: This paper introduces definitions, requirements, models and techniques of QUALITOP Federated Big Data Analytics Learning System (QFLS), a Cloud-based framework for supporting big healthcare data management and analytics from big data lakes . QFLS anatomy and main functionalities are described, along with the main software solutions proposed with the framework.
This paper presents an innovative approach to address the problems researchers face in Emotion Aware Recommender Systems (EARS): the difficulty and cumbersome collecting voluminously good quality emotion-tagged datasets and an effective way to protect users' emotional data privacy. Without enough good-quality emotion-tagged datasets, researchers cannot conduct repeatable affective computing research in EARS that generates personalized recommendations based on users' emotional preferences. Similarly, if we fail to fully protect users' emotional data privacy, users could resist engaging with EARS services. This paper introduced a method that detects affective features in subjective passages using the Generative Pre-trained Transformer Technology, forming the basis of the Affective Index and Affective Index Indicator (AII). Eliminate the need for users to build an affective feature detection mechanism. The paper advocates for a separation of responsibility approach where users protect their emotional profile data while EARS service providers refrain from retaining or storing it. Service providers can update users' Affective Indices in memory without saving their privacy data, providing Affective Aware recommendations without compromising user privacy. This paper offers a solution to the subjectivity and variability of emotions, data privacy concerns, and evaluation metrics and benchmarks, paving the way for future EARS research.
With the rapid rate at which networking technologies are changing, there is a need to regularly update network activity datasets to accurately reflect the current state of network infrastructure/traffic. The uniqueness of this work was that this was the first network dataset collected using Zeek and labelled using the MITRE ATT&CK framework. In addition to identifying attack traffic, the MITRE ATT&CK framework allows for the detection of adversary behavior leading to an attack. It can also be used to develop user profiles of groups intending to perform attacks. This paper also outlined how both the cyber range and hadoop’s big data platform were used for creating this network traffic data repository. The data was collected using Security Onion in two formats: Zeek and PCAPs. Mission logs, which contained the MITRE ATT&CK data, were used to label the network attack data. The data was transferred daily from the Security Onion virtual machine running on a cyber range to the big-data platform, Hadoop’s distributed file system. This dataset, UWF-ZeekData22, is publicly available at datasets.uwf.edu.
Recent studies on indoor positioning using Wi-Fi fingerprinting are motivated by the ubiquity of Wi-Fi networks and their promising positioning accuracy. Machine learning algorithms are commonly leveraged in indoor positioning works. The performance of machine learning based solutions are dependent on the availability, volume, quality, and diversity of related data. Several public datasets have been published in order to foster advancements in Wi-Fi based fingerprinting indoor positioning solutions. These datasets, however, lack dual-band Wi-Fi data within symmetric indoor environments. To fill this gap, this research work presents the UTMInDualSymFi dataset, as a source of dual-band Wi-Fi data, acquired within multiple residential buildings with symmetric deployment of access points. UTMInDualSymFi comprises the recorded dual-band raw data, training and test datasets, radio maps and supporting metadata. Additionally, a statistical radio map construction algorithm is presented. Benchmark performance was evaluated by implementing a machine-learning-based positioning algorithm on the dataset. In general, higher accuracy was observed, on the 5 GHz data scenarios. This systematically collected dataset enables the development and validation of future comprehensive solutions, inclusive of novel preprocessing, radio map construction, and positioning algorithms.
There has recently been an increasing interest in Learning Management Systems (LMSs). It is currently unclear, however, exactly how these systems are perceived by their users. This article analyzes data on user acceptance for two LMSs (Blackboard and Canvas). The respective data are collected using a questionnaire modeled after the Technology Acceptance Model (TAM); it relates several variables that influence system acceptability, allowing for a detailed analysis of the system acceptance. We present analyses at two levels of the questionnaire data: questions and constructs (taken from TAM) as well as on different analysis levels using targeted methods. First, we investigate the differences between the above LMSs using statistical tests (t-test). Second, we provide results at the question level using descriptive indices, such as the mean and the Gini heterogeneity index, and apply methods for ordinal data using the Cumulative Link Mixed Model (CLMM). Next, we apply the same approach at the TAM construct level plus descriptive network analysis (degree centrality and bipartite motifs) to explore the variability of users’ answers and the degree of users’ satisfaction considering the extracted patterns. In the context of TAM, the statistical model is able to analyze LMS acceptance on the question level. As we are also very much interested in identifying LMS acceptance at the construct level, in this article, we provide both statistical analysis as well as network analysis to explore the connection between questionnaire data and relational data. A network analysis approach is particularly useful when analyzing LMS acceptance on the construct level, as this can take the structure of the users’ answers across questions per construct into account. Taken together, these results suggest a higher rate of user acceptance among Canvas users compared to Blackboard both for the question and construct level. Likewise, the descriptive network modeling for Canvas indicates a slightly higher concordance between Canvas users than Blackboard at the construct level.
Conservation management heavily relies on accurate species distribution data. However, distributional information for most species is limited to distributional range maps, which could not have enough resolution to take conservation action and know current distribution status. In many cases, distribution maps are difficult to access in proper data formats for analysis and conservation planning of species. In this study, we addressed this issue by developing Species Distribution Models (SDMs) that integrate species presence data from various citizen science initiatives. This allowed us to systematically construct current distribution maps for 1091 bird species across India. To create these SDMs, we used MaxEnt 3.4.4 (Maximum Entropy) as the base for species distribution modelling and combined it with multiple citizen science datasets containing information on species occurrence and 29 environmental variables. Using this method, we were able to estimate species distribution maps at both a national scale and a high spatial resolution of 1 km2. Thus, the results of our study provide species current species distribution maps for 968 bird species found in India. These maps significantly improve our knowledge of the geographic distribution of about 75% of India’s bird species and are essential for addressing spatial knowledge gaps for conservation issues. Additionally, by superimposing the distribution maps of different species, we can locate hotspots for bird diversity and align conservation action.
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 PowerWorldTM 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.
As deep underground digital twins have not yet been established worldwide, this study extracted keywords from national or city-led digital twin practices and elements of digital twins and through these keywords selected research papers and topics that could contribute to the establishment of deep underground digital twins in the future. We applied the concept of digital twins in geology and underground research to collect 1702 papers from the Web of Science and conducted semantic network analysis and topic modeling. The keywords digital, three dimensions, and real time were placed in the middle and have many links in the word network. Artificial intelligence, deep learning, and neural networks all showed a low degree of centrality. As a result of topic modeling using Latent Dirichlet allocation (LDA), topics related to topography, geological structure, and rock distribution, which are the basic data for building a deep underground digital twin, were noted, and topics related to earthquakes/vibrations, landslides, groundwater, and volcanoes were identified. Energy resources and space utilization have emerged as the main themes.
Data sharing is proposed because the issue of data islands hinders advancement of artificial intelligence technology in the 5G era. Sharing high-quality data has a direct impact on how well machine-learning models work, but there will always be misuse and leakage of data. The field of financial technology, or FinTech, has received a lot of attention and is growing quickly. This field has seen the introduction of new terms as a result of its ongoing expansion. One example of such terminology is “FinTech”. This term is used to describe a variety of procedures utilized frequently in the financial technology industry. This study aims to create a cloud-based intrusion detection system based on IoT federated learning architecture as well as smart contract analysis. This study proposes a novel method for detecting intrusions using a cyber-threat federated graphical authentication system and cloud-based smart contracts in FinTech data. Users are required to create a route on a world map as their credentials under this scheme. We had 120 people participate in the evaluation, 60 of whom had a background in finance or FinTech. The simulation was then carried out in Python using a variety of FinTech cyber-attack datasets for accuracy, precision, recall, F-measure, AUC (Area under the ROC Curve), trust value, scalability, and integrity. The proposed technique attained accuracy of 95%, precision of 85%, RMSE of 59%, recall of 68%, F-measure of 83%, AUC of 79%, trust value of 65%, scalability of 91%, and integrity of 83%.
While ChatGPT has gained global significance and widespread adoption, its exploration within specific cultural contexts, particularly within the Arab world, remains relatively limited. This study investigates the discussions among early Arab users in Arabic tweets related to ChatGPT, focusing on topics, sentiments, and the presence of sarcasm. Data analysis and topic-modeling techniques were employed to examine 34,760 Arabic tweets collected using specific keywords. This study revealed a strong interest within the Arabic-speaking community in ChatGPT technology, with prevalent discussions spanning various topics, including controversies, regional relevance, fake content, and sector-specific dialogues. Despite the enthusiasm, concerns regarding ethical risks and negative implications of ChatGPT’s emergence were highlighted, indicating apprehension toward advanced artificial intelligence (AI) technology in language generation. Region-specific discussions underscored the diverse adoption of AI applications and ChatGPT technology. Sentiment analysis of the tweets demonstrated a predominantly neutral sentiment distribution (92.8%), suggesting a focus on objectivity and factuality over emotional expression. The prevalence of neutral sentiments indicated a preference for evidence-based reasoning and logical arguments, fostering constructive discussions influenced by cultural norms. Sarcasm was found in 4% of the tweets, distributed across various topics but not dominating the conversation. This study’s implications include the need for AI developers to address ethical concerns and the importance of educating users about the technology’s ethical considerations and risks. Policymakers should consider the regional relevance and potential scams, emphasizing the necessity for ethical guidelines and regulations.
Computer vision is a new approach to navigation aiding that assists visually impaired people to travel independently. A deep learning-based solution implemented on a portable device that uses a monocular camera to capture public objects could be a low-cost and handy navigation aid. By recognizing public objects in the street and estimating their distance from the user, visually impaired people are able to avoid obstacles in the outdoor environment and walk safely. In this paper, we created a dataset of public objects in an uncontrolled environment for navigation aiding. The dataset contains three classes of objects which commonly exist on pavements in the city. It was verified that the dataset was of high quality for object detection and distance estimation, and was ultimately utilized as a navigation aid solution.
In this work, we share the dataset collected during the Smart Green Homes (SGH) project. The project’s goal was to develop integrated products and technology solutions for households, as well as to improve the standards of comfort and user satisfaction. This was to be achieved while improving household energy efficiency and reducing the usage of gaseous pollutants, in response to the planet’s sustainability issues. One of the tasks executed within the project was the collection of data from volunteers’ homes, including environmental information and the level of comfort as perceived by the volunteers themselves. While used in the original project, the resulting dataset contains valuable information that could not be explored at the time. We now share this dataset with the community, which can be used for various scenarios. These may include heating appliance optimisation, presence detection and environmental prediction.
Among the emerging applications of remote sensing technologies, the remote detection of plastic litter has observed successful applications in recent years. However, while the number of studies and datasets for spectral characterization of plastic is growing, few studies address plastic litter while being submerged in natural seawater in an outdoor context. This study aims to investigate the feasibility of hyperspectral characterization of submerged plastic litter in less-than-ideal conditions. We present a hyperspectral dataset of eight different polymers in field conditions, taken by an unmanned aerial vehicle (UAV) on different days in a three-week period. The measurements were carried out off the coast of Mytilene, Greece. The team collected the dataset using a Bayspec OCI-F push broom sensor from 25 m and 40 m height above the water. For a contextual background, the dataset also contains optical (RGB) high-resolution orthomosaics.
(1) Background: Bioeconomy aims to reduce dependence on non-renewable resources and foster economic growth through the development of new bio-based products and services. Achieving this goal requires social acceptance and stakeholder engagement in the development of sustainable technologies. The objective of this data article is to provide a dataset derived from a survey with a representative sample of 500 citizens over 18 years old based in the Community of Madrid. (2) Methods: We created a questionnaire on the social acceptance of technologies and bio-based products to later gather the responses using a SurveyMonkey panel for the Community of Madrid through an online CAWI survey; (3) Results: A dataset with a total of 82 columns with all responses is the result of this study. (4) Conclusions: This data article provides not only a valuable representative dataset of citizens of the Community of Madrid but also sufficient resources to replicate the same study in other regions.
After the COVID-19 pandemic the use of face masks has become a common practice in many situations. Partial occlusion of the face due to the use of masks poses new challenges for facial expression recognition because of the loss of significant facial information. Consequently, the identification and classification of facial expressions can be negatively affected when using neural networks in particular. This paper presents a new dataset of virtual characters, with and without face masks, with identical geometric information and spatial location. This novelty will certainly allow researchers a better refinement on lost information due to the occlusion of the mask.
Many previous studies have demonstrated that transcutaneous vagus nerve stimulation (VNS) has the potential to exhibit therapeutic effects similar to its invasive counterpart. An objective assessment of VNS requires a reliable biomarker of successful vagal activation. Although many potential biomarkers have been proposed, most studies have focused on heart rate variability (HRV). Despite the physiological rationale for HRV as a biomarker for assessing vagal stimulation, data on its effects on HRV are equivocal. To further advance this field, future studies investigating VNS should contain adequate methodological specifics that make it possible to compare the results between studies, to replicate studies, and to enhance the safety of study participants. This article describes the design and methodology of a randomized study evaluating the effect of short-term noninvasive stimulation of the auricular branch of the vagus nerve on parameters of HRV. Primary records of rhythmograms of all the subjects, as well as a dataset with clinical, instrumental, and laboratory data of all the current study subjects are in the public domain for possible secondary analysis to all interested researchers. The physiological interpretation of the obtained data is not considered in the article.
The RaspberrySet dataset is a valuable resource for those working in the field of agriculture, particularly in the selection and breeding of ecologically adaptable berry cultivars. This is because long-term changes in temperature and weather patterns have made it increasingly important for crops to be able to adapt to their environment. To assess the suitability of different cultivars or to make yield predictions, it is necessary to describe and evaluate berries’ characteristics at various growth stages. This process is typically carried out visually, but it can be time-consuming and labor-intensive, requiring significant expert knowledge. The RaspberrySet dataset was created to assist with this process, and it includes images of raspberry berries at five different stages of development. These stages are flower buds, flowers, unripe berries, and ripe berries. All these stages of raspberry images classified buds, damaged buds, flowers, unripe berries, and ripe berries and were annotated using ground truth ROI and presented in YOLO format. The dataset includes 2039 high-resolution RGB images, with a total of 46,659 annotations provided by experts using Label Studio software (1.7.1). The images were taken in various weather conditions, at different times of the day, and from different angles, and they include fully visible buds, flowers, berries, and partially obscured buds. This dataset is intended to improve the efficiency of berry breeding and yield estimation and to identify the raspberry phenotype more accurately. It may also be useful for breeding other fruit crops, as it allows for the reliable detection and phenotyping of yield components at different stages of development. By providing a homogenized dataset of images taken on-site at the Institute of Horticulture in Dobele, Latvia, the RaspberrySet dataset offers a valuable resource for those working in horticulture.
Ferroptosis is a unique variety of non-apoptotic cell death, driven by massive lipid oxidation in an iron-dependent manner. Since ferroptosis was introduced as a concept in 2012, it has demonstrated its essential role in the pathogenesis in neurodegenerative diseases and an important role in therapy-resistant cancer cells. Thus, detailed molecular understanding of both canonical and alternative ferroptosis pathways is required. There is a set of widely used chemical agents to modulate ferroptosis using different pathway targets: erastin blocks cystine–glutamate antiporter, system xc-; ML210 directly inactivates GPX4; and L-buthionine sulfoximine (BSO) inhibits γ-glutamylcysteine synthetase, an essential enzyme for glutathione synthesis de novo. Most studies have focused on the lipidomic profiling of model systems undergoing death in a ferroptotic modality. In this study, we developed high-quality shotgun proteome sequencing during ferroptosis induction by three widely used chemical agents (erastin, ML210, and BSO) before and after 24 and 48 h of treatment. Chromato-mass spectra were registered in DDA mode and are suitable for further label-free quantification. Both processed and raw files are publicly available and could be a valuable dynamic proteome map for further ferroptosis investigation.
Irreversible vision loss is a worldwide threat. Developing a computer-aided diagnosis system to detect retinal fundus diseases is extremely useful and serviceable to ophthalmologists. Early detection, diagnosis, and correct treatment could save the eye's vision. Nevertheless, an eye may be afflicted with several diseases if proper care is not taken. A single retinal fundus image might be linked to one or more diseases. Age-related macular degeneration, cataracts, diabetic retinopathy, Glaucoma, and uncorrected refractive errors are the leading causes of visual impairment. Our research team at the center of excellence lab has generated a new dataset called the Retinal Fundus Multi-Disease Image Dataset 2.0 (RFMiD2.0). This dataset includes around 860 retinal fundus images, annotated by three eye specialists, and is a multiclass, multilabel dataset. We gathered images from a research facility in Jalna and Nanded, where patients across Maharashtra come for preventative and therapeutic eye care. Our dataset would be the second publicly available dataset consisting of the most frequent diseases, along with some rarely identified diseases. This dataset is auxiliary to the previously published RFMiD dataset. This dataset would be significant for the research and development of artificial intelligence in ophthalmology.
The promised dataset was obtained from daily Turkish words and phrases pronounced by various people in videos posted on YouTube. The purpose of compiling the dataset was to provide a method for the detection of the spoken word by recognizing patterns or classifying lip movements with supervised, unsupervised, and semi-supervised learning, and machine learning algorithms. Most of the datasets related to lip reading consist of people recorded on camera with fixed backgrounds and the same conditions, but the dataset presented here consists of images compatible with machine learning models developed for real-life challenges. It contains a total of 2335 instances taken from TV series, movies, vlogs, and song clips on YouTube. The images in the dataset vary due to factors such as the way people say words, accents, speaking rate, gender, and age. Furthermore, the instances in the dataset consist of videos with different angles, shadows, resolution, and brightness that are not created manually. The most important feature of our lip reading dataset is that we contribute to the non-synthetic Turkish dataset pool, which does not have wide dataset varieties. Machine learning studies can be carried out in many areas, such as education, security, and social life with this dataset.
