Dataset Of Users Research Articles

The Dark Five: A reconsideration of the Short Dark Tetrad (SD4)

The measurement of ‘dark’ personality traits has been shown to be susceptible to error. In particular, the Dark Tetrad is theorized to fit poorly to the 4-factor structure within the SD4 (Paulhus et al., 2021). The present study examined the structural validity of the SD4 by using a dataset of internet users (n = 604). A robust approach to confirmatory factor analysis revealed that the 4-factor model proposed by Paulhus et al. (2021) did not appropriately reflect the data. Alternatively, an exploratory 5-factor model (The Dark Five; including coalition-building, grandiose exhibitionism, psychopathy, violent voyeurism, and indirect sadism) resulted in better statistical fit than the 4-factor model. Moreover, the Dark Five allowed the successful discrimination of psychopathy and sadism and explained more variance in attachment orientation than had the 4-factor model. Future research using the SD4 to measure ‘dark’ personalities or to explore differences between psychopathy and sadism may therefore benefit by testing the 5-factor model or opting for a different measure of the Dark Tetrad.

Preliminary study on the feasibility of approximating children's engagement level from their emotions estimation by a picture-based, three-model AI in a family-robot cohabitation scenario

Haru4Kids (H4K) is a system that emulates the physical, social, family-oriented robot Haru, designed with the goal of cohabitating with children in their homes for extended periods of time. In a previous experiment [Garcia GA, Perez G, Levinson L, et al. Living with Haru4Kids: study on children's activity and engagement in a family-robot cohabitation scenario. In: 2023 IEEE ROMAN; 2023 Aug. p. 1428–1435], seven families kept H4K for a span of two weeks in their homes. Throughout this period of cohabitation, we collected child-robot interaction data, including images that were later hand-annotated to estimate user engagement. In this present work, we used a novel AI-based, four-stage framework available from Roboflow for the automatic estimation of children's level of engagement from their inferred emotions. We did a deep study of the performance and behaviour of that framework over our dataset of users' pictures and characterized its response in order to understand its advantages and limitations, including the technique used to translate emotions into engagement levels. We also tested a different approach for that mapping, using a machine learning technique based on Support Vector Machine (SVM). The framework yielded promising results just ‘off-the-shelf’: 0.47–0.68 accuracy and 0.46–0.70 F1 using the original mapping, and 0.39–0.75 and 0.37–0.78 respectively using SVM. Therefore, we propose this emotion-based approach for engagement estimation from pictures.

What Factors Revitalize the Street Vitality of Old Cities? A Case Study in Nanjing, China

Urban street vitality has been a perennial focus within the domain of urban planning. This study examined spatial patterns of street vitality in the old city of Nanjing during working days and weekends using real-time user datasets (RTUDs). A spatial autoregressive model (SAM) and a multiscale geographically weighted regression (MGWR) model were employed to quantitatively assess the impact of various factors on street vitality and their spatial heterogeneity. This study revealed the following: (1) the distribution of street vitality in the old city of Nanjing exhibited a structure centered around Xinjiekou, with greater regularity and predictability in street vitality on working days than on weekends; (2) eight variables, such as traffic location, road density, and functional density, are positively associated with street vitality, whereas the green view index is negatively associated with street vitality, and commercial location benefits street vitality at weekends but detracts from street vitality on working days; and (3) the influence of variables such as traffic location and functional density on street vitality is contingent on their spatial position. Based on these results, this study provides new strategies to enhance the street vitality of old cities.

Unfolding the downloads of datasets: A multifaceted exploration of influencing factors

Scientific data are essential to advancing scientific knowledge and are increasingly valued as scholarly output. Understanding what drives dataset downloads is crucial for their effective dissemination and reuse. Our study, analysing 55,473 datasets from 69 data repositories, identifies key factors driving dataset downloads, focusing on interpretability, reliability, and accessibility. We find that while lengthy descriptive texts can deter users due to complexity and time requirements, readability boosts a dataset’s appeal. Reliability, evidenced by factors like institutional reputation and citation counts of related papers, also significantly increases a dataset’s attractiveness and usage. Additionally, our research shows that open access to datasets increases their downloads and amplifies the importance of interpretability and reliability. This indicates that easy access enhances the overall attractiveness and usage of datasets in the scholarly community. By emphasizing interpretability, reliability, and accessibility, this study offers a comprehensive framework for future research and guides data management practices toward ensuring clarity, credibility, and open access to maximize the impact of scientific datasets.

Automatic depression prediction via cross-modal attention-based multi-modal fusion in social networks

Depression has become a worldwide public health concern in recent years. The online social network can be served as a tool to predict latent depressed users. However, existing studies primarily use machine learning or deep learning methods on multi-modal information with simple concatenation, resulting in unsatisfactory prediction. Additionally, the lack of interpretability in extracted features is a challenge. Furthermore, the majority of these studies primarily concentrate on English social networks. To address these issues, we propose a novel unified multi-tasking training model named CrossAMF (Cross-modal Attention-based Multi-modal Fusion). CrossAMF extracts crucial multi-modal features with explainability and effectively fuses representations of different modalities. Our approach employs a sentiment filtering-based XLNet-CNN-BiGRU model and Reduce-VGGNet to extract textual and image features, respectively. We also introduce innovative user behavior metrics previously unexplored. We collect a dataset of Chinese depressed users from Sina microblog with their texts and images, including 5692 depressed users and 9121 normal users. Experimental results demonstrate the significance of each modality to our task, with the combination yielding the best performance. CrossAMF outperforms other state-of-the-art methods with 0.9097 in accuracy and 0.8915 in F1, showing the potential of application in screening out early depression automatically.

Fraud Detection of the Electricity Consumption by combining Deep Learning and Statistical Methods

An important issue for the electricity distribution companies is the non-technical loss (NTL), also known as electricity fraud. This issue has a significant impact on the economies of all countries in the world. In this context, we studied the problem of the imbalance between the electrical energy invoiced and the electrical energy supplied within the Algerian economic sector. This article presents an approach to detecting electrical fraud using a combination of Long Short-Term Memory (LSTM) and robust Exponential and Holt-Winters Smoothing (EHWS) methods in order to enhance the accuracy and efficacy of fraud detection mechanisms. The proposed approach investigates the fraudulent behaviour of electricity consumers and unfolds in several key phases. In the first step, monthly consumption forecasts are made and the model with the most accurate results is selected. Then, the phase of detection of anomalies in economic meters and detection of cases of fraud by economic customers begins. This phase relies on the robust exponential and Holt-Winters Smoothing methods for uncovering irregular patterns indicative of potential fraud instances. The proposed model was trained and evaluated, and several experiments were carried out using a large dataset of real users from the economic sector. The dataset comprised approximately 2,000 customers and encompassed 14 years of monthly electricity usage in Bejaia, Algeria. The results of the experiments demonstrate promising performance, underscoring the efficacy of our proposed solution in effectively detecting instances of fraud, leading us to conclude that this proposition is robust and can help improve the accuracy of locating abnormal consumer behaviour and increase the company's profits.

Applying Multi-Armed Bandit algorithms for music recommendations at Spotify

This study explores the application of multi-armed bandit algorithms in enhancing music recommendation systems, with a focus on Spotify. It delves into the Explore-Then-Commit (ETC), Upper Confidence Bound (UCB), and Thompson Sampling (TS) algorithms, evaluating their efficacy within the Spotify context. The primary objective is to determine which algorithm optimally balances exploration and exploitation to maximize user satisfaction and engagement. The research reveals that the ETC algorithm, with its rigid exploration and exploitation phases, incurs a notably higher regret value. This rigidity can lead to missed opportunities in identifying optimal choices and hinder adaptability to evolving user preferences. Conversely, the UCB and TS algorithms exhibit remarkable adaptability and a flexible balance between exploration and exploitation. This flexibility translates into more personalized and satisfactory user experiences in music recommendations. However, the selection of the most appropriate algorithm should be contingent on the size and characteristics of the specific user dataset, as well as the fine-tuning of algorithm parameters to align with user preferences and behaviors.

GazeIntent: Adapting Dwell-time Selection in VR Interaction with Real-time Intent Modeling

The use of ML models to predict a user's cognitive state from behavioral data has been studied for various applications which includes predicting the intent to perform selections in VR. We developed a novel technique that uses gaze-based intent models to adapt dwell-time thresholds to aid gaze-only selection. A dataset of users performing selection in arithmetic tasks was used to develop intent prediction models (F1 = 0.94). We developed GazeIntent to adapt selection dwell times based on intent model outputs and conducted an end-user study with returning and new users performing additional tasks with varied selection frequencies. Personalized models for returning users effectively accounted for prior experience and were preferred by 63% of users. Our work provides the field with methods to adapt dwell-based selection to users, account for experience over time, and consider tasks that vary by selection frequency.

R/The_Donald Had a Forum: How Socialization in Far-Right Social Media Communities Shapes Identity and Spreads Extreme Rhetoric

Can engagement with far-right social media communities socialize users into a new political identity? This study addresses concerns about the spread of far-right groups on mainstream social media platforms by examining how newcomers are affected by their engagement with these groups. I theorize that changes in social identity expression indicative of socialization will be measurable in the language users use to express themselves on social media, and that the magnitude of this linguistic change will intensify with more frequent far-right engagement. I develop a custom dictionary of far-right-relevant terms sourced from communities like The Daily Stormer and Stormfront. Using an original dataset of Reddit user posting histories from 2015–2017, I test for increases in the frequency of this far-right vocabulary. I find that users who engage often with a far-right community like r/The_Donald begin to sound more like white nationalists within three months. Socialized users also use far-right vocabulary more frequently in other spaces on their platform, contributing to the spread and normalization of far-right rhetoric.

Leveraging datasets for effective mitigation of DDoS attacks in software-defined networking: significance and challenges

Software-Defined Networking (SDN) has emerged as a transformative paradigm for network management, offering centralized control and programmability. However, with the proliferation of Distributed Denial of Service (DDoS) attacks that pose significant threats to network infrastructures, effective mitigation strategies are needed. The subject matter of this study is to explore the importance of datasets in the mitigation of DDoS attacks in SDN environments. The paper discusses the significance of datasets for training machine learning models, evaluating detection mechanisms, and enhancing the resilience of SDN-based defense systems. Goal of the paper is to assist researchers in effectively selecting and usage of datasets for DDoS mitigation in SDN, thereby maximizing benefits and overcoming challenges involved in dataset selection. This paper outlines the challenges associated with dataset collection, labeling, and management, along with potential solutions to address these challenges. Effective detection and mitigation of DDoS attacks in SDN require robust datasets that capture the diverse and evolving nature of attack scenarios. Characterization of tasks for each section is as follows: Importance of datasets in DDoS attack mitigation in SDN, challenges in dataset utilization in DDoS mitigation in SDN, Guidelines for dataset selection, comparison of datasets used and their results and different dataset usage according to the need. Methodology involves collecting results in tabular form based on prior research to analyze the characteristics of existing datasets, techniques for dataset augmentation and enhancement, and evaluating the effectiveness of different datasets in detecting and mitigating DDoS attacks through comprehensive experimentation. Results of our findings indicate that effective detection and mitigation of DDoS attacks in SDN require robust datasets that capture the diverse and evolving nature of attack scenarios. Our findings provide valuable insights into the importance of datasets in enhancing the resilience of SDN infrastructures against DDoS attacks. In conclusion, our findings provide valuable insights into the importance of datasets in enhancing the resilience of SDN infrastructures against DDoS attacks and highlight the need for further research in this critical area. Thorough guidelines for dataset selection and impacts of different datasets used in recent studies, provide research challenges and future directions in this area.

Towards facilitated interpretation of shotgun metagenomics long-read sequencing data analyzed with KMA for the detection of bacterial pathogens and their antimicrobial resistance genes.

Metagenomic sequencing is a promising method that has the potential to revolutionize the world of pathogen detection and antimicrobial resistance (AMR) surveillance in food-producing environments. However, the analysis of the huge amount of data obtained requires performant bioinformatics tools and databases, with intuitive and straightforward interpretation. In this study, based on long-read metagenomics data of chicken fecal samples with a spike-in mock community, we proposed confidence levels for taxonomic identification and AMR gene detection, with interpretation guidelines, to help with the analysis of the output data generated by KMA, a popular k-mer read alignment tool. Additionally, we demonstrated that the completeness and diversity of the genomes present in the reference databases are key parameters for accurate and easy interpretation of the sequencing data. Finally, we explored whether KMA, in a two-step procedure, can be used to link the detected AMR genes to their bacterial host chromosome, both detected within the same long-reads. The confidence levels were successfully tested on 28 metagenomics datasets which were obtained with sequencing of real and spiked samples from fecal (chicken, pig, and buffalo) or food (minced beef and food enzyme products) origin. The methodology proposed in this study will facilitate the analysis of metagenomics sequencing datasets for KMA users. Ultimately, this will contribute to improvements in the rapid diagnosis and surveillance of pathogens and AMR genes in food-producing environments, as prioritized by the EU.

An autonomous blockchain‐based computational broker for e‐science

AbstractBlockchain infrastructures have emerged as a disruptive technology and have led to the realization of cryptocurrencies (peer‐to‐peer payment systems) and smart contracts. They can have a wide range of application areas in e‐Science due to their open, public nature and global accessability in a trustless manner. We propose and implement a smart contract called eBlocBroker, which is an autonomous blockchain‐based middleware system for volunteer computing and providing data resources for e‐Science. The eBlocBroker infrastructure connects requesters who need to combine applications (jobs) with datasets and run them via an Ethereum‐based private blockchain network (Bloxberg) on providers that utilize computational and data resources on clouds or home servers. It uses cloud storage, such as B2DROP, IPFS, or Google Drive, to store and transfer data between requesters and providers. Each provider utilizes the Slurm workload manager to execute jobs submitted through eBlocBroker. In this paper, we demonstrate how an autonomous organization programmed as a smart contract can be used to deploy a marketplace that supports data and computation‐intensive research projects. We propose a cost model implemented as a function in the smart contract which calculates and records computation and dataset usage costs. We develop a Python‐based system to communicate with eBlocBroker and orchestrate jobs' execution on the provider's end. We present eBlocBroker's features, infrastructure, implementation, algorithms and experimental results.

Algorithm-Based Data Generation (ADG) Engine for Dual-Mode User Behavioral Data Analytics

The increasing significance of data analytics in modern information analysis is underpinned by vast amounts of user data. However, it is only feasible to amass sufficient data for various tasks in specific data-gathering contexts that either have limited security information or are associated with older applications. There are numerous scenarios where a domain is too new, too specialized, too secure, or data are too sparsely available to adequately support data analytics endeavors. In such cases, synthetic data generation becomes necessary to facilitate further analysis. To address this challenge, we have developed an Algorithm-based Data Generation (ADG) Engine that enables data generation without the need for initial data, relying instead on user behavior patterns, including both normal and abnormal behavior. The ADG Engine uses a structured database system to keep track of users across different types of activity. It then uses all of this information to make the generated data as real as possible. Our efforts are particularly focused on data analytics, achieved by generating abnormalities within the data and allowing users to customize the generation of normal and abnormal data ratios. In situations where obtaining additional data through conventional means would be impractical or impossible, especially in the case of specific characteristics like anomaly percentages, algorithmically generated datasets provide a viable alternative. In this paper, we introduce the ADG Engine, which can create coherent datasets for multiple users engaged in different activities and across various platforms, entirely from scratch. The ADG Engine incorporates normal and abnormal ratios within each data platform through the application of core algorithms for time-based and numeric-based anomaly generation. The resulting abnormal percentage is compared against the expected values and ranges from 0.13 to 0.17 abnormal data instances in each column. Along with the normal/abnormal ratio, the results strongly suggest that the ADG Engine has successfully completed its primary task.

Minimum labelling requirements for dermatology artificial intelligence-based Software as Medical Device (SaMD): A consensus statement.

Artificial intelligence (AI) holds remarkable potential to improve care delivery in dermatology. End users (health professionals and general public) of AI-based Software as Medical Devices (SaMD) require relevant labelling information to ensure that these devices can be used appropriately. Currently, there are no clear minimum labelling requirements for dermatology AI-based SaMDs. Common labelling recommendations for AI-based SaMD identified in a recent literature review were evaluated by an Australian expert panel in digital health and dermatology via a modified Delphi consensus process. A nine-point Likert scale was used to indicate importance of 10 items, and voting was conducted to determine the specific characteristics to include for some items. Consensus was achieved when more than 75% of the experts agreed that inclusion of information was necessary. There was robust consensus supporting inclusion of all proposed items as minimum labelling requirements; indication for use, intended user, training and test data sets, algorithm design, image processing techniques, clinical validation, performance metrics, limitations, updates and adverse events. Nearly all suggested characteristics of the labelling items received endorsement, except for some characteristics related to performance metrics. Moreover, there was consensus that uniform labelling criteria should apply across all AI categories and risk classes set out by the Therapeutic Goods Administration. This study provides critical evidence for setting labelling standards by the Therapeutic Goods Administration to safeguard patients, health professionals, consumers, industry, and regulatory bodies from AI-based dermatology SaMDs that do not currently provide adequate information about how they were developed and tested.

Covid-19 Vaccination: An Attitude Analysis of Global Users of Social Media Towards Government Communication

Amidst a global pandemic, the key challenge before governments, health institutions and administrative authorities is to communicate and inform the general public about the never-heard of morbidity, virology and immunity in their simplest form and language. However, this can only be possible when they can appropriately predict the perceptions and reactions of public to a given set of communications regarding the disease, preventive measures and the adoption of established principles of users’ perceptions. This article is a study of the users’ perceptions about Covid-19 vaccination. It conducts sentiment analysis in Python on a dataset of global users of the social media channel Twitter. The dataset available at kaggle.com, comprising 51,393 tweets from December 2020 to February 2021 with more than fifteen features, was put to test. The majority of the people (60.8%) expressed their neutral sentiments towards vaccination, while 23.9% had a positive opinion. Further, in order to evaluate the aforementioned analysis, the machine learning pipeline process of model evaluation is also performed. This process includes a split of dataset into training and testing, followed by determining various evaluation parameters including confusion matrix, precision, recall and F1-score. The accuracy of 97.1% depicts the outperformance of the model.

Ensemble methods and semi-supervised learning for information fusion: A review and future research directions

Advances over the past decade at the intersection of information fusion methods and Semi-Supervised Learning (SSL) are investigated in this paper that grapple with challenges related to limited labelled data. To do so, a bibliographic review of papers published since 2013 is presented, in which ensemble methods are combined with new machine learning algorithms. A total of 128 new proposals using SSL algorithms for ensemble construction are identified and classified. All the methods are categorised by approach, ensemble type, and base classifier. Experimental protocols, pre-processing, dataset usage, unlabelled ratios, and statistical tests are also assessed, underlining the major trends, and some shortcomings of particular studies. It is evident from this literature review that foundational algorithms such as self-training and co-training are influencing current developments, and that innovative ensemble techniques are continuing to emerge. Additionally, valuable guidelines are identified in the review for improving research into intrinsically semi-supervised and unsupervised pre-processing methods, especially for regression tasks.

What matters in reinforcement learning for tractography

Recently, deep reinforcement learning (RL) has been proposed to learn the tractography procedure and train agents to reconstruct the structure of the white matter without manually curated reference streamlines. While the performances reported were competitive, the proposed framework is complex, and little is still known about the role and impact of its multiple parts. In this work, we thoroughly explore the different components of the proposed framework, such as the choice of the RL algorithm, seeding strategy, the input signal and reward function, and shed light on their impact. Approximately 7,400 models were trained for this work, totalling nearly 41,000 h of GPU time. Our goal is to guide researchers eager to explore the possibilities of deep RL for tractography by exposing what works and what does not work with the category of approach. As such, we ultimately propose a series of recommendations concerning the choice of RL algorithm, the input to the agents, the reward function and more to help future work using reinforcement learning for tractography. We also release the open source codebase, trained models, and datasets for users and researchers wanting to explore reinforcement learning for tractography.

Problem Spaces and Algorithms in Data Mining

Data mining has been described severally as the best thing to have happened to data and information management, especially these days that the cost of computing technologies and storage media are falling, data gathering tools becoming varied and very efficient and the boom in network computing becoming very rewarding. The challenges presented by the management and meaningful usage of large data sets have stimulated so much research in data mining. Consequently, the birth of a number of algorithms to provide insights to these big data has equally presented more complications in information processing computing. Therefore, this paper presents different problem spaces in data mining, available algorithms to mine these data and then mapping specific algorithms to specific problem spaces. Analysis of datasets from a typical financial institution suggests that no one algorithm is necessarily better than the other, but all have strengths and weaknesses depending on the particular problem spaces in use.

The institutional environment and gig platform transaction cost solutions

Abstract A growing number of labour market participants transact through gig platforms. This choice should reflect a reduction in transaction costs for platform users, compared to costs they meet when using alternative modes of governance. We exploit a unique cross-platform, cross-country data set of gig platform users to test the impact of the institutional environment in one of its dimensions – the strictness of labour market regulation (LMR) – on the ability of gig platforms to reduce users' transaction costs. According to our findings, the regulation indicator of both the user and platform countries influences transaction costs for platform users, even controlling for platform and user characteristics. The platform appears to reduce transaction costs most when users face stricter or weaker LMR, in a U-shaped effect. In the former case, the platform may provide an escape from labour regulations when hiring for tasks, while in the latter case, the platform can economize on the usual transaction costs of private contracting by administrating some types of users' activities.

Rural-urban variation in COVID-19 vaccination uptake in Aotearoa New Zealand: Examining the national roll-out.

This study aimed to understand rural-urban differences in the uptake of COVID-19 vaccinations during the peak period of the national vaccination roll-out in Aotearoa New Zealand (NZ). Using a linked national dataset of health service users aged 12+ years and COVID-19 immunization records, age-standardized rates of vaccination uptake were calculated at fortnightly intervals, between June and December 2021, by rurality, ethnicity, and region. Rate ratios were calculated for each rurality category with the most urban areas (U1) used as the reference. Overall, rural vaccination rates lagged behind urban rates, despite early rapid rural uptake. By December 2021, a rural-urban gradient developed, with age-standardized coverage for R3 areas (most rural) at 77%, R2 81%, R1 83%, U2 85%, and U1 (most urban) 89%. Age-based assessments illustrate the rural-urban vaccination uptake gap was widest for those aged 12-44years, with older people (65+) having broadly consistent levels of uptake regardless of rurality. Variations from national trends are observable by ethnicity. Early in the roll-out, Indigenous Māori residing in R3 areas had a higher uptake than Māori in U1, and Pacific peoples in R1 had a higher uptake than those in U1. The extent of differences in rural-urban vaccine uptake also varied by region.