Reviewerly/exHarmony huggingface dataset:
BASIC STRUCTURE OF SCIENTIFIC ARTICLE–IMRAD METHODOLOGY

&lt;p&gt;&lt;span&gt;The paper will present the basic structure of writing a scientific paper, so-called IMRAD methodology (Introduction, Materials and Methods, Results, Discussion, Conclusion, Acknowledgments, References). The process and direction of writing a scientific paper will be shown with reference to the most important rules in writing a paper, all with the aim of ranking the paper itself. Five basic rules for increasing the visibility of the work itself and basic SEO optimization will be shown through practical examples of presentations with reference to the importance of the abstract itself and keywords. The importance of social networks and ways of promoting the work itself will also be clearly shown.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; IMRAD methodology, scientific article, SEO, abstract, keywords.&lt;/span&gt;&lt;/p&gt;
SCIENTIFIC AND PRACTICAL TRAINING COMPONENT OF HIGHER EDUCATION APPLICANTS BY IMPLEMENTING THE DIGITALIZATION OF THE INFORMATION SPACE

The article deals with the problem of forming a competent understanding of students' scientific research work as a component of the training specialists system in the technical field. The main attention is paid on the experience of technical universities in Ukraine in the implementation of various forms of the scientific research work in the educational process. It is proved that the students’ scientific research work has a two-level end-to-end character. The expectations of respondents regarding the implementation of different forms and methods of the scientific research work into the educational process are described. The self-evaluation of bachelors was based on the personal experience of students participating in various types of scientific activities. The historical experience of introducing forms and methods of student scientific research work based on the use of archival statistical materials is investigated. It is emphasized that the new conditions that have arisen with the spread of the COVID-19 pandemic and the full-scale war started by the russian federation have made certain changes to the conduct of scientific activities with students. It is necessary to revise approaches to its organization in order to integrate into the globalized information society.
Research on the Integration of Natural Phonetic Pronunciation and International Phonetic Alphabet to Improve College Students' Oral English Expression Ability

Phonics is an international mainstream English teaching method, which forms a phonetic corresponding system according to the pronunciation rules of the combination of letters. The International Phonetic Alphabet (IPA) is a system used for labeling English pronunciation, and make the English spoken language reach the international standards. At present, the common problems of Chinese college students are lack of the ability of output and the inadequacy of the oral pronunciation. Combined with the current situation of college students' oral English, this paper aims to explore the advantages and the integration of phonics and International Phonetic Alphabet and explore a new set of education method which is suitable for improving oral English ability and have practical teaching significance.
Legendre pairs of lengths <i>ℓ</i> ≡ 0 (mod 5)

Abstract By assuming a type of balance for length <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>ℓ</m:mi> <m:mo>=</m:mo> <m:mn>87</m:mn> </m:math> \ell =87 and nontrivial subgroups of multiplier groups of Legendre pairs (LPs) for length <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>ℓ</m:mi> <m:mo>=</m:mo> <m:mn>85</m:mn> </m:math> \ell =85 , we find LPs of these lengths. We then study the power spectral density (PSD) values of <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>m</m:mi> </m:math> m compressions of LPs of length <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mn>5</m:mn> <m:mi>m</m:mi> </m:math> 5m . We also formulate a conjecture for LPs of lengths <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>ℓ</m:mi> <m:mo>≡</m:mo> <m:mn>0</m:mn> </m:math> \ell \equiv 0 (mod 5) and demonstrate how it can be used to decrease the search space and storage requirements for finding such LPs. The newly found LPs decrease the number of integers in the range <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mo>≤</m:mo> <m:mn>200</m:mn> </m:math> \le 200 for which the existence question of LPs remains unsolved from 12 to 10.
Self-Attention Mechanism Based Federated Learning Model for Cross Context Recommendation System

The traditional single-context recommendation algorithm is constrained by the sparse connection between users and items and a user/item cold start problem. Based on the review, the text-based cross-context recommendation algorithm extracts user/item comment information in the auxiliary context to alleviate the target context’s data sparsity problem and improve recommendation accuracy. A cross-context recommendation strategy based on self-attention-based federated learning (SAFL) is suggested in this work. In contrast to current algorithms, SAFL fully mixes the target and auxiliary background information. Federated learning addresses crucial challenges including data privacy, data security, data access rights, and access to heterogeneous data by allowing several players to develop a single, strong machine learning model without sharing data. To begin, self-attention mechanism is introduced to model the user’s preference information; then, the information from one field is used to improve the recommendation accuracy of another area; and finally, the information from the two contexts is integrated into the knowledge fusion module and the score prediction module to predict the score. Experiments on the Amazon dataset indicate that MAE and MSE values of SAFL are higher when compared to the current cross-context recommendation model, MAE values rose by 8.4%, 13.2%, and 19.4% across three cross-context datasets.
Occlusion-Aware Feature Recover Model for Occluded Person Re-Identification

Occluded person re-identification (Re-ID) is a challenging task, as various object-to-person (OTP) and person-to-person (PTP) occlusion scenarios cause diverse occlusion interference and target person feature loss problems in person matching. Most existing methods, which utilize auxiliary models to evaluate the unoccluded person parts for occlusion feature elimination, are inefficient and cannot handle the PTP occlusion scenarios and person feature loss problems. To solve these issues, we propose a novel Occlusion-Aware Feature Recover (OAFR) model. OAFR simulates diverse occlusions to facilitate the model perceiving OTP, PTP occlusions and recovers occluded query features with unoccluded retrieved gallery features. Concretely, the Prior Knowledge-based Occlusion Simulation method is firstly introduced to synthesize OTP, PTP occlusions and corresponding occlusion labels, empowering model target person perception and occlusion-aware capability through self-supervised learning. Afterward, the feature recovery module reconstructs occluded query features with corresponding unoccluded local features of the top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> retrieved images by the visibility weighted average scheme, thus recovering the occluded query features to maintain more comprehensive features for better retrieval. Extensive experiments demonstrate that the proposed OAFR achieves superior performance to the state-of-the-art for both holistic and occluded Re-ID. Especially for Occluded-DukeMTMC dataset, OAFR outperforms the state-of-the-art by 6.0% for Rank-1 accuracy and 2.2% for mAP score. The source codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yuanbianGit/OAFR</uri> .
Future Prediction of Close Contacts in IoT-based Contact Tracing System using a New Real-Life Dataset

Contact tracing is an effective method for mitigating the infectious diseases spread and it played a crucial role in reducing COVID-19 outbreak. Since the pandemic, there has been an increased concern regarding people's health in hospital and office settings, as these limited air exchange spaces provide a conductive medium for virus spread. Various technologies were used to recognize close contacts autonomously, in addition, multiple machine learning attempts were carried out to determine proximity in contact tracing. This study, however, proposes a unique concept in contact tracing: forecasting future close contact prior to occurrence in order to regulate and control it rather than tracking past occurrences. For our research, we constructed a completely new real-life dataset that was collected during the pandemic in a hospital infectious ward (Alfred Hospital, Melbourne, Australia) utilizing a Bluetooth Low Energy (BLE) Internet of Things (IoT) system. Our prediction technique considers two types of environments: single transceiver environments and multiple transceivers settings, these transceivers record the nearby tags' BLE received signal strength indicator (RSSI) values. The system employs mathematical models and supervised machine learning (ML) algorithms to solve regression and classification problems for workers' pattern recognition within the environment. The output is compared using different metrics, such as efficiency, which reached more than 80%, root mean square errors and mean absolute errors which were as low as 2.4 and 1.2 respectively in some models.
Graph Soft Actor–Critic Reinforcement Learning for Large-Scale Distributed Multirobot Coordination

Learning distributed cooperative policies for large-scale multirobot systems remains a challenging task in the multiagent reinforcement learning (MARL) context. In this work, we model the interactions among the robots as a graph and propose a novel off-policy actor–critic MARL algorithm to train distributed coordination policies on the graph by leveraging the ability of information extraction of graph neural networks (GNNs). First, a new type of Gaussian policy parameterized by the GNNs is designed for distributed decision-making in continuous action spaces. Second, a scalable centralized value function network is designed based on a novel GNN-based value function decomposition technique. Then, based on the designed actor and the critic networks, a GNN-based MARL algorithm named graph soft actor–critic (G-SAC) is proposed and utilized to train the distributed policies in an effective and centralized fashion. Finally, two custom multirobot coordination environments are built, under which the simulation results are performed to empirically demonstrate both the sample efficiency and the scalability of G-SAC as well as the strong zero-shot generalization ability of the trained policy in large-scale multirobot coordination problems.
A comparative study on the pollution status of heavy metals in soils and sediment from farmlands in Askira Uba, Nigeria

Heavy metals may enter the food chain from soil through mineralization by crops or environmental contamination, as in application of agricultural inputs such as pesticides and fertilizers or in the treatment of soils with sewage sludge cupper content of normal plant tissue. Atomic Absorption Spectroscopic (AAS) method is one of the most commonly used instrument in metal analysis due to its reproducibility of results, short analysis time, lower level of detection. Soil and sediments samples were collected at three distinct stations each, designated as A1, B1, C1 and A2, B2, C2 respectively from Askir/Uba local government area of Borno State, Nigeria. The concentrations of six heavy metals of environmental concern which includes Cd, Cu, Cr, Pb, Ni and Zn were determined by Atomic Absorption Spectrophotometer; Buck scientific model 210GP. The mean concentration of Cd in soil and sediment were (10.7±2.32 mg/kg and 9.62±2.32 mg/kg) and are of toxicological concern. The decrease in concentrations of the metals across sampling locations followed this order Cd &gt; Cu &gt; Cr &gt; Pb &gt; Ni &gt; Zn. The geoaccumulation index of the sediment by each metals are in the following increasing order: B2 &lt; A2 &lt; C2 for Cd and Pb, B2 &lt; C2 &lt; A2 for Cr, A2 &lt; B2 &lt; C2 for Cu, A2 &lt; B2 &lt; C2 for Zn respectively. In general, the order of metal contamination were Zn &lt; Ni &lt; Cu &lt; Pb &lt; Cr &lt; Cd. The results of the findings revealed that the soil and sediment were contaminated with Cadmium, the high level of Cd in the soil were above the recommended values. However, Independent-samples t-test (statically significant at p&lt;0.05) was used to compared the average metal composition in soil and sediment which showed that there was no significant difference on the composition in soil and sediment for all the metals p-value = 0.8939 The aim of the study was to compare the mean level of the soil and sediment with WHO permissible limit, hence evaluate their pollution status by the application of geoaccumulation index.
Survey of adaptive containerization architectures for HPC

Containers offer an array of advantages that benefit research reproducibility and portability. As container tools mature, container security improves, and high-performance computing (HPC) and cloud system tools converge, supercomputing centers are increasingly integrating containers into their workflows. Despite this, most research into containers remains focused on cloud environments.
A Reference Implementation for a Quantum Message Passing Interface

Practical applications of quantum computing are currently limited by the number of qubits that can be set with reasonable fidelity for each system. Therefore, a distributed quantum computing system with multiple quantum computers coherently connected is highly demanding. To realize the inter-node communication of quantum information, the software interface, Quantum Message Passing Interface (QMPI), leveraging the framework built for classical MPI but taking advantage of quantum teleportation to communicate between different quantum nodes was proposed. In this work, we developed a QMPI implementation with a variety of point-to-point and collective operations in Qiskit and characterize its performance by demonstrating the reference implementations on a few benchmark quantum applications. Moreover, we developed a new approach for optimizing collective communications for the distributed quantum programs with multi-controlled Toffoli gates. This approach outperforms current state-of-the-art in terms of fidelity and the number of remote EPR pairs in both simulations and experiments over IBM’s quantum platforms.
An Analysis of Graph Neural Network Memory Access Patterns

Graph Neural Networks (GNNs) are becoming increasingly popular for applying neural networks to graph data. However, as the size of the input graph increases, the GPU memory wall problem becomes an important issue. Since both current solutions to reduce the memory footprint, such as mini-batch approaches and the use of memory-efficient tensor manipulations, have drawbacks, we attempt to solve the problem by expanding the memory size using a virtual memory technology. To overcome the data transfer overhead of virtual memory technology, in this paper we focus on analyzing the memory access pattern of GNNs with the goal of reducing the data transfer latency perceived by the user. A preliminary result of applying optimization techniques guided by our analysis results shows a 40% reduction in the execution time of a combination of training and testing.
AI technologies in the ESL/EFL writing classroom: The villain or the champion?

The impact of AI technologies, particularly ChatGPT, on the ESL/EFL writing classroom is unprecedented. In response to the focus paper by Warschauer and colleagues regarding this issue, this paper shares some insights from the experiences teaching English writing at the post-secondary level in Thailand. Due to the lack of institutional policies on the use of AI technologies, we need to devise our own strategies for our classes. While students are encouraged to use their voices instead of relying on AI, we attempt to adapt our practice by incorporating AI generators in a productive way to help students enhance their potential as L2 writers. However, there has been a debate among writing teachers in our context whether to accept AI tools as a part of teaching and learning.
Implementation of a quantum walk in a cycle of four nodes using an alternative method based on the Swap gate

This work has the objective of implementing quantum random walks in a graph with 4 nodes represented by a circle. It was observed that during the implementation of a discrete-time quantum walk, based on Hadamard's coin, the simulated results diverge from the theoretically expected results. In order to correct the discrepancies between the processed and theoretically expected results we use an alternative method based on the Swap port. To perform the simulation of the algorithms, the Qiskit framework and a real processor provided by IBM through remote access was used. The simulations performed in the real processor showed small fluctuations, however the quantum states were obtained with satisfactory probabilities.
Creating an engaged community of readers

Early Years EducatorVol. 24, No. 5 Early Years Community CornerCreating an engaged community of readersRuth MottramRuth MottramEducation Advisor across all sitesSearch for more papers by this authorRuth MottramPublished Online:10 Nov 2023https://doi.org/10.12968/eyed.2023.24.5.S3AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 2 December 2023Volume 24Issue 5 ISSN (print): 1465-931XISSN (online): 2052-4617 Metrics History Published online 10 November 2023 Published in print 2 December 2023 Information© MA Education LimitedPDF download
Artificial Intelligence Adoption in the Banking Industry: Current State and Future Prospect

AI adoption in the banking industry is a necessity not only to gain a competitive advantage within the industry but more importantly to defend the bank versus technological disruptors that are trying to gain ground in service areas that were previously dominated by banks. This study aims to analyze the current state of AI adoption in the banking industry through a systematic review of related literature. Using the SCOPUS database, thirty-five studies were identified that are relevant to this study. While the number of studies is on increasing trend, the focus of existing literature is predominantly on the customers' and the banks' perspectives. Studies from the regulators' and service providers' perspectives are still sparse despite the importance of these two actors in the adoption of AI in the banking industry. The application of AI has permeated wide areas of the banking function expanding beyond the use of chatbots. Improving customer experience, increasing profitability, and improving competitiveness drive the adoption of AI by banks while people-related issues, technology-related issues, and regulatory-compliance issues deter banks from fully embracing AI. Transparency of algorithms, data privacy, data protection, and fair use of data are the main concerns both of regulators and customers. Results of this study point to the need for more empirical and theoretical research on the regulatory perspective of AI adoption in the banking industry, how to harness the strength of service providers to enable wider and more effective adoption of AI, how institutions can help increase the talent pool for AI-driven technologies, and how to strengthen the synergistic cooperation of the various actors in the adoption of AI in the banking industry.
Adaptive Decision-Support System Model for Automated Analysis and Classification of Crime Reports for E-Government

This study explores the potential of text analysis and classification techniques to improve the operational efficiency and effectiveness of e-government, particularly within law enforcement agencies. It aims to automate the analysis of textual crime reports and deliver timely decision support to policymakers. Given the increasing volume of anonymous and digitized crime reports, conventional crime analysts encounter challenges in efficiently processing these reports, which often lack the filtering or guidance found in detective-led interviews, resulting in a surplus of irrelevant information. Our research involves the development of a Decision Support System (DSS) that integrates Natural Language Processing (NLP) methods, similarity metrics, and machine learning, specifically the Naïve Bayes' classifier, to facilitate crime analysis and categorize reports as pertaining to the same or different crimes. We present a crucial algorithm within the DSS and its evaluation through two studies featuring both small and large datasets, comparing our system's performance with that of a human expert. In the first study, which encompasses ten sets of crime reports covering 2 to 5 crimes each, the binary logistic regression yielded the highest algorithm accuracy at 89%, with the Naive Bayes' classifier trailing slightly at 87%. Notably, the human expert achieved superior performance at 96% when provided with sufficient time. In the second study, featuring two datasets comprising 40 and 60 crime reports discussing 16 distinct crime types for each dataset, our system exhibited the highest classification accuracy at 94.82%, surpassing the crime analyst's accuracy of 93.74%. These findings underscore the potential of our system to augment human analysts' capabilities and enhance the efficiency of law enforcement agencies in the processing and categorization of crime reports.
Study of Improved Crack Toughness of Unsaturated Polymers with Rice Husk Fiber and Sago Flour as Strengthening Materials

The development of environmentally friendly composites from natural fibers is an absolute thing to do to replace non-degradable synthetic composites. Some of the weaknesses of natural fiber composites are low mechanical strength, ease of cracking, no moisture resistance, and high-temperature resistance. One of the things that has been done is to make a combination of synthetic materials as a matrix derived from unsaturated polyester reinforced with natural fibers from crushed rice husk particles and starch from sago flour which is used to reduce the percentage of synthetic materials to be able to form composites that are easily decomposed. From the research, it was found that the strength of crack resistance could be increased with a mixture of polyester reinforced with rice husk fiber and sago flour, obtaining an increase in crack resistance strength until the addition of rice husk with a percentage of 15%. While increasing the RH content above 15%, the crack strength value decreases due to the saturation of the RH molecules in the UP which is no longer bound to the UP molecules. The highest crack strength values occurred for the addition of the percentage of RH and SS to the UP material with the addition of 5% SS, which obtained a cracking force of 550 N while the strength of pure polyester was only 37 N. This shows that RH and SS materials can bond with UP molecules and some molecules of RH and SS can prevent cross-linking of polyester molecules. Meanwhile, the addition of SS up to 10% decreases the fracture force obtained, indicating that not all of the sago starch can bind to the polyester molecules.