2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)

17-20 Dec. 2018

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  • [Title page i]

    Publication Year: 2018, Page(s): 1
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  • [Title page iii]

    Publication Year: 2018, Page(s): 3
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  • [Copyright notice]

    Publication Year: 2018, Page(s): 4
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  • Table of contents

    Publication Year: 2018, Page(s):5 - 28
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  • Preface

    Publication Year: 2018, Page(s):29 - 30
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  • Organizing Committee

    Publication Year: 2018, Page(s): 31
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  • Program Committees

    Publication Year: 2018, Page(s):32 - 43
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  • Keynotes

    Publication Year: 2018, Page(s):44 - 45
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (106 KB)

    Provides an abstract for each of the keynote presentations and may include a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings. View full abstract»

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  • Inner Attention Based bi-LSTMs with Indexing for non-Factoid Question Answering

    Publication Year: 2018, Page(s):1 - 7
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (416 KB) | HTML iconHTML

    In this paper, we focussed on non-factoid question answering problem using a bidirectional LSTM with an inner attention mechanism and indexing for better accuracy. Non factoid QA is an important task and can be significantly applied in constructing useful knowledge bases and extracting valuable information. The advantage of using Deep Learning frameworks in solving these kind of problems is that i... View full abstract»

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  • Localized Deep Norm-CNN Structure for Face Verification

    Publication Year: 2018, Page(s):8 - 15
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1043 KB) | HTML iconHTML

    Face verification is still a big challenging problem due to the different image conditions such as expression, pose, and illumination. To address these challenges, we propose a new Deep Leaning structure called Localized Deep-Norm CNN. Our model focuses on finding the correlations of features inside the sub region of each learning face by adding a localized feature normalization layer. The model c... View full abstract»

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  • Dynamic Analysis of Executables to Detect and Characterize Malware

    Publication Year: 2018, Page(s):16 - 22
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (219 KB) | HTML iconHTML

    Malware detection and remediation is an on-going task for computer security and IT professionals. Here, we examine the use of neural algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored. We examine several deep learning techniques, and liquid state machines baselined against a random forest. The experiments ex... View full abstract»

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  • Prediction of Sorghum Bicolor Genotype from In-Situ Images Using Autoencoder-Identified SNPs

    Publication Year: 2018, Page(s):23 - 31
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (803 KB) | HTML iconHTML

    Extensive genetic and phenotypic research is necessary for any effective plant breeding program. Such studies, however, require an immense amount of time and resources. In order to expedite the breeding process, we provide a novel method for rapid genotype prediction using in-situ images of plants. In this method, significant single nucleotide polymorphisms (SNPs) are first identified using a nove... View full abstract»

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  • Actionable Pattern Mining - A Scalable Data Distribution Method Based on Information Granules

    Publication Year: 2018, Page(s):32 - 39
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (284 KB) | HTML iconHTML

    Actionable patterns are a form of recommendations that gives knowledge required by the user on actions they need to undertake to attain profit or achieve goals. Action rule is one of the methods to mine actionable patterns that are hidden in a large dataset. In the modern era of big data, organizations are collecting massive amounts of data and they keep the data updated constantly, including in m... View full abstract»

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  • Recursive Feature Elimination by Sensitivity Testing

    Publication Year: 2018, Page(s):40 - 47
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (194 KB) | HTML iconHTML

    There is great interest in methods to improve human insight into trained non-linear models. Leading approaches include producing a ranking of the most relevant features, a non-trivial task for non-linear models. We show theoretically and empirically the benefit of a novel version of recursive feature elimination (RFE) as often used with SVMs; the key idea is a simple twist on the kinds of sensitiv... View full abstract»

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  • Reinforcement Learning Algorithms for Uncertain, Dynamic, Zero-Sum Games

    Publication Year: 2018, Page(s):48 - 54
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (334 KB) | HTML iconHTML

    Dynamic zero-sum games are a model of multiagent decision-making that has been well-studied in the mathematical game theory literature. In this paper, we derive a sufficient condition for the existence of a solution to this problem, and then proceed to discuss various reinforcement learning strategies to solve such a dynamic game in the presence of uncertainty where the game matrices at various st... View full abstract»

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  • Exploring Sentence Vector Spaces through Automatic Summarization

    Publication Year: 2018, Page(s):55 - 60
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (251 KB) | HTML iconHTML

    Given vector representations for individual words, it is necessary to compute vector representations of sentences for many applications in a compositional manner, often using artificial neural networks. Relatively little work has explored the internal structure and properties of such sentence vectors. In this paper, we explore the properties of sentence vectors in the context of automatic summariz... View full abstract»

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  • Trademark Design Code Identification Using Deep Neural Networks

    Publication Year: 2018, Page(s):61 - 65
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (318 KB) | HTML iconHTML

    Trademark review and approval is a complex process that involves thorough analysis and review of the design components of the marks including the visual characteristics as well as the textual mark description data specifying the significant aspects of the mark. One of the crucial aspect in review of the trademark application is determining the design codes of the trademarks based on their mark des... View full abstract»

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  • A Multi-objective Rule Optimizer with an Application to Risk Management

    Publication Year: 2018, Page(s):66 - 72
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    Managing risk is important to any E-commerce merchant. Various machine learning (ML) models combined with a rule set as the decision layer is a common practice to manage the risks. Unlike the ML models that can be automatically refreshed periodically based on new risk patterns, rules are generally static and rely on manual updates. To tackle that, this paper presents a data-driven and automated ru... View full abstract»

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  • Learning to Fingerprint the Latent Structure in Question Articulation

    Publication Year: 2018, Page(s):73 - 80
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (804 KB) | HTML iconHTML

    Algorithmic interpretation of question articulation is one of the key steps in a machine-driven question-answering system. Machine understanding of an input question is tightly related to recognition of articulation in the context of the computational capabilities of an underlying processing algorithm. In this paper, a mathematical model to capture and distinguish the latent structure in the artic... View full abstract»

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  • Time Series Prediction of Agricultural Products Price Based on Time Alignment of Recurrent Neural Networks

    Publication Year: 2018, Page(s):81 - 88
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (965 KB) | HTML iconHTML

    We propose a time series prediction system of agricultural products price based on recurrent neural networks and their time alignment. The purpose of the system is to provide a way to make the price movements stable by sharing the information of price between producers and consumers of agricultural products. For the purpose, we designed a time alignment mechanism of recurrent neural networks to pr... View full abstract»

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  • DeepHCF: A Deep Learning Based Hybrid Collaborative Filtering Approach for Recommendation Systems

    Publication Year: 2018, Page(s):89 - 96
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (204 KB) | HTML iconHTML

    The data sparsity is a significant challenge for collaborative filtering methods in recommendation systems for making accurate recommendations. Several approaches have been published to address this issue. Most of them usually use only one source of data to train their model, and other approaches still have lower performance especially when the sparsity of data is very high. In this paper, we use ... View full abstract»

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  • Financial Markets Prediction with Deep Learning

    Publication Year: 2018, Page(s):97 - 104
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    Financial markets are difficult to predict due to its complex systems dynamics. Although there have been some recent studies that use machine learning techniques for financial markets prediction, they do not offer satisfactory performance on financial returns. We propose a novel one-dimensional convolutional neural networks (CNN) model to predict financial market movement. The customized one-dimen... View full abstract»

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  • ROI Detection in Mammogram Images Using Wavelet-Based Haralick and HOG Features

    Publication Year: 2018, Page(s):105 - 109
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (542 KB) | HTML iconHTML

    Digital mammography is a widespread medical imaging technique that is used for early detection and diagnosis of breast cancer. Detecting the region of interest (ROI) helps to locate the abnormal areas, which may be analyzed further by a radiologist or a CAD system. In this paper, a new classification method is proposed for ROI detection in mammography images. Features are extracted using Wavelet t... View full abstract»

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  • Forecasting Residential Energy Consumption: Single Household Perspective

    Publication Year: 2018, Page(s):110 - 117
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    With the development of smart electricity metering technologies, huge amounts of consumption data can be retrieved on a daily and hourly basis. Energy consumption forecasting facilitates electricity demand management and utilities load planning. Most studies have been focussed on commercial customers or residential building-level energy consumption, or have used behavioral and occupancy sensor dat... View full abstract»

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  • Neural Fingerprint Enhancement

    Publication Year: 2018, Page(s):118 - 124
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (340 KB) | HTML iconHTML

    Biometrics fingerprint matching has been done with a heavily hand-tuned and designed process of classical computer vision techniques for several decades. This approach has lead to accurate solutions solving crimes today, and as such little effort has been placed on using deep learning in this domain. Given that convolutional neural networks have shown dominance for most other image based problems,... View full abstract»

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