Paper Group ANR 1560
Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud. Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila. Deep Eikonal Solvers. Deep Reinforcement Learning Based Power control for Wireless Multicast Systems. Point Process Flows. Oversampling Log Messages Using a Sequence Generative Ad …
Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud
| Title | Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud |
| Authors | Hang Guo, Xun Fan, Anh Cao, Geoff Outhred, John Heidemann |
| Abstract | Machine-learning-based anomaly detection (ML-based AD) has been successful at detecting DDoS events in the lab. However published evaluations of ML-based AD have only had limited data and have not provided insight into why it works. To address limited evaluation against real-world data, we apply autoencoder, an existing ML-AD model, to 57 DDoS attack events captured at 5 cloud IPs from a major cloud provider. To improve our understanding for why ML-based AD works or not works, we interpret this data with feature attribution and counterfactual explanation. We show that our version of autoencoders work well overall: our models capture nearly all malicious flows to 2 of the 4 cloud IPs under attacks (at least 99.99%) but generate a few false negatives (5% and 9%) for the remaining 2 IPs. We show that our models maintain near-zero false positives on benign flows to all 5 IPs. Our interpretation of results shows that our models identify almost all malicious flows with non-whitelisted (non-WL) destination ports (99.92%) by learning the full list of benign destination ports from training data (the normality). Interpretation shows that although our models learn incomplete normality for protocols and source ports, they still identify most malicious flows with non-WL protocols and blacklisted (BL) source ports (100.0% and 97.5%) but risk false positives. Interpretation also shows that our models only detect a few malicious flows with BL packet sizes (8.5%) by incorrectly inferring these BL sizes as normal based on incomplete normality learned. We find our models still detect a quarter of flows (24.7%) with abnormal payload contents even when they do not see payload by combining anomalies from multiple flow features. Lastly, we summarize the implications of what we learn on applying autoencoder-based AD in production. |
| Tasks | Anomaly Detection |
| Published | 2019-12-11 |
| URL | https://arxiv.org/abs/1912.05590v2 |
| https://arxiv.org/pdf/1912.05590v2.pdf | |
| PWC | https://paperswithcode.com/paper/peek-inside-the-closed-world-evaluating |
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Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila
| Title | Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila |
| Authors | Khan Faraz, Ahmed Bouridane, Richard Jiang, Tiancheng Xia, Paul Chazot, Abdel Ennaceur |
| Abstract | Gene expression of social actions in Drosophilae has been attracting wide interest from biologists, medical scientists and psychologists. Gene-edited Drosophilae have been used as a test platform for experimental investigation. For example, Parkinson’s genes can be embedded into a group of newly bred Drosophilae for research purpose. However, human observation of numerous tiny Drosophilae for a long term is an arduous work, and the dependence on human’s acute perception is highly unreliable. As a result, an automated system of social action detection using machine learning has been highly demanded. In this study, we propose to automate the detection and classification of two innate aggressive actions demonstrated by Drosophilae. Robust keypoint detection is achieved using selective spatio-temporal interest points (sSTIP) which are then described using the 3D Scale Invariant Feature Transform (3D-SIFT) descriptors. Dimensionality reduction is performed using Spectral Regression Kernel Discriminant Analysis (SR-KDA) and classification is done using the nearest centre rule. The classification accuracy shown demonstrates the feasibility of the proposed system. |
| Tasks | Action Detection, Dimensionality Reduction, Keypoint Detection |
| Published | 2019-09-11 |
| URL | https://arxiv.org/abs/1909.04974v1 |
| https://arxiv.org/pdf/1909.04974v1.pdf | |
| PWC | https://paperswithcode.com/paper/computer-aided-automated-detection-of-gene |
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Deep Eikonal Solvers
| Title | Deep Eikonal Solvers |
| Authors | Moshe Lichtenstein, Gautam Pai, Ron Kimmel |
| Abstract | A deep learning approach to numerically approximate the solution to the Eikonal equation is introduced. The proposed method is built on the fast marching scheme which comprises of two components: a local numerical solver and an update scheme. We replace the formulaic local numerical solver with a trained neural network to provide highly accurate estimates of local distances for a variety of different geometries and sampling conditions. Our learning approach generalizes not only to flat Euclidean domains but also to curved surfaces enabled by the incorporation of certain invariant features in the neural network architecture. We show a considerable gain in performance, validated by smaller errors and higher orders of accuracy for the numerical solutions of the Eikonal equation computed on different surfaces The proposed approach leverages the approximation power of neural networks to enhance the performance of numerical algorithms, thereby, connecting the somewhat disparate themes of numerical geometry and learning. |
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| Published | 2019-03-19 |
| URL | http://arxiv.org/abs/1903.07973v1 |
| http://arxiv.org/pdf/1903.07973v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-eikonal-solvers |
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Deep Reinforcement Learning Based Power control for Wireless Multicast Systems
| Title | Deep Reinforcement Learning Based Power control for Wireless Multicast Systems |
| Authors | Ramkumar Raghu, Pratheek Upadhyaya, Mahadesh Panju, Vaneet Aggarwal, Vinod Sharma |
| Abstract | We consider a multicast scheme recently proposed for a wireless downlink in [1]. It was shown earlier that power control can significantly improve its performance. However for this system, obtaining optimal power control is intractable because of a very large state space. Therefore in this paper we use deep reinforcement learning where we use function approximation of the Q-function via a deep neural network. We show that optimal power control can be learnt for reasonably large systems via this approach. The average power constraint is ensured via a Lagrange multiplier, which is also learnt. Finally, we demonstrate that a slight modification of the learning algorithm allows the optimal control to track the time varying system statistics. |
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| Published | 2019-09-27 |
| URL | https://arxiv.org/abs/1910.05308v2 |
| https://arxiv.org/pdf/1910.05308v2.pdf | |
| PWC | https://paperswithcode.com/paper/deep-reinforcement-learning-based-power |
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Point Process Flows
| Title | Point Process Flows |
| Authors | Nazanin Mehrasa, Ruizhi Deng, Mohamed Osama Ahmed, Bo Chang, Jiawei He, Thibaut Durand, Marcus Brubaker, Greg Mori |
| Abstract | Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature. We propose an intensity-free framework that directly models the point process distribution by utilizing normalizing flows. This approach is capable of capturing highly complex temporal distributions and does not rely on restrictive parametric forms. Comparisons with state-of-the-art baseline models on both synthetic and challenging real-life datasets show that the proposed framework is effective at modeling the stochasticity of discrete event sequences. |
| Tasks | Point Processes |
| Published | 2019-10-18 |
| URL | https://arxiv.org/abs/1910.08281v3 |
| https://arxiv.org/pdf/1910.08281v3.pdf | |
| PWC | https://paperswithcode.com/paper/point-process-flows |
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Oversampling Log Messages Using a Sequence Generative Adversarial Network for Anomaly Detection and Classification
| Title | Oversampling Log Messages Using a Sequence Generative Adversarial Network for Anomaly Detection and Classification |
| Authors | Amir Farzad, T. Aaron Gulliver |
| Abstract | Dealing with imbalanced data is one of the main challenges in machine/deep learning algorithms for classification. This issue is more important with log message data as it is typically very imbalanced and negative logs are rare. In this paper, a model is proposed to generate text log messages using a SeqGAN network. Then features are extracted using an Autoencoder and anomaly detection is done using a GRU network. The proposed model is evaluated with two imbalanced log data sets, namely BGL and Openstack. Results are presented which show that oversampling and balancing data increases the accuracy of anomaly detection and classification. |
| Tasks | Anomaly Detection |
| Published | 2019-12-09 |
| URL | https://arxiv.org/abs/1912.04747v2 |
| https://arxiv.org/pdf/1912.04747v2.pdf | |
| PWC | https://paperswithcode.com/paper/oversampling-log-messages-using-a-sequence |
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A Comparison of CNN and Classic Features for Image Retrieval
| Title | A Comparison of CNN and Classic Features for Image Retrieval |
| Authors | Umut Özaydın, Theodoros Georgiou, Michael Lew |
| Abstract | Feature detectors and descriptors have been successfully used for various computer vision tasks, such as video object tracking and content-based image retrieval. Many methods use image gradients in different stages of the detection-description pipeline to describe local image structures. Recently, some, or all, of these stages have been replaced by convolutional neural networks (CNNs), in order to increase their performance. A detector is defined as a selection problem, which makes it more challenging to implement as a CNN. They are therefore generally defined as regressors, converting input images to score maps and keypoints can be selected with non-maximum suppression. This paper discusses and compares several recent methods that use CNNs for keypoint detection. Experiments are performed both on the CNN based approaches, as well as a selection of conventional methods. In addition to qualitative measures defined on keypoints and descriptors, the bag-of-words (BoW) model is used to implement an image retrieval application, in order to determine how the methods perform in practice. The results show that each type of features are best in different contexts. |
| Tasks | Content-Based Image Retrieval, Image Retrieval, Keypoint Detection, Object Tracking, Video Object Tracking |
| Published | 2019-08-25 |
| URL | https://arxiv.org/abs/1908.09300v1 |
| https://arxiv.org/pdf/1908.09300v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-comparison-of-cnn-and-classic-features-for |
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Advanced Capsule Networks via Context Awareness
| Title | Advanced Capsule Networks via Context Awareness |
| Authors | Nguyen Huu Phong, Bernardete Ribeiro |
| Abstract | Capsule Networks (CN) offer new architectures for Deep Learning (DL) community. Though its effectiveness has been demonstrated in MNIST and smallNORB datasets, the networks still face challenges in other datasets for images with distinct contexts. In this research, we improve the design of CN (Vector version) namely we expand more Pooling layers to filter image backgrounds and increase Reconstruction layers to make better image restoration. Additionally, we perform experiments to compare accuracy and speed of CN versus DL models. In DL models, we utilize Inception V3 and DenseNet V201 for powerful computers besides NASNet, MobileNet V1 and MobileNet V2 for small and embedded devices. We evaluate our models on a fingerspelling alphabet dataset from American Sign Language (ASL). The results show that CNs perform comparably to DL models while dramatically reducing training time. We also make a demonstration and give a link for the purpose of illustration. |
| Tasks | Image Restoration |
| Published | 2019-03-18 |
| URL | http://arxiv.org/abs/1903.07497v2 |
| http://arxiv.org/pdf/1903.07497v2.pdf | |
| PWC | https://paperswithcode.com/paper/advanced-capsule-networks-via-context |
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Aggregating explanation methods for stable and robust explainability
| Title | Aggregating explanation methods for stable and robust explainability |
| Authors | Laura Rieger, Lars Kai Hansen |
| Abstract | Despite a growing literature on explaining neural networks, no consensus has been reached on how to explain a neural network decision or how to evaluate an explanation. Our contributions in this paper are twofold. First, we investigate schemes to combine explanation methods and reduce model uncertainty to obtain a single aggregated explanation. We provide evidence that the aggregation is better at identifying important features, than on individual methods. Adversarial attacks on explanations is a recent active research topic. As our second contribution, we present evidence that aggregate explanations are much more robust to attacks than individual explanation methods. |
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| Published | 2019-03-01 |
| URL | https://arxiv.org/abs/1903.00519v5 |
| https://arxiv.org/pdf/1903.00519v5.pdf | |
| PWC | https://paperswithcode.com/paper/aggregating-explainability-methods-for-neural |
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Ranking in Genealogy: Search Results Fusion at Ancestry
| Title | Ranking in Genealogy: Search Results Fusion at Ancestry |
| Authors | Peng Jiang, Yingrui Yang, Gann Bierner, Fengjie Alex Li, Ruhan Wang, Azadeh Moghtaderi |
| Abstract | Genealogy research is the study of family history using available resources such as historical records. Ancestry provides its customers with one of the world’s largest online genealogical index with billions of records from a wide range of sources, including vital records such as birth and death certificates, census records, court and probate records among many others. Search at Ancestry aims to return relevant records from various record types, allowing our subscribers to build their family trees, research their family history, and make meaningful discoveries about their ancestors from diverse perspectives. In a modern search engine designed for genealogical study, the appropriate ranking of search results to provide highly relevant information represents a daunting challenge. In particular, the disparity in historical records makes it inherently difficult to score records in an equitable fashion. Herein, we provide an overview of our solutions to overcome such record disparity problems in the Ancestry search engine. Specifically, we introduce customized coordinate ascent (customized CA) to speed up ranking within a specific record type. We then propose stochastic search (SS) that linearly combines ranked results federated across contents from various record types. Furthermore, we propose a novel information retrieval metric, normalized cumulative entropy (NCE), to measure the diversity of results. We demonstrate the effectiveness of these two algorithms in terms of relevance (by NDCG) and diversity (by NCE) if applicable in the offline experiments using real customer data at Ancestry. |
| Tasks | Information Retrieval |
| Published | 2019-02-27 |
| URL | http://arxiv.org/abs/1903.00099v1 |
| http://arxiv.org/pdf/1903.00099v1.pdf | |
| PWC | https://paperswithcode.com/paper/ranking-in-genealogy-search-results-fusion-at |
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Learning Landmarks from Unaligned Data using Image Translation
| Title | Learning Landmarks from Unaligned Data using Image Translation |
| Authors | Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi |
| Abstract | We introduce a method for learning landmark detectors from unlabelled video frames and unpaired labels. This allows us to learn a detector from a large collection of raw videos given only a few example annotations harvested from existing data or motion capture. We achieve this by formulating the landmark detection task as one of image translation, learning to map an image of the object to an image of its landmarks, represented as a skeleton. The advantage is that this translation problem can then be tackled by CycleGAN. However, we show that a naive application of CycleGAN confounds appearance and pose information, with suboptimal keypoint detection performance. We solve this problem by introducing an analytical and differentiable renderer for the skeleton image so that no appearance information can be leaked in the skeleton. Then, since cycle consistency requires to reconstruct the input image from the skeleton, we supply the appearance information thus removed by conditioning the generator with a second image of the same object (e.g. another frame from a video). Furthermore, while CycleGAN uses two cycle consistency constraints, we show that the second one is detrimental in this application and we discard it, significantly simplifying the model. We show that these modifications improve the quality of the learned detector leading to state-of-the-art unsupervised landmark detection performance in a number of challenging human pose and facial landmark detection benchmarks. |
| Tasks | Facial Landmark Detection, Keypoint Detection, Motion Capture |
| Published | 2019-07-03 |
| URL | https://arxiv.org/abs/1907.02055v1 |
| https://arxiv.org/pdf/1907.02055v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-landmarks-from-unaligned-data-using |
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Multikernel activation functions: formulation and a case study
| Title | Multikernel activation functions: formulation and a case study |
| Authors | Simone Scardapane, Elena Nieddu, Donatella Firmani, Paolo Merialdo |
| Abstract | The design of activation functions is a growing research area in the field of neural networks. In particular, instead of using fixed point-wise functions (e.g., the rectified linear unit), several authors have proposed ways of learning these functions directly from the data in a non-parametric fashion. In this paper we focus on the kernel activation function (KAF), a recently proposed framework wherein each function is modeled as a one-dimensional kernel model, whose weights are adapted through standard backpropagation-based optimization. One drawback of KAFs is the need to select a single kernel function and its eventual hyper-parameters. To partially overcome this problem, we motivate an extension of the KAF model, in which multiple kernels are linearly combined at every neuron, inspired by the literature on multiple kernel learning. We provide an application of the resulting multi-KAF on a realistic use case, specifically handwritten Latin OCR, on a large dataset collected in the context of the `In Codice Ratio’ project. Results show that multi-KAFs can improve the accuracy of the convolutional networks previously developed for the task, with faster convergence, even with a smaller number of overall parameters. | |
| Tasks | Optical Character Recognition |
| Published | 2019-01-29 |
| URL | http://arxiv.org/abs/1901.10232v1 |
| http://arxiv.org/pdf/1901.10232v1.pdf | |
| PWC | https://paperswithcode.com/paper/multikernel-activation-functions-formulation |
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Query-driven PAC-Learning for Reasoning
| Title | Query-driven PAC-Learning for Reasoning |
| Authors | Brendan Juba |
| Abstract | We consider the problem of learning rules from a data set that support a proof of a given query, under Valiant’s PAC-Semantics. We show how any backward proof search algorithm that is sufficiently oblivious to the contents of its knowledge base can be modified to learn such rules while it searches for a proof using those rules. We note that this gives such algorithms for standard logics such as chaining and resolution. |
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| Published | 2019-06-24 |
| URL | https://arxiv.org/abs/1906.10118v1 |
| https://arxiv.org/pdf/1906.10118v1.pdf | |
| PWC | https://paperswithcode.com/paper/query-driven-pac-learning-for-reasoning |
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SAML-QC: a Stochastic Assessment and Machine Learning based QC technique for Industrial Printing
| Title | SAML-QC: a Stochastic Assessment and Machine Learning based QC technique for Industrial Printing |
| Authors | Azhar Hussain |
| Abstract | Recently, the advancement in industrial automation and high-speed printing has raised numerous challenges related to the printing quality inspection of final products. This paper proposes a machine vision based technique to assess the printing quality of text on industrial objects. The assessment is based on three quality defects such as text misalignment, varying printing shades, and misprinted text. The proposed scheme performs the quality inspection through stochastic assessment technique based on the second-order statistics of printing. First: the text-containing area on printed product is identified through image processing techniques. Second: the alignment testing of the identified text-containing area is performed. Third: optical character recognition is performed to divide the text into different small boxes and only the intensity value of each text-containing box is taken as a random variable and second-order statistics are estimated to determine the varying printing defects in the text under one, two and three sigma thresholds. Fourth: the K-Nearest Neighbors based supervised machine learning is performed to provide the stochastic process for misprinted text detection. Finally, the technique is deployed on an industrial image for the printing quality assessment with varying values of n and m. The results have shown that the proposed SAML-QC technique can perform real-time automated inspection for industrial printing. |
| Tasks | Optical Character Recognition |
| Published | 2019-01-18 |
| URL | http://arxiv.org/abs/1901.07370v1 |
| http://arxiv.org/pdf/1901.07370v1.pdf | |
| PWC | https://paperswithcode.com/paper/saml-qc-a-stochastic-assessment-and-machine |
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A Multi-Turn Emotionally Engaging Dialog Model
| Title | A Multi-Turn Emotionally Engaging Dialog Model |
| Authors | Yubo Xie, Ekaterina Svikhnushina, Pearl Pu |
| Abstract | Open-domain dialog systems (also known as chatbots) have increasingly drawn attention in natural language processing. Some of the recent work aims at incorporating affect information into sequence-to-sequence neural dialog modeling, making the response emotionally richer, while others use hand-crafted rules to determine the desired emotion response. However, they do not explicitly learn the subtle emotional interactions captured in human dialogs. In this paper, we propose a multi-turn dialog system aimed at learning and generating emotional responses that so far only humans know how to do. Compared with two baseline models, offline experiments show that our method performs the best in perplexity scores. Further human evaluations confirm that our chatbot can keep track of the conversation context and generate emotionally more appropriate responses while performing equally well on grammar. |
| Tasks | Chatbot |
| Published | 2019-08-15 |
| URL | https://arxiv.org/abs/1908.07816v2 |
| https://arxiv.org/pdf/1908.07816v2.pdf | |
| PWC | https://paperswithcode.com/paper/190807816 |
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