Exploring the Measurement Lab Open Dataset for Internet Performance Evaluation: The German Internet Landscape

The Measurement Lab (MLab) provides a large and open collection of Internet performance measurements. We make use of it to look at the state of the German Internet by a structured analysis, in which we carve out expressive results from the dataset to identify busy hours and days, the impact of server locations and congestion control protocols, and compare Internet service providers. Moreover, we examine the impact of the COVID-19 lockdown in Germany. We observe that only parts of the Internet show a performance degradation at the beginning of the lockdown and that a large impact in performance depends on the network the servers are located in. Furthermore, the evolution of congestion control algorithms is reflected by performance improvements. For our analysis, we focus on the busy hours. From the end-user perspective, this time is of most interest to identify if the network can support challenging services such as video streaming or cloud gaming at these intervals.

The Inclusion of Children With Special Education Needs

This chapter represents a literature review of inclusion policies and practices for children with special needs into the mainstream education system of Romania. The authors provide a structured analysis of the main inclusion practices, with an emphasis on the criticism of the current practice. The main criticisms come from teachers, informal caregivers, students with disabilities, and stakeholders. The analysis is backed up by official documents (reports and legislation), empirical research, and other papers of Romanian professionals in the field of special and inclusive education. After approximately two decades of inclusive schooling in Romania, the authors conclude that there is still a lot of room for improvement. Inclusion is an uninterrupted process that requests resources, structure, and scientific evidence, all embodied in technical and material means, diverse teaching strategies, and well-trained professors that are able to face the challenges.

Diabetic and Hypertensive Retinopathy Screening in Fundus Images Using Artificially Intelligent Shallow Architectures

Retinal blood vessels are considered valuable biomarkers for the detection of diabetic retinopathy, hypertensive retinopathy, and other retinal disorders. Ophthalmologists analyze retinal vasculature by manual segmentation, which is a tedious task. Numerous studies have focused on automatic retinal vasculature segmentation using different methods for ophthalmic disease analysis. However, most of these methods are computationally expensive and lack robustness. This paper proposes two new shallow deep learning architectures: dual-stream fusion network (DSF-Net) and dual-stream aggregation network (DSA-Net) to accurately detect retinal vasculature. The proposed method uses semantic segmentation in raw color fundus images for the screening of diabetic and hypertensive retinopathies. The proposed method’s performance is assessed using three publicly available fundus image datasets: Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of Retina (STARE), and Children Heart Health Study in England Database (CHASE-DB1). The experimental results revealed that the proposed method provided superior segmentation performance with accuracy (Acc), sensitivity (SE), specificity (SP), and area under the curve (AUC) of 96.93%, 82.68%, 98.30%, and 98.42% for DRIVE, 97.25%, 82.22%, 98.38%, and 98.15% for CHASE-DB1, and 97.00%, 86.07%, 98.00%, and 98.65% for STARE datasets, respectively. The experimental results also show that the proposed DSA-Net provides higher SE compared to the existing approaches. It means that the proposed method detected the minor vessels and provided the least false negatives, which is extremely important for diagnosis. The proposed method provides an automatic and accurate segmentation mask that can be used to highlight the vessel pixels. This detected vasculature can be utilized to compute the ratio between the vessel and the non-vessel pixels and distinguish between diabetic and hypertensive retinopathies, and morphology can be analyzed for related retinal disorders.

Deep learning for detecting distresses in buildings and pavements: a critical gap analysis

Purpose The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection. Design/methodology/approach A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations. Findings The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results. Practical implications The availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources. Originality/value A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.

Management Structure of Product Design of Small and Medium Industries in Thailand to the International Market Education

The purpose of this research were 1) to synthesize the structure of product design management of small and medium industries in Thailand to the international market education, and 2) to verify the consistency of the relationship between marketing demand and corporate strategy, research and development, innovation and technology and design goals. This research is quantitative and qualitative research. The sample group for quantitative research was 500 small and medium business entrepreneurs, 9 key informants divided into 4 groups: business people, government organization group, academic group, and designer data analysis uses a structured analysis. The analysis of the developed structural models was found that the evaluation criteria were consistent with the empirical data. The relative chi-squared probability was 0.306, the relative chi-squared probability was 1.042, the consistency index was 0.957, and the mean square of the estimation of the error was 0.009.

Tourism towards Sustainability and Innovation: A Systematic Literature Review

In this paper, we analyze the progress of tourism towards sustainability and innovation through a systematic literature review summarizing the last five years of research strictly focused on innovation and sustainability applied to tourism. This research comprises a range of theories, practices, methods, and results pursuing innovation and sustainability across different levels, stages, and drivers, and in many tourism contexts. Wide, in-depth, and structured analysis, evaluation, and examination (using the PRISMA and VOSviewer tools) of a final sample of 50 scholarly papers from 27 journals, published between 2017 and the first quarter of 2021, were undertaken. Current publications emphasize qualitative, quantitative, and mixed research methods, as well as statistical and econometric methods, such as descriptive statistics, factor analysis, and structural equation modeling. This study categorizes the four major topics identified, sustainability, innovation, sustainable development, and sustainable tourism, which comprised the contextual dimensions and relevant stages of the subject areas examined. This systematic literature review highlights advances and the significantly increasing overall number of papers over recent years. Currently, sustainability is in a more advanced state compared to innovation. The outcomes highlight that the indicators of sustainability and innovation still need further analysis within the tourism context. However, more concrete process indicators are needed for continuous improvement of the front-end of innovation and sustainable tourism. The results help in better understanding the sustainability and innovation process as applied to tourism. In particular, this study explores further direct linkages between sustainability and innovation and tourism, discussing and providing new future directions aligned with the closing remarks as well as a strategic agenda for future action post-COVID-19.

Toward An Ontology of Strategy in an Enterprise Context

What is meant by "strategy" and what concepts are involved in its creation are not well understood, and there is significant inconsistency in the way they are all used. It was hypothesised that current tools and techniques for ontology development and semantic analysis could be effectively applied to understand better what is meant by the term strategy and understand their nature and relationships. A literature review was conducted to identify how practitioners and academia view the subject, and the results organised using structured analysis. The result is a more complete, internally consistent view of the features of strategy definition. By applying structured analysis, the components and relationships used to form strategy and therefore the requisite structure of strategy in the enterprise context are uncovered. In this way, the specification that makes up a strategy is better understood using conceptual and systems dynamics models. In this way, the nature, and relationships necessary and sufficient to describe the multiple dimensions of strategy are exposed.

Optimalisasi Pelayanan Perpustakaan terhadap Minat Baca Menggunakan Metode K-Means Clustering

Knowledge Discovery in Database (KDD) is a structured analysis process aimed at getting new and correct information, finding patterns from complex data, and being useful. Data mining is at the core of the KDD process. Clustering is a data mining method that is suitable for optimizing library services because it can cluster books effectively and efficiently, with the K-Means algorithm data can be clustered and information from each centroid value of each cluster. Library services can optimize the placement of books so that students can quickly find books according to their reading interest more effectively and can be attracted to other books because they are in one grouping. Meanwhile, the library can prioritize the procurement of the next book. Optimization of library services in the cluster using the K-Means method. Clustering interest in reading has the criteria for the number of books available, borrowed books, and the length of time the books are borrowed. The book data is clustered into 3, namely very interested, in demand, and less desirable. After doing the calculation process from 40 samples of book types, it resulted in 6 iterations, and the final results were 3 clustering, namely cluster 1 of 4 books that were of great interest, cluster 2 of 20 books that were of interest, and cluster 3 of 16 books that were less desirable. This research can be used as a recommendation reference for optimizing library services both for the layout and procurement of books by prioritizing the types of books that are of great interest.