pwc-archive/papers-with-abstracts huggingface dataset:
Recognizing Facial Expressions of Occluded Faces using Convolutional Neural Networks
Authors: Mariana-Iuliana Georgescu, Radu Tudor Ionescu
Date: 2019-11-12 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

In this paper, we present an approach based on convolutional neural networks (CNNs) for facial expression recognition in a difficult setting with severe occlusions. More specifically, our task is to recognize the facial expression of a person wearing a virtual reality (VR) headset which essentially occludes the upper part of the face. In order to accurately train neural networks for this setting, in which faces are severely occluded, we modify the training examples by intentionally occluding the upper half of the face. This forces the neural networks to focus on the lower part of the face and to obtain better accuracy rates than models trained on the entire faces. Our empirical results on two benchmark data sets, FER+ and AffectNet, show that our CNN models' predictions on lower-half faces are up to 13% higher than the baseline CNN models trained on entire faces, proving their suitability for the VR setting. Furthermore, our models' predictions on lower-half faces are no more than 10% under the baseline models' predictions on full faces, proving that there are enough clues in the lower part of the face to accurately predict facial expressions.
Semi-supervised Wrapper Feature Selection by Modeling Imperfect Labels
Authors: Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini
Date: 2019-11-12 00:00:00
Tasks: feature selection

In this paper, we propose a new wrapper feature selection approach with partially labeled training examples where unlabeled observations are pseudo-labeled using the predictions of an initial classifier trained on the labeled training set. The wrapper is composed of a genetic algorithm for proposing new feature subsets, and an evaluation measure for scoring the different feature subsets. The selection of feature subsets is done by assigning weights to characteristics and recursively eliminating those that are irrelevant. The selection criterion is based on a new multi-class $\mathcal{C}$-bound that explicitly takes into account the mislabeling errors induced by the pseudo-labeling mechanism, using a probabilistic error model. Empirical results on different data sets show the effectiveness of our framework compared to several state-of-the-art semi-supervised feature selection approaches.
Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural Networks
Authors: Jonguk Kim, Jeong-Han Yun, Hyoung Chun Kim
Date: 2019-11-12 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

This study proposes an anomaly detection method for operational data of industrial control systems (ICSs). Sequence-to-sequence neural networks were applied to train and predict ICS operational data and interpret their time-series characteristic. The proposed method requires only a normal dataset to understand ICS's normal state and detect outliers. This method was evaluated with SWaT (secure water treatment) dataset, and 29 out of 36 attacks were detected. The reported method also detects the attack points, and 25 out of 53 points were detected. This study provides a detailed analysis of false positives and false negatives of the experimental results.
Artist and style exposure bias in collaborative filtering based music recommendations
Authors: Andres Ferraro, Dmitry Bogdanov, Xavier Serra, Jason Yoon
Date: 2019-11-12 00:00:00
Tasks: Collaborative Filtering, Music Recommendation

Algorithms have an increasing influence on the music that we consume and understanding their behavior is fundamental to make sure they give a fair exposure to all artists across different styles. In this on-going work we contribute to this research direction analyzing the impact of collaborative filtering recommendations from the perspective of artist and music style exposure given by the system. We first analyze the distribution of the recommendations considering the exposure of different styles or genres and compare it to the users' listening behavior. This comparison suggests that the system is reinforcing the popularity of the items. Then, we simulate the effect of the system in the long term with a feedback loop. From this simulation we can see how the system gives less opportunity to the majority of artists, concentrating the users on fewer items. The results of our analysis demonstrate the need for a better evaluation methodology for current music recommendation algorithms, not only limited to user-focused relevance metrics.
How Low Can You Go? Reducing Frequency and Time Resolution in Current CNN Architectures for Music Auto-tagging
Authors: Andres Ferraro, Dmitry Bogdanov, Xavier Serra, Jay Ho Jeon, Jason Yoon
Date: 2019-11-12 00:00:00
Tasks: Information Retrieval, Music Auto-Tagging, Music Information Retrieval, Retrieval

Automatic tagging of music is an important research topic in Music Information Retrieval and audio analysis algorithms proposed for this task have achieved improvements with advances in deep learning. In particular, many state-of-the-art systems use Convolutional Neural Networks and operate on mel-spectrogram representations of the audio. In this paper, we compare commonly used mel-spectrogram representations and evaluate model performances that can be achieved by reducing the input size in terms of both lesser amount of frequency bands and larger frame rates. We use the MagnaTagaTune dataset for comprehensive performance comparisons and then compare selected configurations on the larger Million Song Dataset. The results of this study can serve researchers and practitioners in their trade-off decision between accuracy of the models, data storage size and training and inference times.
A Capsule Network-based Model for Learning Node Embeddings
Authors: Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung
Date: 2019-11-12 00:00:00
Tasks: General Classification, Node Classification

In this paper, we focus on learning low-dimensional embeddings for nodes in graph-structured data. To achieve this, we propose Caps2NE -- a new unsupervised embedding model leveraging a network of two capsule layers. Caps2NE induces a routing process to aggregate feature vectors of context neighbors of a given target node at the first capsule layer, then feed these features into the second capsule layer to infer a plausible embedding for the target node. Experimental results show that our proposed Caps2NE obtains state-of-the-art performances on benchmark datasets for the node classification task. Our code is available at: \url{https://github.com/daiquocnguyen/Caps2NE}.
Grouping Capsules Based Different Types
Authors: Qiang Ren
Date: 2019-11-12 00:00:00

Capsule network was introduced as a new architecture of neural networks, it encoding features as capsules to overcome the lacking of equivariant in the convolutional neural networks. It uses dynamic routing algorithm to train parameters in different capsule layers, but the dynamic routing algorithm need to be improved. In this paper, we propose a novel capsule network architecture and discussed the effect of initialization method of the coupling coefficient $c_{ij}$ on the model. First, we analyze the rate of change of the initial value of $c_{ij}$ when the dynamic routing algorithm iterates. The larger the initial value of $c_{ij}$, the better effect of the model. Then, we proposed improvement that training different types of capsules by grouping capsules based different types. And this improvement can adjust the initial value of $c_{ij}$ to make it more suitable. We experimented with our improvements on some computer vision datasets and achieved better results than the original capsule network
On Policy Gradients
Authors: Mattis Manfred Kämmerer
Date: 2019-11-12 00:00:00

The goal of policy gradient approaches is to find a policy in a given class of policies which maximizes the expected return. Given a differentiable model of the policy, we want to apply a gradient-ascent technique to reach a local optimum. We mainly use gradient ascent, because it is theoretically well researched. The main issue is that the policy gradient with respect to the expected return is not available, thus we need to estimate it. As policy gradient algorithms also tend to require on-policy data for the gradient estimate, their biggest weakness is sample efficiency. For this reason, most research is focused on finding algorithms with improved sample efficiency. This paper provides a formal introduction to policy gradient that shows the development of policy gradient approaches, and should enable the reader to follow current research on the topic.
Detection of speech events and speaker characteristics through photo-plethysmographic signal neural processing
Authors: Guillermo Cámbara, Jordi Luque, Mireia Farrús
Date: 2019-11-12 00:00:00

The use of photoplethysmogram signal (PPG) for heart and sleep monitoring is commonly found nowadays in smartphones and wrist wearables. Besides common usages, it has been proposed and reported that person information can be extracted from PPG for other uses, like biometry tasks. In this work, we explore several end-to-end convolutional neural network architectures for detection of human's characteristics such as gender or person identity. In addition, we evaluate whether speech/non-speech events may be inferred from PPG signal, where speech might translate in fluctuations into the pulse signal. The obtained results are promising and clearly show the potential of fully end-to-end topologies for automatic extraction of meaningful biomarkers, even from a noisy signal sampled by a low-cost PPG sensor. The AUCs for best architectures put forward PPG wave as biological discriminant, reaching $79\%$ and $89.0\%$, respectively for gender and person verification tasks. Furthermore, speech detection experiments reporting AUCs around $69\%$ encourage us for further exploration about the feasibility of PPG for speech processing tasks.
MSDF: A Deep Reinforcement Learning Framework for Service Function Chain Migration
Authors: Ruoyun Chen, Hancheng Lu, Yujiao Lu, Jinxue Liu
Date: 2019-11-12 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Under dynamic traffic, service function chain (SFC) migration is considered as an effective way to improve resource utilization. However, the lack of future network information leads to non-optimal solutions, which motivates us to study reinforcement learning based SFC migration from a long-term perspective. In this paper, we formulate the SFC migration problem as a minimization problem with the objective of total network operation cost under constraints of users' quality of service. We firstly design a deep Q-network based algorithm to solve single SFC migration problem, which can adjust migration strategy online without knowing future information. Further, a novel multi-agent cooperative framework, called MSDF, is proposed to address the challenge of considering multiple SFC migration on the basis of single SFC migration. MSDF reduces the complexity thus accelerates the convergence speed, especially in large scale networks. Experimental results demonstrate that MSDF outperforms typical heuristic algorithms under various scenarios.
Effects of data ambiguity and cognitive biases on the interpretability of machine learning models in humanitarian decision making
Authors: David Paulus, Gerdien de Vries, Bartel Van de Walle
Date: 2019-11-12 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Humanitarian

The effectiveness of machine learning algorithms depends on the quality and amount of data and the operationalization and interpretation by the human analyst. In humanitarian response, data is often lacking or overburdening, thus ambiguous, and the time-scarce, volatile, insecure environments of humanitarian activities are likely to inflict cognitive biases. This paper proposes to research the effects of data ambiguity and cognitive biases on the interpretability of machine learning algorithms in humanitarian decision making.
Investigating Constraint Programming and Hybrid Methods for Real World Industrial Test Laboratory Scheduling
Authors: Tobias Geibinger, Florian Mischek, Nysret Musliu
Date: 2019-11-12 00:00:00
Tasks: Scheduling

In this paper we deal with a complex real world scheduling problem closely related to the well-known Resource-Constrained Project Scheduling Problem (RCPSP). The problem concerns industrial test laboratories in which a large number of tests has to be performed by qualified personnel using specialised equipment, while respecting deadlines and other constraints. We present different constraint programming models and search strategies for this problem. Furthermore, we propose a Very Large Neighborhood Search approach based on our CP methods. Our models are evaluated using CP solvers and a MIP solver both on real-world test laboratory data and on a set of generated instances of different sizes based on the real-world data. Further, we compare the exact approaches with VLNS and a Simulated Annealing heuristic. We could find feasible solutions for all instances and several optimal solutions and we show that using VLNS we can improve upon the results of the other approaches.
Merging-ISP: Multi-Exposure High Dynamic Range Image Signal Processing
Authors: Prashant Chaudhari, Franziska Schirrmacher, Andreas Maier, Christian Riess, Thomas Köhler
Date: 2019-11-12 00:00:00
Tasks: Demosaicking, Vocal Bursts Intensity Prediction

High dynamic range (HDR) imaging combines multiple images with different exposure times into a single high-quality image. The image signal processing pipeline (ISP) is a core component in digital cameras to perform these operations. It includes demosaicing of raw color filter array (CFA) data at different exposure times, alignment of the exposures, conversion to HDR domain, and exposure merging into an HDR image. Traditionally, such pipelines cascade algorithms that address these individual subtasks. However, cascaded designs suffer from error propagation, since simply combining multiple steps is not necessarily optimal for the entire imaging task. This paper proposes a multi-exposure HDR image signal processing pipeline (Merging-ISP) to jointly solve all these subtasks. Our pipeline is modeled by a deep neural network architecture. As such, it is end-to-end trainable, circumvents the use of hand-crafted and potentially complex algorithms, and mitigates error propagation. Merging-ISP enables direct reconstructions of HDR images of dynamic scenes from multiple raw CFA images with different exposures. We compare Merging-ISP against several state-of-the-art cascaded pipelines. The proposed method provides HDR reconstructions of high perceptual quality and it quantitatively outperforms competing ISPs by more than 1 dB in terms of PSNR.
Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation
Authors: Shengze Yu, Xin Wang, Wenwu Zhu, Peng Cui, Jingdong Wang
Date: 2019-01-01 00:00:00

Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing cross-platform recommendation approaches assume all cross-platform information to be consistent with each other and can be aligned. However, there remain two unsolved challenges: i) there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii) data from distinct platforms may have different semantic granularities. In this paper, we propose a cross-platform association model for cross-platform video recommendation, i.e., Disparity-preserved Deep Cross-platform Association (DCA), taking platform-specific disparity and granularity difference into consideration. The proposed DCA model employs a partially-connected multi-modal autoencoder, which is capable of explicitly capturing platform-specific information, as well as utilizing nonlinear mapping functions to handle granularity differences. We then present a cross-platform video recommendation approach based on the proposed DCA model. Extensive experiments for our cross-platform recommendation framework on real-world dataset demonstrate that the proposed DCA model significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics.
Prediction of Missing Semantic Relations in Lexical-Semantic Network using Random Forest Classifier
Authors: Kévin Cousot, Mehdi Mirzapour, Waleed Ragheb
Date: 2019-11-12 00:00:00

This study focuses on the prediction of missing six semantic relations (such as is_a and has_part) between two given nodes in RezoJDM a French lexical-semantic network. The output of this prediction is a set of pairs in which the first entries are semantic relations and the second entries are the probabilities of existence of such relations. Due to the statement of the problem we choose the random forest (RF) predictor classifier approach to tackle this problem. We take for granted the existing semantic relations, for training/test dataset, gathered and validated by crowdsourcing. We describe how all of the mentioned ideas can be followed after using the node2vec approach in the feature extraction phase. We show how this approach can lead to acceptable results.
Network Creation Games with Local Information and Edge Swaps
Authors: Shotaro Yoshimura, Yukiko Yamauchi
Date: 2019-11-12 00:00:00

In the swap game (SG) selfish players, each of which is associated to a vertex, form a graph by edge swaps, i.e., a player changes its strategy by simultaneously removing an adjacent edge and forming a new edge (Alon et al., 2013). The cost of a player considers the average distance to all other players or the maximum distance to other players. Any SG by $n$ players starting from a tree converges to an equilibrium with a constant Price of Anarchy (PoA) within $O(n^3)$ edge swaps (Lenzner, 2011). We focus on SGs where each player knows the subgraph induced by players within distance $k$. Therefore, each player cannot compute its cost nor a best response. We first consider pessimistic players who consider the worst-case global graph. We show that any SG starting from a tree (i) always converges to an equilibrium within $O(n^3)$ edge swaps irrespective of the value of $k$, (ii) the PoA is $\Theta(n)$ for $k=1,2,3$, and (iii) the PoA is constant for $k \geq 4$. We then introduce weakly pessimistic players and optimistic players and show that these less pessimistic players achieve constant PoA for $k \leq 3$ at the cost of best response cycles.
SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery
Authors: Shion Honda, Shoi Shi, Hiroki R. Ueda
Date: 2019-11-12 00:00:00
Tasks: Drug Discovery, Language Modeling, Language Modelling, Small Data Image Classification, Unsupervised Pre-training

In drug-discovery-related tasks such as virtual screening, machine learning is emerging as a promising way to predict molecular properties. Conventionally, molecular fingerprints (numerical representations of molecules) are calculated through rule-based algorithms that map molecules to a sparse discrete space. However, these algorithms perform poorly for shallow prediction models or small datasets. To address this issue, we present SMILES Transformer. Inspired by Transformer and pre-trained language models from natural language processing, SMILES Transformer learns molecular fingerprints through unsupervised pre-training of the sequence-to-sequence language model using a huge corpus of SMILES, a text representation system for molecules. We performed benchmarks on 10 datasets against existing fingerprints and graph-based methods and demonstrated the superiority of the proposed algorithms in small-data settings where pre-training facilitated good generalization. Moreover, we define a novel metric to concurrently measure model accuracy and data efficiency.
Data-Free Point Cloud Network for 3D Face Recognition
Authors: Ziyu, Zhang, Feipeng, Da, Yi, Yu
Date: 2019-11-12 00:00:00
Tasks: 3D Object Classification, Face Recognition, Semantic Parsing

Point clouds-based Networks have achieved great attention in 3D object classification, segmentation and indoor scene semantic parsing. In terms of face recognition, 3D face recognition method which directly consume point clouds as input is still under study. Two main factors account for this: One is how to get discriminative face representations from 3D point clouds using deep network; the other is the lack of large 3D training dataset. To address these problems, a data-free 3D face recognition method is proposed only using synthesized unreal data from statistical 3D Morphable Model to train a deep point cloud network. To ease the inconsistent distribution between model data and real faces, different point sampling methods are used in train and test phase. In this paper, we propose a curvature-aware point sampling(CPS) strategy replacing the original furthest point sampling(FPS) to hierarchically down-sample feature-sensitive points which are crucial to pass and aggregate features deeply. A PointNet++ like Network is used to extract face features directly from point clouds. The experimental results show that the network trained on generated data generalizes well for real 3D faces. Fine tuning on a small part of FRGCv2.0 and Bosphorus, which include real faces in different poses and expressions, further improves recognition accuracy.
Aplib: Tactical Programming of Intelligent Agents
Authors: I. S. W. B. Prasetya
Date: 2019-11-12 00:00:00
Tasks: Automated Theorem Proving

This paper presents aplib, a Java library for programming intelligent agents, featuring BDI and multi agency, but adding on top of it a novel layer of tactical programming inspired by the domain of theorem proving. Aplib is also implemented in such a way to provide the fluency of a Domain Specific Language (DSL). Compared to dedicated BDI agent programming languages such as JASON, 2APL, or GOAL,aplib's embedded DSL approach does mean that \aplib\ programmers will still be limited by Java syntax, but on other hand they get all the advantages that Java programmers get: rich language features (object orientation, static type checking, $\lambda$-expression, libraries, etc), a whole array of development tools, integration with other technologies, large community, etc.
FLAML: A Fast and Lightweight AutoML Library
Authors: Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu
Date: 2019-11-12 00:00:00
Tasks: AutoML, Hyperparameter Optimization

We study the problem of using low computational cost to automate the choices of learners and hyperparameters for an ad-hoc training dataset and error metric, by conducting trials of different configurations on the given training data. We investigate the joint impact of multiple factors on both trial cost and model error, and propose several design guidelines. Following them, we build a fast and lightweight library FLAML which optimizes for low computational resource in finding accurate models. FLAML integrates several simple but effective search strategies into an adaptive system. It significantly outperforms top-ranked AutoML libraries on a large open source AutoML benchmark under equal, or sometimes orders of magnitude smaller budget constraints.