pwc-archive/papers-with-abstracts huggingface dataset:
Artificial Intelligence Systems applied to tourism: A Survey
Authors: Luis Duarte, Jonathan Torres, Vitor Ribeiro, Inês Moreira
Date: 2020-10-27 00:00:00
Tasks: Recommendation Systems, Survey

Artificial Intelligence (AI) has been improving the performance of systems for a diverse set of tasks and introduced a more interactive generation of personal agents. Despite the current trend of applying AI for a great amount of areas, we have not seen the same quantity of work being developed for the tourism sector. This paper reports on the main applications of AI systems developed for tourism and the current state of the art for this sector. The paper also provides an up-to-date survey of this field regarding several key works and systems that are applied to tourism, like Personal Agents, for providing a more interactive experience. We also carried out an in-depth research on systems for predicting traffic human flow, more accurate recommendation systems and even how geospatial is trying to display tourism data in a more informative way and prevent problems before they arise.
Crop and weed classification based on AutoML
Authors: XueTao Jiang, BinBin Yong, Soheila Garshasbi, Jun Shen, Meiyu Jiang, Qingguo Zhou
Date: 2020-10-28 00:00:00
Tasks: AutoML, Classification, Crop Classification, Deep Learning, General Classification

CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature. However, to manually choose and fine-tune the deep learning models becomes laborious and indispensable in most traditional practices and research. Moreover, the classic objective functions are not thoroughly compatible with agricultural farming tasks as the corresponding models suffer from misclassifying crop to weed, often more likely than in other deep learning application domains. In this paper, we applied autonomous machine learning with a new objective function for crop and weed classification, achieving higher accuracy and lower crop killing rate (rate of identifying a crop as a weed). The experimental results show that our method outperforms state-of-the-art applications, for example, ResNet and VGG19.
On Fair Virtual Conference Scheduling: Achieving Equitable Participant and Speaker Satisfaction
Authors: Gourab K Patro, Abhijnan Chakraborty, Niloy Ganguly, Krishna P. Gummadi
Date: 2020-10-24 00:00:00
Tasks: Fairness, Scheduling

The (COVID-19) pandemic-induced restrictions on travel and social gatherings have prompted most conference organizers to move their events online. However, in contrast to physical conferences, virtual conferences face a challenge in efficiently scheduling talks, accounting for the availability of participants from different time-zones as well as their interests in attending different talks. In such settings, a natural objective for the conference organizers would be to maximize some global welfare measure, such as the total expected audience participation across all talks. However, we show that optimizing for global welfare could result in a schedule that is unfair to the stakeholders, i.e., the individual utilities for participants and speakers can be highly unequal. To address the fairness concerns, we formally define fairness notions for participants and speakers, and subsequently derive suitable fairness objectives for them. We show that the welfare and fairness objectives can be in conflict with each other, and there is a need to maintain a balance between these objective while caring for them simultaneously. Thus, we propose a joint optimization framework that allows conference organizers to design talk schedules that balance (i.e., allow trade-offs) between global welfare, participant fairness and the speaker fairness objectives. We show that the optimization problem can be solved using integer linear programming, and empirically evaluate the necessity and benefits of such joint optimization approach in virtual conference scheduling.
Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problems
Authors: Kookjin Lee, Eric J. Parish
Date: 2020-10-28 00:00:00
Tasks: Decoder

This work proposes an extension of neural ordinary differential equations (NODEs) by introducing an additional set of ODE input parameters to NODEs. This extension allows NODEs to learn multiple dynamics specified by the input parameter instances. Our extension is inspired by the concept of parameterized ordinary differential equations, which are widely investigated in computational science and engineering contexts, where characteristics of the governing equations vary over the input parameters. We apply the proposed parameterized NODEs (PNODEs) for learning latent dynamics of complex dynamical processes that arise in computational physics, which is an essential component for enabling rapid numerical simulations for time-critical physics applications. For this, we propose an encoder-decoder-type framework, which models latent dynamics as PNODEs. We demonstrate the effectiveness of PNODEs with important benchmark problems from computational physics.
AM-RRT*: Informed Sampling-based Planning with Assisting Metric
Authors: Daniel Armstrong, André Jonasson
Date: 2020-10-28 00:00:00

In this paper, we present a new algorithm that extends RRT* and RT-RRT* for online path planning in complex, dynamic environments. Sampling-based approaches often perform poorly in environments with narrow passages, a feature common to many indoor applications of mobile robots as well as computer games. Our method extends RRT-based sampling methods to enable the use of an assisting distance metric to improve performance in environments with obstacles. This assisting metric, which can be any metric that has better properties than the Euclidean metric when line of sight is blocked, is used in combination with the standard Euclidean metric in such a way that the algorithm can reap benefits from the assisting metric while maintaining the desirable properties of previous RRT variants - namely probabilistic completeness in tree coverage and asymptotic optimality in path length. We also introduce a new method of targeted rewiring, aimed at shortening search times and path lengths in tasks where the goal shifts repeatedly. We demonstrate that our method offers considerable improvements over existing multi-query planners such as RT-RRT* when using diffusion distance as an assisting metric; finding near-optimal paths with a decrease in search time of several orders of magnitude. Experimental results show planning times reduced by 99.5% and path lengths by 9.8% over existing real-time RRT planners in a variety of environments.
Replay and Synthetic Speech Detection with Res2net Architecture
Authors: Xu Li, Na Li, Chao Weng, Xunying Liu, Dan Su, Dong Yu, Helen Meng
Date: 2020-10-28 00:00:00
Tasks: Feature Engineering, Synthetic Speech Detection

Existing approaches for replay and synthetic speech detection still lack generalizability to unseen spoofing attacks. This work proposes to leverage a novel model structure, so-called Res2Net, to improve the anti-spoofing countermeasure's generalizability. Res2Net mainly modifies the ResNet block to enable multiple feature scales. Specifically, it splits the feature maps within one block into multiple channel groups and designs a residual-like connection across different channel groups. Such connection increases the possible receptive fields, resulting in multiple feature scales. This multiple scaling mechanism significantly improves the countermeasure's generalizability to unseen spoofing attacks. It also decreases the model size compared to ResNet-based models. Experimental results show that the Res2Net model consistently outperforms ResNet34 and ResNet50 by a large margin in both physical access (PA) and logical access (LA) of the ASVspoof 2019 corpus. Moreover, integration with the squeeze-and-excitation (SE) block can further enhance performance. For feature engineering, we investigate the generalizability of Res2Net combined with different acoustic features, and observe that the constant-Q transform (CQT) achieves the most promising performance in both PA and LA scenarios. Our best single system outperforms other state-of-the-art single systems in both PA and LA of the ASVspoof 2019 corpus.
How to Not Get Caught When You Launder Money on Blockchain?
Authors: Cuneyt G. Akcora, Sudhanva Purusotham, Yulia R. Gel, Mitchell Krawiec-Thayer, Murat Kantarcioglu
Date: 2020-09-22 00:00:00

The number of blockchain users has tremendously grown in recent years. As an unintended consequence, e-crime transactions on blockchains has been on the rise. Consequently, public blockchains have become a hotbed of research for developing AI tools to detect and trace users and transactions that are related to e-crime. We argue that following a few select strategies can make money laundering on blockchain virtually undetectable with most of the existing tools and algorithms. As a result, the effective combating of e-crime activities involving cryptocurrencies requires the development of novel analytic methodology in AI.
Secure Transmission by Leveraging Multiple Intelligent Reflecting Surfaces in MISO Systems
Authors: Jian Li, Lan Zhang, Kaiping Xue, Yuguang Fang
Date: 2020-08-09 00:00:00

Due to the broadcast nature of wireless communications, physical layer security has always been a fundamental but challenging problem. Recent advance of Intelligent Reflecting Surface (IRS) introduces a new dimension for secure communications by reconfiguring the transmission environments. In this paper, we devise a secure transmission scheme for multi-user MISO systems by leveraging multiple collaborative IRSs. Specifically, to guarantee the worst-case achievable secrecy rate among multiple legitimate users, we formulate a max-min problem that can be solved by an alternative optimization method to decouple it into multiple sub-problems. Based on semidefinite relaxation and successive convex approximation, each sub-problem can be further converted into convex problem and easily solved. Extensive experimental results demonstrate that our proposed scheme can adapt to complex scenarios for multiple users and achieve significant gain in terms of achievable secrecy rate. The gap between the secrecy rate from our proposed scheme and that from the traditional sum-rate maximization and our results show that the secrecy rate obtained from our scheme converges to that achieved from the traditional sum-rate maximization when increasing the number of elements on IRSs.
The artificial ecosystem: number soup (part II)
Authors: Yu Liu
Date: 2018-01-12 00:00:00

This paper is a follow-up work about the artificial ecosystem model: number soup (Liu and Sumpter, J. Royal Soc. Interface, 2017). It elaborates more details about this model and points out future directions.
A Decade of Evidence of Trend Following Investing in Cryptocurrencies
Authors: Evans Rozario, Samuel Holt, James West, Shaun Ng
Date: 2020-09-25 00:00:00

Cryptocurrency markets have many of the characteristics of 20th century commodities markets, making them an attractive candidate for trend following strategies. We present a decade of evidence from the infancy of bitcoin, showcasing the potential investor returns in cryptocurrency trend following, 255% walkforward annualised returns. We find that cryptocurrencies offer similar returns characteristics to commodities with similar risk-adjusted returns, and strong bear market diversification against traditional equities. Code available at https://github.com/Globe-Research/bittrends.
Rooted NNI moves on tree-based phylogenetic networks
Authors: Péter L. Erdős, Andrew Francis, Tamás Róbert Mezei
Date: 2020-03-16 00:00:00

We show that the space of rooted tree-based phylogenetic networks is connected under rooted nearest-neighbour interchange (rNNI) moves.
(In)Stability for the Blockchain: Deleveraging Spirals and Stablecoin Attacks
Authors: Ariah Klages-Mundt, Andreea Minca
Date: 2019-06-05 00:00:00

We develop a model of stable assets, including non-custodial stablecoins backed by cryptocurrencies. Such stablecoins are popular methods for bootstrapping price stability within public blockchain settings. We derive fundamental results about dynamics and liquidity in stablecoin markets, demonstrate that these markets face deleveraging feedback effects that cause illiquidity during crises and exacerbate collateral drawdown, and characterize stable dynamics of the system under particular conditions. The possibility of such `deleveraging spirals' was first predicted in the initial release of our paper in 2019 and later directly observed during the `Black Thursday' crisis in Dai in 2020. From these insights, we suggest design improvements that aim to improve long-term stability. We also introduce new attacks that exploit arbitrage-like opportunities around stablecoin liquidations. Using our model, we demonstrate that these can be profitable. These attacks may induce volatility in the `stable' asset and cause perverse incentives for miners, posing risks to blockchain consensus. A variant of such attacks also later occurred during Black Thursday, taking the form of mempool manipulation to clear Dai liquidation auctions at near zero prices, costing $8m.
Statistical Comparisons of Classifiers over Multiple Data Sets
Authors: Janez Demsâr
Date: 2006-10-08 00:00:00

While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more algorithms on multiple data sets, which is even more essential to typical machine learning studies, has been all but ignored. This article reviews the current practice and then theoretically and empirically examines several suitable tests. Based on that, we recommend a set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparison of more classifiers over multiple data sets. Results of the latter can also be neatly presented with the newly introduced CD (critical difference) diagrams.
One-class learning towards generalized voice spoofing detection
Authors: You Zhang, Fei Jiang, Zhiyao Duan
Date: 2020-10-27 00:00:00
Tasks: Speaker Verification, text-to-speech, Text to Speech, Voice Anti-spoofing, Voice Conversion

Human voices can be used to authenticate the identity of the speaker, but the automatic speaker verification (ASV) systems are vulnerable to voice spoofing attacks, such as impersonation, replay, text-to-speech, and voice conversion. Recently, researchers developed anti-spoofing techniques to improve the reliability of ASV systems against spoofing attacks. However, most methods encounter difficulties in detecting unknown attacks in practical use, which often have different statistical distributions from known attacks. In this work, we propose an anti-spoofing system to detect unknown logical access attacks (i.e., synthetic speech) using one-class learning. The key idea is to compact the genuine speech representation and inject an angular margin to separate the spoofing attacks in the embedding space. Our system achieves an equal error rate of 2.19% on the evaluation set of ASVspoof 2019 Challenge, outperforming all existing single systems.
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions
Authors: Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar
Date: 2020-10-28 00:00:00
Tasks: regression

Recently, invariant risk minimization (IRM) (Arjovsky et al.) was proposed as a promising solution to address out-of-distribution (OOD) generalization. In Ahuja et al., it was shown that solving for the Nash equilibria of a new class of "ensemble-games" is equivalent to solving IRM. In this work, we extend the framework in Ahuja et al. for linear regressions by projecting the ensemble-game on an $\ell_{\infty}$ ball. We show that such projections help achieve non-trivial OOD guarantees despite not achieving perfect invariance. For linear models with confounders, we prove that Nash equilibria of these games are closer to the ideal OOD solutions than the standard empirical risk minimization (ERM) and we also provide learning algorithms that provably converge to these Nash Equilibria. Empirical comparisons of the proposed approach with the state-of-the-art show consistent gains in achieving OOD solutions in several settings involving anti-causal variables and confounders.
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks
Authors: Julieta Martinez, Jashan Shewakramani, Ting Wei Liu, Ioan Andrei Bârsan, Wenyuan Zeng, Raquel Urtasun
Date: 2020-10-29 00:00:00
Proceeding: CVPR 2021 1
Tasks: image-classification, Image Classification, object-detection, Object Detection, Quantization

Compressing large neural networks is an important step for their deployment in resource-constrained computational platforms. In this context, vector quantization is an appealing framework that expresses multiple parameters using a single code, and has recently achieved state-of-the-art network compression on a range of core vision and natural language processing tasks. Key to the success of vector quantization is deciding which parameter groups should be compressed together. Previous work has relied on heuristics that group the spatial dimension of individual convolutional filters, but a general solution remains unaddressed. This is desirable for pointwise convolutions (which dominate modern architectures), linear layers (which have no notion of spatial dimension), and convolutions (when more than one filter is compressed to the same codeword). In this paper we make the observation that the weights of two adjacent layers can be permuted while expressing the same function. We then establish a connection to rate-distortion theory and search for permutations that result in networks that are easier to compress. Finally, we rely on an annealed quantization algorithm to better compress the network and achieve higher final accuracy. We show results on image classification, object detection, and segmentation, reducing the gap with the uncompressed model by 40 to 70% with respect to the current state of the art.
Fact or Factitious? Contextualized Opinion Spam Detection
Authors: Stefan Kennedy, Niall Walsh, Kirils Sloka, Jennifer Foster, Andrew McCarren
Date: 2020-10-29 00:00:00
Proceeding: ACL 2019 7
Tasks: BIG-bench Machine Learning, Spam detection

In this paper we perform an analytic comparison of a number of techniques used to detect fake and deceptive online reviews. We apply a number machine learning approaches found to be effective, and introduce our own approach by fine-tuning state of the art contextualised embeddings. The results we obtain show the potential of contextualised embeddings for fake review detection, and lay the groundwork for future research in this area.
Detecting Stance in Media on Global Warming
Authors: Yiwei Luo, Dallas Card, Dan Jurafsky
Date: 2020-10-28 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Articles

Citing opinions is a powerful yet understudied strategy in argumentation. For example, an environmental activist might say, "Leading scientists agree that global warming is a serious concern," framing a clause which affirms their own stance ("that global warming is serious") as an opinion endorsed ("[scientists] agree") by a reputable source ("leading"). In contrast, a global warming denier might frame the same clause as the opinion of an untrustworthy source with a predicate connoting doubt: "Mistaken scientists claim [...]." Our work studies opinion-framing in the global warming (GW) debate, an increasingly partisan issue that has received little attention in NLP. We introduce Global Warming Stance Dataset (GWSD), a dataset of stance-labeled GW sentences, and train a BERT classifier to study novel aspects of argumentation in how different sides of a debate represent their own and each other's opinions. From 56K news articles, we find that similar linguistic devices for self-affirming and opponent-doubting discourse are used across GW-accepting and skeptic media, though GW-skeptical media shows more opponent-doubt. We also find that authors often characterize sources as hypocritical, by ascribing opinions expressing the author's own view to source entities known to publicly endorse the opposing view. We release our stance dataset, model, and lexicons of framing devices for future work on opinion-framing and the automatic detection of GW stance.
Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation
Authors: Nick Lawrence, Mingren Shen, Ruiqi Yin, Cloris Feng, Dane Morgan
Date: 2020-10-29 00:00:00
Tasks: Deep Learning, Generative Adversarial Network, Image-to-Image Translation, regression, Translation

The use of accurate scanning transmission electron microscopy (STEM) image simulation methods require large computation times that can make their use infeasible for the simulation of many images. Other simulation methods based on linear imaging models, such as the convolution method, are much faster but are too inaccurate to be used in application. In this paper, we explore deep learning models that attempt to translate a STEM image produced by the convolution method to a prediction of the high accuracy multislice image. We then compare our results to those of regression methods. We find that using the deep learning model Generative Adversarial Network (GAN) provides us with the best results and performs at a similar accuracy level to previous regression models on the same dataset. Codes and data for this project can be found in this GitHub repository, https://github.com/uw-cmg/GAN-STEM-Conv2MultiSlice.
Aggregative Efficiency of Bayesian Learning in Networks
Authors: Krishna Dasaratha, Kevin He
Date: 2019-11-22 00:00:00

When individuals in a social network learn about an unknown state from private signals and neighbors' actions, the network structure often causes information loss. We consider rational agents and Gaussian signals in the canonical sequential social-learning problem and ask how the network changes the efficiency of signal aggregation. Rational actions in our model are log-linear functions of observations and admit a signal-counting interpretation of accuracy. Networks where agents observe multiple neighbors but not their common predecessors confound information, and even a small amount of confounding can lead to much lower accuracy. In a class of networks where agents move in generations and observe the previous generation, we quantify the information loss with an aggregative efficiency index. Aggregative efficiency is a simple function of network parameters: increasing in observations and decreasing in confounding. Later generations contribute little additional information, even with arbitrarily large generations.