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Human-centricity is the core value behind the evolution of manufacturing towards Industry 5.0. Nevertheless, there is a lack of architecture that considers safety, trustworthiness, and human-centricity at its core. Therefore, we propose an architecture that integrates Artificial Intelligence (Active Learning, Forecasting, Explainable Artificial Intelligence), simulated reality, decision-making, and users' feedback, focussing on synergies between humans and machines. Furthermore, we align the proposed architecture with the Big Data Value Association Reference Architecture Model. Finally, we validate it on three use cases from real-world case studies. [ABSTRACT FROM AUTHOR]

We have a simple thesis: the relationship between academic and industry-based cognitive science is broken, but can be fixed. Over the last few decades, there has been a huge increase in the representation of cognitive science in industry. Beyond just machine learning, businesses are increasingly interested in human behavior and cognitive processes. Large proportions of our Ph.D. students, post-docs, and even faculty choose to go through a largely one-way door to corporate jobs in data science, behavioral experimentation, machine learning, user experience, and elsewhere. Currently, people who choose industry careers often lose their social and intellectual networks and their ability to return to tenure-track positions. Valuable insights from industry about memory, decision-making, learning, emotion, distributed cognition, and much more never return to the academic community. We believe that deep, theory driven, theory building work is being done in industry settings–and that the rift between communities makes all our work less effective

Neuroimaging, especially through Functional Magnetic Resonance Imaging (fMRI), plays a pivotal role in understanding brain activity by leveraging blood-oxygen level dependent (BOLD) signals to estimate neural activities across the brain. The interpretation of these signals through functional connectivity (FC) matrices facilitates the application of Graph Neural Networks (GNN) for analyzing brain network structures, offering insights into both normal and abnormal brain functions. Despite the potential of centralized learning methods in this domain, challenges related to data privacy and the feasibility of sharing sensitive medical datasets across institutions limit their application. This study introduces the Federated Graph Learning Framework for Brain Connectome (FGLBC), addressing these concerns. This novel approach enables the collaborative training of GNN models across multiple entities, such as hospitals, without compromising data privacy. The FGLBC framework implements a privacy-preserving local GNN training (PPGT) algorithm that incorporates Differential Privacy (DP) to safeguard sensitive information during model training. Furthermore, we introduce a unique similarity-weighted aggregation (SWA) algorithm that enhances the aggregation process, thereby boosting the global model's utility and performance. Our comprehensive evaluation across benchmark datasets demonstrates that the FGLBC not only preserves user privacy but also achieves or surpasses the performance of existing methods.

Children learn powerful internal models of the world around them from a few years of egocentric visual experience. Can such internal models be learned from a child's visual experience with highly generic learning algorithms or do they require strong inductive biases? Recent advances in collecting large-scale, longitudinal, developmentally realistic video datasets and generic self-supervised learning (SSL) algorithms are allowing us to begin to tackle this nature vs. nurture question. However, existing work typically focuses on image-based SSL algorithms and visual capabilities that can be learned from static images (e.g. object recognition), thus ignoring temporal aspects of the world. To close this gap, here we train self-supervised video models on longitudinal, egocentric headcam recordings collected from a child over a two year period in their early development (6-31 months). The resulting models are highly effective at facilitating the learning of action concepts from a small number of labeled examples; they have favorable data size scaling properties; and they display emergent video interpolation capabilities. Video models also learn more robust object representations than image-based models trained with the exact same data. These results suggest that important temporal aspects of a child's internal model of the world may be learnable from their visual experience using highly generic learning algorithms and without strong inductive biases.

What factors contribute to the growth of children's early vocabulary? One method for exploring this question is investigating predictors (e.g., frequency) that differentiate words learnt earlier from those learnt later. A more comprehensive account requires the examination of multiple language families and multiple levels of linguistic representation (e.g., phonological, morphosyntactic, semantic). Here, we studied 10 predictors of word ages of acquisition across 27 languages and dialects. We found that words that were more frequent, concrete, and associated with babies were learnt earlier, whereas words that had greater length in phonemes and mean length of utterance were learnt later. There was no reliable effect of other morphosyntactic predictors, or of phonological neighbourhood. We found evidence of a tradeoff between a language's morphological complexity and the effect of syntactic complexity for predicates, supporting the competition model. Predictor coefficients revealed broad consistency across all languages, along with variability that reflected language family classifications.

Dependency length minimization is a universally observed quantitative property of natural languages. However, the extent of dependency length minimization, and the cognitive mechanisms through which the language processor achieves this minimization remain unclear. This research offers mechanistic insights by postulating that moving a short preverbal constituent next to the main verb explains preverbal constituent ordering decisions better than global minimization of dependency length in SOV languages. This approach constitutes a least-effort strategy because it's just one operation but simultaneously reduces the length of all preverbal dependencies linked to the main verb. We corroborate this strategy using large-scale corpus evidence across all seven SOV languages that are prominently represented in the Universal Dependency Treebank. These findings align with the concept of bounded rationality, where decision-making is influenced by `quick-yet-economical' heuristics rather than exhaustive searches for optimal solutions. Overall, this work sheds light on the role of bounded rationality in linguistic decision-making and language evolution.

Both prediction and working memory constraints have been established as key factors in characterizing incremental sentence processing difficulty. Here we investigate the less explored question: Whether and how predictive expectation and working memory interact with each other using data from naturalistic reading time corpora. We provide broad-coverage evaluations of two hypotheses that make divergent predictions regarding the interaction of expectation and memory constraints: the Information Locality and Prediction Maintenance hypotheses. We first confirmed the predictions of both expectation- and working memory-based theories. Regarding their interactions, we find support the Information Locality hypothesis: Strong mutual predictability can enhance locality effects. We argue that future theory building in sentence processing should therefore take into consideration both prediction and memory constraints, as well as their potential interaction.

Predicting Click-Through Rate (CTR) is crucial in product and content recommendation, as it involves estimating the likelihood of a user engaging with a specific advertisement or content link. This task encompasses understanding the complex cognitive processes behind human interactions with recommended content. Learning varied feature embeddings that reflect different cognitive responses in various circumstances is significantly important. However, traditional methods typically learn fixed feature representations, leading to suboptimal performance. Some recent approaches attempt to address this issue by learning bit-wise weights or augmented embeddings for feature representations, but suffer from uninformative or redundant features in the context. To tackle this problem, inspired by the Global Workspace Theory in conscious processing, which posits that only a specific subset of the product features are pertinent while the rest can be noisy and even detrimental to human-click behaviors, we propose a CTR model that enables Dynamic Embedding Learning with Truncated Conscious Attention for CTR prediction, termed DELTA. DELTA contains two key components:(I) conscious truncation module (CTM), which utilizes curriculum learning to apply adaptive truncation on attention weights to select the most critical feature in the context;(II) explicit embedding optimization (EEO), which applies an auxiliary task during training that directly and independently propagates the gradient from the loss layer to the embedding layer, thereby optimizing the embedding explicitly via linear feature crossing. Extensive experiments on five challenging CTR datasets demonstrate that DELTA achieves new state-of-the-art performance among current CTR methods.

Language is fundamental in human cognition and communication, helping us to encode the world around us. Adjectives represent a linguistic form used extensively, particularly in the social domain. Adjectives vary in both their valence and their breadth (e.g., “punctual” is narrower than “dependable”). Variations in adjectival breadth have not been studied extensively but may have significant consequences across various domains. The present study explores how subtle distinctions in adjective use may relate to descriptions of experiences that people share. To assess linguistic breadth in online communication, we examine whether online reviews with different star ratings are associated with differences in adjectival breadth. Through an analysis of over 200,000 reviews from Amazon digital music (Study 1) and Yelp restaurants (Study 2), we find evidence that linguistic desirability and breadth of adjectives in reviews positively correlate with their ratings. Specifically, higher-rated reviews tend to use broader and more desirable adjectives. However, this relationship varies between product categories, with high-rated music reviews showing increased linguistic breadth and desirability, while top-rated restaurant reviews demonstrate a decrease in breadth. This paper contributes to understanding linguistic breadth in social media contexts, highlighting how subtle language variations in evaluations can reflect different cognitive and communicative processes.

Previous work has shown that isolated non-canonical sentences with Object-before-Subject (OSV) order are initially harder to process than their canonical counterparts with Subject-before-Object (SOV) order. Although this difficulty diminishes with appropriate discourse context, the underlying cognitive factors responsible for alleviating processing challenges in OSV sentences remain a question. In this work, we test the hypothesis that dependency length minimization is a significant predictor of non-canonical (OSV) syntactic choices, especially when controlling for information status such as givenness and surprisal measures. We extract sentences from the Hindi-Urdu Treebank corpus (HUTB) that contain clearly-defined subjects and objects, systematically permute the preverbal constituents of those sentences, and deploy a classifier to distinguish between original corpus sentences and artificially generated alternatives. The classifier leverages various discourse-based and cognitive features, including dependency length, surprisal, and information status, to inform its predictions. Our results suggest that, although there exists a preference for minimizing dependency length in non-canonical corpus sentences amidst the generated variants, this factor does not significantly contribute in identifying corpus sentences above and beyond surprisal and givenness measures. Notably, discourse predictability emerges as the primary determinant of constituent-order preferences. These findings are further supported by human evaluations involving 44 native Hindi speakers. Overall, this work sheds light on the role of expectation adaptation in word-ordering decisions. We conclude by situating our results within the theories of discourse production and information locality.

Extensive instruction tuning of large language models(LLMs) has proven to be a powerful technique, extending the outstanding performance of general LLMs to new tasks. Consequently, the integration of state-of-the-art(SOTA) general open-source models with specific domains leverages instruction tuning to unlock the emerging capabilities of large language models(LLMs) in those domains. Current practices in instruction tuning often rely on expanding the size of the dataset without a clear strategy to ensure data quality. This can inadvertently introduce noise and degrade model performance. Furthermore, there is currently no unified approach for the quantity, quality, and diversity of instruction tuning data, as well as the methods for instruction tuning. As a result, this severely hampers the practicality and universality of instruction tuning.\\ Addressing these issues, we propose a \textbf{UNI}fied \textbf{F}ramework for \textbf{I}nstruction \textbf{T}uning(\textbf{UNIFIT}), namely Concept Tree generation for instruction tuning data, instruction following difficulty selecting data for high-quality instruction tuning, and the incorporation of random noise embeddings(NE) to enhance model performance during tuning. Through experiments involving multiple models, domains, and orders of magnitude, our proposed instruction tuning framework not only enhances the diversity of instruction tuning data but also achieves a remarkable 60\% reduction in training time consumption, with a mere 6\% of all instruction tuning data, surpassing the performance of using all instruction tuning data by 11\%. This universally applicable instruction tuning framework signifies a substantial advancement in the generality of large language model instruction tuning, marking a revolutionary leap forward and promising efficiency gains while being resource-conscious.

In recent years, with the widespread popularity of location-based social network platforms, the data generated by users on social networks has grown exponentially. There has been a growing focus on the problem of POI (Point-of-Interest) recommendations. Unlike traditional sequence recommendation that primarily considers the temporal dimension, POI recommendation needs to account for the influence of geographical information to a large extent. However, previous works in the graph construction process often only consider the places users have visited, neglecting those they haven't been to. To address this, we propose a Dual Weighted Graph Convolutional Network for POI recommendation called DualPOI. Specifically, we first leverage graph neural networks and attention mechanisms to capture users' local trajectory preferences for visited POIs. A delicately designed spatiotemporal encoder is conducted to model users' local spatiotemporal preferences. Subsequently, using a dual graph convolutional approach, we transfer the user's local preference information to a global scope, thereby modeling novel preferences for unvisited locations. Extensive experiments on four real-world datasets validate the effectiveness of our proposed method in enhancing the accuracy of POI recommendations. Comprehensive ablation studies and parameter analysis further confirm the efficacy of the proposed modules.

Compositionality is considered a key hallmark of humanlanguage. However, most research focuses on item-level compositionality, e.g.,to what extent the meanings of phrases are composed of the meanings of theirsub-parts, rather than on language-level compositionality, which is the degreeto which possible combinations are utilized in practice during language use.Here, we propose a novel way to quantify the degree of language-levelcompositionality and apply it in the case of English adjective-nouncombinations. Using corpus analyses, large language models, and humanacceptability ratings, we find that (1) English only sparsely utilizes thecompositional potential of adjective–noun combinations; and (2) LLMs struggle topredict human acceptability judgments of rare combinations. Taken together, ourfindings shed new light on the role of compositionality in language andhighlight a challenging area for further improving LLMs.

People sometimes explicitly announce that they are being sarcastic. The announcement appears to be particularly common in text-based conversations where prosodic cues are more difficult to identify. In certain cases, the tone of a comment is sufficient to determine non-literal meaning. However, what happens in the absence of these features, or when context forces us to explicitly caveat our sarcasm? In this study, we examined Reddit comments from r/AskReddit for the features that are present in comments tagged with "/s", a convention on the platform for users to denote sarcasm. We found that a host of cues which mimic prosody, and other aspects of figures of speech, were inconsistent predictors of announced sarcasm. In contrast, when talking about sociomoral topics such as politics or race, users were more likely to tag their comments with "/s". This suggests that users are more likely to announce sarcasm in text-based conversations where misinterpretation would be socially detrimental.

The scientific potential of digital game studies in psychology is limited by the observational nature of the data that they investigate. However, digital environments present us with a perfect opportunity to incorporate experimental paradigms in complex interactive and multivariate worlds where each decision made by participants can be tracked and recorded. In this study, we demonstrate an industry-academic research collaboration that offers a proof-of-the-concept on how minor modifications of the game settings could be used to test psychological research questions. We modify the settings of the Zool platform game, where players allocated to the experimental group are provided with more information when in danger of dying in the game. Results of the study show that manipulation does not influence behaviour in the game, such as achieved score or number of deaths, but it changes the overall player's response of whether they will continue playing the game after the disappointing event of losing all their lives, game over event. In line with previous studies, the additional information provided through the experimental manipulation made death in the game more informative to the players.

Various methods of dimensionality reduction have been used to apply a quantitative approach to the study of complex skill acquisition. This work builds upon past approaches, offering a comparative analysis of principal component analysis, logistic regression, and linear discriminant analysis to quantify expertise in the domain of competitive video gaming, or “eSports.” We present a novel, robust dataset of expert and non-expert gameplay data from professional and amateur players of the Super Smash Bros. Melee competitive fighting game. We assess each quantitative model via the metrics of providing accurate expertise classification, predictive utility, and a pragmatic window into the features of complex skill performance that hold the most weight in overall performance outcomes, thereby also providing insights for direction of future training. We posit that linear discriminant analysis provides the best performance for all relevant metrics. The nuances are discussed here, and suggestions for the field are offered for future study of other complex skill domains.

Cognitive science has historically been introduced as a multidisciplinary and, sometimes, an interdisciplinary study of the mind. Recent critical views of the field have questioned the foundational core and its multidisciplinary nature by suggesting that psychology has come to dominate cognitive science. As these are actively debated issues, we need further investigations. This study examines the degree of overlap between cognitive science and psychological science by comparing article keywords and departmental affiliations of authors extracted from flagship journals over the past decade (2012-2022). The results reveal that over 50% of published authors stem from psychology departments. The topics of study between the two remain quite similar as well. However, network analyses found fragmentation in terms of the methodological approaches and a considerable focus by the community of cognitive scientists on formal modeling. Based on the topics and socio-institutional analysis, we suggest that cognitive science is largely (cognitive) psychology. Implications for the field of cognitive science and its claims of multidisciplinarity are discussed.

The idea of curriculum learning, whereby a model is first exposed to simpler examples before an increase in complexity, has long fascinated the AI community. Unfortunately, the experimental successes of curriculum learning have been mixed, particularly applied to natural language, where a vast body of literature appears to evidence its failures. However, recent work has shown that language models trained on transcribed-child-directed-speech (CDS) learn more grammar compared to those trained on Wikipedia. To a lesser extent, the same trend has been observed through training on transcribed speech and simple text data. Motivated by these findings, we revisit the idea of curriculum learning starting from CDS, before moving to simple data, and finally finishing with complex long form text. Unfortunately, through experimentation with an array of models and training step sizes, only in the smallest models trained for the least steps does curriculum learning show any advantage over random sampling.

When individuals are pressed to make decisions quickly, their accuracy tends to decline, which is termed as speed-accuracy trade-off. But does this phenomenon extend to perceptual rating? In other words, do rapid judgments result in more extreme outcomes? To address this question, the study analyzed a global dataset covering 11,481 adult participants' ratings of 120 targets across 45 countries. The hypothesis posited that the rating became more extreme if it took less time. The study firstly identified response time as a significant predictor in extremity of social judgments through a machine learning algorithm, XGBoost, with cultural variables emerging as the second most important predictor. Given the importance of response time, the study employed hierarchical general linear models to investigate whether faster decision-making correlates with more extreme ratings and how this effect varies across diverse cultural contexts. The findings revealed a significant global level effect, also showing considerable variance across eleven regions. This observed phenomenon is termed as the “speed-extremity trade-off,” and is strongest in the Middle East and weakest in East Southeast Asia and Scandinavia.

Social isolation (SI) is a modifiable factor, thought to impact cognitive resilience, with the potential to impact cognition up to ADRD diagnosis and throughout disease duration. MMSE and/or MoCA cognitive function measurements, demographic (including marital and accommodation status SI proxies) and diagnosis data were extracted, using natural language processing, from electronic health records from Oxford NHS patients aged 50+ years. Longitudinal multilevel models were used to predict cognition as a function of the interaction between diagnosis duration, SI proxies and Covid-19, controlling for age, sex and diagnosis. Using MoCA, ‘lifelong single' marital status (

Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either grey or white matter metrics in humans, leaving open the key question as to whether grey or white matter microstructure play distinct or complementary roles supporting cognitive performance. To compare the role of grey and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with grey and white matter measures. Specifically, we compared how grey matter (volume, cortical thickness and surface area) and white matter measures (volume, fractional anisotropy and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study, 5680 female; 6196 male) at 10 years old. We found that grey and white matter metrics bring partly non-overlapping information to predict cognitive performance. The models with only grey or white matter explained respectively 15.4% and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in we additionally found that different metrics within grey and white matter had different predictive power, and that the tracts/regions that were most predictive of cognitive performance differed across metric. These results show that studies focusing on a single metric in either grey or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation.

How does experiencing a rare event, like a car accident or a lottery win, influence decision-makers consecutive decisions? Studying these so-called recency effects holds a long tradition in research on experience-based decision-making. Previous work suggests opposite behavioral patterns after experiencing a positive rare event: People have been found to be more likely to either repeat their choice (positive recency) or to avoid it (negative recency). The effect is thought to persevere for multiple choices and decrease over time. In this study, we provide new insights into recency effects by analyzing people's repeated choices from an extensive database—consisting of 3 million choices by 8,000 participants across 12 different decision-from-experience paradigms collected from 139 studies. We provide a conceptual framework clarifying patterns of positive and negative recency, including how the direction and magnitude of impact change over time.

Utilizing a large-scale human mobility dataset, this study explores the influence of income inequality on status-seeking behaviour. Existing research suggests that income disparity, typically measured using the Gini coefficient, leads to increased status enhancement tendencies. Our study advocates the use of alternative multi-parameter metrics that capture inequality concentrated within specific income distribution segments. The findings of this analysis, based on foot traffic information from approximately 24,000 clothing stores, suggest that income inequality at both the lower and top ends of the income distribution promotes people's status-seeking behaviour, with lower-concentrated inequality exhibiting a larger effect. Furthermore, our data reveal a negative correlation between visits to “high-status” brands and an important element of social capital – civic engagement, indicating community participation could potentially counterbalance the need for status enhancement through consumption. Thus, this research provides a nuanced lens on the complex dynamics between income inequality, status-seeking behaviour, and social capital.

Over the past 30 years there have been numerous large-scale and longitudinal psychiatric research efforts to improve our understanding and treatment of mental health conditions. However, despite the huge effort by the research community and considerable funding, we still lack a causal understanding of most mental health disorders. Consequently, the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than measuring or addressing root causes. This results in a trial-and-error approach that is a poor fit to underlying causality with poor clinical outcomes. Here we discuss how a research framework that originates from exploration of causal factors, rather than symptom groupings, applied to large scale multi-dimensional data can help address some of the current challenges facing mental health research and, in turn, clinical outcomes. Firstly, we describe some of the challenges and complexities underpinning the search for causal drivers of mental health conditions, focusing on current approaches to the assessment and diagnosis of psychiatric disorders, the many-to-many mappings between symptoms and causes, the search for biomarkers of heterogeneous symptom groups, and the multiple, dynamically interacting variables that influence our psychology. Secondly, we put forward a causal-orientated framework in the context of two large-scale datasets arising from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States, and the Global Mind Project which is the largest database in the world of mental health profiles along with life context information from 1.4 million people across the globe. Finally, we describe how analytical and machine learning approaches such as clustering and causal inference can be used on datasets such as these to help elucidate a more causal understanding of mental health conditions to enable diagnostic approaches and preventative solutions that tackle mental health challenges at their root cause.

Drilling operation relies on observing various devices, such as weight indicator, drilling equipment parameters, and logging display screen. This leads to high work intensity and difficulty in ensuring wellbore engineering safety and drilling speed, especially in complex well sections. To address these problems, this paper presents a smart driller pilot system for future automatic drilling. The paper introduces the working principle, calculation model, hardware composition, functional architecture, and field application of the system. The system can optimize parameters, provide drilling risk tips, engineering early warnings, expert instructions, and other functions in text, graphic, and voice modes. It aims to improve the driller's precise operation and reduce the drilling accident and complexity rate. Field tests of 65 wells show that the system can increase drilling speed by 2% and decrease the drilling complexity rate by 3%. [ABSTRACT FROM AUTHOR]

The ever-growing volume and complexity of Big Data pose challenges for traditional classification tasks. This paper explores the potential of Neutrosophic Sets (NS), a powerful framework for handling uncertainty, in building robust classification models for Big Data prediction using Machine Learning (ML) techniques. We provide a detailed background on NS and discuss its advantages over Fuzzy Sets. We then propose a methodology that integrates NS with relevant ML algorithms for classification. We evaluate the performance of our Neutrosophic-based model on a Big Data source. The results are analyzed to assess the effectiveness of the Neutrosophic approach for Big Data prediction. This research contributes to the advancement of uncertainty management in Big Data classification and paves the way for further exploration of Neutrosophic-based ML models for various prediction tasks. Results show that the Neutrosophic Neural Networks (NNs) model achieved commendable performance across various metrics, with an accuracy of 79.08%, precision of 74.58%, recall of 77.64%, and an F1-score of 75.63%. These metrics indicate that the Neutrosophic NNs model effectively balances the trade-offs between precision and recall, providing a robust classification performance in the context of the evaluated dataset. [ABSTRACT FROM AUTHOR]

This study aims to provide a medical information management system using multi-source heterogeneous big data to improve medical service quality and efficiency, with a motivation on its potential in medical insurance DRG payment. The system framework uses Back Propagation Neural Network (BPNN) technology to efficiently process and analyze multi-source medical data. Comparative experiments and parameter adjustments evaluated the system's performance. Results show that the BPNN model achieved excellent accuracy (92.5%), recall (93%), and F1 value (92.8%) on the test data set, outperforming other models such as PSO(88%), CNN(89%), and RNN(90%). The system's response speed was also significantly improved, with an average response time of 0.38 seconds, compared to 0.89 seconds for traditional systems. A 72-hour stability test confirmed the system's reliability and ability to meet user needs. The proposed system demonstrates improved performance and user experience, making it a promising solution for medical information management and DRG payment applications. [ABSTRACT FROM AUTHOR]

Big data management systems are in demand today in almost all industries, being also a foundation for artificial intelligence training. The use of heterogeneous polystores in big data systems has led to the fact that tools within the same system have different data granularity and access control models. The harmonization of these components by the security administrator and the implementation of a common access policy are now carried out by hand. This leads to an increasing number of vulnerabilities, which in turn become frequent causes of data leaks. The current situation in the field of automation and analysis of access control in big data systems reveals the lack of automation solutions for polystore-based systems. This paper addresses the problem of automated access control analysis in big data management systems. We formulate and discuss the main contradiction between the requirement of scalability and flexibility of access control and the increased workload on the security administrator, aggravated by the use of different data and access control models in system components. To solve this problem, we propose a new automated method for analyzing security policies based on a graph model, which reduces the number of potential vulnerabilities caused by incorrect management of big data systems. The proposed method uses the data lifecycle model of the system, its current settings, and the required security policy. The use of two-pass analysis (from data sources to data receivers and back) allows us to solve two problems: the analysis of the access control system for potential vulnerabilities and the check for business logic vulnerabilities. As an example, we consider the use of a developed prototype tool for security policy analysis in a big data management system. [ABSTRACT FROM AUTHOR]

In order to solve the problem of strain modal identification under strain monitoring signals with poor vibration modal information, this paper proposes a strain mode identification method with statistical stability significance. This method removes noise, vehicle-induced effects, and temperature effects from the original dynamic strain signal, retaining only vibration-related components, and obtaining a statistically stable high quality bridge strain power spectrum, thereby identifying high quality strain mode parameters. Furthermore, in order to verify the confidence level of the strain modes obtained by this method, this paper adopts the interval estimation method to estimate the power spectrum, natural frequency, damping ratio, and modal shape after statistical processing. The credibility of strain modes has been estimated by interval estimation. The confidence interval of 95% confidence for each modal parameter is obtained, achieving the confidence-level evaluation of corresponding variable modal parameter identification. In response to practical engineering problems, this paper evaluates the actual bridge data of Tongji Road Bridge in Shanghai, and explains the abnormal phenomena that occurred in the data evaluation based on the measured diseases, verifying the practicality of this method. [ABSTRACT FROM AUTHOR]

Data integration combines information from different sources to provide a comprehensive view for making informed business decisions. The ETL (Extract, Transform, and Load) process is essential in data integration. In the past two decades, modeling the ETL process has become a priority for effectively managing information. This paper aims to explore ETL approaches to help researchers and organizational stakeholders overcome challenges, especially in Big Data integration. It offers a comprehensive overview of ETL methods, from traditional to Big Data, and discusses their advantages, limitations, and the primary trends in Big Data integration. The study emphasizes that many technologies have been integrated into ETL steps for data collection, storage, processing, querying, and analysis without proper modeling. Therefore, more generic and customized design modeling of the ETL steps should be carried out to ensure reusability and flexibility. The paper summarizes the exploration of ETL modeling, focusing on Big Data scalability and processing trends. It also identifies critical dilemmas, such as ensuring compatibility across multiple sources and dealing with large volumes of Big Data. Furthermore, it suggests future directions in Big Data integration by leveraging advanced artificial intelligence processing and storage systems to ensure consistency, efficiency, and data integrity. [ABSTRACT FROM AUTHOR]

A key challenge in monitoring, managing, and mitigating global health crises is the need to coordinate clinical decision-making with systems outside of healthcare. In the 21st century, human engagement with Internet-connected ubiquitous devices generates an enormous amount of big data, which can be used to address complex, intersectoral problems via participatory epidemiology and mHealth approaches that can be operationalized with digital citizen science. These big data – which traditionally exist outside of health systems – are underutilized even though their usage can have significant implications for prediction and prevention of communicable and non-communicable diseases. To address critical challenges and gaps in big data utilization across sectors, a Digital Citizen Science Observatory (DiScO) is being developed by the Digital Epidemiology and Population Health Laboratory by scaling up existing digital health infrastructure. DiScO's development is informed by the Smart Framework, which leverages ubiquitous devices for ethical surveillance. The Observatory will be operationalized by implementing a rapidly adaptable, replicable, and scalable progressive web application that repurposes jurisdiction-specific cloud infrastructure to address crises across jurisdictions. The Observatory is designed to be highly adaptable for both rapid data collection as well as rapid responses to emerging and existing crises. Data sovereignty and decentralization of technology are core aspects of the observatory, where citizens can own the data they generate, and researchers and decision-makers can re-purpose digital health infrastructure. The ultimate aim of DiScO is to transform health systems by breaking existing jurisdictional silos in addressing global health crises. [ABSTRACT FROM AUTHOR]

Summary: Cloud Computing (CC) and big data are growing technology in the business. Big data is demonstrated in terms of volume, variety, and velocity. CC is employed for storing, processing, and accessing data. Many cryptographic techniques have been developed to enhance big data security in cloud computing. However, security and privacy are the primary concerns in protecting data, as it is highly sensitive. Yet, it faces the major problems of inefficient performance, increased time consumption, and lack of data confidentiality and integrity. To address this issue, proposed Extreme Learning with Projection Relational Algebraic Secured Data Transmission (ELPRA‐SDT) is introduced to secure data transactions from cloud users to cloud servers with enhanced data confidentiality and reduced time consumption for big cloud data. The proposed ELPRA‐SDT consists of two major processes namely registration and key generation. At first, the user's IP address is registered employing a transitive advanced set relation theory graph model in a cloud server (CS) for retrieving the numerous services. The CS generates private and public keys for each registered user's IP address using the Transitive Operational and Time Synchronized Random Winternitz Key generation model. After, the user sends a request to the CS for acquiring data. The CS validates the requested user based on security policy attributes. Second, the Projection Relational Algebraic Signcryption and Unsigncryption algorithm performs signature verification to ensure secure data access for protecting the data. Results of experiments carried out by using Coburg Intrusion Detection Data Sets‐001 dataset in Java. ELPRA‐SDT method is more efficient and more suitable for providing security and privacy to network traces in the Cloud. The result shows maximum performance with data confidentiality by 10% and data integrity by 13%. In addition, delay is reduced by 32%, and data delivery time and communication complexity is decreased by 28% and 24% to other existing methods. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Location Taxon Methods Results Conclusions Surveying for a species of concern ahead of proposed activities that alter its habitat is routine practice in conservation and management. Such surveys may accumulate large datasets that could further elucidate trends in abundance and distribution. However, the as‐needed surveying of proposed activities may impart a sample site selection bias on the data if used for another purpose. Management of a threatened, forest‐nesting seabird offered an example of this. Here we assessed how resource management planning and survey requirements can bias clearance monitoring survey data collected prior to proposed timber harvests, if those data are used for other purposes.Oregon and Washington, USA.Marbled Murrelet (Brachyramphus marmoratus).To assess how timber planning and other factors influenced marbled murrelet survey location selection, we used logistic regression models to examine habitat associations of marbled murrelet survey sites (n = 9178) encompassing proposed timber harvests, and the survey stations (n = 38,923) therein, across the murrelet's inland range in Washington and Oregon, USA between 1989 and 2021. We then simulated the effect this selective sampling might have on assessments of occupancy trends.Most habitat characteristics considered did influence where surveys were located, with distance to roads often being the strongest predictor of survey location. The strength of selection for each covariate changed over time, such that a habitat characteristic strongly influenced location selection in a year but was less influential in another year. The simulation analysis suggested that the non‐random selection of survey sites could profoundly bias assessments of occupancy trends.When using these clearance monitoring survey data– or any data–beyond their original purpose, careful consideration should be given to the scope of inference provided and analytical methods used, to ensure that observed trends are the product of biological processes and not biased by sampling artefacts. [ABSTRACT FROM AUTHOR]

Big Data platforms store vast amounts of information, necessitating robust security measures, including risk-based approaches. Risk assessment, a key part of Information Security Management Systems (ISMS), involves evaluating threats, vulnerabilities, and documenting risks through risk registers. Organizations face the challenge of allocating resources effectively to implement controls that mitigate these risks. This involves calculating risk scores before and after control implementation and prioritizing them—an NP-Complete (Nondeterministic Polynomial-time Complete) problem. This paper presents a mathematical model for solving this using the Z3 Satisfiability Modulo Theories (SMT) solver. The model enables risk-based planning for security implementation in big data platforms. The results demonstrate the feasibility of the approach, with the system processing up to 11 risks (almost 40 million permutations) efficiently, compared to brute force methods, which struggle beyond six risks (720 permutations). [ABSTRACT FROM AUTHOR]

Background: Premature ovarian insufficiency/failure (POI/POF) is a significant issue for women of reproductive age. Anti-Müllerian hormone (AMH) is a potential biomarker of ovarian reserve, but its clinical value in diagnosing and predicting POI/POF remains unclear. This study aimed to analyze the correlation between AMH and basal follicle-stimulating hormone (FSH) levels in women aged 18 to 40 and evaluate AMH's predictive value for POI/POF. Methods: A total of 21,143 participants aged 18-40 who visited the gynecology department or underwent physical examinations at the International Peace Maternity and Child Health Hospital in Shanghai, China, from July 2016 to June 2021 were enrolled. Demographic information and laboratory test results were collected, including age, FSH, AMH, E2 and test dates. Participants were grouped by FSH and AMH levels, and subgroup analyses were performed to investigate the relationship between these hormones and age. The AMH level associated with POI risk was evaluated using restricted cubic splines (RCS) and logistic regression. Clinical benefit was assessed by decision curve analysis (DCA). Results: Participants with higher FSH levels had significantly lower median AMH levels and vice versa(p<0.001). At AMH ≥ 0.5 ng/mL, FSH levels were normal or slightly elevated with age. At AMH level below 0.5ng/ml,basal FSH increased significantly with age. At FSH <10 IU/L, AMH levels show a trend of rising and then decreasing with age, reaching a peak at approximately 25 years old and gradually decreasing with age. At FSH ≥10 IU/L, AMH levels show a gradual downward trend with age, and at FSH >40 IU/L, AMH levels remain very low to undetectable values. The RCS showed that the risk of POI/POF in the overall population sharply increased until serum AMH reached a low level (below 0.5ng/ml). DCA showed that a low AMH level had good clinical diagnostic utility in predicting POI/POF. Conclusion: Our analysis of a large dataset suggests that serum AMH levels are inversely correlated with FSH levels and that AMH is a good predictor of POI until it drops to a low level. [ABSTRACT FROM AUTHOR]

We introduce an open-source Python package for the analysis of large-scale electrophysiological data, named SyNCoPy, which stands for Systems Neuroscience Computing in Python. The package includes signal processing analyses across time (e.g., time-lock analysis), frequency (e.g., power spectrum), and connectivity (e.g., coherence) domains. It enables user-friendly data analysis on both laptop-based and high-performance computing systems. SyNCoPy is designed to facilitate trial-parallel workflows (parallel processing of trials), making it an ideal tool for large-scale analysis of electrophysiological data. Based on parallel processing of trials, the software can support very large-scale datasets via innovative out-of-core computation techniques. It also provides seamless interoperability with other standard software packages through a range of file format importers and exporters and open file formats. The naming of the user functions closely follows the well-established FieldTrip framework, which is an open-source MATLAB toolbox for advanced analysis of electrophysiological data. [ABSTRACT FROM AUTHOR]

Wind energy is a promising renewable source, necessitating effective monitoring of wind turbine (WT) conditions for reliable and cost-effective energy production, amidst environmental challenges. Condition monitoring of WTs employs traditional methods, signal processing, and emerging artificial intelligence (AI) approaches. AI-driven techniques excel in data-driven decision-making, addressing big data challenges in condition monitoring. This review paper presents a comprehensive overview of all streams of condition monitoring associated with WT, offering detailed insights into the related tasks. It also provides details on AI-based approaches and their application in executing various tasks within condition monitoring for WT. Finally, the study summarises the current trends, advantages, and disadvantages of AI-based techniques for real-world decision making in condition monitoring. This systematic review covers fundamentals to future developments in AI-driven approaches in condition monitoring for WT, serving as a valuable resource for readers and novice researchers in this field. [ABSTRACT FROM AUTHOR]

Background: Big data and AI applications now play a major role in many health contexts. Much research has already been conducted on ethical and social challenges associated with these technologies. Likewise, there are already some studies that investigate empirically which values and attitudes play a role in connection with their design and implementation. What is still in its infancy, however, is the comparative investigation of the perspectives of different stakeholders. Methods: To explore this issue in a multi-faceted manner, we conducted semi-structured interviews as well as focus group discussions with patients and clinicians. These empirical methods were used to gather interviewee's views on the opportunities and challenges of medical AI and other data-intensive applications. Results: Different clinician and patient groups are exposed to medical AI to differing degrees. Interviewees expect and demand that the purposes of data processing accord with patient preferences, and that data are put to effective use to generate social value. One central result is the shared tendency of clinicians and patients to maintain individualistic ascriptions of responsibility for clinical outcomes. Conclusions: Medical AI and the proliferation of data with import for health-related inferences shape and partially reconfigure stakeholder expectations of how these technologies relate to the decision-making of human agents. Intuitions about individual responsibility for clinical outcomes could eventually be disrupted by the increasing sophistication of data-intensive and AI-driven clinical tools. Besides individual responsibility, systemic governance will be key to promote alignment with stakeholder expectations in AI-driven and data-intensive health settings. [ABSTRACT FROM AUTHOR]