pwc-archive/papers-with-abstracts huggingface dataset:
End-To-End Measure for Text Recognition
Authors: Gundram Leifert, Roger Labahn, Tobias Grüning, Svenja Leifert
Date: 2019-08-26 00:00:00
Tasks: Information Retrieval, Line Detection, Retrieval

Measuring the performance of text recognition and text line detection engines is an important step to objectively compare systems and their configuration. There exist well-established measures for both tasks separately. However, there is no sophisticated evaluation scheme to measure the quality of a combined text line detection and text recognition system. The F-measure on word level is a well-known methodology, which is sometimes used in this context. Nevertheless, it does not take into account the alignment of hypothesis and ground truth text and can lead to deceptive results. Since users of automatic information retrieval pipelines in the context of text recognition are mainly interested in the end-to-end performance of a given system, there is a strong need for such a measure. Hence, we present a measure to evaluate the quality of an end-to-end text recognition system. The basis for this measure is the well established and widely used character error rate, which is limited -- in its original form -- to aligned hypothesis and ground truth texts. The proposed measure is flexible in a way that it can be configured to penalize different reading orders between the hypothesis and ground truth and can take into account the geometric position of the text lines. Additionally, it can ignore over- and under- segmentation of text lines. With these parameters it is possible to get a measure fitting best to its own needs.
A Methodology to Select Topology Generators for WANET Simulations (Extended Version)
Authors: Michael O'Sullivan, Leonardo Aniello, Vladimiro Sassone
Date: 2019-08-26 00:00:00

Many academic and industrial research works on WANETs rely on simulations, at least in the first stages, to obtain preliminary results to be subsequently validated in real settings. Topology generators (TG) are commonly used to generate the initial placement of nodes in artificial WANET topologies, where those simulations take place. The significance of these experiments heavily depends on the representativeness of artificial topologies. Indeed, if they were not drawn fairly, obtained results would apply only to a subset of possible configurations, hence they would lack of the appropriate generality required to port them to the real world. Although using many TGs could mitigate this issue by generating topologies in several different ways, that would entail a significant additional effort. Hence, the problem arises of what TGs to choose, among a number of available generators, to maximise the representativeness of generated topologies and reduce the number of TGs to use. In this paper, we address that problem by investigating the presence of bias in the initial placement of nodes in artificial WANET topologies produced by different TGs. We propose a methodology to assess such bias and introduce two metrics to quantify the diversity of the topologies generated by a TG with respect to all the available TGs, which can be used to select what TGs to use. We carry out experiments on three well-known TGs, namely BRITE, NPART and GT-ITM. Obtained results show that using the artificial networks produced by a single TG can introduce bias.
Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results
Authors: Alexander Mey, Marco Loog
Date: 2019-08-26 00:00:00
Tasks: Survey

Semi-supervised learning is a setting in which one has labeled and unlabeled data available. In this survey we explore different types of theoretical results when one uses unlabeled data in classification and regression tasks. Most methods that use unlabeled data rely on certain assumptions about the data distribution. When those assumptions are not met in reality, including unlabeled data may actually decrease performance. Studying such methods, it therefore is particularly important to have an understanding of the underlying theory. In this review we gather results about the possible gains one can achieve when using semi-supervised learning as well as results about the limits of such methods. More precisely, this review collects the answers to the following questions: What are, in terms of improving supervised methods, the limits of semi-supervised learning? What are the assumptions of different methods? What can we achieve if the assumptions are true? Finally, we also discuss the biggest bottleneck of semi-supervised learning, namely the assumptions they make.
Neuromorphic Electronic Systems for Reservoir Computing
Authors: Fatemeh Hadaeghi
Date: 2019-08-26 00:00:00
Tasks: Computational Efficiency, regression

This chapter provides a comprehensive survey of the researches and motivations for hardware implementation of reservoir computing (RC) on neuromorphic electronic systems. Due to its computational efficiency and the fact that training amounts to a simple linear regression, both spiking and non-spiking implementations of reservoir computing on neuromorphic hardware have been developed. Here, a review of these experimental studies is provided to illustrate the progress in this area and to address the technical challenges which arise from this specific hardware implementation. Moreover, to deal with challenges of computation on such unconventional substrates, several lines of potential solutions are presented based on advances in other computational approaches in machine learning. Keywords: Analog Microchips, FPGA, Memristors, Neuromorphic Architectures, Reservoir Computing
Accelerated Motion-Aware MR Imaging via Motion Prediction from K-Space Center
Authors: Christoph Jud, Damien Nguyen, Alina Giger, Robin Sandkühler, Miriam Krieger, Tony Lomax, Rares Salomir, Oliver Bieri, Philippe C. Cattin
Date: 2019-08-26 00:00:00
Tasks: Image Reconstruction, motion prediction

Motion has been a challenge for magnetic resonance (MR) imaging ever since the MR has been invented. Especially in volumetric imaging of thoracic and abdominal organs, motion-awareness is essential for reducing motion artifacts in the final image. A recently proposed MR imaging approach copes with motion by observing the motion patterns during the acquisition. Repetitive scanning of the k-space center region enables the extraction of the patient motion while acquiring the remaining part of the k-space. Due to highly redundant measurements of the center, the required scanning time of over 11 min and the reconstruction time of 2 h exceed clinical applicability though. We propose an accelerated motion-aware MR imaging method where the motion is inferred from small-sized k-space center patches and an initial training phase during which the characteristic movements are modeled. Thereby, acquisition times are reduced by a factor of almost 2 and reconstruction times by two orders of magnitude. Moreover, we improve the existing motion-aware approach with a systematic temporal shift correction to achieve a sharper image reconstruction. We tested our method on 12 volunteers and scanned their lungs and abdomen under free breathing. We achieved equivalent to higher reconstruction quality using the motion-prediction compared to the slower existing approach.
Customizable Architecture Search for Semantic Segmentation
Authors: Yiheng Zhang, Zhaofan Qiu, Jingen Liu, Ting Yao, Dong Liu, Tao Mei
Date: 2019-08-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: GPU, Image Segmentation, Segmentation, Semantic Segmentation

In this paper, we propose a Customizable Architecture Search (CAS) approach to automatically generate a network architecture for semantic image segmentation. The generated network consists of a sequence of stacked computation cells. A computation cell is represented as a directed acyclic graph, in which each node is a hidden representation (i.e., feature map) and each edge is associated with an operation (e.g., convolution and pooling), which transforms data to a new layer. During the training, the CAS algorithm explores the search space for an optimized computation cell to build a network. The cells of the same type share one architecture but with different weights. In real applications, however, an optimization may need to be conducted under some constraints such as GPU time and model size. To this end, a cost corresponding to the constraint will be assigned to each operation. When an operation is selected during the search, its associated cost will be added to the objective. As a result, our CAS is able to search an optimized architecture with customized constraints. The approach has been thoroughly evaluated on Cityscapes and CamVid datasets, and demonstrates superior performance over several state-of-the-art techniques. More remarkably, our CAS achieves 72.3% mIoU on the Cityscapes dataset with speed of 108 FPS on an Nvidia TitanXp GPU.
Annals of Library and Information Studies. A bibliometric analysis of the journal and a comparison with the top library and information studies journals in Asia and worldwide (2011_2017)
Authors: Juan Jose Prieto-Gutierrez, Francisco Segado-Boj
Date: 2019-08-26 00:00:00
Tasks: Articles

This paper presents a thorough bibliometric analysis of research published in Annals of Library and Information Studies (ALIS), an India-based journal, for the period 2011_2017. Specifically, it compares this journal's trends with those of other library and information science (LIS) journals from the same geographical area (India, and Asia as a whole) and with the 10 highest-rated LIS journals worldwide. The source of the data used was the multidisciplinary database Scopus. To perform this comparison, ALIS' production was analyzed in order to identify authorship patterns; for example, authors' countries of residence, co-authorship trends, and collaboration networks. Research topics were identified through keyword analysis, while performance was measured by examining the number of citations articles received. This study provides substantial information. The research lines detected through examining the keywords in ALIS articles were determined to be similar to those for the top LIS journals in both Asia and worldwide. Specifically, ALIS authors are focusing on metrics, bibliometrics, and social networking, which follows global trends. Notably, however, collaboration among Asia-based journals was found to be lower than that in the top-indexed journals in the LIS field. The results obtained present a roadmap for expanding the research in this field.
Uncertainty-Aware Anticipation of Activities
Authors: Yazan Abu Farha, Juergen Gall
Date: 2019-08-26 00:00:00

Anticipating future activities in video is a task with many practical applications. While earlier approaches are limited to just a few seconds in the future, the prediction time horizon has just recently been extended to several minutes in the future. However, as increasing the predicted time horizon, the future becomes more uncertain and models that generate a single prediction fail at capturing the different possible future activities. In this paper, we address the uncertainty modelling for predicting long-term future activities. Both an action model and a length model are trained to model the probability distribution of the future activities. At test time, we sample from the predicted distributions multiple samples that correspond to the different possible sequences of future activities. Our model is evaluated on two challenging datasets and shows a good performance in capturing the multi-modal future activities without compromising the accuracy when predicting a single sequence of future activities.
Learning Disentangled Representations via Independent Subspaces
Authors: Maren Awiszus, Hanno Ackermann, Bodo Rosenhahn
Date: 2019-08-26 00:00:00
Tasks: Disentanglement, Semantic Segmentation

Image generating neural networks are mostly viewed as black boxes, where any change in the input can have a number of globally effective changes on the output. In this work, we propose a method for learning disentangled representations to allow for localized image manipulations. We use face images as our example of choice. Depending on the image region, identity and other facial attributes can be modified. The proposed network can transfer parts of a face such as shape and color of eyes, hair, mouth, etc.~directly between persons while all other parts of the face remain unchanged. The network allows to generate modified images which appear like realistic images. Our model learns disentangled representations by weak supervision. We propose a localized resnet autoencoder optimized using several loss functions including a loss based on the semantic segmentation, which we interpret as masks, and a loss which enforces disentanglement by decomposition of the latent space into statistically independent subspaces. We evaluate the proposed solution w.r.t. disentanglement and generated image quality. Convincing results are demonstrated using the CelebA dataset.
Error Bounded Foreground and Background Modeling for Moving Object Detection in Satellite Videos
Authors: Junpeng Zhang, Xiuping Jia, Jiankun Hu
Date: 2019-08-26 00:00:00
Tasks: Moving Object Detection, object-detection, Object Detection

Detecting moving objects from ground-based videos is commonly achieved by using background subtraction techniques. Low-rank matrix decomposition inspires a set of state-of-the-art approaches for this task. It is integrated with structured sparsity regularization to achieve background subtraction in the developed method of Low-rank and Structured Sparse Decomposition (LSD). However, when this method is applied to satellite videos where spatial resolution is poor and targets' contrast to the background is low, its performance is limited as the data no longer fits adequately either the foreground structure or the background model. In this paper, we handle these unexplained data explicitly and address the moving target detection from space as one of the pioneer studies. We propose a technique by extending the decomposition formulation with bounded errors, named Extended Low-rank and Structured Sparse Decomposition (E-LSD). This formulation integrates low-rank background, structured sparse foreground and their residuals in a matrix decomposition problem. We provide an effective solution by introducing an alternative treatment and adopting the direct extension of Alternating Direction Method of Multipliers (ADMM). The proposed E-LSD was validated on two satellite videos, and experimental results demonstrate the improvement in background modeling with boosted moving object detection precision over state-of-the-art methods.
Transductive Data-Selection Algorithms for Fine-Tuning Neural Machine Translation
Authors: Alberto Poncelas, Gideon Maillette de Buy Wenniger, Andy Way
Date: 2019-08-26 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, NMT, Translation

Machine Translation models are trained to translate a variety of documents from one language into another. However, models specifically trained for a particular characteristics of the documents tend to perform better. Fine-tuning is a technique for adapting an NMT model to some domain. In this work, we want to use this technique to adapt the model to a given test set. In particular, we are using transductive data selection algorithms which take advantage the information of the test set to retrieve sentences from a larger parallel set. In cases where the model is available at translation time (when the test set is provided), it can be adapted with a small subset of data, thereby achieving better performance than a generic model or a domain-adapted model.
Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation
Authors: Pengjie Ren, Zhumin Chen, Christof Monz, Jun Ma, Maarten de Rijke
Date: 2019-08-26 00:00:00

Background Based Conversations (BBCs) have been introduced to help conversational systems avoid generating overly generic responses. In a BBC, the conversation is grounded in a knowledge source. A key challenge in BBCs is Knowledge Selection (KS): given a conversational context, try to find the appropriate background knowledge (a text fragment containing related facts or comments, etc.) based on which to generate the next response. Previous work addresses KS by employing attention and/or pointer mechanisms. These mechanisms use a local perspective, i.e., they select a token at a time based solely on the current decoding state. We argue for the adoption of a global perspective, i.e., pre-selecting some text fragments from the background knowledge that could help determine the topic of the next response. We enhance KS in BBCs by introducing a Global-to-Local Knowledge Selection (GLKS) mechanism. Given a conversational context and background knowledge, we first learn a topic transition vector to encode the most likely text fragments to be used in the next response, which is then used to guide the local KS at each decoding timestamp. In order to effectively learn the topic transition vector, we propose a distantly supervised learning schema. Experimental results show that the GLKS model significantly outperforms state-of-the-art methods in terms of both automatic and human evaluation. More importantly, GLKS achieves this without requiring any extra annotations, which demonstrates its high degree of scalability.
A Convolutional Neural Network with Mapping Layers for Hyperspectral Image Classification
Authors: Rui Li, Zhibin Pan, Yang Wang, Ping Wang
Date: 2019-08-26 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

In this paper, we propose a convolutional neural network with mapping layers (MCNN) for hyperspectral image (HSI) classification. The proposed mapping layers map the input patch into a low dimensional subspace by multilinear algebra. We use our mapping layers to reduce the spectral and spatial redundancy and maintain most energy of the input. The feature extracted by our mapping layers can also reduce the number of following convolutional layers for feature extraction. Our MCNN architecture avoids the declining accuracy with increasing layers phenomenon of deep learning models for HSI classification and also saves the training time for its effective mapping layers. Furthermore, we impose the 3-D convolutional kernel on convolutional layer to extract the spectral-spatial features for HSI. We tested our MCNN on three datasets of Indian Pines, University of Pavia and Salinas, and we achieved the classification accuracy of 98.3%, 99.5% and 99.3%, respectively. Experimental results demonstrate that the proposed MCNN can significantly improve the classification accuracy and save much time consumption.
NETR-Tree: An Eifficient Framework for Social-Based Time-Aware Spatial Keyword Query
Authors: Xiuqi Huang, Yuanning Gao, Xiaofeng Gao, Guihai Chen
Date: 2019-08-26 00:00:00
Tasks: Network Embedding

The development of global positioning system stimulates the popularity of location-based social network (LBSN) services. With a large volume of data containing locations, texts, check-in information, and social relationships, spatial keyword queries in LBSNs have become increasingly complex. In this paper, we identify and solve the Social-based Time-aware Spatial Keyword Query (STSKQ) that returns the top-k objects by considering geo-spatial score, keywords similarity, visiting time score, and social relationship effect. To tackle STSKQ, we propose a two-layer hybrid index structure called Network Embedding Time-aware R-tree (NETR-Tree). In the user layer, we exploit the network embedding strategy to measure the relationship effect in users' relationship network. In the location layer, we build a Time-aware R-tree (TR-tree) considered spatial objects' spatiotemporal check-in information, and present a corresponding query processing algorithm. Finally, extensive experiments on two different real-life LBSNs demonstrate the effectiveness and efficiency of our methods, compared with existing state-of-the-art methods.
Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation
Authors: Ozan Öktem, Camille Pouchol, Olivier Verdier
Date: 2019-08-26 00:00:00

Patient movement in emission tomography deteriorates reconstruction quality because of motion blur. Gating the data improves the situation somewhat: each gate contains a movement phase which is approximately stationary. A standard method is to use only the data from a few gates, with little movement between them. However, the corresponding loss of data entails an increase of noise. Motion correction algorithms have been implemented to take into account all the gated data, but they do not scale well, especially not in 3D. We propose a novel motion correction algorithm which addresses the scalability issue. Our approach is to combine an enhanced ML-EM algorithm with deep learning based movement registration. The training is unsupervised, and with artificial data. We expect this approach to scale very well to higher resolutions and to 3D, as the overall cost of our algorithm is only marginally greater than that of a standard ML-EM algorithm. We show that we can significantly decrease the noise corresponding to a limited number of gates.
Mocycle-GAN: Unpaired Video-to-Video Translation
Authors: Yang Chen, Yingwei Pan, Ting Yao, Xinmei Tian, Tao Mei
Date: 2019-08-26 00:00:00
Tasks: Image-to-Image Translation, Motion Estimation, Translation, Unsupervised Image-To-Image Translation

Unsupervised image-to-image translation is the task of translating an image from one domain to another in the absence of any paired training examples and tends to be more applicable to practical applications. Nevertheless, the extension of such synthesis from image-to-image to video-to-video is not trivial especially when capturing spatio-temporal structures in videos. The difficulty originates from the aspect that not only the visual appearance in each frame but also motion between consecutive frames should be realistic and consistent across transformation. This motivates us to explore both appearance structure and temporal continuity in video synthesis. In this paper, we present a new Motion-guided Cycle GAN, dubbed as Mocycle-GAN, that novelly integrates motion estimation into unpaired video translator. Technically, Mocycle-GAN capitalizes on three types of constrains: adversarial constraint discriminating between synthetic and real frame, cycle consistency encouraging an inverse translation on both frame and motion, and motion translation validating the transfer of motion between consecutive frames. Extensive experiments are conducted on video-to-labels and labels-to-video translation, and superior results are reported when comparing to state-of-the-art methods. More remarkably, we qualitatively demonstrate our Mocycle-GAN for both flower-to-flower and ambient condition transfer.
Partially-supervised Mention Detection
Authors: Lesly Miculicich, James Henderson
Date: 2019-08-26 00:00:00
Proceeding: COLING (CRAC) 2020 12
Tasks: coreference-resolution, Coreference Resolution

Learning to detect entity mentions without using syntactic information can be useful for integration and joint optimization with other tasks. However, it is common to have partially annotated data for this problem. Here, we investigate two approaches to deal with partial annotation of mentions: weighted loss and soft-target classification. We also propose two neural mention detection approaches: a sequence tagging, and an exhaustive search. We evaluate our methods with coreference resolution as a downstream task, using multitask learning. The results show that the recall and F1 score improve for all methods.