Merging Information Research Articles

Optical Rules to Mitigate the Parallax-Related Registration Error in See-Through Head-Mounted Displays for the Guidance of Manual Tasks

Head-mounted displays (HMDs) are hands-free devices particularly useful for guiding near-field tasks such as manual surgical procedures. See-through HMDs do not significantly alter the user’s direct view of the world, but the optical merging of real and virtual information can hinder their coherent and simultaneous perception. In particular, the coherence between the real and virtual content is affected by a viewpoint parallax-related misalignment, which is due to the inaccessibility of the user-perceived reality through the semi-transparent optical combiner of the OST Optical See-Through (OST) display. Recent works demonstrated that a proper selection of the collimation optics of the HMD significantly mitigates the parallax-related registration error without the need for any eye-tracking cameras and/or for any error-prone alignment-based display calibration procedures. These solutions are either based on HMDs that projects the virtual imaging plane directly at arm’s distance, or they require the integration on the HMD of additional lenses to optically move the image of the observed scene to the virtual projection plane of the HMD. This paper describes and evaluates the pros and cons of both the suggested solutions by providing an analytical estimation of the residual registration error achieved with both solutions and discussing the perceptual issues generated by the simultaneous focalization of real and virtual information.

Digital Twin Technologies for Autonomous Environmental Control and Life Support Systems

Environmental control and life support systems will require enhanced self-awareness and self-sufficiency as human spaceflights are designed to reach further destinations. These requirements have led to the development of autonomous technologies and systems to enable more Earth independence, while at the same time relying more heavily on the knowledge contained in their computational models (as opposed to the knowledge of ground control experts). For environmental control and life support systems, these consist of disparate models often tailored to specific subsystems and use cases, such as temperature control and CO2 removal. Therefore, there is a need for technologies supporting the integration of existing models. We propose to extend existing digital twin frameworks to integrate models and serve as a tool for answering onboard queries during operation. Toward this vision, we identify research directions for three types of technologies: i) streamlining the process of merging information models without redundancies by leveraging model-based systems engineering languages; ii) the calibration of simulation models that requires a better understanding of how to encode domain knowledge into a probabilistic representation of subsystem states and model parameters; and iii) automating the query understanding process and constructing a mapping between information and simulation models.

Rural revitalization helps rural e-commerce level development efficiency-Based on DEA model

Abstract The rural revitalization strategy is underway and can provide the promotion of high-quality growing rural e-commerce as well as supply the facilities and industrial support required for its growth. The complete score of the rural e-commerce development level was assessed using the entropy value technique by analyzing data from 31 Chinese provinces, municipalities, and autonomous regions. The efficiency of rural e-commerce growth was evaluated using the BCC model and the super-efficient DEA model within the DEA model. The study examined the effects of rural revival on the effectiveness of electronic commerce in rural development. Both input and output views were considered when selecting the related factors. After that, the productivity value was examined by merging information on the growth of rural e-commerce between 2016 and 2020. The research indicates that between 2015 and 2022, the comprehensive score of rural e-commerce growth experienced a high-quality increase of 173.50%. Furthermore, 8 out of 31 provinces, municipalities, and autonomous areas had DEA-achieved e-commerce development efficiency, with a mean complete efficiency score of 0.700. However, the mean value of total efficiency decreased by 35.29% when the progress level of rural rejuvenation was eliminated. Apart from providing support for infrastructure construction, rural revitalization can also provide policy assurances and an industrial base for the expansion of rural electronic commerce.

Professioni nella Care economy e occupazione: evidenze su dati amministrativi

The paper analyses the evolution of hiring in the care economy professions. To this end, a dataset is used that merges information from sample and administrative sources on labour market flows and occupations. Empirical analyses show that over the period 2012-2021 there was an increase in hiring in the care economy sector, albeit with a cyclical dynamic mainly linked to the Covid-19 emergency. However, these occupations are associated with a penalty in terms of work intensity compared to the average for other occupations. Finally, in care economy professions, there is a reduction in gender inequality when measured by the average number of days worked during the year

Apple Leaf Disease Detection Based on Lightweight YOLOv8-GSSW

HighlightsModify the YOLOV8 backbone network to reduce model calculation complexity and parameter amount.Integrating the Slim-Neck design and the SA attention mechanism into the neck network to enhance the attention of target features and improve the weight of the target.Adoption of the WIoU loss function over the original CIoU to address the sample imbalance problem and accelerate model convergence.Abstract. In response to the challenges posed by the excessive number of parameters and the high computational complexity inherent in existing algorithms for apple leaf disease recognition, this study introduces an enhanced, lightweight detection algorithm, YOLOv8-GSSW. Building upon the YOLOv8n as the foundational model, this research innovates in several key areas. Initially, the algorithm employs a lightweight GSConv convolutional module to replace the standard convolutional modules within the backbone network, significantly reducing both the model’s computational complexity and its parameter count. Furthermore, the incorporation of the Slim-Neck design concept into the neck network module serves to bolster the model’s capabilities in feature processing and learning. This enhancement is further augmented by the integration of the SA attention mechanism within Slim-Neck, which amplifies the attention to target features and increases their weight by merging information across both spatial and channel dimensions. Finally, the original CIoU loss function is substituted with the WIoU loss function to address the issue of sample imbalance and accelerate the model’s convergence. Experimental results underscore the YOLOv8-GSSW algorithm’s superior performance, achieving a mean average precision (mAP) of 87.3% with a parameter volume of 2.7×106 and a model weight of 5.4 MB. Relative to the YOLOv8n benchmark model, this represents a 15.6% reduction in parameter volume and an 11.5% decrease in model size. The modifications introduced in this algorithm not only facilitate more convenient application in practical scenarios for apple leaf disease detection but also render the algorithm well-suited for deployment on edge devices. Keywords: Apple leaves, Disease detection, Lightweight, YOLOv8n.

SDSNet: Building Extraction in High-Resolution Remote Sensing Images Using a Deep Convolutional Network with Cross-Layer Feature Information Interaction Filtering

Building extraction refers to the automatic identification and separation of buildings from the background in remote sensing images. It plays a significant role in urban planning, land management, and disaster monitoring. Deep-learning methods have shown advantages in building extraction, but they still face challenges such as variations in building types, object occlusions, and complex backgrounds. To address these issues, SDSNet, a deep convolutional network that incorporates global multi-scale feature extraction and cross-level feature fusion, is proposed. SDSNet consists of three modules: semantic information extraction (SIE), multi-level merge (MLM), and semantic information fusion (SIF). The SIE module extracts contextual information and improves recognition of multi-scale buildings. The MLM module filters irrelevant details guided by high-level semantic information, aiding in the restoration of edge details for buildings. The SIF module combines filtered detail information with extracted semantic information for refined building extraction. A series of experiments conducted on two distinct public datasets for building extraction consistently demonstrate that SDSNet outperforms the state-of-the-art deep-learning models for building extraction tasks. On the WHU building dataset, the overall accuracy (OA) and intersection over union (IoU) achieved impressive scores of 98.86% and 90.17%, respectively. Meanwhile, on the Massachusetts dataset, SDSNet achieved OA and IoU scores of 94.05% and 71.6%, respectively. SDSNet exhibits a unique advantage in recovering fine details along building edges, enabling automated and intelligent building extraction. This capability effectively supports urban planning, resource management, and disaster monitoring.

Time Series Manufacturing Data Edge Monitoring and Visualization to Support Industrial Maintenance Teams

Traditional manufacturing control systems such as Manufacturing Execution Systems (MES) or SCADA (Supervisory Control And Data Acquisition) were not designed for Industry 4.0 paradigm. Industry 4.0 implies that more data variables must be automatically monitored and data must be captured at a higher frequency: from one value of a few key variables to values of several variables captured at frequencies of seconds. Thus, new architectures and tools are required to merge Information Technology (IT) and Operation Technology (OT) fields and to meet Industry 4.0 requirements. This paper proposes a lightweight architecture based on micro-services and time series data requirements to connect to manufacturing process controllers, and to capture, store, monitor and visualize relevant data about the process. Moreover, a reference implementation based on Open Source tools is presented. This implementation has been validated by members of the maintenance team of a factory from Lecta, a paper manufacturer. The implementation has proven to be a new valuable tool providing further insights and customized alarms of the manufacturing process.

YWnet: A convolutional block attention-based fusion deep learning method for complex underwater small target detection

Underwater target detection holds a noteworthy role in the field of marine exploration. However, it is difficult to extract useful feature information from blurred images with complex backgrounds, resulting in suboptimal and unsatisfactory target detection in conventional models. Among them, YOLOv5 leverages the advantages of fast detection performs better in detecting underwater samples. Nevertheless, YOLOv5 still faces difficulties including missed and incorrect detections due to the underwater environment's small scale of objects, dense distribution of organisms, and occlusion. To address these challenges, we propose a novel YoLoWaternet (YWnet) model that builds upon the YOLOv5 framework for complex underwater species detection with three main innovations: 1) A convolutional block attention module (CBAM) is introduced to enhance feature extraction for blurry images in the initial stages of the network and a new feature fusion network called the CRFPN is created to transfer important information and detect underwater targets. 2) A novel feature extraction module is presented, namely, the skip residual C3 module (SRC3), by effectively merging information from various scales to minimize the loss of original data during transmission. 3) Regression and classification algorithms are separated using the decoupled head to improve the effectiveness of detection and the EIoU loss function is employed to accelerate the convergence speed. Finally, the experimental results demonstrate that YWnet achieves remarkable accuracies of 73.2% mAp and 39.3% mAp50–95 on the underwater dataset, surpassing YOLOv5 by 2.3% and 2.4%, respectively. Furthermore, the proposed fusion model outperforms nine state-of-the-art baseline models on the undersea dataset and has generalization capabilities in other datasets.

Fourier ptychographic layer-based imaging of hazy environments

Macroscopic Fourier ptychographic imaging method can significantly improve the resolution of imaging systems. Nevertheless, under far-field complex environment conditions, such as strong scattering medium (e.g.,hazy), the inhomogeneous distribution of the particles inside the hazy cause random interference to the propagation of the photons carrying the target information, resulting in distortion and blurring of the wavefront information. This severely impacts the quality of the final reconstructed image. In this paper, we propose an improved Resnet network to extract and enhance the features from distorted intensity images in hazy environment, and effectively merge information through multi-scale feature fusion. We successfully recover the target intensity information sequences hidden by different concentrations of hazy and even dynamic non-uniform hazy, and present the final Fourier ptychographic reconstruction results. To the best of our knowledge, we are the first to demonstrate the feasibility of far-field Fourier ptychographic imaging through hazy environments using deep learning methods. Experimental results indicate that it can effectively overcome the images blurring and distortion problems caused by the scattering of hazy environment, significantly improving the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) of final macroscopic Fourier ptychographic reconstructed images under different hazy levels. This study not only reveals the great value of deep learning in solving the imaging problems of complex scenes such as dynamic non-uniform strong scattering media, but also effectively suppresses the adverse effect of hazy scattering environment on macroscopic Fourier ptychographic imaging, which greatly promotes the progress and development of the application of the Fourier ptychographic imaging technology in the field of imaging complex environments in the long distance.

Systematic Literature Review of Interior Design in Virtual Reality Environment

The purpose of the current paper is to review the potential interior design in virtual reality environment. Therefore, in this systematic literature review, we analyse findings on virtual reality interior design and disseminate this information as a written overview of current studies. Six papers were selected after 146 papers from Scopus databases were shortlisted and subjected to various inclusion and exclusion criteria. Virtual reality has the potential to be a tool for interior designers when it comes to design. Future research can gather more thorough data by incorporating conference articles and merging information from various databases.

A Novel Privacy-Preserving Graph Convolutional Network via Secure Matrix Multiplication

Graph convolutional network (GCN) is one of the most representative methods in the realm of graph neural networks (GNNs). In the convolution process, GCN combines the structural information of the networks with the features of nodes. In practice, the structure information of the networks and the features of nodes may be controlled by different parties, which can render GCN ineffective when information sharing is constrained by privacy concerns or licensing issues. Therefore, it is of significant importance to design an effective and secure scheme for GCN that can collaboratively merge information from both parties while safeguarding their sensitive data. In this paper, we introduce S-GCN (i.e., Secure GCN Scheme), which employs secure matrix multiplication (SMM) to compute the product of two matrices in a privacy-preserving manner. The S-GCN scheme requires frequent utilization of SMM to merge information from both parties, resulting in high time and space complexity. To address this issue, we introduce SF-GCN (i.e., Secure and Fast GCN Scheme), which minimizes the use of SMM. Additionally, both S-GCN and SF-GCN may be susceptible to privacy breaches when dealing with dense networks. Hence, we further enhance security by introducing differential privacy through the Laplacian mechanism. Experimental results demonstrate that the proposed schemes do not significantly reduce accuracy in downstream tasks, and, more importantly, effectively protect the privacy information of both parties.

Migrating 120,000 Legacy Publications from Several Systems into a Current Research Information System Using Advanced Data Wrangling Techniques

This article describes a complex CRIS (current research information system) implementation project involving the migration of around 120,000 legacy publication records from three different systems. The project, undertaken by Tampere University, encountered several challenges in data diversity, data quality, and resource allocation. To handle the extensive and heterogenous dataset, innovative approaches such as machine learning techniques and various data wrangling tools were used to process data, correct errors, and merge information from different sources. Despite significant delays and unforeseen obstacles, the project was ultimately successful in achieving its goals. The project served as a valuable learning experience, highlighting the importance of data quality and standardized practices, and the need for dedicated resources in handling complex data migration projects in research organizations. This study stands out for its comprehensive documentation of the data wrangling and migration process, which has been less explored in the context of CRIS literature.

OpenOrgs

Building a connected open scholarly communication system requires unambiguously identifying research-related entities. It is not a simple task: for example, the same organization can have a range of names (eg. legal name, a shortened version, an abbreviation, in national language or in English), as well as varied metadata in other sources. Additionally, persistent IDs might not be helpful when several data sources (eg. ROR, ISNI, EC PIC) utilize various PID schemas to identify organizations. Due to this ambiguity, there are efficiency issues with information sharing, discoverability of research outputs, keeping track of activities, and ultimately building an integrated open scholarly communication system and OS services (Artini et al., 2022).A new tool called OpenOrgs was developed to address this old issue: the disambiguation of organizations engaged in the research process (Pavone, 2021) as well as the parentchild relationships between departments and organizations. OpenOrgs tackles the ambiguity in the data that OpenAIRE collects from several research organization registries (eg. ROR), as well as other sources including institutional repositories, scientific journals, and CRIS systems, and aggregates them to populate the OpenAIRE Graph (OpenAIRE Graph, n.d.). To make up for the lack of information and increase the organization's discoverability and recognition, OpenOrgs combines automated processes with human curation. Numerous data sources are used to gather and merge information on organizations. Their metadata are automatically compared and combined, then these suggested identities are manually checked by data curators assigned at a national or multi-national level.These two steps work as follows:1. The deduplication algorithm (De Bonis, 2022) suggests a similarity between organizations that emerge in various sources by comparing their metadata (eg. the organization name, URL, country).2. The automatic procedure is then verified by a manual curation process. By indicating whether two or more entities pertain to the same organization or not, data curators can clear up any ambiguity surrounding duplicates detected using the automated approach. Furthermore, they can themselves suggest new duplicates unidentified by the algorithm and make up for the information shortage by editing metadata description of the organization, compensating the lack of information from sources and enhancing the organization records' completeness and discoverability, for example by adding a persistent identifier, an alternative name (OpenAIRE, 2023), or establishing parent- child relationships (eg. university and departments). As of now, there are more than 70 registered data curators from over 40 countries, with more than 100,000 curated organizations.OpenOrgs offers a number of advantages for researchers, Research Performing Organizations (RPOs), Research Funding Organizations (RFOs), and all other stakeholders of Open Science services. It improves the findability of digital objects for academics and provides RPOs with a consistent showcase of the overall scientific production. It offers RFOs consistent data on the impact of resources. Finally, OpenOrgs offers functional and up-to-date services to all parties involved in Open Science.In the OpenAIRE ecosystem, OpenOrgs plays an important role. For example, OpenAIRE Explore displays the curated metadata from OpenOrgs, giving researchers quick and easy access to details about the organizations involved in the research process (OpenAIRE EXPLORE, n.d.). These data are also used by the OpenAIRE Monitor service, which tracks and monitors research activities and Open Science trends of organizations (OpenAIRE MONITOR, n.d.). This integration improves these organizations’ discoverability and recognition even more, fostering a more open and cooperative research environment. Therefore, rather than just a tool, OpenOrgs is a game-changer for the research community, and we believe it will contribute positively to build and maintain an integrated open scholarly communication system in the years to come.

Some general fusion and transformation frames for merging basic uncertain information

The Basic Uncertain Information (BUI) is a recently introduced type of uncertain data that has rapidly undergone development and practical application. The existing aggregation operators designed for BUI solely encompass the weighted mean and Choquet integral. The present study puts forth a set of general information fusion frameworks and methodologies aimed at gathering BUI granules. The first mode yields BUI granules as its output, whereas the subsequent two modes generate outputs in the form of interval values. The paper includes numerical examples and applications that correspond to the presented findings. The present study conducts an analysis of various mathematical properties pertaining to the three BUI fusion modes that have been proposed. These properties include idempotency, monotonicities, certainty derived inclusion, certainty monotonicity, homogeneities, non-symmetricity, comonotone additivities, and continuities. The proposals and analyses presented in this work are of a general nature and have the potential to inspire various practical specifications.

Using a Bayesian hierarchical approach to study the association between non-pharmaceutical interventions and the spread of Covid-19 in Germany

Non-Pharmaceutical Interventions (NPIs) are community mitigation strategies, aimed at reducing the spread of illnesses like the coronavirus pandemic, without relying on pharmaceutical drug treatments. This study aims to evaluate the effectiveness of different NPIs across sixteen states of Germany, for a time period of 21 months of the pandemic. We used a Bayesian hierarchical approach that combines different sub-models and merges information from complementary sources, to estimate the true and unknown number of infections. In this framework, we used data on reported cases, hospitalizations, intensive care unit occupancy, and deaths to estimate the effect of NPIs. The list of NPIs includes: “contact restriction (up to 5 people)”, “strict contact restriction”, “curfew”, “events permitted up to 100 people”, “mask requirement in shopping malls”, “restaurant closure”, “restaurants permitted only with test”, “school closure” and “general behavioral changes”. We found a considerable reduction in the instantaneous reproduction number by “general behavioral changes”, “strict contact restriction”, “restaurants permitted only with test”, “contact restriction (up to 5 people)”, “restaurant closure” and “curfew”. No association with school closures could be found. This study suggests that some public health measures, including general behavioral changes, strict contact restrictions, and restaurants permitted only with tests are associated with containing the Covid-19 pandemic. Future research is needed to better understand the effectiveness of NPIs in the context of Covid-19 vaccination.

Nonlinear RNN with noise-immune: A robust and learning-free method for hyperspectral image target detection

While the recurrent neural network (RNN) has achieved remarkable performance on dynamic and control tasks, its applications to image processing, particularly target detection are limited. Challenges arise from differences between the two domains, such as the way for merging time information into static problems and variances of dynamic and static solving methods. To this end, we first extend the existing constrained energy minimization (CEM)-based detection scheme to a dynamic version, e.g. dynamic reinforced CEM (DRCEM), which injects the dynamic information. After that, aided by the rigorous mathematical derivation and optimization theory, the DRCEM is merged into the RNN solution framework. To enhance the robustness and convergence of the existing RNN solutions for improving DRCEM performance, the nonlinear and bounded-constraint RNN (NBCRNN) is designed by developing a novel nonlinear activation function, then applying the proposed model to implement the DRCEM scheme. The corresponding theorem results reveal the proposed model possesses global convergence and enhanced robustness. Compared to state-of-the-art works, the DRCEM solved by the NBCRNN model detection method achieves better detection accuracy, with 1.82% improvement in terms of the Kappa coefficient, and reduces the residual error from 10−4 to 10−7. Furthermore, our detection method is able to preserve the detection accuracy in presence of noise perturbated. To the best of our knowledge, it is the first work to develop the zeroing-type RNN for hyperspectral image target detection. The code and models are publicly available at Github DRCEM_NBCRNN Code Implementation.

Arbeitsmarktdaten: Gesetzesänderungen gefordert

Abstract In recent years, access to German research data on labour markets and social insurances has improved substantially. Nevertheless, reforms are urgently needed to catch up with other countries. Among the most important measures, the German government should legalise the merging of information from heterogeneous data sources for research, make more register available for research, harmonise the handling of data protection, and make remote desktop data accessible more broadly. Research data availability is essential for a functioning and informed democracy that aims to use its resources wisely and to learn from experience.

IEFM and IDS: Enhancing 3D environment perception via information encoding in indoor point cloud semantic segmentation

The point cloud semantic segmentation plays a significant role in the understanding of 3D environment. However, current 3D point cloud segmentation methods pay little attention in the inevitable blurring and loss of point information within deep network, which considerably impairs the segmentation performance, particularly in the complex and colorful indoor scenes. To overcome the inevitable loss of point information (position, color and normal vector), in this paper, we propose a brand-new Information Encoding and Fusion Module (IEFM), comprising a point Information Encoding Method (IEM) and an optimized Multi-Encoding Fusion Method (EFM). In terms of constructing the innovative bias standardization of point information and effectively merging other information encodings into position encoding, IEFM adaptively complements the loss of the point information and thereby achieving enhanced environment perception ability. Additionally, although the proposed IEFM is capable of managing multi-class point information, the encoding interference resulting from the coexistence of multiple information is quite unavoidable, leading to detrimental consequences for the overall semantic segmentation performance. Therefore, to reduce the information encoding interference problem, we further innovatively propose the Information Distribution Strategy (IDS), in terms of hierarchically distribute kinds of point information, so that the interference of multiple information encodings will be extremely mitigated and achieving more accurate indoor point cloud semantic segmentation. Benefiting from the modular design, the proposed IEFM and IDS can be easily inserted in existing point-based point cloud segmentation models. The experimental results have shown the effectiveness of our proposed methods across multiple state-of-the-art models and benchmarks (S3DIS and ScanNet), achieving competitive performance of 77.4% mIoU on the large-scale S3DIS benchmark.

Ipsilateral Lesion Detection Refinement for Tomosynthesis.

Computer-aided detection (CAD) frameworks for breast cancer screening have been researched for several decades. Early adoption of deep-learning models in CAD frameworks has shown greatly improved detection performance compared to traditional CAD on single-view images. Recently, studies have improved performance by merging information from multiple views within each screening exam. Clinically, the integration of lesion correspondence during screening is a complicated decision process that depends on the correct execution of several referencing steps. However, most multi-view CAD frameworks are deep-learning-based black-box techniques. Fully end-to-end designs make it very difficult to analyze model behaviors and fine-tune performance. More importantly, the black-box nature of the techniques discourages clinical adoption due to the lack of explicit reasoning for each multi-view referencing step. Therefore, there is a need for a multi-view detection framework that can not only detect cancers accurately but also provide step-by-step, multi-view reasoning. In this work, we present Ipsilateral-Matching-Refinement Networks (IMR-Net) for digital breast tomosynthesis (DBT) lesion detection across multiple views. Our proposed framework adaptively refines the single-view detection scores based on explicit ipsilateral lesion matching. IMR-Net is built on a robust, single-view detection CAD pipeline with a commercial development DBT dataset of 24675 DBT volumetric views from 8034 exams. Performance is measured using location-based, case-level receiver operating characteristic (ROC) and case-level free-response ROC (FROC) analysis.

Nexus of environmental, social, and governance performance in China‐listed companies: Disclosure and green bond issuance

AbstractThis study establishes a connection between existing literature on corporate environmental, social, and governance (ESG) matters, information disclosure, and green bonds by employing the issuance of green bonds to indicate investors' acknowledgment of a company's sustainable activities. Prior research has not adequately addressed the relationship between ESG performance, disclosure, and green bond issuance. A novel dataset has been compiled by merging ESG performance and information disclosure data of Chinese‐listed companies spanning from 2016 to 2020, alongside green bond issuance information from the Wind and Hexun databases. Concurrently, China's pursuit of carbon neutrality necessitates the establishment of a green, low‐carbon, and circular economy framework. ESG has surfaced as an indispensable instrument for appraising a company's sustainability and advocating the enhancement of its information disclosure systems, thereby aiding investors in forming well‐informed judgments. This study scrutinizes the correlation between each facet of ESG performance, disclosure, and green bond issuance, from the perspective of companies listed in emerging markets. The findings corroborate that companies exhibiting commendable ESG practices are more predisposed to issuing green bonds, particularly when such practices are suitably disclosed. The identified evidence will spur the enforcement of obligatory ESG disclosure regulations, further propelling China's advancement in ecological civilization and green finance.