pwc-archive/papers-with-abstracts huggingface dataset:
Fingerprint Presentation Attack Detection Based on Local Features Encoding for Unknown Attacks
Authors: Lázaro J. González-Soler, Marta Gomez-Barrero, Leonardo Chang, Airel Pérez-Suárez, Christoph Busch
Date: 2019-08-27 00:00:00

Fingerprint-based biometric systems have experienced a large development in the last years. Despite their many advantages, they are still vulnerable to presentation attacks (PAs). Therefore, the task of determining whether a sample stems from a live subject (i.e., bona fide) or from an artificial replica is a mandatory issue which has received a lot of attention recently. Nowadays, when the materials for the fabrication of the Presentation Attack Instruments (PAIs) have been used to train the PA Detection (PAD) methods, the PAIs can be successfully identified. However, current PAD methods still face difficulties detecting PAIs built from unknown materials or captured using other sensors. Based on that fact, we propose a new PAD technique based on three image representation approaches combining local and global information of the fingerprint. By transforming these representations into a common feature space, we can correctly discriminate bona fide from attack presentations in the aforementioned scenarios. The experimental evaluation of our proposal over the LivDet 2011 to 2015 databases, yielded error rates outperforming the top state-of-the-art results by up to 50\% in the most challenging scenarios. In addition, the best configuration achieved the best results in the LivDet 2019 competition (overall accuracy of 96.17\%).
Mobile Video Action Recognition
Authors: Yuqi Huo, Xiaoli Xu, Yao Lu, Yulei Niu, Zhiwu Lu, Ji-Rong Wen
Date: 2019-08-27 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Video action recognition, which is topical in computer vision and video analysis, aims to allocate a short video clip to a pre-defined category such as brushing hair or climbing stairs. Recent works focus on action recognition with deep neural networks that achieve state-of-the-art results in need of high-performance platforms. Despite the fast development of mobile computing, video action recognition on mobile devices has not been fully discussed. In this paper, we focus on the novel mobile video action recognition task, where only the computational capabilities of mobile devices are accessible. Instead of raw videos with huge storage, we choose to extract multiple modalities (including I-frames, motion vectors, and residuals) directly from compressed videos. By employing MobileNetV2 as backbone, we propose a novel Temporal Trilinear Pooling (TTP) module to fuse the multiple modalities for mobile video action recognition. In addition to motion vectors, we also provide a temporal fusion method to explicitly induce the temporal context. The efficiency test on a mobile device indicates that our model can perform mobile video action recognition at about 40FPS. The comparative results on two benchmarks show that our model outperforms existing action recognition methods in model size and time consuming, but with competitive accuracy.
Incremental Improvement of a Question Answering System by Re-ranking Answer Candidates using Machine Learning
Authors: Michael Barz, Daniel Sonntag
Date: 2019-08-27 00:00:00
Tasks: Answer Selection, BIG-bench Machine Learning, Question Answering, Re-Ranking, Sentence, Sentence Embeddings

We implement a method for re-ranking top-10 results of a state-of-the-art question answering (QA) system. The goal of our re-ranking approach is to improve the answer selection given the user question and the top-10 candidates. We focus on improving deployed QA systems that do not allow re-training or re-training comes at a high cost. Our re-ranking approach learns a similarity function using n-gram based features using the query, the answer and the initial system confidence as input. Our contributions are: (1) we generate a QA training corpus starting from 877 answers from the customer care domain of T-Mobile Austria, (2) we implement a state-of-the-art QA pipeline using neural sentence embeddings that encode queries in the same space than the answer index, and (3) we evaluate the QA pipeline and our re-ranking approach using a separately provided test set. The test set can be considered to be available after deployment of the system, e.g., based on feedback of users. Our results show that the system performance, in terms of top-n accuracy and the mean reciprocal rank, benefits from re-ranking using gradient boosted regression trees. On average, the mean reciprocal rank improves by 9.15%.
Enabling Hyper-Personalisation: Automated Ad Creative Generation and Ranking for Fashion e-Commerce
Authors: Sreekanth Vempati, Korah T Malayil, Sruthi V, Sandeep R
Date: 2019-08-27 00:00:00

Homepage is the first touch point in the customer's journey and is one of the prominent channels of revenue for many e-commerce companies. A user's attention is mostly captured by homepage banner images (also called Ads/Creatives). The set of banners shown and their design, influence the customer's interest and plays a key role in optimizing the click through rates of the banners. Presently, massive and repetitive effort is put in, to manually create aesthetically pleasing banner images. Due to the large amount of time and effort involved in this process, only a small set of banners are made live at any point. This reduces the number of banners created as well as the degree of personalization that can be achieved. This paper thus presents a method to generate creatives automatically on a large scale in a short duration. The availability of diverse banners generated helps in improving personalization as they can cater to the taste of larger audience. The focus of our paper is on generating wide variety of homepage banners that can be made as an input for user level personalization engine. Following are the main contributions of this paper: 1) We introduce and explain the need for large scale banner generation for e-commerce 2) We present on how we utilize existing deep learning based detectors which can automatically annotate the required objects/tags from the image. 3) We also propose a Genetic Algorithm based method to generate an optimal banner layout for the given image content, input components and other design constraints. 4) Further, to aid the process of picking the right set of banners, we designed a ranking method and evaluated multiple models. All our experiments have been performed on data from Myntra (http://www.myntra.com), one of the top fashion e-commerce players in India.
Cooperative Cross-Stream Network for Discriminative Action Representation
Authors: Jingran Zhang, Fumin Shen, Xing Xu, Heng Tao Shen
Date: 2019-08-27 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Triplet

Spatial and temporal stream model has gained great success in video action recognition. Most existing works pay more attention to designing effective features fusion methods, which train the two-stream model in a separate way. However, it's hard to ensure discriminability and explore complementary information between different streams in existing works. In this work, we propose a novel cooperative cross-stream network that investigates the conjoint information in multiple different modalities. The jointly spatial and temporal stream networks feature extraction is accomplished by an end-to-end learning manner. It extracts this complementary information of different modality from a connection block, which aims at exploring correlations of different stream features. Furthermore, different from the conventional ConvNet that learns the deep separable features with only one cross-entropy loss, our proposed model enhances the discriminative power of the deeply learned features and reduces the undesired modality discrepancy by jointly optimizing a modality ranking constraint and a cross-entropy loss for both homogeneous and heterogeneous modalities. The modality ranking constraint constitutes intra-modality discriminative embedding and inter-modality triplet constraint, and it reduces both the intra-modality and cross-modality feature variations. Experiments on three benchmark datasets demonstrate that by cooperating appearance and motion feature extraction, our method can achieve state-of-the-art or competitive performance compared with existing results.
A hybrid parametric-deep learning approach for sound event localization and detection
Authors: Andres Perez-Lopez, Eduardo Fonseca, Xavier Serra
Date: 2019-08-27 00:00:00
Tasks: Sound Event Localization and Detection

This work describes and discusses an algorithm submitted to the Sound Event Localization and Detection Task of DCASE2019 Challenge. The proposed methodology relies on parametric spatial audio analysis for source localization and detection, combined with a deep learning-based monophonic event classifier. The evaluation of the proposed algorithm yields overall results comparable to the baseline system. The main highlight is a reduction of the localization error on the evaluation dataset by a factor of 2.6, compared with the baseline performance.
TERA: the Toxicological Effect and Risk Assessment Knowledge Graph
Authors: Erik Bryhn Myklebust, Ernesto Jimenez-Ruiz, Jiaoyan Chen, Raoul Wolf, Knut Erik Tollefsen
Date: 2019-08-27 00:00:00

Ecological risk assessment requires large amounts of chemical effect data from laboratory experiments. Due to experimental effort and animal welfare concerns it is desired to extrapolate data from existing sources. To cover the required chemical effect data several data sources need to be integrated to enable their interoperability. In this paper we introduce the Toxicological Effect and Risk Assessment (TERA) knowledge graph, which aims at providing such integrated view, and the data preparation and steps followed to construct this knowledge graph. We also present the applications of TERA for chemical effect prediction and the potential applications within the Semantic Web community.
Learning-Based Video Game Development in MLP@UoM: An Overview
Authors: Ke Chen
Date: 2019-08-27 00:00:00
Tasks: BIG-bench Machine Learning

In general, video games not only prevail in entertainment but also have become an alternative methodology for knowledge learning, skill acquisition and assistance for medical treatment as well as health care in education, vocational/military training and medicine. On the other hand, video games also provide an ideal test bed for AI researches. To a large extent, however, video game development is still a laborious yet costly process, and there are many technical challenges ranging from game generation to intelligent agent creation. Unlike traditional methodologies, in Machine Learning and Perception Lab at the University of Manchester (MLP@UoM), we advocate applying machine learning to different tasks in video game development to address several challenges systematically. In this paper, we overview the main progress made in MLP@UoM recently and have an outlook on the future research directions in learning-based video game development arising from our works.
Proactive Intention Recognition for Joint Human-Robot Search and Rescue Missions through Monte-Carlo Planning in POMDP Environments
Authors: Dimitri Ognibene, Lorenzo Mirante, Letizia Marchegiani
Date: 2019-08-27 00:00:00
Tasks: Intent Detection, Position

Proactively perceiving others' intentions is a crucial skill to effectively interact in unstructured, dynamic and novel environments. This work proposes a first step towards embedding this skill in support robots for search and rescue missions. Predicting the responders' intentions, indeed, will enable exploration approaches which will identify and prioritise areas that are more relevant for the responder and, thus, for the task, leading to the development of safer, more robust and efficient joint exploration strategies. More specifically, this paper presents an active intention recognition paradigm to perceive, even under sensory constraints, not only the target's position but also the first responder's movements, which can provide information on his/her intentions (e.g. reaching the position where he/she expects the target to be). This mechanism is implemented by employing an extension of Monte-Carlo-based planning techniques for partially observable environments, where the reward function is augmented with an entropy reduction bonus. We test in simulation several configurations of reward augmentation, both information theoretic and not, as well as belief state approximations and obtain substantial improvements over the basic approach.
Heuristic design of fuzzy inference systems: A review of three decades of research
Authors: Varun Ojha, Ajith Abraham, Vaclav Snasel
Date: 2019-08-27 00:00:00
Tasks: Evolutionary Algorithms, Vocal Bursts Type Prediction

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multi-objective fuzzy systems (MFS), which is in view that some of them are linked to each other. The heuristic design of GFS uses evolutionary algorithms for optimizing both Mamdani-type and Takagi-Sugeno-Kang-type fuzzy systems. Whereas, the NFS combines the FIS with neural network learning systems to improve the approximation ability. An HFS combines two or more low-dimensional fuzzy logic units in a hierarchical design to overcome the curse of dimensionality. An EFS solves the data streaming issues by evolving the system incrementally, and an MFS solves the multi-objective trade-offs like the simultaneous maximization of both interpretability and accuracy. This paper offers a synthesis of these dimensions and explores their potentials, challenges, and opportunities in FIS research. This review also examines the complex relations among these dimensions and the possibilities of combining one or more computational frameworks adding another dimension: deep fuzzy systems.
Multi-Layer Softmaxing during Training Neural Machine Translation for Flexible Decoding with Fewer Layers
Authors: Raj Dabre, Atsushi Fujita
Date: 2019-08-27 00:00:00
Tasks: Decoder, Machine Translation, Translation

This paper proposes a novel procedure for training an encoder-decoder based deep neural network which compresses NxM models into a single model enabling us to dynamically choose the number of encoder and decoder layers for decoding. Usually, the output of the last layer of the N-layer encoder is fed to the M-layer decoder, and the output of the last decoder layer is used to compute softmax loss. Instead, our method computes a single loss consisting of NxM losses: the softmax loss for the output of each of the M decoder layers derived using the output of each of the N encoder layers. A single model trained by our method can be used for decoding with an arbitrary fewer number of encoder and decoder layers. In practical scenarios, this (a) enables faster decoding with insignificant losses in translation quality and (b) alleviates the need to train NxM models, thereby saving space. We take a case study of neural machine translation and show the advantage and give a cost-benefit analysis of our approach.
Synthetic patches, real images: screening for centrosome aberrations in EM images of human cancer cells
Authors: Artem Lukoyanov, Isabella Haberbosch, Constantin Pape, Alwin Kraemer, Yannick Schwab, Anna Kreshuk
Date: 2019-08-27 00:00:00

Recent advances in high-throughput electron microscopy imaging enable detailed study of centrosome aberrations in cancer cells. While the image acquisition in such pipelines is automated, manual detection of centrioles is still necessary to select cells for re-imaging at higher magnification. In this contribution we propose an algorithm which performs this step automatically and with high accuracy. From the image labels produced by human experts and a 3D model of a centriole we construct an additional training set with patch-level labels. A two-level DenseNet is trained on the hybrid training data with synthetic patches and real images, achieving much better results on real patient data than training only at the image-level. The code can be found at https://github.com/kreshuklab/centriole_detection.
HRGE-Net: Hierarchical Relational Graph Embedding Network for Multi-view 3D Shape Recognition
Authors: Xin Wei, Ruixuan Yu, Jian Sun
Date: 2019-08-27 00:00:00
Tasks: 3D Shape Classification, 3D Shape Recognition, Graph Embedding, Retrieval

View-based approach that recognizes 3D shape through its projected 2D images achieved state-of-the-art performance for 3D shape recognition. One essential challenge for view-based approach is how to aggregate the multi-view features extracted from 2D images to be a global 3D shape descriptor. In this work, we propose a novel feature aggregation network by fully investigating the relations among views. We construct a relational graph with multi-view images as nodes, and design relational graph embedding by modeling pairwise and neighboring relations among views. By gradually coarsening the graph, we build a hierarchical relational graph embedding network (HRGE-Net) to aggregate the multi-view features to be a global shape descriptor. Extensive experiments show that HRGE-Net achieves stateof-the-art performance for 3D shape classification and retrieval on benchmark datasets.
VAE-based Domain Adaptation for Speaker Verification
Authors: Xueyi Wang, Lantian Li, Dong Wang
Date: 2019-08-27 00:00:00
Tasks: Domain Adaptation, Speaker Verification

Deep speaker embedding has achieved satisfactory performance in speaker verification. By enforcing the neural model to discriminate the speakers in the training set, deep speaker embedding (called `x-vectors`) can be derived from the hidden layers. Despite its good performance, the present embedding model is highly domain sensitive, which means that it often works well in domains whose acoustic condition matches that of the training data (in-domain), but degrades in mismatched domains (out-of-domain). In this paper, we present a domain adaptation approach based on Variational Auto-Encoder (VAE). This model transforms x-vectors to a regularized latent space; within this latent space, a small amount of data from the target domain is sufficient to accomplish the adaptation. Our experiments demonstrated that by this VAE-adaptation approach, speaker embeddings can be easily transformed to the target domain, leading to noticeable performance improvement.
On NMT Search Errors and Model Errors: Cat Got Your Tongue?
Authors: Felix Stahlberg, Bill Byrne
Date: 2019-08-27 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Machine Translation, NMT, Translation

We report on search errors and model errors in neural machine translation (NMT). We present an exact inference procedure for neural sequence models based on a combination of beam search and depth-first search. We use our exact search to find the global best model scores under a Transformer base model for the entire WMT15 English-German test set. Surprisingly, beam search fails to find these global best model scores in most cases, even with a very large beam size of 100. For more than 50% of the sentences, the model in fact assigns its global best score to the empty translation, revealing a massive failure of neural models in properly accounting for adequacy. We show by constraining search with a minimum translation length that at the root of the problem of empty translations lies an inherent bias towards shorter translations. We conclude that vanilla NMT in its current form requires just the right amount of beam search errors, which, from a modelling perspective, is a highly unsatisfactory conclusion indeed, as the model often prefers an empty translation.
Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention
Authors: Yang Liu, Runnan He, Kuanquan Wang, Qince Li, Qiang Sun, Na Zhao, Henggui Zhang
Date: 2019-08-27 00:00:00
Tasks: Denoising, Diagnostic

Heart disease is one of the most common diseases causing morbidity and mortality. Electrocardiogram (ECG) has been widely used for diagnosing heart diseases for its simplicity and non-invasive property. Automatic ECG analyzing technologies are expected to reduce human working load and increase diagnostic efficacy. However, there are still some challenges to be addressed for achieving this goal. In this study, we develop an algorithm to identify multiple abnormalities from 12-lead ECG recordings. In the algorithm pipeline, several preprocessing methods are firstly applied on the ECG data for denoising, augmentation and balancing recording numbers of variant classes. In consideration of efficiency and consistency of data length, the recordings are padded or truncated into a medium length, where the padding/truncating time windows are selected randomly to sup-press overfitting. Then, the ECGs are used to train deep neural network (DNN) models with a novel structure that combines a deep residual network with an attention mechanism. Finally, an ensemble model is built based on these trained models to make predictions on the test data set. Our method is evaluated based on the test set of the First China ECG Intelligent Competition dataset by using the F1 metric that is regarded as the harmonic mean between the precision and recall. The resultant overall F1 score of the algorithm is 0.875, showing a promising performance and potential for practical use.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Authors: Nils Reimers, Iryna Gurevych
Date: 2019-08-27 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Clustering, Linear-Probe Classification, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Embedding, Sentence Embeddings, STS, Transfer Learning, Triplet

BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10,000 sentences requires about 50 million inference computations (~65 hours) with BERT. The construction of BERT makes it unsuitable for semantic similarity search as well as for unsupervised tasks like clustering. In this publication, we present Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT. We evaluate SBERT and SRoBERTa on common STS tasks and transfer learning tasks, where it outperforms other state-of-the-art sentence embeddings methods.