Visual object tracking is essentially crucial for unmanned aerial vehicles (UAVs). Despite the substantial progress, most of the existing UAV trackers are designed for well-conditioned daytime data, while for the scenarios in challenging weather condition, e.g. foggy or nighttime environment, the tremendous domain gap leads to significant performance degradation. To address this issue, in this paper, we propose a novel robust UAV tracker termed LVPTrack, which conducts high quality label-aligned visual prompt tuning to adapt to various challenging weather conditions. Specifically, we first synthesize the sequential foggy and nighttime video frames to assist the model training. A domain adaptive teacher-student network is utilized to distill the hierarchical visual semantic of the target objects in cross-domain scenarios. Then we propose a target-aware pseudo-label voting (PLV) strategy to alleviate the target-level misalignment in the dual domains. Furthermore, we propose a dynamic aggregated prompt (DAP) module to facilitate the appearance variation adaptation of the target object in challenging scenarios. Extensive experiments demonstrate that our tracker achieves superior performance over existing state-of-the-art UAV trackers.

Hyperspectral remote sensing images contain abundant spectral information and have a broad application in various areas, such as precision agriculture and geological exploration. However, due to the existence of many bands and the correlation between these bands, it is easy to cause the curse of dimensionality. Therefore, how to efficiently extract spectral and spatial features should be addressed. In this paper, a novel and efficient sharpened cosine similarity operation is applied in hyperspectral images classification to enhance the ability of feature extraction. To validate the efficiency of the proposed method, experiments are conducted on a real hyperspectral dataset, e.g., the University of Pavia (UP). Quantitative and qualitative results demonstrate that sharpened cosine distance operation can efficiently extract discriminative features and achieve a better classification performance.

Point clouds semantic labeling is an important task in 3D computer vision. Current major researches focus on fully supervised learning. However, point-by-point manual annotations are expensive and time-consuming. To this end, we propose a general point clouds deep active learning framework to ease the annotation burden for researchers. In this work, we propose a conditional random field (CRF) based pseudo labels generation to provide more supervised information for deep neural network (DNN) and employ Golden Loss Correction (GLC) to correct pseudo labeled data training loss. Finally, we propose the Modified Margin acquisition function which can select the most valuable points for labeling. We demonstrate the improvements provided by our proposed method on the S3DIS benchmark.

Security Service Chaining (SSC) has recently shown great potential to address cloud security problems. A key point to implement SSC is Virtualized Security Functions (VSF) placement, which is a special kind of VNF placement. However, the existing solutions of VNF placement have not considered traffic reachability problem and policy conflict problem, which should be addressed for SSC. In this paper, we study the issue of VSF placement for SSC in cloud, and propose a solution named MCE (Map, Check reachability, and Eliminate conflict). In the framework of MCE, we first formulate an optimization model for VSF and VL mapping, which is NP-hard and can be solved by existing mapping algorithms. Next, we propose to use HSA method to find and delete some improper mapping results where traffic reachability can't be satisfied. Finally, we propose a scheme named BSIS-RC (Bit Sequence Intersection and Subtraction based Rule Computation), which is based on our work on the formula expression of security policies, the definition of policy spaces, bit sequence subtraction rule and the definition of policy relationships. BSIS-RC can check and eliminate policy conflicts quickly and effectively. We combine MCE with three existing mapping algorithms and compare the performance of six solutions through simulations. Results show that, compared with three solutions not considering the problems of traffic reachability and policy conflict, MCE can improve 38% of the SSC request success rate on average and reduce 15% of the total bandwidth consumption per SSC request on average. Moreover, among the three MCE solutions with three different mapping algorithms, MCE with Genetic algorithm has the best performance.

Multi-path TCP has recently shown great potential to take advantage of the rich path diversity in data center networks (DCN) to increase transmission throughput. However, the small flows, which take a large fraction of data center traffic, will easily get a timeout when split onto multiple paths. Moreover, the dynamic congestions and node failures in DCN will exacerbate the reorder problem of parallel multi-path transmissions for large flows. In this paper, we propose DC2-MTCP (Data Center Coded Multi-path TCP), which employs a fast and light-weight coding method to address the above challenges while maintaining the benefit of parallel multi-path transmissions. To meet the high flow performance in DCN, we insert a very low ratio of coded packets with a careful selection of the packets to be coded. We further present a progressive decoding algorithm to decode the packets online with a low time complexity. Extensive ns2-based simulations show that with two orders of magnitude lower coding delay, DC2-MTCP can reduce on average 40% flow completion time for small flows and increase 30% flow throughput for large flows compared to the peer schemes in varying network conditions.