Reviewerly/exHarmony huggingface dataset:
Numerical Simulation and Design of Ensemble Learning Based Improved Software Development Effort Estimation System

This research paper proposes a novel approach to improving software development effort estimation by integrating ensemble learning algorithms with numerical simulation techniques. The objective of this study is to design an ensemble learning-based software development effort estimation system that leverages the strengths of multiple algorithms to enhance accuracy and reliability. The proposed system combines the power of ensemble learning, which involves aggregating predictions from multiple models, with numerical simulation techniques that enable the modelling and analysis of complex software development processes. A diverse set of software development projects is collected, encompassing various domains, sizes, and complexities. Ensemble learning algorithms such as Random Forest, Gradient Boosting, Bagging, and AdaBoost are selected for their ability to capture different aspects of the data and produce robust predictions. The proposed system architecture is presented, illustrating the flow of data and components. A model training and evaluation pipeline is developed, enabling the integration of ensemble learning and numerical simulation modules. The system combines the predictions generated by the ensemble models with the simulation results to produce more accurate and reliable effort estimates. The experimental setup involves a comprehensive evaluation of the proposed system. A real-world dataset comprising historical project data is utilized, and various performance metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), are employed to assess the effectiveness of the system. The results and analysis demonstrate that the ensemble learning-based effort estimation system outperforms traditional techniques, showcasing its potential to enhance project planning and resource allocation.
Efficient Fraud Detection in Ethereum Blockchain through Machine Learning and Deep Learning Approaches

Background: This paper tackles the critical challenge of detecting fraudulent transactions within the Ethereum blockchain using machine learning techniques. With the burgeoning importance of blockchain, ensuring its security against fraudulent activities is crucial to prevent significant monetary losses. We utilized a public dataset comprising 9,841 Ethereum transactions, characterized by attributes such as gas price, transaction fee, and timestamp.Methods: Our approach is bifurcated into two core phases: data preprocessing and predictive modeling. In the data preprocessing phase, we meticulously process the dataset and extract pivotal features from transactions, setting the stage for efficient predictive modeling.Findings: For predictive modeling, we employed several machine learning algorithms to discern between fraudulent and legitimate transactions. Our evaluation encompassed algorithms like decision trees, logistic regression, gradient boosting, XGBoost, and an innovative hybrid model that melds random forests with deep neural networks (DNN).Novelty: Our findings underscore that the proposed model boasts a precision rate of 97.16%, marking a substantial leap in fraudulent transaction detection on the Ethereum blockchain in comparison to prevailing methodologies. This paper augments the current efforts aimed at bolstering the security of blockchain transactions using sophisticated analytical strategies..
Secure and Transparent Supply Chain Management using Blockchain and IoT

Blockchain technology has emerged as a disruptive force across various industries, and its integration with the Internet of Things (IoT) has unlocked new avenues for supply chain management. The conventional supply chain systems often encounter challenges related to privacy, security, and data integrity. In contrast, blockchain's decentralized and tamper-proof nature ensures a secure, auditable, and transparent record of product movement within the supply chain. By leveraging the immutable properties of blockchain, the system enhances product traceability, authenticity, and accountability while significantly reducing operational costs. IoT devices are vulnerable to attack as due to low processing power, storage limitations etc. Blockchain integrated with IoT provides a solution faced by the several industries. Blockchains and smart contracts are technology that has gained massive attention. The integration of blockchain addresses these shortcomings by providing robust data security and integrity, minimizing the risk of unauthorized access or alteration. This paper presents a system that helps the industrialist to have an access to agricultural data and supply of crops data to farmer. As industries continue to embrace digitization and connectivity, the presented system offers a significant step towards a more streamlined and secure future for agricultural information sharing. This system will be effective for the supply chain management for the trusted delivery.
A Deep Learning based Model using Review Associated Feature Extraction Approach for Sentiment Analysis

With the advancement of internet technologies, in the present days, online forums, social media platforms and e-commerce sites have made the product reviews process very easy. There are a lot of mobile applications, websites and forums where consumers used to share and circulate their opinions, experiences, ideas and views regarding products, brands and services. In consequence, online user reviews have become a deciding factor for many consumers prior to purchasing their selected items. The sentiment analysis is a technique to extract sentiments, feelings and insights from customer reviews and public texts. Therefore, plenty of businesses perform sentiment analysis in order to more thoroughly comprehend of their customer opinions and suggestions regarding their products and services. Furthermore, a number of scientific researchers also have a keen interest in classifying customer reviews into a set of labels employing text classification techniques. The objective of the this research work is to develop an approach to extract review associated features using Part-of-Speech (POS) tagging and design a CNN model to classify the reviews' sentiment as positive or negative. In this paper, an approach to extract review associated feature has been presented. Natural Language Processing (NLP) techniques are utilized for data preprocessing to remove uninformative data from reviews. Deep learning model CNN is used for sentiment classification and Amazon mobile reviews dataset is used for the experiment. The proposed model is experimentally evaluated and provides enhanced performance than other models also provides improved accuracy of 97.23% on Amazon mobile review dataset.
A Real Time Employee Attendance Monitoring System using ANN

Face recognition refers to the technology that examines and contrasts a person's face characteristics to recognise or verify their identity. Recently, this technology has drawn a lot of attention due to the potential uses it may have in security, marketing, and law enforcement. Face recognition involves studying a picture or video of a person's face to identify features like the space between their eyes, the contour of their nose, and the curve of their mouth. The person's identity is then established or verified by comparing these characteristics to a database of previously saved pictures. A series of techniques called facial recognition algorithms are used to identify and authenticate persons based on the features of their faces. These algorithms compare a person's facial attributes to those in a database of recognised faces by looking at things like the shape of their face, the distance between their eyes, and other distinctive facial features. There are many different types of face recognition algorithms, including geometric-based algorithms, appearance-based algorithms, and hybrid algorithms that combine both approaches. Geometric-based algorithms employ the geometry of face traits to identify and validate people, while appearance-based algorithms use image processing techniques to compare the patterns and textures of facial features. Recent advances in deep learning have significantly improved the accuracy of facial recognition algorithms. Artificial Neural Network (ANN) has shown to be highly effective and have been used in a range of applications, including mobile devices, security, and surveillance. Face recognition algorithms provide advantages, but there are also moral dilemmas with regard to its application, such as potential biases and privacy difficulties. As technology advances, it is imperative to address these problems and ensure that face recognition algorithms are used ethically and responsibly.
Enhanced Spam Detection System for Twitter Social Networking Platform

Twitter social site is one of the most popular Online Social Networking Site (OSN) used by popular people such as Ministers, businessman, large companies, actors to share their information. In this site, around 500 million of tweets are posted monthly by the total 313 million Twitter active users. The widespread of Twitter has drawn the interest of spammers. These malicious actors exploit the platform for various nefarious purposes, including monitoring authentic users, disseminating harmful software, and promoting their agendas through URLs embedded in tweets. They engage in tactics like secret following and unfollowing legitimate users, all with the intent of gathering sensitive information.To resolve this problem, a secure spam detection based on machine learning approach is designed. The designed used stop word removal, word to vector model to refined and dimensionally reduced the data. To enhance the quality of the data Cosine similarity is also been applied to measure the similarity score among the tweets and based upon that Artificial Neural Network (ANN) is trained. Later on, it is used to test the efficiency by examining the performance parameters in terms of precision, recall and F-measure. Also, the comparative analysis has been performed to present the efficiency of the work. The average precision, recall and F measure of proposed spam detection model of 0.9252, 0.6107 and 0.734 are obtained.
Emotional Tendency Analysis of Twitter Data Streams

The web now seems to be an alive and dynamic arena in which billions of people across the globe connect, share, publish, and engage in a broad range of everyday activities. Using social media, individuals may connect and communicate with each other at any time and from any location. More than 500 million individuals across the globe post their thoughts and opinions on the internet every day. There is a huge amount of information created from a variety of social media platforms in a variety of formats and languages throughout the globe. Individuals define emotions as powerful feelings directed toward something or someone as a result of internal or external events that have a personal meaning. Emotional recognition in text has several applications in human-computer interface and natural language processing (NLP). Emotion classification has previously been studied using bag-of words classifiers or deep learning methods on static Twitter data. For real-time textual emotion identification, the proposed model combines a mix of keyword-based and learning-based models, as well as a real-time Emotional Tendency Analysis
Development of a novel robot-assisted vocabulary learning system using pure synthetic data

This study investigates the application of deep learning methods trained on synthetic data for the robust detection of vocabulary flashcards, an essential aspect of robot-assisted language learning (RALL). Despite its importance, flashcard detection has not been extensively researched in RALL systems, and it poses significant challenges due to the extensive data collection and annotation needed, especially given the large number of classes involved. These models need to generalise well to different real-world environments. To address these issues, a novel robotic platform designed for flashcard-based vocabulary learning is proposed, supported by a synthetic data generation pipeline using high dynamic range images (HDRIs) and synthetic human actors. The proposed pipeline offers an efficient data generation method and significantly enhances model generalisability to various environments. The proposed method was evaluated with five object detection models based on several challenging real-world datasets, each containing more than 200 class labels. The object detection models trained based on the proposed synthetic datasets (HDRI and HDRI+Humans) demonstrated outstanding performance, achieving median mean average precision (mAP) scores of 0.797 and 0.778. The proposed method achieves this performance without needing to be trained with real data. A comprehensive analysis comparing the proposed method with other synthetic data generation techniques is presented, and its potential for improving vocabulary flashcard detection in RALL systems is highlighted. The data and models are available at https://github.com/aimlab-mu/aimrobot.
On complementing unsupervised learning with uncertainty quantification

Pseudo-label-based semi-supervised learning (SSL) recently has achieved significant success in unlabeled data utilization. Recent success on pseudo-label-based SSL methods crucially hinges on thresholded pseudo-labeling and consistency regularization for the unlabeled data. However, most of the existing methods do not measure and incorporate the uncertainties due to the noisy pseudo-labels or out-of-distribution unlabeled samples. Therefore, the model's discernment becomes noisier in real-life applications that involve a substantial amount of out-of-distribution unannotated data. This leads to slow convergence in the training process and poor generalization performance. Inspired by the recent developments in SSL, our goal in this paper is to propose a novel unsupervised uncertainty-aware objective and threshold-mediated pseudo-labeling scheme that rely on uncertainty quantification from aleatoric and epistemic sources. By incorporating recent techniques in SSL, our proposed uncertainty-aware framework can mitigate the issue of confirmation bias and the impact of noisy pseudo-labels, resulting in improved training efficiency and enhanced generalization performance within the SSL domain. Despite its simplicity and computational efficiency, our approach demonstrates improved performance compared to state-of-the-art SSL methods on challenging datasets, such as CIFAR-100 and the real-world dataset Semi-iNat.
Exploring blockchain-based metaverses: Data collection and valuation of virtual lands using machine learning techniques

In recent years, the concept of the metaverse has evolved significantly, with the aim of defining richer immersive and interactive environments that can support various types of virtual experiences and interactions among users. This evolution has given rise to several metaverse platforms that utilize blockchain technology and non-fungible tokens (NFTs) to establish ownership of metaverse elements and attach features and information to them. This article seeks to delve into the complexity and heterogeneity of the data involved in these metaverse platforms and highlight some of the dynamics and features that make them unique. Additionally, the paper introduces a metaverse analysis tool developed by the authors, which leverages machine learning techniques to collect and analyze daily data, including blockchain transactions, platform-specific metadata, and social media trends. The experimental results of our approach are presented with a use-case scenario focused on the trading of digital parcels, commonly referred to as metaverse real estate. This scenario allows us to demonstrate the effectiveness of our tool and showcase the potential of using machine learning techniques to analyze and gain insights into the metaverse ecosystem.
Branch-cut phase unwrapping method based on deep learning

As a typical representative of the phase unwrapping algorithm, the branch-cut method has good anti-noise performance in the phase unwrapping process. However, the branch-cut generated by the traditional branch-cut method cannot accurately block error propagation. This study proposes a branch-cut phase unwrapping method based on deep learning, which transforms the traditional problem of connecting residual points into a semantic segmentation learning problem, to improve the blocking effect of the branch-cut to the error. The article analyzes the performance of modulation and residue maps in representing phase errors and finds that residue maps have a poor perception of error information in shadow areas in the wrapped phase map. Therefore, a new branch-cut connection criterion is proposed that combines modulation maps and sets the bound of shadow and non-shadow areas as part of branch-cut. Positive and negative residual points are connected by curves along the path of poor modulation, and their polarities are accumulated to maintain branch-cut balance. By using the new branch-cut connection criterion to provide learning samples and using the U-Net++ network structure as the deployment network of branch-cut, the network generates a branch-cut for phase unwrapping, which phase unwrapping path bypasses the branch-cut. The experimental results show that the deployment of branch-cut using the U-Net++ network structure can achieve the expected training effect, and the branch-cut generated based on the new connection principle can more effectively block the propagation of errors during phase unwrapping.
Sistem Informasi Laporan Kinerja Pegawai Non PNSD Menggunakan Metode Management By Objectives (MBO)

In carrying out government activities, Sako District is supported by elements of the Civil Servant Apparatus and Non PNSD (Honorary) Employees who are arranged in various positions with different duties and authorities. Currently, the process for managing PNS performance data already has a special system that refers to the assessment system from the central government, while non-PNS honorary employees still do not have a separate system and only rely on records in the performance report book. This causes several deficiencies, namely, the potential for errors in the process of recording performance data, also the potential for damage and loss of data (notebooks), as well as the potential for fraudulent practices because these data cannot be accessed or seen widely. Through this research, the authors see a solution, namely by building an Information System for Non PNSD Employee Performance Reports at the Sako District Office, Palembang City using the Management by Objectives (MBO) method where each employee will be assessed based on daily work achievements which will later be accumulated at each end. year, with the aim of assessing if employees achieve certain work targets they will still be retained as temporary employees in Sako Palembang District.
Comodule Estimation of Cognitive Sensor Networks Based on Partial Clustering for Partial Observed Data

The proposed study is on the partial clustering algorithms for cognitive sensor networks that deal with partially observed data. The proposed algorithms aim to estimate clusters in the presence of missing values and leverage data imputation techniques to fill in the gaps in the target and station device matrices. A modified loss function is introduced to shape the cluster centers, and robust Non-negative Matrix Factorization (NMF) algorithms are utilized to enhance the robustness of the clustering process. This research contributes to the field of cognitive sensor networks by providing insights into the challenges of partial clustering and presenting effective algorithms to address them. The proposed methods have the potential to enhance the performance of clustering tasks in various domains, including sensor networks, by accounting for missing data and producing accurate cluster reconstructions.
Sentiment Analysis Related National Social Security Agency for Employment in Indonesia: Hybrid Method Using Lexicon Based and Naive Bayes Classifier Approaches

&#x0D; &#x0D; &#x0D; &#x0D; The National Social Security Agency (BPJS) for Employment is the Social Security Administering Agency with the goal of ensuring that each participant or member of the family receives adequate necessities. In its implementation, there is information that is spread, particularly on Twitter, regarding the Ministry of Health's decision, namely regarding Old Age Security (JHT), which can only be distributed/taken after the participant turns 56 years old, causing both pros and cons among the public. Based on unanalyzed tweets on Twitter, it is necessary to do extensive research to collect relevant information based on netizens' viewpoints. This research describes sentiment analysis of tweets from Twitter using the terms JHT, BPJSTK, and BPJS, which yield 4154 data tweets. We employ two approaches in this study: Lexicon Based and Nave Bayes Classifier. According to this study, the accuracy of the testing data is 92% for the Lexicon Based and 95% for the Nave Bayes Classifier. This study concluded that the JHT at BPJS Employment received unfavorable attitudes and negative reactions among users who addressed the rejection of new restrictions where JHT, could only be dispensed or taken when participants at BPJS Employment were 56 years old.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Reliability and Validity of the Electronic English Placement Test (EPT) conducted by the Language Centre, Public Authority for Applied Education and Training

This study aims to investigate the validity and reliability of the electronic English language test administered by the Language Centre, the Public Authority for Applied Education and Training. 5 EFL teachers were interviewed by the researcher using a semi-structured interview. 10 EFL freshmen students were asked to perform the EPT for the second time and then participated in a semi-structured interview as well. The researcher used the mixed method design in his study, the quantitative and qualitative methods, which added strength to the data analysis. The finding showed that EPT had deficient levels of validity and reliability. The reasons behind this result varied from ignoring writing, listening, and speaking when designing the EPT to poor instruction procedures and proctoring. The lack of new and advanced computers did affect the validity and reliability in a significant way. This study ends with recommendations to improve EPT's validity and reliability levels.