Reviewerly/exHarmony huggingface dataset:
Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers

The single image super-resolution task is one of the most examined inverse problems in the past decade. In the recent years, Deep Neural Networks (DNNs) have shown superior performance over alternative methods when the acquisition process uses a fixed known downscaling kernel---typically a bicubic kernel. However, several recent works have shown that in practical scenarios, where the test data mismatch the training data (e.g. when the downscaling kernel is not the bicubic kernel or is not available at training), the leading DNN methods suffer from a huge performance drop. Inspired by the literature on generalized sampling, in this work we propose a method for improving the performance of DNNs that have been trained with a fixed kernel on observations acquired by other kernels. For a known kernel, we design a closed-form correction filter that modifies the low-resolution image to match one which is obtained by another kernel (e.g. bicubic), and thus improves the results of existing pre-trained DNNs. For an unknown kernel, we extend this idea and propose an algorithm for blind estimation of the required correction filter. We show that our approach outperforms other super-resolution methods, which are designed for general downscaling kernels.
ELECTRICITY: An Efficient Multi-camera Vehicle Tracking System for Intelligent City

City-scale multi-camera vehicle tracking is an important task in the intelligent city and traffic management. It is quite challenging with large scale variance, frequent occlusion and appearance variance caused by viewing perspective difference. In this paper, we propose ELECTRICITY, an efficient multi-camera vehicle tracking system with aggregation loss and fast multi-target cross-camera tracking strategy. The proposed system contains four main modules. Firstly, we extract tracklets under single camera view through object detection and multi-object tracking modules which shared the detection features. After that, we match the generated tracklets through a multicamera re-identification module. Finally, we eliminate isolated tracklets and synchronize tracking ids according to the re-identification results. The proposed system wins the first place in the City-Scale Multi-Camera Vehicle Tracking of AI City 2020 Challenge (Track 3) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> with a score of 0.4585.
Neuromorphic Camera Guided High Dynamic Range Imaging

Reconstruction of high dynamic range image from a single low dynamic range image captured by a frame-based conventional camera, which suffers from over- or under-exposure, is an ill-posed problem. In contrast, recent neuromorphic cameras are able to record high dynamic range scenes in the form of an intensity map, with much lower spatial resolution, and without color. In this paper, we propose a neuromorphic camera guided high dynamic range imaging pipeline, and a network consisting of specially designed modules according to each step in the pipeline, which bridges the domain gaps on resolution, dynamic range, and color representation between two types of sensors and images. A hybrid camera system has been built to validate that the proposed method is able to reconstruct quantitatively and qualitatively high-quality high dynamic range images by successfully fusing the images and intensity maps for various real-world scenarios.
Towards Transferable Targeted Attack

An intriguing property of adversarial examples is their transferability, which suggests that black-box attacks are feasible in real-world applications. Previous works mostly study the transferability on non-targeted setting. However, recent studies show that targeted adversarial examples are more difficult to transfer than non-targeted ones. In this paper, we find there exist two defects that lead to the difficulty in generating transferable examples. First, the magnitude of gradient is decreasing during iterative attack, causing excessive consistency between two successive noises in accumulation of momentum, which is termed as noise curing. Second, it is not enough for targeted adversarial examples to just get close to target class without moving away from true class. To overcome the above problems, we propose a novel targeted attack approach to effectively generate more transferable adversarial examples. Specifically, we first introduce the Poincar\'{e} distance as the similarity metric to make the magnitude of gradient self-adaptive during iterative attack to alleviate noise curing. Furthermore, we regularize the targeted attack process with metric learning to take adversarial examples away from true label and gain more transferable targeted adversarial examples. Experiments on ImageNet validate the superiority of our approach achieving 8\% higher attack success rate over other state-of-the-art methods on average in black-box targeted attack.
Few-Shot Open-Set Recognition Using Meta-Learning

The problem of open-set recognition is considered. While previous approaches only consider this problem in the context of large-scale classifier training, we seek a unified solution for this and the low-shot classification setting. It is argued that the classic softmax classifier is a poor solution for open-set recognition, since it tends to overfit on the training classes. Randomization is then proposed as a solution to this problem. This suggests the use of meta-learning techniques, commonly used for few-shot classification, for the solution of open-set recognition. A new oPen sEt mEta LEaRning (PEELER) algorithm is then introduced. This combines the random selection of a set of novel classes per episode, a loss that maximizes the posterior entropy for examples of those classes, and a new metric learning formulation based on the Mahalanobis distance. Experimental results show that PEELER achieves state of the art open set recognition performance for both few-shot and large-scale recognition. On CIFAR and miniImageNet, it achieves substantial gains in seen/unseen class detection AUROC for a given seen-class classification accuracy.
SP-NAS: Serial-to-Parallel Backbone Search for Object Detection

Advanced object detectors usually adopt a backbone network designed and pretrained by ImageNet classification. Recently neural architecture search (NAS) has emerged to automatically design a task-specific backbone to bridge the gap between the tasks of classification and detection. In this paper, we propose a two-phase serial-to-parallel architecture search framework named SP-NAS towards a flexible task-oriented detection backbone. Specifically, the serial-searching round aims at finding a sequence of serial blocks with optimal scale and output channels in the feature hierarchy by a Swap-Expand-Reignite search algorithm; the parallel-searching phase then assembles several sub-architectures along with the previous searched backbone into a more powerful parallel-structured backbone. We efficiently search a detection backbone by exploring a network morphism strategy on multiple detection benchmarks. The resulting architectures achieve SOTA results, i.e. top performance (LAMR: 0.055) on the automotive detection leaderboard of EuroCityPersons benchmark, improving 2.3% mAP with less FLOPS than NAS-FPN on COCO, and reaching 84.1% AP50 on VOC better than DetNAS and Auto-FPN in terms of both accuracy and speed.
SurvLIME: A method for explaining machine learning survival models

A new method called SurvLIME for explaining machine learning survival models is proposed. It can be viewed as an extension or modification of the well-known method LIME. The main idea behind the proposed method is to apply the Cox proportional hazards model to approximate the survival model at the local area around a test example. The Cox model is used because it considers a linear combination of the example covariates such that coefficients of the covariates can be regarded as quantitative impacts on the prediction. Another idea is to approximate cumulative hazard functions of the explained model and the Cox model by using a set of perturbed points in a local area around the point of interest. The method is reduced to solving an unconstrained convex optimization problem. A lot of numerical experiments demonstrate the SurvLIME efficiency.
A hybrid method integrating an elite genetic algorithm with tabu search for the quadratic assignment problem

The Quadratic Assignment Problem (QAP) is one of the most studied classical combinatorial optimization problems. QAP has many practical applications. Designing enhanced meta-heuristic approaches for the QAP is an active research area. In this work, we propose a hybrid algorithm (EGATS) that combines an elite genetic algorithm and tabu search to solve the QAP. In the optimization process, EGATS employs two kinds of elite crossovers, repeated 2-exchange mutation, and tabu search to strike a balance between exploitation and exploration. We evaluated the performance of EGATS through computational experiments on 135 well-known benchmark instances from the quadratic assignment problem library, QAPLIB. EGATS obtained the best-known solution for 131 instances. Compared to other popular meta-heuristic algorithms in the literature, EGATS is a competitive method for the QAP.
Deep Back-ProjectiNetworks for Single Image Super-Resolution

Previous feed-forward architectures of recently proposed deep super-resolution networks learn the features of low-resolution inputs and the non-linear mapping from those to a high-resolution output. However, this approach does not fully address the mutual dependencies of low- and high-resolution images. We propose Deep Back-Projection Networks (DBPN), the winner of two image super-resolution challenges (NTIRE2018 and PIRM2018), that exploit iterative up- and down-sampling layers. These layers are formed as a unit providing an error feedback mechanism for projection errors. We construct mutually-connected up- and down-sampling units each of which represents different types of low- and high-resolution components. We also show that extending this idea to demonstrate a new insight towards more efficient network design substantially, such as parameter sharing on the projection module and transition layer on projection step. The experimental results yield superior results and in particular establishing new state-of-the-art results across multiple data sets, especially for large scaling factors such as 8×.
Fine-grained emotion classification of Chinese microblogs based on graph convolution networks

Microblogs are widely used to express people’s opinions and feelings in daily life. Sentiment analysis (SA) can timely detect personal sentiment polarities through analyzing text. Deep learning approaches have been broadly used in SA but still have not fully exploited syntax information. In this paper, we propose a syntax-based graph convolution network (GCN) model to enhance the understanding of diverse grammatical structures of Chinese microblogs. In addition, a pooling method based on percentile is proposed to improve the accuracy of the model. In experiments, for Chinese microblogs emotion classification categories including happiness, sadness, like, anger, disgust, fear, and surprise, the F-measure of our model reaches 82.32% and exceeds the state-of-the-art algorithm by 5.90%. The experimental results show that our model can effectively utilize the information of dependency parsing to improve the performance of emotion detection. What is more, we annotate a new dataset for Chinese emotion classification, which is open to other researchers.
FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation

Deep Neural Networks (DNNs) are widely being adopted for safety-critical applications, e.g., healthcare and autonomous driving. Inherently, they are considered to be highly error-tolerant. However, recent studies have shown that hardware faults that impact the parameters of a DNN (e.g., weights) can have drastic impacts on its classification accuracy. In this paper, we perform a comprehensive error resilience analysis of DNNs subjected to hardware faults (e.g., permanent faults) in the weight memory. The outcome of this analysis is leveraged to propose a novel error mitigation technique which squashes the high- intensity faulty activation values to alleviate their impact. We achieve this by replacing the unbounded activation functions with their clipped versions. We also present a method to systematically define the clipping values of the activation functions that result in increased resilience of the networks against faults. We evaluate our technique on the AlexNet and the VGG-16 DNNs trained for the CIFAR-10 dataset. The experimental results show that our mitigation technique significantly improves the resilience of the DNNs to faults. For example, the proposed technique offers on average 68.92% improvement in the classification accuracy of resilience-optimized VGG-16 model at 1 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> fault rate, when compared to the base network without any fault mitigation.
Blockchain voting: Publicly verifiable online voting protocol without trusted tallying authorities

Abstract In organizing elections, a difficult problem is to achieve trust of all voters in the tallying process. Practical elections often lead to recounting of the submitted votes and raise questions about the validity of many submitted votes. There are even situations, when the opposition raises concerns about the validity of the whole election process due to insufficient transparency in the verification of the votes and in tallying. To solve this problem, the present paper proposes a new voting protocol based on the blockchain technology. There are several main advantages of the protocol. It does not rely on a trusted tallying authority. All votes are submitted with complete proofs of validity and are available for public access in an encrypted form. We propose a new encryption mechanism to guarantee that nobody can decrypt the votes, but everyone can verify the validity of the votes as well as the outcome of the tallying process by using the homomorphic property of the encryption. This makes the results of the election publicly verifiable. Our protocol enables all voters to store, verify, and tally all submitted votes which are added to a blockchain database. They allow every voter to rank each candidate by assigning different scores to them, rather than voting for only one candidate. Each vote is encrypted using a new encryption mechanism before submission. For each encrypted score in the vote, proofs are generated and stored. Everyone can use these proofs to verify the correctness and the eligibility of each submission without decrypting the content of the vote. This ensures the validity of the submitted votes in the counting process and at the same time maintains confidentiality. The security and performance analyses included in this paper demonstrate the feasibility of the proposed protocol for implementation in real elections.
Improved Overlap-based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson’s Disease

Classification of imbalanced datasets has attracted substantial research interest over the past decades. Imbalanced datasets are common in several domains such as health, finance, security and others. A wide range of solutions to handle imbalanced datasets focus mainly on the class distribution problem and aim at providing more balanced datasets by means of resampling. However, existing literature shows that class overlap has a higher negative impact on the learning process than class distribution. In this paper, we propose overlap-based undersampling methods for maximizing the visibility of the minority class instances in the overlapping region. This is achieved by the use of soft clustering and the elimination threshold that is adaptable to the overlap degree to identify and eliminate negative instances in the overlapping region. For more accurate clustering and detection of overlapped negative instances, the presence of the minority class at the borderline areas is emphasized by means of oversampling. Extensive experiments using simulated and real-world datasets covering a wide range of imbalance and overlap scenarios including extreme cases were carried out. Results show significant improvement in sensitivity and competitive performance with well-established and state-of-the-art methods.
BDCN: Bi-Directional Cascade Network for Perceptual Edge Detection

Exploiting multi-scale representations is critical to improve edge detection for objects at different scales. To extract edges at dramatically different scales, we propose a bi-directional cascade network (BDCN) architecture, where an individual layer is supervised by labeled edges at its specific scale, rather than directly applying the same supervision to different layers. Furthermore, to enrich multi-scale representations learned by each layer of BDCN, we introduce a scale enhancement module (SEM), which utilizes dilated convolution to generate multi-scale features, instead of using deeper CNNs. These new approaches encourage the learning of multi-scale representations in different layers and detect edges that are well delineated by their scales. Learning scale dedicated layers also results in a compact network with a fraction of parameters. We evaluate our method on three datasets, i.e., BSDS500, NYUDv2, and Multicue, and achieve ODS F-measure of 0.832, 2.7 percent higher than current state-of-the-art on the BSDS500 dataset. We also applied our edge detection result to other vision tasks. Experimental results show that, our method further boosts the performance of image segmentation, optical flow estimation, and object proposal generation.
Price discovery in Bitcoin: The impact of unregulated markets

We analyse minute-level multi-dimensional information flows within and between bitcoin spot and derivatives. We show that perpetual swaps and futures traded on the unregulated exchanges Huobi, OKEx and BitMEX are much the strongest instruments for bitcoin price discovery and we examine potential determinants of their leadership strength. Prices on the regulated CME bitcoin futures and the US-based spot exchanges react to, rather than lead, price movements on the unregulated exchanges and they may do so relatively slowly. In a multi-dimensional setting including the main price leaders within futures, perpetuals and spot markets, the CME futures have a very minor effect on price discovery, even less than the spot exchanges Bitfinex, Bitstamp and Coinbase. Our findings highlight the persistent problems stemming from inconsistent regulation in bitcoin spot and derivatives markets, including insufficient price stability and lack of resistance to manipulative trading. We conclude that the SEC are correct to maintain such issues as their main concern for bitcoin ETF applications.
An Efficient Hybrid Signcryption Scheme With Conditional Privacy-Preservation for Heterogeneous Vehicular Communication in VANETs

Vehicular ad hoc networks (VANETs) ensure improvement in road safety and traffic management by allowing the vehicles and infrastructure that are connected to them to exchange safety messages. Due to the open wireless communication channels, security and privacy issues are a major concern in VANETs. A typical attack consists of a malicious third party intercepting, modifying and retransmitting messages. Heterogeneous vehicular communication in VANETs occurs when vehicles (only) or vehicles and other infrastructure communicate using different cryptographic techniques. To address the security and privacy issues in heterogeneous vehicular communication, some heterogeneous signcryption schemes have been proposed. These schemes simultaneously satisfy the confidentiality, authentication, integrity and non-repudiation security requirements. They however fail to properly address the efficiency with respect to the computational cost involved in unsigncrypting ciphertexts, which is often affected by the speeds at which vehicles travel in VANETs. In this paper, we propose an efficient conditional privacy-preserving hybrid signcryption (CPP-HSC) scheme that uses bilinear pairing to satisfy the security requirements of heterogeneous vehicular communication in a single logical step. Our scheme ensures the transmission of a message from a vehicle with a background of an identity-based cryptosystem (IBC) to a receiver with a background of a public-key infrastructure (PKI). Furthermore, it supports a batch unsigncryption method, which allows the receiver to speed up the process by processing multiple messages simultaneously. The security of our CPP-HSC scheme ensures the indistinguishability against adaptive chosen ciphertext attack (IND-CCA2) under the intractability assumption of q-bilinear Diffie-Hellman inversion (q-BDHI) problem and the existential unforgeability against adaptive chosen message attack (EUF-CMA) under the intractability assumption of q-strong Diffie-Hellman (q-SDH) problem in the random oracle model (ROM). The performance analysis indicates that our scheme has an improvement over the existing related schemes with respect to the computational cost without an increase in the communication cost.
The voice of optimization

We introduce the idea that using optimal classification trees (OCTs) and optimal classification trees with-hyperplanes (OCT-Hs), interpretable machine learning algorithms developed by Bertsimas and Dunn (Mach Learn 106(7):1039–1082, 2017), we are able to obtain insight on the strategy behind the optimal solution in continuous and mixed-integer convex optimization problem as a function of key parameters that affect the problem. In this way, optimization is not a black box anymore. Instead, we redefine optimization as a multiclass classification problem where the predictor gives insights on the logic behind the optimal solution. In other words, OCTs and OCT-Hs give optimization a voice. We show on several realistic examples that the accuracy behind our method is in the 90–100% range, while even when the predictions are not correct, the degree of suboptimality or infeasibility is very low. We compare optimal strategy predictions of OCTs and OCT-Hs and feedforward neural networks (NNs) and conclude that the performance of OCT-Hs and NNs is comparable. OCTs are somewhat weaker but often competitive. Therefore, our approach provides a novel insightful understanding of optimal strategies to solve a broad class of continuous and mixed-integer optimization problems.