Existing topic modeling approaches possess several issues, including the overfitting issue of Probablistic Latent Semantic Indexing (pLSI), failure capturing rich topical correlations among topics in Dirichlet Allocation (LDA), and high inference complexity. In this paper, we provide a new method to overcome pLSI by using amortized with word embedding as input, instead prior LDA. For generative model, large number free latent variables is root overfitting. To reduce parameters, replaces...

Although existing Graph Neural Networks (GNNs) based on message passing achieve state-of-the-art, the over-smoothing issue, node similarity distortion issue and dissatisfactory link prediction performance can't be ignored. This paper summarizes these issues as interference between topology attribute for first time. By leveraging recently proposed optimization perspective of GNNs, this is analyzed ascribed to that learned representation in GNNs essentially compromises attribute. To alleviate...

Many efforts have been paid to enhance Graph Convolutional Network from the perspective of propagation under philosophy that ``Propagation is essence GCNNs". Unfortunately, its adverse effect over-smoothing, which makes performance dramatically drop. To prevent many variants are presented. However, can't provide an intuitive and unified interpretation their on over-smoothing. In this paper, we aim at providing a novel explanation question "Why do attributes propagate in GCNNs?''. not only...

In this article, the homogenous polynomially membership functions dependent (HPMFD) matrices are used to study interval type-2 Takagi-Sugeno fuzzy systems. First, some necessary notations of HPMFD introduced. Next, based on these notations, time derivative is discussed and a switching method proposed ensure that negative. Then, controller designed new stabilization conditions obtained by using Lyapunov function. end, simulations show in article less conservative than existing ones literatures.

Semi-supervised classification is a fundamental technology to process the structured and unstructured data in machine learning field. The traditional attribute-graph based semi-supervised methods propagate labels over graph which usually constructed from features, while convolutional neural networks smooth node attributes, i.e., real topology. In this paper, they are interpreted perspective of propagation, accordingly categorized into symmetric asymmetric propagation methods. From both...

Motivated by the capability of Generative Adversarial Network on exploring latent semantic space and capturing variations in data distribution, adversarial learning has been adopted network embedding to improve robustness. However, this important ability is lost existing adversarially regularized methods, because their results are directly compared samples drawn from perturbation (Gaussian) distribution without any rectification real data. To overcome vital issue, a novel Joint Embedding...

Most attempts on extending Graph Neural Networks (GNNs) to Heterogeneous Information (HINs) implicitly take the direct assumption that multiple homogeneous attributed networks induced by different meta-paths are complementary. The doubts about hypothesis of complementary motivate an alternative consensus. That is, aggregated node attributes shared essential for representations, while specific ones in each network should be discarded. In this paper, a novel Bottleneck (HGIB) is proposed...

In this paper, we present an ant colony-based heuristic to solve QoS (quality of service) constrained multicast routing problems. Our algorithm considers multiple metrics, such as bandwidth, delay, delay jitter and packet loss rate, find the tree that minimizes total cost. We also explore scalability algorithm. tests show can optimal (or near-optimal) solutions quickly it has good scalability.

The success of graph convolutional neural networks (GCNNs) based semi-supervised node classification is credited to the attribute smoothing (propagating) over topology. However, attributes may be interfered by utilization topology information. This distortion will induce a certain amount misclassifications nodes, which can correctly predicted with only attributes. By analyzing impact edges in propagations, simple edges, connect two nodes similar attributes, should given priority during...

Recently, a switching method is applied to deal with the membership function-dependent Lyapunov-Krasovskii functional (LKF) for fuzzy systems time delay; however, Lyapunov matrices are only linear dependent on grades of which leads (Wang and Lam, 2019). In this article, dependence extended homogenous polynomially function (HPMFD) polynomial matrix switching, based obtained result contains previous one as special case. Furthermore, in order fully use introduced variables without speial...

Temporal convolution networks (TCNs) are recently proposed to be used in the short-term load forecasting (STLF) tasks modern smart grids, however, TCNs have two shortcomings, i.e., redundant convolutional operation and equal input importance problems. Therefore, we propose a novel TCN-based backbone model, called PhaCIA-TCNs, achieve more accurate forecasting, where parallel hybrid activated (PhaC) attention (IA) resolve above problems, respectively. Specifically, IA is highlight important...

Ant colony algorithm is one kind of new heuristic biological modelling method which has the ability parallel processing and global searching, but its convergence speed slow because poor pheromone on early path. In this paper, discuss a combines genetic algorithm. Genetic added to ant algorithm's every generation in proposed Making use advantage whole quick convergence, quickened. mutation mechanism improves avoid being trapped local optimal. The simulation shows that effective solving...

By interpreting Graph Neural Networks (GNNs) as the message passing from spatial perspective, their success is attributed to Laplacian smoothing. However, it also leads serious over-smoothing issue by stacking many layers. Recently, efforts have been paid overcome this in semi-supervised learning. Unfortunately, more unsupervised node representation learning task due lack of supervision information. Thus, most or self-supervised GNNs often employ \textit{one-layer GCN} encoder. Essentially,...

While remarkable progress has been made to pedestrian detection in recent years, robust the wild e.g., under surveillance scenarios with occlusions, remains a challenging problem. In this paper, we present novel approach for joint and body part via semantic relationship learning unconstrained scenarios. Specifically, propose Body Part Indexed Feature (BPIF) representation encode between individual parts (i.e., head, head-shoulder, upper body, whole body) highlight per features, providing...

Medical image segmentation is an important and complex task in clinical practices, but the widely used U-Net usually cannot achieve satisfactory performances some challenging cases. Therefore, advanced variants of are proposed using multi-scale attention mechanisms. Different from existing works where independently, this work, we integrate them together propose a collaborative guided feature fusion with enhanced convolution based (EC-CaM-UNet) model for more accurate medical segmentation,...

Edge-preserving image smoothing is one of the fundamental tasks in field computer graphics and vision. Recently, L0 gradient minimization (LGM) has been proposed for this purpose. In contrast to total variation (TV) model which employs L1 norm gradient, LGM adopts yields much better results piecewise constant image. However, as an improvement model, also suffers, even more seriously, from staircasing effect not robust noise. order overcome these drawbacks, paper, we propose by prefiltering...

In this paper, a new kind of automated fault diagnosis algorithm is proposed. It data-mining with ontology-based. This doesn't only find low-level rules which usually not interesting , but also can high-level described by concepts. we use ontologies provide mechanism, in data mining, domain specific knowledge may be included to aid the discovery process and multi-level classification rules, are So it more for enterprise. Finally, study case given explain practical application bases on...

As the current level of people-counting, on consideration cost acquisition equipment and accuracy, customer-counting system is constructed based RBF neural net using technology infrared photoelectric sensor. For better differential count, extant data segmentation method feature extraction improved passenger-counting continuous space-time sequence. Compared with traditional sensor, accuracy real-time in this situation customers who entry at same time can be identified lower error rate. It...

Routing optimization is a key technology in the intelligent warehouse logistics. In order to get an optimal route for vehicle, routing complex global dynamic environment studied. A new evolutionary ant colony algorithm based on RFID and knowledge-refinement proposed. The gets environmental information timely through updates map at same time. It adopts elite kept, fallback, pheromones limitation adjustment strategy. current population space optimized experiential knowledge. experimental...

Genetic algorithm offers the common frame of resolving optimization problem by imitating biological evolution based on natural selection. However it has some drawbacks such as slow convergence and being premature. In genetic algorithm, individual generated operation is a bit random even sometimes more inferior than its parents. So new operator - negative selection that can filtrate bad-quality introduced to speed up improve global searching ability. With this operator, proposed. Furthermore...