Paper Group ANR 75
An HTM based cortical algorithm for detection of seismic waves. Representing Sentences as Low-Rank Subspaces. Traffic Lights with Auction-Based Controllers: Algorithms and Real-World Data. A Question Answering Approach to Emotion Cause Extraction. Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Fac …
An HTM based cortical algorithm for detection of seismic waves
| Title | An HTM based cortical algorithm for detection of seismic waves |
| Authors | Ruggero Micheletto, Ahyi Kim |
| Abstract | Recognizing seismic waves immediately is very important for the realization of efficient disaster prevention. Generally these systems consist of a network of seismic detectors that send real time data to a central server. The server elaborates the data and attempts to recognize the first signs of an earthquake. The current problem with this approach is that it is subject to false alarms. A critical trade-off exists between sensitivity of the system and error rate. To overcame this problems, an artificial neural network based intelligent learning systems can be used. However, conventional supervised ANN systems are difficult to train, CPU intensive and prone to false alarms. To surpass these problems, here we attempt to use a next-generation unsupervised cortical algorithm HTM. This novel approach does not learn particular waveforms, but adapts to continuously fed data reaching the ability to discriminate between normality (seismic sensor background noise in no-earthquake conditions) and anomaly (sensor response to a jitter or an earthquake). Main goal of this study is test the ability of the HTM algorithm to be used to signal earthquakes automatically in a feasible disaster prevention system. We describe the methodology used and give the first qualitative assessments of the recognition ability of the system. Our preliminary results show that the cortical algorithm used is very robust to noise and that can successfully recognize synthetic earthquake-like signals efficiently and reliably. |
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| Published | 2017-07-06 |
| URL | http://arxiv.org/abs/1707.01642v1 |
| http://arxiv.org/pdf/1707.01642v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-htm-based-cortical-algorithm-for-detection |
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Representing Sentences as Low-Rank Subspaces
| Title | Representing Sentences as Low-Rank Subspaces |
| Authors | Jiaqi Mu, Suma Bhat, Pramod Viswanath |
| Abstract | Sentences are important semantic units of natural language. A generic, distributional representation of sentences that can capture the latent semantics is beneficial to multiple downstream applications. We observe a simple geometry of sentences – the word representations of a given sentence (on average 10.23 words in all SemEval datasets with a standard deviation 4.84) roughly lie in a low-rank subspace (roughly, rank 4). Motivated by this observation, we represent a sentence by the low-rank subspace spanned by its word vectors. Such an unsupervised representation is empirically validated via semantic textual similarity tasks on 19 different datasets, where it outperforms the sophisticated neural network models, including skip-thought vectors, by 15% on average. |
| Tasks | Semantic Textual Similarity |
| Published | 2017-04-18 |
| URL | http://arxiv.org/abs/1704.05358v1 |
| http://arxiv.org/pdf/1704.05358v1.pdf | |
| PWC | https://paperswithcode.com/paper/representing-sentences-as-low-rank-subspaces |
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Traffic Lights with Auction-Based Controllers: Algorithms and Real-World Data
| Title | Traffic Lights with Auction-Based Controllers: Algorithms and Real-World Data |
| Authors | Shumeet Baluja, Michele Covell, Rahul Sukthankar |
| Abstract | Real-time optimization of traffic flow addresses important practical problems: reducing a driver’s wasted time, improving city-wide efficiency, reducing gas emissions and improving air quality. Much of the current research in traffic-light optimization relies on extending the capabilities of traffic lights to either communicate with each other or communicate with vehicles. However, before such capabilities become ubiquitous, opportunities exist to improve traffic lights by being more responsive to current traffic situations within the current, already deployed, infrastructure. In this paper, we introduce a traffic light controller that employs bidding within micro-auctions to efficiently incorporate traffic sensor information; no other outside sources of information are assumed. We train and test traffic light controllers on large-scale data collected from opted-in Android cell-phone users over a period of several months in Mountain View, California and the River North neighborhood of Chicago, Illinois. The learned auction-based controllers surpass (in both the relevant metrics of road-capacity and mean travel time) the currently deployed lights, optimized static-program lights, and longer-term planning approaches, in both cities, measured using real user driving data. |
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| Published | 2017-02-03 |
| URL | http://arxiv.org/abs/1702.01205v1 |
| http://arxiv.org/pdf/1702.01205v1.pdf | |
| PWC | https://paperswithcode.com/paper/traffic-lights-with-auction-based-controllers |
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A Question Answering Approach to Emotion Cause Extraction
| Title | A Question Answering Approach to Emotion Cause Extraction |
| Authors | Lin Gui, Jiannan Hu, Yulan He, Ruifeng Xu, Qin Lu, Jiachen Du |
| Abstract | Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural networks, we propose a new mechanism to store relevant context in different memory slots to model context information. Our proposed approach can extract both word level sequence features and lexical features. Performance evaluation shows that our method achieves the state-of-the-art performance on a recently released emotion cause dataset, outperforming a number of competitive baselines by at least 3.01% in F-measure. |
| Tasks | Emotion Classification, Question Answering, Reading Comprehension |
| Published | 2017-08-18 |
| URL | http://arxiv.org/abs/1708.05482v2 |
| http://arxiv.org/pdf/1708.05482v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-question-answering-approach-to-emotion |
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Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection
| Title | Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection |
| Authors | Yue Wu, Qiang Ji |
| Abstract | Cascade regression framework has been shown to be effective for facial landmark detection. It starts from an initial face shape and gradually predicts the face shape update from the local appearance features to generate the facial landmark locations in the next iteration until convergence. In this paper, we improve upon the cascade regression framework and propose the Constrained Joint Cascade Regression Framework (CJCRF) for simultaneous facial action unit recognition and facial landmark detection, which are two related face analysis tasks, but are seldomly exploited together. In particular, we first learn the relationships among facial action units and face shapes as a constraint. Then, in the proposed constrained joint cascade regression framework, with the help from the constraint, we iteratively update the facial landmark locations and the action unit activation probabilities until convergence. Experimental results demonstrate that the intertwined relationships of facial action units and face shapes boost the performances of both facial action unit recognition and facial landmark detection. The experimental results also demonstrate the effectiveness of the proposed method comparing to the state-of-the-art works. |
| Tasks | Facial Action Unit Detection, Facial Landmark Detection |
| Published | 2017-09-23 |
| URL | http://arxiv.org/abs/1709.08129v1 |
| http://arxiv.org/pdf/1709.08129v1.pdf | |
| PWC | https://paperswithcode.com/paper/constrained-joint-cascade-regression |
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Crowd Sourcing Image Segmentation with iaSTAPLE
| Title | Crowd Sourcing Image Segmentation with iaSTAPLE |
| Authors | Dmitrij Schlesinger, Florian Jug, Gene Myers, Carsten Rother, Dagmar Kainmüller |
| Abstract | We propose a novel label fusion technique as well as a crowdsourcing protocol to efficiently obtain accurate epithelial cell segmentations from non-expert crowd workers. Our label fusion technique simultaneously estimates the true segmentation, the performance levels of individual crowd workers, and an image segmentation model in the form of a pairwise Markov random field. We term our approach image-aware STAPLE (iaSTAPLE) since our image segmentation model seamlessly integrates into the well-known and widely used STAPLE approach. In an evaluation on a light microscopy dataset containing more than 5000 membrane labeled epithelial cells of a fly wing, we show that iaSTAPLE outperforms STAPLE in terms of segmentation accuracy as well as in terms of the accuracy of estimated crowd worker performance levels, and is able to correctly segment 99% of all cells when compared to expert segmentations. These results show that iaSTAPLE is a highly useful tool for crowd sourcing image segmentation. |
| Tasks | Semantic Segmentation |
| Published | 2017-02-21 |
| URL | http://arxiv.org/abs/1702.06461v1 |
| http://arxiv.org/pdf/1702.06461v1.pdf | |
| PWC | https://paperswithcode.com/paper/crowd-sourcing-image-segmentation-with |
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Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container
| Title | Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container |
| Authors | Nataniel Ruiz, James M. Rehg |
| Abstract | Face detection is a very important task and a necessary pre-processing step for many applications such as facial landmark detection, pose estimation, sentiment analysis and face recognition. Not only is face detection an important pre-processing step in computer vision applications but also in computational psychology, behavioral imaging and other fields where researchers might not be initiated in computer vision frameworks and state-of-the-art detection applications. A large part of existing research that includes face detection as a pre-processing step uses existing out-of-the-box detectors such as the HoG-based dlib and the OpenCV Haar face detector which are no longer state-of-the-art - they are primarily used because of their ease of use and accessibility. We introduce Dockerface, a very accurate Faster R-CNN face detector in a Docker container which requires no training and is easy to install and use. |
| Tasks | Face Detection, Face Recognition, Facial Landmark Detection, Pose Estimation, Sentiment Analysis |
| Published | 2017-08-15 |
| URL | http://arxiv.org/abs/1708.04370v2 |
| http://arxiv.org/pdf/1708.04370v2.pdf | |
| PWC | https://paperswithcode.com/paper/dockerface-an-easy-to-install-and-use-faster |
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Efficient, Safe, and Probably Approximately Complete Learning of Action Models
| Title | Efficient, Safe, and Probably Approximately Complete Learning of Action Models |
| Authors | Roni Stern, Brendan Juba |
| Abstract | In this paper we explore the theoretical boundaries of planning in a setting where no model of the agent’s actions is given. Instead of an action model, a set of successfully executed plans are given and the task is to generate a plan that is safe, i.e., guaranteed to achieve the goal without failing. To this end, we show how to learn a conservative model of the world in which actions are guaranteed to be applicable. This conservative model is then given to an off-the-shelf classical planner, resulting in a plan that is guaranteed to achieve the goal. However, this reduction from a model-free planning to a model-based planning is not complete: in some cases a plan will not be found even when such exists. We analyze the relation between the number of observed plans and the likelihood that our conservative approach will indeed fail to solve a solvable problem. Our analysis show that the number of trajectories needed scales gracefully. |
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| Published | 2017-05-24 |
| URL | http://arxiv.org/abs/1705.08961v1 |
| http://arxiv.org/pdf/1705.08961v1.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-safe-and-probably-approximately |
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Density Estimation for Geolocation via Convolutional Mixture Density Network
| Title | Density Estimation for Geolocation via Convolutional Mixture Density Network |
| Authors | Hayate Iso, Shoko Wakamiya, Eiji Aramaki |
| Abstract | Nowadays, geographic information related to Twitter is crucially important for fine-grained applications. However, the amount of geographic information avail- able on Twitter is low, which makes the pursuit of many applications challenging. Under such circumstances, estimating the location of a tweet is an important goal of the study. Unlike most previous studies that estimate the pre-defined district as the classification task, this study employs a probability distribution to represent richer information of the tweet, not only the location but also its ambiguity. To realize this modeling, we propose the convolutional mixture density network (CMDN), which uses text data to estimate the mixture model parameters. Experimentally obtained results reveal that CMDN achieved the highest prediction performance among the method for predicting the exact coordinates. It also provides a quantitative representation of the location ambiguity for each tweet that properly works for extracting the reliable location estimations. |
| Tasks | Density Estimation |
| Published | 2017-05-08 |
| URL | http://arxiv.org/abs/1705.02750v1 |
| http://arxiv.org/pdf/1705.02750v1.pdf | |
| PWC | https://paperswithcode.com/paper/density-estimation-for-geolocation-via |
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Geometric Semantic Genetic Programming Algorithm and Slump Prediction
| Title | Geometric Semantic Genetic Programming Algorithm and Slump Prediction |
| Authors | Juncai Xu, Zhenzhong Shen, Qingwen Ren, Xin Xie, Zhengyu Yang |
| Abstract | Research on the performance of recycled concrete as building material in the current world is an important subject. Given the complex composition of recycled concrete, conventional methods for forecasting slump scarcely obtain satisfactory results. Based on theory of nonlinear prediction method, we propose a recycled concrete slump prediction model based on geometric semantic genetic programming (GSGP) and combined it with recycled concrete features. Tests show that the model can accurately predict the recycled concrete slump by using the established prediction model to calculate the recycled concrete slump with different mixing ratios in practical projects and by comparing the predicted values with the experimental values. By comparing the model with several other nonlinear prediction models, we can conclude that GSGP has higher accuracy and reliability than conventional methods. |
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| Published | 2017-09-18 |
| URL | http://arxiv.org/abs/1709.06114v2 |
| http://arxiv.org/pdf/1709.06114v2.pdf | |
| PWC | https://paperswithcode.com/paper/geometric-semantic-genetic-programming |
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On Adaptive Estimation for Dynamic Bernoulli Bandits
| Title | On Adaptive Estimation for Dynamic Bernoulli Bandits |
| Authors | Xue Lu, Niall Adams, Nikolas Kantas |
| Abstract | The multi-armed bandit (MAB) problem is a classic example of the exploration-exploitation dilemma. It is concerned with maximising the total rewards for a gambler by sequentially pulling an arm from a multi-armed slot machine where each arm is associated with a reward distribution. In static MABs, the reward distributions do not change over time, while in dynamic MABs, each arm’s reward distribution can change, and the optimal arm can switch over time. Motivated by many real applications where rewards are binary, we focus on dynamic Bernoulli bandits. Standard methods like $\epsilon$-Greedy and Upper Confidence Bound (UCB), which rely on the sample mean estimator, often fail to track changes in the underlying reward for dynamic problems. In this paper, we overcome the shortcoming of slow response to change by deploying adaptive estimation in the standard methods and propose a new family of algorithms, which are adaptive versions of $\epsilon$-Greedy, UCB, and Thompson sampling. These new methods are simple and easy to implement. Moreover, they do not require any prior knowledge about the dynamic reward process, which is important for real applications. We examine the new algorithms numerically in different scenarios and the results show solid improvements of our algorithms in dynamic environments. |
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| Published | 2017-12-08 |
| URL | http://arxiv.org/abs/1712.03134v2 |
| http://arxiv.org/pdf/1712.03134v2.pdf | |
| PWC | https://paperswithcode.com/paper/on-adaptive-estimation-for-dynamic-bernoulli |
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Could you guess an interesting movie from the posters?: An evaluation of vision-based features on movie poster database
| Title | Could you guess an interesting movie from the posters?: An evaluation of vision-based features on movie poster database |
| Authors | Yuta Matsuzaki, Kazushige Okayasu, Takaaki Imanari, Naomichi Kobayashi, Yoshihiro Kanehara, Ryousuke Takasawa, Akio Nakamura, Hirokatsu Kataoka |
| Abstract | In this paper, we aim to estimate the Winner of world-wide film festival from the exhibited movie poster. The task is an extremely challenging because the estimation must be done with only an exhibited movie poster, without any film ratings and box-office takings. In order to tackle this problem, we have created a new database which is consist of all movie posters included in the four biggest film festivals. The movie poster database (MPDB) contains historic movies over 80 years which are nominated a movie award at each year. We apply a couple of feature types, namely hand-craft, mid-level and deep feature to extract various information from a movie poster. Our experiments showed suggestive knowledge, for example, the Academy award estimation can be better rate with a color feature and a facial emotion feature generally performs good rate on the MPDB. The paper may suggest a possibility of modeling human taste for a movie recommendation. |
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| Published | 2017-04-07 |
| URL | http://arxiv.org/abs/1704.02199v1 |
| http://arxiv.org/pdf/1704.02199v1.pdf | |
| PWC | https://paperswithcode.com/paper/could-you-guess-an-interesting-movie-from-the |
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Sentiment Identification in Code-Mixed Social Media Text
| Title | Sentiment Identification in Code-Mixed Social Media Text |
| Authors | Souvick Ghosh, Satanu Ghosh, Dipankar Das |
| Abstract | Sentiment analysis is the Natural Language Processing (NLP) task dealing with the detection and classification of sentiments in texts. While some tasks deal with identifying the presence of sentiment in the text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as positive, negative and neutral. Whenever there is a presence of sentiment in the text, it has a source (people, group of people or any entity) and the sentiment is directed towards some entity, object, event or person. Sentiment analysis tasks aim to determine the subject, the target and the polarity or valence of the sentiment. In our work, we try to automatically extract sentiment (positive or negative) from Facebook posts using a machine learning approach.While some works have been done in code-mixed social media data and in sentiment analysis separately, our work is the first attempt (as of now) which aims at performing sentiment analysis of code-mixed social media text. We have used extensive pre-processing to remove noise from raw text. Multilayer Perceptron model has been used to determine the polarity of the sentiment. We have also developed the corpus for this task by manually labeling Facebook posts with their associated sentiments. |
| Tasks | Sentiment Analysis, Subjectivity Analysis |
| Published | 2017-07-04 |
| URL | http://arxiv.org/abs/1707.01184v1 |
| http://arxiv.org/pdf/1707.01184v1.pdf | |
| PWC | https://paperswithcode.com/paper/sentiment-identification-in-code-mixed-social |
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Camera-Aware Multi-Resolution Analysis (CAMRA) for Raw Sensor Data Compression
| Title | Camera-Aware Multi-Resolution Analysis (CAMRA) for Raw Sensor Data Compression |
| Authors | Y. Lee, K. Hirakawa, T. Nguyen |
| Abstract | We propose a novel lossless and lossy compression scheme for color filter array~(CFA) sampled images based on the wavelet transform of them. Our analysis suggests that the wavelet coefficients of HL and LH subbands are highly correlated. Hence, we decorrelate Mallat wavelet packet decomposition to further sparsify the coefficients. In addition, we develop a camera processing pipeline for compressing CFA sampled images aimed at maximizing the quality of the color images constructed from the compressed CFA sampled images. We validated our theoretical analysis and the performance of the proposed compression scheme using images of natural scenes captured in a raw format. The experimental results verify that our proposed method improves coding efficiency relative to the standard and the state-of-the-art compression schemes CFA sampled images. |
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| Published | 2017-09-25 |
| URL | http://arxiv.org/abs/1709.08739v1 |
| http://arxiv.org/pdf/1709.08739v1.pdf | |
| PWC | https://paperswithcode.com/paper/camera-aware-multi-resolution-analysis-camra |
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Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification
| Title | Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification |
| Authors | Xavier Roynard, Jean-Emmanuel Deschaud, François Goulette |
| Abstract | This paper introduces a new Urban Point Cloud Dataset for Automatic Segmentation and Classification acquired by Mobile Laser Scanning (MLS). We describe how the dataset is obtained from acquisition to post-processing and labeling. This dataset can be used to learn classification algorithm, however, given that a great attention has been paid to the split between the different objects, this dataset can also be used to learn the segmentation. The dataset consists of around 2km of MLS point cloud acquired in two cities. The number of points and range of classes make us consider that it can be used to train Deep-Learning methods. Besides we show some results of automatic segmentation and classification. The dataset is available at: http://caor-mines-paristech.fr/fr/paris-lille-3d-dataset/ |
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| Published | 2017-11-30 |
| URL | http://arxiv.org/abs/1712.00032v2 |
| http://arxiv.org/pdf/1712.00032v2.pdf | |
| PWC | https://paperswithcode.com/paper/paris-lille-3d-a-large-and-high-quality |
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