pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning Testing: Survey, Landscapes and Horizons
Authors: Jie M. Zhang, Mark Harman, Lei Ma, Yang Liu
Date: 2019-06-19 00:00:00
Tasks: Autonomous Driving, BIG-bench Machine Learning, Fairness, Machine Translation, Survey, Translation

This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. It covers 144 papers on testing properties (e.g., correctness, robustness, and fairness), testing components (e.g., the data, learning program, and framework), testing workflow (e.g., test generation and test evaluation), and application scenarios (e.g., autonomous driving, machine translation). The paper also analyses trends concerning datasets, research trends, and research focus, concluding with research challenges and promising research directions in ML testing.
Multi-Span Acoustic Modelling using Raw Waveform Signals
Authors: Patrick von Platen, Chao Zhang, Philip Woodland
Date: 2019-06-21 00:00:00
Tasks: Acoustic Modelling, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Traditional automatic speech recognition (ASR) systems often use an acoustic model (AM) built on handcrafted acoustic features, such as log Mel-filter bank (FBANK) values. Recent studies found that AMs with convolutional neural networks (CNNs) can directly use the raw waveform signal as input. Given sufficient training data, these AMs can yield a competitive word error rate (WER) to those built on FBANK features. This paper proposes a novel multi-span structure for acoustic modelling based on the raw waveform with multiple streams of CNN input layers, each processing a different span of the raw waveform signal. Evaluation on both the single channel CHiME4 and AMI data sets show that multi-span AMs give a lower WER than FBANK AMs by an average of about 5% (relative). Analysis of the trained multi-span model reveals that the CNNs can learn filters that are rather different to the log Mel filters. Furthermore, the paper shows that a widely used single span raw waveform AM can be improved by using a smaller CNN kernel size and increased stride to yield improved WERs.
Physics-Informed Echo State Networks for Chaotic Systems Forecasting
Authors: Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
Date: 2019-04-09 00:00:00

We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve supervised learning tasks while ensuring that their predictions do not violate physical laws. This is achieved by introducing an additional loss function during the training of the ESNs, which penalizes non-physical predictions without the need of any additional training data. This approach is demonstrated on a chaotic Lorenz system, where the physics-informed ESNs improve the predictability horizon by about two Lyapunov times as compared to conventional ESNs. The proposed framework shows the potential of using machine learning combined with prior physical knowledge to improve the time-accurate prediction of chaotic dynamical systems.
Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining
Authors: Rajeev Yasarla, Vishal M. Patel
Date: 2019-06-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Single Image Deraining

Single image de-raining is an extremely challenging problem since the rainy image may contain rain streaks which may vary in size, direction and density. Previous approaches have attempted to address this problem by leveraging some prior information to remove rain streaks from a single image. One of the major limitations of these approaches is that they do not consider the location information of rain drops in the image. The proposed Uncertainty guided Multi-scale Residual Learning (UMRL) network attempts to address this issue by learning the rain content at different scales and using them to estimate the final de-rained output. In addition, we introduce a technique which guides the network to learn the network weights based on the confidence measure about the estimate. Furthermore, we introduce a new training and testing procedure based on the notion of cycle spinning to improve the final de-raining performance. Extensive experiments on synthetic and real datasets to demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods. Code is available at: https://github.com/rajeevyasarla/UMRL--using-Cycle-Spinning
A Review of Machine Learning Applications in Fuzzing
Authors: Gary J Saavedra, Kathryn N. Rodhouse, Daniel M. Dunlavy, Philip W Kegelmeyer
Date: 2019-06-13 00:00:00
Tasks: BIG-bench Machine Learning

Fuzzing has played an important role in improving software development and testing over the course of several decades. Recent research in fuzzing has focused on applications of machine learning (ML), offering useful tools to overcome challenges in the fuzzing process. This review surveys the current research in applying ML to fuzzing. Specifically, this review discusses successful applications of ML to fuzzing, briefly explores challenges encountered, and motivates future research to address fuzzing bottlenecks.
Seq-SetNet: Exploring Sequence Sets for Inferring Structures
Authors: Fusong Ju, Jianwei Zhu, Guozheng Wei, Qi Zhang, Shiwei Sun, Dongbo Bu
Date: 2019-06-06 00:00:00
Tasks: Multiple Sequence Alignment, Protein Secondary Structure Prediction, Protein Structure Prediction

Sequence set is a widely-used type of data source in a large variety of fields. A typical example is protein structure prediction, which takes an multiple sequence alignment (MSA) as input and aims to infer structural information from it. Almost all of the existing approaches exploit MSAs in an indirect fashion, i.e., they transform MSAs into position-specific scoring matrices (PSSM) that represent the distribution of amino acid types at each column. PSSM could capture column-wise characteristics of MSA, however, the column-wise characteristics embedded in each individual component sequence were nearly totally neglected. The drawback of PSSM is rooted in the fact that an MSA is essentially an unordered sequence set rather than a matrix. Specifically, the interchange of any two sequences will not affect the whole MSA. In contrast, the pixels in an image essentially form a matrix since any two rows of pixels cannot be interchanged. Therefore, the traditional deep neural networks designed for image processing cannot be directly applied on sequence sets. Here, we proposed a novel deep neural network framework (called Seq-SetNet) for sequence set processing. By employing a {\it symmetric function} module to integrate features calculated from preceding layers, Seq-SetNet are immune to the order of sequences in the input MSA. This advantage enables us to directly and fully exploit MSAs by considering each component protein individually. We evaluated Seq-SetNet by using it to extract structural information from MSA for protein secondary structure prediction. Experimental results on popular benchmark sets suggests that Seq-SetNet outperforms the state-of-the-art approaches by 3.6% in precision. These results clearly suggest the advantages of Seq-SetNet in sequence set processing and it can be readily used in a wide range of fields, say natural language processing.
Deployable probabilistic programming
Authors: David Tolpin
Date: 2019-06-20 00:00:00
Tasks: Decision Making, Probabilistic Programming

We propose design guidelines for a probabilistic programming facility suitable for deployment as a part of a production software system. As a reference implementation, we introduce Infergo, a probabilistic programming facility for Go, a modern programming language of choice for server-side software development. We argue that a similar probabilistic programming facility can be added to most modern general-purpose programming languages. Probabilistic programming enables automatic tuning of program parameters and algorithmic decision making through probabilistic inference based on the data. To facilitate addition of probabilistic programming capabilities to other programming languages, we share implementation choices and techniques employed in development of Infergo. We illustrate applicability of Infergo to various use cases on case studies, and evaluate Infergo's performance on several benchmarks, comparing Infergo to dedicated inference-centric probabilistic programming frameworks.
Integration of TensorFlow based Acoustic Model with Kaldi WFST Decoder
Authors: Minkyu Lim, Ji-Hwan Kim
Date: 2019-06-21 00:00:00
Tasks: Decoder, speech-recognition, Speech Recognition

While the Kaldi framework provides state-of-the-art components for speech recognition like feature extraction, deep neural network (DNN)-based acoustic models, and a weighted finite state transducer (WFST)-based decoder, it is difficult to implement a new flexible DNN model. By contrast, a general-purpose deep learning framework, such as TensorFlow, can easily build various types of neural network architectures using a tensor-based computation method, but it is difficult to apply them to WFST-based speech recognition. In this study, a TensorFlow-based acoustic model is integrated with a WFST-based Kaldi decoder to combine the two frameworks. The features and alignments used in Kaldi are converted so they can be trained by the TensorFlow model, and the DNN-based acoustic model is then trained. In the integrated Kaldi decoder, the posterior probabilities are calculated by querying the trained TensorFlow model, and a beam search is performed to generate the lattice. The advantages of the proposed one-pass decoder include the application of various types of neural networks to WFST-based speech recognition and WFST-based online decoding using a TensorFlow-based acoustic model. The TensorFlow based acoustic models trained using the RM, WSJ, and LibriSpeech datasets show the same level of performance as the model trained using the Kaldi framework.
Predicting Confusion from Eye-Tracking Data with Recurrent Neural Networks
Authors: Shane D. Sims, Vanessa Putnam, Cristina Conati
Date: 2019-06-19 00:00:00
Tasks: Data Augmentation, Sensitivity, Specificity

Encouraged by the success of deep learning in a variety of domains, we investigate the suitability and effectiveness of Recurrent Neural Networks (RNNs) in a domain where deep learning has not yet been used; namely detecting confusion from eye-tracking data. Through experiments with a dataset of user interactions with ValueChart (an interactive visualization tool), we found that RNNs learn a feature representation from the raw data that allows for a more powerful classifier than previous methods that use engineered features. This is evidenced by the stronger performance of the RNN (0.74/0.71 sensitivity/specificity), as compared to a Random Forest classifier (0.51/0.70 sensitivity/specificity), when both are trained on an un-augmented dataset. However, using engineered features allows for simple data augmentation methods to be used. These same methods are not as effective at augmentation for the feature representation learned from the raw data, likely due to an inability to match the temporal dynamics of the data.
Pyramid U-Network for Skeleton Extraction From Shape Points
Authors: Rowel Atienza
Date: 2019-06-17 00:00:00
Proceeding: IEEE 2019 CVPR Workshop 2019 6

The knowledge about the skeleton of a given geometric shape has many practical applications such as shape animation, shape comparison, shape recognition, and estimating structural strength. Skeleton extraction becomes a more challenging problem when the topology is represented in point cloud domain. In this paper, we present the network architecture, PSPU-SkelNet, for TeamPH which ranked 3rd in Point SkelNetOn 2019 challenge. PSPU-SkelNet is a pyramid of three U-Nets that predicts the skeleton from a given shape point cloud. PSPU-SkelNet achieves a Chamfer Distance (CD) of 2.9105 on the final test dataset. The code of PSPU SkelNet is available at https://github.com/roatienza/skelnet.
A Conditional Generative Adversarial Network for Rendering Point Clouds
Authors: Rowel Atienza
Date: 2019-06-17 00:00:00
Proceeding: IEEE 2019 CVPR Workshop 2019 6
Tasks: Generative Adversarial Network, Surface Reconstruction

In computer graphics, point clouds from laser scanning devices are difficult to render into photo-realistic images due to lack of information they carry about color, normal, lighting, and connection between points. Rendering a point cloud after surface mesh reconstruction generally results into poor image quality with many noticeable artifacts. In this paper, we propose a conditional generative adversarial network that directly renders a point cloud given the azimuth and elevation angles of camera viewpoint. The proposed method, called pc2pix, renders point clouds into objects with higher class similarity with the ground truth as compared to images from surface reconstruction. pc2pix is also significantly faster, more robust to noise and can operate on a lower number of points. The code is available at: https://github.com/roatienza/pc2pix.
Multiple Context Features in Siamese Networks for Visual Object Tracking
Authors: Henrique Morimitsu
Date: 2018-09-01 00:00:00
Proceeding: ECCV Workshops 2018 2018 9
Tasks: Object Tracking, Visual Object Tracking

Siamese networks have been successfully utilized to learn a robust matching function between pairs of images. Visual object tracking methods based on siamese networks have been gaining popularity recently due to their robustness and speed. However, existing siamese approaches are still unable to perform on par with the most accurate trackers. In this paper, we propose to extend the SiamFC tracker to extract features at multiple context and semantic levels from very deep networks. We show that our approach effectively extracts complementary features for siamese matching from different layers, which provides a significant performance boost when fused. Experimental results on VOT and OTB datasets show that our multi-context tracker is comparable to the most accurate methods, while still being faster than most of them. In particular, we outperform several other state-of-the-art siamese methods.
Deep iris representation with applications in iris recognition and cross-sensor iris recognition
Authors: abhishek, akanksha
Date: 2016-09-25 00:00:00
Proceeding: 2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) 2016 9
Tasks: Iris Recognition

Despite significant advances in iris recognition (IR), the efficient and robust IR at scale and in non-ideal conditions
presents serious performance issues and is still ongoing research topic. Deep Convolution Neural Networks (DCNN)
are powerful visual models that have reported state-of-theart performance in several domains. In this paper, we
propose deep learning based method termed as DeepIrisNet for iris representation. The proposed approach bases on very
deep architecture and various tricks from recent successful CNNs. Experimental analysis reveal that proposed
DeepIrisNet can model the micro-structures of iris very effectively and provides robust, discriminative, compact,
and very easy-to-implement iris representation that obtains state-of-the-art accuracy. Furthermore, we evaluate our iris
representation for cross-sensor IR. The experimental results demonstrate that DeepIrisNet models obtain a significant
improvement in cross-sensor recognition accuracy too.
Entropy and Graph Based Modelling of Document Coherence using Discourse Entities: An Application
Authors: Casper Petersen, Christina Lioma, Jakob Grue Simonsen, Birger Larsen
Date: 2015-07-29 00:00:00
Tasks: Clustering, Information Retrieval, Reranking, Retrieval, Sentence

We present two novel models of document coherence and their application to information retrieval (IR). Both models approximate document coherence using discourse entities, e.g. the subject or object of a sentence. Our first model views text as a Markov process generating sequences of discourse entities (entity n-grams); we use the entropy of these entity n-grams to approximate the rate at which new information appears in text, reasoning that as more new words appear, the topic increasingly drifts and text coherence decreases. Our second model extends the work of Guinaudeau & Strube [28] that represents text as a graph of discourse entities, linked by different relations, such as their distance or adjacency in text. We use several graph topology metrics to approximate different aspects of the discourse flow that can indicate coherence, such as the average clustering or betweenness of discourse entities in text. Experiments with several instantiations of these models show that: (i) our models perform on a par with two other well-known models of text coherence even without any parameter tuning, and (ii) reranking retrieval results according to their coherence scores gives notable performance gains, confirming a relation between document coherence and relevance. This work contributes two novel models of document coherence, the application of which to IR complements recent work in the integration of document cohesiveness or comprehensibility to ranking [5, 56].
Hybrid Approach for Single Text Document Summarization using Statistical and Sentiment Features
Authors: Chandra Shekhar Yadav, Aditi Sharan
Date: 2016-01-03 00:00:00
Tasks: Document Summarization, Extractive Summarization, Sentence, Sentiment Analysis

Summarization is a way to represent same information in concise way with equal sense. This can be categorized in two type Abstractive and Extractive type. Our work is focused around Extractive summarization. A generic approach to extractive summarization is to consider sentence as an entity, score each sentence based on some indicative features to ascertain the quality of sentence for inclusion in summary. Sort the sentences on the score and consider top n sentences for summarization. Mostly statistical features have been used for scoring the sentences. We are proposing a hybrid model for a single text document summarization. This hybrid model is an extraction based approach, which is combination of Statistical and semantic technique. The hybrid model depends on the linear combination of statistical measures : sentence position, TF-IDF, Aggregate similarity, centroid, and semantic measure. Our idea to include sentiment analysis for salient sentence extraction is derived from the concept that emotion plays an important role in communication to effectively convey any message hence, it can play a vital role in text document summarization. For comparison we have generated five system summaries Proposed Work, MEAD system, Microsoft system, OPINOSIS system, and Human generated summary, and evaluation is done using ROUGE score.
A Food Photography App with Image Recognition for Thai Food
Authors: Ukrit Tiankaew, Peerapon Chunpongthong, Vacharapat Mettanant
Date: 2018-07-01 00:00:00
Proceeding: ICT-ISPC 2018 7
Tasks: Image Recognition, Transfer Learning

In this paper, we present a food photography application for smart phones, which can recognise 13 types of Thai food from photos. With this feature, the application can easily help users calculate their calories and make some suggestion, just by keep taking a photo of food they are eating. Our application uses React Native for the front-end, and Python-Flask for the back-end. For image recognition, we design a deep convolutional neural network to learn from our dataset. Moreover, we compare the result of our model with another model adapted from the famous one of Karen Simonyan andAndrew Zisserman called VGG19. We use transfer learning from the pre-trained VGG19, implementing with Keras and Tensorflow. Our result shows that the transfer learning model is better. It give us approximately 82% test accuracy or 18% top-1 error rate. Using top-3 and top-5 scores, The model reports 2.6% top-3 error rate and 1.3% top-5 error rate, which works well in our application.
Recipe recognition with large multimodal food dataset
Authors: Xin Wang, Devinder Kumar, Nicolas Thome, Matthieu Cord, Frederic Precioso
Date: 2015-07-03 00:00:00
Proceeding: IEEE 2015 7
Tasks: Image Classification, Text Classification, Transfer Learning

This paper deals with automatic systems for image recipe recognition. For this purpose, we compare and evaluate leading vision-based and text-based technologies on a new very large multimodal dataset (UPMC Food-101) containing about 100,000 recipes for a total of 101 food categories. Each item in this dataset is represented by one image plus textual information. We present deep experiments of recipe recognition on our dataset using visual, textual information and fusion. Additionally, we present experiments with text-based embedding technology to represent any food word in a semantical continuous space. We also compare our dataset features with a twin dataset provided by ETHZ university: we revisit their data collection protocols and carry out transfer learning schemes to highlight similarities and differences between both datasets. Finally, we propose a real application for daily users to identify recipes. This application is a web search engine that allows any mobile device to send a query image and retrieve the most relevant recipes in our dataset.
Rethinking Planar Homography Estimation Using Perspective Fields
Authors: Rui Zeng, Simon Denman, Sridha Sridharan, Clinton Fookes
Date: 2019-05-26 00:00:00
Proceeding: ACCV 2018 2019 5
Tasks: Homography Estimation

Planar homography estimation refers to the problem of computing a bijective linear mapping of pixels between two images. While this problem has been studied with convolutional neural networks (CNNs), existing methods simply regress the location of the four corners using a dense layer preceded by a fully-connected layer. This vector representation damages the spatial structure of the corners since they have a clear spatial order. Moreover, four points are the minimum required to compute the homography, and so such an approach is susceptible to perturbation. In this paper, we propose a conceptually simple, reliable, and general framework for homography estimation. In contrast to previous works, we formulate this problem as a perspective field (PF), which models the essence of the homography - pixel-to-pixel bijection. The PF is naturally learned by the proposed fully convolutional residual network, PFNet, to keep the spatial order of each pixel. Moreover, since every pixels’ displacement can be obtained from the PF, it enables robust homography estimation by utilizing dense correspondences. Our experiments demonstrate the proposed method outperforms traditional correspondence-based approaches and state-of-the-art CNN approaches in terms of accuracy while also having a smaller network size. In addition, the new parameterization of this task is general and can be implemented by any fully convolutional network (FCN) architecture.