pwc-archive/papers-with-abstracts huggingface dataset:
PIANOTREE VAE: Structured Representation Learning for Polyphonic Music
Authors: Ziyu Wang, Yiyi Zhang, Yixiao Zhang, Junyan Jiang, Ruihan Yang, Junbo Zhao, Gus Xia
Date: 2020-08-17 00:00:00
Tasks: Music Generation, Representation Learning

The dominant approach for music representation learning involves the deep unsupervised model family variational autoencoder (VAE). However, most, if not all, viable attempts on this problem have largely been limited to monophonic music. Normally composed of richer modality and more complex musical structures, the polyphonic counterpart has yet to be addressed in the context of music representation learning. In this work, we propose the PianoTree VAE, a novel tree-structure extension upon VAE aiming to fit the polyphonic music learning. The experiments prove the validity of the PianoTree VAE via (i)-semantically meaningful latent code for polyphonic segments; (ii)-more satisfiable reconstruction aside of decent geometry learned in the latent space; (iii)-this model's benefits to the variety of the downstream music generation.
Semi-Supervised Learning with GANs for Device-Free Fingerprinting Indoor Localization
Authors: Kevin M. Chen, Ronald Y. Chang
Date: 2020-08-17 00:00:00
Tasks: Generative Adversarial Network, Indoor Localization

Device-free wireless indoor localization is a key enabling technology for the Internet of Things (IoT). Fingerprint-based indoor localization techniques are a commonly used solution. This paper proposes a semi-supervised, generative adversarial network (GAN)-based device-free fingerprinting indoor localization system. The proposed system uses a small amount of labeled data and a large amount of unlabeled data (i.e., semi-supervised), thus considerably reducing the expensive data labeling effort. Experimental results show that, as compared to the state-of-the-art supervised scheme, the proposed semi-supervised system achieves comparable performance with equal, sufficient amount of labeled data, and significantly superior performance with equal, highly limited amount of labeled data. Besides, the proposed semi-supervised system retains its performance over a broad range of the amount of labeled data. The interactions between the generator, discriminator, and classifier models of the proposed GAN-based system are visually examined and discussed. A mathematical description of the proposed system is also presented.
Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints
Authors: Dmitri Goldenberg, Javier Albert, Lucas Bernardi, Pablo Estevez
Date: 2020-08-14 00:00:00

Promotions and discounts have become key components of modern e-commerce platforms. For online travel platforms (OTPs), popular promotions include room upgrades, free meals and transportation services. By offering these promotions, customers can get more value for their money, while both the OTP and its travel partners may grow their loyal customer base. However, the promotions usually incur a cost that, if uncontrolled, can become unsustainable. Consequently, for a promotion to be viable, its associated costs must be balanced by incremental revenue within set financial constraints. Personalized treatment assignment can be used to satisfy such constraints. This paper introduces a novel uplift modeling technique, relying on the Knapsack Problem formulation, that dynamically optimizes the incremental treatment outcome subject to the required Return on Investment (ROI) constraints. The technique leverages Retrospective Estimation, a modeling approach that relies solely on data from positive outcome examples. The method also addresses training data bias, long term effects, and seasonality challenges via online-dynamic calibration. This approach was tested via offline experiments and online randomized controlled trials at Booking .com - a leading OTP with millions of customers worldwide, resulting in a significant increase in the target outcome while staying within the required financial constraints and outperforming other approaches.
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering
Authors: Debolina Paul, Saptarshi Chakraborty, Didong Li, David Dunson
Date: 2020-08-17 00:00:00
Tasks: Clustering, Computational Efficiency, Dimensionality Reduction

Even with the rise in popularity of over-parameterized models, simple dimensionality reduction and clustering methods, such as PCA and k-means, are still routinely used in an amazing variety of settings. A primary reason is the combination of simplicity, interpretability and computational efficiency. The focus of this article is on improving upon PCA and k-means, by allowing non-linear relations in the data and more flexible cluster shapes, without sacrificing the key advantages. The key contribution is a new framework for Principal Elliptical Analysis (PEA), defining a simple and computationally efficient alternative to PCA that fits the best elliptical approximation through the data. We provide theoretical guarantees on the proposed PEA algorithm using Vapnik-Chervonenkis (VC) theory to show strong consistency and uniform concentration bounds. Toy experiments illustrate the performance of PEA, and the ability to adapt to non-linear structure and complex cluster shapes. In a rich variety of real data clustering applications, PEA is shown to do as well as k-means for simple datasets, while dramatically improving performance in more complex settings.
WSRNet: Joint Spotting and Recognition of Handwritten Words
Authors: George Retsinas, Giorgos Sfikas, Petros Maragos
Date: 2020-08-17 00:00:00
Tasks: Binarization, Keyword Spotting

In this work, we present a unified model that can handle both Keyword Spotting and Word Recognition with the same network architecture. The proposed network is comprised of a non-recurrent CTC branch and a Seq2Seq branch that is further augmented with an Autoencoding module. The related joint loss leads to a boost in recognition performance, while the Seq2Seq branch is used to create efficient word representations. We show how to further process these representations with binarization and a retraining scheme to provide compact and highly efficient descriptors, suitable for keyword spotting. Numerical results validate the usefulness of the proposed architecture, as our method outperforms the previous state-of-the-art in keyword spotting, and provides results in the ballpark of the leading methods for word recognition.
Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining
Authors: Jiaying Liu, Feng Xia, Lei Wang, Bo Xu, Xiangjie Kong, Hanghang Tong, Irwin King
Date: 2020-08-17 00:00:00
Tasks: Representation Learning

The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2.
Decomposition-Based Multi-Objective Evolutionary Algorithm Design under Two Algorithm Frameworks
Authors: Lie Meng Pang, Hisao Ishibuchi, Ke Shang
Date: 2020-08-17 00:00:00
Tasks: Vocal Bursts Valence Prediction

The development of efficient and effective evolutionary multi-objective optimization (EMO) algorithms has been an active research topic in the evolutionary computation community. Over the years, many EMO algorithms have been proposed. The existing EMO algorithms are mainly developed based on the final population framework. In the final population framework, the final population of an EMO algorithm is presented to the decision maker. Thus, it is required that the final population produced by an EMO algorithm is a good solution set. Recently, the use of solution selection framework was suggested for the design of EMO algorithms. This framework has an unbounded external archive to store all the examined solutions. A pre-specified number of solutions are selected from the archive as the final solutions presented to the decision maker. When the solution selection framework is used, EMO algorithms can be designed in a more flexible manner since the final population is not necessarily to be a good solution set. In this paper, we examine the design of MOEA/D under these two frameworks. We use an offline genetic algorithm-based hyper-heuristic method to find the optimal configuration of MOEA/D in each framework. The DTLZ and WFG test suites and their minus versions are used in our experiments. The experimental results suggest the possibility that a more flexible, robust and high-performance MOEA/D algorithm can be obtained when the solution selection framework is used.
Understanding Brain Dynamics for Color Perception using Wearable EEG headband
Authors: Mahima Chaudhary, Sumona Mukhopadhyay, Marin Litoiu, Lauren E Sergio, Meaghan S Adams
Date: 2020-08-17 00:00:00
Tasks: Classification, Dimensionality Reduction, EEG, Electroencephalogram (EEG), feature selection, General Classification

The perception of color is an important cognitive feature of the human brain. The variety of colors that impinge upon the human eye can trigger changes in brain activity which can be captured using electroencephalography (EEG). In this work, we have designed a multiclass classification model to detect the primary colors from the features of raw EEG signals. In contrast to previous research, our method employs spectral power features, statistical features as well as correlation features from the signal band power obtained from continuous Morlet wavelet transform instead of raw EEG, for the classification task. We have applied dimensionality reduction techniques such as Forward Feature Selection and Stacked Autoencoders to reduce the dimension of data eventually increasing the model's efficiency. Our proposed methodology using Forward Selection and Random Forest Classifier gave the best overall accuracy of 80.6\% for intra-subject classification. Our approach shows promise in developing techniques for cognitive tasks using color cues such as controlling Internet of Thing (IoT) devices by looking at primary colors for individuals with restricted motor abilities.
Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI
Authors: Carlo Russo, Sidong Liu, Antonio Di Ieva
Date: 2020-08-17 00:00:00
Tasks: Brain Tumor Segmentation, Tumor Segmentation

Magnetic Resonance Imaging (MRI) is used in everyday clinical practice to assess brain tumors. Several automatic or semi-automatic segmentation algorithms have been introduced to segment brain tumors and achieve an expert-like accuracy. Deep Convolutional Neural Networks (DCNN) have recently shown very promising results, however, DCNN models are still far from achieving clinically meaningful results mainly because of the lack of generalization of the models. DCNN models need large annotated datasets to achieve good performance. Models are often optimized on the domain dataset on which they have been trained, and then fail the task when the same model is applied to different datasets from different institutions. One of the reasons is due to the lack of data standardization to adjust for different models and MR machines. In this work, a 3D Spherical coordinates transform during the pre-processing phase has been hypothesized to improve DCNN models' accuracy and to allow more generalizable results even when the model is trained on small and heterogeneous datasets and translated into different domains. Indeed, the spherical coordinate system avoids several standardization issues since it works independently of resolution and imaging settings. Both Cartesian and spherical volumes were evaluated in two DCNN models with the same network structure using the BraTS 2019 dataset. The model trained on spherical transform pre-processed inputs resulted in superior performance over the Cartesian-input trained model on predicting gliomas' segmentation on tumor core and enhancing tumor classes (increase of 0.011 and 0.014 respectively on the validation dataset), achieving a further improvement in accuracy by merging the two models together. Furthermore, the spherical transform is not resolution-dependent and achieve same results on different input resolution.
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation
Authors: Hongyu Ren, Yuke Zhu, Jure Leskovec, Anima Anandkumar, Animesh Garg
Date: 2020-08-17 00:00:00
Tasks: Variational Inference

Real-world tasks often exhibit a compositional structure that contains a sequence of simpler sub-tasks. For instance, opening a door requires reaching, grasping, rotating, and pulling the door knob. Such compositional tasks require an agent to reason about the sub-task at hand while orchestrating global behavior accordingly. This can be cast as an online task inference problem, where the current task identity, represented by a context variable, is estimated from the agent's past experiences with probabilistic inference. Previous approaches have employed simple latent distributions, e.g., Gaussian, to model a single context for the entire task. However, this formulation lacks the expressiveness to capture the composition and transition of the sub-tasks. We propose a variational inference framework OCEAN to perform online task inference for compositional tasks. OCEAN models global and local context variables in a joint latent space, where the global variables represent a mixture of sub-tasks required for the task, while the local variables capture the transitions between the sub-tasks. Our framework supports flexible latent distributions based on prior knowledge of the task structure and can be trained in an unsupervised manner. Experimental results show that OCEAN provides more effective task inference with sequential context adaptation and thus leads to a performance boost on complex, multi-stage tasks.
Multi-Task Learning for Interpretable Weakly Labelled Sound Event Detection
Authors: Soham Deshmukh, Bhiksha Raj, Rita Singh
Date: 2020-08-17 00:00:00
Tasks: Event Detection, Multiple Instance Learning, Multi-Task Learning, Sound Event Detection, Task 2

Weakly Labelled learning has garnered lot of attention in recent years due to its potential to scale Sound Event Detection (SED) and is formulated as Multiple Instance Learning (MIL) problem. This paper proposes a Multi-Task Learning (MTL) framework for learning from Weakly Labelled Audio data which encompasses the traditional MIL setup. To show the utility of proposed framework, we use the input TimeFrequency representation (T-F) reconstruction as the auxiliary task. We show that the chosen auxiliary task de-noises internal T-F representation and improves SED performance under noisy recordings. Our second contribution is introducing two step Attention Pooling mechanism. By having 2-steps in attention mechanism, the network retains better T-F level information without compromising SED performance. The visualisation of first step and second step attention weights helps in localising the audio-event in T-F domain. For evaluating the proposed framework, we remix the DCASE 2019 task 1 acoustic scene data with DCASE 2018 Task 2 sounds event data under 0, 10 and 20 db SNR resulting in a multi-class Weakly labelled SED problem. The proposed total framework outperforms existing benchmark models over all SNRs, specifically 22.3 %, 12.8 %, 5.9 % improvement over benchmark model on 0, 10 and 20 dB SNR respectively. We carry out ablation study to determine the contribution of each auxiliary task and 2-step Attention Pooling to the SED performance improvement. The code is publicly released
Edge Network-Assisted Real-Time Object Detection Framework for Autonomous Driving
Authors: Seung Wook Kim, Keunsoo Ko, Haneul Ko, Victor C. M. Leung
Date: 2020-08-17 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, object-detection, Object Detection, Real-Time Object Detection

Autonomous vehicles (AVs) can achieve the desired results within a short duration by offloading tasks even requiring high computational power (e.g., object detection (OD)) to edge clouds. However, although edge clouds are exploited, real-time OD cannot always be guaranteed due to dynamic channel quality. To mitigate this problem, we propose an edge network-assisted real-time OD framework~(EODF). In an EODF, AVs extract the region of interests~(RoIs) of the captured image when the channel quality is not sufficiently good for supporting real-time OD. Then, AVs compress the image data on the basis of the RoIs and transmit the compressed one to the edge cloud. In so doing, real-time OD can be achieved owing to the reduced transmission latency. To verify the feasibility of our framework, we evaluate the probability that the results of OD are not received within the inter-frame duration (i.e., outage probability) and their accuracy. From the evaluation, we demonstrate that the proposed EODF provides the results to AVs in real-time and achieves satisfactory accuracy.
MIDAS: Multi-agent Interaction-aware Decision-making with Adaptive Strategies for Urban Autonomous Navigation
Authors: Xiaoyi Chen, Pratik Chaudhari
Date: 2020-08-17 00:00:00
Tasks: Autonomous Navigation, Decision Making

Autonomous navigation in crowded, complex urban environments requires interacting with other agents on the road. A common solution to this problem is to use a prediction model to guess the likely future actions of other agents. While this is reasonable, it leads to overly conservative plans because it does not explicitly model the mutual influence of the actions of interacting agents. This paper builds a reinforcement learning-based method named MIDAS where an ego-agent learns to affect the control actions of other cars in urban driving scenarios. MIDAS uses an attention-mechanism to handle an arbitrary number of other agents and includes a "driver-type" parameter to learn a single policy that works across different planning objectives. We build a simulation environment that enables diverse interaction experiments with a large number of agents and methods for quantitatively studying the safety, efficiency, and interaction among vehicles. MIDAS is validated using extensive experiments and we show that it (i) can work across different road geometries, (ii) results in an adaptive ego policy that can be tuned easily to satisfy performance criteria such as aggressive or cautious driving, (iii) is robust to changes in the driving policies of external agents, and (iv) is more efficient and safer than existing approaches to interaction-aware decision-making.
Feature Selection Methods for Cost-Constrained Classification in Random Forests
Authors: Rudolf Jagdhuber, Michel Lang, Jörg Rahnenführer
Date: 2020-08-14 00:00:00
Tasks: Classification, feature selection, General Classification, Model Selection

Cost-sensitive feature selection describes a feature selection problem, where features raise individual costs for inclusion in a model. These costs allow to incorporate disfavored aspects of features, e.g. failure rates of as measuring device, or patient harm, in the model selection process. Random Forests define a particularly challenging problem for feature selection, as features are generally entangled in an ensemble of multiple trees, which makes a post hoc removal of features infeasible. Feature selection methods therefore often either focus on simple pre-filtering methods, or require many Random Forest evaluations along their optimization path, which drastically increases the computational complexity. To solve both issues, we propose Shallow Tree Selection, a novel fast and multivariate feature selection method that selects features from small tree structures. Additionally, we also adapt three standard feature selection algorithms for cost-sensitive learning by introducing a hyperparameter-controlled benefit-cost ratio criterion (BCR) for each method. In an extensive simulation study, we assess this criterion, and compare the proposed methods to multiple performance-based baseline alternatives on four artificial data settings and seven real-world data settings. We show that all methods using a hyperparameterized BCR criterion outperform the baseline alternatives. In a direct comparison between the proposed methods, each method indicates strengths in certain settings, but no one-fits-all solution exists. On a global average, we could identify preferable choices among our BCR based methods. Nevertheless, we conclude that a practical analysis should never rely on a single method only, but always compare different approaches to obtain the best results.
Playing Catan with Cross-dimensional Neural Network
Authors: Quentin Gendre, Tomoyuki Kaneko
Date: 2020-08-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Catan is a strategic board game having interesting properties, including multi-player, imperfect information, stochastic, complex state space structure (hexagonal board where each vertex, edge and face has its own features, cards for each player, etc), and a large action space (including negotiation). Therefore, it is challenging to build AI agents by Reinforcement Learning (RL for short), without domain knowledge nor heuristics. In this paper, we introduce cross-dimensional neural networks to handle a mixture of information sources and a wide variety of outputs, and empirically demonstrate that the network dramatically improves RL in Catan. We also show that, for the first time, a RL agent can outperform jsettler, the best heuristic agent available.
AlphaNet: Improving Long-Tail Classification By Combining Classifiers
Authors: Nadine Chang, Jayanth Koushik, Aarti Singh, Martial Hebert, Yu-Xiong Wang, Michael J. Tarr
Date: 2020-08-17 00:00:00
Tasks: Classification, Long-tail Learning, Transfer Learning

Methods in long-tail learning focus on improving performance for data-poor (rare) classes; however, performance for such classes remains much lower than performance for more data-rich (frequent) classes. Analyzing the predictions of long-tail methods for rare classes reveals that a large number of errors are due to misclassification of rare items as visually similar frequent classes. To address this problem, we introduce AlphaNet, a method that can be applied to existing models, performing post hoc correction on classifiers of rare classes. Starting with a pre-trained model, we find frequent classes that are closest to rare classes in the model's representation space and learn weights to update rare class classifiers with a linear combination of frequent class classifiers. AlphaNet, applied to several models, greatly improves test accuracy for rare classes in multiple long-tailed datasets, with very little change to overall accuracy. Our method also provides a way to control the trade-off between rare class and overall accuracy, making it practical for long-tail classification in the wild.
Artificial Neural Networks and Fault Injection Attacks
Authors: Shahin Tajik, Fatemeh Ganji
Date: 2020-08-17 00:00:00

This chapter is on the security assessment of artificial intelligence (AI) and neural network (NN) accelerators in the face of fault injection attacks. More specifically, it discusses the assets on these platforms and compares them with ones known and well-studied in the field of cryptographic systems. This is a crucial step that must be taken in order to define the threat models precisely. With respect to that, fault attacks mounted on NNs and AI accelerators are explored.