Reviewerly/exHarmony huggingface dataset:
Multiple Target Track-Before-Detect Using Sequential ML-PDA

The maximum likelihood probabilistic data association (ML-PDA) algorithm is the optimal detection algorithm in the maximum likelihood sense, but it has a key limitation that it is restricted to the single-target case. In this paper, we propose another ML-PDA algorithm for multi-target scenarios, called the Sequential ML-PDA (SML-PDA) algorithm. The SML-PDA algorithm can achieve multi-target detection and tracking without knowing the number of targets in advance. The simulation results prove the rationality of the SML-PDA algorithm. And the SML-PDA algorithm is applied to the measured data to validate its performance in real scenarios.
Face Mask Detection Alert System for COVID Prevention Using Deep Learning

Machine learning and deep learning are aspects of artificial intelligence (AI) that help us to make the applications more accurate at predicting some real-life phenomenon without being specifically programmed or designed to do so. In this book chapter, we have discussed the COVID-19 virus, the importance of using face masks, and the existing models of face mask detection. The book chapter is analyzing the current model of sending an alert when a person is detected not wearing a mask and also comments on the improvements that can be done in the model. With the use of a ‘convolutional neural network,’ we are making this model more efficient. We are attempting to briefly explain the model of face mask detection using various python libraries and deep learning efficiently.
Breast Cancer Early Detection Comparison with Deep Learning and Machine Learning Models: A Case of Study

Breast cancer is one of the most widespread in the female population, being able to predict its developments and capturing the inputs of the onset of the disease is one of the main objectives that science is pursuing. Clinical Decision Support Systems (CDSS) in recent decades are extensively using these technological tools, such as Machine Learning (ML) and Deep Learning (DL). In this paper, two of the main methods of these subset of AI are compared: an ensemble-type algorithm, XGBoost (or Extreme Gradient Boosting) and a deep neural network (DNN) are applied to the data of a study conducted on an Indonesian population. The results obtained are very interesting as despite being tabular, binary categorical and multiclass data, the DNN model achieves performance and results much higher than the well-known XGB used in literature for data of this type.
Improving Naturalness in Transparent Augmented Reality with Image Gamma and Black Level

In this study, we used a custom-built Optical See-Through Augmented Reality (OST AR) system to conduct a psychophysical experiment to determine the preferred gamma and black level for high naturalness perception in OST-AR. We utilized 6 different fruit stimuli and 11 different backgrounds to do this experiment. We used two-way ANOVA to analyze the data and concluded that only the effect of different fruits on virtual objects&#x2019; gamma preference for high naturalness is considered statistically significant. Surprisingly, all ANOVA analyses indicate background&#x2019;s color does not contribute to observers&#x2019; gamma or black level preference for naturalness. We found that gamma preference has a strong correlation with the average lightness of the virtual stimuli. There is no clear correlation between chroma, hue, and gamma preference in terms of naturalness perception. This finding suggests that the background can be ignored in future imaging pipelines emphasizing high naturalness appearance in Augmented Reality.
Digital Restoration of Lost Art: Applying the Colorization Transformer to the Ghent Altarpiece Panels

In recent years, image processing has proven to be a great tool to help document, preserve, and restore art pieces, especially visual art. One example is using colorization techniques when the original color information of the image has been lost or is unavailable. To expand on that, we used Ghent Altarpiece as the study case. One of the panels of this polyptych has been lost, and only an archival photograph exists. Using the state-of-the-art colorization method, we want to digitally restore what has been lost in its original form. In this work, we proposed a pipeline that consists of a colorization transformer (ColTran) trained on the captured patch-based imaging data of the Ghent Altarpiece. We evaluated the proposed pipeline and addressed its strengths and weaknesses. Moreover, we presented planned future steps and improvements for this project.
A Mitigation Method for Optical-Turbulence-Induced Errors and Optimal Target Design in Vision-Based Displacement Measurement

Computer vision-based displacement measurement techniques are increasingly used for structural health monitoring. However, the vision sensors employed are easily affected by optical turbulence when capturing images of the structure, resulting in displacement measurement errors that significantly reduce the accuracy required in engineering applications. Hence, this paper develops a multi-measurement point method to address this problem by mitigating optical-turbulence errors with spatial randomness. Then, the effectiveness of the proposed method in mitigating optical-turbulence errors is verified by static target experiments, in which the RMSE correction rate can reach up to 82%. Meanwhile, the effects of target size and the number of measurement points on the proposed method are evaluated, and the optimal target design criteria are proposed to improve our method’s performance in mitigating optical-turbulence errors under different measurement conditions. Additionally, extensive dynamic target experiments reveal that the proposed method achieves an RMSE correction rate of 69% after mitigating the optical-turbulence error. The experimental results demonstrate that the proposed method improves the visual displacement measurement accuracy and retains the detailed information of the displacement measurement results.
PERANCANGAN SISTEM INFORMASI E-MARKETPLACE EVENT ONLINE UNTUK MENINGKATKAN PEMASARAN DIGITAL BERBASIS MOBILE

An event is an activity carried out to celebrate a certain moment, whether it is held in public or private. In recent years, Events have been used as a business object and already have a sizeable market. Online events are expected to continue to be a trend that will develop in the future. However, on the other hand, there are still problems faced by both the organizers and the general public, namely difficulties in disseminating and obtaining information related to an event and coordination of ticket sales that has not been maximized. The purpose of this research is to provide an information system solution for organizing an online event in the form of designing a marketplace information system that can be used as a forum for all events spread across various platforms to be centralized into one platform. The information system design method used is the waterfall method with the concept of gamification to maintain and increase user loyalty. The results of this study are in the form of an information system design that can handle event sales and purchase transactions in which users and event organizers can be connected to each other.
An Enhanced Deep Learning Technique to Measure the Impact of Cryptocurrency on the World Payment system using Random Forest

Cryptocurrency is a technology that uses an encrypted peer-to-peer network to facilitate digital barter. Bitcoin, the first and most popular cryptocurrency, is paving the way as a disruptive technology to long-standing and unchanging financial payment systems. While cryptocurrencies are unlikely to replace traditional fiat currency, they have the potential to alter how Internet-connected global markets interact with one another, removing the restrictions that exist around traditional national currencies and exchange rates. Technology advances at a breakneck pace, and a technology's success is almost entirely determined by the market it tries to improve. Cryptocurrencies have the potential to change digital trade marketplaces by enabling a fee-free trading mechanism. A SWOT analysis of Bitcoin is offered, which highlights some of the recent events and movements that may have an impact on whether Bitcoin contributes to a paradigm change in economics. Cryptocurrency is a relatively new payment option, and users are naturally drawn to it because it offers privacy. To measure the impact of cryptocurrency on the world payment system, we use a Cryptocurrency extra data – Bitcoin. The proposed algorithm uses Random Forest Algorithm for prediction. The RFPA has achieved a 0.073 MSE. The RFPA has achieved the best results as it can handle huge datasets with a lot of dimensionality. It improves the model's accuracy and eliminates the problem of overfitting. When compared to other algorithms, it takes less time to train.
Autoencoder Feature Based Kinship Verification using Machine Learning Classifiers

Verification of kinship in an automatic way is a new challenge in vision on the computer that intends to determine if two given individuals have any kinship relationship based on their facial features. Verification of Kinship by facial pictures is difficult since face images have a lot of intra-class variations due to age, genetic and gender difference. Videos can provide more information as they have additional sources of information as compared to single image verification. In this paper, deep learning based Supervised Mixed Norm regularization Autoencoder (SMNAE) is suggested for verification of kinship in unconfined videos. This proposed SMNAE based approach employs the spatial information to test kinship in the video frames. The Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) classifiers are used to Classify the decision borderline among kin and non-kin classes. The efficacy of this proposed approach is evaluated on seven kin relationships using a kinship video (KIVI) face dataset of kin pairs. According to experimental results SMNAE spatial features with DT classifier produce 70.14% of f1score, which performs much better than other classifiers. It gives improvement in f1 score of +24.52%, +22.05% than SVM and RF classifiers respectively. Furthermore, when compared to existing systems, the proposed SMNAE spatial features with DT framework performs exceptionally well in kinship verification.
Video-based Salient Object Detection: A Survey

Salient object detection aims to detect most important regions from the natural visual scenes. Saliency detection task has attracted great attention of computer vision researchers in static visual scenes due to its significant applicability and challenging problem. Recently, salient object detection has been explored in understanding and modelling significant regions detection over dynamic scenes. This salient object detection is referred to as video-based salient object detection. In this direction, several salient object detection models have been proposed in the literature which are focused on characteristics of real dynamic scenes. Video-based salient object detection model finds the motion-related salient regions in dynamic scenes. These approaches take the motion feature and spatiotemporal constraint jointly from video sequences for saliency detection. Toward this end, this study introduces a review on video-based salient object detection. This review mainly focuses on recent progress in salient object detection in dynamic scenes. In this study, we provide a comprehensive review that presents various most challenging scenarios of salient object detection approaches in dynamic scenes and summarizes the more challenging problems of the existing methods. Further, we discuss the issues in video-based saliency detection and provide future research directions. Moreover, the study and evaluation of various benchmark video saliency detection datasets and evaluation measures are briefly presented.
A Survey on Consensus Algorithms in Blockchain Based on Post Quantum Cryptosystems

Blockchain is a distributed, decentralized ledger mechanism with a various features. It is used in diverse applications nowadays, from financial sector to password management. It does not have any central tool to validate and verify the transactions, yet every transaction in the Blockchain is secured and verified. This is achieved because of a consensus algorithm in the network. Latest study reports show that emergence of quantum computing will impact the overall safety of Blockchain and its internal working. Attacks using Grover's and Shor's algorithm can threaten the hashing and public key schemes in cryptography. It will force a complete redesign of the Blockchain systems to withstand quantum attacks. So there is a need to develop quantum-safe Blockchain systems. Consensus mechanisms like Proof of Work (PoW) and Signing algorithms like Elliptic Curve Cryptography used in Blockchain can be modified to overcome quantum attacks. Here a comparative study and analysis of different consensus algorithms developed using post-quantum cryptosystems are done, which can be implemented in Blockchain systems to make them quantum safe.
The Impact of Social Media Usage on the Organisation’s Reputation Risk through its Cybersecurity

The utilisation of social media among organisations is growing tremendously and is seen to be moving forward in line with the rapidly evolving technology in Malaysia.It is inevitable that social media not only provides benefits to organisations but also exposes organisations to various risks.Local organisations often get public attention when an issue related to the organisation goes viral on social media, exposing them to reputation risk and eventually affecting their reputation.Changes in the reputation of organisations affect relationships with their stakeholders.Most organisations today are aware of the importance of using social media.Despite the beneficial usage of social media, organisations encounter social media risks that require attention and must be catered to wisely.Due to the vulnerability of social media, there is a high possibility of being attacked, which indicates the need for effective cybersecurity in organisations.The aim of this conceptual paper is to discuss the relationship between social media usage and organisation's reputation risk, in which organisation's cybersecurity is predictably act as a mediator in the relationship between the variables.
Genetic Peculiarities, Gold-Containing Volcanogenic Deposits of the Non-Ferrous Metals of the Paleoisland-Arc Structures of the Lesser Caucasus and East Pontides according to Gas-Fluid Inclusions Data and Isotopic Investigations

The geological settings of deposits exploration in Turkey, Azerbaijan, Armenia and Georgia have been determined in the article.Most of them can be related to the Kuroko type and they are differed by ore accumulation nature: The copper-zinc deposits formed apparently in the conditions of the deep sea basin are known in Turkey but to the east -we have only epigenetic deposits examples.Moreover Madneuli deposit is developed in Bolnisi region.It is an example of polyformational deposit and therefore is unique.The barite deposits, barite-polymetallic veins, gold-bearing secondary quartzites and large-scale stockworks of copper ores are concentrated in a confined space (known as biclinal structure on large volcano slope).Gadabey and Alaverdi ore districts are of interest because there are copper, copper-zinc and barite-sulphide ores in the Jurassic volcanic depressions but copper-porphyry deposits -Garadag and Tekhut are known in upstanding wedgeshaped blocks limiting volcanic depressions.All deposits of Gadabey and Alaverdi ore districts including copper-porphyry are commercially important.The solutions from which the sulphides deposited were similar to seawater according to salinity and were weakly acidic; copper-containing sulphides were formed at maximum 410-390 °C temperatures but barite-sulphide ones -at ~ 280 °C.In all probability the fluid pressure didn't exceed 200 bar in the epigenetic deposits.The stable accumulation of hydrothermal-sedimentary deposits occurred at the bottom of the sea which depth reached 2-3 km.Apparently the fluid boiling didn't occur and mineral zonation in deposits being similar to modern "ore hills" can be explained by redistribution of the ore-forming components as a result of "hills" destruction, their diffusion from the lower levels up to the upper ones in the process of ores "washout" by ore-bearing fluids.
Entangled Photon Generation from a Three-Level Laser with a Parametric Amplifier and Coupled to a Thermal Reservoir

In this article the investigation of squeezing and statistical properties of light resulting by a non-degenerate three-level laser with the parametric amplifier and coupled to a thermal reservoir have been occurred. With the aid of master equation, stochastic differential equations were obtained. Applying solutions of resulting differential equations, quadrature variance, the mean and variance of photon number, the photon number correlation are calculated. However, the two-mode driving light has no effect on the squeezing properties of the cavity modes. On the other hand, parametric amplifier and thermal reservoir increase the mean and variance of photon number. Furthermore, employing the same solutions, we also obtain anti normally ordered characteristic function defined in the Heisenberg picture. For a linear gain coefficient of (A = 100), for a cavity damping constant of K= 0:8, µ = 0 and for thermal reservoir th = 0, the maximum intra cavity photon entanglement is found at steady state and at threshold to be 60%.
A Gated Multiscale Multitask Learning Model Using Time-Frequency Representation for Health Assessment and Remaining Useful Life Prediction

Health assessment and remaining useful life prediction are usually seen as separate tasks in industrial systems. Some multitask models use common features to handle these tasks synchronously, but they lack the usage of the representation in different scales and time-frequency domain. A lack of balance also exists among these scales. Therefore, a gated multiscale multitask learning model known as GMM-Net is proposed in this paper. By using the time-frequency representation, GMM-Net can obtain features of different scales via different kernels and compose the features by a gating network. A detailed loss function whose weight can be searched in a smaller scale is designed. The model is tested with different weights in the total loss function, and an optimal weight is found. Using this optimal weight, it is observed that the proposed method converges to a smaller loss and has a smaller model size than long short-term memory (LSTM) and gated recurrent unit (GRU) with less training time. The experiment results demonstrate the effectiveness of the proposed method.
SYNTACTICAL ERRORS OF NARRTIVE ESSAY MADE BY EFL STUDENTS LOW ACHIEVEMENTS

This research aims to find syntactic errors and factors of errors made in the essay narrative in semester V. The method used in this study is qualitative, and the approach used in this research is descriptive research. Data was obtained through documentation and analyzed using Surface Strategy Taxonomy. This research took data based on the category of grades B and C. The results showed that there were four types of errors made by students in descriptive writing. The total errors were 41 errors which were divided into 42% error misinformation, error addition of 35%, error addition 19%%, and misconception misordering as much as 4%. Based on data analysis, a standard error made by students is misformation. In the results of the error factor, the researcher found the most errors in the intralingual error factor.Students are still influenced by their habits in making sentences using their mother tongue (Indonesian). Students make errors in writing, especially in the use of tenses. Most of the students wrote incomplete and ungrammatical sentences. The teacher should find the best method based on the condition of the students to teach English, especially in teaching tenses. Since tenses and grammar are essential for language learning, they should be taught cohesively.
Application of Internet of Things Technology in Power Security Control

People's work, study and daily life are closely related to electricity, which puts forward higher requirements for power safety control. How to ensure power safety has also become the focus of social attention. The use of Internet of things technology in power security can improve work efficiency, reduce resource waste and avoid safety accidents. Starting from the importance of power security management and control, this paper explores the application of Internet of things technology in power security management and control under the background of big data, and investigates the impact of the use of Internet of things technology on power security management and control.
Group-based Sparse Coding with Adaptive Dictionary Learning for Image Denoising

Group sparse coding (GSC) is a powerful mechanism that has achieved great success in many low-level vision tasks, showing great potential in image denoising. Traditional group sparse coding generally uses overcomplete dictionaries and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{1}$</tex> -norm to regularize sparse coefficients. But this is only an estimate of the solution, which cannot obtain a sparse solution and has a high computational cost. In this paper, we use a GSC framework with adaptive dictionary learning for image denoising. In order to improve the accuracy of obtaining sparse coefficients, the dictionary used in this paper is learned from the input image, which can be obtained by applying SVD once for each patch group. Then use ADMM algorithm to solve the objective function. Experimental results show that the PSNR value of our approach not only is competitive with many advanced image denoising methods but also achieves better visual effects.
Design and Implementation of English Online Translation Cloud Platform

Nowadays, the globalized economic growth has become a general trend, and the society's demand for the translation industry is constantly increasing. The emergence of the English online translation platform has surpassed the limitations of the traditional translation model, and the efficiency and quality have been simultaneously improved. This paper aims to study the design and implementation of the English online translation cloud platform. On the basis of analyzing the cloud computing system, the performance requirements of the system and the server-side operating environment, this English online translation cloud platform is designed, and the performance of the platform is analyzed. The test results show that this platform can effectively meet the requirements of features and functions, the page design is simple, and the modules are designed according to the characteristics of each module, which basically meets the expected performance requirements.