Data, Volume 8, Issue 10 (October 2023) – 14 articles

The impacts of the coronavirus disease 2019 (COVID-19) pandemic have been extremely severe, with both economic and health crises experienced worldwide. Based on the panel regression model, this study examined the trends and correlations in the number of COVID-19-related deaths and the number of COVID-19-infected cases in all 37 regions of the Tamil Nadu state in India, in August 2020. The fixed effects model had the greatest R² value of 78% and exhibited significant results. The slope coefficient was also highly significant, showing a considerable variation in the relationship between new COVID-19 cases and deaths. Additionally, for every unit increase in COVID-19-infected cases, the death rate increased by 0.02%. Full article

This paper describes a dataset collected in an industrial setting using a mobile unit resembling an industrial vehicle equipped with several sensors. Wi-Fi interfaces collect signals from available Access Points (APs), while motion sensors collect data regarding the mobile unit’s movement (orientation and displacement). The distinctive features of this dataset include synchronous data collection from multiple sensors, such as Wi-Fi data acquired from multiple interfaces (including a radio map), orientation provided by two low-cost Inertial Measurement Unit (IMU) sensors, and displacement (travelled distance) measured by an absolute encoder attached to the mobile unit’s wheel. Accurate ground-truth information was determined using a computer vision approach that recorded timestamps as the mobile unit passed through reference locations. We assessed the quality of the proposed dataset by applying baseline methods for dead reckoning and Wi-Fi fingerprinting. The average positioning error for simple dead reckoning, without using any other absolute positioning technique, is 8.25 m and 11.66 m for IMU1 and IMU2, respectively. The average positioning error for simple Wi-Fi fingerprinting is 2.19 m when combining the RSSI information from five Wi-Fi interfaces. This dataset contributes to the fields of Industry 4.0 and mobile sensing, providing researchers with a resource to develop, test, and evaluate indoor tracking solutions for industrial vehicles. Full article

Cybersecurity social networking is a new scientific and engineering discipline that was interdisciplinary in its early days, but is now transdisciplinary. The issues of reviewing and analyzing of principal tasks related to information collection, monitoring of social networks, assessment methods, and preventing and combating cybersecurity threats are, therefore, essential and pending. There is a need to design certain methods, models, and program complexes aimed at estimating risks related to the cyberspace of social networks and the support of their activities. This study considers a risk to be the combination of consequences of a given event (or incident) with a probable occurrence (likelihood of occurrence) involved, while risk assessment is a general issue of identification, estimation, and evaluation of risk. The findings of the study made it possible to elucidate that the technique of cognitive modeling for risk assessment is part of a comprehensive cybersecurity approach included in the requirements of basic IT standards, including IT security risk management. The study presents a comprehensive approach in the field of cybersecurity in social networks that allows for consideration of all the elements that constitute cybersecurity as a complex, interconnected system. The ultimate goal of this approach to cybersecurity is the organization of an uninterrupted scheme of protection against any impacts related to physical, hardware, software, network, and human objects or resources of the critical infrastructure of social networks, as well as the integration of various levels and means of protection. Full article

Islamic content is a broad and diverse domain that encompasses various sources, topics, and perspectives. However, there is a lack of comprehensive and reliable datasets that can facilitate conducting studies on Islamic content. In this paper, we present fatwaset, the first public Arabic dataset of Islamic fatwas. It contains Islamic fatwas that we collected from various trusted and authenticated sources in the Islamic fatwa domain, such as agencies, religious scholars, and websites. Fatwaset is a rich resource as it does not only contain fatwas but also includes a considerable set of their surrounding metadata. It can be used for many natural language processing (NLP) tasks, such as language modeling, question answering, author attribution, topic identification, text classification, and text summarization. It can also support other domains that are related to Islamic culture, such as philosophy and language art. We describe the methodology and criteria we used to select the content, as well as the challenges and limitations we faced. Additionally, we perform an Exploratory Data Analysis (EDA), which investigates the dataset from different perspectives. The results of the EDA reveal important information that greatly benefits researchers in this area. Full article

Fish in the Volga-Kama River System (the largest river system in Europe) are important as a crucial food source for local populations; fish have the highest trophic level among hydrobionts. The purpose of this research is to describe the diversity of non-indigenous and native fish in the Volga and Kama Rivers, in the European part of Russia. This dataset encompasses data from June 2001 to September 2021 and comprises 1888 records (36,376 individual observations) for littoral and pelagic habitats from 143 sampling sites, representing 52 species from 42 genera in 22 families. The dataset has a Darwin Core standard format and has been fully released in the Global Biodiversity Information Facility (GBIF) under CC-BY 4.0 International license. The data are validated with several international databases such as FishBase, Eschmeyer’s Catalog of Fishes, the Barcode of Life Data System, and the SAS.Planet geoinformations system. Newly established populations have been found for several species belonging to the following Actinopteri families: Alosidae, Anguillidae, Cichlidae, Ehiravidae, Gobiidae, Odontobutidae, Syngnathidae, and Xenocyprididae. Therefore, this dataset can be used in the particular taxon species distribution analysis, which are especially important for non-indigenous species. Full article

Machine learning is a crucial tool for both academic and real-world applications. Classification problems are often used as the preferred showcase in this space, which has led to a wide variety of datasets being collected and utilized for a myriad of applications. Unfortunately, there is very little standardization in how these datasets are collected, processed, and disseminated. As new learning paradigms like lifelong or meta-learning become more popular, the demand for merging tasks for at-scale evaluation of algorithms has also increased. This paper provides a methodology for processing and cleaning datasets that can be applied to existing or new classification tasks as well as implements these practices in a collection of diverse classification tasks called USC-DCT. Constructed using 107 classification tasks collected from the internet, this collection provides a transparent and standardized pipeline that can be useful for many different applications and frameworks. While there are currently 107 tasks, USC-DCT is designed to enable future growth. Additional discussion provides explanations of applications in machine learning paradigms such as transfer, lifelong, or meta-learning, how revisions to the collection will be handled, and further tips for curating and using classification tasks at this scale. Full article

In this paper, we describe a 13-year (2009–2022) dataset of legacy POP concentrations (DDT_tot and sumPCB₁₄ from 2016 isomers and congeners concentrations are also reported) in the planktonic crustaceans of Lake Maggiore (≥450 µm size fraction). The data were collected in the framework of a monitoring program finalized to assess the presence of pollutants in the lake biota, including zooplankton organisms directly preyed by fish. The data report both concentration of DDT_tot and sumPCB₁₄ in the zooplankton and the standing stock density and biomass of the population in each season. The dataset allows for detecting changes in the concentration over the long term and within a year, thus providing evidence for the seasonal and the plurennial variations in the presence of these pollutants in the lake. They also provide a basis for further studies aimed at modeling paths and the fate of persistent organic pollutants, for which the amount of toxicants stocked in the zooplankton compartment linked to fish is a crucial estimate. Full article

This dataset collects tabular and geographical information about all hydrogeological disasters (landslides and floods) that occurred in Italy from 2013 to 2022 that caused such severe impacts as to require the declaration of national-level emergencies. The severity and spatiotemporal extension of each emergency are characterized in terms of duration and timing, funds requested by local administrations, funds approved by the national government, and municipalities and provinces hit by the event (further subdivided between those included in the emergency and those not, depending on whether relevant impacts were ascertained). Italian exposure to hydrogeological risk is portrayed strikingly: in the covered period, 123 emergencies affected Italy, all regions were struck at least once, and some provinces were struck more than 10 times. Damage declared by local institutions adds up to EUR 11,000,000,000, while national recovery funds add up to EUR 1,000,000,000. The dataset may foster further research on risk assessment, econometric analysis, public policy support, and decision-making implementation. Moreover, it provides systematic evidence helpful in raising awareness about hydrogeological risks affecting Italy. Full article

The rapid expansion of distributed generation leads to the integration of an increasing number of energy generation sources. However, integrating these sources into electrical distribution networks presents specific challenges to ensure that the distribution networks can effectively accommodate the associated distributed energy and power. Thus, it is crucial to evaluate the electrical effects of power along the conductors, components, and loads. Power-flow analysis is a well-established numerical methodology for assessing parameters and quantities within power systems during steady-state operation. The University of São Paulo’s Cidade Universitária “Armando de Salles Oliveira” (CUASO) campus in São Paulo, Brazil, features an underground power distribution system. The Institute of Energy and Environment (IEE) leads the integration of several distributed generation (DG) sources, including a biogas plant, photovoltaic installations, and a small wind turbine, into one of the CUASO’s feeders, referred to as “USP-105”. Load-flow simulations were conducted using the PowerWorld^TM Simulator v.23, considering the interconnection of these sources. This dataset provides comprehensive information and computational files utilized in the simulations. It serves as a valuable resource for reanalysis, didactic purposes, and the dissemination of technical insights related to DG implementation. Full article

Currently, several devices (such as laser scanners, Kinect, time of flight cameras, medical imaging equipment (CT, MRI, intraoral scanners)), and technologies (e.g., photogrammetry) are capable of generating 3D point clouds. Each point cloud type has its unique structure or characteristics, but they have a common point: they may be loaded with errors. Before further data processing, these unwanted portions of the data must be removed with filtering and outlier detection. There are several algorithms for detecting outliers, but their performances decrease when the size of the point cloud increases. The industry has a high demand for efficient algorithms to deal with large point clouds. The most commonly used algorithm is the radius outlier filter (ROL or ROR), which has several improvements (e.g., statistical outlier removal, SOR). Unfortunately, this algorithm is also limited since it is slow on a large number of points. This paper introduces a novel algorithm, based on the idea of the ROL filter, that finds outliers in huge point clouds while its time complexity is not exponential. As a result of the linear complexity, the algorithm can handle extra large point clouds, and the effectiveness of this is demonstrated in several tests. Full article

In recent years, blockchain research has drawn attention from all across the world. It is a decentralized competence that is spread out and uncertain. Several nations and scholars have already successfully applied blockchain in numerous arenas. Blockchain is essential in delicate situations because it secures data and keeps it from being altered or forged. In addition, the market’s increased demand for data is driving demand for data scaling across all industries. Researchers from many nations have used blockchain in various sectors over time, thus bringing extreme focus to this newly escalating blockchain domain. Every research project begins with in-depth knowledge about the working domain, and new interest information about blockchain is quite scattered. This study analyzes academic literature on blockchain technology, emphasizing three key aspects: blockchain storage, scalability, and availability. These are critical areas within the broader field of blockchain technology. This study employs CiteSpace and VOSviewer to understand the current state of research in these areas comprehensively. These are bibliometric analysis tools commonly used in academic research to examine patterns and relationships within scientific literature. Thus, to visualize a way to store data with scalability and availability while keeping the security of the blockchain in sync, the required research has been performed on the storage, scalability, and availability of data in the blockchain environment. The ultimate goal is to contribute to developing secure and efficient data storage solutions within blockchain technology. Full article

In the context of exponential demographic growth, the imbalance between human resources and public health problems impels us to envision other solutions to the difficulties faced in the diagnosis, prevention, and large-scale management of the most common diseases. Cardiovascular diseases represent the leading cause of morbidity and mortality worldwide. A large-scale screening program would make it possible to promptly identify patients with high cardiovascular risk in order to manage them adequately. Optical coherence tomography angiography (OCT-A), as a window into the state of the cardiovascular system, is a rapid, reliable, and reproducible imaging examination that enables the prompt identification of at-risk patients through the use of automated classification models. One challenge that limits the development of computer-aided diagnostic programs is the small number of open-source OCT-A acquisitions available. To facilitate the development of such models, we have assembled a set of images of the retinal microvascular system from 499 patients. It consists of 814 angiocubes as well as 2005 en face images. Angiocubes were captured with a swept-source OCT-A device of patients with varying overall cardiovascular risk. To the best of our knowledge, our dataset, Retinal oct-Angiography and cardiovascular STAtus (RASTA), is the only publicly available dataset comprising such a variety of images from healthy and at-risk patients. This dataset will enable the development of generalizable models for screening cardiovascular diseases from OCT-A retinal images. Full article

The paper is devoted to the problem of generating artificial datasets for data envelopment analysis (DEA), which can be used for testing DEA models and methods. In particular, the papers that applied DEA to big data often used synthetic data generation to obtain large-scale datasets because real datasets of large size, available in the public domain, are extremely rare. This paper proposes the algorithm which takes as input some real dataset and complements it by artificial efficient and inefficient units. The generation process extends the efficient part of the frontier by inserting artificial efficient units, keeping the original efficient frontier unchanged. For this purpose, the algorithm uses the assurance region method and consistently relaxes weight restrictions during the iterations. This approach produces synthetic datasets that are closer to real ones, compared to other algorithms that generate data from scratch. The proposed algorithm is applied to a pair of small real-life datasets. As a result, the datasets were expanded to 50K units. Computational experiments show that artificially generated DMUs preserve isotonicity and do not increase the collinearity of the original data as a whole. Full article

Age estimation from facial images has gained significant attention due to its practical applications such as public security. However, one of the major challenges faced in this field is the limited availability of comprehensive training data. Moreover, due to the gradual nature of aging, similar-aged faces tend to share similarities despite their race, gender, or location. Recent studies on age estimation utilize convolutional neural networks (CNN), treating every facial region equally and disregarding potentially informative patches that contain age-specific details. Therefore, an attention module can be used to focus extra attention on important patches in the image. In this study, tests are conducted on different attention modules, namely CBAM, SENet, and Self-attention, implemented with a convolutional neural network. The focus is on developing a lightweight model that requires a low number of parameters. A merged dataset and other cutting-edge datasets are used to test the proposed model’s performance. In addition, transfer learning is used alongside the scratch CNN model to achieve optimal performance more efficiently. Experimental results on different aging face databases show the remarkable advantages of the proposed attention-based CNN model over the conventional CNN model by attaining the lowest mean absolute error and the lowest number of parameters with a better cumulative score. Full article