Reviewerly/exHarmony huggingface dataset:
Machine Learning, Neural Networks and Econometric Models for Prediction the Realized Volatility of Bitcoin and E-Mini S&amp;P500

The article compares ARMA, boosting, neural network models, GARCH and HAR models, and proposes a new efficient method for forecasting one day ahead of the realized volatility of financial series. The GARCH and HAR models are taken as comparable models, which showed the best results in Manevich etÂ al. (2023). In addition, the transfer learning phenomenon for boosting and neural network models is investigated. Bitcoin and E-mini S&P 500 are chosen as examples. Realized volatility is calculated from intraday (intraday - 24 hours) data. The calculation is based on the closing values of the internal five-minute intervals. All days of the week are almost equally present among the days selected for forecasting. The comparison is made on the observation interval from 01.01.2018 to 29.12.2021. Since there could be (and have been) structural changes in the markets during this interval, the models are evaluated in sliding windows of 399 days length. For each time series, we compare three-parameter enumeration boostings, about 10 different neural network architectures, ARMA models, a newly designed method, and different transfer learning and training sample expansion options. It is shown that GARCH and HAR models are inferior to other listed methods and models. CNN architecture is the most suitable for financial time series forecasting. The proposed method shows the results comparable to neural networks and boostings in precision. Transfer learning shows improvement in some cases, especially for neural networks, and needs to be studied in more detail. ARMA models outperform other listed methods in some cases. The smallest relative errors for the Bitcoin and E-mini S&P 500 realized volatility predictions are 25.28% and 30.74%, respectively.
Assisted Sign Language Tutoring System Using a Bioinspired Virtual Controlled Robotic Arm

The central aim of this study was to develop a virtual robotic trainer platform for teaching Mexican Sign Language (MSL) to deaf and non-deaf people. The MSL-Virtual Robotic Trainer (MSL-VRT) design was inspired by a human upper limb (hand, forearm, and wrist). It has 25 degrees of freedom (DOF) to perform finger-spelling/dactilology of words considering one-handed static signs (21 signs) of the MSL alphabet. The design of MSL-VRT uses Solidworks TM, and the assembly was then exported to SimulinkTM/SimscapeTM included in Matlab for numerical evaluation of the control action that regulates the MSL-VRT motion. The controller design uses adaptive gains proportional-integral-derivative (PID) forms with the estimation of the derivatives for the tracking errors provided by the supertwisting algorithm (STA). The gains of these controllers were adjusted to attain the designed reference trajectories composed of a superposition of sigmoid functions. The gains are adjusted in an adaptive way to enforce the convergence of the tracking errors to provide an accurate reproduction of the signs in the MSL-VRT. A numerical evaluation confirms the performance of the tutoring device in reproducing the sequence of characters obtained from texts. A comparison with the traditional non-adaptive PID was included to verify that the adaptive form performs better in the tracking trajectory task leading to a better tutoring outcome.
Gender Inequality and the Marketisation Hypothesis: Some Evidence from Sub-Saharan Africa

The marketisation hypothesis states that growth in services sector reduces gender inequality. In recent years, the African service sector has grown considerably, yet we still observe relatively high gender inequality on the continent. Using a new dataset on gender inequality and panel data analysis, we study the effect of service sector shares on gender inequality in 31 sub-Saharan African countries during the 1990-2014 period. Consistent with the predictions of the marketisation hypothesis, we find a negative correlation. Further analysis shows the channels through which the marketisation process is effective in reducing gender inequality in SSA. We observe a non-linear association between the marketisation process and female labour force participation. The findings imply that SSA is still in the early stages of transitioning from agriculture to services sector hence some "female marginalisation" exists within the formal labour market.
Assessing Earthquake Early Warning Performance Based on Operational Aspects

Earthquake early warning (EEW) technology was developed to rapidly alert the public of earthquake risks after P-wave detection and before the onset of strong tremors. This technology estimates earthquake location, magnitude, and expected intensity based on initial P-wave information. The EEW system applies various theoretical and empirical technologies, such as earth science, geology, engineering, and communication. Moreover, we should be verified for the modified system to whether well working, when revising a setting or criteria by the policy. However, there is no standard for verification of EEW system, and each reviewer may give a different evaluation. Therefore, A standard of review is essential for the stable operation of integrated EEW systems. The present study proposes an assessment method from the perspective of the system operator. The proposed model enables uniform evaluation, regardless of changes in the method or technology of the EEW system.
Services interfaces for interoperability of signaling computer interlocking on borders

&lt;p&gt;Technological developments in the field of railway signaling have allowed more and more flexibility in the management of rail traffic, especially with computer interlocking. However, differences in signaling principles from one country to another as well as differences in the structure of interlocking software and communication protocols depending on suppliers lead to interoperability difficulties at the borders between computer interlocking. Some deployed projects deal with interoperability issues regarding the communication of signaling information between the train and interlocking like the European rail traffic management system (ERTMS) project. Unfortunately, the interoperability between interlocking themselves is still not achieved. Some deployed projects deal with interoperability issues regarding the communication of signaling information between the train and interlocking like the ERTMS project. Unfortunately, the interoperability between interlocking themselves is still not achieved. This article draws up a proposed model for interfacing at the interlocking boundaries based on service-oriented architecture (SOA). In addition, to ensure the coupling of SOA services to the internal functions of the signaling computer interlocking, a distributed architecture of programmable logic controller according to the IEC 61499 standard is suggested.&lt;/p&gt;
Multimodal video abstraction into a static document using deep learning

&lt;p&gt;&lt;span lang="EN-US"&gt;Abstraction is a strategy that gives the essential points of a document in a short period of time. The video abstraction approach proposed in this research is based on multi-modal video data, which comprises both audio and visual data. Segmenting the input video into scenes and obtaining a textual and visual summary for each scene are the major video abstraction procedures to summarize the video events into a static document. To recognize the shot and scene boundary from a video sequence, a hybrid features method was employed, which improves detection shot performance by selecting strong and flexible features. The most informative keyframes from each scene are then incorporated into the visual summary. A hybrid deep learning model was used for abstractive text summarization. The BBC archive provided the testing videos, which comprised BBC Learning English and BBC News. In addition, a news summary dataset was used to train a deep model. The performance of the proposed approaches was assessed using metrics like Rouge for textual summary, which achieved a 40.49% accuracy rate. While precision, recall, and F-score used for visual summary have achieved (94.9%) accuracy, which performed better than the other methods, according to the findings of the experiments.&lt;/span&gt;&lt;/p&gt;
Sentiment analysis in SemEval: a review of sentiment identification approaches

&lt;div class="page" title="Page 1"&gt;&lt;div class="layoutArea"&gt;&lt;div class="column"&gt;&lt;div class="page" title="Page 1"&gt;&lt;div class="layoutArea"&gt;&lt;div class="column"&gt;&lt;p&gt;ocial media platforms are becoming the foundations of social interactions including messaging and opinion expression. In this regard, sentiment analysis techniques focus on providing solutions to ensure the retrieval and analysis of generated data including sentiments, emotions, and discussed topics. International competitions such as the International Workshop on Semantic Evaluation (SemEval) have attracted many researchers and practitioners with a special research interest in building sentiment analysis systems. In our work, we study top-ranking systems for each SemEval edition during the 2013-2021 period, a total of 658 teams participated in these editions with increasing interest over years. We analyze the proposed systems marking the evolution of research trends with a focus on the main components of sentiment analysis systems including data acquisition, preprocessing, and classification. Our study shows an active use of preprocessing techniques, an evolution of features engineering and word representation from lexicon-based approaches to word embeddings, and the dominance of neural networks and transformers over the classification phasefostering the use of ready-to-use models. Moreover, we provide researchers with insights based on experimented systems which will allow rapid prototyping of new systems and help practitioners build for future SemEval editions.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;
Context-aware recommender system for multi-user smart home

&lt;span lang="EN-US"&gt;Smart home is one of the most important applications of the internet of things (IoT). Smart home makes life simpler, easier to control, saves energy based on user’s behavior and interaction with the home appliances. Many existing approaches have designed a smart home system using data mining algorithms. However, these approaches do not consider multiusers that exist in the same location and time (which needs a complex control). They also use centralized mining algorithm, then the system’s efficiency is reduced when datasets increase. Therefore, in this paper, we firstly build a context-aware recommender system that considers multi-user’s preferences and solves their conflicts by using unsupervised algorithms to deliver useful recommendation services. Secondly, we improve smart home’s responsive using parallel computing. The results reveal that the proposed method is better than existing approaches.&lt;/span&gt;
An optimized deep learning model for optical character recognition applications

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recognition applications; the proposed method was evaluated for performance in terms of computational accuracy, convergence analysis, and cost.
Flagging clickbait in Indonesian online news websites using fine-tuned transformers

Click counts are related to the amount of money that online advertisers paid to news sites. Such business models forced some news sites to employ a dirty trick of click-baiting, i.e., using hyperbolic and interesting words, sometimes unfinished sentences in a headline to purposefully tease the readers. Some Indonesian online news sites also joined the party of clickbait, which indirectly degrade other established news sites' credibility. A neural network with a pre-trained language model multilingual bidirectional encoder representations from transformers (BERT) that acted as an embedding layer is then combined with a 100 node-hidden layer and topped with a sigmoid classifier was trained to detect clickbait headlines. With a total of 6,632 headlines as a training dataset, the classifier performed remarkably well. Evaluated with 5-fold cross-validation, it has an accuracy score of 0.914, an F1-score of 0.914, a precision score of 0.916, and a receiver operating characteristic-area under curve (ROC-AUC) of 0.92. The usage of multilingual BERT in the Indonesian text classification task was tested and is possible to be enhanced further. Future possibilities, societal impact, and limitations of clickbait detection are discussed.
Apply deep learning to improve the question analysis model in the Vietnamese question answering system

Question answering (QA) system nowadays is quite popular for automated answering purposes, the meaning analysis of the question plays an important role, directly affecting the accuracy of the system. In this article, we propose an improvement for question-answering models by adding more specific question analysis steps, including contextual characteristic analysis, pos-tag analysis, and question-type analysis built on deep learning network architecture. Weights of extracted words through question analysis steps are combined with the best matching 25 (BM25) algorithm to find the best relevant paragraph of text and incorporated into the QA model to find the best and least noisy answer. The dataset for the question analysis step consists of 19,339 labeled questions covering a variety of topics. Results of the question analysis model are combined to train the question-answering model on the data set related to the learning regulations of Industrial University of Ho Chi Minh City. It includes 17,405 pairs of questions and answers for the training set and 1,600 pairs for the test set, where the robustly optimized BERT pre-training approach (RoBERTa) model has an F1-score accuracy of 74%. The model has improved significantly. For long and complex questions, the mode has extracted weights and correctly provided answers based on the question’s contents.
K-means variations analysis for translation of English Tafseer Al-Quran text

Text mining is a powerful modern technique used to obtain interesting information from huge datasets. Text clustering is used to distinguish between documents that have the same themes or topics. The absence of the datasets ground truth enforces the use of clustering (unsupervised learning) rather than others, such as classification (supervised learning). The “no free lunch” (NFL) theorem supposed that no algorithm outperformed the other in a variety of conditions (several datasets). This study aims to analyze the k-means cluster algorithm variations (three algorithms (k-means, mini-batch k-means, and k-medoids) at the clustering process stage. Six datasets were used/analyzed in chapter Al-Baqarah English translation (text) of 286 verses at the preprocessing stage. Moreover, feature selection used the term frequency–inverse document frequency (TF-IDF) to get the weighting term. At the final stage, five internal cluster validations metrics were implemented silhouette coefficient (SC), Calinski-Harabasz index (CHI), C-index (CI), Dunn’s indices (DI) and Davies Bouldin index (DBI) and regarding execution time (ET). The experiments proved that k-medoids outperformed the other two algorithms in terms of ET only. In contrast, no algorithm is superior to the other in terms of the clustering process for the six datasets, which confirms the NFL theorem assumption.
Sensor and internet of things based integrated inundation mitigation for smart city

&lt;span lang="EN-US"&gt;Flooding is a natural phenomenon that often occurs in tropical countries. Drainage design is one of the efforts to prevent floods, however, when the rainfall is high, there are still several inundation points that occur. This requires comprehensive handling to reduce the impact of these inundations, to get an adaptive solution, the use of internet of things based (IoT) tools is one of the alternatives proposed. This study proposes an IoT-based flood inundation monitoring system, which includes a water level reader, a web-based inundation monitoring system, a flood inundation area and depth reporting system as evaluation materials for the government city. The sensor module that we propose is a series of sensors in a hollow cylinder design to reduce water ripples. The server application is displayed in the form of an interactive area mapping which is divided into 4 layers for 4 different analyzes so that central officers can quickly coordinate with field officers to carry out mitigation actions in the affected area. The module requires a low cost and easy installation process compared to a liquid sensor, besides that the display in the form of a web makes it easier for officers to access monitoring applications anywhere compared to geographic information system based (GIS) applications. This research has been carried out and tested in one of the major cities in Indonesia.&lt;/span&gt;
Multi-label learning by extended multi-tier stacked ensemble method with label correlated feature subset augmentation

&lt;p&gt;Classification is one of the basic and most important operations that can be used in data science and machine learning applications. Multi-label classification is an extension of the multi-class problem where a set of class labels are associated with a particular instance at a time. In a multiclass problem, a single class label is associated with an instance at a time. However, there are many different stacked ensemble methods that have been proposed and because of the complexity associated with the multi-label problems, there is still a lot of scope for improving the prediction accuracy. In this paper, we are proposing the novel extended multi-tier stacked ensemble (EMSTE) method with label correlation&lt;br /&gt;by feature subset selection technique and then augmenting those feature subsets while constructing the intermediate dataset for improving the prediction accuracy in the generalization phase of the stacking. The performance effect of the proposed method has been compared with existing methods and showed that our proposed method outperforms the other methods.&lt;/p&gt;
New hybrid ensemble method for anomaly detection in data science

Anomaly detection is a significant research area in data science. Anomaly detection is used to find unusual points or uncommon events in data streams. It is gaining popularity not only in the business world but also in different of other fields, such as cyber security, fraud detection for financial systems, and healthcare. Detecting anomalies could be useful to find new knowledge in the data. This study aims to build an effective model to protect the data from these anomalies. We propose a new hyper ensemble machine learning method that combines the predictions from two methodologies the outcomes of isolation forest-k-means and random forest using a voting majority. Several available datasets, including KDD Cup-99, Credit Card, Wisconsin Prognosis Breast Cancer (WPBC), Forest Cover, and Pima, were used to evaluate the proposed method. The experimental results exhibit that our proposed model gives the highest realization in terms of receiver operating characteristic performance, accuracy, precision, and recall. Our approach is more efficient in detecting anomalies than other approaches. The highest accuracy rate achieved is 99.9%, compared to accuracy without a voting method, which achieves 97%.
An Innovative Approach for Automatic Character Recognition of Indian Languages

evelopment of OCRs for Indian script is an active area of activity today. Optical character recognition (OCR) is the mechanical or electronic translation of images of handwritten, typewritten or printed text (usually captured by a scanner) into machine-editable text. In simple words OCR is a visual recognition process that turns printed or written text into an electronic character-based file. OCR is a field of research in pattern recognition, artificial intelligence and machine vision. Though academic research in the field continues, the focus on OCR has shifted to implementation of proven techniques. A lot of work had been carried out for OCR at international scenario but in Indian context a concrete approach for character recognition is still required as scripts of Indian languages are from the group of most complex scripts and it is very hard to recognize them. Indian scripts present great challenges to an OCR designer due to the large number of letters in the alphabet, the sophisticated ways in which they combine, and the complicated graphemes they result in. The problem is compounded by the unstructured manner in which popular fonts are designed. There is a lot of common structure in the different Indian scripts. All existing OCR systems developed for various Indian scripts do not provide sufficient efficiency due to various factors. The objective of this paper is to discuss a more efficient character recognition technique. This paper introduces a new technical approach to recognize Indian script characters which are unpredictable due to different problems in other OCR‟s.
An optimized discrete wavelet transform compression technique for image transferring over wireless multimedia sensor network

Transferring images in a wireless multimedia sensor network (WMSN) knows a fast development in both research and fields of application. Nevertheless, this area of research faces many problems such as the low quality of the received images after their decompression, the limited number of reconstructed images at the base station, and the high-energy consumption used in the process of compression and decompression. In order to fix these problems, we proposed a compression method based on the classic discrete wavelet transform (DWT). Our method applies the wavelet compression technique multiple times on the same image. As a result, we found that the number of received images is higher than using the classic DWT. In addition, the quality of the received images is much higher compared to the standard DWT. Finally, the energy consumption is lower when we use our technique. Therefore, we can say that our proposed compression technique is more adapted to the WMSN environment.