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2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献

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Classifying Educational Lectures in Low-Resource Languages 低资源语言教学讲座分类
Gihad N. Sohsah, Onur Güzey, Zaina Tarmanini
Classifying educational resources such as videos and articles can be challenging in low-resource languages due to lack of appropriate tools and sufficient labeled data. To overcome this problem, a crosslingual classification method that utilizes resources created in one high-resource language, such as English, to perform classification in many low-resource languages, is proposed. Data scarcity issue is prevented by transferring information from highresources languages to the low-resources ones. First, word embeddings are extracted using one of the frameworks proposed previously, then classifiers are trained using the highresource language documents. Two versions of the method that use different higher-level composition functions are implemented and compared.
由于缺乏适当的工具和足够的标记数据,在资源匮乏的语言中,对视频和文章等教育资源进行分类可能具有挑战性。为了克服这一问题,提出了一种跨语言分类方法,该方法利用一种高资源语言(如英语)中创建的资源来对许多低资源语言进行分类。通过将信息从资源丰富的语言传递到资源贫乏的语言,避免了数据短缺问题。首先,使用前面提出的框架之一提取词嵌入,然后使用高资源语言文档训练分类器。实现并比较了使用不同高级组合函数的两个版本的方法。
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引用次数: 1
Robust Kernel Embedding of Conditional and Posterior Distributions with Applications 条件和后验分布的鲁棒核嵌入及其应用
M. Nawaz, Omar Arif
This paper proposes a novel non-parametric method to robustly embed conditional and posterior distributions to reproducing Kernel Hilbert space (RKHS). Robust embedding is obtained by the eigenvalue decomposition in the RKHS. By retaining only the leading eigenvectors, the noise in data is methodically disregarded. The non-parametric conditional and posterior distribution embedding obtained by our method can be applied to a wide range of Bayesian inference problems. In this paper, we apply it to heterogeneous face recognition and zero-shot object recognition problems. Experimental validation shows that our method produces better results than the comparative algorithms.
提出了一种新的非参数方法,将条件分布和后验分布鲁棒嵌入到核希尔伯特空间(RKHS)的再现中。在RKHS中通过特征值分解得到鲁棒嵌入。通过只保留主要特征向量,数据中的噪声被有条不紊地忽略。该方法得到的非参数条件和后验分布嵌入可以应用于广泛的贝叶斯推理问题。本文将其应用于异构人脸识别和零射击目标识别问题。实验验证表明,该方法比比较算法的效果更好。
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引用次数: 2
Cross-Document Knowledge Discovery Using Semantic Concept Topic Model 基于语义概念主题模型的跨文档知识发现
Xin Li, W. Jin
Topic models employ the Bag-of-Words (BOW) representation, which break terms into constituent words and treat words as surface strings without assuming predefined knowledge about word meaning. In this paper, we propose the Semantic Concept Latent Dirichlet Allocation (SCLDA) and Semantic Concept Hierarchical Dirichlet Process (SCHDP) based approaches by representing text as meaningful concepts rather than words, using a new model known as Bag-of-Concepts (BOC). We propose new algorithms of applying SCLDA and SCHDP into the Concept Chain Queries (CCQ) problem. The algorithms are focused on discovering new semantic relationships between two concepts across documents where relationships found reveal semantic paths linking two concepts across multiple text units. The experiments demonstrate the search quality has been greatly improved, compared with using other LDA or HDP based approaches.
主题模型采用词袋(BOW)表示,它将术语分解为组成词,并将词作为表面字符串处理,而不需要假定关于单词含义的预定义知识。在本文中,我们提出了基于语义概念潜狄利克雷分配(SCLDA)和语义概念分层狄利克雷过程(SCHDP)的方法,通过使用一种称为概念袋(BOC)的新模型将文本表示为有意义的概念而不是单词。我们提出了将SCLDA和SCHDP应用于概念链查询(CCQ)问题的新算法。这些算法的重点是发现跨文档的两个概念之间的新的语义关系,这些关系揭示了跨多个文本单元连接两个概念的语义路径。实验表明,与其他基于LDA或HDP的方法相比,该方法的搜索质量有了很大的提高。
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引用次数: 3
An Oblivious Routing-Based Power Flow Calculation Method for Loss Minimization of Smart Power Networks: A Theoretical Perspective 基于遗忘路由的智能电网损耗最小化潮流计算方法:一个理论视角
Kianoosh G. Boroojeni, M. Amini, S. S. Iyengar
Power loss minimization plays an important role in the appropriate operation of power networks. Line power loss occurs when the power is transmitted through the lines of a network due to the permittivity of lines medium. Transmission loss may increase the dispatch cost of all of the obtained power flows based on market contracts. Hence, the independent system operators should use loss minimization methods to facilitate the implementation of the contracted power transactions. Loss minimization also will improve the security and stability of power network. In this paper, we present a novel loss minimization scheme based on oblivious network design, referred to as oblivious routing-based power flow method. The method is built on the bottom-up oblivious network routing scheme which offers multiple paths from several sources (generation units) to the specific destinations (electric load demands). Although there is limited information regarding other line flows and the current status of network, the routing scheme mathematically guarantees that the power flow solution is an approximation of the optimal solution with a specific competitiveness ratio. In fact, the Our main focus is on the power flow calculation while optimizing power losses. Compared with the recently developed power flow methods, our approach does not depend on the network topology and its performance for both radial and non-radial networks is accurate. Hence, it is suitable to use the propose approach for large-scale loss minimization while determining the power flows. This paper mainly focuses on the theoretical aspect of the proposed method. As our method is based on a novel concept from computer science discipline, we provide sufficient explanation about the preliminaries of oblivious routing scheme.
在电网合理运行中,功率损耗最小化具有重要意义。由于线路介质的介电常数,当电力通过网络的线路传输时,就会发生线路功率损耗。根据市场契约,输电损耗会增加所有获得的潮流的调度成本。因此,独立系统运营商应采用损失最小化的方法来促进合同电力交易的实施。损耗最小化也将提高电网的安全性和稳定性。本文提出了一种新的基于遗忘网络设计的损耗最小化方案,即基于遗忘路由的潮流法。该方法建立在自下而上的遗忘网络路由方案上,该方案提供了从多个源(发电机组)到特定目的地(电力负荷需求)的多条路径。虽然关于其他线路流和网络当前状态的信息有限,但该路由方案在数学上保证潮流解是具有特定竞争比的最优解的近似值。实际上,我们的主要重点是在优化功率损耗的同时进行潮流计算。与现有的潮流分析方法相比,该方法不依赖于网络拓扑结构,对径向网络和非径向网络的性能都是准确的。因此,在确定潮流时,该方法适用于大规模的损耗最小化。本文主要对所提出方法的理论方面进行了研究。由于我们的方法是基于计算机科学学科的一个新概念,我们提供了充分的解释,不经意路由方案的初步。
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引用次数: 3
Decoding Epileptogenesis in a Reduced State Space 在简化状态空间中解码癫痫发生
François G. Meyer, Alexander M. Benison, Zachariah Smith, D. Barth
We describe here the recent results of a multidisciplinary effort to design a biomarker that can actively and continuously decode the progressive changes in neuronal organization leading to epilepsy, a process known as epileptogenesis. Using an animal model of acquired epilepsy, we chronically record hippocampal evoked potentials elicited by an auditory stimulus. Using a set of reduced coordinates, our algorithm can identify universal smooth low-dimensional configurations of the auditory evoked potentials that correspond to distinct stages of epileptogenesis. We use a hidden Markov model to learn the dynamics of the evoked potential, as it evolves along these smooth low-dimensional subsets. We provide experimental evidence that the biomarker is able to exploit subtle changes in the evoked potential to reliably decode the stage of epileptogenesis and predict whether an animal will eventually recover from the injury, or develop spontaneous seizures.
我们在这里描述了一项多学科努力设计的生物标志物的最新结果,该生物标志物可以主动和持续地解码导致癫痫的神经元组织的进行性变化,这一过程被称为癫痫发生。使用获得性癫痫动物模型,我们长期记录由听觉刺激引起的海马诱发电位。使用一组简化的坐标,我们的算法可以识别出与癫痫发生不同阶段对应的听觉诱发电位的普遍光滑低维配置。我们使用隐马尔可夫模型来学习诱发电位的动态,因为它沿着这些光滑的低维子集演变。我们提供的实验证据表明,该生物标志物能够利用诱发电位的细微变化来可靠地解码癫痫发生阶段,并预测动物最终是否会从损伤中恢复,还是会发生自发性癫痫发作。
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引用次数: 1
ECG Biometric Identification Using Wavelet Analysis Coupled with Probabilistic Random Forest 基于小波分析和概率随机森林的心电生物特征识别
Robin Tan, M. Perkowski
A novel algorithm is proposed in this study for improving the accuracy and robustness of human biometric identification using electrocardiograms (ECG) from mobile devices. The algorithm combines the advantages of both fiducial and non-fiducial ECG features and implements a fully automated, two-stage cascaded classification system using wavelet analysis coupled with probabilistic random forest machine learning. The proposed algorithm achieves a high identification accuracy of 99.43% for the MIT-BIH Arrhythmia database, 99.98% for the MIT-BIH Normal Sinus Rhythm database, 100% for the ECG data acquired from an ECG sensor integrated into a mobile phone, and 98.79% for the PhysioNet Human-ID database acquired from multiple tests within a 6-month span. These results demonstrate the effectiveness and robustness of the proposed algorithm for biometric identification, hence supporting its practicality in applications such as remote healthcare and cloud data security.
本研究提出了一种新的算法,用于提高使用移动设备的心电图(ECG)进行人体生物特征识别的准确性和鲁棒性。该算法结合了基准和非基准ECG特征的优点,并使用小波分析和概率随机森林机器学习实现了全自动的两阶段级联分类系统。该算法对MIT-BIH心律失常数据库的识别准确率为99.43%,对MIT-BIH正常窦性心律数据库的识别准确率为99.98%,对集成在手机上的心电传感器采集的心电数据的识别准确率为100%,对6个月内多次测试获得的PhysioNet Human-ID数据库的识别准确率为98.79%。这些结果证明了所提出的生物特征识别算法的有效性和鲁棒性,因此支持其在远程医疗保健和云数据安全等应用中的实用性。
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引用次数: 13
Improving Speed Independent Performance of Fault Diagnosis Systems through Feature Mapping and Normalization 通过特征映射和归一化提高故障诊断系统的速度无关性
A. Raghunath, K. T. Sreekumar, C. S. Kumar, K. I. Ramachandran
High accuracy fault diagnosis systems are extremely important for effective condition based maintenance (CBM) of rotating machines. In this work, we develop a fault diagnosis system using time and frequency domain statistical features as input to a backend support vector machine (SVM) classifier. We evaluate the performance of the baseline system for speed dependent and speed independent performance. We show how feature mapping and feature normalization can help in enhancing the speed independent performance of machine fault diagnosis systems. We first perform feature mapping using locality constrained linear coding (LLC) which maps the input features to a higher dimensional feature space to be used as input to an SVM classifier (LLC-SVM). It is seen that there is a significant improvement in the speed independent performance of the fault identification system. We obtain an improvement of 11.81% absolute and 10.53% absolute respectively for time and frequency domain LLC-SVM systems compared to the respective baseline systems. We then explore variance normalization considering the speed specific variations as noise to further improve the performance of the fault diagnosis system. We obtain a performance improvement of 8.20% absolute and 6.71% absolute respectively over the time and frequency domain LLC-SVM systems. It may be noted that that the variance normalized LLC-SVM system outperforms.
高精度的故障诊断系统对于有效的旋转机械状态维修至关重要。在这项工作中,我们开发了一个故障诊断系统,使用时域和频域统计特征作为后端支持向量机(SVM)分类器的输入。我们对基线系统的性能进行了速度依赖和速度独立性能的评估。我们展示了特征映射和特征归一化如何有助于提高机器故障诊断系统的速度无关性能。我们首先使用局域约束线性编码(LLC)进行特征映射,该编码将输入特征映射到高维特征空间,作为支持向量机分类器(LLC-SVM)的输入。可以看出,故障识别系统的速度无关性有了明显的提高。与各自的基线系统相比,我们获得了时域和频域LLC-SVM系统分别提高了11.81%和10.53%的绝对精度。在此基础上,我们进一步探讨了将速度特定变化作为噪声的方差归一化,以进一步提高故障诊断系统的性能。与时域和频域LLC-SVM系统相比,我们获得了8.20%和6.71%的绝对性能提升。值得注意的是,方差归一化的LLC-SVM系统优于方差归一化的LLC-SVM系统。
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引用次数: 7
A Study on Effects of Different Control Period of Neural Network Based Reference Modified PID Control for DC-DC Converters 基于神经网络的参考修正PID控制对DC-DC变换器不同控制周期的影响研究
H. Maruta, Hironobu Taniguchi, F. Kurokawa
This paper studies about computational burden of a reference modified PID with a neural network prediction for dc-dc converters. Flexible control methods are required to realize a superior transient response since the converter has a nonlinear behavior. However, the computational burden becomes a problem to implement the control to computation devices. In this paper, the neural network is adopted to improve the transient response of output voltage of the dc-dc converter under the consideration of its computational burden. The neural network computation part has a longer computation period than the PID main control part. It can be possible since the neural network gives more than one predictions which are required for the reference modification for each main control period. Therefore, the reference modification can be adopted on every main control period. From results, it is confirmed that the proposed method can improve the transient response effectively with reducing computational burden of neural network control.
本文研究了基于神经网络预测的参考修正PID对dc-dc变换器的计算量问题。由于变换器具有非线性特性,需要灵活的控制方法来实现良好的暂态响应。然而,计算量的增加成为实现对计算设备控制的一个问题。本文在考虑到dc-dc变换器计算量大的情况下,采用神经网络改善其输出电压的暂态响应。与PID主控制部分相比,神经网络计算部分的计算周期更长。这是可能的,因为神经网络给出了每个主要控制周期的参考修正所需的多个预测。因此,每个主要控制周期都可以采用参考修正。结果表明,该方法可以有效地改善系统的暂态响应,减少神经网络控制的计算量。
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引用次数: 2
k-Means Partition of Monthly Average Insolation Period Data for Turkey 土耳其月平均日照期数据的k-Means分割
M. Yesilbudak, I. Colak, R. Bayindir
Solar power penetration has made the site-specific energy ratings an essential necessity for utilities, independent systems operators and regional transmission organizations. Since, it leads to the reliable and efficient energy production with the increased levels of solar power integration. This study concentrates on the partitional clustering analysis of monthly average insolation period data for the 75 provinces in Turkey. Together with the k-means clustering algorithm, we use Pearson Correlation, Cosine, Squared Euclidean and City-Block distance measures for the high-dimensional neighborhood measurement and utilize the silhouette width for validating the achieved clustering results. In consequence of comparing the star glyph plots with the k-means clustering results, the most productive and the most unfavorable places among all provinces are mined on the basis of monthly average insolation period.
太阳能的渗透使得特定地点的能源等级对公用事业、独立系统运营商和区域输电组织来说是必不可少的。因此,随着太阳能集成水平的提高,它将导致可靠和高效的能源生产。本研究对土耳其75个省的月平均日照期数据进行了分区聚类分析。结合k-means聚类算法,我们使用Pearson Correlation、Cosine、Squared Euclidean和City-Block距离度量进行高维邻域度量,并利用轮廓宽度验证获得的聚类结果。将星形图与k-means聚类结果进行比较,根据月平均日照时间挖掘出各省中最高产和最不利的地方。
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引用次数: 3
Feedforward Neural Networks for Predicting the Duration of Maintained Software Projects 预测软件项目维护周期的前馈神经网络
C. López-Martín
Once a software project has been developed and delivered, any modification to it corresponds to maintenance. Software maintenance (SM) involves modifications to keep a software project usable in a changed or a changing environment, reactive modifications to correct discovered faults, and modifications to improve performance or maintainability. Since the duration of SM should be predicted, in this study, after a statistical analysis of projects maintained on several platforms and programming languages generations, data sets were selected for training and testing multilayer feedforward neural networks (i.e., multilayer perceptron, MLP). These data sets were obtained from the International Software Benchmarking Standards Group. Results based on Wilcoxon statistical tests show that prediction accuracy with the MLP is statistically better than that with the statistical regression models when software projects were maintained on (1) Mid Range platform and coded in programming languages of third generation, and (2) Multi platform and coded in programming languages of fourth generation.
一旦软件项目被开发并交付,对它的任何修改都对应于维护。软件维护(SM)包括修改以保持软件项目在变化或不断变化的环境中可用,修改以纠正发现的错误,以及修改以提高性能或可维护性。由于SM的持续时间是需要预测的,因此在本研究中,在对多个平台和编程语言世代维护的项目进行统计分析后,选择数据集进行多层前馈神经网络(即多层感知机,MLP)的训练和测试。这些数据集是从国际软件基准标准组获得的。基于Wilcoxon统计检验的结果表明,当软件项目在(1)Mid Range平台上以第三代编程语言进行维护,(2)Multi平台上以第四代编程语言进行编码时,MLP的预测精度在统计学上优于统计回归模型。
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引用次数: 2
期刊
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)
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