Clustering of Web Users Using Session-based Similarity Measures

One important research topic in web usage mining is the clustering of web users based on their common properties. Informative knowledge obtained from web user clusters were used for many applications, such as the prefetching of pages between web clients and proxies. This paper presents an approach for measuring similarity of interests among web users from their past access behaviors. The similarity measures are based on the user sessions extracted from the user\u27s access logs. A multi-level scheme for clustering a large number of web users is proposed, as an extension to the method proposed in our previous work (2001). Experiments were conducted and the results obtained show that our clustering method is capable of clustering web users with similar interest

A Solution to the Ambiguity Problem in Depth Contouring

Depth contours on a chart are important for safe navigation. The ambiguity problem can appear when points of equal depth are joined in contouring. Unreasonable solutions may mistake a shallow area for a deep one, which could result in a potential danger for navigation. A solution is presented to solve the ambiguity problem using constrained lines formed by two shallow depths. The constrained lines are used to limit the joining of the points with equal depth. Experimental results demonstrate that the proposed solution can reduce the dangers of producing non-existent deep areas in bathymetric contouring.Las isobatas en una carta son importantes para la seguridad de la navegaciôn. El problema de ambiguedad puede aparecer cuando puntos de igual profundidad se unen en el trazado de la isobata. Soluciones no razonadas pueden confundir un area somera por una profunda, lo que podria resultar en un peligro potencial a la navegaciôn. Una soluciôn se présenta para resolver el problema de ambigüedad utilizando lineas forzadas formadas por dos profundidades s orneras. Las lineas forzadas se ut Uizan para limitar la union de puntos con igual profundidad. Los resultados expérimentales demuestran que la soluciôn propuesta puede reducir los peligros de producir areas profundas no existentes en los contornos batimétricos.Sur une carte, les isobathes sont importantes en ce qui concerne la sécurité de la navigation. Le problème de l'ambiguïté peut apparaître lorsque des points de profondeur égale se rejoignent sur le tracé de l'isobathe. Certaines solutions non fondées rationnellement peuvent prendre par erreur une zone peu profonde pour une zone profonde, ce qui peut entraîner un danger potentiel pour la navigation. Une solution est présentée pour résoudre le problème de l’ambiguïté en utilisant des lignes contraintes formées par deux faibles profondeurs. Les lignes contraintes sont utilisées pour limiter la réunion de points d’une égale profondeur. Des résultats expérimentaux ont montré que la solution proposée peut réduire les dangers liés à la création de zones profondes non existantes dans le tracé bathymétrique

Pattern identification of biomedical images with time series: contrasting THz pulse imaging with DCE-MRIs

Objective We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. Methods Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. Validation Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. Results Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. Conclusion The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community

Correlation Filter Selection for Visual Tracking Using Reinforcement Learning

Correlation filter has been proven to be an effective tool for a number of approaches in visual tracking, particularly for seeking a good balance between tracking accuracy and speed. However, correlation filter based models are susceptible to wrong updates stemming from inaccurate tracking results. To date, little effort has been devoted towards handling the correlation filter update problem. In this paper, we propose a novel approach to address the correlation filter update problem. In our approach, we update and maintain multiple correlation filter models in parallel, and we use deep reinforcement learning for the selection of an optimal correlation filter model among them. To facilitate the decision process in an efficient manner, we propose a decision-net to deal target appearance modeling, which is trained through hundreds of challenging videos using proximal policy optimization and a lightweight learning network. An exhaustive evaluation of the proposed approach on the OTB100 and OTB2013 benchmarks show that the approach is effective enough to achieve the average success rate of 62.3% and the average precision score of 81.2%, both exceeding the performance of traditional correlation filter based trackers.Comment: 13 pages, 11 figure

Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems

The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with broad real-world applications. Recently, neural networks have gained popularity in this research area because they provide strong heuristic solutions to TSPs. Compared to autoregressive neural approaches, non-autoregressive (NAR) networks exploit the inference parallelism to elevate inference speed but suffer from comparatively low solution quality. In this paper, we propose a novel NAR model named NAR4TSP, which incorporates a specially designed architecture and an enhanced reinforcement learning strategy. To the best of our knowledge, NAR4TSP is the first TSP solver that successfully combines RL and NAR networks. The key lies in the incorporation of NAR network output decoding into the training process. NAR4TSP efficiently represents TSP encoded information as rewards and seamlessly integrates it into reinforcement learning strategies, while maintaining consistent TSP sequence constraints during both training and testing phases. Experimental results on both synthetic and real-world TSP instances demonstrate that NAR4TSP outperforms four state-of-the-art models in terms of solution quality, inference speed, and generalization to unseen scenarios.Comment: 14 pages, 5 figure

A review on the rule-based filtering structure with applications on computational biomedical images

concepts in the filtering structure. It is crucial for understanding and discussing different principles associated with fuzzy filter design procedures. A number of typical fuzzy multichannel filtering approaches are provided in order to clarify the different fuzzy filter designs and compare different algorithms. In particular, in most practical applications (i.e., biomedical image analysis), the emphasis is placed primarily on fuzzy filtering algorithms, with the main advantages of restoration of corrupted medical images and the interpretation capability, along with the capability of edge preservation and relevant image information for accurate diagnosis of diseases

Main control factors of rock burst and its disaster evolution mechanism

With the gradual transfer of shallow coal mining to deep coal mining in China, the rock burst disasters are becoming an increasingly serious problem. In the process of rock burst mechanism cognition to rock burst prevention engineering, the primary task is to clarify the main factors of rock burst and to identify its risk level. In this paper, four kinds of objective factors i.e., coal rock impact tendency, mining depth, hard roof and geological structure, and three kinds of human factors i.e., coal pillar, goaf and mining unloading effect, were proposed. And the disaster evolution mechanism of each factor was discussed in detail. In terms of objective controlling factors, the impact tendency is the inherent attribute of coal/rock to accumulate deformation energy and induce impact failure. The mining depth is positively correlated with the deformation energy accumulated in the surrounding rock of the roadway, which is an essential condition for the occurrence of rock burst. The impact dynamic load and kinetic energy formed by large-scale hard roof periodic fracture are the 'fuse' to rock burst. The influence of geological structure on rock burst is significant. For fault structure, the two walls will relatively ‘rebound’ under the sudden unloading caused by mining disturbance. And the equivalent elastic modulus of the thinning area of the coal seam becomes larger, and the advanced abutment pressure is distributed in a 'double peak' pattern, which expands the impact influence range. In terms of subjective controlling factors, coal pillar is a high stress concentration area, and its size, dip angle and relative position will directly affect the probability and strength of rock burst. The goaf will induce a sudden release of energy accumulated in the stress concentration area, especially under large mining height and insufficient roof collapse conditions. Mining unloading will lead to the rapid “migration” of the stress concentration area and release a large amount of energy stored in the coal/rock, which is an important external inducement of rock burst. On this basis, the differences of main control factors of rock burst disaster in the main rock burst mining area, such as Xinwen, Luxi, Erdos, Binchang, Xinjiang and Gansu were compared and analyzed. The study emphasized the importance of identifying the main control factors and their influence degree of rock burst from an entire mine, a panel to a working face. Also, it constructed the engineering management path of rock burst from energy-reducing, energy-releasing, energy-damping to energy-resisting