Towards building a monitoring platform for a challenge-oriented smart specialisation with RIS3-MCAT

• URL: http://arxiv.org/abs/2401.10900v1
• Date: Tue, 19 Dec 2023 07:58:39 GMT
• Title: Towards building a monitoring platform for a challenge-oriented smart specialisation with RIS3-MCAT
• Abstract summary: RIS3-MCAT is an interactive platform that facilitates access to R&I project data in formats that allow for sophisticated analyses. Its conceptualisation, development framework and use are presented in this paper.
• Score: 0.031208736245509724
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the new research and innovation (R&I) paradigm, aimed at a transformation towards more sustainable, inclusive and fair pathways to address societal and environmental challenges, and at generating new patterns of specialisation and new trajectories for socioeconomic development, it is essential to provide monitoring systems and tools to map and understand the contribution of R&I policies and projects. To address this transformation, we present the RIS3-MCAT platform, the result of a line of work aimed at exploring the potential of open data, semantic analysis, and data visualisation, for monitoring challenge-oriented smart specialisation in Catalonia. RIS3-MCAT is an interactive platform that facilitates access to R&I project data in formats that allow for sophisticated analyses of a large volume of texts, enabling the detailed study of thematic specialisations and challenges beyond classical classification systems. Its conceptualisation, development framework and use are presented in this paper.

Related papers

• A Framework and Prototype for a Navigable Map of Datasets in Engineering Design and Systems Engineering [0.0]
  This paper proposes a systematic framework for a "Map of Datasets in EDSE"<n>The framework is built upon a multi-dimensional taxonomy designed to classify engineering datasets by domain, lifecycle stage, data type, and format.<n>An analysis of the current data landscape reveals underrepresented areas ("data deserts") in early-stage design and system architecture, as well as relatively well-represented areas ("data oases") in predictive maintenance and autonomous systems.
  arXiv  Detail & Related papers  (2026-03-16T17:08:20Z)
• Integrative Analysis of Risk Management Methodologies in Data Science Projects [0.0]
  This study aims to conduct a comparative analysis of the main risk management methodologies applied to data science projects.<n>The study examines widely adopted risk management standards ISO 31000, PMBOK Risk Management and NIST RMF, as well as frameworks specific to data science.<n>The findings reveal that traditional approaches provide limited coverage of emerging risks, whereas contemporary models propose multidimensional structures.
  arXiv  Detail & Related papers  (2025-12-02T13:06:52Z)
• Deep Research: A Systematic Survey [118.82795024422722]
  Deep Research (DR) aims to combine the reasoning capabilities of large language models with external tools, such as search engines.<n>This survey presents a comprehensive and systematic overview of deep research systems.
  arXiv  Detail & Related papers  (2025-11-24T15:28:28Z)
• Graph Foundation Models: A Comprehensive Survey [66.74249119139661]
  Graph Foundation Models (GFMs) aim to bring scalable, general-purpose intelligence to structured data.<n>This survey provides a comprehensive overview of GFMs, unifying diverse efforts under a modular framework.<n>GFMs are poised to become foundational infrastructure for open-ended reasoning over structured data.
  arXiv  Detail & Related papers  (2025-05-21T05:08:00Z)
• Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directions [0.37865171120254354]
  Stance detection is essential for understanding subjective content across various platforms such as social media, news articles, and online reviews.<n>Recent advances in Large Language Models (LLMs) have revolutionized stance detection by introducing novel capabilities.<n>We present a novel taxonomy for LLM-based stance detection approaches, structured along three key dimensions.<n>Key applications in stance detection, political analysis, public health monitoring, and social media moderation are discussed.
  arXiv  Detail & Related papers  (2025-05-13T11:47:49Z)
• A Survey on Progress in LLM Alignment from the Perspective of Reward Design [24.47393138031339]
  Large language models (LLMs) with human values and intentions represents a core challenge in AI research.<n>This study conducts a comprehensive investigation of reward mechanisms in LLM alignment through a systematic theoretical framework.<n>This research establishes a systematic classification framework that reveals evolutionary trends in reward modeling.
  arXiv  Detail & Related papers  (2025-05-05T14:15:02Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [59.52058740470727]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• A Survey on Knowledge-Oriented Retrieval-Augmented Generation [45.65542434522205]
  Retrieval-Augmented Generation (RAG) has gained significant attention in recent years. RAG combines large-scale retrieval systems with generative models. We discuss the key characteristics of RAG, such as its ability to augment generative models with dynamic external knowledge.
  arXiv  Detail & Related papers  (2025-03-11T01:59:35Z)
• Composed Multi-modal Retrieval: A Survey of Approaches and Applications [81.54640206021757]
  Composed Multi-modal Retrieval (CMR) emerges as a pivotal next-generation technology.<n>CMR enables users to query images or videos by integrating a reference visual input with textual modifications.<n>This paper provides a comprehensive survey of CMR, covering its fundamental challenges, technical advancements, and applications.
  arXiv  Detail & Related papers  (2025-03-03T09:18:43Z)
• A Survey of Model Architectures in Information Retrieval [64.75808744228067]
  We focus on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation. We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs) We conclude by discussing emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains beyond traditional search paradigms.
  arXiv  Detail & Related papers  (2025-02-20T18:42:58Z)
• Knowledge Integration Strategies in Autonomous Vehicle Prediction and Planning: A Comprehensive Survey [0.0]
  This survey examines the integration of knowledge-based approaches into autonomous driving systems. We systematically review methodologies for incorporating domain knowledge, traffic rules, and commonsense reasoning into these systems.
  arXiv  Detail & Related papers  (2025-02-13T19:32:41Z)
• Graph Foundation Models for Recommendation: A Comprehensive Survey [55.70529188101446]
  Large language models (LLMs) are designed to process and comprehend natural language, making both approaches highly effective and widely adopted. Recent research has focused on graph foundation models (GFMs) GFMs integrate the strengths of GNNs and LLMs to model complex RS problems more efficiently by leveraging the graph-based structure of user-item relationships alongside textual understanding.
  arXiv  Detail & Related papers  (2025-02-12T12:13:51Z)
• A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models [15.190033208947051]
  Retrieval-augmented generation (RAG) has emerged as a promising solution to customize large language models (LLMs) This survey presents a systematic analysis of Graph-based Retrieval-Augmented Generation (GraphRAG)
  arXiv  Detail & Related papers  (2025-01-21T06:25:21Z)
• A Survey of Financial AI: Architectures, Advances and Open Challenges [0.6798775532273751]
  Financial AI empowers sophisticated approaches to financial market forecasting, portfolio optimization, and automated trading. This survey provides a systematic analysis of these developments across three primary dimensions.
  arXiv  Detail & Related papers  (2024-11-01T04:16:00Z)
• A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions [0.0]
  RAG combines retrieval mechanisms with generative language models to enhance the accuracy of outputs. Recent research breakthroughs are discussed, highlighting novel methods for improving retrieval efficiency. Future research directions are proposed, focusing on improving the robustness of RAG models.
  arXiv  Detail & Related papers  (2024-10-03T22:29:47Z)
• Finding frames with BERT: A transformer-based approach to generic news frame detection [0.0]
  We introduce a transformer-based approach for generic news frame detection in Anglophone online content. We discuss the composition of the training and test datasets, the model architecture, and the validation of the approach.
  arXiv  Detail & Related papers  (2024-08-30T22:05:01Z)
• On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey [82.49623756124357]
  Zero-shot image recognition (ZSIR) aims at empowering models to recognize and reason in unseen domains. This paper presents a broad review of recent advances in element-wise ZSIR. We first attempt to integrate the three basic ZSIR tasks of object recognition, compositional recognition, and foundation model-based open-world recognition into a unified element-wise perspective.
  arXiv  Detail & Related papers  (2024-08-09T05:49:21Z)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• A Survey on Retrieval-Augmented Text Generation for Large Language Models [1.4579344926652844]
  Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning advancements. This paper organizes the RAG paradigm into four categories: pre-retrieval, retrieval, post-retrieval, and generation. It outlines RAG's evolution and discusses the field's progression through the analysis of significant studies.
  arXiv  Detail & Related papers  (2024-04-17T01:27:42Z)
• A Survey on 3D Skeleton Based Person Re-Identification: Approaches, Designs, Challenges, and Future Directions [71.99165135905827]
  Person re-identification via 3D skeletons is an important emerging research area that triggers great interest in the pattern recognition community. With distinctive advantages for many application scenarios, a great diversity of 3D skeleton based person re-identification methods have been proposed in recent years. This paper provides a systematic survey on current SRID approaches, model designs, challenges, and future directions.
  arXiv  Detail & Related papers  (2024-01-27T04:52:24Z)
• A Survey on Interpretable Cross-modal Reasoning [64.37362731950843]
  Cross-modal reasoning (CMR) has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics. This survey delves into the realm of interpretable cross-modal reasoning (I-CMR) This survey presents a comprehensive overview of the typical methods with a three-level taxonomy for I-CMR.
  arXiv  Detail & Related papers  (2023-09-05T05:06:48Z)
• Geometric Deep Learning for Structure-Based Drug Design: A Survey [83.87489798671155]
  Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, have significantly propelled the field forward.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)
• Retrieval-Enhanced Machine Learning [110.5237983180089]
  We describe a generic retrieval-enhanced machine learning framework, which includes a number of existing models as special cases. REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization. REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
  arXiv  Detail & Related papers  (2022-05-02T21:42:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.