OpenRaiser/Intern-Atlas huggingface dataset:
Hyperspectral Image Restoration via Global Total Variation Regularized Local nonconvex Low-Rank matrix Approximation
Venue: arXiv cs.CV 2020
Year: 2020

Several bandwise total variation (TV) regularized low-rank (LR)-based models have been proposed to remove mixed noise in hyperspectral images (HSIs). Conventionally, the rank of LR matrix is approximated using nuclear norm (NN). The NN is defined by adding all singular values together, which is essentially a $L_1$-norm of the singular values. It results in non-negligible approximation errors and thus the resulting matrix estimator can be significantly biased. Moreover, these bandwise TV-based methods exploit the spatial information in a separate manner. To cope with these problems, we propose a spatial-spectral TV (SSTV) regularized non-convex local LR matrix approximation (NonLLRTV) method to remove mixed noise in HSIs. From one aspect, local LR of HSIs is formulated using a non-convex $L_γ$-norm, which provides a closer approximation to the matrix rank than the traditional NN. From another aspect, HSIs are assumed to be piecewisely smooth in the global spatial domain. The TV regularization is effective in preserving the smoothness and removing Gaussian noise. These facts inspire the integration of the NonLLR with TV regularization. To address the limitations of bandwise TV, we use the SSTV regularization to simultaneously consider global spatial structure and spectral correlation of neighboring bands. Experiment results indicate that the use of local non-convex penalty and global SSTV can boost the preserving of spatial piecewise smoothness and overall structural information.
Swarm Programming Using Moth-Flame Optimization and Whale Optimization Algorithms
Venue: arXiv cs.NE 2020
Year: 2020

Automatic programming (AP) is an important area of Machine Learning (ML) where computer programs are generated automatically. Swarm Programming (SP), a newly emerging research area in AP, automatically generates the computer programs using Swarm Intelligence (SI) algorithms. This paper presents two grammar-based SP methods named as Grammatical Moth-Flame Optimizer (GMFO) and Grammatical Whale Optimizer (GWO). The Moth-Flame Optimizer and Whale Optimization algorithm are used as search engines or learning algorithms in GMFO and GWO respectively. The proposed methods are tested on Santa Fe Ant Trail, quartic symbolic regression, and 3-input multiplexer problems. The results are compared with Grammatical Bee Colony (GBC) and Grammatical Fireworks algorithm (GFWA). The experimental results demonstrate that the proposed SP methods can be used in automatic computer program generation.
A Hybrid Method for Training Convolutional Neural Networks
Venue: arXiv cs.NE 2020
Year: 2020

Artificial Intelligence algorithms have been steadily increasing in popularity and usage. Deep Learning, allows neural networks to be trained using huge datasets and also removes the need for human extracted features, as it automates the feature learning process. In the hearth of training deep neural networks, such as Convolutional Neural Networks, we find backpropagation, that by computing the gradient of the loss function with respect to the weights of the network for a given input, it allows the weights of the network to be adjusted to better perform in the given task. In this paper, we propose a hybrid method that uses both backpropagation and evolutionary strategies to train Convolutional Neural Networks, where the evolutionary strategies are used to help to avoid local minimas and fine-tune the weights, so that the network achieves higher accuracy results. We show that the proposed hybrid method is capable of improving upon regular training in the task of image classification in CIFAR-10, where a VGG16 model was used and the final test results increased 0.61%, in average, when compared to using only backpropagation.
Hybrid 2-stage Imperialist Competitive Algorithm with Ant Colony Optimization for Solving Multi-Depot Vehicle Routing Problem
Venue: arXiv cs.NE 2020
Year: 2020

The Multi-Depot Vehicle Routing Problem (MDVRP) is a real-world model of the simplistic Vehicle Routing Problem (VRP) that considers how to satisfy multiple customer demands from numerous depots. This paper introduces a hybrid 2-stage approach based on two population-based algorithms - Ant Colony Optimization (ACO) that mimics ant behaviour in nature and the Imperialist Competitive Algorithm (ICA) that is based on geopolitical relationships between countries. In the proposed hybrid algorithm, ICA is responsible for customer assignment to the depots while ACO is routing and sequencing the customers. The algorithm is compared to non-hybrid ACO and ICA as well as four other state-of-the-art methods across 23 common Cordreaus benchmark instances. Results show clear improvement over simple ACO and ICA and demonstrate very competitive results when compared to other rival algorithms.
BCI-Controlled Hands-Free Wheelchair Navigation with Obstacle Avoidance
Venue: arXiv cs.HC 2020
Year: 2020

Brain-Computer interfaces (BCI) are widely used in reading brain signals and converting them into real-world motion. However, the signals produced from the BCI are noisy and hard to analyze. This paper looks specifically towards combining the BCI's latest technology with ultrasonic sensors to provide a hands-free wheelchair that can efficiently navigate through crowded environments. This combination provides safety and obstacle avoidance features necessary for the BCI Navigation system to gain more confidence and operate the wheelchair at a relatively higher velocity. A population of six human subjects tested the BCI-controller and obstacle avoidance features. Subjects were able to mentally control the destination of the wheelchair, by moving the target from the starting position to a predefined position, in an average of 287.12 seconds and a standard deviation of 48.63 seconds after 10 minutes of training. The wheelchair successfully avoided all obstacles placed by the subjects during the test.
The critical locus of overparameterized neural networks
Venue: arXiv cs.LG 2020
Year: 2020

Many aspects of the geometry of loss functions in deep learning remain mysterious. In this paper, we work toward a better understanding of the geometry of the loss function $L$ of overparameterized feedforward neural networks. In this setting, we identify several components of the critical locus of $L$ and study their geometric properties. For networks of depth $\ell \geq 4$, we identify a locus of critical points we call the star locus $S$. Within $S$ we identify a positive-dimensional sublocus $C$ with the property that for $p \in C$, $p$ is a degenerate critical point, and no existing theoretical result guarantees that gradient descent will not converge to $p$. For very wide networks, we build on earlier work and show that all critical points of $L$ are degenerate, and give lower bounds on the number of zero eigenvalues of the Hessian at each critical point. For networks that are both deep and very wide, we compare the growth rates of the zero eigenspaces of the Hessian at all the different families of critical points that we identify. The results in this paper provide a starting point to a more quantitative understanding of the properties of various components of the critical locus of $L$.
DeepFIR: Addressing the Wireless Channel Action in Physical-Layer Deep Learning
Venue: arXiv cs.NI 2020
Year: 2020

Deep learning can be used to classify waveform characteristics (e.g., modulation) with accuracy levels that are hardly attainable with traditional techniques. Recent research has demonstrated that one of the most crucial challenges in wireless deep learning is to counteract the channel action, which may significantly alter the waveform features. The problem is further exacerbated by the fact that deep learning algorithms are hardly re-trainable in real time due to their sheer size. This paper proposes DeepFIR, a framework to counteract the channel action in wireless deep learning algorithms without retraining the underlying deep learning model. The key intuition is that through the application of a carefully-optimized digital finite input response filter (FIR) at the transmitter's side, we can apply tiny modifications to the waveform to strengthen its features according to the current channel conditions. We mathematically formulate the Waveform Optimization Problem (WOP) as the problem of finding the optimum FIR to be used on a waveform to improve the classifier's accuracy. We also propose a data-driven methodology to train the FIRs directly with dataset inputs. We extensively evaluate DeepFIR on a experimental testbed of 20 software-defined radios, as well as on two datasets made up by 500 ADS-B devices and by 500 WiFi devices and a 24-class modulation dataset. Experimental results show that our approach (i) increases the accuracy of the radio fingerprinting models by about 35%, 50% and 58%; (ii) decreases an adversary's accuracy by about 54% when trying to imitate other device's fingerprints by using their filters; (iii) achieves 27% improvement over the state of the art on a 100-device dataset; (iv) increases by 2x the accuracy of the modulation dataset.
View Invariant Human Body Detection and Pose Estimation from Multiple Depth Sensors
Venue: arXiv cs.CV 2020
Year: 2020

Point cloud based methods have produced promising results in areas such as 3D object detection in autonomous driving. However, most of the recent point cloud work focuses on single depth sensor data, whereas less work has been done on indoor monitoring applications, such as operation room monitoring in hospitals or indoor surveillance. In these scenarios multiple cameras are often used to tackle occlusion problems. We propose an end-to-end multi-person 3D pose estimation network, Point R-CNN, using multiple point cloud sources. We conduct extensive experiments to simulate challenging real world cases, such as individual camera failures, various target appearances, and complex cluttered scenes with the CMU panoptic dataset and the MVOR operation room dataset. Unlike most of the previous methods that attempt to use multiple sensor information by building complex fusion models, which often lead to poor generalization, we take advantage of the efficiency of concatenating point clouds to fuse the information at the input level. In the meantime, we show our end-to-end network greatly outperforms cascaded state-of-the-art models.
Towards Robustness against Unsuspicious Adversarial Examples
Venue: arXiv cs.LG 2020
Year: 2020

Despite the remarkable success of deep neural networks, significant concerns have emerged about their robustness to adversarial perturbations to inputs. While most attacks aim to ensure that these are imperceptible, physical perturbation attacks typically aim for being unsuspicious, even if perceptible. However, there is no universal notion of what it means for adversarial examples to be unsuspicious. We propose an approach for modeling suspiciousness by leveraging cognitive salience. Specifically, we split an image into foreground (salient region) and background (the rest), and allow significantly larger adversarial perturbations in the background, while ensuring that cognitive salience of background remains low. We describe how to compute the resulting non-salience-preserving dual-perturbation attacks on classifiers. We then experimentally demonstrate that our attacks indeed do not significantly change perceptual salience of the background, but are highly effective against classifiers robust to conventional attacks. Furthermore, we show that adversarial training with dual-perturbation attacks yields classifiers that are more robust to these than state-of-the-art robust learning approaches, and comparable in terms of robustness to conventional attacks.
Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients
Venue: arXiv cs.LG 2020
Year: 2020

The potential of Reinforcement Learning (RL) has been demonstrated through successful applications to games such as Go and Atari. However, while it is straightforward to evaluate the performance of an RL algorithm in a game setting by simply using it to play the game, evaluation is a major challenge in clinical settings where it could be unsafe to follow RL policies in practice. Thus, understanding sensitivity of RL policies to the host of decisions made during implementation is an important step toward building the type of trust in RL required for eventual clinical uptake. In this work, we perform a sensitivity analysis on a state-of-the-art RL algorithm (Dueling Double Deep Q-Networks)applied to hemodynamic stabilization treatment strategies for septic patients in the ICU. We consider sensitivity of learned policies to input features, embedding model architecture, time discretization, reward function, and random seeds. We find that varying these settings can significantly impact learned policies, which suggests a need for caution when interpreting RL agent output.
Blockchain BFT Protocol for Complete Asynchronous Networks
Venue: arXiv cs.CR 2020
Year: 2020

Ethereum Research team has proposed a family of Casper blockchain consensus protocols for Ethereum 2.0. It has been shown in the literature that Casper Friendly Finality Gadget (Casper FFG) for Ethereum 2.0's beacon network cannot achieve liveness property in partially synchronous networks such as the Internet environment. The "Correct-by-Construction" family of Casper blockchain consensus protocols (CBC Casper) has been proposed as a finality gadget for the future release of Ethereum 2.0 blockchain. Unfortunately, neither constructive finality rule nor satisfactory liveness property has been obtained for CBC Casper, and it is commonly believed that CBC Casper could not achieve liveness property in asynchronous networks. This paper provides the first probabilistic CBC Casper protocol that achieves liveness property against (n-1)/3 Byzantine participants in complete asynchronous networks.
LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation
Venue: arXiv cs.CL 2020
Year: 2020

Recent trends in NLP research have raised an interest in linguistic code-switching (CS); modern approaches have been proposed to solve a wide range of NLP tasks on multiple language pairs. Unfortunately, these proposed methods are hardly generalizable to different code-switched languages. In addition, it is unclear whether a model architecture is applicable for a different task while still being compatible with the code-switching setting. This is mainly because of the lack of a centralized benchmark and the sparse corpora that researchers employ based on their specific needs and interests. To facilitate research in this direction, we propose a centralized benchmark for Linguistic Code-switching Evaluation (LinCE) that combines ten corpora covering four different code-switched language pairs (i.e., Spanish-English, Nepali-English, Hindi-English, and Modern Standard Arabic-Egyptian Arabic) and four tasks (i.e., language identification, named entity recognition, part-of-speech tagging, and sentiment analysis). As part of the benchmark centralization effort, we provide an online platform at ritual.uh.edu/lince, where researchers can submit their results while comparing with others in real-time. In addition, we provide the scores of different popular models, including LSTM, ELMo, and multilingual BERT so that the NLP community can compare against state-of-the-art systems. LinCE is a continuous effort, and we will expand it with more low-resource languages and tasks.
GPU Acceleration of Sparse Neural Networks
Venue: arXiv cs.DC 2020
Year: 2020

In this paper, we use graphics processing units(GPU) to accelerate sparse and arbitrary structured neural networks. Sparse networks have nodes in the network that are not fully connected with nodes in preceding and following layers, and arbitrary structure neural networks have different number of nodes in each layers. Sparse Neural networks with arbitrary structures are generally created in the processes like neural network pruning and evolutionary machine learning strategies. We show that we can gain significant speedup for full activation of such neural networks using graphical processing units. We do a prepossessing step to determine dependency groups for all the nodes in a network, and use that information to guide the progression of activation in the neural network. Then we compute activation for each nodes in its own separate thread in the GPU, which allows for massive parallelization. We use CUDA framework to implement our approach and compare the results of sequential and GPU implementations. Our results show that the activation of sparse neural networks lends very well to GPU acceleration and can help speed up machine learning strategies which generate such networks or other processes that have similar structure.
Day of the week submission effect for accepted papers in Physica A, PLOS ONE, Nature and Cell
Venue: arXiv cs.DL 2020
Year: 2020

The particular day of the week when an event occurs seems to have unexpected consequences. For example, the day of the week when a paper is submitted to a peer reviewed journal correlates with whether that paper is accepted. Using an econometric analysis (a mix of log-log and semi-log based on undated and panel structured data) we find that more papers are submitted to certain peer review journals on particular weekdays than others, with fewer papers being submitted on weekends. Seasonal effects, geographical information as well as potential changes over time are examined. This finding rests on a large (178 000) and reliable sample; the journals polled are broadly recognized (Nature, Cell, PLOS ONE and Physica A). Day of the week effect in the submission of accepted papers should be of interest to many researchers, editors and publishers, and perhaps also to managers and psychologists.
Memory-Augmented Relation Network for Few-Shot Learning
Venue: arXiv cs.CV 2020
Year: 2020

Metric-based few-shot learning methods concentrate on learning transferable feature embedding that generalizes well from seen categories to unseen categories under the supervision of limited number of labelled instances. However, most of them treat each individual instance in the working context separately without considering its relationships with the others. In this work, we investigate a new metric-learning method, Memory-Augmented Relation Network (MRN), to explicitly exploit these relationships. In particular, for an instance, we choose the samples that are visually similar from the working context, and perform weighted information propagation to attentively aggregate helpful information from the chosen ones to enhance its representation. In MRN, we also formulate the distance metric as a learnable relation module which learns to compare for similarity measurement, and augment the working context with memory slots, both contributing to its generality. We empirically demonstrate that MRN yields significant improvement over its ancestor and achieves competitive or even better performance when compared with other few-shot learning approaches on the two major benchmark datasets, i.e. miniImagenet and tieredImagenet.
How not to secure wireless sensor networks revisited: Even if you say it twice it's still not secure
Venue: arXiv cs.CR 2020
Year: 2020

Two recent papers describe almost exactly the same group key establishment protocol for wireless sensor networks. Quite part from the duplication issue, we show that both protocols are insecure and should not be used - a member of a group can successfully impersonate the key generation centre and persuade any other group member to accept the wrong key value. This breaks the stated objectives of the schemes.
Finding Universal Grammatical Relations in Multilingual BERT
Venue: arXiv cs.CL 2020
Year: 2020

Recent work has found evidence that Multilingual BERT (mBERT), a transformer-based multilingual masked language model, is capable of zero-shot cross-lingual transfer, suggesting that some aspects of its representations are shared cross-lingually. To better understand this overlap, we extend recent work on finding syntactic trees in neural networks' internal representations to the multilingual setting. We show that subspaces of mBERT representations recover syntactic tree distances in languages other than English, and that these subspaces are approximately shared across languages. Motivated by these results, we present an unsupervised analysis method that provides evidence mBERT learns representations of syntactic dependency labels, in the form of clusters which largely agree with the Universal Dependencies taxonomy. This evidence suggests that even without explicit supervision, multilingual masked language models learn certain linguistic universals.
PriLok: Citizen-protecting distributed epidemic tracing
Venue: arXiv cs.DC 2020
Year: 2020

Contact tracing is an important instrument for national health services to fight epidemics. As part of the COVID-19 situation, many proposals have been made for scaling up contract tracing capacities with the help of smartphone applications, an important but highly critical endeavor due to the privacy risks involved in such solutions. Extending our previously expressed concern, we clearly articulate in this article, the functional and non-functional requirements that any solution has to meet, when striving to serve, not mere collections of individuals, but the whole of a nation, as required in face of such potentially dangerous epidemics. We present a critical information infrastructure, PriLock, a fully-open preliminary architecture proposal and design draft for privacy preserving contact tracing, which we believe can be constructed in a way to fulfill the former requirements. Our architecture leverages the existing regulated mobile communication infrastructure and builds upon the concept of "checks and balances", requiring a majority of independent players to agree to effect any operation on it, thus preventing abuse of the highly sensitive information that must be collected and processed for efficient contact tracing. This is enforced with a largely decentralised layout and highly resilient state-of-the-art technology, which we explain in the paper, finishing by giving a security, dependability and resilience analysis, showing how it meets the defined requirements, even while the infrastructure is under attack.
In-memory eigenvector computation in time O(1)
Venue: arXiv cs.CC 2020
Year: 2020

In-memory computing with crosspoint resistive memory arrays has gained enormous attention to accelerate the matrix-vector multiplication in the computation of data-centric applications. By combining a crosspoint array and feedback amplifiers, it is possible to compute matrix eigenvectors in one step without algorithmic iterations. In this work, time complexity of the eigenvector computation is investigated, based on the feedback analysis of the crosspoint circuit. The results show that the computing time of the circuit is determined by the mismatch degree of the eigenvalues implemented in the circuit, which controls the rising speed of output voltages. For a dataset of random matrices, the time for computing the dominant eigenvector in the circuit is constant for various matrix sizes, namely the time complexity is O(1). The O(1) time complexity is also supported by simulations of PageRank of real-world datasets. This work paves the way for fast, energy-efficient accelerators for eigenvector computation in a wide range of practical applications.