Heterogeneous Information Sources Research Articles

Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex.

Dynamic processes involving biomolecules are essential for the function of the cell. Here, we introduce an integrative method for computing models of these processes based on multiple heterogeneous sources of information, including time-resolved experimental data and physical models of dynamic processes. We first compute integrative structure models at fixed time points and then optimally select and connect these snapshots into a series of trajectories that optimize the likelihood of both the snapshots and transitions between them. The method is demonstrated by application to the assembly process of the human Nuclear Pore Complex in the context of the reforming nuclear envelope during mitotic cell division, based on live-cell correlated electron tomography, bulk fluorescence correlation spectroscopy-calibrated quantitative live imaging, and a structural model of the fully-assembled Nuclear Pore Complex. Modeling of the assembly process improves the model precision over static integrative structure modeling alone. The method is applicable to a wide range of time-dependent systems in cell biology, and is available to the broader scientific community through an implementation in the open source Integrative Modeling Platform software.

Ontological model of structure and parameters of components in high-tech product modernization projects

The subject of the article is models representing the structure and parameters of components in high-tech products based on the analysis of technical documentation in modernization projects. The purpose of the proposed research is to enhance the quality of modernization processes of high-tech products by forming an ontological model of the product with innovative components, considering the diversity of information support. The article addresses the following tasks: studying the main methods and technologies of system representation of complex product structure; forming a model of structural-functional decomposition of a high-tech product, creating an ontological model of the structure and parameters of a high-tech product based on technical documentation. The following methods are applied: systemic approach, methods of functional-structural decomposition, set theory, ontology construction methods, semantic models. The following results were obtained: The main methods of system representation of complex product structure were investigated, based on the following principles: decomposition of complex product architecture, layering of complex product representation, multivariate synthesis of component architecture. The main directions and advantages of using 3D technologies for solving design tasks in modernization projects of high-tech product components are considered. The decomposition of a high-tech product into component parts and partial parameters is proposed, taking into account functional, structural, and parametric characteristics. An ontological model of the structure and parameters of a high-tech product is formed based on a set of technical documentation and considering additional heterogeneous sources of information. Innovative elements are identified, the description of which may be fuzzy. Conclusions. The proposed ontological model can serve as a basis for finding similar solutions regarding the design of innovative components in the precedent database, which is properly structured. In the absence of similar design solutions, innovative component design can be carried out using 3D technologies based on supplementing fuzzy information in the semantic model.

MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.

Introduction: MicroRNAs (miRNAs) are a class of non-coding RNA molecules that play a crucial role in the regulation of diverse biological processes across various organisms. Despite not encoding proteins, miRNAs have been found to have significant implications in the onset and progression of complex human diseases. Methods: Conventional methods for miRNA functional enrichment analysis have certain limitations, and we proposed a novel method called MiRNA Set Enrichment Analysis based on Multi-source Heterogeneous Information Fusion (MHIF-MSEA). Three miRNA similarity networks (miRSN-DA, miRSN-GOA, and miRSN-PPI) were constructed in MHIF-MSEA. These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. These miRNA similarity networks were fused into a single similarity network with the averaging method. This fused network served as the input for the random walk with restart algorithm, which expanded the original miRNA list. Finally, MHIF-MSEA performed enrichment analysis on the expanded list. Results and Discussion: To determine the optimal network fusion approach, three case studies were introduced: colon cancer, breast cancer, and hepatocellular carcinoma. The experimental results revealed that the miRNA-miRNA association network constructed using miRSN-DA and miRSN-GOA exhibited superior performance as the input network. Furthermore, the MHIF-MSEA model performed enrichment analysis on differentially expressed miRNAs in breast cancer and hepatocellular carcinoma. The achieved p-values were 2.17e(-75) and 1.50e(-77), and the hit rates improved by 39.01% and 44.68% compared to traditional enrichment analysis methods, respectively. These results confirm that the MHIF-MSEA method enhances the identification of enriched miRNA sets by leveraging multiple sources of heterogeneous information, leading to improved insights into the functional implications of miRNAs in complex diseases.

The unresolved need for dependable guarantees on security, sovereignty, and trust in data ecosystems

Data ecosystems emerged as a new paradigm to facilitate the automated and massive exchange of data from heterogeneous information sources between different stakeholders. However, the corresponding benefits come with unforeseen risks as sensitive information is potentially exposed, questioning data ecosystem reliability. Consequently, data security is of utmost importance and, thus, a central requirement for successfully realizing data ecosystems. Academia has recognized this requirement, and current initiatives foster sovereign participation via a federated infrastructure where participants retain local control over what data they offer to whom. However, recent proposals place significant trust in remote infrastructure by implementing organizational security measures such as certification processes before the admission of a participant. At the same time, the data sensitivity incentivizes participants to bypass the organizational security measures to maximize their benefit. This issue significantly weakens security, sovereignty, and trust guarantees and highlights that organizational security measures are insufficient in this context. In this paper, we argue that data ecosystems must be extended with technical means to (re)establish dependable guarantees. We underpin this need with three representative use cases for data ecosystems, which cover personal, economic, and governmental data, and systematically map the lack of dependable guarantees in related work. To this end, we identify three enablers of dependable guarantees, namely trusted remote policy enforcement, verifiable data tracking, and integration of resource-constrained participants. These enablers are critical for securely implementing data ecosystems in data-sensitive contexts.

Онтолого-ориентированная геоаналитика для определения мест концентрации ДТП на участках улично-дорожной сети.

Geoanalytics is an important tool in analyzing the urban environment. This work focuses on the development of geoanalytics tools by ontology-oriented integration of information from heterogeneous sources to identify potentially accident-prone sections of the road network, which can be used to build routes along minimally dangerous roads. The heterogeneity of a large number of sources is caused by the variety of types of information provided, collected both centrally by road and emergency services, and distributed with the help of volunteers. The novelty of this work is the combination of heterogeneous sources of information in a single analytical system and conducting a geoanalysis of the frequency of accidents on individual road sections, determining the most frequent type and cause of accidents to build a route bypassing sections of the road network with a high concentration of accidents.

The influence of current results in a event­oriented data collection system

The article discusses synchronous and asynchronous procedures in the implementation of the microservices management algorithm in an adaptive system for processing large data flows when collecting information on the main event-oriented approach in the implementation of the architecture of a software system that processes information in real-time. This approach is important when processing large volumes of data from heterogeneous information sources, especially when the task is to minimize the total processing time of large data streams and reduce the total number of calls to data sources. The proposed approach makes it possible, based on its adaptability, to manage the selection of the composition and the number of calls of microservices to the sources, by the events that occur during the collection of information. Thus, it is possible to determine the choice of sources based on the assessment of the effectiveness of obtaining relevant information from them. This is especially important when processing large data flows from heterogeneous information sources when the task is to minimize the total time of collection and processing of large data flows. In turn, this poses the task of minimizing the number of requests to information sources to obtain a sufficient number of data units that are relevant to the search query. The creation of effective big data processing systems requires constant development of approaches to the architecture of building software applications. The event-oriented microservice architecture of the system makes it possible to adapt the operation of the system to the loads on individual microservices and the efficiency of their work by forming and responding to relevant events based on the analysis of relevant events that occur during the collection and initial processing of the received data. Depending on the specific task, it is possible to use both a synchronous and an asynchronous microservice management algorithm. The article provides an analysis of the effectiveness of obtaining relevant data depending on the degree of consideration in the evaluation of results and the formation of events, both the depth of the previous history of the results of requests and the size of their quantitative impact. The use of event-oriented microservice architecture can be especially effective when developing various information and analytical systems that need, according to user requests, to access various sources of information, analyze their data for relevance according to the request, and process large data streams.

LMKG: A large-scale and multi-source medical knowledge graph for intelligent medicine applications

Medical Knowledge Graph (KG) has shown great potential in various healthcare scenarios, such as drug recommendation and clinical decision support system. The factors that determine the role of a medical KG in practical applications are the scale, coverage, and quality of the medical knowledge it can provide. Most existing medical KGs are extracted from a single or a few information sources. However, medical knowledge extracted from insufficient information sources is usually highly incomplete or even biased, which results in a lack of data completeness and may lessen their effectiveness in real-world scenarios. Besides, the coverage of entity and relation types is inadequate in most previous works, which also might restrict their potential usage in future applications. In this paper, we build a unified system that can extract and manage medical knowledge from heterogeneous information sources. We first employ named entity recognition and relation extraction methods to extract knowledge triplets from medical texts. Then we propose a hierarchical entity alignment framework for further knowledge refinement. Based on our system, we construct a large-scale, high-quality, multi-source, and multi-lingual medical KG named LMKG, which includes 13 entity types and 17 relation types, and contains 403,784 entity and 1,225,097 relation instances. We conduct extensive experiments to evaluate the quality of LMKG. Experimental results show that LMKG can effectively enhance the performance of both upstream and downstream intelligent medicine applications. We have publicly released the KG resources and corresponding management service interface to facilitate research and applications in the medical field.

A rigorous possibility approach for the geotechnical reliability assessment supported by external database and local experience

Reliability analyses based on probability theory are widely applied in geotechnical engineering, and several analytical or numerical methods have been built upon the concept of failure occurrence. Nevertheless, common geotechnical engineering real-world problems deal with scarce or sparse information where experimental data are not always available to a sufficient extent and quality to infer a reliable probability distribution function.This paper rigorously combines Fuzzy Clustering and Possibility Theory for deriving a data-driven, quantitative, reliability approach, in addition to fully probability-oriented assessments, when useful but heterogeneous sources of information are available.The proposed non-probabilistic approach is mathematically consistent with the failure probability, when ideal random data are considered. Additionally, it provides a robust tool to account for epistemic uncertainties when data are uncertain, scarce, and sparse. The Average Cumulative Function transformation is used to obtain possibility distributions inferred from the fuzzy clustering of an indirect database. Target Reliability Index Values, consistent with the prescribed values provided by Eurocode 0, are established.Moreover, a Degree of Understanding tier system based on the practitioner’s local experience is also proposed. The proposed methodology is detailed and discussed for two numerical examples using national-scale databases, highlighting the potential benefits compared to traditional probabilistic approaches.

Modeling and efficiency analysis of blockchain agriculture products E-commerce cold chain traceability system based on Petri net

The cold chain supply chain of e-commerce has the characteristics of long chain, dispersed production, heterogeneous information sources, etc., which is easy to cause the upstream and downstream information of the supply chain to be broken and opaque. Traditional traceability data is stored in each node enterprise, and there are problems such as low cooperation trust and poor authenticity in the upstream and downstream data transmission process of the supply chain, resulting in consumers' trust crisis on the authenticity of traceability information. Blockchain traceability system through the establishment of multi-party participation, joint maintenance of distributed database, and the use of cryptography and consensus mechanism to establish trust relationships, with data can not be tampered with, sharing of high credibility. On the basis of stochastic Petri net theory, this study constructed a blockchain e-commerce cold chain traceability model centering on the actual application of blockchain technology in logistics traceability to improve the reliability and validity of e-commerce cold chain traceability. Correlation matrix and invariants were used to analyze the validity of the model, and an isomorphic Markov chain was constructed to analyze the effectiveness evolution of the model. This study aims to deepen the understanding of the cold chain traceability system of blockchain e-commerce from the three aspects of the best optimization link, the best synergism scale, and the adaptive ability of the system to provide reference for the improvement of cold chain traceability ability by blockchain technology.

Invisible, aesthetic, and enrolled listeners across storytelling modalities: Immersive preference as situated player type

Immersive storytelling includes narrative experiences that take place in the public spaces of theaters, theme parks, museums, and historical reenactment sites; on tabletops, where role-playing games are collaboratively played; in fictional spaces superimposed on the real world during a live-action role-playing (larping) event; and in hybrid, asynchronous, and transmediated spaces that blend the digital, the virtual, and the real. Understood as a kind of boundary object, a player typology can incorporate multiple perspectives and heterogeneous sources of information, producing ‘ideal types’, which provide a framework for observation and discussion across immersive modalities. This paper offers a typology of situated immersive preference, in which narrative immersion and embodied immersion are understood to vary independently of one another. Along the vertical axis listener-players of immersive storytelling experiences are classified as narratively attached, narratively detached, or narratively opposed. Across the horizontal axis of embodied engagement, listener-players are classified as invisible, aesthetic, or enrolled. Nine ideal types emerge at the intersection of these narrative and embodied preferences. Why someone might fit into one category rather than another reflects comfort rather than personality. Situated immersive types are understood to be fluid and temporary configurations. The degree to which players are willing to engage and are comfortable with what they are being asked to do may differ dramatically from experience to experience and from day to day, and even during a single session, reflecting how they feel at a given moment, which is affected by who they are with and how they are perceived and treated by others. This framework for understanding immersive preferences calls for the design of more widely inclusive story worlds.

Distribution of Age of Information in Status Update Systems With Heterogeneous Information Sources: An Absorbing Markov Chain-Based Approach

In this letter, we obtain the exact distributions of the Age of Information (AoI) and Peak AoI (PAoI) in a non-preemptive multi-source status update system for (i) Generate-At-Will (GAW) servers with probabilistic transmissions (ii) Random Arrival with Single Buffer (RA-SB) servers employing probabilistic buffer management, using absorbing Continuous-Time Markov Chains (CTMC). For both servers, the information sources are allowed to have different relative urgencies, phase-type service time distributions, and transmission error probabilities, for the sake of generality. Numerical examples are presented to validate the proposed analytical model.

FURSformer: Semantic Segmentation Network for Remote Sensing Images with Fused Heterogeneous Features

Semantic segmentation of remote sensing images poses a formidable challenge within this domain. Our investigation commences with a pilot study aimed at scrutinizing the advantages and disadvantages of employing a Transformer architecture and a CNN architecture in remote sensing imagery (RSI). Our objective is to substantiate the indispensability of both local and global information for RSI analysis. In this research article, we harness the potential of the Transformer model to establish global contextual understanding while incorporating an additional convolution module for localized perception. Nonetheless, a direct fusion of these heterogeneous information sources often yields subpar outcomes. To address this limitation, we propose an innovative hierarchical fusion feature information module that this model can fuse Transformer and CNN features using an ensemble-to-set approach, thereby enhancing information compatibility. Our proposed model, named FURSformer, amalgamates the strengths of the Transformer architecture and CNN. The experimental results clearly demonstrate the effectiveness of this approach. Notably, our model achieved an outstanding accuracy of 90.78% mAccuracy on the DLRSD dataset.

Assessing risk for butterflies in the context of climate change, demographic uncertainty, and heterogeneous data sources

AbstractOngoing declines in insect populations have led to substantial concern and calls for conservation action. However, even for relatively well studied groups, like butterflies, information relevant to species‐specific status and risk is scattered across field guides, the scientific literature, and agency reports. Consequently, attention and resources have been spent on a minuscule fraction of insect diversity, including a few well studied butterflies. Here we bring together heterogeneous sources of information for 396 butterfly species to provide the first regional assessment of butterflies for the 11 western US states. For 184 species, we use monitoring data to characterize historical and projected trends in population abundance. For another 212 species (for which monitoring data are not available, but other types of information can be collected), we use exposure to climate change, development, geographic range, number of host plants, and other factors to rank species for conservation concern. A phylogenetic signal is apparent, with concentrations of declining and at‐risk species in the families Lycaenidae and Hesperiidae. A geographic bias exists in that many species that lack monitoring data occur in the more southern states where we expect that impacts of warming and drying trends will be most severe. Legal protection is rare among the taxa with the highest risk values: of the top 100 species, one is listed as threatened under the US Endangered Species Act and one is a candidate for listing. Among the many taxa not currently protected, we highlight a short list of species in decline, including Vanessa annabella, Thorybes mexicanus, Euchloe ausonides, and Pholisora catullus. Notably, many of these species have broad geographic ranges, which perhaps highlights a new era of insect conservation in which small or fragmented ranges will not be the only red flags that attract conservation attention.

Flexible Data Refreshing Architecture for Health Information System Integration

Background: Having a consistent and unified view of heterogeneous distributed medical information sources is an inevitable need of health informatics. Integrating medical information of patients or about a disease, a treatment or side effects of a drug, etc, is very useful to help medical education, to achieve medical research goals and to provide the computer- based decision support systems. Contribution: This article proposes a flexible incremental update method for the materialized part of the integration system. It permits us to manage the integration system according to the characteristics of the data sources which can change. Method: This paper presents a hybrid data integration approach in which the materialized part of the system in mediator is the object indexation structure based on an instance classification of the sources objects which correspond to the global schema. The object identifier of each object in the indexation structure is materialized together with the attributes which are needed for the incremental updating of this indexation (classifying attributes). Results: The main idea of this paper is to develop a hybrid data integration framework, which represents a new aspect of a hybrid method focusing on flexible data refreshing. Conclusion: This hybrid approach implements a vertical hybrid approach. It means that at the mediator level, some data of each object are materialized and others are virtual.

Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-Based Recruitment

The presence of decision-making algorithms in society is rapidly increasing nowadays, while concerns about their transparency and the possibility of these algorithms becoming new sources of discrimination are arising. There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. All these four Human-Centric requirements are closely related to each other. With the aim of studying how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, we propose a fictitious case study focused on automated recruitment: FairCVtest. We train automatic recruitment algorithms using a set of multimodal synthetic profiles including image, text, and structured data, which are consciously scored with gender and racial biases. FairCVtest shows the capacity of the Artificial Intelligence (AI) behind automatic recruitment tools built this way (a common practice in many other application scenarios beyond recruitment) to extract sensitive information from unstructured data and exploit it in combination to data biases in undesirable (unfair) ways. We present an overview of recent works developing techniques capable of removing sensitive information and biases from the decision-making process of deep learning architectures, as well as commonly used databases for fairness research in AI. We demonstrate how learning approaches developed to guarantee privacy in latent spaces can lead to unbiased and fair automatic decision-making process. Our methodology and results show how to generate fairer AI-based tools in general, and in particular fairer automated recruitment systems.

Dynamic SFC Embedding Algorithm Assisted by Federated Learning in Space–Air–Ground-Integrated Network Resource Allocation Scenario

Traditional terrestrial wireless communication networks cannot support the requirements for high-quality services for artificial intelligence applications such as smart cities. The space-air-ground integrated network (SAGIN) provides a solution to address this challenge. However, SAGIN is heterogeneous, time-varying, and multi-dimensional information sources, making it difficult for traditional network architectures to support resource allocation in large-scale complex network environments. This paper proposes a service provision method based on Service Function Chaining (SFC) to solve this problem. Network Function Virtualization (NFV) is essential for efficient resource allocation in SAGIN to meet the resource requirements of user service requests. We propose a federation learning (FL) based algorithm to solve the embedding problem of SFCs in SAGIN. The algorithm considers different characteristics of nodes and resource load to balance resource consumption. Then an SFC scheduling mechanism is proposed that allows SFC reconfiguration to reduce the service blocking rate. Simulation results show that our proposed FL-VNFE algorithm is more advantageous compared to other algorithms, with 12.9%, 2.52%, and 10.5% improvement in long-term average revenue, acceptance rate, and long-term average revenue-cost ratio, respectively.

Regional characterization of vs30 with hybrid geotechnical and geological data

Regional characterization of soil properties requires not only geostatistical models considering the spatial variability of soil properties but also methods that account for various sources of heterogeneous information. This paper presents a multi-conditional random field approach to characterize the regional characteristics of the average shear-wave velocity in the top 30 meters of subsoil (vs30) based on hybrid geotechnical and geological data. The workflow for integrating multiple sources of soil properties information in a random field model for regional vs30 mapping is developed. A detailed two-dimensional (2D) synthetic digital soil field is generated to assess and verify this workflow. With the generated synthetic field, parametric studies on the investigation plans, the value of Markov Bayes coefficient B, element size, and a predefined grid of secondary data are performed. Practice on whether to incorporate secondary data for vs30 mapping and the determination of coefficient B are provided.

Knowledge Graph-Based Explainable Artificial Intelligence for Business Process Analysis

For critical operational decisions (e.g. consulting services), explanations and interpretable results of powerful Artificial Intelligence (AI) systems are becoming increasingly important. Knowledge graphs possess a semantic model that integrates heterogeneous information sources and represents knowledge elements in a machine-readable form. The integration of knowledge graphs and machine learning methods represents a new form of hybrid intelligent systems that benefit from each other’s strengths. Our research aims at an explainable system with a specific knowledge graph architecture that generates human-understandable results even when no suitable domain experts are available. Against this background, the interpretability of a knowledge graph-based explainable AI approach for business process analysis is focused. We design a framework of interpretation, show how interpretable models are generated by a single case study and evaluate the applicability of our approach by different expert interviews. Result paths on weaknesses and improvement measures related to a business process are used to produce stochastic decision trees, which improve the interpretability of results. This can lead to interesting consulting self-services for clients or be applied as a device for accelerating classical consulting projects.

AgroGenome: Interactive Genomic-Based Web Server Developed Based on Data Collected for Accessions Stored in Polish Genebank

New intensive farming systems have resulted in a narrowing of the genetic diversity used in breeding programs. Breeders are looking for new sources of variation of specific traits to make genetic progress in adaptation to changing environmental conditions. Genomics-based plant germplasm research seeks to apply the techniques of genomics to germplasm characterization. Using these new methods and obtained data, plant breeders can increase the rate of genetic gains in specific breeding programs. Due to the complexity of heterogeneous sources of information, it is necessary to collect large quantities of referenced data. Molecular platforms are becoming increasingly important for the development of strategic germplasm resources for more effective molecular breeding of new cultivars. Following this trend in plant breeding, the AgroGenome portal for precise breeding programs was developed based on data collected for accessions stored in the Polish Genebank. It combines passport data of genotypes, phenotypic characteristics and interactive GWAS analysis visualization on the Manhattan plots based on GWAS results and on JBrowse interface. The AgroGenome portal can be utilized by breeders or researchers to explore diversity among investigated genomes. It is especially important to identify markers for tracking specific traits and identify QTL. The AgroGenome portal facilitates the exploitation and use of plant genetic resources stored in the Polish Genebank.

The research project “CHARMING PISTOIA”: an integrated HBIM project for preservation and maintenance of heritage structures

The implementation of proper strategies of conservation of heritage structures requires the collection of heterogeneous sources of information (historical evolution, past restoration works, experimental data, monitoring systems results, etc.). The handling of this data needs an effective informative modelling, able to collect different type of data by using 3D parametric model. Heritage Building Information Modelling (HBIM) can be considered a promising tool for the conservation of monuments which have to be protected along the centuries, thanks to its capability of archiving and handling the building information. This paper discusses this topic by describing the research project “CHARMING PISTOIA” (Cultural HeritAge infoRmation ModellING for PISTOIA: from monitoring data to digital twin). The goal of the project is to deliver tools and methodologies to improve conservation of historical constructions by ensuring the optimal use of the resources required for the tasks of Control & Preservation of heritage buildings. It proposed a three steps approach: i) the design of the HBIM to collect the information on the structure and to export them in computational models; ii) the integration of a Structural Health Monitoring (SHM) system to ensure the steady flow of structural information and its updating through Bayesian inference; iii). the definition of robust computational models for the Joint Vulnerability Assessment of the structure and the works of art inside. In this context, the Pieve of Sant'Andrea in Pistoia (Italy) and its masterpiece, the Pulpit of Giovanni Pisano, represents an emblematic case study to meet the challenges of the research. In this paper the preliminary stages of the project are reported and discussed together with the expected results.