Paper Group ANR 327
Forecasting the 2017-2018 Yemen Cholera Outbreak with Machine Learning. Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems. Cascade Feature Aggregation for Human Pose Estimation. Artistic Enhancement and Style Transfer of Image Edges using Directional Pseudo-coloring. Optimizing Software Effort Estimation Models Using …
Forecasting the 2017-2018 Yemen Cholera Outbreak with Machine Learning
| Title | Forecasting the 2017-2018 Yemen Cholera Outbreak with Machine Learning |
| Authors | Rohil Badkundri, Victor Valbuena, Srikusmanjali Pinnamareddy, Brittney Cantrell, Janet Standeven |
| Abstract | The ongoing Yemen cholera outbreak has been deemed one of the worst cholera outbreaks in history, with over a million people impacted and thousands dead. Triggered by a civil war, the outbreak has been shaped by various political, environmental, and epidemiological factors and continues to worsen. While cholera has several effective treatments, the untimely and inefficient distribution of existing medicines has been the primary cause of cholera mortality. With the hope of facilitating resource allocation, various mathematical models have been created to track the Yemeni outbreak and identify at-risk administrative divisions, called governorates. Existing models are not powerful enough to accurately and consistently forecast cholera cases per governorate over multiple timeframes. To address the need for a complex, reliable model, we offer the Cholera Artificial Learning Model (CALM); a system of 4 extreme-gradient-boosting (XGBoost) machine learning models that forecast the number of new cholera cases a Yemeni governorate will experience from a time range of 2 weeks to 2 months. CALM provides a novel machine learning approach that makes use of rainfall data, past cholera cases and deaths data, civil war fatalities, and inter-governorate interactions represented across multiple time frames. Additionally, the use of machine learning, along with extensive feature engineering, allows CALM to easily learn complex non-linear relations apparent in an epidemiological phenomenon. CALM is able to forecast cholera incidence 2 weeks to 2 months in advance within a margin of just 5 cholera cases per 10,000 people in real-world simulation. |
| Tasks | Feature Engineering |
| Published | 2019-02-16 |
| URL | http://arxiv.org/abs/1902.06739v1 |
| http://arxiv.org/pdf/1902.06739v1.pdf | |
| PWC | https://paperswithcode.com/paper/forecasting-the-2017-2018-yemen-cholera |
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Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems
| Title | Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems |
| Authors | Meysam Sadeghi, Erik G. Larsson |
| Abstract | We show that end-to-end learning of communication systems through deep neural network (DNN) autoencoders can be extremely vulnerable to physical adversarial attacks. Specifically, we elaborate how an attacker can craft effective physical black-box adversarial attacks. Due to the openness (broadcast nature) of the wireless channel, an adversary transmitter can increase the block-error-rate of a communication system by orders of magnitude by transmitting a well-designed perturbation signal over the channel. We reveal that the adversarial attacks are more destructive than jamming attacks. We also show that classical coding schemes are more robust than autoencoders against both adversarial and jamming attacks. The codes are available at [1]. |
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| Published | 2019-02-22 |
| URL | http://arxiv.org/abs/1902.08391v1 |
| http://arxiv.org/pdf/1902.08391v1.pdf | |
| PWC | https://paperswithcode.com/paper/physical-adversarial-attacks-against-end-to |
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Cascade Feature Aggregation for Human Pose Estimation
| Title | Cascade Feature Aggregation for Human Pose Estimation |
| Authors | Zhihui Su, Ming Ye, Guohui Zhang, Lei Dai, Jianda Sheng |
| Abstract | Human pose estimation plays an important role in many computer vision tasks and has been studied for many decades. However, due to complex appearance variations from poses, illuminations, occlusions and low resolutions, it still remains a challenging problem. Taking the advantage of high-level semantic information from deep convolutional neural networks is an effective way to improve the accuracy of human pose estimation. In this paper, we propose a novel Cascade Feature Aggregation (CFA) method, which cascades several hourglass networks for robust human pose estimation. Features from different stages are aggregated to obtain abundant contextual information, leading to robustness to poses, partial occlusions and low resolution. Moreover, results from different stages are fused to further improve the localization accuracy. The extensive experiments on MPII datasets and LIP datasets demonstrate that our proposed CFA outperforms the state-of-the-art and achieves the best performance on the state-of-the-art benchmark MPII. |
| Tasks | Pose Estimation |
| Published | 2019-02-21 |
| URL | https://arxiv.org/abs/1902.07837v3 |
| https://arxiv.org/pdf/1902.07837v3.pdf | |
| PWC | https://paperswithcode.com/paper/improvement-multi-stage-model-for-human-pose |
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Artistic Enhancement and Style Transfer of Image Edges using Directional Pseudo-coloring
| Title | Artistic Enhancement and Style Transfer of Image Edges using Directional Pseudo-coloring |
| Authors | Shouvik Mani |
| Abstract | Computing the gradient of an image is a common step in computer vision pipelines. The image gradient quantifies the magnitude and direction of edges in an image and is used in creating features for downstream machine learning tasks. Typically, the image gradient is represented as a grayscale image. This paper introduces directional pseudo-coloring, an approach to color the image gradient in a deliberate and coherent manner. By pseudo-coloring the image gradient magnitude with the image gradient direction, we can enhance the visual quality of image edges and achieve an artistic transformation of the original image. Additionally, we present a simple style transfer pipeline which learns a color map from a style image and then applies that color map to color the edges of a content image through the directional pseudo-coloring technique. Code for the algorithms and experiments is available at https://github.com/shouvikmani/edge-colorizer. |
| Tasks | Style Transfer |
| Published | 2019-06-19 |
| URL | https://arxiv.org/abs/1906.07981v1 |
| https://arxiv.org/pdf/1906.07981v1.pdf | |
| PWC | https://paperswithcode.com/paper/artistic-enhancement-and-style-transfer-of |
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Optimizing Software Effort Estimation Models Using Firefly Algorithm
| Title | Optimizing Software Effort Estimation Models Using Firefly Algorithm |
| Authors | Nazeeh Ghatasheh, Hossam Faris, Ibrahim Aljarah, Rizik M. H. Al-Sayyed |
| Abstract | Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received a considerable amount of attention from researchers and became a challenge for software industry. In the last two decades, many researchers and practitioners proposed statistical and machine learning-based models for software effort estimation. In this work, Firefly Algorithm is proposed as a metaheuristic optimization method for optimizing the parameters of three COCOMO-based models. These models include the basic COCOMO model and other two models proposed in the literature as extensions of the basic COCOMO model. The developed estimation models are evaluated using different evaluation metrics. Experimental results show high accuracy and significant error minimization of Firefly Algorithm over other metaheuristic optimization algorithms including Genetic Algorithms and Particle Swarm Optimization. |
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| Published | 2019-01-08 |
| URL | http://arxiv.org/abs/1903.02079v1 |
| http://arxiv.org/pdf/1903.02079v1.pdf | |
| PWC | https://paperswithcode.com/paper/optimizing-software-effort-estimation-models |
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Offline identification of surgical deviations in laparoscopic rectopexy
| Title | Offline identification of surgical deviations in laparoscopic rectopexy |
| Authors | Arnaud Huaulmé, Pierre Jannin, Fabian Reche, Jean-Luc Faucheron, Alexandre Moreau-Gaudry, Sandrine Voros |
| Abstract | Objective: A median of 14.4% of patient undergone at least one adverse event during surgery and a third of them are preventable. The occurrence of adverse events forces surgeons to implement corrective strategies and, thus, deviate from the standard surgical process. Therefore, it is clear that the automatic identification of adverse events is a major challenge for patient safety. In this paper, we have proposed a method enabling us to identify such deviations. We have focused on identifying surgeons’ deviations from standard surgical processes due to surgical events rather than anatomic specificities. This is particularly challenging, given the high variability in typical surgical procedure workflows. Methods: We have introduced a new approach designed to automatically detect and distinguish surgical process deviations based on multi-dimensional non-linear temporal scaling with a hidden semi-Markov model using manual annotation of surgical processes. The approach was then evaluated using cross-validation. Results: The best results have over 90% accuracy. Recall and precision were superior at 70%. We have provided a detailed analysis of the incorrectly-detected observations. Conclusion: Multi-dimensional non-linear temporal scaling with a hidden semi-Markov model provides promising results for detecting deviations. Our error analysis of the incorrectly-detected observations offers different leads in order to further improve our method. Significance: Our method demonstrated the feasibility of automatically detecting surgical deviations that could be implemented for both skill analysis and developing situation awareness-based computer-assisted surgical systems. |
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| Published | 2019-09-24 |
| URL | https://arxiv.org/abs/1909.10790v2 |
| https://arxiv.org/pdf/1909.10790v2.pdf | |
| PWC | https://paperswithcode.com/paper/offline-identification-of-surgical-deviations |
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Universal Rules for Fooling Deep Neural Networks based Text Classification
| Title | Universal Rules for Fooling Deep Neural Networks based Text Classification |
| Authors | Di Li, Danilo Vasconcellos Vargas, Sakurai Kouichi |
| Abstract | Recently, deep learning based natural language processing techniques are being extensively used to deal with spam mail, censorship evaluation in social networks, among others. However, there is only a couple of works evaluating the vulnerabilities of such deep neural networks. Here, we go beyond attacks to investigate, for the first time, universal rules, i.e., rules that are sample agnostic and therefore could turn any text sample in an adversarial one. In fact, the universal rules do not use any information from the method itself (no information from the method, gradient information or training dataset information is used), making them black-box universal attacks. In other words, the universal rules are sample and method agnostic. By proposing a coevolutionary optimization algorithm we show that it is possible to create universal rules that can automatically craft imperceptible adversarial samples (only less than five perturbations which are close to misspelling are inserted in the text sample). A comparison with a random search algorithm further justifies the strength of the method. Thus, universal rules for fooling networks are here shown to exist. Hopefully, the results from this work will impact the development of yet more sample and model agnostic attacks as well as their defenses, culminating in perhaps a new age for artificial intelligence. |
| Tasks | Text Classification |
| Published | 2019-01-22 |
| URL | http://arxiv.org/abs/1901.07132v2 |
| http://arxiv.org/pdf/1901.07132v2.pdf | |
| PWC | https://paperswithcode.com/paper/universal-rules-for-fooling-deep-neural |
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Structure learning in graphical models by covariance queries
| Title | Structure learning in graphical models by covariance queries |
| Authors | Gábor Lugosi, Jakub Truszkowski, Vasiliki Velona, Piotr Zwiernik |
| Abstract | We study the problem of recovering the structure underlying large Gaussian graphical models. In high-dimensional problems it is often too costly to store the entire sample covariance matrix. We propose a new input model in which one can query single entries of the sample covariance matrix. We present computationally efficient algorithms for structure recovery in Gaussian graphical models with low query and computational complexity. Our algorithms work in a regime of tree-like graphs and, more generally, for graphs of small treewidth. Our results demonstrate that for large classes of graphs, the structure of the corresponding Gaussian graphical models can be determined much faster than even computing the empirical covariance matrix. |
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| Published | 2019-06-22 |
| URL | https://arxiv.org/abs/1906.09501v1 |
| https://arxiv.org/pdf/1906.09501v1.pdf | |
| PWC | https://paperswithcode.com/paper/structure-learning-in-graphical-models-by |
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A Staged Approach to Evolving Real-world UAV Controllers
| Title | A Staged Approach to Evolving Real-world UAV Controllers |
| Authors | Gerard David Howard, Alberto Elfes |
| Abstract | A testbed has recently been introduced that evolves controllers for arbitrary hover-capable UAVs, with evaluations occurring directly on the robot. To prepare the testbed for real-world deployment, we investigate the effects of state-space limitations brought about by physical tethering (which prevents damage to the UAV during stochastic tuning), on the generality of the evolved controllers. We identify generalisation issues in some controllers, and propose an improved method that comprises two stages: in the first stage, controllers are evolved as normal using standard tethers, but experiments are terminated when the population displays basic flight competency. Optimisation then continues on a much less restrictive tether, effectively free-flying, and is allowed to explore a larger state-space envelope. We compare the two methods on a hover task using a real UAV, and show that more general solutions are generated in fewer generations using the two-stage approach. A secondary experiment undertakes a sensitivity analysis of the evolved controllers. |
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| Published | 2019-05-26 |
| URL | https://arxiv.org/abs/1905.10762v1 |
| https://arxiv.org/pdf/1905.10762v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-staged-approach-to-evolving-real-world-uav |
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Decomposing Temperature Time Series with Non-Negative Matrix Factorization
| Title | Decomposing Temperature Time Series with Non-Negative Matrix Factorization |
| Authors | Peter Weiderer, Ana Maria Tomé, Elmar Wolfgang Lang |
| Abstract | During the fabrication of casting parts sensor data is typically automatically recorded and accumulated for process monitoring and defect diagnosis. As casting is a thermal process with many interacting process parameters, root cause analysis tends to be tedious and ineffective. We show how a decomposition based on non-negative matrix factorization (NMF), which is guided by a knowledge-based initialization strategy, is able to extract physical meaningful sources from temperature time series collected during a thermal manufacturing process. The approach assumes the time series to be generated by a superposition of several simultaneously acting component processes. NMF is able to reverse the superposition and to identify the hidden component processes. The latter can be linked to ongoing physical phenomena and process variables, which cannot be monitored directly. Our approach provides new insights into the underlying physics and offers a tool, which can assist in diagnosing defect causes. We demonstrate our method by applying it to real world data, collected in a foundry during the series production of casting parts for the automobile industry. |
| Tasks | Time Series |
| Published | 2019-04-03 |
| URL | http://arxiv.org/abs/1904.02217v1 |
| http://arxiv.org/pdf/1904.02217v1.pdf | |
| PWC | https://paperswithcode.com/paper/decomposing-temperature-time-series-with-non |
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Understanding and Partitioning Mobile Traffic using Internet Activity Records Data – A Spatiotemporal Approach
| Title | Understanding and Partitioning Mobile Traffic using Internet Activity Records Data – A Spatiotemporal Approach |
| Authors | Kashif Sultan, Hazrat Ali, Haris Anwaar, Kabo Poloko Nkabiti, Adeel Ahamd, Zhongshan Zhang |
| Abstract | The internet activity records (IARs) of a mobile cellular network posses significant information which can be exploited to identify the network’s efficacy and the mobile users’ behavior. In this work, we extract useful information from the IAR data and identify a healthy predictability of spatio-temporal pattern within the network traffic. The information extracted is helpful for network operators to plan effective network configuration and perform management and optimization of network’s resources. We report experimentation on spatiotemporal analysis of IAR data of the Telecom Italia. Based on this, we present mobile traffic partitioning scheme. Experimental results of the proposed model is helpful in modelling and partitioning of network traffic patterns. |
| Tasks | |
| Published | 2019-07-30 |
| URL | https://arxiv.org/abs/1908.07653v1 |
| https://arxiv.org/pdf/1908.07653v1.pdf | |
| PWC | https://paperswithcode.com/paper/understanding-and-partitioning-mobile-traffic |
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Enforcing Encoder-Decoder Modularity in Sequence-to-Sequence Models
| Title | Enforcing Encoder-Decoder Modularity in Sequence-to-Sequence Models |
| Authors | Siddharth Dalmia, Abdelrahman Mohamed, Mike Lewis, Florian Metze, Luke Zettlemoyer |
| Abstract | Inspired by modular software design principles of independence, interchangeability, and clarity of interface, we introduce a method for enforcing encoder-decoder modularity in seq2seq models without sacrificing the overall model quality or its full differentiability. We discretize the encoder output units into a predefined interpretable vocabulary space using the Connectionist Temporal Classification (CTC) loss. Our modular systems achieve near SOTA performance on the 300h Switchboard benchmark, with WER of 8.3% and 17.6% on the SWB and CH subsets, using seq2seq models with encoder and decoder modules which are independent and interchangeable. |
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| Published | 2019-11-09 |
| URL | https://arxiv.org/abs/1911.03782v1 |
| https://arxiv.org/pdf/1911.03782v1.pdf | |
| PWC | https://paperswithcode.com/paper/enforcing-encoder-decoder-modularity-in |
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Generative adversarial network based on chaotic time series
| Title | Generative adversarial network based on chaotic time series |
| Authors | Makoto Naruse, Takashi Matsubara, Nicolas Chauvet, Kazutaka Kanno, Tianyu Yang, Atsushi Uchida |
| Abstract | Generative adversarial network (GAN) is gaining increased importance in artificially constructing natural images and related functionalities wherein two networks called generator and discriminator are evolving through adversarial mechanisms. Using deep convolutional neural networks and related techniques, high-resolution, highly realistic scenes, human faces, among others have been generated. While GAN in general needs a large amount of genuine training data sets, it is noteworthy that vast amounts of pseudorandom numbers are required. Here we utilize chaotic time series generated experimentally by semiconductor lasers for the latent variables of GAN whereby the inherent nature of chaos can be reflected or transformed into the generated output data. We show that the similarity in proximity, which is a degree of robustness of the generated images with respects to a minute change in the input latent variables, is enhanced while the versatility as a whole is not severely degraded. Furthermore, we demonstrate that the surrogate chaos time series eliminates the signature of generated images that is originally observed corresponding to the negative autocorrelation inherent in the chaos sequence. We also discuss the impact of utilizing chaotic time series in retrieving images from the trained generator. |
| Tasks | Time Series |
| Published | 2019-05-24 |
| URL | https://arxiv.org/abs/1905.10163v1 |
| https://arxiv.org/pdf/1905.10163v1.pdf | |
| PWC | https://paperswithcode.com/paper/generative-adversarial-network-based-on-1 |
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Generic Intent Representation in Web Search
| Title | Generic Intent Representation in Web Search |
| Authors | Hongfei Zhang, Xia Song, Chenyan Xiong, Corby Rosset, Paul N. Bennett, Nick Craswell, Saurabh Tiwary |
| Abstract | This paper presents GEneric iNtent Encoder (GEN Encoder) which learns a distributed representation space for user intent in search. Leveraging large scale user clicks from Bing search logs as weak supervision of user intent, GEN Encoder learns to map queries with shared clicks into similar embeddings end-to-end and then finetunes on multiple paraphrase tasks. Experimental results on an intrinsic evaluation task - query intent similarity modeling - demonstrate GEN Encoder’s robust and significant advantages over previous representation methods. Ablation studies reveal the crucial role of learning from implicit user feedback in representing user intent and the contributions of multi-task learning in representation generality. We also demonstrate that GEN Encoder alleviates the sparsity of tail search traffic and cuts down half of the unseen queries by using an efficient approximate nearest neighbor search to effectively identify previous queries with the same search intent. Finally, we demonstrate distances between GEN encodings reflect certain information seeking behaviors in search sessions. |
| Tasks | Multi-Task Learning |
| Published | 2019-07-24 |
| URL | https://arxiv.org/abs/1907.10710v1 |
| https://arxiv.org/pdf/1907.10710v1.pdf | |
| PWC | https://paperswithcode.com/paper/generic-intent-representation-in-web-search |
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A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well
| Title | A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well |
| Authors | Nicolas Garneau, Mathieu Godbout, David Beauchemin, Audrey Durand, Luc Lamontagne |
| Abstract | In this paper, we reproduce the experiments of Artetxe et al. (2018b) regarding the robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. We show that the reproduction of their method is indeed feasible with some minor assumptions. We further investigate the robustness of their model by introducing four new languages that are less similar to English than the ones proposed by the original paper. In order to assess the stability of their model, we also conduct a grid search over sensible hyperparameters. We then propose key recommendations applicable to any research project in order to deliver fully reproducible research. |
| Tasks | Word Embeddings |
| Published | 2019-12-03 |
| URL | https://arxiv.org/abs/1912.01706v2 |
| https://arxiv.org/pdf/1912.01706v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-robust-self-learning-method-for-fully-2 |
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