ALBERT

Description: Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an essential step in diagnosis and treatment planning to maximize the likelihood of successful treatment. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, making it an important tool for effective diagnosis which is requisite to replace the existing manual detection system where patients rely on the skills and expertise of a human. In order to solve this problem, a brain tumor segmentation & detection system is proposed where experiments are tested on the collected BraTS 2018 dataset. This dataset contains four different MRI modalities for each patient as T1, T2, T1Gd, and FLAIR, and as an outcome, a segmented image and ground truth of tumor segmentation, i.e., class label, is provided. A fully automatic methodology to handle the task of segmentation of gliomas in pre-operative MRI scans is developed using a U-Net-based deep learning model. The first step is to transform input image data, which is further processed through various techniques—subset division, narrow object region, category brain slicing, watershed algorithm, and feature scaling was done. All these steps are implied before entering data into the U-Net Deep learning model. The U-Net Deep learning model is used to perform pixel label segmentation on the segment tumor region. The algorithm reached high-performance accuracy on the BraTS 2018 training, validation, as well as testing dataset. The proposed model achieved a dice coefficient of 0.9815, 0.9844, 0.9804, and 0.9954 on the testing dataset for sets HGG-1, HGG-2, HGG-3, and LGG-1, respectively.

Description: Currently, most middleware application developers have two choices when designing or implementing Application Programming Interface (API) services; i.e., they can either stick with Representational State Transfer (REST) or explore the emerging GraphQL technology. Although REST is widely regarded as the standard method for API development, GraphQL is believed to be revolutionary in overcoming the main drawbacks of REST, especially data-fetching issues. Nevertheless, doubts still remain, as there are no investigations with convincing results in evaluating the performance of the two services. This paper proposes a new research methodology to evaluate the performance of REST and GraphQL API services with two main ideas as novelties. The first novel method is the evaluation of the two services is performed on the real ongoing operation of the management information system, where massive and intensive query transactions take place on a complex database with many relationships. The second is the fair and independent performance evaluation results obtained by distributing client requests and synchronizing the service responses on the two virtually separated parallel execution paths for each API service, respectively. The performance evaluation was investigated using basic measures of QoS (Quality of Services), i.e., response time, throughput, CPU load, and memory usage. We use the term efficiency in comparing the evaluation results to capture differences in their performance measures. The statistical hypothesis parameters test using the two-tails paired t-test, and boxplot visualization was also given to confirm the significance of the comparison results. The results showed REST is still faster up to 50.50% in response time and 37.16% for throughput, while GraphQL is very efficient in resource utilization, i.e., 37.26% for CPU load and 39.74% for memory utilization. Therefore, GraphQL is the right choice when data requirements change frequently, and resource utilization is the most important consideration. REST is used when some data are frequently accessed and called by multiple requests.

Description: Prolonged exposure to ultraviolet (UV) radiation is linked to skin cancer. Children are more vulnerable to UV harmful effects compared to adults. Children’s active involvement in using Internet of Things (IoT) devices to collect and analyze real-time UV radiation data is suggested to increase their awareness of UV protection. This quasi-experimental pre-test post-test control group study implemented light sensors in a STEM inquiry-based learning environment focusing on UV radiation and protection in primary education. This exploratory, small-scale study investigated the effect of a STEM environment implementing IoT devices on 6th graders’ knowledge, attitudes, and behaviors about UV radiation and protection. Participants were 31 primary school students. Experimental group participants (n = 15) attended four eighty-minute inquiry-based lessons on UV radiation and protection and used sensors to measure and analyze UV radiation in their school. Data sources included questionnaires on UV knowledge, attitudes, and behaviors administered pre- and post-intervention. Statistically significant learning gains were found only for the experimental group (t14 = −3.64, p = 0.003). A statistically significant positive behavioral change was reported for experimental group participants six weeks post-intervention. The study adds empirical evidence suggesting the value of real-time data-driven approaches implementing IoT devices to positively influence students’ knowledge and behaviors related to socio-scientific problems affecting their health.

Description: Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select effective features. However, most of them are not effective and scalable enough to select effective features from large medical datasets as well as small ones. Therefore, in this paper, a binary moth-flame optimization (B-MFO) is proposed to select effective features from small and large medical datasets. Three categories of B-MFO were developed using S-shaped, V-shaped, and U-shaped transfer functions to convert the canonical MFO from continuous to binary. These categories of B-MFO were evaluated on seven medical datasets and the results were compared with four well-known binary metaheuristic optimization algorithms: BPSO, bGWO, BDA, and BSSA. In addition, the convergence behavior of the B-MFO and comparative algorithms were assessed, and the results were statistically analyzed using the Friedman test. The experimental results demonstrate a superior performance of B-MFO in solving the feature selection problem for different medical datasets compared to other comparative algorithms.

Description: Augmented reality (AR) and virtual reality (VR) are two technologies with the potential to enhance learning quality by activating episodic student memory. In this manuscript we outline the pedagogical benefits of AR and VR as well as the rationale, design, development, and pilot evaluation results from an in-service teacher online professional development program (OPD) on AR and VR linked with research activities. More specifically, we examined the “Augmented and Virtual Reality in Teaching and Learning” OPD course aimed at K-12 educators in a European country combining pedagogically informed methods such as inquiry-based learning and digital storytelling. Findings from a mixed-method research using questionnaires and focus groups indicate that AR- and VR-based pedagogical scenarios can be integrated effectively into everyday school teaching practice. The study also outlines implications and limitations that policy makers, education leaders, and educators need to consider for efficient institution-wide deployment of AR and VR technologies.

Description: In this paper, we demonstrate the multiple points of innovation when combining blockchain technology with Internet of Things (IoT) and security frameworks. The deployment and use of IoT device networks in smart city environments has produced an enormous amount of data. The fact that those data are possessed by multiple sources that use independent systems for data collection, storage, and use impedes the exploitation of their value. Blockchains, as distributed ledgers, can be used for addressing the development of a universal system for data collection and distribution. Smart contracts can be used to automate all the processes of such a network, while at the same time, blockchain and the InterPlanetary File System (IPFS) protect sensitive data through anonymity and distributed storage. An innovative and open IoT blockchain market of applications, data, and services is proposed that: (i) provides the framework upon which objects and people can exchange value in form of virtual currencies, for assets (data and services) received; (ii) defines the motivation incentives according to social and business context for humans and smart objects to interact. The specific marketplace is piloted through a cross-border trial between Santander and Fujisawa, in the context of the M-Sec project, validating thus the interoperability, efficiency, and data protection principles.

Description: In the last year, educational experiences have become increasingly challenging due to teaching classes remotely. For this reason, it has been necessary to develop educational strategies that accompany the use of new technologies to maintain student interest. One of these methodologies is gamification, implemented in school environments more frequently due to the pandemic and whose impact on student motivation and engagement needs to be explored. The literature shows that student participation in these contexts should be increased when applying this methodology, where teachers can also provide greater support. This research proposes improving these aspects by developing a gamification strategy that can be easily replicated in other environments. This study was carried out for a chemistry course at a university in central Mexico. This proposal details the short-, medium-, and long-term bonuses which stimulated and motivated students and achieved specific objectives. We also present the quantitative results of a questionnaire applied to 48 engineering students to identify their perceptions of how gamification could increase motivation and engagement in learning the subject of chemistry. In addition, pre- and post-knowledge tests were applied to determine whether there were changes in the learning outcomes. The results indicated that gamification increased student motivation and engagement, improved attitudes, promoted actions such as keeping the camera on during lectures and regular attendance, and improved student grades. This study fills the need for planning strategies to help improve student motivation in online classes and proposes an instrument to measure the results. It can be helpful to those interested in applying or adapting it in other disciplines.

Description: Serverless computing is a new concept allowing developers to focus on the core functionality of their code, while abstracting away the underlying infrastructure. Even though there are existing commercial serverless cloud providers and open-source solutions, dealing with the explosive growth of new Internet of Things (IoT) devices requires more efficient bandwidth utilization, reduced latency, and data preprocessing closer to the source, thus reducing the overall data volume and meeting privacy regulations. Moving serverless computing to the edge of the network is a topic that is actively being researched with the aim of solving these issues. This study presents a systematic mapping review of current progress made to this effect, analyzing work published between 1 January 2015 and 1 September 2021. Using a document selection methodology which emphasizes the quality of the papers obtained through querying several popular databases with relevant search terms, we have included 64 entries, which we then further categorized into eight main categories. Results show that there is an increasing interest in this area with rapid progress being made to solve the remaining open issues, which have also been summarized in this paper. Special attention is paid to open-source efforts, as well as open-access contributions.

Description: Attempts to facilitate and streamline systems architecting have resulted in a great number of reusable principles, practices, mechanisms, frameworks, and tools. Such a practice is the use of architectural viewpoints and views. However, as systems change, these practices should also evolve. The increasing scale and complexity of systems resulting from an ever-growing pool of human needs and breakthroughs may lead, in some cases, to an increased gap between the abstraction activities attempting to capture the whole of a system, and the instantiation activities that produce concrete and detailed descriptions of a system’s architecture. To address this issue, this article introduces a new notion, that of architectural glimpse statements, fundamental questions acting as the building blocks for architectural views and products. This notion can help architects ask the right questions in the right manner to create fundamental statements, the elaboration on which can lead directly to concrete architectural products. Working on top of standardized and common approaches, the article introduces a language for the creation of architectural glimpse statements using the 5W1H maxim. Based on this language, a tool and guidelines are also provided to facilitate the usage of glimpses. Finally, the overall methodology is demonstrated in two case studies.

Description: The augmented reality game-based learning (ARGBL) approach is an advantageous pathway for the development and enhancement of teaching and learning processes. To this end, this paper presents the design and development of an ARGBL application for the implementation of physics experiments in the fifth grade of a Greek primary school. The purpose of the ARGBL system is twofold: to educate and entertain. For this reason, a treasure hunt game was implemented, which allows students to interact with a digital world and to manipulate virtual objects with the use of an augmented reality (AR) device. Then, according to the instructions, students have to collect all the materials to conduct the AR educational experiment. Overall, the evaluation of the system’s usability by 17 users (both students and teachers) was very promising, indicating that the ARGBL application has the potential to be an easy-to-use educational tool for improving not only the teaching of physics experiments in primary school but also the learning process, by positively affecting the students’ motivation and engagement.