancing the efficiency of the reselection step. Experimental results obtained through dual-platform experiments using python and ROS demonstrate that the FI-RRT* algorithm has a higher planning speed.
Smart Contracts and Reputation-Based Access Control Solutions for the Internet of Things

With the increase of IoT (Internet of Things) application scenarios, traditional access control technology can no longer meet the security needs of device access control in IoT scenarios. There are problems such as inadequate security of stored information, difficulty in controlling access rights at a fine-grained level, and untimely handling of malicious access behavior in IoT. To address these problems, an ABAC(attribute based access control) model is used as the basis for access control, and a smart contract based on attribute and reputation access control model is proposed. The solution divides the access control process into four parts and uses a combination of smart contracts and the ABAC model to achieve traceable, automated and trusted access control to the entire IoT resources. Finally, the effectiveness and compatibility of the smart contract and reputation-based access control model is verified through simulation experiments and performance tests.
Generation of Defective Lithium Battery Electrode Samples Based on Diffusion Models

In the domain of fold defect detection in lithium batteries, gathering a sufficient number of defect samples for training deep learning models is often challenging due to the low incidence rate of fold defects. This limitation hinders the accuracy of subsequent defect detection models. To address this issue, we propose a novel method that utilizes diffusion models to generate non-paired image data and enhance industrial defect samples. We replace the commonly used U-Net backbone with transformers, as the inductive bias of U-Net is not essential for diffusion models. Our research investigates the impact of transformers on latent patches to improve the quality of generated samples. By leveraging defect-free samples from industrial production and a limited quantity of defect samples, our approach enhances the quality of defect samples. The empirical evidence from our study supports the use of diffusion models with transformer-based enhancements (referred to as DiT) for generating high-quality defect samples. This research effectively tackles the challenge of generating non-paired image data, specifically for industrial defect image generation, enabling improved training of deep learning models for industrial defect detection.
An Analysis Model of English Text Coherence Based on RST Dependency Relationship

Text coherence analysis is an important and challenging task that is essential for subtasks such as automatic summarisation, viewpoint extraction and machine translation in natural language processing (NLP). A large body of previous work has used linguistic features such as lexical, syntactic and entity features to capture relatively shallow coherence features, while ignoring deeper logically relevant features hidden in the text. We propose an end-to-end English text coherence analysis model (RGCM for short) that incorporates RST and graph convolutional neural networks (GCN). The intra- and inter-sentence logical relations of the input text are first mapped onto a discourse relationship tree, and then transformed into an RST-dependent context graph by certain pruning strategies. Subsequently, we propose a GCN-based text coherence assessment framework to capture intra- and inter-sentence interactions to assess text coherence. We conducted experiments on two different coherence assessment datasets and achieved accuracy rates of 95.5% and 97.8% respectively.
Blockchain-Based Student Information Management System

With the advancement of information technology, Blockchain, as a new scattered database, is being widely used in a variety of situations. Currently, Chinese universities face numerous challenges in information management. Genuineness is difficult to establish, and data is easily lost and tampered with. In this paper, we propose Hyperledger Fabric, a Blockchain-based student information management system, to address the difficulty of being in charge of university documents. The test results show that the created system may alleviate the force of unifying student information storage, address the problem of traditional information management systems that are simple to tamper with, difficult to exchange, and easy to leak privacy, among other things. It provides technical assistance for the implementation of college student information management.
Research on Communication and Perception Signal Classification Based on BP Neural Network

The article considers the waveform classification and coexistence between communication signal and radar signal in the complicated scenarios. For a given performance requirement of complex communication scene, this article designs classification and predictive models such that the detection probability of the communication and radar signal is maximized. While the original detection algorithm is not ideal, five representative features are extracted and detailed Time-Frequency analysis is done to obtain a low-error-rate and stable classification method. The proposed model based on BP neutral network is designed to be robust to original imperfect and unitary algorithm. Simulation results verify that the signals can be perfectly classified and the accuracy rate is more than 95% with this proposed approach.