Reviewerly/exHarmony huggingface dataset:
Support Vector Machine for Multiclass Classification of Redundant Instances

In recent years, support vector machine has become one of the most important classification techniques in pattern recognition, machine learning, and data mining due to its superior classification effect and solid theoretical base. However, its training time will increase dramatically as the number of samples increases, and training will become more sophisticated when dealing with problems involving multiple classifications. A quick training data reduction approach MOIS appropriate for multi-classification tasks is presented as a solution for the aforementioned issues. While eliminating redundant training samples, the boundary samples that play a vital role are chosen in order to considerably reduce training data and the problem of unequal distribution between categories. The experimental results demonstrate that MOIS may maintain or even improve the classification performance of support vector machines while substantially enhancing training efficiency. On the Opt digit dataset, the suggested method improves classification accuracy from 98.94% to 99.05%, while training time is reduced to 15% of the original; in HCL2000, the proposed method improves classification accuracy from 98.94% to 99.05%. When the accuracy rate is marginally increased (from 99.29% to 99.30%) on the first 100 categories dataset, the training time is dramatically reduced to less than 6% of the original. Additionally, MOIS has a high operational efficiency.
Auto-encoder and Graph Neural Networks-Based Hybrid Model for Link Prediction on Complex Network

Among the many network data mining tasks, link prediction is crucial. Due to the in-depth development of graph neural network research, related models can learn essential features of the network more effectively and have achieved good prediction results in tasks such as link prediction. However, unlike the CNN model in deep learning, the existing graph neural network model only aggregates the first-order neighbor information of nodes and does not fully consider the topological structure characteristics between neighbor nodes. On this basis, a motif-based graph neural network link prediction model is proposed. The model adopts the self-encoder structure. In the encoding process, the adjacency matrix of the node is constructed through the motif, and then the motif neighborhood of the node is obtained. Next, the neighbor information is aggregated according to the neighborhood of each type of motif, and the node is obtained through nonlinear transformation. Finally, concatenate the representations of nodes under each type of motif. However, since different motif structures have other importance in the network, the attention network gives the attention weights expressing different motifs. The vector representation of nodes is provided by connecting the attention network. During decoding, the network is reconstructed by computing the similarity between nodes. Investigational results on several citation collaborator networks show that the proposed method surpasses most baseline algorithms on two metrics, effectively improving the accuracy of network link prediction.
Improved Cat Swarm Optimization Algorithm for Load Balancing in the Cloud Computing Environment

Recently, cloud computing has gained recognition as a powerful tool for providing clients with flexible platforms, software services, and cost-effective infrastructures.Cloud computing is a form of distributed computing that allows users to store and process data in a virtual environment instead of a physical server.This is beneficial because it allows businesses to quickly scale up or down their computing capacity, reducing the need to invest in expensive hardware.As cloud tasks continue to grow exponentially and the usage of cloud services increases, scheduling these tasks across diverse virtual machines poses a challenging NP-hard optimization problem with substantial requirements, including optimal resource utilization levels, a short execution time, and a reasonable implementation cost.The issue has consequently been addressed using a variety of metaheuristic algorithms.In this paper, we propose a new loadbalancing approach using the Cat Swarm Optimization (CSO) algorithm in order to distribute the load among the various servers within a data center.Statistical analyses indicate that our algorithm is superior to previous research with regard to energy consumption, makespan, and time required up to 30%, 35%, and 40%, respectively.
Multi-feature Fusion for Relation Extraction using Entity Types and Word Dependencies

Most existing methods do not make full use of different types of information sources to extract effective features for relation extraction.This paper proposes a multi-feature fusion model based on raw input sentences and external knowledge sources, which deeply integrates diverse lexical, semantic, and syntactic features into deep neural network models.Specifically, our model extracts lexical features of different granularity from the original input text representation, entity type features from the entity annotation information of the corpus, and dependency features from the dependency trees.Meanwhile, the dimension-based attention mechanism is proposed to enrich the diversity of entity type features and enhance their discriminability.Different features enable the model to comprehensively utilize various types of information, so this paper fuses these features and train a classifier for relation extraction.The experimental results show that the proposed model outperforms the existing state-of-the-art baselines on the TACRED Revisited, Re-TACRED, and SemEval datasets, with macro-average F1 scores of 81.2%, 90.2%, and 89.4%, respectively, improving the performance by 1.4%, 4.4%, and 2% on average, which indicates the effectiveness of multi-feature fusion modeling.
A Hybrid Cryptography Method using Extended Letters in Arabic and Persian Language

Cryptography is widely used in information security systems.In encryption, the goal is to hide information in such a way that only the sender and receiver are aware of the existence of communication and information.Encryption takes place in various media, such as image, sound and text.Today, the rapid growth of network technologies and digital tools has made digital delivery fast and easy.However, the distribution of digital data in public networks such as the Internet has various challenges due to copyright infringement, forgery, coding and fraud.Therefore, methods of protecting digital data, especially sensitive data, are very necessary.Accordingly, in this article, a combined solution is used based on the technique of stretching the letters and making minor changes in the letters that have closed spaces so that the bits related to the hidden text can be inserted into a Persian or Arabic language.For this purpose, a new solution has been designed, in which the cover text is similar to the normal text, with the difference that, in addition to the extended letters that are longer due to the status of the secret message, it also has some prepositions, which have spaces.They are empty and closed.Of course, this difference in the closed space between the original letters and the changed letters will be very slight, and as a result, there will not be much difference between them that the normal user can feel the change.Finally, the proposed solution has been evaluated with the help of the MATLAB program, and according to the rate parameter of encryption capacity, the results show that the proposed method has an average encryption capacity of more than 50% compared to other common solutions.
Continuous Software Engineering for Augmented Reality

Continuous software engineering is a new trend that has attracted increasing attention from the research community in recent years.In software engineering there are "continuous" stages that are used depending on the number of artifact repositories such as databases, meta data, virtual machines, networks and servers, various logs, and reports.Augmented Reality (AR) technology is currently growing rapidly.We can find this technology in various fields of life, but unfortunately sustainable software engineering for Augmented Reality is not found.The method shown in previous research is a general method in software engineering so that a theory is needed for sustainable software engineering for AR considering that AR is not just an ordinary application but there are 3D elements and specific components that must be met so that it can be called AR.The main idea behind this research is to find a continuous pattern from the stages of the existing method so far.For example, in general the stages of system development are planning, analysis, design, implementation and maintenance.Then after the application has been built, does it finish there?As we know software always grows and develops according to human needs.Therefore, there are continuous stages that must be patterned so that the life cycle process can be maintained.In this paper we present our initial findings about the continuous stages of continuous software engineering namely continuous planning, continuous analysis, continuous design, continuous programming, continuous integration, and continuous maintenance.
A Hybrid Federated Learning Framework and Multi-Party Communication for Cyber-Security Analysis

The term "Internet of Things" (IoT) describes a global system of electronically linked devices and sensors capable of two-way communication and data sharing.IoT provides various advantages, including improved efficiency and production and lower operating expenses.Concern about data breaches is constantly present, for example, since devices with sensors capture and send confidential data that might have dire effects if leaked.Hence, this research proposed a novel hybrid federated learning framework with multi-party communication (FLbMPC) to address the cyber-security challenges.The proposed approach comprises four phases: data collection and standardization, model training, data aggregation, and attack detection.The research uses the UNSW-NB15 cyber-security dataset, which was collected and standardized using the z-score normalization approach.Federated learning was used to train the local models of each IoT device with their respective subsets of data.The MPC method is used to aggregate the encrypted local models into a global model while maintaining the confidentiality of the local models.Finally, in the attack detection phase, the global model compares real-time sensor data and predicted values to identify cyber-attacks.The experiment findings show that the suggested model outperforms the current methods in terms of accuracy, precision, f-measure and recall.
A New Approach Method for Multi Classification of Lung Diseases using X-Ray Images

Lung disease is one of the most common diseases in today's society.This lung disease's treatment is frequently postponed.This is usually due to a lack of understanding about proper treatment and a lack of clear information about lung disease.Reading the correct X-ray images, which is usually done by experts who are familiar with these X-rays, is one method of detecting lung disease.However, the results of this diagnosis are dependent on the expert's practice schedule and take a long time.This study aims to classify lung disease images using preprocessing, augmentation, and multimachine learning methods, with the goal of achieving high classification performance accuracy with multi-class lung disease.The classification ExtraTrees was obtained from experimental results with unbalanced datasets using a balancing process with augmentation.Precision, Recall, Fi-Score, and Accuracy are 100% for training and testing data 89% for Precision, 88% for Recall, 87 for Fi-Score, and 85% for Accuracy outperform other machine learning models such as Kneighbors, Support Vector Machine (SVM), and Random Forest in classifying lung diseases.The conclusion from this research is that the machine learning approach can detect several lung diseases using X-ray images.
Hubungan Antara Keterampilan Metakognisi dan Motivasi Belajar dengan Hasil Belajar IPA Siswa Kelas VIII SMP Negeri di Kecamatan Ujung Pandang Kota Makassar

This study aims to (i) analyze how metacognitive skills are; (ii) analyze how learning motivation is; (iii) analyze how science learning outcomes (iv) analyze the relationship between metacognitive skills and science learning outcomes; (v) analyze the relationship between learning motivation and science learning outcomes; (vi) analyze the relationship between metacognitive skills and learning motivation with learning outcomes together with science learning outcomes for class VIII SMP Negeri Makassar City students. This research is an ex-post facto research. The population in the study was all students of class VIII of SMP Negeri in Ujung Pandang sub-district, Makassar City in the 2021-2022 academic year with a total of 717 students. The research sample was 345 students using cluster random sampling. Data collection techniques are questionnaires and documentation. Data were analyzed by descriptive and inferential statistical techniques. The results showed that (i) students' metacognition skills were classified as moderate, (ii) students' learning motivation was classified as moderate (iii) students' science learning outcomes were in the medium category, (iv) there was a relationship between metacognitive skills and science learning outcomes, (v) there were there is a relationship between learning motivation and learning outcomes in Biology, (vi) there is a relationship between metacognitive skills and learning motivation with science learning outcomes for eighth grade students of SMP Negeri in Ujung Pandang District, Makassar City.
The Use of English Song for Improving Students' Vocabulary Mastery: Action Research

The goal of this study was to promote students' vocabulary mastery using English song at MTs Negeri 2 Bandar Lampung's eighth grade. The thirty-two participants in this study were made up of 11 males and 19 females. This study was carried out with the use of classroom action research. The action research model, according to Kemmis and Taggart, consists of two cycles. Planning, action, observation, and reflection were the four steps in each cycle. Before beginning cycle 1, the writer conducted a pre-cycle to determine the students' vocabulary competence as well as their vocabulary challenges. In this study, the writer worked with an English teacher at MTs Negeri 2 Bandar Lampung to collect data from students in the eighth (VIII C) grade. The writer's goal for this study was to increase the students' learning activities and vocabulary knowledge. The students' improvement in learning activities was evident from the average score of students' learning activities in cycle 1 of 7.16 with the criteria quite active, while in cycle 2 of 7.83 with the criteria quite active. As a result, there was a 0.67 point improvement in students' learning activity between cycles 1 and 2. Meanwhile, the students' vocabulary mastery improved, as seen by the average pre-cycle score of 66.25, with 12 students passing the Minimum Mastery Criterion. The average score for the first cycle was 70.87, with 13 students 40.62% passing the Minimum Mastery Criterion, while the average score for the second cycle was 81.37, with 26 students 81.37% passing the Minimum Mastery Criterion. Based on the findings of the data analysis, it is possible to conclude that Classroom Action Research using English song can promote students' learning activities and vocabulary acquisition.
Hubungan Kecerdasan Emosional Dan Motivasi Belajar Dengan Hasil Belajar IPA Peserta Didik Kelas VIII Kepulauan Selayar

This study aims to determine: (1) the description of emotional intelligence, learning motivation, and science learning outcomes for students in class VIII SMPN in Benteng District, (2) The relationship between emotional intelligence and science learning outcomes for students in class VIII SMPN in Benteng Regency. Benteng Regency, (3) the relationship between learning motivation and science learning outcomes for class VIII SMPN students in Benteng Regency, (4) the relationship between emotional intelligence and learning motivation on science learning outcomes for VIII SMPN students in Benteng Regency. This research is an ex post facto research which is correlational. Sampling by means of random sampling. Data were analyzed by descriptive statistics and inferential statistical analysis. The conclusions in this study are as follows. (1) The emotional intelligence of class VIII SMP Negeri in Benteng Subdistrict, Selayar Islands Regency is in the medium qualification. Motivation to learn is in moderate to high qualifications, and student learning outcomes are in low, medium to high qualifications. (2) There is a significant influence of emotional intelligence directly on learning outcomes. (3) There is a significant effect of learning motivation directly on learning outcomes. (4) There is a significant influence between emotional intelligence and learning motivation together on learning outcome.
Performance Boosting Mislabels Correction with Semi-Supervised Learning and Deep Feature Similarity Measurements

In this paper, dataset is applied from the perspective of semi-supervised learning, using a small amount of clean annotated data and combining a large amount of misannotated data for training. Clothing1M was used in the experiments. Therefore, the purpose of this study is to tackle the problem of noisy datasets to boost the models&#x2019; performance. From the perspective of semi-supervised learning, the clean dataset is treated as the labeled dataset, and the remaining noisy data are regarded as the unlabeled data. The initial model was trained on the labeled dataset first, and then the model was used to perform feature extraction on the unlabeled dataset. The &#x201C;prototypes&#x201D; for each category can be obtained via feature matching and clustering. As a result, the dual screening scheme is proposed to take the model&#x2019;s predictions and the predictions from the prototypes method into account, reducing the impact of noisy data. The clean dataset after screening and the remaining data with noisy labels were trained by MixMatch to further enhance the robustness of models. Experimental results show that the proposed methods can boost the classification performance by 3% in accuracy, and outperform the state-of-the-art method by 1%. It achieves (1) cost reduction in labeling, (2) impact mitigation of noisy data via the dual screening scheme, and (3) performance boosting by semi-supervised learning.
Lexical Cohesion Used In Donald Trump’s Campaign Speech

The title of this research is "Lexical Cohesion Used in Donald Trump's Campaign Speeches". Lexical cohesion is one of the most important tools for bringing text together. Lexical cohesion is grouped into five types. Due to the large number of types, research on lexical cohesion needs to be carried out and the problems to be studied are: the types and uses of the most common types found in Donald Trump's campaign speeches. The theory used is the theory of lexical cohesion types taken from Cohesion in English by Halliday and Hassan (1976). This study uses four of Donald Trump's speeches as data sources. Data collection is carried out in the form of library research, which searches for and downloads data sources and then reads the relevant data included in it. All data is grouped into the appropriate type group. The data that has been collected is analyzed descriptively and frequency. The results of the study show that five types of lexical cohesion are found in Donald Trump's campaign speeches. The five types of lexical cohesion found are repetition, synonym, superordinate, general words, and collocation. The mostly type of lexical cohesion found is repetition.
An Extended Vector Polar Histogram Method Using Omni-Directional LiDAR Information

This study presents an extended vector polar histogram (EVPH) method for efficient robot navigation using omni-directional LiDAR data. Although the conventional vector polar histogram (VPH) method is a powerful technique suitable for LiDAR sensors, it is limited in its sensing range by the single LiDAR sensor to a semicircle. To address this limitation, the EVPH method incorporates multiple LiDAR sensor’s data for omni-directional sensing. First off, in the EVPH method, the LiDAR sensor coordinate systems are directly transformed into the robot coordinate system to obtain an omni-directional polar histogram. Several techniques are also employed in this process, such as minimum value selection and linear interpolation, to generate a uniform omni-directional polar histogram. The resulting histogram is modified to represent the robot as a single point. Subsequently, consecutive points in the histogram are grouped to construct a symbol function for excluding concave blocks and a threshold function for safety. These functions are combined to determine the maximum cost value that generates the robot’s next heading angle. Robot backward motion is made feasible based on the determined heading angle, enabling the calculation of the velocity vector for time-efficient and collision-free navigation. To assess the efficacy of the proposed EVPH method, experiments were carried out in two environments where humans and obstacles coexist. The results showed that, compared to the conventional method, the robot traveled safely and efficiently in terms of the accumulated amount of rotations, total traveling distance, and time using the EVPH method. In the future, our plan includes enhancing the robustness of the proposed method in congested environments by integrating parameter adaptation and dynamic object estimation methods.
A Survey on Cybercrime Using Social Media

There is growing interest in automating crime detection and prevention for large populations as a result of the increased usage of social media for victimization and criminal activities. This area is frequently researched due to its potential for enabling criminals to reach a large audience. While several studies have investigated specific crimes on social media, a comprehensive review paper that examines all types of social media crimes, their similarities, and detection methods is still lacking. The identification of similarities among crimes and detection methods can facilitate knowledge and data transfer across domains. The goal of this study is to collect a library of social media crimes and establish their connections using a crime taxonomy. The survey also identifies publicly accessible datasets and offers areas for additional study in this area.
Implementasi Metode Analytical Hierarchy Process (AHP) untuk Analisis Pemilihan Aplikasi Sekuritas Saham Pemula

In the current era of modernization, everything can be accessed very easily via the internet and digitization, this has become a demand for all sectors to be more adaptive in the development of the times, one of which is in terms of the capital market which is now an important part of financial world. Technological progress is a factor that has a very broad effect, there are many conveniences that can be obtained for potential investors, issuers and prospective issuers to get updated information from the capital market. The digitization process does not only affect trading patterns in the capital market, but in terms of business and products in a company that is produced. The selection of stock securities applications includes application design, application ease, application features, and application speed. This is very important because it affects when we make transactions in the capital market to avoid things that are not desirable. The method used is the Analytical Hierarchy Process method with application design criteria, application ease, application features, and application speed. From the results of processing stock security application selection data using Microsoft Excel 2010 and Expert Choice 11 software, the results show that the first rank is IPOT with a value of 0.570 or 57%, then POEMS with a value of 0.217 or 21.7% and finally AJAIB with a value of 0.214 or 21. ,4%