基于部分加权损失函数的RefineDet

针对目标检测网络单阶改进目标检测器（RefineDet）对类间不平衡数据集中小样本类别检测性能差的问题,提出一种部分加权损失函数SWLoss。首先,以每个训练批量中不同类别样本数量的倒数作为启发式的类间样本平衡因子,对分类损失中的不同类别进行加权,从而提高对小样本类别学习的关注程度;然后引入多任务平衡因子对分类损失和回归损失进行加权,缩小两个任务学习速率的差异;最后,在目标类别样本数量存在大幅差异的Pascal VOC 2007数据集和点阵字符数据集上进行实验。结果表明,与原始RefineDet相比,基于SWLoss的RefineDet明显提高了小样本类别的检测精度,它在两个数据集上的平均精度均值（mAP）分别提高了1.01、9.86个百分点;与基于损失平衡函数和加权成对损失的RefineDet相比,基于SWLoss的RefineDet在两个数据集上的mAP分别提高了0.68、4.73和0.49、1.48个百分点。     

利用置信规则库构建WSN节点故障检测模型

在无线传感器网络(wireless sensor network, WSN)节点故障检测领域的研究过程中,故障检测准确率会受节点数据的不确定性和专家知识模糊性的影响。针对这一问题,本文提出了一种基于置信规则库(belief rule base, BRB)的WSN节点故障检测方法。首先,根据WSN工作原理及节点工作特性描述WSN节点故障检测过程;然后,从空间和时间2个维度对节点数据提取特征,建立基于空间和时间相关性的WSN节点故障检测模型;最后,利用Intel Lab Data无线传感器数据集进行案例研究以验证模型的有效性。结果证明,本文方法能够统筹利用专家知识和节点数据实现WSN节点故障检测。     

基于C/S体系结构的网络多媒体教室管理系统

多媒体教室管理系统是典型的信息管理系统(MIS),其开发主要包括后台数据库的建立和维护以及前端应用程序的开发两个方面。该系统采用了面向对象的程序设计语言Delphi6.0实现。     

Web检索查询意图分类技术综述

查询分类是近年来信息检索领域的研究热点,并且在很多领域得到了广泛地关注。主要讨论根据查询的意图进行分类的研究工作,从查询分类的诞生背景、关键技术、所使用的分类方法和评价方法方面进行综述评论,提出了查询意图分类面临的问题和挑战。认为缺乏权威的评测标准、在大规模数据集上的未经全面测试的性能、如何准确地获取查询的特征以及如何证明分类体系的完备性和独立性是目前查询意图分类研究的关键问题。     

Multi-scale-based joint super-resolution and inverse tone-mapping with data synthesis for UHD HDR video

The performance of current joint super-resolution (SR) and inverse tone-mapping (ITM) frameworks is limited since they only account for information in small local receptive fields. Moreover, the SDR images in the existing dataset for joint SR-ITM are oversaturated compared to the HDR ground truths. The models trained on this dataset produce HDR images with color shift. This paper proposes a multi-scale-based joint SR-ITM model to reconstruct HR HDR videos. Image features are downsampled to different resolutions to increase the local receptive fields. Thus the proposed model can react to more complex patterns of input images. And we design a novel multi-path residual dense block (MRDB) as the model’s fundamental component to extract features. The proposed MRDBs can improve performance by combining dense feature learning and novel multi-path residual learning. Experiments show that the proposed model outperforms the state-of-the-art methods in terms of quantitative and qualitative results. Furthermore, we propose a data synthesis pipeline to generate perceptually identical SDR images from their HDR counterparts. These SDR-HDR pairs are used to create a new dataset. Experiments demonstrate that models trained with our new dataset prevent color shift and preserve creative intent.     

Passive infrared sensor dataset and deep learning models for device-free indoor localization and tracking

Location estimation or localization is one of the key components in IoT applications such as remote health monitoring and smart homes. Amongst device-free localization technologies, passive infrared (PIR) sensors are one of the promising options due to their low cost, low energy consumption, and good accuracy. However, most of the existing systems are complexly designed and difficult to deploy in real life, in addition, there is no public dataset available for researchers to benchmark their proposed localization and tracking methods. In this paper, we propose a system and a dataset collected from our PIR system consisting of commercial-of-the-shelf (COTS) sensors without any modification. Our dataset includes profile data of 36 classes that have over 1,000 samples of different walking directions and test data consisting of multiple scenarios with a sequence length of over 2,000 timesteps. To evaluate our system and dataset, we implement various deep learning methods such as CNN, RNN, and CNN–RNN. Our results prove the applicability and feasibility of our system and illustrate the viability of deep learning methods for PIR-based localization and tracking. We also show that our dataset can be converted for coordinate estimation so that deep learning methods and particle filter approaches can be applied to estimate coordinates. As a result, the best performer achieves a distance error of 0.25 m.     

面向大规模数据集的近邻传播聚类

近邻传播聚类在计算过程中需构建相似度矩阵,该矩阵的规模随样本数急剧增长,限制了算法在大规模数据集上的直接应用。为此,提出一种改进的近邻传播聚类算法,利用数据点的局部分布,借鉴半监督聚类的思想构造稀疏化的相似度矩阵,并对聚类结果中的簇代表点再次或多次聚类,直至得到合适的簇划分。实验结果表明,该算法在处理能力和运算速度上优于原算法。     

p范数正则化支持向量机分类算法

L2范数罚支持向量机(Support vector machine,SVM)是目前使用最广泛的分类器算法之一,同时实现特征选择和分类器构造的L1范数和L0范数罚SVM算法也已经提出.但是,这两个方法中,正则化阶次都是事先给定,预设p=2或p=1.而我们的实验研究显示,对于不同的数据,使用不同的正则化阶次,可以改进分类算法的预测准确率.本文提出p范数正则化SVM分类器算法设计新模式,正则化范数的阶次p可取范围为02范数罚SVM,L1范数罚SVM和L0范数罚SVM.     

基于遗传规划集成学习的网络作弊检测

网络作弊检测是搜索引擎的重要挑战之一,该文提出基于遗传规划的集成学习方法 (简记为GPENL)来检测网络作弊。该方法首先通过欠抽样技术从原训练集中抽样得到t个不同的训练集;然后使用c个不同的分类算法对t个训练集进行训练得到t*c个基分类器;最后利用遗传规划得到t*c个基分类器的集成方式。新方法不仅将欠抽样技术和集成学习融合起来提高非平衡数据集的分类性能,还能方便地集成不同类型的基分类器。在WEBSPAM-UK2006数据集上所做的实验表明无论是同态集成还是异态集成,GPENL均能提高分类的性能,且异态集成比同态集成更加有效;GPENL比AdaBoost、Bagging、RandomForest、多数投票集成、EDKC算法和基于Prediction Spamicity的方法取得更高的F-度量值。     

A Study on the Prediction of Cancer Using Whole-Genome Data and Deep Learning

The number of patients diagnosed with cancer continues to increasingly rise, and has nearly doubled in 20 years. Therefore, predicting cancer occurrence has a significant impact on reducing medical costs, and preventing cancer early can increase survival rates. In the data preprocessing step, since individual genome data are used as input data, they are classified as individual genome data. Subsequently, data embedding is performed in character units, so that it can be used in deep learning. In the deep learning network schema, using preprocessed data, a character-based deep learning network learns the correlation between individual feature data and predicts cancer occurrence. To evaluate the objective reliability of the method proposed in this study, various networks published in other studies were compared and evaluated using the TCGA dataset. As a result of comparing various networks published in other studies using the same data, excellent results were obtained in terms of accuracy, sensitivity, and specificity. Thus, the superiority of the effectiveness of deep learning networks in predicting cancer occurrence using individual whole-genome data was demonstrated. From the results of the confusion matrix, the validity of the model for predicting the cancer using an individual’s whole-genome data and the deep learning proposed in this study was proven. In addition, the AUC, which is the area under the ROC curve, which judges the efficiency of diagnosis as a performance evaluation index of the model, was found to be 90% or more, good classification results were derived. The objectives of this study were to use individual genome data for 12 cancers as input data to analyze the whole genome pattern, and to not separately use reference genome sequence data of normal individuals. In addition, several mutation types, including SNV, DEL, and INS, were applied.