July 27, 2019

3061 words 15 mins read

Paper Group ANR 464

Ontological Multidimensional Data Models and Contextual Data Qality. Bootstrap Robust Prescriptive Analytics. Fingerprint Extraction Using Smartphone Camera. Adapting general-purpose speech recognition engine output for domain-specific natural language question answering. End-to-end Planning of Fixed Millimeter-Wave Networks. NeST: A Neural Network …

Ontological Multidimensional Data Models and Contextual Data Qality


Title	Ontological Multidimensional Data Models and Contextual Data Qality
Authors	Leopoldo Bertossi, Mostafa Milani
Abstract	Data quality assessment and data cleaning are context-dependent activities. Motivated by this observation, we propose the Ontological Multidimensional Data Model (OMD model), which can be used to model and represent contexts as logic-based ontologies. The data under assessment is mapped into the context, for additional analysis, processing, and quality data extraction. The resulting contexts allow for the representation of dimensions, and multidimensional data quality assessment becomes possible. At the core of a multidimensional context we include a generalized multidimensional data model and a Datalog+/- ontology with provably good properties in terms of query answering. These main components are used to represent dimension hierarchies, dimensional constraints, dimensional rules, and define predicates for quality data specification. Query answering relies upon and triggers navigation through dimension hierarchies, and becomes the basic tool for the extraction of quality data. The OMD model is interesting per se, beyond applications to data quality. It allows for a logic-based, and computationally tractable representation of multidimensional data, extending previous multidimensional data models with additional expressive power and functionalities.
Tasks
Published	2017-04-01
URL	http://arxiv.org/abs/1704.00115v2
PDF	http://arxiv.org/pdf/1704.00115v2.pdf
PWC	https://paperswithcode.com/paper/ontological-multidimensional-data-models-and
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Bootstrap Robust Prescriptive Analytics


Title	Bootstrap Robust Prescriptive Analytics
Authors	Dimitris Bertsimas, Bart Van Parys
Abstract	We address the problem of prescribing an optimal decision in a framework where its cost depends on uncertain problem parameters $Y$ that need to be learned from data. Earlier work by Bertsimas and Kallus (2014) transforms classical machine learning methods that merely predict $Y$ from supervised training data $[(x_1, y_1), \dots, (x_n, y_n)]$ into prescriptive methods taking optimal decisions specific to a particular covariate context $X=\bar x$. Their prescriptive methods factor in additional observed contextual information on a potentially large number of covariates $X=\bar x$ to take context specific actions $z(\bar x)$ which are superior to any static decision $z$. Any naive use of limited training data may, however, lead to gullible decisions over-calibrated to one particular data set. In this paper, we borrow ideas from distributionally robust optimization and the statistical bootstrap of Efron (1982) to propose two novel prescriptive methods based on (nw) Nadaraya-Watson and (nn) nearest-neighbors learning which safeguard against overfitting and lead to improved out-of-sample performance. Both resulting robust prescriptive methods reduce to tractable convex optimization problems and enjoy a limited disappointment on bootstrap data. We illustrate the data-driven decision-making framework and our novel robustness notion on a small news vendor problem as well as a small portfolio allocation problem.
Tasks	Decision Making
Published	2017-11-27
URL	http://arxiv.org/abs/1711.09974v1
PDF	http://arxiv.org/pdf/1711.09974v1.pdf
PWC	https://paperswithcode.com/paper/bootstrap-robust-prescriptive-analytics
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Framework

Fingerprint Extraction Using Smartphone Camera


Title	Fingerprint Extraction Using Smartphone Camera
Authors	Saksham Gupta, Sukhad Anand, Atul Rai
Abstract	In the previous decade, there has been a considerable rise in the usage of smartphones.Due to exorbitant advancement in technology, computational speed and quality of image capturing has increased considerably. With an increase in the need for remote fingerprint verification, smartphones can be used as a powerful alternative for fingerprint authentication instead of conventional optical sensors. In this research, wepropose a technique to capture finger-images from the smartphones and pre-process them in such a way that it can be easily matched with the optical sensor images.Effective finger-image capturing, image enhancement, fingerprint pattern extraction, core point detection and image alignment techniques have been discussed. The proposed approach has been validated on FVC 2004 DB1 & DB2 dataset and the results show the efficacy of the methodology proposed. The method can be deployed for real-time commercial usage.
Tasks	Image Enhancement
Published	2017-08-02
URL	http://arxiv.org/abs/1708.00884v1
PDF	http://arxiv.org/pdf/1708.00884v1.pdf
PWC	https://paperswithcode.com/paper/fingerprint-extraction-using-smartphone
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Adapting general-purpose speech recognition engine output for domain-specific natural language question answering


Title	Adapting general-purpose speech recognition engine output for domain-specific natural language question answering
Authors	C. Anantaram, Sunil Kumar Kopparapu
Abstract	Speech-based natural language question-answering interfaces to enterprise systems are gaining a lot of attention. General-purpose speech engines can be integrated with NLP systems to provide such interfaces. Usually, general-purpose speech engines are trained on large `general’ corpus. However, when such engines are used for specific domains, they may not recognize domain-specific words well, and may produce erroneous output. Further, the accent and the environmental conditions in which the speaker speaks a sentence may induce the speech engine to inaccurately recognize certain words. The subsequent natural language question-answering does not produce the requisite results as the question does not accurately represent what the speaker intended. Thus, the speech engine’s output may need to be adapted for a domain before further natural language processing is carried out. We present two mechanisms for such an adaptation, one based on evolutionary development and the other based on machine learning, and show how we can repair the speech-output to make the subsequent natural language question-answering better. \|
Tasks	Question Answering, Speech Recognition
Published	2017-10-12
URL	http://arxiv.org/abs/1710.06923v1
PDF	http://arxiv.org/pdf/1710.06923v1.pdf
PWC	https://paperswithcode.com/paper/adapting-general-purpose-speech-recognition
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End-to-end Planning of Fixed Millimeter-Wave Networks


Title	End-to-end Planning of Fixed Millimeter-Wave Networks
Authors	Tim Danford, Onur Filiz, Jing Huang, Brian Karrer, Manohar Paluri, Guan Pang, Vish Ponnampalam, Nicolas Stier-Moses, Birce Tezel
Abstract	This article discusses a framework to support the design and end-to-end planning of fixed millimeter-wave networks. Compared to traditional techniques, the framework allows an organization to quickly plan a deployment in a cost-effective way. We start by using LiDAR data—basically, a 3D point cloud captured from a city—to estimate potential sites to deploy antennas and whether there is line-of-sight between them. With that data on hand, we use combinatorial optimization techniques to determine the optimal set of locations and how they should communicate with each other, to satisfy engineering (e.g., latency, polarity), design (e.g., reliability) and financial (e.g., total cost of operation) constraints. The primary goal is to connect as many people as possible to the network. Our methodology can be used for strategic planning when an organization is in the process of deciding whether to adopt a millimeter-wave technology or choosing between locations, or for operational planning when conducting a detailed design of the actual network to be deployed in a selected location.
Tasks	Combinatorial Optimization
Published	2017-05-20
URL	http://arxiv.org/abs/1705.07249v1
PDF	http://arxiv.org/pdf/1705.07249v1.pdf
PWC	https://paperswithcode.com/paper/end-to-end-planning-of-fixed-millimeter-wave
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Framework

NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm


Title	NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm
Authors	Xiaoliang Dai, Hongxu Yin, Niraj K. Jha
Abstract	Deep neural networks (DNNs) have begun to have a pervasive impact on various applications of machine learning. However, the problem of finding an optimal DNN architecture for large applications is challenging. Common approaches go for deeper and larger DNN architectures but may incur substantial redundancy. To address these problems, we introduce a network growth algorithm that complements network pruning to learn both weights and compact DNN architectures during training. We propose a DNN synthesis tool (NeST) that combines both methods to automate the generation of compact and accurate DNNs. NeST starts with a randomly initialized sparse network called the seed architecture. It iteratively tunes the architecture with gradient-based growth and magnitude-based pruning of neurons and connections. Our experimental results show that NeST yields accurate, yet very compact DNNs, with a wide range of seed architecture selection. For the LeNet-300-100 (LeNet-5) architecture, we reduce network parameters by 70.2x (74.3x) and floating-point operations (FLOPs) by 79.4x (43.7x). For the AlexNet and VGG-16 architectures, we reduce network parameters (FLOPs) by 15.7x (4.6x) and 30.2x (8.6x), respectively. NeST’s grow-and-prune paradigm delivers significant additional parameter and FLOPs reduction relative to pruning-only methods.
Tasks	Network Pruning
Published	2017-11-06
URL	http://arxiv.org/abs/1711.02017v3
PDF	http://arxiv.org/pdf/1711.02017v3.pdf
PWC	https://paperswithcode.com/paper/nest-a-neural-network-synthesis-tool-based-on
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Framework

Leveraging Native Language Speech for Accent Identification using Deep Siamese Networks


Title	Leveraging Native Language Speech for Accent Identification using Deep Siamese Networks
Authors	Aditya Siddhant, Preethi Jyothi, Sriram Ganapathy
Abstract	The problem of automatic accent identification is important for several applications like speaker profiling and recognition as well as for improving speech recognition systems. The accented nature of speech can be primarily attributed to the influence of the speaker’s native language on the given speech recording. In this paper, we propose a novel accent identification system whose training exploits speech in native languages along with the accented speech. Specifically, we develop a deep Siamese network-based model which learns the association between accented speech recordings and the native language speech recordings. The Siamese networks are trained with i-vector features extracted from the speech recordings using either an unsupervised Gaussian mixture model (GMM) or a supervised deep neural network (DNN) model. We perform several accent identification experiments using the CSLU Foreign Accented English (FAE) corpus. In these experiments, our proposed approach using deep Siamese networks yield significant relative performance improvements of 15.4 percent on a 10-class accent identification task, over a baseline DNN-based classification system that uses GMM i-vectors. Furthermore, we present a detailed error analysis of the proposed accent identification system.
Tasks	Speech Recognition
Published	2017-12-25
URL	http://arxiv.org/abs/1712.08992v2
PDF	http://arxiv.org/pdf/1712.08992v2.pdf
PWC	https://paperswithcode.com/paper/leveraging-native-language-speech-for-accent
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Framework

K-sets+: a Linear-time Clustering Algorithm for Data Points with a Sparse Similarity Measure


Title	K-sets+: a Linear-time Clustering Algorithm for Data Points with a Sparse Similarity Measure
Authors	Cheng-Shang Chang, Chia-Tai Chang, Duan-Shin Lee, Li-Heng Liou
Abstract	In this paper, we first propose a new iterative algorithm, called the K-sets+ algorithm for clustering data points in a semi-metric space, where the distance measure does not necessarily satisfy the triangular inequality. We show that the K-sets+ algorithm converges in a finite number of iterations and it retains the same performance guarantee as the K-sets algorithm for clustering data points in a metric space. We then extend the applicability of the K-sets+ algorithm from data points in a semi-metric space to data points that only have a symmetric similarity measure. Such an extension leads to great reduction of computational complexity. In particular, for an n * n similarity matrix with m nonzero elements in the matrix, the computational complexity of the K-sets+ algorithm is O((Kn + m)I), where I is the number of iterations. The memory complexity to achieve that computational complexity is O(Kn + m). As such, both the computational complexity and the memory complexity are linear in n when the n * n similarity matrix is sparse, i.e., m = O(n). We also conduct various experiments to show the effectiveness of the K-sets+ algorithm by using a synthetic dataset from the stochastic block model and a real network from the WonderNetwork website.
Tasks
Published	2017-05-11
URL	http://arxiv.org/abs/1705.04249v1
PDF	http://arxiv.org/pdf/1705.04249v1.pdf
PWC	https://paperswithcode.com/paper/k-sets-a-linear-time-clustering-algorithm-for
Repo
Framework


Title	Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach
Authors	Lei Lin, Zhengbing He, Srinivas Peeta
Abstract	This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored; GCNNreg-DDGF is a regular GCNN-DDGF model which contains the convolution and feedforward blocks, and GCNNrec-DDGF additionally contains a recurrent block from the Long Short-term Memory neural network architecture to capture temporal dependencies in the bike-sharing demand series. Furthermore, four types of GCNN models are proposed whose adjacency matrices are based on various bike-sharing system data, including Spatial Distance matrix (SD), Demand matrix (DE), Average Trip Duration matrix (ATD), and Demand Correlation matrix (DC). These six types of GCNN models and seven other benchmark models are built and compared on a Citi Bike dataset from New York City which includes 272 stations and over 28 million transactions from 2013 to 2016. Results show that the GCNNrec-DDGF performs the best in terms of the Root Mean Square Error, the Mean Absolute Error and the coefficient of determination (R2), followed by the GCNNreg-DDGF. They outperform the other models. Through a more detailed graph network analysis based on the learned DDGF, insights are obtained on the black box of the GCNN-DDGF model. It is found to capture some information similar to details embedded in the SD, DE and DC matrices. More importantly, it also uncovers hidden heterogeneous pairwise correlations between stations that are not revealed by any of those matrices.
Tasks
Published	2017-12-13
URL	http://arxiv.org/abs/1712.04997v2
PDF	http://arxiv.org/pdf/1712.04997v2.pdf
PWC	https://paperswithcode.com/paper/predicting-station-level-hourly-demands-in-a
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Framework

Conjunctions of Among Constraints


Title	Conjunctions of Among Constraints
Authors	Victor Dalmau
Abstract	Many existing global constraints can be encoded as a conjunction of among constraints. An among constraint holds if the number of the variables in its scope whose value belongs to a prespecified set, which we call its range, is within some given bounds. It is known that domain filtering algorithms can benefit from reasoning about the interaction of among constraints so that values can be filtered out taking into consideration several among constraints simultaneously. The present pa- per embarks into a systematic investigation on the circumstances under which it is possible to obtain efficient and complete domain filtering algorithms for conjunctions of among constraints. We start by observing that restrictions on both the scope and the range of the among constraints are necessary to obtain meaningful results. Then, we derive a domain flow-based filtering algorithm and present several applications. In particular, it is shown that the algorithm unifies and generalizes several previous existing results.
Tasks
Published	2017-06-15
URL	http://arxiv.org/abs/1706.05059v1
PDF	http://arxiv.org/pdf/1706.05059v1.pdf
PWC	https://paperswithcode.com/paper/conjunctions-of-among-constraints
Repo
Framework

Cognitive Subscore Trajectory Prediction in Alzheimer’s Disease


Title	Cognitive Subscore Trajectory Prediction in Alzheimer’s Disease
Authors	Lev E. Givon, Laura J. Mariano, David O’Dowd, John M. Irvine, Abraham R. Schneider
Abstract	Accurate diagnosis of Alzheimer’s Disease (AD) entails clinical evaluation of multiple cognition metrics and biomarkers. Metrics such as the Alzheimer’s Disease Assessment Scale - Cognitive test (ADAS-cog) comprise multiple subscores that quantify different aspects of a patient’s cognitive state such as learning, memory, and language production/comprehension. Although computer-aided diagnostic techniques for classification of a patient’s current disease state exist, they provide little insight into the relationship between changes in brain structure and different aspects of a patient’s cognitive state that occur over time in AD. We have developed a Convolutional Neural Network architecture that can concurrently predict the trajectories of the 13 subscores comprised by a subject’s ADAS-cog examination results from a current minimally preprocessed structural MRI scan up to 36 months from image acquisition time without resorting to manual feature extraction. Mean performance metrics are within range of those of existing techniques that require manual feature selection and are limited to predicting aggregate scores.
Tasks	Feature Selection, Trajectory Prediction
Published	2017-06-26
URL	http://arxiv.org/abs/1706.08491v2
PDF	http://arxiv.org/pdf/1706.08491v2.pdf
PWC	https://paperswithcode.com/paper/cognitive-subscore-trajectory-prediction-in
Repo
Framework

Motion Segmentation via Global and Local Sparse Subspace Optimization


Title	Motion Segmentation via Global and Local Sparse Subspace Optimization
Authors	Michael Ying Yang, Hanno Ackermann, Weiyao Lin, Sitong Feng, Bodo Rosenhahn
Abstract	In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model. Since the feature trajectories in practice are high-dimensional and contain a lot of noise, we firstly apply the sparse PCA to represent the original trajectories with a low-dimensional global subspace, which consists of the orthogonal sparse principal vectors. Subsequently, the local subspace separation will be achieved via automatically searching the sparse representation of the nearest neighbors for each projected data. In order to refine the local subspace estimation result and deal with the missing data problem, we propose an error estimation to encourage the projected data that span a same local subspace to be clustered together. In the end, the segmentation of different motions is achieved through the spectral clustering on an affinity matrix, which is constructed with both the error estimation and sparse neighbors optimization. We test our method extensively and compare it with state-of-the-art methods on the Hopkins 155 dataset and Freiburg-Berkeley Motion Segmentation dataset. The results show that our method is comparable with the other motion segmentation methods, and in many cases exceed them in terms of precision and computation time.
Tasks	Motion Segmentation
Published	2017-01-24
URL	http://arxiv.org/abs/1701.06944v1
PDF	http://arxiv.org/pdf/1701.06944v1.pdf
PWC	https://paperswithcode.com/paper/motion-segmentation-via-global-and-local
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Framework

Maximum-Likelihood Augmented Discrete Generative Adversarial Networks


Title	Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Authors	Tong Che, Yanran Li, Ruixiang Zhang, R Devon Hjelm, Wenjie Li, Yangqiu Song, Yoshua Bengio
Abstract	Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather restricted. The fundamental reason is the difficulty of back-propagation through discrete random variables combined with the inherent instability of the GAN training objective. To address these problems, we propose Maximum-Likelihood Augmented Discrete Generative Adversarial Networks. Instead of directly optimizing the GAN objective, we derive a novel and low-variance objective using the discriminator’s output that follows corresponds to the log-likelihood. Compared with the original, the new objective is proved to be consistent in theory and beneficial in practice. The experimental results on various discrete datasets demonstrate the effectiveness of the proposed approach.
Tasks
Published	2017-02-26
URL	http://arxiv.org/abs/1702.07983v1
PDF	http://arxiv.org/pdf/1702.07983v1.pdf
PWC	https://paperswithcode.com/paper/maximum-likelihood-augmented-discrete
Repo
Framework

emLam – a Hungarian Language Modeling baseline


Title	emLam – a Hungarian Language Modeling baseline
Authors	Dávid Márk Nemeskey
Abstract	This paper aims to make up for the lack of documented baselines for Hungarian language modeling. Various approaches are evaluated on three publicly available Hungarian corpora. Perplexity values comparable to models of similar-sized English corpora are reported. A new, freely downloadable Hungar- ian benchmark corpus is introduced.
Tasks	Language Modelling
Published	2017-01-26
URL	http://arxiv.org/abs/1701.07880v1
PDF	http://arxiv.org/pdf/1701.07880v1.pdf
PWC	https://paperswithcode.com/paper/emlam-a-hungarian-language-modeling-baseline
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Framework

Unsupervised patient representations from clinical notes with interpretable classification decisions


Title	Unsupervised patient representations from clinical notes with interpretable classification decisions
Authors	Madhumita Sushil, Simon Šuster, Kim Luyckx, Walter Daelemans
Abstract	We have two main contributions in this work: 1. We explore the usage of a stacked denoising autoencoder, and a paragraph vector model to learn task-independent dense patient representations directly from clinical notes. We evaluate these representations by using them as features in multiple supervised setups, and compare their performance with those of sparse representations. 2. To understand and interpret the representations, we explore the best encoded features within the patient representations obtained from the autoencoder model. Further, we calculate the significance of the input features of the trained classifiers when we use these pretrained representations as input.
Tasks	Denoising
Published	2017-11-14
URL	http://arxiv.org/abs/1711.05198v1
PDF	http://arxiv.org/pdf/1711.05198v1.pdf
PWC	https://paperswithcode.com/paper/unsupervised-patient-representations-from
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