Reviewerly/exHarmony huggingface dataset:
Cross-Cutting Methodologies in Learning 3D Modeling

The training of 3D modeling professionals from the complexity of the software itself or the trends of the professional sector, often focuses on the mastery of a single main tool, perhaps complementing it with other more specific tools for texturing or digital sculpting. Students, when entering the labor market, even if they master the techniques in a software, often need to improve or expand their professional profile by learning other tools, adapting their previous knowledge transversely, almost having to start from scratch. In addition to this, there is a certain lack of unity, from the nomenclature, information architecture, or interaction, to make equivalent operations between these softwares. The creation of didactic materials that allow the acquisition of knowledge from the transversal mapping would help to reduce or optimize this adaptation process, while reinforcing the professional profile of 3D modeling students by providing them with complementary skills.
Using a Social Robot to Aid Online Learning: Identifying Teachers’ Media and Digital Competencies, Barriers and Opportunities

Although the benefits of using social robots to increase engagement and boost learning are well documented (Belpaeme et al. in Sci Robot 3, 2018 [1]), less is known regarding the competencies to effectively incorporate them as a tool in online education, with the challenges and benefits thereof. This paper investigates the implementation of social robots as a pedagogical tool to support educators during online learning. Based on (Ferrés and Piscitelli in Comunicar 38:75–82, 2012 [2]) framework of media and digital competencies for teachers, this study investigates these skills, and the barriers and opportunities presented for teachers when using a social robot for online distance education. By means of a thematic analysis of in-depth interviews with educators in Chile, we identified main media and digital skills: basic programming; managing information and digital data; designing interactive activities; and developing work teams. The main barriers and benefits encountered are also provided. We conclude by providing advice for practitioners and avenues for future research.
Adoption of Virtual Academic Profiles for Knowledge Transfer in Research Communication

Technological development and the professional use of the Web have given new impetus to the communicative work of researchers, with direct benefits in terms of transferring and promoting knowledge, in particular scientific publications, and visibility for the author and his work. However, there is resistance and mistrust regarding their use, as well as a lack of knowledge about the most appropriate services. This research contributes to the study of the popularity and willingness of researchers to adopt digital academic services that allow the creation of a virtual profile, and its relationship with the web visibility obtained. Overlapping of 203 active profile accounts shows which and how many services are used by advertising and public relation researchers. The study represents the first exploration of the two scientific subfields and confirms ResearchGate and Google Scholar as the most popular services, as well as the existence of trends regarding their adoption in terms of scientific subfield and gender of researchers. The results reveal that the number of virtual profiles in digital scholarly services influences the web visibility of scientists in terms of researcher agent and their scientific production.
Improved seagull optimization algorithm combined with random division method to solve dynamic optimization problems

The numerical solution of dynamic optimization problem is often sought for chemical processes, but the discretization of control variables is a difficult problem. Therefore, we propose improved seagull optimization algorithm (ISOA) combined with random division method to solve dynamic optimization problems. Firstly, based on the analysis of the seagull optimization algorithm, this paper introduces the cognitive part in the process of a seagull’s attack behavior to make the group approach the best position. Secondly, this paper uses the 14 benchmark test functions to verify ISOA. Finally, the improved seagull optimization algorithm is combined with the random division method to solve two chemical dynamic optimization problems. The experimental results show that ISOA algorithm has better performance in function optimization.
A Fast and Accurate Human Pose Estimation Method Based on Multi-Scale Feature Fusion Grid Structure

Human pose estimation (HPE) is a research hotspot in the field of computer vision. Most of the existing approaches first generate low-resolution representation from high-resolution representation through continuous serial downsampling, and then reconstruct high-resolution results from low-resolution features through continuous serial upsampling, which loses a lot of effective feature information and leads to slow model inference. In this paper, the Fast Accuracy Network (FANet), a framework that enables fast and high-accuracy HPE, is proposed. The innovation lies in that, first of all, a grid structure is proposed and adopted, which can be regarded as a set of deep paths and shallow paths. The structure uses multiple high-resolution and low-resolution branch pairs to perform skip-level connections at different scale-space levels so that the information can be exchanged between different resolution representations for many times. The feature information fusion of multi-scale space is realized to obtain more abundant feature information. Second, an improved bottleneck block is proposed to extract effective feature information with fewer parameters, ensuring that the computational burden is reduced without sacrificing accuracy performance. The experimental results show that, compared with other current models, FANet has faster inference speed on the premise of a slight improvement in accuracy performance.
Two‐stage local attention network for salient object detection in remote sensing images

With the development of earth observation satellites, it is very meaningful to study methods for intelligently processing remote sensing images (RSIs). As an important pretreatment technique, salient object detection (SOD) can produce accurate saliency predictions in natural scenes. However, due to the complex object types, high noise levels, and different imaging angles in RSIs, it is still difficult to generate accurate saliency predictions in such images. This paper proposes a SOD method for RSIs, namely, the two-stage local attention network (TLANet). It uses two-stage attention (TSA) to capture the relationships between the pixels of features at different scales and uses an adjacent semantic refinement (ASR) module to enhance the flow of information across scales. In addition, a cascade structure is introduced to the decoder so that more semantic features can guide the feature decoding process. Moreover, a new loss function is proposed to improve the prediction effects obtained for salient edges. Through comprehensive experiments conducted on two RSIs datasets, it is demonstrated that the TLANet is superior to the state-of-the-art methods in both qualitative and quantitative comparisons.
Phase sensitivity in single-shot speckle interferometry using a custom integrating glass-sphere for the measurement of femtometric wavelength change

Phase sensitivity single-shot speckle interferometry for detection of wavelength changes in the femtometric scale depends on the optical path differences (OPDs) produced by the multiple reflections inside an integrating sphere. In this work the OPDs are calculated using a ray-tracing program of a glass sphere coated with white paint, which shows that the resulting OPDs are distributed randomly over the speckle field-of-view but with a characteristic curve that depends strongly on the dimensions of the optical setup, the internal reflection coefficient, and the diameter of the integrating sphere that combined produce the final phase sensitivity of the optical setup. It is also shown that singularities removal of vortices is necessary for the phase-change calculation. The ray-tracing simulations of the proposed optical setup are confirmed by the experimental results of the optical implementation.
Private Cloud Deployment of Land Resource Management Information Framework based on Cyclic Random Forest Algorithm

This paper proposes a model definition method for CRFs that integrates multiple cues, such as image spectrum, directional gradient histogram, and multi-scale and multi-directional Texton texture, for the deployment of a private cloud of land resource management information framework. Using the above characteristics, the cyclic random forest ion algorithm is selected to define the CRFs correlation potential function; the key technical nodes such as the overall positioning, security management and synchronous application of the core business of land resource management “One Map” database construction are discussed comprehensively, which effectively improves the The city's land and resources management is precise, standardized and scientific.
A Design and Implementation of Bangla Next Word Predictor Based on Personalized Federated Learning Leveraging Model Agnostic Meta Learning and Semantic Analysis

Next word prediction for technological devices has become a contentious issue in predictive analytics. Word pre-diction requires a huge amount of data from a vast number of different users, which introduces issues like heterogeneous dataset management and privacy concerns, thus raising the importance of Federated Learning (FL) as it can learn from a large amount of sparsely distributed data while ensuring privacy. Further investigations on related studies have led us to contribute in this area based on the Bangla language due to the minimal appearance of this language in the domain of word prediction based on FL. In consideration of implementing our work, some of the prime issues have been noted where avoiding data poisoning attacks, prioritizing user preferences during the global update, and focusing on fast convergence to reach a high-quality model in a few training rounds have become unavoidable matters of contention. Besides this, generating initial models which can be easily adapted by the current or new users is also an important research problem. Based on these, we have proposed a structure that combines semantic analysis and personalized FL using Model-Agnostic Meta-Learning (MAML) to obtain the desired goals. Extensive evaluations have been done on Non-IID data for both FL and personalized FL with MAML on the next word prediction model, which justifiably achieved 83.05% and 86.23% test accuracy respectively on the server model after ten communication rounds across ten clients. Apart from that, the effective results of semantic approaches show an impressive outcome as intended. Thus, the limitations and the challenges of FL on next word prediction conceptually allowed us to use a variety of algorithms to turn them into advanced ones for further usage.
Recent Trends in Intelligent Data Health Records

The importance of various AI concepts for telemedicine aims, as well as their current and prospective future applications, will be examined in this article. Patients will be observed, healthcare information technology will be discussed, intelligent computer diagnosis will be discussed, simulation and training systems will be examined, and data analysis and collaboration will be investigated. Artificial intelligence (AI) has been around for decades and is employed in a broad range of applications. In healthcare, the technology can be used in a variety of ways, such as providing a system for analysing medical data to identify sources of errors and developing solutions based on existing procedure results, as well as improving procedures by incorporating computerised intelligence into medical devices and tools. The capacity to spot patterns quickly and with calculated accuracy aids in finding the best practice and treatment options for medical therapies.
Automatic Recognition of Activities of Daily Living for Elderly to Support Independent Living

In this preliminary study, we explore the potential for automatically recognizing activities of daily living by identifying the folding patterns of various laundry items such as shirts, jeans, and towels. Laundry is an important aspect of personal hygiene for the elderly population. The study is performed by creating a model to identify these laundry items with high accuracy using data collected from wearable sensors. Time-domain and frequency domain features are extracted from data sets and are fed to Support Vector classifiers using fine Gaussian kernels in MATLAB. Multiple locations on the body and multiple sets of data at each location provide ample data for training the system. The overall classification rate is 78% for detecting all laundry items. Activities of Daily Living will be further explored to recognize cognitive health states by analyzing the activity pattern changes over an extended period.
Optimizing Resource Allocation in Cloud

As an emerging technology, Cloud computing is vastly superior to traditional self-hosting computing methods because it separates computations from infrastructure. Every cloud provider must provide task scheduling, a resource allocation function that involves work being delegated to computational resources within the cloud system through a broker. Only a few of cloud task scheduling methods are accessible, such as Sufferage, Max-Min, and Min-Min; nevertheless, each is constrained by performance trade-offs. We propose a new task scheduling algorithm MinPlus for resource allocation, which exceeds several existing algorithms in terms of performance. Experiments are conducted for the MinPlus algorithm with the free planform CloudSim, and results are compared to existing algorithms that show decreased turnaround time, enhanced resource load balancing, and increased throughput.
Network2U: Cloud Business Templates

Businesses are transitioning towards using cloud services for many of their networking and system needs. However, designing and implementing these cloud services can be costly for business owners. In this paper, we propose Network2U, a business cloud network infrastructure service built upon Google Cloud. Network2U can be fit into several types of business by extending two sample templates. Current templates include a school system where hundreds of students will connect to a network, with strict security requirements regarding student data. The other template is a hybrid workforce finance company, where its users will connect to the network from different locations, and customer data security is key. These templates provide the different business types with all the systems, networking and security services needed to run their business. Network2U is for small or medium business owners who cannot invest in the large cost of getting a business cloud network infrastructure implemented.
Didactic Tool for Teaching Quality of Service Algorithms in Communication Networks

Nowadays, information technology opens great opportunities to transform teaching and learning. Currently, different platforms have been deployed to offer teaching services. These platforms can be built from computer systems that collect and analyze data, to those systems that interact with users and allow them to learn from these data. For example, a system can build animations to visualize and understand the operation of an industrial process or an algorithm. This paper presents a digital tool to support the teaching and learning of quality of service (QoS) algorithms in the communication networks. Quality of service is an important topic in the computers networks courses due to the current high demand for quality multimedia content on the internet. Our digital teaching tool tries to support the teaching/learning processes in this important communication networks topic.
2D Orthographic Drawings to 3D Conversion using Machine Learning

3D AutoCAD drawing is very common in the manufacturing industry. Most of the companies are using 3D models to produce parts and products as it gives us substantial accuracy and precision. However, the process of producing 3D models drawing is tedious, time-consuming, and requires a lot of human effort. Indeed, there is a need to have an automated process to construct the 3D models using the image of the 2D view. Not only it reduces human effort, save time and cost, it is also increasing the accuracy and reduces error due to machine learning generally having higher consistency and precision than a human being. Therefore, this project aims to develop software that can convert 2D orthographic drawings into 3D CAD models using machine learning to reduce human intervention during the conversion process.
An Automated System to Calculate Marks from Answer Scripts

During evaluating exam scripts, teachers have to write down marks for each question on the top sheet of the answer script and then sum those marks to calculate the total marks - which is a common source of error. This literature has focused on automating the script marks calculation process using multiple handwritten characters' recognition. Traditional solutions for identifying handwritten characters focus on labeling a single character using suggestion based solution. So, existing solutions are not enough to address the problem that this study has focused on. In this regard, the proposed framework has tried to develop a hybrid approach. The proposed solution first identifies the connected components, based on the pixel color as red, through a depth-first search based solution. Then each connected component is passed through a learning architecture to generate a numeric digit suggestion combining heuristic and machine learning, making a hybrid system. The proposed system is very modular and flexible to be implemented and modified. The extensive analysis presented in the evaluation section shows the merit of the proposed framework in automating the addressed problem.
Analysis of Predicting Bitcoin Price using Deep Learning Technique

The major aim of this work is to uncover the accuracy of the Bitcoin price in any fiat/flat currency that can be predicted in advance. Bitcoin is a form of cryptocurrency and is now one of the most popular types of investments in the stock market. And bitcoin is the only form of cryptocurrency that has been on the rise in the last few years, and sometimes a sudden collapse without knowing the impact behind it in the stock market. To utilize the long short-term memory for predicting the bitcoin value in advance. Many researchers used RNN for this bitcoin prediction and observed that it lacks in consistency, to overcome this issue LSTM and ARIMA are used to ensure the accuracy and yields better prediction in terms of time series and proves that it is superior to existing state of art techniques.
Access Delegation Framework for Private Decentralized Patient Health Records Sharing System Based on Blockchain

Patient health records(PHRs) are crucial and sensitive as they contain essential information and are frequently shared among healthcare entities. This information must remain correct, up to date, private and accessible only to the authorized entities. Moreover, access must also be assured during health emergency crises such as the recent outbreak, which represents the greatest test of the flexibility and the efficiency of PHR sharing among healthcare providers, which ended up an immense interruption to the healthcare industry. Moreover, the right to privacy is the most fundamental right for a patient. Hence, the patient health records in the healthcare sector have faced issues with privacy breaches, insider outside attacks, and unauthorized access to crucial patients' records. As a result, it pushes more patients to demand more control, security, and a smoother experience when they want to access their health records. Furthermore, the lack of interoperability among the healthcare system and providers and the added weight of cyber-attacks on an already overwhelmed system have called for an immediate solution. In this work, we developed a secured blockchain framework that safeguards patients' full control over their health data which can be stored in their private IPFS and later shared with an authorized provider. Furthermore, the system ensures privacy and security while handling patient data, which can only be shared with the patients. The proposed Security and privacy analysis show promising results in providing time savings, enhanced confidentiality, and less disruption in patient-provider interactions.
Panorama of Peruvian Kindergarten Education Teachers Trained in ICT Use before COVID-19 Pandemic

This study with a quantitative, descriptive and trend approach aims to determine the panorama of kindergarten teachers who received training on the use of ICT before the covid-19 pandemic. To do this, three Surveys to Peruvian Teachers (ENDO) carried out by Ministerio de Educación del Perú before the pandemic (in 2014, 2016 and 2018) were analyzed. The total of kindergarten education teachers who participated in each year, respectively, were: 1593 (74.2 % = urban, 25.8 % = rural), 1759 (76.8 % = urban, 23.2 % = rural) and 2935 (82.2 % = urban, 17.8 % = rural). After the analysis, it can be concluded percentages, in general, are worrying: there is a sensible decrease from 2014 to 2018 in the number of trained teachers; there was a predilection for traditional model versus virtual learning, and also that quality of training they received to ICT use in the teaching-learning process is between regular and good.
Data Normalization Technique for Energy Efficient Drones: An Ensemble Learning Approach

Frequent wildfires in the western part of the United States are affecting the local economy, flora and fauna, air quality, and health of the local community. Over the years, various techniques have been utilized for the early detection of wildfires, that include satellites, lookout towers, and drones. Among various techniques, drones are gaining popularity due to recent advancements in drone technology, multi-role adaptability, and lower cost of operation. However, to be effective during disaster management and monitoring, the endurance and longevity of the drone's flight time are essential. A transistor-embedded photovoltaics (PV) panel-powered drone can enable such essential qualities required in the drone. Such a power source requires an efficient algorithm for switching the configuration of the PV panel for them to be effective during different lighting and operating conditions. Machine learning classification techniques such as Random Forest for activating the algorithm have shown effectiveness in detecting the presence of shade. However, with a larger number of properties for training, supervised ML classification can result in increased memory usage and, in some cases, lower accuracy. In this paper, we propose a novel normalization technique to reduce the number of properties required to train a machine learning model. After applying the normalization technique, We observed the performance of the Random Forest classification model increased to 90.1 % for shade detection in the PV module, along with a 7.535 % reduction in memory usage.