Paper Group ANR 500
Forecasting the Success of Television Series using Machine Learning. Android Botnet Detection using Convolutional Neural Networks. Projecting “better than randomly”: How to reduce the dimensionality of very large datasets in a way that outperforms random projections. Constructing the F-Graph with a Symmetric Constraint for Subspace Clustering. Arch …
Forecasting the Success of Television Series using Machine Learning
| Title | Forecasting the Success of Television Series using Machine Learning |
| Authors | Ramya Akula, Zachary Wieselthier, Laura Martin, Ivan Garibay |
| Abstract | Television is an ever-evolving multi billion dollar industry. The success of a television show in an increasingly technological society is a vast multi-variable formula. The art of success is not just something that happens, but is studied, replicated, and applied. Hollywood can be unpredictable regarding success, as many movies and sitcoms that are hyped up and promise to be a hit end up being box office failures and complete disappointments. In current studies, linguistic exploration is being performed on the relationship between Television series and target community of viewers. Having a decision support system that can display sound and predictable results would be needed to build confidence in the investment of a new TV series. The models presented in this study use data to study and determine what makes a sitcom successful. In this paper, we use descriptive and predictive modeling techniques to assess the continuing success of television comedies: The Office, Big Bang Theory, Arrested Development, Scrubs, and South Park. The factors that are tested for statistical significance on episode ratings are character presence, director, and writer. These statistics show that while characters are indeed crucial to the shows themselves, the creation and direction of the shows pose implication upon the ratings and therefore the success of the shows. We use machine learning based forecasting models to accurately predict the success of shows. The models represent a baseline to understanding the success of a television show and how producers can increase the success of current television shows or utilize this data in the creation of future shows. Due to the many factors that go into a series, the empirical analysis in this work shows that there is no one-fits-all model to forecast the rating or success of a television show. |
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| Published | 2019-10-18 |
| URL | https://arxiv.org/abs/1910.12589v1 |
| https://arxiv.org/pdf/1910.12589v1.pdf | |
| PWC | https://paperswithcode.com/paper/forecasting-the-success-of-television-series |
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Android Botnet Detection using Convolutional Neural Networks
| Title | Android Botnet Detection using Convolutional Neural Networks |
| Authors | Sina Hojjatinia, Sajad Hamzenejadi, Hadis Mohseni |
| Abstract | Today, Android devices are able to provide various services. They support applications for different purposes such as entertainment, business, health, education, and banking services. Because of the functionality and popularity of Android devices as well as the open-source policy of Android OS, they have become a suitable target for attackers. Android Botnet is one of the most dangerous malwares because an attacker called Botmaster can control that remotely to perform destructive attacks. A number of researchers have used different well-known Machine Learning (ML) methods to recognize Android Botnets from benign applications. However, these conventional methods are not able to detect new sophisticated Android Botnets. In this paper, we propose a novel method based on Android permissions and Convolutional Neural Networks (CNNs) to classify Botnets and benign Android applications. Being the first developed method that uses CNNs for this aim, we also proposed a novel method to represent each application as an image which is constructed based on the co-occurrence of used permissions in that application. The proposed CNN is a binary classifier that is trained using these images. Evaluating the proposed method on 5450 Android applications consist of Botnet and benign samples, the obtained results show the accuracy of 97.2% and recall of 96% which is a promising result just using Android permissions. |
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| Published | 2019-11-27 |
| URL | https://arxiv.org/abs/1911.12457v1 |
| https://arxiv.org/pdf/1911.12457v1.pdf | |
| PWC | https://paperswithcode.com/paper/android-botnet-detection-using-convolutional |
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Projecting “better than randomly”: How to reduce the dimensionality of very large datasets in a way that outperforms random projections
| Title | Projecting “better than randomly”: How to reduce the dimensionality of very large datasets in a way that outperforms random projections |
| Authors | Michael Wojnowicz, Di Zhang, Glenn Chisholm, Xuan Zhao, Matt Wolff |
| Abstract | For very large datasets, random projections (RP) have become the tool of choice for dimensionality reduction. This is due to the computational complexity of principal component analysis. However, the recent development of randomized principal component analysis (RPCA) has opened up the possibility of obtaining approximate principal components on very large datasets. In this paper, we compare the performance of RPCA and RP in dimensionality reduction for supervised learning. In Experiment 1, study a malware classification task on a dataset with over 10 million samples, almost 100,000 features, and over 25 billion non-zero values, with the goal of reducing the dimensionality to a compressed representation of 5,000 features. In order to apply RPCA to this dataset, we develop a new algorithm called large sample RPCA (LS-RPCA), which extends the RPCA algorithm to work on datasets with arbitrarily many samples. We find that classification performance is much higher when using LS-RPCA for dimensionality reduction than when using random projections. In particular, across a range of target dimensionalities, we find that using LS-RPCA reduces classification error by between 37% and 54%. Experiment 2 generalizes the phenomenon to multiple datasets, feature representations, and classifiers. These findings have implications for a large number of research projects in which random projections were used as a preprocessing step for dimensionality reduction. As long as accuracy is at a premium and the target dimensionality is sufficiently less than the numeric rank of the dataset, randomized PCA may be a superior choice. Moreover, if the dataset has a large number of samples, then LS-RPCA will provide a method for obtaining the approximate principal components. |
| Tasks | Dimensionality Reduction, Malware Classification |
| Published | 2019-01-03 |
| URL | http://arxiv.org/abs/1901.00630v1 |
| http://arxiv.org/pdf/1901.00630v1.pdf | |
| PWC | https://paperswithcode.com/paper/projecting-better-than-randomly-how-to-reduce |
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Constructing the F-Graph with a Symmetric Constraint for Subspace Clustering
| Title | Constructing the F-Graph with a Symmetric Constraint for Subspace Clustering |
| Authors | Kai Xu, Xiao-Jun Wu, Wen-Bo Hu |
| Abstract | Based on further studying the low-rank subspace clustering (LRSC) and L2-graph subspace clustering algorithms, we propose a F-graph subspace clustering algorithm with a symmetric constraint (FSSC), which constructs a new objective function with a symmetric constraint basing on F-norm, whose the most significant advantage is to obtain a closed-form solution of the coefficient matrix. Then, take the absolute value of each element of the coefficient matrix, and retain the k largest coefficients per column, set the other elements to 0, to get a new coefficient matrix. Finally, FSSC performs spectral clustering over the new coefficient matrix. The experimental results on face clustering and motion segmentation show FSSC algorithm can not only obviously reduce the running time, but also achieve higher accuracy compared with the state-of-the-art representation-based subspace clustering algorithms, which verifies that the FSSC algorithm is efficacious and feasible. |
| Tasks | Motion Segmentation |
| Published | 2019-12-17 |
| URL | https://arxiv.org/abs/1912.07871v1 |
| https://arxiv.org/pdf/1912.07871v1.pdf | |
| PWC | https://paperswithcode.com/paper/constructing-the-f-graph-with-a-symmetric |
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Architecture Search by Estimation of Network Structure Distributions
| Title | Architecture Search by Estimation of Network Structure Distributions |
| Authors | Anton Muravev, Jenni Raitoharju, Moncef Gabbouj |
| Abstract | The influence of deep learning is continuously expanding across different domains, and its new applications are ubiquitous. The question of neural network design thus increases in importance, as traditional empirical approaches are reaching their limits. Manual design of network architectures from scratch relies heavily on trial and error, while using existing pretrained models can introduce redundancies or vulnerabilities. Automated neural architecture design is able to overcome these problems, but the most successful algorithms operate on significantly constrained design spaces, assuming the target network to consist of identical repeating blocks. We propose a probabilistic representation of a neural network structure under the assumption of independence between layer types. The probability matrix (prototype) can describe general feedforward architectures and is equivalent to the population of models, while being simple to interpret and analyze. We construct an architecture search algorithm, inspired by the estimation of distribution algorithms, to take advantage of this representation. The probability matrix is tuned towards generating high-performance models by repeatedly sampling the architectures and evaluating the corresponding networks. Our algorithm is shown to discover models which are competitive with those produced by existing architecture search methods, both in accuracy and computational costs, despite the conceptual simplicity and the comparatively limited scope of achievable designs. |
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| Published | 2019-08-19 |
| URL | https://arxiv.org/abs/1908.06886v1 |
| https://arxiv.org/pdf/1908.06886v1.pdf | |
| PWC | https://paperswithcode.com/paper/architecture-search-by-estimation-of-network |
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Concavifiability and convergence: necessary and sufficient conditions for gradient descent analysis
| Title | Concavifiability and convergence: necessary and sufficient conditions for gradient descent analysis |
| Authors | Thulasi Tholeti, Sheetal Kalyani |
| Abstract | Convergence of the gradient descent algorithm has been attracting renewed interest due to its utility in deep learning applications. Even as multiple variants of gradient descent were proposed, the assumption that the gradient of the objective is Lipschitz continuous remained an integral part of the analysis until recently. In this work, we look at convergence analysis by focusing on a property that we term as concavifiability, instead of Lipschitz continuity of gradients. We show that concavifiability is a necessary and sufficient condition to satisfy the upper quadratic approximation which is key in proving that the objective function decreases after every gradient descent update. We also show that any gradient Lipschitz function satisfies concavifiability. A constant known as the concavifier analogous to the gradient Lipschitz constant is derived which is indicative of the optimal step size. As an application, we demonstrate the utility of finding the concavifier the in convergence of gradient descent through an example inspired by neural networks. We derive bounds on the concavifier to obtain a fixed step size for a single hidden layer ReLU network. |
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| Published | 2019-05-28 |
| URL | https://arxiv.org/abs/1905.11620v1 |
| https://arxiv.org/pdf/1905.11620v1.pdf | |
| PWC | https://paperswithcode.com/paper/concavifiability-and-convergence-necessary |
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Learning and Optimization with Bayesian Hybrid Models
| Title | Learning and Optimization with Bayesian Hybrid Models |
| Authors | Elvis A. Eugene, Xian Gao, Alexander W. Dowling |
| Abstract | Bayesian hybrid models fuse physics-based insights with machine learning constructs to correct for systematic bias. In this paper, we compare Bayesian hybrid models against physics-based glass-box and Gaussian process black-box surrogate models. We consider ballistic firing as an illustrative case study for a Bayesian decision-making workflow. First, Bayesian calibration is performed to estimate model parameters. We then use the posterior distribution from Bayesian analysis to compute optimal firing conditions to hit a target via a single-stage stochastic program. The case study demonstrates the ability of Bayesian hybrid models to overcome systematic bias from missing physics with less data than the pure machine learning approach. Ultimately, we argue Bayesian hybrid models are an emerging paradigm for data-informed decision-making under parametric and epistemic uncertainty. |
| Tasks | Calibration, Decision Making |
| Published | 2019-12-12 |
| URL | https://arxiv.org/abs/1912.06269v1 |
| https://arxiv.org/pdf/1912.06269v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-and-optimization-with-bayesian |
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Benchmarking unsupervised near-duplicate image detection
| Title | Benchmarking unsupervised near-duplicate image detection |
| Authors | Lia Morra, Fabrizio Lamberti |
| Abstract | Unsupervised near-duplicate detection has many practical applications ranging from social media analysis and web-scale retrieval, to digital image forensics. It entails running a threshold-limited query on a set of descriptors extracted from the images, with the goal of identifying all possible near-duplicates, while limiting the false positives due to visually similar images. Since the rate of false alarms grows with the dataset size, a very high specificity is thus required, up to $1 - 10^{-9}$ for realistic use cases; this important requirement, however, is often overlooked in literature. In recent years, descriptors based on deep convolutional neural networks have matched or surpassed traditional feature extraction methods in content-based image retrieval tasks. To the best of our knowledge, ours is the first attempt to establish the performance range of deep learning-based descriptors for unsupervised near-duplicate detection on a range of datasets, encompassing a broad spectrum of near-duplicate definitions. We leverage both established and new benchmarks, such as the Mir-Flick Near-Duplicate (MFND) dataset, in which a known ground truth is provided for all possible pairs over a general, large scale image collection. To compare the specificity of different descriptors, we reduce the problem of unsupervised detection to that of binary classification of near-duplicate vs. not-near-duplicate images. The latter can be conveniently characterized using Receiver Operating Curve (ROC). Our findings in general favor the choice of fine-tuning deep convolutional networks, as opposed to using off-the-shelf features, but differences at high specificity settings depend on the dataset and are often small. The best performance was observed on the MFND benchmark, achieving 96% sensitivity at a false positive rate of $1.43 \times 10^{-6}$. |
| Tasks | Content-Based Image Retrieval, Image Retrieval |
| Published | 2019-07-03 |
| URL | https://arxiv.org/abs/1907.02821v1 |
| https://arxiv.org/pdf/1907.02821v1.pdf | |
| PWC | https://paperswithcode.com/paper/benchmarking-unsupervised-near-duplicate |
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Self-Teaching Networks
| Title | Self-Teaching Networks |
| Authors | Liang Lu, Eric Sun, Yifan Gong |
| Abstract | We propose self-teaching networks to improve the generalization capacity of deep neural networks. The idea is to generate soft supervision labels using the output layer for training the lower layers of the network. During the network training, we seek an auxiliary loss that drives the lower layer to mimic the behavior of the output layer. The connection between the two network layers through the auxiliary loss can help the gradient flow, which works similar to the residual networks. Furthermore, the auxiliary loss also works as a regularizer, which improves the generalization capacity of the network. We evaluated the self-teaching network with deep recurrent neural networks on speech recognition tasks, where we trained the acoustic model using 30 thousand hours of data. We tested the acoustic model using data collected from 4 scenarios. We show that the self-teaching network can achieve consistent improvements and outperform existing methods such as label smoothing and confidence penalization. |
| Tasks | Speech Recognition |
| Published | 2019-09-09 |
| URL | https://arxiv.org/abs/1909.04157v1 |
| https://arxiv.org/pdf/1909.04157v1.pdf | |
| PWC | https://paperswithcode.com/paper/self-teaching-networks |
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Deep Spherical Quantization for Image Search
| Title | Deep Spherical Quantization for Image Search |
| Authors | Sepehr Eghbali, Ladan Tahvildari |
| Abstract | Hashing methods, which encode high-dimensional images with compact discrete codes, have been widely applied to enhance large-scale image retrieval. In this paper, we put forward Deep Spherical Quantization (DSQ), a novel method to make deep convolutional neural networks generate supervised and compact binary codes for efficient image search. Our approach simultaneously learns a mapping that transforms the input images into a low-dimensional discriminative space, and quantizes the transformed data points using multi-codebook quantization. To eliminate the negative effect of norm variance on codebook learning, we force the network to L_2 normalize the extracted features and then quantize the resulting vectors using a new supervised quantization technique specifically designed for points lying on a unit hypersphere. Furthermore, we introduce an easy-to-implement extension of our quantization technique that enforces sparsity on the codebooks. Extensive experiments demonstrate that DSQ and its sparse variant can generate semantically separable compact binary codes outperforming many state-of-the-art image retrieval methods on three benchmarks. |
| Tasks | Image Retrieval, Quantization |
| Published | 2019-06-07 |
| URL | https://arxiv.org/abs/1906.02865v1 |
| https://arxiv.org/pdf/1906.02865v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-spherical-quantization-for-image-search-1 |
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Automatic Lesion Boundary Segmentation in Dermoscopic Images with Ensemble Deep Learning Methods
| Title | Automatic Lesion Boundary Segmentation in Dermoscopic Images with Ensemble Deep Learning Methods |
| Authors | Manu Goyal, Amanda Oakley, Priyanka Bansal, Darren Dancey, Moi Hoon Yap |
| Abstract | Early detection of skin cancer, particularly melanoma, is crucial to enable advanced treatment. Due to the rapid growth in the numbers of skin cancers, there is a growing need of computerized analysis for skin lesions. The state-of-the-art public available datasets for skin lesions are often accompanied with very limited amount of segmentation ground truth labeling as it is laborious and expensive. The lesion boundary segmentation is vital to locate the lesion accurately in dermoscopic images and lesion diagnosis of different skin lesion types. In this work, we propose the use of fully automated deep learning ensemble methods for accurate lesion boundary segmentation in dermoscopic images. We trained the Mask-RCNN and DeepLabv3+ methods on ISIC-2017 segmentation training set and evaluate the performance of the ensemble networks on ISIC-2017 testing set. Our results showed that the best proposed ensemble method segmented the skin lesions with Jaccard index of 79.58% for the ISIC-2017 testing set. The proposed ensemble method outperformed FrCN, FCN, U-Net, and SegNet in Jaccard Index by 2.48%, 7.42%, 17.95%, and 9.96% respectively. Furthermore, the proposed ensemble method achieved an accuracy of 95.6% for some representative clinically benign cases, 90.78% for the melanoma cases, and 91.29% for the seborrheic keratosis cases on ISIC-2017 testing set, exhibiting better performance than FrCN, FCN, U-Net, and SegNet. |
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| Published | 2019-02-02 |
| URL | https://arxiv.org/abs/1902.00809v2 |
| https://arxiv.org/pdf/1902.00809v2.pdf | |
| PWC | https://paperswithcode.com/paper/automatic-lesion-boundary-segmentation-in |
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Decision Making guided by Emotion A computational architecture
| Title | Decision Making guided by Emotion A computational architecture |
| Authors | Dominique Béroule, Pascale Gisquet-Verrier |
| Abstract | A computational architecture is presented, in which “swift and fuzzy” emotional channels guide a “slow and precise” decision-making channel. Reported neurobiological studies first provide hints on the representation of both emotional and cognitive dimensions across brain structures, mediated by the neuromodulation system. The related model is based on Guided Propagation Networks, the inner flows of which can be guided through modulation. A key-channel of this model grows from a few emotional cues, and is aimed at anticipating the consequences of ongoing possible actions. Current experimental results of a computer simulation show the integrated contribution of several emotional influences, as well as issues of accidental all-out emotions. |
| Tasks | Decision Making |
| Published | 2019-11-22 |
| URL | https://arxiv.org/abs/1911.09948v1 |
| https://arxiv.org/pdf/1911.09948v1.pdf | |
| PWC | https://paperswithcode.com/paper/decision-making-guided-by-emotion-a |
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Gyroscope-aided Relative Pose Estimation for Rolling Shutter Cameras
| Title | Gyroscope-aided Relative Pose Estimation for Rolling Shutter Cameras |
| Authors | Chang-Ryeol Lee, Ju Hong Yoon, Min-Gyu Park, Kuk-Jin Yoon |
| Abstract | The rolling shutter camera has received great attention due to its low cost imaging capability, however, the estimation of relative pose between rolling shutter cameras still remains a difficult problem owing to its line-by-line image capturing characteristics. To alleviate this problem, we exploit gyroscope measurements, angular velocity, along with image measurement to compute the relative pose between rolling shutter cameras. The gyroscope measurements provide the information about instantaneous motion that causes the rolling shutter distortion. Having gyroscope measurements in one hand, we simplify the relative pose estimation problem and find a minimal solution for the problem based on the Grobner basis polynomial solver. The proposed method requires only five points to compute relative pose between rolling shutter cameras, whereas previous methods require 20 or 44 corresponding points for linear and uniform rolling shutter geometry models, respectively. Experimental results on synthetic and real data verify the superiority of the proposed method over existing relative pose estimation methods. |
| Tasks | Pose Estimation |
| Published | 2019-04-14 |
| URL | http://arxiv.org/abs/1904.06770v1 |
| http://arxiv.org/pdf/1904.06770v1.pdf | |
| PWC | https://paperswithcode.com/paper/gyroscope-aided-relative-pose-estimation-for |
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META$^\mathbf{2}$: Memory-efficient taxonomic classification and abundance estimation for metagenomics with deep learning
| Title | META$^\mathbf{2}$: Memory-efficient taxonomic classification and abundance estimation for metagenomics with deep learning |
| Authors | Andreas Georgiou, Vincent Fortuin, Harun Mustafa, Gunnar Rätsch |
| Abstract | Metagenomic studies have increasingly utilized sequencing technologies in order to analyze DNA fragments found in environmental samples.One important step in this analysis is the taxonomic classification of the DNA fragments. Conventional read classification methods require large databases and vast amounts of memory to run, with recent deep learning methods suffering from very large model sizes. We therefore aim to develop a more memory-efficient technique for taxonomic classification. A task of particular interest is abundance estimation in metagenomic samples. Current attempts rely on classifying single DNA reads independently from each other and are therefore agnostic to co-occurence patterns between taxa. In this work, we also attempt to take these patterns into account. We develop a novel memory-efficient read classification technique, combining deep learning and locality-sensitive hashing. We show that this approach outperforms conventional mapping-based and other deep learning methods for single-read taxonomic classification when restricting all methods to a fixed memory footprint. Moreover, we formulate the task of abundance estimation as a Multiple Instance Learning (MIL) problem and we extend current deep learning architectures with two different types of permutation-invariant MIL pooling layers: a) deepsets and b) attention-based pooling. We illustrate that our architectures can exploit the co-occurrence of species in metagenomic read sets and outperform the single-read architectures in predicting the distribution over taxa at higher taxonomic ranks. |
| Tasks | Multiple Instance Learning |
| Published | 2019-09-28 |
| URL | https://arxiv.org/abs/1909.13146v2 |
| https://arxiv.org/pdf/1909.13146v2.pdf | |
| PWC | https://paperswithcode.com/paper/deep-multiple-instance-learning-for-taxonomic |
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Deep Neural Networks Abstract Like Humans
| Title | Deep Neural Networks Abstract Like Humans |
| Authors | Alex Gain, Hava Siegelmann |
| Abstract | Deep neural networks (DNNs) have revolutionized AI due to their remarkable performance in pattern recognition, comprising of both memorizing complex training sets and demonstrating intelligence by generalizing to previously unseen data (test sets). The high generalization performance in DNNs has been explained by several mathematical tools, including optimization, information theory, and resilience analysis. In humans, it is the ability to abstract concepts from examples that facilitates generalization; this paper thus researches DNN generalization from that perspective. A recent computational neuroscience study revealed a correlation between abstraction and particular neural firing patterns. We express these brain patterns in a closed-form mathematical expression, termed the `Cognitive Neural Activation metric’ (CNA) and apply it to DNNs. Our findings reveal parallels in the mechanism underlying abstraction in DNNs and those in the human brain. Beyond simply measuring similarity to human abstraction, the CNA is able to predict and rate how well a DNN will perform on test sets, and determines the best network architectures for a given task in a manner not possible with extant tools. These results were validated on a broad range of datasets (including ImageNet and random labeled datasets) and neural architectures. | |
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| Published | 2019-05-27 |
| URL | https://arxiv.org/abs/1905.11515v1 |
| https://arxiv.org/pdf/1905.11515v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-neural-networks-abstract-like-humans |
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