Reviewerly/exHarmony huggingface dataset:
Automatic POS tagging of Arabic words using the YAMCHA machine learning tool

The process of automatically giving the proper POS tag to each word in a text based on context is known as automatic POS tagging. The majority of NLP applications require this process as a crucial step. This study intends to propose a machine learning-based Arabic POS tagger. YAMCHA tool is the machine learning system employed in this study. YAMCHA utilizes Support Vector Machines as a machine learning algorithm. SVM classifies data with high accuracy because it makes use of part of data in training process. As a result, in order to train the system, a substantial amount of annotated data must be evaluated at the POS level. A corpus of 100,039 words is utilized in this study. It was divided into training and testing parts, totaling 64,608 and 35,431 words, respectively. A tag set of 48 morphological tags were used in training and testing. To reach the best result in the automatic POS tagging, the system was trained multiple times with changing the range of linguistic information used in training process, and then new texts were tested and evaluated. The least error rate achieved was 11.4%. This rate was reached when the preceding word of the target one was considered in the training process without considering its POS tag (F: −1‥0: 0‥).
Neural Networks for Bilingual Machine Translation Model

Machine translation can be involved in statistical-based, corpus-based or dataset-based machine translation systems, in addition to linguistic systems. This paper objects to develop a bilingual English to Arabic translation model with quality for continuous improvement and flexible to be expanded multi-lingual other language pairs. This in addition to create an integrated translation environment that incorporates computer-assisted facilities to enhance the quality of automatically produced texts, increase translators' productivity and help their professional capabilities. Therefore, a machine translation model based on neural networks will be developed. Consequently, bilingual dictionaries will be involved, after cleaning and removing non-alphanumeric texts using linguistic modification tasks for the proposed machine translation model. Therefore, encoder and decoder models are involved for such machine translation. Finally, the training model is used to inference on new input to translate and therefore, the testing phase of the proposed machine translation model will be evaluated.
Optimization of Certainty Factor Algorithm to Overcome Uncertainty in Expert System Identification of Pests and Diseases of Cocoa

Lampung Province is one of the regions with the highest cocoa production, but many factors can interfere with cocoa production. One of them is the factor of pests and diseases of cocoa that cannot be identified and prevented beforehand. An expert system is a system that adopts expert knowledge or experts in a particular field. However, the diagnosis usually contains uncertainty in the form of answers and statements used. Certainty Factor (CF) algorithm, is one of the algorithms that is able to overcome uncertainty by providing a value for the level of trust from experts and users. However, because the CF value is based on the level of confidence in the symptoms experienced by the user, it creates a problem, namely the consistency of the user's answers. So, it is necessary to make improvements to the CF algorithm, to overcome the inconsistency of user answers through the use of Consistency Ratio (CR). Through the use of CR, inconsistencies are overcome through comparisons between the consistency index and the random index. Based on the results of the accuracy test, the proposed model is able to produce an accuracy rate of 86.67% in the diagnosis of pests and diseases on cocoa plants.
A Business Recommender System Based on Zones and Commercial Data

In many countries people target different places to open a business and succeed in it. They may choose an unsuccessful business or the location does not need the type of this business. In this paper, we aim to improve the opportunity of choosing a correct business and location. We suggest an approach based on many principles of machine learning. The approach uses a prediction model based on analysing data about zones (areas) and their commercial services. The zones are classified using K-Means clustering method that depends on the number of same businesses and their costs averages in an area. To show the novelty of our work, we developed a system that implements the approach principles for several zones in Nablus city. We evaluate the work by running several test cases to show the system ability in recommending kinds of businesses.
Logo Detection Using You Only Look Once (YOLO) Method

The growing amount of documents necessitates automatic document classification since manual classification takes longer. Logos allow for defining the source of a document rapidly and accurately. Logos are part of the identity attached to companies, organizations, institutions, and individuals. The placement of the logo on each document varies, as does the image quality of a document. This research aimed to identify the logo on a document in the form of a college diploma. This study utilized diplomas from fifteen universities. This system utilizes the You Only Look Once algorithm version 4 (YOLOv4) by implementing the Darknet Framework. The ultimate YOLOv4 model's results indicated that the system could detect and recognize logos with a mean Average Precision (mAP) of 93.73%.
Twitter Sentiment Analysis in Indonesian Language using Naive Bayes Classification Method

Social media is a very large source of data and has continuous and very short changes, the time span of which changes also has considerable relevance for various fields. The purpose of this study as a sentiment analysis aimed at twitter status discussing daily topics to determine the attitude of opinion from tweets posted on a trending topic online using the Naive Bayes Classifier method. With various series of steps carried out, namely data collection from Twitter, preprocessing, classification process with the Naives Bayes Classifier algorithm, and evaluation of the results determined by this method. The results at the time of testing, namely in the form of the highest accuracy value obtained in testing 40 tweet data by 82% with a time of 19.92 seconds, for the highest precision value in testing 10, 20, and 50 tweet data by 100% with a time of 14, 85 seconds, for the highest recall value in testing 40 tweet data of 81.82% with a time of 16.09 seconds, for the highest F1-Score value in testing 40 tweet data of 88.52% with a time of 16.38 seconds.
Anomaly Detection in Cyber-Physical Systems based on BiGRU-VAE

Various problems inevitably arise in cyber-physical systems, such as equipment failure, performance degradation, etc. Untimely detection of an abnormal state caused by a cyber-attack or a failure to operate devices in a cyber-physical system can lead to severe losses for the entire system. This paper proposes a method based on a deep bidirectional gated recurrent unit and variational autoencoder model to detect anomalies in a cyber-physical system. Experiments on a real dataset have shown the effectiveness of the proposed method in detecting anomalies in a cyber-physical system. Comparison with known methods showed the most accurate results according to the precision, recall, and F-measure metrics and amounted to 99.87%, 77.39%, and 87.20%, respectively.
SCAS-AI: A Strategy to Semi-Automate the Initial Selection Task in Systematic Literature Reviews

Context: There are several initiatives to semi-automate the initial selection of studies task for Systematic Literature Reviews (SLR) to reduce effort and potential bias. Objective: We propose a strategy called SCAS-AI to semi-automate the initial selection task. This strategy improves the original SCAS strategy with Artificial Intelligence (AI) resources (fuzzy logic and genetic algorithm) for studies selection. Method: We evaluated the SCAS-AI strategy through a quasi-experiment with SLRs in Software Engineering (SE). Results: In general, the SCAS-AI strategy improved the results achieved using the original SCAS strategy in reducing the effort of the initial selection task. The effort reduction applying SCAS-AI was 39.1%. In addition, the errors percentage was 0.3% for studies automatically excluded (false negative – loss of evidence) and 3.3% for studies automatically included (false positive – evidence later excluded during the full-text reading). Conclusion: The results show the potential of the investigated AI techniques to support the initial selection task for SLRs in SE.
The Guidelines of Building a Treebank for Modern Standard Arabic

Treebanks are one of the most needed and used linguistic resources in the fields of Natural language processing (NLP) and Natural language understanding (NLU). Arabic has only two constituency-based treebanks and a number of dependency treebanks. The current research presents the guidelines for building a parsed Arabic treebank for Modern Standard Arabic (MSA). The guidelines show, firstly the choice of the grammar formalism, then the genre and size of the treebank, and finally the annotation layers of the treebank. The study also shows that using the traditional Arabic grammar syntactic theory to describe the Arabic syntax has proven to be more suitable than using any of the modern syntax theories. Working with the traditional Arabic grammar also helps avoid the errors that the available treebank fell in as a result of using guidelines that don't suit the Arabic grammar. The study adopts three layers of annotations: the morphological layer, the syntactic layer, and the grammatical function layer. The resultant tree is a very detailed and rich syntactic tree, which is preferable by the researcher over having a huge amount of data poorly and shallowly annotated.
Turnstile Access based on Facial Recognition and Vaccine Passport Verification

This paper aims to provide a system ensuring turnstile access based on facial recognition and vaccine passport verification in order to enable touch-free entrance to buildings, universities, offices, etc. The algorithm of the proposed method is comprised of two essential parts: YOLO algorithm for face detection and CNN for face recognition. After successful user authentication, there are two important criteria that should be met for granting access to the person: Person should not be an active COVID-19 patient and Person should have a valid vaccine passport. The proposed method results 95.57% accuracy rate for face detection with YOLO algorithm and 70% for face recognition with CNN.
Deep Learning in Arabic Text Summarization: Approaches, Datasets, and Evaluation Metrics

Recently, there is a massive amount of data available on the internet. Hence, it is quite difficult for the users to go through all the available online information to generate a precise summary manually. Automatic Text Summarization (ATS) systems provide a solution to this problem as they produce a shorter and manageable version of the input text while keeping the most important information. Deep learning has achieved good results in Natural Language Processing (NLP) tasks and the use of deep learning techniques specifically in Automatic Text Summarization (ATS) has increased in English language. However, there is still a shortage of studies evaluating these techniques in Arabic language. In this research work, we review several articles that address the usage of deep learning with Arabic language. Specifically, we study the available models, datasets, and evaluation metrics for extractive and abstractive Arabic text summarization. We reviewed 12 research papers and found that most of the studies employed deep learning for the abstractive summarization type.
Quantitative and Semantic Analysis of Texts in Turkic Languages using Universal Declaration of Human Rights (UDHR) as a Corpus

Thanks to Web, ubiquitous digital technologies and the increasing usage of digital environment by humans for work, entertainment, education and other activities, huge amounts of textual data is generated and available online. Text is the most informative and at the same time most sophisticated data type in terms of its comprehension by machines. The Text Analytics is a field that involves number of computer science disciplines to process textual data and transforms it into computer readable format suitable for another field of study Natural Language Processing to extract meaning.This research paper is an attempt to apply broad variety of statistical analysis methods to the corpora of several Turkic languages using Universal Declaration of Human Rights as a Corpus. Quantitative Text Analysis as a research area is focused on understanding the human language through statistics and numbers. As the language is the most effective tool to describe the social world, the Quantitative Text Analysis enables social exploration of the rial world at the scale.
ARML: Augmented Reality Mathematics Learning for Remedial Students

The focus of this research is to develop Mathematics Augmented Reality Content that combined mathematics learning with multimedia elements for remedial students. Students in this category have a limited cognitive ability and usually struggle to proceed in conventional learning, and teachers must modify the teaching to their abilities. Initially, a preliminary investigation (PI) was carried out to learn more about the methods and styles of mathematic learning, as well as how frequently teachers include mobile learning into their teaching and learning procedures. Identifying augmented reality (AR) components and mathematics materials for remedial students to be integrated with the application, designing, and developing the Mathematics Augmented Reality Content for Remedial Students, and assessing the application are the goals of this study. The five steps of the ADDIE Model: Analysis, Design, Development, Implementation, and Evaluation are used in its application. Expert review to assess the design and User Acceptance Test to evaluate the effectiveness of the application are the two forms of testing done to assess the AR application. The outcomes demonstrate the efficacy of mathematics augmented reality content for remedial students as a learning tool for these children. Augmented reality technology produces a beautiful combination that integrates both virtual and actual benefits for the environment, as well as benefits to both teachers and students as a result of this project, by integrating the best of both worlds.
Software Reuse and Evolution in JavaScript Applications

JavaScript (JS) is one of the most popular programming languages on GitHub. Most JavaScript applications are reusing third-party components to acquire various functionalities. Despite the benefits offered by software reuse there are still challenges, during the evolution of JavaScript applications, related to the management and maintenance of the third-party dependencies. Our key objective is to explore the evolution of library dependencies constraints in the context of JavaScript applications in terms of (a) the changeability (i.e., number of removed, added, or maintained libraries) (b) the update frequency of the library dependencies. For this purpose, we conducted a case study on the 86 most forked JavaScript applications hosted on GitHub and analyzed reuse data from a total of 2.363 successive releases. In general, 39% of the packages introduced in the first version of the project are being reused in the entire project’s lifetime. The number of package dependencies slightly grows over time, while several other are being permanently removed. Regarding the evolution of third-party applications, it is observed that developers do not update the dependencies constraints to a most recent version, waiting to reach probably “breaking points” when the updates will be inevitable.
A Blockchain-based Document Verification Model in Freshers Hiring Process

A quick growth of information sharing and transferring has been observed recently. Everything is getting digitized, and everyone requires a simple and straightforward method. As a result, the number of fake documents generated for job applications is increasing, and checking all the documents manually is not a good option as it takes a lot of time. Therefore, digitizing documents is becoming an increasingly popular option for companies and individuals as it is the most secure and least time-consuming way of verifying documents. As Blockchain is a decentralized system that guarantees the protection of data kept in it, this article proposes a solution based on Federated Blockchain technology that allows specific organizations to submit candidates' original documents. It validates the student's submitted document hash value by comparing the existing cryptographic hash in the Blockchain. SHA-512 is used to generate the hash values for the documents. This technique is incredibly efficient, consumes less time, and is less expensive to execute all types of verifications.
Performance of CPUs and GPUs on Deep Learning Models For Heterogeneous Datasets

Deep learning is a branch of Artificial Intelligence (AI) where neural networks are trained to learn patterns from large amounts of data. The primary issue raised by the growth in data volume and diversity of neural networks is selecting hardware accelerators that are effective and appropriate for the specified dataset and selected neural network. This paper studies the performance of CPU and GPU based on the input data size, size of data batches and type of neural network chosen. Four datasets were chosen for benchmark testing, these included a csv data file, a textual dataset and two image datasets. Suitable neural networks were chosen for given data sets. Tests were performed on Intel i5 9th gen CPU and NVIDIA GeForce GTX 1650 GPU. The results show that performance of CPU and GPU doesn't depend on the data format, but rather depends on the type of architecture of the neural network. Neural networks which support parallelization, provide performance boost in GPU s compared to CPUs. When ANN architecture was used, CPUs performed 1.2 times better than GPUs in terms of execution time. With deeper CNN models GPUs performed 8.8 times and with RNNs 4.90 times faster than CPU s. Linear relation between dataset size and training time was observed and GPUs outdid CPUs when batch size was increased irrespective of NN architecture.
A Fast and Effective Tree-based Mining Technique for Extraction of High Utility Itemsets

The most important phase in the discovery of common item-sets is identifying relationships between the items. Frequent-pattern growth (FP-growth), one of the traditional association mining techniques, excels at generating frequent item sets. For the purpose of eliminating item sets with high infrequency, both mining algorithms employ the support-confidence framework. Due to its inability to take into account the affected utility element, the support-confidence framework falls short in important applications including e-commerce, web mining, and healthcare. For circumvent the drawbacks of conventional algorithms, utility-based mining methods must be developed. Utility-based mining algorithms have recently developed with the significant advancements in association mining. For the purpose of performing active utility mining, the proposed technique is described. It employed the utility mining algorithms by tree structure building. The experimental section shows experimental findings from benchmark datasets and illustrates the effectiveness of the proposed methodology.
An Improved Network Segmentation Performance in Lesion Segmentation based on Mask R-CNN

Computational intelligence has grown increasingly common in the analysis of healthcare data in recent years. The deep learning algorithms, in instance, have such a number of fruitful implications in the analysis of medical images. This research study has presented a double awareness deep convolution network-based system for end-to-end healthcare lesion classification. Several fully supervised and poorly supervised extracted features are included in this framework. The issue of the high cost of pixel-level labels with lesions in the medical pictures is alleviated by the weakly supervised segmentation module's use of bounding-box labels of lesion regions, which allows for correct lesion segmentation to be accomplished. In addition to this, a dual attention mechanism has been included for the purpose of improving the network's capacity for visual feature learning. The dual attention mechanism, which consists of both channel and spatial attention, may assist in drawing the network's attention to the extraction of features from significant locations. It is possible for it to considerably minimize the gaps between ground-truth labels and pseudo labels, in comparison to the current standard approach of weakly supervised segmentation using pseudo labels. Final results show that our proposed approach beat the competitors on to an oral disease collection, while our approach was extended to incorporate dermatological disease categorization in the ultimate analysis.
Handwritten Mathematical Symbol Recognition using Neural Network Architectures

Mathematical symbol recognition is a topic of attention that convert physical documents into digital format. Despite the existing techniques to recognize handwritten characters and symbols, recognition accuracy is unstable. The main objective of this work is to build an intelligent system to recognize handwritten characters or symbols written in different styles with improved and stable accuracy. The proposed system can read handwritten mathematical characters or symbols as input and recognize them with corresponding characters or symbol names. The proposed implementation uses various machine learning and deep learning algorithms like Logistic Regression, Convolutional Neural networks, and Dense net. The dataset used for this research is 46 MB, which contains images of numerical values from 0 to 9, mathematical symbols, and alphabets that are available in the Kaggle open-source platform. Each data category has around 500 plus handwritten images. The implementation uses various machine learning and deep learning algorithms like Logistic Regression, Convolutional Neural networks, and Dense net to address the symbol recognition challenges. The comparative study is implemented with these algorithms and the Dense net has presented exceptional results during the training and testing phase with an accuracy of 99% and 94.2% respectively. This improved accuracy is due to the utilization of Densenet over other CNN architectures, as the DenseNet concatenates the output of the predecessor layer with the successor layer and it weakens the vanishing gradient problem. Also, the Dynamic feature propagation helps in the regulated flow of information in the dense network architecture.
Dual Axis Solar Tracking System

The extending interest in energy the, ceaseless decline in existing wellsprings of non-renewable energy source, also, the creating concern in regards to ecologicaldefilement has pushed humanity to examine new advancement for the making of electrical energy using unblemished, economical sources, similar to daylight-based energy, wind energy, etc. Among the forward-thinking manageable power sources, sun oriented based energy has inconceivable Changes to electric power have the possibility to be made, preparing it to guarantee critical piece of the planet's electrical