Reviewerly/exHarmony huggingface dataset:
Ecosystem-Centric Approach to Intelligent Product Development: Paving the Way for Scalable and Adaptive Solutions

This paper explores the evolution from traditional to modern methodologies for the development of intelligent products. The traditional linear and sequential approach, often characterized by limited toolsets and hardware-centric programming, fails to address the complexities and dynamic requirements of today's digital ecosystems. To bridge this gap, we propose an ecosystem-centric approach to intelligent product development that emphasizes scalability, flexibility, and integration of advanced technologies. Our approach fosters the use of artificial intelligence, machine learning, and advanced data analytics techniques, providing a model for creating scalable and adaptive solutions. We also delve into the necessity of DevSecOps methodology, incorporating cloud-based solutions such as Integration PaaS services, and embracing IoT technologies. This paper contributes by highlighting the benefits of this approach, including improved functionality, reduced development lifecycle, enhanced supply strategy, and cost savings.
Robust extended Kalman smoothing under model uncertainties

This paper proposes a robust extended Kalman smoothing approach in the case that there is a modeling mismatch between the given nominal nonlinear state-space model and the actual one. The resulting algorithm follows a linearization process and takes the form of a robust fixed-lag smoother. At each time step the computation of the smoothing gain of the linearized model is required. The latter is derived from a dynamic game: one player selects the least favorable model in a prescribed “ball’’ centered about the nominal linearized model in Kullback-Leibler (KL) divergence topology, and the other selects the optimal smoothing gain. Finally, some numerical experiments show the effectiveness of the proposed algorithm.
Self-regulation of Learning and Personality: a Predictive Study in University Students from the Ecuadorian Highlands

This research analyzes the psychometric properties, the influence of sociodemographic variables, the level of association and prediction between Zimmerman’s self-regulation model of learning and Zuckerman’s psychobiological model of personality in university students from the Ecuadorian highlands. A total of 394 undergraduate students from the cities of Quito, Ambato and Loja were surveyed by non-probabilistic sampling. Data were analyzed by descriptive statistics, reliability methods, content and construct validation, Spearman’s correlation and multiple linear regression using software and technological tools. The results show: a) reliability and validity of the ISLP and ZKPQ-50cc instruments, b) gender and age influence on personality traits, while study modality influences self-regulation of learning and personality, c) personality traits reveal low and medium relationships with self-regulation of learning and d) personality traits of: activity, aggression-hostility and neuroticism-anxiety influence on self-regulation of learning.
Supplementing Real Data with Transferable Textures on Face Presentation Attack Detection

Face Presentation Attack Detection (PAD) is the branch of biometrics that detects spoofing in identity verification systems. Therefore, face PAD aims to stop attacks from bad agents trying to impersonate other people, or conceal their identity, for personal gain. One of the problems facing this technology is that current Deep-Learning based methods need thousands of images of real faces and attacks to train properly. However, obtaining that amount of images can be a very difficult and time consuming task. In this work, we aim to find out whether the Transferable-Textures technique can be used in face PAD to artificially increase the number of images in the dataset with similar performance to capturing new images. For this purpose, we trained and tested Siamese Networks on the wildly adopted CASIA-FASD dataset. Our results indicate that networks trained with Transferable Textures produced similar performance to those trained with captured images only. This demonstrates that this technique can be used to supplement manually captured attacks without any performance loss.
Advancements in Virtual Reality for Elderly Care: A Two-Decade Journey

In the context of the global aging population, there is a critical need for effective and innovative care strategies for older adults. Virtual reality (VR) has emerged as a promising tool for improving the lives of older adults. However, the scope and direction of research in this field have not been fully explored. This bibliometric study aims to analyze the development and current trends in research on the use of VR in elderly care over the past 20 years. A systematic search was conducted in Scopus, applying inclusion and exclusion criteria, resulting in the selection of 1622 articles. Using VOSViewer software, these articles were analyzed, revealing significant patterns in keywords, authors, citations, and publication venues. The findings indicate a growing interest in this topic, with a notable focus on aging, cognition, therapeutic exercise, and randomized controlled trials. Future directions for VR research include spatial navigation, neuropsychological testing, and physical exercises. Notably, the journals Frontiers in Aging Neuroscience and Journal of NeuroEngineering and Rehabilitation have high citation rates for their VR-related articles. This study provides a comprehensive overview of VR research in elderly care, identifying emerging trends and highlighting areas that warrant further attention.
Postponing the decay of entanglement and quantum coherence for maximally entangled mixed states under the action of correlated noise channels

We investigate the dynamics of a maximally entangled mixed state (MEMS) under the action of correlated noise channels. The channel acts in a way that its successive uses are correlated. We have studied the MEMS properties, including quantum coherence and entanglement. For partially correlated channels, both the entanglement and coherence of MEMS are found to decay much slower than those of the memoryless channels. Moreover, we observe a freezing effect of coherence for phase damping as well as depolarizing channels and freezing of entanglement for phase-damping channels with perfect memory. For amplitude damping and depolarizing channels, memory helps in either delaying the sudden death of entanglement or slowing the decay rate of coherence. These observations suggest that memory channels perform better than memoryless channels in maintaining the integrity of quantum states and have utility in quantum information processing protocols.
Temporal silhouette: validation of stream clustering robust to concept drift

Abstract Stream clustering is required in applications where data is generated continuously or periodically and must be processed considering its temporal nature. In the absence of a ground truth, internal validation is the only option to evaluate the quality of performances. Traditional internal validation is commonly used also in stream clustering, even in spite of the fact that it becomes inconsistent in the event of data evolution. Recent trends opt for incremental approaches, but these are closer to change detection rather than validation methods and limit themselves by imposing online validation on online analysis. In this work we study the impact of concept drift in the validation of stream clustering and propose the Temporal Silhouette index, therefore making internal validation conform to streaming data. We conduct tests with more than 200 datasets and contrast performances of four popular stream clustering algorithms with seven validation methods (three static internal, three incremental internal, one external) and the proposed index. Results show the suitability of the Temporal Silhouette index for stream clustering validation in the event of concept drift and different types of outliers. The demand for reliable unsupervised learning in applications that process data in streams is ever-increasing, and such reliability inevitably requires the use of validation. This fact highlights the significance of the novel approach proposed in this work.
The new Ukrainian school reform: Achievements, developments and challenges

Abstract This article describes the New Ukrainian School reform and its conceptual framework. The reform builds on advanced pedagogical experience, best education practices, European competency frameworks and the promotion of national values. The system‐wide reform focuses on competency‐based content for teacher professional development, safe learning environments, inclusion and the digitalisation of education. Also, it addresses the internal and external quality assessment of education. The reform is financed through central government grants and the budgets of local governments. To test the newly developed content, the reform was piloted at 143 schools one year ahead of the nationwide roll‐out. The schools are a representative sample of schools in Ukraine. The pilot study involved monitoring student learning outcomes, teacher preparedness to implement the reform, and organisation of education environments. Methods used for the pilot study included a survey, test, pedagogical observations, and secondary analysis of statistics. The national implementation of the reform started in 2017 and is planned to be completed by 2030. Russia's full‐scale invasion of Ukraine in 2022 has contributed to obstacles and risks such as learning losses, lacking school safety, absence of funding for the development of schools and for printing textbooks. We describe measures undertaken in Ukraine to promote the reform and good results. A range of challenges remain, such as rebuilding destroyed schools, optimising the school network in line with demographic changes and organising shelters at schools. Additional challenges include the promotion of digital technologies at schools, closing learning gaps and the provision of psychological support at school.
Kroatische Sprichwortvarianten bei der Erstellung des kroatischen parömiologischen Thesaurus

Abstract The project establishing the Croatian paremiological minimum and optimum began in 2014 during which valuable data was collected from 1,585 informants, who completed two sets of field questionnaires – the first and the second final text presentation of the proverbs occurring most frequently in the three biggest Croatian corpora (see Aleksa Varga and Keglević 2020a). The questionnaires were analyzed using the model analysis developed by Chlosta and Grzybek (see Chlosta and Grzybek 2005, 2015 and Grzybek 2012), based on a 4-code system. Since part of the analysis of both studies regarding the zero variant and the familiarity of Croatian proverbs have already been published (see Aleksa Varga and Keglević 2020b), the scope of this paper lies in analyzing the variations of the first, second, or third degree. As one of the goals of the research, apart from establishing the Croatian paremiological minimum and/or optimum, is also the construction of an electronic Croatian paremiological thesaurus, it is of utmost importance to analyze the proverb variations from the field questionnaires for inclusion in the database as well; i.e., to determine if they appear frequently enough alongside or instead of the proposed zero variant. Therefore, the present paper analyzes the Croatian proverb variants of the first, second, and third degree from the quantitative, qualitative, and corpus linguistic point of view, providing suggestions on further steps in constructing the Croatian proverbial thesaurus.
A Detailed Analysis on Temporal Detection of Pixel Motion in Video Data

Detection of motion of pixel is a significant step in several applications. This paper emphasizes the research methodologies that use time as a parameter for detection of pixel motion viz. Temporal Difference, Optical Flow, Subtraction of background and representation using kinematic features. The first approach of difference of frame or temporal difference is the easiest to perform but has the drawback that it is poor at extraction of enough relevant feature pixels. Detection of moving objects in the first approach is done from the difference between the frame which is existing and the frame which is the reference frame. The second approach i.e. the optical flow technique makes use of flow vectors of moving objects over time for detection of regions which are moving in an image. This approach gives a perfect result in several conditions of illuminations but has the drawback of being complex in its implementation. The memory required for implementation is relatively large. The third approach i.e. the subtraction of background is the most widely used technique. It is relatively easier to implement but it has the drawback that it cannot handle sudden changes in lighting. The fourth approach i.e. the representation of kinematic features is based upon the idea that the information of optical flow method can be converted into a representation, which improves the classification, using the features of kinematic. The major advantage of the technique of using kinematic features is that it is robust against the changes in scale. Each technique has its own pros and cons. Hence, each of these four techniques analysed in this paper can be used to detect pixel motion.
Market Basket Analysis of a Health Food Store in Thailand

This article presents a market basket analysis of a health food store in Thailand. The analysis identifies data attributes that frequently occur together in the dataset. Frequent occurrences of data attributes representing customer purchasing behaviors are extracted as association rules using the frequent pattern growth algorithm. The generated associations are evaluated using standard measures based on occurrence counts and an additional financial measure. Marketing strategies in the form of cross-selling pairs of specific products are then designed based on the data attributes appearing in the significant associations. The cross-selling products are offered at discounted prices and promoted in marketing campaigns. A break-even analysis is performed to estimate the required number of additional sales volumes from each marketing campaign to compensate for the discounted prices. The presented use case demonstrates the effectiveness of extending the market basket analysis to include a financial measure that can lead to practical marketing campaigns.
Fine-grained load balancing with proactive prediction and adaptive rerouting in data center

Though the existing load balancing designs successfully make full use of available parallel paths and attain high bisection network bandwidth, they reroute flows regardless of their dissimilar performance requirements. But traffic in modern data center networks exhibits short bursts characteristic, which can easily lead to network congestion. The short flows suffer from the problems of large queuing delay and packet reordering, while the long flows fail to obtain high throughput due to low link utilization and packet reordering. In order to solve these inefficiency, we designed a fine-grained load balancing method (FLB), which uses an active monitoring mechanism to split traffic, and flexibly transfers flowlet to non-congested path, effectively reducing the negative impact of burst flow on network performance. Besides, to avoid packet reordering, FLB leverages the probe packets to estimate the end-to-end delay, thus excluding paths that potentially cause packet reordering. The test results of NS2 simulation show that FLB significantly reduces the average and tail flow completion time of flows by up to 59% and 56% compared to the state-of-the-art multi-path transmission scheme with less computational overhead, as well as increases the throughput of long flow.
Socioeconomic Impact of Disseminated Technologies of Development Projects on the Daily Farming in Low Rainfall-Prone Areas of Rwanda: Case of Innovafrica Project

This research study investigated the socioeconomic impact of introducing disseminated technologies of development projects on daily farming in low rainfall-prone regions of Rwanda. The study focused on the InnovAfrica Project in Kirehe District. The study found that a significant proportion of respondents had adopted and actively used agricultural technologies promoted by the InnovAfrica Project. The technologies included forage storage, irrigation systems, and drought-resistant seeds. The study also found that the InnovAfrica Project positively influenced food security in the study area. Respondents reported increased access to adequate and diverse food supply, better coping mechanisms during food shortages or adverse weather conditions, and an overall enhancement of food security. Moreover, participation in the InnovAfrica Project also resulted in positive changes in socioeconomic conditions. Respondents reported improved household income, better access to education, and increased access to healthcare services. This positive impact contributed to an overall increase in well-being. Recommendations from the research include enhancing technology dissemination, providing financial support or access to credit for farmers, promoting sustainable food security practices, sustaining and expanding the InnovAfrica Project, and conducting regular monitoring and evaluation. Keywords: Socioeconomic, Disseminated Technologies, Development Projects, Daily Farming, Low Rainfall-Prone
Artificial algae optimizer with hybrid deep learning based yoga posture recognition model

Numerous people are interested in learning yoga due to the increased tension levels in the modern lifestyle, and there are a variety of techniques or resources available. Yoga is practiced in yoga centers, by personal instructors, and through books, the Internet, recorded videos, etc. As the aforementioned resources may not always be available, a large number of people will opt for self-study in fast-paced lifestyles. Self-learning makes it impossible to recognize an incorrect posture. Incorrect poses will have a negative effect on the patient’s health, causing severe agony and long-term chronic issues. Computer vision (CV)-related techniques derive pose features and conduct pose analysis using non-invasive CV methods. The application of machine learning (ML) and artificial intelligence (AI) techniques to an inter-disciplinary field like yoga becomes quite difficult. Due to its potent feature learning ability, deep learning (DL) has recently achieved an impressive level of performance in classifying yoga poses. In this paper, an artificial algae optimizer with hybrid deep learning-based yoga pose estimation (AAOHDL-YPE) model is presented. The presented AAOHDL-YPE model analyzes yoga video clips to estimate pose. Utilizing Part Confidence Map and Part Affinity Field with bipartite equivalent and parsing, OpenPose can be employed to determine the joint location. The deep belief network (DBN) model is then used for Yoga recognition. Finally, the AAO algorithm is utilized to enhance the EfficientNet model’s recognition performance. The results of a comprehensive experimentation analysis reveal that the AAOHDL-YPE technique produces superior results in comparison to existing methods.
A Helicopter Path Planning Method Based on AIXM Dataset

ICAO has emphasized that aeronautical information agencies should provide digitized aeronautical data and information, and realize that aeronautical data exchanging internationally in AIM. The AIXM structured aeronautical information dataset will be the main source of aeronautical basic data in the aeronautical information exchange network. In this article, the authors first analyze the spatio-temporal attributes of AIXM dataset and design the query method of AIXM structured obstacle data based on the research of AIXM coding specification. Secondly, the helicopter path planning is taken as the research scenario. Using the AIXM obstacle dataset and route dataset, combining the helicopter performance constraints to construct the envelope frame for collision judgment, and a new path planning method with improving the classical A* algorithm based on the AIXM dataset is proposed. The proposed method is validated and visualized. The validation results show that the proposed method reduces the frequency of helicopter turning, and ensures the safe distance between the flight path and the obstacles.
An Analysis of the Relationship Between Temperature Rise and GDP Using Machine Learning Techniques

Research has been conducted using random forest, gradient boosting tree, decision tree, and linear regression algorithms, using cumulative GDP data and GDP for each agricultural, industrial, manufacturing, and service sector. The goal is to analyze the relationship between these factors on the temperature rise in nine countries. First, the decision tree and linear regression algorithms are used for preliminary relationship analysis. Then, the more complex algorithms, i.e., random forest and gradient boosting tree, are developed for further predictions. It is found that the best algorithm trained with the training data is the gradient boosting tree algorithm using 100 trees. However, when tested for accuracy with the testing data, it has a more significant error than the error using training data. This result concludes that the model produces accurate predictions only for the training data.
AIOps With Multi Cloud

The rapid adoption of cloud computing and the increasing complexity of IT infrastructures have presented new challenges for managing and monitoring enterprise systems. AIOps (Artificial Intelligence for IT Operations) has emerged as a powerful approach to address these challenges by leveraging the capabilities of artificial intelligence and machine learning. This research paper explores the integration of AIOps with multi-cloud environments, focusing on the benefits, challenges, and best practices of using AIOps techniques to enhance IT operations in a multi-cloud setting
Artificial Intelligence Application with Contact Tracing for Post COVID -19 Epidemic Management

INTRODUCTION: Post COVID -19 epidemics is in a critical situation which has to be properly managed with right preventive and curative measures to protect the economy and welfare of the Human beings.&#x0D; OBJECTIVES: Effective management of this terrific situation may be possible through the help of contact tracing and its application of AI mechanism. Here the authors as taken the available data for the testing of the significance of AI approach for contract tracing proper management of the post COVID epidemic situation.&#x0D; METHODS: Here contact tracing data are collected analysed interpreted and validity is tested with the help of statistical tools like egression, coefficient and Annova for the testing of the available data with its further application.&#x0D; R ESULTS: AI application creates more awareness, vaccination, self-testing, isolation and intake medicine&#x0D; CONCLUSION: Artificial Intelligence &amp;social media plays a vital role for the creation of social awareness and proper manage of post COVID-19 epidemics.