Paper Group ANR 1697
Finding Rats in Cats: Detecting Stealthy Attacks using Group Anomaly Detection. An adaptive stigmergy-based system for evaluating technological indicator dynamics in the context of smart specialization. Scanning Probe State Recognition With Multi-Class Neural Network Ensembles. Dimensionality Reduction of Complex Metastable Systems via Kernel Embed …
Finding Rats in Cats: Detecting Stealthy Attacks using Group Anomaly Detection
| Title | Finding Rats in Cats: Detecting Stealthy Attacks using Group Anomaly Detection |
| Authors | Aditya Kuppa, Slawomir Grzonkowski, Muhammad Rizwan Asghar, Nhien-An Le-Khac |
| Abstract | Advanced attack campaigns span across multiple stages and stay stealthy for long time periods. There is a growing trend of attackers using off-the-shelf tools and pre-installed system applications (such as \emph{powershell} and \emph{wmic}) to evade the detection because the same tools are also used by system administrators and security analysts for legitimate purposes for their routine tasks. To start investigations, event logs can be collected from operational systems; however, these logs are generic enough and it often becomes impossible to attribute a potential attack to a specific attack group. Recent approaches in the literature have used anomaly detection techniques, which aim at distinguishing between malicious and normal behavior of computers or network systems. Unfortunately, anomaly detection systems based on point anomalies are too rigid in a sense that they could miss the malicious activity and classify the attack, not an outlier. Therefore, there is a research challenge to make better detection of malicious activities. To address this challenge, in this paper, we leverage Group Anomaly Detection (GAD), which detects anomalous collections of individual data points. Our approach is to build a neural network model utilizing Adversarial Autoencoder (AAE-$\alpha$) in order to detect the activity of an attacker who leverages off-the-shelf tools and system applications. In addition, we also build \textit{Behavior2Vec} and \textit{Command2Vec} sentence embedding deep learning models specific for feature extraction tasks. We conduct extensive experiments to evaluate our models on real-world datasets collected for a period of two months. The empirical results demonstrate that our approach is effective and robust in discovering targeted attacks, pen-tests, and attack campaigns leveraging custom tools. |
| Tasks | Anomaly Detection, Group Anomaly Detection, Sentence Embedding |
| Published | 2019-05-16 |
| URL | https://arxiv.org/abs/1905.07273v2 |
| https://arxiv.org/pdf/1905.07273v2.pdf | |
| PWC | https://paperswithcode.com/paper/finding-rats-in-cats-detecting-stealthy |
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An adaptive stigmergy-based system for evaluating technological indicator dynamics in the context of smart specialization
| Title | An adaptive stigmergy-based system for evaluating technological indicator dynamics in the context of smart specialization |
| Authors | A. L. Alfeo, F. P. Appio, M. G. C. A. Cimino, A. Lazzeri, A. Martini, G. Vaglini |
| Abstract | Regional innovation is more and more considered an important enabler of welfare. It is no coincidence that the European Commission has started looking at regional peculiarities and dynamics, in order to focus Research and Innovation Strategies for Smart Specialization towards effective investment policies. In this context, this work aims to support policy makers in the analysis of innovation-relevant trends. We exploit a European database of the regional patent application to determine the dynamics of a set of technological innovation indicators. For this purpose, we design and develop a software system for assessing unfolding trends in such indicators. In contrast with conventional knowledge-based design, our approach is biologically-inspired and based on self-organization of information. This means that a functional structure, called track, appears and stays spontaneous at runtime when local dynamism in data occurs. A further prototyping of tracks allows a better distinction of the critical phenomena during unfolding events, with a better assessment of the progressing levels. The proposed mechanism works if structural parameters are correctly tuned for the given historical context. Determining such correct parameters is not a simple task since different indicators may have different dynamics. For this purpose, we adopt an adaptation mechanism based on differential evolution. The study includes the problem statement and its characterization in the literature, as well as the proposed solving approach, experimental setting and results. |
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| Published | 2019-01-02 |
| URL | http://arxiv.org/abs/1901.00553v1 |
| http://arxiv.org/pdf/1901.00553v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-adaptive-stigmergy-based-system-for |
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Scanning Probe State Recognition With Multi-Class Neural Network Ensembles
| Title | Scanning Probe State Recognition With Multi-Class Neural Network Ensembles |
| Authors | O. Gordon, P. D’Hondt, L. Knijff, S. Freeney, F. Junqueira, P. Moriarty, I. Swart |
| Abstract | One of the largest obstacles facing scanning probe microscopy is the constant need to correct flaws in the scanning probe in situ. This is currently a manual, time-consuming process that would benefit greatly from automation. Here we introduce a convolutional neural network protocol that enables automated recognition of a variety of desirable and undesirable scanning probe tip states on both metal and non-metal surfaces. By combining the best performing models into majority voting ensembles, we find that the desirable states of H:Si(100) can be distinguished with a mean precision of 0.89 and an average receiver-operator-characteristic curve area of 0.95. More generally, high and low-quality tips can be distinguished with a mean precision of 0.96 and near perfect area-under-curve of 0.98. With trivial modifications, we also successfully automatically identify undesirable, non-surface-specific states on surfaces of Au(111) and Cu(111). In these cases we find mean precisions of 0.95 and 0.75 and area-under-curves of 0.98 and 0.94, respectively. |
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| Published | 2019-03-21 |
| URL | http://arxiv.org/abs/1903.09101v1 |
| http://arxiv.org/pdf/1903.09101v1.pdf | |
| PWC | https://paperswithcode.com/paper/scanning-probe-state-recognition-with-multi |
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Dimensionality Reduction of Complex Metastable Systems via Kernel Embeddings of Transition Manifolds
| Title | Dimensionality Reduction of Complex Metastable Systems via Kernel Embeddings of Transition Manifolds |
| Authors | Andreas Bittracher, Stefan Klus, Boumediene Hamzi, Péter Koltai, Christof Schütte |
| Abstract | We present a novel kernel-based machine learning algorithm for identifying the low-dimensional geometry of the effective dynamics of high-dimensional multiscale stochastic systems. Recently, the authors developed a mathematical framework for the computation of optimal reaction coordinates of such systems that is based on learning a parametrization of a low-dimensional transition manifold in a certain function space. In this article, we enhance this approach by embedding and learning this transition manifold in a reproducing kernel Hilbert space, exploiting the favorable properties of kernel embeddings. Under mild assumptions on the kernel, the manifold structure is shown to be preserved under the embedding, and distortion bounds can be derived. This leads to a more robust and more efficient algorithm compared to previous parametrization approaches. |
| Tasks | Dimensionality Reduction |
| Published | 2019-04-18 |
| URL | https://arxiv.org/abs/1904.08622v2 |
| https://arxiv.org/pdf/1904.08622v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-kernel-based-method-for-coarse-graining |
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VoteNet+ : An Improved Deep Learning Label Fusion Method for Multi-atlas Segmentation
| Title | VoteNet+ : An Improved Deep Learning Label Fusion Method for Multi-atlas Segmentation |
| Authors | Zhipeng Ding, Xu Han, Marc Niethammer |
| Abstract | In this work, we improve the performance of multi-atlas segmentation (MAS) by integrating the recently proposed VoteNet model with the joint label fusion (JLF) approach. Specifically, we first illustrate that using a deep convolutional neural network to predict atlas probabilities can better distinguish correct atlas labels from incorrect ones than relying on image intensity difference as is typical in JLF. Motivated by this finding, we propose VoteNet+, an improved deep network to locally predict the probability of an atlas label to differs from the label of the target image. Furthermore, we show that JLF is more suitable for the VoteNet framework as a label fusion method than plurality voting. Lastly, we use Platt scaling to calibrate the probabilities of our new model. Results on LPBA40 3D MR brain images show that our proposed method can achieve better performance than VoteNet. |
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| Published | 2019-11-01 |
| URL | https://arxiv.org/abs/1911.00582v2 |
| https://arxiv.org/pdf/1911.00582v2.pdf | |
| PWC | https://paperswithcode.com/paper/votenet-an-improved-deep-learning-label |
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A Faster Algorithm Enumerating Relevant Features over Finite Fields
| Title | A Faster Algorithm Enumerating Relevant Features over Finite Fields |
| Authors | Mikito Nanashima |
| Abstract | We consider the problem of enumerating relevant features hidden in other irrelevant information for multi-labeled data, which is formalized as learning juntas. A $k$-junta function is a function which depends on only $k$ coordinates of the input. For relatively small $k$ w.r.t. the input size $n$, learning $k$-junta functions is one of fundamental problems both theoretically and practically in machine learning. For the last two decades, much effort has been made to design efficient learning algorithms for Boolean junta functions, and some novel techniques have been developed. However, in real world, multi-labeled data seem to be obtained in much more often than binary-labeled one. Thus, it is a natural question whether these techniques can be applied to more general cases about the alphabet size. In this paper, we expand the Fourier detection techniques for the binary alphabet to any finite field $\mathbb{F}_q$, and give, roughly speaking, an $O(n^{0.8k})$-time learning algorithm for $k$-juntas over $\mathbb{F}_q$. Note that our algorithm is the first non-trivial (i.e., non-brute force) algorithm for such a class even in the case where $q=3$ and we give an affirmative answer to the question posed by Mossel et al. Our algorithm consists of two reductions: (1) from learning juntas to LDME which is a variant of the learning with errors (LWE) problems introduced by Regev, and (2) from LDME to the light bulb problem (LBP) introduced by L.Valiant. Since the reduced problem (i.e., LBP) is a kind of binary problem regardless of the alphabet size of the original problem (i.e., learning juntas), we can directly apply the techniques for the binary case in the previous work. |
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| Published | 2019-03-15 |
| URL | https://arxiv.org/abs/1903.06412v2 |
| https://arxiv.org/pdf/1903.06412v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-faster-algorithm-enumerating-relevant |
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Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs
| Title | Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs |
| Authors | Marek Petrik, Reazul Hasan Russell |
| Abstract | Robust MDPs (RMDPs) can be used to compute policies with provable worst-case guarantees in reinforcement learning. The quality and robustness of an RMDP solution are determined by the ambiguity set—the set of plausible transition probabilities—which is usually constructed as a multi-dimensional confidence region. Existing methods construct ambiguity sets as confidence regions using concentration inequalities which leads to overly conservative solutions. This paper proposes a new paradigm that can achieve better solutions with the same robustness guarantees without using confidence regions as ambiguity sets. To incorporate prior knowledge, our algorithms optimize the size and position of ambiguity sets using Bayesian inference. Our theoretical analysis shows the safety of the proposed method, and the empirical results demonstrate its practical promise. |
| Tasks | Bayesian Inference |
| Published | 2019-02-20 |
| URL | http://arxiv.org/abs/1902.07605v1 |
| http://arxiv.org/pdf/1902.07605v1.pdf | |
| PWC | https://paperswithcode.com/paper/beyond-confidence-regions-tight-bayesian |
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Bayesian Optimization under Heavy-tailed Payoffs
| Title | Bayesian Optimization under Heavy-tailed Payoffs |
| Authors | Sayak Ray Chowdhury, Aditya Gopalan |
| Abstract | We consider black box optimization of an unknown function in the nonparametric Gaussian process setting when the noise in the observed function values can be heavy tailed. This is in contrast to existing literature that typically assumes sub-Gaussian noise distributions for queries. Under the assumption that the unknown function belongs to the Reproducing Kernel Hilbert Space (RKHS) induced by a kernel, we first show that an adaptation of the well-known GP-UCB algorithm with reward truncation enjoys sublinear $\tilde{O}(T^{\frac{2 + \alpha}{2(1+\alpha)}})$ regret even with only the $(1+\alpha)$-th moments, $\alpha \in (0,1]$, of the reward distribution being bounded ($\tilde{O}$ hides logarithmic factors). However, for the common squared exponential (SE) and Mat'{e}rn kernels, this is seen to be significantly larger than a fundamental $\Omega(T^{\frac{1}{1+\alpha}})$ lower bound on regret. We resolve this gap by developing novel Bayesian optimization algorithms, based on kernel approximation techniques, with regret bounds matching the lower bound in order for the SE kernel. We numerically benchmark the algorithms on environments based on both synthetic models and real-world data sets. |
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| Published | 2019-09-16 |
| URL | https://arxiv.org/abs/1909.07040v1 |
| https://arxiv.org/pdf/1909.07040v1.pdf | |
| PWC | https://paperswithcode.com/paper/bayesian-optimization-under-heavy-tailed |
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From Fully Supervised to Zero Shot Settings for Twitter Hashtag Recommendation
| Title | From Fully Supervised to Zero Shot Settings for Twitter Hashtag Recommendation |
| Authors | Abhay Kumar, Nishant Jain, Suraj Tripathi, Chirag Singh |
| Abstract | We propose a comprehensive end-to-end pipeline for Twitter hashtags recommendation system including data collection, supervised training setting and zero shot training setting. In the supervised training setting, we have proposed and compared the performance of various deep learning architectures, namely Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Transformer Network. However, it is not feasible to collect data for all possible hashtag labels and train a classifier model on them. To overcome this limitation, we propose a Zero Shot Learning (ZSL) paradigm for predicting unseen hashtag labels by learning the relationship between the semantic space of tweets and the embedding space of hashtag labels. We evaluated various state-of-the-art ZSL methods like Convex combination of Semantic Embedding (ConSE), Embarrassingly Simple Zero-Shot Learning (ESZSL) and Deep Embedding Model for Zero-Shot Learning (DEM-ZSL) for the hashtag recommendation task. We demonstrate the effectiveness and scalability of ZSL methods for the recommendation of unseen hashtags. To the best of our knowledge, this is the first quantitative evaluation of ZSL methods to date for unseen hashtags recommendations from tweet text. |
| Tasks | Zero-Shot Learning |
| Published | 2019-06-11 |
| URL | https://arxiv.org/abs/1906.04914v1 |
| https://arxiv.org/pdf/1906.04914v1.pdf | |
| PWC | https://paperswithcode.com/paper/from-fully-supervised-to-zero-shot-settings |
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Context-Dependent Semantic Parsing over Temporally Structured Data
| Title | Context-Dependent Semantic Parsing over Temporally Structured Data |
| Authors | Charles Chen, Razvan Bunescu |
| Abstract | We describe a new semantic parsing setting that allows users to query the system using both natural language questions and actions within a graphical user interface. Multiple time series belonging to an entity of interest are stored in a database and the user interacts with the system to obtain a better understanding of the entity’s state and behavior, entailing sequences of actions and questions whose answers may depend on previous factual or navigational interactions. We design an LSTM-based encoder-decoder architecture that models context dependency through copying mechanisms and multiple levels of attention over inputs and previous outputs. When trained to predict tokens using supervised learning, the proposed architecture substantially outperforms standard sequence generation baselines. Training the architecture using policy gradient leads to further improvements in performance, reaching a sequence-level accuracy of 88.7% on artificial data and 74.8% on real data. |
| Tasks | Semantic Parsing, Time Series |
| Published | 2019-05-01 |
| URL | http://arxiv.org/abs/1905.00245v1 |
| http://arxiv.org/pdf/1905.00245v1.pdf | |
| PWC | https://paperswithcode.com/paper/context-dependent-semantic-parsing-over |
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Syllable-based Neural Named Entity Recognition for Myanmar Language
| Title | Syllable-based Neural Named Entity Recognition for Myanmar Language |
| Authors | Hsu Myat Mo, Khin Mar Soe |
| Abstract | Named Entity Recognition (NER) for Myanmar Language is essential to Myanmar natural language processing research work. In this work, NER for Myanmar language is treated as a sequence tagging problem and the effectiveness of deep neural networks on NER for Myanmar language has been investigated. Experiments are performed by applying deep neural network architectures on syllable level Myanmar contexts. Very first manually annotated NER corpus for Myanmar language is also constructed and proposed. In developing our in-house NER corpus, sentences from online news website and also sentences supported from ALT-Parallel-Corpus are also used. This ALT corpus is one part of the Asian Language Treebank (ALT) project under ASEAN IVO. This paper contributes the first evaluation of neural network models on NER task for Myanmar language. The experimental results show that those neural sequence models can produce promising results compared to the baseline CRF model. Among those neural architectures, bidirectional LSTM network added CRF layer above gives the highest F-score value. This work also aims to discover the effectiveness of neural network approaches to Myanmar textual processing as well as to promote further researches on this understudied language. |
| Tasks | Named Entity Recognition |
| Published | 2019-03-12 |
| URL | http://arxiv.org/abs/1903.04739v1 |
| http://arxiv.org/pdf/1903.04739v1.pdf | |
| PWC | https://paperswithcode.com/paper/syllable-based-neural-named-entity |
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SiamMan: Siamese Motion-aware Network for Visual Tracking
| Title | SiamMan: Siamese Motion-aware Network for Visual Tracking |
| Authors | Wenzhang Zhou, Longyin Wen, Libo Zhang, Dawei Du, Tiejian Luo, Yanjun Wu |
| Abstract | In this paper, we present a novel siamese motion-aware network (SiamMan) for visual tracking, which consists of the siamese feature extraction subnetwork, followed by the classification, regression, and localization branches in parallel. The classification branch is used to distinguish the foreground from background, and the regression branch is adopt to regress the bounding box of target. To reduce the impact of manually designed anchor boxes to adapt to different target motion patterns, we design the localization branch, which aims to coarsely localize the target to help the regression branch to generate accurate results. Meanwhile, we introduce the global context module into the localization branch to capture long-range dependency for more robustness in large displacement of target. In addition, we design a multi-scale learnable attention module to guide these three branches to exploit discriminative features for better performance. The whole network is trained offline in an end-to-end fashion with large-scale image pairs using the standard SGD algorithm with back-propagation. Extensive experiments on five challenging benchmarks, i.e., VOT2016, VOT2018, OTB100, UAV123 and LTB35, demonstrate that SiamMan achieves leading accuracy with high efficiency. Code can be found at https://isrc.iscas.ac.cn/gitlab/research/siamman. |
| Tasks | Visual Tracking |
| Published | 2019-12-11 |
| URL | https://arxiv.org/abs/1912.05515v2 |
| https://arxiv.org/pdf/1912.05515v2.pdf | |
| PWC | https://paperswithcode.com/paper/siamman-siamese-motion-aware-network-for |
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EKFPnP: Extended Kalman Filter for Camera Pose Estimation in a Sequence of Images
| Title | EKFPnP: Extended Kalman Filter for Camera Pose Estimation in a Sequence of Images |
| Authors | Mohammad Amin Mehralian, Mohsen Soryani |
| Abstract | In real-world applications the Perspective-n-Point (PnP) problem should generally be applied in a sequence of images which a set of drift-prone features are tracked over time. In this paper, we consider both the temporal dependency of camera poses and the uncertainty of features for the sequential camera pose estimation. Using the Extended Kalman Filter (EKF), a priori estimate of the camera pose is calculated from the camera motion model and then corrected by minimizing the reprojection error of the reference points. Experimental results, using both simulated and real data, demonstrate that the proposed method improves the robustness of the camera pose estimation, in the presence of noise, compared to the state-of-the-art. |
| Tasks | Pose Estimation |
| Published | 2019-06-25 |
| URL | https://arxiv.org/abs/1906.10324v1 |
| https://arxiv.org/pdf/1906.10324v1.pdf | |
| PWC | https://paperswithcode.com/paper/ekfpnp-extended-kalman-filter-for-camera-pose |
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The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers
| Title | The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers |
| Authors | Alex X. Lu, Amy X. Lu, Wiebke Schormann, Marzyeh Ghassemi, David W. Andrews, Alan M. Moses |
| Abstract | Understanding if classifiers generalize to out-of-sample datasets is a central problem in machine learning. Microscopy images provide a standardized way to measure the generalization capacity of image classifiers, as we can image the same classes of objects under increasingly divergent, but controlled factors of variation. We created a public dataset of 132,209 images of mouse cells, COOS-7 (Cells Out Of Sample 7-Class). COOS-7 provides a classification setting where four test datasets have increasing degrees of covariate shift: some images are random subsets of the training data, while others are from experiments reproduced months later and imaged by different instruments. We benchmarked a range of classification models using different representations, including transferred neural network features, end-to-end classification with a supervised deep CNN, and features from a self-supervised CNN. While most classifiers perform well on test datasets similar to the training dataset, all classifiers failed to generalize their performance to datasets with greater covariate shifts. These baselines highlight the challenges of covariate shifts in image data, and establish metrics for improving the generalization capacity of image classifiers. |
| Tasks | |
| Published | 2019-06-17 |
| URL | https://arxiv.org/abs/1906.07282v3 |
| https://arxiv.org/pdf/1906.07282v3.pdf | |
| PWC | https://paperswithcode.com/paper/the-cells-out-of-sample-coos-dataset-and |
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Encoding high-cardinality string categorical variables
| Title | Encoding high-cardinality string categorical variables |
| Authors | Patricio Cerda, Gaël Varoquaux |
| Abstract | Statistical models usually require vector representations of categorical variables, using for instance one-hot encoding. This strategy breaks down when the number of categories grows, as it creates high-dimensional feature vectors. Additionally, for string entries, one-hot encoding does not capture information in their representation.Here, we seek low-dimensional encoding of high-cardinality string categorical variables. Ideally, these should be: scalable to many categories; interpretable to end users; and facilitate statistical analysis. We introduce two encoding approaches for string categories: a Gamma-Poisson matrix factorization on substring counts, and the min-hash encoder, for fast approximation of string similarities. We show that min-hash turns set inclusions into inequality relations that are easier to learn. Both approaches are scalable and streamable. Experiments on real and simulated data show that these methods improve supervised learning with high-cardinality categorical variables. We recommend the following: if scalability is central, the min-hash encoder is the best option as it does not require any data fit; if interpretability is important, the Gamma-Poisson factorization is the best alternative, as it can be interpreted as one-hot encoding on inferred categories with informative feature names. Both models enable autoML on the original string entries as they remove the need for feature engineering or data cleaning. |
| Tasks | AutoML, Feature Engineering |
| Published | 2019-07-03 |
| URL | https://arxiv.org/abs/1907.01860v4 |
| https://arxiv.org/pdf/1907.01860v4.pdf | |
| PWC | https://paperswithcode.com/paper/encoding-high-cardinality-string-categorical |
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