International Journal of Software Engineering and Knowledge Engineering

Crowd simulation explores crowd behavior in virtual environments, which has been extensively studied in many areas, such as safety and civil engineering, transportation, social science, and entertainment industry. In this paper, an improved potential field method is proposed to achieve the real-time crowd simulation, which is composed of the global navigation with Dijkstra's algorithm and the potential field based local navigation. First, a region separation is performed to divide the environment into a set of triangles, and thus a topological graph can be built with the triangles as vertices. Then a velocity-density model is introduced for improving the speed controlling mechanism and solving the "maximum speed dilemma" which means the velocity of an individual derived by potential field will be stuck into the maximum due to the ill speed control. Since the movement of an individual in the crowd is influenced by the socio-psychological forces, the individuals' actions express the group attributes. In order to represent the group attributes in the crowd, the repulsive potential function is improved in this paper. Experiments have been carried out and the results show that the improved potential field based method can simulate the crowd in real time and avoid the "maximum speed dilemma".

A major challenge in document clustering is the extremely high dimensionality as well as the sparsity of the sample matrix. In this paper, we propose a new short-text oriented analysis approach to cluster short text automatically and extract the hot topics from each cluster. Different from the previous studies focused on long text, our analysis approach mainly focused on short-text cases. The approach consists of three stages: Firstly, generate feature vector for each sample so as to obtain the whole high-dimensional Vector Space Model; Secondly, use Singular Value Decomposition to achieve the dimensions reduction; Lastly, apply cosine similarity and k-means method to cluster samples on the low-dimensional matrix and extract the hot topics for each cluster. The experimental results show that our analysis approach can deal with the short-text samples and find out the hot topics efficiently and effectively.

Depth images, in particular depth maps estimated from stereo vision, may have a substantial amount of outliers and result in inaccurate 3D modelling and reconstruction. To address this challenging issue, in this paper, a graph-cut based multiple depth maps integration approach is proposed to obtain smooth and watertight surfaces. First, confidence maps for the depth images are estimated to suppress noise, based on which reliable patches covering the object surface are determined. These patches are then exploited to estimate the path weight for 3D geodesic distance computation, where an adaptive regional term is introduced to deal with the "shorter-cuts" problem caused by the effect of the minimal surface bias. Finally, the adaptive regional term and the boundary term constructed using patches are combined in the graph-cut framework for more accurate and smoother 3D modelling. We demonstrate the superior performance of our algorithm on the well-known Middlebury multi-view database and additionally on real-world multiple depth images captured by Kinect. The experimental results have shown that our method is able to preserve the object protrusions and details while maintaining surface smoothness.

Facing the important issues of safety analysis and assessment for the train service state, an online quantified safety assessment method based on the safety region estimation and hybrid intelligence technologies was proposed in this paper. First, the previous researches on the safety analysis and assessment were briefly reviewed for the train itself and its key equipment, and the existential problems were further pointed out. Then, using the safety monitoring data and the safety region estimation theory, a new online safety assessment method with data-driven was put forward, which was followed by a detailed description of the concrete implementation steps including the EMD (Local Mean Decomposition) and EM (Energy Moment) based safety risk evaluation index selection, Interval Type 2 Fuzzy C-Means (IT2FCM) clustering based safety region boundary calculation modeling and safety risk grading. Finally, in order to verify its performance through experiments, the above method was applied in analyzing and evaluating service states of the rolling bearings, the key equipment of the train, on the basis of mass field data. The experimental results indicate that this method is valid.

This research focuses on the identification of relevant experience required for solving IT (Information Technology) problems in small- to medium-sized enterprises. To achieve this, we integrated information retrieval techniques with clustering and optimization techniques to design and develop a custom-built Experience Management system for IT management support. We have built and evaluated our system on three different publicly available data sets: Princeton, Parallels and GoDaddy. Results support that it is possible to provide the right mix of automation and manual activity for IT experience management while achieving a high accuracy.

Cloud computing has been a mainstream solution for the processing and storage of mass data, as well as an exciting area for research. As a novel business model, cloud computing has dramatically changed the provision of services and IT capacity by means of the advanced techniques. In recent years, with the increasing research interests and rapid growth of publications, some review papers provide detailed analysis on cloud computing area. In this paper, a bibliometric-based approach is presented and implemented to quantitatively review the progress in global cloud computing research with the related literature during 2007–2013 from the databases of Science Citation Index Expanded (SCI-E), Conference Proceedings Citation Index–Science (CPCI-S), and IEEEXplore. Our work is motivated by the purpose of tracing global advancement in terms of research content, geographic distribution and issue time of the related publications, rather than a specific technological area in cloud computing research. By investigating the characteristics of publications such as keywords, output, geographic distribution and affiliation, we draw some valuable conclusions to guide the further research. The experimental results show that the top 5 active research points of cloud computing concentrate on virtualization, security, mobile cloud, distributed computing, and scheduling. From the location-time aspect, China, USA, and India have published most of the papers, dominate cloud computing research and keep a high level on the international research cooperation. And there is a great increase in publication outputs especially in China and USA. Meanwhile, the analysis results demonstrate the top 3 high-cited research institutes of the University of Melbourne, University of California. Berkeley and University of Vienna in cloud computing research. The mobile cloud will be a future research hotspot and promising application field.

Class temporal specification is a kind of important program specifications especially for object-oriented programs, which specifies that interface methods of a class should be called in a particular sequence. Currently, most existing approaches mine this kind of specifications based on finite state automaton. Observed that finite state automaton is a kind of deterministic models with inability to tolerate noise. In this paper, we propose to mine class temporal specifications relying on a probabilistic model extending from Markov chain. To the best of our knowledge, this is the first work of learning specifications from object-oriented programs dynamically based on probabilistic models. Different from similar works, our technique does not require annotating programs. Additionally, it learns specifications in an online mode, which can refine existing models continuously. Above all, we talk about problems regarding noise and connectivity of mined models and a strategy of computing thresholds is proposed to resolve them. To investigate our technique's feasibility and effectiveness, we implemented our technique in a prototype tool ISpecMiner and used it to conduct several experiments. Results of the experiments show that our technique can deal with noise effectively and useful specifications can be learned. Furthermore, our method of computing thresholds provides a strong assurance for mined models to be connected.

Data stream associative classification poses many challenges to the data mining community. In this paper, we address four major challenges posed, namely, infinite length, extraction of knowledge with single scan, processing time, and accuracy. Since data streams are infinite in length, it is impractical to store and use all the historical data for training. Mining such streaming data for knowledge acquisition is a unique opportunity and even a tough task. A streaming algorithm must scan data once and extract knowledge. While mining data streams, processing time, and accuracy have become two important aspects. In this paper, we propose PSTMiner which considers the nature of data streams and provides an efficient classifier for predicting the class label of real data streams. It has greater potential when compared with many existing classification techniques. Additionally, we propose a compact novel tree structure called PSTree (Prefix Streaming Tree) for storing data. Extensive experiments conducted on 24 real datasets from UCI repository and synthetic datasets from MOA (Massive Online Analysis) show that PSTMiner is consistent. Empirical results show that performance of PSTMiner is highly competitive in terms of accuracy and performance time when compared with other approaches under windowed streaming model.