Reviewerly/exHarmony huggingface dataset:
Utilization of Merge-Sorting Method to Improve Stitching Efficiency in Multi-Scene Image Stitching

Aiming at the problem of single stitching scenes (ordered or disordered images), which are time-consuming and involve complex computation during the image stitching, a multi-scene image stitching method based on merge sorting is proposed called merge-sorting image stitching (MSIS). We established an image stitching model based on merge sorting, directly stitching the matched images without traversing all the images and sorting them. Compared with the traditional method, this method significantly reduces the computational time taken matching irrelevant image pairs and improves the efficiency of image registration and stitching. The experimental results show that, compared with the binary tree-based model, our method reduces the time required to complete image stitching by about 10% and the distortion of the stitched images.
Improving financial literacy through interactive read-aloud with children’s picture books

The current study aimed to explore the effects of interactive read-aloud with children’s picture books on third-grade elementary school students’ financial literacy attitude and behavior. A pretest-posttest control group quasi-experimental research design was employed. The sample of the current research consisted of 46 third-grade elementary school students. We randomly assigned two preexisting third-grade elementary school classrooms to the treatment and control groups that appeared similar considering the pretest scores of the groups. While interactive read-aloud was used in the treatment group, just reading read-aloud activities occurred in the control group. The implementation process took four weeks. Before and after the implementation, the measurement tool was administered to the students in the groups. A one-way analysis of covariance was used for the posttest scores of the students in the groups. The analysis revealed that there was a statistically significant difference in favor of the treatment group.
Improving the Digital and Pedagogical Competence in Engineering Educational Context

This paper presents the evaluation results of an online training course in Online Engineering Education and Pedagogy (OEP). The course consisted of a series of modules implemented on a mix of online communication synchrony strategy of synchronous activities carried out on the Zoom platform, together with work on a Moodle-based LMS platform. Based on the IGIP Curriculum offered for the TU Dresden and the demands for effective online education, a training course for the academic staff of an Engineering Faculty at a Chilean University was implemented for a second time. The course was called "Introduction to the online teaching and learning in engineering" and was offered in e-learning modality in 2021 with the participation of a group of more than 50 academics. The main goal of the study was to identify the perception of the participants of the training course (academic staff) about their experiences and valuation of the competencies developed during the course. The research question that guided the research process was how the participants evaluate: (1) the course in general, (2) the didactic design of the course, (3) the usefulness of the acquired knowledge for their teaching practice, (4) the teaching competencies, and (5) a self-evaluation about their participation and their learning process. The participants correspond to the careers of Mechanical Engineering, Electrical Engineering, Industrial Engineering, Mining Engineering, and Computer Engineering.
Image Classification of Natural Disasters Using Different Deep Learning Models

Natural disasters continue to affect the world through wildfires, cyclones, earthquakes, and floods. The advent of photography has provided us with valuable images of how disasters happen and their impact. Many deep-learning models have been developed to classify images. However, the classification of natural disasters still lags. Through transfer learning, eleven existing deep learning models and two optimizers were adapted, analyzed and tested on images based on natural disasters. We explore the impact of data augmentation on deep learning model performance. Based on experimental results, ResNet-50 coupled with SGDM optimizer achieved an accuracy of 98.6%. However, AlexNet converge faster in 4109 seconds, compared to all adopted deep learning models.
Security Threats in Healthcare Systems—A Bibliometric Study

Healthcare cybersecurity has surfaced as the industry’s greatest serious extortions. The potential dangers of Web-based healthcare systems have rapidly increased, attracting the hacker community. When patient health records are digitized, there is a risk that sensitive health information will be compromised, and sensitive data may be leaked to hackers and used against them. Health data are a valuable asset in the cybercriminal world, making it a target for hacking. Because the well-being of sick people is jeopardized, healthcare entities are more in the offing to pay ransomware burdens.
An Ontology for Software Patterns: Application to Blockchain-Based Software Development

Ensuring the quality of software design is usually a difficult task. In the blockchain field, the design of an application is particularly important as flaws can lead to critical vulnerabilities and cost overheads. To assist practitioners in this task, software patterns can be used (solutions to repeatable problems in a given context). Some blockchain patterns exist but they are scattered, and described in many different notations and templates. As a result, practitioners can be lost in the selection of adequate blockchain-based patterns. This paper fills the gap by proposing a blockchain-based software pattern ontology. The ontology is composed of two distinct subspaces: first, a set of classes and individuals related to blockchain-based patterns, based on a previous Systematic Literature Review (SLR). It notably reuses a taxonomy of blockchain design patterns, that helps to classify these patterns in relevant categories. Along that, another subspace has been created to further organize the knowledge related to software patterns and allow inferences. A tool is proposed along with the ontology to assist practitioners in finding blockchain-based patterns that fit their needs. An evaluation is performed to assess the usability and the relevancy of the ontology.
Introduction to Presentation Attacks in Signature Biometrics and Recent Advances

Applications based on biometric authentication have received a lot of interest in the last years due to the breathtaking results obtained using personal traits such as face or fingerprint. However, it is important not to forget that these biometric systems have to withstand different types of possible attacks. This chapter carries out an analysis of different Presentation Attack (PA) scenarios for on-line handwritten signature verification. The main contributions of this chapter are: (i) an updated overview of representative methods for Presentation Attack Detection (PAD) in signature biometrics; (ii) a description of the different levels of PAs existing in on-line signature verification regarding the amount of information available to the impostor, as well as the training, effort, and ability to perform the forgeries; and (iii) an evaluation of the system performance in signature biometrics under different scenarios considering recent publicly available signature databases, DeepSignDB ( https://github.com/BiDAlab/DeepSignDB ) and SVC2021_EvalDB ( https://github.com/BiDAlab/SVC2021_EvalDB ), ( https://competitions.codalab.org/competitions/27295 ). This work is in line with recent efforts in the Common Criteria standardization community towards security evaluation of biometric systems.
Delineation of The Early 2024 Election Map: Sentiment Analysis Approach to Twitter Data

As a democratic country, the people hold an important role in determining power in Indonesia. The closest political agenda in Indonesia is the 2024 Election. A survey has been conducted by several private survey agencies regarding the 2024 political map which has revealed the top five names, namely Prabowo Subianto, Ganjar Pranowo, Anies Baswedan, Sandiaga Uno, and Ridwan Kamil. This study aims to describe the initial map of the 2024 Election through a sentiment analysis approach to Twitter data. This study uses tweet data that mentions five political figures during 2021. In general, the demographic condition of Twitter users that pros or cons to five political figures, among them: located on the Java, in the age group 19â€“29 years old, and male.Â The sentiment analysis method used is supervised learning with different methods for each figure. The difference in methods adjusts the best evaluation value given in each figure. The results showed that the highest positive sentimental tweets and the highest number of pro accounts was about Ganjar Pranowo. On the other hand, the highest negative sentiment and the highest number of contra accounts was about Prabowo Subianto. Many words that often appear on a figure's positive sentiment are expressions of hope, prayer, and support. On negative tweets, the word that comes up a lot relating to the work field or work region of the figures.Â
Lossy Image Compression-A Comparison Between Wavelet Transform, Principal Component Analysis, K-Means and Autoencoders

For many years, lossless image compression has been a promising topic of study. Various techniques have been created over time to obtain an approximation of the reduced data size. While discrete wavelet transform (DWT) and discrete cosine transform (DCT) have historically been employed for the purpose of compressing images, various machine learning methods and deep learning networks are now being offered. In this research, we conduct a comparative analysis of conventional and contemporary lossy image compression techniques on the Kodak Dataset, including Autoencoders, Principal Component Analysis (PCA), K-Means, and Discrete Wavelet Transform (DWT). The metrics used for the evaluation of the proposed study are Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), Compression Ratio (CR), and Structural Similarity Index (SSIM).
FPL Demo: A Flexible and Scalable Quantum-Classical Interface based on FPGAs

This demonstration shows a Quantum-Classical interface (QC-IF) for quantum computing implemented on multiple FPGAs. Quantum computers need a controller to transmit/receive microwave to/from quantum devices. In order to explore various quantum devices, the controller requires flexibility to transmit/receive various wave shapes. In addition to that, scalability is also required for large-scale quantum computers. FPGAs are attractive platforms; however, some challenges exist in implementing QC-IF on FPGAs in terms of required specifications from physics, such as treating data and realizing scalability. This work demonstrates an implementation of QC-IF on FPGA with high bandwidth memory to treat large-volume data in high throughput. Furthermore, the IEEE1588-like clock synchronization mechanism is implemented to make multiple FPGAs synchronized.
A-U3D: A Unified 2D/3D CNN Accelerator on the Versal Platform for Disparity Estimation

3-Dimensional (3D) convolutional neural networks (CNN) are widely used in the field of disparity estimation. However, 3D CNN is more computationally dense than 2D CNN due to the increase in the disparity dimension. To enable more practical applications in autonomous driving, robotics, and other scenarios on embedded devices, we propose a unified 2D/3D CNN accelerator (A-U3D) design. This design unifies 3D standard / transposed convolution into 2D standard convolution, respectively. Our processing unit can support 2D and 3D convolution in the same mode without additional structures. Based on PSMNet, a 3D-based CNN for disparity estimation, we build a heterogeneous multi-core system integrated with A-U3D in conjunction with CPU, DSP, and AI Engines on the Xilinx Versal ACAP platform. Running the pruned 8-bit model, our A-U3D system achieves 0.289s latency, which is 11.5 × faster than the state-of-the-art solution on the same platform, and reaches an end-to-end (E2E) performance of 10.1 frames per second (FPS). Our proposed system explores the feasibility of deploying 3D CNNs with large workloads on FPGA.
Learning from a Distance

With the onslaught of the global COVID19 pandemic, universities were forced to quickly pivot to exclusively remote and virtual service options. To further complicate the situation, many international student populations at these institutions were forced to study remotely in their home countries due to the pandemic and visa restrictions. In Canada and Ontario, International students make up a major revenue source for post-secondary institutions, making the need to find viable solutions to continue to serve these populations essential to their financial stability.The Ontario Council of University Libraries (OCUL) runs a shared virtual reference service called Ask a Librarian (Ask). This paper assessed the global pandemic's impact through a comparative study of the service before, during, and after the pandemic's height. Using IP addresses, this study evaluated the impact of geographical location on the user’s access to virtual library resources, as well as identified any barriers, shifts, or trends in the service. The COVID-19 pandemic has changed the face of education and remote learning indefinitely. The hope of this study is to assess the overall success and pitfalls of our current virtual reference services and suggest future improvement areas.
Atmospheric Correction of Landsat Image

Purpose: The satellite imagery such as Landsat contains water vapour and gases that do interfere with analytical process to lower the result. The research focused on atmospheric corrected and un-corrected image on Normalized Difference Vegetation Index determination. The aim of the paper was atmospheric correction on satellite imagery and the objectives considered were to: (1) discuss the types of atmospheric correction (2) determine the Normalized Difference Vegetation Index using atmospheric corrected image (3) highlight the Normalized Difference Vegetation Index without atmospheric corrected image.&#x0D; Methodology: The following materials were used for the study, they are Landsat imagery, ArcGIS 10.7 and Idrisi 32 software. Remote sensing and Geographical Information Systems (GIS), were factored in the process of Normalize Difference Vegetation Index (NDVI) determination using bands 4 and 3. Additive rescaling formular was used to extract the temperature values. Analysis of variance was also conducted on the two NDVIs and presented in regression table.&#x0D; Findings: The study found that atmospheric corrected NDVI started from 0.02 to 1.0 on the scale of measurement while uncorrected NDVI ranges from &lt;61.00 to 201.&#x0D; Conclusion: The study conclude that corrected satellite imagery gave a good reflectance of the earth features than uncorrected image.&#x0D; Recommendation: The study recommend that for image studies to be carried out, there should be an atmospheric correction to have a precise result.&#x0D;
Cloud based ERP systems and Data Security for Cloud based ERP Applications - SAP S/4HANA

In this research, we will talk about data security in cloud-based ERP. We utilized SAP S/4HANA cloud (Public edition) in this research. It is an analysis of security issues connected to cloud data and associated topics. The paper will examine data in detail. Worldwide protection techniques and strategies are employed to guarantee the highest level of data protection by lowering risks and threats in Cloud ERP. Although many apps benefit from having access to data on the cloud ERP, doing so exposes data to applications that may already have security flaws. Now-a-days many Organizations preferring Cloud based ERPs than on premises ERP so, we decided to Explore the Cloud based ERP. The paper will also discuss data security issues for Data-at-Rest and Data-in- Transit. All tiers of SaaS (Software as a Service), PaaS (Platform as a Service) are used in the study. In this research we also explore the solutions for different security issues in Cloud ERP. Key Words: Data Security, Cloud Computing, SAP S/4HANA, Cloud ERP
FedDdrl: Federated Double Deep Reinforcement Learning for Heterogeneous IoT with Adaptive Early Client Termination and Local Epoch Adjustment

Federated learning (FL) is a technique that allows multiple clients to collaboratively train a global model without sharing their sensitive and bandwidth-hungry data. This paper presents a joint early client termination and local epoch adjustment for FL. We consider the challenges of heterogeneous Internet of Things (IoT) environments including non-independent and identically distributed (non-IID) data as well as diverse computing and communication capabilities. The goal is to strike the best tradeoff among three conflicting objectives, namely global model accuracy, training latency and communication cost. We first leverage the balanced-MixUp technique to mitigate the influence of non-IID data on the FL convergence rate. A weighted sum optimization problem is then formulated and solved via our proposed FL double deep reinforcement learning (FedDdrl) framework, which outputs a dual action. The former indicates whether a participating FL client is dropped, whereas the latter specifies how long each remaining client needs to complete its local training task. Simulation results show that FedDdrl outperforms the existing FL scheme in terms of overall tradeoff. Specifically, FedDdrl achieves higher model accuracy by about 4% while incurring 30% less latency and communication costs.
Security Analysis for Threats to Patient Data in the Medical Internet of Things

The current model of the healthcare system, which is being changed, is based on hospitals and specialists. The new model will be less centralised and will put more attention on each patient. The Medical Internet of Things (MIoT) makes it possible for machines to talk to each other and for humans to intervene in real time. These changes will soon make medical care much easier to get, cheaper, and more reliable. The promise of the Medical Internet of Things (MIoT) is that it will make patients happier with their healthcare overall by making it easier for them to get personalized e-health services no matter where they are or what time it is. The Medical Internet of Things (MIoT), also called the healthcare Internet of Things, is made up of medical devices and software that are all connected to each other. Many Internet of Things (IoT) applications are based on the management of personally identifiable information, such as a patient's name, address, medical history, and health conditions. This is one reason why IoT applications have a special place in the field of healthcare services. When it comes to the Internet of Things (IoT), protecting the privacy and safety of patients without compromising data security is one of the hardest things to do. The privacy and safety of the information that IoT devices collect is still a big concern, both when the information is being sent to the cloud for storage and when it is being sent there. This is because there is a lot of data and it is important and sensitive. In this paper, we look at the technical flaws and research challenges for future work in security and privacy, as well as the current challenges and needs related to the flow of data in the MIoT. To proposed approach for Security Analysis for Threats to Patient Data in the Medical Internet of Things
BLOCKCHAIN TECHNOLOGIES AND MICROSERVICES FOR OPEN LEARNING COMMUNITIES. A SOFTWARE ARCHITECTURE PERSPECTIVE

It is a well-known fact that innovative e-learning concepts have pushed IT technologies to new levels of development or, reciprocally, innovative or even disrupting technologies have opened new possibilities for elearning. There are many examples of IT technologies that contributed to new or improved learning paradigms and styles, such as social networks, mobile devices, augmented and virtual reality, MOOCs and distributed computing. Recently, several research works discuss how the blockchain concepts and technologies can be applied not only in cryptocurrency, but also on elearning and educational processes with an essential impact, e.g. de-centralization of resources, open learning, tokenization of elearning, authenticity and security of information and resources. The current research paper will address how blockchain can be utilized with microservice based architectures, based on similarities, to support the most modern trends in education, such as open learning communities. In the first part of the paper, the main concepts and mechanisms behind the blockchain technologies are reviewed and explained in comparison with concepts and characteristics of microservices, considered a similar architectural pattern. Blockchain relies on the existing algorithms (such as cryptography) and distributed computing to bring new concepts such as ledgers and smart contracts. Likewise, microservices have roots on Service-Oriented Architecture (SOA) and Application Programming Interfaces (API). Microservices represent a new arhitectural pattern, to change the traditional way of software development resulting in "application monoliths" to a true modularization by means of composition of standalone software components (microservices), having well-defined functionality, securely exposed to other microservices or applications. In the second part, a software architecture leveraging blockchain smart contract and microservices to support open learning communities, unique identity, secure storage and retrieval of resources will be proposed and described.
INFORMATION AND DOCUMENTATION THROUGH NEW TECHNOLOGIES IN E-LEARNING PROCESS

The paper presents the results of research projects developed in the field of Information management by the National Institute for Research and Develop Considering the high-paced growth of information generated in the current Information Society, a high-quality e-learning process must deliver a significant amount of content to a large number of users. The paper presents the development, from concept to release, of a platform destined for promoting culture at different levels of users. It shows how the e-learning process could be organized around the integrated information regarding one or more cultural objectives. Technical innovations and the exponential expansion of online resources changed the way society works. Nowadays the classic method of learning, based on books, source code samples, magazines and journals, is losing ground in favor of e-Learning which is based on internet resources such as documents, applications and tools that are accessed using a personal computer or any another smart device. Considering the high-paced growth of information generated in the current Information Society, a high-quality e-learning process must deliver a significant amount of content to a large number of users. Technology is now facilitating the distribution of knowledge through software tools, in order to create a wide variety of learning possibilities for all sorts of users. A platform for information and documentation that integrates the IT&amp;C technologies to deliver cultural information, with facilities that appeal to the public, is a qualitative and quantitative point of access. The technical considerations for the implementations of this project are also described in the paper.