Tootfinder

User Association in the Presence of Jamming in Wireless Networks Using the Whittle Index
Pramod N Chine, Suven Jagtiani, Mandar R Nalavade, Gaurav S Kasbekar
https://arxiv.org/abs/2507.04968

User Association in the Presence of Jamming in Wireless Networks Using the Whittle Index
In wireless networks, algorithms for user association, i.e., the task of choosing the base station (BS) that every arriving user should join, significantly impact the network performance. A wireless network with multiple BSs, operating on non-overlapping channels, is considered. The channels of the BSs are susceptible to jamming by attackers. During every time slot, a user arrives with a certain probability. There exists a holding cost in each slot for every user associated with a BS. The goal …

STRive: An association rule-based system for the exploration of spatiotemporal categorical data
Mauro Diaz, Luis Sante, Joel Perca, Jo\~ao Victor da Silva, Nivan Ferreira, Jorge Poco
https://arxiv.org/abs/2509.02732

STRive: An association rule-based system for the exploration of spatiotemporal categorical data
Effectively analyzing spatiotemporal data plays a central role in understanding real-world phenomena and informing decision-making. Capturing the interaction between spatial and temporal dimensions also helps explain the underlying structure of the data. However, most datasets do not reveal attribute relationships, requiring additional algorithms to extract meaningful patterns. Existing visualization tools often focus either on attribute relationships or spatiotemporal analysis, but rarely supp…

Self-Supervised Real-Time Tracking of Military Vehicles in Low-FPS UAV Footage
Markiyan Kostiv, Anatolii Adamovskyi, Yevhen Cherniavskyi, Mykyta Varenyk, Ostap Viniavskyi, Igor Krashenyi, Oles Dobosevych
https://arxiv.org/abs/2507.05229

Self-Supervised Real-Time Tracking of Military Vehicles in Low-FPS UAV Footage
Multi-object tracking (MOT) aims to maintain consistent identities of objects across video frames. Associating objects in low-frame-rate videos captured by moving unmanned aerial vehicles (UAVs) in actual combat scenarios is complex due to rapid changes in object appearance and position within the frame. The task becomes even more challenging due to image degradation caused by cloud video streaming and compression algorithms. We present how instance association learning from single-frame annota…