Reviewerly/exHarmony huggingface dataset:
Convolutional Deblurring for Natural Imaging

In this paper, we propose a novel design of image deblurring in the form of one-shot convolution filtering that can directly convolve with naturally blurred images for restoration. The problem of optical blurring is a common disadvantage to many imaging applications that suffer from optical imperfections. Despite numerous deconvolution methods that blindly estimate blurring in either inclusive or exclusive forms, they are practically challenging due to high computational cost and low image reconstruction quality. Both conditions of high accuracy and high speed are prerequisites for high-throughput imaging platforms in digital archiving. In such platforms, deblurring is required after image acquisition before being stored, previewed, or processed for high-level interpretation. Therefore, on-the-fly correction of such images is important to avoid possible time delays, mitigate computational expenses, and increase image perception quality. We bridge this gap by synthesizing a deconvolution kernel as a linear combination of finite impulse response (FIR) even-derivative filters that can be directly convolved with blurry input images to boost the frequency fall-off of the point spread function (PSF) associated with the optical blur. We employ a Gaussian low-pass filter to decouple the image denoising problem for image edge deblurring. Furthermore, we propose a blind approach to estimate the PSF statistics for two Gaussian and Laplacian models that are common in many imaging pipelines. Thorough experiments are designed to test and validate the efficiency of the proposed method using 2054 naturally blurred images across six imaging applications and seven state-of-the-art deconvolution methods.
On Two-to-One Mappings Over Finite Fields

Two-to-one (2-to-1) mappings over finite fields play an important role in symmetric cryptography. In particular they allow to design APN functions, bent functions and semi-bent functions. In this paper we provide a systematic study of two-to-one mappings that are defined over finite fields. We characterize such mappings by means of the Walsh transforms. We also present several constructions, including an AGW-like criterion, constructions with the form of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x^{r}h(x^{(q-1)/d})$ </tex-math></inline-formula> , those from permutation polynomials, from linear translators and from APN functions. Then we present 2-to-1 polynomial mappings in classical classes of polynomials: linearized polynomials and monomials, low degree polynomials, Dickson polynomials and Muller-Cohen-Matthews polynomials, etc. Lastly, we show applications of 2-to-1 mappings over finite fields for constructions of bent Boolean and vectorial bent functions, semi-bent functions, planar functions and permutation polynomials. In all those respects, we shall review what is known and provide several new results.
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets

Mode connectivity is a surprising phenomenon in the loss landscape of deep nets. Optima---at least those discovered by gradient-based optimization---turn out to be connected by simple paths on which the loss function is almost constant. Often, these paths can be chosen to be piece-wise linear, with as few as two segments. We give mathematical explanations for this phenomenon, assuming generic properties (such as dropout stability and noise stability) of well-trained deep nets, which have previously been identified as part of understanding the generalization properties of deep nets. Our explanation holds for realistic multilayer nets, and experiments are presented to verify the theory.
Revisiting offline evaluation for implicit-feedback recommender systems

Recommender systems are typically evaluated in an offline setting. A subset of the available user-item interactions is sampled to serve as test set, and some model trained on the remaining data points is then evaluated on its performance to predict which interactions were left out. Alternatively, in an online evaluation setting, multiple versions of the system are deployed and various metrics for those systems are recorded. Systems that score better on these metrics, are then typically preferred. Online evaluation is effective, but inefficient for a number of reasons. Offline evaluation is much more efficient, but current methodologies often fail to accurately predict online performance. In this work, we identify three ways to improve and extend current work on offline evaluation methodologies. More specifically, we believe there is much room for improvement in temporal evaluation, off-policy evaluation, and moving beyond using just clicks to evaluate performance.
Adversarial Black-Box Attacks on Automatic Speech Recognition Systems Using Multi-Objective Evolutionary Optimization

Fooling deep neural networks with adversarial input have exposed a significant vulnerability in the current state-of-the-art systems in multiple domains. Both black-box and white-box approaches have been used to either replicate the model itself or to craft examples which cause the model to fail. In this work, we propose a framework which uses multi-objective evolutionary optimization to perform both targeted and un-targeted black-box attacks on Automatic Speech Recognition (ASR) systems. We apply this framework on two ASR systems: Deepspeech and Kaldi-ASR, which increases the Word Error Rates (WER) of these systems by upto 980%, indicating the potency of our approach. During both un-targeted and targeted attacks, the adversarial samples maintain a high acoustic similarity of 0.98 and 0.97 with the original audio.
Improved payload capacity in LSB image steganography uses dilated hybrid edge detection

This research proposed the dilated hybrid edge detection on the three most significant bits (MSB) pixels of cover images with the aim of expanding the edge area so as to increase the data embedding capacity in image steganography. This technique can perform extraction without the need to save edge detection on the original cover image. This is because edge detection is performed on 3-bits MSB images, whereas messages are embedded at x LSB and y LSB values, where x is the number of LSB bits replaced in the edge area and y is the number of bits replaced in the non-edge area, where x and y cannot reach 3-bits MSB, so the edge detection of the cover image and the stego image will be the same. Based on the results of tests carried out, the proposed steganography technique succeeded in improving the quality of imperceptibility, where the PSNR value increased by about 1 to 2 dB when compared to the previous method. Similarly, the message embedding capacity can be increased due to a wider edge area, with the same image dataset obtained an average increase of 4,350–12,364 pixels of the edge area, however, the quality of the stego image imperceptibility can be maintained.
Dynamic Network Pruning with Interpretable Layerwise Channel Selection

Dynamic network pruning achieves runtime acceleration by dynamically determining the inference paths based on different inputs. However, previous methods directly generate continuous decision values for each weight channel, which cannot reflect a clear and interpretable pruning process. In this paper, we propose to explicitly model the discrete weight channel selections, which encourages more diverse weights utilization, and achieves more sparse runtime inference paths. Meanwhile, with the help of interpretable layerwise channel selections in the dynamic network, we can visualize the network decision paths explicitly for model interpretability. We observe that there are clear differences in the layerwise decisions between normal and adversarial examples. Therefore, we propose a novel adversarial example detection algorithm by discriminating the runtime decision features. Experiments show that our dynamic network achieves higher prediction accuracy under the similar computing budgets on CIFAR10 and ImageNet datasets compared to traditional static pruning methods and other dynamic pruning approaches. The proposed adversarial detection algorithm can significantly improve the state-of-the-art detection rate across multiple attacks, which provides an opportunity to build an interpretable and robust model.
A Plain-Image-Related Chaotic Image Encryption Algorithm Based on DNA Sequence Operation and Discrete Logarithm

According to the research works of the literatures on chaotic cryptanalysis, many recent chaotic image encryption algorithms cannot resist chosen-plaintext attack.Although some chaotic image encryption algorithms introduce plain-image information, they still violate some design requirements of modern cryptosystems.Therefore, after analyzing many literatures related to chaotic image encryption, we propose a plain-image-related chaotic image encryption algorithm based on the deoxyribonucleic acid (DNA) sequence operation and discrete logarithm.As we know, the discrete logarithm calculation is a complex non-linear calculation, introducing it can increase the complexity of the encryption process.More importantly, discrete logarithm values are significantly different under different generators, and the multiplicative group adopted in the proposed algorithm has as many as 128 generators.Therefore, by using two generators determined by the secret key and the hash value of the plain-image respectively, we not only expand the key space, but also greatly improve the plain-image sensitivity and plain-image relevancy of the encryption process.In addition, the proposed algorithm improves the way of using the hash value of the plain-image.In this way, once the secret key is selected, chaotic sequences can be generated in advance and reused to improve the encryption efficiency.Similarly, the proposed algorithm also introduces the DNA sequence operation with high parallelism to further improves the encryption efficiency.Simulation tests and comparative analyses show that the proposed algorithm can not only resist common attacks, but also outperform some recent chaotic image encryption algorithms in many aspects.
Control Flow Graph Embedding Based on Multi-Instance Decomposition for Bug Localization

During software maintenance, bug report is an effective way to identify potential bugs hidden in a software system. It is a great challenge to automatically locate the potential buggy source code according to a bug report. Traditional approaches usually represent bug reports and source code from a lexical perspective to measure their similarities. Recently, some deep learning models are proposed to learn the unified features by exploiting the local and sequential nature, which overcomes the difficulty in modeling the difference between natural and programming languages. However, only considering local and sequential information from one dimension is not enough to represent the semantics, some multi-dimension information such as structural and functional nature that carries additional semantics has not been well-captured. Such information beyond the lexical and structural terms is extremely vital in modeling program functionalities and behaviors, leading to a better representation for identifying buggy source code. In this paper, we propose a novel model named CG-CNN, which is a multi-instance learning framework that enhances the unified features for bug localization by exploiting structural and sequential nature from the control flow graph. Experimental results on widely-used software projects demonstrate the effectiveness of our proposed CG-CNN model.
Subspace Distribution Adaptation Frameworks for Domain Adaptation

Domain adaptation tries to adapt a model trained from a source domain to a different but related target domain. Currently, prevailing methods for domain adaptation rely on either instance reweighting or feature transformation. Unfortunately, instance reweighting has difficulty in estimating the sample weights as the dimension increases, whereas feature transformation sometimes fails to make the transformed source and target distributions similar when the cross-domain discrepancy is large. In order to overcome the shortcomings of both methodologies, in this article, we model the unsupervised domain adaptation problem under the generalized covariate shift assumption and adapt the source distribution to the target distribution in a subspace by applying a distribution adaptation function. Accordingly, we propose two frameworks: Bregman-divergence-embedded structural risk minimization (BSRM) and joint structural risk minimization (JSRM). In the proposed frameworks, the subspace distribution adaptation function and the target prediction model are jointly learned. Under certain instantiations, convex optimization problems are derived from both frameworks. Experimental results on the synthetic and real-world text and image data sets show that the proposed methods outperform the state-of-the-art domain adaptation techniques with statistical significance.
Operational efficiency effects of blockchain technology implementation in firms

Purpose The purpose of this study is to evaluate the effect of blockchain technology on firms’ operational efficiency in the context of China. Design/methodology/approach The authors use panel data for blockchain-based companies listed on stock exchanges in China (Shanghai, Shenzhen and Hong Kong) between 2014 and 2018. The operational efficiency of firms that deploy blockchain technology is evaluated using ordinary least squares and system generalized method of moments estimation. Findings Results suggest that companies’ current year performance exceeds the previous year performance because of blockchain deployment in firms’ operations. Firms with higher financial leverage and return on assets reap more benefits from blockchain. Larger and older firms benefit less from blockchain implementation. Stochastic frontier estimation suggests that, on average, firms attain a 57.76 per cent technical efficiency level, or, put differently, they operate 42.24 per cent below their maximum level of potential output. Originality/value Blockchain can benefit firms in terms of consensus, security and trust, spurring the evolution of a new form of organizational dynamics. This study explores the theory of transactional cost analysis under blockchain technology. In addition, this study hypothesizes and empirically demonstrates the significant impacts of blockchain technology on corporations’ operational efficiency, using audited, externally reported financial data. Industry professionals can reap benefits from this research by noticing the magnitude of changes in firms’ financial parameters attributable to blockchain adoption.
Inductive Relation Prediction by Subgraph Reasoning

The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i.e., embeddings) of entities and relations. However, these embedding-based methods do not explicitly capture the compositional logical rules underlying the knowledge graph, and they are limited to the transductive setting, where the full set of entities must be known during training. Here, we propose a graph neural network based relation prediction framework, GraIL, that reasons over local subgraph structures and has a strong inductive bias to learn entity-independent relational semantics. Unlike embedding-based models, GraIL is naturally inductive and can generalize to unseen entities and graphs after training. We provide theoretical proof and strong empirical evidence that GraIL can represent a useful subset of first-order logic and show that GraIL outperforms existing rule-induction baselines in the inductive setting. We also demonstrate significant gains obtained by ensembling GraIL with various knowledge graph embedding methods in the transductive setting, highlighting the complementary inductive bias of our method.
CrossTransformers: spatially-aware few-shot transfer

Given new tasks with very little data$-$such as new classes in a classification problem or a domain shift in the input$-$performance of modern vision systems degrades remarkably quickly. In this work, we illustrate how the neural network representations which underpin modern vision systems are subject to supervision collapse, whereby they lose any information that is not necessary for performing the training task, including information that may be necessary for transfer to new tasks or domains. We then propose two methods to mitigate this problem. First, we employ self-supervised learning to encourage general-purpose features that transfer better. Second, we propose a novel Transformer based neural network architecture called CrossTransformers, which can take a small number of labeled images and an unlabeled query, find coarse spatial correspondence between the query and the labeled images, and then infer class membership by computing distances between spatially-corresponding features. The result is a classifier that is more robust to task and domain shift, which we demonstrate via state-of-the-art performance on Meta-Dataset, a recent dataset for evaluating transfer from ImageNet to many other vision datasets.
Detection of Road Objects With Small Appearance in Images for Autonomous Driving in Various Traffic Situations Using a Deep Learning Based Approach

Effectively detecting road objects in various environments would significantly improve driving safety for autonomous vehicles.However, small objects, low illumination, and blurred outline in images strongly limit the performance of current road object detection methods.To solve these problems, this paper proposed a novel deep learning anchor-free approach based on CenterNet.The atrous spatial pyramid pooling (ASPP) was used to extract features from multiple scales to improve the detection performance while not increasing the computational cost and the number of parameters.The space to depth algorithm was then adopted in our proposed approach to optimize the traditional downsampling process.A large-scale naturalistic driving dataset (BDD100K) was used to examined the effectiveness of our proposed approach.The experimental results show that our proposed approach can effectively improve the detection performance on small objects in various traffic situations.
Tensor Low-Rank Constraint and $l_0$ Total Variation for Hyperspectral Image Mixed Noise Removal

Several methods based on Total Variation (TV) have been proposed for Hyperspectral Image (HSI) denoising. However, the TV terms of these methods just use various l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norms and penalize image gradient magnitudes, having a negative influence on the preprocessing of HSI denoising and further HSI classification task. In this paper, a novel l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> Total Variation (l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> TV) is first introduced and analyzed for the HSI noise removal framework to preserve more information for classification. We propose a novel Tensor low-rank constraint and l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> Total Variation (TLR-l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> TV) model in this paper. l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> TV directly controls the number of non-zero gradients and focuses on recovering the sharp image edges. The spectral-spatial information among all bands is exploited uniformly for removing mixed noise, which facilitates the subsequent classification after denoising. Including the Weighted Sum of Weighted Nuclear Norm (WSWNN) and the Weighted Sum of Weighted Tensor Nuclear Norm (WSWTNN), we propose two TLR-l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> TV-based algorithms, namely WSWNN-l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> TV and WSWTNN-l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> TV. The Alternating Direction Method of Multipliers (ADMM) and the Augmented Lagrange Multiplier (ALM) are employed to solve the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> TV model and TLR-l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> TV model, respectively. In both simulated and real data, the proposed models achieve superior performances in mixed noise removal of HSI. Especially, HSI classification accuracy is improved more effectively after denoising by the proposed TLR-l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> TV method.
Small-Object Detection Based on YOLO and Dense Block via Image Super-Resolution

Small-object detection is a basic and challenging problem in computer vision tasks. It is widely used in pedestrian detection, traffic sign detection, and other fields. This paper proposes a deep learning small-object detection method based on image super-resolution to improve the speed and accuracy of small-object detection. First, we add a feature texture transfer (FTT) module at the input end to improve the image resolution at this end as well as to remove the noise in the image. Then, in the backbone network, using the Darknet53 framework, we use dense blocks to replace residual blocks to reduce the number of network structure parameters to avoid unnecessary calculations. Then, to make full use of the features of small targets in the image, the neck uses a combination of SPPnet and PANnet to complete this part of the multi-scale feature fusion work. Finally, the problem of image background and foreground imbalance is solved by adding the foreground and background balance loss function to the YOLOv4 loss function part. The results of the experiment conducted using our self-built dataset show that the proposed method has higher accuracy and speed compared with the currently available small-target detection methods.