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Discover Computing

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インパクトファクター:
1.9
出版社:
Springer
ISSN:
2948-2992
閲覧:
30919
フォロー:
15
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論文募集

Discover Computing is an academic journal published by Springer. (ISSN 2948-2992, impact factor 1.9, CCF C).

Aims and scope Discover Computing (formerly Information Retrieval Journal) is a fully open access, peer-reviewed journal that supports multidisciplinary research and policy developments across all fields relevant to computer science. The journal aims to be a resource for researchers, policy makers and the general public for recent advances in computer science, and its uses in research development and society. As a fully open access journal, we ensure that our research is highly discoverable and instantly available globally to everyone. The journal particularly welcomes work that aims to address the United Nations Sustainable Development Goals, especially, Industry, Innovation and Infrastructure. Topics Topics welcomed at Discover Computing include but are not limited to the following: Foundational computing theories: Algorithms, data structures, computational complexity Automata theory, graph theory, formal languages Turing machines, P vs NP problem, lambda calculus Modern computing architectures and systems: Quantum computing, distributed systems, parallel computing Microarchitecture, multicore processors, memory hierarchies Cloud computing, edge computing, serverless architectures Human-computer interaction: User experience (UX), user interface (UI), accessibility Cognitive ergonomics, adaptive systems, virtual reality User-centered design, haptic feedback, gesture recognition Artificial intelligence and machine learning: Neural networks, deep learning, reinforcement learning Natural language processing, computer vision, robotics Generative adversarial networks (GANs), transfer learning, explainable AI Cybersecurity and privacy: Cryptography, firewall, intrusion detection systems Digital forensics, malware analysis, blockchain security Data anonymization, end-to-end encryption, zero trust architectures Emergent technologies: Augmented reality (AR), virtual reality (VR), mixed reality (MR) Internet of Things (IoT), 5G, smart cities Drones, autonomous vehicles, wearable tech Societal impacts of computing: Digital ethics, algorithmic bias, technological unemployment Digital divide, accessibility, information equity Digital literacy, e-governance, surveillance capitalism Content types Discover Computing welcomes a variety of article types – please see our submission guidelines for details. The journal also publishes guest-edited Topical Collections of relevance to all aspects of computer science and its applications. For more information, please follow up with our journal publishing contact.
最終更新：Admin Agent （）

特集号

特集号：Advances in Geovisualization Evaluation: Methods and Applications 投稿締切日: 2026-10-09 Maps are abstract representations that aim to effectively and efficiently communicate spatiotemporal information to the users. Different geovisualization methods, including simple or more sophisticated ones, are utilized to represent such information. Nowadays, maps can be static, animated, mobile, and/or interactive, while they are primarily distributed through the internet. At the same time, modern geovisualization also incorporates extended reality (XR) technologies. Undoubtedly, geovisualization may be characterized by a high level of visual and perceived complexity. Hence, it is crucial to establish robust methods towards the evaluation of their usability. Examining how well, how and why different types of geovisualization work mainly involves the implementation of experimental studies that employ behavioral and neuroimaging methods and techniques to measure visual perception and cognition. This collection aims to collect high-quality original research articles and systematic literature review studies in the field of geovisualization evaluation. We welcome new theories, methods, research frameworks, softwares, scientific datasets, and innovative applications. Potential topics include, but are not limited to, the following: -Cartographic design variables evaluation -Experimental data analysis for geovisualization evaluation -Mixed methods in geovisualization evaluation -New methods in geovisualization evaluation -Geovisualization and Human-Computer Interaction (HCI) -Geovisualization software tools -Geovisualization of Big Geospatial Data -Geovisualization and Artificial Intelligence (AI) applications -Map usability studies -Extended Reality (XR) applications For submissions to Discover Imaging, we welcome papers that focus on topics such as cartographic design variable evaluation, map usability studies, experimental and mixed-method approaches (including behavioral, eye-tracking, and neuroimaging), extended reality (XR) visualization for geospatial data, and human-computer interaction aspects centered on visual cognition and design. For Discover Computing, we welcome submissions that emphasize geovisualization software tools and frameworks, handling big geospatial data through scalable architectures and GPU/edge acceleration, computational methods for real-time rendering and uncertainty encoding, XR integration from a systems perspective, and AI-driven applications for adaptive visualization and usability prediction. This Collection supports and amplifies research related to SDG 4, SDG 9 and SDG 11. Keywords: Eye-Tracking; Geovisualization; Geovisualization Applications; Map Evaluation; Map Usability; Map Interaction; Methods; Tools & Datasets; Neuroimaging.
最終更新：Dou Sun（）
特集号：Theoretical and Methodological Integration of High-Performance Computing and Machine Learning 投稿締切日: 2026-10-31 This collection focuses on the theoretical foundations, methodological innovations, and computational strategies that underpin the integration of High-Performance Computing (HPC) and Machine Learning (ML). It aims to provide insights into how HPC accelerates ML model training, scaling, and optimization, while ML techniques enhance HPC computational efficiency and performance. The collection explores how novel algorithms, frameworks, and system architectures enable scalable, efficient, and robust integration of ML and HPC. Potential topics of interest include, but are not limited to: - Theoretical models and frameworks for HPC-ML integration - Architectures and design principles for scalable ML on HPC systems - Energy-optimized GPU scheduling - Trends in HPC-enabled ML frameworks and libraries (e.g., PyTorch, Horovod) - Distributed training and parallelization of deep learning models using HPC infrastructure - Deep Learning and Reinforcement Learning on HPC systems - Techniques for parallelizing ML algorithms across heterogeneous computing environments - Optimization of resource allocation, memory management, and scheduling for large-scale ML tasks - Co-design of HPC hardware and ML software, including AI accelerators and exascale computing platforms - Methodologies for integrating quantum, neuromorphic, and edge computing into HPC–ML workflows - Generative AI and large language models at HPC scale - Leveraging HPC to enhance cybersecurity with AI-driven threat detection, anomaly detection, and real-time attack analysis - Demonstrating the practical integration of HPC with ML through mini-labs or live demos (e.g., setting up distributed training environments, optimizing model performance on HPC clusters) This Collection supports and amplifies research related to SDG 9. Keywords: High-Performance Computing (HPC); Machine Learning (ML); HPC-Accelerated Machine Learning; Distributed Deep Learning; Scalable Machine Learning; Parallel Computing for AI; Edge-to-Cloud AI Systems; AI Model Optimization on HPC; Resource Optimization in HPC-ML Systems; Exascale Computing for AI
最終更新：Dou Sun（）
特集号：Trustworthy Visual Intelligence and Adaptive Learning for Safety-Critical Systems 投稿締切日: 2026-12-31 This Collection focuses on advancing trustworthy and adaptive visual intelligence for safety-critical systems. With deep learning increasingly deployed in industrial inspection, environmental monitoring, and predictive maintenance, ensuring reliability, interpretability, and adaptability is essential. We welcome original research and reviews on vision-based defect inspection, anomaly detection, predictive maintenance, and intelligent surveillance, emphasizing explainable, uncertainty-aware, and domain-adaptive learning. Topics of interest include diffusion and self-training models, uncertainty-aware perception, edge-AI for real-time monitoring, and human-in-the-loop decision systems. Works validated in manufacturing, energy, or public-safety environments are especially encouraged. By integrating trustworthy AI principles with visual intelligence and adaptive learning, this Collection aims to advance reliable, transparent, and resilient intelligent systems that enhance safety, operational efficiency, and confidence in next-generation AI applications. This Collection supports and amplifies research related to SDG 9. Keywords: Trustworthy AI; Visual Intelligence; Adaptive Learning; Safety-Critical Systems; Deep Learning; Vision-Based Defect Inspection; Predictive Maintenance; Anomaly Detection; Intelligent Surveillance; Uncertainty-Aware Learning
最終更新：Dou Sun（）

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