Reviewerly/exHarmony huggingface dataset:
Recent advances in deep learning based computer vision

Deep learning is a hot topic in current AI research and significant progress has been made in a variety of fields, including computer vision and natural language processing. Traditional architecture of deep learning normally based on convolutional neural networks. In recent years, impressive results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Thus, neural networks with Transformer architecture have been introduced to computer vision tasks and even challenge mainstream of traditional convolutional neural network. In this paper, it will review recent improvements in computer vision based on deep learning method. It'll go over some of the most important research in deep learning models. After that, it focus on advances in four key computer vision tasks: picture classification, object identification, semantic segmentation, and human posture estimation. It'll go through the most recent developments in these fields. Finally, it will provide a conclution of recent computer vision advances and discuss future possibility.
Selection of the Best Color Space for Image Steganography with the Least Significant Bit Method

Various kinds of security techniques have been developed to protect and maintain data confidentiality in order to avoid security problems, one of which is steganography techniques. Steganography techniques are used so that message protection or hiding secret messages does not raise suspicion for the attacker. The most commonly used steganographic embedding method is the least significant bit with image media. Current technological developments and advances make many different color spaces. However, it is not yet known the best color space for steganographic images in digital images. In this study, the performance and image quality of the eight best color spaces were compared, namely RGB, HSV, HSI, XYZ, LAB, YIQ, YUV, and YCbCr. The images used as experiments in this study are pictures of lena and baboons. The comparison results obtained with the evaluation criteria of image quality with eight measurement methods, namely NAE, NCC, MSE, PSNR, AD, MD, SC, and SSIM. Resulting that the image in the Hue Saturation Value (HSV) color space has better performance than other color spaces. This can be seen from the results of the test which got the best six values out of eight parameters for the flax image, namely HSV 1.36E-05, NCC 0.999997, MSE 0.0190582, PNSR 65.364, AD 0.0019302, and SC 1. While the baboon image got the five best values, namely MSE 0.0607897, PNSR 60.3265, MD 181, SC 1, and SSIM 0.999992.
Cryptocurrency Sentiment Analysis on the Twitter Platform Using Support Vector Machine (SVM) Algorithm

Cryptocurrencies have emerged in recent years and have continued to grow until they have become very popular, widespread, and surrounded by various pros and cons of their innovative developments. This study uses sentiment analysis on Twitter towards cryptocurrencies to create positive and negative trends based on comments by classifying data on Twitter. The dataset used is a Tweet related to cryptocurrency in June 2022. To get positive or negative sentiment of this research using SVM classification method with the following stages. The dataset goes through the stages of preprocessing, labeling, imbalance handling, train test split, TF-IDF weighting to transform the data from text to numeric which are then used for sentiment analysis using SVM. This research resulted in sentiment analysis based on the TextBlob library to determine positive sentiment and negative sentiment. The accuracy of the application of SVM in this study is known through the use of k-fold cross validation and three methods, namely undersampling, without imbalance handling and oversampling and three comparisons of train test splits, namely 90:10, 80:20, and 70:30. In k-fold cross validation, an accuracy of 93. 19% was obtained. While the other three methods, obtained three highest accuracy where the accuracy is in a ratio of 80:20, namely 94.64% for undersampling, 93.42% for data without imbalance handling, and 93.40% for oversampling. The best accuracy is in the data that goes through the undersampling process with a ratio of 80:20 which is of 94.64% and the sentiment analysis result is a positive sentiment.
Development of User Management in Ihya Digital Ecosystem Using Iterative Incremental Method

Information systems have an essential role in the development of technology and information in Indonesia. Information systems are employed and utilized in many facets of society and organizations. Digitizing and utilizing this information system will often form an integrated relationship between several digital sectors into an ecosystem or what is commonly called a digital ecosystem. Digital ecosystem provides benefits for users by exchanging data between systems within it since only one user account could access several systems or services in the ecosystem. The development of user management in the digital ecosystem will serve as a solution to accommodate the increasing number of users of the digital ecosystem. Iterative incremental development methods are used in the development of user management in Ihya digital ecosystem. The system was also tested using scenario tests, user acceptance tests, and load tests. Based on the results of the tests, the Ihya digital ecosystem user management module has met user needs with a User Acceptance Test value of 82.5%. Further development can be done in this research, including using APIs in the data exchange process and adding a two-factor authentication feature to improve data security.
Comparison of Bagging and Boosting in Imbalanced Multilabel of Al-Quran Dataset

This paper address multi-label text classification in the case of Al-Quran in English translation. This research built from two models which are Bagging and Boosting in comparison. Those models are chosen because previous research gives a good performance, especially in the imbalanced dataset. Imbalanced data is one problem in classification and this research focuses on that problem. This research started by preprocessing data, then TF-IDF is used for feature extraction. To measure the prediction of classification, this research uses the Hamming Loss to see the performance. Based the several experiments, the best performance result is obtained by Random Forest with a Hamming Loss value of 0.1019. Then, For the fastest computational time is given by Extreme Gradient Boosting (XGBoost) with 224.76 seconds for running the system. Thus, Bagging and Boosting have their advantage in classification.
Application of Artificial Neural Network in Teaching Quality Evaluation

Nowadays, Teaching quality evaluation are facing challenges. Artificial Neural Network is conductive to developing a new system of evaluation. Facial recognition helps to record attendance in class; emotion analysis and behavior analysis contribute to get real-time feedback of students' emotion, engagement and activity in class.
Evaluation of Sampling Algorithms for a Pairwise Subjective Assessment Methodology

Subjective assessment tests are often employed to evaluate image processing systems, notably image and video compression, super-resolution among others and have been used as an indisputable way to provide evidence of the performance of an algorithm or system. While several methodologies can be used in a subjective quality assessment test, pairwise comparison tests are nowadays attracting a lot of attention due to their accuracy and simplicity. However, the number of comparisons in a pairwise comparison test increases quadratically with the number of stimuli and thus often leads to very long tests, which is impractical for many cases. However, not all the pairs contribute equally to the final score and thus, it is possible to reduce the number of comparisons without degrading the final accuracy. To do so, pairwise sampling methods are often used to select the pairs which provide more information about the quality of each stimuli. In this paper, a reliable and much-needed evaluation procedure is proposed and used for already available methods in the literature, especially considering the case of subjective evaluation of image and video codecs. The results indicate that an appropriate selection of the pairs allows to achieve very reliable scores while requiring the comparison of a much lower number of pairs.
Optimization of Hijaiyah Letter Handwriting Recognition Model Based on Deep Learning

Hijaiyah handwriting recognition is a challenging research topic. There have been many works and research on character recognition from various languages, but the accuracy value is still being done to improve. Meanwhile, the dataset of handwritten characters with hijaiyah letters is still limited. This study proposes a convolution neural network to recognize and classify hijaiyah writing. The datasets used in this study were Hijja and AHCD. In enhancing the advanced model that has been done previously, we propose the addition of the Adam optimization. In addition, in this study, we have processed both Hijja and AHCD datasets with a composition of 60:20:20. This sophisticated model can improve and be better than the previous model with 91% accuracy results on the Hijja dataset and 98% accuracy on the AHCD dataset. Future work of this work can be made into an application so that the results model that has been built can be used in mobile-based applications.
NSCT-DWT-SVD Composite Digital Watermarking Algorithm based on QR Code

In order to enhance the security of the watermark and to ensure the stability of the algorithm against attacks and the concealment of the watermark, a NSCT-DWT-SVD composite digital watermarking algorithm based on QR code is proposed. First, QR code encoding is conducted on the original watermark image to generate a QR code watermark image. In order to ensure the security of the watermark information, Arnold scrambling and encryption is performed on the QR code watermark information. Second, the original Lena image is carried out NSCT transform. The low frequency part that stores more information are selected for DWT. Then the low-frequency images with strong image resistance are performed SVD. The encrypted watermark information are embedded into the decomposed singular values. Through the experimental verification of the algorithm, the algorithm has strong concealment and strong resistance to conventional attacks on the basis of ensuring the security of watermarking. Especially for the rotation attack and JPEG compression attack, the NC values are above 0.92 and 1, respectively.
Clustering of COVID-19 Vaccination Recipients in DKI Jakarta Using The K-Medoids Algorithm

The COVID-19 pandemic has become a serious problem that has attacked various aspects of life such as social, economic, religious, and others. The government has held a COVID-19 vaccination program as an effort to deal with the COVID-19 problem since January 13, 2021. Many problems occurred due to difficulties in dividing the vaccination recipient areas. This is due to the large number of regions with different conditions for each region. One of the efforts to assist the process of processing large vaccination data is data mining techniques and using the clustering method with the K-medoids algorithm. In this study, data on COVID-19 vaccination was grouped in the East Jakarta area using the K-medoids algorithm clustering method. The calculation is carried out using the Euclidean Distance equation and the value of S > 0. The grouped area categories are at the kelurahan level which will then be divided into several clusters. The clustering process was carried out with RapidMiner on 267 kelurahan data on four main attributes, namely the number of targets, the number of vaccine doses 1, the number of vaccine doses 2, and the number of vaccine doses 3. The clustering process was carried out in 6 simulations with variations of k medoids as much as 2 to 7. The results of clustering show the best number of clusters obtained inthe simulation is cluster 6 with the smallest Davies Bouldin Index (DBI) value of 0.209.The clusters obtained are clusters 0 to cluster 5.The cluster that is prioritized in giving vaccinations is cluster 2 with 67 items because its members are areas in DKI Jakarta and give a high score in cases of COVID-19 compared to other clusters.
Hand Gesture-based Control of Autonomous UAV for Mobile Disinfectant Dispenser

Autonomous unmanned aerial vehicles (UAVs) have witnessed a rapid increase in their utilization in various applications and will continue to do so in the coming decades. These UAVs, also known as drones, are designed to either assist humans or perform tasks that involve people. Drones of today have grown to be faster and less expensive by integrating several technologies, supported by hybrid algorithms, and perform various tedious, challenging, filthy and hazardous tasks. The deployment of machine learning and other AI-based algorithms enhances drones’ autonomous and vision capabilities. Today, part of an effort to curtail the spread of COVID-19, this research has designed, developed and built a mobile disinfectant dispenser based on autonomous quadrotor UAV. It is a “flying dispenser”, able to detect a person’s hand gestures from afar, based on machine learning (ML), to fly and maneuver towards the person and finally spray disinfectant on his/her hand. In order to identify various hand motions for maneuvering, this research studies and improves the ML algorithms and carries out various experiments to improve the drones’ response time and maneuvering performance, for the final objective of taking precautions to protect humans from Covid-19.
Construction of digital twin model of other-excited DC motor and application of simulation experiments

For the study of digital twin models, this paper analyzes the electromagnetic characteristics, mechanical characteristics, thermodynamic characteristics, and mutual coupling relationship of the other-excited DC motor, and constructs a mathematical model of ”electric-mechanical-thermal” integration suitable for digital twin technology. The model is built under the experimental data parameters and experimental information of the solid DC motor, which increases the generality, accuracy and timeliness of the model. The digital twin simulation system is built and used in simulation experiments on high-speed motor accidents using 3dsMax, Unity3D, and C#. The results show that the digital twin model is reasonably constructed, and the operation is stable and reliable in the built digital twin system, which can well map the various experimental data conditions of the real DC motor, and the simulation experiment is well applied, also solve the barrier that the real experiment can’t conduct experimental operation on the high-speed motor accident, the experiment can be conducted anytime and anywhere, which is conducive to improving the efficiency, safety and accuracy of engineering teaching and saving resources.
A Length Adaptive Memory Management Framework in High Speed Acquisition System

In the age of information explosion, the number of network packets generated every day is monotonically increasing. In order to analyze valuable data and improve the storage performance of traffic acquisition systems, a high-performance data processing system architecture is needed. In this paper, we propose designing a high-speed data acquisition system based on data characteristics and comparing the features and capabilities of standard memory management methods. In addition, we propose a length-adaptive memory management framework based on a hot-cold domain structure. We conclude that this system architecture is experimentally proven to have reasonable practicality and usability.
Effective Visualization and Analysis of Recommender Systems

Recommender system exists everywhere in the business world. From Goodreads to TikTok, customers of internet products become more addicted to the products thanks to the technology. Industrial practitioners focus on increasing the technical accuracy of recommender systems while at same time balancing other factors such as diversity and serendipity. In spite of the length of the research and development history of recommender systems, there has been little discussion on how to take advantage of visualization techniques to facilitate the algorithmic design of the technology. In this paper, we use a series of data analysis and visualization techniques such as Takens Embedding, Determinantal Point Process and Social Network Analysis to help people develop effective recommender systems by predicting intermediate computational cost and output performance. Our work is pioneering in the field, as to our limited knowledge, there have been few publications (if any) on visualization of recommender systems.
Image Segmentation for Hexagonally Sampled Images Using Statistical Region Merging

The statistical region merging (SRM) method for image segmentation is based on some solid probabilistic and statistical principles. It produces good segmentation results, and is efficient in term of the computational time. The original SRM algorithm is for Cartesian images sampled by square lattices (sqL). Because hexagonal lattices (hexL) have the advantage that each lattice point in a hexL has six equidistant adjacent lattice points, in this paper, we perform image segmentation for hexagonally sampled images using SRM. We first convert the SRM algorithm from sqLs to hexLs. Then we use some test images to compare the corresponding segmentation effect for hexLs versus sqLs. The experimental results have shown that a hexL exhibits evidently better image segmentation effect than the corresponding sqL (with the same spatial sampling rate as the hexL) using the usual 4-connectivity. Finally, we point out that CT image segmentation may benefit from using hexLs since they provide better image reconstruction effect than sqLs.
Cooperative Positioning of Single Target Based on TDOA Using UAV Cluster

Passive localization based on Time Difference Of Arrival (TDOA) has attracted more attention because of its simple equipment and high positioning accuracy. UAV cluster with high mobility and security has repeatedly appeared in modern battlefields and plays an important role in passive positioning. This paper focuses on the localization solution algorithms of single emitter target by UAV cluster based on TDOA. Firstly, some improved methods of Chan algorithm and Taylor algorithm are simulated in the presence of sensor locating errors. Then a new method based on particle swarm optimization and Taylor cooperation (Taylor-PSO-Mod) is proposed, whose positioning accuracy is close to the Cramer-Rao Lower Bound (CRLB), and it solves the initial value problem of Taylor algorithm.
Concept of a Fuzzy Expert System for Story Point Estimations in Agile Projects

Estimating efforts in software projects has always been a challenging task. Great uncertainty in early phases of projects create a need for sound methods. Yet, complex mathematical models tend to be less popular amongst practitioners, making expert-based methods the most commonly used form of estimation methods. As personnel capacity and expert knowledge are known to be expensive, the need for alternative solutions, which offer reliable results whilst lowering costs, arises. Thus, this paper proposes a concept, based on fuzzy logic and agile methods incorporated into an expert system, with the goal of handling uncertainty, increasing acceptance and aiding young professionals.
The Impact of Interactive technology by using Artificial Intelligence on the Formation of Social Interactions: A Study in Tourism Industry 4.0

Artificial intelligence and interactive technology can be effective in shaping social interaction between people by identifying them as users, and consequently lead to a positive personalization in the industry. The purpose of this article is to investigate the impact of interactive and intelligent technologies to create personalization and to design a different collective experience in the tourism industry. The interactive technology in this research interacts with the user in the form of an intelligent system embedded in the body of a building. In this research, firstly, the formation of cognition and experience in humans has been investigated, as a means to effectively interact between computers and humans. Findings show that human perceptions are divided into linguistic and non-linguistic types, and sensory perceptions are part of these non-linguistic cognition. This positive emotional perception also has a positive effect on a person's mood and social interactions. By examining the moods and feelings of users through faces and analyzing them based on the intelligent building system, this study defines new methods for adapting the space to users' emotions and the impact of these changes on users' social interactions with each other.
Intelligent Substation Switch Traffic Scheduling Model Based on Time-Sensitive Network

In order to solve the contradiction between data traffic growth and real-time control information in intelligent substation, this paper proposes a time-sensitive network (TSN)based intelligent substation switch traffic scheduling model, which uses new technologies to dispatch data link layer traffic. This technology adopts the idea of monopolizing and sharing the time slot window for traffic transmission time, using the mechanism of frame preemption in the traffic transmission channel, realizing traffic classification according to the traffic characteristics, and is used to improve the traffic scheduling mechanism of the process layer of the intelligent substation on the traditional switch, improve the effect of determining the time-sensitive traffic delay scheduling, provide traffic transmission services with low latency jitter for the Ethernet network of the intelligent substation, and put forward a new idea for the improvement of the service quality (QoS) of the power system.
Forward Kinematic Based Approach Using Sensor Fusion for Tracking the Human Limb

A vision-inertial sensor fusion algorithm that utilises a vision system to calibrate a forward kinematic model of the arm, mounted with Inertial Measurement Units (IMU) is proposed. This model utilises the data from both IMU and vision system to track the position and orientation of the arm. The algorithm was initially tested for drift error whilst working only with the IMU. Further tests are carried out to verify the improvement using the new algorithm. The results are compared with body tracking data obtained from the vision system and a baseline measurement of points in the real world. The results show that the proposed algorithm improves the overall accuracy of the system whilst operating at a higher frequency matching the output of the IMU sensors.