pwc-archive/papers-with-abstracts huggingface dataset:
Cryptocurrencies Are Becoming Part of the World Global Financial Market
Authors: Marcin Wątorek, Jarosław Kwapień, Stanisław Drożdż
Date: 2023-02-18 00:00:00

In this study the cross-correlations between the cryptocurrency market represented by the two most liquid and highest-capitalized cryptocurrencies: bitcoin and ethereum, on the one side, and the instruments representing the traditional financial markets: stock indices, Forex, commodities, on the other side, are measured in the period: January 2020--October 2022. Our purpose is to address the question whether the cryptocurrency market still preserves its autonomy with respect to the traditional financial markets or it has already aligned with them in expense of its independence. We are motivated by the fact that some previous related studies gave mixed results. By calculating the $q$-dependent detrended cross-correlation coefficient based on the high frequency 10 s data in the rolling window, the dependence on various time scales, different fluctuation magnitudes, and different market periods are examined. There is a strong indication that the dynamics of the bitcoin and ethereum price changes since the March 2020 Covid-19 panic is no longer independent. Instead, it is related to the dynamics of the traditional financial markets, which is especially evident now in 2022, when the bitcoin and ethereum coupling to the US tech stocks is observed during the market bear phase. It is also worth emphasizing that the cryptocurrencies have begun to react to the economic data such as the Consumer Price Index readings in a similar way as traditional instruments. Such a spontaneous coupling of the so far independent degrees of freedom can be interpreted as a kind of phase transition that resembles the collective phenomena typical for the complex systems. Our results indicate that the cryptocurrencies cannot be considered as a safe haven for the financial investments.
Learning high-dimensional causal effect
Authors: Aayush Agarwal, Saksham Bassi
Date: 2023-03-01 00:00:00
Tasks: Causal Inference, Vocal Bursts Intensity Prediction

The scarcity of high-dimensional causal inference datasets restricts the exploration of complex deep models. In this work, we propose a method to generate a synthetic causal dataset that is high-dimensional. The synthetic data simulates a causal effect using the MNIST dataset with Bernoulli treatment values. This provides an opportunity to study varieties of models for causal effect estimation. We experiment on this dataset using Dragonnet architecture (Shi et al. (2019)) and modified architectures. We use the modified architectures to explore different types of initial Neural Network layers and observe that the modified architectures perform better in estimations. We observe that residual and transformer models estimate treatment effect very closely without the need for targeted regularization, introduced by Shi et al. (2019).
Combining Generative Artificial Intelligence (AI) and the Internet: Heading towards Evolution or Degradation?
Authors: Gonzalo Martínez, Lauren Watson, Pedro Reviriego, José Alberto Hernández, Marc Juarez, Rik Sarkar
Date: 2023-02-17 00:00:00
Tasks: Image Generation

In the span of a few months, generative Artificial Intelligence (AI) tools that can generate realistic images or text have taken the Internet by storm, making them one of the technologies with fastest adoption ever. Some of these generative AI tools such as DALL-E, MidJourney, or ChatGPT have gained wide public notoriety. Interestingly, these tools are possible because of the massive amount of data (text and images) available on the Internet. The tools are trained on massive data sets that are scraped from Internet sites. And now, these generative AI tools are creating massive amounts of new data that are being fed into the Internet. Therefore, future versions of generative AI tools will be trained with Internet data that is a mix of original and AI-generated data. As time goes on, a mixture of original data and data generated by different versions of AI tools will populate the Internet. This raises a few intriguing questions: how will future versions of generative AI tools behave when trained on a mixture of real and AI generated data? Will they evolve with the new data sets or degenerate? Will evolution introduce biases in subsequent generations of generative AI tools? In this document, we explore these questions and report some very initial simulation results using a simple image-generation AI tool. These results suggest that the quality of the generated images degrades as more AI-generated data is used for training thus suggesting that generative AI may degenerate. Although these results are preliminary and cannot be generalised without further study, they serve to illustrate the potential issues of the interaction between generative AI and the Internet.
Multi-task neural networks by learned contextual inputs
Authors: Anders T. Sandnes, Bjarne Grimstad, Odd Kolbjørnsen
Date: 2023-03-01 00:00:00
Tasks: Multi-Task Learning

This paper explores learned-context neural networks. It is a multi-task learning architecture based on a fully shared neural network and an augmented input vector containing trainable task parameters. The architecture is interesting due to its powerful task adaption mechanism, which facilitates a low-dimensional task parameter space. Theoretically, we show that a scalar task parameter is sufficient for universal approximation of all tasks, which is not necessarily the case for more common architectures. Evidence towards the practicality of such a small task parameter space is given empirically. The task parameter space is found to be well-behaved, and simplifies workflows related to updating models as new data arrives, and training new tasks when the shared parameters are frozen. Additionally, the architecture displays robustness towards cases with few data points. The architecture's performance is compared to similar neural network architectures on ten datasets.
An Incremental Gray-box Physical Adversarial Attack on Neural Network Training
Authors: Rabiah Al-qudah, Moayad Aloqaily, Bassem Ouni, Mohsen Guizani, Thierry Lestable
Date: 2023-02-20 00:00:00
Tasks: Adversarial Attack

Neural networks have demonstrated remarkable success in learning and solving complex tasks in a variety of fields. Nevertheless, the rise of those networks in modern computing has been accompanied by concerns regarding their vulnerability to adversarial attacks. In this work, we propose a novel gradient-free, gray box, incremental attack that targets the training process of neural networks. The proposed attack, which implicitly poisons the intermediate data structures that retain the training instances between training epochs acquires its high-risk property from attacking data structures that are typically unobserved by professionals. Hence, the attack goes unnoticed despite the damage it can cause. Moreover, the attack can be executed without the attackers' knowledge of the neural network structure or training data making it more dangerous. The attack was tested under a sensitive application of secure cognitive cities, namely, biometric authentication. The conducted experiments showed that the proposed attack is effective and stealthy. Finally, the attack effectiveness property was concluded from the fact that it was able to flip the sign of the loss gradient in the conducted experiments to become positive, which indicated noisy and unstable training. Moreover, the attack was able to decrease the inference probability in the poisoned networks compared to their unpoisoned counterparts by 15.37%, 14.68%, and 24.88% for the Densenet, VGG, and Xception, respectively. Finally, the attack retained its stealthiness despite its high effectiveness. This was demonstrated by the fact that the attack did not cause a notable increase in the training time, in addition, the Fscore values only dropped by an average of 1.2%, 1.9%, and 1.5% for the poisoned Densenet, VGG, and Xception, respectively.
Privacy-Preserving Tree-Based Inference with TFHE
Authors: Jordan Frery, Andrei Stoian, Roman Bredehoft, Luis Montero, Celia Kherfallah, Benoit Chevallier-Mames, Arthur Meyre
Date: 2023-02-13 00:00:00
Tasks: Privacy Preserving

Privacy enhancing technologies (PETs) have been proposed as a way to protect the privacy of data while still allowing for data analysis. In this work, we focus on Fully Homomorphic Encryption (FHE), a powerful tool that allows for arbitrary computations to be performed on encrypted data. FHE has received lots of attention in the past few years and has reached realistic execution times and correctness. More precisely, we explain in this paper how we apply FHE to tree-based models and get state-of-the-art solutions over encrypted tabular data. We show that our method is applicable to a wide range of tree-based models, including decision trees, random forests, and gradient boosted trees, and has been implemented within the Concrete-ML library, which is open-source at https://github.com/zama-ai/concrete-ml. With a selected set of use-cases, we demonstrate that our FHE version is very close to the unprotected version in terms of accuracy.
Unnoticeable Backdoor Attacks on Graph Neural Networks
Authors: Enyan Dai, Minhua Lin, Xiang Zhang, Suhang Wang
Date: 2023-02-11 00:00:00
Tasks: Backdoor Attack, Graph Classification, Node Classification

Graph Neural Networks (GNNs) have achieved promising results in various tasks such as node classification and graph classification. Recent studies find that GNNs are vulnerable to adversarial attacks. However, effective backdoor attacks on graphs are still an open problem. In particular, backdoor attack poisons the graph by attaching triggers and the target class label to a set of nodes in the training graph. The backdoored GNNs trained on the poisoned graph will then be misled to predict test nodes to target class once attached with triggers. Though there are some initial efforts in graph backdoor attacks, our empirical analysis shows that they may require a large attack budget for effective backdoor attacks and the injected triggers can be easily detected and pruned. Therefore, in this paper, we study a novel problem of unnoticeable graph backdoor attacks with limited attack budget. To fully utilize the attack budget, we propose to deliberately select the nodes to inject triggers and target class labels in the poisoning phase. An adaptive trigger generator is deployed to obtain effective triggers that are difficult to be noticed. Extensive experiments on real-world datasets against various defense strategies demonstrate the effectiveness of our proposed method in conducting effective unnoticeable backdoor attacks.
Safety without alignment
Authors: András Kornai, Michael Bukatin, Zsolt Zombori
Date: 2023-02-27 00:00:00
Tasks: Ethics

Currently, the dominant paradigm in AI safety is alignment with human values. Here we describe progress on developing an alternative approach to safety, based on ethical rationalism (Gewirth:1978), and propose an inherently safe implementation path via hybrid theorem provers in a sandbox. As AGIs evolve, their alignment may fade, but their rationality can only increase (otherwise more rational ones will have a significant evolutionary advantage) so an approach that ties their ethics to their rationality has clear long-term advantages.
Safe deep learning-based global path planning using a fast collision-free path generator
Authors: Shirin Chehelgami, Erfan Ashtari, Mohammad Amin Basiri, Mehdi Tale Masouleh, Ahmad Kalhor
Date: 2023-02-27 00:00:00
Proceeding: Robotics and Autonomous Systems 2023 2

In this research, a global path planning method based on recurrent neural networks by means of a new Loss function is presented, which regardless of the complexity of the configuration space, generates the path in a relatively constant time. The new Loss function is defined in such a way that in addition to learning the input data of the network, it creates an adjustable safety margin around the obstacles and ultimately creates a safe path. Moreover, a new global path planning method is also introduced, which is used to create the dataset required to train the proposed neural network. The convergence of this method is mathematically proven and it is shown that this method can also produce a suboptimal path in a much shorter time than the common methods of global path planning reported in the literature. In short, the main purpose of this research consists in providing a method which can create a suboptimal, fast and safe path for a mobile robot from any random starting point to any random destination in a known environment. First, the proposed methods will be implemented for different two-dimensional environments consisting of convex and non-convex obstacles, considering the robot as a point-mass, and then it will be implemented in a simulation environment, AI2THOR. Compared to classical global path planning algorithms, such as RRT and A*, the proposed approach demonstrates better performance in complex and challenging environments.
On the Robustness of the Acoustic Scale in the Low-Redshift Clustering of Matter
Authors: Daniel J. Eisenstein, Hee-Jong Seo, Martin White
Date: 2006-04-17 00:00:00
Tasks: Clustering

We discuss the effects of non-linear structure formation on the signature of acoustic oscillations in the late-time galaxy distribution. We argue that the dominant non-linear effect is the differential motion of pairs of tracers separated by 150 Mpc. These motions are driven by bulk flows and cluster formation and are much smaller than the acoustic scale itself. We present a model for the non-linear evolution based on the distribution of pairwise Lagrangian displacements that provides a quantitative model for the degradation of the acoustic signature, even for biased tracers in redshift space. The Lagrangian displacement distribution can be calibrated with a significantly smaller set of simulations than would be needed to construct a precise power spectrum. By connecting the acoustic signature in the Fourier basis with that in the configuration basis, we show that the acoustic signature is more robust than the usual Fourier-space intuition would suggest because the beat frequency between the peaks and troughs of the acoustic oscillations is a very small wavenumber that is well inside the linear regime. We argue that any possible shift of the acoustic scale is related to infall on 150 Mpc scale, which is O(0.5%) fractionally at first-order even at z=0. For the matter, there is a first-order cancellation such that the mean shift is O(10^{-4}). However, galaxy bias can circumvent this cancellation and produce a sub-percent systematic bias.
A Two-Stage Generative Adversarial Networks With Semantic Content Constraints for Adversarial Example Generation
Authors: Jianyi Liu, Yu Tian, Ru Zhang, (Member, IEEE), YOUQIANG SUN, AND CHAN WANG
Date: 2020-11-11 00:00:00
Proceeding: IEEE 2020 11

Deep neural networks (DNNs) have achieved great success in various applications due to
their strong expressive power. However, recent studies have shown that DNNs are vulnerable to adversarial
examples, and these manipulated instances can mislead DNN into making false predictions. The existing
methods of generating adversarial examples include pixel-level perturbation or spatial transformation of
images, which cannot consider concurrently with the semantic quality of adversarial examples or success rate
of attack. These methods are computationally bulky and slow to generate the adversarial examples. To solve
this kind of issue, a two-stage generative adversarial networks (TSGAN) with semantic content constraints
is proposed in this paper. The first-stage uses the original example dataset to train generator G, which can
help the generator learn the distribution of real examples. Then, the example semantic quality constraint loss
function, the adversarial loss function and the distance loss function are adopted in the second-stage, so that
the generator G can continue to learn to search the distribution of the adversarial examples, and train the new
generator Gadv. The adversarial examples generated by generator Gadv are better fit the distribution of real
examples, and have targeted black-box attack capability. The experiments show that the adversarial examples
generated by TSGAN can achieve the success rate of attack at 98.40% in target model, 29.40% success rate
in defense-oriented model. And 77.58% success rate is obtained in the transfer test attack. The results show
that the adversarial examples generated by the proposed model, which has a highly attack success rate and
more difficult to defense. Meanwhile, the improved adversarial examples have stronger transfer ability than
the existing models. The proposed model can effectively reduce the expression of target category features of
the adversarial examples, and the generated adversarial examples have better semantic quality than others.
The Defense of Adversarial Example with Conditional Generative Adversarial Networks
Authors: Fangchao Yu, Li Wang, Xianjin Fang, and Youwen Zhang
Date: 2020-08-25 00:00:00
Proceeding: Security and Communication Networks 2020 8
Tasks: Generative Adversarial Network, Image-to-Image Translation

Deep neural network approaches have made remarkable progress in many machine learning tasks. However, the latest research indicates that they are vulnerable to adversarial perturbations. An adversary can easily mislead the network models by adding well-designed perturbations to the input. The cause of the adversarial examples is unclear. Therefore, it is challenging to build a defense mechanism. In this paper, we propose an image-to-image translation model to defend against adversarial examples. The proposed model is based on a conditional generative adversarial network, which consists of a generator and a discriminator. The generator is used to eliminate adversarial perturbations in the input. The discriminator is used to distinguish generated data from original clean data to improve the training process. In other words, our approach can map the adversarial images to the clean images, which are then fed to the target deep learning model. The defense mechanism is independent of the target model, and the structure of the framework is universal. A series of experiments conducted on MNIST and CIFAR10 show that the proposed method can defend against multiple types of attacks while maintaining good performance.
Text line segmentation of historical documents: a survey
Authors: Laurence Likforman-Sulem, Abderrazak Zahour & Bruno Taconet
Date: 2006-12-28 00:00:00
Proceeding: International Journal of Document Analysis and Recognition (IJDAR) 2006 12
Tasks: Segmentation, Survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines), automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade and dedicated to documents of historical interest.
Composition of Dynamic Control Objectives Based on Differential Games
Authors: Joshua Shay Kricheli, Aviran Sadon, Shai Arogeti, Shimon Regev, Gera Weiss
Date: 2021-07-15 00:00:00
Proceeding: 29th Mediterranean Conference on Control and Automation (MED) 2021 7

We present a divide and conquer design approach for handling control problems with dynamically changing objectives. The approach is based on associating each objective with a virtual input and then considering a game between a set of (virtual) players, each trying to achieve an objective by controlling the associated virtual input. By guarantying a Nash equilibrium for this game, we synthesize a combined controller that establishes a stable balance between the objectives. To demonstrate the use of the approach, we describe how a controller for a non-linear system (quadrotor) can be designed to handle competing objectives that change in time. The example shows how our approach allows to model each of the objectives separately and to combine them using game theory.
SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method
Authors: Hengzhe Zhang, Aimin Zhou, Qi Chen, Bing Xue, Mengjie Zhang
Date: 2023-02-07 00:00:00
Proceeding: IEEE Transactions on Evolutionary Computation 2023 2
Tasks: Ensemble Learning, Penn Machine Learning Benchmark, regression, Symbolic Regression

Ensemble learning methods have been widely used in machine learning in recent years due to their high predictive performance. With the development of genetic programming-based symbolic regression methods, many papers begin to choose a popular ensemble learning method, random forests, as the baseline competitor. Instead of considering them as competitors, an alternative idea might be to consider symbolic regression as an enhancement technique for random forest. Genetic programming-based symbolic regression methods which fit a smooth function are complementary to the piecewise nature of decision trees, as the smooth variation is common in regression problems. In this article, we propose to form an ensemble model with symbolic regression-based decision trees to address this issue. Furthermore, we design a guided mutation operator to speed up the search on high-dimensional problems, a multi-fidelity evaluation strategy to reduce the computational cost and an ensemble selection mechanism to improve predictive performance. Finally, experimental results on a regression benchmark with 120 datasets show that the proposed ensemble model outperforms 25 existing symbolic regression and ensemble learning methods. Moreover, the proposed method can provide notable insights on an XGBoost hyperparameter performance prediction task, which is an important application area of ensemble learning methods.
An Evolutionary Forest for Regression
Authors: Hengzhe Zhang, Aimin Zhou, Hu Zhang
Date: 2021-11-20 00:00:00
Proceeding: IEEE Transactions on Evolutionary Computation 2021 11
Tasks: Penn Machine Learning Benchmark, regression

Random forest (RF) is a type of ensemble-based machine learning method that has been applied to a variety of machine learning tasks in recent years. This article proposes an evolutionary approach to generate an oblique RF for regression problems. More specifically, our method induces an oblique RF by transforming the original feature space to a new feature space through the evolutionary feature construction method. To speed up the searching process, the proposed method evaluates each set of features based on a decision tree (DT) rather than an RF. In order to obtain an RF, we archive top-performing features and corresponding trees during the search. In this way, both the features and the forest can be constructed simultaneously in a single run. The proposed evolutionary forest is applied to 117 benchmark problems with different characteristics and compared with some state-of-the-art regression methods, including several variants of the RF and gradient boosted DTs (GBDTs). The experimental results suggest that the proposed method outperforms the existing RF and GBDT methods.