pwc-archive/papers-with-abstracts huggingface dataset:
V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs
Authors: Christian M. M. Frey, Matthias Schubert
Date: 2022-07-22 00:00:00
Tasks: Common Sense Reasoning, Disentanglement, Knowledge Graphs, Learning Theory, Link Prediction

Semantic Web or Knowledge Graphs (KG) emerged to one of the most important information source for intelligent systems requiring access to structured knowledge. One of the major challenges is the extraction and processing of unambiguous information from textual data. Following the human perception, overlapping semantic linkages between two named entities become clear due to our common-sense about the context a relationship lives in which is not the case when we look at it from an automatically driven process of a machine. In this work, we are interested in the problem of Relational Resolution within the scope of KGs, i.e, we are investigating the inherent semantic of relationships between entities within a network. We propose a new adaptive AutoEncoder, called V-Coder, to identify relations inherently connecting entities from different domains. Those relations can be considered as being ambiguous and are candidates for disentanglement. Likewise to the Adaptive Learning Theory (ART), our model learns new patterns from the KG by increasing units in a competitive layer without discarding the previous observed patterns whilst learning the quality of each relation separately. The evaluation on real-world datasets of Freebase, Yago and NELL shows that the V-Coder is not only able to recover links from corrupted input data, but also shows that the semantic disclosure of relations in a KG show the tendency to improve link prediction. A semantic evaluation wraps the evaluation up.
From Single Aircraft to Communities: A Neutral Interpretation of Air Traffic Complexity Dynamics
Authors: Ralvi Isufaj, Marsel Omeri, Miquel Angel Piera, Jaume Saez Valls, Christian Eduardo Verdonk Gallego
Date: 2022-07-15 00:00:00
Tasks: Decision Making, Fairness, Management

Present air traffic complexity metrics are defined considering the interests of different management layers of ATM. These layers have different objectives which in practice compete to maximize their own goals, which leads to fragmented decision making. This fragmentation together with competing KPAs requires transparent and neutral air traffic information to pave the way for an explainable set of actions. In this paper, we introduce the concept of single aircraft complexity, to determine the contribution of each aircraft to the overall complexity of air traffic. Furthermore, we describe a methodology extending this concept to define complex communities, which are groups of interdependent aircraft that contribute the majority of the complexity in a certain airspace. In order to showcase the methodology, a tool that visualizes different outputs of the algorithm is developed. Through use-cases based on synthetic and real historical traffic, we first show that the algorithm can serve to formalize controller decisions as well as guide controllers to better decisions. Further, we investigate how the provided information can be used to increase transparency of the decision makers towards different airspace users, which serves also to increase fairness and equity. Lastly, a sensitivity analysis is conducted in order to systematically analyse how each input affects the methodology.
Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images
Authors: Zeba Ghaffar, Pir Masoom Shah, Hikmat Khan, Syed Farhan Alam Zaidi, Abdullah Gani, Izaz Ahmad Khan, Munam Ali Shah, Saif ul Islam
Date: 2022-07-01 00:00:00

The ongoing COVID-19 pandemic has already taken millions of lives and damaged economies across the globe. Most COVID-19 deaths and economic losses are reported from densely crowded cities. It is comprehensible that the effective control and prevention of epidemic/pandemic infectious diseases is vital. According to WHO, testing and diagnosis is the best strategy to control pandemics. Scientists worldwide are attempting to develop various innovative and cost-efficient methods to speed up the testing process. This paper comprehensively evaluates the applicability of the recent top ten state-of-the-art Deep Convolutional Neural Networks (CNNs) for automatically detecting COVID-19 infection using chest X-ray images. Moreover, it provides a comparative analysis of these models in terms of accuracy. This study identifies the effective methodologies to control and prevent infectious respiratory diseases. Our trained models have demonstrated outstanding results in classifying the COVID-19 infected chest x-rays. In particular, our trained models MobileNet, EfficentNet, and InceptionV3 achieved a classification average accuracy of 95\%, 95\%, and 94\% test set for COVID-19 class classification, respectively. Thus, it can be beneficial for clinical practitioners and radiologists to speed up the testing, detection, and follow-up of COVID-19 cases.
CCTV-Exposure: An open-source system for measuring user's privacy exposure to mapped CCTV cameras based on geo-location (Extended Version)
Authors: Hannu Turtiainen, Andrei Costin, Timo Hamalainen
Date: 2022-07-02 00:00:00

In this work, we present CCTV-Exposure -- the first CCTV-aware solution to evaluate potential privacy exposure to closed-circuit television (CCTV) cameras. The objective was to develop a toolset for quantifying human exposure to CCTV cameras from a privacy perspective. Our novel approach is trajectory analysis of the individuals, coupled with a database of geo-location mapped CCTV cameras annotated with minimal yet sufficient meta-information. For this purpose, CCTV-Exposure model based on a Global Positioning System (GPS) tracking was applied to estimate individual privacy exposure in different scenarios. The current investigation provides an application example and validation of the modeling approach. The methodology and toolset developed and implemented in this work provide time-sequence and location-sequence of the exposure events, thus making possible association of the exposure with the individual activities and cameras, and delivers main statistics on individual's exposure to CCTV cameras with high spatio-temporal resolution.
Machine Learning-Based Approaches For Breast Cancer Detection in Microwave Imaging
Authors: Humza Sami
Date: 2021-02-10 00:00:00
Proceeding: IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium) 2021 2
Tasks: BIG-bench Machine Learning, Binary Classification, Breast Cancer Detection

Detection of breast cancer at an early stage can significantly reduce the mortality rate. Microwave imaging is a promising detection tool for harmless and non-ionizing screening of breast cancer. In this work, a fast and accurate machine learning algorithm is proposed for the prediction of the breast lesion using microwave signals. Machine learning has proved itself reliable in the field of biomedical application where the diagnosis of the disease is desired. The support vector machine (SVM) algorithm with the linear and polynomial kernel is trained and tested on raw backscattered signals data. SVM with third-degree polynomial kernel obtained 99.7% accuracy that outperforms the existing conventional machine learning binary classification algorithms. Thus, the prediction of tumor presence would help the radiologist to diagnose tumor correctly at early stages.
Multimodal Neurons in Artificial Neural Networks
Authors: Gabriel Goh, Nick Cammarata, Chelsea Voss, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, Chris Olah
Date: 2021-09-02 00:00:00
Proceeding: Distill 2021 9

In 2005, a letter published in Nature described human neurons responding to specific people, such as Jennifer Aniston or Halle Berry . The exciting thing wasn’t just that they selected for particular people, but that they did so regardless of whether they were shown photographs, drawings, or even images of the person’s name. The neurons were multimodal. As the lead author would put it: "You are looking at the far end of the transformation from metric, visual shapes to conceptual… information." Quiroga's full quote, from reads: "I think that’s the excitement to these results. You are looking at the far end of the transformation from metric, visual shapes to conceptual memory-related information. It is that transformation that underlies our ability to understand the world. It’s not enough to see something familiar and match it. It’s the fact that you plug visual information into the rich tapestry of memory that brings it to life." We elided the portion discussing memory since it was less relevant.

We report the existence of similar multimodal neurons in artificial neural networks. This includes neurons selecting for prominent public figures or fictional characters, such as Lady Gaga or Spiderman.
Color information for region segmentation
Authors: Yu-Ichi Ohta, Takeo Kanade, Toshiyuki Sakai
Date: 2004-10-04 00:00:00
Proceeding: ScienceDirect 2004 10
Tasks: Segmentation

In color image processing various kinds of color features can be calculated from the tristimuli R, G, and B. We attempt to derive a set of effective color features by systematic experiments of region segmentation. An Ohlander-type segmentation algorithm by recursive thresholding is employed as a tool for the experiment. At each step of segmenting a region, new color features are calculated for the pixels in that region by the Karhunen Loeve transformation of R, G, and B data. By analyzing more than 100 color features which are thus obtained during segmenting eight kinds of color pictures, we have found that a set of color features,  (R + G + B)3, R - B, and (2G - R - B)3, are effective. These three features are significant in this order and in many cases a good segmentation can be achieved by using only the first two. The effectiveness of our color feature set is discussed by a comparative study with various other sets of color features which are commonly used in image analysis. The comparison is performed in terms of both the quality of segmentation results and the calculation involved in transforming data of R, G, and B to other forms.
Feature Visualization
Authors: Chris Olah, Alexander Mordvintsev, Ludwig Schubert
Date: 2020-01-10 00:00:00
Proceeding: Distill 2020 1

There is a growing sense that neural networks need to be interpretable to humans. The field of neural network interpretability has formed in response to these concerns. As it matures, two major threads of research have begun to coalesce: feature visualization and attribution. 

This article focuses on feature visualization. While feature visualization is a powerful tool, actually getting it to work involves a number of details. In this article, we examine the major issues and explore common approaches to solving them. We find that remarkably simple methods can produce high-quality visualizations. Along the way we introduce a few tricks for exploring variation in what neurons react to, how they interact, and how to improve the optimization process.
Differentiable Image Parameterizations
Authors: Alexander Mordvintsev, Nicola Pezzotti, Ludwig Schubert, Chris Olah
Date: 2018-07-25 00:00:00
Proceeding: Distill 2018 7
Tasks: Image Generation

Typically, we parameterize the input image as the RGB values of each pixel, but that isn’t the only way. As long as the mapping from parameters to images is differentiable, we can still optimize alternative parameterizations with gradient descent. 

Differentiable image parameterizations invite us to ask “what kind of image generation process can we backpropagate through?” The answer is quite a lot, and some of the more exotic possibilities can create a wide range of interesting effects, including 3D neural art, images with transparency, and aligned interpolation. Previous work using specific unusual image parameterizations has shown exciting results — we think that zooming out and looking at this area as a whole suggests there’s even more potential.
The Building Blocks of Interpretability
Authors: Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, Alexander Mordvintsev
Date: 2018-03-06 00:00:00
Proceeding: Distill 2018 3

In this article, we treat existing interpretability methods as fundamental and composable building blocks for rich user interfaces. We find that these disparate techniques now come together in a unified grammar, fulfilling complementary roles in the resulting interfaces. Moreover, this grammar allows us to systematically explore the space of interpretability interfaces, enabling us to evaluate whether they meet particular goals. We will present interfaces that show what the network detects and explain how it develops its understanding, while keeping the amount of information human-scale. For example, we will see how a network looking at a labrador retriever detects floppy ears and how that influences its classification.
LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor
Authors: Alberto Viale, Alberto Marchisio, Maurizio Martina, Guido Masera, Muhammad Shafique
Date: 2022-08-03 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Lane Detection

Autonomous Driving (AD) related features represent important elements for the next generation of mobile robots and autonomous vehicles focused on increasingly intelligent, autonomous, and interconnected systems. The applications involving the use of these features must provide, by definition, real-time decisions, and this property is key to avoid catastrophic accidents. Moreover, all the decision processes must require low power consumption, to increase the lifetime and autonomy of battery-driven systems. These challenges can be addressed through efficient implementations of Spiking Neural Networks (SNNs) on Neuromorphic Chips and the use of event-based cameras instead of traditional frame-based cameras. In this paper, we present a new SNN-based approach, called LaneSNN, for detecting the lanes marked on the streets using the event-based camera input. We develop four novel SNN models characterized by low complexity and fast response, and train them using an offline supervised learning rule. Afterward, we implement and map the learned SNNs models onto the Intel Loihi Neuromorphic Research Chip. For the loss function, we develop a novel method based on the linear composition of Weighted binary Cross Entropy (WCE) and Mean Squared Error (MSE) measures. Our experimental results show a maximum Intersection over Union (IoU) measure of about 0.62 and very low power consumption of about 1 W. The best IoU is achieved with an SNN implementation that occupies only 36 neurocores on the Loihi processor while providing a low latency of less than 8 ms to recognize an image, thereby enabling real-time performance. The IoU measures provided by our networks are comparable with the state-of-the-art, but at a much low power consumption of 1 W.
Evolutionary bagging for ensemble learning
Authors: Giang Ngo, Rodney Beard, Rohitash Chandra
Date: 2022-08-04 00:00:00
Tasks: BIG-bench Machine Learning, Diversity, Ensemble Learning, Evolutionary Algorithms

Ensemble learning has gained success in machine learning with major advantages over other learning methods. Bagging is a prominent ensemble learning method that creates subgroups of data, known as bags, that are trained by individual machine learning methods such as decision trees. Random forest is a prominent example of bagging with additional features in the learning process. Evolutionary algorithms have been prominent for optimisation problems and also been used for machine learning. Evolutionary algorithms are gradient-free methods that work with a population of candidate solutions that maintain diversity for creating new solutions. In conventional bagged ensemble learning, the bags are created once and the content, in terms of the training examples, are fixed over the learning process. In our paper, we propose evolutionary bagged ensemble learning, where we utilise evolutionary algorithms to evolve the content of the bags in order to iteratively enhance the ensemble by providing diversity in the bags. The results show that our evolutionary ensemble bagging method outperforms conventional ensemble methods (bagging and random forests) for several benchmark datasets under certain constraints. We find that evolutionary bagging can inherently sustain a diverse set of bags without reduction in performance accuracy.
Adaptive Latent Factor Analysis via Generalized Momentum-Incorporated Particle Swarm Optimization
Authors: Jiufang Chen, Ye Yuan
Date: 2022-08-04 00:00:00

Stochastic gradient descent (SGD) algorithm is an effective learning strategy to build a latent factor analysis (LFA) model on a high-dimensional and incomplete (HDI) matrix. A particle swarm optimization (PSO) algorithm is commonly adopted to make an SGD-based LFA model's hyper-parameters, i.e, learning rate and regularization coefficient, self-adaptation. However, a standard PSO algorithm may suffer from accuracy loss caused by premature convergence. To address this issue, this paper incorporates more historical information into each particle's evolutionary process for avoiding premature convergence following the principle of a generalized-momentum (GM) method, thereby innovatively achieving a novel GM-incorporated PSO (GM-PSO). With it, a GM-PSO-based LFA (GMPL) model is further achieved to implement efficient self-adaptation of hyper-parameters. The experimental results on three HDI matrices demonstrate that the GMPL model achieves a higher prediction accuracy for missing data estimation in industrial applications.
Neuro-symbolic computing with spiking neural networks
Authors: Dominik Dold, Josep Soler Garrido, Victor Caceres Chian, Marcel Hildebrandt, Thomas Runkler
Date: 2022-08-04 00:00:00
Tasks: Graph Embedding, Graph Neural Network, Knowledge Graphs, Open-Ended Question Answering

Knowledge graphs are an expressive and widely used data structure due to their ability to integrate data from different domains in a sensible and machine-readable way. Thus, they can be used to model a variety of systems such as molecules and social networks. However, it still remains an open question how symbolic reasoning could be realized in spiking systems and, therefore, how spiking neural networks could be applied to such graph data. Here, we extend previous work on spike-based graph algorithms by demonstrating how symbolic and multi-relational information can be encoded using spiking neurons, allowing reasoning over symbolic structures like knowledge graphs with spiking neural networks. The introduced framework is enabled by combining the graph embedding paradigm and the recent progress in training spiking neural networks using error backpropagation. The presented methods are applicable to a variety of spiking neuron models and can be trained end-to-end in combination with other differentiable network architectures, which we demonstrate by implementing a spiking relational graph neural network.
Hybrid cuckoo search algorithm for the minimum dominating set problem
Authors: Belkacem Zouilekh, Sadek Bouroubi
Date: 2022-06-28 00:00:00
Tasks: Game of Chess

The notions of dominating sets of graphs began almost 400 years ago with the game of chess, which sparked the analysis of dominating sets of graphs, at first relatively loosely until the beginnings of the 1960s, when the issue was given mathematical description. It's among the most important problems in graph theory, as well as an NP-Complete problem that can't be solved in polynomial time. As a result, we describe a new hybrid cuckoo search technique to tackle the MDS problem in this work. Cuckoo search is a well-known metaheuristic famed for its capacity for exploring a large area of the search space, making it useful for diversification. However, to enhance performance, we incorporated intensification techniques in addition to the genetic crossover operator in the suggested approach. The comparison of our method with the corresponding state-of-the-art techniques from the literature is presented in an exhaustive experimental test. The suggested algorithm outperforms the present state of the art, according to the obtained results.
Modular Grammatical Evolution for the Generation of Artificial Neural Networks
Authors: Khabat Soltanian, Ali Ebnenasir, Mohsen Afsharchi
Date: 2022-08-04 00:00:00

This paper presents a novel method, called Modular Grammatical Evolution (MGE), towards validating the hypothesis that restricting the solution space of NeuroEvolution to modular and simple neural networks enables the efficient generation of smaller and more structured neural networks while providing acceptable (and in some cases superior) accuracy on large data sets. MGE also enhances the state-of-the-art Grammatical Evolution (GE) methods in two directions. First, MGE's representation is modular in that each individual has a set of genes, and each gene is mapped to a neuron by grammatical rules. Second, the proposed representation mitigates two important drawbacks of GE, namely the low scalability and weak locality of representation, towards generating modular and multi-layer networks with a high number of neurons. We define and evaluate five different forms of structures with and without modularity using MGE and find single-layer modules with no coupling more productive. Our experiments demonstrate that modularity helps in finding better neural networks faster. We have validated the proposed method using ten well-known classification benchmarks with different sizes, feature counts, and output class count. Our experimental results indicate that MGE provides superior accuracy with respect to existing NeuroEvolution methods and returns classifiers that are significantly simpler than other machine learning generated classifiers. Finally, we empirically demonstrate that MGE outperforms other GE methods in terms of locality and scalability properties.
The Role of Morphological Variation in Evolutionary Robotics: Maximizing Performance and Robustness
Authors: Jonata Tyska Carvalho, Stefano Nolfi
Date: 2022-08-04 00:00:00

Exposing an Evolutionary Algorithm that is used to evolve robot controllers to variable conditions is necessary to obtain solutions which are robust and can cross the reality gap. However, we do not yet have methods for analyzing and understanding the impact of the varying morphological conditions which impact the evolutionary process, and therefore for choosing suitable variation ranges. By morphological conditions, we refer to the starting state of the robot, and to variations in its sensor readings during operation due to noise. In this article, we introduce a method that permits us to measure the impact of these morphological variations and we analyze the relation between the amplitude of variations, the modality with which they are introduced, and the performance and robustness of evolving agents. Our results demonstrate that (i) the evolutionary algorithm can tolerate morphological variations which have a very high impact, (ii) variations affecting the actions of the agent are tolerated much better than variations affecting the initial state of the agent or of the environment, and (iii) improving the accuracy of the fitness measure through multiple evaluations is not always useful. Moreover, our results show that morphological variations permit generating solutions which perform better both in varying and non-varying conditions.