MoWLD: A Robust Motion Image Descriptor for Violence Detection

Abstract Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in designing an algorithm that can detect violence in surveillance videos with high performance. Existing methods typically apply the Bagof-Words (BoW) model on local spatiotemporal descriptors. However, traditional spatiotemporal features are not discriminative enough, and also the BoW model roughly assigns each feature vector to only one visual word and therefore ignores the spatial relationships among the features. To tackle these problems, in this paper we propose a novel Motion Weber Local Descriptor (MoWLD) in the spirit of the well-known WLD and make it a powerful and robust descriptor for motion images. We extend the WLD spatial descriptions by adding a temporal component to the appearance descriptor, which implicitly captures local motion information as well as low-level image appear information. To eliminate redundant and irrelevant features, the nonparametric Kernel Density Estimation (KDE) is employed on the MoWLD descriptor. In order to obtain more discriminative features, we adopt the sparse coding and max pooling scheme to further process the selected MoWLDs. Experimental results on three benchmark datasets have demonstrated the superiority of the proposed approach over the state-of-the-arts

Weighted Multi-Skill Resource Constrained Project Scheduling: A Greedy and Parallel Scheduling Approach

This study addresses the Weighted Multi-Skill Resource Constrained Project Scheduling Problem (W-MSRCSPSP) with the aim of minimizing software project makespan. Unlike previous works, our investigation regards heterogeneous resources characterized by varying skill proficiency levels. Another major problem with existing methodologies is the potential underutilization of human resources due to varying task durations. This work introduces an innovative scheduling approach known as the Greedy and Parallel Scheduling (GPS) algorithm to handle the said issues. GPS focuses on assigning the most suitable resources available to project activities at each scheduling point. The fundamental goal of our proposed approach is to reduce resource wastage while efficiently allocating surplus resources, if any, to project tasks, ultimately leading to a decrease in the makespan. To empirically evaluate the efficacy of the GPS algorithm, we conduct a comparative analysis against the Parallel Scheduling Scheme (PSS). The advantage of our proposed approach lies in its ability to optimize the utilization of available resources, resulting in accelerated project completion. Results from extensive simulations substantiate this claim, demonstrating that the GPS scheme outperforms the PSS approach in minimizing project duration

Learning the consensus and complementary information for large-scale multi-view clustering

The multi-view data clustering has attracted much interest from researchers, and the large-scale multi-view clustering has many important applications and significant research value. In this article, we fully make use of the consensus and complementary information, and exploit a bipartite graph to depict the duality relationship between original points and anchor points. To be specific, representative anchor points are selected for each view to construct corresponding anchor representation matrices, and all views' anchor points are utilized to construct a common representation matrix. Using anchor points also reduces the computation complexity. Next, the bipartite graph is built by fusing these representation matrices, and a Laplacian rank constraint is enforced on the bipartite graph. This will make the bipartite graph have k connected components to obtain accurate clustering labels, where the bipartite graph is specifically designed for a large-scale dataset problem. In addition, the anchor points are also updated by dictionary learning. The experimental results on the four benchmark image processing datasets have demonstrated superior performance of the proposed large-scale multi-view clustering algorithm over other state-of-the-art multi-view clustering algorithms. [Abstract copyright: Copyright © 2024 Elsevier Ltd. All rights reserved.

An Ensemble Method for High-Dimensional Multilabel Data

Multilabel learning is now receiving an increasing attention from a variety of domains and many learning algorithms have been witnessed. Similarly, the multilabel learning may also suffer from the problems of high dimensionality, and little attention has been paid to this issue. In this paper, we propose a new ensemble learning algorithms for multilabel data. The main characteristic of our method is that it exploits the features with local discriminative capabilities for each label to serve the purpose of classification. Specifically, for each label, the discriminative capabilities of features on positive and negative data are estimated, and then the top features with the highest capabilities are obtained. Finally, a binary classifier for each label is constructed on the top features. Experimental results on the benchmark data sets show that the proposed method outperforms four popular and previously published multilabel learning algorithms

CNN-Based Smoker Classification and Detection in Smart City Application

To better regulate smoking in no-smoking areas, we present a novel AI-based surveillance system for smart cities. In this paper, we intend to solve the issue of no-smoking area surveillance by introducing a framework for an AI-based smoker detection system for no-smoking areas in a smart city. Moreover, this research will provide a dataset for smoker detection problems in indoor and outdoor environments to help future research on this AI-based smoker detection system. The newly curated smoker detection image dataset consists of two classes, Smoking and NotSmoking. Further, to classify the Smoking and NotSmoking images, we have proposed a transfer learning-based solution using the pre-trained InceptionResNetV2 model. The performance of the proposed approach for predicting smokers and not-smokers was evaluated and compared with other CNN methods on different performance metrics. The proposed approach achieved an accuracy of 96.87% with 97.32% precision and 96.46% recall in predicting the Smoking and NotSmoking images on a challenging and diverse newly-created dataset. Although, we trained the proposed method on the image dataset, we believe the performance of the system will not be affected in real-time

HROM: Learning High-Resolution Representation and Object-Aware Masks for Visual Object Tracking

Siamese network-based trackers consider tracking as features cross-correlation between the target template and the search region. Therefore, feature representation plays an important role for constructing a high-performance tracker. However, all existing Siamese networks extract the deep but low-resolution features of the entire patch, which is not robust enough to estimate the target bounding box accurately. In this work, to address this issue, we propose a novel high-resolution Siamese network, which connects the high-to-low resolution convolution streams in parallel as well as repeatedly exchanges the information across resolutions to maintain high-resolution representations. The resulting representation is semantically richer and spatially more precise by a simple yet effective multi-scale feature fusion strategy. Moreover, we exploit attention mechanisms to learn object-aware masks for adaptive feature refinement, and use deformable convolution to handle complex geometric transformations. This makes the target more discriminative against distractors and background. Without bells and whistles, extensive experiments on popular tracking benchmarks containing OTB100, UAV123, VOT2018 and LaSOT demonstrate that the proposed tracker achieves state-of-the-art performance and runs in real time, confirming its efficiency and effectiveness

Visual Tracking via Hierarchical Deep Reinforcement Learning

Visual tracking has achieved great progress due to numerous different algorithms. However, deep trackers based on classification or Siamese network still have their specific limitations. In this work, we show how to teach machines to track a generic object in videos like humans, who can use a few search steps to perform tracking. By constructing a Markov decision process in Deep Reinforcement Learning (DRL), our agents can learn to determine hierarchical decisions on tracking mode and motion estimation. To be specific, our Hierarchical DRL framework is composed of a Siamese-based observation network which models the motion information of an arbitrary target, a policy network for mode switch and an actor-critic network for box regression. This tracking strategy is more in line with human behavior paradigm, and is effective and efficient to cope with fast motion, background clutter and large deformations. Extensive experiments on the GOT-10k, OTB-100, UAV-123, VOT and LaSOT tracking benchmarks, demonstrate that the proposed tracker achieves state-of-the-art performance while running in real-time

Generalizing infrastructure inspection: step transfer learning aided extreme learning machine for automated crack detection in concrete structures.

Identification of damage and selection of a restoration strategy in concrete structures is contingent upon automatic inspection for crack detection and assessment. Most research on deep learning models for autonomous inspection has focused solely on measuring crack dimensions, omitting the generalization power of a model. This research utilizes a novel step transfer learning (STL) added extreme learning machine (ELM) approach to develop an automatic assessment strategy for surface cracks in concrete structures. STL is helpful in mining generalized abstract features from different sets of source images, and ELM helps the proposed model overcome the optimization limitations of traditional artificial neural networks. The proposed model achieved at least 2.5%, 4.8%, and 0.8% improvement in accuracy, recall, and precision, respectively, in comparison to the other studies, indicating that the proposed model could aid in the automated inspection of concrete structures, ensuring high generalization ability

Identification of Species-Specific MicroRNAs Provides Insights into Dynamic Evolution of MicroRNAs in Plants

MicroRNAs (miRNAs) are an important class of regulatory small RNAs that program gene expression, mainly at the post-transcriptional level. Although sporadic examples of species-specific miRNAs (termed SS-miRNAs) have been reported, a genome-scale study across a variety of distant species has not been assessed. Here, by comprehensively analyzing miRNAs in 81 plant species phylogenetically ranging from chlorophytes to angiosperms, we identified 8048 species-specific miRNAs from 5499 families, representing over 61.2% of the miRNA families in the examined species. An analysis of the conservation from different taxonomic levels supported the high turnover rate of SS-miRNAs, even over short evolutionary distances. A comparison of the intrinsic features between SS-miRNAs and NSS-miRNAs (non-species-specific miRNAs) indicated that the AU content of mature miRNAs was the most striking difference. Our data further illustrated a significant bias of the genomic coordinates towards SS-miRNAs lying close to or within genes. By analyzing the 125,267 putative target genes for the 7966 miRNAs, we found the preferentially regulated functions of SS-miRNAs related to diverse metabolic processes. Collectively, these findings underscore the dynamic evolution of miRNAs in the species-specific lineages

Rapid response with good toleration of sirolimus for life‐threatening neonatal lymphatic malformations

ABSTRACT Introduction Lymphatic malformations (LMs) are rare vascular anomalies predominantly affecting infants, which can be debilitating or life‐threatening when complicated with intralesional bleeding or infection. Effective and safe management strategies are essential in such cases. Case presentation We report a case series involving four Chinese neonates with life‐threatening LMs, initially treated with oral sirolimus. All patients achieved rapid relief and sustained remission, using a lower sirolimus dosage than previously recommended. Furthermore, adverse events were rarely recorded during follow‐up. Conclusion Sirolimus can be considered a promising choice for neonates with intricate and life‐threatening LMs. Initiation with a reduced sirolimus dose is advisable