Learning Dynamic Classes of Events using Stacked Multilayer Perceptron Networks

People often use a web search engine to find information about events of interest, for example, sport competitions, political elections, festivals and entertainment news. In this paper, we study a problem of detecting event-related queries, which is the first step before selecting a suitable time-aware retrieval model. In general, event-related information needs can be observed in query streams through various temporal patterns of user search behavior, e.g., spiky peaks for popular events, and periodicities for repetitive events. However, it is also common that users search for non-popular events, which may not exhibit temporal variations in query streams, e.g., past events recently occurred, historical events triggered by anniversaries or similar events, and future events anticipated to happen. To address the challenge of detecting dynamic classes of events, we propose a novel deep learning model to classify a given query into a predetermined set of multiple event types. Our proposed model, a Stacked Multilayer Perceptron (S-MLP) network, consists of multilayer perceptron used as a basic learning unit. We assemble stacked units to further learn complex relationships between neutrons in successive layers. To evaluate our proposed model, we conduct experiments using real-world queries and a set of manually created ground truth. Preliminary results have shown that our proposed deep learning model outperforms the state-of-the-art classification models significantly.Comment: Neu-IR '16 SIGIR Workshop on Neural Information Retrieval, 6 pages, 4 figure

ACO-GCN: A FAULT DETECTION FUSION ALGORITHM FOR WIRELESS SENSOR NETWORK NODES

Wireless Sensor Network (WSN) has become a solution for real-time monitoring environments and is widely used in various fields. A substantial number of sensors in WSNs are prone to succumb to failures due to faulty attributes, complex working environments, and their hardware, resulting in transmission error data. To resolve the existing problem of fault detection in WSN, this paper presents a WSN node fault detection method based on ant colony optimization-graph convolutional network (ACO-GCN) models, which consists of an input layer, a space-time processing layer, and an output layer. First, the users apply the random search algorithm and the search strategy of the ant colony algorithm (ACO) to find the optimal path and locate the WSN node failures to grasp the overall situation. Then, the WSN fault node information obtained by the GCN model is learned. During the data training process, where the WSN fault node is used for error prediction, the weights and thresholds of the network are further adjusted to increase the accuracy of fault diagnosis. To evaluate the performance of the ACO-GCN model, the results show that the ACO-GCN model significantly improves the fault detection rate and reduces the false alarm rate compared with the benchmark algorithms. Moreover, the proposed ACO-GCN fusion algorithm can identify fault sensors more effectively, improve the service quality of WSN and enhance the stability of the system

A comprehensive study of sparse codes on abnormality detection

Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes. While much emphasis is put on discriminative dictionary construction, there are no comparative studies of sparse codes regarding abnormality detection. We comprehensively study two types of sparse codes solutions - greedy algorithms and convex L1-norm solutions - and their impact on abnormality detection performance. We also propose our framework of combining sparse codes with different detection methods. Our comparative experiments are carried out from various angles to better understand the applicability of sparse codes, including computation time, reconstruction error, sparsity, detection accuracy, and their performance combining various detection methods. Experiments show that combining OMP codes with maximum coordinate detection could achieve state-of-the-art performance on the UCSD dataset [14].Comment: 7 page