Reviewerly/exHarmony huggingface dataset:
Everyday engagement with mobile phones in an urban slum in Delhi

This ethnographic case study presents findings of an 18-month research study focusing on the ways in which families residing in an urban slum were using mobile phones and how this use supported literacy practices. Data collection included participant observations and interviews with 42 participants including parents, children and community members. Results of the data analysis indicated that in this urban slum, most participants owned a mobile phone which provided multiple entry points to learning. The phones ushered in new ways of brokering knowledge where children acted as ‘experts’ and enabled parents to perform everyday tasks while parents mediated as cultural brokers and fostered religious and cultural practices and knowledge of the mother tongue. The implications of the study point to the evolving nature of literacy practices, the versatility of the device, the uneven landscape of smartphone use and the limitations posed by the schooling contexts.
Symphonic tableau Birds and water by Gh. Ciobanu: programming trends in contemporary music

The article examines a creation by Ghenadie Ciobanu, composer from the Republic of Moldova, in the context of the contemporary programming tendencies. The work highlights a system of means operating at different scale levels of composition and concretizes the program design. The connotative layer containing motifs-symbols and reinforcing the associative aspect of the author's concept becomes important.
CaliProb: Probability-based Calibration Model for Robust Predictions in Environments with Data Biases

Various studies have focused on solving data bias problems in artificial intelligence. However, conventional approaches have primarily focused on data biases originating from imbalanced data distributions between sensor nodes and algorithms owing to overfitting. However, methods handling the biases between sensors have rarely been considered. In general, when data are trained using multiple heterogeneous devices, a device bias occurs due to process, voltage, and temperature variations, differences in the preprocessing methods for each device and the aging of specific devices, resulting in a data bias. In this study, we prove that a data bias can occur due to a device bias, which deteriorates the training performance. In response, we propose the probability-based calibration (CaliProb) model. This model compares the predicted probabilities for each piece of data, selects the data with the best training performance as the reference data, and calibrates the data bias using these reference data. We experimentally verify the CaliProb model. It exhibits an accuracy greater than that of the uncalibrated general model in situations with low and high data biases and achieves an accuracy that is approximately 12.2% higher than that of the Non-calibrated model in a case with abundant data bias.
Design of Lightweight Cryptography based Deep Learning Model for Side Channel Attacks

Depending on a device's encryption mechanism, a wide variety of tangible details could be exposed. These leaks are used in side-channel analysis, which is used to get keys. Due to deep learning's sensitivity to the characteristics of the data being processed, using such algorithms can significantly improve the accuracy and efficiency of side channel analysis. However, classic neural networks are now used for the vast majority of the work that is being done. When the number of nodes in a network grows, so does the efficiency with which key recovery can function. However, the method's computing complexity grows in direct proportion. Overfitting, inadequate capacity for feature extraction, and inefficient training are all potential issues. In this study, we develop a compact convolutional neural network by enhancing a previously existing combination of neural networks. Novel neural network along with previous neural network both have their own implementations of the side-channel analysis used in comparative trials. Statistically, the new network has better accuracy, quicker convergence, and more robustness. As part of the research, heatmaps were provided as a means of data visualisation. The critical interval concentration is higher and the heat value is higher in the new network. Conventional neural networks, which serve as the foundation for various kinds of neural networks, perform much worse than side channel studies based on feature fusion networks.
Evaluation of RTT as an Estimation of Interactivity Time for QoE Evaluation in Remote Desktop Environments

In recent years, there has been a notable surge in the utilization of remote desktop services, largely driven by the emergence of new remote work models introduced during the pandemic. Traditional evaluation of the quality of experience (QoE) of users in remote desktop environments has relied on measures such as round-trip time (RTT). However, these measures are insufficient to capture all the factors that influence QoE. This study evaluated RTT and interactivity time in an enterprise environment over a period of 6 months and analysed the suitability of using RTT drawing previously unexplored connections between RTT, interactivity, and QoE. The results indicate that RTT is an insufficient indicator of QoE in productive environments with low RTT values. We outline some precise measures of interactivity needed to capture all the factors that contribute to QoE in remote desktop environments.
Dealing with Anti-Patterns When Migrating from Monoliths to Microservices: Challenges and Research Directions

Microservices refers to a highly effective architectural style for organizations to build cloud-native applications. However, in most real-world cases, the adoption of microservices is not a deliberate choice made by organizations. Instead, the shift towards microservices is compelled by the urgent necessity to overcome the constraints imposed by traditional monolithic architectures. As a result, organizations typically seek to migrate their existing systems to microservices incrementally and with caution to ensure safe migration. To achieve this particular goal, the industry has provided various strategies and patterns. However, many practitioners overlook a very important aspect while migrating to microservices, which is the presence of anti-patterns and their potential evolution during the migration phases. Therefore, studies and investigations are needed to assist the migration task to microservices against anti-patterns. Accordingly, this paper brings attention to a research gap concerning the impact of anti-patterns on migrating to microservices. This gap is addressed through the formulation of four research questions. The main aim is to inspire the academic community to conduct additional investigations in this area, aiming to improve migration quality and facilitate the development of more efficient architectures.
Using Serverless FaaS for Invoking Long-Running Jobs

Function-as-a-Service (FaaS) is a popular computing model which abstracts out a lot of infrastructure intricacies from developers for building serverless applications, supported by all major cloud providers and many open-source software frameworks. Albeit, the numerous advantages a FaaS platform entails with it, there are a few perils associated with it as well, one of which is a timeout. Every function invocation has a default timeout, after which if the program running in the FaaS platform is still running, is terminated by the FaaS platform. This makes it's a difficult to run long running jobs/batches on a FaaS platform. This document depicts exploiting the usage of Function as a Service (FaaS) platforms for triggering and long running jobs and batches, and post processing the output of the jobs/batches.
Comparison of a FaaS- and SaaS-Based ETL Process in a Cloud Environment

The goal of this paper is to analyze experiences from a FaaS-based (function as a service) cloud-native implementation of an ETL process (extract, transform, load) based on AWS Lambda functions. The actual implementation is outlined and the experiences from that implementation are evaluated. This results in an overview of pros and cons of a cloud-native implementation in general and determines best practices for other implementations in other cloud environments or business domains.
Cloud Identity Management Mechanisms and Issues

It is widely recognized that cloud-based services have experienced a significant increase in popularity, accompanied by notable success. However, the security risks persist as a consequence of this progress. Cloud Service Providers (CSPs) need to challenge those security risks to make users trust their services cloud. Identity management is a key concern within the cloud environment, where several efforts have been and will continue to be made in order to ensure that no one other than the right user gets access to these services. Various identity management protocols and mechanisms have been implemented by Cloud Service Providers (CSPs). This paper provides a comprehensive overview of these commonly used protocols, including Credentials, Multi-Factor Authentication (MFA), and Single Sign-On (SSO). The main focus is placed on analyzing their features, potential security vulnerabilities, and proposed mitigations.
Comparative Experimentation of MLOps Power on Microsoft Azure, Amazon Web Services, and Google Cloud Platform

Any organization that wants to remain competitive can profit greatly from automating the creation of machine learning systems. To offer models as quickly as possible, many processes and branches are made easier, risk is decreased, and productivity is increased. MLOps, which stands for Machine Learning Operations, is the strategic process that adopts effective techniques for processing, testing, and deploying ML systems in production. It is what makes it all feasible. In this paper, we will study the experimentation of MLOps on the three primary Cloud providers: Microsoft Azure (Azure), Amazon Web Services (AWS), and Google Cloud Platform (GCP). We will then compare the MLOps power of these three platforms.
Feature Selection on Grouping Students Into Lab Specializations for the Final Project Using Fuzzy C-Means

The student’s Final Project is critical as a requirement to graduate from the University. In the PSTI at Mataram University, each student is required to choose a specialization lab to focus on the final project topic that they will work on. From the questionnaire, 57.7% of students answered that it is difficult to select a lab, and others answered that they prefer to determine the labs based on the grades of the courses that represent each lab. This research aimed to group and analyze students in the final project specialization lab by using the main method, namely Fuzzy C-Means (FCM). The methods used were FCM for clustering, Silhouette Coefficient for analysis of cluster quality results, Pearson Correlation, and Principal Component Analysis for the feature selection processing. The results of this study showed that the FCM method followed by a method for feature selection has better results than previous studies that used the K-Means method without feature selection; with this research result using 131 data, the cluster validation result is 0.501, after feature selection using Pearson correlation is 0.534. Thus, Fuzzy C-Means followed by the right feature selection method can group students into specialization laboratories with good results and can be further developed.
Harnessing Blockchain in BPS Microdata Dissemination

Towards achieving BPS-Statistics Indonesia missions, the dissemination process of statistical products must be carried out well. One of BPS-Statistics Indonesia statistical products is the microdata. In this case, the activity of disseminating microdata should be conducted through the implementation of feasible best practices. Related to that, in a fast-paced of the ever-changing world that heavily relies on the evolution of technology, the process of bringing out the best efforts in disseminating microdata must as well follow the rhythm of the moving technology to meet the current needs of the digital society, because otherwise it will be obsolete as time goes by. One of the important issues is the limitation of the existing system in tracking microdata to ensure its authenticity and integrity, in case where the users have purchased the microdata from BPS-Statistics Indonesia. In addressing this traceability issue, a solution through the implementation of the cutting-edge Blockchain technology is considered. A design is proposed to incorporate Blockchain into the existing mechanism of BPS-Statistics Indonesia microdata dissemination. Therefore, a system architecture and a schema for smart contract utilization are proposed to reinforce the microdata tracking.
Adaptive multi-strategy particle swarm optimization for solving NP-hard optimization problems

Particle Swarm Optimization algorithm (PSO) has been widely utilized for addressing optimization problems due to its straightforward implementation and efficiency in tackling various test functions and engineering optimization problems. Nevertheless, PSO encounters issues like premature convergence and a lack of diversity, particularly when confronted with complex high-dimensional optimization tasks. In this study, we propose an enhanced version of the Island Model Particle Swarm Optimization (IMPSO), where island models are integrated into the PSO algorithm based on several migration strategies. The first contribution consists in applying a new selection and replacement strategies based on tabu search technique, while the second contribution consists in proposing a dynamic migration rate relying on the Biogeography-Based Optimization technique. To assess and validate the effectiveness of the proposed method, several unconstrained benchmark functions are applied. The obtained results confirm that the approach yield better performance than the old version of IMPSO for solving NP-hard optimization problems. Compared to the performance of other well-known evolutionary algorithms, the proposed approach is more efficient and effective.
New Trends in Artificial Intelligence

Nowadays, with society 5.0, where technology has reached its highest level and smart societies have been formed, artificial intelligence has entered our lives in almost every field. With artificial intelligence, which is related to many disciplines, machines that resemble the human thinking and learning system are produced. In this way, artificial intelligence is used in cases where workforce and potential processing capacity are insufficient. In this study, the development process of artificial intelligence, new trends with artificial intelligence technologies are presented and the areas of use of artificial intelligence are included with examples. Finally, information about the future of artificial intelligence is given. It is expected that this study, which explains the basic principles of artificial intelligence, will be useful to its readers.
Design and Implementation of an Interactive Visualization Dashboard for Monitoring the Flood Vulnerability and Mapping

This study aims to build a web-based interactive visualization dashboard from granular flood vulnerability index estimation maps using data from satellite imagery. The approach used to build this visualization dashboard is a two-dimensional (2D) approach created with the qgis2web python plugin facilitated with a JavaScript leaflet. Raw data from satellite imagery consisting of indicators of the causes of flooding are extracted in comma-separated value (CSV) format. Furthermore, the data is integrated based on its spatial attributes and stored in Geographic JavaScript Object Notation (GeoJSON) format to produce a visualization of the flood vulnerability index map. In web views, dashboards are built by utilizing hypertext markup language (HTML), cascading style sheets (CSS), and JavaScript (JS). This interactive dashboard has several useful features in helping the process of monitoring the flood vulnerability of an area such as zoom, "show me where I am", measure distance, search, legend, and change year. Thus, the flood vulnerability estimation map dashboard is expected to assist the government in monitoring areas with extreme flood vulnerability and support the decision-making process related to mitigation of areas that have high flood vulnerability.
The Mousty Formation (Brabant Massif, Belgium): state of the art

The Mousty Formation has unique and particularly interesting lithological, mineralogical, sedimentological, metamorphic and tectonic characteristics which have been the subject of much research in connection with the new geological map of Wallonia, but also within the framework of a large amount of academic research, sometimes unpublished. An effort to synthesise and upgrade it in an updated framework seemed necessary. After a history of its definition, mapping and outcrop areas, the following topics are successively addressed in detail and illustrated with diagrams and photos: its lithostratigraphy with the probable discovery of its base near the Court-St-Etienne anticline, its well-constrained biostratigraphy and chronostratigraphy, a new estimate of its minimum thickness, its sedimentology—a subject little addressed until now—, and the resulting depositional sedimentary environment. Its geochemistry, never addressed, is comparable to black shale international standards, except for abnormally high manganese and very low calcium contents; high radon levels in buildings are related to the black slate presence in outcrops. Its mineralogy is rich in manganese-bearing metamorphic minerals, its metamorphism in which we go beyond the strict framework of the formation to deal with the entire southern outcropping rim of the Brabant Massif. Finally, the relationship between the Mousty Formation and the tectonic is discussed, with additional field data, in the context of the innovative and unifying concept of a low-angle extensional detachment called the Asquempont Detachment System. All these observations, some old and some very recent, discussed in the updated framework of the geology of the Caledonian basement of Brabant and in the global stratigraphy and palaeogeography of the lower Palaeozoic, allow us to renew in depth the vision we had of the Mousty Formation and of its place in the Brabant Massif.
Complying with the US Department of Defense

The benefits of STIG (Security Technical Implementation Guide) compliance for 3D printers.
EFFECT OF THE CONVECTIVE BOUNDARY LAYER ON RADIOSONDE FLIGHT PATH OVER BAGHDAD AIRPORT STATION (CASE STUDY)

This research was aimed to investigate effect of the convection conditions resulted from surface heating at different weather condition on movement track of radiosonde. Data used in this study divided to surface local weather from Baghdad airport station and reanalysis weather surface data from European center medium weather forecast (ECMWF) that contain also advance parameter parameters such as sensible heat flux and boundary layer height and other, all have large accurate spatial distribution inside and outside Baghdad province. Third type data from radiosonde used to observed atmospheric upper layer through selected days from winter season (January month) and summer season (July month) all at time 12AM. Software installed in radiosonde used to recoded wind speed, temperature and other parameter such as global position system (GPS) all plotted and represented by ArcGISpro. Results show different in the radiosone path at convection and nonconventional conditions. Convection constraint movement of radiosonde path (wind speed profiles very low don’t reach to 30m/s in upper layer, this is opposite to nonconventional case, it reach to more than 65m/s) because turbulent and random in different directions, this case is opposite to the nonconventional condition there is large free movement, where balloon reach to large and upper distance in nonconventional (Jan. cases).
A white light interferometry stitching measurement method based on feature extraction

The white light interferometry technique can measure the three-dimensional topography of ultra-precise complex surface with sub-nanometer resolution and precision, and has been widely concerned by academia and industry since it was proposed. The measurement range of white light interferometry technology is usually limited by the size of the optical field of view of the system, and the surface of the measured object can only be measured within the limited range of the camera's single imaging. In order to obtain the microscopic surface topography with a larger field of view, it is usually necessary to conduct stitching measurement. In this paper, a measurement method of aperture stitching of white light interferometry based on feature extraction is presented. The first step is subaperture partitioning and data preprocessing. The second step is feature extraction. The feature points are extracted by the Speeded-Up Robust Features (SURF) algorithm. The third step is to identify and filter wrong match points in the feature extraction algorithm using the Random Sample Consensus (RANSAC) algorithm. The fourth step is to use the improved Iterative Closest Point (ICP) algorithm to accurately register the point cloud, and perform data fusion in the overlapping region to complete the reconstruction of the microscopic three-dimensional surface topography. This method is used to measure the microstructure of quartz vibratory beam accelerometer. The experimental results show that the proposed algorithm has a faster speed than the traditional algorithm while maintaining high precision.
An exploration of public key infrastructure applications across diverse domains: a comparative analysis

This article delves into the vital role of Public Key Infrastructure (PKI) in securing and authenticating communications across a multitude of fields. PKI has evolved from a mere technical concept into a cornerstone of secure digital communications, playing a central role in various domains such as web security, healthcare, finance, the Internet of Things (IoT), and government services. PKI employs cryptographic techniques and digital certificates to establish trust, ensure data integrity, and enable secure communications, thus acting as the backbone of digital security. In the wake of the digital revolution, the demand for reliable and robust security solutions has skyrocketed. The diversity and scale of modern digital platforms necessitate adaptable security solutions, a challenge which PKI tackles through its flexible implementation. Despite sharing core principles, the implementation of PKI demonstrates divergences influenced by factors such as scale, complexity, resource constraints, regulatory environments, and trust models. This article offers an extensive comparison of PKI's utilization across various domains, highlighting the commonalities and divergences. It explores how PKI is tailored to meet the unique requirements and challenges of each sector and discusses the certificate lifecycle management in varying contexts. Moreover, it provides an analysis of the current state of PKI applications and challenges, offering insights into the evolving landscape of threats and technologies. Not only does the article address the current state of PKI, but it also presents a forward-looking perspective on its potential future developments. As the digital landscape continues to evolve and expand, it is crucial to anticipate the emerging challenges and devise strategies for proactive adaptation. This article thus serves as a comprehensive resource for understanding the role and impact of PKI in the contemporary digital infrastructure. Ultimately, the article seeks to underline the importance of PKI and highlight the need for continued research and development in this area. As our reliance on digital communications and transactions continues to grow, the role of PKI in safeguarding these interactions becomes increasingly significant. This comprehensive review serves as a valuable resource for researchers, practitioners, and policymakers in understanding the diverse applications of PKI and its critical role in securing the digital world.