pwc-archive/papers-with-abstracts huggingface dataset:
A Creative Dialog Generator for Fallout 4
Authors: Khalid Alnajjar, Mika Hämäläinen
Date: 2019-08-01 00:00:00
Proceeding: The 14th International Conference on the Foundations of Digital Games 2019 8

This software demonstration describes a mod for Fallout 4 that will adapt in-game dialog to the context of the current state of the game. The dialog is generated by a computationally creative back-end software during the game play. The mod solves the problem of Fallout 4 not supporting dynamically generated dialog by showing dialog in an overlay application on top of the game window
Secured Wireless Communication using Fuzzy Logic based High Speed Public-Key Cryptography (FLHSPKC)
Authors: Arindam Sarkar, J. K. Mandal
Date: 2012-11-09 00:00:00
Tasks: Management

In this paper secured wireless communication using fuzzy logic based high speed public key cryptography (FLHSPKC) has been proposed by satisfying the major issues likes computational safety, power management and restricted usage of memory in wireless communication. Wireless Sensor Network (WSN) has several major constraints likes inadequate source of energy, restricted computational potentiality and limited memory. Though conventional Elliptic Curve Cryptography (ECC) which is a sort of public key cryptography used in wireless communication provides equivalent level of security like other existing public key algorithm using smaller parameters than other but this traditional ECC does not take care of all these major limitations in WSN. In conventional ECC consider Elliptic curve point p, an arbitrary integer k and modulus m, ECC carry out scalar multiplication kP mod m, which takes about 80% of key computation time on WSN. In this paper proposed FLHSPKC scheme provides some novel strategy including novel soft computing based strategy to speed up scalar multiplication in conventional ECC and which in turn takes shorter computational time and also satisfies power consumption restraint, limited usage of memory without hampering the security level. Performance analysis of the different strategies under FLHSPKC scheme and comparison study with existing conventional ECC methods has been done.
The Fuzzy Vault for fingerprints is Vulnerable to Brute Force Attack
Authors: Preda Mihailescu
Date: 2007-08-22 00:00:00

The \textit{fuzzy vault} approach is one of the best studied and well accepted ideas for binding cryptographic security into biometric authentication. The vault has been implemented in connection with fingerprint data by Uludag and Jain. We show that this instance of the vault is vulnerable to brute force attack. An interceptor of the vault data can recover both secret and template data using only generally affordable computational resources. Some possible alternatives are then discussed and it is suggested that cryptographic security may be preferable to the one - way function approach to biometric security.
Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping
Authors: Thai Duong, Nikhil Das, Michael Yip, Nikolay Atanasov
Date: 2020-02-05 00:00:00
Tasks: Autonomous Navigation

This paper focuses on real-time occupancy mapping and collision checking onboard an autonomous robot navigating in an unknown environment. We propose a new map representation, in which occupied and free space are separated by the decision boundary of a kernel perceptron classifier. We develop an online training algorithm that maintains a very sparse set of support vectors to represent obstacle boundaries in configuration space. We also derive conditions that allow complete (without sampling) collision-checking for piecewise-linear and piecewise-polynomial robot trajectories. We demonstrate the effectiveness of our mapping and collision checking algorithms for autonomous navigation of an Ackermann-drive robot in unknown environments.
Vehicle Ego-Lane Estimation with Sensor Failure Modeling
Authors: Augusto Luis Ballardini, Daniele Cattaneo, Rubén Izquierdo, Ignacio Parra Alonso, Andrea Piazzoni, Miguel Ángel Sotelo, Domenico Giorgio Sorrenti
Date: 2020-02-05 00:00:00

We present a probabilistic ego-lane estimation algorithm for highway-like scenarios that is designed to increase the accuracy of the ego-lane estimate, which can be obtained relying only on a noisy line detector and tracker. The contribution relies on a Hidden Markov Model (HMM) with a transient failure model. The proposed algorithm exploits the OpenStreetMap (or other cartographic services) road property lane number as the expected number of lanes and leverages consecutive, possibly incomplete, observations. The algorithm effectiveness is proven by employing different line detectors and showing we could achieve much more usable, i.e. stable and reliable, ego-lane estimates over more than 100 Km of highway scenarios, recorded both in Italy and Spain. Moreover, as we could not find a suitable dataset for a quantitative comparison with other approaches, we collected datasets and manually annotated the Ground Truth about the vehicle ego-lane. Such datasets are made publicly available for usage from the scientific community.
FastGAE: Scalable Graph Autoencoders with Stochastic Subgraph Decoding
Authors: Guillaume Salha, Romain Hennequin, Jean-Baptiste Remy, Manuel Moussallam, Michalis Vazirgiannis
Date: 2020-02-05 00:00:00

Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node embedding methods, but suffer from scalability issues. In this paper, we introduce FastGAE, a general framework to scale graph AE and VAE to large graphs with millions of nodes and edges. Our strategy, based on an effective stochastic subgraph decoding scheme, significantly speeds up the training of graph AE and VAE while preserving or even improving performances. We demonstrate the effectiveness of FastGAE on various real-world graphs, outperforming the few existing approaches to scale graph AE and VAE by a wide margin.
Deep Radial-Basis Value Functions for Continuous Control
Authors: Kavosh Asadi, Neev Parikh, Ronald E. Parr, George D. Konidaris, Michael L. Littman
Date: 2020-02-05 00:00:00
Tasks: continuous-control, Continuous Control, Reinforcement Learning, Reinforcement Learning (RL)

A core operation in reinforcement learning (RL) is finding an action that is optimal with respect to a learned value function. This operation is often challenging when the learned value function takes continuous actions as input. We introduce deep radial-basis value functions (RBVFs): value functions learned using a deep network with a radial-basis function (RBF) output layer. We show that the maximum action-value with respect to a deep RBVF can be approximated easily and accurately. Moreover, deep RBVFs can represent any true value function owing to their support for universal function approximation. We extend the standard DQN algorithm to continuous control by endowing the agent with a deep RBVF. We show that the resultant agent, called RBF-DQN, significantly outperforms value-function-only baselines, and is competitive with state-of-the-art actor-critic algorithms.
Locally-Adaptive Nonparametric Online Learning
Authors: Ilja Kuzborskij, Nicolò Cesa-Bianchi
Date: 2020-02-05 00:00:00
Proceeding: NeurIPS 2020 12

One of the main strengths of online algorithms is their ability to adapt to arbitrary data sequences. This is especially important in nonparametric settings, where performance is measured against rich classes of comparator functions that are able to fit complex environments. Although such hard comparators and complex environments may exhibit local regularities, efficient algorithms, which can provably take advantage of these local patterns, are hardly known. We fill this gap by introducing efficient online algorithms (based on a single versatile master algorithm) each adapting to one of the following regularities: (i) local Lipschitzness of the competitor function, (ii) local metric dimension of the instance sequence, (iii) local performance of the predictor across different regions of the instance space. Extending previous approaches, we design algorithms that dynamically grow hierarchical $\epsilon$-nets on the instance space whose prunings correspond to different "locality profiles" for the problem at hand. Using a technique based on tree experts, we simultaneously and efficiently compete against all such prunings, and prove regret bounds each scaling with a quantity associated with a different type of local regularity. When competing against "simple" locality profiles, our technique delivers regret bounds that are significantly better than those proven using the previous approach. On the other hand, the time dependence of our bounds is not worse than that obtained by ignoring any local regularities.
Wasserstein Exponential Kernels
Authors: Henri De Plaen, Michaël Fanuel, Johan A. K. Suykens
Date: 2020-02-05 00:00:00

In the context of kernel methods, the similarity between data points is encoded by the kernel function which is often defined thanks to the Euclidean distance, a common example being the squared exponential kernel. Recently, other distances relying on optimal transport theory - such as the Wasserstein distance between probability distributions - have shown their practical relevance for different machine learning techniques. In this paper, we study the use of exponential kernels defined thanks to the regularized Wasserstein distance and discuss their positive definiteness. More specifically, we define Wasserstein feature maps and illustrate their interest for supervised learning problems involving shapes and images. Empirically, Wasserstein squared exponential kernels are shown to yield smaller classification errors on small training sets of shapes, compared to analogous classifiers using Euclidean distances.
$ε$-shotgun: $ε$-greedy Batch Bayesian Optimisation
Authors: George De Ath, Richard M. Everson, Jonathan E. Fieldsend, Alma A. M. Rahat
Date: 2020-02-05 00:00:00
Tasks: Bayesian Optimisation

Bayesian optimisation is a popular, surrogate model-based approach for optimising expensive black-box functions. Given a surrogate model, the next location to expensively evaluate is chosen via maximisation of a cheap-to-query acquisition function. We present an $\epsilon$-greedy procedure for Bayesian optimisation in batch settings in which the black-box function can be evaluated multiple times in parallel. Our $\epsilon$-shotgun algorithm leverages the model's prediction, uncertainty, and the approximated rate of change of the landscape to determine the spread of batch solutions to be distributed around a putative location. The initial target location is selected either in an exploitative fashion on the mean prediction, or -- with probability $\epsilon$ -- from elsewhere in the design space. This results in locations that are more densely sampled in regions where the function is changing rapidly and in locations predicted to be good (i.e close to predicted optima), with more scattered samples in regions where the function is flatter and/or of poorer quality. We empirically evaluate the $\epsilon$-shotgun methods on a range of synthetic functions and two real-world problems, finding that they perform at least as well as state-of-the-art batch methods and in many cases exceed their performance.
If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills
Authors: Ziang Xiao, Michelle X. Zhou, Wenxi Chen, Huahai Yang, Changyan Chi
Date: 2020-02-05 00:00:00
Tasks: Chatbot

Interview chatbots engage users in a text-based conversation to draw out their views and opinions. It is, however, challenging to build effective interview chatbots that can handle user free-text responses to open-ended questions and deliver engaging user experience. As the first step, we are investigating the feasibility and effectiveness of using publicly available, practical AI technologies to build effective interview chatbots. To demonstrate feasibility, we built a prototype scoped to enable interview chatbots with a subset of active listening skills - the abilities to comprehend a user's input and respond properly. To evaluate the effectiveness of our prototype, we compared the performance of interview chatbots with or without active listening skills on four common interview topics in a live evaluation with 206 users. Our work presents practical design implications for building effective interview chatbots, hybrid chatbot platforms, and empathetic chatbots beyond interview tasks.
Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents
Authors: Ruixue Zhang, Wei Yang, Luyun Lin, Zhengkai Tu, Yuqing Xie, Zihang Fu, Yuhao Xie, Luchen Tan, Kun Xiong, Jimmy Lin
Date: 2020-02-05 00:00:00

Techniques for automatically extracting important content elements from business documents such as contracts, statements, and filings have the potential to make business operations more efficient. This problem can be formulated as a sequence labeling task, and we demonstrate the adaption of BERT to two types of business documents: regulatory filings and property lease agreements. There are aspects of this problem that make it easier than "standard" information extraction tasks and other aspects that make it more difficult, but on balance we find that modest amounts of annotated data (less than 100 documents) are sufficient to achieve reasonable accuracy. We integrate our models into an end-to-end cloud platform that provides both an easy-to-use annotation interface as well as an inference interface that allows users to upload documents and inspect model outputs.
Geosocial Location Classification: Associating Type to Places Based on Geotagged Social-Media Posts
Authors: Elad Kravi, Benny Kimelfeld, Yaron Kanza, Roi Reichart
Date: 2020-02-05 00:00:00
Tasks: General Classification, Text Classification, Vocal Bursts Type Prediction

Associating type to locations can be used to enrich maps and can serve a plethora of geospatial applications. An automatic method to do so could make the process less expensive in terms of human labor, and faster to react to changes. In this paper we study the problem of Geosocial Location Classification, where the type of a site, e.g., a building, is discovered based on social-media posts. Our goal is to correctly associate a set of messages posted in a small radius around a given location with the corresponding location type, e.g., school, church, restaurant or museum. We explore two approaches to the problem: (a) a pipeline approach, where each message is first classified, and then the location associated with the message set is inferred from the individual message labels; and (b) a joint approach where the individual messages are simultaneously processed to yield the desired location type. We tested the two approaches over a dataset of geotagged tweets. Our results demonstrate the superiority of the joint approach. Moreover, we show that due to the unique structure of the problem, where weakly-related messages are jointly processed to yield a single final label, linear classifiers outperform deep neural network alternatives.
Analyzing the Dependency of ConvNets on Spatial Information
Authors: Yue Fan, Yongqin Xian, Max Maria Losch, Bernt Schiele
Date: 2020-02-05 00:00:00
Tasks: image-classification, Image Classification, Object Recognition

Intuitively, image classification should profit from using spatial information. Recent work, however, suggests that this might be overrated in standard CNNs. In this paper, we are pushing the envelope and aim to further investigate the reliance on spatial information. We propose spatial shuffling and GAP+FC to destroy spatial information during both training and testing phases. Interestingly, we observe that spatial information can be deleted from later layers with small performance drops, which indicates spatial information at later layers is not necessary for good performance. For example, test accuracy of VGG-16 only drops by 0.03% and 2.66% with spatial information completely removed from the last 30% and 53% layers on CIFAR100, respectively. Evaluation on several object recognition datasets (CIFAR100, Small-ImageNet, ImageNet) with a wide range of CNN architectures (VGG16, ResNet50, ResNet152) shows an overall consistent pattern.
Discontinuous Constituent Parsing with Pointer Networks
Authors: Daniel Fernández-González, Carlos Gómez-Rodríguez
Date: 2020-02-05 00:00:00
Tasks: Dependency Parsing, Part-Of-Speech Tagging, Sentence

One of the most complex syntactic representations used in computational linguistics and NLP are discontinuous constituent trees, crucial for representing all grammatical phenomena of languages such as German. Recent advances in dependency parsing have shown that Pointer Networks excel in efficiently parsing syntactic relations between words in a sentence. This kind of sequence-to-sequence models achieve outstanding accuracies in building non-projective dependency trees, but its potential has not been proved yet on a more difficult task. We propose a novel neural network architecture that, by means of Pointer Networks, is able to generate the most accurate discontinuous constituent representations to date, even without the need of Part-of-Speech tagging information. To do so, we internally model discontinuous constituent structures as augmented non-projective dependency structures. The proposed approach achieves state-of-the-art results on the two widely-used NEGRA and TIGER benchmarks, outperforming previous work by a wide margin.
Understanding the Decision Boundary of Deep Neural Networks: An Empirical Study
Authors: David Mickisch, Felix Assion, Florens Greßner, Wiebke Günther, Mariele Motta
Date: 2020-02-05 00:00:00
Tasks: Autonomous Driving, image-classification, Image Classification

Despite achieving remarkable performance on many image classification tasks, state-of-the-art machine learning (ML) classifiers remain vulnerable to small input perturbations. Especially, the existence of adversarial examples raises concerns about the deployment of ML models in safety- and security-critical environments, like autonomous driving and disease detection. Over the last few years, numerous defense methods have been published with the goal of improving adversarial as well as corruption robustness. However, the proposed measures succeeded only to a very limited extent. This limited progress is partly due to the lack of understanding of the decision boundary and decision regions of deep neural networks. Therefore, we study the minimum distance of data points to the decision boundary and how this margin evolves over the training of a deep neural network. By conducting experiments on MNIST, FASHION-MNIST, and CIFAR-10, we observe that the decision boundary moves closer to natural images over training. This phenomenon even remains intact in the late epochs of training, where the classifier already obtains low training and test error rates. On the other hand, adversarial training appears to have the potential to prevent this undesired convergence of the decision boundary.
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
Authors: Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu ji, Guihong Cao, Daxin Jiang, Ming Zhou
Date: 2020-02-05 00:00:00
Proceeding: Findings (ACL) 2021 8
Tasks: Dependency Parsing, Entity Typing, Question Answering, Relation Classification, Relation Extraction

We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models when injecting knowledge. However, when multiple kinds of knowledge are injected, the historically injected knowledge would be flushed away. To address this, we propose K-Adapter, a framework that retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused model. Taking RoBERTa as the backbone model, K-Adapter has a neural adapter for each kind of infused knowledge, like a plug-in connected to RoBERTa. There is no information flow between different adapters, thus multiple adapters can be efficiently trained in a distributed way. As a case study, we inject two kinds of knowledge in this work, including (1) factual knowledge obtained from automatically aligned text-triplets on Wikipedia and Wikidata and (2) linguistic knowledge obtained via dependency parsing. Results on three knowledge-driven tasks, including relation classification, entity typing, and question answering, demonstrate that each adapter improves the performance and the combination of both adapters brings further improvements. Further analysis indicates that K-Adapter captures versatile knowledge than RoBERTa.
Sharpe Ratio Analysis in High Dimensions: Residual-Based Nodewise Regression in Factor Models
Authors: Mehmet Caner, Marcelo Medeiros, Gabriel Vasconcelos
Date: 2020-02-05 00:00:00
Tasks: regression, Time Series Analysis

We provide a new theory for nodewise regression when the residuals from a fitted factor model are used. We apply our results to the analysis of the consistency of Sharpe ratio estimators when there are many assets in a portfolio. We allow for an increasing number of assets as well as time observations of the portfolio. Since the nodewise regression is not feasible due to the unknown nature of idiosyncratic errors, we provide a feasible-residual-based nodewise regression to estimate the precision matrix of errors which is consistent even when number of assets, p, exceeds the time span of the portfolio, n. In another new development, we also show that the precision matrix of returns can be estimated consistently, even with an increasing number of factors and p>n. We show that: (1) with p>n, the Sharpe ratio estimators are consistent in global minimum-variance and mean-variance portfolios; and (2) with p>n, the maximum Sharpe ratio estimator is consistent when the portfolio weights sum to one; and (3) with p<<n, the maximum-out-of-sample Sharpe ratio estimator is consistent.
Proximity Preserving Binary Code using Signed Graph-Cut
Authors: Inbal Lav, Shai Avidan, Yoram Singer, Yacov Hel-Or
Date: 2020-02-05 00:00:00
Tasks: graph partitioning

We introduce a binary embedding framework, called Proximity Preserving Code (PPC), which learns similarity and dissimilarity between data points to create a compact and affinity-preserving binary code. This code can be used to apply fast and memory-efficient approximation to nearest-neighbor searches. Our framework is flexible, enabling different proximity definitions between data points. In contrast to previous methods that extract binary codes based on unsigned graph partitioning, our system models the attractive and repulsive forces in the data by incorporating positive and negative graph weights. The proposed framework is shown to boil down to finding the minimal cut of a signed graph, a problem known to be NP-hard. We offer an efficient approximation and achieve superior results by constructing the code bit after bit. We show that the proposed approximation is superior to the commonly used spectral methods with respect to both accuracy and complexity. Thus, it is useful for many other problems that can be translated into signed graph cut.