Learning state machine-based string edit kernels

During the past few years, several works have been done to derive string kernels from probability distributions. For instance, the Fisher kernel uses a generative model M (e.g. a hidden Markov model) and compares two strings according to how they are generated by M. On the other hand, the marginalized kernels allow the computation of the joint similarity between two instances by summing conditional probabilities. In this paper, we adapt this approach to edit distance-based conditional distributions and we present a way to learn a new string edit kernel. We show that the practical computation of such a kernel between two strings x and x^′ built from an alphabet Σ requires (i) to learn edit probabilities in the form of the parameters of a stochastic state machine and (ii) to calculate an infinite sum over Σ^* by resorting to the intersection of probabilistic automata as done for rational kernels. We show on a handwritten character recognition task that our new kernel outperforms not only the state of the art string kernels and string edit kernels but also the standard edit distance used by a neighborhood-based classifier.     

How older people account for their experiences with interactive technology

We present a qualitative study, undertaken over a period of nine months, of older people facing the challenges of learning to use interactive technology, specifically personal computers (PCs) and the internet. We examine the range of causal explanations (attributions) voiced by the group in accounting for their difficulties with it. A discourse analysis of these data reveals some factors (anxiety, age-related issues, being too busy to learn and the need for a purpose for the new tools) that support the work of other researchers, while other themes (issues around alienation, identity and agency) deepen understanding of this domain. The implications of the results for how we approach understanding the difficulties faced by older people in this context are discussed.     

SCI简介──投稿指南(1)

准确评价科研成果早已在世界范围内受到普遍关注，近年来在我国也引起社会各方面的重视，而科学期刊的地位直接影响着所发表科研成果的地位，因而选择什么学术期刊发表学术成果是一项很重要的任务。通过对SCI的了解可以得到答案。     

高职学生数学学习中的习得性无助研究

数学作为高职学生一门必修的基础课程,对其后续学习有着至关重要的作用,本文研究了数学学习中习得性无助产生的原因,并提出相应的教学干预措施。     

Optimized sampling distribution based on nonparametric learning for improved compressive sensing performance

In this work, an optimized nonparametric learning approach for obtaining the data-guided sampling distribution is proposed, where a probability density function (pdf) is learned in a nonparametric manner based on past measurements from similar types of signals. This learned sampling distribution is then used to better optimize the sampling process based on the underlying signal characteristics. A realization of this stochastic learning approach for compressive sensing of imaging data is introduced via a stochastic Monte Carlo optimization strategy to learn a nonparametric sampling distribution based on visual saliency. Experiments were performed using different types of signals such as fluorescence microscopy images and laser range measurements. Results show that the proposed optimized sampling method which is based on nonparametric stochastic learning outperforms significantly the previously proposed approach. The proposed method is achieves higher reconstruction signal to noise ratios at the same compression rates across all tested types of signals.     

Statistical voice activity detection based on sparse representation over learned dictionary

In this paper, we present a novel approach to voice activity detection (VAD) based on the sparse representation of an input noisy speech over a learned dictionary. First, we investigate the relationship between the signal detection and the sparse representation based on the Bayesian framework. Second, we derive the decision rule and an adaptive threshold based on a likelihood ratio test, by modeling the non-zero elements in the sparse representation as a Gaussian distribution. The experimental results show that the proposed approach outperforms the current statistical model-based methods, such as Gaussian, Laplacian, and Gamma, under white, babble, and vehicle noise conditions.     

Probabilistic Kalman filter for moving object tracking

Kalman filter has been successfully applied to tracking moving objects in real-time situations. However, the filter cannot take into account the existing prior knowledge to improve its predictions. In the moving object tracking, the trajectories of multiple targets in the same environment could be available, which can be viewed as the prior knowledge for the tracking procedure. This paper presents the probabilistic Kalman filter (PKF) that is able to take into account the stored trajectories to improve tracking estimation. The PKF has an extra stage after two steps of the Kalman filter to refine the estimated position of the targets. The refinement is obtained by applying the Viterbi algorithm to a probabilistic graph, that is constructed based on the observed trajectories. The graph is built in the offline situation and could be adapted in the online tracking. The proposed tracker has higher accuracy compared to the standard Kalman filter and could handle widespread problems such as occlusion. Another significant achievement of the proposed tracker is to track an object with anomalous behaviors by drawing an inference based on the constructed probabilistic graph. The PKF was applied to several manually-built videos and several other video-bases containing severe occlusions, which demonstrates a significant performance in comparison with other state-of-the-art trackers.     

基于逆向习得推理的网络异常行为检测模型

针对网络异常行为检测中因数据不平衡而导致召回率低的问题，提出一种基于逆向习得推理（ALI）的网络异常行为检测模型。首先，去除数据集中用离散数据表示的特征项，并对处理后的数据集进行归一化以提高模型的收敛速度与精度；然后，提出改进的ALI模型，通过ALI训练算法用仅由正样本所构成的数据对其进行训练，并利用已训练完成的改进ALI模型处理检测数据以生成处理后的检测数据集；最后，依据异常检测函数计算检测数据与处理后的检测数据之间的距离来判断数据是否异常。与单类支持向量机（OC-SVM）、深层结构能量模型（DSEBM）、深度自编码高斯混合模型（DAGMM）和生成对抗网络异常检测模型（AnoGAN）的对比实验结果表明，所提模型的准确率提升了5.8~17.4个百分点，召回率提升了1.4~31.4个百分点，F₁值提升了14.18~19.7个百分点。可知所提出的基于逆向习得推理的网络异常行为检测模型在数据不平衡时仍具有较高的召回率和检测精度。     

Multiplicative noise removal via sparse and redundant representations over learned dictionaries and total variation

In this paper, we propose a new three-stage model for multiplicative noise removal. In the first stage, sparse and redundant representation is used to approximate the log-image. The K-SVD algorithm is used to train a redundant dictionary, which can describe the log-image sparsity. Then in the second stage, we use the total variation (TV) method to amend the image obtained. At last, via an exponential function and bias correction, the result is transformed back from the log-domain to the real one. Our method combines the advantages of sparse and redundant representation over trained dictionary and TV method. Experimental results show that the new model is more effective to filter out multiplicative noise than the state-of-the-art models.