Reviewerly/exHarmony huggingface dataset:
LeakGAN-Based Causality Extraction in the Financial Field

Causality extraction model can quickly extract causality in text. It can be applied to event prediction, question-answering systems, and scenario generation. The traditional causality extraction pays more attention to the extraction of entities and ignores the deep semantic representation between cause entities and effect entities of the text. So the accuracy of causality extraction is low in the end. A causal relationship extraction model based on LeakGAN is proposed to solve this problem. The core task of the model is to analyze the causality existing in the review text ( this paper focuses on explicit one-cause and one-effect analysis). It also realizes deep extraction under semantic enhancement. Four levels of causality extraction model based on LeakGAN are proposed: data preprocessing, pre-training, Bi-LSTM + Attention, and LeakGAN learning. Experimental results show that the model can improve the accuracy of causality extraction.
Colour Coherence Entropy for Ceramic Fragment Analysis

Thousands of broken porcelain tiles are unearthed every year and it is difficult to sort out the individual pieces from the jumble of tiles. Digital restoration of ancient artifacts reduces the reliance on heavy human labour for identification in the restoration process. Image recognition technology is one of the effective means of digital restoration, and the ability to extract the features of the image determines the recognition result. In this work, we aim to cluster a given collection of pieces into sets that came from different caremic objects. Features are extracted from each piece and compared to perform clustering. Color coherence vectors (CCV) put the consistency of the same color into consideration, add spatial information to improve the performance of image recognition, which provide an idea of partition for content-based image retrieval (CBIR). This paper proposed an runtime improved CCV, colour coherence entropy (CCE), based on CCV and entropy. CCV divides the quantized colors into coherent part and incoherent part. The entropy is used to describe the distribution of those two parts to reduce the dimensionality of the data. As a hybrid feature of image retrieval, CCE reduces the data dimension from the original 64 or even higher to 2, while preserving the original features as much as possible through the entropy function. In a database of 10,000 images, CCE was 94.3% faster than CCV in retrieval computation efficiency. In 93 clustered image datasets, the retrieval accuracy of CCE was 25.1% higher than that of the one-dimensional entropy difference method. Considering the balance between computational efficiency and retrieval accuracy, CCE has a good performance for image retrieval pre-processing in CBIR and is suitable for massive image retrieval for ceramic fragment analysis.
The role of quintin polynomial and firefly algorithm in mobile root path planning optimization

The emergence of mobile robot cannot be overemphasized as it is seen as essential but extreme technology taking into considerations, recent industrialisation. Automation has become a part of the process in order to obtain pathway of collision. The firefly algorithm has been identified to work on the principles of behaviour simulation. Two algorithms are implemented in this regard: Pseudo code of the basic firefly and Pseudo code of the firefly algorithm method that is proposed. To be considered for integration, the firefly algorithm method proposed is taken into consideration. A run of suggested mobile robot environment have been run. In conclusion, metaheuristic algorithms, is inspired by nature and are known as powerful tools especially in tackling global optimisation problem.
Social Media Sentiment Analysis on Third Booster Dosage for COVID-19 Vaccination: A Holistic Machine Learning Approach

Over a period of more than two years the public health has been experiencing legitimate threat due to COVID-19 virus infection. This article represents a holistic machine learning approach to get an insight of social media sentiment analysis on third booster dosage for COVID-19 vaccination across the globe. Here in this work, researchers have considered Twitter responses of people to perform the sentiment analysis. Large number of tweets on social media require multiple terabyte sized database. The machine learned algorithm-based sentiment analysis can actually be performed by retrieving millions of twitter responses from users on daily basis. Comments regarding any news or any trending product launch may be ascertained well in twitter information. Our aim is to analyze the user tweet responses on third booster dosage for COVID-19 vaccination. In this sentiment analysis, the user sentiment responses are firstly categorized into positive sentiment, negative sentiment, and neutral sentiment. A performance study is performed to quickly locate the application and based on their sentiment score the application can distinguish the positive sentiment, negative sentiment and neutral sentiment-based tweet responses once clustered with various dictionaries and establish a powerful support on the prediction. This paper surveys the polarity activity exploitation using various machine learning algorithms viz. Naïve Bayes (NB), K- Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), and Valence Aware wordbook and sEntiment thinker (VADER) on the third booster dosage for COVID-19 vaccination. The VADER sentiment analysis predicts 97% accuracy, 92% precision, and 95% recall compared to other existing machine learning models.
Accelerating Distributed Knowledge Graph System Based on Non-Volatile Memory

Deploying large scale knowledge graphs on distributed systems has become an industry trend for their high scalability and availability. There are some distributed graph databases that prefer to adopt the NoSQL data models like the key-value store as their storage engines for its scalability and practicability. Therefore, an upper-level graph query language (GQL) statement will be translated into a group of the native and hybrid kinds of key-value (KV) operations. To accelerate the KV operations generated form upper-level knowledge graph queries, we propose a high performance knowledge graph system with a non-volatile memory based queries booster (KGB). KGB mainly contains a neighbors queries auxiliary index for reducing KVs searching cost, a fast Raft algorithm for efficient KVs operations, and a KV tuning mechanism to acquire extra performance promotion for knowledge graph application scenarios. Experiments show that KGB can effectively reduce the latency and achieve higher performance promotion for knowledge graph system.
A Study on Quantum Cryptography and Its Need

With the increase of e-payments methods and the essential requirement to keep everything secret during online transactions, the demand of cryptography has increased a lot. In cryptography, factorization of huge integers or calculation of the exponents of a large prime number using hands or by normal computers is very hard but quantum computer can easily solve them within a lesser time. There is a need to move from classical cryptography to quantum cryptography to cope up with the advancement of technology and to maintain the demand of high level of information security. This chapter focuses on quantum cryptography, its need, and quantum key distribution that allows two parties to secretly share their information under the nose of an attacker with high computational power. This chapter also focusses on BB84, Ekert 91 protocol and the detection of eavesdropper in quantum key distribution based on photon polarization. Also, discuss the quantum masked authentication protocols in detail.
Identifikasi Keterampilan Berbahasa Siswa Kelas III dalam Pembelajaran Bahasa Indonesia

Language is a communication tool between humans to be able to share experiences, learn from each other and improve intellectual abilities. To be able to communicate well, it is necessary to master the 4 language skills, namely listening skills, speaking skills, reading skills and writing skills. Based on the results of observations and interviews, a problem was found, namely the low mastery of language skills by the third grade students at SDN 2 Plantaran. Factors that cause these problems come from students who lack interest and motivation in following Indonesian language learning. This can be seen from the results of observations that have been made by researchers during the implementation of learning. This study aims to describe how the implementation of Indonesian language learning for class III students, describe the factors that influence difficulties in mastering language skills, and describe strategies as a solution to overcome the problems of class III students' difficulties in mastering language skills. This research was conducted through descriptive qualitative research. Data collection techniques used are interviews, observation and documentation.&#x0D; &#x0D; Keywords: Indonesian Language Learning, Language Skills
A Statistical Approach for Extractive Hindi Text Summarization Using Machine Translation

Automatic text summarization (ATS) is a challenging problem for Indian languages. The outcomes of ATS research are the development of summarization systems that consistently generate high-accuracy summaries and have extensive coverage to handle a wide range of languages. Many ATS systems and techniques are available now in a variety of rich-resource languages. However, for Indian languages such as Hindi, less attention has been received due to resource limitations. To address this issue, in this work, we propose an extractive summarization technique for Hindi using different machine translation engines. For this purpose, we have used translated corpus in Hindi for benchmark datasets for BBC News stories, CNN News stories, and the DUC 2004 datasets and used a statistical approach for summary generation using sentence ranking. We have computed the F-score and ROUGE-3 metrics to evaluate the proposed system which has been found comparable for different machine translation engines.
Penerapan Metode Pembelajaran CIRC Pada Kemampuan Membaca Pemahaman Siswa Sekolah Dasar

The low level of students' reading ability has an impact on students' difficulty in understanding reading. The current phenomenon of students' reading comprehension is still low. Basically, students have difficulty understanding the content of the reading such as: 1) students have difficulty in answering questions. 2) students find it difficult to find the main idea. 3) students find it difficult to make a summary of the text read. This study explains the results of the application of the CIRC learning method to students' reading comprehension skills. This research uses qualitative methods obtained from literature studies. According to a literature review from relevant journals, it was obtained that the application of the CIRC method succeeded in improving students' reading comprehension skills in elementary school.&#x0D; &#x0D; Keywords: CIRC method, reading comprehension ability&#x0D;
Kesulitan Belajar Siswa dalam Mata Pelajaran Bahasa Indonesia di Sekolah Dasar

This study aims to describe students’ learning difficulties in the Indonesian language learning process at the Elementary School and to explore the factors that cause learning difficulties Indonesian in Elementary School. This study uses a kualitattive research design with case studies. The subjects in this study were students of Elementary School. The data collection techniques used in this study are Observation and Interview. Analysis techniques using miles and huberman models are reduction, display, and verification. The result of this study is that there are internal and external factors of students in learning Indonesian, especially in students' reading learning materials. The internal factor is the lack of motivation and interest in reading from the student itself, students are still not motivated in following the indonesian language learning process and students' interest in learning Indonesian is still lacking. The external factors are first: the lack of creativity of the teacher in the learning process takes place, so that it makes students saturated and less pleasant in following the learning process Indonesian, then the methods used by teachers are also not varied and less attractive to students. Second: parents' lack of attention and encouragement towards a child's learning achievement, especially in the process of learning to read. Based on the results of observations and interviews conducted in Elementary School it can be concluded that there so many students who are still having difficulties in the process of learning to read. &#x0D; &#x0D; Keywords: Learning Difficulty, Reading Difficulties, Elementary School
Studi Literatur: Analisis Media Pembelajaran Bahasa Indonesia Guna Meningkatkan Keterampilan Literasi Siswa SD

The purpose of this article is to discuss Indonesian language learning media used in elementary schools to help children develop reading skills. This is a literature review using content analysis techniques. To improve students' reading skills in elementary schools, researchers study relevant research from national, international journals, and other written works that are relevant to the topic of articles on Indonesian language learning media. Researchers go through several stages in reviewing previous research, including: 1) Sorting out relevant research; 2) Collecting data and information for review; 3) Determining the state of the art from research that has been analyzed; 4) Developing the results of information analysis and stringing them into a conclusion. The results of this study can be concluded that the existence of learning media is very important in improving reading skills, especially at the elementary school level. It is necessary to use Indonesian language learning media in order to achieve the learning objectives desired by the teacher&#x0D; &#x0D; Keywords: Learning Media, Indonesian Language, Reading Skills, Elementary School&#x0D;
Mining categorical Covid-19 data using chi-square and logistic regression algorithms

This paper provide a general overview on different kinds of mining categorical covid-19 data Using Chi-Square and Logistic Regression Algorithms. Our study methodology are categorized into three steps, the first one is data preprocessing & feature extraction approaches, the second is feature selection by using chi-square approach,and finally classification by using Logistic Regression Algorithm approaches. Some techniques are illustrated in details while the others with general explanation. In this research we used two datasets (dataset1 & dataset2) in order to test and train our program . The final result after applying chi-square and logistic regression algorihm to the aforementioned data set , the category of each input case will be determines by the likelihood of belonging to one of the two categories in the data set.
Real-time recognition and decision making of objects using deep learning ENet based UAV images

Unmanned Aerial Vehicles (UAVs) have been found to have many uses in the maintenance and oversight of civil infrastructure assets. They contribute to scheduled bridge checkups, crisis control, electricity transmission cable oversight and traffic analysis. With more and more uses of UAVs being introduced, a greater focus on individuality and freedom regarding governance of these devices is required to ensure security, competency, and precision. The subject of this study outlines the method and policies to be followed for teaching the principals of the (efficient Neural Network) ENet architecture, machine learning, and using OpenCV to implement semantic segmentation on a collection of images obtained through aerial photography for identification of objects. Possible utilizations of UAVs in the area of transportation are mentioned as well along with the precision and efficiency of training for the application of the ENet architecture, machine learning, and OpenCV to implement semantic segmentation, the optimization selection of operational parameters, and the machine learning and ENet architecture teaching methods and policies drafting process. Through analysis of the object identification results, it was proven that by adhering to a specific set of parameters, the ENet architecture and machine learning procedures can successfully identify objects with an accuracy of 99% when there is no distortion. In addition, using a combination of all three technologies mentioned, it is possible to not only classify objects, but the device is also capable of automated tracking and detection of the objects by real-time processing of streamed videos by the UAVs. The novelty, that the ENET was applied for large class members difference distance among the same objects family.
Enhancement and colorized a noisy image using MATLAB and AI libraries

The main reason of images corruption is noise. The process of removing noise from distorted images is a challenge. In this paper, the effect of some filters used in filtering some distorted images, such as mean and median, was studied depending on their ability to reconstruct images affected by noise. After that, artificial intelligence and deep learning libraries are used to colorize the images. The noise generated within these images will be removed using these algorithms. the results of this experiment will be shown which algorithm is most preferable for impulse noise removal in digital gray images. To evaluate performance, the quality of the improved images is measured by means of statistical quantitative measures: root mean square error (RMSE) and peak signal to noise ratio (PSNR).
A Bio-Inspired Model for Robust Navigation Assistive Devices: A Proof of Concept

This paper proposes an implementation and evaluation in a real-world environment of a new bio-inspired predictive navigation model for mobility control, suitable especially for visually impaired people. This model relies on the interactions between formal models of three types of neurons identified in the mammals' brain implied in navigation tasks (namely place cells, grid cells, and head direction cells) to construct a topological model of the environment under the form of a decentralized navigation graph. The proposed model, previously tested in virtual environments, demonstrated a high tolerance to motion drift and robustness to environment changes. This paper presents an implementation of this navigation model, based on a stereoscopic camera, and evaluates its possibilities to map and guide a person in an unknown real environment. The evaluation results confirm the effectiveness of the proposed bio-inspired navigation model to build a path map and guide a person through this path, while remaining robust to environment changes, and estimating traveled distances with an error rate below 2% over test paths, up to 100 m. These results open the way toward efficient wearable assistive devices for visually impaired people navigation.
An Innovative Recommendation System for a Knowledge Transfer Matchmaking Platform

The phenomenon of aging, which has become more prevalent in our society in recent decades, has raised a number of concerns about the well-being of the elderly. Studies show that a significant percentage of retired seniors suffer from depression as a result of inactivity and a poor social environment. In order to provide seniors an opportunity to reintegrate into a healthy work environment, a knowledge transfer platform was created, with the goal of allowing seniors to share their knowledge with organizations that required the experience of a specialist. The paper presents a hybrid system that can recommend mentors for a certain assignment to companies, based on their abilities. Using AI agents, a combination of a matchmaking system and a collaborative filter calculates the similarity between the corporate profile and candidate profiles. The functionality of the system has been tested on different scenarios.
Analysis of Students’ Fitness and Health Using Data Mining

The level of physical development of students’ has greatly improved as living standards have continued to rise. According to the previous study, students are dealing with a variety of issues such as stress, sadness, anxiety, disinterest, and behavioral issues. These issues may be caused by a lack of physical education as well as the numerous expectations placed on their shoulders. Because of numerous difficulties reported among students, awareness over physical fitness and health management is fast growing. On a daily basis, most modern university campuses save statistical information trails from varied databases addressing many aspects of student life. However, aggregating these data to get a comprehensive picture of a student, using these data to accurately anticipate student health, and applying such predictions to promote positive student involvement with the universities are all complicated tasks. This paper proposes a use case for Internet of things technology, particularly data mining techniques such as the random forest algorithm, decision tree algorithm, voting classifier, and LSTM are implemented to monitor fitness and health management among students. Here, data is processed through random forest because the random forest model has the greater precision, performance, and classification effect. As a result, this paper is beneficial to schools and colleges in terms of mastering students' health of students and scientifically preventing health issues among students. The trials reveal that the AugmentED model has a high level of accuracy in predicting pupils’ wellness.
Authorship Identification Through Stylometry Analysis Using Text Processing and Machine Learning Algorithms

The project aims to detect the identity of an anonymous author of a defamatory blog post or comment. A dataset samples containing list of authors is acquired, and then predict the anonymous author by using a custom machine learning model. The main task in this proposal is to build an authorship analysis model that will match a sample to the defamatory BlogSpot and reveal the anonymous author. Text preprocessing methods along with a combination of machine learning algorithms such as SDG classifier are employed. Stylometry analysis gives the clarity about the text information like text length, vocabulary and style of text. By this we can use this technique for authorization purpose. The project consists of building a model that can learn authorship style and then scale the model to handle hundreds of such cases. Stylometry analysis plays a major role in this project. An accuracy of 79% is obtained with 40 classes, which was found improving with lesser number of classes.
Implications of Deep Circuits in Improving Quality of Quantum Question Answering

Question Answering (QA) has proved to be an arduous challenge in the area of natural language processing (NLP) and artificial intelligence (AI). Many attempts have been made to develop complete solutions for QA as well as improving significant sub-modules of the QA systems to improve the overall performance through the course of time. Questions are the most important piece of QA, because knowing the question is equivalent to knowing what counts as an answer (Harrah in Philos Sci, 1961 [1]). In this work, we have attempted to understand questions in a better way by using Quantum Machine Learning (QML). The properties of Quantum Computing (QC) have enabled classically intractable data processing. So, in this paper, we have performed question classification on questions from two classes of SelQA (Selection-based Question Answering) dataset using quantum-based classifier algorithms-quantum support vector machine (QSVM) and variational quantum classifier (VQC) from Qiskit (Quantum Information Science toolKIT) for Python. We perform classification with both classifiers in almost similar environments and study the effects of circuit depths while comparing the results of both classifiers. We also use these classification results with our own rule-based QA system and observe significant performance improvement. Hence, this experiment has helped in improving the quality of QA in general.