In cross-modal retrieval tasks, unsupervised hash code learning still faces key challenges, including the difficulty of modeling shared semantic structures across modalities and the inability to adaptively balance multiple supervision objectives during optimization. To address these issues, we propose a novel Unsupervised Dynamic Weighted Cluster-Cooperative Hashing (UDCH) framework, which jointly models feature-level alignment and cluster-level semantic structure to guide consistency learning across modalities under label-free conditions. Specifically, we design an instance-level contrastive loss in the feature branch to align the embedding spaces of images and texts, while employing K-Means clustering to generate pseudo-labels and construct a cluster-center contrast mechanism that captures semantic grouping information. Furthermore, we integrate cross-modal feature similarity to construct a high-order structure matrix, enabling fine-grained structural supervision. To enhance the synergy of multi-objective optimization, we introduce a dynamic weighting strategy that adaptively adjusts the contributions of the feature and cluster branches based on the degree of modal alignment and semantic compactness. Extensive experiments on multiple cross-modal retrieval benchmarks demonstrate that UDCH achieves superior semantic alignment and retrieval performance under unsupervised settings, validating the effectiveness of multi-level semantic modeling and adaptive collaboration mechanisms in unsupervised hashing tasks.

We present MobiFuse, a high-precision depth perception system on mobile devices that combines dual RGB and Time-of-Flight (ToF) cameras. To achieve this, we leverage physical principles from various environmental factors to propose the Depth Error Indication (DEI) modality, characterizing the depth error of ToF and stereo-matching. Furthermore, we employ a progressive fusion strategy, merging geometric features from ToF and stereo depth maps with depth error features from the DEI modality to create precise depth maps. Additionally, we create a new ToF-Stereo depth dataset,RealToF, to train and validate our model. Our experiments demonstrate that MobiFuse excels over baselines by significantly reducing depth measurement errors by up to 77.7%. It also showcases strong generalization across diverse datasets and proves effectiveness in two downstream tasks: 3D reconstruction and 3D segmentation. The demo video of MobiFuse in real-life scenarios is available at the de-identified YouTube link.

Abstract To effectively address common issues such as cattle being obscured by fences and images prone to colour shifts and high brightness in a ranch setting, this paper proposes an algorithm for counting cows based on multi‐scale perception and image correlation. The algorithm first adjusts the model output scale to enhance cattle detection under current conditions. It incor‐porates efficient Partial Convolution (PConv) to replace 3 × 3 convolutions in the Neck segment of the YOLOv7 network, boost‐ing computational speed and reducing complexity. To streamline feature fusion, Dynamic Head (DyHead) unifies multiple at‐tentional operations in the Neck segment, enhancing efficiency. Additionally, it introduces a novel bounding box similarity metric Minimum Point DioU (MPDIoU) based on minimum point distance, encompassing factors from existing loss functions, while simplifying computations. Experimental results demonstrate the algorithm significantly improves detection, achieving 98.8% accuracy, 99.0% recall, and a 92.1% mAP value. Compared with mainstream SOTA models, Precision increases by 0.4%, Recall by 2.0%, and mAP value by 2.2%. Model size decreases by 23.9%, parameter count by 23.0%, and computational load by 6.1%. the algorithm shows improvements across all indices, meeting the challenge of real‐time cattle counting in ranches under complex conditions.

Modelling musical performers’ individual playing styles based on audio features is important for music education, music expression analysis and music generation. In violin performance, the perception of playing styles are mainly affected by the characteristic musical timbre, which is mostly determined by performers, instruments and recording conditions. To verify if timbre features can describe a performer’s style adequately, we examine a violinist identification method based on note-level timbre feature distributions. We first apply it using solo datasets to recognise professional violinists, then use it to identify master players from commercial concerto recordings. The results show that the designed features and method work very well for both datasets. The identification accuracy with the solo dataset using MFCCs and spectral constrast features are 0.94 and 0.91 respectively. Significantly lower but promising results are reported with the concerto dataset. Results suggest that the selected timbre features can model performers’ individual playing reasonably objectively, regardless of the instrument they play.

Time-series sensor data are used to create prediction models, called city profiles, for understanding city dynamics in the smart-city sector. These city profiles are typically created and updated by the Cloud using reported raw sensor data. However, continuously reporting raw sensor data is not energy efficient for boundary computing resources of a network. Thus, this work considers edge servers that are deployed on the boundary computing resources of a network to collaborate with the Cloud for adaptively mitigate city profiling tasks (i.e., creating city profiles) across an edge server and the Cloud. By maintaining the local city profiles on the edge or the global city profiles on the Cloud, either an edge or the Cloud can dynamically respond to user queries. However, there is a trade-off between the energy consumption of an edge and the response accuracy of the city profiles. This work designs an adaptive city profiling and synchronization approach for edges to decide when, where (i.e., an edge or the Cloud), and how to update and synchronize local and global city profiles such that the energy consumption of the edge is reduced while the accuracy of a city profile can be guaranteed. Extensive simulations are conducted using a real-world temperature dataset to evaluate the performance of the proposed approach. The simulation results indicate an average energy saving of 60% of edges compared with a typical Cloud-based approach while the required accuracy is fulfilled.