Reviewerly/exHarmony huggingface dataset:
The 7-Phases Preprocessing Based On Extractive Text Summarization

Extractive text summarization is an approach to automatic text summarization whose main purpose is to reduce the size of the document while preserving the information of the original document. In this study, 7-phase preprocessing is proposed as a preprocessing composition to clean text data so that it is ready to enter data into the summary method such as machine learning. The composition of 7-phase preprocessing is data frame by sentence, remove title from data frame, lower caseing, remove punctuations, remove stop words, tokenizing and stemming. The preprocessing model was tested on the DUC 2002 dataset which is a news document. The performance of the proposed preprocessing stage for extractive summarization showed superior results compared to the combination of other comparison preprocessing stages with ROUGE-1 measurements (recall, precision, and F-1).
Analysis on Optimal Resource Management Strategies: A Virtual Machine Migration Perspective

To save energy, reduce resource usage, and ensure cloud data center quality of service (QoS), virtual machine migration has become a key requirement with the rapid expansion of cloud environments. Dynamic migration of virtual machines is a successful way to meet growing demand for resources such as processing, connectivity, and storage. This proposal examines the design of control algorithms and their performance models used for migration within a local area network (LAN) or within a data center. The existing methods are investigated to accommodate large numbers of cloud users, improve computing infrastructure, and reduce time and energy spent in cloud data centers. User mobility helps reduce network overhead during VM migration. A key element of this proposal will show you how to optimize data deduplication and peer-to-peer (P2P) file sharing to help further improve the efficiency of data migration for VM storage.
Text Normalization on Code-Mixed Twitter Text using Language Detection

Presence of code-mixed language become a challenge for NLP research that focused on Twitter text normalization. Some challenges include normalize text containing words with more than one language. But recent method for text normalization still has problems related to language, either on identifying language or normalize a word. This research report covers the solution that can be given to overcome those problems. The approach is using language detection module alongside with transformer model. A BERT model tagger was used as a language detection, and two ByT5 models was used as a normalization. The research shows that proposed method has ERR score 1.01 percent lower than baseline.
Processing of Wisconsin Breast Cancer Data using Ebola Optimization Algorithm with Mixture Kernel SVM

Being a deadly disease, breast cancer is becoming the more progressive one in providing higher mortality for females around the world. Thereby, the need for an appropriate strategy is always required for earlier breast cancer diagnosis. The physicians utilize the Computer-Aided Diagnosis (CAD) tool for effective and tireless detection of such cancers. In this regard, the work is intended to design a CAD system for breast cancer diagnosis in a timely manner. The implementation starts with the use of Wisconsin Breast Cancer dataset. After performing preprocessing and visual analysis of the input dataset, feature selection is performed to improve the efficiency of the CAD system. This can be done by using the recently evolved Ebola Optimization Algorithm (EOA). This algorithm is based on an effective approach used in the propagation of the Ebola virus among individuals. After feature selection, the dominant features are then classified with the aid of a mixture Kernel Support Vector Machine (mK-SVM) algorithm. Additionally, the work utilized the Linear SVM, and KNN algorithms for the experimental analysis and comparison. As a result, the mK-SVM together with EOA provides maximum accuracy of 97.19% in classifying the input as either benign severity or malignant case.
Android-based Matrix Learning Media to Increase Student Interest in Learning

Lecturers must utilize technology to create creative, effective learning materials and boost student interest in learning since students often find traditional teaching methods like diktat or modules boring and unable to drive learning interest. This research aims to produce a valid android-based matrix learning media to increase student interest in learning. The subjects of this study were students of the Informatics Engineering Study Program at the Banjarmasin State Polytechnic. Learning media was developed using the Construct 3 application. Research has succeeded in producing android-based matrix learning media. Based on the media experts' test, the application has met the “Good” category with a percentage of 69.4%. In contrast, the test by material experts gave the result that this learning media has met the criteria for the content of the material with the “Very Good” category at number 82, 7%. Therefore, the learning media can be said to have been valid and can be used. Based on the N-gain test, it is concluded that this android-based matrix learning media has increased student interest in learning with a coefficient of 0.73, which is in the “High” change category.
Sentiment Analysis on Cryptocurrency Based on Tweets and Retweets Using Support Vector Machines and Chi-Square

The high public interest in cryptocurrencies is increasing over time. Cryptocurrencies are gaining worldwide attention because of their unique and unpredictable nature. Nowadays, cryptocurrencies can be used for various things, one of which is for investment. In investing in cryptocurrencies, various analyzes are needed to consider trade flows. One way to analyze it is by analyzing public sentiment which consists of positive and negative sentiments. However, there has been no previous research on the use of machine learning approaches and feature selection in sentiment analysis of Twitter tweets and retweets. The research was conducted by analyzing public sentiment using a machine learning algorithm, namely the Support Vector Machine. In addition to using the Support Vector Machine, Chi-square is also used in feature selection to help reduce noise in a sentence. Retweets will considerably improve sentiment label classification. The performance of sentiment analysis can be improved across the board by at least 91%. The sentiment analysis findings from this study are positive. This implies that a trader can take cryptocurrencies like Bitcoin, Ethereum, and Ripple into account while deciding on the type of investment.
Optimization Analysis of Neural Network Algorithms Using Bagging Techniques on Classification of Date Fruit Types

To perform an optimization One of the machine learning algorithms is by using Ensemble Learning. The main problem in this study is to increase the accuracy of the neural network algorithm in classifying data. This study aims to optimize the neural network algorithm in classifying. There are many optimization algorithms in classifying. In this study, we tried to optimize using the bagging technique for classification. This study uses types of dates as a case study. The results of the model evaluation show that the neural network algorithm plus the bagging technique produces an accuracy rate of 96%, and this has a higher accuracy of 1% compared to the neural network algorithm alone.
Detecting Online Outlier for Data Streams using Recursive Residual

Outlier analysis is a statistical procedure that involves the identification of anomalous observations. Outlier detection is important in many fields and involved in numerous applications since if the outliers are not detected then it can lead us to a wrong decision. There are a wide variety of techniques that can be used to identify outliers in datasets. Most of the existing methods are based on static data, while in case of data stream these methods are computational inefficiency and require big memory storage. This is due to the data stream being a dataset that is generated in a real-time data stream and requires real-time data analysis. The recursive least squares approach is mainly used in many regression analysis and the outlier detection process is referred to as supervised outlier detection. In this paper, we propose an online outlier detection algorithm using recursive residuals via recursive least squares method. The recursive residuals are sequentially calculated on the distance of the observation and the prediction values. To estimate online parameters in this research, we apply a recursive least squares method to predict an iterative model. In recursive least squares method a single new data point is analyzed each algorithm iteration to improve the estimation of our model parameters. Then, our simulation is performed on historical data for Tesla stock for a given period. Results that our online algorithm can demonstrate its ability to maintain high accuracy and find better outliers with low computational cost for anomaly detection.
A Systematic Literature Review Enhanced Felder Silverman Learning Style Models (FSLSM)

Student's learning style was identified and considered a critical factor in personalizing the learning process to meet student's learning preferences, specifically in intelligent e-learning system development. Learning style theories state the importance of student's profiles during learning. One of them is Felder-Silverman Learning Style Model (FSLSM). However, FSLSM has a lack of comprehensive literature review about how to improve intelligent e-learning system performance. Thus, this study analyzed and classified prior studies between 2011 and 2020 regarding FSLSM implementation and intelligent e-learning system development, including the trend. This study categorized several techniques found: identification system technique, recommendation system technique, recommendation learning object, and evaluation system technique. This study shows that FSLSM enhancement in e-learning may improve system quality using a recommendation technique. Furthermore, combining identification, recommendation, and evaluation system techniques may improve the effectiveness and efficiency of the learning process in an intelligent e-learning system.
Detecting Toxic Comments Using Convolutional Neural Network Approach

In the most significant issue now plaguing social networking platforms and online communities is toxicity identification. Therefore, it is necessary to create an automatic hazardous identification system to block and restrict individual from certain online environments. We introduce multichannel Convolutional Neural Network (CNN) approach in this paper for the detection of toxic comments in a multi-label context. With the help of pre-trained word embeddings, the suggested model produces word vectors. Also, to model input words with long-term dependency, this hybrid model extracts local characteristics using a variety of filters and kernel sizes. Then, to forecast multi-label categories, we integrate numerous channels with three layers as fully linked, normalization, and an output layer. The results of the experiments show that the suggested model performs where we are presenting the fresh modeling CNN approach to detect the toxicity of textual content present on the social media platforms and we categorized the toxicity into positive and negative impact on our society.
Quantum Image Classification on NISQ Devices

Quantum computing is an emerging computing field that is expected to make a huge impact on several scopes of science and technology. In this paper, we investigate the role of quantum computing in image classification, as an important branch of machine learning with widely used applications in healthcare, military, and IR4.0. In particular, we systemically compare the performance of two well-known classical image classification systems, i.e., Support Vector Machine (SVM) and Convolutions Neural Network (CNN), with equivalent quantum image classification algorithms, i.e., Quantum Support Vector Machine (Q-SVM) and Quantum Convolutional Neural Network (Q-CNN). Both quantum and classical algorithms are implemented on available Noisy-Intermediate Scale Quantum (NISQ) devices using MNIST dataset. Performance of models were compared regarding accuracy and training time. The results show that classical algorithms outperform the quantum algorithms for the given tasks. However, we observe that large-scale fault-tolerant quantum computing can effectively perform image classification tasks in the future.
Nonlinear set membership filter with state estimation constraints via consensus-ADMM

This paper considers the state estimation problem for nonlinear dynamic systems with unknown but bounded noises. Set membership filter (SMF) is a popular algorithm to solve this problem. In the set membership setting, we investigate the filter problem where the state estimation requires to be constrained by a linear or nonlinear equality. We propose a consensus alternating direction method of multipliers (ADMM) based SMF algorithm for nonlinear dynamic systems. To deal with the difficulty of nonlinearity, instead of linearizing the nonlinear system, a semi-infinite programming (SIP) approach is used to transform the nonlinear system into a linear one, which allows us to obtain a more accurate estimation ellipsoid. For the solution of the SIP, an ADMM algorithm is proposed to handle the state estimation constraints, and each iteration of the algorithm can be solved efficiently. Finally, the proposed filter is applied to typical numerical examples to demonstrate its effectiveness.
Semantic Segmentation of Landsat Satellite Imagery

Currently, practitioners of remote-sensing and geographic information system use mathematical and statistical-based methods in making land cover segmentation. However, this method requires a lengthy process and staff expertise. Urban planners need applications that are not only fast in performing spatial analysis but also do not require special skills. This study proposes a land cover classification application with one of the deep learning methods for semantic segmentation, namely DeepLabV3+. This method will be compared with Iterative Self-Organizing Clustering (ISOCLUST) and Object Based Image Analysis (OBIA) in Karawang, Indonesia, as the case study. The results showed that the DeepLabV3+ accuracy was 95% which is higher than OBIA (80%). Although ISOCLUST more accurate that is usually used as ground truth dataset, this method takes a lot of time as it is semi-automatic compared to DeepLabV3+ which only takes about one minute.
Utilization of Linguistic Data for Learner Assessment on e-Learning: Instrument and Processing

The increasingly massive use of e-Learning illustrates the speed and need for innovation in learning. According to the National Higher Education Standards (SN-Dikti), constructive alignment is required between learning outcomes, processes, and assessments to properly implement learning in e-Learning. Assessment is an essential component of e-Learning. Unfortunately, the problem of assessment in e-Learning is still found. One of them is the limitations of e-Learning in accommodating and processing various assessment data, primarily linguistic. Even though the variety of assessment data, both numerical, linguistic, and a combination of the two, supports a comprehensive assessment. On the other hand, the accommodation of linguistic data raises problems regarding how to process of unifying linguistic data is carried out. Research related to linguistic data using computing with words has been carried out, but it still needs more precise results from the unification of the linguistic data. Therefore, this study proposes providing an assessment instrument to accommodate linguistic data in e-Learning, as well as showing how to process of unifying linguistic data is carried out using 2-Tuple Fuzzy Linguistic. This approach can avoid the loss of assessment information by presenting more informative and precise results in a 2-tuple (s, α) where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> indicates the ability level, and α shows a comparison of abilities with other learners and the potential of the learner to achieve higher abilities. This proposal has the potential to be applied in a learner assessment system for higher education e-Learning.
Evaluating the integration of Blockchain Technologies in Supply Chain Management: a case study of sustainable fishing

As a consequence of the Global pandemic, Supply Change Management (SCM) is becoming more complex due to market uncertainty across value chains; from sourcing materials to logistics and production. With the development of contemporary technology, blockchain may allay this worry by providing the SCM industry with automated software solutions. Blockchain is an emerging technology that supports a distributed and transparent approach to transactions between various entities. Due to increased digital usage across many sectors, the technology is being adopted more commonly in real-world business applications that aim to achieve transparency and security along a distributed chain of processes. Examining how these applications are deployed, based on the respective domain creates opportunities for future research and in advancing current thought processes of supply chain practitioners. This research aims to assess the fishing industry and provide a solution to trace the complete seafood lifecycle by capturing, recording, and tracking all relevant activities and data (e.g., video, photo, documents) from "bait to plate" and provide an open and immutable history record for each transaction in the supply chain of this lifecycle. The research offers valuable insight for supply chain practitioners into how blockchain technology has the potential to disrupt existing supply chain deployments and highlights some challenges of its successful adoption. Emerging blockchain applications aim to help businesses, including supply-chain transparency for a wide range of products
Model Implementation of Application Programming Interface for E-Goverment Data Integration

The challenge in implementing information systems to realize good e-government governance is to minimize the burden on information system users but not reduce data validity. The Jombang Regency Agriculture Office, as part of the Jombang district government, seeks to apply information technology in its activities. Over time, seven applications are in their scope of work. The problem stems from the existence of many complaints from employees about the amount of data that must be input into the application, even though a lot of the same or almost the same data is input into the various applications, so it is felt necessary to carry out the integration between existing applications. By using observation and interviews, research data were collected. The Service Oriented Architecture approach is used in the Application Programming Interface (API) application to carry out the integration process between these applications. The conclusion from this research is that API can integrate the systems to eliminate repetitive work that can burden existing employees.
Design of Hybrid Glitch-Reduction Techniques for Loop Unrolled SIMON Block Cypher

The growing demand for the Internet of Things in the application fields with minimum latency requirements is emerging at a rapid rate. Securing these applications not only requires the design of lightweight crypto primitives with minimal code footprint but with shorter execution times. However, despite being a vital performance indicator for deterministic time-bound applications, this has not received much attention and has often been sub-prioritised. Low-latency block cyphers employing loop unrolling design techniques are a favourable choice for securing real-time IoT applications. However, although loop unrolling increases the speed of the overall design, glitches between the unrolled round functions increase its dynamic power and energy consumption, making the cyphers unfit for low-power IoT devices. In this paper, the hybrid glitch-reduction techniques designed using different combinational and sequential circuits have been proposed. These techniques have been devised for the SIMON block cypher because of its hardware efficiency. Furthermore, the high-speed loop unrolling technique for SIMON64/128 block cypher has been analysed for low-latency behaviour in light of various trade-offs between different design metrics. These techniques have been simulated and analysed in Xilinx ISE for Artix-7 and Spartan-6 FPGA boards regarding various metrics such as power, area, latency, throughput and critical path. The results demonstrate that the proposed approaches for SIMON64/128 block cypher produces better results certifying their use for high-speed IoT applications.
Predicting Breast Cancer Malignancy using Deep Neural Networks

A lot of people are scared of cancer since it’s so deadly. However, if caught and treated early, cancer has a high chance of being cured. The ability of computer-assisted diagnosis to serve as a primary screening test for many illnesses, including cancer, has contributed to its rise in popularity in recent years. Deep learning is an artificial intelligence technology that gives computers intelligence by programming them to think like people. In this study, we explore the feasibility of training a deep neural network to provide such a prediction for breast cancer. Information is taken from a UCI-supplied dataset on breast cancer in Wisconsin. Over fitting is prevented by the early halting mechanism and the dropout layers in the neural network model, which together allow for an F1 score of more than 97.
Text Classification and Categorization through Deep Learning

Text classification is one of the important fields in Natural Language Processing (NLP). It assigns text documents into at least two categories in the domain by submitting and deriving a set of features to describe each document and to select the correct category for each one for a set of pre-defined tags or categories based on content. It is even used in several real-life applications such as engineering, science, and marketing and it can be quite effective in addressing problems with labeled data. There are certain Deep Learning (DL) algorithms that can be handy in categorizing text data such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes. This paper illustrates how the text in each document is reviewed and grouped into different sets through the above-mentioned techniques. That way, it will determine which method is best suited for higher accuracy and what possible problems the deep learning model faces using text classification and categorization so that new solutions can be invented to resolve these issues without interfering with the processes in the future.
Small Object Difficulty (SOD) Modeling for Objects Detection in Satellite Images

Recent increases in aerial image access and volume, increases in computational power, and interest in applications have opened the door to scaling up object detection to production. Aerial data sets are very large in size, and each frame of the data set contains a huge number of dense and small objects. Deep learning applications for aerial imagery are behind due to a high variety between datasets (e.g. object sizes, class distributions, object feature uniformity, image acquisition, distance, weather conditions), the size of objects in satellite imagery, and the subsequent failure of state-of-the-art architecture to capture small objects, local features, and region proposals for densely overlapped objects in satellite images. In this paper, we provide a novel pipeline that improves the back-end through spatial pyramid pooling, a partial cross-stage network, a region proposal network via heatmap-based region proposals, and object localization and identification through a novel image difficulty score t hat adapts the Overall focal loss measure based on the image difficulty. Our proposed model outperformed the state-of-the-art method in mAP by 1.8% and 2.3% in the DOTA and DIOR data sets, respectively.