May 7, 2019

3435 words 17 mins read

Paper Group ANR 60

A Hybrid Citation Retrieval Algorithm for Evidence-based Clinical Knowledge Summarization: Combining Concept Extraction, Vector Similarity and Query Expansion for High Precision. On Improving Informativity and Grammaticality for Multi-Sentence Compression. Labeled Multi-Bernoulli Tracking for Industrial Mobile Platform Safety. Joint Visual Denoisin …

A Hybrid Citation Retrieval Algorithm for Evidence-based Clinical Knowledge Summarization: Combining Concept Extraction, Vector Similarity and Query Expansion for High Precision


Title	A Hybrid Citation Retrieval Algorithm for Evidence-based Clinical Knowledge Summarization: Combining Concept Extraction, Vector Similarity and Query Expansion for High Precision
Authors	Kalpana Raja, Andrew J Sauer, Ravi P Garg, Melanie R Klerer, Siddhartha R Jonnalagadda
Abstract	Novel information retrieval methods to identify citations relevant to a clinical topic can overcome the knowledge gap existing between the primary literature (MEDLINE) and online clinical knowledge resources such as UpToDate. Searching the MEDLINE database directly or with query expansion methods returns a large number of citations that are not relevant to the query. The current study presents a citation retrieval system that retrieves citations for evidence-based clinical knowledge summarization. This approach combines query expansion, concept-based screening algorithm, and concept-based vector similarity. We also propose an information extraction framework for automated concept (Population, Intervention, Comparison, and Disease) extraction. We evaluated our proposed system on all topics (as queries) available from UpToDate for two diseases, heart failure (HF) and atrial fibrillation (AFib). The system achieved an overall F-score of 41.2% on HF topics and 42.4% on AFib topics on a gold standard of citations available in UpToDate. This is significantly high when compared to a query-expansion based baseline (F-score of 1.3% on HF and 2.2% on AFib) and a system that uses query expansion with disease hyponyms and journal names, concept-based screening, and term-based vector similarity system (F-score of 37.5% on HF and 39.5% on AFib). Evaluating the system with top K relevant citations, where K is the number of citations in the gold standard achieved a much higher overall F-score of 69.9% on HF topics and 75.1% on AFib topics. In addition, the system retrieved up to 18 new relevant citations per topic when tested on ten HF and six AFib clinical topics.
Tasks	Information Retrieval
Published	2016-09-06
URL	http://arxiv.org/abs/1609.01597v1
PDF	http://arxiv.org/pdf/1609.01597v1.pdf
PWC	https://paperswithcode.com/paper/a-hybrid-citation-retrieval-algorithm-for
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On Improving Informativity and Grammaticality for Multi-Sentence Compression


Title	On Improving Informativity and Grammaticality for Multi-Sentence Compression
Authors	Elaheh ShafieiBavani, Mohammad Ebrahimi, Raymond Wong, Fang Chen
Abstract	Multi Sentence Compression (MSC) is of great value to many real world applications, such as guided microblog summarization, opinion summarization and newswire summarization. Recently, word graph-based approaches have been proposed and become popular in MSC. Their key assumption is that redundancy among a set of related sentences provides a reliable way to generate informative and grammatical sentences. In this paper, we propose an effective approach to enhance the word graph-based MSC and tackle the issue that most of the state-of-the-art MSC approaches are confronted with: i.e., improving both informativity and grammaticality at the same time. Our approach consists of three main components: (1) a merging method based on Multiword Expressions (MWE); (2) a mapping strategy based on synonymy between words; (3) a re-ranking step to identify the best compression candidates generated using a POS-based language model (POS-LM). We demonstrate the effectiveness of this novel approach using a dataset made of clusters of English newswire sentences. The observed improvements on informativity and grammaticality of the generated compressions show that our approach is superior to state-of-the-art MSC methods.
Tasks	Language Modelling, Sentence Compression
Published	2016-05-07
URL	http://arxiv.org/abs/1605.02150v1
PDF	http://arxiv.org/pdf/1605.02150v1.pdf
PWC	https://paperswithcode.com/paper/on-improving-informativity-and-grammaticality
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Labeled Multi-Bernoulli Tracking for Industrial Mobile Platform Safety


Title	Labeled Multi-Bernoulli Tracking for Industrial Mobile Platform Safety
Authors	Tharindu Rathnayake, Reza Hoseinnezhad, Ruwan Tennakoon, Alireza Bab-Hadiashar
Abstract	This paper presents a track-before-detect labeled multi-Bernoulli filter tailored for industrial mobile platform safety applications. We derive two application specific separable likelihood functions that capture the geometric shape and colour information of the human targets who are wearing a high visible vest. These likelihoods are then used in a labeled multi-Bernoulli filter with a novel two step Bayesian update. Preliminary simulation results show that the proposed solution can successfully track human workers wearing a luminous yellow colour vest in an industrial environment.
Tasks
Published	2016-04-20
URL	http://arxiv.org/abs/1604.05966v2
PDF	http://arxiv.org/pdf/1604.05966v2.pdf
PWC	https://paperswithcode.com/paper/labeled-multi-bernoulli-tracking-for
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Joint Visual Denoising and Classification using Deep Learning


Title	Joint Visual Denoising and Classification using Deep Learning
Authors	Gang Chen, Yawei Li, Sargur N. Srihari
Abstract	Visual restoration and recognition are traditionally addressed in pipeline fashion, i.e. denoising followed by classification. Instead, observing correlations between the two tasks, for example clearer image will lead to better categorization and vice visa, we propose a joint framework for visual restoration and recognition for handwritten images, inspired by advances in deep autoencoder and multi-modality learning. Our model is a 3-pathway deep architecture with a hidden-layer representation which is shared by multi-inputs and outputs, and each branch can be composed of a multi-layer deep model. Thus, visual restoration and classification can be unified using shared representation via non-linear mapping, and model parameters can be learnt via backpropagation. Using MNIST and USPS data corrupted with structured noise, the proposed framework performs at least 20% better in classification than separate pipelines, as well as clearer recovered images. The noise model and the reproducible source code is available at {\url{https://github.com/ganggit/jointmodel}}.
Tasks	Denoising
Published	2016-12-04
URL	http://arxiv.org/abs/1612.01075v1
PDF	http://arxiv.org/pdf/1612.01075v1.pdf
PWC	https://paperswithcode.com/paper/joint-visual-denoising-and-classification
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A Survey of Stealth Malware: Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions


Title	A Survey of Stealth Malware: Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions
Authors	Ethan M. Rudd, Andras Rozsa, Manuel Günther, Terrance E. Boult
Abstract	As our professional, social, and financial existences become increasingly digitized and as our government, healthcare, and military infrastructures rely more on computer technologies, they present larger and more lucrative targets for malware. Stealth malware in particular poses an increased threat because it is specifically designed to evade detection mechanisms, spreading dormant, in the wild for extended periods of time, gathering sensitive information or positioning itself for a high-impact zero-day attack. Policing the growing attack surface requires the development of efficient anti-malware solutions with improved generalization to detect novel types of malware and resolve these occurrences with as little burden on human experts as possible. In this paper, we survey malicious stealth technologies as well as existing solutions for detecting and categorizing these countermeasures autonomously. While machine learning offers promising potential for increasingly autonomous solutions with improved generalization to new malware types, both at the network level and at the host level, our findings suggest that several flawed assumptions inherent to most recognition algorithms prevent a direct mapping between the stealth malware recognition problem and a machine learning solution. The most notable of these flawed assumptions is the closed world assumption: that no sample belonging to a class outside of a static training set will appear at query time. We present a formalized adaptive open world framework for stealth malware recognition and relate it mathematically to research from other machine learning domains.
Tasks
Published	2016-03-19
URL	http://arxiv.org/abs/1603.06028v2
PDF	http://arxiv.org/pdf/1603.06028v2.pdf
PWC	https://paperswithcode.com/paper/a-survey-of-stealth-malware-attacks
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Master’s Thesis : Deep Learning for Visual Recognition


Title	Master’s Thesis : Deep Learning for Visual Recognition
Authors	Rémi Cadène, Nicolas Thome, Matthieu Cord
Abstract	The goal of our research is to develop methods advancing automatic visual recognition. In order to predict the unique or multiple labels associated to an image, we study different kind of Deep Neural Networks architectures and methods for supervised features learning. We first draw up a state-of-the-art review of the Convolutional Neural Networks aiming to understand the history behind this family of statistical models, the limit of modern architectures and the novel techniques currently used to train deep CNNs. The originality of our work lies in our approach focusing on tasks with a low amount of data. We introduce different models and techniques to achieve the best accuracy on several kind of datasets, such as a medium dataset of food recipes (100k images) for building a web API, or a small dataset of satellite images (6,000) for the DSG online challenge that we’ve won. We also draw up the state-of-the-art in Weakly Supervised Learning, introducing different kind of CNNs able to localize regions of interest. Our last contribution is a framework, build on top of Torch7, for training and testing deep models on any visual recognition tasks and on datasets of any scale.
Tasks
Published	2016-10-18
URL	http://arxiv.org/abs/1610.05567v1
PDF	http://arxiv.org/pdf/1610.05567v1.pdf
PWC	https://paperswithcode.com/paper/masters-thesis-deep-learning-for-visual
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On Feature based Delaunay Triangulation for Palmprint Recognition


Title	On Feature based Delaunay Triangulation for Palmprint Recognition
Authors	Zanobya N. Khan, Rashid Jalal Qureshi, Jamil Ahmad
Abstract	Authentication of individuals via palmprint based biometric system is becoming very popular due to its reliability as it contains unique and stable features. In this paper, we present a novel approach for palmprint recognition and its representation. To extract the palm lines, local thresholding technique Niblack binarization algorithm is adopted. The endpoints of these lines are determined and a connection is created among them using the Delaunay triangulation thereby generating a distinct topological structure of each palmprint. Next, we extract different geometric as well as quantitative features from the triangles of the Delaunay triangulation that assist in identifying different individuals. To ensure that the proposed approach is invariant to rotation and scaling, features were made relative to topological and geometrical structure of the palmprint. The similarity of the two palmprints is computed using the weighted sum approach and compared with the k-nearest neighbor. The experimental results obtained reflect the effectiveness of the proposed approach to discriminate between different palmprint images and thus achieved a recognition rate of 90% over large databases.
Tasks
Published	2016-02-05
URL	http://arxiv.org/abs/1602.01927v1
PDF	http://arxiv.org/pdf/1602.01927v1.pdf
PWC	https://paperswithcode.com/paper/on-feature-based-delaunay-triangulation-for
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Feedback-Controlled Sequential Lasso Screening


Title	Feedback-Controlled Sequential Lasso Screening
Authors	Yun Wang, Xu Chen, Peter J. Ramadge
Abstract	One way to solve lasso problems when the dictionary does not fit into available memory is to first screen the dictionary to remove unneeded features. Prior research has shown that sequential screening methods offer the greatest promise in this endeavor. Most existing work on sequential screening targets the context of tuning parameter selection, where one screens and solves a sequence of $N$ lasso problems with a fixed grid of geometrically spaced regularization parameters. In contrast, we focus on the scenario where a target regularization parameter has already been chosen via cross-validated model selection, and we then need to solve many lasso instances using this fixed value. In this context, we propose and explore a feedback controlled sequential screening scheme. Feedback is used at each iteration to select the next problem to be solved. This allows the sequence of problems to be adapted to the instance presented and the number of intermediate problems to be automatically selected. We demonstrate our feedback scheme using several datasets including a dictionary of approximate size 100,000 by 300,000.
Tasks	Model Selection
Published	2016-08-21
URL	http://arxiv.org/abs/1608.06010v2
PDF	http://arxiv.org/pdf/1608.06010v2.pdf
PWC	https://paperswithcode.com/paper/feedback-controlled-sequential-lasso
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Collaborative Layer-wise Discriminative Learning in Deep Neural Networks


Title	Collaborative Layer-wise Discriminative Learning in Deep Neural Networks
Authors	Xiaojie Jin, Yunpeng Chen, Jian Dong, Jiashi Feng, Shuicheng Yan
Abstract	Intermediate features at different layers of a deep neural network are known to be discriminative for visual patterns of different complexities. However, most existing works ignore such cross-layer heterogeneities when classifying samples of different complexities. For example, if a training sample has already been correctly classified at a specific layer with high confidence, we argue that it is unnecessary to enforce rest layers to classify this sample correctly and a better strategy is to encourage those layers to focus on other samples. In this paper, we propose a layer-wise discriminative learning method to enhance the discriminative capability of a deep network by allowing its layers to work collaboratively for classification. Towards this target, we introduce multiple classifiers on top of multiple layers. Each classifier not only tries to correctly classify the features from its input layer, but also coordinates with other classifiers to jointly maximize the final classification performance. Guided by the other companion classifiers, each classifier learns to concentrate on certain training examples and boosts the overall performance. Allowing for end-to-end training, our method can be conveniently embedded into state-of-the-art deep networks. Experiments with multiple popular deep networks, including Network in Network, GoogLeNet and VGGNet, on scale-various object classification benchmarks, including CIFAR100, MNIST and ImageNet, and scene classification benchmarks, including MIT67, SUN397 and Places205, demonstrate the effectiveness of our method. In addition, we also analyze the relationship between the proposed method and classical conditional random fields models.
Tasks	Object Classification, Scene Classification
Published	2016-07-19
URL	http://arxiv.org/abs/1607.05440v1
PDF	http://arxiv.org/pdf/1607.05440v1.pdf
PWC	https://paperswithcode.com/paper/collaborative-layer-wise-discriminative
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Comparison of 14 different families of classification algorithms on 115 binary datasets


Title	Comparison of 14 different families of classification algorithms on 115 binary datasets
Authors	Jacques Wainer
Abstract	We tested 14 very different classification algorithms (random forest, gradient boosting machines, SVM - linear, polynomial, and RBF - 1-hidden-layer neural nets, extreme learning machines, k-nearest neighbors and a bagging of knn, naive Bayes, learning vector quantization, elastic net logistic regression, sparse linear discriminant analysis, and a boosting of linear classifiers) on 115 real life binary datasets. We followed the Demsar analysis and found that the three best classifiers (random forest, gbm and RBF SVM) are not significantly different from each other. We also discuss that a change of less then 0.0112 in the error rate should be considered as an irrelevant change, and used a Bayesian ANOVA analysis to conclude that with high probability the differences between these three classifiers is not of practical consequence. We also verified the execution time of “standard implementations” of these algorithms and concluded that RBF SVM is the fastest (significantly so) both in training time and in training plus testing time.
Tasks	Quantization
Published	2016-06-02
URL	http://arxiv.org/abs/1606.00930v1
PDF	http://arxiv.org/pdf/1606.00930v1.pdf
PWC	https://paperswithcode.com/paper/comparison-of-14-different-families-of
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Temporal Clustering of Time Series via Threshold Autoregressive Models: Application to Commodity Prices


Title	Temporal Clustering of Time Series via Threshold Autoregressive Models: Application to Commodity Prices
Authors	Sipan Aslan, Ceylan Yozgatligil, Cem Iyigun
Abstract	This study aimed to find temporal clusters for several commodity prices using the threshold non-linear autoregressive model. It is expected that the process of determining the commodity groups that are time-dependent will advance the current knowledge about the dynamics of co-moving and coherent prices, and can serve as a basis for multivariate time series analyses. The clustering of commodity prices was examined using the proposed clustering approach based on time series models to incorporate the time varying properties of price series into the clustering scheme. Accordingly, the primary aim in this study was grouping time series according to the similarity between their Data Generating Mechanisms (DGMs) rather than comparing pattern similarities in the time series traces. The approximation to the DGM of each series was accomplished using threshold autoregressive models, which are recognized for their ability to represent nonlinear features in time series, such as abrupt changes, time-irreversibility and regime-shifting behavior. Through the use of the proposed approach, one can determine and monitor the set of co-moving time series variables across the time dimension. Furthermore, generating a time varying commodity price index and sub-indexes can become possible. Consequently, we conducted a simulation study to assess the effectiveness of the proposed clustering approach and the results are presented for both the simulated and real data sets.
Tasks	Time Series
Published	2016-05-03
URL	http://arxiv.org/abs/1605.00779v1
PDF	http://arxiv.org/pdf/1605.00779v1.pdf
PWC	https://paperswithcode.com/paper/temporal-clustering-of-time-series-via
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Computer Aided Detection of Oral Lesions on CT Images


Title	Computer Aided Detection of Oral Lesions on CT Images
Authors	Shaikat Galib, Fahima Islam, Muhammad Abir, Hyoung-Koo Lee
Abstract	Oral lesions are important findings on computed tomography (CT) images. In this study, a fully automatic method to detect oral lesions in mandibular region from dental CT images is proposed. Two methods were developed to recognize two types of lesions namely (1) Close border (CB) lesions and (2) Open border (OB) lesions, which cover most of the lesion types that can be found on CT images. For the detection of CB lesions, fifteen features were extracted from each initial lesion candidates and multi layer perceptron (MLP) neural network was used to classify suspicious regions. Moreover, OB lesions were detected using a rule based image processing method, where no feature extraction or classification algorithm were used. The results were validated using a CT dataset of 52 patients, where 22 patients had abnormalities and 30 patients were normal. Using non-training dataset, CB detection algorithm yielded 71% sensitivity with 0.31 false positives per patient. Furthermore, OB detection algorithm achieved 100% sensitivity with 0.13 false positives per patient. Results suggest that, the proposed framework, which consists of two methods, has the potential to be used in clinical context, and assist radiologists for better diagnosis.
Tasks	Computed Tomography (CT)
Published	2016-11-29
URL	http://arxiv.org/abs/1611.09769v1
PDF	http://arxiv.org/pdf/1611.09769v1.pdf
PWC	https://paperswithcode.com/paper/computer-aided-detection-of-oral-lesions-on
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Convolutional Experts Constrained Local Model for Facial Landmark Detection


Title	Convolutional Experts Constrained Local Model for Facial Landmark Detection
Authors	Amir Zadeh, Tadas Baltrušaitis, Louis-Philippe Morency
Abstract	Constrained Local Models (CLMs) are a well-established family of methods for facial landmark detection. However, they have recently fallen out of favor to cascaded regression-based approaches. This is in part due to the inability of existing CLM local detectors to model the very complex individual landmark appearance that is affected by expression, illumination, facial hair, makeup, and accessories. In our work, we present a novel local detector – Convolutional Experts Network (CEN) – that brings together the advantages of neural architectures and mixtures of experts in an end-to-end framework. We further propose a Convolutional Experts Constrained Local Model (CE-CLM) algorithm that uses CEN as local detectors. We demonstrate that our proposed CE-CLM algorithm outperforms competitive state-of-the-art baselines for facial landmark detection by a large margin on four publicly-available datasets. Our approach is especially accurate and robust on challenging profile images.
Tasks	Facial Landmark Detection
Published	2016-11-26
URL	http://arxiv.org/abs/1611.08657v5
PDF	http://arxiv.org/pdf/1611.08657v5.pdf
PWC	https://paperswithcode.com/paper/convolutional-experts-constrained-local-model
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Incremental One-Class Models for Data Classification


Title	Incremental One-Class Models for Data Classification
Authors	Takoua Kefi, Riadh Ksantini, M. Becha Kaaniche, Adel Bouhoula
Abstract	In this paper we outline a PhD research plan. This research contributes to the field of one-class incremental learning and classification in case of non-stationary environments. The goal of this PhD is to define a new classification framework able to deal with very small learning dataset at the beginning of the process and with abilities to adjust itself according to the variability of the incoming data which create large scale datasets. As a preliminary work, incremental Covariance-guided One-Class Support Vector Machine is proposed to deal with sequentially obtained data. It is inspired from COSVM which put more emphasis on the low variance directions while keeping the basic formulation of incremental One-Class Support Vector Machine untouched. The incremental procedure is introduced by controlling the possible changes of support vectors after the addition of new data points, thanks to the Karush-Kuhn-Tucker conditions, that have to be maintained on all previously acquired data. Comparative experimental results with contemporary incremental and non-incremental one-class classifiers on numerous artificial and real data sets show that our method results in significantly better classification performance.
Tasks
Published	2016-10-15
URL	http://arxiv.org/abs/1610.04725v1
PDF	http://arxiv.org/pdf/1610.04725v1.pdf
PWC	https://paperswithcode.com/paper/incremental-one-class-models-for-data
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A modified single and multi-objective bacteria foraging optimization for the solution of quadratic assignment problem


Title	A modified single and multi-objective bacteria foraging optimization for the solution of quadratic assignment problem
Authors	Saeid Parvandeh, Parya Soltani, Mohammadreza Boroumand, Fahimeh Boroumand
Abstract	Non-polynomial hard (NP-hard) problems are challenging because no polynomial-time algorithm has yet been discovered to solve them in polynomial time. The Bacteria Foraging Optimization (BFO) algorithm is one of the metaheuristics algorithms that is mostly used for NP-hard problems. BFO is inspired by the behavior of the bacteria foraging such as Escherichia coli (E-coli). The aim of BFO is to eliminate those bacteria that have weak foraging properties and maintain those bacteria that have breakthrough foraging properties toward the optimum. Despite the strength of this algorithm, most of the problems reaching optimal solutions are time-demanding or impossible. In this paper, we modified single objective BFO by adding a mutation operator and multi-objective BFO (MOBFO) by adding mutation and crossover from genetic algorithm operators to update the solutions in each generation, and local tabu search algorithm to reach the local optimum solution. Additionally, we used a fast nondominated sort algorithm in MOBFO to find the best-nondominated solutions in each generation. We evaluated the performance of the proposed algorithms through a number of single and multi-objective Quadratic Assignment Problem (QAP) instances. The experimental results show that our approaches outperform some previous optimization algorithms in both convergent and divergent solutions.
Tasks
Published	2016-06-13
URL	https://arxiv.org/abs/1606.04055v2
PDF	https://arxiv.org/pdf/1606.04055v2.pdf
PWC	https://paperswithcode.com/paper/bacteria-foraging-algorithm-with-genetic
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