pwc-archive/papers-with-abstracts huggingface dataset:
Encrypted Internet traffic classification using a supervised Spiking Neural Network
Authors: Ali Rasteh, Florian Delpech, Carlos Aguilar-Melchor, Romain Zimmer, Saeed Bagheri Shouraki, Timothée Masquelier
Date: 2021-01-24 00:00:00
Tasks: General Classification, Traffic Classification

Internet traffic recognition is an essential tool for access providers since recognizing traffic categories related to different data packets transmitted on a network help them define adapted priorities. That means, for instance, high priority requirements for an audio conference and low ones for a file transfer, to enhance user experience. As internet traffic becomes increasingly encrypted, the mainstream classic traffic recognition technique, payload inspection, is rendered ineffective. This paper uses machine learning techniques for encrypted traffic classification, looking only at packet size and time of arrival. Spiking neural networks (SNN), largely inspired by how biological neurons operate, were used for two reasons. Firstly, they are able to recognize time-related data packet features. Secondly, they can be implemented efficiently on neuromorphic hardware with a low energy footprint. Here we used a very simple feedforward SNN, with only one fully-connected hidden layer, and trained in a supervised manner using the newly introduced method known as Surrogate Gradient Learning. Surprisingly, such a simple SNN reached an accuracy of 95.9% on ISCX datasets, outperforming previous approaches. Besides better accuracy, there is also a very significant improvement on simplicity: input size, number of neurons, trainable parameters are all reduced by one to four orders of magnitude. Next, we analyzed the reasons for this good accuracy. It turns out that, beyond spatial (i.e. packet size) features, the SNN also exploits temporal ones, mostly the nearly synchronous (within a 200ms range) arrival times of packets with certain sizes. Taken together, these results show that SNNs are an excellent fit for encrypted internet traffic classification: they can be more accurate than conventional artificial neural networks (ANN), and they could be implemented efficiently on low power embedded systems.
Annealed Stein Variational Gradient Descent
Authors: Francesco D'Angelo, Vincent Fortuin
Date: 2021-01-24 00:00:00
Proceeding: pproximateinference AABI Symposium 2021 1

Particle based optimization algorithms have recently been developed as sampling methods that iteratively update a set of particles to approximate a target distribution. In particular Stein variational gradient descent has gained attention in the approximate inference literature for its flexibility and accuracy. We empirically explore the ability of this method to sample from multi-modal distributions and focus on two important issues: (i) the inability of the particles to escape from local modes and (ii) the inefficacy in reproducing the density of the different regions. We propose an annealing schedule to solve these issues and show, through various experiments, how this simple solution leads to significant improvements in mode coverage, without invalidating any theoretical properties of the original algorithm.
FakeFlow: Fake News Detection by Modeling the Flow of Affective Information
Authors: Bilal Ghanem, Simone Paolo Ponzetto, Paolo Rosso, Francisco Rangel
Date: 2021-01-24 00:00:00
Proceeding: EACL 2021 2
Tasks: Articles, Fake News Detection

Fake news articles often stir the readers' attention by means of emotional appeals that arouse their feelings. Unlike in short news texts, authors of longer articles can exploit such affective factors to manipulate readers by adding exaggerations or fabricating events, in order to affect the readers' emotions. To capture this, we propose in this paper to model the flow of affective information in fake news articles using a neural architecture. The proposed model, FakeFlow, learns this flow by combining topic and affective information extracted from text. We evaluate the model's performance with several experiments on four real-world datasets. The results show that FakeFlow achieves superior results when compared against state-of-the-art methods, thus confirming the importance of capturing the flow of the affective information in news articles.
NeurT-FDR: Controlling FDR by Incorporating Feature Hierarchy
Authors: Lin Qiu, Nils Murrugarra-Llerena, Vítor Silva, Lin Lin, Vernon M. Chinchilli
Date: 2021-01-24 00:00:00

Controlling false discovery rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science. Existing methods rely on the test-level covariates while ignoring possible hierarchy among the covariates. This strategy may not be optimal for complex large-scale problems, where hierarchical information often exists among those test-level covariates. We propose NeurT-FDR which boosts statistical power and controls FDR for multiple hypothesis testing while leveraging the hierarchy among test-level covariates. Our method parametrizes the test-level covariates as a neural network and adjusts the feature hierarchy through a regression framework, which enables flexible handling of high-dimensional features as well as efficient end-to-end optimization. We show that NeurT-FDR has strong FDR guarantees and makes substantially more discoveries in synthetic and real datasets compared to competitive baselines.
Analytical Characterization and Design Space Exploration for Optimization of CNNs
Authors: Rui Li, Yufan Xu, Aravind Sukumaran-Rajam, Atanas Rountev, P. Sadayappan
Date: 2021-01-24 00:00:00
Tasks: BIG-bench Machine Learning

Moving data through the memory hierarchy is a fundamental bottleneck that can limit the performance of core algorithms of machine learning, such as convolutional neural networks (CNNs). Loop-level optimization, including loop tiling and loop permutation, are fundamental transformations to reduce data movement. However, the search space for finding the best loop-level optimization configuration is explosively large. This paper develops an analytical modeling approach for finding the best loop-level optimization configuration for CNNs on multi-core CPUs. Experimental evaluation shows that this approach achieves comparable or better performance than state-of-the-art libraries and auto-tuning based optimizers for CNNs.
Estimating the Total Volume of Queries to a Search Engine
Authors: Fabrizio Lillo, Salvatore Ruggieri
Date: 2021-01-24 00:00:00

We study the problem of estimating the total number of searches (volume) of queries in a specific domain, which were submitted to a search engine in a given time period. Our statistical model assumes that the distribution of searches follows a Zipf's law, and that the observed sample volumes are biased accordingly to three possible scenarios. These assumptions are consistent with empirical data, with keyword research practices, and with approximate algorithms used to take counts of query frequencies. A few estimators of the parameters of the distribution are devised and experimented, based on the nature of the empirical/simulated data. For continuous data, we recommend using nonlinear least square regression (NLS) on the top-volume queries, where the bound on the volume is obtained from the well-known Clauset, Shalizi and Newman (CSN) estimation of power-law parameters. For binned data, we propose using a Chi-square minimization approach restricted to the top-volume queries, where the bound is obtained by the binned version of the CSN method. Estimations are then derived for the total number of queries and for the total volume of the population, including statistical error bounds. We apply the methods on the domain of recipes and cooking queries searched in Italian in 2017. The observed volumes of sample queries are collected from Google Trends (continuous data) and SearchVolume (binned data). The estimated total number of queries and total volume are computed for the two cases, and the results are compared and discussed.
Online Memory Leak Detection in the Cloud-based Infrastructures
Authors: Anshul Jindal, Paul Staab, Jorge Cardoso, Michael Gerndt, Vladimir Podolskiy
Date: 2021-01-24 00:00:00

A memory leak in an application deployed on the cloud can affect the availability and reliability of the application. Therefore, to identify and ultimately resolve it quickly is highly important. However, in the production environment running on the cloud, memory leak detection is a challenge without the knowledge of the application or its internal object allocation details. This paper addresses this challenge of online detection of memory leaks in cloud-based infrastructure without having any internal application knowledge by introducing a novel machine learning based algorithm Precog. This algorithm solely uses one metric i.e the system's memory utilization on which the application is deployed for the detection of a memory leak. The developed algorithm's accuracy was tested on 60 virtual machines manually labeled memory utilization data provided by our industry partner Huawei Munich Research Center and it was found that the proposed algorithm achieves the accuracy score of 85\% with less than half a second prediction time per virtual machine.
Exploring ensembles and uncertainty minimization in denoising networks
Authors: Xiaoqi Ma
Date: 2021-01-24 00:00:00
Tasks: Denoising, Ensemble Learning, Image Denoising

The development of neural networks has greatly improved the performance in various computer vision tasks. In the filed of image denoising, convolutional neural network based methods such as DnCNN break through the limits of classical methods, achieving better quantitative results. However, the epistemic uncertainty existing in neural networks limits further improvements in their performance over denoising tasks. Therefore, we develop and study different solutions to minimize uncertainty and further improve the removal of noise. From the perspective of ensemble learning, we implement manipulations to noisy images from the point of view of spatial and frequency domains and then denoise them using pre-trained denoising networks. We propose a fusion model consisting of two attention modules, which focus on assigning the proper weights to pixels and channels. The experimental results show that our model achieves better performance on top of the baseline of regular pre-trained denoising networks.
Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation
Authors: Michael Rebsamen, Christian Rummel, Mauricio Reyes, Roland Wiest, Richard McKinley
Date: 2020-11-05 00:00:00
Tasks: 3D Medical Imaging Segmentation, Anatomy, Brain Morphometry, Brain Segmentation, Deep Learning, Diffeomorphic Medical Image Registration

Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurological disorders. Diffeomorphic registration‐based cortical thickness (DiReCT) is a known technique to derive such measures from non‐surface‐based volumetric tissue maps. ANTs provides an open‐source method for estimating cortical thickness, derived by applying DiReCT to an atlas‐based segmentation. In this paper, we propose DL+DiReCT, a method using high‐quality deep learning‐based neuroanatomy segmentations followed by DiReCT, yielding accurate and reliable cortical thickness measures in a short time. We evaluate the methods on two independent datasets and compare the results against surface‐based measures from FreeSurfer. Good correlation of DL+DiReCT with FreeSurfer was observed (r = .887) for global mean cortical thickness compared to ANTs versus FreeSurfer (r = .608). Experiments suggest that both DiReCT‐based methods had higher sensitivity to changes in cortical thickness than Freesurfer. However, while ANTs showed low scan‐rescan robustness, DL+DiReCT showed similar robustness to Freesurfer. Effect‐sizes for group‐wise differences of healthy controls compared to individuals with dementia were highest with the deep learning‐based segmentation. DL+DiReCT is a promising combination of a deep learning‐based method with a traditional registration technique to detect subtle changes in cortical thickness.
The Role of Cost in the Integration of Security Features in Integrated Circuits for Smart Cards
Authors: Nikolaos Athanasios Anagnostopoulos
Date: 2021-01-25 00:00:00

This essay investigates the role of cost in the development and production of secure integrated circuits. Initially, I make a small introduction on hardware attacks on smart cards and some of the reasons behind them. Subsequently, I introduce the production phases of chips that are integrated to smart cards and try to identify the costs affecting each one of them. I proceed to identify how adding security features on such integrated circuits may affect the costs of their development and production. I then make a more thorough investigation on the costs of developing a hardware attack for such chips and try to estimate the potential damages and losses of such an attack. I also go on to examine potential ways of reducing the cost of production for secure chips, while identifying the difficulties in adopting them. This essay ends with the conclusion that adding security features to chips meant to be used for secure applications is well worth it, because the costs of developing attacks are of comparable amounts to the costs of developing and producing a chip and the potential damages and losses caused by such attacks can be way higher than these costs. Therefore, although the production and development of integrated circuits come at a certain cost and security introduces further additional costs, security is inherently unavoidable in such chips. Finally, I additionally identify that security is an evolving concept and does not aim to make a chip totally impenetrable, as this may be impossible, but to lower the potential risks, including that of being compromised, to acceptable levels. Thus, a balance needs be found between the level of security and the levels of cost and risk.
Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters
Authors: Xianfei Hui, Baiqing Sun, Hui Jiang, Indranil SenGupta
Date: 2021-01-22 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

In this paper we implement a combination of data-science and fuzzy theory to improve the classical Barndorff-Nielsen and Shephard model, and implement this to analyze the S&P 500 index. We pre-process the index data based on fuzzy theory. After that, S&P 500 stock index data for the past ten years are analyzed, and a deterministic parameter is extracted using various machine and deep learning methods. The results show that the new model, where fuzzy parameters are incorporated, can incorporate the long-term dependence in the classical Barndorff-Nielsen and Shephard model. The modification is based on only a few changes compared to the classical model. At the same time, the resulting analysis effectively captures the stochastic dynamics of the stock index time series.
QFold: Quantum Walks and Deep Learning to Solve Protein Folding
Authors: P A M Casares, Roberto Campos, M A Martin-Delgado
Date: 2021-01-25 00:00:00
Tasks: Deep Learning, Protein Folding

Predicting the 3D structure of proteins is one of the most important problems in current biochemical research. In this article, we explain how to combine recent deep learning advances with the well known technique of quantum walks applied to a Metropolis algorithm. The result, QFold, is a fully scalable hybrid quantum algorithm that, in contrast to previous quantum approaches, does not require a lattice model simplification and instead relies on the much more realistic assumption of parameterization in terms of torsion angles of the amino acids. We compare it with its classical analog for different annealing schedules and find a polynomial quantum advantage, and implement a minimal realization of the quantum Metropolis in the IBMQ Casablanca quantum system.
Consistent specification testing under spatial dependence
Authors: Abhimanyu Gupta, Xi Qu
Date: 2021-01-25 00:00:00
Tasks: regression

We propose a series-based nonparametric specification test for a regression function when data are spatially dependent, the `space' being of a general economic or social nature. Dependence can be parametric, parametric with increasing dimension, semiparametric or any combination thereof, thus covering a vast variety of settings. These include spatial error models of varying types and levels of complexity. Under a new smooth spatial dependence condition, our test statistic is asymptotically standard normal. To prove the latter property, we establish a central limit theorem for quadratic forms in linear processes in an increasing dimension setting. Finite sample performance is investigated in a simulation study, with a bootstrap method also justified and illustrated, and empirical examples illustrate the test with real-world data.
Generating Simulation-Based Contacts Matrices for Disease Transmission Modelling at Special Settings
Authors: Mahdi M. Najafabadi, Ali Asgary, Mohammadali Tofighi, Ghassem Tofighi
Date: 2021-01-25 00:00:00

Since a significant amount of disease transmission occurs through human-to-human or social contact, understanding who interacts with whom in time and space is essential for disease transmission modeling, prediction, and assessment of prevention strategies in different environments and special settings. Thus, measuring contact mixing patterns, often in the form of a contacts matrix, has been a key component of heterogeneous disease transmission modeling research. Several data collection techniques estimate or calculate a contacts matrix at different geographical scales and population mixes based on surveys and sensors. This paper presents a methodology for generating a contacts matrix by using high fidelity simulations which mimic actual workflow and movements of individuals in time and space. Results of this study show that such simulations can be a feasible, flexible, and reasonable alternative method for estimating social contacts and generating contacts mixing matrices for various settings under different conditions.
Back-Projection Pipeline
Authors: Pablo Navarrete Michelini, Hanwen Liu, Yunhua Lu, Xingqun Jiang
Date: 2021-01-25 00:00:00
Tasks: Raindrop Removal, Rain Removal, Super-Resolution

We propose a simple extension of residual networks that works simultaneously in multiple resolutions. Our network design is inspired by the iterative back-projection algorithm but seeks the more difficult task of learning how to enhance images. Compared to similar approaches, we propose a novel solution to make back-projections run in multiple resolutions by using a data pipeline workflow. Features are updated at multiple scales in each layer of the network. The update dynamic through these layers includes interactions between different resolutions in a way that is causal in scale, and it is represented by a system of ODEs, as opposed to a single ODE in the case of ResNets. The system can be used as a generic multi-resolution approach to enhance images. We test it on several challenging tasks with special focus on super-resolution and raindrop removal. Our results are competitive with state-of-the-arts and show a strong ability of our system to learn both global and local image features.
A Primer for Neural Arithmetic Logic Modules
Authors: Bhumika Mistry, Katayoun Farrahi, Jonathon Hare
Date: 2021-01-23 00:00:00

Neural Arithmetic Logic Modules have become a growing area of interest, though remain a niche field. These modules are neural networks which aim to achieve systematic generalisation in learning arithmetic and/or logic operations such as $\{+, -, \times, \div, \leq, \textrm{AND}\}$ while also being interpretable. This paper is the first in discussing the current state of progress of this field, explaining key works, starting with the Neural Arithmetic Logic Unit (NALU). Focusing on the shortcomings of the NALU, we provide an in-depth analysis to reason about design choices of recent modules. A cross-comparison between modules is made on experiment setups and findings, where we highlight inconsistencies in a fundamental experiment causing the inability to directly compare across papers. To alleviate the existing inconsistencies, we create a benchmark which compares all existing arithmetic NALMs. We finish by providing a novel discussion of existing applications for NALU and research directions requiring further exploration.
Automatic Preference Based Multi-objective Evolutionary Algorithm on Vehicle Fleet Maintenance Scheduling Optimization
Authors: Yali Wang, Steffen Limmer, Markus Olhofer, Michael Emmerich, Thomas Baeck
Date: 2021-01-23 00:00:00
Tasks: Diversity, Scheduling

A preference based multi-objective evolutionary algorithm is proposed for generating solutions in an automatically detected knee point region. It is named Automatic Preference based DI-MOEA (AP-DI-MOEA) where DI-MOEA stands for Diversity-Indicator based Multi-Objective Evolutionary Algorithm). AP-DI-MOEA has two main characteristics: firstly, it generates the preference region automatically during the optimization; secondly, it concentrates the solution set in this preference region. Moreover, the real-world vehicle fleet maintenance scheduling optimization (VFMSO) problem is formulated, and a customized multi-objective evolutionary algorithm (MOEA) is proposed to optimize maintenance schedules of vehicle fleets based on the predicted failure distribution of the components of cars. Furthermore, the customized MOEA for VFMSO is combined with AP-DI-MOEA to find maintenance schedules in the automatically generated preference region. Experimental results on multi-objective benchmark problems and our three-objective real-world application problems show that the newly proposed algorithm can generate the preference region accurately and that it can obtain better solutions in the preference region. Especially, in many cases, under the same budget, the Pareto optimal solutions obtained by AP-DI-MOEA dominate solutions obtained by MOEAs that pursue the entire Pareto front.
Neuromorphic adaptive spiking CPG towards bio-inspired locomotion of legged robots
Authors: Pablo Lopez-Osorio, Alberto Patino-Saucedo, Juan P. Dominguez-Morales, Horacio Rostro-Gonzalez, Fernando Perez-Peña
Date: 2021-01-24 00:00:00

In recent years, locomotion mechanisms exhibited by vertebrate animals have been the inspiration for the improvement in the performance of robotic systems. These mechanisms include the adaptability of their locomotion to any change registered in the environment through their biological sensors. In this regard, we aim to replicate such kind of adaptability in legged robots through a Spiking Central Pattern Generator. This Spiking Central Pattern Generator generates different locomotion (rhythmic) patterns which are driven by an external stimulus, that is, the output of a Force Sensitive Resistor connected to the robot to provide feedback. The Spiking Central Pattern Generator consists of a network of five populations of Leaky Integrate-and-Fire neurons designed with a specific topology in such a way that the rhythmic patterns can be generated and driven by the aforementioned external stimulus. Therefore, the locomotion of the end robotic platform (any-legged robot) can be adapted to the terrain by using any sensor as input. The Spiking Central Pattern Generator with adaptive learning has been numerically validated at software and hardware level, using the Brian 2 simulator and the SpiNNaker neuromorphic platform for the latest. In particular, our experiments clearly show an adaptation in the oscillation frequencies between the spikes produced in the populations of the Spiking Central Pattern Generator while the input stimulus varies. To validate the robustness and adaptability of the Spiking Central Pattern Generator, we have performed several tests by variating the output of the sensor. These experiments were carried out in Brian 2 and SpiNNaker; both implementations showed a similar behavior with a Pearson correlation coefficient of 0.905.