基于反向选择的网络异常学习行为识别方法

网络学习中,异常学习行为不易及时被察觉和纠正,可能会导致严重的学习问题.网络异常学习行为具有多样性和不确定性,难以通过规则直接界定.借鉴生物免疫系统识别病原体的原理设计的反向选择算法,能自适应识别未知异常,并具有实时性、动态性、多样性、鲁棒性等特征.借助主成分分析法从网络学习行为日志数据中抽取行为特征,构成多维空间的学习行为向量,通过优化训练集改进了反向选择算法并设计了基于该算法的网络异常学习行为识别方法.在真实数据集上的实验结果表明:该方法的识别率优于朴素高斯贝叶斯、决策树、支持向量机等常用算法,能够及时对异常学习行为进行早期预警,为干预和改进学习效果提供客观依据.该方法不需要人工干预,能识别未知的异常行为,具有多样性和较高的自适应性.

时间驱动的异常学习相关滤波器的目标跟踪

为了解决传统的相关滤波跟踪算法在复杂环境中容易跟踪失败的问题,本文提出时间驱动的异常学习相关滤波器,旨在提高模型在复杂环境下的适应性,实现安全有效的目标跟踪。通过引入结合异常学习的时间正则项,该模型不仅可以结合滤波器响应相似度和时间域特征搜索到目标,达到抑制异常的效果,还可以提高外观模型在时域中的鲁棒性,缓解时间滤波器退化。另外,本文采用交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)算法实现模型的优化过程,大大减少模型的计算复杂度。大量的实验结果证实了所提出的跟踪算法性能的优越性。

高中学生异常学习心理和行为浅析

拥有一个健康乐观、积极进取的心理状态 ,对高中学生未来的发展至关重要。但在实际调查中 ,他们当中存在程度不同的异常学习心理和行为 ,其诱因有社会、学校教育、家庭、学生个性心理等因素。因此要从多方面采取措施 。

初中学生学习表现异常情况的分析及引导策略

当前不少初中学生不同程度地存在着各种学习表现异常的情况,这些障碍如果不及时纠正,将会导致心理障碍,形成各种问题,影响初中学生健全人格的形成和发展,本文阐述了初中学生学习表现异常的四种情况,并运用心理学、教育学的原理,对目前初中学生学习表现异常的原因进行分析,在此基础上提出一些相应的引导策略。

基于隐马尔可夫模型的学习行为评估

在网络教学过程中,为了强化学生对知识的灵活运用,教师通常会引入虚拟实验实训、在线测试之类的系统对学生进行知识训练。以数据库课程SQL在线测试系统为例,为了发现抄袭和异常的学习行为,引入隐马尔可夫模型,对正常的学习行为进行建模,并使用滑动窗口技术解决学习序列长度不一而影响输出概率的问题。实验结果表明,评估模型对抄袭和异常学习行为的识别率比普通方法高,准确率达到93%。

一种在线学习监督辅助系统的设计与研究

为提高学习者的在线学习效率,针对学习者在线学习时的异常学习行为,提出一种包括学习端、教学端和后台管理端的在线学习监督辅助系统,通过人脸活体检测模块、页面检测模块、应用程序识别模块、流量检测模块等进行学习者异常学习行为检测,并提出学习信用度概念,通过学习信用度自适应控制检测频率,节约网络资源。该系统对线上课程不管是理论课还是实际操作课均能进行有效监督。

中学生异常心理问题分析及对策

随着社会的发展、独生子女时代的到来,以及社会发展所产生的一系列的社会问题,中学生的心理问题也越来越严重。有一少部分学生的心理问题是由于特殊的、非生理性的原因导致的,有的是生理原因、社会原因等内外因共同作用之下产生的,其称为异常心理问题,这类心理问题往往很复杂,也更难以解决。

药物成瘾过程的心理-神经理论模型

药物成瘾一般被界定为强迫性药物寻求和药物摄入的行为模式,是一个由偶尔用药逐渐过渡到强迫性用药模式的过程。成瘾过程伴随着一系列脑机能和心理机能的改变,但对药物滥用如何导致这些改变以及这些改变如何诱发成瘾却存在着不同的解释。本文在简要评述拮抗过程模型和异常学习模型的基础上,重点讨论了诱因-易感化模型的主要观点及其行为实验和神经心理学证据,并提出成瘾理论研究进一步整合的可能性。

Three-dimensional gravity inversion based on 3D U-Net++

The gravity inversion is to restore genetic density distribution of the underground target to be explored for explaining the internal structure and distribution of the Earth.In this paper,we propose a new 3D gravity inversion method based on 3D U-Net++.Compared with two-dimensional gravity inversion,three-dimensional(3D)gravity inversion can more precisely describe the density distribution of underground space.However,conventional 3D gravity inversion method input is two-dimensional,the input and output of the network proposed in our method are three-dimensional.In the training stage,we design a large number of diversifi ed simulation model-data pairs by using the random walk method to improve the generalization ability of the network.In the test phase,we verify the network performance by using the model-data pairs generated by the simulation.To further illustrate the eff ectiveness of the algorithm,we apply the method to the inversion of the San Nicolas mining area,and the inversion results are basically consistent with the borehole measurement results.Moreover,the results of the 3D U-Net++inversion and the 3D U-Net inversion are compared.The density models of the 3D U-Net++inversion have higher resolution,more concentrated inversion results,and a clearer boundary of the density model.

Anomaly detection of earthquake precursor data using long short-term memory networks

Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.

ADS-B Anomaly Data Detection Model Based on Deep Learning and Difference of Gaussian Approach

Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.

Anomaly detection and segmentation based on multi-student teacher network

In automated industrial inspection,it is often necessary to train models on anomaly-free images and perform anomaly detection on products,which is also an important and challenging task in computer vision.The student-teacher network trains students to regress the output of the teacher,and uses the difference between the output of the student network and the pre-trained teacher network to locate anomalies,which has achieved advanced results in the field of abnormal segmentation.However,it is slow to predict a picture,and no anomaly detection is performed.A multi-student teacher network is proposed,which uses multiple student networks to jointly regress the output of the teacher network,and the minimum square difference between the output of students and teachers in each dimension is selected as the difference value.The information in the middle layer of the network is used to represent each area of the image and calculate the anomaly distance for anomaly segmentation,and the maximum abnormal score is used to represent the abnormal degree of the image for abnormal detection.Experiments results on MVTec anomaly detection show that the algorithm predicts a picture in 0.17 s and can output anomaly detection results at the same time,with image AUROC reaching 91.1%and Pixel AUROC reaching 94.5%.On the wall tile dataset produced by taking pictures of real scenes,image AUROC reached 89.7%,and Pixel AUROC reached 89.1%.Compared with the original student-teacher network,the proposed method can quickly complete anomaly segmentation and anomaly detection tasks at the same time with better accuracy,and it also has better results in real applications.

Approach to Anomaly Traffic Detection in a Local Network

The research intends to solve the problem of the occupation of bandwidth of local network by abnormal traffic which affects normal user's network behaviors.Firstly,a new algorithm in this paper named danger-theory-based abnormal traffic detection was presented.Then an advanced ID3 algorithm was presented to classify the abnormal traffic.Finally a new model of anomaly traffic detection was built upon the two algorithms above and the detection results were integrated with firewall.The firewall limits the bandwidth based on different types of abnormal traffic.Experiments show the outstanding performance of the proposed approach in real-time property,high detection rate,and unsupervised learning.

Study of Structural, Electrical and Magnetic Properties of Manganese Doped Cobalt Ferrite Nanoparticles with Non-stoichiometric Composition

The nanoparticles of Co1＋xMnxFe2-xO4 （0≤x ≤ 0.5） ferrite system are synthesized by solid-state reaction route using planetary ball milling technique to investigate structural, electrical and magnetic properties. The X-ray diffraction patterns confirm the inverse spinel structure with residual oxide phases. Three distinct regions of frequency response on dielectric constant are observed Co1.2sMn0.5Fe1.75O4 as determined by the Wayne Kerr Impedance Analyzer. The first two regions of frequency response 1.13-4.5 MHz and 4.5-6.5 MHz exhibit the normal behavior but the last region 6.5-10.5 MHz indicates its anomalous behavior due to concurrent contribution of O^2-, Fe^3＋, Co^2＋ and Mn^3＋ ions in the relaxation process for sintering effects （sintered at 700℃）. This anomalous behavior is found to be pronounced and significant for the sample of composition Co1.25Mn0.25Fe1.75O4, which may be suitable to be used in the frequency band filter over wide range of frequencies. The single peak of imaginary part of dielectric constant （ε＂） indicates that the conduction process in this sample is due to the grain boundary resistance. The pronounced increase of capacitance （C） as observed from 100 ℃ to 125 ~C in temperature dependent measurement （30-125℃） is expected to eause from the change of polarization across the grain boundary due to redistribution of ions by the thermal agitation. The variation of resistance （R） with temperature （30-125 ℃） is found to exhibit semieonducting behavior that resulted from the p-type carriers （Co^2＋/Co^3＋）. A significant increase of Z from 105 ℃ with the increase of temperature indicates the signature of phase transition from ferrimagnetic-to-ferromagnetic, which may be ascribed to the increase of Co content. The appearance of the single semicircular arc in the Cole-Cole plot may be attributed to the contribution of grain boundary resistance and correspond to the parallel equivalent circuit of resistor-capacitor （R-C） combination with single relaxation time. Saturation magnetization of Co1.25Mn0.25Fe1.75O4 and Co1.375Mn0.375Fe1.625O4 is found to be greater than the literature value （61.5 emu/g） of un-doped cobalt ferrite in the measurement of their initial magnetization using Lakeshore vibrating sample magnetometer. The negative real part of AC permeability of Co1.5Mn0.5Fe1.5O4 signifies the diamagnetic behavior in the frequency range 0.13-25.2 MHz and expected to cause from the formation of magnetic dipoles opposite to the applied field due to Mn^2＋ in the B site. The samples are expected to be suitable for dielectric heating and high frequency applications.

Outlier detection by means of robust regression estimators for use in engineering science

This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates (MSR) were computed and used as comparison criteria. The results showed that the least median of squares (LMS) and least trimmed squares (LTS) were the most successful methods for data that included leverage points, masking and swamping effects or critical and concentrated outliers. We recommend using LMS and LTS as diagnostic tools to classify outliers, because they remain robust even when applied to models that are heavily contaminated or that have a complicated structure of outliers.