AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This paper proposes Scenemash, a system that generates multimodal summaries of multiple alternative routes between locations in a city by jointly analysing distributions of a large number of semantic concepts detected in the visual content and latent topics extracted from associated text.
An integrated framework combining Topic-RoBERTa, topic-level semantic network analysis, and Graph Attention Networks (GAT) to extract experience topics and model their semantic associations and offer valuable insights for tourism management and service optimization is presented.
The need for task-specific designs that support cross-city preference transfer, semantic grounding, and scalable reasoning over unseen destination inventories is highlighted, with results highlighting the need for task-specific designs that support cross-city preference transfer, semantic grounding, and scalable reasoning over unseen destination inventories.
Comprehending the mobility of entities in urban environments is crucial for developing and maintaining efficient and livable cities. This survey presents fundamental concepts, methods, and challenges related to knowledge discovery from urban trajectory data, including data acquisition, preprocessing, mining, and practical applications. Unlike previous studies, our survey offers a holistic and integrated view of these steps, ensuring that each stage is optimized to contribute to the overall performance of the mobility knowledge discovery process. Moreover, we identify operational and technical challenges, discussing potential future directions that can unlock new opportunities and advancements in this field.
Abstract. Digital platform capitalism depends on geographic data to connect users with nearby goods and services, thus playing a role in both representing and shaping commercial landscapes. OpenStreetMap (OSM) represents a vast and low-cost geographic data source for platform capitalism; however, OSM is crowdsourced and exhibits uneven development across space, especially in rural areas. OSM researchers have observed that the map is built by a mix of local and remote volunteers, as well as corporate editors paid to identify and fill data gaps; however, deeper place-based inquiries are needed in order to better understand the process by which OSM contributors grow the map and how fully their contributions are actually representing small towns and villages. I model such a case study by using the OSM history files, contributor profile pages, and the geographies of contributor interest to construct “the story of OpenStreetMap” in the small agricultural town of Othello, Washington, USA. In addition to identifying trends in local and corporate mapping activity, I evaluate the map’s completeness by performing a walking survey of Othello’s commercial areas and comparing the visible landscape with the town’s representation in OSM. At small scales, Othello appears data-rich due to contributors who have traced building footprints and road information; however, a substantial percentage of civic and commercial institutions are still missing from the map, especially small businesses. Platforms that use OSM to connect Othello’s residents with commodities or resources may be strengthening the position of national or global chain businesses at the expense of local commerce.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.