2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献

Tree-Structured Curriculum Learning Based on Semantic Similarity of Text 基于文本语义相似度的树状结构课程学习

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-20 DOI: 10.1109/ICMLA.2017.00-27

Sanggyu Han, Sung-Hyon Myaeng

引用次数: 8

Predicting Psychosis Using the Experience Sampling Method with Mobile Apps 使用手机应用程序的经验抽样方法预测精神病

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-18 DOI: 10.1109/ICMLA.2017.00-84

D. Stamate, Andrea Katrinecz, W. Alghamdi, D. Ståhl, P. Delespaul, J. Os, S. Guloksuz

{"title":"Predicting Psychosis Using the Experience Sampling Method with Mobile Apps","authors":"D. Stamate, Andrea Katrinecz, W. Alghamdi, D. Ståhl, P. Delespaul, J. Os, S. Guloksuz","doi":"10.1109/ICMLA.2017.00-84","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-84","url":null,"abstract":"Smart phones have become ubiquitous in the recent years, which opened up a new opportunity for rediscovering the Experience Sampling Method (ESM) in a new efficient form using mobile apps, and provides great prospects to become a low cost and high impact mHealth tool for psychiatry practice. The method is used to collect longitudinal data of participants' daily life experiences, and is ideal to capture fluctuations in emotions (momentary mental states) as an early indicator for later mental health disorder. In this study ESM data of patients with psychosis and controls were used to examine emotion changes and identify patterns. This paper attempts to determine whether aggregated ESM data, in which statistical measures represent the distribution and dynamics of the original data, are able to distinguish patients from controls. Variable importance, recursive feature elimination and ReliefF methods were used for feature selection. Model training and tuning, and testing were performed in nested cross-validation, and were based on algorithms such as Random Forests, Support Vector Machines, Gaussian Processes, Logistic Regression and Neural Networks. ROC analysis was used to post-process these models. Stability of model performances was studied using Monte Carlo simulations. The results provide evidence that pattern in mood changes can be captured with the combination of techniques used. The best results were achieved by SVM with radial kernel, where the best model performed with 82% accuracy and 82% sensitivity.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"26 1","pages":"667-673"},"PeriodicalIF":0.0,"publicationDate":"2017-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81882553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Direct Multiclass Boosting Using Base Classifiers' Posterior Probabilities Estimates 基于基分类器后验概率估计的直接多类提升

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-18 DOI: 10.1109/ICMLA.2017.0-154

M. Bourel, B. Ghattas

引用次数: 0

Human Action Recognition from Body-Part Directional Velocity Using Hidden Markov Models 基于隐马尔可夫模型的人体动作识别

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-18 DOI: 10.1109/ICMLA.2017.00-14

Sid Ahmed Walid Talha, A. Fleury, S. Ambellouis

引用次数: 3

Realistic Traffic Generation for Web Robots 现实的流量生成网络机器人

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-15 DOI: 10.1109/ICMLA.2017.0-161

Kyle Brown, Derek Doran

{"title":"Realistic Traffic Generation for Web Robots","authors":"Kyle Brown, Derek Doran","doi":"10.1109/ICMLA.2017.0-161","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-161","url":null,"abstract":"Critical to evaluating the capacity, scalability, and availability of web systems are realistic web traffic generators. Web traffic generation is a classic research problem, no generator accounts for the characteristics of web robots or crawlers that are now the dominant source of traffic to a web server. Administrators are thus unable to test, stress, and evaluate how their systems perform in the face of ever increasing levels of web robot traffic. To resolve this problem, this paper introduces a novel approach to generate synthetic web robot traffic with high fidelity. It generates traffic that accounts for both the temporal and behavioral qualities of robot traffic by statistical and Bayesian models that are fitted to the properties of robot traffic seen in web logs from North America and Europe. We evaluate our traffic generator by comparing the characteristics of generated traffic to those of the original data. We look at session arrival rates, inter-arrival times and session lengths, comparing and contrasting them between generated and real traffic. Finally, we show that our generated traffic affects cache performance similarly to actual traffic, using the common LRU and LFU eviction policies.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"341 1","pages":"178-185"},"PeriodicalIF":0.0,"publicationDate":"2017-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73754074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Hierarchical, Bulk-Synchronous Stochastic Gradient Descent Algorithm for Deep-Learning Applications on GPU Clusters 面向GPU集群深度学习应用的分层、批量同步随机梯度下降算法

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-56

Guojing Cong, Onkar Bhardwaj

引用次数: 8

Advanced ECHMM-Based Machine Learning Tools for Complex Big Data Applications 用于复杂大数据应用的先进的基于echmm的机器学习工具

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-86

A. Cuzzocrea, E. Mumolo, G. Vercelli

{"title":"Advanced ECHMM-Based Machine Learning Tools for Complex Big Data Applications","authors":"A. Cuzzocrea, E. Mumolo, G. Vercelli","doi":"10.1109/ICMLA.2017.00-86","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-86","url":null,"abstract":"We present a novel approach for accurate characterization of workloads, which is relevant in the context of complex big data applications.Workloads are generally described with statistical models and are based on the analysis of resource requests measurements of a running program. In this paper we propose to consider the sequence of virtual memory references generated from a program during its execution as a temporal series, and to use spectral analysis principles to process the sequence. However, the sequence is time-varying, so we employed processing approaches based on Ergodic Continuous Hidden Markov Models (ECHMMs) which extend conventional stationary spectral analysis approaches to the analysis of time-varying sequences. In this work, we describe two applications of the proposed approach: the on-line classification of a running process and the generation of synthetic traces of a given workload. The first step was to show that ECHMMs accurately describe virtual memory sequences; to this goal a different ECHMM was trained for each sequence and the related run-time average process classification accuracy, evaluated using trace driven simulations over a wide range of traces of SPEC2000, was about 82%. Then, a single ECHMM was trained using all the sequences obtained from a given running application; again, the classification accuracy has been evaluated using the same traces and it resulted about 76%. As regards the synthetic trace generation, a single ECHMM characterizing a given application has been used as a stochastic generator to produce benchmarks for spanning a large application space.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"38 1","pages":"655-660"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75660425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Time-Sensitive Behavior Prediction in a Health Social Network 健康社会网络中的时间敏感行为预测

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.000-4

Amnay Amimeur, Nhathai Phan, D. Dou, David Kil, B. Piniewski

引用次数: 1

Anomaly Prediction Based on k-Means Clustering for Memory-Constrained Embedded Devices 基于k均值聚类的内存受限嵌入式设备异常预测

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-182

Yuto Kitagawa, Tasuku Ishigoka, Takuya Azumi

{"title":"Anomaly Prediction Based on k-Means Clustering for Memory-Constrained Embedded Devices","authors":"Yuto Kitagawa, Tasuku Ishigoka, Takuya Azumi","doi":"10.1109/ICMLA.2017.0-182","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-182","url":null,"abstract":"This paper proposes an anomaly prediction method based on k-means clustering that assumes embedded devices with memory constraints to predict control system anomalies. With this method, by checking control system behavior, it is possible to predict anomalies. However, continuing clustering is difficult because data accumulate in memory similar to existing k-means clustering method, which is problematic for embedded devices with low memory capacity. Therefore, we also propose k-means clustering to continue clustering for infinite stream data. The proposed k-means clustering method is based on online k-means clustering of sequential processing. The proposed k-means clustering method only stores data required for anomaly prediction and releases other data from memory. Experimental results show that anomalies can be predicted by k-means clustering, and the proposed method can predict anomalies similar to standard k-means clustering while reducing memory consumption. Moreover, the proposed k-means clustering demonstrates better results of anomaly prediction than existing online k-means clustering.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"22 1","pages":"26-33"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74774948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Exploring the Impact of Clone Refactoring on Test Code Size in Object-Oriented Software 探讨面向对象软件中克隆重构对测试代码大小的影响

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00098

M. Badri, L. Badri, Oussama Hachemane, Alexandre Ouellet

{"title":"Exploring the Impact of Clone Refactoring on Test Code Size in Object-Oriented Software","authors":"M. Badri, L. Badri, Oussama Hachemane, Alexandre Ouellet","doi":"10.1109/ICMLA.2017.00098","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00098","url":null,"abstract":"This paper aims at exploring the impact of clone refactoring on the test code size, in terms of number of operations, in object-oriented software. We investigated three research questions: (1) the impact of clone refactoring on three important source code attributes (coupling, complexity and size) that are related to unit testability of classes, (2) the impact of clone refactoring on the test code size, and (3) the variations after clone refactoring in the source code attributes that have the most important impact on the test code size. We used linear regression and three popular machine learning techniques (i.e., k-Nearest Neighbors, Naïve Bayes and Random Forest) to develop predictive and explanatory models. We used data collected from an open source Java software system (ANT) that has been refactored using clone-refactoring techniques. The analyses indicate that there is a strong and positive relationship between clone refactoring and the reduction of the test code size. Results show that: (1) the source code attributes of refactored classes have been significantly improved, (2) the test code size of refactored classes has been significantly reduced, and (3) the variations of the test code size are more influenced by the variations of the complexity and size of refactored classes compared to coupling.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"63 1","pages":"586-592"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75030536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10