Reviewerly/exHarmony huggingface dataset:
Computational Analysis of a Mobile Path-Planning via Quarter-Sweep Two-Parameter Over-Relaxation

Over the years, self-reliant navigation has risen to the forefront of research topics. Improving the path-planning competencies is an extremely important component in achieving excellent autonomous navigation. This paper describes a refinement of the proficiency of mobile path-planning through a computational approach, i.e. the quarter-sweep two-parameter over-relaxation (QSTOR), to solving path-planning problems iteratively. The solution of Laplace’s equation (otherwise known as the harmonic functions) is the source for producing the potential function of the configuration space of the mobile robot. Numerical experiments illustrate that, in a given environment, a mobile robot is able to steer towards a particular destination with a smooth and ideal path from any beginning location. Furthermore, it is shown that in terms of the iterations number and computational time, the QSTOR iterative technique outperforms its predecessors in addressing mobile path-planning issues.
Towards a Hybrid Federated Social Networking Architecture. A Case of Zimbabwe’s Higher Education

The study had its motivation established on the readiness of Zimbabwean universities to adopt a Hybrid Federated Social Networking framework (FSNS). In that respect, the study pursued a mixed method research design, based on a pragmatist philosophy, which therefore provided a basis for an exploratory multiple case study pursuing five universities in Zimbabwe. In addition to that, ethnography, interviews and survey were adopted for data gathering. A total of 45 respondents were exposed to purposive sampling technique, while 748 contributed to an online survey. Findings from ethnography revealed that universities were ready to embrace the future of social media technology borne of a hybrid FSNS. The survey noted that there is a strong positive relationship between FSNS adoption and opportunities presented by eLearning in higher education. However, there are some inborne bottlenecks identified in the Zimbabwean higher education environment, which includes poor networking resources, lack of clear eLearning policies as well as the underlying technical architecture of the social media sites which includes agreed protocols, shared infrastructure. In line with that the study proffers a set of recommendations, as well as a proposed hybrid FSNS framework for adoption in higher education.
Deep Convolutional Neural Networks for Real-Time Human Detection and Tracking on UAVs Embedded Systems

Human detection for critical missions with unmanned aerial vehicle (UAV) support becomes more and more important in the actual context when tension at borders builds up for an increasing number of countries. Although convolutional neural networks are continuously evolving, the required computational resources pose a great problem when implemented on portable embedded systems such as UAVs, with limited processing power and autonomy. This demand becomes even more drastic when running real-time human detection and tracking. This paper proposes an improved implementation of the YOLOv.7, trained on a custom dataset, for real-time human detection and tracking with confidence scores above 80% on NVIDIA Jetson TX2 neural processing unit equipped on DJI Matrice 100 UAV. The authors created a YOLOv.7 model running independently on an embedded system for real-time human detection and tracking.
Lean production in the decrease time expenses at publication of a scientific journal

The results of work on the optimization of processes in the editorial office of a scientific journal (reviewing, working out the text of articles by the authors according to the comments of reviewers, literary editing, layout, coordination with the authors) are presented. During the work, non-production losses were analyzed and reduced. The aim of the project was to optimally use the available resources to reduce time costs in the production of the scientific journal. The task of the project was to analyze the sequence, time and distribution law of the editorial and publishing processes of the scientific journal, and on their basis to develop recommendations for eliminating losses and implement their activities of the editorial staff of the scientific journal. As a result of the project: several times reduced losses when reviewing manuscripts; the average time for reviewing articles is maintained with a significant increase in the flow of incoming articles over a short period of time; the average time of delay in the release of magazines has been reduced. In the course of the work, it was revealed that lean production methods (lean methods) are also applicable to complex formalized processes that, at their core, clearly do not create added value and do not deal with physical production, and process optimization significantly affects customer satisfaction. The results of the Gantt diagram before and after of lean productions had been presented.
Biopolymer Cryogels for Transient Ecology‐Drones

Transient Robotics As described by Fabian Wiesemüller, Mirko Kovač, and colleagues in article number 2300037, transient robots are biodegradable tools for assessing environmental health and autonomously creating ecological models. After collecting the targeted environmental data, the aerial robot lands and completes its service life. The structure of the robot, made from renewable biopolymers, degrades and the stored nutrients are reintroduced into the carbon cycle. This circular economy approach towards robot manufacturing minimizes the environmental footprint.
Data Exchange Mechanism for Real-Time Object Detection in Cloud-Edge IoT System

Real-time target detection based on massive surveillance video data plays a key role in the smart grid, especially in power vision applications. With the indepth construction of power Internet of Things, centralized cloud applications pose challenges to real-time applications due to high bandwidth costs, resulting in delays and loads. Edge computing attempts to perform all the tasks on the edge. However, limited resources on the edge generally cannot guarantee real-time tasks, while complex intelligence algorithms are difficult to fully deploy. Given that both cloud computing solution and edge-only computing solution cannot achieve a good effect, this paper proposes a data exchange mechanism for real-time object detection in a cloud-edge IoT system, which achieves a balance of object detection task executing between delay and accuracy by utilizing the designed intelligent task scheduler. Experimental results performed in real environments using real surveillance video datasets show that by using our cloud-edge collaboration mechanism, the task response time of the system is only about 12% of cloud computing solution while ensuring query accuracy within the practical scope. The system also improves the query accuracy by about 59% compared to edge computing solutions, while the task response time was reduced to its 72%.
A Rate-Distortion-Classification approach for lossy image compression

In lossy image compression, the objective is to achieve minimal signal distortion while compressing images to a specified bit rate. The increasing demand for visual analysis applications, particularly in classification tasks, has emphasized the significance of considering semantic distortion in compressed images. To bridge the gap between image compression and visual analysis, we propose a Rate-Distortion-Classification (RDC) model for lossy image compression, offering a unified framework to optimize the trade-off between rate, distortion, and classification accuracy. The RDC model is extensively analyzed both statistically on a multi-distribution source and experimentally on the widely used MNIST dataset. The findings reveal that the RDC model exhibits desirable properties, including monotonic non-increasing and convex functions, under certain conditions. This work provides insights into the development of human-machine friendly compression methods and Video Coding for Machine (VCM) approaches, paving the way for end-to-end image compression techniques in real-world applications.
A Comparative Analysis of Vocabulary Grade in Korean Textbooks in 2015 Curriculum Middle School

Objectives The purpose of this study is to analyze the educational vocabulary grades of expository and narratives contained in the entire volume of middle school Korean textbooks of nine publishers of the 2015 curriculum to obtain implications for the development of Korean textbooks and vocabulary education in middle school.&#x0D; Methods To do this, the study built a corpus of expository and narrative texts in all Korean language textbooks for grades 1-3 in middle school, extracted 120 high-frequency content words from each text using the Mecab morphological analyzer in Python after preprocessing, and analyzed them according to the educational vocabulary grade presented in previous research. The study also calculated the vocabulary grade by text type and grade, extracting the top 50 high-frequency words from each text.&#x0D; Results In the corpus of expository texts, the vocabulary grade was 1st grade (80.9%) &gt; 2nd grade (10.8%) &gt; 3rd grade (6.7%) &gt; 4th grade (0.8%) = 0th grade (0.8%) in descending order. In the corpus of narrative texts, the vocabulary grade was 1st grade (95.8%) &gt; 2nd grade (3.3%) &gt; 0th grade (0.9%) in descending order. There were no 3rd or 4th grade words in the narrative text corpus. When comparing by grade, in the corpus of expository texts, the 1st and 3rd grades had the order of 1st grade (84%) &gt; 2nd grade (8%) = 3rd grade (8%) in descending order, and there were no 4th grade words extracted from either grade. In the 2nd grade, the order was 1st grade (84%) &gt; 2nd grade (8%) &gt; 3rd grade (4%) = 4th grade (4%) in descending order, and the proportion of 4th grade words was the highest among all grades. In the corpus of narrative texts, the 1st grade had only 1st grade words (100%), the 2nd grade had 1st grade words (96%) and 2nd grade words (4%), and the 3rd grade had 1st grade words (98%) and 0th grade words (2%). As with the corpus of expository texts, the 2nd grade had a higher grade of words than the 3rd grade in the narrative text corpus.&#x0D; Conclusions Based on the research results, the vocabulary grade of expository and narrative texts in middle school Korean language textbooks is appropriate and provided suggestions on how to utilize the results for the development of new textbooks and vocabulary education.
Immutable Livestock Tracking and Compliance Logs Using Blockchain

A supply chain is designed to maintain the quality of sensitive goods throughout their production process. The life cycle of a product requires different organizations, people, activities, information, and resources involved in the processing of a product or service from supply to customer.This paper describes a solution from e-Livestock (a software development company focused on livestock tracking) in collaboration with nChain (a blockchain technology development company), which enables small livestock producers to monitor and control the status of cuttle and collaborate with local regulatory entities. This solution uses the Bitcoin SV Blockchain to provide a tamper-proof and immutable sequential record of events such as vaccinations, asset ownership, and health tracking. Deployed in September 2022, it aims to prevent corruption, fraud, and human error by offering supply chain process improvements, achieving an end-to-end traceable model.
Quasi-Periodic Image Noise Filtering Using Inverse Laplace Filter

In the process of image acquisition a lot of different types of noise can arise. Filtering this noise is a well-known problem in image processing. Quasi-periodic noise filtering has proven to be a challenging problem in this topic. This paper presents a novel automated approach for filtering quasi-periodic noise using Laplace distribution for modelling Fourier transform coefficients, Inverse Laplace filter and Median filter. Results show that this approach can filter the noise even when the noise is more present in the image than the original content of the image. Average increase in the amount of the signal of the image is 50%.
Simplification of Arabic text: A hybrid approach integrating machine translation and transformer-based lexical model

The process of text simplification (TS) is crucial for enhancing the comprehension of written material, especially for people with low literacy levels and those who struggle to understand written content. In this study, we introduce the first automated approach to TS that combines word-level and sentence-level simplification techniques for Arabic text. We employ three models: a neural machine translation model, an Arabic-BERT-based lexical model, and a hybrid model that combines both methods to simplify the text. To evaluate the models, we created and utilized two Arabic datasets, namely EW-SEW and WikiLarge, comprising 82,585 and 249 sentence pairs, respectively. As resources were scarce, we made these datasets available to other researchers. The EW-SEW dataset is a commonly used English TS corpus that aligns each sentence in the original English Wikipedia (EW) with a simpler reference sentence from Simple English Wikipedia (SEW). In contrast, the WikiLarge dataset has eight simplified reference sentences for each of the 249 test sentences. The hybrid model outperformed the other models, achieving a BLEU score of 55.68, a SARI score of 37.15, and an FBERT score of 86.7% on the WikiLarge dataset, demonstrating the effectiveness of the combined approach.
Forecasting Euro Banknotes in Circulation with Structural Time Series Models in Times of the COVID-19 Pandemic

As part of the Eurosystem’s annual banknote production planning, the national central banks draw up forecasts estimating the volumes of national-issued banknotes in circulation for the three years ahead. As at the end of 2021, more than 80 per cent of euro banknotes in circulation (cumulated net issuance) had been issued by the national central banks of France, Germany, Italy and Spain (collectively referred to as the “4 NCBs”). To date, the 4 NCBs have been using ARIMAX models to forecast the banknotes issued nationally in circulation by denomination (“benchmark models”). This paper presents the structural time series models developed by the 4 NCBs as an additional forecasting tool. According to the forecast accuracy measures employed, the Structural Time series Models (“STSMs”) outperform the benchmark models at each of the 4 NCBs and for most of the denominations. However, it should be borne in mind that the statistical informative value of this comparison is limited by the short projection period of just 12 months.
Evaluation of Machine Learning Algorithms for Message Spam Filtering

Over the years, we have expanded the incorporation of technology into our day to day lives in numerous ways such as phones and computers. These devices allow for humans to stay connected and have instantaneous communication. Not only can messages be sent in an instant but can be sent on a massive scale. This is commonly used by advertisers and what we commonly view as spam mail. With a growing presence of spam follows an increasing need for methods to combat this issue. This is the aim of many machine learning algorithms with the task of sorting through data in a specific file and identifying whether it is spam or not. This work will compile data from various machine learning algorithms and test each one’s accuracy. The algorithms will be tested using a dataset containing many text messages some of which being spam and some not spam which will be referred to as ham. The accuracy score of these algorithms were output in the code when ran. Overall, this paper will dwell on spam detection and the accuracy of varying machine learning algorithms.
Why You Should Be Able to Make Your Own Individualized, Digital Nano-Currency

Increasing opportunities for access to traditional financial systems.
Marathi Extractive Text Summarization using Latent Semantic Analysis and Fuzzy Algorithms

Extractive text summarization involves the retention of only the most important sentences in a document. In the past, multiple approaches involving both statistical and machine learning-based methods have been used for this task. The crucial step in extractive text summarization is getting the right ranking order of sentences in the document in terms of their importance. Singular value decomposition or SVD algorithm based on latent semantic analysis focuses on recognizing the sections in the document which are related in terms of their semantic nature. Fuzzy algorithms involve reasoning of the priority order of the sentences using fuzzy logic unlike the use of discrete values. While significant work has been done for extractive text summarization in English and other foreign languages, there is ample scope for improving the performance of systems when dealing with Marathi text. In this paper, SVD and fuzzy algorithms are proposed for performing extractive text summarization on Marathi documents. Work is done upon the modeling principle, data flow, and parameters of these algorithms such that they are best suited for the task. An analysis of the characteristics of both these techniques is conducted to compare their benefits and shortcomings. The performance of both the algorithms is evaluated on a document dataset using standard performance metrics including the ROUGE metric. An unbiased comparison of both these techniques is carried out to inform the applicability of them, especially when working with Marathi or in general, non-English text.
Short-range and Long-range Obstacle Detection Method for a Delivery Robot Based on Multi-sensor Fusion

The development of an effective obstacle perception system is critical for preventing potential collisions between an autonomous delivery vehicle and obstacles in its path. The position of obstacles, which can be determined by their 3D location and yaw value, is vital in facilitating reliable path planning for the vehicle. However, most conventional approaches to obstacle detection rely on a single sensory system, leading to blind spots where obstacles may go undetected due to hardware limitations. This paper proposes a novel approach that fuses three sensors - rotating LiDAR, horizontal LiDAR, and a camera sensor - to create a robust obstacle detection system. This new system enables the detection of short-and long-range obstacles previously undetectable due to hardware limitations. The camera sensor is also utilized to classify the detected objects, thereby enhancing the overall reliability of the perception system. The paper proposed a unique fusion method to detect and classify obstacles for the delivery vehicle.
Sensitivity Analysis for an Ensemble Transfer Learning scheme for Time Series Forecasting – Case-Study of a Shallow Neural Network Achitecture

Transfer learning (TL), applied in the context of time-series forecasting, is an important hot topic nowadays in machine learning. This paper addresses the gap identified in most recent survey papers that empirical studies, currently missing, are very necessary to come up with guidelines for TL approaches and TL method design selections that can be used by practitioners. In this perspective, this paper does overall suggest the skeleton of a comprehensive sensitivity analysis methodology for TL schemes w.r.t. to a given machine learning model at hand. As a first step, five relevant TL performance metrics are suggested and comprehensively defined. Then, the core steps of a comprehensive TL-related sensitivity analysis are formulated. For illustration, an mini-sensitivity analysis is conducted on an MLP shallow network. Although relatively small, this mini-sensitivity analysis does confirm the usability of the suggested methodology as it does already, nevertheless, highlight some interesting insights. The end-product of this project can culminate in designing a pretrained model that can be useful to time-series forecasting practitioners.
The Mediated effect of Teacher Professionalism on the Relation Between Daycare center Director’s Servant Leadership and Teacher’s Play support Competency

Objectives The purpose of this study was to examine the mediating effect of teacher’s professionalism on the relation between daycare center director’s servant leadership and teacher’s play support competency.&#x0D; Methods Two hundred fifty teachers from Chung-buk, Chung-nam and Seoul participated in this study. Collected data was analyzed using SPSS version 21.0 program for frequency, description analysis and correlation analysis. AMOS version 23.0 program was used for structural equation analysis. Bootstrapping was performed to analyze mediating effect of teacher’s professionalism on the relation between director’s servant leadership and teacher’s play support competency.&#x0D; Results There is the positive correlation among the variables. director’s servant leadership has a negative influence on teacher’s play support competency but has a positive influence on the teacher professionalism. Teacher professionalism has a positive influence on the teacher’s play support competency. the mediating effect of teacher professionalism on the relation between the director’s servant leadership and the teacher’s play support competency has verified.&#x0D; Conclusions The results of this study verified that the most important element for teacher’s play support competency is improving teacher’s professionalism. This result have a evidence for directionality. The key element for the play-centered curriculum is supporting teachers to improve their professionalism when the director managing a daycare center using the servant leadership approach.
A multi‐faceted analysis of the performance variability of virtual machines

Abstract Cloud computing and virtualization solutions allow one to rent the virtual machines (VMs) needed to run applications on a pay‐per‐use basis, but rented VMs do not offer any guarantee on their performance. Cloud platforms are known to be affected by performance variability , but a better understanding is still required. This article moves in that direction and presents an in‐depth, multi‐faceted study on the performance variability of VMs. Unlike previous studies, our assessment covers a wide range of factors: 16 VM types from 4 well‐known cloud providers, 10 benchmarks, and 28 different metrics. We present four new contributions. First, we introduce a new benchmark suite ( VMBS ) that let researchers and practitioners systematically collect a diverse set of performance data. Second, we present a new indicator, called V I , that allows for measuring variability in the performance of VMs. Third, we illustrate an analysis of the collected data across four different dimensions: resources , isolation , time , and cost . Fourth, we present multiple predictive models based on machine learning (ML) that aim to forecast future performance and detect time patterns. Our experiments provide important insights on the resource variability of VMs, highlighting differences and similarities between various cloud providers. To the best of our knowledge, this is the widest analysis ever conducted on the topic.
The Correlation Between Studentsâ€™ Vocabulary Mastery and Reading Comprehension at the Eighth-Grade Students of SMPN 2 Sojol

The research aims to determine whether there was a correlation between vocabulary mastery and reading comprehension. The study used a simple random sampling technique, namely, taken 30 students in class VIII B. However, the correlation criterion between 0.60 and 0.80 was considered very high. The researchers concluded that rxy = 0.797 and N 30 at a 5% correlation significance is 0,361. Therefore, based on the Pearson Product Moment formula, compared to the rtable, the study found that rcount higher than rtable (0.361), the average Ho was rejected, and Ha was accepted. To sum up, the final results exhibited a correlation between students' vocabulary mastery and reading comprehension.