Reviewerly/exHarmony huggingface dataset:
Perbedaan Nilai Indeks Eritrosit Pada Sampel Darah Vena Dengan Pengambilan Posisi Duduk Dan Posisi Berbaring

Authors:
raw_author_name: Denny Juraijin
raw_affiliation_string: IKesT Muhammadyah Palembang
raw_author_name: Nurhidayanti Nurhidayanti
raw_affiliation_string: Program Studi D IV Teknologi Laboratorium Medis Institut Ilmu Kesehatan dan   Teknologi Muhammadiyah Palembang

Abstract: The erythrocyte index or Mean Cospuscular Value is an average value that can provide information about the average erythrocyte and the amount of hemoglobin per-erythrocyte. The erythrocyte index consists of Mean Corpuscular Volume (MCV) or the average volume of an erythrocyte, Mean Corpusculara Hemoglobin (MCH) or the amount of hemoglobin per-erythrocyte, Mean Corpuscular Hemoglobin Concentration (MCHC) or the concentration of hemoglobin obtained per-erythrocyte. Erythrocyte index examination is more specifically used to determine what type of anemia is suffered by someone who experiences it. This study aims to determine the difference in the results of the examination of erythrocyte index values in venous blood sampling by taking sitting and lying positions. This research method is Crossectional, research conducted at Bunda Hospital Palembang. The population taken was female students of the S.Tr study program. Medical Laboratory Technology Muhammadiyah Palembang, Samples used in this study amounted to 31 samples using Purposive Sampling. The study obtained the results of MCV sitting position 84.88 and lying position 84.34 fl, MCH obtained an average value in sitting position 28.09 and lying position 28.05 pg / cell and MCHC examination obtained an average value in sitting position 33.05 and lying position 33.22 g / dl which means there are differences in erythrocyte index examination in venous blood sampling with sitting position and lying position. It can be concluded that venous blood sampling in the sitting position is not recommended for erythrocyte index examination, because the examination results are different in the sitting position.
Teachers’ Strategies and Challenges to Promote Students’ Active English Learning in SMK Muhammadiyah 1 Palu

Authors:
raw_author_name: Sriati Usman
raw_affiliation_string: Universitas Tadulako
raw_author_name: Abdul Kamaruddin
raw_affiliation_string: Universitas Tadulako

Abstract: Student engagement has an important aspect in achieving excellent learning outcomes for learners and further improving their abilities. So teachers must have strategies to encourage student involvement. The aim of this research was to investigate the strategies the teachers use in activating students active learning in teaching and learning process and the challenges the teachers face in promoting students active learning. The researcher used qualitative research. The place of the research was in SMKS Muhammdiyah 1 Palu that located in Letjend Seoprapto. The Subject of the research were all English teachers at SMK Muhammadiyah 1 Palu that consist of four teachers. The data collection techniques that wil be used are: In depth Interview, Observation, and Documentation. The data will be analyzed in 3 steps, are Data reduction, data display, and conclusion. After conducting a research in SMK Muhammadiyah 1 Palu, it is found that in stimulating students’ active English learning, the teachers use six strategies; Questionning &amp; discussion, visual based instruction, rolep lay, game, project based learning, and discovery based learning.From those six strategies the teachers used in the previous explanation, there are four strategies that are used dominantly; questioning &amp; discussion, visual based instruction, project based learning, and discovery based learning.researcher proposes that future researchers interested in the same issue might do in-depth research on teacher ways to encourage students' active learning in English. For instance, the specific strategy that can stimulate the specific skill.
THE EFFECT OF IMMERSIVE TECHNOLOGY ON ENHANCING STUDENT LEARNING:

Authors:
raw_author_name: Kharisma Alivia Nastiti
raw_affiliation_string: Universitas Indonesia
raw_author_name: Harry Budi Santoso
raw_affiliation_string: Universitas Indonesia
raw_author_name: Kasiyah Junus
raw_affiliation_string: Universitas Indonesia
raw_author_name: Mubarik Ahmad
raw_affiliation_string: Universitas Indonesia
raw_author_name: Endina Putri Purwandari
raw_affiliation_string: Universitas Indonesia

Abstract: Education is one of the fundamental factors influencing national development. Educators are required to constantly explore the newest and creative strategies to deliver topics to students and promote students’ engagement in the learning process. One technology that has numerous potentials to facilitate the education process is immersive technology. Immersive technology has the potential to improve student achievement by enabling better learning outcomes. This study aims (1) to analyze the trends and impact of immersive technology usage in education; (2) to identify immersive technology that supports students’ learning processes in the pandemic era; and (3) to identify the usage of learning theories in educational application development that implement immersive technology to enhance students’ learning outcomes. This study examined seventeen selected studies after adopting the selection process of the systematic literature review process by Kitchenham. The selected studies were published between 2018 and 2022 by Emerald Insight, IEEE Xplore, and Scopus. There are three kinds of immersive technology identified in this study: virtual reality, augmented reality, and mixed reality. The study showed the nine types of students’ learning outcomes, and the results showed that using immersive technology significantly improved student learning compared to traditional methods. Furthermore, thirteen learning theories were adopted as the basis for developing educational applications. Future research directions are also suggested to continue developing promising and limited new technologies to enhance the variety of education-oriented applications. Immersive technologies designed for learning are still on a long journey, including considerations of readiness and potential effects that may arise for users such as privacy and security concern.
Implementation of Association Rules Algorithm to Identify Popular Topping Combinations in Orders

Authors:
raw_author_name: Sania Fitri Octavia
raw_affiliation_string: Universitas Islam Negeri Sultan Syarif Kasim Riau

Abstract: Association rule is a data mining technique to find associative rules between a combination of items. This research aims to apply association rules algorithm in identifying popular topping combinations in food orders. This application aims to help restaurant owners or food businesses understand their customers' preferences and optimize their menu offerings. Data obtained from kaggle, the association rules algorithm is applied to this dataset to identify patterns or combinations of toppings that often appear together in orders. The results of this study show toppings with chocolate as a popular item in orders. These findings can provide valuable insights for food business owners in structuring their menus and determining attractive offers for customers. This study also applied a comparison between the apriori, fp- growth and eclat algorithms, with the result that the best item transaction rule was found: a combination of dill &amp; unicorn toppings with chocolate with 60% confidence. Overall, the application of eclat algorithm in this study provides the best performance with higher execution speed, thus providing insight into customer preferences regarding topping combinations in food orders. Despite the shortcomings of the data form from this study, it is expected to help business owners in optimizing their offerings, increasing customer satisfaction, and improving their business performance.
Comparison of K-Means, BIRCH and Hierarchical Clustering Algorithms in Clustering OCD Symptom Data

Authors:
raw_author_name: Haykal Alya Mubarak
raw_affiliation_string: Universitas Islam Negeri Sultan Syarif Kasim Riau

Abstract: The hallmarks of Obsessive-Compulsive Disorder (OCD) are intrusive, anxiety-inducing thoughts (called obsessions) and associated repeated activities (called compulsions). To understand the patterns and relationships between OCD data that have been obtained, data will be grouped (clustering). In clustering using several clustering algorithms, namely K-Means, BIRCH, In this work, hierarchical clustering was used to identify the optimal cluster value comparison, and the Davies Bouldin Index (DBI) was used to confirm the results. Then the results of the best cluster value in processing OCD data are using the BIRCH algorithm in the K10 experiment which gets a value of 1.3. While the K-Means algorithm obtained the best cluster at K10 with a value obtained of 1.36 and the Hierarchical clustering algorithm also at the K10 value of 2.03. Thus in this study, the comparison results of the application of 3 clustering algorithms obtained results, namely the BIRCH algorithm shows the value of the resulting cluster is the best in clustering OCD data. This means that the BIRCH algorithm can be used to cluster OCD data more accurately and efficiently.
Perbandingan Hasil Recovery Tools Mobile Forensic Di Smartphone Android Menggunakan Metode National Institute Of Justice (NIJ)

Authors:
raw_author_name: Trihastuti Yuniati
raw_affiliation_string: Institut Teknologi Telkom Purwokerto

Abstract: Penggunaan smartphone di Indonesia mencapai 192,15 juta pengguna aktif pada tahun 2022, menjadikannya pasar terbesar keempat di dunia. Namun, pertumbuhan ini juga berdampak pada meningkatnya tindak kejahatan siber, tercatat sebanyak 8,831 kasus selama tahun tersebut. Penelitian tentang efektivitas alat pemulihan mobile forensic pada smartphone Android dapat menjadi acuan pemilihan alat uji mobile forensic baik untuk kebutuhan penyelidikan maupun akademis. Pada penelitian ini dilakukan pengujian terhadap dua tools mobile forensic, yaitu Andriller yang merupakan aplikasi open source, dan MOBILedit Forensic Express Pro yang merupakan aplikasi berbayar, untuk mengetahui seberapa tingkat recovery yang mampu dilakukan oleh kedua tools tersebut. Penelitian dengan metode National Institute of Justice (NIJ) menemukan bahwa kedua software yang digunakan dapat digunakan untuk analisis mobile forensic tanpa akses root ke smartphone. Hasil pengujian menunjukkan MOBILedit Forensic Express Pro memiliki tingkat pemulihan data yang lebih tinggi (83,33%) dibandingkan dengan Andriller (50%). Dari hasil ini diharapkan dapat membantu dalam memilih alat atau software mobile forensic dalam proses penyelidikan maupun kebutuhan akademis. Kata kunci: digital forensik, mobile forensic, national institute of justice ------------------------------------------------------------------------- The usage of smartphones in Indonesia reached 192.15 million active users in 2022, making it the fourth largest market in the world. However, this growth has also led to an increase in cybercrime, with 8,831 cases reported during that year. A research on the effectiveness of mobile forensic recovery tools on Android smartphones, can be a reference for selecting mobile forensic testing tools for both investigative and academic needs. In this research, two mobile forensic tools were tested, namely Andriller, which is an open source application, and MOBILedit Forensic Express Pro, which is a paid application, to find out what level of recovery these two tools are capable of. Research using the National Institute of Justice (NIJ) method found that the two software used can be used for mobile forensic analysis without root access to a smartphone. The test results indicated that MOBILedit Forensic Express Pro achieved a higher data recovery rate (83.33%) compared to Andriller (50%). These findings are expected to aid in selecting suitable mobile forensic tools for investigative and academic purposes. Keywords: digital forensik, mobile forensic, national institute of justice
Real-Time Traffic Light Recognition with Lightweight State Recognition and Ratio-Preserving Zero Padding

Authors:
raw_author_name: Jihwan Choi
raw_affiliation_string: Department of Semiconductor System Engineering, Kumoh National Institute of Technology, 61, Daehak-ro, Gumi-si 39177, Gyeongsangbuk-do, Republic of Korea
raw_author_name: Harim Lee
raw_affiliation_string: School of Electronic Engineering, Kumoh National Institute of Technology, 61, Daehak-ro, Gumi-si 39177, Gyeongsangbuk-do, Republic of Korea

Abstract: As online shopping is becoming mainstream, driven by the social impact of Coronavirus disease-2019 (COVID-19) as well as the development of Internet services, the demand for autonomous delivery mobile robots is rapidly increasing. This trend has brought the autonomous mobile robot market to a new turning point, with expectations that numerous mobile robots will be driving on roads with traffic. To achieve these expectations, autonomous mobile robots should precisely perceive the situation on roads with traffic. In this paper, we revisit and implement a real-time traffic light recognition system with a proposed lightweight state recognition network and ratio-preserving zero padding, which is a two-stage system consisting of a traffic light detection (TLD) module and a traffic light status recognition (TLSR) module. For the TLSR module, this work proposes a lightweight state recognition network with a small number of weight parameters, because the TLD module needs more weight parameters to find the exact location of traffic lights. Then, the proposed effective and lightweight network architecture is constructed by using skip connection, multifeature maps with different sizes, and kernels of appropriately tuned sizes. Therefore, the network has a negligible impact on the overall processing time and minimal weight parameters while maintaining high performance. We also propose to utilize a ratio-preserving zero padding method for data preprocessing for the TLSR module to enhance recognition accuracy. For the TLD module, extensive evaluations with varying input sizes and backbone network types are conducted, and then appropriate values for those factors are determined, which strikes a balance between detection performance and processing time. Finally, we demonstrate that our traffic light recognition system, utilizing the TLD module’s determined parameters, the proposed network architecture for the TLSR module, and the ratio-preserving zero padding method can reliably detect the location and state of traffic lights in real-world videos recorded in Gumi and Deagu, Korea, while maintaining at least 30 frames per second for real-time operation.
Copy-Move Forgery Detection and Localization Framework for Images Using Stationary Wavelet Transform and Hybrid Dilated Adaptive VGG16 with Optimization Strategy

Authors:
raw_author_name: Prabhu Bevinamarad
raw_author_name: Prakash Unki

Abstract: Due to the availability of low-cost electronic devices and advanced image editing tools, changing the semantic meaning of a particular image has become straightforward by employing various image manipulation techniques like image copy-move, image splicing and removal operations. The tampered images with this sophisticated software are rich in visualization, making the modifications invisible to the naked eye. Detecting these image alterations is laborious, time-consuming, and often yields inappropriate results. The current techniques use conventional square, slide regular, and artifacts procedures to identify image deviations to combat image forgery practices. Still, these techniques exhibit problems related to generalization, training and testing, and model complexity. So, in this paper, a novel image forgery detection and localization framework is implemented using stationary wavelet transform (SWT), and a Hybrid Dilated Adaptive VGG16 model with optimization is introduced to classify forgery images and localize the forgery regions present in an image. Initially, the proposed framework processes the input image with SWT to decompose an image into different subband and further divide it into patches. After that, the hybrid dilated adaptive VGG16 Network (HDA-VGG16Net) is built to extract the deep image features from the patches. Later, the Hybridized Tuna Swarm with Bald Eagle Search Optimization (HTS-BESO) technique is applied to optimize the VGG16 parameters. Finally, feature matching is formed using multi-similarity searching to recognize whether the input image is forged or original by locating forgery regions. The evaluation results are compared with existing forgery detection approaches to ensure the efficiency of the developed model by considering multiple performance measures.
When Handcrafted Features Meet Deep Features: An Empirical Study on Component-Level Image Classification

Authors:
raw_author_name: Tauseef Khan
raw_author_name: Ayatullah Faruk Mollah

Abstract: Scene text detection from natural images has been a prime focus from last few decades. Classification of foreground object components is an essential task in many scene text detection approaches under uncontrollable environment. As it heavily relies upon robust and discriminating features, several features have been engineered for component-level text non-text classification. Competency of such feature descriptors particularly in respect of deep features needs to be examined. In this paper, we present prospective feature descriptors applicable to component-level text non-text classification and examine their performance along with convolutional neural network based deep features. Series of experiments have been carried out on publicly available benchmark dataset(s) of multi-script document-type, scene-type, and combined text vs. non-text components. Interestingly, feature combination is found to put well-demonstrated deep features into tough competition on most datasets under consideration. For instance, on the combined text non-text classification problem, CNN based deep features yield 97.6%, whereas aggregated features produce an accuracy of 98.4%. Similar findings are obtained on other experiments as well. Along with the quantitative figures, results have been analyzed and insightful discussion is made to ascertain the conjectures drawn herein. This study may cater the need of leveraging potentially strong handcrafted feature descriptors.
Non-Gaussianity detection of single-mode rotationally symmetric quantum states via cumulant method

Authors:
raw_author_name: Shao-Hua Xiang
raw_affiliation_string: College of Physics, Electronics and Intelligent Manufacturing, Huaihua University, Huaihua, 418008, CHINA
raw_author_name: Li-Jun Huang
raw_affiliation_string: College of Physics, Electronics and Intelligent Manufacturing, Huaihua University, Huaihua, 418008, CHINA
raw_author_name: Xian-Wu Mi
raw_affiliation_string: College of Physics, Electronics and Intelligent Manufacturing, Huaihua University, Huaihua, 418008, CHINA

Abstract: Abstract The non-Gaussianity of quantum states incarnates an important resource for improving the performance of continuous-variable quantum information protocols. We propose a novel criterion of non-Gaussianity for single-mode rotationally symmetric quantum states via the squared Frobenius norm of higher-order cumulant matrix for the quadrature distribution function. As an application, we study the non-Gaussianities of three classes of single-mode symmetric non-Gaussian states: a mixture of vacuum and Fock states, single-photon added thermal states, and even/odd Schrödinger cat states. It is shown that such a criterion is a faithful and effective one for revealing non-Gaussianity. We further extend this criterion to two cases of symmetric multi-mode non-Gaussian states and non-symmetric single-mode non-Gaussian states, respectively.
Field-enriched A* search algorithm for robot motion in path planning

Authors:
raw_author_name: Dongyue Lei
raw_affiliation_string: Centrale Pekin/School of General Engineering, Beihang University, Beijing, China

Abstract: Path planning in unknown or complex environments is a central issue in the fields of automation and robotics. To address this global path planning problem while maintaining the smoothness of robot motion, as well as to ensure safety by keeping away from obstacles, this paper propose an innovative method called Field-Enriched A* Planner (FEAP), which incorporates the repulsive potential field into the heuristic computation of the A* search algorithm. The research modified the heuristic mechanism of the A* algorithm by incorporating both attraction and repulsion factors from the Artificial Potential Field method, thereby enhancing the influence of obstacles in the decision-making process. The repulsive potential field enhances obstacle avoidance, ensuring smoother robot motion during the path planning process. The simulation experiments are conducted on the proposed method and compare it with common path planning algorithms. Results from the experimental trials demonstrate that the new method can effectively enhance the smoothness of robot motion while ensuring a safe path is identified.
Prediction of CBR by Deep Artificial Neural Networks with Hyperparameter Optimization by Simulated Annealing

Authors:
raw_author_name: Ehsan Noroozinejad Farsangi
raw_affiliation_string: Urban Transformations Research Centre (UTRC), Western Sydney University, Parramatta, NSW, Australia

Abstract: Abstract The construction of pavements requires the complete identification of the soils in place and of the added materials. This identification consists in determining the class of the soils and in evaluating their bearing capacity through the California bearing ratio (CBR) index. Obtaining the CBR index is very costly in terms of time and financial resources, especially when it is a large-scale project. It thus leaves prospects of obtaining it by simpler processes; hence, it arises the need to find simpler processes compared to classical processes. This study develops models for predicting the CBR index from physical properties that are less complex to obtain, based on deep neural networks. To achieve this, three databases were used. A first database consists of the proportion of fines, the Atterberg limits and the Proctor references of the soils. A second database uses the methylene blue value instead of the Atterberg limits, and a third database uses only the proportion of fines and the Proctor soil reference. On each of the databases, a deep neural network model was developed using dense layers, regularization layers, residual blocks and parallelization in TensorFlow to predict the CBR value. Each model was formed by combining several deep neural networks developed according to specific architectures. To expedite training, the simulated annealing method was employed to optimize hyperparameters and define the optimal configuration for each network. The predictions obtained are correlated with the true values from 83.6 to 96.5%. In terms of performance, the models have a mean deviation ranging from 3.74 to 5.96%, a maximum deviation ranging from 12.43 to 16.2% and a squared deviation ranging from 0.781 to 2.189. The results suggest that the variable VBS has a negative impact on the accuracy of the networks in predicting the CBR index. The developed models respect the confidence threshold (± 10%) and can be used to set up a local or regional geotechnical platform.