pwc-archive/papers-with-abstracts huggingface dataset:
Explainable Video Action Reasoning via Prior Knowledge and State Transitions
Authors: Tao Zhuo, Zhiyong Cheng, Peng Zhang, Yongkang Wong, Mohan Kankanhalli
Date: 2019-08-28 00:00:00
Tasks: Action Analysis, Attribute

Human action analysis and understanding in videos is an important and challenging task. Although substantial progress has been made in past years, the explainability of existing methods is still limited. In this work, we propose a novel action reasoning framework that uses prior knowledge to explain semantic-level observations of video state changes. Our method takes advantage of both classical reasoning and modern deep learning approaches. Specifically, prior knowledge is defined as the information of a target video domain, including a set of objects, attributes and relationships in the target video domain, as well as relevant actions defined by the temporal attribute and relationship changes (i.e. state transitions). Given a video sequence, we first generate a scene graph on each frame to represent concerned objects, attributes and relationships. Then those scene graphs are linked by tracking objects across frames to form a spatio-temporal graph (also called video graph), which represents semantic-level video states. Finally, by sequentially examining each state transition in the video graph, our method can detect and explain how those actions are executed with prior knowledge, just like the logical manner of thinking by humans. Compared to previous works, the action reasoning results of our method can be explained by both logical rules and semantic-level observations of video content changes. Besides, the proposed method can be used to detect multiple concurrent actions with detailed information, such as who (particular objects), when (time), where (object locations) and how (what kind of changes). Experiments on a re-annotated dataset CAD-120 show the effectiveness of our method.
Initialization for Network Embedding: A Graph Partition Approach
Authors: Wenqing Lin, Feng He, Faqiang Zhang, Xu Cheng, Hongyun Cai
Date: 2019-08-28 00:00:00
Tasks: General Classification, graph partitioning, Link Prediction, Network Embedding, Node Classification

Network embedding has been intensively studied in the literature and widely used in various applications, such as link prediction and node classification. While previous work focus on the design of new algorithms or are tailored for various problem settings, the discussion of initialization strategies in the learning process is often missed. In this work, we address this important issue of initialization for network embedding that could dramatically improve the performance of the algorithms on both effectiveness and efficiency. Specifically, we first exploit the graph partition technique that divides the graph into several disjoint subsets, and then construct an abstract graph based on the partitions. We obtain the initialization of the embedding for each node in the graph by computing the network embedding on the abstract graph, which is much smaller than the input graph, and then propagating the embedding among the nodes in the input graph. With extensive experiments on various datasets, we demonstrate that our initialization technique significantly improves the performance of the state-of-the-art algorithms on the evaluations of link prediction and node classification by up to 7.76% and 8.74% respectively. Besides, we show that the technique of initialization reduces the running time of the state-of-the-arts by at least 20%.
Exploiting Multiple Embeddings for Chinese Named Entity Recognition
Authors: Canwen Xu, Feiyang Wang, Jialong Han, Chenliang Li
Date: 2019-08-28 00:00:00
Tasks: Chinese Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Identifying the named entities mentioned in text would enrich many semantic applications at the downstream level. However, due to the predominant usage of colloquial language in microblogs, the named entity recognition (NER) in Chinese microblogs experience significant performance deterioration, compared with performing NER in formal Chinese corpus. In this paper, we propose a simple yet effective neural framework to derive the character-level embeddings for NER in Chinese text, named ME-CNER. A character embedding is derived with rich semantic information harnessed at multiple granularities, ranging from radical, character to word levels. The experimental results demonstrate that the proposed approach achieves a large performance improvement on Weibo dataset and comparable performance on MSRA news dataset with lower computational cost against the existing state-of-the-art alternatives.
CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing
Authors: Shifeng Zhang, Ajian Liu, Jun Wan, Yanyan Liang, Guogong Guo, Sergio Escalera, Hugo Jair Escalante, Stan Z. Li
Date: 2019-08-28 00:00:00
Tasks: Face Anti-Spoofing, Face Recognition

Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects ($\le\negmedspace170$) and modalities ($\leq\negmedspace2$), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of $1,000$ subjects with $21,000$ videos and each sample has $3$ modalities (i.e., RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/face-anti-spoofing/welcome/challengecvpr2019?authuser=0
Machine-learning techniques for the optimal design of acoustic metamaterials
Authors: Andrea Bacigalupo, Giorgio Gnecco, Marco Lepidi, Luigi Gambarotta
Date: 2019-08-28 00:00:00
Tasks: Band Gap, BIG-bench Machine Learning

Recently, an increasing research effort has been dedicated to analyse the transmission and dispersion properties of periodic acoustic metamaterials, characterized by the presence of local resonators. Within this context, particular attention has been paid to the optimization of the amplitudes and center frequencies of selected stop and pass bands inside the Floquet-Bloch spectra of the acoustic metamaterials featured by a chiral or antichiral microstructure. Novel functional applications of such research are expected in the optimal parametric design of smart tunable mechanical filters and directional waveguides. The present paper deals with the maximization of the amplitude of low-frequency band gaps, by proposing suitable numerical techniques to solve the associated optimization problems. Specifically, the feasibility and effectiveness of Radial Basis Function networks and Quasi-Monte Carlo methods for the interpolation of the objective functions of such optimization problems are discussed, and their numerical application to a specific acoustic metamaterial with tetrachiral microstructure is presented. The discussion is motivated theoretically by the high computational effort often needed for an exact evaluation of the objective functions arising in band gap optimization problems, when iterative algorithms are used for their approximate solution. By replacing such functions with suitable surrogate objective functions constructed applying machine-learning techniques, well performing suboptimal solutions can be obtained with a smaller computational effort. Numerical results demonstrate the effective potential of the proposed approach. Current directions of research involving the use of additional machine-learning techniques are also presented.
How big can style be? Addressing high dimensionality for recommending with style
Authors: Diogo Goncalves, Liweu Liu, Ana Magalhães
Date: 2019-08-28 00:00:00
Tasks: Recommendation Systems

Using embeddings as representations of products is quite commonplace in recommender systems, either by extracting the semantic embeddings of text descriptions, user sessions, collaborative relationships, or product images. In this paper, we present an approach to extract style embeddings for using in fashion recommender systems, with a special focus on style information such as textures, prints, material, etc. The main issue of using such a type of embeddings is its high dimensionality. So, we propose feature reduction solutions alongside the investigation of its influence in the overall task of recommending products of the same style based on their main image. The feature reduction we propose allows for reducing the embedding vector from 600k features to 512, leading to a memory reduction of 99.91\% without critically compromising the quality of the recommendations.
Self-supervised blur detection from synthetically blurred scenes
Authors: Aitor Alvarez-Gila, Adrian Galdran, Estibaliz Garrote, Joost Van de Weijer
Date: 2019-08-28 00:00:00
Tasks: Segmentation, Self-Supervised Learning

Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labour intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically blurred images. This allows us to perform self-supervised learning over the generated image and ground truth blur mask pairs using CNNs, defining a framework that can be employed in purely self-supervised, weakly supervised or semi-supervised configurations. Interestingly, experimental results of such setups over the largest blur segmentation datasets available show that this approach achieves state of the art results in blur segmentation, even without ever observing any real blurred image.
Emotion Recognition in Low-Resource Settings: An Evaluation of Automatic Feature Selection Methods
Authors: Fasih Haider, Senja Pollak, Pierre Albert, Saturnino Luz
Date: 2019-08-28 00:00:00
Tasks: Emotion Recognition, feature selection

Research in automatic affect recognition has seldom addressed the issue of computational resource utilization. With the advent of ambient intelligence technology which employs a variety of low-power, resource-constrained devices, this issue is increasingly gaining interest. This is especially the case in the context of health and elderly care technologies, where interventions may rely on monitoring of emotional status to provide support or alert carers as appropriate. This paper focuses on emotion recognition from speech data, in settings where it is desirable to minimize memory and computational requirements. Reducing the number of features for inductive inference is a route towards this goal. In this study, we evaluate three different state-of-the-art feature selection methods: Infinite Latent Feature Selection (ILFS), ReliefF and Fisher (generalized Fisher score), and compare them to our recently proposed feature selection method named `Active Feature Selection' (AFS). The evaluation is performed on three emotion recognition data sets (EmoDB, SAVEE and EMOVO) using two standard acoustic paralinguistic feature sets (i.e. eGeMAPs and emobase). The results show that similar or better accuracy can be achieved using subsets of features substantially smaller than the entire feature set. A machine learning model trained on a smaller feature set will reduce the memory and computational resources of an emotion recognition system which can result in lowering the barriers for use of health monitoring technology.
Onto Word Segmentation of the Complete Tang Poems
Authors: Chao-Lin Liu
Date: 2019-08-28 00:00:00

We aim at segmenting words in the Complete Tang Poems (CTP). Although it is possible to do some research about CTP without doing full-scale word segmentation, we must move forward to word-level analysis of CTP for conducting advanced research topics. In November 2018 when we submitted the manuscript for DH 2019 (ADHO), we collected only 2433 poems that were segmented by trained experts, and used the segmented poems to evaluate the segmenter that considered domain knowledge of Chinese poetry. We trained pointwise mutual information (PMI) between Chinese characters based on the CTP poems (excluding the 2433 poems, which were used exclusively only for testing) and the domain knowledge. The segmenter relied on the PMI information to the recover 85.7% of words in the test poems. We could segment a poem completely correct only 17.8% of the time, however. When we presented our work at DH 2019, we have annotated more than 20000 poems. With a much larger amount of data, we were able to apply biLSTM models for this word segmentation task, and we segmented a poem completely correct above 20% of the time. In contrast, human annotators completely agreed on their annotations about 40% of the time.
Persuading Voters: It's Easy to Whisper, It's Hard to Speak Loud
Authors: Matteo Castiglioni, Andrea Celli, Nicola Gatti
Date: 2019-08-28 00:00:00

We focus on the following natural question: is it possible to influence the outcome of a voting process through the strategic provision of information to voters who update their beliefs rationally? We investigate whether it is computationally tractable to design a signaling scheme maximizing the probability with which the sender's preferred candidate is elected. We focus on the model recently introduced by Arieli and Babichenko (2019) (i.e., without inter-agent externalities), and consider, as explanatory examples, $k$-voting rule and plurality voting. There is a sharp contrast between the case in which private signals are allowed and the more restrictive setting in which only public signals are allowed. In the former, we show that an optimal signaling scheme can be computed efficiently both under a $k$-voting rule and plurality voting. In establishing these results, we provide two general (i.e., applicable to settings beyond voting) contributions. Specifically, we extend a well known result by Dughmi and Xu (2017) to more general settings, and prove that, when the sender's utility function is anonymous, computing an optimal signaling scheme is fixed parameter tractable w.r.t. the number of receivers' actions. In the public signaling case, we show that the sender's optimal expected return cannot be approximated to within any factor under a $k$-voting rule. This negative result easily extends to plurality voting and problems where utility functions are anonymous.
Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks
Authors: Yuting Ye, Xuwu Wang, Jiangchao Yao, Kunyang Jia, Jingren Zhou, Yanghua Xiao, Hongxia Yang
Date: 2019-08-28 00:00:00
Tasks: General Classification, Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction, Node Classification, Triplet

Low-dimensional embeddings of knowledge graphs and behavior graphs have proved remarkably powerful in varieties of tasks, from predicting unobserved edges between entities to content recommendation. The two types of graphs can contain distinct and complementary information for the same entities/nodes. However, previous works focus either on knowledge graph embedding or behavior graph embedding while few works consider both in a unified way. Here we present BEM , a Bayesian framework that incorporates the information from knowledge graphs and behavior graphs. To be more specific, BEM takes as prior the pre-trained embeddings from the knowledge graph, and integrates them with the pre-trained embeddings from the behavior graphs via a Bayesian generative model. BEM is able to mutually refine the embeddings from both sides while preserving their own topological structures. To show the superiority of our method, we conduct a range of experiments on three benchmark datasets: node classification, link prediction, triplet classification on two small datasets related to Freebase, and item recommendation on a large-scale e-commerce dataset.
Classical Chinese Sentence Segmentation for Tomb Biographies of Tang Dynasty
Authors: Chao-Lin Liu, Yi Chang
Date: 2019-08-28 00:00:00
Tasks: BIG-bench Machine Learning, Sentence, Sentence segmentation

Tomb biographies of the Tang dynasty provide invaluable information about Chinese history. The original biographies are classical Chinese texts which contain neither word boundaries nor sentence boundaries. Relying on three published books of tomb biographies of the Tang dynasty, we investigated the effectiveness of employing machine-learning methods for algorithmically identifying the pauses and terminals of sentences in the biographies. We consider the segmentation task as a classification problem. Chinese characters that are and are not followed by a punctuation mark are classified into two categories. We applied a machine-learning-based mechanism, the conditional random fields (CRF), to classify the characters (and words) in the texts, and we studied the contributions of selected types of lexical information to the resulting quality of the segmentation recommendations. This proposal presented at the DH 2018 conference discussed some of the basic experiments and their evaluations. By considering the contextual information and employing the heuristics provided by experts of Chinese literature, we achieved F1 measures that were better than 80%. More complex experiments that employ deep neural networks helped us further improve the results in recent work.
Techniques for Inverted Index Compression
Authors: Giulio Ermanno Pibiri, Rossano Venturini
Date: 2019-08-28 00:00:00

The data structure at the core of large-scale search engines is the inverted index, which is essentially a collection of sorted integer sequences called inverted lists. Because of the many documents indexed by such engines and stringent performance requirements imposed by the heavy load of queries, the inverted index stores billions of integers that must be searched efficiently. In this scenario, index compression is essential because it leads to a better exploitation of the computer memory hierarchy for faster query processing and, at the same time, allows reducing the number of storage machines. The aim of this article is twofold: first, surveying the encoding algorithms suitable for inverted index compression and, second, characterizing the performance of the inverted index through experimentation.
Attention-based Fusion for Outfit Recommendation
Authors: Katrien Laenen, Marie-Francine Moens
Date: 2019-08-28 00:00:00

This paper describes an attention-based fusion method for outfit recommendation which fuses the information in the product image and description to capture the most important, fine-grained product features into the item representation. We experiment with different kinds of attention mechanisms and demonstrate that the attention-based fusion improves item understanding. We outperform state-of-the-art outfit recommendation results on three benchmark datasets.
BioFaceNet: Deep Biophysical Face Image Interpretation
Authors: Sarah Alotaibi, William Smith
Date: 2019-08-28 00:00:00
Tasks: Decoder

In this paper we present BioFaceNet, a deep CNN that learns to decompose a single face image into biophysical parameters maps, diffuse and specular shading maps as well as estimating the spectral power distribution of the scene illuminant and the spectral sensitivity of the camera. The network comprises a fully convolutional encoder for estimating the spatial maps with a fully connected branch for estimating the vector quantities. The network is trained using a self-supervised appearance loss computed via a model-based decoder. The task is highly underconstrained so we impose a number of model-based priors. Skin spectral reflectance is restricted to a biophysical model, we impose a statistical prior on camera spectral sensitivities, a physical constraint on illumination spectra, a sparsity prior on specular reflections and direct supervision on diffuse shading using a rough shape proxy. We show convincing qualitative results on in-the-wild data and introduce a benchmark for quantitative evaluation on this new task.
STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control
Authors: Yanan Wang, Tong Xu, Xin Niu, Chang Tan, Enhong Chen, Hui Xiong
Date: 2019-08-28 00:00:00
Tasks: Graph Neural Network, Management, Multi-agent Reinforcement Learning, Q-Learning, Reinforcement Learning

The development of intelligent traffic light control systems is essential for smart transportation management. While some efforts have been made to optimize the use of individual traffic lights in an isolated way, related studies have largely ignored the fact that the use of multi-intersection traffic lights is spatially influenced and there is a temporal dependency of historical traffic status for current traffic light control. To that end, in this paper, we propose a novel SpatioTemporal Multi-Agent Reinforcement Learning (STMARL) framework for effectively capturing the spatio-temporal dependency of multiple related traffic lights and control these traffic lights in a coordinating way. Specifically, we first construct the traffic light adjacency graph based on the spatial structure among traffic lights. Then, historical traffic records will be integrated with current traffic status via Recurrent Neural Network structure. Moreover, based on the temporally-dependent traffic information, we design a Graph Neural Network based model to represent relationships among multiple traffic lights, and the decision for each traffic light will be made in a distributed way by the deep Q-learning method. Finally, the experimental results on both synthetic and real-world data have demonstrated the effectiveness of our STMARL framework, which also provides an insightful understanding of the influence mechanism among multi-intersection traffic lights.
On the overestimation of widely applicable Bayesian information criterion
Authors: Toru Imai
Date: 2019-08-28 00:00:00
Tasks: Model Selection

A widely applicable Bayesian information criterion (Watanabe, 2013) is applicable for both regular and singular models in the model selection problem. This criterion tends to overestimate the log marginal likelihood. We identify an overestimating term of a widely applicable Bayesian information criterion. Adjustment of the term gives an asymptotically unbiased estimator of the leading two terms of asymptotic expansion of the log marginal likelihood. In numerical experiments on regular and singular models, the adjustment resulted in smaller bias than the original criterion.