pwc-archive/papers-with-abstracts huggingface dataset:
Iterative VAE as a predictive brain model for out-of-distribution generalization
Authors: Victor Boutin, Aimen Zerroug, Minju Jung, Thomas Serre
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS Workshop SVRHM 2020 12
Tasks: Out-of-Distribution Generalization

Our ability to generalize beyond training data to novel, out-of-distribution, image degradations is a hallmark of primate vision. The predictive brain, exemplified by predictive coding networks (PCNs), has become a prominent neuroscience theory of neural computation. Motivated by the recent successes of variational autoencoders (VAEs) in machine learning, we rigorously derive a correspondence between PCNs and VAEs. This motivates us to consider iterative extensions of VAEs (iVAEs) as plausible variational extensions of the PCNs. We further demonstrate that iVAEs generalize to distributional shifts significantly better than both PCNs and VAEs. In addition, we propose a novel measure of recognizability for individual samples which can be tested against human psychophysical data. Overall, we hope this work will spur interest in iVAEs as a promising new direction for modeling in neuroscience.
SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge
Authors: Nandana Mihindukulasooriya, Mohnish Dubey, Alfio Gliozzo, Jens Lehmann, Axel-Cyrille Ngonga Ngomo, Ricardo Usbeck
Date: 2020-12-01 00:00:00
Tasks: Knowledge Base Question Answering, Prediction, Question Answering, Type prediction, Vocal Bursts Type Prediction

Each year the International Semantic Web Conference accepts a set of Semantic Web Challenges to establish competitions that will advance the state of the art solutions in any given problem domain. The SeMantic AnsweR Type prediction task (SMART) was part of ISWC 2020 challenges. Question type and answer type prediction can play a key role in knowledge base question answering systems providing insights that are helpful to generate correct queries or rank the answer candidates. More concretely, given a question in natural language, the task of SMART challenge is, to predict the answer type using a target ontology (e.g., DBpedia or Wikidata).
One-Pixel Attack Deceives Computer-Assisted Diagnosis of Cancer
Authors: Joni Korpihalkola, Tuomo Sipola, Samir Puuska, Tero Kokkonen
Date: 2020-12-01 00:00:00
Tasks: Diagnostic, whole slide images

Computer vision and machine learning can be used to automate various tasks in cancer diagnostic and detection. If an attacker can manipulate the automated processing, the results can be devastating and in the worst case lead to wrong diagnosis and treatment. In this research, the goal is to demonstrate the use of one-pixel attacks in a real-life scenario with a real pathology dataset, TUPAC16, which consists of digitized whole-slide images. We attack against the IBM CODAIT's MAX breast cancer detector using adversarial images. These adversarial examples are found using differential evolution to perform the one-pixel modification to the images in the dataset. The results indicate that a minor one-pixel modification of a whole slide image under analysis can affect the diagnosis by reversing the automatic diagnosis result. The attack poses a threat from the cyber security perspective: the one-pixel method can be used as an attack vector by a motivated attacker.
DNA mixture deconvolution using an evolutionary algorithm with multiple populations, hill-climbing, and guided mutation
Authors: Søren B. Vilsen, Torben Tvedebrink, Poul Svante Eriksen
Date: 2020-12-01 00:00:00

DNA samples crime cases analysed in forensic genetics, frequently contain DNA from multiple contributors. These occur as convolutions of the DNA profiles of the individual contributors to the DNA sample. Thus, in cases where one or more of the contributors were unknown, an objective of interest would be the separation, often called deconvolution, of these unknown profiles. In order to obtain deconvolutions of the unknown DNA profiles, we introduced a multiple population evolutionary algorithm (MEA). We allowed the mutation operator of the MEA to utilise that the fitness is based on a probabilistic model and guide it by using the deviations between the observed and the expected value for every element of the encoded individual. This guided mutation operator (GM) was designed such that the larger the deviation the higher probability of mutation. Furthermore, the GM was inhomogeneous in time, decreasing to a specified lower bound as the number of iterations increased. We analysed 102 two-person DNA mixture samples in varying mixture proportions. The samples were quantified using two different DNA prep. kits: (1) Illumina ForenSeq Panel B (30 samples), and (2) Applied Biosystems Precision ID Globalfiler NGS STR panel (72 samples). The DNA mixtures were deconvoluted by the MEA and compared to the true DNA profiles of the sample. We analysed three scenarios where we assumed: (1) the DNA profile of the major contributor was unknown, (2) DNA profile of the minor was unknown, and (3) both DNA profiles were unknown. Furthermore, we conducted a series of sensitivity experiments on the ForenSeq panel by varying the sub-population size, comparing a completely random homogeneous mutation operator to the guided operator with varying mutation decay rates, and allowing for hill-climbing of the parent population.
Gaussian Process Based Message Filtering for Robust Multi-Agent Cooperation in the Presence of Adversarial Communication
Authors: Rupert Mitchell, Jan Blumenkamp, Amanda Prorok
Date: 2020-12-01 00:00:00

In this paper, we consider the problem of providing robustness to adversarial communication in multi-agent systems. Specifically, we propose a solution towards robust cooperation, which enables the multi-agent system to maintain high performance in the presence of anonymous non-cooperative agents that communicate faulty, misleading or manipulative information. In pursuit of this goal, we propose a communication architecture based on Graph Neural Networks (GNNs), which is amenable to a novel Gaussian Process (GP)-based probabilistic model characterizing the mutual information between the simultaneous communications of different agents due to their physical proximity and relative position. This model allows agents to locally compute approximate posterior probabilities, or confidences, that any given one of their communication partners is being truthful. These confidences can be used as weights in a message filtering scheme, thereby suppressing the influence of suspicious communication on the receiving agent's decisions. In order to assess the efficacy of our method, we introduce a taxonomy of non-cooperative agents, which distinguishes them by the amount of information available to them. We demonstrate in two distinct experiments that our method performs well across this taxonomy, outperforming alternative methods. For all but the best informed adversaries, our filtering method is able to reduce the impact that non-cooperative agents cause, reducing it to the point of negligibility, and with negligible cost to performance in the absence of adversaries.
Boosting the Performance of Semi-Supervised Learning with Unsupervised Clustering
Authors: Boaz Lerner, Guy Shiran, Daphna Weinshall
Date: 2020-12-01 00:00:00
Tasks: Clustering, Semi-Supervised Image Classification

Recently, Semi-Supervised Learning (SSL) has shown much promise in leveraging unlabeled data while being provided with very few labels. In this paper, we show that ignoring the labels altogether for whole epochs intermittently during training can significantly improve performance in the small sample regime. More specifically, we propose to train a network on two tasks jointly. The primary classification task is exposed to both the unlabeled and the scarcely annotated data, whereas the secondary task seeks to cluster the data without any labels. As opposed to hand-crafted pretext tasks frequently used in self-supervision, our clustering phase utilizes the same classification network and head in an attempt to relax the primary task and propagate the information from the labels without overfitting them. On top of that, the self-supervised technique of classifying image rotations is incorporated during the unsupervised learning phase to stabilize training. We demonstrate our method's efficacy in boosting several state-of-the-art SSL algorithms, significantly improving their results and reducing running time in various standard semi-supervised benchmarks, including 92.6% accuracy on CIFAR-10 and 96.9% on SVHN, using only 4 labels per class in each task. We also notably improve the results in the extreme cases of 1,2 and 3 labels per class, and show that features learned by our model are more meaningful for separating the data.
A Statistical Real-Time Prediction Model for Recommender System
Authors: Md Rifat Arefin, Minhas Kamal, Kishan Kumar Ganguly, Tarek Salah Uddin Mahmud
Date: 2020-12-01 00:00:00
Tasks: Prediction, Recommendation Systems

Recommender system has become an inseparable part of online shopping and its usability is increasing with the advancement of these e-commerce sites. An effective and efficient recommender system benefits both the seller and the buyer significantly. We considered user activities and product information for the filtering process in our proposed recommender system. Our model has achieved inspiring result (approximately 58% true-positive and 13% false-positive) for the data set provided by RecSys Challenge 2015. This paper aims to describe a statistical model that will help to predict the buying behavior of a user in real-time during a session.
A Multi-intersection Vehicular Cooperative Control based on End-Edge-Cloud Computing
Authors: Mingzhi Jiang, Tianhao Wu, Zhe Wang, Yi Gong, Lin Zhang, Ren Ping Liu
Date: 2020-12-01 00:00:00
Tasks: Cloud Computing, Management

Cooperative Intelligent Transportation Systems (C-ITS) will change the modes of road safety and traffic management, especially at intersections without traffic lights, namely unsignalized intersections. Existing researches focus on vehicle control within a small area around an unsignalized intersection. In this paper, we expand the control domain to a large area with multiple intersections. In particular, we propose a Multi-intersection Vehicular Cooperative Control (MiVeCC) to enable cooperation among vehicles in a large area with multiple unsignalized intersections. Firstly, a vehicular end-edge-cloud computing framework is proposed to facilitate end-edge-cloud vertical cooperation and horizontal cooperation among vehicles. Then, the vehicular cooperative control problems in the cloud and edge layers are formulated as Markov Decision Process (MDP) and solved by two-stage reinforcement learning. Furthermore, to deal with high-density traffic, vehicle selection methods are proposed to reduce the state space and accelerate algorithm convergence without performance degradation. A multi-intersection simulation platform is developed to evaluate the proposed scheme. Simulation results show that the proposed MiVeCC can improve travel efficiency at multiple intersections by up to 4.59 times without collision compared with existing methods.
Analysis of Drifting Features
Authors: Fabian Hinder, Jonathan Jakob, Barbara Hammer
Date: 2020-12-01 00:00:00
Tasks: feature selection

The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time. We are interested in an identification of those features, that are most relevant for the observed drift. We distinguish between drift inducing features, for which the observed feature drift cannot be explained by any other feature, and faithfully drifting features, which correlate with the present drift of other features. This notion gives rise to minimal subsets of the feature space, which are able to characterize the observed drift as a whole. We relate this problem to the problems of feature selection and feature relevance learning, which allows us to derive a detection algorithm. We demonstrate its usefulness on different benchmarks.
Problems of representation of electrocardiograms in convolutional neural networks
Authors: Iana Sereda, Sergey Alekseev, Aleksandra Koneva, Alexey Khorkin, Grigory Osipov
Date: 2020-12-01 00:00:00

Using electrocardiograms as an example, we demonstrate the characteristic problems that arise when modeling one-dimensional signals containing inaccurate repeating pattern by means of standard convolutional networks. We show that these problems are systemic in nature. They are due to how convolutional networks work with composite objects, parts of which are not fixed rigidly, but have significant mobility. We also demonstrate some counterintuitive effects related to generalization in deep networks.
Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information
Authors: Filippo Simini, Gianni Barlacchi, Massimiliano Luca, Luca Pappalardo
Date: 2020-12-01 00:00:00

The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propose the Deep Gravity model, an effective method to generate flow probabilities that exploits many variables (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those variables and mobility flows. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity has good geographic generalization capability, achieving a significant increase in performance (especially in densely populated regions of interest) with respect to the classic gravity model and models that do not use deep neural networks or geographic data. We also show how flows generated by Deep Gravity may be explained in terms of the geographic features using explainable AI techniques.
A Unified Deep Speaker Embedding Framework for Mixed-Bandwidth Speech Data
Authors: Weicheng Cai, Ming Li
Date: 2020-12-01 00:00:00
Tasks: Bandwidth Extension, image-classification, Image Classification

This paper proposes a unified deep speaker embedding framework for modeling speech data with different sampling rates. Considering the narrowband spectrogram as a sub-image of the wideband spectrogram, we tackle the joint modeling problem of the mixed-bandwidth data in an image classification manner. From this perspective, we elaborate several mixed-bandwidth joint training strategies under different training and test data scenarios. The proposed systems are able to flexibly handle the mixed-bandwidth speech data in a single speaker embedding model without any additional downsampling, upsampling, bandwidth extension, or padding operations. We conduct extensive experimental studies on the VoxCeleb1 dataset. Furthermore, the effectiveness of the proposed approach is validated by the SITW and NIST SRE 2016 datasets.
Mixed Information Flow for Cross-domain Sequential Recommendations
Authors: Muyang Ma, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Lifan Zhao, Jun Ma, Maarten de Rijke
Date: 2020-12-01 00:00:00
Tasks: Sequential Recommendation, Transfer Learning

Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains. Previous studies have investigated the flow of behavioral information by exploring the connection between items from different domains. The flow of knowledge (i.e., the connection between knowledge from different domains) has so far been neglected. In this paper, we propose a mixed information flow network for cross-domain sequential recommendation to consider both the flow of behavioral information and the flow of knowledge by incorporating a behavior transfer unit and a knowledge transfer unit. The proposed mixed information flow network is able to decide when cross-domain information should be used and, if so, which cross-domain information should be used to enrich the sequence representation according to users' current preferences. Extensive experiments conducted on four e-commerce datasets demonstrate that mixed information flow network is able to further improve recommendation performance in different domains by modeling mixed information flow.
ClimaText: A Dataset for Climate Change Topic Detection
Authors: Francesco S. Varini, Jordan Boyd-Graber, Massimiliano Ciaramita, Markus Leippold
Date: 2020-12-01 00:00:00
Tasks: Fact Checking, Question Answering, Sentence, Sentiment Analysis

Climate change communication in the mass media and other textual sources may affect and shape public perception. Extracting climate change information from these sources is an important task, e.g., for filtering content and e-discovery, sentiment analysis, automatic summarization, question-answering, and fact-checking. However, automating this process is a challenge, as climate change is a complex, fast-moving, and often ambiguous topic with scarce resources for popular text-based AI tasks. In this paper, we introduce \textsc{ClimaText}, a dataset for sentence-based climate change topic detection, which we make publicly available. We explore different approaches to identify the climate change topic in various text sources. We find that popular keyword-based models are not adequate for such a complex and evolving task. Context-based algorithms like BERT \cite{devlin2018bert} can detect, in addition to many trivial cases, a variety of complex and implicit topic patterns. Nevertheless, our analysis reveals a great potential for improvement in several directions, such as, e.g., capturing the discussion on indirect effects of climate change. Hence, we hope this work can serve as a good starting point for further research on this topic.
Consistent Representation Learning for High Dimensional Data Analysis
Authors: Stan Z. Li, Lirong Wu, Zelin Zang
Date: 2020-12-01 00:00:00
Tasks: Clustering, Dimensionality Reduction, Representation Learning, Vocal Bursts Intensity Prediction

High dimensional data analysis for exploration and discovery includes three fundamental tasks: dimensionality reduction, clustering, and visualization. When the three associated tasks are done separately, as is often the case thus far, inconsistencies can occur among the tasks in terms of data geometry and others. This can lead to confusing or misleading data interpretation. In this paper, we propose a novel neural network-based method, called Consistent Representation Learning (CRL), to accomplish the three associated tasks end-to-end and improve the consistencies. The CRL network consists of two nonlinear dimensionality reduction (NLDR) transformations: (1) one from the input data space to the latent feature space for clustering, and (2) the other from the clustering space to the final 2D or 3D space for visualization. Importantly, the two NLDR transformations are performed to best satisfy local geometry preserving (LGP) constraints across the spaces or network layers, to improve data consistencies along with the processing flow. Also, we propose a novel metric, clustering-visualization inconsistency (CVI), for evaluating the inconsistencies. Extensive comparative results show that the proposed CRL neural network method outperforms the popular t-SNE and UMAP-based and other contemporary clustering and visualization algorithms in terms of evaluation metrics and visualization.
Farthest sampling segmentation of triangulated surfaces
Authors: Victoria Hernández-Mederos, Dimas Martínez, Jorge Estrada-Sarlabous, Valia Guerra-Ones
Date: 2020-12-01 00:00:00
Tasks: Clustering, Segmentation

In this paper we introduce Farthest Sampling Segmentation (FSS), a new method for segmentation of triangulated surfaces, which consists of two fundamental steps: the computation of a submatrix $W^k$ of the affinity matrix $W$ and the application of the k-means clustering algorithm to the rows of $W^k$. The submatrix $W^k$ is obtained computing the affinity between all triangles and only a few special triangles: those which are farthest in the defined metric. This is equivalent to select a sample of columns of $W$ without constructing it completely. The proposed method is computationally cheaper than other segmentation algorithms, since it only calculates few columns of $W$ and it does not require the eigendecomposition of $W$ or of any submatrix of $W$. We prove that the orthogonal projection of $W$ on the space generated by the columns of $W^k$ coincides with the orthogonal projection of $W$ on the space generated by the $k$ eigenvectors computed by Nystr\"om's method using the columns of $W^k$ as a sample of $W$. Further, it is shown that for increasing size $k$, the proximity relationship among the rows of $W^k$ tends to faithfully reflect the proximity among the corresponding rows of $W$. The FSS method does not depend on parameters that must be tuned by hand and it is very flexible, since it can handle any metric to define the distance between triangles. Numerical experiments with several metrics and a large variety of 3D triangular meshes show that the segmentations obtained computing less than the 10% of columns $W$ are as good as those obtained from clustering the rows of the full matrix $W$.
Improving cluster recovery with feature rescaling factors
Authors: Renato Cordeiro de Amorim, Vladimir Makarenkov
Date: 2020-12-01 00:00:00
Tasks: Clustering

The data preprocessing stage is crucial in clustering. Features may describe entities using different scales. To rectify this, one usually applies feature normalisation aiming at rescaling features so that none of them overpowers the others in the objective function of the selected clustering algorithm. In this paper, we argue that the rescaling procedure should not treat all features identically. Instead, it should favour the features that are more meaningful for clustering. With this in mind, we introduce a feature rescaling method that takes into account the within-cluster degree of relevance of each feature. Our comprehensive simulation study, carried out on real and synthetic data, with and without noise features, clearly demonstrates that clustering methods that use the proposed data normalization strategy clearly outperform those that use traditional data normalization.