Reviewerly/exHarmony huggingface dataset:
On the Security Relevance of Initial Weights in Deep Neural Networks

Recently, a weight-based attack on stochastic gradient descent inducing overfitting has been proposed. We show that the threat is broader: A task-independent permutation on the initial weights suffices to limit the achieved accuracy to for example 50% on the Fashion MNIST dataset from initially more than 90%. These findings are supported on MNIST and CIFAR. We formally confirm that the attack succeeds with high likelihood and does not depend on the data. Empirically, weight statistics and loss appear unsuspicious, making it hard to detect the attack if the user is not aware. Our paper is thus a call for action to acknowledge the importance of the initial weights in deep learning.
Low-Light Image Enhancement Combined with Attention Map and U-Net Network

The quality of images obtained in low-light environments will degrade and exhibit poor results, so researchers have focused on improving low-light images and enhancing them. This paper recommends a different method for low-illumination image enhancement. Our method combines traditional methods and CNN to achieve image brightness enhancement, color recovery and denoising. The attention map that we propose can help avoid overexposure in bright regions and enhance dark regions. Through a large number of subjective and objective experiments, the method proposed in this paper can not only enhance low-illuminance images, but also require less inference time.
WITHDRAWN: Online English translation information retrieval based on FPGA and Internet of Things

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
DIBR‐synthesised video quality assessment by measuring geometric distortion and spatiotemporal inconsistency

Depth-image-based rendering (DIBR), as the most popular view synthesis method, is commonly used in the application of multi-view and free-viewpoint videos. However, the quality evaluation of DIBR-synthesised videos remains largely unexplored, which may hinder the development of more advanced view synthesis technology. With this motivation, this Letter presents a new quality metric for DIBR-synthesised videos. Specifically, the disoccluded regions are first detected based on an adaptive threshold to quantify geometric distortions. An energy-based sequence mapping strategy is proposed to portray spatiotemporal inconsistency by calculating first-order and second-order similarities in the gradient magnitude domain and the Laplace-of-Gaussian domain, respectively. Finally, the overall quality score is generated by pooling the scores of geometric distortion and spatiotemporal inconsistency. Experimental results demonstrate that the proposed metric outperforms the state-of-the-art metrics dedicated to DIBR-synthesised images and videos.
Digit Image Recognition Using an Ensemble of One-Versus-All Deep Network Classifiers

In multiclass deep network classifiers, the burden of classifying samples of different classes is put on a single classifier. As the result, the optimum classification accuracy is not obtained. Also, training times are large due to running the CNN training on single CPU/GPU. However, it is known that using ensembles of classifiers increases the performance. Also, the training times can be reduced by running each member of the ensemble on a separate processor. Ensemble learning has been used in the past for traditional methods to a varying extent and is a hot topic. With the advent of deep learning, ensemble learning has been applied to the former as well. However, an area which is unexplored and has potential is one-versus-all (OVA) deep ensemble learning. In this paper, we explore it and show that by using OVA ensembles of deep networks, improvements in performance of deep networks can be obtained. As shown in this paper, the classification capability of deep networks can be further increased by using an ensemble of binary classification (OVA) deep networks. We implement a novel technique for the case of digit image recognition and test and evaluate it on the same. In the proposed approach, a single OVA deep network classifier is dedicated to each category. Subsequently, OVA deep network ensembles have been investigated. Every network in an ensemble has been trained by an OVA training technique using the stochastic gradient descent with momentum algorithm (SGDMA). For classification of a test sample, the sample is presented to each network in the ensemble. After prediction score voting, the network with the largest score is assumed to have classified the sample. The experimentation has been done on the MNIST digit dataset, the USPS + digit dataset, and MATLAB digit image dataset. Our proposed technique outperforms the baseline on digit image recognition for all datasets.
An Efficient Residual-Based Method for Railway Image Dehazing

Trains shuttle in semiopen environments, and the surrounding environment plays an important role in the safety of train operation. The weather is one of the factors that affect the surrounding environment of railways. Under haze conditions, railway monitoring and staff vision could be blurred, threatening railway safety. This paper tackles image dehazing for railways. The contributions of this paper for railway video image dehazing are as follows: (1) this paper proposes an end-to-end residual block-based haze removal method that consists of two subnetworks, namely fine-grained and coarse-grained network can directly generate the clean image from input hazy image, called RID-Net (Railway Image Dehazing Network). (2) The combined loss function (per-pixel loss and perceptual loss functions) is proposed to achieve both low-level features and high-level features so to generate the high-quality restored images. (3) We take the full-reference criterion (PSNR&SSIM), object detection, running time, and sensory vision to evaluate the proposed dehazing method. Experimental results on railway synthesized dataset, benchmark indoor dataset, and real-world dataset demonstrate our method has superior performance compared to the state-of-the-art methods.
Comparison of the number of nodes explored by cyclic best first search with depth contour and best first search

Search strategies are used in Branch-and-bound (B&B) algorithms to determine the order to process the nodes in a search tree. A search strategy that has seen frequent success in B&B algorithms is Best First Search (BFS). However, a new search strategy called Cyclic Best First Search with depth contour (CBFS-depth) has been shown to consistently process fewer nodes than BFS on several combinatorial optimization problems. In this paper, we study the properties of search trees under which CBFS-depth may outperform BFS in terms of the average number of nodes explored to prove optimality. To facilitate the study, a search tree model is proposed for minimization problems solved by B&B algorithms which is based on the distributions of nodes in the tree with different lower bounds. Using this model, we estimate the expected number of nodes expanded by CBFS-depth. Additionally, we apply B&B algorithms using both search strategies to trees that are randomly generated according to the proposed model in order to study how the average number of nodes expanded by each search strategy is affected by the search tree. Finally, both search strategies are tested on existing optimization problems to validate the observations from the randomly generated search trees.
Defining a Software Maintainability Dataset: Collecting, Aggregating and Analysing Expert Evaluations of Software Maintainability

Before controlling the quality of software systems, we need to assess it. In the case of maintainability, this often happens with manual expert reviews. Current automatic approaches have received criticism because their results often do not reflect the opinion of experts or are biased towards a small group of experts. We use the judgments of a significantly larger expert group to create a robust maintainability dataset. In a large scale survey, 70 professionals assessed code from 9 open and closed source Java projects with a combined size of 1.4 million source lines of code. The assessment covers an overall judgment as well as an assessment of several subdimensions of maintainability. Among these subdimensions, we present evidence that understandability is valued the most by the experts. Our analysis also reveals that disagreement between evaluators occurs frequently. Significant dissent was detected in 17% of the cases. To overcome these differences, we present a method to determine a consensus, i.e. the most probable true label. The resulting dataset contains the consensus of the experts for more than 500 Java classes. This corpus can be used to learn precise and practical classifiers for software maintainability.
Combination of Convolutional Neural Network Architecture and its Learning Method for <scp>Rotation‐Invariant</scp> Handwritten Digit Recognition

This letter presents several combinations of a convolutional neural network (CNN) and its learning method for rotation‐invariant digit recognition. Rotation data augmentation is widely used for improving rotation invariance. Data augmentation commonly assigns the same label to all augmented images of the same source. However, this learning method causes some collisions between original and rotated digits. Thus, this letter presents three types of rotation‐invariance learning methods and applies them to five popular CNN architectures. Experimental results indicate that multi‐task learning on ResNet‐50 is the best combination. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Research on Power Transformer Test Plan based on Big Data Processing Algorithm

Abstract There are many power transformer parameters, and their changes are closely related to factors such as grid operation and meteorological environment. It is urgent to use effective big data technology to mine and analyze a large amount of relevant data and extract information to improve the timeliness and accuracy of power transformer experiments. First, based on a large number of fault and defect samples, the corresponding relationship between the key performance of the power transformer and the state quantity is mined through the confidence of the association rule, and then the time series of the state quantity of the power transformer is characterized by big data through the big data processing algorithm. The characteristic root spectrum distribution and circle rate of big data containing time series models are studied, the history and current state information of state quantities are analyzed, and the key performance experiments and abnormal detection of power transformers are realized. Taking a 500kV substation as an example, the transformer load, online monitoring and environmental meteorological data fusion constitute the key performance big data, and the big data processing algorithm is used to compare the spectral properties of the historical and current time period matrices to realize the key performance experiment of the transformer And anomaly detection. The research results show that the big data processing algorithm is effective for analyzing the operating status of power transformers, and provides a new way of thinking for the application of big data technology in power transformer status experiments.
Explaining Results of Path Queries on Graphs

Many graph query languages use, at their core, path queries that yield node pairs that are connected by a path of interest. For the end-user, such node pairs only give limited insight as to why this query result is obtained, as the pair does not directly identify the underlying path of interest. To address this limitation of path queries, we propose the single-path semantics, which evaluates path queries to, for each node pair (m, n), a single path from m to n satisfying the conditions of the query. To put our proposal in practice, we provide an efficient algorithm for evaluating context-free path queries, a particular powerful type of path queries, using the single-path semantics. Additionally, we perform a short evaluation of our techniques that shows that the single-path semantics is practically feasible, even when query results grow large.
Bayesian Extended Target Tracking with Automotive Radar using Learned Spatial Distribution Models

We apply the concept of random set cluster processes in combination with a learned measurement model to extended target tracking. The spatial distribution of measurements generated by a target vehicle is learned via a variational Gaussian mixture (VGM) model. The VGM is then interpreted as the measurement likelihood of a Multi-Bernoulli (MB) distribution. We derive a closed-form Bayesian recursion for tracking an extended target by the use of random set cluster process. This formulation is particularly successful for sparse and noisy measurements, and is applied to automotive Radio Detection and Ranging (RADAR) detections. Last, we provide a large-scale evaluation of our approach based on the data published in the Nuscenes data set.
Shape Memory Alloy Haptic Compression Garment for Media Augmentation in Virtual Reality Environment

Compression-based haptics instrumented into soft, wearable systems have the potential to enhance/augment gaming and entertainment experiences. Here, we present a computer-mediated Shape Memory Alloy (SMA) garment capable of delivering spatially and temporally dynamic compression and thermal stimuli for the purposes of enhancing a movie-watching affective experience based on 4 different emotions: sadness, love, gratitude, fear. Preliminary findings, still pending publication, were obtained from an MTurk survey (n=53) that seek to gather how different emotions can/should be represented physically through a haptic garment. A proposed study in a virtual reality (VR) environment is also introduced as a next step to evaluate how the presence/absence of haptics changes a user's video-watching experience, including immersion, enjoyment, presence, and felt emotional intensity, evaluated through objective (biometrics) and subjective (self-report questionnaire and qualitative interview) measures.
DOOM: a Training-Free, Real-Time Video Flow Identification Method for Encrypted Traffic

The internet traffic is mainly dominated by videos, limiting video transmission is thus required to manage the traffic when network congestion occurs. Besides, service providers want to guarantee video transmission for customers' satisfaction. However, traffic encryption is becoming increasingly common, making conventional payload-based methods unsuitable, while traffic analysis (TA) still works. In this paper, we implement and evaluate machine learning (ML)-based TA for video flow identification and propose a method named detecting ON-OFF mode (DOOM), which is a training-free, real-time and lightweight video flow identification method suitable for Gateway or Internet Backbone Provider (IBP) middle-boxes.
Indicative Conditionals in Objective Contexts

A‐62 conversation can be conceived as aiming to circumscribe a set of possibilities that are relevant to the goals of the conversation. This set of possibilities may be conceived as determined by the goals and objective circumstances of the interlocutors and not by their propositional attitudes. An indicative conditional can be conceived as circumscribing a set of possibilities that have a certain property: If the set of relevant possibilities is subsequently restricted to one in which the antecedent holds, then it will be restricted as well to one in which the consequent holds. We will identify a number of desiderata concerning the validity of arguments; we will develop a formally precise semantics for conditionals conceived in this way that satisfies the desiderata, and we will present a deductive calculus that is sound and complete with respect to the semantics. Finally, we will argue that the semantics compares well, both formally and foundationally, with two other semantic theories of indicative conditionals that satisfy the desiderata, namely, those of Gillies and Bledin.
Gaze Preserving CycleGANs for Eyeglass Removal and Persistent Gaze Estimation

A driver’s gaze is critical for determining theirattention, state, situational awareness, and readiness to take over control from partially automated vehicles. Estimating the gaze direction is the most obvious way to gauge a driver’s state under ideal conditions when limited to using non-intrusive imaging sensors. Unfortunately, the vehicular environment introduces a variety of challenges that are usually unaccounted for - harsh illumination, nighttime conditions, and reflective eyeglasses. Relying on head pose alone under such conditions can prove to be unreliable and erroneous. In this study, we offer solutions to address these problems encountered in the real world. To solve issues with lighting, we demonstrate that using an infrared camera with suitable equalization and normalization suffices. To handle eyeglasses and their corresponding artifacts, we adopt image-to-image translation using generative adversarial networks to pre-process images prior to gaze estimation. Our proposed Gaze Preserving CycleGAN (GPCycleGAN) is trained to preserve the driver’s gaze while removing potential eyeglasses from face images. GPCycleGAN is based on the well-known CycleGAN approach - with the addition of a gaze classifier and a gaze consistency loss for additional supervision. Our approach exhibits improved performance, interpretability, robustness and superior qualitative results on challenging real-world datasets.
A multi-level approach with visual information for encrypted H.265/HEVC videos

High-efficiency video coding (HEVC) encryption has been proposed to encrypt syntax elements for the purpose of video encryption. To achieve high video security, to the best of our knowledge, almost all of the existing HEVC encryption algorithms mainly encrypt the whole video, such that the user without permissions cannot obtain any viewable information. However, these encryption algorithms cannot meet the needs of customers who need part of the information but not the full information in the video. In many cases, such as professional paid videos or video meetings, users would like to observe some visible information in the encrypted video of the original video to satisfy their requirements in daily life. Aiming at this demand, this paper proposes a multi-level encryption scheme that is composed of lightweight encryption, medium encryption and heavyweight encryption, where each encryption level can obtain a different amount of visual information. First, we employ AES–CTR to generate a pseudo-random number sequence. Then, the main syntax elements in the H.265/HEVC encoding process are encrypted by a pseudorandom sequence. In the lightweight encryption level, the syntax element of the luma intraprediction model is chosen for encryption. In the medium encryption level, the syntax element of the discrete cosine transform (DCT) coefficient sign is employed for scrambling encryption. In the heavyweight encryption level, syntax elements of both the luma intraprediction model and the DCT coefficient sign are encrypted simultaneously by the pseudorandom sequence. It is found that both encrypting the luma intraprediction model (IPM) and scrambling the syntax element of the DCT coefficient sign can achieve the performance of a distorted video in which there is still residual visual information, while encrypting both of them can implement the intensity of encryption and one cannot gain any visual information. The experimental results meet our expectations appropriately, indicating that there is a different amount of visual information in each encryption level. Meanwhile, users can flexibly choose the encryption level according to their various requirements.
Partitioning the internet using Anycast catchments

In anycast deployments, knowing how traffic will be distributed among the locations is challenging. In this paper, we propose a technique for partitioning the Internet using passive measurements of existing anycast deployments such that all IP addresses within a partition are routed to the same location for an arbitrary anycast deployment. One IP address per partition may then represent the entire partition in subsequent measurements of specific anycast deployments. We implement a practical version of our technique and apply it to production traffic from an anycast authoritative DNS service of a major CDN and demonstrate that the resulting partitions have low error even up to 2 weeks after they are generated.
Minimal Rolling Shutter Absolute Pose with Unknown Focal Length and Radial Distortion

The internal geometry of most modern consumer cameras is not adequately described by the perspective projection. Almost all cameras exhibit some radial lens distortion and are equipped with electronic rolling shutter that induces distortions when the camera moves during the image capture. When focal length has not been calibrated offline, the parameters that describe the radial and rolling shutter distortions are usually unknown. While, for global shutter cameras, minimal solvers for the absolute camera pose and unknown focal length and radial distortion are available, solvers for the rolling shutter were missing. We present the first minimal solutions for the absolute pose of a rolling shutter camera with unknown rolling shutter parameters, focal length, and radial distortion. Our new minimal solvers combine iterative schemes designed for calibrated rolling shutter cameras with fast generalized eigenvalue and Gröbner basis solvers. In a series of experiments, with both synthetic and real data, we show that our new solvers provide accurate estimates of the camera pose, rolling shutter parameters, focal length, and radial distortion parameters. The implementation of our solvers is available at .
PQual

The increasing complexity of web pages has brought a number of solutions to offer simpler or lighter versions of these pages. The qualitative evaluation of the new versions is commonly carried out relying on user studies. In addition to the associated costs, running user studies might become extremely challenging due to health, travel, and financial constraints. Moreover, user studies are prone to subjectivity, which makes it difficult to compare the results of different studies. In this paper, we propose PQual, a tool that enables the automation of the qualitative evaluation of web pages using computer vision. Results show that PQual computes comparable similarity scores to the scores provided by human raters. In addition, it can effectively evaluate all the functionality of a web page, whereas humans might skip many of the functional elements during the evaluation.