Reviewerly/exHarmony huggingface dataset:
AGWO: Advanced GWO in multi-layer perception optimization

The Multi-Layer Perceptron (MLP) has been applied into many real-world problems as one of the most extensively used Neural Networks (NNs). It often suffers from local stagnation and premature convergence problems when treating the specific datasets. This paper proposes an Advanced Grey Wolf Optimization algorithm (AGWO) with elastic, circling and attacking mechanisms to alleviate those problems. The performance of the proposed AGWO is tested on the IEEE CEC 2014. Meanwhile, the efficiency of AGWO and five noted heuristic algorithms in training MLP are investigated by 7 classification datasets and 3 function approximation datasets. The experimental results show that AGWO is superior to other algorithms in terms of local stagnation and premature convergence. Simultaneously, training MLP by means of AGWO is clearly superior to the other heuristic algorithms in local optimum avoidance and computational accuracy.
LAMP-HQ: A Large-Scale Multi-pose High-Quality Database and Benchmark for NIR-VIS Face Recognition

Near-infrared-visible (NIR-VIS) heterogeneous face recognition matches NIR to corresponding VIS face images. However, due to the sensing gap, NIR images often lose some identity information so that the NIR-VIS recognition issue is more difficult than conventional VIS face recognition. Recently, NIR-VIS heterogeneous face recognition has attracted considerable attention in the computer vision community because of its convenience and adaptability in practical applications. Various deep learning-based methods have been proposed and substantially increased the recognition performance, but the lack of NIR-VIS training samples leads to the difficulty of the model training process. In this paper, we propose a new $$\mathbf{L} {} \mathbf{a} $$ rge-Scale $$\mathbf{M} $$ ulti- $$\mathbf{P} $$ ose $$\mathbf{H} $$ igh- $$\mathbf{Q} $$ uality NIR-VIS database ‘ $$\mathbf{LAMP}-HQ $$ ’ containing 56,788 NIR and 16,828 VIS images of 573 subjects with large diversities in pose, illumination, attribute, scene and accessory. We furnish a benchmark along with the protocol for NIR-VIS face recognition via generation on LAMP-HQ, including Pixel2-Pixel, CycleGAN, ADFL, PCFH, and PACH. Furthermore, we propose a novel exemplar-based variational spectral attention network to produce high-fidelity VIS images from NIR data. A spectral conditional attention module is introduced to reduce the domain gap between NIR and VIS data and then improve the performance of NIR-VIS heterogeneous face recognition on various databases including the LAMP-HQ.
The EMV Standard: Break, Fix, Verify

EMV is the international protocol standard for smartcard payment and is used in over 9 billion cards worldwide. Despite the standard’s advertised security, various issues have been previously uncovered, deriving from logical flaws that are hard to spot in EMV’s lengthy and complex specification, running over 2,000 pages.We formalize a comprehensive symbolic model of EMV in Tamarin, a state-of-the-art protocol verifier. Our model is the first that supports a fine-grained analysis of all relevant security guarantees that EMV is intended to offer. We use our model to automatically identify flaws that lead to two critical attacks: one that defrauds the cardholder and a second that defrauds the merchant. First, criminals can use a victim’s Visa contactless card to make payments for amounts that require cardholder verification, without knowledge of the card’s PIN. We built a proof-of-concept Android application and successfully demonstrated this attack on real-world payment terminals. Second, criminals can trick the terminal into accepting an unauthentic offline transaction, which the issuing bank should later decline, after the criminal has walked away with the goods. This attack is possible for implementations following the standard, although we did not test it on actual terminals for ethical reasons. Finally, we propose and verify improvements to the standard that prevent these attacks, as well as any other attacks that violate the considered security properties. The proposed improvements can be easily implemented in the terminals and do not affect the cards in circulation.
Word-class embeddings for multiclass text classification

Pre-trained word embeddings encode general word semantics and lexical regularities of natural language, and have proven useful across many NLP tasks, including word sense disambiguation, machine translation, and sentiment analysis, to name a few. In supervised tasks such as multiclass text classification (the focus of this article) it seems appealing to enhance word representations with ad-hoc embeddings that encode task-specific information. We propose (supervised) word-class embeddings (WCEs), and show that, when concatenated to (unsupervised) pre-trained word embeddings, they substantially facilitate the training of deep-learning models in multiclass classification by topic. We show empirical evidence that WCEs yield a consistent improvement in multiclass classification accuracy, using six popular neural architectures and six widely used and publicly available datasets for multiclass text classification. One further advantage of this method is that it is conceptually simple and straightforward to implement. Our code that implements WCEs is publicly available at https://github.com/AlexMoreo/word-class-embeddings .
GraphPi: High Performance Graph Pattern Matching through Effective Redundancy Elimination

Graph pattern matching, which aims to discover structural patterns in graphs, is considered one of the most fundamental graph mining problems in many real applications. Despite previous efforts, existing systems face two main challenges. First, inherent symmetry existing in patterns can introduce a large amount of redundant computation. Second, different matching orders for a pattern have significant performance differences and are quite hard to predict. When these factors are mixed, this problem becomes extremely complicated. High efficient pattern matching remains an open problem currently. To address these challenges, we propose GraphPi, a high performance distributed pattern matching system. GraphPi utilizes a new algorithm based on 2-cycles in group theory to generate multiple sets of asymmetric restrictions, where each set can eliminate redundant computation completely. We further design an accurate performance model to determine the optimal matching order and asymmetric restriction set for efficient pattern matching. We evaluate GraphPi on Tianhe-2A supercomputer. Results show that GraphPi outperforms the state-of-the-art system, by up to $ 105\times$ for 6 real-world graph datasets on a single node. We also scale GraphPi to 1,024 computing nodes (24,576 cores).
Multi-path dilated convolution network for haze and glow removal in nighttime images

In this paper, we address the single-image haze removal problem in nighttime scenes. The night haze removal is a severely ill-posed problem due to the presence of various visible night light sources with varying colors and non-uniform illumination. These light sources are of different shapes and introduce a noticeable amount of glow in the night scenes. To overcome these effects, we introduce a deep learning-based DeGlow–DeHaze iterative model which accounts for varying colors and glows. The proposed model is a linear combination of three terms: the direct transmission attenuation, airlight and glow. First, a multi-path dilated convolution DeGlow network is introduced to interactively learn the local features with different reception fields and reduce the glow effect. The glow term is estimated by a binary mask that informs the location of the illumination source. As a result, the nighttime image is only left with only direct transmission and airlight terms. Finally, a separate post-processing DeHaze network is included to remove the haze effect from the reduced glow image. For our model training, we collected the night hazy images from internal and external resources, synthesized transmission maps from the NYU depth datasets, and consequently restored the haze-free images. The quantitative and qualitative evaluations show the effectiveness of our model on several real and synthetic images and compare our results with existing night haze models. The experimental results demonstrate that our multi-path CNN model outperforms other state-of-the-art methods in terms of PSNR (19.25 dB), SSIM (0.9958) evaluation parameters and computation time.
Securing Data Sharing from the Sky: Integrating Blockchains into Drones in 5G and Beyond

5G and beyond (B5G) networks significantly promote the popularity and ubiquity of drones by providing high-throughput and low-latency communication. In B5G drone networks, data sharing among drones has great potential to improve and enrich civilian and commercial applications, such as surveillance monitoring. Nevertheless, a series of security challenges arise such as data privacy leakage due to the lack of reliable centralized supervision. In this article, we employ the permissioned blockchain technology to propose a decentralized data management system. The permissioned blockchain deployed at pre-selected ground base stations is particularly suitable and practical for ensuring efficient peer-to-peer data sharing in B5G drone networks. However, the ground base stations working as miners are widely deployed without strong security protection, which may be compromised to produce maliciously manipulated results during the block verification in the permissioned blockchain. Hence, the miner selection is crucial for the permissioned blockchain-based B5G drone networks. We therefore introduce credit as a metric and propose a secure credit-based miner selection scheme using a four-weight subjective logic model. Numerical results demonstrate that the proposed schemes are effective for secure data sharing in permissioned blockchain-based B5G drone networks.
Robust and discriminative image representation: fractional-order Jacobi-Fourier moments

Robust and discriminative image representation is a long-lasting battle in the computer vision and pattern recognition. Moment-based image representation model is effective in satisfying the core conditions of semantic description, due to its geometric invariance and independence. However, moment-based descriptors suffer from a contradiction between the robustness and discriminability, which limits the further improvement of description quality. In this paper, a set of generic moments along with a novel representation framework are proposed to mitigate this troublesome contradiction. We first define a new set of orthogonal moments, named Fractional-order Jacobi-Fourier Moments (FJFM), which is characterized by the generic nature and time-frequency analysis capability. We then develop a new framework to improve both the robustness and discriminability of image representation, called Mixed Low-order Moment Feature (MLMF), by fully exploiting the time-frequency property of FJFM. Extensive experimental results and a real-world application are provided to demonstrate the superior performance of our FJFM-based MLMF, with respect to robustness and discriminability.
Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments

This letter presents a data-driven decentralized trajectory optimization approach for multi-robot motion planning in dynamic environments. When navigating in a shared space, each robot needs accurate motion predictions of neighboring robots to achieve predictive collision avoidance. These motion predictions can be obtained among robots by sharing their future planned trajectories with each other via communication. However, such communication may not be available nor reliable in practice. In this letter, we introduce a novel trajectory prediction model based on recurrent neural networks (RNN) that can learn multi-robot motion behaviors from demonstrated trajectories generated using a centralized sequential planner. The learned model can run efficiently online for each robot and provide interaction-aware trajectory predictions of its neighbors based on observations of their history states. We then incorporate the trajectory prediction model into a decentralized model predictive control (MPC) framework for multi-robot collision avoidance. Simulation results show that our decentralized approach can achieve a comparable level of performance to a centralized planner while being communication-free and scalable to a large number of robots. We also validate our approach with a team of quadrotors in real-world experiments.
Fooling deep neural detection networks with adaptive object-oriented adversarial perturbation

Deep learning has shown superiority in dealing with complicated and professional tasks (e.g., computer vision, audio, and language processing). However, research works have confirmed that Deep Neural Networks (DNNs) are vulnerable to carefully crafted adversarial perturbations, which cause DNNs confusion on specific tasks. In object detection domain, the background has little contributions to object classification, and the crafted adversarial perturbations added to the background do not improve the adversary effect in fooling deep neural detection models yet induce substantial distortions in generated examples. Based on such situation, we introduce an adversarial attack algorithm named Adaptive Object-oriented Adversarial Method (AO2AM). It aims to fool deep neural object detection networks with the adversarial examples by applying the adaptive cumulation of object-based gradients and adding the adaptive object-based adversarial perturbations merely onto objects rather than the whole frame of input images. AO2AM can effectively make the representations of generated adversarial samples close to the decision boundary in the latent space, and force deep neural detection networks to yield inaccurate locations and false classification in the process of object detection. Compared with existing adversarial attack methods which generate adversarial perturbations acting on the global scale of the original inputs, the adversarial examples produced by AO2AM can effectively fool deep neural object detection networks and maintain a high structural similarity with corresponding clean inputs. Performing adversarial attacks on Faster R-CNN, AO2AM gains attack success rate (ASR) over 98.00% on pre-processed Pascal VOC 2007&2012 (Val), and reaches SSIM over 0.870. In Fooling SSD, AO2AM receives SSIM exceeding 0.980 on L2 norm constraint. On SSIM and Mean Attack Ratio, AO2AM outperforms adversarial attack methods based on global scale perturbations.
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems

Graph neural networks (GNNs) are gaining popularity as a promising approach to machine learning on graphs. Unlike traditional graph workloads where each vertex/edge is associated with a scalar, GNNs attach a feature tensor to each vertex/edge. This additional feature dimension, along with consequently more complex vertex- and edge-wise computations, has enormous implications on locality and parallelism, which existing graph processing systems fail to exploit. This paper proposes FeatGraph to accelerate GNN workloads by co-optimizing graph traversal and feature dimension computation. FeatGraph provides a flexible programming interface to express diverse GNN models by composing coarse-grained sparse templates with fine-grained user-defined functions (UDFs) on each vertex/edge. FeatGraph incorporates optimizations for graph traversal into the sparse templates and allows users to specify optimizations for UDFs with a feature dimension schedule (FDS). FeatGraph speeds up end-to-end GNN training and inference by up to 32× on CPU and 7× on GPU.
Metis: Learning to Schedule Long-Running Applications in Shared Container Clusters at Scale

Online cloud services are increasingly deployed as long-running applications (LRAs) in containers. Placing LRA containers is known to be difficult as they often have sophisticated resource interferences and I/O dependencies. Existing schedulers rely on operators to manually express the container scheduling requirements as placement constraints and strive to satisfy as many constraints as possible. Such schedulers, however, fall short in performance as placement constraints only provide qualitative scheduling guidelines and minimizing constraint violations does not necessarily result in the optimal performance.In this work, we present Metis, a general-purpose scheduler that learns to optimally place LRA containers using deep reinforcement learning (RL) techniques. This eliminates the complex manual specification of placement constraints and offers, for the first time, concrete quantitative scheduling criteria. As directly training an RL agent does not scale, we develop a novel hierarchical learning technique that decomposes a complex container placement problem into a hierarchy of subproblems with significantly reduced state and action space. We show that many subproblems have similar structures and can hence be solved by training a unified RL agent offline. Large-scale EC2 deployment shows that compared with the traditional constraint-based schedulers, Metis improves the throughput by up to 61%, optimizes various performance metrics, and easily scales to a large cluster where 3K containers run on over 700 machines.
CCANet: Class-Constraint Coarse-to-Fine Attentional Deep Network for Subdecimeter Aerial Image Semantic Segmentation

Semantic segmentation is important for the understanding of subdecimeter aerial images. In recent years, deep convolutional neural networks (DCNNs) have been used widely for semantic segmentation in the field of remote sensing. However, because of the highly complex subdecimeter resolution of aerial images, inseparability often occurs among some geographic entities of interest in the spectral domain. In addition, the semantic segmentation methods based on DCNNs mostly obtain context information using extra information within the added receptive field. However, the context information obtained this way is not explicit. We propose a novel class-constraint coarse-to-fine attentional (CCA) deep network, which enables the formation of class information constraints to obtain explicit long-range context information. Further, the performance of subdecimeter aerial image semantic segmentation can be improved, particularly for fine-structured geographic entities. Based on coarse-to-fine technology, we obtained a coarse segmentation result and constructed an image class feature library. We propose the use of the attention mechanism to obtain strong class-constrained features. Consequently, pixels of different geographic entities can adaptively match the corresponding categories in the class feature library. Additionally, we employed a novel loss function, CCA-loss to realize end-to-end training. The experimental results obtained using two popular open benchmarks, International Society for Photogrammetry and Remote Sensing (ISPRS) 2-D semantic labeling Vaihingen data set and Institute of Electrical and Electronics Engineers (IEEE) Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest Zeebrugge data set, validated the effectiveness and superiority of our proposed model. The proposed method achieved state-of-the-art performance on the IEEE GRSS Data Fusion Contest Zeebrugge data set.
Wavelet transform analytics for RF-based UAV detection and identification system using machine learning

In this work, we performed a thorough comparative analysis on a radio frequency (RF) based drone detection and identification system (DDI) under wireless interference, such as WiFi and Bluetooth, by using machine learning algorithms, and a pre-trained convolutional neural network-based algorithm called SqueezeNet, as classifiers. In RF signal fingerprinting research, the transient and steady state of the signals can be used to extract a unique signature from an RF signal. By exploiting the RF control signals from unmanned aerial vehicles (UAVs) for DDI, we considered each state of the signals separately for feature extraction and compared the pros and cons for drone detection and identification. Using various categories of wavelet transforms (discrete wavelet transform, continuous wavelet transform, and wavelet scattering transform) for extracting features from the signals, we built different models using these features. We studied the performance of these models under different signal-to-noise ratio (SNR) levels. By using the wavelet scattering transform to extract signatures (scattergrams) from the steady state of the RF signals at 30 dB SNR, and using these scattergrams to train SqueezeNet, we achieved an accuracy of 98.9% at 10 dB SNR.
Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems

In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the above-mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real-time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural network-based vehicle classification system to improve robustness of vehicle classification in real-time applications. We present a vehicle dataset comprising of 10,000 images categorized into six-common vehicle classes considering adverse illuminous conditions to achieve robustness in real-time vehicle classification systems. Initially, pretrained AlexNet, GoogleNet, Inception-v3, VGG, and ResNet are fine-tuned on self-constructed vehicle dataset to evaluate their performance in terms of accuracy and convergence. Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine-tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. Finally, a comparison study has been carried out between the proposed and existing vehicle classification methods to evaluate the effectiveness of the proposed vehicle classification system. Consequently, our proposed system achieved 99.68%, 99.65%, and 99.56% accuracy, precision, and F1-score on our self-constructed dataset.