Clustering ensemble integrates multiple base clustering results to improve the stability and robustness of the single clustering method. It consists of two principal steps: a generation step, which is about the creation of base clusterings, and a consensus function, which is the integration of all clusterings obtained in the generation step. However, most of the existing base clustering algorithms used in the generation step are shallow clustering algorithms such as k-means. These shallow clustering algorithms do not work well or even fail when dealing with large-scale, high-dimensional unstructured data. The emergence of deep clustering algorithms provides a solution to address this challenge. Deep clustering combines the unsupervised commonality of deep representation learning to address complex high-dimensional data clustering, which has achieved excellent performance in many fields. In light of this, we introduce deep clustering into clustering ensemble and propose an improved selective deep-learning-based clustering ensemble algorithm (ISDCE). ISDCE exploits the deep clustering algorithm with different initialization parameters to generate multiple diverse base clusterings. Next, ISDCE constructs ensemble quality and diversity evaluation metrics of base clusterings to select higher-quality and rich-diversity candidate base clusterings. Finally, a weighted graph partition consensus function is utilized to aggregate the candidate base clusterings to obtain a consensus clustering result. Extensive experimental results on various types of datasets demonstrate that ISDCE performs significantly better than existing clustering ensemble approaches.

Unmanned aerial vehicle (UAV)-based object detection methods are widely used in traffic detection due to their high flexibility and extensive coverage. In recent years, with the increasing complexity of the urban road environment, UAV object detection algorithms based on deep learning have gradually become a research hotspot. However, how to further improve algorithmic efficiency in response to the numerous and rapidly changing road elements, and thus achieve high-speed and accurate road object detection, remains a challenging issue. Given this context, this paper proposes the high-efficiency multi-object detection algorithm for UAVs (HeMoDU). HeMoDU reconstructs a state-of-the-art, deep-learning-based object detection model and optimizes several aspects to improve computational efficiency and detection accuracy. To validate the performance of HeMoDU in urban road environments, this paper uses the public urban road datasets VisDrone2019 and UA-DETRAC for evaluation. The experimental results show that the HeMoDU model effectively improves the speed and accuracy of UAV object detection.

Hyperspectral remote sensing technology is swiftly evolving, prioritizing affordability, enhanced portability, seamless integration, sophisticated intelligence, and immediate processing capabilities. The leading model for classifying hyperspectral images, which relies on convolutional neural networks (CNNs), has proven to be highly effective when run on advanced computing platforms. Nonetheless, the high degree of parameterization inherent in CNN models necessitates considerable computational and storage resources, posing challenges to their deployment in processors with limited capacity like drones and satellites. This paper focuses on advancing lightweight models for hyperspectral image classification and introduces EBCNN, a novel binary convolutional neural network. EBCNN is designed to effectively regulate backpropagation gradients and minimize gradient discrepancies to optimize BNN performance. EBCNN incorporates an adaptive gradient scaling module that utilizes a multi-scale pyramid squeeze attention (PSA) mechanism during the training phase, which can adjust training gradients flexibly and efficiently. Additionally, to address suboptimal training issues, EBCNN employs a dynamic curriculum learning strategy underpinned by a confidence-aware loss function, Superloss, enabling progressive binarization and enhancing its classification effectiveness. Extensive experimental evaluations conducted on five esteemed public datasets confirm the effectiveness of EBCNN. These analyses highlight a significant enhancement in the classification accuracy of hyperspectral images, achieved without incurring additional memory or computational overheads during the inference process.

Multimodal Large Language Models (MLLMs), by expanding the model's capabilities to perceive and interact through multi-modalities, have significantly enhanced performance across various tasks. The perception of vision is an important modality developed into LLM, enabling research in vision-language to continuously lead the cutting-edge advancements in the MLLM community. However, the standard pre-training pipeline on image-text pairs results in a limited model understanding of relationships between multiple images and texts, as well as visual details. Additionally, the setting of fine-tuning with a frozen visual backbone hinders the enhancement of visual representations on new data. These two issues lead to suboptimal performance in models for demonstrative instruction following about multiple images. This work introduces a novel framework called MLoEM, which first converts long multimodal data into an interleaved image-instruction format, and then adopts a fully autoregressive architecture model, allowing for more robust and coherent learning from naturally occurring multimodal documents than pair-based pipeline. Additionally, we incorporate the Low-Rank Adaptation (LoRA) fine-tuning method, enhancing visual representations while maintaining the stability of previously learned knowledge. Finally, we utilize ensemble methods to enhance model performance on tasks. To alleviate the storage overhead issue of parallel ensembles with large models, we design an ensemble approach that shares the MLLM while only switching the LoRA matrices. In the experiments, the proposed MLoEM shows superior performance on the testing set.

Hyperspectral image cross-scene classification (HSICC) tasks are confronted with tremendous challenges due to spectral shift phenomena across scenes and the tough work of obtaining labels. Unsupervised domain adaptation has proven its effectiveness in tackling this issue, but it has a fatal limitation of intending to narrow the disparity between source and target domains by utilizing fully labeled source data and unlabeled target data. However, it is costly even to attain labels from source domains in many cases, rendering sufficient labeling as used in prior work impractical. In this work, we investigate an extreme and realistic scenario where unsupervised domain adaptation methods encounter sparsely labeled source data when handling HSICC tasks, namely, few-shot unsupervised domain adaptation. We propose an end-to-end refined bi-directional prototypical contrastive learning (RBPCL) framework for overcoming the HSICC problem with only a few labeled samples in the source domain. RBPCL captures category-level semantic features of hyperspectral data and performs feature alignment through in-domain refined prototypical self-supervised learning and bi-directional cross-domain prototypical contrastive learning, respectively. Furthermore, our framework introduces the class-balanced multicentric dynamic prototype strategy to generate more robust and representative prototypes. To facilitate prototype contrastive learning, we employ a Siamese-style distance metric loss function to aggregate intra-class features while increasing the discrepancy of inter-class features. Finally, extensive experiments and ablation analysis implemented on two public cross-scene data pairs and three pairs of self-collected ultralow-altitude hyperspectral datasets under different illumination conditions verify the effectiveness of our method, which will further enhance the practicality of hyperspectral intelligent sensing technology.

With the rapid development of the Internet of Things (IoT), data centers are facing growing performance demands for flow deadline requirements, other to the low latency and high throughput. To address congestion issues that affect the performance of data center networks, this paper proposes D2C4, a deadline-aware data-credit coupled congestion control scheme. By tightly coupled data-credit integration using an ECN-based feedback loop, D2C4 minimizes credit waste while a credit-driven scheduler meets IoT applications’ flow deadlines. The key innovations of D2C4 include: 1) Data-credit coupling for performance gains; 2) ECN-based credit rate control reducing waste; 3) Deadline-sensitive credit-based flow scheduling to meet flow deadline demands. We conduct extensive experiments of D2C4 through small-scale DPDK-based tests and large-scale OMNeT++ simulations. Experimental results show that D2C4 outperforms existing protocols in flow completion time, throughput, deadline miss ratio, and packet drop.