The paper evaluates on the development level of modern service industry of Anhui Province.Firstly,it confirms the comprehensive evaluation index system on the development level of modern service industry,and then takes the statistics of 2005,establishes the evaluation model through PCA(Principal Component Analysis).Then,it indicates that the development level of modern service industry of An'hui province is disparate obviously to eastern provinces,is middle in middle provinces,and a little better than western provinces.Finally,proposes measures to promote modern service industry of Anhui Province.
In recommendation systems, items are likely to be exposed to various users and we would like to learn about the familiarity of a new user with an existing item. This can be formulated as an anomaly detection (AD) problem distinguishing between "common users" (nominal) and "fresh users" (anomalous). Considering the sheer volume of items and the sparsity of user-item paired data, independently applying conventional single-task detection methods on each item quickly becomes difficult, while correlations between items are ignored. To address this multi-task anomaly detection problem, we propose collaborative anomaly detection (CAD) to jointly learn all tasks with an embedding encoding correlations among tasks. We explore CAD with conditional density estimation and conditional likelihood ratio estimation. We found that: $i$) estimating a likelihood ratio enjoys more efficient learning and yields better results than density estimation. $ii$) It is beneficial to select a small number of tasks in advance to learn a task embedding model, and then use it to warm-start all task embeddings. Consequently, these embeddings can capture correlations between tasks and generalize to new correlated tasks.
The purpose of gesture recognition is to recognize meaningful movements of human bodies, and gesture recognition is an important issue in computer vision. In this paper, we present a multimodal gesture recognition method based on 3D densely convolutional networks (3D-DenseNets) and improved temporal convolutional networks (TCNs). The key idea of our approach is to find a compact and effective representation of spatial and temporal features, which orderly and separately divide task of gesture video analysis into two parts: spatial analysis and temporal analysis. In spatial analysis, we adopt 3D-DenseNets to learn short-term spatio-temporal features effectively. Subsequently, in temporal analysis, we use TCNs to extract temporal features and employ improved Squeeze-and-Excitation Networks (SENets) to strengthen the representational power of temporal features from each TCNs' layers. The method has been evaluated on the VIVA and the NVIDIA Gesture Dynamic Hand Gesture Datasets. Our approach obtains very competitive performance on VIVA benchmarks with the classification accuracies of 91.54%, and achieve state-of-the art performance with 86.37% accuracy on NVIDIA benchmark.
The increasing demand for home fitness solutions underscores the need for interactive displays that enhance user experiences. This study introduces a technology that autonomously adjusts display height using the skeletal information of demonstrators from videos, catering to home fitness needs. A user study involving thirty participants compared fixed height, manual adjustment, and automatic adjustment conditions. Head flexion angles and NASA-TLX survey responses were used for evaluation. Results showed a significant reduction in head flexion angles with automatic adjustment, promoting proper spinal alignment. NASA-TLX responses indicated lower mental, effort, and frustration ratings, along with improved performance and perceived support in the automatic adjustment condition compared to other conditions. These findings confirm that motion-based height adjustment improves posture and enhances the overall interactive experience. This research demonstrates the feasibility of integrating responsive ergonomics into interactive displays and suggests the importance of further personalization, conducting diverse user studies, and refining algorithms to fully leverage the potential of this technology.
Coloring the animation sketch sequence is a challenging task in computer vision, since the information contained in the line sketches is too sparse, and the colors need to be uniform between continuous frames. Most of the existing colorization algorithms are only for one image, which can be considered a color filling algorithm. Such algorithms only give a color result that fits within a reasonable range and does not apply to the coloring of frame sequences. This paper proposes an end-to-end two-stage optical flow colorization network to solve the animation frame sequence colorization problem. The first stage network finds the direction of color pixel flow from the texture change between a given reference frame and the next frame of line artwork, then completes the initial coloring, while the second stage network performs color correction and clarification on the output of the first stage. Since our algorithm does not directly colorize the image but finds the path of color change to colorize it, it ensures a consistent color space for the sequence frames after colorization. We conduct experiments on the animation dataset and the results show that our algorithm is effective.
<p>We propose an unsupervised blind fusion network that operates on a single HSI and RGB image pair and requires neither known degradation models nor any training data. Our method takes full advantage of an unrolling network and coordinate encoding to provide a state-of-the-art HSI reconstruction. It can also estimate the degradation parameters relatively accurately through the neural representation and implicit regularization of the degradation model.</p>
In this paper we present a novel technology named dynamic relay handover (DRH) for leveraging DSR and AODV for the benefit of providing a guaranteed network performance. The key points of DRH are the connectivity checking of the existing relay node and the assignment of a new relay node. In simulation of this algorithm based on OPNET Modeler 10.5, the effect of DRH on the network performance and the invulnerability is estimated according to the mostly used network performance indices and some new indices we propose. The simulation results indicate that as a backup relay mode for the paired slot relay, DRH can largely improve the invulnerability of JTIDS, while the extra cost on the network resources is little.
Remarkably easy implementation and guaranteed convergence has made the EM algorithm one of the most used algorithms for mixture modeling. On the downside, the E-step is linear in both the sample size and the number of mixture components, making it impractical for large-scale data. Based on the variational EM framework, we propose a fast alternative that uses component-specific data partitions to obtain a sub-linear E-step in sample size, while the algorithm still maintains provable convergence. Our approach builds on previous work, but is significantly faster and scales much better in the number of mixture components. We demonstrate this speedup by experiments on large-scale synthetic and real data.
Long-term development of Shaanxi elonomy is directly affected by whether realizing of sustainable development of Shaanxi's forestry is possible, Problems existing in Shaanxi's Forstry are analysed and some solutions are given.
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