2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第2页

A Hybrid Machine Learning Approach for Planning Safe Trajectories in Complex Traffic-Scenarios 混合机器学习方法在复杂交通场景下的安全轨迹规划

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0095

Amit Chaulwar, M. Botsch, W. Utschick

{"title":"A Hybrid Machine Learning Approach for Planning Safe Trajectories in Complex Traffic-Scenarios","authors":"Amit Chaulwar, M. Botsch, W. Utschick","doi":"10.1109/ICMLA.2016.0095","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0095","url":null,"abstract":"Planning of safe trajectories with interventions in both lateral and longitudinal dynamics of vehicles has huge potential for increasing the road traffic safety. Main challenges for the development of such algorithms are the consideration of vehicle nonholonomic constraints and the efficiency in terms of implementation, so that algorithms run in real time in a vehicle. The recently introduced Augmented CL-RRT algorithm is an approach that uses analytical models for trajectory planning based on the brute force evaluation of many longitudinal acceleration profiles to find collision-free trajectories. The algorithm considers nonholonomic constraints of the vehicle in complex road traffic scenarios with multiple static and dynamic objects, but it requires a lot of computation time. This work proposes a hybrid machine learning approach for predicting suitable acceleration profiles in critical traffic scenarios, so that only few acceleration profiles are used with the Augmented CL-RRT to find a safe trajectory while reducing the computation time. This is realized using a convolutional neural network variant, introduced as 3D-ConvNet, which learns spatiotemporal features from a sequence of predicted occupancy grids generated from predictions of other road traffic participants. These learned features together with hand-designed features of the EGO vehicle are used to predict acceleration profiles. Simulations are performed to compare the brute force approach with the proposed approach in terms of efficiency and safety. The results show vast improvement in terms of efficiency without harming safety. Additionally, an extension to the Augmented CL-RRT algorithm is introduced for finding a trajectory with low severity of injury, if a collision is already unavoidable.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122264999","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: 11

Automatic Species Recognition Based on Improved Birdsong Analysis 基于改进鸟鸣分析的物种自动识别

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0037

Joshua Knapp, Guangzhi Qu, Feng Zhang

引用次数: 4

Preference Aware Recommendation Based on Categorical Information 基于分类信息的偏好感知推荐

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0155

Zhiwei Rao, Jiangchao Yao, Ya Zhang, Rui Zhang

{"title":"Preference Aware Recommendation Based on Categorical Information","authors":"Zhiwei Rao, Jiangchao Yao, Ya Zhang, Rui Zhang","doi":"10.1109/ICMLA.2016.0155","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0155","url":null,"abstract":"Contextual aware matrix factorization has been widely used in recommender systems by learning latent feature vectors of users and items along with contextual information. While most of them add identical bias for each type of side information to represent systematic tendencies in users' rating behaviors, they are not able to capture the preference unique to users or items. In this paper, we propose a probabilistic generative model which allows the bias to vary among different types of users or items. We first use Gaussian Mixture Components to cluster the users (or items) based on corresponding latent feature vectors respectively. Biases are then distributed on these clusters along with categorical side information. Finally, they are jointed with latent feature vectors of the users and items to affect the generation of observed ratings. Experiments on MovieLens-100K and MovieLens-1M data sets have shown promising results compared with state-of-the-art contextual aware recommendation approaches. We also qualitatively analyze the preferences of users and items and demonstrate differences in preference among both users and items.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133977283","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

A Big Data Analytics Framework for Supporting Multidimensional Mining over Big Healthcare Data 支持医疗大数据多维挖掘的大数据分析框架

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0090

Mario Alessandro Bochicchio, A. Cuzzocrea, L. Vaira

{"title":"A Big Data Analytics Framework for Supporting Multidimensional Mining over Big Healthcare Data","authors":"Mario Alessandro Bochicchio, A. Cuzzocrea, L. Vaira","doi":"10.1109/ICMLA.2016.0090","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0090","url":null,"abstract":"Nowadays, a great deal of attention is being devoted to big data analytics in complex healthcare environments. Fetal growth curves, which are a classical case of big healthcare data, are used in prenatal medicine to early detect potential fetal growth problems, estimate the perinatal outcome and promptly treat possible complications. However, the currently adopted curves and the related diagnostic techniques have been criticized because of their poor precision. New techniques, based on the idea of customized growth curves, have been proposed in literature. In this perspective, the problem of building customized or personalized fetal growth curves by means of big data techniques is discussed in this paper. The proposed framework introduces the idea of summarizing the massive amounts of (input) big data via multidimensional views on top of which well-known Data Mining methods like clustering and classification are applied. This overall defines a multidimensional mining approach, targeted to complex healthcare environments. A preliminary analysis on the effectiveness of the framework is also proposed.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125214295","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: 18

A Nonnegative Tensor Factorization Approach for Three-Dimensional Binary Wafer-Test Data 三维二元晶圆试验数据的非负张量分解方法

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0151

T. Siegert, R. Schachtner, G. Pöppel, E. Lang

引用次数: 2

Validation of a Quantifier-Based Fuzzy Classification System for Breast Cancer Patients on External Independent Cohorts 基于量化因子的乳腺癌患者模糊分类系统的外部独立队列验证

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0101

D. Soria, J. Garibaldi

{"title":"Validation of a Quantifier-Based Fuzzy Classification System for Breast Cancer Patients on External Independent Cohorts","authors":"D. Soria, J. Garibaldi","doi":"10.1109/ICMLA.2016.0101","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0101","url":null,"abstract":"Recent studies in breast cancer domains have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a variety of unsupervised learning techniques. Consensus among the clustering algorithms has been used to categorise patients into these specific groups, but often at the expenses of not classifying all patients. It is known that fuzzy methodologies can provide linguistic based classification rules to ease those from consensus clustering. The objective of this study is to present the validation of a recently developed extension of a fuzzy quantification subsethood-based algorithm on three sets of newly available breast cancer data. Results show that our algorithm is able to reproduce the seven biological classes previously identified, preserving their characterisation in terms of marker distributions and therefore their clinical meaning. Moreover, because our algorithm constitutes the fundamental basis of the newly developed Nottingham Prognostic Index Plus (NPI+), our findings demonstrate that this new medical decision making tool can help moving towards a more tailored care in breast cancer.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131261360","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

Machine Learning for Plant Disease Incidence and Severity Measurements from Leaf Images 基于叶片图像的植物疾病发生率和严重程度测量的机器学习

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0034

Ernest Mwebaze, Godliver Owomugisha

{"title":"Machine Learning for Plant Disease Incidence and Severity Measurements from Leaf Images","authors":"Ernest Mwebaze, Godliver Owomugisha","doi":"10.1109/ICMLA.2016.0034","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0034","url":null,"abstract":"In many fields, superior gains have been obtained by leveraging the computational power of machine learning techniques to solve expert tasks. In this paper we present an application of machine learning to agriculture, solving a particular problem of diagnosis of crop disease based on plant images taken with a smartphone. Two pieces of information are important here, the disease incidence and disease severity. We present a classification system that trains a 5 class classification system to determine the state of disease of a plant. The 5 classes represent a health class and 4 disease classes. We further extend the classification system to classify different severity levels for any of the 4 diseases. Severity levels are assigned classes 1 - 5, 1 being a healthy plant, 5 being a severely diseased plant. We present ways of extracting different features from leaf images and show how different extraction methods result in different performance of the classifier. We finally present the smartphone-based system that uses the classification model learnt to do real-time prediction of the state of health of a farmers garden. This works by the farmer uploading an image of a plant in his garden and obtaining a disease score from a remote server.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133249202","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: 96

Uncovering the Landscape of Fraud and Spam in the Telephony Channel 揭露诈骗和垃圾邮件在电话频道的景观

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0153

A. Marzuoli, H. Kingravi, David Dewey, Robert S. Pienta

{"title":"Uncovering the Landscape of Fraud and Spam in the Telephony Channel","authors":"A. Marzuoli, H. Kingravi, David Dewey, Robert S. Pienta","doi":"10.1109/ICMLA.2016.0153","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0153","url":null,"abstract":"Robocalling, voice phishing, and caller ID spoofing are common cybercrime techniques used to launch scam campaigns through the telephony channel, which unsuspecting users have long trusted. More reliable than online complaints, a telephony honeypot provides complete, accurate and timely information about unwanted phone calls across the United States. Our first goal is to provide a large-scale data-driven analysis of the telephony spam and fraud ecosystem. Our second goal is to uniquely identify bad actors potentially operating several phone numbers. We collected about 40,000 unsolicited calls. Our results show that only a few bad actors, robocallers or telemarketers, are responsible for the majority of the spam and scam calls, and that they can be uniquely identified based on audio features from their calls. This discovery has major implications for law enforcement and businesses that are presently engaged in combatting the rise of telephony fraud. In particular, since our system allows endusers to detect fraudulent behavior and tie it back to existing fraud and spam campaigns, it can be used as the first step towards designing and deploying intelligent defense strategies.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"213 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123158860","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

System-Level Test Case Prioritization Using Machine Learning 使用机器学习的系统级测试用例优先级

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0065

R. Lachmann, Sandro Schulze, Manuel Nieke, C. Seidl, Ina Schaefer

{"title":"System-Level Test Case Prioritization Using Machine Learning","authors":"R. Lachmann, Sandro Schulze, Manuel Nieke, C. Seidl, Ina Schaefer","doi":"10.1109/ICMLA.2016.0065","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0065","url":null,"abstract":"Regression testing is the common task of retesting software that has been changed or extended (e.g., by new features) during software evolution. As retesting the whole program is not feasible with reasonable time and cost, usually only a subset of all test cases is executed for regression testing, e.g., by executing test cases according to test case prioritization. Although a vast amount of methods for test case prioritization exist, they mostly require access to source code (i.e., white-box). However, in industrial practice, system-level testing is an important task that usually grants no access to source code (i.e., black-box). Hence, for an effective regression testing process, other information has to be employed. In this paper, we introduce a novel technique for test case prioritization for manual system-level regression testing based on supervised machine learning. Our approach considers black-box meta-data, such as test case history, as well as natural language test case descriptions for prioritization. We use the machine learning algorithm SVM Rank to evaluate our approach by means of two subject systems and measure the prioritization quality. Our results imply that our technique improves the failure detection rate significantly compared to a random order. In addition, we are able to outperform a test case order given by a test expert. Moreover, using natural language descriptions improves the failure finding rate.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123172170","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: 47

Toward Parametric Security Analysis of Machine Learning Based Cyber Forensic Biometric Systems 基于机器学习的网络法医生物识别系统参数安全性分析

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0110

Koosha Sadeghi, Ayan Banerjee, Javad Sohankar, S. Gupta

引用次数: 11