Utility Of Data Research Articles

Seeding innovation: the role of internal and external digital data in agri-food product innovation

PurposeSince previous literature provides fragmented and conflicting results about the use of digital data for product innovation, the article aims to comprehensively explore and shed light on how agri-food firms utilise external and internal digital data sources when dealing with different product innovations, such as incremental, architecture and radical innovation.Design/methodology/approachThis paper adopts an exploratory multiple-case study and a theory-building process, focussing on the agri-food industry. We collected primary and secondary data from eight manufacturing companies.FindingsThe findings of this research show an empirical framework of six agri-food firms’ digital data utilisation behaviours: the supervisor, the passive supervisor, the developer, the passive developer, the pathfinder and the conjunction behaviour. These digital data utilisation behaviours vary according to a combination of data sources, such as internal data related to inside phenomenon measures (e.g. data generated by sensors installed in the production plan) or external data (e.g., market trends, overall sector sales), and innovation purposes.Practical implicationsThis article offers guiding principles that assist agri-food companies when utilising internal and external digital data sources for specific product innovation outcomes such as incremental, architectural and radical innovation.Originality/valueThe significance of external and internal data sources in stimulating product innovation has garnered substantial attention within academic discussions, highlighting the critical importance of analysing digital data for driving such innovation. Nonetheless, the predominant approach is to study a single innovation outcome through the lens of digital technology. In contrast, our study stands out by adopting a fundamental perspective on data sources, enabling a more nuanced explanation of the overall product innovation outcomes within the agri-food sector.

Assessing the Copyright Infringement Risk of Generative AI Created Works

The rapid evolution of the Internet and big data technology has ushered in significant advancements in data production and utilization. However, this progress has brought to the forefront the intricate issue of copyright protection. This is particularly evident with the widespread adoption of generative artificial intelligence (AI), where concerns regarding data source protection, data processing standards, and the boundaries of data application have garnered considerable attention from both academia and industry. Big data mining techniques coupled with generative AI algorithms offer robust support for data collection and processing, but they also introduce risks of data infringement and pose challenges regarding algorithmic compliance and ownership rights over generated works. Addressing these issues is imperative. This paper recommends enhancing legal compliance standards throughout each stage of the generative AI process, recalibrating the scope of acceptable copyright use, and establishing a regulatory framework for generative AI works. These measures are crucial for fostering the sustainable and orderly advancement of the generative AI industry.

Predictors of valproic acid steady-state serum levels in adult and pediatric psychiatric inpatients: a comparative analysis.

Valproic acid (VPA) is commonly used as a second-line mood stabilizer or augmentative agent in severe mental illnesses. However, population pharmacokinetic studies specific to psychiatric populations are limited, and clinical predictors for the precision application of VPA remain undefined. To identify steady-state serum VPA level predictors in pediatric/adolescent and adult psychiatric inpatients. We analyzed data from 634 patients and 1,068 steady-state therapeutic drug monitoring (TDM) data points recorded from 2015 to 2021. Steady-state VPA levels were obtained after tapering during each hospitalization episode. Electronic patient records were screened for routine clinical parameters and co-medication. Generalized additive mixed models were employed to identify independent predictors. Most TDM episodes involved patients with psychotic disorders, including schizophrenia (29.2%) and schizoaffective disorder (17.3%). Polypharmacy was common, with the most frequent combinations being VPA + quetiapine and VPA + promethazine. Age was significantly associated with VPA levels, with pediatric/adolescent patients (< 18 years) demonstrating higher dose-adjusted serum levels of VPA (β = 7.6±2.34, p < 0.001) after accounting for BMI. Women tended to have higher adjusted VPA serum levels than men (β = 5.08±1.62, p < 0.001). The formulation of VPA (Immediate-release vs. extended-release) showed no association with VPA levels. Co-administration of diazepam exhibited a dose-dependent decrease in VPA levels (F = 15.7, p < 0.001), suggesting a potential pharmacokinetic interaction. This study highlights the utility of population-specific pharmacokinetic data for VPA in psychiatric populations. Age, gender, and co-administration of diazepam were identified as predictors of VPA levels. Further research is warranted to establish additional predictors and optimize the precision application of VPA in psychiatric patients.

Refining ICESAT-2 ATL13 Altimetry Data for Improving Water Surface Elevation Accuracy on Rivers

The application of ICESAT-2 altimetry data in river hydrology critically depends on the accuracy of the mean water surface elevation (WSE) at a virtual station (VS) where satellite observations intersect solely with water. It is acknowledged that the ATL13 product has noise elevations of the adjacent land, resulting in biased high mean WSEs at VSs. Earlier studies have relied on human intervention or water masks to resolve this. Both approaches are unsatisfactory solutions for large river basins where the issue becomes pronounced due to many tributaries and meanders. There is no automated procedure to partition the truly representative water height from the totality of the along-track ICESAT-2 photon segments (portions of photon points along a beam) for increasing precision of the mean WSE at VSs. We have developed an automated approach called “auto-segmentation”. The accuracy of our method was assessed by comparing the ATL13-derived WSEs with direct water level observations at 10 different gauging stations on 37 different dates along the Lower Murray River, Australia. The concordance between the two datasets is significantly high and without detectable bias. In addition, we evaluated the effects of four methods for calculating the mean WSEs at VSs after auto-segmentation processing. Our results reveal that all methods perform almost equally well, with the same R2 value (0.998) and only subtle variations in RMSE (0.181–0.189 m) and MAE (0.130–0.142 m). We also found that the R2, RMSE and MAE are better under the high flow condition (0.999, 0.124 and 0.111 m) than those under the normal-low flow condition (0.997, 0.208 and 0.160 m). Overall, our auto-segmentation method is an effective and efficient approach for deriving accurate mean WSEs at river VSs. It will contribute to the improvement of ICESAT-2 ATL13 altimetry data utility on rivers.

Image Data Stream Organization and Online Analysis Application Based on Data Cube Technology

Abstract. This study aims to explore the important role of data-like cube structures in modern remote sensing data processing and data analysis through ArcPy and Python multiprocessing techniques. A multi-scale spatial data cube is innovatively developed to improve the efficiency of remote sensing data management and optimize data analysis. The core of this study is to define and implement grid cells of different sizes that form the basis of data cube, and to quantify the efficient coverage of specific areas using Python multiprocessing techniques. Experiments were conducted in Hainan Province, and efficient data coverage of the whole Hainan Province was realized using the grid data method, which significantly reduced the amount of remote sensing data and processing time required. This shows that the method has successfully improving data coverage capacity and utilization efficiency. The results of this study not only demonstrate the effective application of data-like cubes in remote sensing data processing and analysis, but also provide new perspectives and methods for future complex spatial data analysis and large-scale remote sensing data processing.

An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems

Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications.

Implementation of the electronic health record in the German healthcare system: an assessment of the current status and future development perspectives considering the potentials of health data utilisation by representatives of different stakeholder groups

IntroductionThe digitalisation of the German healthcare system enables a wide range of opportunities to utilize healthcare data. The implementation of the EHR in January 2021 was a significant step, but compared to other European countries, the implementation of the EHR in the German healthcare system is still at an early stage. The aim of this paper is to characterise the structural factors relating to the adoption of the EHR in more detail from the perspective of representatives of stakeholders working in the German healthcare system and to identify existing barriers to implementation and the need for change.MethodsQualitative expert interviews were conducted with one representative from each of the stakeholder groups health insurance, pharmacies, healthcare research, EHR development and panel doctors.ResultsThe interviews with the various stakeholders revealed that the implementation process of the EHR is being delayed by a lack of a viable basis for decision-making, existing conflicts of interest and insufficient consideration of the needs of patients and service providers, among other things.DiscussionThe current status of EHR implementation is due to deficiency in legal regulations as well as structural problems and the timing of the introduction. For instance, the access rights of various stakeholders to the EHR data and the procedure in the event of a technical failure of the telematics infrastructure are remain unclear. In addition, insufficient information and communication measures have not led to the desired acceptance of EHR use among patients and service providers.

Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL

In the context of the increasing integration of Internet of Things technologies and the growing importance of data lakes, the need for robust cybersecurity measures to protect privacy without compromising data utility becomes key. Aiming to address the privacy–security challenge in such digital ecosystems, this study explores the application of Fully Homomorphic Encryption (FHE) using the Microsoft SEAL library. FHE allows for operations on encrypted data, offering a promising opportunity for maintaining data confidentiality during processing. Our research employs systematic experimental tests on datasets to evaluate the performance of homomorphic encryption in terms of CPU usage and execution time, executed across traditional PC configurations and a NVIDIA Jetson Nano device to assess the scalability and practicality of FHE in edge computing. The results reveal a performance disparity between computing environments, with the PC showing stable performance and the Jetson Nano revealing the limitations of edge devices in handling encryption tasks due to computational and memory constraints.

Assessing the utility of shellfish sanitation monitoring data for long-term estuarine water quality analysis

Regular testing of coastal waters for fecal coliform bacteria by shellfish sanitation programs could provide data to fill large gaps in existing coastal water quality monitoring, but research is needed to understand the opportunities and limitations of using these data for inference of long-term trends. In this study, we analyzed spatiotemporal trends from multidecadal fecal coliform concentration observations collected by a shellfish sanitation program, and assessed the feasibility of using these monitoring data to infer long-term water quality dynamics. We evaluated trends in fecal coliform concentrations for a 20-year period (1999–2021) using data collected from spatially fixed sampling sites (n = 466) in North Carolina (USA). Findings indicated that shellfish sanitation data can be used for long-term water quality inference under relatively stationary management conditions, and that salinity trends can be used to investigate management-driven bias in fecal coliform observations collected in a particular area.

Fourier-Matsubara series expansion for imaginary-time correlation functions.

A Fourier-Matsubara series expansion is derived for imaginary-time correlation functions that constitutes the imaginary-time generalization of the infinite Matsubara series for equal-time correlation functions. The expansion is consistent with all known exact properties of imaginary-time correlation functions and opens up new avenues for the utilization of quantum Monte Carlo simulation data. Moreover, the expansion drastically simplifies the computation of imaginary-time density-density correlation functions with the finite temperature version of the self-consistent dielectric formalism. Its existence underscores the utility of imaginary-time as a complementary domain for many-body physics.

Anonymity‐Aware Framework for Designing Recommender Systems

Due to increasing secondary use of data, recommender systems using anonymized data are in demand. However, implementing a recommender system requires complicated data processing and programming, and the relationship between anonymization level and recommendation quality has not been investigated. Therefore, this study proposes a framework that facilitates the development of recommender systems. Additionally, a method is proposed for quantitatively evaluating recommendation quality when the anonymization level is varied. The proposed method promotes data utilization by recommender systems and determination of compensation for providing data based on anonymization level. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Multi-view Self-supervised Learning and Multi-scale Feature Fusion for Automatic Speech Recognition

To address the challenges of the poor representation capability and low data utilization rate of end-to-end speech recognition models in deep learning, this study proposes an end-to-end speech recognition model based on multi-scale feature fusion and multi-view self-supervised learning (MM-ASR). It adopts a multi-task learning paradigm for training. The proposed method emphasizes the importance of inter-layer information within shared encoders, aiming to enhance the model’s characterization capability via the multi-scale feature fusion module. Moreover, we apply multi-view self-supervised learning to effectively exploit data information. Our approach is rigorously evaluated on the Aishell-1 dataset and further validated its effectiveness on the English corpus WSJ. The experimental results demonstrate a noteworthy 4.6%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} reduction in character error rate, indicating significantly improved speech recognition performance . These findings showcase the effectiveness and potential of our proposed MM-ASR model for end-to-end speech recognition tasks.

Towards decision-making support for complex socio-technical system safety assessment: A hybrid model combining FRAM and dynamic Bayesian networks

Effectively managing system safety and resilience in critical infrastructures requires addressing emergent risks and critical resonances. This study introduces a quantitative model that merges Monte Carlo Simulation (MCS) of the Functional Resonance Analysis Method (FRAM) and Dynamic Bayesian Network (DBN) to assess technological, organizational, and human performance variability in complex social-technical systems. FRAM identifies system taxonomy and functional variability, while MCS pinpoints critical coupling, supplying prior probabilities for functional resonance. This information feeds into the DBN, facilitating the modeling of causal relationships and probabilistic inferences regarding risk uncertainties and system resonances. The model underwent rigorous testing and validation on a preheater cyclone system within the cement industry. This process involved the utilization of historical field data gathered from six prominent cement industries and input from thirty subject matter experts. The integrated approach deeply analyzes emerging risk indicators, unveiling interactions within organizational, human, and technical subsets, alongside performance variability. Furthermore, the model incorporates system learning parameters, aiding decision-making under uncertainty. These findings advance system safety and resilience management, offering insights for risk assessment and accident prevention across diverse scenarios in complex socio-technical systems.

Navigating business transformation and strategic decision-making in multinational energy corporations with geodata

In the dynamic landscape of multinational energy corporations, navigating business transformation and strategic decision-making requires innovative approaches. Geodata, encompassing spatial and location-based information, emerges as a pivotal asset in this endeavor. This paper outlines the integration of geodata into the fabric of energy corporations, facilitating informed decisions and operational excellence. The introduction elucidates the significance of geodata amidst evolving business paradigms within the energy sector. It explores the transformative potential of geospatial intelligence in fostering agility and resilience, crucial attributes for multinational corporations. Understanding business transformation delves into the fundamental concepts and drivers reshaping the energy industry. Drawing from case studies, it illustrates how corporations leverage strategic shifts to adapt to market dynamics and emerging technologies. The subsequent section emphasizes the pivotal role of geodata in strategic decision-making processes. It elucidates the diverse applications of geospatial information in optimizing operations, mitigating risks, and capitalizing on market opportunities. Integrating geodata into business processes outlines the methodologies and technologies underpinning effective utilization of spatial data. It underscores the importance of robust data management and geospatial analysis techniques for deriving actionable insights. Leveraging geodata for operational excellence explores how energy corporations optimize asset management, streamline supply chains, and enhance risk assessment through geospatial analytics. Case studies and best practices offer real-world examples of successful geodata integration, highlighting key lessons and future trends. Challenges and considerations encompass the complexities of data quality, regulatory compliance, and organizational change management. In conclusion, the paper underscores the transformative potential of geodata in shaping the future trajectory of multinational energy corporations, advocating for a strategic embrace of geospatial intelligence in decision-making processes. Keywords: Business Transformation, Strategic Decision-Making, Multinational Energy Corporations,  Geodata.

Sentiment-driven Movie Recommendation System: A Machine Learning Approach

Pioneering method for enhancing movie recommendation systems through the integration of sentiment analysis of micro-blogging data. Leveraging natural language processing (NLP) techniques, the proposed system extracts sentiment from tweets and other micro-blogging sources pertinent to movies systematic approach encompassing the collection, preprocessing, and analysis of movie review data tailored for sentiment analysis tasks. Key aspects covered include the acquisition of publicly available datasets, methodologies for web scraping, preprocessing techniques, strategies for sentiment labeling, methods for data augmentation, procedures for data splitting, optimal data storage formats, and ethical considerations inherent in data collection and utilization. By offering a comprehensive guide, furnish both researchers and practitioners with the necessary tools to proficiently manage movie review data while navigating the ethical intricacies associated with its acquisition and application.

A Comprehensive Dataset of Four Provincial Legislative Assembly Members

Abstract This research note reports on a new dataset about legislators in four Canadian provinces since the establishment of their colonial assemblies in the eighteenth century. Over 7,000 legislators from Ontario, Quebec, New Brunswick, and Nova Scotia are included, with consolidated information drawn from multiple sources about parliamentarians’ years of birth and death, religion, electoral performance, kinship, and several other biographical indicators. We also illustrate the utility of such data with the help of a few descriptive examples drawn from the four provinces. We believe this consolidated dataset offers several opportunities for future research on representation, legislative activities and party politics.

BFL-SA: Blockchain-based federated learning via enhanced secure aggregation

Federated learning, involving a central server and multiple clients, aims to keep data local but raises privacy concerns like data exposure and participation privacy. Secure aggregation, especially with pairwise masking, preserves privacy without accuracy loss. Yet, issues persist like security against malicious models, central server fault tolerance, and trust in decryption keys. Resolving these challenges is vital for advancing secure federated learning systems. In this paper, we present BFL-SA, a blockchain-based federated learning scheme via enhanced secure aggregation, which addresses key challenges by integrating blockchain consensus, publicly verifiable secret sharing, and an overdue gradients aggregation module. These enhancements significantly boost security and fault tolerance while improving the efficiency of data utilization in the secure aggregation process. After security analysis, we have demonstrated that BFL-SA achieves secure aggregation even in malicious models. Through experimental comparative analysis, BFL-SA exhibits rapid secure aggregation speed and achieves 100% model aggregation accuracy.

A Lightweight_PAEKS-based energy scheduling model considering priority in MicroGrid

Priority-based energy scheduling is proposed, whereby the data utility of measured user information in smart meters, including the priority and power demand of power consumers and the maximum power supply of power suppliers, is leveraged to ensure that in the case of limited power resources within islanded microgrids, energy is allocated to power consumers in the order of high to low priority. Nonetheless, in terms of data utility, most existing strategies only consider high-priority power consumers, neglecting the priority of power suppliers and low-priority power consumers, thus damaging the fairness of low-priority consumers and the interests of power suppliers, to the detriment of these groups. Moreover, smart meters are vulnerable to data integrity attacks, which may result in the manipulation of measured user information during the forwarding process, thereby disrupting normal priority-based energy scheduling. Therefore, focusing on data utility without considering data security is insufficient. Researchers have extensively investigated how to secure smart meters within advanced metering infrastructure (AMI), primarily employing cryptography-based methods to encrypt user information in measured meters and decrypt it at the microgrid central controller (MGCC), thereby protecting the data confidentiality of user information during the forwarding process and preventing data tampering. However, the prevailing cryptographic methods used to secure smart meters are extremely complex, and make smart meters and local controllers on the forwarding path unable to obtain any user information in which embedded consumers’ priority and cannot leverage the data utility of priority to forward information accordingly. In other words, existing models cannot simultaneously achieve data confidentiality and data utility during the forwarding process. To solve these issues, this study investigates a Lightweight_PAEKS-based energy scheduling model considering priority in microgrid (LPESCP). As a lightweight solution, the LPESCP comprehensively considers data confidentiality and data utility, as well as the priority and fairness of all users, to ensure the security of smart meters and optimize energy scheduling. In particular, the data utility problem is solved by using an optimization model that aims to maximize the global satisfaction degree of all users in terms of priority and fairness. The Lightweight_PAEKS and Paillier encryption scheme are used so that nodes on the forwarding path can successfully match priority-related keyword and forward information in order of the keyword corresponding emergency coefficient indicating the urgency levels of information, ensuring data utility, while do not know the specific keyword, emergency coefficient and other information, ensuring data confidentiality. To verify the effectiveness of the LPESCP, experiments are conducted for three cases generated based on random numbers. The results show that the LPESCP model can effectively ensure energy supply to consumers, comprehensively considering data confidentiality and utility as well as priority and fairness of all users. In addition, the global satisfaction degree and time overhead are introduced as metrics to verify that the LPESCP strategy is more effective in providing greater global satisfaction degree and lower time overhead than existing priority-based energy scheduling models.

Uncertainty-Aware Contrastive Learning for semi-supervised named entity recognition

Named entity recognition (NER) based on deep neural networks has shown competitive performance when trained on large-scale human-annotated data. However, they face challenges in low-resource settings, where the available labeled data are scarce. A typical solution is pseudo-labeling which assigns pseudo-labels to the certain (i.e., high confidence) tokens of unlabeled sentences while discards the uncertain (i.e., low confidence) ones. But there still have two potential challenges: (1) discarding the uncertain tokens leads to low utilization of unlabeled data; (2) the intrinsic quality-quantity trade-off issue of pseudo-labeling with confidence threshold. In this work, we propose an innovative method named Uncertainty-Aware Contrastive Learning (UACL) for semi-supervised named entity recognition. Specifically, UACL first utilizes a Gaussian-based class-wise token separation mechanism to dynamically distinguish certain and uncertain tokens, which can self-adaptively adjust the confidence threshold to balance the quantity and quality of pseudo-labeled certain tokens. Then we perform pseudo-supervised learning based on certain tokens and contrastive learning based on uncertain ones, which not only improves the utilization of unlabeled data, but also provides uncertainty-aware guidance information for model training. Furthermore, our method leverages uncertain tokens to optimize token representation, leading to improving performance. The extensive experimental results on four benchmarks demonstrate that the performance of our proposed approach surpasses that of previously leading low-resource baselines.

DPGazeSynth: Enhancing Eye-tracking Virtual Reality Privacy with Differentially Private Data Synthesis

As spatial computing devices increasingly integrate eye-tracking technology to enhance virtual reality (VR) experiences, the imperative to protect sensitive eye-tracking data against privacy risks, such as user re-identification, has become more pressing. Existing privacy-preserving mechanisms face challenges in balancing the dual demands of privacy and utility in the context of VR applications. This paper presents DPGazeSynth, a novel framework designed to fortify privacy protections while ensuring the utility of eye-tracking data. DPGazeSynth addresses the unique requirements of gaze path synthesis, especially the differentiation between fixations and saccades. Our approach introduces a semi-synthetic method based on the Markov Chain model to accurately maintain data correlations for analytical tasks. We demonstrate that DPGazeSynth provides robust differential privacy guarantees, and our comprehensive experiments on two real-world datasets validate its effectiveness in safeguarding against re-identification attacks. The results showcase DPGazeSynth's better performance over existing solutions like Kalεido and establish its potential as a reliable foundation for future research aimed at reconciling privacy concerns with the demands of complex trajectory data analysis in eye-tracking applications.