pwc-archive/papers-with-abstracts huggingface dataset:
Brain Tumor Detection and Classification Using a New Evolutionary Convolutional Neural Network
Authors: Amin Abdollahi Dehkordi, Mina Hashemi, Mehdi Neshat, Seyedali Mirjalili, Ali Safaa Sadiq
Date: 2022-04-26 00:00:00
Tasks: image-classification, Image Classification, Specificity

A definitive diagnosis of a brain tumour is essential for enhancing treatment success and patient survival. However, it is difficult to manually evaluate multiple magnetic resonance imaging (MRI) images generated in a clinic. Therefore, more precise computer-based tumour detection methods are required. In recent years, many efforts have investigated classical machine learning methods to automate this process. Deep learning techniques have recently sparked interest as a means of diagnosing brain tumours more accurately and robustly. The goal of this study, therefore, is to employ brain MRI images to distinguish between healthy and unhealthy patients (including tumour tissues). As a result, an enhanced convolutional neural network is developed in this paper for accurate brain image classification. The enhanced convolutional neural network structure is composed of components for feature extraction and optimal classification. Nonlinear L\'evy Chaotic Moth Flame Optimizer (NLCMFO) optimizes hyperparameters for training convolutional neural network layers. Using the BRATS 2015 data set and brain image datasets from Harvard Medical School, the proposed model is assessed and compared with various optimization techniques. The optimized CNN model outperforms other models from the literature by providing 97.4% accuracy, 96.0% sensitivity, 98.6% specificity, 98.4% precision, and 96.6% F1-score, (the mean of the weighted harmonic value of CNN precision and recall).
Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression
Authors: Marco Virgolin, Peter A. N. Bosman
Date: 2022-04-26 00:00:00
Tasks: regression, Symbolic Regression

Currently, the genetic programming version of the gene-pool optimal mixing evolutionary algorithm (GP-GOMEA) is among the top-performing algorithms for symbolic regression (SR). A key strength of GP-GOMEA is its way of performing variation, which dynamically adapts to the emergence of patterns in the population. However, GP-GOMEA lacks a mechanism to optimize coefficients. In this paper, we study how fairly simple approaches for optimizing coefficients can be integrated into GP-GOMEA. In particular, we considered two variants of Gaussian coefficient mutation. We performed experiments using different settings on 23 benchmark problems, and used machine learning to estimate what aspects of coefficient mutation matter most. We find that the most important aspect is that the number of coefficient mutation attempts needs to be commensurate with the number of mixing operations that GP-GOMEA performs. We applied GP-GOMEA with the best-performing coefficient mutation approach to the data sets of SRBench, a large SR benchmark, for which a ground-truth underlying equation is known. We find that coefficient mutation can help re-discovering the underlying equation by a substantial amount, but only when no noise is added to the target variable. In the presence of noise, GP-GOMEA with coefficient mutation discovers alternative but similarly-accurate equations.
A PAC-Bayes oracle inequality for sparse neural networks
Authors: Maximilian F. Steffen, Mathias Trabs
Date: 2022-04-26 00:00:00
Tasks: regression

We study the Gibbs posterior distribution for sparse deep neural nets in a nonparametric regression setting. The posterior can be accessed via Metropolis-adjusted Langevin algorithms. Using a mixture over uniform priors on sparse sets of network weights, we prove an oracle inequality which shows that the method adapts to the unknown regularity and hierarchical structure of the regression function. The estimator achieves the minimax-optimal rate of convergence (up to a logarithmic factor).
On automatic calibration of the SIRD epidemiological model for COVID-19 data in Poland
Authors: Piotr Błaszczyk, Konrad Klimczak, Adam Mahdi, Piotr Oprocha, Paweł Potorski, Paweł Przybyłowicz, Michał Sobieraj
Date: 2022-04-26 00:00:00
Tasks: GPU

We propose a novel methodology for estimating the epidemiological parameters of a modified SIRD model (acronym of Susceptible, Infected, Recovered and Deceased individuals) and perform a short-term forecast of SARS-CoV-2 virus spread. We mainly focus on forecasting number of deceased. The procedure was tested on reported data for Poland. For some short-time intervals we performed numerical test investigating stability of parameter estimates in the proposed approach. Numerical experiments confirm the effectiveness of short-term forecasts (up to 2 weeks) and stability of the method. To improve their performance (i.e. computation time) GPU architecture was used in computations.
Learning reversible symplectic dynamics
Authors: Riccardo Valperga, Kevin Webster, Victoria Klein, Dmitry Turaev, Jeroen S. W. Lamb
Date: 2022-04-26 00:00:00

Time-reversal symmetry arises naturally as a structural property in many dynamical systems of interest. While the importance of hard-wiring symmetry is increasingly recognized in machine learning, to date this has eluded time-reversibility. In this paper we propose a new neural network architecture for learning time-reversible dynamical systems from data. We focus in particular on an adaptation to symplectic systems, because of their importance in physics-informed learning.
Discussion of Multiscale Fisher's Independence Test for Multivariate Dependence
Authors: Duyeol Lee, Helal El-Zaatari, Michael R. Kosorok, Xinyi Li, Kai Zhang
Date: 2022-04-26 00:00:00

The multiscale Fisher's independence test (MULTIFIT hereafter) proposed by Gorsky & Ma (2022) is a novel method to test independence between two random vectors. By its design, this test is particularly useful in detecting local dependence. Moreover, by adopting a resampling-free approach, it can easily accommodate massive sample sizes. Another benefit of the proposed method is its ability to interpret the nature of dependency. We congratulate the authors, Shai Gorksy and Li Ma, for their very interesting and elegant work. In this comment, we would like to discuss a general framework unifying the MULTIFIT and other tests and compare it with the binary expansion randomized ensemble test (BERET hereafter) proposed by Lee et al. (In press). We also would like to contribute our thoughts on potential extensions of the method.
Confidence Band Estimation for Survival Random Forests
Authors: Sarah Elizabeth Formentini, Wei Liang, Ruoqing Zhu
Date: 2022-04-26 00:00:00
Tasks: valid

Survival random forest is a popular machine learning tool for modeling censored survival data. However, there is currently no statistically valid and computationally feasible approach for estimating its confidence band. This paper proposes an unbiased confidence band estimation by extending recent developments in infinite-order incomplete U-statistics. The idea is to estimate the variance-covariance matrix of the cumulative hazard function prediction on a grid of time points. We then generate the confidence band by viewing the cumulative hazard function estimation as a Gaussian process whose distribution can be approximated through simulation. This approach is computationally easy to implement when the subsampling size of a tree is no larger than half of the total training sample size. Numerical studies show that our proposed method accurately estimates the confidence band and achieves desired coverage rate. We apply this method to veterans' administration lung cancer data.
Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering
Authors: Minghao Zhao, Le Wu, Yile Liang, Lei Chen, Jian Zhang, Qilin Deng, Kai Wang, Xudong Shen, Tangjie Lv, Runze Wu
Date: 2022-04-26 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Recent years have witnessed the great accuracy performance of graph-based Collaborative Filtering (CF) models for recommender systems. By taking the user-item interaction behavior as a graph, these graph-based CF models borrow the success of Graph Neural Networks (GNN), and iteratively perform neighborhood aggregation to propagate the collaborative signals. While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate popularity bias of recommender systems?" To answer this question, we first investigate the two-fold performances w.r.t. accuracy and novelty for existing graph-based CF methods. The empirical results show that symmetric neighborhood aggregation adopted by most existing graph-based CF models exacerbate the popularity bias and this phenomenon becomes more serious as the depth of graph propagation increases. Further, we theoretically analyze the cause of popularity bias for graph-based CF. Then, we propose a simple yet effective plugin, namely r-AdjNorm, to achieve an accuracy-novelty trade-off by controlling the normalization strength in the neighborhood aggregation process. Meanwhile, r-AdjNorm can be smoothly applied to the existing graph-based CF backbones without additional computation. Finally, experimental results on three benchmark datasets show that our proposed method can improve novelty without sacrificing accuracy under various graph-based CF backbones.
Understanding User Satisfaction with Task-oriented Dialogue Systems
Authors: Clemencia Siro, Mohammad Aliannejadi, Maarten de Rijke
Date: 2022-04-26 00:00:00
Tasks: Conversational Recommendation, Task-Oriented Dialogue Systems

$ $Dialogue systems are evaluated depending on their type and purpose. Two categories are often distinguished: (1) task-oriented dialogue systems (TDS), which are typically evaluated on utility, i.e., their ability to complete a specified task, and (2) open domain chatbots, which are evaluated on the user experience, i.e., based on their ability to engage a person. What is the influence of user experience on the user satisfaction rating of TDS as opposed to, or in addition to, utility? We collect data by providing an additional annotation layer for dialogues sampled from the ReDial dataset, a widely used conversational recommendation dataset. Unlike prior work, we annotate the sampled dialogues at both the turn and dialogue level on six dialogue aspects: relevance, interestingness, understanding, task completion, efficiency, and interest arousal. The annotations allow us to study how different dialogue aspects influence user satisfaction. We introduce a comprehensive set of user experience aspects derived from the annotators' open comments that can influence users' overall impression. We find that the concept of satisfaction varies across annotators and dialogues, and show that a relevant turn is significant for some annotators, while for others, an interesting turn is all they need. Our analysis indicates that the proposed user experience aspects provide a fine-grained analysis of user satisfaction that is not captured by a monolithic overall human rating.
Cross-media Scientific Research Achievements Query based on Ranking Learning
Authors: Benzhi Wang, Meiyu Liang, Ang Li
Date: 2022-04-26 00:00:00
Tasks: Decision Making

With the advent of the information age, the scale of data on the Internet is getting larger and larger, and it is full of text, images, videos, and other information. Different from social media data and news data, scientific research achievements information has the characteristics of many proper nouns and strong ambiguity. The traditional single-mode query method based on keywords can no longer meet the needs of scientific researchers and managers of the Ministry of Science and Technology. Scientific research project information and scientific research scholar information contain a large amount of valuable scientific research achievement information. Evaluating the output capability of scientific research projects and scientific research teams can effectively assist managers in decision-making. In view of the above background, this paper expounds on the research status from four aspects: characteristic learning of scientific research results, cross-media research results query, ranking learning of scientific research results, and cross-media scientific research achievement query system.
A Review-aware Graph Contrastive Learning Framework for Recommendation
Authors: Jie Shuai, Kun Zhang, Le Wu, Peijie Sun, Richang Hong, Meng Wang, Yong Li
Date: 2022-04-26 00:00:00
Tasks: Contrastive Learning, Recommendation Systems, Representation Learning

Most modern recommender systems predict users preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the auxiliary review information accompanied with user ratings, many of the existing review-based recommendation models enriched user/item embedding learning ability with historical reviews or better modeled user-item interactions with the help of available user-item target reviews. Though significant progress has been made, we argue that current solutions for review-based recommendation suffer from two drawbacks. First, as review-based recommendation can be naturally formed as a user-item bipartite graph with edge features from corresponding user-item reviews, how to better exploit this unique graph structure for recommendation? Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better? To this end, in this paper, we propose a novel Review-aware Graph Contrastive Learning (RGCL) framework for review-based recommendation. Specifically, we first construct a review-aware user-item graph with feature-enhanced edges from reviews, where each edge feature is composed of both the user-item rating and the corresponding review semantics. This graph with feature-enhanced edges can help attentively learn each neighbor node weight for user and item representation learning. After that, we design two additional contrastive learning tasks (i.e., Node Discrimination and Edge Discrimination) to provide self-supervised signals for the two components in recommendation process. Finally, extensive experiments over five benchmark datasets demonstrate the superiority of our proposed RGCL compared to the state-of-the-art baselines.
Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining
Authors: Di wu, Siyuan Li, Jie Yang, Mohamad Sawan
Date: 2022-04-20 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Electromyography (EMG), Gesture Recognition, Representation Learning

Deep learning associated with neurological signals is poised to drive major advancements in diverse fields such as medical diagnostics, neurorehabilitation, and brain-computer interfaces. The challenge in harnessing the full potential of these signals lies in the dependency on extensive, high-quality annotated data, which is often scarce and expensive to acquire, requiring specialized infrastructure and domain expertise. To address the appetite for data in deep learning, we present Neuro-BERT, a self-supervised pre-training framework of neurological signals based on masked autoencoding in the Fourier domain. The intuition behind our approach is simple: frequency and phase distribution of neurological signals can reveal intricate neurological activities. We propose a novel pre-training task dubbed Fourier Inversion Prediction (FIP), which randomly masks out a portion of the input signal and then predicts the missing information using the Fourier inversion theorem. Pre-trained models can be potentially used for various downstream tasks such as sleep stage classification and gesture recognition. Unlike contrastive-based methods, which strongly rely on carefully hand-crafted augmentations and siamese structure, our approach works reasonably well with a simple transformer encoder with no augmentation requirements. By evaluating our method on several benchmark datasets, we show that Neuro-BERT improves downstream neurological-related tasks by a large margin.
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated Learning
Authors: Yuwei Sun, Hiroshi Esaki, Hideya Ochiai.
Date: 2020-12-16 00:00:00
Proceeding: IEEE Open Journal of the Communications Society (Conference version: IJCNN) 2020 12
Tasks: Federated Learning, Intrusion Detection, Network Intrusion Detection, Personalized Federated Learning

Predominant network intrusion detection systems (NIDS) aim to identify malicious traffic patterns based on a handcrafted dataset of rules. Recently, the application of machine learning in NIDS helps alleviate the enormous effort of human observation. Federated learning (FL) is a collaborative learning scheme concerning distributed data. Instead of sharing raw data, it allows a participant to share only a trained local model. Despite the success of existing FL solutions, in NIDS, a network's traffic data distribution does not always fit into the single global model of FL; some networks have similarities with each other but other networks do not. We propose Segmented-Federated Learning (Segmented-FL), where by employing periodic local model evaluation and network segmentation, we aim to bring similar network environments to the same group. A comparison between FL and our method was conducted against a range of metrics including the weighted precision, recall, and F1 score, using a collected dataset from 20 massively distributed networks within 60 days. By studying the optimized hyperparameters of Segmented-FL and employing three evaluation methods, it shows that Segmented-FL has better performance in all three types of intrusion detection tasks, achieving validation weighted F1 scores of 0.964, 0.803, and 0.912 with Method A, Method B, and Method C respectively. For each method, this scheme shows a gain of 0.1%, 4.0% and 1.1% in performance compared with FL.
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANs
Authors: Yuwei Sun, Hideya Ochiai, Hiroshi Esaki
Date: 2020-09-28 00:00:00
Proceeding: International Joint Conference on Neural Networks (IJCNN) 2020 9
Tasks: Federated Learning, Intrusion Detection, Network Intrusion Detection, Personalized Federated Learning

Traditional approaches to cybersecurity issues usually protect users from attacks after the occurrence of specific types of attacks. Besides, patterns of recent cyberattacks tend to be changeable, which add up to unpredictability of them. On the other hand, machine learning, as a new method used to detect intrusion, is attracting more and more attention. Moreover, through the sharing of local training data, the centralized learning approach has proven to improve a model's performance. In this research, a segmented federated learning is proposed, different from a collaborative learning based on single global model in a traditional federated learning model, it keeps multiple global models which allow each segment of participants to conduct collaborative learning separately and rearranges the segmentation of participants dynamically as well. Furthermore, these multiple global models interact with each other for updating parameters, thus being adaptable to various participants' LANs. A dataset covering two months' traffic data from 20 participants' LANs in the LAN-Security Monitoring Project is used. We adopt three types of knowledge-based methods for labeling network events and train a CNN model based on the dataset. At last, we achieve validation accuracies of 0.923, 0.813 and 0.877 individually with these labeling methods.
POINTVIEW-GCN: 3D SHAPE CLASSIFICATION WITH MULTI-VIEW POINT CLOUDS
Authors: Seyed Saber Mohammadi, Yiming Wang, Alessio Del Bue
Date: 2021-09-22 00:00:00
Proceeding: IEEE International Conference on Image Processing 2021 9
Tasks: 3D Point Cloud Classification, 3D Shape Classification, Classification, Object

We address 3D shape classification with partial point cloud inputs captured from multiple viewpoints around the object.
Different from existing methods that perform classification on the complete point cloud by first registering multi-view capturing, we propose PointView-GCN with multi-level Graph Convolutional Networks (GCNs) to hierarchically aggregate
the shape features of single-view point clouds, in order to encode both the geometrical cues of an object and their multi-
view relations. With experiments on our novel single-view datasets, we prove that PointView-GCN produces a more de-
scriptive global shape feature which stably improves the classification accuracy by ∼ 5% compared to the classifiers with
single-view point clouds, and outperforms the state-of-the-art methods with the complete point clouds on ModelNet40.
Machine learning identification of organic compounds using visible light
Authors: Thulasi Bikku, Rubén A. Fritz, Yamil J. Colón, Felipe Herrera
Date: 2022-04-06 00:00:00
Tasks: BIG-bench Machine Learning

Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical response of materials encodes enough electronic and vibrational information for remote chemical identification. This has been exploited using the fingerprint region of infrared absorption spectra, which involves a dense set of absorption peaks that are unique to individual molecules, thus facilitating chemical identification. However, optical identification using visible light has not been realized. Using decades of experimental refractive index data in the scientific literature of pure organic compounds and polymers over a broad range of frequencies from the ultraviolet to the far-infrared, we develop a machine learning classifier that can accurately identify organic species based on a single-wavelength dispersive measurement in the visible spectral region, away from absorption resonances. The optical classifier proposed here could be applied to autonomous material identification protocols or applications.
Analysis of a Spatially Correlated Vehicular Network Assisted by Cox-distributed Vehicle Relays
Authors: Chang-Sik Choi, François Baccelli
Date: 2022-04-26 00:00:00
Tasks: Point Processes

In vehicle-to-all (V2X) communications, roadside units (RSUs) play an essential role in connecting various network devices. In some cases, users may not be well-served by RSUs due to congestion, attenuation, or interference. In these cases, vehicular relays associated with RSUs can be used to serve those users. This paper uses stochastic geometry to model and analyze a spatially correlated heterogeneous vehicular network where both RSUs and vehicular relays serve network users such as pedestrians or other vehicles. We present an analytical model where the spatial correlation between roads, RSUs, relays, and users is systematically modeled via Cox point processes. Assuming users are associated with either RSUs or relays, we derive the association probability and the coverage probability of the typical user. Then, we derive the user throughput by considering interactions of links unique to the proposed network. This paper gives practical insights into designing spatially correlated vehicular networks assisted by vehicle relays. For instance, we express the network performance such as the user association, SIR coverage probability, and the network throughput as the functions of network key geometric variables. In practice, this helps one to optimize the network so as to achieve ultra reliability or maximum user throughput of a spatially correlated vehicular networks by varying key aspects such as the relay density or the bandwidth for relays.
Investigating the Optimal Neural Network Parameters for Decoding
Authors: Joshua Tshifhiwa Maumela
Date: 2022-04-20 00:00:00

Neural Networks have been proved to work as decoders in telecommunications, so the ways of making it efficient will be investigated in this thesis. The different parameters to maximize the Neural Network Decoder's efficiency will be investigated. The parameters will be tested for inversion errors only.