Paper Group ANR 306
Analysis Of Multi Field Of View Cnn And Attention Cnn On H&E Stained Whole-slide Images On Hepatocellular Carcinoma. A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children. Optimal Randomized First-Order Methods for Least-Squares Problems. ActiLabel: A Combinatorial Transfer Learning Framework for Acti …
Analysis Of Multi Field Of View Cnn And Attention Cnn On H&E Stained Whole-slide Images On Hepatocellular Carcinoma
| Title | Analysis Of Multi Field Of View Cnn And Attention Cnn On H&E Stained Whole-slide Images On Hepatocellular Carcinoma |
| Authors | Mehmet Burak Sayıcı, Rikiya Yamashita, Jeanne Shen |
| Abstract | Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Whole-slide imaging which is a method of scanning glass slides have been employed for diagnosis of HCC. Using high resolution Whole-slide images is infeasible for Convolutional Neural Network applications. Hence tiling the Whole-slide images is a common methodology for assigning Convolutional Neural Networks for classification and segmentation. Determination of the tile size affects the performance of the algorithms since small field of view can not capture the information on a larger scale and large field of view can not capture the information on a cellular scale. In this work, the effect of tile size on performance for classification problem is analysed. In addition, Multi Field of View CNN is assigned for taking advantage of the information provided by different tile sizes and Attention CNN is assigned for giving the capability of voting most contributing tile size. It is found that employing more than one tile size significantly increases the performance of the classification by 3.97% and both algorithms are found successful over the algorithm which uses only one tile size. |
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| Published | 2020-02-12 |
| URL | https://arxiv.org/abs/2002.04836v2 |
| https://arxiv.org/pdf/2002.04836v2.pdf | |
| PWC | https://paperswithcode.com/paper/analysis-of-multi-field-of-view-cnn-and |
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A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children
| Title | A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children |
| Authors | Michael A. Skinner, Lakshmi Raman, Neel Shah, Abdelaziz Farhat, Sriraam Natarajan |
| Abstract | The increased use of electronic health records has made possible the automated extraction of medical policies from patient records to aid in the development of clinical decision support systems. We adapted a boosted Statistical Relational Learning (SRL) framework to learn probabilistic rules from clinical hospital records for the management of physiologic parameters of children with severe cardiac or respiratory failure who were managed with extracorporeal membrane oxygenation. In this preliminary study, the results were promising. In particular, the algorithm returned logic rules for medical actions that are consistent with medical reasoning. |
| Tasks | Relational Reasoning |
| Published | 2020-01-13 |
| URL | https://arxiv.org/abs/2001.04432v1 |
| https://arxiv.org/pdf/2001.04432v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-preliminary-approach-for-learning |
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Optimal Randomized First-Order Methods for Least-Squares Problems
| Title | Optimal Randomized First-Order Methods for Least-Squares Problems |
| Authors | Jonathan Lacotte, Mert Pilanci |
| Abstract | We provide an exact analysis of a class of randomized algorithms for solving overdetermined least-squares problems. We consider first-order methods, where the gradients are pre-conditioned by an approximation of the Hessian, based on a subspace embedding of the data matrix. This class of algorithms encompasses several randomized methods among the fastest solvers for least-squares problems. We focus on two classical embeddings, namely, Gaussian projections and subsampled randomized Hadamard transforms (SRHT). Our key technical innovation is the derivation of the limiting spectral density of SRHT embeddings. Leveraging this novel result, we derive the family of normalized orthogonal polynomials of the SRHT density and we find the optimal pre-conditioned first-order method along with its rate of convergence. Our analysis of Gaussian embeddings proceeds similarly, and leverages classical random matrix theory results. In particular, we show that for a given sketch size, SRHT embeddings exhibits a faster rate of convergence than Gaussian embeddings. Then, we propose a new algorithm by optimizing the computational complexity over the choice of the sketching dimension. To our knowledge, our resulting algorithm yields the best known complexity for solving least-squares problems with no condition number dependence. |
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| Published | 2020-02-21 |
| URL | https://arxiv.org/abs/2002.09488v2 |
| https://arxiv.org/pdf/2002.09488v2.pdf | |
| PWC | https://paperswithcode.com/paper/optimal-randomized-first-order-methods-for |
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ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition
| Title | ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition |
| Authors | Parastoo Alinia, Iman Mirzadeh, Hassan Ghasemzadeh |
| Abstract | Sensor-based human activity recognition has become a critical component of many emerging applications ranging from behavioral medicine to gaming. However, an unprecedented increase in the diversity of sensor devices in the Internet-of-Things era has limited the adoption of activity recognition models for use across different domains. We propose ActiLabel a combinatorial framework that learns structural similarities among the events in an arbitrary domain and those of a different domain. The structural similarities are captured through a graph model, referred to as the it dependency graph, which abstracts details of activity patterns in low-level signal and feature space. The activity labels are then autonomously learned by finding an optimal tiered mapping between the dependency graphs. Extensive experiments based on three public datasets demonstrate the superiority of ActiLabel over state-of-the-art transfer learning and deep learning methods. |
| Tasks | Activity Recognition, Human Activity Recognition, Transfer Learning |
| Published | 2020-03-16 |
| URL | https://arxiv.org/abs/2003.07415v1 |
| https://arxiv.org/pdf/2003.07415v1.pdf | |
| PWC | https://paperswithcode.com/paper/actilabel-a-combinatorial-transfer-learning |
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Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness
| Title | Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness |
| Authors | Huijie Feng, Chunpeng Wu, Guoyang Chen, Weifeng Zhang, Yang Ning |
| Abstract | Recently smoothing deep neural network based classifiers via isotropic Gaussian perturbation is shown to be an effective and scalable way to provide state-of-the-art probabilistic robustness guarantee against $\ell_2$ norm bounded adversarial perturbations. However, how to train a good base classifier that is accurate and robust when smoothed has not been fully investigated. In this work, we derive a new regularized risk, in which the regularizer can adaptively encourage the accuracy and robustness of the smoothed counterpart when training the base classifier. It is computationally efficient and can be implemented in parallel with other empirical defense methods. We discuss how to implement it under both standard (non-adversarial) and adversarial training scheme. At the same time, we also design a new certification algorithm, which can leverage the regularization effect to provide tighter robustness lower bound that holds with high probability. Our extensive experimentation demonstrates the effectiveness of the proposed training and certification approaches on CIFAR-10 and ImageNet datasets. |
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| Published | 2020-02-17 |
| URL | https://arxiv.org/abs/2002.07246v1 |
| https://arxiv.org/pdf/2002.07246v1.pdf | |
| PWC | https://paperswithcode.com/paper/regularized-training-and-tight-certification |
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Declarative Memory-based Structure for the Representation of Text Data
| Title | Declarative Memory-based Structure for the Representation of Text Data |
| Authors | Sumant Pushp, Pragya Kashmira, Shyamanta M Hazarika |
| Abstract | In the era of intelligent computing, computational progress in text processing is an essential consideration. Many systems have been developed to process text over different languages. Though, there is considerable development, they still lack in understanding of the text, i.e., instead of keeping text as knowledge, many treat text as a data. In this work we introduce a text representation scheme which is influenced by human memory infrastructure. Since texts are declarative in nature, a structural organization would foster efficient computation over text. We exploit long term episodic memory to keep text information observed over time. This not only keep fragments of text in an organized fashion but also reduces redundancy and stores the temporal relation among them. Wordnet has been used to imitate semantic memory, which works at word level to facilitate the understanding about individual words within text. Experimental results of various operation performed over episodic memory and growth of knowledge infrastructure over time is reported. |
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| Published | 2020-02-25 |
| URL | https://arxiv.org/abs/2002.10665v1 |
| https://arxiv.org/pdf/2002.10665v1.pdf | |
| PWC | https://paperswithcode.com/paper/declarative-memory-based-structure-for-the |
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Multi-Task Siamese Neural Network for Improving Replay Attack Detection
| Title | Multi-Task Siamese Neural Network for Improving Replay Attack Detection |
| Authors | Patrick von Platen, Fei Tao, Gokhan Tur |
| Abstract | Automatic speaker verification systems are vulnerable to audio replay attacks which bypass security by replaying recordings of authorized speakers. Replay attack detection (RA) detection systems built upon Residual Neural Networks (ResNet)s have yielded astonishing results on the public benchmark ASVspoof 2019 Physical Access challenge. With most teams using fine-tuned feature extraction pipelines and model architectures, the generalizability of such systems remains questionable though. In this work, we analyse the effect of discriminative feature learning in a multi-task learning (MTL) setting can have on the generalizability and discriminability of RA detection systems. We use a popular ResNet architecture optimized by the cross-entropy criterion as our baseline and compare it to the same architecture optimized by MTL using Siamese Neural Networks (SNN). It can be shown that SNN outperform the baseline by relative 26.8 % Equal Error Rate (EER). We further enhance the model’s architecture and demonstrate that SNN with additional reconstruction loss yield another significant improvement of relative 13.8 % EER. |
| Tasks | Multi-Task Learning, Speaker Verification |
| Published | 2020-02-16 |
| URL | https://arxiv.org/abs/2002.07629v1 |
| https://arxiv.org/pdf/2002.07629v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-task-siamese-neural-network-for |
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A Scalable Method for Scheduling Distributed Energy Resources using Parallelized Population-based Metaheuristics
| Title | A Scalable Method for Scheduling Distributed Energy Resources using Parallelized Population-based Metaheuristics |
| Authors | Hatem Khalloof, Wilfried Jakob, Shadi Shahoud, Clemens Duepmeier, Veit Hagenmeyer |
| Abstract | Recent years have seen an increasing integration of distributed renewable energy resources into existing electric power grids. Due to the uncertain nature of renewable energy resources, network operators are faced with new challenges in balancing load and generation. In order to meet the new requirements, intelligent distributed energy resource plants can be used. However, the calculation of an adequate schedule for the unit commitment of such distributed energy resources is a complex optimization problem which is typically too complex for standard optimization algorithms if large numbers of distributed energy resources are considered. For solving such complex optimization tasks, population-based metaheuristics – as, e.g., evolutionary algorithms – represent powerful alternatives. Admittedly, evolutionary algorithms do require lots of computational power for solving such problems in a timely manner. One promising solution for this performance problem is the parallelization of the usually time-consuming evaluation of alternative solutions. In the present paper, a new generic and highly scalable parallel method for unit commitment of distributed energy resources using metaheuristic algorithms is presented. It is based on microservices, container virtualization and the publish/subscribe messaging paradigm for scheduling distributed energy resources. Scalability and applicability of the proposed solution are evaluated by performing parallelized optimizations in a big data environment for three distinct distributed energy resource scheduling scenarios. Thereby, unlike all other optimization methods in the literature, the new method provides cluster or cloud parallelizability and is able to deal with a comparably large number of distributed energy resources. The application of the new proposed method results in very good performance for scaling up optimization speed. |
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| Published | 2020-02-18 |
| URL | https://arxiv.org/abs/2002.07505v1 |
| https://arxiv.org/pdf/2002.07505v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-scalable-method-for-scheduling-distributed |
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CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media
| Title | CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media |
| Authors | Alberto Barron-Cedeno, Tamer Elsayed, Preslav Nakov, Giovanni Da San Martino, Maram Hasanain, Reem Suwaileh, Fatima Haouari |
| Abstract | We describe the third edition of the CheckThat! Lab, which is part of the 2020 Cross-Language Evaluation Forum (CLEF). CheckThat! proposes four complementary tasks and a related task from previous lab editions, offered in English, Arabic, and Spanish. Task 1 asks to predict which tweets in a Twitter stream are worth fact-checking. Task 2 asks to determine whether a claim posted in a tweet can be verified using a set of previously fact-checked claims. Task 3 asks to retrieve text snippets from a given set of Web pages that would be useful for verifying a target tweet’s claim. Task 4 asks to predict the veracity of a target tweet’s claim using a set of Web pages and potentially useful snippets in them. Finally, the lab offers a fifth task that asks to predict the check-worthiness of the claims made in English political debates and speeches. CheckThat! features a full evaluation framework. The evaluation is carried out using mean average precision or precision at rank k for ranking tasks, and F1 for classification tasks. |
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| Published | 2020-01-21 |
| URL | https://arxiv.org/abs/2001.08546v1 |
| https://arxiv.org/pdf/2001.08546v1.pdf | |
| PWC | https://paperswithcode.com/paper/checkthat-at-clef-2020-enabling-the-automatic |
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Efficient reinforcement learning control for continuum robots based on Inexplicit Prior Knowledge
| Title | Efficient reinforcement learning control for continuum robots based on Inexplicit Prior Knowledge |
| Authors | Junjia Liu, Jiaying Shou, Zhuang Fu, Hangfei Zhou, Rongli Xie, Jun Zhang, Jian Fei, Yanna Zhao |
| Abstract | Compared to rigid robots that are often studied in reinforcement learning, the physical characteristics of some sophisticated robots such as software or continuum are more complicated. Moreover, recent reinforcement learning methods are data-inefficient and can not be directly deployed to the robot without simulation. In this paper, we propose an efficient reinforcement learning method based on inexplicit prior knowledge in response to such problems. The method is firstly corroborated by simulation and employed directly in the real world. By using our method, we can achieve visual active tracking and distance maintenance of a tendon-driven robot which will be critical in minimally-invasive procedures. |
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| Published | 2020-02-26 |
| URL | https://arxiv.org/abs/2002.11573v1 |
| https://arxiv.org/pdf/2002.11573v1.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-reinforcement-learning-control-for |
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Comparative analysis of machine learning models for Ammonia Capture of Ionic Liquids
| Title | Comparative analysis of machine learning models for Ammonia Capture of Ionic Liquids |
| Authors | Shahaboddin Shamshirband, Narjes Nabipour, Masoud Hadipoor, Alireza Baghban, Amir Mosavi |
| Abstract | Industry uses various solvents in the processes of refrigeration and ventilation. Among them, the Ionic liquids (ILs) as the relatively new solvents, are known for their proven eco-friendly characteristics. In this research, a comprehensive literature review was carried out to deliver an insight into the ILs and the prediction models used for estimating the ammonia solubility in ILs. Furthermore, a number of advanced machine learning methods, i.e. multilayer perceptron (MLP) and a combination of particle swarm optimization (PSO) and adaptive neuro-fuzzy inference system (ANFIS) models are used to estimate the solubility of ammonia in various ionic liquids. Affecting parameters were molecular weight, critical temperature and pressure of ILs. Furthermore, the salability is also predicted using the two-equation of states. Down the line, some comparisons were drawn between experimental and modeling results which is rarely done. The study shows that the equations of states are not able estimate the solubility of ammonia accurately, by contrast, artificial intelligence methods have produced promising results. |
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| Published | 2020-02-19 |
| URL | https://arxiv.org/abs/2003.06224v1 |
| https://arxiv.org/pdf/2003.06224v1.pdf | |
| PWC | https://paperswithcode.com/paper/comparative-analysis-of-machine-learning |
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The reproducing Stein kernel approach for post-hoc corrected sampling
| Title | The reproducing Stein kernel approach for post-hoc corrected sampling |
| Authors | Liam Hodgkinson, Robert Salomone, Fred Roosta |
| Abstract | Stein importance sampling is a widely applicable technique based on kernelized Stein discrepancy, which corrects the output of approximate sampling algorithms by reweighting the empirical distribution of the samples. A general analysis of this technique is conducted for the previously unconsidered setting where samples are obtained via the simulation of a Markov chain, and applies to an arbitrary underlying Polish space. We prove that Stein importance sampling yields consistent estimators for quantities related to a target distribution of interest by using samples obtained from a geometrically ergodic Markov chain with a possibly unknown invariant measure that differs from the desired target. The approach is shown to be valid under conditions that are satisfied for a large number of unadjusted samplers, and is capable of retaining consistency when data subsampling is used. Along the way, a universal theory of reproducing Stein kernels is established, which enables the construction of kernelized Stein discrepancy on general Polish spaces, and provides sufficient conditions for kernels to be convergence-determining on such spaces. These results are of independent interest for the development of future methodology based on kernelized Stein discrepancies. |
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| Published | 2020-01-25 |
| URL | https://arxiv.org/abs/2001.09266v1 |
| https://arxiv.org/pdf/2001.09266v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-reproducing-stein-kernel-approach-for |
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Boosting rare benthic macroinvertebrates taxa identification with one-class classification
| Title | Boosting rare benthic macroinvertebrates taxa identification with one-class classification |
| Authors | Fahad Sohrab, Jenni Raitoharju |
| Abstract | Insect monitoring is crucial for understanding the consequences of rapid ecological changes, but taxa identification currently requires tedious manual expert work and cannot be scaled-up efficiently. Deep convolutional neural networks (CNNs), provide a viable way to significantly increase the biomonitoring volumes. However, taxa abundances are typically very imbalanced and the amounts of training images for the rarest classes are simply too low for deep CNNs. As a result, the samples from the rare classes are often completely missed, while detecting them has biological importance. In this paper, we propose combining the trained deep CNN with one-class classifiers to improve the rare species identification. One-class classification models are traditionally trained with much fewer samples and they can provide a mechanism to indicate samples potentially belonging to the rare classes for human inspection. Our experiments confirm that the proposed approach may indeed support moving towards partial automation of the taxa identification task. |
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| Published | 2020-02-12 |
| URL | https://arxiv.org/abs/2002.10420v1 |
| https://arxiv.org/pdf/2002.10420v1.pdf | |
| PWC | https://paperswithcode.com/paper/boosting-rare-benthic-macroinvertebrates-taxa |
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Online detection of local abrupt changes in high-dimensional Gaussian graphical models
| Title | Online detection of local abrupt changes in high-dimensional Gaussian graphical models |
| Authors | Hossein Keshavarz, George Michailidis |
| Abstract | The problem of identifying change points in high-dimensional Gaussian graphical models (GGMs) in an online fashion is of interest, due to new applications in biology, economics and social sciences. The offline version of the problem, where all the data are a priori available, has led to a number of methods and associated algorithms involving regularized loss functions. However, for the online version, there is currently only a single work in the literature that develops a sequential testing procedure and also studies its asymptotic false alarm probability and power. The latter test is best suited for the detection of change points driven by global changes in the structure of the precision matrix of the GGM, in the sense that many edges are involved. Nevertheless, in many practical settings the change point is driven by local changes, in the sense that only a small number of edges exhibit changes. To that end, we develop a novel test to address this problem that is based on the $\ell_\infty$ norm of the normalized covariance matrix of an appropriately selected portion of incoming data. The study of the asymptotic distribution of the proposed test statistic under the null (no presence of a change point) and the alternative (presence of a change point) hypotheses requires new technical tools that examine maxima of graph-dependent Gaussian random variables, and that of independent interest. It is further shown that these tools lead to the imposition of mild regularity conditions for key model parameters, instead of more stringent ones required by leveraging previously used tools in related problems in the literature. Numerical work on synthetic data illustrates the good performance of the proposed detection procedure both in terms of computational and statistical efficiency across numerous experimental settings. |
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| Published | 2020-03-16 |
| URL | https://arxiv.org/abs/2003.06961v1 |
| https://arxiv.org/pdf/2003.06961v1.pdf | |
| PWC | https://paperswithcode.com/paper/online-detection-of-local-abrupt-changes-in |
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Automating Botnet Detection with Graph Neural Networks
| Title | Automating Botnet Detection with Graph Neural Networks |
| Authors | Jiawei Zhou, Zhiying Xu, Alexander M. Rush, Minlan Yu |
| Abstract | Botnets are now a major source for many network attacks, such as DDoS attacks and spam. However, most traditional detection methods heavily rely on heuristically designed multi-stage detection criteria. In this paper, we consider the neural network design challenges of using modern deep learning techniques to learn policies for botnet detection automatically. To generate training data, we synthesize botnet connections with different underlying communication patterns overlaid on large-scale real networks as datasets. To capture the important hierarchical structure of centralized botnets and the fast-mixing structure for decentralized botnets, we tailor graph neural networks (GNN) to detect the properties of these structures. Experimental results show that GNNs are better able to capture botnet structure than previous non-learning methods when trained with appropriate data, and that deeper GNNs are crucial for learning difficult botnet topologies. We believe our data and studies can be useful for both the network security and graph learning communities. |
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| Published | 2020-03-13 |
| URL | https://arxiv.org/abs/2003.06344v1 |
| https://arxiv.org/pdf/2003.06344v1.pdf | |
| PWC | https://paperswithcode.com/paper/automating-botnet-detection-with-graph-neural |
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