July 28, 2019

3138 words 15 mins read

Paper Group ANR 336

Unsupervised learning of dynamical and molecular similarity using variance minimization. Sentiment analysis of twitter data. Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions. Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets. Hierarchical Scene Parsing by Weakly Supervised Learning wi …

Unsupervised learning of dynamical and molecular similarity using variance minimization


Title	Unsupervised learning of dynamical and molecular similarity using variance minimization
Authors	Brooke E. Husic, Vijay S. Pande
Abstract	In this report, we present an unsupervised machine learning method for determining groups of molecular systems according to similarity in their dynamics or structures using Ward’s minimum variance objective function. We first apply the minimum variance clustering to a set of simulated tripeptides using the information theoretic Jensen-Shannon divergence between Markovian transition matrices in order to gain insight into how point mutations affect protein dynamics. Then, we extend the method to partition two chemoinformatic datasets according to structural similarity to motivate a train/validation/test split for supervised learning that avoids overfitting.
Tasks
Published	2017-12-20
URL	http://arxiv.org/abs/1712.07704v1
PDF	http://arxiv.org/pdf/1712.07704v1.pdf
PWC	https://paperswithcode.com/paper/unsupervised-learning-of-dynamical-and
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Sentiment analysis of twitter data


Title	Sentiment analysis of twitter data
Authors	Hamid Bagheri, Md Johirul Islam
Abstract	Social networks are the main resources to gather information about people’s opinion and sentiments towards different topics as they spend hours daily on social media and share their opinion. In this technical paper, we show the application of sentimental analysis and how to connect to Twitter and run sentimental analysis queries. We run experiments on different queries from politics to humanity and show the interesting results. We realized that the neutral sentiments for tweets are significantly high which clearly shows the limitations of the current works.
Tasks	Sentiment Analysis
Published	2017-11-15
URL	http://arxiv.org/abs/1711.10377v2
PDF	http://arxiv.org/pdf/1711.10377v2.pdf
PWC	https://paperswithcode.com/paper/sentiment-analysis-of-twitter-data
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Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions


Title	Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions
Authors	Daniel Lopez Martinez, Ognjen Rudovic, Rosalind Picard
Abstract	Pain is a personal, subjective experience that is commonly evaluated through visual analog scales (VAS). While this is often convenient and useful, automatic pain detection systems can reduce pain score acquisition efforts in large-scale studies by estimating it directly from the participants’ facial expressions. In this paper, we propose a novel two-stage learning approach for VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs) to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels from face images. The estimated scores are then fed into the personalized Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by each person. Personalization of the model is performed using a newly introduced facial expressiveness score, unique for each person. To the best of our knowledge, this is the first approach to automatically estimate VAS from face images. We show the benefits of the proposed personalized over traditional non-personalized approach on a benchmark dataset for pain analysis from face images.
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Published	2017-06-22
URL	http://arxiv.org/abs/1706.07154v2
PDF	http://arxiv.org/pdf/1706.07154v2.pdf
PWC	https://paperswithcode.com/paper/personalized-automatic-estimation-of-self
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Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets


Title	Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets
Authors	Junyu Luo, Yong Xu, Chenwei Tang, Jiancheng Lv
Abstract	The inverse mapping of GANs’(Generative Adversarial Nets) generator has a great potential value.Hence, some works have been developed to construct the inverse function of generator by directly learning or adversarial learning.While the results are encouraging, the problem is highly challenging and the existing ways of training inverse models of GANs have many disadvantages, such as hard to train or poor performance.Due to these reasons, we propose a new approach based on using inverse generator ($IG$) model as encoder and pre-trained generator ($G$) as decoder of an AutoEncoder network to train the $IG$ model. In the proposed model, the difference between the input and output, which are both the generated image of pre-trained GAN’s generator, of AutoEncoder is directly minimized. The optimizing method can overcome the difficulty in training and inverse model of an non one-to-one function.We also applied the inverse model of GANs’ generators to image searching and translation.The experimental results prove that the proposed approach works better than the traditional approaches in image searching.
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Published	2017-03-29
URL	http://arxiv.org/abs/1703.10094v2
PDF	http://arxiv.org/pdf/1703.10094v2.pdf
PWC	https://paperswithcode.com/paper/learning-inverse-mapping-by-autoencoder-based
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Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions


Title	Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions
Authors	Ruimao Zhang, Liang Lin, Guangrun Wang, Meng Wang, Wangmeng Zuo
Abstract	This paper investigates a fundamental problem of scene understanding: how to parse a scene image into a structured configuration (i.e., a semantic object hierarchy with object interaction relations). We propose a deep architecture consisting of two networks: i) a convolutional neural network (CNN) extracting the image representation for pixel-wise object labeling and ii) a recursive neural network (RsNN) discovering the hierarchical object structure and the inter-object relations. Rather than relying on elaborative annotations (e.g., manually labeled semantic maps and relations), we train our deep model in a weakly-supervised learning manner by leveraging the descriptive sentences of the training images. Specifically, we decompose each sentence into a semantic tree consisting of nouns and verb phrases, and apply these tree structures to discover the configurations of the training images. Once these scene configurations are determined, then the parameters of both the CNN and RsNN are updated accordingly by back propagation. The entire model training is accomplished through an Expectation-Maximization method. Extensive experiments show that our model is capable of producing meaningful scene configurations and achieving more favorable scene labeling results on two benchmarks (i.e., PASCAL VOC 2012 and SYSU-Scenes) compared with other state-of-the-art weakly-supervised deep learning methods. In particular, SYSU-Scenes contains more than 5000 scene images with their semantic sentence descriptions, which is created by us for advancing research on scene parsing.
Tasks	Scene Labeling, Scene Parsing, Scene Understanding
Published	2017-09-27
URL	http://arxiv.org/abs/1709.09490v2
PDF	http://arxiv.org/pdf/1709.09490v2.pdf
PWC	https://paperswithcode.com/paper/hierarchical-scene-parsing-by-weakly
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Automatic Error Analysis of Human Motor Performance for Interactive Coaching in Virtual Reality


Title	Automatic Error Analysis of Human Motor Performance for Interactive Coaching in Virtual Reality
Authors	Felix Hülsmann, Stefan Kopp, Mario Botsch
Abstract	In the context of fitness coaching or for rehabilitation purposes, the motor actions of a human participant must be observed and analyzed for errors in order to provide effective feedback. This task is normally carried out by human coaches, and it needs to be solved automatically in technical applications that are to provide automatic coaching (e.g. training environments in VR). However, most coaching systems only provide coarse information on movement quality, such as a scalar value per body part that describes the overall deviation from the correct movement. Further, they are often limited to static body postures or rather simple movements of single body parts. While there are many approaches to distinguish between different types of movements (e.g., between walking and jumping), the detection of more subtle errors in a motor performance is less investigated. We propose a novel approach to classify errors in sports or rehabilitation exercises such that feedback can be delivered in a rapid and detailed manner: Homogeneous sub-sequences of exercises are first temporally aligned via Dynamic Time Warping. Next, we extract a feature vector from the aligned sequences, which serves as a basis for feature selection using Random Forests. The selected features are used as input for Support Vector Machines, which finally classify the movement errors. We compare our algorithm to a well established state-of-the-art approach in time series classification, 1-Nearest Neighbor combined with Dynamic Time Warping, and show our algorithm’s superiority regarding classification quality as well as computational cost.
Tasks	Feature Selection, Time Series, Time Series Classification
Published	2017-09-26
URL	http://arxiv.org/abs/1709.09131v1
PDF	http://arxiv.org/pdf/1709.09131v1.pdf
PWC	https://paperswithcode.com/paper/automatic-error-analysis-of-human-motor
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Title	Firing Cell: An Artificial Neuron with a Simulation of Long-Term-Potentiation-Related Memory
Authors	Jacek Bialowas, Beata Grzyb, Pawel Poszumski
Abstract	We propose a computational model of neuron, called firing cell (FC), properties of which cover such phenomena as attenuation of receptors for external stimuli, delay and decay of postsynaptic potentials, modification of internal weights due to propagation of postsynaptic potentials through the dendrite, modification of properties of the analog memory for each input due to a pattern of short-time synaptic potentiation or long-time synaptic potentiation (LTP), output-spike generation when the sum of all inputs exceeds a threshold, and refraction. The cell may take one of the three forms: excitatory, inhibitory, and receptory. The computer simulations showed that, depending on the phase of input signals, the artificial neuron’s output frequency may demonstrate various chaotic behaviors.
Tasks
Published	2017-04-21
URL	http://arxiv.org/abs/1704.06593v1
PDF	http://arxiv.org/pdf/1704.06593v1.pdf
PWC	https://paperswithcode.com/paper/firing-cell-an-artificial-neuron-with-a
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Indoor Frame Recovery from Refined Line Segments


Title	Indoor Frame Recovery from Refined Line Segments
Authors	Luanzheng Guo, Jun Chu
Abstract	An important yet challenging problem in understanding indoor scene is recovering indoor frame structure from a monocular image. It is more difficult when occlusions and illumination vary, and object boundaries are weak. To overcome these difficulties, a new approach based on line segment refinement with two constraints is proposed. First, the line segments are refined by four consecutive operations, i.e., reclassifying, connecting, fitting, and voting. Specifically, misclassified line segments are revised by the reclassifying operation, some short line segments are joined by the connecting operation, the undetected key line segments are recovered by the fitting operation with the help of the vanishing points, the line segments converging on the frame are selected by the voting operation. Second, we construct four frame models according to four classes of possible shooting angles of the monocular image, the natures of all frame models are introduced via enforcing the cross ratio and depth constraints. The indoor frame is then constructed by fitting those refined line segments with related frame model under the two constraints, which jointly advance the accuracy of the frame. Experimental results on a collection of over 300 indoor images indicate that our algorithm has the capability of recovering the frame from complex indoor scenes.
Tasks
Published	2017-04-30
URL	http://arxiv.org/abs/1705.00279v1
PDF	http://arxiv.org/pdf/1705.00279v1.pdf
PWC	https://paperswithcode.com/paper/indoor-frame-recovery-from-refined-line
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Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks


Title	Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks
Authors	Mohammad Tariqul Islam, Md Abdul Aowal, Ahmed Tahseen Minhaz, Khalid Ashraf
Abstract	Chest X-Rays (CXRs) are widely used for diagnosing abnormalities in the heart and lung area. Automatically detecting these abnormalities with high accuracy could greatly enhance real world diagnosis processes. Lack of standard publicly available dataset and benchmark studies, however, makes it difficult to compare various detection methods. In order to overcome these difficulties, we have used a publicly available Indiana CXR, JSRT and Shenzhen dataset and studied the performance of known deep convolutional network (DCN) architectures on different abnormalities. We find that the same DCN architecture doesn’t perform well across all abnormalities. Shallow features or earlier layers consistently provide higher detection accuracy compared to deep features. We have also found ensemble models to improve classification significantly compared to single model. Combining these insight, we report the highest accuracy on chest X-Ray abnormality detection on these datasets. We find that for cardiomegaly detection, the deep learning method improves the accuracy by a staggering 17 percentage point compared to rule based methods. We applied the techniques to the problem of tuberculosis detection on a different dataset and achieved the highest accuracy. Our localization experiments using these trained classifiers show that for spatially spread out abnormalities like cardiomegaly and pulmonary edema, the network can localize the abnormalities successfully most of the time. One remarkable result of the cardiomegaly localization is that the heart and its surrounding region is most responsible for cardiomegaly detection, in contrast to the rule based models where the ratio of heart and lung area is used as the measure. We believe that through deep learning based classification and localization, we will discover many more interesting features in medical image diagnosis that are not considered traditionally.
Tasks	Anomaly Detection
Published	2017-05-27
URL	http://arxiv.org/abs/1705.09850v3
PDF	http://arxiv.org/pdf/1705.09850v3.pdf
PWC	https://paperswithcode.com/paper/abnormality-detection-and-localization-in
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MAGIX: Model Agnostic Globally Interpretable Explanations


Title	MAGIX: Model Agnostic Globally Interpretable Explanations
Authors	Nikaash Puri, Piyush Gupta, Pratiksha Agarwal, Sukriti Verma, Balaji Krishnamurthy
Abstract	Explaining the behavior of a black box machine learning model at the instance level is useful for building trust. However, it is also important to understand how the model behaves globally. Such an understanding provides insight into both the data on which the model was trained and the patterns that it learned. We present here an approach that learns if-then rules to globally explain the behavior of black box machine learning models that have been used to solve classification problems. The approach works by first extracting conditions that were important at the instance level and then evolving rules through a genetic algorithm with an appropriate fitness function. Collectively, these rules represent the patterns followed by the model for decisioning and are useful for understanding its behavior. We demonstrate the validity and usefulness of the approach by interpreting black box models created using publicly available data sets as well as a private digital marketing data set.
Tasks
Published	2017-06-22
URL	http://arxiv.org/abs/1706.07160v3
PDF	http://arxiv.org/pdf/1706.07160v3.pdf
PWC	https://paperswithcode.com/paper/magix-model-agnostic-globally-interpretable
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A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction


Title	A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction
Authors	Abdelhadi Azzouni, Guy Pujolle
Abstract	Network Traffic Matrix (TM) prediction is defined as the problem of estimating future network traffic from the previous and achieved network traffic data. It is widely used in network planning, resource management and network security. Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that is well-suited to learn from experience to classify, process and predict time series with time lags of unknown size. LSTMs have been shown to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose a LSTM RNN framework for predicting short and long term Traffic Matrix (TM) in large networks. By validating our framework on real-world data from GEANT network, we show that our LSTM models converge quickly and give state of the art TM prediction performance for relatively small sized models.
Tasks	Time Series
Published	2017-05-16
URL	http://arxiv.org/abs/1705.05690v3
PDF	http://arxiv.org/pdf/1705.05690v3.pdf
PWC	https://paperswithcode.com/paper/a-long-short-term-memory-recurrent-neural
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Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)


Title	Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)
Authors	Ronald Salloum, Yuzhuo Ren, C. -C. Jay Kuo
Abstract	In this work, we propose a technique that utilizes a fully convolutional network (FCN) to localize image splicing attacks. We first evaluated a single-task FCN (SFCN) trained only on the surface label. Although the SFCN is shown to provide superior performance over existing methods, it still provides a coarse localization output in certain cases. Therefore, we propose the use of a multi-task FCN (MFCN) that utilizes two output branches for multi-task learning. One branch is used to learn the surface label, while the other branch is used to learn the edge or boundary of the spliced region. We trained the networks using the CASIA v2.0 dataset, and tested the trained models on the CASIA v1.0, Columbia Uncompressed, Carvalho, and the DARPA/NIST Nimble Challenge 2016 SCI datasets. Experiments show that the SFCN and MFCN outperform existing splicing localization algorithms, and that the MFCN can achieve finer localization than the SFCN.
Tasks	Multi-Task Learning
Published	2017-09-06
URL	http://arxiv.org/abs/1709.02016v1
PDF	http://arxiv.org/pdf/1709.02016v1.pdf
PWC	https://paperswithcode.com/paper/image-splicing-localization-using-a-multi
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Heuristic Optimization for Automated Distribution System Planning in Network Integration Studies


Title	Heuristic Optimization for Automated Distribution System Planning in Network Integration Studies
Authors	Alexander Scheidler, Leon Thurner, Martin Braun
Abstract	Network integration studies try to assess the impact of future developments, such as the increase of Renewable Energy Sources or the introduction of Smart Grid Technologies, on large-scale network areas. Goals can be to support strategic alignment in the regulatory framework or to adapt the network planning principles of Distribution System Operators. This study outlines an approach for the automated distribution system planning that can calculate network reconfiguration, reinforcement and extension plans in a fully automated fashion. This allows the estimation of the expected cost in massive probabilistic simulations of large numbers of real networks and constitutes a core component of a framework for large-scale network integration studies. Exemplary case study results are presented that were performed in cooperation with different major distribution system operators. The case studies cover the estimation of expected network reinforcement costs, technical and economical assessment of smart grid technologies and structural network optimisation.
Tasks
Published	2017-11-09
URL	http://arxiv.org/abs/1711.03331v2
PDF	http://arxiv.org/pdf/1711.03331v2.pdf
PWC	https://paperswithcode.com/paper/heuristic-optimization-for-automated
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Deep Learning for Fatigue Estimation on the Basis of Multimodal Human-Machine Interactions


Title	Deep Learning for Fatigue Estimation on the Basis of Multimodal Human-Machine Interactions
Authors	Yuri Gordienko, Sergii Stirenko, Yuriy Kochura, Oleg Alienin, Michail Novotarskiy, Nikita Gordienko
Abstract	The new method is proposed to monitor the level of current physical load and accumulated fatigue by several objective and subjective characteristics. It was applied to the dataset targeted to estimate the physical load and fatigue by several statistical and machine learning methods. The data from peripheral sensors (accelerometer, GPS, gyroscope, magnetometer) and brain-computing interface (electroencephalography) were collected, integrated, and analyzed by several statistical and machine learning methods (moment analysis, cluster analysis, principal component analysis, etc.). The hypothesis 1 was presented and proved that physical activity can be classified not only by objective parameters, but by subjective parameters also. The hypothesis 2 (experienced physical load and subsequent restoration as fatigue level can be estimated quantitatively and distinctive patterns can be recognized) was presented and some ways to prove it were demonstrated. Several “physical load” and “fatigue” metrics were proposed. The results presented allow to extend application of the machine learning methods for characterization of complex human activity patterns (for example, to estimate their actual physical load and fatigue, and give cautions and advice).
Tasks
Published	2017-12-30
URL	http://arxiv.org/abs/1801.06048v1
PDF	http://arxiv.org/pdf/1801.06048v1.pdf
PWC	https://paperswithcode.com/paper/deep-learning-for-fatigue-estimation-on-the
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A Shared Task on Bandit Learning for Machine Translation


Title	A Shared Task on Bandit Learning for Machine Translation
Authors	Artem Sokolov, Julia Kreutzer, Kellen Sunderland, Pavel Danchenko, Witold Szymaniak, Hagen Fürstenau, Stefan Riezler
Abstract	We introduce and describe the results of a novel shared task on bandit learning for machine translation. The task was organized jointly by Amazon and Heidelberg University for the first time at the Second Conference on Machine Translation (WMT 2017). The goal of the task is to encourage research on learning machine translation from weak user feedback instead of human references or post-edits. On each of a sequence of rounds, a machine translation system is required to propose a translation for an input, and receives a real-valued estimate of the quality of the proposed translation for learning. This paper describes the shared task’s learning and evaluation setup, using services hosted on Amazon Web Services (AWS), the data and evaluation metrics, and the results of various machine translation architectures and learning protocols.
Tasks	Machine Translation
Published	2017-07-27
URL	http://arxiv.org/abs/1707.09050v1
PDF	http://arxiv.org/pdf/1707.09050v1.pdf
PWC	https://paperswithcode.com/paper/a-shared-task-on-bandit-learning-for-machine
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