pwc-archive/papers-with-abstracts huggingface dataset:
Zero-Shot Audio Classification Based on Class Label Embeddings
Authors: Huang Xie, Tuomas Virtanen
Date: 2019-05-06 00:00:00
Tasks: Audio Classification, Classification, General Classification, Zero-shot Audio Classification, Zero-Shot Learning

This paper proposes a zero-shot learning approach for audio classification based on the textual information about class labels without any audio samples from target classes. We propose an audio classification system built on the bilinear model, which takes audio feature embeddings and semantic class label embeddings as input, and measures the compatibility between an audio feature embedding and a class label embedding. We use VGGish to extract audio feature embeddings from audio recordings. We treat textual labels as semantic side information of audio classes, and use Word2Vec to generate class label embeddings. Results on the ESC-50 dataset show that the proposed system can perform zero-shot audio classification with small training dataset. It can achieve accuracy (26 % on average) better than random guess (10 %) on each audio category. Particularly, it reaches up to 39.7 % for the category of natural audio classes.
Fast and Reliable Architecture Selection for Convolutional Neural Networks
Authors: Lukas Hahn, Lutz Roese-Koerner, Klaus Friedrichs, Anton Kummert
Date: 2019-05-06 00:00:00
Tasks: Bayesian Optimisation, BIG-bench Machine Learning, Neural Architecture Search

The performance of a Convolutional Neural Network (CNN) depends on its hyperparameters, like the number of layers, kernel sizes, or the learning rate for example. Especially in smaller networks and applications with limited computational resources, optimisation is key. We present a fast and efficient approach for CNN architecture selection. Taking into account time consumption, precision and robustness, we develop a heuristic to quickly and reliably assess a network's performance. In combination with Bayesian optimisation (BO), to effectively cover the vast parameter space, our contribution offers a plain and powerful architecture search for this machine learning technique.
FaceShapeGene: A Disentangled Shape Representation for Flexible Face Image Editing
Authors: Sen-Zhe Xu, Hao-Zhi Huang, Shi-Min Hu, Wei Liu
Date: 2019-05-06 00:00:00
Tasks: Image Manipulation

Existing methods for face image manipulation generally focus on editing the expression, changing some predefined attributes, or applying different filters. However, users lack the flexibility of controlling the shapes of different semantic facial parts in the generated face. In this paper, we propose an approach to compute a disentangled shape representation for a face image, namely the FaceShapeGene. The proposed FaceShapeGene encodes the shape information of each semantic facial part separately into a 1D latent vector. On the basis of the FaceShapeGene, a novel part-wise face image editing system is developed, which contains a shape-remix network and a conditional label-to-face transformer. The shape-remix network can freely recombine the part-wise latent vectors from different individuals, producing a remixed face shape in the form of a label map, which contains the facial characteristics of multiple subjects. The conditional label-to-face transformer, which is trained in an unsupervised cyclic manner, performs part-wise face editing while preserving the original identity of the subject. Experimental results on several tasks demonstrate that the proposed FaceShapeGene representation correctly disentangles the shape features of different semantic parts. %In addition, we test our system on several novel part-wise face editing tasks. Comparisons to existing methods demonstrate the superiority of the proposed method on accomplishing novel face editing tasks.
Image Captioning with Clause-Focused Metrics in a Multi-Modal Setting for Marketing
Authors: Philipp Harzig, Dan Zecha, Rainer Lienhart, Carolin Kaiser, René Schallner
Date: 2019-05-06 00:00:00
Tasks: Descriptive, Image Captioning, Marketing, Multi-Task Learning

Automatically generating descriptive captions for images is a well-researched area in computer vision. However, existing evaluation approaches focus on measuring the similarity between two sentences disregarding fine-grained semantics of the captions. In our setting of images depicting persons interacting with branded products, the subject, predicate, object and the name of the branded product are important evaluation criteria of the generated captions. Generating image captions with these constraints is a new challenge, which we tackle in this work. By simultaneously predicting integer-valued ratings that describe attributes of the human-product interaction, we optimize a deep neural network architecture in a multi-task learning setting, which considerably improves the caption quality. Furthermore, we introduce a novel metric that allows us to assess whether the generated captions meet our requirements (i.e., subject, predicate, object, and product name) and describe a series of experiments on caption quality and how to address annotator disagreements for the image ratings with an approach called soft targets. We also show that our novel clause-focused metrics are also applicable to other image captioning datasets, such as the popular MSCOCO dataset.
Missing Data Imputation with Adversarially-trained Graph Convolutional Networks
Authors: Indro Spinelli, Simone Scardapane, Aurelio Uncini
Date: 2019-05-06 00:00:00
Tasks: Denoising, Imputation, Missing Values

Missing data imputation (MDI) is a fundamental problem in many scientific disciplines. Popular methods for MDI use global statistics computed from the entire data set (e.g., the feature-wise medians), or build predictive models operating independently on every instance. In this paper we propose a more general framework for MDI, leveraging recent work in the field of graph neural networks (GNNs). We formulate the MDI task in terms of a graph denoising autoencoder, where each edge of the graph encodes the similarity between two patterns. A GNN encoder learns to build intermediate representations for each example by interleaving classical projection layers and locally combining information between neighbors, while another decoding GNN learns to reconstruct the full imputed data set from this intermediate embedding. In order to speed-up training and improve the performance, we use a combination of multiple losses, including an adversarial loss implemented with the Wasserstein metric and a gradient penalty. We also explore a few extensions to the basic architecture involving the use of residual connections between layers, and of global statistics computed from the data set to improve the accuracy. On a large experimental evaluation, we show that our method robustly outperforms state-of-the-art approaches for MDI, especially for large percentages of missing values.
Lesion Segmentation in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets
Authors: Jie Xing, Zheren Li, Biyuan Wang, Yuji Qi, Bingbin Yu, Farhad G. Zanjani, Aiwen Zheng, Remco Duits, Tao Tan
Date: 2019-05-06 00:00:00
Tasks: Diagnostic, Lesion Segmentation

Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the breast cancer. The area and the shape information of a lesion is very helpful for clinicians to make diagnostic decisions. In this study we propose a new deep-learning scheme, semi-pixel-wise cycle generative adversarial net (SPCGAN) for segmenting the lesion in 2D ultrasound. The method takes the advantage of a fully convolutional neural network (FCN) and a generative adversarial net to segment a lesion by using prior knowledge. We compared the proposed method to a fully connected neural network and the level set segmentation method on a test dataset consisting of 32 malignant lesions and 109 benign lesions. Our proposed method achieved a Dice similarity coefficient (DSC) of 0.92 while FCN and the level set achieved 0.90 and 0.79 respectively. Particularly, for malignant lesions, our method increases the DSC (0.90) of the fully connected neural network to 0.93 significantly (p$<$0.001). The results show that our SPCGAN can obtain robust segmentation results. The framework of SPCGAN is particularly effective when sufficient training samples are not available compared to FCN. Our proposed method may be used to relieve the radiologists' burden for annotation.
Learning with Learned Loss Function: Speech Enhancement with Quality-Net to Improve Perceptual Evaluation of Speech Quality
Authors: Szu-Wei Fu, Chien-Feng Liao, Yu Tsao
Date: 2019-05-06 00:00:00
Tasks: Speech Enhancement

Utilizing a human-perception-related objective function to train a speech enhancement model has become a popular topic recently. The main reason is that the conventional mean squared error (MSE) loss cannot represent auditory perception well. One of the typical hu-man-perception-related metrics, which is the perceptual evaluation of speech quality (PESQ), has been proven to provide a high correlation to the quality scores rated by humans. Owing to its complex and non-differentiable properties, however, the PESQ function may not be used to optimize speech enhancement models directly. In this study, we propose optimizing the enhancement model with an approximated PESQ function, which is differentiable and learned from the training data. The experimental results show that the learned surrogate function can guide the enhancement model to further boost the PESQ score (in-crease of 0.18 points compared to the results trained with MSE loss) and maintain the speech intelligibility.
Distributional Semantics and Linguistic Theory
Authors: Gemma Boleda
Date: 2019-05-06 00:00:00

Distributional semantics provides multi-dimensional, graded, empirically induced word representations that successfully capture many aspects of meaning in natural languages, as shown in a large body of work in computational linguistics; yet, its impact in theoretical linguistics has so far been limited. This review provides a critical discussion of the literature on distributional semantics, with an emphasis on methods and results that are of relevance for theoretical linguistics, in three areas: semantic change, polysemy and composition, and the grammar-semantics interface (specifically, the interface of semantics with syntax and with derivational morphology). The review aims at fostering greater cross-fertilization of theoretical and computational approaches to language, as a means to advance our collective knowledge of how it works.
Evolutionary Optimisation of Real-Time Systems and Networks
Authors: Leandro Soares Indrusiak, Robert I. Davis, Piotr Dziurzanski
Date: 2019-05-06 00:00:00
Tasks: Scheduling

The design space of networked embedded systems is very large, posing challenges to the optimisation of such platforms when it comes to support applications with real-time guarantees. Recent research has shown that a number of inter-related optimisation problems have a critical influence over the schedulability of a system, i.e. whether all its application components can execute and communicate by their respective deadlines. Examples of such optimization problems include task allocation and scheduling, communication routing and arbitration, memory allocation, and voltage and frequency scaling. In this paper, we advocate the use of evolutionary approaches to address such optimization problems, aiming to evolve individuals of increased fitness over multiple generations of potential solutions. We refer to plentiful evidence that existing real-time schedulability tests can be used effectively to guide evolutionary optimisation, either by themselves or in combination with other metrics such as energy dissipation or hardware overheads. We then push that concept one step further and consider the possibility of using evolutionary techniques to evolve the schedulability tests themselves, aiming to support the verification and optimisation of systems which are too complex for state-of-the-art (manual) derivation of schedulability tests.
POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion
Authors: Wen Chen, Pipei Huang, Jiaming Xu, Xin Guo, Cheng Guo, Fei Sun, Chao Li, Andreas Pfadler, Huan Zhao, Binqiang Zhao
Date: 2019-05-06 00:00:00

Increasing demand for fashion recommendation raises a lot of challenges for online shopping platforms and fashion communities. In particular, there exist two requirements for fashion outfit recommendation: the Compatibility of the generated fashion outfits, and the Personalization in the recommendation process. In this paper, we demonstrate these two requirements can be satisfied via building a bridge between outfit generation and recommendation. Through large data analysis, we observe that people have similar tastes in individual items and outfits. Therefore, we propose a Personalized Outfit Generation (POG) model, which connects user preferences regarding individual items and outfits with Transformer architecture. Extensive offline and online experiments provide strong quantitative evidence that our method outperforms alternative methods regarding both compatibility and personalization metrics. Furthermore, we deploy POG on a platform named Dida in Alibaba to generate personalized outfits for the users of the online application iFashion. This work represents a first step towards an industrial-scale fashion outfit generation and recommendation solution, which goes beyond generating outfits based on explicit queries, or merely recommending from existing outfit pools. As part of this work, we release a large-scale dataset consisting of 1.01 million outfits with rich context information, and 0.28 billion user click actions from 3.57 million users. To the best of our knowledge, this dataset is the largest, publicly available, fashion related dataset, and the first to provide user behaviors relating to both outfits and fashion items.
The Missing Data Encoder: Cross-Channel Image Completion\\with Hide-And-Seek Adversarial Network
Authors: Arnaud Dapogny, Matthieu Cord, Patrick Perez
Date: 2019-05-06 00:00:00
Tasks: Colorization, Occlusion Handling, Representation Learning

Image completion is the problem of generating whole images from fragments only. It encompasses inpainting (generating a patch given its surrounding), reverse inpainting/extrapolation (generating the periphery given the central patch) as well as colorization (generating one or several channels given other ones). In this paper, we employ a deep network to perform image completion, with adversarial training as well as perceptual and completion losses, and call it the ``missing data encoder'' (MDE). We consider several configurations based on how the seed fragments are chosen. We show that training MDE for ``random extrapolation and colorization'' (MDE-REC), i.e. using random channel-independent fragments, allows a better capture of the image semantics and geometry. MDE training makes use of a novel ``hide-and-seek'' adversarial loss, where the discriminator seeks the original non-masked regions, while the generator tries to hide them. We validate our models both qualitatively and quantitatively on several datasets, showing their interest for image completion, unsupervised representation learning as well as face occlusion handling.
DeepCheck: A Non-intrusive Control-flow Integrity Checking based on Deep Learning
Authors: Jiliang Zhang, Wuqiao Chen, Yuqi Niu
Date: 2019-05-06 00:00:00

Code reuse attack (CRA) is a powerful attack that reuses existing codes to hijack the program control flow. Control flow integrity (CFI) is one of the most popular mechanisms to prevent against CRAs. However, current CFI techniques are difficult to be deployed in real applications due to suffering several issues such as modifying binaries or compiler, extending instruction set architectures (ISA) and incurring unacceptable runtime overhead. To address these issues, we propose the first deep learning-based CFI technique, named DeepCheck, where the control flow graph (CFG) is split into chains for deep neural network (DNN) training. Then the integrity features of CFG can be learned by DNN to detect abnormal control flows. DeepCheck does not interrupt the application and hence incurs zero runtime overhead. Experimental results on Adobe Flash Player, Nginx, Proftpd and Firefox show that the average detection accuracy of DeepCheck is as high as 98.9%. In addition, 64 ROP exploits created by ROPGadget and Ropper are used to further test the effectiveness, which shows that the detection success rate reaches 100%.
UFRGS Participation on the WMT Biomedical Translation Shared Task
Authors: Felipe Soares, Karin Becker
Date: 2019-05-06 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

This paper describes the machine translation systems developed by the Universidade Federal do Rio Grande do Sul (UFRGS) team for the biomedical translation shared task. Our systems are based on statistical machine translation and neural machine translation, using the Moses and OpenNMT toolkits, respectively. We participated in four translation directions for the English/Spanish and English/Portuguese language pairs. To create our training data, we concatenated several parallel corpora, both from in-domain and out-of-domain sources, as well as terminological resources from UMLS. Our systems achieved the best BLEU scores according to the official shared task evaluation.
A Large Parallel Corpus of Full-Text Scientific Articles
Authors: Felipe Soares, Viviane Pereira Moreira, Karin Becker
Date: 2019-05-06 00:00:00
Proceeding: LREC 2018 5
Tasks: Articles, Machine Translation, Sentence, Translation

The Scielo database is an important source of scientific information in Latin America, containing articles from several research domains. A striking characteristic of Scielo is that many of its full-text contents are presented in more than one language, thus being a potential source of parallel corpora. In this article, we present the development of a parallel corpus from Scielo in three languages: English, Portuguese, and Spanish. Sentences were automatically aligned using the Hunalign algorithm for all language pairs, and for a subset of trilingual articles also. We demonstrate the capabilities of our corpus by training a Statistical Machine Translation system (Moses) for each language pair, which outperformed related works on scientific articles. Sentence alignment was also manually evaluated, presenting an average of 98.8% correctly aligned sentences across all languages. Our parallel corpus is freely available in the TMX format, with complementary information regarding article metadata.
P-ODN: Prototype based Open Deep Network for Open Set Recognition
Authors: Yu Shu, Yemin Shi, Yao-Wei Wang, Tiejun Huang, Yonghong Tian
Date: 2019-05-06 00:00:00
Tasks: Open Set Learning, Triplet

Most of the existing recognition algorithms are proposed for closed set scenarios, where all categories are known beforehand. However, in practice, recognition is essentially an open set problem. There are categories we know called "knowns", and there are more we do not know called "unknowns". Enumerating all categories beforehand is never possible, consequently it is infeasible to prepare sufficient training samples for those unknowns. Applying closed set recognition methods will naturally lead to unseen-category errors. To address this problem, we propose the prototype based Open Deep Network (P-ODN) for open set recognition tasks. Specifically, we introduce prototype learning into open set recognition. Prototypes and prototype radiuses are trained jointly to guide a CNN network to derive more discriminative features. Then P-ODN detects the unknowns by applying a multi-class triplet thresholding method based on the distance metric between features and prototypes. Manual labeling the unknowns which are detected in the previous process as new categories. Predictors for new categories are added to the classification layer to "open" the deep neural networks to incorporate new categories dynamically. The weights of new predictors are initialized exquisitely by applying a distances based algorithm to transfer the learned knowledge. Consequently, this initialization method speed up the fine-tuning process and reduce the samples needed to train new predictors. Extensive experiments show that P-ODN can effectively detect unknowns and needs only few samples with human intervention to recognize a new category. In the real world scenarios, our method achieves state-of-the-art performance on the UCF11, UCF50, UCF101 and HMDB51 datasets.
Extracting human emotions at different places based on facial expressions and spatial clustering analysis
Authors: Yuhao Kang, Qingyuan Jia, Song Gao, Xiaohuan Zeng, Yueyao Wang, Stephan Angsuesser, Yu Liu, Xinyue Ye, Teng Fei
Date: 2019-05-06 00:00:00
Tasks: Clustering

The emergence of big data enables us to evaluate the various human emotions at places from a statistic perspective by applying affective computing. In this study, a novel framework for extracting human emotions from large-scale georeferenced photos at different places is proposed. After the construction of places based on spatial clustering of user generated footprints collected in social media websites, online cognitive services are utilized to extract human emotions from facial expressions using the state-of-the-art computer vision techniques. And two happiness metrics are defined for measuring the human emotions at different places. To validate the feasibility of the framework, we take 80 tourist attractions around the world as an example and a happiness ranking list of places is generated based on human emotions calculated over 2 million faces detected out from over 6 million photos. Different kinds of geographical contexts are taken into consideration to find out the relationship between human emotions and environmental factors. Results show that much of the emotional variation at different places can be explained by a few factors such as openness. The research may offer insights on integrating human emotions to enrich the understanding of sense of place in geography and in place-based GIS.
RSL19BD at DBDC4: Ensemble of Decision Tree-based and LSTM-based Models
Authors: Chih-Hao Wang, Sosuke Kato, Tetsuya Sakai
Date: 2019-05-06 00:00:00

RSL19BD (Waseda University Sakai Laboratory) participated in the Fourth Dialogue Breakdown Detection Challenge (DBDC4) and submitted five runs to both English and Japanese subtasks. In these runs, we utilise the Decision Tree-based model and the Long Short-Term Memory-based (LSTM-based) model following the approaches of RSL17BD and KTH in the Third Dialogue Breakdown Detection Challenge (DBDC3) respectively. The Decision Tree-based model follows the approach of RSL17BD but utilises RandomForestRegressor instead of ExtraTreesRegressor. In addition, instead of predicting the mean and the variance of the probability distribution of the three breakdown labels, it predicts the probability of each label directly. The LSTM-based model follows the approach of KTH with some changes in the architecture and utilises Convolutional Neural Network (CNN) to perform text feature extraction. In addition, instead of targeting the single breakdown label and minimising the categorical cross entropy loss, it targets the probability distribution of the three breakdown labels and minimises the mean squared error. Run 1 utilises a Decision Tree-based model; Run 2 utilises an LSTM-based model; Run 3 performs an ensemble of 5 LSTM-based models; Run 4 performs an ensemble of Run 1 and Run 2; Run 5 performs an ensemble of Run 1 and Run 3. Run 5 statistically significantly outperformed all other runs in terms of MSE (NB, PB, B) for the English data and all other runs except Run 4 in terms of MSE (NB, PB, B) for the Japanese data (alpha level = 0.05).