Reviewerly/exHarmony huggingface dataset:
An Investigation on Disparity Responds of Machine Learning Algorithms to Data Normalization Method

Data normalization can be useful in eliminating the effect of inconsistent ranges in some machine learning (ML) techniques and in speeding up the optimization process in others. Many studies apply different methods of data normalization with an aim to reduce or eliminate the impact of data variance on the accuracy rate of ML-based models. However, the significance of this impact aligning with the mathematical concept of the ML algorithms still needs more investigation and tests. To identify that, this work proposes an investigation methodology involving three different ML algorithms, which are support vector machine (SVM), artificial neural network (ANN), and Euclidean-based K-nearest neighbor (E-KNN). Throughout this work, five different datasets have been utilized, and each has been taken from different application fields with different statistical properties. Although there are many data normalization methods available, this work focuses on the min-max method, because it actively eliminates the effect of inconsistent ranges of the datasets. Moreover, other factors that are challenging the process of min-max normalization, such as including or excluding outliers or the least significant feature, have also been considered in this work. The finding of this work shows that each ML technique responds differently to the min-max normalization. The performance of SVM models has been improved, while no significant improvement happened to the performance of ANN models. It is been concluded that the performance of E-KNN models may improve or degrade with the min-max normalization, and it depends on the statistical properties of the dataset.
In situ measurement and management of soil, air, noise and water pollution in and around the Limestone mining area of Yerraguntla, YSR kadapa, Andhra Pradesh, India for the sustainable development

For emerging countries, mining has been a vital factor in employment, economic development, infrastructure, and supply of essential raw materials for Nation’s Gross domestic product (GDP) growth. The Limestone mine industry is serving as a viable route for economic transformation in India. Limestone exploration causes major damage to the environment at Yerraguntla industrial zone, YSR Kadapa district, Andhra Pradesh, India. The main objective of this study is to evaluate the environmental Pollution parameter that causes Air, Water, Noise, and Soil pollution in and around limestone quarries started in the early 1984. The present study estimated Air Quality Index (AQI) as 76 based on the air quality sub-index approach using four pollutants (PM10, PM2.5, SO2, NOx) for a period of 24 hrs by taking one sample per hour during the post monsoon. Water Quality Index (WQI) obtained as 303.91 from fourteen physicochemical parameters (pH, EC, fluoride, Total alkalinity etc.) measured from water samples. Soil quality was determined using four physicochemical parameters (pH, EC, WHC, Calcium and Magnesium) from the soil samples collected from ten sampling stations. The obtained pH range was (7.6 to 9.4), EC of the soil was determined as 4,140 µs/cm, the water retention capacity of the soil, ranges from (17.68 to 97.68) %, and the Calcium (Ca2+) and Magnesium (Mg2+) ranged from 74.5 to 272.75 mEq/L. Noise levels were determined as 76.64 dB in the mine’s, 58.16 dB in the cement industry, and 52.285 dB in the mine surrounding villages. This study can help mining sector management’s in developing a sustainable Environmental Management frame work to meet the world sustainable development goals (SDGs).
Lung cancer detection using image processing and deep learning

This project is about the detection of lung cancer by training a model of deep neural networks using histopathological lung cancer tissue images. Deferent models have been proposed for detecting lung cancer cells automatically involving Inception V3, Random Forest, and convolutional neural network (CNN). The deep convolutional neural network has been trained to extract important features that facilitate build detection and diagnosis of lung cancer cells more efficiently and accurately. The proposed method in this project has accomplished promising and satisfactory results in terms of accuracy, precision, recall, F-score, and specificity measure in lung cancer detection. Furthermore, it has been applied on dataset which contains 178,000 photos. The accuracy values that are obtained are accuracy 97.09%, precision 96.89%, recall 97.31%, F-score measure 97.09%, and specificity measure 96.88%.
Natural language processing for Albanian: a state-of-the-art survey

&lt;span lang="EN-US"&gt;Due to its wide applicability, natural language processing (NLP) has attracted significant research efforts to the machine learning and deep learning research community. Despite this, research works investigating NLP for the Albanian language are still limited. However, to the best of our knowledge, there is no literature review available, which presents a clear picture of what has been studied, argued, and established in the area. The main objective of this survey is to comprehensively review, analyze and discuss the state-of-the-art in NLP for the Albanian language. Here, we present an extensive study concerning the contribution of several authors that have contributed to the application of NLP to the Albanian language. Also, we present an overview of research carried out in the typical applications of NLP for the Albanian language. Finally, some future challenges and limitations of the area are discussed.&lt;/span&gt;
Implementation of the C4.5 algorithm for micro, small, and medium enterprises classification

&lt;span lang="EN-US"&gt;The coronavirus disease-19 (COVID-19) pandemic has spread to various countries including Indonesia. Thus, implementing large-scale social restrictions &lt;/span&gt;&lt;em&gt;&lt;span lang="EN-US"&gt;(Bahasa: Pembatasan Sosial Berskala Besar (PSBB))&lt;/span&gt;&lt;/em&gt;&lt;span lang="EN-US"&gt; has resulted in the paralysis of the economy in Indonesia. including micro, small, and medium enterprises (MSMEs) have decreased turnover and even went out of business. The Department of Cooperatives and Small and Medium Enterprises (SMEs) in Pesawaran Regency, Lampung, oversees 3,808 MSMEs, whose development should be monitored as a basis for determining policies. However, there are problems in classifying MSMEs according to their categories because they have to check the existing data one by one, so it takes a long time. Therefore, this study proposed the C4.5 algorithm to solve this problem. In addition, this research compared with the naïve Bayes algorithm to find out which algorithm had a good performance and is suitable for this case. The results showed that 91% of MSMEs were included in the micro category, 8% was in a small category, and 1% was in the medium category. Based on the results, it explained that the C4.5 algorithm was bigger than naïve Bayes with a difference in the value of 3.79%. It had an accuracy value of 99.2%. Meanwhile, naive Bayes was 95.41%.&lt;/span&gt;
Internet of things based real-time coronavirus 2019 disease patient health monitoring system

&lt;span lang="EN-US"&gt;The coronavirus disease (COVID-19) outbreak has led to many infected worldwide and has become a global crisis. COVID-19 manifests in the form of shortness of breath, coughing and fever. More people are getting infected and healthcare systems worldwide are overwhelmed as healthcare workers become exhausted and infected. Thus, remote monitoring for COVID-19 patients is required. An internet of things (IoT) based real-time health monitoring system for COVID-19 patients was proposed. It features monitoring of five physiological parameters, namely electrocardiogram (ECG), heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2) and body temperature. These vitals are processed by the main controller and transmitted to the cloud for storage. Healthcare professionals can read real-time patient vitals on the web-based dashboard which is equipped with an alert service. The proposed system was able to transmit and display all parameters in real-time accurately without any packet loss or transmission errors. The accuracy of body temperature readings, RR, SpO2 and HR, is up to 99.7%, 100%, 97.97% and 98.34%, respectively. Alerts were successfully sent when the parameters reached unsafe levels. With the proposed system, healthcare professionals can remotely monitor COVID-19 patients with greater ease, lessen their exposure to the pathogen, and improve patient monitoring.&lt;/span&gt;
Bridging the gap between the semantic web and big data: answering SPARQL queries over NoSQL databases

Nowadays, the database field has gotten much more diverse, and as a result, a variety of non-relational (NoSQL) databases have been created, including JSON-document databases and key-value stores, as well as extensible markup language (XML) and graph databases. Due to the emergence of a new generation of data services, some of the problems associated with big data have been resolved. In addition, in the haste to address the challenges of big data, NoSQL abandoned several core databases features that make them extremely efficient and functional, for instance the global view, which enables users to access data regardless of how it is logically structured or physically stored in its sources. In this article, we propose a method that allows us to query non-relational databases based on the ontology-based access data (OBDA) framework by delegating SPARQL protocol and resource description framework (RDF) query language (SPARQL) queries from ontology to the NoSQL database. We applied the method on a popular database called Couchbase and we discussed the result obtained.
Machine English Translation Evaluation System Based on BP Neural Network Algorithm

In order to solve the problems of machine translation efficiency and translation quality, this paper proposes an English translation evaluation system based on the BP neural network algorithm. This method provides users with a more intelligent machine translation service experience. With the help of the BP neural network algorithm, taking English online translation as the research object, Google's translation quality is the best, with an error frequency of only 167, while Baidu translation and iFLYTEK translation in China have a high error rate of 266 and 301, respectively, which is much higher than Google translation. A model of machine translation evaluation based on the neural network algorithm is proposed to better solve the disadvantages of traditional English machine translation. The results show that the machine translation system based on the neural network algorithm can further optimize the problems existing in machine translation, such as insufficient use of information and large scale of model parameters, and further improve the performance of neural network machine translation.
Data augmentation by combining feature selection and color features for image classification

&lt;span lang="EN-US"&gt;Image classification is an essential task in computer vision with various applications such as bio-medicine, industrial inspection. In some specific cases, a huge training data is required to have a better model. However, it is true that full label data is costly to obtain. Many basic pre-processing methods are applied for generating new images by translation, rotation, flipping, cropping, and adding noise. This could lead to degrade the performance. In this paper, we propose a method for data augmentation based on color features information combining with feature selection. This combination allows improving the classification accuracy. The proposed approach is evaluated on several texture datasets by using local binary patterns features.&lt;/span&gt;
Data storage model in low-cost mobile applications

&lt;span lang="EN-US"&gt;Mobile applications that have data transactions between users require a database relational database management system (RDBMS) and RESTful API operating on the hosting service so that all users can access the data. Renting a hosting service is not cheap and creating a RESTful API takes plenty of time. As an alternative to hosting, a free version of the Cloud Firestore service gives full access rights to the database and has an application programming interface (API) to manage data or access data. However, the free version of Cloud Firestore has limitations in terms of storage capacity, read, write, and delete processes. Therefore, redesigning process of the database was carried out into a low-cost version of the database model consisting of SQLite database and a low-cost version of the NoSQL database to overcome this problem. The goal is to reduce storage space usage and read, write, and delete processing on Cloud Firestore. The low-cost version of the database was tested with 6,030 data. The results obtained were savings of 47.27% storage usage, 83.08% write usage, 91.26% read process usage, and 83.19% delete process usage compared to the test results of the relational database model.&lt;/span&gt;
General and Domain-adaptive Chinese Spelling Check with Error-consistent Pretraining

The lack of label data is one of the significant bottlenecks for Chinese Spelling Check. Existing researches use the automatic generation method by exploiting unlabeled data to expand the supervised corpus. However, there is a big gap between the real input scenario and automatically generated corpus. Thus, we develop a competitive general speller ECSpell, which adopts the Error-consistent masking strategy to create data for pretraining. This error-consistency masking strategy is used to specify the error types of automatically generated sentences consistent with the real scene. The experimental result indicates that our model outperforms previous state-of-the-art models on the general benchmark. Moreover, spellers often work within a particular domain in real life. Due to many uncommon domain terms, experiments on our built domain-specific datasets show that general models perform terribly. Inspired by the common practice of input methods, we propose to add an alterable user dictionary to handle the zero-shot domain-adaption problem. Specifically, we attach a User Dictionary guided inference module (UD) to a general token classification-based speller. Our experiments demonstrate that ECSpell UD , namely, ECSpell combined with UD, surpasses all the other baselines broadly, even approaching the performance on the general benchmark. 1
Deep learning for cancer tumor classification using transfer learning and feature concatenation

&lt;span lang="EN-US"&gt;Deep convolutional neural networks (CNNs) represent one of the &lt;br /&gt; state-of-the-art methods for image classification in a variety of fields. Because the number of training dataset images in biomedical image classification is limited, transfer learning with CNNs is frequently applied. Breast cancer is one of most common types of cancer that causes death in women. Early detection and treatment of breast cancer are vital for improving survival rates. In this paper, we propose a deep neural network framework based on the transfer learning concept for detecting and classifying breast cancer histopathology images. In the proposed framework, we extract features from images using three pre-trained CNN architectures: VGG-16, ResNet50, and Inception-v3, and concatenate their extracted features, and then feed them into a fully connected (FC) layer to classify benign and malignant tumor cells in the histopathology images of the breast cancer. In comparison to the other CNN architectures that use a single CNN and many conventional classification methods, the proposed framework outperformed all other deep learning architectures and achieved an average accuracy of 98.76%.&lt;/span&gt;
A simplified and novel technique to retrieve color images from hand-drawn sketch by human

&lt;span lang="EN-US"&gt;With the increasing adoption of human-computer interaction, there is a growing trend of extracting the image through hand-drawn sketches by humans to find out correlated objects from the storage unit. A review of the existing system shows the dominant use of sophisticated and complex mechanisms where the focus is more on accuracy and less on system efficiency. Hence, this proposed system introduces a simplified extraction of the related image using an attribution clustering process and a cost-effective training scheme. The proposed method uses K-means clustering and bag-of-attributes to extract essential information from the sketch. The proposed system also introduces a unique indexing scheme that makes the retrieval process faster and results in retrieving the highest-ranked images. Implemented in MATLAB, the study outcome shows the proposed system offers better accuracy and processing time than the existing feature extraction technique.&lt;/span&gt;
Best practice framework for information technology security governance in Indonesian government

&lt;span lang="EN-US"&gt;Information technology security is crucial for a digital government system to have so that the continuity of business processes can run smoothly. However, the current best practice of information security governance in the Indonesian national government is still inadequate according to various related studies still siloed and scattered and leading to vulnerabilities in the various digital services provided. Therefore, this study aims to develop a best practice framework for managing information security that is aligned with the needs of Indonesia's digital government. This research started by looking for the main framework of information security governance. Then the main components that resulted from that were benchmarked with other Information Security Governance (ISG) best practices from different countries. Finally, it ended up complementing them with information security parameters, other related components, and recommendations, particularly in the Indonesian context, so that the main components and their respective constituent sub-components can be obtained according to the needs of the Indonesian e-government. The cause-and-effect analysis concept analyses the data linkages between the six central components and their respective sub-components. This study concludes that each of main components and sub-components supports each other so that all these things must be carried out in a balanced and continuous manner.&lt;/span&gt;
Phishing Email Detection Using Improved RCNN with Multilevel Vectors and Attention Mechanism

Abstract: The Phishing emails are the common threats in the present world which leads to the financial losses and stealing the sensitive information like credit card details, username and password etc. Due to increase in the rate of phishing emails there is a need to introduce the effective phishing detection technology. In order to detect the phishing emails many methods, techniques and technologies are introduced .Improved RCNN with multilevel vectors and attention mechanism is one such technology. Firstly, the email structure is analyzed and then by using RCNN model with multilevel vectors ,attention mechanism phishing content is detected .we introduce noise to test the effectiveness.
A Dialogue System that Models User's Opinion Based on Information Content

When designing rule-based dialogue systems, the need for elaborate design by the designer is a challenge for dialogue systems. One way to reduce the cost of creating content is to generate utterances from data collected in an objective and reproducible manner. This study focuses on rule-based dialogue systems using survey data, and more specifically on opinion dialogue where the system models the user. In opinion dialogue, there has not been much study of topic transition methods for modeling users while maintaining their motivation for dialogue. To model them, we adopted information content. Our contribution includes the design of a rule-based dialogue system that does not require elaborate design. We also reported an appropriate topic transition method based on information content. This is confirmed by the influence of the user's personality characteristics. The content of questions can give the user a sense of the system's intention to understand them. We also reported the possibility that the system's rational intention contributes to the user's motivation for dialogue with the system.
PENGARUH PENERAPAN MODEL PEMBELAJARAN DISCOVERY LEARNING TERHADAP HASIL BELAJAR KOGNITIF PESERTA DIDIK

The education system is having problems, especially the low quality of education. This matter could be caused by the ineffective learning process, so we need an steps that can improve students' understanding of the subject matter. Process changed to use the learning model with Discovery Learning. This study aims used determine the effect of using the model Learning Discovery Learning about cognitive learning outcomes of students in the subject matter chemistry class X IPA SMA Kristen YABT Manokwari. The research method used is a Quasi Experimental with the form of Time Series Design. Sampling technique using saturated sampling with a sample size of fifteen people. Data collection tools is a test of cognitive learning outcomes. Based on the results of data processing obtained, the average score The pre-test is 53.78 and the average score post-test is 68, and the N-Gain result is obtained shows an increase in the learning ability of students between before being treated and after being given treatment that is 0.73. Based on the Paired Sample T-test, it was found that there is an effect of using the learning model Discovery Learning on the results cognitive learning of students on the subject matter of chemical bonding class X IPA SMA Kristen YABT Manokwari with a large influence of 30%. Keywords: Learning model, discovery learning, outcomes learning, cognitive, Quasi Experimental
Cross-domain semantic alignment: Concrete concepts are more abstract than you think

We can easily evaluate similarities between concepts within semantic domains, e.g., doctor and nurse, or violin and piano. Here, we show that people are also able to evaluate similarities across domains, e.g., aligning doctors with pianos and nurses with violins. We argue that understanding how people do this is important for understanding conceptual organization and the ubiquity of metaphorical language. We asked people to answer questions of the form "If a nurse were an animal, they would be a(n)…" (Experiment 1 and 2), and asked them to explain the basis for their response (Experiment 1). People converged to a surprising degree (e.g., 20% answered "cat"). In Experiment 3, we presented people with cross-domain mappings of the form "If a nurse were an animal, they would be a cat” and asked them to indicate how good each mapping was. The results showed that the targets people chose and their goodness ratings of a given response were predicted by similarity along abstract semantic dimensions such as valence, speed, and genderedness. Reliance on such dimensions was also the most common explanation for their responses. Altogether, we show that people can evaluate similarity between very different domains in predictable ways, suggesting that either seemingly concrete concepts are represented along relatively abstract dimensions (e.g., weak-strong) or that they can be readily projected onto these dimensions.