Reviewerly/exHarmony huggingface dataset:
Pengaruh Penggunaan Peta Digital ArcGIS Sebagai Media Pembelajaran IPS Terhadap Motivasi Belajar Peserta Didik

One of the main factors contributing to students’ low motivation in learning is the need for more innovative use of learning media. Innovative learning media is crucial to sustaining students' interest and preventing them from getting bored easily. This research focuses on using ArcGIS digital maps as social studies learning media and its impact on students' learning motivation at SMPN 225 Jakarta. This study aims to investigate the effect of ArcGIS digital maps on the learning motivation of SMPN 225 Jakarta students. A quasi-experimental method with a non-equivalent control group design was used for this study, and data collection was carried out using questionnaires and documentation. The questionnaire utilized to collect data on the influence of ArcGIS digital maps as social studies learning media on students’ learning motivation. The samples for this study were drawn from class VIII E as the control group and class VIII H as the experimental group. The hypothesis testing technique used for the analysis was the T or difference test. The research results indicate that using ArcGIS digital maps as social studies learning media significantly affected student learning motivation in the experimental class, with a sig value of 0.000 &lt; 0.05. The control class that used conventional maps also significantly affected student learning motivation, with a sig value of 0.000 &lt; 0.05. The motivational results of the experimental and control groups were not significantly different, with a sig value of 0.392 &gt; 0.05. Keywords: Learning motivation, ArcGIS digital maps, social studies learning.
Efektivitas Pemanfaatan Media Facebook pada Laman Notifikasi Beranda untuk Meningkatkan Kemampuan Menulis Puisi Siswa Kelas X SMA Nurul Iman

The research was carried out which aims to describe the use, application, and use of facebook media on the notification page of the homepage in writing poetry by students of class X SMA Nurul Iman. This study uses a quantitative descriptive method with a population of 180 people and a set sample of 27 people. The total sample is 15% of the total population. The highest score obtained is 90 while the lowest score is 75. Based on the results of data analysis, the average utilization, application, and use of facebook media on the notification page homepage by class X SMA Nurul Iman students is 81.85 (85%) which is categorized as "good" . The ability to use the Facebook home page by class X SMA Nurul Iman tests based on a poetry writing test on Facebook media on the home page notification is equal to 69.00 which is included in the "fairly capable" category. This is evidenced by the results of the poetry writing test on Facebook media on the home notification page showing his ability with an average score of 81.85 (good).&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; The calculation of the score for writing poetry on Facebook media on the homepage notification page for class X students of SMA Nurul Iman in the 2021-2022 academic year is the same as 81.85. The average score of class X students at SMA Nurul Imanfor the academic year 2021-2022 is the same as 81.85 which is qualified into the category B (Good).&#x0D; &#x0D; &#x0D; &#x0D;
Phoneme-Based Multi-task Assessment of Affective Vocal Bursts

Affective speech analysis is an ongoing topic of research. A relatively new problem in this field is the analysis of affective vocal bursts, which are non-verbal vocalisations such as laughs or sighs. The current state of the art in the analysis of affective vocal bursts is predominantly based on wav2vec2 or HuBERT features. In this paper, we investigate the application of the wav2vec2 successor data2vec and the extension wav2vec2phoneme in combination with a multi-task learning pipeline to tackle different analysis problems at once, e.g., type of burst, country of origin, and conveyed emotion. Finally, we present an ablation study to validate our approach. We discovered that data2vec appears to be the best option if time and lightweightness are critical factors. On the other hand, wav2vec2phoneme is the most appropriate choice if overall performance is the primary criterion.
Optimization of credit scorecard combinations based on quantum annealing algorithm

With the development of the times, the usage of loan-related business is increasing. However, most of the credit score card portfolio methods used nowadays are manually designed and supplemented with computer calculations, so the bank yields are low. Therefore, it is of great interest to study the application of quantum computers in credit score card portfolio optimization for better rating of customer credit ratings. Based on the given credit score card data, a linear programming model is developed. Firstly, the data is processed into two sets of one-dimensional data by Python and the working range of the two sets of data is found. Secondly, after deriving the objective function expressions according to the given conditions, the data were visualized by building a 3D grid matrix with the pass rate and bad debt rate as the XOY plane and the final benefit to the bank as the Z axis. Subsequently, the decision variables in the linear programming model are converted into binary variables, and the original model is transformed into a QUBO model by converting the constraints into binary variables. After analyzing the QUBO model, the quantum annealing method is chosen to solve the optimal solution of the objective function. And based on this, the case of three selected credit score card combinations and any three credit score cards are considered.
Texture and pixel intensity characterization-based image segmentation with morphology and watershed techniques

&lt;p&gt;Image segmentation is an image processing technique that concentrated on finding and locating the parts of an image such as objects and boundaries. The purpose of locating these parts is for use in further processing analysis of an image such as recognition tasks, and content-based image retrieval. This paper introduces the segmentation procedure using a proposed template of features with watershed or morphology operations. Features template based on segmentation process conveys pixels’ intensities property perceived by the threshold value of histogram representation and texture feature where the regions are characterized by their texture content using standard deviation (SD) filtering. Wiener filter and histogram equalization (HE) techniques are used as preprocessing operations to enhance the image quality. The edge detector operator is hybridized to boost the segmentation process. Some statistical metrics are used for assessing and analyzing the performance of the stages in the proposed work. As a result, this proposed template of features achieved more performance with watershed and morphology segmentation.&lt;/p&gt;
Enhancing attendance tracking using animated QR codes: a case study

This research paper explores the effectiveness of quick response (QR) code-based attendance systems with the added security measure of generating two QR codes per second. With traditional attendance tracking methods being time-consuming and inefficient, QR codes have become increasingly popular as a quick and efficient alternative. However, one concern with QR code-based attendance systems is the potential for fraud and misuse. To address this issue, this study proposes generating two QR codes per second to ensure that only the current and legitimate QR code is recognized. The purpose of this study is to assess the impact of this technology on student attendance rates, the accuracy and reliability of attendance data, and the overall user experience for both students and instructors. Through data analysis and surveys, we found that the use of QR codes with the added security measure resulted in increased student attendance rates, improved accuracy and reliability of attendance data, and a positive user experience for both students and instructors. This research provides practical insights for educational institutions considering the implementation of QR code-based attendance systems and contributes to the growing body of literature on the use of QR codes in education.
Application of artificial neural networks for personality traits prediction based on handwriting

The automatization of personality traits prediction still brings considerable research these days, especially when the detection could be achieved without administrating personality assessment instruments. With the evolution of computational intelligence, a variety of deep machine learning techniques were developed and proposed for that purpose. Nevertheless, proposing robust and rapid systems to solve this problem remains a challenging task. The process of feature extraction is the main key. This paper presents an effective method for extracting five graphological features from handwriting ensuring the prediction of personality traits based on the big five personality traits model. We started by collecting both handwriting samples and big five questionnaires, then the feature extraction process, after that the data preparation and finished with the application of several popular deep machine learning models to achieve the prediction. Experimental results indicate the remarkable performance of the multi-layer perceptron (MLP) compared to other classifiers, the model was 100% precise and classification accuracy attained 100% for trained data and 72.73% for new tested data. With only 100 participants, we strongly believe that our proposed method is simple and promising, and better results will be attained with a larger dataset.
Categorical encoder based performance comparison in pre-processing imbalanced multiclass classification

The contribution of this study is to offer suggestions for coding techniques for categorical predictor variables and comprehensive test scenarios to obtain significant performance results for imbalanced multiclass classification problems. We modify scenarios in the data mining process with the sample, explore, modify, model, and assess (SEMMA) framework coupled with statistical hypothesis testing to generalize the model performance evaluation conclusions as enhanced-SEMMA. We selected four open-source data sets with unequal class distributions and categorical predictors. Ordinal, nominal, dirichlet, frequency, target, leave one, one hot, dummy, binary, and hashing encoder methods are used. We use the grid-search technique to find the best hyperparameters. The F1-Score and area under the curve (AUC) are evaluated to select the optimal model. In all datasets with 10-fold stratified cross-validation and 95% to 99% accuracy for each dataset, the results show that support vector machine (SVM) outperforms the decision tree (DT) K-nearest neighbor (KNN), Naïve Bayes (NB), logistic regression (LR), and random forest (RF) algorithms. Probability-based or binary encodings, such as target, Dirichlet, dummy, one-hot, or binary, are best for situations with less than 3% of minor class proportions. Nominal or ordinal encoders are preferred for data with a minor class proportion of more than 3%.
A scoping review of topic modelling on online data

With &lt;span&gt;the increasing prevalence of unstructured online data generated (e.g., social media, online forums), mining them is important since they provide a genuine viewpoint of the public. Due to this significant advantage, topic modelling has become more important than ever. Topic modelling is a natural language processing (NLP) technique that mainly reveals relevant topics hidden in text corpora. This paper aims to review recent research trends in topic modelling and state-of-the-art techniques used when dealing with online data. Preferred reporting items for systematic reviews and meta-analysis (PRISMA) methodology was used in this scoping review. This study was conducted on recent research works published from 2020 to 2022. We constructed 5 research questions for the interest of many researchers. 36 relevant papers revealed that more work on non-English languages is needed, common pre-processing techniques were applied to all datasets regardless of language e.g., stop word removal; latent dirichlet allocation (LDA) is the most used modelling technique and also one of the best performing; and the produced result is most evaluated using topic coherence. In conclusion, topic modelling has largely benefited from LDA, thus, it is interesting to see if this trend continues in the future across languages.&lt;/span&gt;
Overview of the Positioning Technology of the Sweeping Robot

Nowadays, the sweeping robot has been widely used in every household, the positioning technology of the sweeping robot, as one of the key technologies, is constantly updated, different technologies have different advantages and disadvantages and characteristics. Therefore, it is very necessary to summarize and summarize the positioning of the sweeping robot. Firstly, according to the existing review, according to the dimension of positioning technology, it is divided into four parts: simultaneous localization and mapping (S LAM), beacon-based positioning technology, environmental map matching positioning technology, and dead reckoning, and summarizes it. Finally, the future development prospect of the sweeping robot positioning technology is summarized.
Implementing The Field Trip Method With Youtube Media To Improve Description Text Writing Skills

Writing is one way to express ideas and ideas about what has been seen, heard or experienced. To be able to produce or express flanguage through writing is an ability that necessitates encoding processes, and writing is an active and productive linguistic activity.This research was carried out because of the low desire to write and the lack of knowledge about writing descriptive texts. The benefits that can be obtained from the use of media through YouTube, among others, the teacher will find it easier to convey learning materials in a more interesting way, avoid the lecture method in delivering material, can facilitate various types of children's learning methods, students will be more enthusiastic about participating in learning with In presenting videos, the teacher will more easily invite students to be involved in learning activities. the purpose of this study is to determine the effect of using youtube media with the field trip method. The methodology of this investigation is experimental. This study employs a pre-experimental design known as the One-Group Pretest-Posttest Plan. The outcomes demonstrate an elevated dependence on Youtube media when composing descriptive texts using the field trip approach.
The Implementation Of Scientific Approach Based On English Learning Strategy (SABELS) To The Students’ Ability In Reading Comprehension

This research aims to apply The Scientific Approach-Based English Learning Strategy (SABELS) on the students’ ability in comprehending text fourth semester students of STIKOM Tunas Bangsa. This research is descriptive qualitative, because the researcher used descriptive method to analize the data and the data are in the form of word. This research conducted in STIKOM Tunas Bangsa Pematangsiantar. The researcher used three techniques to collecting the data. Those are observation, interview and study document. The researcher did two observations in class. The subject of the interview is the teacher as implementer the teaching learning process. The research findings are (1) the Implementation of the Scientific Approach in the Teaching of Reading Comprehension to the fourth semester students at STIKOM Tunas Bangsa was good, because was appropriate with the regulation, (2) in applying scientific approach, the teacher used five phase, those are observing, questioning, experimenting, associating, and communicating. The teacher used authentic assessment in the evaluation. Based on the second meeting, the English teacher uses writing test to assess the cognitive aspect of the students. Finally, by knowing the result, it is better if English teachers apply the SABELS to improve the students’ achievement.
Design and development of MemoryTrail virtual reality game to study brain and memory processes in a fun and interactive manner

We designed and developed a virtual reality game, MemoryTrail VR, to learn about brain anatomy and the nervous system using a serious games design and assessment framework with a focus on purpose that was reflected in five elements: content, fiction and narrative, mechanics, aesthetics and graphics, and framing. We added a sixth element, motivation and memory, with the aim to bridge the gap between student engagement and the content being taught and, in doing so, aid in the retention of knowledge. This was executed through the introduction of knowledge interweaved in a storyline that was relatable to our undergraduate students from a local university in Singapore. The 21-item questionnaire data validated that MemoryTrail VR was a serious game designed with components which were mainly coherent and cohesive with its purpose. We found that using a structured framework to design and evaluate the game enabled us to collate meaningful feedback and identify specific areas for improvement for the next version of the game with the goal of eventually developing a serious game that is theory- and evidence-driven. There was a significant increase in the percentage of students who answered conceptual questions correctly when comparing the pre- and in-game quizzes, providing evidence of learning achieved by the students because of the gameplay.
Non-same-scale feature attention network based on BPD for medical image segmentation

The accuracy of diagnosis in medical systems requires automatic image segmentation techniques to provide accurate segmented images of lesions. Segmented images need to be more accurate not only in terms of shape size but also in terms of position. In recent years, a large number of deep learning algorithms have worked tirelessly on this goal. In the field of medical image segmentation, although the prediction images generated by traditional algorithms may not exhibit ideal performance, it is important to note that these methods still provide valuable information regarding edge features. Thus, our goal is to develop a combined approach that integrates traditional algorithms with deep learning techniques. By harnessing the rich edge feature information offered by traditional algorithms, we can enhance the accuracy of image segmentation achieved through deep learning. We propose the Non-same-scale feature attention network based on BPD for medical image segmentation (BPD-NSSFA). First, the network acquires a feature map with rich edge information through Boundary-to-Pixel Direction (BPD) and sends the feature map together with the original image into the backbone network to complete feature extraction and feature fusion. At the bottleneck layer, we use ASPP to expand the receptive field to focus on the associations between more feature information. Finally, we create a Non-same-Scale Feature Attention Block for feature fusion and supervise the fusion process using a deep supervision mechanism. To validate the effectiveness of our network, we select seven different datasets of varying sizes to test the performance of the network. From the experimental results, our network demonstrates superior performance compared to current state-of-the-art methods in lesion localization, edge processing, and noise robustness. Additionally, ablative experiments confirm the rationality of the network structure.
Cluster-Re-Supervision: Bridging the Gap Between Image-Level and Pixel-Wise Labels for Weakly Supervised Medical Image Segmentation

Weakly supervised learning, releasing deep learning from highly labor-intensive pixel-wise annotations, has gained great attention, especially for medical image segmentation. With only image-level labels, pixel-wise segmentation/localization usually is achieved based on class activation maps (CAMs) containing the most discriminative regions. One common consequence of CAM-based approaches is incomplete foreground segmentation, i.e. under-segmentation/false negatives. Meanwhile, suffering from relatively limited medical imaging data, class-irrelevant tissues can hardly be suppressed during classification, resulting in incorrect background identification, i.e. over-segmentation/false positives. The above two issues are determined by the loose-constraint nature of image-level labels penalizing on the entire image space, and thus how to develop pixel-wise constraints based on image-level labels is the key for performance improvement which is under-explored. In this paper, based on unsupervised clustering, we propose a new paradigm called cluster-re-supervision to evaluate the contribution of each pixel in CAMs to final classification and thus generate pixel-wise supervision (i.e., clustering maps) for CAMs refinement on both over- and under-segmentation reduction. Furthermore, based on self-supervised learning, an inter-modality image reconstruction module, together with random masking, is designed to complement local information in feature learning which helps stabilize clustering. Experimental results on two popular public datasets demonstrate the superior performance of the proposed weakly-supervised framework for medical image segmentation. More importantly, cluster-re-supervision is independent of specific tasks and highly extendable to other applications.
Technological Challenges in Accounting and Finance (Big Data)

The term Big Data refers to a collection of very large records, some of which are unstructured Like images and too large to be processed using basic Database management tools. Big data refers to an explosion in the quantity (and sometimes quality) of available and potentially relevant data. In this new and exciting world, sample size is no longer properly measured in "number of observations" but in, say, megabytes.
Single-case study: Effectiveness of multilayer model to improve vocabulary knowledge of deaf students

This study aims to determine the effectiveness of implementing a multilayer model in lesson plan (LP) to increase the vocabulary knowledge of deaf students. This study is single-subject research with an A-B-A design (baseline-intervention-maintenance). The intervention instrument used was a LP that consisted of four units, each containing four words taught using multilayers. The instrument was validated and scored 4.6 on a scale of 1-5. The subjects who participated in the study were two students with severe deafness. Two teachers and two observers were involved in this study. The data were analyzed by inspecting the graph, intracondition, and intercondition. The percentage of non-overlapping data (PND) from the inter-condition was used to indicate the effectiveness of the intervention. It was found that the mean value of PND B/A for all LP from subject-1 was 96.25%, and from subject-2 was 100%. Thus, the multilayer model is very effective in improving vocabulary knowledge. The mean of PND A/B was 93.75%, and subject-2 achieved 89.87%, which means that the subject's vocabulary knowledge is maintained even without intervention.