Reviewerly/exHarmony huggingface dataset:
Mapping of Manado State Polytechnic campus area with web-based Geographic Information System (GIS): A Small Unmanned Aerial Vehicle (SUAV) application

Technology is continuously developing, especially in mapping and information technology. Geographic Information System (GIS) is a system used to collect, store, analyze, and display geographic data. Geographic data is information about location or geography, such as maps, satellite imagery, digital elevation models, or data associated with a particular location. This study aims to produce a web-based GIS map by visualizing spatial data containing each room's location, parking area, and landscape in the Manado State Polytechnic campus area. The method used in this study was to collect location data using a Small Unmanned Aerial Vehicle (SUAV) to take aerial photos, Ground Control Point (GCP), and tagging photos. Data processing uses Agisoft Metashape Software to combine all aerial photographs to become an exclusive photo and ArcGIS for programming. The result is creating a digital map that makes it easy for users to get the position of buildings with a visual display along with the information contained therein.
Designing A Web-Based Employee Payment Information System For The Nutritional Culture And Tourism Office

Employees were previously paid directly by people and. Now. Employee payments are made instantly. The world of labor is also affected by technology advancements, which can move quite quickly.Time makes it possible for humans to do their work quickly in general. Today, a lot of businesses use technology to expedite work. Payroll information system that can help with data processing payroll for better management of payroll computations, pay stubs, and necessary reports. Based on these issues, research was done to learn the specifications needed to create an employee payroll information system for the plan. This design's outcome isemployee payroll was digitized using a web-based information system. This system was created to facilitate and minimize the company's performance in processing payroll data. processes used to calculate employee salaries. The Culture Department continues to prepare payroll reports manually, and there are still numerous challenges involved. So, in order to construct a based application, this is required. The website makes use of MySQL to make employees' job more productive. Additionally, this method enforces absenteeism for workers, and if they are ill while on leave or without notice, their pay will be withheld in accordance with the guidelines of the North Sumatra culture and tourism agency.
Modified ResNet50 model and semantic segmentation based image co-saliency detection

Co-salient object identification is a new and emerging branch of the visual saliency detection technique that tries to find salient pattern appearing in several image groups. The proposed work has the potential to benefit a wide variety of important applications including the detection of objects of interest, more robust object recognition and animation synthesis, handling input image query, 3D object reconstruction, object co-segmentation etc. To build modified ResNet50 model, the hyperparameters are adjusted in the current work to increase accuracy while minimizing loss. The modified network is trained on HOG features to mine more significant features along with their corresponding ground truth images. For a more streamlined outcome, the proposed system was built using the SGDM optimizer. During testing among the relevant and irrelevant image the network generates appropriate co-saliency map of relevant images. Integrating the associated and prominent characteristics of the image yields the appropriate ground truth for each image. The proposed method reports better F1 value 98.7% and MAE score 0.089 value when compared with SOTA model.
Two influential textbooks – “Mee” and “Mellor”.

I am a member of the &amp;nbsp;Royal Society of &amp;nbsp;Chemistry’s Historical group. Amongst other activities, it publishes two editions&amp;nbsp;of a newsletter each year for its members.
Applications of Active Learning in Predictive Maintenance

Nowadays, the common choice in maintenance strategies is predictive maintenance (PdM), deprecating the corrective and preventive kinds. Even with various machine learning techniques to get advanced predictive models to achieve PdM, difficulties remain in the data acquisition process. While there is a plethora of unlabeled data from sensors, most of those available techniques can only process labeled data, i.e, supervised learning. To combat the fact that the availability of the labeleddata is limited, this paper proposes the use of Active Learning to label and annotate the informative instances while minimizing overall processing time. This approach maintains high performance and decreases the number of labeled instances, with support from experimental results and a discussion of the applicability of this method.
APPLICATION OF MACROMEDIA FLASH LEARNING MEDIA IN INCREASING STUDENTS' INTEREST IN ICT SUBJECTS AT SMK NEGERI 6 SERANG IN ACADEMIC YEAR 2022/2023

This study aims to obtain information on increasing student interest in Information and Communication Technology subjects at SMK Negeri 6 Serang City by using macromedia flash learning media. The method used in this study is a qualitative descriptive method, namely a research method that seeks to collect data from the phenomenon being investigated. This research was conducted at SMK Negeri 6 Kota Serang and the objects studied were limited to students of class X TKJ 1 and X TKJ 2 with a total of 36 students per class. Collecting data using the method of observation, interviews, questionnaires and documentation. Based on the research results, it is known that most of the students (83.3%) are very interested in learning to use learning media, especially macromedia flash. Macromedia flash is a media presentation that combines audio-visual technology such as sound, animation and text that can be used in the teaching and learning process. With Macromedia Flash it is easier for students to understand and accept the material taught by the teacher. This can be seen from the results of observations of the learning process which shows that students are more enthusiastic and enthusiastic, as well as active and creative in learning. This is supported by the results of interviews and questionnaires with students who explained that students really enjoyed learning to use Macromedia Flash because it looks attractive and easy to understand.
Usability evaluation of online transportation using Nielsen model

Maxim is one of the online transportation services in Indonesia. Based on the feedback provided by users on the App Store and Google Play store, there are still shortcomings, such as the application often having errors when used, GPS accuracy is not precise, and the system feeling difficult when canceling orders. Based on these problems, usability testing is needed. The method used in this research is usability testing based on the standard Nielsen Model conducted by five respondents. In this study, the Usability System Scale questionnaire was also filled in to get the satisfaction value carried out by 100 respondents using Maxim. The usability test results show that the respondents complained that the payment methods were not varied and there was no customer service chat. This usability test gives results on each indicator: the learnability value of 92%, the efficiency value of 0.42 goals/sec, the error value of 0.1, and the satisfaction value of 57.2.
A unified and constructive framework for the universality of neural networks

Abstract One of the reasons why many neural networks are capable of replicating complicated tasks or functions is their universal approximation property. Though the past few decades have seen tremendous advances in theories of neural networks, a single constructive and elementary framework for neural network universality remains unavailable. This paper is an effort to provide a unified and constructive framework for the universality of a large class of activation functions including most of the existing ones. At the heart of the framework is the concept of neural network approximate identity (nAI). The main result is as follows: any nAI activation function is universal in the space of continuous functions on compacta. It turns out that most of the existing activation functions are nAI, and thus universal. The framework induces several advantages over the contemporary counterparts. First, it is constructive with elementary means from functional analysis, probability theory, and numerical analysis. Second, it is one of the first unified and constructive attempts that is valid for most of the existing activation functions. Third, it provides new proofs for most activation functions. Fourth, for a given activation and error tolerance, the framework provides precisely the architecture of the corresponding one-hidden neural network with a predetermined number of neurons and the values of weights/biases. Fifth, the framework allows us to abstractly present the first universal approximation with a favorable non-asymptotic rate. Sixth, our framework also provides insights into the developments, and hence providing constructive derivations, of some of the existing approaches.
Performance Evaluation of Feature Fusion Approaches for Indian Sign Language Recognition System

Objectives: To recognize and analyze the Indian sign language (ISL) gestures in simple background using various features and Machine learning classifiers. Methods: a) Data pre-processing: Contour matching approach for hand region segmentation b) Feature extraction: Local features like gradient & key point descriptors were extracted using HOG, SIFT, SURF, LBP, FAST, feature fusion is done by concatenating features of HOG with LBP, SIFT with FAST, BOVW model with SURF. c) Model development: SVM, Random Forest, Logistic regression, Naïve Bayes were trained on large dataset and was experimented with hyper parameter tuning. The experiment was performed on 2 standard datasets which consist of alpha numerals (A-Z & 1-9) in simple black background. Findings: Our research demonstrates that our model consistently achieved a remarkable 100% accuracy rate when utilizing feature fusion techniques on both of the datasets employed in this study. The research findings underscore the need to consider a more comprehensive approach for gesture recognition. Relying solely on distinct features extracted from a single algorithm is shown to be insufficient in addressing the challenges posed by the diverse nature of sign shapes, varying illumination conditions, and different orientations. This emphasizes the importance of exploring hybrid or multi-algorithmic strategies to enhance the accuracy and robustness of gesture recognition systems. Novelty: This research introduces a novel perspective on ISL gesture recognition by emphasizing gestures against a simple black background. The innovative application of feature fusion, combining various elements like hand shape and orientation enhances accuracy. The inclusion of hand segmentation using contour matching algorithm and experimentation on benchmark data adds another layer of novelty, highlighting practical applicability. The experimental results show that SVM has given better results when used with different combinations of feature extractors. Keywords: ISL, Feature Fusion, Keypoint Descriptors, HOG, SIFT, SURF, LBP, FAST
Improving English Language Skills in Early Childhood at SPS Mawar Kebonagung Through English Day Activities

In the eyes of society, universities are considered only to teach theory without any form of application when viewed from the perspective of social life. This can be seen from the surrounding conditions where not many university graduates can apply this theory in the field. This activity aims to bridge learning at SPS Mawar, Kebonagung Subdistrict, Pasuruan City, with a service learning approach in the English Day program to provide training for SPS Mawar Kebonagung Early Age Children (AUD) in improving their English language skills by introducing several new vocabulary through singing and fun game. This activity involved a lecturer, four PGRI Wiranegara University students, AUD SPS Mawar students, and teachers. A participatory approach is also used in this service activity to attract students' attention and make it easier for students to remember new vocabulary. Students are directly involved in the learning process. The success of providing English language teaching can increase AUD's ability or vocabulary in English by providing new inspiration in teaching children English skills with fun learning.
PENGARUH SISTEM INFORMASI AKUNTANSI DAN SISTEM PENGENDALIAN INTERNAL TERHADAP KINERJA PEGAWAI KANTOR CAMAT MEDAN MARELAN

This research aims to determine the effect of accounting information systems and internal control systems on employee performance at the Medan Marelan Subdistrict Office. This type of research is quantitative research. The population used was all employees at the Medan Marelan Subdistrict Office. The sampling technique in this research used a purposive sampling method, with a sample of 36 people. The data source used is primary data obtained through distributing questionnaires to respondents. Data analysis in this research uses descriptive, data quality analysis tests, classical assumption tests, and hypothesis tests with the help of the SPSS version 23.0 program. The research results show that (1) the accounting information system has a positive and significant effect on employee performance at the Medan Marelan Subdistrict Office; (2) the internal control system has a positive and significant effect on employee performance at the Medan Marelan Subdistrict Office Employee Performance, Accounting Information Systems; Internal Control System.&#x0D; &#x0D; &#x0D; Keywords: Employee Performance, Accounting Information Systems; Internal Control System&#x0D;
Unveiling the Key Demands: Exploring Tourism Student Needs at a Vocational School

Tourism plays a pivotal role by substantially boosting foreign exchange, highlighting its immense significance for the region. This study is aimed at exploring the target needs concerning the communicative competencies of tenth-grade tourism students at vocational institutions in Makassar. To delineate these needs, open-ended questionnaires focusing on three specific target need dimensions were directed toward the teaching faculty. The subsequent data were meticulously processed through reduction, display, and conclusion phases. The distilled findings shed light on three cardinal facets of students' target needs: 1. Necessities: A realm encompassing pivotal skills such as speaking, listening, writing, and reading in tandem with the indispensable teaching materials. 2. Lacks: This underscores the discernible voids in students' grammar and lexical repertoire. 3. Wants: A domain where students manifest an ardent aspiration to master oral communication skills. These revelations underscore the intertwined complexities of target needs in the pedagogy of the English language. Additionally, this investigative endeavor empowers educators in Makassar's vocational institutions with a compass to navigate the intricate landscape of tourism students' requisites. Such profound insights germinate the seeds for sculpting potent English language curricula, harmoniously resonating with the students' ambitions while adeptly bridging their skill deficits. In summation, this research not only augments the caliber of tourism pedagogy but also unfurls a roadmap for educators, equipping them with the acumen to meticulously tailor their instructional strategies, ensuring a holistic address of students' target needs.
Genetic Programming with Convolutional Operators for Albatross Nest Detection from Satellite Imaging

Conservation efforts in remote areas, such as population monitoring, are expensive and laborious. Recent advances in satellite resolution have made it possible to achieve sub-40 cm resolution and see small objects. This study aimed to count potential southern royal albatross nests based on the remote Campbell Island, 700 km south of New Zealand. The southern royal albatross population is declining, and due to its remoteness, there is an urgent need to develop new remote sensing methods for assessing the population. This paper proposes a new tree-based genetic programming (GP) approach for binary image segmentation by extracting shallow convolutional features. An ensemble of these GP segmentation models and individual GP models were compared with a state-of-the-art nnU-Net segmentation model trained on manually labelled images. The ensemble of shallow GP segmentation trees provided significantly more interpretable models, using <1% the number of convolutions while achieving performance similar to that of the nnU-Net models. Overall, the GP ensemble achieved a per-pixel F1 score of 75.44% and 123 out of 166 correctly identified nest-like points in the test set compared with the nnU-Net methods, which achieved a per-pixel F1 score of 74.49% and 129 out of 166 correctly identified nest-like points. This approach improves the practicality of machine learning and remote sensing for monitoring endangered species in hard-to-reach regions.
Development and application of novel sun-tracking control system for parabolic trough solar collector on distributed energy

A distributed energy system with multi-source cooperative heating that relies primarily on trough solar thermal heating with high efficiency is designed due to low tracking accuracy in conventional engineering applications, leading to inefficient solar energy utilization. A high-precision mathematical model for single-axis tracking of parabolic troughs is developed based on the solar position algorithm (SPA). The quantitative calculation of operating characteristics and cosine efficiency is carried out, and the influence of the condenser arrangement on system performance is studied. The entire simulation model is built based on System Advisor Model (SAM), using the data from a typical meteorological year. And the performance and economic analysis are accomplished. An automatic tracking strategy with intermittent control considering multi-mode switching and boundary conditions is designed, cooperating with a worm gear reducer to realize the closed-loop real-time tracking of the sun&apos;s position by the multi-axis synchronous drive system. High-precision algorithms is realized through embedded programming with a PLC controller. The actual operation data shows that the system has the advantages of high tracking accuracy, simplicity of structure, and convenient maintenance, which are significant for the application of clean energy in the field of distributed heating.
Guided Sampling by Neighborhood Information and Matching Scores for Multi-Structure Data

The success of most robust model estimation methods heavily relies on their used data sampling algorithms. This paper proposes a novel sampling algorithm, called Guided Sampling by Neighborhood Information and Matching Scores (NIMS), to efficiently sample promising hypotheses for fitting multi-structure data. Specifically, NIMS follows a specific sampling process. First, the proposed NIMS randomly selects a data point. Then, NIMS selects the neighbors of the selected data by using the neighborhood information to remove most of the outlier neighbors. Finally, NIMS samples a data subset using matching scores from the selected neighbors, which encourages NIMS to sample inliers from the selected neighbors. Experimental results on the publicly available <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AdelaideRMF</i> dataset demonstrate that the proposed NIMS outperforms several state-of-the-artsampling algorithms.
On Signal Analysis with ANN and PSO Techniques

The work presents some results of signal analysis, periodic and aperiodic behavior, with artificial intelligence techniques. Two paradigms are considered, based on artificial neural networks (ANN) and – the second one – based on particle swarm optimization (PSO). The first approach promotes linear neural networks architecture, ADALINE for single signals and MADALINE for multiple signals case. The second approach promotes the swarm intelligence, used here for the minimization of objective function based on mean squared error (MSE). For periodic signals, the trigonometric Fourier series is considered. For aperiodic signals, the discrete Fourier transform is promoted. Both techniques could be applied for a more general problem of model parameter estimation. The computer-based experiments are made with various signal-to-noise ratios, to study the effects on the converge rate on the estimation process. At least for the considered signals, the ANN based solution provides better results. The framework could be extended to the analysis problem based on other base functions, e.g., wavelets.
A Short Comparation between PSO and QPSO for Signal Processing Applications

The aim of the work is to make an evaluation of two versions of Particle Swarm Optimization (PSO), in the framework of signal analysis and model parameters estimations. The versions of PSO are with constraint coefficients and inertia (PSO-COIN) and the version based on quantum models (QPSO). Both versions are members of the evolutionary computation domain and have bioinspired roots. Two case studies are considered. The first case study is represented by two test functions, chosen from a benchmark suite used for the evaluation of the optimization algorithms. The second case is the parameter estimation problem, in the context of the signal analysis framework, and under various signal-to-noise ratios to study the effects on the converge rates of the estimation processes. The computer-based experiments show comparable performances for PSO algorithms, for short time simulation, e.g., 100 iterations. After that, the version of PSO-COIN gives better results.
Aspects of Artificial Intelligence Conversion from PC to Pumpkin boards

The objective of the paper is to present a way to develop and train a neural network in Matlab, in order to run it on a customized evaluation board. The essence of the development of this route is, on one hand, the fact that the customized Pumpkin board has associated software dedicated to running artificial neural networks on the Graphics Processing Unit - GPU, which gives a high speed of simulating neural networks thanks to the parallel processing feature and on the other hand, the Matlab environment has important resources for the development, characterization and optimization of artificial neural networks - ANN. The considered artificial neural network is of the “deep network” category, with fifteen layers and the data structure is specific to this category, being different from the “shallow network” category. The data presented for training and running the network are of image type with eight-bit greyscale representation and consists of digits from 0 to 9. The classification results obtained by running the neural network in Matlab and on the considered Pumpkin board are compared.