Reviewerly/exHarmony huggingface dataset:
Real-Time Emotion Recognition Using CNN and OpenCV

In the educational environment, real-time emotion detection can provide valuable information for teachers and students to understand students' levels of engagement, satisfaction, and attention during the learning process. By using CNN as an efficient and flexible model and utilizing the OpenCV open-source library in implementing the model into the system, the system becomes a very useful tool for improving teacher-student communication in integrated learning. The evaluation results show that this model has a good performance in classifying emotions in general, although there are differences in performance between the different emotion classes. Pruning was also carried out using TensorFlow Model Optimization (tfmot) to reduce model complexity and produce a more efficient model. This model can provide better insight to teachers about student response and involvement in the learning process. The results open up opportunities for further development in improving the accuracy and reliability of the model. With efficient and reliable implementation, this model can be a valuable tool in supporting the learning process in various learning environments.
Design of the Bontocani Health Center Service Application: A Proposed Method

Indonesia is a country that has 273 million people with uneven population distribution. Bontocani District is one of the areas in South Sulawesi with one health center and three auxiliary health centers, with an average expedition time of about 120 minutes to reach a health facility. In addition, road access to health facilities is also quite challenging in certain conditions, like rain. Therefore, web and Android-based applications are needed to make it easier for people to obtain health services. Clinical teleconsultation is an option to make it easier for people to get a diagnosis. This paper proposes a design of Android and web-based clinical teleconsultation applications for the Community Health centers in Indonesia. In the future, this health center information system can also be held in other health center and integrated with fellow health centers in one district and even the province to get better patient care.
Spelling Correction Using Encoder-Decoder and Damerau-Levenshtein Distance

A spell checker is a tool for detecting and correcting various spelling errors. Using memory and pattern recognition skills, humans find it easy to correct spelling errors. In contrast, for a machine to utilize these functions to perform a similar task, we need to train it to recognize patterns. One of the critical tasks of NLP has been to devise solutions for denoising and correcting misspelt words in a document and ease typing for typists by providing support infrastructure. In recent years, Long Short-term Memory (LSTM) has been utilized for similar operations because it aids the decision-making process by utilizing contextual information. To tackle this problem and to provide a new understanding of this problem, we propose a mixture model that uses an LSTM model to correct the spelling in the first go and then pass it to a probabilistic model that can then make edits to it. After getting the output from the LSTM model, which utilizes the encoder-decoder architecture, the probability model calculates the DL distance up to 2 to find the closest word in the English dictionary and replace the passed word.
A Novel Invoice Automation System

OCR has been employed since the inception of the machine learning era, but it charged a lot of effort and money to develop. OCR models with improved accuracy have been produced numerous benefits to recent advancements in machine learning and deep learning that involved models and methodologies. Receiving invoices using a software tool to read and analyse them, record the processed data in an accounts payable system, match it with purchase orders and receipts created in connection with the invoice, process the invoice, and release payments to the appropriate party. Optical character recognition (OCR) is a method for extracting text from scanned documents and images. The receipt or document must be clear and language should be readable in order to extract such data effectively and more accurately. Here the proposed model is primarily focusing on pre-processing of the receipts so that the information from these can be easily comprehensible by the model and extraction accuracy will be escalated. The techniques associated with the proposed model are OpenCV, tesseract and google OCR to compare the accuracy after proposed pre-processing methods. The accuracy of OCR extraction after enhancement of the receipts is 20-30% higher than normal OCR output.
AnomalyBAGS: Bagging-based Ensemble Learning with Time-Series-to-Image Transformations for Robust Anomaly Detection

This work presents AnomalyBAGS, a novel method for improving the robustness of anomaly detection in time series data by using bagging-based ensemble learning combined with time-series-to-image transformations. With the increase of time series data in various domains, such as finance, healthcare, and network systems, the need for efficient anomaly detection is of major importance. Traditional methods often cannot handle the dynamics and nuances of time series data, so more advanced methods need to be explored. AnomalyBAGS harnesses the potential of ensemble learning by combining the strengths of multiple models to increase accuracy and performance. The method focuses on creating custom bagging ensembles by applying unique modifications to the training dataset of each member ensuring holistic consolidation of insights from multiple models. The results highlight the central role of selecting and combining the right data transformation techniques, with certain coding methods performing particularly well on specific datasets. Finally, this research not only advances the field of anomaly detection in time series data, but also provides a valuable reference tool for practitioners seeking to select the most appropriate data transformation methods for their specific datasets.
Application of YOLO Object Detector for Horse Stress Monitoring During Transportation

The emerging field of ubiquitous computing leads to new application and processing speed requirements in various every day aspects. When it comes to image processing tasks, which require many computations, the limited resources of embedded systems make those tasks rather challenging. This paper explores an implementation of an object detection task deployed on an embedded device NVIDIA Jetson Nano GPGPU: YOLO object detector detects the nostrils of horses using an infrared camera during transportation. Results of the proposed algorithm can directly be used for calculating the breathing rate and to find out if the horse is currently under stress. We compared YOLOv5 and YOLOv8 as object detection models. In the proposed implementation on the NVIDIA Jetson Nano GPGPU the inference time is around 40ms, which fulfills real-time processing requirements for the given environment. Both YOLO versions tested show high accuracy, while the additional robustness tests indicate a slight advantage of YOLOv5 comparing to YOLOv8.
Application of Methods for Detecting the Boundaries of an Object in an Image to Search for a Pilot Line

The article considers the possibility of using computer vision for the task of detecting a pilot line on a pipe. The pilot line is applied with paint during the production process to the surface of the pipe being welded from the sheet. The line is rotated at an angle of 90 degrees (at the three o'clock position) relative to the weld and parallel to it. Thanks to it, it is possible to position the installation of local heat treatment of the weld, due to which residual stresses formed during welding are removed. The method of image classification based on the weighted average brightness of the image is considered, three methods of detecting the boundaries of an object in the image are analyzed (the threshold binarization method, the Canny Edge Detector edge detection operator and the Gabor filter), as well as an algorithm for selecting straight lines using Line Segment Detector. An assessment of the quality of work of each of the methods in solving the task was carried out. The best result was shown by the edge detection operator Canny Edge Detector. A Python program was developed using the OpenCV library, the Kivy framework and the KivyMD add-on.
Technologies for the Formation of Functional Literacy

The article describes the concept of functional literacy, provides approaches to understanding its components, and also presents a technology aimed at developing this skill using digital tools. One of the problems of school education is the formalism of knowledge and the inability of students to apply subject knowledge to solve life problems in various fields, in connection with which the issue of forming and increasing the level of functional literacy of modern schoolchildren becomes relevant. This work has a systematic and complex character and should be carried out within the framework of both classroom and extracurricular activities. The article focuses on the formation and improvement of the level of functional literacy in computer science lessons as a science characterized by interdisciplinarity, fundamentality and practice orientation. A technology for the formation of functional literacy based on B. Bloom's hierarchy of educational goals which includes six levels is proposed, the article also presents technologies and tools for the implementation of educational goals in accordance with the types of activities at each level.
UAV Path Planning in Three-Dimensional Complex Environments

In the face of the complex three-dimensional (3D) flight environment under the constraints of terrain, no-fly zone, height limit, weather and other threats, the traditional reinforcement learning unmanned aerial vehicle (UAV) path planning algorithm has the problems of unreachable target points and dimension explosion. This paper proposes a path planning method for UAV in complex 3D environment. Based on the traditional reinforcement learning Q-Learning algorithm and the principle of artificial potential field (APF) algorithm, this method modifies the reward function generation mechanism in Q-Learning algorithm. The improved Q-Learning algorithm generates a dynamic reward function by judging the action of each step combined with environmental information. The reward function combines the good performance of the artificial potential field algorithm to make the reward accumulation process smoother. Finally, the typical complex mountain area scene is selected for simulation experiments. The experimental results show that the path planning algorithm designed in this paper can carry out feasible path planning for UAV in complex 3D environment.
Implementing the Johnson Algorithm: A New Approach to ONOS Intent Path Forwarding

In this paper, we assess the ONOS-Intent Path Forwarding (ONOS-IPF) version 2, representing a pivotal progression in intent-based path discovery within network infrastructures. By substituting the Dijkstra algorithm with the Johnson algorithm, ONOS-IPFv2 pioneers an approach for pinpointing paths from a singular source to multiple endpoints, a departure from the antecedent one-to-one model. Our empirical results indicate that, though ONOS-IPFv2 doesn't eclipse its predecessor in performance metrics, it adeptly incorporates intent in packet forwarding and unveils a refined mechanism for path discovery and provisioning anchored in bandwidth considerations. Regarding end-to-end latency, ONOS-IPFv2 is congruent with its forerunner, particularly for packets exceeding 3,200 bytes, and remains on par with the default ONOS packet forwarding application. The integration of the Johnson algorithm heralds an auspicious trajectory for network strategies, enriching our comprehension and propelling advancements in the realm of networking technologies.”
Character-Based String Matching Similarity Algorithms for Madurese Spelling Correction: A Preliminary Study

The Madurese language lacks of spelling correction study. Spelling correction suggests a list of corrections for misspelled words or strings. Its process consists of two steps, i.e., generating a list of suggestion strings likely to be the expected correct string and then ranking them. Henceforth, the ones considered the best match are those on the top list. This paper compares five character-based string matching similarity metrics for Madurese spelling correction. The metrics are also known as edit distance metrics, as they work by computing the similarity between two strings based on the number of edit operations needed to transform a string to another. Experiment results show that Hamming performs the best by achieving 90% correction accuracy, followed by Damerau-Levensthein at 88%, Levensthein at 86%, Jaro-Winkler at 73%, and Jaro at 72%. However, the computation time of Hamming is longer than Levensthein and more comparable to Damerau-Levensthein. Hence, implementing those three metrics is worthwhile for further study of Madurese spelling correction.
Swimming Style Recognition for Video Sequences Using Deep Learning

Today, deep learning is pivotal in automating computer vision tasks previously handled by humans. Convolutional Neural Networks (CNNs) excel in signal, image, and video processing. This study conducted experiments to create a CNN model classifying swimming video sequences (freestyle, breaststroke, butterfly stroke). Datasets were assembled from YouTube videos adhering to specific criteria for underwater views and pool-side monitoring. The study addressed challenges like camera viewpoint and noise. A 2DCNN architecture with two convolutional layers served as the baseline model, enhanced with a third convolutional layer. It achieved remarkable accuracy (99.29%), precision (99.38%), and recall (99.37%) using an input size of 64x64 for the swimClass0 dataset. This model was selected for validation against the UCF15 benchmark, achieving 97.11% accuracy, 97.11% precision, and 97.08% recall. The results highlight the superior performance of the proposed model compared to the comparative study.
Adaptive inference procedures for the concentration parameter of a Fisher-von Mises-Langevin distribution

This paper focuses on the estimation and testing of a shape parameter or concentration parameter κ of a directional distribution named Fisher-von Mises- Langevin distribution. The estimators for κ using standard procedures cannot be obtained in closed or explicit forms. We derive asymptotic expressions up to first order for the bias, mean squared error and variance of the maximum likelihood estimator of κ when its mean direction is unknown. Using these expressions in a selective manner, several nearly-unbiased estimators for κ are proposed. A simulation study is carried out to compare the biases and percentage risk improvement of these new proposed estimators. The proposed estimators are seen to offer substantial improvements in terms of bias and percentage risk improvement over the earlier ones. The simulation study shows that these tests achieve size quite close to their nominal values and have quite good power performances. A nonparametric bootstrap confidence intervals for κ is presented. Finally these estimators and test procedures are applied on a real data set. ‘MATLAB’ functions are developed for practical use.
Automatic Text Categorization to Standard Classification of Indonesian Business Fields (KBLI) 2020

Indonesia used a Standard Classification of Indonesian Business Fields (KBLI) to classify and categorize economic activities and businesses based on their primary functions and outputs. Practically, to determine the economic activity of a worker, a questionnaire is designed using open-ended questions that generate free-form text responses, and then the enumerator manually selects the appropriate category of KBLI. In this study, we develop text classification models using machine learning and transfer learning approach to automatically assign the category of KBLI from respondents' economic activity descriptions through free-form text responses. The dataset consists of a pair of respondents' job descriptions and the category of KBLI derived from the tourism survey. Based on the evaluation, our transfer learning model from pre-trained IndoBERT trained on a large monolingual Indonesian corpus outperformed machine learning models but with slight differences.
Hierarchical CNN Automated Hierarchy Creation Using Output Prediction Analysis With Misprediction Matrix

Computer vision applications have been widely employed in critical areas. However, the classification performance of these applications has been hindered by low accuracy when dealing with small objects that possess low resolutions and are dominated by their surrounding object environment. This limitation arises from the fact that small objects are constrained by their limited pixel count, resulting in insufficient information for accurately describing the real object. Thus, the inclusion of external information, such as grouping similar classes into distinct groups, can effectively ascertain the overall context of the object class for classification purposes. Subsequently, a hierarchical-based CNN is constructed, incorporating different contextual information pertaining to various classes, thereby generating coarse-to-fine levels to enhance the classification. However, most datasets do not come with multi-level labels and manually creating them is tedious and requires expertise on the domain. In this study, we propose a discriminative detector for object context to facilitate the grouping of object classes. The analysis and evaluation of the misprediction matrix information aid in identifying the appropriate similar groups to be formed. The experimental results on the CIFAR-100 dataset containing 50,000 training images and 10,000 test images demonstrate the enhanced image classification accuracy achieved by our proposed model by almost 6% compared to the baseline model.
Designing Big Data Management Policy for Statistics Indonesia

Technological developments and urbanization are becoming a reality we must face today. Both of these phenomena bring challenges that must be faced related to data. The International Data Corporation (IDC) estimates that the volume of data worldwide will increase fifty times from 2010 to 2020. It is estimated that the volume of global storage will double every 12–18 months. Most of the data is unstructured. This situation is known as the big data phenomenon. The challenges related to big data are opportunities for statistical organizations to produce more accurate and fast official statistics. In implementing big data as one of the new data sources, Statistics Indonesia (BPS) needs to pay attention to big data management aspects because there are risks that can lead to lawsuits, decreased reputation, and loss of public trust. Traditional governance is insufficient to address the new issues big data brings. A literature study on data management and governance at BPS shows no specific research on big data management. This research seeks to answer these problems by providing solutions by designing policy artifacts. Policy is important in implementing information and communication technology and is the foundation for building all information technology services. The definition of policy used in this study is a document established by the organization's leadership board that explains what must be done and what cannot be done, must be obeyed, is binding, and contains elements of sanctions for policy violations. The methodology used in this study is the Design Science Research Methodology (DSRM). The conclusions from this study indicate that a new artifact has been produced in the form of a big data management policy that meets the criteria of completeness, ease of implementation, ease of understanding, and provides environmental benefits for BPS.
Developing Interpretive Text Analysis for Corporate Culture Measurement

Corporate culture is crucial to an organization's success, as it influences teamwork, productivity, efficiency, and customer service ability. However, there has yet to be a consensus on measuring corporate culture. Most tools used for measuring corporate culture are self-reported questionnaires, which are prone to faking. Projective evaluation methods, however, are less popular due to time-intensive administration and scoring, and the need for uniformity in interpretation. These disadvantages of projective assessment could be reduced through machine-based text analysis. We developed a text-analysis projective assessment for corporate culture using a case study of one Indonesia's State-Owned Enterprise, which has recently undergone significant organizational changes, including digital transformation. The processes of developing the tools are operationalizing the variables, creating a corpus for training the model, and evaluating the model. We found that the combination of Label Powerset and Linear SVC performs best among all combinations of multi-label classification approach and algorithm. Moreover, we discuss the possibilities of future works to improve the tool.
Deep Neural Networks for Intelligent Word Recognition

Optical character recognition (OCR) technologies are widely used to turn scanned documents into digital text. However, they still struggle to recognize handwritten writing, particularly when the document contains low-quality images or unusual fonts. Intelligent word recognition (IWR) is a more advanced form of OCR that recognizes entire words or phrases instead of character-by-character. We introduce a deep learning model for IWR in this research that can precisely extract printed and handwritten text from images. Our NMIWR model is built using a combination of CNN, LSTM and CTC layers, which can learn the characteristics of both forms of text. We train ourmodel with a custom dataset that contains a diverse range of handwritten and printed text samples. Our results reveal that our model demonstrates a remarkable accuracy of up to 97% in recognizing both forms of text.
Indo LEGO-ABSA: A Multitask Generative Aspect Based Sentiment Analysis for Indonesian Language

Aspect-based sentiment analysis is a method in natural language processing aimed at identifying and understanding sentiments related to specific aspects of an entity. Aspects are words or phrases that represent an aspect or attribute of a particular entity. Earlier studies have applied generative pretrained language model for aspect-based sentiment analysis. An example of this is the LEGO-ABSA framework, which effectively utilized these models, specifically in English-based aspect-based sentiment analysis. LEGO-ABSA uses a multitask learning and prompting approach to enhance model performance. However, the application of this approach has not been done in the context of Indonesian language. Therefore, this research aims to implement the multitask learning and prompting approach in aspect-based sentiment analysis for Indonesian language using generative pretrained language model. In this study, the Indo LEGO-ABSA model is developed, which is an aspect-based sen-timent analysis model utilizing generative pretrained language model and trained with multitask learning and prompting. Indo LEGO-ABSA is trained with a hotel domain dataset in the Indonesian language. The obtained results include an f1-score of 79.55% for the Aspect Sentiment Triplet Extraction, 86.09% for Unified Aspect-based Sentiment Analysis, 79.85% for Aspect Opinion Pair Extraction, 87.45% for Aspect Term Extraction, and 88.09% for Opinion Term Extraction. Indo LEGO-ABSA adopts the LEGO-ABSA framework that employs the T5 model, specifically mT5, by applying multitask learning to train all tasks within aspect-based sentiment analysis. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> All works can be visited in https://github.com/rdyzakya/IndoLEGO-ABSA