Driven by the challenges associated with training large models in heterogeneous networks, where a one-size-fits-all training strategy can result in inefficiencies, we propose a versatile Federated Split Learning (FSL) algorithm designed for dynamic resource allocation. Conventional methods often encounter resource limitations, particularly in settings with varying computational abilities. Our algorithm overcomes this issue by intelligently distributing model components among nodes according to their specific computational capabilities, thereby enhancing resource utilization.A key feature of our approach is the inclusion of an auxiliary classifier layer that enables each client to calculate losses and locally update parameters, while only sending activation values to the central server. This mechanism not only reduces communication overhead but also speeds up the overall training process. Additionally, we implement an earlyexit inference strategy that adapts based on task complexity and available resources, further enhancing inference efficiency.
This work addresses a route planning problem constrained by a bus road network that includes the schedules of all buses. Given a query with a starting bus stop and a set of Points of Interest (POIs) to visit, our goal is to find an optimal route on the bus network that allows the user to visit all specified POIs from the starting stop with minimal travel time, which includes both bus travel time and waiting time at bus stops. Although this problem resembles a variant of the Traveling Salesman Problem, it cannot be effectively solved using existing solutions due to the complex nature of bus networks, particularly the constantly changing bus travel times and user waiting times. In this paper, we first propose a modified graph structure to represent the bus network, accommodating the varying bus travel times and their arrival schedules at each stop. Initially, we suggest a brute-force exploration algorithm based on the Dijkstra principle to evaluate all potential routes and determine the best one; however, this approach is too costly for large bus networks. To address this, we introduce the EA-Star algorithm, which focuses on computing the shortest route for promising POI visit sequences. The algorithm includes a terminal condition that halts evaluation once the optimal route is identified, avoiding the need to evaluate all possible POI sequences. During the computation of the shortest route for each POI visiting sequence, it employs the $A^{*}$ algorithm on the modified graph structure, narrowing the search space toward the destination and improving search efficiency. Experiments using New York bus network datasets demonstrate the effectiveness of our approach.
Compute Express Link (CXL), a cache-coherent interconnect protocol for CPUs and PCIe devices, has emerged as a promising solution for memory expansion. In this work, we develop a CXL simulator built upon GEM5 and QEMU to assess the system-wide performance impacts based on varying attributes of CXL devices, such as memory capacity and latency. The simulator directs memory traffic to either system memory or the CXL memory expander, tracks cache hit/miss ratios, and projects application performance. Our simulation study demonstrates that the simulator accurately predicts application performance.
Multiple Object Tracking (MOT) has applications across many fields, yet occlusion-related issues continue to impact its effectiveness. These occlusions lead to information loss, reducing model performance and causing re-identification errors. In response, this study introduces a Dual View Sharing Multi Object Tracking (DVSMOT) method. Results show that this approach minimizes information loss due to occlusion, improves re-identification accuracy, and enhances tracking precision in experimental trials compared to traditional methods.
The rapid growth of the internet and digital technologies has expanded access to digital content while increasing copyright infringement through illegal content-sharing websites. These platforms cause significant financial losses to copyright holders and persist despite government monitoring efforts. This study proposes an automated method to analyze piracy site characteristics and classify sites involved in copyright infringement. By addressing the evolving nature of such platforms, the approach enhances detection and contributes to combating online piracy effectively.
