Reviewerly/exHarmony huggingface dataset:
Optimal Mobile Node Path Optimization Based on Ant Colony Algorithm

With the progress of science and technology and artificial intelligence, path planning and optimal path solving have gradually become hot topics. Considering the frequent deadlock problem of ant colony algorithm in path planning problem, and the long running time and low computational efficiency of ant colony algorithm. The research of node ant colony technology is based on Darwin's theory of evolution. To begin, based on the previous research on ant colony algorithm and the model learning of path search, a simple ant colony algorithm model is proposed to deal with the blind search problem in the blank grid. When the goal influence factor and the barrier impact factor are combined into the heuristic function, the global search capabilities increase and impasse is averted. The next phase is to use Darwin's theory of evolution to improve the pheromone workup of the ant colonies algorithm in order to speed up and lower the predicted time cost. Experiments on raster maps of various sizes show that the evolutionary ant colony technique suggested in this study boosts iteration speed, improves search efficiency, finds the best path, and avoids stalemate.
Adaptive ICT-based evaluation system in teaching and learning process

Evaluation is an essential part of education. In addition to helping track student progress and learning processes, evaluations play a significant critical and important role in developing the individual's personality and forming a realistic self-image. Therefore, evaluations must be accurate, reliable, encompassing of changes, and supportive of developmental paths. Assessments can take various forms and can be carried out in-person, verbally, in writing, or through practical tasks, but they can also be conducted online, in a digital format, within a suitable framework. Regardless of these, one common element, the assessing educator is the adult who plans, integrates, and conducts the periodic control and evaluation process. Thus, there is also a generational difference inherently present and this affects the entire teaching and learning process, thereby influencing the evaluation process significantly, and reducing its efficiency. In this study, we present a self-developed system that increases the efficiency of the control and evaluation process by assisting the educator's work and reducing the communication gap due to generational differences. In addition to describing the applied technologies during the system's demonstration, we also discuss the methods and operating processes, which we conclude by evaluating the test results.
Research on the Life Scaffold of Enterprise Organization

The essential research elements of Enterprise organization engineering include four essential life elements: enterprise organization gene, enterprise organization seed cell, enterprise organization life scaffold, and enterprise organization growth factor. The in-depth research of these four essential life elements of enterprise organization constitutes the core theoretical content of Enterprise organization engineering. Chapter 12 of this book systematically analyzes enterprise organization life scaffold, includes significance of enterprise organization engineering research (determining the vital work of Enterprise organization engineering, determining the research program of Enterprise organization engineering, determining the difficulty of Enterprise organization engineering work), human tissue engineering life scaffold (meaning of human tissue engineering life scaffold, life scaffold material sources, human synthetic degradable polymers, natural synthetic degradable polymers), meaning of life scaffold in enterprise organizations (preliminary definition of life scaffolding, the essential difference between life scaffold of enterprise organization and human life scaffold, essential differences between life scaffold and office facilities, essential differences between life scaffolds and production facilities), life scaffold components (basic office system, modern management information system, modern production systems), basic idea of life scaffolding application research (life scaffold identification research, life scaffolding planning, generation and activation research).
HHO-SMOTe: Efficient Sampling Rate for Synthetic Minority Oversampling Technique Based on Harris Hawk Optimization

Classifying imbalanced datasets presents a significant challenge in the field of machine learning, especially with big data, where instances are unevenly distributed among classes, leading to class imbalance issues that affect classifier performance. Synthetic Minority Over-sampling Technique (SMOTE) is an effective oversampling method that addresses this by generating new instances for the under-represented minority class. However, SMOTE's efficiency relies on the sampling rate for minority class instances, making optimal sampling rates crucial for solving class imbalance. In this paper, we introduce HHO-SMOTe, a novel hybrid approach that combines the Harris Hawk optimization (HHO) search algorithm with SMOTE to enhance classification accuracy by determining optimal sample rates for each dataset. We conducted extensive experiments across diverse datasets to comprehensively evaluate our binary classification model. The results demonstrated our model's exceptional performance, with an AUC score exceeding 0.96, a high G-means score of 0.95 highlighting its robustness, and an outstanding F1-score consistently exceeding 0.99. These findings collectively establish our proposed approach as a formidable contender in the domain of binary classification models.
Preventing computer crime by knowing the legal regulations that ensure the protection of computer systems

Our intention in this study is to emphasise a few aspects relating to the need to support cyber education and to preventive goals, by informing people about the strategies in place at Union and/or national level, which reflect current realities, in relation to which each person, active entities and, last but not least, the authorities will have to act so as to identify early on any possible risks. We have also aimed to identify legislation that criminalises acts of cyber fraud, in which respect we have referred to certain articles in the criminal codes of several European countries. We consider that cyber education and legal education, regardless of the social level one belongs to, the social roles one takes on at a given time, and/or of age, are essential to setting the foundations for a cyber security culture.
Evolutionary Machine Learning in Environmental Science

This chapter reviews the use of Evolutionary Machine Learning (EML) in environmental science. We cover the various steps of the machine learning pipelineMachine learning pipelines, also addressing topics like model robustness, interpretabilityInterpretability, and human-competitivenessHuman-competitiveness. Environmental science is an interdisciplinary field mainly dedicated to climate changeClimate change, natural resource management, conservation biology, and sustainability. We review applications such as forest monitoring, optimization of photovoltaic installations, improvement of traffic flow, and reduction of waste in animal farms, among others.
Evolutionary Regression and Modelling

Regression and modelling, which identify the relationship between the dependent and independent variables, play an important role in knowledge discovery from data. Symbolic regressionSymbolic regression goes a step further by learning explicitly symbolic models from data that are potentially interpretable. This chapter provides an overview of evolutionary computation techniques for regression and modelling including coefficient learning and symbolic regressionSymbolic regression. We introduce the ideas behind various evolutionary computation methods for regression and present a review of the efforts on enhancing learning capability, generalisation, interpretabilityInterpretability and imputationImputation of missing dataMissing data in evolutionary computation for regression.
The Analysis of Functional Morpheme in Hansel and Gretel Short Story

&lt;div class="WordSection1"&gt;&lt;p&gt;The purpose of the research is to identify what functional morpheme is and how to recognize it in “Hansel and Gretel” short story. A functional morpheme is a part of free morpheme. A morpheme is the smallest unit in English linguistics that forms a word. There are two kinds of morpheme; they are free morpheme and bound morpheme. A morpheme that can stand on its own is called free, and ones that cannot are bound. A free morpheme has two types; they are lexical and functional morpheme. Functional morpheme is a morpheme that is commonly used in some articles to connect words or sentences. A functional morpheme is a part of Morphology. Functional morpheme is mostly used in English and it can be found in an English story. It is used because it gives influences the sentences. Without functional morpheme, sometimes the sentences will have no meaning or read awkwardly so it can give a different meaning to the sentences themselves. The data of the research was taken from “Hansel and Gretel” short story and the results of students’ analysis. The participants of the research were the 1st semester of English Department students. A qualitative descriptive method was used to do the research. After doing the research, there are some results found in the research, they are (1) two items in the article section; (2) four items for the conjunction part; (3) eight items for the preposition part; and (4) eight items in the pronoun section.&lt;/p&gt;&lt;/div&gt;
Students’ Perception on Utilization of Technology in Listening Learning

&lt;div class="WordSection1"&gt;&lt;p&gt;The learning process in listening practice is only limited to one type of text. Students have difficulty adapting to various types of audio material. Learning focuses more on speaking and writing skills than listening. As a result, students are less trained in active listening. A deep understanding of these issues helps to develop more effective learning strategies and supports students in overcoming these obstacles. Technological developments in education have influenced students' learning styles. This research aims to describe how students perceive the utilization of technology, specifically podcasts, to enhance their listening skills. The research employed a qualitative research method, specifically a case study, to gather data through questionnaires and interviews. The participants were EFL learners focusing on Listening for Professional Purposes, with 40 students completing the questionnaires and 10 representative students being interviewed. Findings reveal that students had a favorable response to utilizing podcasts as a means of enhancing their listening skills. They appreciated the simplicity, flexibility, engagement, and enjoyment of employing podcasts as media of learning. Findings from this research help lecturer develop curriculum and learning materials that better suit with students’ preferences and needs. This research allowed for the creation of more interesting and technology-relevant materials that are more acceptable to students.&lt;/p&gt;&lt;/div&gt;
The Ability To Write Scientific Articles among University Students Views from Five Institutions in Indonesia

&lt;div class="WordSection1"&gt;&lt;p&gt;The low ability to write scientific articles in students must be addressed immediately. This factor is increasingly prominent when the learning model used is not in accordance with the learning outcomes of the course. This research aims to measure the ability to write scientific articles of university students in Indonesia before and after the application of the Genre-Based Writing Model synthesized with Critical Literacy. A quantitative approach was used in this research with a quasi-experimental research method. The data sources of this research were students from four universities in Indonesia with a total of 144 students. The research data were the students' scientific articles. The data collection technique in this research was the test technique. Based on the results of the research, students' abilities before being treated with the Genre-Based Writing model synthesized with Critical Literacy were in the sufficient category, while after being treated with the model students' abilities were in the excellent category. In other words, there was a significant increase in students' ability to write scientific articles between before and after being treated with the Genre-Based Writing model synthesised with Critical Literacy.&lt;/p&gt;&lt;/div&gt;
Evolutionary Classification

Classification is a supervised machine learning process that categories an instance based on a number of features. The process of classification involves several stages, including data preprocessing (such as feature selectionFeature selection and feature construction)Feature construction, model training and evaluation. Evolutionary computation has been widely applied to all these stages to improve the performance and explainabilityExplainability of the built classification models, where term for this research area is Evolutionary Classification. This chapter introduces the fundamental concepts of evolutionary classification, followed by the key ideas using evolutionary computation techniques to address existing classification challenges such as multi-class classification, unbalanced dataUnbalanced data, explainable/interpretable classifiers andTransfer learning transfer learning.
Evolutionary Neural Network Architecture Search

Deep Neural Networks (DNNs) have been remarkably successful in numerous scenarios of machine learning. However, the typical design for DNN architectures is manual, which highly relies on the domain knowledge and experience of neural networks. Neural architecture search (NAS) methods are often considered an effective way to achieve automated design of DNN architectures. There are three approaches to realizing NAS: reinforcement learningReinforcement Learning approaches, gradient-based approaches, and evolutionary computation approaches. Among them, evolutionary computation-based NASNeural Architecture Search (NAS) (ENAS) has received much attention. This chapter will detail ENAS in terms of four aspects. First, we will present an overall introduction to NAS and the commonly used approaches to NASNeural Architecture Search (NAS). Following that, we will introduce the core components of ENAS and discuss the details of how to design an ENAS algorithm with a focus on search space, search strategy, and performance evaluation of the ENAS algorithm. Moreover, detailed implementations of these components will be presented to help readers implement an ENAS algorithm step by step. We will discuss state-of-the-art ENAS methods with the three core components. Finally, we will provide five major challenges and identify corresponding future directions.
Ethereum Blockchain-Based Medicine Supply Chain

The medicine supply chain is a process of transferring medicine across various stakeholders. The manual process of the supply chain needs help to locate the source of the medicine accurately. In addition, many people consume the medicine without validating it, which generates a risk for patients. To address this, in this paper, we have designed an Ethereum-based smart contract for tracking medicine in real-time accurately. We have also developed a smart contract to verify the medicine and achieve medicine compliance. Initially, the BCN verifies the credentials of each stakeholder and adds them to the BCN. Afterward, Admin can assign the roles to each stakeholder. The supplier can initiate collecting raw forms and send them to the manufacturer with the raw form ID. The manufacturer can verify the raw forms of medicine with its ID and manufacture the medicine accordingly. Further, this medicine verifies by the medical authority nodes and added medicine in the BCN. Later on, this medicine was transferred across other stakeholders, and locate the source of the medicine accurately. We have also validated our smart contract with vulnerability analysis. We have used the Oyente tool to verify our smart contract validity. Thus, the proposed scheme achieves real-time traceability, transparency, immutability, and medicine compliance.
Satellite-Based Quantum Key Distribution in the Presence of Bypass Channels

The security of prepare-and-measure satellite-based quantum key distribution (QKD), under restricted eavesdropping scenarios, is addressed. We particularly consider cases where the eavesdropper, Eve, has limited access to the transmitted signal by Alice and/or Bob’s receiver station. This restriction is modeled by lossy channels between relevant parties, where the transmissivity of such channels can, in principle, be bounded by monitoring techniques. An artifact of such lossy channels is the possibility of having bypass channels, those that are not accessible to Eve but that may not necessarily be characterized by the users either. This creates interesting unexplored scenarios for analyzing QKD security. In this paper, we obtain generic bounds on the key rate in the presence of bypass channels and apply them to continuous-variable QKD protocols with Gaussian encoding with direct and reverse reconciliation. We find regimes of operation in which the above restrictions on Eve can considerably improve system performance. We also develop customized bounds for several protocols in the BB84 family and show that, in certain regimes, even the simple protocol of BB84 with weak coherent pulses is able to offer positive key rates at high channel losses, which would otherwise be impossible under an unrestricted Eve. In this case, the limitation on Eve would allow Alice to send signals with larger intensities than the optimal value under an ideal Eve, which effectively reduces the effective channel loss. In all these cases, the part of the transmitted signal that does not reach Eve can play a nontrivial role in specifying the achievable key rate. Our work opens up new security frameworks for spaceborne quantum communications systems.10 MoreReceived 20 December 2022Revised 28 April 2023Accepted 25 September 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040320Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum communication, protocols & technologyQuantum Information, Science & Technology
COMPARATIVE ANALYSIS OF ACCURACY OF RANDOM FOREST AND GRADIENT BOOSTING CLASSIFIER ALGORITHM FOR DIABETES CLASSIFICATION

Diabetes is a disease characterized by high blood sugar (glucose) levels. If blood sugar is not controlled properly, it can cause various critical diseases, one of which is diabetes. The purpose of this study was to determine the results of a comparison of the accuracy values ​​of the Random Forest Algorithm and the Gradient Boosting Classifier Algorithm in the classification of diabetes which will be tested for accuracy, Precision, Recall, and F1 score performance. The method used in this study was descriptive and the data source used the Pima Indians Diabetes Dataset from Kaggle. Based on data analysis using a ratio of 80:20, the Random Forest Algorithm has an accuracy of 79% obtained from the results of the confusion matrix. From the confusion matrix results, the results obtained were AUC 0.835, Recall 78%, and Precision 90%. Based on the results of Recall and Precision, an F1 score of 83% was obtained. Whereas the Boosting Classifier Algorithm has an accuracy result obtained from the results of the confusion matrix which is 81%. From the confusion matrix results, the AUC results were 0.877, Recall 83%, and Precision 67%. Based on the results of Recall and Precision, an F1 score of 74% was obtained. In this study, the accuracy evaluation results obtained were through the results of the Confusion matrix and the AUC value. These results indicate that the Gradient Boosting Classifier Algorithm has a more excellent accuracy evaluation result compared to the Random Forest Algorithm.
A Novel Multidimensional Reference Model for Heterogeneous Textual Datasets using Context, Semantic and Syntactic Clues

With the advent of technology and use of latest devices, they produce voluminous data. Out of it, 80% of the data are unstructured and remaining 20% are structured and semi-structured. The produced data are in heterogeneous format and without following any standards. Among heterogeneous (structured, semi-structured and unstructured) data, textual data are nowadays used by industries for prediction and visualization of future challenges. Extracting useful information from it is really challenging for stakeholders due to lexical and semantic matching. Few studies have been solving this issue by using ontologies and semantic tools, but the main limitations of proposed work were the less coverage of multidimensional terms. To solve this problem, this study aims to produce a novel multidimensional reference model using linguistics categories for heterogeneous textual datasets. The categories in such context, semantic and syntactic clues are focused along with their score. The main contribution of MRM is that it checks each tokens with each term based on indexing of linguistic categories such as synonym, antonym, formal, lexical word order and co-occurrence. The experiments show that the percentage of MRM is better than the state-of-the-art single dimension reference model in terms of more coverage, linguistics categories and heterogeneous datasets.
An Enhanced CoD System Leveraging Blockchain, Smart Contracts, and NFTs: A New Approach for Trustless Transactions

The global transportation of goods has evolved in response to varied economic demands. The rapid progression of modern scientific and technological innovations offers a shift from traditional shipping paradigms. Current systems, whether domestic like Cash-on-Delivery (CoD) or international such as Letter-of-Credit, necessitate trust-building through an intermediary—be it a carrier or a financial institution. While these conventional systems provide certain benefits, they inherently present several challenges and potential vulnerabilities, affecting both sellers and buyers. The introduction of blockchain technology and smart contracts has been explored as a viable alternative to bypass these intermediaries. However, simply removing the shipping intermediary presents its own set of issues, particularly when disputes arise. Notably, the shipper remains unaffected in situations of contention. Consequently, some models are now incorporating the shipper’s role, either as a singular entity or in collaboration with others. Yet, a considerable number of these models still depend on an external trusted party for conflict resolution. Our study introduces a unique framework, blending the robustness of blockchain, the enforceability of smart contracts, and the authenticity assurance of NFTs. This system creates a streamlined CoD operation encompassing the seller, shipper, and buyer, using NFTs to produce digital receipts, guaranteeing both proof-of-purchase and a security deposit. Furthermore, our system provides an inherent mechanism for dispute resolution. Key contributions of our work including i) The design of a novel CoD system anchored on blockchain and smart contract capabilities; ii) The incorporation of Ethereum-based NFT (specifically, ERC721) for securely logging package information; iii) The development of smart contracts that facilitate NFT generation and transfer between transactional entities; and iv) Performance evaluation and deployment of these contracts across multiple EVM-compatible platforms such as BNB Smartchain, Fantom, Celo, and Polygon, establishing the optimal environment for our innovative system.