Entity-level sentiment prediction in Danmaku video interaction

The Journal of Supercomputing - Sentiment analysis in Danmaku video interaction aims at measuring public mood in respect of the video, which is helpful for the potential applications in behavioral...     

An online multiple object tracker based on structure keeper net

We propose a novel online multiple object tracker taking structure information into account. State-of-the-art multi-object tracking (MOT) approaches commonly focus on discriminative appearance features, while neglect in different levels structure information and the core of data association. Addressing this, we design a new tracker fully exploiting structure information and encoding such information into the cost function of the graph matching model. Firstly, a new measurement is proposed to compare the structure similarity of two graphs whose nodes are equal. With this measurement, we define a complete matching which performs association in high efficiency. Secondly, for incomplete matching scenarios, a structure keeper net (SKnet) is designed to adaptively establish the graph for matching. Finally, we conduct extensive experiments on benchmarks including MOT2015 and MOT17. The results demonstrate the competitiveness and practicability of our tracker.

     

Anomaly detection via a combination model in time series data

Since the time series data have the characteristics of a large amount of data and non-stationarity, we usually cannot obtain a satisfactory result by a single-model-based method to detect anomalies in time series data. To overcome this problem, in this paper, a combination-model-based approach is proposed by combining a similarity-measurement-based method and a model-based method for anomaly detection. First, the process of data representation is performed to generate a new data form to arrive at the purpose of reducing data volume. Furthermore, due to the anomalies being generally caused by changes in amplitude and shape, we take both the original time series data and their amplitude change data into consideration of the process of data representation to capture the shape and morphological features. Then, the results of data representation are employed to establish a model for anomaly detection. Compared with the state-of-the-art methods, experimental studies on a large number of datasets show that the proposed method can significantly improve the performance of anomaly detection with higher data anomaly resolution.

     

Sensitive attribute privacy preservation of trajectory data publishing based on l-diversity

Distributed and Parallel Databases - The widely application of positioning technology has made collecting the movement of people feasible for knowledge-based decision. Data in its original form...     

FHUQI-Miner: Fast high utility quantitative itemset mining

Applied Intelligence - High utility itemset mining is a popular pattern mining task, which aims at revealing all sets of items that yield a high profit in a transaction database. Although this task...     

Dual local learning regularized nonnegative matrix factorization and its semi-supervised extension for clustering

Neural Computing and Applications - Nonnegative matrix factorization (NMF) has received considerable attention in data representation due to its strong interpretability. However, traditional NMF...     

Image cipher using image filtering with 3D DNA-based confusion and diffusion strategy

Neural Computing and Applications - In this paper, an image cipher is presented based on DNA sequence operations, image filtering and memrisitve chaotic system. Firstly, plain image is preprocessed...     

Pricing Exotic Option Under Jump-Diffusion Models by the Quadrature Method

This paper extends the quadrature method to price exotic options under jump-diffusion models. We compute the transition density of jump-extended models using convolution integrals. Furthermore, a simpler and more efficient lattice grid is introduced to implement the recursion more directly in matrix form. It can be shown that a lot of running time can be saved. At last, we apply the developed approach to the different jump-extended models to demonstrate its universality and provide a detailed comparison for the discrete path-dependent options to demonstrate its advantages in terms of speed and accuracy.

     

混合高斯模型结合五帧差分的运动目标检测

在复杂背景下,光照变化、目标短暂遮挡以及背景运动等因素会导致运动目标检测精度较低.提出一种结合改进混合高斯模型和改进五帧差分的运动目标检测算法,首先在混合高斯模型中加入自适应学习率以及背景学习速率更新策略有效解决传统背景更新速率恒定而出现的残影现象;然后利用改进的五帧差分法克服运动目标短暂遮挡问题,并加入光照阈值判别因素,有效减弱光照变化带来的影响;最后对两者结果进行或运算进而得到最终检测结果.实验结果表明,该融合算法在复杂环境下具有一定的抗干扰能力,运动目标检测精度较高.     

工业远程监测系统中自组织网络结构研究与实现

针对工业远程监控系统覆盖地域广、监控对象的通讯环境差异大的问题,探讨了异构网络融合技术和自组织网络结构,提出了根据工业现场环境对监控系统的要求,采用多种通讯方式相结合、多种网络结构共存的方法组建工业远程监控网络;以供热管网远程监控系统为应用对象论证了方法的可行性,详细阐述了系统的自组织协议和软、硬件设计方案;该方案有效地利用了各种通讯方式的优点,避开了现场环境的限制,提高了系统的可靠性,降低了系统的建设、运行成本。