In the visual Internet of Things (VIoT), smart surveillance is an important component of the multimodality person retrieval (MPR) task. Capturing discriminative pedestrian information from images aids in identifying target individuals through sketch or text descriptions, improving the reliability of VIoT systems. Current granularity-level matching methods face challenges with information redundancy and modality differences in MPR tasks. In this article, we propose a novel approach for intra and intermodality saliency feature learning by multigranularity feature selection and relation (MFSR). First, to reduce redundant information within each modality, the intramodality feature selection module (IFSM) employs the adaptive weighting mechanism to enhance salient pedestrian features while suppressing irrelevant features. Second, the multigranularity feature relation module (MFRM) aligns cross-modality salient person features by increasing the similarity scores of local and global features across modalities, to reduce differences between multimodality visual-text pairs. Finally, the cross-modality similarity matching (CSM) loss is designed to enhance consistency in visual-text pairs of the same identity, ensuring compact intraclass features by minimizing discrepancies between cross-modality similarity distributions and identity-matching distributions. Experimental results show that our approach achieves state-of-the-art performance on benchmark datasets.

Text-based Person Retrieval (TPR) plays a pivotal role in video surveillance systems for safeguarding public safety. As a fine-grained retrieval task, TPR faces the significant challenge of precisely capturing highly discriminative features across image and text modalities. Existing methods primarily focus on establishing modality-shared feature spaces to bridge cross-modal discrepancies. However, these methods are prone to disturbances from irrelevant information, such as background noises in the visual modality, and often over-emphasize specific local regions while neglecting the capture of diverse discriminative modal features, thereby limiting the robustness of cross-modal matching. In this paper, we introduce a novel framework, termed the Diverse Co-saliency Feature Learning Network (DCFL), which mines the co-saliency information between image and text modalities and enhances the diversity of cross-modal discriminative features while mitigating the interference of noise. Specifically, to construct cross-modal co-saliency features, we devise the Intra-modal Saliency Feature Learning (ISFL) and Cross-modal Saliency Feature Matching (CSFM) modules. ISFL employs a weighted mask mechanism to guide the model in reducing the impact of noise information in both modalities. Complementing ISFL, CSFM establishes consistent relationships between saliency features across modalities, leveraging text descriptions to align pedestrian-relevant visual regions. Furthermore, we propose the Diverse Co-saliency Feature Mining (DCFM) to bolster the diversity of discriminative co-saliency features across both image and text modalities. This module integrates a diversity regularization term, enabling the extraction of varied visual cues and capturing comprehensive features of the target individual. Extensive benchmark experiments demonstrate a substantial superiority of our approach over the state-of-the-art methods. The code will be released publicly.

Learning-enabled spectroscopic analysis, promising for automated real-time analysis of chemicals, is facing several challenges. First, a typical machine learning model requires a large number of training samples that physical systems can not provide. Second, it requires the testing samples to be in range with the training samples, which often is not the case in the real world. Further, a spectroscopy device is limited by its memory size, computing power, and battery capacity. That requires highly efficient learning models for on-site analysis. In this paper, by analyzing multi-gas mixtures and multi-molecule suspensions, we first show that orders of magnitude reduction of data dimension can be achieved as the number of principal components that need to be retained is the same as the independent constituents in the mixture. From this principle, we designed highly compact models in which the essential principal components can be directly extracted from the interrelations between the individual chemical properties and principal components; and only a few training samples are required. Our model can predict the constituent concentrations that have not been seen in the training dataset and provide estimations of measurement noises. This approach can be extended as an effectively standardized method for principle component extraction.

Multispectral object detection for autonomous driving is multi-object localization and classification task on visible and thermal modalities. In this scenario, modality differences lead to the lack of object information in a single modality and the misalignment of cross-modality information. To alleviate these problems, most existing methods extract information based on a single scale (e.g., these methods mainly focus on detecting significant cars or pedestrians), which leads to insufficient performance in capturing multi-scale discriminative information (e.g., small bicycles and blurred pedestrians) and safety hazards in the driving process. In this paper, we propose a Multi-Scale Aggregation Network (MSANet) consisting of two parts Multi-Scale Aggregation Transformer (MSAT) and the Cross-modal Merging Fusion Mechanism (CMFM), which combined with the advantages of Transformer and CNN to extract rich image information from two modalities by mining both local and global context dependencies. Firstly, to reduce the lack of information in a single modality, we design a novel MSAT module to extract rich details and texture from multi-scale. Secondly, to alleviate feature misalignment caused by modality differences, the CMFM is utilized to aggregate complementary information on multiple levels. Comprehensive experiments on two benchmarks demonstrate that our approach shows better results than several state-of-the-art methods. The code is available athttps://github.com/ysh-strive/MSANet.

Fecal coliform bacteria are commonly used as an indicator to reflect the fecal contamination level in the water. To manage a healthy and thriving aquaculture industry, the Canadian Shellfish Sanitation Program (CSSP) was established in 1948. As a part of the CSSP mandate, fecal coliform bacteria levels in shellfish growing habitat have been monitored at nearly 15, 000 shellfish harvesting sites across the six coastal provinces of Canada over 40 years (1980–2019). The irregular sparseness in the measurement data presented a critical challenge for reliable analysis of fecal contamination patterns along Canada's coastline. This paper illustrates a preprocessing approach to handle the irregular sparseness in the measurement data of fecal coliform bacteria levels and demonstrates the effectiveness of data filtering, pooling, binning, partitioning, and the application of functional principal component analysis. We managed to transform the irregularly sparse measurements at a site into surrogate variables without missing data that represent the differences in the site's contamination amplitude and seasonal variation from the average. The surrogate variables will be used for downstream analyses, such as associating contamination with climate change.

Abstract QoS‐aware based web service recommendation is one of the crucial solutions to help users find high‐quality web services. To accurately predict the QoS values of candidate services, it is usually required to collect historical QoS data of users (QoS data for short). If these collected QoS data are improperly processed, QoS data privacy may be threatened. However, how to accurately predict the QoS values of candidate services while protecting QoS data privacy has not been well studied. In response to the situation, we propose a hybrid web service recommendation mechanism, which is divided into three parts. In the first part, the QoS data privacy preservation algorithm, which called DVO, is proposed based on keeping the cosine similarity of QoS data unchanged, that is, to realize the confusion of QoS data while ensuring the availability of QoS data remains unchanged. In the second part, a hybrid matrix factorization model based on location information and service features, which called LCLMF, is proposed to improve the accuracy of QoS values prediction. According to DVO and LCLMF, the DVO + LCLMF is designed in the third part, which can accurately predict QoS values while protecting QoS data privacy. The experimental results show that DVO + LCLMF can accurately predict the QoS values of candidate services on the basis of attaining QoS data privacy protection.

Elastic scaling is one of the techniques to deal with the sudden change of the number of tasks and the long average waiting time of tasks in the container cluster. The unreasonable resource supply may lead to the low comprehensive resource utilization rate of the cluster. Therefore, balancing the relationship between the average waiting time of tasks and the comprehensive resource utilization rate of the cluster based on the number of tasks is the key to elastic scaling. In this paper, an adaptive scaling algorithm based on the queuing model called ACEA is proposed. This algorithm uses the hybrid multiserver queuing model (M/M/s/K) to quantitatively describe the relationship among number of tasks, average waiting time of tasks, and comprehensive resource utilization rate of cluster and builds the cluster performance model, evaluation function, and quality of service (QoS) constraints. Particle swarm optimization (PSO) is used to search feasible solution space determined by the constraint relation of ACEA quickly, so as to improve the dynamic optimization performance and convergence timeliness of ACEA. The experimental results show that the algorithm can ensure the comprehensive resource utilization rate of the cluster while the average waiting time of tasks meets the requirement.