Multiple Object Tracking through Background Learning

This paper discusses about the new approach of multiple object track-ing relative to background information.The concept of multiple object tracking through background learning is based upon the theory of relativity,that involves a frame of reference in spatial domain to localize and/or track any object.Thefield of multiple object tracking has seen a lot of research,but researchers have considered the background as redundant.However,in object tracking,the back-ground plays a vital role and leads to definite improvement in the overall process of tracking.In the present work an algorithm is proposed for the multiple object tracking through background learning.The learning framework is based on graph embedding approach for localizing multiple objects.The graph utilizes the inher-ent capabilities of depth modelling that assist in prior to track occlusion avoidance among multiple objects.The proposed algorithm has been compared with the recent work available in literature on numerous performance evaluation measures.It is observed that our proposed algorithm gives better performance.

BACKGROUND KNOWLEDGE AND SECONDARY KNOWLEDGE BASES IN LEARNINGS YSTEMS

This paper presents the differences and relations between background knowledge and domain theories in learning systems. The roles they play during learning procedures are discussed. It is emphasized that background knowledge plays an important role in enhancing the ability of a learning system. An explanation based learning system with domain theory in primary knowledge base and background knowledge in secondary knowledge base is introduced as an example. It shows how background knowledge can be used to solve some of the problems caused by incomplete domain theory in an explanation based learning system. The system can accomplish knowledge level learning through purely deductive approach. At last the acquisition of background knowledge is briefly discussed.

Adaptive learning algorithm based on mixture Gaussian background

The key problem of the adaptive mixture background model is that the parameters can adaptively change according to the input data. To address the problem, a new method is proposed. Firstly, the recursive equations are inferred based on the maximum likelihood rule. Secondly, the forgetting factor and learning rate factor are redefined, and their still more general formulations are obtained by analyzing their practical functions. Lastly, the convergence of the proposed algorithm is proved to enable the estimation converge to a local maximum of the data likelihood function according to the stochastic approximation theory. The experiments show that the proposed learning algorithm excels the formers both in converging rate and accuracy.

Using Cooperative Learning Strategies to Cultural Background Teaching in College English

The aim of this innovation project is toconduct a pilot by employing cooperative learning strategies, peer assessment and self-assessment in the process of teaching cultural background in College English Integrated Course to change teachers' role as a dominator as that to explore a feasible yet effective way which will help students to learn the cultural background and obtained some cognitive progress in performance and achievements. According to the findings obtained from the survey, we can see that by employing of the cooperative learning strategy, students' enthusiasm, participation and learning effectiveness have been greatly enhanced. What's more, the application of cooperative learning strategy in teaching cultural background not only motivated students, enhanced students' critical thinking but also reduced teachers' heavy workload. It is a win-win situation both for teacher and the students.

A Summary of Some Teaching Methodologies as well as Some Linguistic and Learning Theories

This paper tries to summarize some main schools 0f teaching methodologies abroad and some main learning theories abroad. From this paper, we can know the main learning theories, the basic theories of them and the leading figures. It can help us understand the characteristics of each school of the teaching methodologies and learning theories.

Adaptive learning rate GMM for moving object detection in outdoor surveillance for sudden illumination changes

A dynamic learning rate Gaussian mixture model（GMM）algorithm is proposed to deal with the problem of slow adaption of GMM in the case of moving object detection in the outdoor surveillance,especially in the presence of sudden illumination changes.The GMM is mostly used for detecting objects in complex scenes for intelligent monitoring systems.To solve this problem,a mixture Gaussian model has been built for each pixel in the video frame,and according to the scene change from the frame difference,the learning rate of GMM can be dynamically adjusted.The experiments show that the proposed method gives good results with an adaptive GMM learning rate when we compare it with GMM method with a fixed learning rate.The method was tested on a certain dataset,and tests in the case of sudden natural light changes show that our method has a better accuracy and lower false alarm rate.

Tunable structure priors for Bayesian rule learning for knowledge integrated biomarker discovery

AIM To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.METHODS Bayesian rule learning(BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks(BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs. In this paper, we extended the Bayesian score to include informative structure priors, which encodes our prior domain knowledge about the dataset. We call this extension of BRL as BRL_p. The structure prior has a λ hyperparameter that allows the user to tune the degree of incorporation of the prior knowledge in the model learning process. We studied the effect of λ on model learning using a simulated dataset and a real-world lung cancer prognostic biomarker dataset, by measuring the degree of incorporation of our specified prior knowledge. We also monitored its effect on the model predictive performance. Finally, we compared BRL_p to other stateof-the-art classifiers commonly used in biomedicine.RESULTS We evaluated the degree of incorporation of prior knowledge into BRL_p, with simulated data by measuring the Graph Edit Distance between the true datagenerating model and the model learned by BRL_p. We specified the true model using informative structurepriors. We observed that by increasing the value of λ we were able to increase the influence of the specified structure priors on model learning. A large value of λ of BRL_p caused it to return the true model. This also led to a gain in predictive performance measured by area under the receiver operator characteristic curve(AUC). We then obtained a publicly available real-world lung cancer prognostic biomarker dataset and specified a known biomarker from literature [the epidermal growth factor receptor(EGFR) gene]. We again observed that larger values of λ led to an increased incorporation of EGFR into the final BRL_p model. This relevant background knowledge also led to a gain in AUC.CONCLUSION BRL_p enables tunable structure priors to be incorporated during Bayesian classification rule learning that integrates data and knowledge as demonstrated using lung cancer biomarker data.

Intelligent Deep Learning Based Automated Fish Detection Model for UWSN

An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces.It has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of marine life.Underwater Wireless Sensor Networks(UWSNs)are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions.In this scenario,it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies.Several models have been developed earlier for fish detection.However,they lack robustness to accommodate considerable differences in scenes owing to poor luminosity,fish orientation,structure of seabed,aquatic plantmovement in the background and distinctive shapes and texture of fishes from different genus.With this motivation,the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN,named IDLAFD-UWSN model.The presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos,particularly in blurred and crowded environments.IDLAFD-UWSN model makes use of Mask Region Convolutional Neural Network(Mask RCNN)with Capsule Network as a baseline model for fish detection.Besides,in order to train Mask RCNN,background subtraction process using GaussianMixtureModel(GMM)model is applied.This model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate regions.Finally,Wavelet Kernel Extreme Learning Machine(WKELM)model is utilized as a classifier model.The performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under different aspects with the maximum accuracy of 98%and 97%on the applied blurred and crowded datasets respectively.

A machine-learning-based electron density (MLED) model in the inner magnetosphere

Plasma density is an important factor in determining wave-particle interactions in the magnetosphere.We develop a machine-learning-based electron density(MLED)model in the inner magnetosphere using electron density data from Van Allen Probes between September 25,2012 and August 30,2019.This MLED model is a physics-based nonlinear network that employs fundamental physical principles to describe variations of electron density.It predicts the plasmapause location under different geomagnetic conditions,and models separately the electron densities of the plasmasphere and of the trough.We train the model using gradient descent and backpropagation algorithms,which are widely used to deal effectively with nonlinear relationships among physical quantities in space plasma environments.The model gives explicit expressions with few parameters and describes the associations of electron density with geomagnetic activity,solar cycle,and seasonal effects.Under various geomagnetic conditions,the electron densities calculated by this model agree well with empirical observations and provide a good description of plasmapause movement.This MLED model,which can be easily incorporated into previously developed radiation belt models,promises to be very helpful in modeling and improving forecasting of radiation belt electron dynamics.

MOTIVATION IN ENGLISH STUDY:Creating a Good Language Learning Environment

基于学生英语学习的动力、经济条件、家庭状况和中国经济快速的腾飞急切需要越来越多能娴熟掌握外语, 特别是英语的现状考虑,营造一个良好的语言学习环境对于学生掌握、学会英语至关重要。当前, 倘若学生尽可能早的掌握外语, 这样会他们在与外国人交流和就业中更加自信。与次同时, 诸多大学和高校较以前而言更加注重英语教学和学习, 竭尽全力开展各式各样的教学改革, 旨在全面提高英语教学和学习, 并呼吁教师和学生家长协助学生解决英语学习中遇到的困难和问题, 给学生学习英语创造一个良好的学习氛围, 达到激发学生学好英语的热情这样一个目的。总的来讲, 在学生的英语学习中, 形成一个良好的语言学习氛围会对学生产生较大的影响。任何一个教师和学生都应意识到, 创建一个良好的语言学习环境在激发学生学习外语中扮演着举足轻重的角色。

家庭背景对大学生在线学习成效的影响机制研究——信息技术获取的中介作用

在线教育过程中优质教育资源共享带来的“公平”与信息技术获取缺失导致的“不公平”交织在一起,引起学者对在线教育公平问题的探讨和辨析。本研究以VanDijk的信息技术获取模型为理论基础,基于大学生在线调查数据,使用多元线性回归和非参数百分位Bootstrap方法分析了家庭背景对大学生在线学习成效的影响机制。研究发现:(1)不同家庭背景的大学生群体间存在多道数字鸿沟;(2)数字鸿沟会转化为教育结果鸿沟,家庭背景通过信息技术获取间接影响在线学习满意度和学习效果;(3)以动机态度和信息技术使用为代表的“新数字鸿沟”对在线学习效果的影响显著大于“旧数字鸿沟”。本研究加深了家庭背景这一结构性因素对在线学习成效影响机制的理解,揭示了在线教育不平等的机制。这为促进教育公平提供了新的思路和经验证据,同时也回应了信息技术对教育结果影响的争议。

在线教学视频中背景音乐的作用:基于对照实验的实证分析

教学视频已经成为在线学习的主要构成要素。但教学视频中背景音乐的作用效果学界还没有一致的结论。研究以背景音乐偏好为调节变量,采用2(陈述性vs程序性)×2(有音乐vs无音乐)的实验设计,尝试探明不同知识类型下背景音乐对学习者的学习情绪、学习成绩及认知负荷的影响。结果发现:背景音乐能有效激发学习者积极的学业情绪;能显著提升高音乐偏好学习者的迁移成绩,但对低音乐偏好和无音乐偏好学习者,其保持和迁移成绩不存在显著性影响;背景音乐对学习者的认知负荷不存在显著性影响。据此,从在线教学视频设计和在线学习平台两个层面提出优化建议,以期改善在线学习体验和学习效果。

基于YOLOv5的复杂背景下植物叶片检测研究

对植物叶片进行检测是研究植物表型性状的基础,但真实环境下叶片间相互遮挡、叶片边缘特征不明显、幼叶目标过小以及外部环境如光照条件等因素影响会对叶片检测效果造成很大的障碍。针对复杂背景下的叶片检测,该研究提出了一种基于改进YOLOv5模型植物叶片检测方法。通过在骨干网络中引入空洞卷积,使得网络可以捕获到更广阔范围的上下文信息;利用双向连接的加权特征金字塔网络,以增强目标叶片特征提取并更好地融合特征信息;利用注意力机制,通过动态地调整注意力分布,以提高边缘特征表达能力。测试结果表明,在Plant Village数据集筛选的葡萄叶片图像以及自拍摄葡萄生长叶片上测试改进算法的可行性,改进的YOLOv5模型其叶片检测mAP比原生模型提高了5.8%,遮挡叶片检测精度提高了7.09%。叶片检测效果有显著提升。该研究提出的方法可以有效解决复杂背景下植物叶片检测效果不佳的问题,为植物表型研究提供技术支撑。

信息化背景下TBL在药店经营与管理课程中的应用

目的 将信息化技术与任务驱动教学法融合并应用于药店经营与管理课程的教学来提升教学效果。方法 本研究以任务驱动法为整体设计思路,结合信息化技术手段,对课程进行模块化设计,并对教学效果进行分析。结果 本研究通过将信息化技术手段与任务驱动教学法融合,达到了增强学生学习的主观能动性、提高教学效果的目的。结论 教师要改变传统的教学观,将信息化技术与教学方法进行很好的融合,从而更好的服务学生。

数字化教育背景下混合式学习在高校体育课中的实证研究

采用文献资料法、问卷调查法、访谈法对数字化背景下混合式学习在高校体育课中的运用进行实证研究。“混合式学习模式”是数字化背景下传统教学模式的延伸,结合新课改的具体要求,进行了教学跟踪。混合式学习与体育课结合的模式,是一种能够培养学生学习兴趣、促进学生个性化发展的新型教学模式,可以解决教学过程中出现的诸多难题。研究表明:采用混合式学习模式可以促进师生之间的交流,学生和教师共同转变学习与教育的态度和观念,学生综合成绩与学习兴趣都有较大提高。

高管文理科教育背景与企业创新

本文旨在从CEO文理科教育背景差异对企业创新影响的角度提供一些经验证据。基于双重/无偏机器学习方法,本文研究发现:理工科教育背景的CEO对企业创新具有正向的促进效应,即相对于人文社科教育背景的CEO,理工科教育背景的CEO能显著提高企业创新。同时,高学历理工科教育背景的CEO对企业创新产出质量的正向影响更加明显。相对于高新企业而言,在一般企业中上述差异反而更显著。进一步机制研究表明,相对于人文社科背景的CEO,理工科教育背景的CEO倾向于通过加强企业创新团队建设、提高创新投入强度等途径促进企业创新。本文首次为企业高管的文理科教育背景差异对企业创新行为影响提供了经验证据。

人工智能背景下高校商务英语教学新型场域构建研究

随着人工智能技术的快速发展和应用,高校商务英语教学正面临着新的挑战和机遇。传统教学模式难以满足学生对实际商务实践能力的需求,且与人工智能技术融合的教学模式还存在改进空间。因此,探索在人工智能背景下构建高校商务英语教学新型场域具有重要意义,旨在促进学生学习体验的提升和专业能力的培养,为高校商务英语教学的创新和改进提供理论支持和实践指导。本研究旨在探讨人工智能背景下高校商务英语教学新型场域构建的策略和实践,以提升学生的语言实践能力和专业素养。

基于融合编码器与双解码器的半监督疲劳裂纹分割

针对现有基于深度学习的疲劳裂纹分割算法依赖于大量像素级标记的问题,提出了一种半监督疲劳裂纹分割网络SFD-Net.SFD-Net利用对比学习方法进行半监督训练,以减少对大量像素级标记的依赖.同时,它采用融合编码器和双解码器的设计,旨在更好地捕捉裂纹区域的特征并提高分割准确性.通过引入改进注意力模块和边界优化模块,增强了对裂纹特征的表示,提高了裂纹边界的分割质量.在公开的疲劳裂纹数据集上对SFD-Net进行性能验证.结果表明:相较于使用相同标记比例的全监督算法,SFD-Net的分割性能有明显提升;仅使用25%的标记数据时,SFD-Net的交并比(IoU)达到70.6%,超过了使用100%标记数据的其他全监督算法的平均IoU(69.1%);同时,与其他先进的半监督方法相比,SFD-Net在所有标记数据比例下均取得了最高的预测精度.

基于轻量级UNet的复杂背景字符语义分割网络

针对传统复杂背景字符分割算法的不足,提出了一种基于轻量级UNet的复杂背景字符语义分割网络.网络结构基于UNet,在特征提取模块中,将传统卷积变为深度可分离卷积,减少了网络特征提取模块的参数量以及计算量,并引入残差学习模块解决网络退化问题.在自制数据集以及H-DIBCO2018公开数据集上展开实验,并与FCN8s、AttationUNet和UNet进行比较.实验结果表明:所提出的网络可同时兼顾计算效率与分割精度,具有实用性.

结合帧间差异检测的固定场景视频压缩与重建

近年来,高清和超高清监控摄像头的广泛部署促使了各类监控等固定场景类视频数据量的急剧增加。对视频的存储和传输造成了巨大压力。为了进一步去除固定场景类视频中的冗余数据,本文提出了一种新颖的压缩与重建方法。通过背景提取和结合帧间前景差异检测的前景提取与压缩方法,大量去除视频中的数据冗余。实验结果表明,本文方法与MPEG-4相比,在更高的压缩率上实现了更高的视频重建性能,与H.264、H.265和DCVC-DC相比,本文所提方法在压缩性能上依次分别提升了82.75%、76.19%和59.56%,并且保持了较高的视频重建水平,从而有效地缓解了固定场景类视频的存储和传输压力。