Reviewerly/exHarmony huggingface dataset:
PERFORMANCE ANALYSIS OF THE IMBALANCED DATA METHOD ON INCREASING THE CLASSIFICATION ACCURACY OF THE MACHINE LEARNING HYBRID METHOD

This study analyzes the performance of hybrid methods in improving accuracy on imbalanced data using Dengue Hemorrhagic Fever Case Data from 2017 to 2021 in Bandung City. The attributes used in this study consist of Total Population, Total Male, Elementary School Graduation, Junior High School Graduation, High School Graduation, College Graduation, Rainfall, Average Temperature, Humidity, Male Cases, Number of Cases, and Class. This research combines five Machine Learning methods, such as Decision Tree, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbor, and Naïve Bayes. Hybrid Methods used in this research are Voting and Stacking methods. The oversampling methods used to handle imbalanced data in this study are Random Oversampling and Adasyn. The results show that Voting and Stacking without Random Oversampling and Adasyn get the same accuracy of 88,88%. While using Random Oversampling, voting gets an accuracy of 95,37% and stacking gets an accuracy of 96,29%. While using Adasyn, voting gets an accuracy of 94,44% and stacking gets an accuracy of 97,22%. Based on the results obtained, it can be concluded that the Random Oversampling and Adasyn Method can improve the performance of the Machine Learning hybrid method on imbalanced data. The contribution of this research is to provide information on the study and analysis of the implementation of the Random Oversampling and Adasyn methods in improving the performance of the Voting and Stacking methods in hybrid classification.
REDESIGNING USER INTERFACE OF TEMAN BUS APPLICATION USING GOAL-DIRECTED DESIGN

In these modern era, it is very easy to create an investment portfolio, an investment portfolio can be formed by buying a shares. By creating portfolio investation, our can expect score return which makes it an advantage. However, there is also a risk value that will be obtained when buy shares. In the formation of an investment portfolio for active investors, it is expected to create an optimal investment portfolio, there are many models that can be used to create an optimal investment portfolio. In this final project, will discusses the use of the Black-Litterman Model in optimizing portfolios for active investors. The Black-Litterman model is one of the models that can be used to optimize the investment portfolio, the Black-Litterman model provides additional information for the return and risk values based on the views of experts. This model builds on the MV and CAPM using a Bayesian framework that allows investors to effectively incorporate their views of the market into the allocation process asset. Based on several tests that have been carried out during the research with different days, the value of the investor's view will always be different every day, the value of an optimistic view will have a good impact on the stock, even though the stock price is falling, if the investor's view is expressed optimism, the weight of the shares will remain high.
Effective Quantum Key Distribution Using Modified Key Sifting Scheme for Cloud Storage Security

Cloud storage has gained extensive attention due to its low cost, global scalability, and improved agility.Data privacy is an important issue in cloud storage because breaches of personal information or business secrets are occurred by malicious hackers or unauthorized cloud operators.To address this issue, a Modified Key Sifting Scheme (MKSS) is proposed for developing an effective Quantum Key Distribution (QKD) with Two-Way Identity Authentication (TWIA) to provide security in the cloud storage.The Tree Parity Machine (TPM) is used for proposing a MKSS which used to minimize the key distribution time based on the synchronization.This MKSS-QKDTWIA considers four different objects such as data user, data owner, server and data storage in a communication scenario.The performance of the MKSS-QKDTWIA is analyzed by means of key computation time, processing time, encryption & decryption time, and memory utilization.Existing research such as Modified Random Fibonacci Cryptographic (MRFC), Software Stack-QKD (SS-QKD) and Enhanced Cloud Security Model based on QKD Protocol (ECSM-QKDP) are used to evaluate the performance of MKSS-QKDTWIA.The key computation time of MKSS-QKDTWIA for 50 attribute sets in each key is 16.7s which is less when compared to the MRFC, SS-QKD and ECSM-QKDP.
Cross Domain Hybrid Feature Fusion based Sarcastic Opinion Recognition Over E-Commerce Reviews Using Adversarial Transfer Learning

Sarcasm detection is a challenge in review based e-commerce product recommendation systems.Failure to detect sarcasm causes assignment of directionally opposite ratings to reviews and faults the recommendation systems.This work proposes a cross domain hybrid feature fusion based sarcastic opinion recognition system.Hybrid deep feature integrating content and context is formed and feature vector volume is expanded using cross domain adversarial learning.The hybrid features are used for sarcasm detection in two combinations.First combination is hybrid features with bi-directional long short term memory (LSTM)and second is hybrid features with traditional machine learning classifiers.Hybrid feature with bi-directional LSTM is able to capture temporal relation between sentences of the review for better identification of sarcasm.Traditional machine learning classifiers trained with these hybrid features are also able to provide higher accuracy of sarcastic detection in reviews.The effectiveness of the proposed cross domain hybrid feature fusion features is tested against various experimental setups with Bi-directional LSTM and traditional machine learning classifiers.The proposed solution is able to achieve peak accuracy of 95% in classifying e-commerce reviews.
Lightweight Authentication Enhancement Using Arnold Chaotic Map and Markov-Chain for Internet of Things Applications

The Internet of Things (IoT) was a new concept that connected various physical things to the internet.The IoT is fast expanding and will soon significantly impact everyday life.Although the increasing number of connected IoT devices is simplifying life, they risk our data.Radiofrequency identification (RFID) assists in the automated identification of connected IoT devices.Furthermore, the primary challenges for RFID tag-connected technology are privacy and security.With the improving safety of solutions RFID for many apps, RFID necessitates a centralized database widening.Blockchain techniques are speedily establishing themselves together as a new distributed and decentralized replacement.And it provides enhanced transparency, data security, immutability, dependability, and lower maintenance costs.Because of its inherent benefits, RFID is projected to play a significant part in enabling identifying technology on the IoT.However, due to its association with sensor technology, it has the potential to be applied in a wide range of industries.Security, however, is one of the more complex components of creating an RFID system.RFID Security focuses around authentication and privacy concerns.From the previous works, we found that the computing cost and uptime are higher.To solve these problems, we offer improving lightweight authentication using Arnold Chaotic map and Markov chain for IoT applications.The proposed method uses random algorithms (Markova chain, Arnold map chaotic, and ECC), which add high security.We also used blockchain technology to transfer keys through a non-security channel.Based on parameters such as (communication cost, throughput rate, storage requirements, transmission delay, and computational cost) the results of the proposed RFID protocol were compared with several RFID-based security solutions.As a result, the proposed lightweight scheme is more efficient and secure than existing RFID systems in terms of performance, according to simulation results, and is fully compatible with actual applications.
Planning the Optimal 3D Quadcopter Trajectory Using a Delivery System-Based Hybrid Algorithm

Path planning is one of the most crucial aspects of implementing unmanned aerial vehicle (UAV) missions.Therefore, it is essential to figure out the optimal path from the starting point to the target point in different scenarios.In this paper, we propose employing a hybrid algorithm, named FPA-GA, generated by combining the flower pollination algorithm (FPA) and genetic algorithm (GA), to find an optimum path in a real modern building's environment.In addition, a cubic polynomial algorithm via two points was proposed to make the route adequate and smooth.Because the GA has a good capability for exploring the search space, it is employed for exploration while the FPA and GA are employed to increase the exploitation capability in the proposed algorithm.Five different scenarios are utilized to evaluate FPA-GA's ability to find the optimal path in a variety of situations, and then the proposed algorithm's performance is compared with that of seven other algorithms: GA, FPA, bat algorithm (BA), particle swarm optimization (PSO), whale optimization algorithm (WOA), improved whale optimization algorithm (IWOA), and IWOA-PSO.The best path, the mean path length, the standard derivation, and the worst path length are the four parameters used to compress data.In all scenarios, the results demonstrate that FPA-GA is capable of locating the shortest and most collision-free paths, and the hybrid algorithm FPA-GA is always superior to other algorithms.The proposed algorithm provided the mean path length enhancement in all scenarios, where the maximum enhancement equals (33.6%), (23.1%), (55.5%), (29.8%), (50.5%), (53.9%), and (26.4%) compared with GA, FPA, BA, PSO, WOA, IWOA, and IWOA-PSO, respectively.On the other hand, the proposed algorithm is guaranteed to find the best path in all scenarios, where the standard deviation of FPA-GA is always less than that of other algorithms.
Handwritten Character Recognition Using Morlet Stacked Sparse Auto Encoder based Feature Dimension Reduction

Handwritten character recognition (HCR) is a growing field in the applications of pattern recognition, image processing, communication technologies, and so on.But, the identification of handwritten characters affected because of different styles of writers, or even one writer's style differed according to the conditions.Moreover, the huge amount of features from the handwritten characters also affect the classification.To address the aforementioned issues, the hybrid feature extraction (HFE) with morlet stacked sparse auto-encoder (MSSAE) based feature dimensionality reduction is proposed for improving the classification of HCR.Here, the HFE is the combination of different textural and shape features such as histogram of oriented gradients (HOG), gray-level co-occurrence matrix (GLCM), discrete wavelet transform (DWT), and skeleton features.The morlet wavelet activation function is used in the MSSAE to enhance the dimensionality reduction ability that used for an effective reduction of feature dimensions.The reduction of feature dimension using MSSAE is used to improve the classification using multi-class support vector machine (MSVM).In this research, the real-time indigenous languages i.e., English and Kannada handwritten characters from the Chars74K dataset are used for the analysis i.e., offline recognition.On the other hand, the online recognition of Kannada characters is also done for analyzing the HFE-MSSAE method.The HFE-MSSAE method is analyzed in terms of accuracy, precision, recall, CSI and F-measure.The existing researches namely convolutional neural network (CNN), hybrid feature based long short term memory (HF-LSTM) and elephant herding optimization -long short term memory (EHO-LSTM) are used to evaluate the HFE-MSSAE method.The accuracy of the HFE-MSSAE for Kannada is 96.73% which is higher than the HF-LSTM and EHO-LSTM.
A Novel Association Rule Mining Model for Generating Positive and Negative Association Rules with Hybridized Meta-Heuristic Development

Association rule mining (ARM) is to discover entire association rules with the confidence and support that are accordingly equivalent or above user-specific lower confidence and support.The existing suggested ARM methods can do the operations on the static datasets only, where the operation of the complete dataset must be accessible in the initial process of mining.Classical ARM methods are designed well on working with static data, and so, they cannot be used instantaneously for mining the association rules in data.To accomplish this, a novel framework is developed for association rule mining with the aid of an adaptive concept.Initially, the required data is collected from the benchmark datasets.The next step is to develop the novel association rule mining algorithm (NARMA), where the parameters are tuned by Modified Scavenger rate-based Dingo Billiards Optimization (MCR-DBO) by the hybridization of Dingo Optimizer (DOX) and Billiards-Inspired Optimization (BIO).This algorithm is used to identify the distinct itemsets of data.Consequently, the resultant dataset is given for finding the most eminent factors, where the itemsets are categorized into positive association rules as well as negative association rules.When the minimal threshold values are fixed with a minimum support count of 0.3, the proposed model achieved an average 81% confidence for generated top 1000 association rules from WebDocs dataset by minimizing the execution time and memory requirements.The outcomes disclose that the designed approach competently mines both the positive and negative rules and outperformed other recommended techniques.
A Literature Review of Block Chain Application in Social Media

Abstract: The importance of online social networks (OSNs) in people’s daily lives is growing, however all widely used OSNs are centralised, which brings up a number of management, security, and privacy concerns. The potential to address the aforementioned problems is provided by a architecture technology which is known as blockchain. In the below work, an Online Social Network based on block-chain is created and its decentralised operation is demonstrated. To decentralise data, a large volume of typically low-security requirements can be stored in the Interplanetary Filesystem (IPFS). Users may self-manage the OSN in a democratic manner thanks to a decentralised autonomous organisation that has been built for user autonomy
Featured Services of BDS-3 with Applications in Geoscience

BeiDou Global Navigation Satellite System (BDS-3) was formally commissioned to provide satellite navigation services worldwide in 2020. It not only has the normal positioning, navigation and timing (PNT) functions, but also provides several kinds of featured services. The paper &amp;#160;focuses on the featured services of BDS-3 and their applications in various geoscience fields. First, the featured services of BDS-3 and their performances are introduced. Then different application examples are described and analyzed based on the Geostationary Orbit (GEO) satellite-based featured services and Middle Earth Orbit (MEO) satellite-based featured services. Finally, some possible improvements to BDS in the future are discussed.
Prawne implikacje wykorzystywania sztucznej inteligencji w handlu algorytmicznym na rynku kapitałowym

The article is focused on the use of artificial intelligence in algorithmic trading in capital markets. The purpose of the study is to answer the questions of whether 1) the existing laws relating to algorithmic trading can successfully regulate such uses; 2) the hypothetical extension application of the draft Artificial Intelligence Act and the recognition of the use of artificial intelligence in algorithmic trading as a high-risk system would increase the safety and stability of the capital market; 3) capital markets law can be a model for other sectoral regulations on the use of artificial intelligence. The research method adopted in the article is the analysis of binding EU legislative and executive acts, the draft of the Act on Artificial Intelligence, as well as the practice of supervisory authorities – the Financial Supervision Authority and the European Banking Authority. The article formulates conclusions de lege ferenda.
Stance and Gender Detection in Spanish Tweets

In this paper, we present a deep learning based system for the user profiling and stance detection tasks in Twitter. Stance detection consists in automatically determining from text whether the author is in favor of a given target, against this target, or whether neither inference is likely. The proposed system assembles Convolutional Neural Networks and Long Short-Term Memory neural networks. We use this system to address, with minor changes, both problems. We explore embeddings and one-hot vectors at character level to select the best tweet representation. We test our approach in the Stance and Gender Detection in Tweets on Catalan Independence track proposed at IberEval 2017 workshop. With the proposed approach, we achieve state-of-the-art results for the Stance detection subtask and the best results published until now for the Gender detection subtask.
Full Inflection Learning Using Deep Neural Networks

In this paper we present a deep learning architecture capable of predicting the full inflectional paradigms from the uninflected, dictionary form of a word and the full orthographic syllabification. We use Romanian as a case study, since its morphology is rife with stem alternations (non-concatenative inflectional processes) and phonological ambiguity between hiatus and diphthongs. We show that Sequence to Sequence model receiving n-grams as input can solve this problem as good as, if not better than, the state of the art.
Contrastive Reasons Detection and Clustering from Online Polarized Debates

This work tackles the problem of unsupervised modeling and extraction of the main contrastive sentential reasons conveyed by divergent viewpoints on polarized issues. It proposes a pipeline approach centered around the detection and clustering of phrases, assimilated to argument facets using a novel Phrase Author Interaction Topic-Viewpoint model. The evaluation is based on the informativeness, the relevance and the clustering accuracy of extracted reasons. The pipeline approach shows a significant improvement over state-of-the-art methods in contrastive summarization on online debate datasets.
The Effectivness of Using snake and Ladder Game to Teach Speaking Skill

Abstract&#x0D; English language has four skills: writing, listening, reading, and speaking, there are so many young people who are not confident, can not even speak English. at SMPN 1 Jombang have problems in speaking skills. The purposes of this study are to determine: the speaking skill of students before treatment. 2. the speaking skill of students after treatment. the effectiveness of the snake and ladder games media on teach speaking skills. This study used a quantitative method with a total sample was 31 students. however, children who has tooken the pre-test and post-test is 25 from the experimental class (8J). Sampling was carried out in grades 8J at SMPN 1 Jombang. The overall sampling technique used probability sampling with random sampling type, The technique of collecting data was using pre-test and post-test, in proving and analyzing the data was using validity test, descriptive statistics and paired sample test. The results experimental class before and after treatment are: 1). Pre test in the experimental class with the respondents is 25 with an average value of 43 and the lowest value of 40 and the highest score of 53. 2). The post test in experimental the average score in the experimental class was 71.72 with the lowest score of 60 and the highest score of 80. 3). Paired sample test shows that the snake and ladder game media is effective for learning English, especially for developing students' speaking skills, from the calculation of the data is 0,000 &lt; 0,05 it means that the results of hypothesis testing, Ha is accepted, Ho is rejected.&#x0D; &#x0D; &#x0D; Keyword : effectiveness, speaking, snake and ladder games.&#x0D;
QUESTION ANSWERING FOR SUMEDANG LARANG KINGDOM USING THE MULTILAYER PERCEPTRON ALGORITHM

In Indonesia, there were many kingdoms in the past, one of which was the Sumedang Larang Kingdom. Sumedang Larang is an Islamic kingdom under the control of the Pajajaran Kingdom. Through history, a nation will be able to recognize the origin of its own nation. Therefore, teaching about history is very important to be instilled from an early age. Through the rapid development of technology, teaching today is not only in the form of formal teaching, but also informal teaching. Nowadays, informal teaching can be done through various media, one of the media that is often used is gadgets. The utilization of gadgets as learning media allows a person to learn independently. One form of history learning by utilizing gadgets can be in the form of a Question Answering System (QAS). QAS allows users to ask questions using natural language and the system will answer the questions. Therefore, our research aims to help introduce the history of the Sumedang Larang kingdom to the public. We build a QAS by utilizing the n-gram model, Multilayer Perceptron (MLP) algorithm, and ontology. N-gram is used to cut words/sentences and convert them into a matrix, while MLP is used to classify texts, and ontology is used as knowledge representation. In this study, the system was able to answer 35 questions out of 61 questions, so it had an accuracy of 57.37%.
KLASIFIKASI TUMBUHAN ANGIOSPERMAE MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR BERDASARKAN PADA BENTUK DAUN

About 250,000 living plant species are plants of the Angiosperms group. The diversity of Angiosperm plants in Indonesia makes it difficult for someone to carry out the process of identifying plants into the monocotyledonous or dicotyledonous class. This is the background for doing this research by classifying Angiosperms plants using the K-Nearest Neighbor algorithm. Research related to classifying manga plants using the Backpropagation algorithm with feature extraction using GLCM produces an accuracy of 49%. Furthermore, the classification of plants using the K-Nearest Neighbor algorithm with leaf morphological features extraction resulted in an accuracy of 92%. There is also a classification of citrus plants using the K-Nearest Neighbor algorithm with leaf texture feature extraction using GLCM resulting in an accuracy of 81.48%. In this study, the K-Nearest Neighbor classification algorithm is used with feature extraction using a population matrix with the aim of examining the level of classification accuracy using population matrix feature extraction. The data used is a digital leaf image which will extract its shape features using a population matrix feature by doing image pre-processing and detection first. The data is then divided into training data of 70%, and test data of 30%. The results of this study are the accuracy of the test data obtained by 85% with the number of neighbors k=1. After testing, an evaluation of the model will be carried out using a confusion matrix, which consists of accuracy, precision, recall, and f-1 score.