Reviewerly/exHarmony huggingface dataset:
HDR-Like Image from Pseudo-Exposure Image Fusion: A Genetic Algorithm Approach

Image distortion during multiple-exposure capture is a common scenario, and most of the existing legacy image content is in the low dynamic range (LDR). For both of these problems, pseudo multiple exposure image generation from a single image is a possible solution. Hence, in this article, we propose a novel method for high dynamic range (HDR)-like image generation by fusing pseudo-multi-exposure images that are produced from only one single image. The proposed pseudo-exposure image generation method is based on intensity mapping function (IMF). First, our system learns the relation between different exposure images with the help of genetic algorithm and generates long and short exposure images. In the second step, we fuse images with different exposures to generate the HDR-like image. Our method outperforms several inverse tone mapping operator (ITMO)-based methods. The simplicity and good performance of the proposed method makes it suitable for mobile phones and other consumer electronics. We also develop a demo software to validate the proposed method.
Declutter and Focus: Empirically Evaluating Design Guidelines for Effective Data Communication

Data visualization design has a powerful effect on which patterns we see as salient and how quickly we see them. The visualization practitioner community prescribes two popular guidelines for creating clear and efficient visualizations: declutter and focus. The declutter guidelines suggest removing non-critical gridlines, excessive labeling of data values, and color variability to improve aesthetics and to maximize the emphasis on the data relative to the design itself. The focus guidelines for explanatory communication recommend including a clear headline that describes the relevant data pattern, highlighting a subset of relevant data values with a unique color, and connecting those values to written annotations that contextualize them in a broader argument. We evaluated how these recommendations impact recall of the depicted information across cluttered, decluttered, and decluttered+focused designs of six graph topics. Undergraduate students were asked to redraw previously seen visualizations, to recall their topics and main conclusions, and to rate the varied designs on aesthetics, clarity, professionalism, and trustworthiness. Decluttering designs led to higher ratings on professionalism, and adding focus to the design led to higher ratings on aesthetics and clarity. They also showed better memory for the highlighted pattern in the data, as reflected across redrawings of the original visualization and typed free-response conclusions, though we do not know whether these results would generalize beyond our memory-based tasks. The results largely empirically validate the intuitions of visualization designers and practitioners. The stimuli, data, analysis code, and Supplementary Materials are available at https://osf.io/wes9u/.
Optimization of Graph Neural Networks with Natural Gradient Descent

In this work, we propose to employ information-geometric tools to optimize a graph neural network architecture such as the graph convolutional networks. More specifically, we develop optimization algorithms for the graph-based semi-supervised learning by employing the natural gradient information in the optimization process. This allows us to efficiently exploit the geometry of the underlying statistical model or parameter space for optimization and inference. To the best of our knowledge, this is the first work that has utilized the natural gradient for the optimization of graph neural networks that can be extended to other semi-supervised problems. Efficient computations algorithms are developed and extensive numerical studies are conducted to demonstrate the superior performance of our algorithms over existing algorithms such as ADAM and SGD.
Deep sparse autoencoder prediction model based on adversarial learning for cross-domain recommendations

Online recommender systems generally suffer from severe data sparsity problems, and this are particularly prevalent in newly launched systems that do not have sufficient amounts of data. Cross-domain recommendations can provide us with some new ideas for assisting with user recommendations in sparse target domains by transferring knowledge from a source domain with rich data. In this paper, a deep sparse autoencoder prediction model based on adversarial learning for cross-domain recommendations (DSAP-AL) is proposed to improve the accuracy of rating predictions in similar cross-domain recommender systems. Specifically, joint matrix factorization and adversarial network learning models are adopted to integrate and align user and item latent factor spaces in a unified pattern. Then, a deep sparse autoencoder is represented and modeled by transferring the latent factors and interlayer weights. Furthermore, a domain factor adaptation algorithm is proposed to capture robust user and item factors, and the learned regularization constraints are added to the objective function, thereby alleviating the data sparsity issue. Experimental results on four real-world datasets demonstrate that, even without overlapping entities (users or items) in the source and target domains, the proposed DSAP-AL method achieves competitive performance relative to other state-of-the-art individual and cross domain approaches. Moreover, the DSAP-AL model is not only effective for scenarios with sparse data but also robust for noise-containing recommendations.
Image Copy-Move Forgery Detection Algorithms Based on Spatial Feature Domain

Currently, digital image forgery (DIF) become more active due to the advent of powerful image processing tools. On a daily, many images are exchanged through the internet, which makes them susceptible to such effects. One of the most popular of the passive image forgery techniques is copy-move forgery. In the Copy-move forgery, the basic process is copy/paste from one area to another in the same image. In this paper, the proposed image copy-move forgery detection (IC-MFDs) involves five stages: image preprocessing, dividing the image into overlapping blocks, calculating the mean and standard deviation of each block, feature vectors are then sorted lexicographically, then feeding the feature vector to the Support Vector Machine (SVM) classifier to identify the image as authentic or forged. Experiments are performed on a standard dataset of copy move forged images MICC-F220 to evaluate the proposed technique. The findings indicate that the proposed IC-MFDs can be extremely accurate in terms of Detection Accuracy (98.44). We also compare some state-of-the-art approaches with our proposed IC-MFDs. It's noted that the findings obtained are better than these approaches.
Argumentation schemes in AI and Law

In this paper we describe the impact that Walton’s conception of argumentation schemes had on AI and Law research. We will discuss developments in argumentation in AI and Law before Walton’s schemes became known in that community, and the issues that were current in that work. We will then show how Walton’s schemes provided a means of addressing all of those issues, and so supplied a unifying perspective from which to view argumentation in AI and Law.
Detection and rectification of arbitrary shaped scene texts by using text keypoints and links

Detection and recognition of scene texts of arbitrary shapes remain a grand challenge due to the super-rich text shape variation in text line orientations, lengths, curvatures, etc. This paper presents a mask-guided multi-task network that detects and rectifies scene texts of arbitrary shapes reliably. Three types of keypoints are detected which specify the centre line and so the shape of text instances accurately. In addition, four types of keypoint links are detected of which the horizontal links associate the detected keypoints of each text instance and the vertical links predict a pair of landmark points (for each keypoint) along the upper and lower text boundary, respectively. Scene texts can be located and rectified by linking up the associated landmark points (giving localization polygon boxes) and transforming the polygon boxes via thin plate spline, respectively. Extensive experiments over several public datasets show that the use of text keypoints is tolerant to the variation in text orientations, lengths, and curvatures, and it achieves competitive scene text detection and rectification performance as compared with state-of-the-art methods.
Risk-Driven Behavioral Biometric-based One-Shot-cum-Continuous User Authentication Scheme

The paper presents a risk-driven behavioral biometric-based user authentication scheme for smartphones. Our scheme delivers one-shot-cum-continuous authentication, thus not only authenticates users at the start of the application sign-in process but also, throughout the active user session. The scheme leverages the widely used PIN/password-based authentication technology by giving flexibility to users to enter any random 8-digit alphanumeric text, instead of pre-configured PIN/Passwords. Internally, the scheme exploits two behavioral biometric traits, i.e., touch-timing-differences of the entered strokes and the hand-movement gesture recorded during the random text entry, to authenticate users. And, for the entire user session, the scheme continuously authenticates the user by computing the risk-score every time the user initiates a sensitive activity. If the risk-score is higher than the predefined threshold, the current user session terminates. Afterward, the scheme requests the user to re-authenticate. Thus, our scheme serves three main objectives: Firstly, it offers users the flexibility to enter an 8 − digit random alphanumeric text as their secret enhancing the usability of PIN/password-based schemes. Secondly, it strengthens the security of PIN/password-based schemes as verification decision is not binary, and mimicking the invisible touch-timings and hand-movements simultaneously, could be extremely difficult as our security analysis determined. Lastly, the scheme does not require any dedicated device (e.g., a smart token for OTP generation) for 2-factor authentication. The results obtained on 11,400 user-samples (collected by 3 days in-the-wild testing) and user-experience responses (received from the Software Usability Scale4 survey) of 95 testers demonstrate our scheme as an accurate and acceptable user authentication scheme.
Assessing and Comparing Interpretability Techniques for Artificial Neural Networks Breast Cancer Classification

Breast cancer is the most common type of cancer among women. Thankfully, early detection and treatment improvements helped decrease the number of deaths. Data Mining techniques have always assisted BC tasks whether it is screening, diagnosis, prognosis, treatment, monitoring, and/or management. Nowadays, the use of Data Mining is witnessing a new era. In fact, the main objective is no longer to replace humans but to enhance their capabilities, which is why Artificial Intelligence is now referred to as Intelligence Augmentation. In this context, interpretability is used to help domain experts learn new patterns and machine learning experts debug their models. This paper aims to investigate three black-boxes interpretation techniques: Feature Importance, Partial Dependence Plot, and LIME when applied to two types of feed-forward Artificial Neural Networks: Multilayer perceptrons, and Radial Basis Function Network, trained on the Wisconsin Original dataset for breast cancer diagnosis. Results showed that local LIME explanations were instance-level interpretations that came in line with the global interpretations of the other two techniques. Global/local interpretability techniques can thus be combined to define the trustworthiness of a black-box model.
Long-Term Temporally Consistent Unpaired Video Translation from Simulated Surgical 3D Data

Research in unpaired video translation has mainly focused on short-term temporal consistency by conditioning on neighboring frames. However for transfer from simulated to photorealistic sequences, available information on the underlying geometry offers potential for achieving global consistency across views. We propose a novel approach which combines unpaired image translation with neural rendering to transfer simulated to photorealistic surgical abdominal scenes. By introducing global learnable textures and a lighting-invariant view-consistency loss, our method produces consistent translations of arbitrary views and thus enables long-term consistent video synthesis. We design and test our model to generate video sequences from minimally-invasive surgical abdominal scenes. Because labeled data is often limited in this domain, photorealistic data where ground truth information from the simulated domain is preserved is especially relevant. By extending existing image-based methods to view-consistent videos, we aim to impact the applicability of simulated training and evaluation environments for surgical applications. Code and data: http://opencas.dkfz.de/video-sim2real.
Monitoring-Based Differential Privacy Mechanism Against Query Flooding-Based Model Extraction Attack

Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through public queries. Though there are some protection options such as differential privacy (DP) and monitoring, which are considered promising techniques to mitigate this attack, we still find that the vulnerability persists. In this article, we propose an adaptive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">query-flooding parameter duplication</i> (QPD) attack. The adversary can infer the model information with black-box access and no prior knowledge of any model parameters or training data via QPD. We also develop a defense strategy using DP called monitoring-based DP (MDP) against this new attack. In MDP, we first propose a novel real-time <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">model extraction status assessment</i> scheme called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Monitor</i> to evaluate the situation of the model. Then, we design a method to guide the differential privacy budget allocation called APBA adaptively. Finally, all DP-based defenses with MDP could dynamically adjust the amount of noise added in the model response according to the result from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Monitor</i> and effectively defends the QPD attack. Furthermore, we thoroughly evaluate and compare the QPD attack and MDP defense performance on real-world models with DP and monitoring protection.
Unravelling Spatial Privacy Risks of Mobile Mixed Reality Data

Previously, 3D data---particularly, spatial data---have primarily been utilized in the field of geo-spatial analyses, or robot navigation (e.g. self-automated cars) as 3D representations of geographical or terrain data (usually extracted from lidar). Now, with the increasing user adoption of augmented, mixed, and virtual reality (AR/MR/VR; we collectively refer to as MR) technology on user mobile devices, spatial data has become more ubiquitous. However, this ubiquity also opens up a new threat vector for adversaries: aside from the traditional forms of mobile media such as images and video, spatial data poses additional and, potentially, latent risks to users of AR/MR/VR. Thus, in this work, we analyse MR spatial data using various spatial complexity metrics---including a cosine similarity-based, and a Euclidean distance-based metric---as heuristic or empirical measures that can signify the inference risk a captured space has. To demonstrate the risk, we utilise 3D shape recognition and classification algorithms for spatial inference attacks over various 3D spatial data captured using mobile MR platforms: i.e. Microsoft HoloLens, and Android with Google ARCore. Our experimental evaluation and investigation shows that the cosine similarity-based metric is a good spatial complexity measure of captured 3D spatial maps and can be utilised as an indicator of spatial inference risk.
Bayesian Estimation of Graph Signals

We consider the problem of recovering random graph signals from nonlinear measurements. For this case, closed-form Bayesian estimators are usually intractable and even numerical evaluation of these estimators may be hard to compute for large networks. In this paper, we propose a graph signal processing (GSP) framework for random graph signal recovery that utilizes the information of the structure behind the data. First, we develop the GSP-linear minimum mean-squared-error (GSP-LMMSE) estimator, which minimizes the mean-squared error (MSE) among estimators that are represented as an output of a graph filter. The GSP-LMMSE estimator is based on diagonal covariance matrices in the graph frequency domain, and thus, has reduced complexity compared with the LMMSE estimator. This property is especially important when using the sample-mean versions of these estimators that are based on a training dataset. We then state conditions under which the low-complexity GSP-LMMSE estimator coincides with the optimal LMMSE estimator. Next, we develop the approximated parametrization of the GSP-LMMSE estimator by shift-invariant graph filters by solving a weighted least squared (WLS) problem. We present three implementations of the parametric GSP-LMMSE estimator for typical graph filters. Parametric graph filters are more robust to outliers and to network topology changes. In our simulations, we evaluate the performance of the proposed GSP-LMMSE estimators for the problem of state estimation in power systems, which can be interpreted as a graph signal recovery task. We show that the proposed sample-GSP estimators outperform the sample-LMMSE estimator for a limited training dataset and that the parametric GSP-LMMSE estimators are more robust to topology changes in the form of adding/removing vertices/edges.
A market of black boxes: The political economy of Internet surveillance and censorship in Russia

In recent years, the Russian Internet has developed according to strong centralizing and State-controlling tendencies, both in terms of legal instruments and technical infrastructure. This strategy implies a strong push to develop Russian-made technical solutions for censorship and traffic interception. Thus, a promising market has opened for Russian vendors of software and hardware solutions for traffic surveillance and filtering. Drawing from a mixed-methods approach and perspectives grounded primarily in Science and Technology Studies (STS), infrastructure studies and the political economy of information networks, this paper aims at exploring the flourishing sector of Russian industry of censorship and surveillance. We focus on two kinds of “black boxes” and examine their influence on the market of Internet Service Providers: surveillance systems known as SORM (System for Operative Investigative Activities), and traffic filtering solutions used to block access to websites that have been blacklisted by Roskomnadzor, the Russian federal watchdog for media and telecommunications. This research sheds light on the vivid debates around controversial technologies which Internet actors must adopt in order to avoid government fines, but which are expensive and complex to implement and raise a number of ethical and political concerns.
Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

Network traffic prediction plays a vital role in effective network management, load evaluation and security warning. Extreme learning machine has the advantages of fast convergence speed and strong generalization ability. Also, it does not easily fall into local optima. The evolutionary algorithm can be used to optimize the number of its hidden layer nodes. However, most of the existing evolutionary algorithms have some adjustable parameters which depend on subjective experience or prior knowledge. Hence, this can affect the model prediction accuracy. Given this context, this paper proposes a network traffic prediction mechanism based on optimized Variational Mode Decomposition (VMD) and Integrated Extreme Learning Machine (ELM). A Scalable Artificial Bee Colony (SABC) algorithm which has fewer adjustable parameters and can thus guarantee the accuracy and stability of the prediction mechanism is also proposed. It can be used in the optimization selection of VMD, Phase Space Reconstruction (PSR) and ELM to achieve higher prediction performance. Finally, we utilize Mackey-Glass, Lorenz chaotic time series of recognized benchmark and a WIDE backbone actual network traffic data to prove the validity of the proposed network traffic prediction mechanism.
Deep Shearlet Residual Learning Network for Single Image Super-Resolution

Recently, the residual learning strategy has been integrated into the convolutional neural network (CNN) for single image super-resolution (SISR), where the CNN is trained to estimate the residual images. Recognizing that a residual image usually consists of high-frequency details and exhibits cartoon-like characteristics, in this paper, we propose a deep shearlet residual learning network (DSRLN) to estimate the residual images based on the shearlet transform. The proposed network is trained in the shearlet transform-domain which provides an optimal sparse approximation of the cartoon-like image. Specifically, to address the large statistical variation among the shearlet coefficients, a dual-path training strategy and a data weighting technique are proposed. Extensive evaluations on general natural image datasets as well as remote sensing image datasets show that the proposed DSRLN scheme achieves close results in PSNR to the state-of-the-art deep learning methods, using much less network parameters.
High-Accuracy Recognition and Localization of Moving Targets in an Indoor Environment Using Binocular Stereo Vision

To obtain effective indoor moving target localization, a reliable and stable moving target localization method based on binocular stereo vision is proposed in this paper. A moving target recognition extraction algorithm, which integrates displacement pyramid Horn–Schunck (HS) optical flow, Delaunay triangulation and Otsu threshold segmentation, is presented to separate a moving target from a complex background, called the Otsu Delaunay HS (O-DHS) method. Additionally, a stereo matching algorithm based on deep matching and stereo vision is presented to obtain dense stereo matching points pairs, called stereo deep matching (S-DM). The stereo matching point pairs of the moving target were extracted with the moving target area and stereo deep matching point pairs, then the three dimensional coordinates of the points in the moving target area were reconstructed according to the principle of binocular vision’s parallel structure. Finally, the moving target was located by the centroid method. The experimental results showed that this method can better resist image noise and repeated texture, can effectively detect and separate moving targets, and can match stereo image points in repeated textured areas more accurately and stability. This method can effectively improve the effectiveness, accuracy and robustness of three-dimensional moving target coordinates.