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A continued challenge for preservation is objective data to make informed collection decisions. When considering a shared national print system, this challenge relates to decisions of withdrawal or retention since catalog partners may not have data regarding the condition of others' volumes. This conundrum led to a national research initiative funded by the Mellon Foundation, "Assessing the Physical Condition of the National Collection." The project captured and analyzed condition data from 500 "identical" volumes from five American research libraries to explore the following: What is the condition of book collections from 1840-1940? Can condition be predicted by catalog or physical parameters? What assessment tools might indicate a book's life expectancy? Filling gaps in knowledge about the physicality of our collections is helping identify at-risk collections and explain the cases of dissimilar "same" volumes based on the impact of paper composition. Predictive modeling and assessment tools are also used to improve the understanding of what is typical for specific eras. [ABSTRACT FROM AUTHOR]

In 3D building models, a large number of texture maps with different sizes increase the number of model data loading and drawing batches, which greatly reduces the drawing efficiency of the model. Therefore, this paper proposes a texture set mapping method based on vertex importance. Firstly, based on the 2D space boxing algorithm, the texture maps are merged and a series of Mipmap texture maps are generated, and then the vertex curvature, texture variability and location information of each vertex are calculated, normalized, and weighted to get the importance of each vertex, and then finally, different Mipmap-level textures are remapped according to the importance of the vertices. The experiment proves that the algorithm in this paper can reduce the amount of texture data on the one hand, and avoid the rendering pressure brought by the still large amount of data after merging on the other hand, so as to improve the rendering efficiency of the model. [ABSTRACT FROM AUTHOR]

Research classification is an important aspect of conducting research projects because it allows researchers to efficiently identify papers that are in line with the latest research in each field and relevant to projects. There are different approaches to the classification of research papers, such as subject-based, methodology-based, text-based, and machine learning-based. Each approach has its advantages and disadvantages, and the choice of classification method depends on the specific research question and available data. The classification of scientific literature helps to better organize and structure the vast amount of information and knowledge generated in scientific research. It enables researchers and other interested parties to access relevant information in a fast and efficient manner. Classification methods allow easier and more accurate extraction of scientific knowledge to be used as a basis for scientific research in each subject area. In this regard, this paper aims to propose a research classification model using data mining methods and techniques. To test the model, we selected scientific articles on digital workplace accessibility for the disabled retrieved from Scopus and Web of Science repositories. We believe that the classification model is universal and can be applied in other scientific fields. [ABSTRACT FROM AUTHOR]

Quantum Machine Learning is an interdisciplinary field that combines the principles of quantum physics, quantum computers, and machine learning to enhance computational performance. In recent years, there have been significant advancements in the field of quantum machine learning. Researchers have developed quantum algorithms for various tasks. Furthermore, quantum machine learning has been applied to various domains, such as chemistry, cryptography, and finance. Previous bibliometric surveys in this area covered the period from 2014 to 2020. However, as demonstrated in this paper, the pace of publication has increased significantly from 2018 to the present. Therefore, it has become essential to provide a new analysis of the current state of research in this field. In this paper, we deployed visualization tools to analyze co-authorships, co-occurrences, and keyword density in this research area. Our study covers and analyzes a total of 918 publications from the Web of Science database and 1171 publications from the Scopus database from 2006 to 2022. Following the analysis, we identify research questions, opportunities, and research gaps in the field of Quantum Machine Learning. [ABSTRACT FROM AUTHOR]

In view of the low identification accuracy of the crack-detection technology of asphalt pavement under the current conditions of complex pavement (subject to strong light, water on the road, debris, and so on), an asphalt pavement crack-detection algorithm based on improved YOLOv5s was proposed by building the data set for asphalt pavement cracks. The first step was to make the following improvements to the original YOLOv5s model according to the characteristics of the asphalt pavement crack data set: The k -means++ algorithm was used to recluster the anchor points of the crack data set, and the initial anchor frame matching the crack characteristics of the asphalt pavement was obtained to replace the default initial anchor frame in the YOLOv5 original model. In the prediction part of the original model, the Convolutional Block Attention Module (CBAM) was added in the order of first channel and then space to improve the detection ability of the model to small cracks. The CIoU_Loss function was used as the regression loss function of the model to replace the GIoU_Loss function in the original model to improve the positioning accuracy of the anchor frame. The second step was to perform an ablation experiment on the improved YOLOv5s model. This would prove that each improvement scheme could increase detection ability without conflict. The final step was to compare the improved YOLOv5s model with various classic target detection models in the data set of this paper: the Crack Forest Data set (CFD), the Crack500 data set, and the Crack200 data set. The results showed that the detection of the improved YOLOv5s model on each data set was better than other target detection models. The mAP@0.5 and mAP@[0.5:0.95] of this model on the data set of this paper were 90.58% and 56.08%, respectively, which were much higher than other target detection models. These findings indicate that the improved YOLOv5s model had better detection under complex pavement conditions and could provide a theoretical basis for the automatic detection of asphalt pavement cracks. [ABSTRACT FROM AUTHOR]

In this Perspective paper we explore the potential of integrating radiological imaging with other data types, a critical yet underdeveloped area in comparison to the fusion of other multi-omic data. Radiological images provide a comprehensive, three-dimensional view of cancer, capturing features that would be missed by biopsies or other data modalities. This paper explores the complexities and challenges of incorporating medical imaging into data integration models, in the context of precision oncology. We present the different categories of imaging-omics integration and discuss recent progress, highlighting the opportunities that arise from bringing together spatial data on different scales. [ABSTRACT FROM AUTHOR]

This paper illustrates how years 1 and 2 students were guided to engage in data modelling and statistical reasoning through interdisciplinary mathematics and science investigations drawn from an Australian 3-year longitudinal study: Interdisciplinary Mathematics and Science Learning (https://imslearning.org/). The project developed learning sequences for 12 inquiry-based investigations involving 35 teachers and cohorts of between 25 and 70 students across years 1 through 6. The research used a design-based methodology to develop, implement, and refine a 4-stage pedagogical cycle based on students' problem posing, data generation, organisation, interpretation, and reasoning about data. Across the stages of the IMS cycle, students generated increasingly sophisticated representations of data and made decisions about whether these supported their explanations, claims about, and solutions to scientific problems. The teacher's role in supporting students' statistical reasoning was analysed across two learning sequences: Ecology in year 1 and Paper Helicopters in year 2 involving the same cohort of students. An explicit focus on data modelling and meta-representational practices enabled the year 1 students to form statistical ideas, such as distribution, sampling, and aggregation, and to construct a range of data representations. In year 2, students engaged in tasks that focused on ordering and aggregating data, measures of central tendency, inferential reasoning, and, in some cases, informal ideas of variability. The study explores how a representation-focused interdisciplinary pedagogy can support the development of data modelling and statistical thinking from an early age. [ABSTRACT FROM AUTHOR]

Most Penman-Monteith-Leuning (PML) evapotranspiration (ET) modeling studies are dominated by consideration of meteorological, energy, and land use information, etc., but the dynamic coupling of soil moisture content (SM), especially in terms of improving accuracy through assimilation, lacks sufficient attention. This paper proposes a research framework for the dynamic coupling simulation of PML model and SM based on data assimilation, i.e., the remote sensing monitored SM is combined with soil evaporation of PML to obtain high-precision time-continuous SM data through data assimilation; simultaneously, dynamical soil evaporation coefficients are generated based on the assimilated SM to improve the simulation accuracy of the PML model. The new scheme was validated at a typical irrigation zone in north China and showed obvious improvements in both SM and ET simulations. Moreover, the effect of the assimilation of SM on the simulation accuracy of ET for different crop growth periods is further analyzed. This research provides a new idea for the coupling simulation of the SM and PML models. [ABSTRACT FROM AUTHOR]

This document is an introduction to a special issue of the journal Lexicography, focusing on the intersection of lexicography, artificial intelligence (AI), and dictionary users. The issue features four selected papers presented at a conference on Asian lexicography in 2023. The first direction discussed is the integration of AI, specifically GPT models, into lexicography to enhance productivity and efficiency. The second direction explores the role of lexicography in the era of large-scale language models (LLMs) and the need to expand linguistic resources and address ethical judgments. The document also highlights the impact of AI on dictionary users and the utility of dictionaries in comparison to AI technology. The volume includes papers on the contribution of GPT models to dictionary writing, the cognitive benefits of dictionary use for language learners, the inclusion of cultural information in bilingual dictionaries, and the compilation and use of a Korean synonym dictionary. [Extracted from the article]

This paper proposes a profit maximization model for a data consumer when it buys personal data from data providers (by obtaining consent) through data brokers and provides their new services to data providers (i.e., service consumers). To observe the behavioral models of data providers, the data consumer, and service consumers, this paper proposes the willingness-to-sell model of personal data of data providers (which is affected by data providers' behavior related to explicit consent), the service quality model obtained by the collected personal data from the data consumer's perspective, and the willingness-to-pay model of service consumers regarding provided new services from the data consumer. Particularly, this paper jointly considers the behavior of data providers and service users under a limited budget. With parameters inspired by real-world surveys on data providers, this paper shows various numerical results to check the feasibility of the proposed models. [ABSTRACT FROM AUTHOR]

Framing of the research. Artificial intelligence (AI) is transforming the way organisations manage human resources, injecting new capabilities into human resource management (HRM). There is a pressing need to examine new and more effective approaches to human resource development (HRD). Purpose of the paper. This paper aims to shed light on current knowledge of AI in the HRD domain, developing a comprehensive view of its role in the employee's journey. Methodology. Keyword co-occurrence analysis and bibliographic coupling analysis were performed on a total of 151 papers published between 2002 and 2022. A similarity visualisation programme (VOSviewer) was used to showcase the results visually. Results. The findings highlight the top five authors, sources, papers, and institutions in terms of the prolificacy of contributions in the field. The relevant contribution of this study is the identification and classification of the main topics and research streams in the academic literature. Five main bibliographic clusters are identified, unveiling the five most prominent topics in the field: i) AI in HR and contextual factors; ii) AI in education and future skills; iii) AI Coaching with chatbots; iv) AI in HR recruitment and training; v) AI in soft skills development. Research limitations. It should be acknowledged that the findings are rooted in one database, Scopus, and only publications in English were considered. Managerial implications. We offer three theoretical and institutional implications for advancing further research on AI in HRD. Furthermore, we outline six major takeaways and future lines of research stemming from our findings, resulting in a novel framework that can also be of practical interest to companies. Originality of the paper. This is the first bibliometric study in the HRD and AI field from the viewpoint of personal development. Thus, we provide a first systematisation of the contributions developed in the last twenty years in this novel field of research. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper argues the importance of considering domain knowledge when dealing with missing data in healthcare. We identify fundamental missingness scenarios in healthcare facilities and show how they impact the missing data analysis methods. Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into the missing data methods. In this paper, we argue that the remedy is to identify the key scenarios that lead to data missingness and investigate their theoretical implications. Based on this proposal, we first introduce an analysis framework where we investigate how different observation agents, such as physicians, influence the data availability and then scrutinize each scenario with respect to the steps in the missing data analysis. We apply this framework to the case study of observational data in healthcare facilities. We identify ten fundamental missingness scenarios and show how they influence the identification step for missing data graphical models, inverse probability weighting estimation, and exponential tilting sensitivity analysis. To emphasize how domain-informed analysis can improve method reliability, we conduct simulation studies under the influence of various missingness scenarios. We compare the results of three common methods in medical data analysis: complete-case analysis, Missforest imputation, and inverse probability weighting estimation. The experiments are conducted for two objectives: variable mean estimation and classification accuracy. We advocate for our analysis approach as a reference for the observational health data analysis. Beyond that, we also posit that the proposed analysis framework is applicable to other medical domains. [ABSTRACT FROM AUTHOR]

Traditional machine learning-based entity extraction methods rely heavily on feature engineering by experts, and the generalization ability of the model is poor. Prototype networks, on the other hand, can effectively use a small amount of labeled data to train models while using category prototypes to enhance the generalization ability of the models. Therefore, this paper proposes a prototype network-based named entity recognition (NER) method, namely the FSPN-NER model, to solve the problem of difficult recognition of sensitive data in data-sparse text. The model utilizes the positional coding model (PCM) to pre-train the data and perform feature extraction, then computes the prototype vectors to achieve entity matching, and finally introduces a boundary detection module to enhance the performance of the prototype network in the named entity recognition task. The model in this paper is compared with LSTM, BiLSTM, CRF, Transformer and their combination models, and the experimental results on the test dataset show that the model outperforms the comparative models with an accuracy of 84.8%, a recall of 85.8% and an F1 value of 0.853. [ABSTRACT FROM AUTHOR]

Before vaccine development during the COVID-19 pandemic, Non-Pharmaceutical Interventions (NPIs) were the only solutions to mitigate COVID-19 infections. Governments continued to use them even after starting vaccine administration. In this research, we review different big data analytics models that assess and optimize the effectiveness of NPIs. These models are categorized into three big data analytics groups: descriptive, which measures the infection rate changes caused by NPIs; predictive, which predicts the future of the pandemic by implementing several NPIs; and data-driven prescriptive, which suggests optimal control policies. We further analyze each method's basic assumptions, limitations, and applicability during different pandemic phases and under different scenarios. This review of COVID-19 NPI evaluation methods will be beneficial for decision-makers to know which model to select for policy-making in possible future pandemics, which are more likely recently due to globalization. Finally, we suggest some future research directions. [ABSTRACT FROM AUTHOR]

In its regulatory enterprise to improve the conditions of data sharing and reuse, the European Union has enacted new legislation: the Data Governance Act (DGA). The DGA envisages new forms of sharing public sector data (PSD). Based on a legal analysis of the DGA and an in-depth study of data governance literature, this paper highlights what is at stake in the new regulatory framework and argues that more than the mere openness of more PSD will be necessary to ensure that the European Union policy goals are achieved, especially those concerning enhancing innovation for the common good. From this perspective, the paper argues that the public data trust model of data governance and the Responsible Research and Innovation approach offer two powerful tools for public sector data governance. In this context, this paper contributes to the debate about new data governance models and discusses tools and frameworks enabling the use of data for the common good. It also provides insights to public administration practitioners aiming to implement a framework for increased and sustainable PSD sharing. [ABSTRACT FROM AUTHOR]

Introduction: Behaviourally informed soft policies, such as nudges, have become popular in areas like health, environment, and energy use as cost-effective instruments to change behaviour and decision-making. However, the effectiveness of soft policies in the transport sector is modest at best. One reason for this relative ineffectiveness might be their one-size-fits-all nature, and personalizing soft interventions has been suggested to increase their effectiveness. The Trans-theoretical Model (TTM) suggests that people progress through five stages of behavioural change, from pre-contemplating a behaviour to maintaining the behaviour, and behavioural interventions could be designed for specific stages. However, it is not always feasible to conduct surveys to place people at different stages of the TTM. Methods: This paper explores whether it is possible to use multimodality data taken from a travel diary to place people at different stages of the TTM. The analysis uses an existing dataset from 826 respondents that includes self-reported TTM stages regarding cycling and data on multimodality. In the analysis, the multimodality data are used to allocate respondents to categories and assign them to TTM stages. The performances of the stage assignment approaches are evaluated using the self-reported TTM data and confusion matrices. Findings: The accuracy of the allocation of participants to TTM stages using multimodality data is approximately 75%. The accuracy is higher for early stages (pre-contemplation) and later stages (maintenance) of the TTM. A data-driven approach to dealing with multimodality data performs slightly better than an approach that relies on pre-defined categorization. Conclusion: The paper suggests that it will be possible in the future to personalise behavioural interventions according to the stages of the TTM even in the absence of self-reported survey data that classifies people to TTM stages if objective multimodality data are available. [ABSTRACT FROM AUTHOR]

This paper employs the instrumental variable method and the intermediary model to explore the influence of monocentric and polycentric spatial structures of innovation agglomeration on energy efficiency. This paper uses data from 2010 to 2021 in China, and also panel data regression models were used. The findings indicate that the monocentric spatial structure of provincial system innovation agglomeration is primarily concentrated in northeastern and western regions of China, whereas the polycentric spatial structure is mainly distributed in eastern coastal areas. A monocentric spatial structure inhibits the enhancement of energy efficiency, but there is an environmental paradox of excessive resource agglomeration. In contrast, a polycentric spatial structure and its strengthening tendency facilitate the improvement of energy efficiency. A monocentric spatial structure has an inhibitory impact on the improvement of energy efficiency in the three sectors of agriculture, industry, and construction, while a polycentric spatial structure aids it. A monocentric spatial structure further hinders the improvement of energy efficiency by impeding the upgrading of the industrial structure, while the upgrading of the industrial structure driven by the polycentric spatial structure plays a role in enhancing energy efficiency. The policy recommendations of this paper are intended to help coordinate the pace of innovation development between cities, adjust the spatial structure of innovation agglomeration between regions, and promote the upgrading of the comprehensive industrial structure, thereby improving overall energy efficiency. [ABSTRACT FROM AUTHOR]

Understanding electrical load profiles and detecting anomaly behaviors are important to the smart grid system. However, current load identification and anomaly analysis are based on static analysis, and less consideration is given to anomaly findings under load change conditions. This paper proposes a deep variational autoencoder network (DVAE) for load profiles, along with anomaly analysis services, and introduces auto-time series data updating strategies based on sliding window adjustment. DVAE can help reconstruct the load curve and measure the difference between the original and the newer curve, whose measurement indicators include reconstruction probability and Pearson similarity. Meanwhile, the design of the sliding window strategy updates the data and DVAE model in a time-series manner. Experiments were carried out based on datasets from the U.S. Department of Energy and from Southeast China. The results showed that the proposed services could result in a 5% improvement in the AUC value, which helps to identify the anomaly behavior. [ABSTRACT FROM AUTHOR]

Earlier methods of signature based threat detection are no longer enough to detect threats. Fencing your network and access is also ineffective in stopping malicious actors because the IT infrastructure is continuously being shifted to the cloud. Continuous data collection, monitoring and watching out for any malicious behaviors can detect zero day or unknown threats as well. This paper focuses on one of the most important and widely used collections of such tools which are built around Elasticsearch (ES). This paper explains Elasticsearch and its ecosystem of tools like Filebeat and Kibana. A test bed is set up consisting of Apache Web server, Elasticsearch, Filebeat and Kibana. Also the machine learning (ML) capabilities of Elasticsearch are demonstrated with manually injected anomalies in the metric data collected for the web server. [ABSTRACT FROM AUTHOR]

Object detection is a computer vision task that identifies and locates one or more effective targets from image or video data. The accuracy of object detection heavily depends on the size and the diversity of the utilized dataset. However, preparing and labeling an adequate dataset to guarantee a high level of reliability can be time-consuming and labor-intensive, because the process of building data requires manually setting up the environment and capturing the dataset while keeping its variety in scenarios. There have been several efforts on object detection that take a long time to prepare the input data for training the models. To deal with this problem, synthetic data have emerged as a potential for the replacement of real-world data in data preparation for model training. In this paper, we provide a technique that can generate an enormous synthetic dataset with little human labor. Concretely, we have simulated the environment by applying the pyBullet library and capturing various types of input images. In order to examine its performance on the training model, we integrated a YOLOv5 object detection model to investigate the dataset. The output of the conducted model was deployed in a simulation robot system to examine its potential. YOLOv5 can reach a high accuracy of object detection at 93.1% mAP when solely training on our generated data. Our research provides a novelistic method to facilitate the understanding of the data generation process in preparing datasets for deep learning models. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for fault detection and isolation (FDI) in transient conditions using a multiway principal component analysis (MPCA) approach where practical data have been augmented with simulated data to conduct FDI when there are insufficient practical data. The motivation for using a heated two-tank system is due to the fact that it resembles a basic process in terms of controllable variables, noise, disturbances, and changes in operating points. Normal and faulty condition data of the practical heated two-tank system as well as a Simulink® model of the heated two-tank system were used. The MPCA technique has enhanced ability to detect and isolate faults in transient conditions compared to classic principal component analysis (PCA). MPCA, however, requires a vast amount of normal process transient conditions data to train the model to then enable meaningful fault detection and isolation. In this study, the practical normal transient conditions data are augmented with simulated normal transient conditions data to meet the requirement of a large amount of data. Utilising different datasets for the training of the MPCA model, the fault detection and isolation performance was evaluated with various metrics. This paper presents positive results towards the implementation of MPCA for fault detection in transient conditions. [ABSTRACT FROM AUTHOR]

There is broad consensus that audio description (AD) is a modality of intersemiotic translation, but there are different views in relation to how AD can be more precisely conceptualised. While Benecke (Audiodeskription als partielle Translation. Modell und Methode, LIT, Berlin, 2014) characterises AD as 'partial translation', Braun (T 28: 302–313, 2016) hypothesises that what audio describers appear to 'omit' from their descriptions can normally be inferred by the audience, drawing on narrative cues from dialogue, mise-en-scène, kinesis, music or sound effects. The study reported in this paper tested this hypothesis using a corpus of material created during the H2020 MeMAD project. The MeMAD project aimed to improve access to audiovisual (AV) content through a combination of human and computer-based methods of description. One of the MeMAD workstreams addressed human approaches to describing visually salient cues. This included an analysis of the potential impact of omissions in AD, which is the focus of this paper. Using a corpus of approximately 500 audio described film extracts we identified the visual elements that can be considered essential for the construction of the filmic narrative and then performed a qualitative analysis of the corresponding audio descriptions to determine how these elements are verbally represented and whether any omitted elements could be inferred from other cues that are accessible to visually impaired audiences. We then identified the most likely source of these inferences and the conditions upon which retrieval could be predicated, preparing the ground for future reception studies to test our hypotheses with target audiences. In this paper, we discuss the methodology used to determine where omissions occur in the analysed audio descriptions, consider worked examples from the MeMAD500 film corpus, and outline the findings of our study namely that various strategies are relevant to inferring omitted information, including the use of proximal and distal contextual cues, and reliance on the application of common knowledge and iconic scenarios. To conclude, consideration is given to overcoming significant omissions in human-generated AD, such as using extended AD formats, and mitigating similar gaps in machine-generated descriptions, where incorporating dialogue analysis and other supplementary data into the computer model could resolve many omissions. [ABSTRACT FROM AUTHOR]

The meta-learning method proposed in this paper addresses the issue of small-sample regression in the application of engineering data analysis, which is a highly promising direction for research. By integrating traditional regression models with optimization-based data augmentation from meta-learning, the proposed deep neural network demonstrates excellent performance in optimizing glass fiber reinforced plastic (GFRP) for wrapping concrete short columns. When compared with traditional regression models, such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Radial Basis Function Neural Networks (RBFNN), the meta-learning method proposed here performs better in modeling small data samples. The success of this approach illustrates the potential of deep learning in dealing with limited amounts of data, offering new opportunities in the field of material data analysis. [ABSTRACT FROM AUTHOR]

Applying a parametric approach, in this paper we studied a new class of multidimensional extremization models with data uncertainty. Concretely, first, we derived the robust conditions of necessary optimality. Thereafter, we established robust sufficient optimality conditions by employing the various forms of convexity of the considered functionals. In addition, we formulated an illustrative example to validate the theoretical results. [ABSTRACT FROM AUTHOR]

One of the major challenges that existing spatial data is facing is the fragmentation of its representation of indoor and outdoor space. As studies in the use of omnidirectional images in representing space and providing Location-based Services (LBS) has been increasing, the representation of the different scales of space, both in indoors and outdoors, has yet to be addressed. This study aims to develop a data model for generating a multi-scale image-based representation of space using omnidirectional images based spatial relationships. This paper identifies the different scales of space that are represented in spatial data and extends previous approaches of using omnidirectional images in providing indoor LBS towards representing the other scales of space, particularly in outdoor space. Using a sample data, we present an experimental implementation to demonstrate the potential of the proposed data model. Results show that apart from the realistic visualization that image data provides, basic spatial functions can be performed on the image data constructed based on the proposed data model. [ABSTRACT FROM AUTHOR]

In this paper, a fractional active disturbance rejection control (FADRC) scheme is proposed for remotely operated vehicles (ROVs) to enhance high-precision positioning and docking control in the presence of ocean current disturbances and model uncertainties. The scheme comprises a double closed-loop fractional-order PI λ D μ controller (DFOPID) and a model-assisted finite-time sliding-mode extended state observer (MFSESO). Among them, DFOPID effectively compensates for non-matching disturbances, while its fractional-order term enhances the dynamic performance and steady-state accuracy of the system. MFSESO contributes to enhancing the estimation accuracy through the integration of sliding-mode technology and model information, ensuring the finite-time convergence of observation errors. Numerical simulations and pool experiments have shown that the proposed control scheme can effectively resist disturbances and successfully complete high-precision tasks in the absence of an accurate model. This underscores the independence of this control scheme on accurate model data of an operational ROV. Meanwhile, it also has the advantages of a simple structure and easy parameter tuning. The FADRC scheme presented in this paper holds practical significance and can serve as a valuable reference for applications involving ROVs. [ABSTRACT FROM AUTHOR]

The International Hydrographic Organization (IHO) has established a new hydrographic information standard, the S-100 standard. This standard applies the ISO 19100 series to the legacy hydrographic exchange standard in order to overcome the limitations of existing hydrographic information expression. The first version of the S-100 standard was released in 2010, and the latest version, 5.0.0, was released in 2022. The International Maritime Organization (IMO) officially adopted the IHO-s S-100 for hydrographic information exchange. The S-100 standard is the base standard, and each hydrographic information is developed as a document called the product specification. The product specification is being developed and led by various standardization organizations. The product specifications developed by each standardization organization are based on S-100, but they are defined in various ways according to the characteristics of information and expression methods. In this paper, the different product specifications of IHO's S-12x series and IALA's S-2xx series are analyzed in terms of features and classes from the data model point of view. The analysis of the feature, attribute, and feature relationship of each product specification was conducted by reviewing both the Data Classification and Encoding Guide (DCEG) and the entire document of the product specification. This study has identified areas of overlap in S-100 product specifications and has explored potential solutions to address these issues. [ABSTRACT FROM AUTHOR]

Model-based systems engineering (MBSE) is a modeling approach used in industry to support the formalization, analysis, design, checking and verification of systems. In MBSE modeling, domain knowledge is the basis of the modeling. However, modeling does not happen overnight; it requires systematic training and a significant investment of resources. Unfortunately, many domain experts lack the expertise required for modeling, even though they know the domain well. The question arises about how to provide system modelers with domain knowledge at the right time to support the efficient completion of modeling. Since MBSE research that integrates AI is just beginning to take off, no public dataset is available. In this paper, aerospace SysML models are constructed based on spacecraft-related domain knowledge to form SysML model data. The validation rules are studied to validate the SysML model data, and combined with the concept of the recommended system, a recommendation method for the MBSE modeling process based on the knowledge and SysML model is proposed. A GLOVE language model is pre-trained by using domain knowledge and general knowledge; the model data are also used to fine-tune the GLOVE language model combined with the pre-training to recommend some domain development processes. The recommendation list is manually quality-verified and fed into the pre-training phase, while new requirement texts are continuously added in the fine-tuning phase, resulting in a more relevant and accurate recommendation list. Experiments show that the incremental recommender system can not only effectively recommend SysML models, but also improve the quality and efficiency of MBSE development. [ABSTRACT FROM AUTHOR]

Process industry suffers from production management in terms of efficiency promotion and waste reduction in large scale manufacturing due to poor organization of the intricate relational databases. In order to enhance the suitability of intelligent manufacturing systems in process industry, this study proposed an innovative top-down structure Knowledge Graph (KG) for process fault diagnosis, and papermaking was taken as a case study. The KG consists of a normalized seven-step-built ontology, which extracted instances of papermaking knowledge via Protégé software. The exported OWL file was imported into Neo4j software for visualization of the KG. The application in papermaking drying process for fault diagnosis shows that it can depict the material and energy flows throughout the process with a clearer relationship visualization than traditional measures. They also enable rationale search for faults and identification of their potential causes. The built KG efficiently manages the vast knowledge of the process, stores unstructured data, and promotes the intelligent development of process with high reusability and dynamicity that can rapidly import new production knowledge as well as flexibly self-updating. [ABSTRACT FROM AUTHOR]

The presented paper introduces a novel path planning algorithm designed for generating low-cost trajectories that fulfill mission requirements expressed in Linear Temporal Logic (LTL). The proposed algorithm is particularly effective in environments where cost functions encompass the entire configuration space. A core contribution of this paper is the presentation of a refined approach to sampling-based path planning algorithms that aligns with the specified mission objectives. This enhancement is achieved through a multi-layered framework approach, enabling a simplified discrete abstraction without relying on mesh decomposition. This abstraction is especially beneficial in complex or high-dimensional environments where mesh decomposition is challenging. The discrete abstraction effectively guides the sampling process, influencing the selection of vertices for extension and target points for steering in each iteration. To further improve efficiency, the algorithm incorporates a deep learning-based extension, utilizing training data to accurately model the optimal trajectory distribution between two points. The effectiveness of the proposed method is demonstrated through simulated tests, which highlight its ability to identify low-cost trajectories that meet specific mission criteria. Comparative analyses also confirm the superiority of the proposed method compared to existing methods. [ABSTRACT FROM AUTHOR]

Binary neural network (BNN) delivers increased compute intensity and reduces memory/data requirements for computation. Scalable BNN enables inference in a limited time due to different constraints. This paper explores the application of Scalable BNN in oblivious inference, a service provided by a server to mistrusting clients. Using this service, a client can obtain the inference result on his/her data by a trained model held by the server without disclosing the data or learning the model parameters. Two contributions of this paper are: (1) we devise lightweight cryptographic protocols explicitly designed to exploit the unique characteristics of BNNs. (2) we present an advanced dynamic exploration of the runtime-accuracy tradeoff of scalable BNNs in a single-shot training process. While previous works trained multiple BNNs with different computational complexities (which is cumbersome due to the slow convergence of BNNs), we train a single BNN that can perform inference under various computational budgets. Compared to CryptFlow2, the state-of-the-art technique in the oblivious inference of non-binary DNNs, our approach reaches 3× faster inference while keeping the same accuracy. Compared to XONN, the state-of-the-art technique in the oblivious inference of binary networks, we achieve 2× to 12× faster inference while obtaining higher accuracy. [ABSTRACT FROM AUTHOR]

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The application of Artificial Intelligence (AI) and Machine Learning (ML) models is increasingly leveraged to automate and optimize Data Centre (DC) operations. However, the interpretability and transparency of these complex models pose critical challenges. Hence, this paper explores the Shapley Additive exPlanations (SHAP) values model explainability method for addressing and enhancing the critical interpretability and transparency challenges of predictive maintenance models. This method computes and assigns Shapley values for each feature, then quantifies and assesses their impact on the model's output. By quantifying the contribution of each feature, SHAP values can assist DC operators in understanding the underlying reasoning behind the model's output in order to make proactive decisions. As DC operations are dynamically changing, we additionally investigate how SHAP can capture the temporal behaviors of feature importance in the dynamic DC environment over time. We validate our approach with selected predictive models using an actual dataset from a High-Performance Computing (HPC) DC sourced from the Enea CRESCO6 cluster in Italy. The experimental analyses are formalized using summary, waterfall, force, and dependency explanations. We delve into temporal feature importance analysis to capture the features' impact on model output over time. The results demonstrate that model explainability can improve model transparency and facilitate collaboration between DC operators and AI systems, which can enhance the operational efficiency and reliability of DCs by providing a quantitative assessment of each feature's impact on the model's output. [ABSTRACT FROM AUTHOR]

The problem of finding a maximum size matching in a graph (known as the maximum matching problem) is one of the most classical problems in computer science. Despite a significant body of work dedicated to the study of this problem in the data stream model, the state-of-the-art single-pass semi-streaming algorithm for it is still a simple greedy algorithm that computes a maximal matching, and this way obtains 1 / 2 -approximation. Some previous works described two/three-pass algorithms that improve over this approximation ratio by using their second and third passes to improve the above mentioned maximal matching. One contribution of this paper continues this line of work by presenting new three-pass semi-streaming algorithms that work along these lines and obtain improved approximation ratios of 0.6111 and 0.5694 for triangle-free and general graphs, respectively. Unfortunately, a recent work Konrad and Naidu (Approximation, randomization, and combinatorial optimization. Algorithms and techniques, APPROX/RANDOM 2021, August 16–18, 2021. LIPIcs, vol 207, pp 19:1–19:18, 2021. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.19) shows that the strategy of constructing a maximal matching in the first pass and then improving it in further passes has limitations. Additionally, this technique is unlikely to get us closer to single-pass semi-streaming algorithms obtaining a better than 1 / 2 -approximation. Therefore, it is interesting to come up with algorithms that do something else with their first pass (we term such algorithms non-maximal-matching-first algorithms). No such algorithms were previously known, and the main contribution of this paper is describing such algorithms that obtain approximation ratios of 0.5384 and 0.5555 in two and three passes, respectively, for general graphs. The main significance of our results is not in the numerical improvements, but in demonstrating the potential of non-maximal-matching-first algorithms. [ABSTRACT FROM AUTHOR]

Despite the complexity of thermodynamic models in predicting wax precipitation, they have not shown satisfactory results in the literature. This paper proposes the least-squares support vector machine optimized by the genetic algorithm to forecast wax precipitation based on the mole fractions, specific gravity, and temperature of crude oil samples. The genetic algorithm was used to prevent sticking in local optima in optimizing the parameters of the predictive model. A dataset of 88 experimental wax precipitation samples was used to evaluate the performance of the proposed method. The results revealed that the suggested algorithm could predict wax precipitation with an accuracy of 0.939 and 0.997 before and after sensitivity analysis, respectively. Furthermore, sensitivity analysis improved the Coefficient of Determination, Mean Squared Error, and Root Mean Squared Error from 0.939, 0.768, and 0.877 to 0.997, 0.041, and 0.202, respectively. The results were compared with the results of previous data-driven and mathematical models that showed the outperformance of the proposed method. The contribution of this paper can be summarized as demonstrating the ability of intelligent models to predict wax precipitation without a need for complex thermodynamic models. Also, it was shown that a proper feature selection could improve the model's performance. [ABSTRACT FROM AUTHOR]

The role and adoption of 3D city models have been changing from a data endpoint to a centralised data source that is used for a variety of different analyses in different sectors. This change has not yet been fully completed and the transition process is still very noticeable at certain places. For example, data required for city-scale analyses are often missing, incorrect, or not stored in a standard way. A subset of these data (E.g. shell volume, shell area & footprint area) can be approximated from lower LoD shapes (LoD2.2 or lower) in the 3D city models. However, these models frequently simplify reality and therefore these approximations are not accurate. This paper proposes computing these data by voxelising Building Information Modelling (BIM) models representing the same buildings as the 3D city model. It is shown that a subset of these approximations (shell volume & footprint area) are more accurate than values computed from lower LoD shapes. Storing these data as attributes of the building models in 3D city models can improve the ease of use and the outcome of city-scale analyses. The computed values from BIM models can also be assigned to outputs of BIM to Geo conversions. This overturns the accuracy loss of the geometry caused by the conversion in which geometry is significantly generalised and simplified. [ABSTRACT FROM AUTHOR]

Time‐to‐detection (TTD) occupancy models are increasingly used to study site occupancy of organisms. Occupancy is a reduced representation of abundance (distinguishing only between 0 and >0 individuals), which is also often a quantity of interest. In this paper, we present a novel framework for TTD occupancy models that address limitations of existing approaches. Our approach incorporates factors that accommodate detection heterogeneity among sites/visits and inter‐visit dependency, allowing for the relaxation of some restrictive assumptions inherent in previous models. As a result, our framework offers a robust and versatile tool for analysing various ecological data sets.We employ a maximum likelihood approach to estimate model parameters and conduct inference for the proposed TTD occupancy models. A key feature of these models is the introduction of a community parameter. This parameter characterizes the similarity of detectabilities, ranging from complete independence to complete identity, across multiple site visits. This framework disentangles the detection rate, abundance and occupancy, similar to the popular N‐mixture model. For situations where abundance estimation is not the primary goal, a family of mixed gamma exponential TTD models is developed, which generally exhibit more stable numerical properties compared to N‐mixture type TTD models.The performance of the proposed models and some reduced models is evaluated through simulation studies. The results indicate that the N‐mixture TTD model tends to considerably overestimate the occupancy probability when the community parameter is less than one, a condition necessary to satisfy the strict closure population assumption. On the other hand, the standard exponential TTD occupancy model underestimates the occupancy probability in the presence of unobserved detection heterogeneity and inter‐visit dependency. An analysis of 63 bird species in the Karoo region of South Africa demonstrates the enhanced flexibility of the proposed TTD occupancy models for data fitting.This paper demonstrates the importance of employing more flexible and general models to accurately capture the complexities of ecological systems or survey data. We provide R‐code to fit all considered models to data. The proposed TTD model framework contributes to enhancing our understanding of species occupancy distributions. [ABSTRACT FROM AUTHOR]

The pupil of the eye provides a rich source of information for cognitive scientists, as it can index a variety of bodily states (e.g., arousal, fatigue) and cognitive processes (e.g., attention, decision-making). As pupillometry becomes a more accessible and popular methodology, researchers have proposed a variety of techniques for analyzing pupil data. Here, we focus on time series-based, signal-to-signal approaches that enable one to relate dynamic changes in pupil size over time with dynamic changes in a stimulus time series, continuous behavioral outcome measures, or other participants' pupil traces. We first introduce pupillometry, its neural underpinnings, and the relation between pupil measurements and other oculomotor behaviors (e.g., blinks, saccades), to stress the importance of understanding what is being measured and what can be inferred from changes in pupillary activity. Next, we discuss possible pre-processing steps, and the contexts in which they may be necessary. Finally, we turn to signal-to-signal analytic techniques, including regression-based approaches, dynamic time-warping, phase clustering, detrended fluctuation analysis, and recurrence quantification analysis. Assumptions of these techniques, and examples of the scientific questions each can address, are outlined, with references to key papers and software packages. Additionally, we provide a detailed code tutorial that steps through the key examples and figures in this paper. Ultimately, we contend that the insights gained from pupillometry are constrained by the analysis techniques used, and that signal-to-signal approaches offer a means to generate novel scientific insights by taking into account understudied spectro-temporal relationships between the pupil signal and other signals of interest. [ABSTRACT FROM AUTHOR]

With the exponential growth of various data interactions on network systems, network intrusions are also increasing. The emergence of edge computing technology brings a new solution to network security. However, due to the difficulty of processing massive and unbalanced data at the edge, higher accuracy requirements are necessary for deployed detection models. This paper proposes a multi-classification model for network intrusion detection based on reconstruction and feature matching. This model can be deployed on small-scale edge nodes, effectively identifying various attack behaviors through the utilization of reconstruction errors and adaptive scaling. Furthermore, we proposed a model transfer method based on feature matching to enhance the training and detection efficiency of multi-classification models under different data distribution conditions. The proposed model has been evaluated on the CICIDS2017 dataset in terms of accuracy, recall, precision and F1 score. The model demonstrates high accuracy for normal flows in the network, majority class attacks, and minority class attacks, achieving an overall multi-class accuracy of 99.81%, outperforming similar models. Furthermore, this model demonstrates faster convergence and training speed after feature matching, exhibiting better robustness and outstanding performance at the edge. [ABSTRACT FROM AUTHOR]

In various real-world domains, the problem of imbalanced data poses a significant challenge since it affects the efficiency and trustworthiness of machine learning models. This article investigates Explainable Artificial Intelligence (XAI) methods for studying models created on imbalanced datasets. The main objective of this paper is to assess models trained on DOSMOTE resampled balanced datasets. Using XAI techniques, the study seeks to understand better inner processes that lead to model decisions. The methodology involves combining DOSMOTE resampling with XAI to provide holistic evaluation through both qualitative and quantitative analysis. It should be noted that F1-Scores of balanced datasets improve significantly: from 76% to 87% for Web-Phishing; and from 58% to 73% for Hayes-Roth. This research highlights the need for XAI in enhancing interpretability of models trained on resampled imbalanced data sets. It also shows how resampling affects decision making in a model while performing and recommends investigating other resampling techniques or combinations with XAI methods aimed at improving model interpretability and transparency. [ABSTRACT FROM AUTHOR]

Satellite remote sensing provides the changes information of Earth surface on large spatial scale in a long time series and has been widely used in ecology. However, the possible impact from human activities generally occurs on a smaller spatial scale and could be detected in a longer time, which requires the remote sensing data having the both higher spatial and temporal resolution. Meanwhile, the development of the spatiotemporal data fusion algorithm provides an opportunity for the requirements. In this paper, based on deep learning, we proposed a residual convolutional neural network (Res-CNN) model to improve the fusion result considerably with brand-new network architecture to fuse the NDVI retrievals from Landsat 8 and MODIS images. Experiments conducted in two different areas demonstrate improvements by comparing them with existing algorithms. The model performance was evaluated by a linear regression between predictions and observations and quantified by determination coefficients (R2), regressive ecoefficiency (slope). The two excellent models, ESTARFM and FSDAF, were compared with the new model on their performance. The results showed that the predicted NDVI had the higher exploitational on the variability in the Landsat-based NDVI with the R2 of 0.768 and 0.807 at the urban and grassland sites. The predicted NDVI was well consistent with the observations with the slope of 1.01 and 0.989, and the R-RMSE of 95.76% and 93.58% at the urban and grassland sites respectively. This study demonstrated that the Res-CNN model developed in this paper exhibits higher accuracy and stronger robustness than the traditional models. This research is full implications because it not only provides a model on the spatio-temporal data fusion, but also can provide the data of a long time series for the management and utilization of agriculture and grassland ecosystems on the regional scale. [ABSTRACT FROM AUTHOR]

In modern times, large amounts of data with diverse characteristics, such as volume, variety, etc., are generated daily. For enterprises such as E-commerce, which is escalating nowadays, massive amounts of unstructured data have increased significantly, making it essential to manage high data availability and consistency in conventional databases. To establish extreme data availability and consistency, opting for the most effective data model is necessary to ensure that data accuracy and consistency are achieved and can be easily accessed and manipulated by the intended users. The development of data modelling approaches has made it possible to use massive quantities of data without degrading performance or loosening the conventional ACID properties. This paper emphasizes the importance of efficient data modelling. It provides practical guidelines for structuring RDBMS data models and for NoSQL data models, considering the types of data access patterns and highlighting their advantages and limitations in terms of e-commerce enterprises. [ABSTRACT FROM AUTHOR]

In the cloud-edge-device collaboration architecture, data types are diverse, and there are differences in storage resources and computing resources at all levels, which brings new challenges to data management. The existing data models or simple superposition of data models are difficult to meet the requirements of multimodal data management and collaborative management in the cloud-edge-device. Therefore, research on multimodal data modeling technology for cloud-edge-device collaboration has become an important issue. The core is how to efficiently obtain the query results that meet the needs of the application from the cloud-edge-device architecture. Starting from the data types of the three-layer data of cloud-edge-device, in this paper we propose a multimodal data modeling technology for cloudedge-device collaboration, give the definition of multimodal data model based on tuples, and design six base classes to achieve a unified representation of multimodal data. The basic data operation architecture of cloud-edge-device collaborative query is also proposed to meet the query requirements of cloud-edge-device business scenarios. The integrity constraints of the multimodal data model are given, which lays a theoretical foundation for query optimization. Finally, a demonstration application of the multimodal data model for cloud-edge-device collaboration is given, and the proposed data model storage method is verified from the three aspects of data storage time, storage space, and query time. The experimental results show that the proposed scheme can effectively represent the multimodal data in the cloud-edge-device collaboration architecture. [ABSTRACT FROM AUTHOR]

This paper proposes a newnetwork structure, namely the ProNet network. Retinal medical image segmentation can help clinical diagnosis of related eye diseases and is essential for subsequent rational treatment. The baselinemodel of the ProNet network is UperNet (Unified perceptual parsing Network), and the backbone network is ConvNext (Convolutional Network). A network structure based on depth-separable convolution and 1 x 1 convolution is used, which has good performance and robustness. We further optimise ProNet mainly in two aspects. One is data enhancement using increased noise and slight angle rotation, which can significantly increase the diversity of data and help the model better learn the patterns and features of the data and improve the model's performance. Meanwhile, it can effectively expand the training data set, reduce the influence of noise and abnormal data in the data set on the model, and improve the accuracy and reliability of the model. Another is the loss function aspect, and we finally use the focal loss function. The focal loss function is well suited for complex tasks such as object detection. The function will penalise the loss carried by samples that the model misclassifies, thus enabling better training of the model to avoid these errors while solving the category imbalance problem as a way to improve image segmentation density and segmentation accuracy. From the experimental results, the evaluation metrics mIoU (mean Intersection overUnion) enhanced by 4.47%, and mDice enhanced by 2.92% compared to the baseline network. Better generalization effects and more accurate image segmentation are achieved. [ABSTRACT FROM AUTHOR]

This paper introduces the modified Kies Topp-Leone (MKTL) distribution for modeling data on the (0, 1) or [0, 1] interval. The shapes of the density and hazard rate functions manifest desirable shapes, making the MKTL distribution suitable for modeling data with different characteristics at the unit interval. Twelve different estimation methods are utilized to estimate the distribution parameters, and Monte Carlo simulation experiments are executed to assess the performance of the methods. The simulation results suggest that the maximum likelihood method is the superior method. The usefulness of the new distribution is illustrated by utilizing three data sets, and its performance is juxtaposed with that of other competing models. The findings affirm the superiority of the MKTL distribution over the other candidate models. Applying the developed quantile regression model using the new distribution disclosed that it offers a competitive fit over other existing regression models. [ABSTRACT FROM AUTHOR]

This paper presents the exponentiated alpha-power log-logistic (EAPLL) distribution, which extends the log-logistic distribution. The EAPLL distribution emphasizes its suitability for survival data modeling by providing analytical simplicity and accommodating both monotone and non-monotone failure rates. We derive some of its mathematical properties and test eight estimation methods using an extensive simulation study. To determine the best estimation approach, we rank mean estimates, mean square errors, and average absolute biases on a partial and overall ranking. Furthermore, we use the EAPLL distribution to examine three real-life survival data sets, demonstrating its superior performance over competing log-logistic distributions. This study adds vital insights to survival analysis methodology and provides a solid framework for modeling various survival data scenarios. [ABSTRACT FROM AUTHOR]

Quantile regression has been widely used in many fields because of its robustness and comprehensiveness. However, it remains challenging to perform the quantile regression (QR) of streaming data by a conventional methods, as they are all based on the assumption that the memory can fit all the data. To address this issue, this paper proposes a Bayesian QR approach for streaming data, in which the posterior distribution was updated by utilizing the aggregated statistics of current and historical data. In addition, theoretical results are presented to confirm that the streaming posterior distribution is theoretically equivalent to the orcale posterior distribution calculated using the entire dataset together. Moreover, we provide an algorithmic procedure for the proposed method. The algorithm shows that our proposed method only needs to store the parameters of historical posterior distribution of streaming data. Thus, it is computationally simple and not storage-intensive. Both simulations and real data analysis are conducted to illustrate the good performance of the proposed method [ABSTRACT FROM AUTHOR]