Search Results (127)

This research addresses the long-standing dependence on printed maps for navigation and highlights the limitations of existing digital services like Google Street View and Google Street View Player in providing comprehensive solutions for route analysis and understanding. The absence of a systematic approach to route analysis, issues related to insufficient street view images, and the lack of proper image mapping for desired roads remain unaddressed by current applications, which are predominantly client-based. In response, we propose an innovative automatic system designed to generate videos depicting road routes between two geographic locations. The system calculates and presents the route conventionally, emphasizing the path on a two-dimensional representation, and in a multimedia format. A prototype is developed based on a cloud-based client–server architecture, featuring three core modules: frames acquisition, frames analysis and elaboration, and the persistence of metadata information and computed videos. The tests, encompassing both real-world and synthetic scenarios, have produced promising results, showcasing the efficiency of our system. By providing users with a real and immersive understanding of requested routes, our approach fills a crucial gap in existing navigation solutions. This research contributes to the advancement of route planning technologies, offering a comprehensive and user-friendly system that leverages cloud computing and multimedia visualization for an enhanced navigation experience. Full article

This paper seeks to address the limitations of conventional remote sensing data dissemination algorithms, particularly their inability to model fine-grained multi-modal heterogeneous feature correlations and adapt to dynamic network topologies under resource constraints. This paper proposes multi-modal-MAPPO, a novel multi-modal deep reinforcement learning (MDRL) framework designed for a proactive data push in large-scale integrated satellite–terrestrial networks (ISTNs). By integrating satellite cache states, user cache states, and multi-modal data attributes (including imagery, metadata, and temporal request patterns) into a unified Markov decision process (MDP), our approach pioneers the application of the multi-actor-attention-critic with parameter sharing (MAPPO) algorithm to ISTNs push tasks. Central to this framework is a dual-branch actor network architecture that dynamically fuses heterogeneous modalities: a lightweight MobileNet-v3-small backbone extracts semantic features from remote sensing imagery, while parallel branches—a multi-layer perceptron (MLP) for static attributes (e.g., payload specifications, geolocation tags) and a long short-term memory (LSTM) network for temporal user cache patterns—jointly model contextual and historical dependencies. A dynamically weighted attention mechanism further adapts modality-specific contributions to enhance cross-modal correlation modeling in complex, time-varying scenarios. To mitigate the curse of dimensionality in high-dimensional action spaces, we introduce a multi-dimensional discretization strategy that decomposes decisions into hierarchical sub-policies, balancing computational efficiency and decision granularity. Comprehensive experiments against state-of-the-art baselines (MAPPO, MAAC) demonstrate that multi-modal-MAPPO reduces the average content delivery latency by 53.55% and 29.55%, respectively, while improving push hit rates by 0.1718 and 0.4248. These results establish the framework as a scalable and adaptive solution for real-time intelligent data services in next-generation ISTNs, addressing critical challenges in resource-constrained, dynamic satellite–terrestrial environments. Full article

As open science initiatives address the crisis in scholarly communication driven by commercialisation, diamond open access publishing—promoting equity for authors and readers—has emerged as a focal point in open access scholarly publishing. This study examines the landscape of institutional publishing in Croatia, focusing on the community-owned diamond open access model. Through the DIAMAS project survey, which targeted 251 institutional publishers and achieved a response rate of 77, the research identifies the distinct features of Croatian institutional publishing. Institutional publishers are characterised by governance structures, funding challenges, voluntary staffing, and alignment with open science principles. Notable traits include reliance on public funding, use of the national open access journal platform, and a strong diamond open access publishing tradition. Key findings emphasise the critical role of national infrastructure, services, and multilingual publishing. Persistent challenges include meeting indexing criteria, advancing open science practices, and ensuring metadata quality. This study provides a comprehensive mapping of Croatian institutional publishers, offering insights into their strengths and weaknesses while proposing strategies for improvement. The findings underscore the importance of national policy frameworks, capacity building, and international collaboration to ensure the sustainability and visibility of Croatian institutional publishing. Full article

The amount of software engineering data is constantly growing, as more and more developers employ online services to store their code, keep track of bugs, or even discuss issues. The data residing in these services can be mined to address different research challenges; therefore, certain initiatives have been established to encourage sharing research datasets collecting them. In this work, we investigate the effect of such an initiative; we create a directory that includes the papers and the corresponding datasets of the data track of the Mining Software Engineering (MSR) conference. Specifically, our directory includes metadata and citation information for the papers of all data tracks, throughout the last twelve years. We also annotate the datasets according to the data source and further assess their compliance to the FAIR principles. Using our directory, researchers can find useful datasets for their research, or even design methodologies for assessing their quality, especially in the software engineering domain. Moreover, the directory can be used for analyzing the citations of data papers, especially with regard to different data categories, as well as for examining their FAIRness score throughout the years, along with its effect on the usage/citation of the datasets. Full article

Cultural ecosystem services provide intangible benefits such as recreation and aesthetic enjoyment but are difficult to quantify compared to provisioning or regulating ecosystem services. Recent technologies offer alternative indicators, such as social media data, to identify popular locations and their features. This study demonstrates how large volumes of citizen science and social media data can be analyzed to reveal patterns of human interactions with nature through unconventional, scalable methods. By applying spatial statistical methods, data from the citizen science platform iNaturalist are analyzed and compared with ground-truth visitation data. To minimize data bias, records are grouped by taxonomic information and applied to the metropolitan area of Seoul, South Korea (2005–2022). The taxonomic information included in the iNaturalist data were investigated using a standard global biodiversity database. The results show citizen science data effectively quantify public preferences for scenic locations, offering a novel approach to mapping cultural ecosystem services when traditional data are unavailable. This method highlights the potential of large-scale citizen-generated data for conservation, urban planning, and policy development. However, challenges like bias in user-generated content, uneven ecosystem coverage, and the over- or under-representation of locations remain. Addressing these issues and integrating additional metadata—such as time of visit, demographics, and seasonal trends—could provide deeper insights into human–nature interactions. Overall, the proposed method opens up new possibilities for using non-traditional data sources to assess and map ecosystem services, providing valuable information for conservation efforts, urban planning, and environmental policy development. Full article

Urban settlements can support significant biodiversity and provide a wide range of ecosystem services. Remote sensing (RS) offers valuable tools for monitoring and conserving urban biodiversity. Our research, funded by the Italian Recovery and Resilience Plan (National Biodiversity Future Centre—Urban Biodiversity), undertakes a systematic scientific review to assess the current status and future prospects of urban biodiversity evaluation using RS. An extensive literature search of indexed peer-reviewed papers published between 2008 and 2023 was conducted on the Scopus database, using a selective choice of keywords. After screening the titles, abstracts, and keywords of 500 articles, 117 relevant papers were retained for meta-data analysis. Our analysis incorporated technical (e.g., sensor, platform, algorithm), geographic (e.g., country, city extent, population) and ecological (biodiversity target, organization level, biome) meta-data, examining their frequencies, temporal trends (Generalized Linear Model—GLM), and covariations (Cramer’s V). The rise in publications over time is linked to the increased availability of imagery, enhanced computing power, and growing awareness of the importance of urban biodiversity. Most research focused on the Northern Hemisphere and large metropolitan areas, with smaller cities often overlooked. Consequently, data coverage is predominantly concentrated on Mediterranean and temperate habitats, with limited attention given to boreal, desert, and tropical biomes. A strong association was observed between the source of RS data (e.g., satellite missions), pixel size, and the purpose of its use (e.g., modeling, detection). This research provides a comprehensive summary of RS applications for evaluating urban biodiversity with a focus on the biomes studied, biodiversity targets, and ecological organization levels. This work can provide information on where future studies should focus their efforts on the study of urban biodiversity using remote sensing instruments in the coming years. Full article

This paper presents a new decision support method aimed at prioritizing processing for an intelligent road problem reporting service. The proposed method uses advanced georeferencing technology to extract the longitude and latitude coordinates in the metadata of photos taken with the smartphone application to capture the complaint scene. This method not only maps out the processing times, but also applies a spatiotemporal clustering technique to link the complaint types and locations with the actual complaint processing times. A validation study of the frequency of reported locations per priority reveals that the complaint-processing prioritization method developed in this study aligns realistically with actual field complaint processing. Furthermore, recognizing the significance of location in processing complaints, the georeferencing technique appears suitable for identifying complaint locations for each report and incorporating this into the decision-making framework. Full article

With the development of the data-sharing system in recent years, financial management systems and their privacy have sparked great interest. Existing financial data-sharing systems store metadata, which include a hash value and database index on the blockchain, and store high-capacity actual data in the center database. However, current data-sharing systems largely depend on centralized systems, which are susceptible to distributed denial-of-service (DDoS) attacks and present a centralized attack vector. Furthermore, storing data in a local center database has a high risk of information disclosure and tampering. In this paper, we propose the ChainMaker Privacy Computing (CPC) system, a new decentralized data-sharing system for secure financial data, to solve this problem. It provides a series of financial data information and a data structure rather than actual data on the blockchain to protect the privacy of data. We utilize a smart contract to establish a trusted platform for the local database to obtain encrypted data. We design a resource catalog to provide a trusted environment of data usage in the privacy computing system that is visible for members on the blockchain. Based on cipher-policy attribute-based encryption (CP-ABE), We design a CPC-CP-ABE algorithm to enable fine-grained access control through attribute based encryption. Finally, We propose an efficient scheme that allows authenticated data-sharing systems to perform Boolean searches on encrypted data information. The results of experiment show that the CPC system can finish trusted data sharing to all organizations on the blockchain. Full article

Efforts to establish Pakistan’s National Spatial Data Infrastructure (NSDI) have been underway for the past 15 years, and therefore it is necessary to gauge the current progress to channelize efforts into areas that need improvement. This article assessed Pakistan’s NSDI implementation efforts through well-established approaches, including the SDI readiness model, organizational aspects, and state of play. The data were collected from Spatial Data Infrastructure (SDI) and Geographic Information System (GIS) experts. The findings underscored challenges related to human resources, SDI education/culture, long-term vision, lack of awareness of geoinformation (GI), sustainable funding, metadata availability, online geospatial services, and geospatial standards hindering NSDI development in Pakistan. However, certain factors exhibit favorable standings, such as the legal framework for NSDI establishment, web connectivity, geospatial software availability, the unavailability of core spatial datasets, and institutional leadership. Thus, to enhance the development of NSDI in Pakistan, recommendations include bolstering financial and human resources, improving online geospatial presence, and fostering a long-term vision for NSDI. Full article

Streaming platforms have revolutionized the digital entertainment industry, but challenges and research opportunities remain to be addressed. One current concern is the lack of transparency in the business model of video streaming platforms, which makes it difficult for content creators to access viewing metrics and receive payments without the intermediary of third parties. Additionally, there is no way to trace payment transactions. This article presents a computational architecture based on blockchain technology to enable transparency in audience management and payments in video streaming platforms. Smart contracts will define the business rules of the streaming services, while middleware will integrate the metadata of the streaming platforms with the proposed computational solution. The proposed solution has been validated through data transactions on different blockchain networks and interviews with content creators from video streaming platforms. The results confirm the viability of the proposed solution in enhancing transparency and auditability in the realm of audience control services and payments on video streaming platforms. Full article

The need for urban societies to move continues to grow with the intensity of their various activities. One of the challenges in micromobility in cities based on bike, scooter, or public scooter systems is determining the potential yet realistic demand for such services. This article aims to present the level of demand for eco-friendly non-motorized transport systems and identify the motivations driving users of public transport to choose these eco-friendly Personal Mobility Devices (PMD). The authors propose a methodology based on the integration of four components: a case study, an analysis of participatory budget projects, a metadata analysis of bikesharing (BSS) in Szczecin, and a meta plan. They utilized a case study method and conducted a survey based on a custom questionnaire. An analysis of participatory budget projects and data from the operation of bikesharing in Szczecin was performed using statistical methods. Applying the heuristic tool meta plan, they outlined the directions for the development and support of bikesharing as an example of micromobility in Szczecin. The research identified connections between the socio-demographic structure of respondents and attributes of bike communication. This study highlighted periods of intensive system use and locations with the highest exploitation. It showed that the demand for BSS depends on weather conditions, traveler convenience, the density and coherence of bike infrastructure with important locations for residents, transfer connections, and safety. The presented research results can assist planners and authorities in Szczecin and other cities in expanding bikesharing. Full article

NICE—Noise Interactive Catalogue Explorer—is a web service developed for rapid-qualitative glitch analysis in gravitational wave data. Glitches are transient noise events that can smother the gravitational wave signal in data recorded by gravitational wave interferometer detectors. NICE provides interactive graphical tools to support detector noise characterization activities, in particular, the analysis of glitches from past and current observing runs, passing from glitch population visualization to individual glitch characterization. The NICE back-end API consists of a multi-database structure that brings order to glitch metadata generated by external detector characterization tools so that such information can be easily requested by gravitational wave scientists. Another novelty introduced by NICE is the interactive front-end infrastructure focused on glitch instrumental and environmental origin investigation, which uses labels determined by their time–frequency morphology. The NICE domain is intended for integration with the Advanced Virgo, Advanced LIGO, and KAGRA characterization pipelines and it will interface with systematic classification activities related to the transient noise sources present in the Virgo detector. Full article

The improvement of search engines for geospatial data on the World Wide Web has been a subject of research, particularly concerning the challenges in discovering and utilizing geospatial web services. Despite the establishment of standards by the Open Geospatial Consortium (OGC), the implementation of these services varies significantly among providers, leading to issues in dataset discoverability and usability. This paper presents a proof of concept for a search engine tailored to geospatial services in Switzerland. It addresses challenges such as scraping data from various OGC web service providers, enhancing metadata quality through Natural Language Processing, and optimizing search functionality and ranking methods. Semantic augmentation techniques are applied to enhance metadata completeness and quality, which are stored in a high-performance NoSQL database for efficient data retrieval. The results show improvements in dataset discoverability and search relevance, with NLP-extracted information contributing significantly to ranking accuracy. Overall, the GeoHarvester proof of concept demonstrates the feasibility of improving the discoverability and usability of geospatial web services through advanced search engine techniques. Full article

The conect4children (c4c) initiative was established to facilitate the development of new drugs and other therapies for paediatric patients. It is widely recognised that there are not enough medicines tested for all relevant ages of the paediatric population. To overcome this, it is imperative that clinical data from different sources are interoperable and can be pooled for larger post hoc studies. c4c has collaborated with the Clinical Data Interchange Standards Consortium (CDISC) to develop cross-cutting data resources that build on existing CDISC standards in an effort to standardise paediatric data. The natural next step was an extension to disease-specific data items. c4c brought together several existing initiatives and resources relevant to disease-specific data and analysed their use for standardising disease-specific data in clinical trials. Several case studies that combined disease-specific data from multiple trials have demonstrated the need for disease-specific data standardisation. We identified three relevant initiatives. These include European Reference Networks, European Joint Programme on Rare Diseases, and Pistoia Alliance. Other resources reviewed were National Cancer Institute Enterprise Vocabulary Services, CDISC standards, pharmaceutical company-specific data dictionaries, Human Phenotype Ontology, Phenopackets, Unified Registry for Inherited Metabolic Disorders, Orphacodes, Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP), and Observational Medical Outcomes Partnership. The collaborative partners associated with these resources were also reviewed briefly. A plan of action focussed on collaboration was generated for standardising disease-specific paediatric clinical trial data. A paediatric data standards multistakeholder and multi-project user group was established to guide the remaining actions—FAIRification of metadata, a Phenopackets pilot with RDCA-DAP, applying Orphacodes to case report forms of clinical trials, introducing CDISC standards into European Reference Networks, testing of the CDISC Pediatric User Guide using data from the mentioned resources and organisation of further workshops and educational materials. Full article

Geospatial data, especially remote sensing (RS) data, are of significant importance for public services and production activities. Expertise is critical in processing raw data, generating geospatial information, and acquiring domain knowledge and other remote sensing applications. However, existing geospatial service platforms are more oriented towards the professional users in the implementation process and final application. Building appropriate geographic applications for non-professionals remains a challenge. In this study, a geospatial data service architecture is designed that links desktop geographic information system (GIS) software and cloud-based platforms to construct an efficient user collaboration platform. Based on the scalability of the platform, four web apps with different themes are developed. Data in the fields of ecology, oceanography, and geology are uploaded to the platform by the users. In this pilot phase, the gap between non-specialized users and experts is successfully bridged, demonstrating the platform’s powerful interactivity and visualization. The paper finally evaluates the capability of building spatial data infrastructures (SDI) based on GeoNode and discusses the current limitations. The support for three-dimensional data, the improvement of metadata creation and management, and the fostering of an open geo-community are the next steps. Full article