pwc-archive/papers-with-abstracts huggingface dataset:
mpNet: variable depth unfolded neural network for massive MIMO channel estimation
Authors: Taha Yassine, Luc Le Magoarou
Date: 2020-08-07 00:00:00

Massive multiple-input multiple-output (MIMO) communication systems have a huge potential both in terms of data rate and energy efficiency, although channel estimation becomes challenging for a large number of antennas. Using a physical model allows to ease the problem by injecting a priori information based on the physics of propagation. However, such a model rests on simplifying assumptions and requires to know precisely the configuration of the system, which is unrealistic in practice.In this paper we present mpNet, an unfolded neural network specifically designed for massive MIMO channel estimation. It is trained online in an unsupervised way. Moreover, mpNet is computationally efficient and automatically adapts its depth to the signal-to-noise ratio (SNR). The method we propose adds flexibility to physical channel models by allowing a base station (BS) to automatically correct its channel estimation algorithm based on incoming data, without the need for a separate offline training phase.It is applied to realistic millimeter wave channels and shows great performance, achieving a channel estimation error almost as low as one would get with a perfectly calibrated system. It also allows incident detection and automatic correction, making the BS resilient and able to automatically adapt to changes in its environment.
Knowing Depth Quality In Advance: A Depth Quality Assessment Method For RGB-D Salient Object Detection
Authors: Xuehao Wang, Shuai Li, Chenglizhao Chen, Aimin Hao, Hong Qin
Date: 2020-08-07 00:00:00
Tasks: object-detection, Object Detection, RGB-D Salient Object Detection, RGB Salient Object Detection, Salient Object Detection

Previous RGB-D salient object detection (SOD) methods have widely adopted deep learning tools to automatically strike a trade-off between RGB and D (depth), whose key rationale is to take full advantage of their complementary nature, aiming for a much-improved SOD performance than that of using either of them solely. However, such fully automatic fusions may not always be helpful for the SOD task because the D quality itself usually varies from scene to scene. It may easily lead to a suboptimal fusion result if the D quality is not considered beforehand. Moreover, as an objective factor, the D quality has long been overlooked by previous work. As a result, it is becoming a clear performance bottleneck. Thus, we propose a simple yet effective scheme to measure D quality in advance, the key idea of which is to devise a series of features in accordance with the common attributes of high-quality D regions. To be more concrete, we conduct D quality assessments for each image region, following a multi-scale methodology that includes low-level edge consistency, mid-level regional uncertainty and high-level model variance. All these components will be computed independently and then be assembled with RGB and D features, applied as implicit indicators, to guide the selective fusion. Compared with the state-of-the-art fusion schemes, our method can achieve a more reasonable fusion status between RGB and D. Specifically, the proposed D quality measurement method achieves steady performance improvements for almost 2.0\% in general.
Recursive Multi-model Complementary Deep Fusion forRobust Salient Object Detection via Parallel Sub Networks
Authors: Zhen-Yu Wu, Shuai Li, Chenglizhao Chen, Aimin Hao, Hong Qin
Date: 2020-08-07 00:00:00
Tasks: Diversity, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Fully convolutional networks have shown outstanding performance in the salient object detection (SOD) field. The state-of-the-art (SOTA) methods have a tendency to become deeper and more complex, which easily homogenize their learned deep features, resulting in a clear performance bottleneck. In sharp contrast to the conventional ``deeper'' schemes, this paper proposes a ``wider'' network architecture which consists of parallel sub networks with totally different network architectures. In this way, those deep features obtained via these two sub networks will exhibit large diversity, which will have large potential to be able to complement with each other. However, a large diversity may easily lead to the feature conflictions, thus we use the dense short-connections to enable a recursively interaction between the parallel sub networks, pursuing an optimal complementary status between multi-model deep features. Finally, all these complementary multi-model deep features will be selectively fused to make high-performance salient object detections. Extensive experiments on several famous benchmarks clearly demonstrate the superior performance, good generalization, and powerful learning ability of the proposed wider framework.
Depth Quality Aware Salient Object Detection
Authors: Chenglizhao Chen, Jipeng Wei, Chong Peng, Hong Qin
Date: 2020-08-07 00:00:00
Tasks: Object, object-detection, Object Detection, RGB-D Salient Object Detection, RGB Salient Object Detection, Salient Object Detection

The existing fusion based RGB-D salient object detection methods usually adopt the bi-stream structure to strike the fusion trade-off between RGB and depth (D). The D quality usually varies from scene to scene, while the SOTA bi-stream approaches are depth quality unaware, which easily result in substantial difficulties in achieving complementary fusion status between RGB and D, leading to poor fusion results in facing of low-quality D. Thus, this paper attempts to integrate a novel depth quality aware subnet into the classic bi-stream structure, aiming to assess the depth quality before conducting the selective RGB-D fusion. Compared with the SOTA bi-stream methods, the major highlight of our method is its ability to lessen the importance of those low-quality, no-contribution, or even negative-contribution D regions during the RGB-D fusion, achieving a much improved complementary status between RGB and D.
Adversarial Examples on Object Recognition: A Comprehensive Survey
Authors: Alex Serban, Erik Poll, Joost Visser
Date: 2020-08-07 00:00:00
Tasks: BIG-bench Machine Learning, Object, Object Recognition, Survey

Deep neural networks are at the forefront of machine learning research. However, despite achieving impressive performance on complex tasks, they can be very sensitive: Small perturbations of inputs can be sufficient to induce incorrect behavior. Such perturbations, called adversarial examples, are intentionally designed to test the network's sensitivity to distribution drifts. Given their surprisingly small size, a wide body of literature conjectures on their existence and how this phenomenon can be mitigated. In this article we discuss the impact of adversarial examples on security, safety, and robustness of neural networks. We start by introducing the hypotheses behind their existence, the methods used to construct or protect against them, and the capacity to transfer adversarial examples between different machine learning models. Altogether, the goal is to provide a comprehensive and self-contained survey of this growing field of research.
Fighting Deepfake by Exposing the Convolutional Traces on Images
Authors: Luca Guarnera, Oliver Giudice, Sebastiano Battiato
Date: 2020-08-07 00:00:00
Tasks: DeepFake Detection, Face Swapping, Image Generation

Advances in Artificial Intelligence and Image Processing are changing the way people interacts with digital images and video. Widespread mobile apps like FACEAPP make use of the most advanced Generative Adversarial Networks (GAN) to produce extreme transformations on human face photos such gender swap, aging, etc. The results are utterly realistic and extremely easy to be exploited even for non-experienced users. This kind of media object took the name of Deepfake and raised a new challenge in the multimedia forensics field: the Deepfake detection challenge. Indeed, discriminating a Deepfake from a real image could be a difficult task even for human eyes but recent works are trying to apply the same technology used for generating images for discriminating them with preliminary good results but with many limitations: employed Convolutional Neural Networks are not so robust, demonstrate to be specific to the context and tend to extract semantics from images. In this paper, a new approach aimed to extract a Deepfake fingerprint from images is proposed. The method is based on the Expectation-Maximization algorithm trained to detect and extract a fingerprint that represents the Convolutional Traces (CT) left by GANs during image generation. The CT demonstrates to have high discriminative power achieving better results than state-of-the-art in the Deepfake detection task also proving to be robust to different attacks. Achieving an overall classification accuracy of over 98%, considering Deepfakes from 10 different GAN architectures not only involved in images of faces, the CT demonstrates to be reliable and without any dependence on image semantic. Finally, tests carried out on Deepfakes generated by FACEAPP achieving 93% of accuracy in the fake detection task, demonstrated the effectiveness of the proposed technique on a real-case scenario.
Impact of meta-roles on the evolution of organisational institutions
Authors: Amir Hosein Afshar Sedigh, Martin K. Purvis, Bastin Tony Roy Savarimuthu, Maryam A. Purvis, Christopher K. Frantz
Date: 2020-08-07 00:00:00

This paper investigates the impact of changes in agents' beliefs coupled with dynamics in agents' meta-roles on the evolution of institutions. The study embeds agents' meta-roles in the BDI architecture. In this context, the study scrutinises the impact of cognitive dissonance in agents due to unfairness of institutions. To showcase our model, two historical long-distance trading societies, namely Armenian merchants of New-Julfa and the English East India Company are simulated. Results show how change in roles of agents coupled with specific institutional characteristics leads to changes of the rules in the system.
Review of Swarm Intelligence-based Feature Selection Methods
Authors: Mehrdad Rostami, Kamal Berahmand, Saman Forouzandeh
Date: 2020-08-07 00:00:00
Tasks: Dimensionality Reduction, feature selection

In the past decades, the rapid growth of computer and database technologies has led to the rapid growth of large-scale datasets. On the other hand, data mining applications with high dimensional datasets that require high speed and accuracy are rapidly increasing. An important issue with these applications is the curse of dimensionality, where the number of features is much higher than the number of patterns. One of the dimensionality reduction approaches is feature selection that can increase the accuracy of the data mining task and reduce its computational complexity. The feature selection method aims at selecting a subset of features with the lowest inner similarity and highest relevancy to the target class. It reduces the dimensionality of the data by eliminating irrelevant, redundant, or noisy data. In this paper, a comparative analysis of different feature selection methods is presented, and a general categorization of these methods is performed. Moreover, in this paper, state-of-the-art swarm intelligence are studied, and the recent feature selection methods based on these algorithms are reviewed. Furthermore, the strengths and weaknesses of the different studied swarm intelligence-based feature selection methods are evaluated.
Incremental maintenance of overgrounded logic programs with tailored simplifications
Authors: Giovambattista Ianni, Francesco Pacenza, Jessica Zangari
Date: 2020-08-06 00:00:00

The repeated execution of reasoning tasks is desirable in many applicative scenarios, such as stream reasoning and event processing. When using answer set programming in such contexts, one can avoid the iterative generation of ground programs thus achieving a significant payoff in terms of computing time. However, this may require some additional amount of memory and/or the manual addition of operational directives in the declarative knowledge base at hand. We introduce a new strategy for generating series of monotonically growing propositional programs. The proposed overgrounded programs with tailoring (OPTs) can be updated and reused in combination with consecutive inputs. With respect to earlier approaches, our tailored simplification technique reduces the size of instantiated programs. A maintained OPT slowly grows in size from an iteration to another while the update cost decreases, especially in later iterations. In this paper we formally introduce tailored embeddings, a family of equivalence-preserving ground programs which are at the theoretical basis of OPTs and we describe their properties. We then illustrate an OPT update algorithm and report about our implementation and its performance. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
Phonological Features for 0-shot Multilingual Speech Synthesis
Authors: Marlene Staib, Tian Huey Teh, Alexandra Torresquintero, Devang S Ram Mohan, Lorenzo Foglianti, Raphael Lenain, Jiameng Gao
Date: 2020-08-06 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

Code-switching---the intra-utterance use of multiple languages---is prevalent across the world. Within text-to-speech (TTS), multilingual models have been found to enable code-switching. By modifying the linguistic input to sequence-to-sequence TTS, we show that code-switching is possible for languages unseen during training, even within monolingual models. We use a small set of phonological features derived from the International Phonetic Alphabet (IPA), such as vowel height and frontness, consonant place and manner. This allows the model topology to stay unchanged for different languages, and enables new, previously unseen feature combinations to be interpreted by the model. We show that this allows us to generate intelligible, code-switched speech in a new language at test time, including the approximation of sounds never seen in training.
From the logistic-sigmoid to nlogistic-sigmoid: modelling the COVID-19 pandemic growth
Authors: Oluwasegun A. Somefun, Kayode Akingbade, Folasade Dahunsi
Date: 2020-08-06 00:00:00
Tasks: COVID-19 Modelling, Time Series, Time Series Analysis

Real-world growth processes, such as epidemic growth, are inherently noisy, uncertain and often involve multiple growth phases. The logistic-sigmoid function has been suggested and applied in the domain of modelling such growth processes. However, existing definitions are limiting, as they do not consider growth as restricted in two-dimension. Additionally, as the number of growth phases increase, the modelling and estimation of logistic parameters becomes more cumbersome, requiring more complex tools and analysis. To remedy this, we introduce the nlogistic-sigmoid function as a compact, unified modern definition of logistic growth for modelling such real-world growth phenomena. Also, we introduce two characteristic metrics of the logistic-sigmoid curve that can give more robust projections on the state of the growth process in each dimension. Specifically, we apply this function to modelling the daily World Health Organization published COVID-19 time-series data of infection and death cases of the world and countries of the world to date. Our results demonstrate statistically significant goodness of fit greater than or equal to 99% for affected countries of the world exhibiting patterns of either single or multiple stages of the ongoing COVID-19 outbreak, such as the USA. Consequently, this modern logistic definition and its metrics, as a machine learning tool, can help to provide clearer and more robust monitoring and quantification of the ongoing pandemic growth process.
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents
Authors: Abdul Mueed Hafiz, Ghulam Mohiuddin Bhat
Date: 2020-08-06 00:00:00
Tasks: Multi-agent Reinforcement Learning, OpenAI Gym, Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

Deep Q-Network (DQN) based multi-agent systems (MAS) for reinforcement learning (RL) use various schemes where in the agents have to learn and communicate. The learning is however specific to each agent and communication may be satisfactorily designed for the agents. As more complex Deep QNetworks come to the fore, the overall complexity of the multi-agent system increases leading to issues like difficulty in training, need for higher resources and more training time, difficulty in fine-tuning, etc. To address these issues we propose a simple but efficient DQN based MAS for RL which uses shared state and rewards, but agent-specific actions, for updation of the experience replay pool of the DQNs, where each agent is a DQN. The benefits of the approach are overall simplicity, faster convergence and better performance as compared to conventional DQN based approaches. It should be noted that the method can be extended to any DQN. As such we use simple DQN and DDQN (Double Q-learning) respectively on three separate tasks i.e. Cartpole-v1 (OpenAI Gym environment) , LunarLander-v2 (OpenAI Gym environment) and Maze Traversal (customized environment). The proposed approach outperforms the baseline on these tasks by decent margins respectively.
Data Minimization for GDPR Compliance in Machine Learning Models
Authors: Abigail Goldsteen, Gilad Ezov, Ron Shmelkin, Micha Moffie, Ariel Farkash
Date: 2020-08-06 00:00:00
Tasks: BIG-bench Machine Learning

The EU General Data Protection Regulation (GDPR) mandates the principle of data minimization, which requires that only data necessary to fulfill a certain purpose be collected. However, it can often be difficult to determine the minimal amount of data required, especially in complex machine learning models such as neural networks. We present a first-of-a-kind method to reduce the amount of personal data needed to perform predictions with a machine learning model, by removing or generalizing some of the input features. Our method makes use of the knowledge encoded within the model to produce a generalization that has little to no impact on its accuracy. This enables the creators and users of machine learning models to acheive data minimization, in a provable manner.
The Chess Transformer: Mastering Play using Generative Language Models
Authors: David Noever, Matt Ciolino, Josh Kalin
Date: 2020-08-02 00:00:00

This work demonstrates that natural language transformers can support more generic strategic modeling, particularly for text-archived games. In addition to learning natural language skills, the abstract transformer architecture can generate meaningful moves on a chessboard. With further fine-tuning, the transformer learns complex gameplay by training on 2.8 million chess games in Portable Game Notation. After 30,000 training steps, OpenAI's Generative Pre-trained Transformer (GPT-2) optimizes weights for 774 million parameters. This fine-tuned Chess Transformer generates plausible strategies and displays game formations identifiable as classic openings, such as English or the Slav Exchange. Finally, in live play, the novel model demonstrates a human-to-transformer interface that correctly filters illegal moves and provides a novel method to challenge the transformer's chess strategies. We anticipate future work will build on this transformer's promise, particularly in other strategy games where features can capture the underlying complex rule syntax from simple but expressive player annotations.
Distributed optimization for nonrigid nano-tomography
Authors: Viktor Nikitin, Vincent De Andrade, Azat Slyamov, Benjamin J. Gould, Yuepeng Zhang, Vandana Sampathkumar, Narayanan Kasthuri, Doga Gursoy, Francesco De Carlo
Date: 2020-07-11 00:00:00
Tasks: Distributed Optimization, Optical Flow Estimation

Resolution level and reconstruction quality in nano-computed tomography (nano-CT) are in part limited by the stability of microscopes, because the magnitude of mechanical vibrations during scanning becomes comparable to the imaging resolution, and the ability of the samples to resist beam damage during data acquisition. In such cases, there is no incentive in recovering the sample state at different time steps like in time-resolved reconstruction methods, but instead the goal is to retrieve a single reconstruction at the highest possible spatial resolution and without any imaging artifacts. Here we propose a joint solver for imaging samples at the nanoscale with projection alignment, unwarping and regularization. Projection data consistency is regulated by dense optical flow estimated by Farneback's algorithm, leading to sharp sample reconstructions with less artifacts. Synthetic data tests show robustness of the method to Poisson and low-frequency background noise. Applicability of the method is demonstrated on two large-scale nano-imaging experimental data sets.
Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets
Authors: Sandesh Ghimire, Satyananda Kashyap, Joy T. Wu, Alexandros Karargyris, Mehdi Moradi
Date: 2020-08-04 00:00:00

Chest radiography is the most common medical image examination for screening and diagnosis in hospitals. Automatic interpretation of chest X-rays at the level of an entry-level radiologist can greatly benefit work prioritization and assist in analyzing a larger population. Subsequently, several datasets and deep learning-based solutions have been proposed to identify diseases based on chest X-ray images. However, these methods are shown to be vulnerable to shift in the source of data: a deep learning model performing well when tested on the same dataset as training data, starts to perform poorly when it is tested on a dataset from a different source. In this work, we address this challenge of generalization to a new source by forcing the network to learn a source-invariant representation. By employing an adversarial training strategy, we show that a network can be forced to learn a source-invariant representation. Through pneumonia-classification experiments on multi-source chest X-ray datasets, we show that this algorithm helps in improving classification accuracy on a new source of X-ray dataset.
Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Belt
Authors: Mohsen Shahhosseini, Guiping Hu, Sotirios V. Archontoulis, Isaiah Huber
Date: 2020-07-28 00:00:00
Tasks: BIG-bench Machine Learning, Crop Yield Prediction, Feature Importance

This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.