Reviewerly/exHarmony huggingface dataset:
An Efficient Algorithm for the Piecewise Affine-Linear Mumford-Shah Model Based on a Taylor Jet Splitting

We propose an algorithm to efficiently compute approximate solutions of the piecewise affine Mumford-Shah model. The algorithm is based on a novel reformulation of the underlying optimization problem in terms of Taylor jets. A splitting approach leads to linewise segmented jet estimation problems for which we propose an exact and efficient solver. The proposed method has the combined advantages of prior algorithms: it directly yields a partition, it does not need an initialization procedure, and it is highly parallelizable. The experiments show that the algorithm has lower computation times and that the solutions often have lower functional values than the state-of-the-art.
Analysis on Hybrid Approach for (K, N) Secret Sharing in Visual Cryptography

Visual cryptography (VC) is an emerging area used for data hiding, authentication, multimedia security and transmission of data. Parameters mainly focused in VC are pixel expansion, number of frames, type of images and number of undisclosed message images. Recent research shows many applications uses visual cryptography concept in authentication, secure transaction, secret sharing of information. But visual cryptography shows limited security for VC shares results in lack of security measure. In this paper work different VC (K, N) secret sharing techniques discussed and following analysis shows hybrid approach algorithms used in recent years that can be used for various applications in further implementation with high confidential secret sharing of data.
Constructive Lazy Wolf Search Algorithm for Feature Selection in Classification

Data mining integrates statistical analysis, machine learning and database technology to extract hidden patterns and relationships from data. The presence of irrelevant, redundant and inconsistent attributes in the data ushers poor classification accuracy. In this paper, a novel bio-inspired heuristic swarm optimization algorithm for feature selection, namely Constructive Lazy Wolf Search Algorithm is proposed based on the backbone of the Wolf Search Algorithm. It is based on the behavior of the real wolves, which search for their food and consequently survive the attacks of the threats by avoiding them. Based on the study conducted on the behavior of wolves two natural factors, namely laziness and health are introduced for attaining highest efficiency. Restricting and controlling the wolves’ behavior by allowing only healthy and constructive lazy wolves to take part in the search reduces the search time and complexity required to search for the best fitness. The proposed algorithm is then applied on a prisoner dataset for crime propensity prediction along with a few benchmark datasets to prove the stability in the improved performance compared with other bio-inspired optimization algorithms. The accuracy achieved by fine-tuning the proposed algorithm was 98.19% providing accurate crime prevention.
A Computational System for Temporal Visual Analysis of Labour Accident Data

This paper presents a system that employs Information Visualization techniques to visually analyse labour accident data, which represents a serious social problem. Our system uses associated geographical and temporal data information to create a hierarchical interactive layout that focuses on highlighting the evolution of accident occurrences in localities over time. We present the results of applying our proposed system on data provided by the Brazilian Federal Labour Prosecution Office, demonstrating the potential of our strategy in analysing the evolution of occurrences over time, highlighting trends, seasonal events, abnormal behaviour, as well as in comparing the behaviour of localities in the same/distinct hierarchical levels, among other tasks. We believe that the proposed system provides an effective environment that may guide the governors in creating public policies to reduce these accidents and grant safety for employees, and that may encourage citizen participation and transparency in governments.
Content-Adaptive Superpixel Segmentation Via Image Transformation

We propose simple and efficient method that produces content-adaptive superpixels, i.e. smaller segments in content-dense areas and larger segments in content-sparse areas. Previous adaptive methods distribute superpixels over the image according to image content. In contrast, we transform the image itself to redistribute the content density uniformly across the image area. This transformation is guided by a significance map, which characterizes the `importance' of each pixel. Arbitrary superpixel algorithm can be utilized to segment the transformed image into regular superpixels, providing a suitable representation for subsequent tasks. Regular superpixels in the transformed image induce content-adaptive superpixels in the original image facilitating the improved segmentation accuracy.
Frictionless Web Payments with Cryptographic Cardholder Authentication

The 3-D Secure protocol, introduced 20 years ago, aims at reducing online credit card fraud by authenticating the cardholder. Version 2 of the protocol, not yet deployed, addresses usability problems that have hindered the deployment of version 1 by introducing a frictionless flow for low risk transactions. But the frictionless flow does not authenticate the cardholder. Instead, it requires the merchant to send information to the issuer through a back channel, potentially violating the cardholder's privacy. The paper analyzes the usability, privacy and security provided by 3-D Secure 2 and proposes an alternative protocol, simpler and less expensive to implement, where the cardholder is authenticated with a cryptographic credential stored in the cardholder's browser with zero friction. The scheme can take advantage of a native bank app in the cardholder's device to further authenticate the cardholder by fingerprint scanning or face recognition as made available by the device, and can be used for credit card purchases made on the merchant's web site or on a merchant app.
Deep Semantic Segmentation Using Nir as Extra Physical Information

Deep neural networks for semantic segmentation are most often trained with RGB color images, which encode the radiation visible to the human eyes. In this paper, we study if additional physical scene information, specifically Near-Infrared (NIR) images, improve the performance of neural networks. NIR information can be captured with conventional silicon-based cameras and provide complementary information to visible images regarding object boundaries and materials. In addition, extending the networks' input from a three to a four channel layer is trivial with respect to changes to the architecture and additional parameters. We perform experiments on several state-of-the-art neural networks trained both on RGB alone and on RGB plus NIR and show that the additional image channel consistently improves semantic segmentation accuracy over conventional RGB input even for powerful architectures.
Machine Learning to Support the Presentation of Complex Pathway Graphs

Visualization of biological mechanisms by means of pathway graphs is necessary to better understand the often complex underlying system. Manual layout of such pathways or maps of knowledge is a difficult and time consuming process. Node duplication is a technique that makes layouts with improved readability possible by reducing edge crossings and shortening edge lengths in drawn diagrams. In this article, we propose an approach using Machine Learning (ML) to facilitate parts of this task by training a Support Vector Machine (SVM) with actions taken during manual biocuration. Our training input is a series of incremental snapshots of a diagram describing mechanisms of a disease, progressively curated by a human expert employing node duplication in the process. As a test of the trained SVM models, they are applied to a single large instance and 25 medium-sized instances of hand-curated biological pathways. Finally, in a user validation study, we compare the model predictions to the outcome of a node duplication questionnaire answered by users of biological pathways with varying experience. We successfully predicted nodes for duplication and emulated human choices, demonstrating that our approach can effectively learn human-like node duplication preferences to support curation of pathway diagrams in various contexts.
Feature Fusion and Adversary Occlusion Networks for Object Detection

Current object detection techniques have difficulties in detecting small objects and a low level of accuracy in detecting occluded objects. To solve these problems, this paper proposes an object detection framework named FFAN which is based on Faster R-CNN that introduces a feature fusion network and an adversary occlusion network into the structure. The feature fusion network combines a feature map of low resolution and high semantic information with a feature map of high resolution and low semantic information using the deconvolution operation to increase the ability to extract low-level features in the network. FFAN then generates a single advanced feature map with high resolution and high semantic information which is used to predict the detection of small objects in the image more effectively. The adversary occlusion network creates occlusion on a deep feature map of the object, and generates an adversary training sample that is difficult for the detector to discriminate. At the same time, the detector classifies accurately the generated occluded adversary samples by self-learning. The two compete with and learn from each other to further improve the performance of the algorithm. We train FFAN on the PASCAL VOC 2007, PASCAL VOC 2012, MS COCO and KITTI datasets. A number of quantitative and qualitative experiments show that FFAN achieves a state-of-the-art detection accuracy.
A New Algorithm for Learning Large Bayesian Network Structure From Discrete Data

Learning the structure of Bayesian networks (BNs) from high dimensional discrete data is common nowadays but a challenging task, due to the large parameter space, the acyclicity constraint placed on the graphical structures and the difficulty in searching for a sparse structure. In this article, we propose a sparse structure learning algorithm (SSLA) to solve this problem. The algorithm uses the negative log-likelihood function of multi-logit regression as a loss function, adding the adaptive group lasso as a penalty term for sparsity, with a new penalty term to ensure that the learned graph is a directed acyclic graph. A block coordinate descent algorithm (BCD) combining with the alternating direction multiplier method (ADMM) algorithm is developed to solve the proposed model. The learned graph is proved theoretically to be a Bayesian network. In order to evaluate the proposed SSLA and compare with its competitors, we conducted intensive simulation studies and applied them to the benchmark Bayesian networks. The results indicate that the SSLA is superior to the hill climbing (HC) algorithm, the CD algorithm and the BFO-B algorithm respectively, and is competitive with K2 algorithm when the order of the nodes is given.
Geometry-Aware GAN for Face Attribute Transfer

In this paper, the geometry-aware GAN is proposed to address the issue of facial attribute transfer with unpaired data. To tackle the unpaired training sample problem, the CycleGAN architecture is applied, where the bilateral mappings between the source and target domains are learned. The deformation flow is learned to capture the geometric variation between two domains. We first warp the source face into desired pose and shape according to the flow. Then, the transfer sub-network is designed to refine the results by hallucinating new components on the warped image. The attribute is removed by the reconstruction sub-network, coupled with the warping process. Experiments on benchmark demonstrate the advantages of our method compared to baselines.
Single Image Noise Level Estimation Using Dark Channel Prior

Noise level is required as an input parameter in various image processing applications. In this work, we use the dark channel prior (DCP) to estimate the noise level of an image degraded by additive white Gaussian noise. We develop an approximate model of the probability density function of the dark channel of the noisy image. Using this model, the noise level is determined with the maximum likelihood estimation method from the dark channel intensity values of the noisy image. The results show that our method is faster than the state-of-the-art methods by about two orders of magnitude while providing slightly inferior estimation performance.
Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack

A database Intrusion Detection System (IDS) based on Role-based Access Control (RBAC) mechanism that has capability of learning and adaptation learns SQL transaction patterns represented by roles to detect insider attacks. In this paper, we parameterize the rules for partitioning the entire query set into multiple areas with simple chromosomes and propose an ensemble of multiple deep learning models that can effectively model the tree structural characteristics of SQL transactions. Experimental results on a large synthetic query dataset verify that it quantitatively outperforms other ensemble methods and machine learning methods including deep learning models, in terms of 10-fold cross validation and chi-square validation.
A Fidelity-Assured Rate Distortion Optimization Method for Perceptual-Based Video Coding

Rate-distortion optimization (RDO) is one of the essential method to improve the video coding efficiency. The main target of RDO is to find the optimal tradeoff between the reconstructed video quality and encoding rate. In traditional video coding standards, e.g. the emerging H.266/Versatile Video Coding(VVC), the H.265/High Efficiency Video Coding (HEVC) and H.264/Advanced Video Coding (AVC), sum of squared error (SSE) is used as the distortion criterion because SSE can represent the image fidelity efficiently. Based on the existing research on human visual characteristic, the perceptual visual quality is not consistent with image fidelity. Accordingly, we propose a video coding method to improve the subjective quality while avoiding great fidelity degradation. Experimental results demonstrate that the proposed method is efficient in preserving subjective qualities with only a little fidelity degradation when comparing to existing subjective quality based rate distortion optimization methods.
Predicting Learners' Demographics Characteristics

Author Profiling (AP), which aims to predict an author's demographics characteristics automatically by using texts written by the author, is an important mechanism for many applications, as well as highly challenging. In this research, we analyse various previous machine learning models for AP, with respect to their potential for our research problem. Based on this, we propose a Deep Learning Architecture to predict the demographics characteristics of the learners in MOOCs, incorporating multi-feature representations and ensemble learning methods. Specifically, we employ a novel pipeline, combining the most successful deep learning classifiers, Convolution Neural Networks, Recurrent Neural Networks and Recursive Neural Networks, to learn from a text. Moreover, beside the state-of-the-art training involving character and word-level input, we additionally propose phrase-level input. With this approach, we aim at deepening our understanding of the writing style of learners, and thus, predict the author profile with high accuracy. In this paper, we propose the model and architecture, and report on initial tests of our model on a large dataset from the FutureLearn platform, to predict the demographics characteristics of the learners.
New approaches to plastic language: Prolegomena to a computer-aided approach to pictorial semiotics

Abstract In this paper we summarize observations bridging the declared aspirations of pictorial semiotics and its real achievements. Pictorial semiotics is here understood as the general study of pictures as signs and it constituted a fundamental step beyond the art historical captivation with individual images. In the first part of our contribution we present a review of the most important methods that have been proposed as an answer to deal with several pictorial problems (multiple instances, segmentation, non-figurative meaning). In the second part, we offer some positive and on-going implementations designed to remedy the shortcomings observed. What is suggested is the proof of concept that human researchers need the assistance of computing methodologies. However, computers can only do their job once an adequate phenomenology of human experience is fed into the process.
Real-time Obstacle Avoidance and Person Following Based on Adaptive Window Approach

Autonomous mobile robots need safe navigation capabilities in order to interact with humans in a dynamic environment. As a step toward this goal, we are applying a realtime obstacle avoidance method for a robot to follow behind a person. This paper presents a laser-based people tracking and following while at the same time avoiding obstacles method. A novel feature of our approach is it considers potential risks for a robot to follow a person in an unknown environment. We implement this method in the Robot Operating System framework and verify it in both virtual and real environments.
Learning Personalized Modular Network Guided by Structured Knowledge

The dominant deep learning approaches use a "one-size-fits-all" paradigm with the hope that underlying characteristics of diverse inputs can be captured via a fixed structure. They also overlook the importance of explicitly modeling feature hierarchy. However, complex real-world tasks often require discovering diverse reasoning paths for different inputs to achieve satisfying predictions, especially for challenging large-scale recognition tasks with complex label relations. In this paper, we treat the structured commonsense knowledge (e.g. concept hierarchy) as the guidance of customizing more powerful and explainable network structures for distinct inputs, leading to dynamic and individualized inference paths. Give an off-the-shelf large network configuration, the proposed Personalized Modular Network (PMN) is learned by selectively activating a sequence of network modules where each of them is designated to recognize particular levels of structured knowledge. Learning semantic configurations and activation of modules to align well with structured knowledge can be regarded as a decision-making procedure, which is solved by a new graph-based reinforcement learning algorithm. Experiments on three semantic segmentation tasks and classification tasks show our PMN can achieve superior performance with the reduced number of network modules while discovering personalized and explainable module configurations for each input.
SID: Sensor-Assisted Image Deblurring System for Mobile Devices

Handheld mobile photography is often affected by motion blur due to the difficulty of keeping the camera's stable. The existing processing method is usually a high-cost deblurring process of a computer, which seriously affects the user experience, and the deblurring effect is poor due to the lack of information on camera motion. Inspire by edging computing's ongoing efforts in automatically and collaboratively process more types of resources in the edge and cloud. In this paper, we present SID, a sensor-assisted image deblurring system for mobile devices. Using information about camera motion acquired from built-in sensors from smartphones (e.g. accelerometers, gyroscopes, and magnetometers), then estimating the point spread function, and combining the image's segmentation smoothing characteristics with spatial adaptation to image Deblurring. The image is preprocessed by the p-m nonlinear diffusion model, which preserves the characteristics of the image. The fuzzy image confirmation avoids the damage to the original high quality image. Wiener-Hoff optimization is used to optimize the effect of image restoration. We evaluated 400 photos with varying degrees of blur and size. Compared to traditional blind and unblind deconvolution methods, our algorithm shows significant advantages in both deblurring and processing delays.
Using C3D to Detect Rear Overtaking Behavior

Avoiding traffic accidents is critical since the death of traffic accidents is the eighth among the top ten leading causes of death in 2018. This paper proposes a light-weight convolutional 3D (C3D) network with five 3D convolution layers and two fully-connected layers to predict overtaking behavior. This network utilizes the last layer of convolution layer to learn the overtaking object location in the final frame. Based on NVIDIA Jetson TX2, the proposed C3D network achieves 91.46% accuracy to detect overtaking behavior on rainy days. To generate this excellent deep learning model, we use an efficient labeling tool, called ezLabel, which is a free SaaS for academia group with 96,000 opened image data samples for deep learning. ezLabel owns outstanding route prediction and fitting functions, which speeds up with the factor of ten compared to traditional tools. Users only label the object in its first frame and in its final frame, and then ezLabel labels the object in all frames in between and fits the bounding box to the object. The ezLabel can be used to label objects captured with any moving or static cameras efficiently.
A Fuzzy Approach to Visual Servoing with A Bagging Method for Wheeled Mobile Robot

Visual servoing is a vision-based control method with a mechanism of closed-loop, which uses feedback information extracted from the camera to control the motion of a robot. In this paper, we propose a fuzzy-based visual servoing integrated with a bagging method for the wheeled mobile robot (WMR). Previous studies have shown that the value of the mixture parameter for the image Jacobian matrix affects the performance of image-based visual servoing(IBVS). However, the mixture parameter value is constant in most visual servoing methods. To address this problem, we propose a fuzzy-based method to adjust the mixture parameter during the process of visual servoing. Meanwhile, in order to reduce the effect of image noise and the computational complexity of the pseudoinverse matrix, we propose a bagging method to calculate the inverse kinematics, instead of using the Moore-Penrose pseudoinverse method. The results of simulation and real experiments demonstrate the effectiveness of the proposed IBVS method.
Quantifying attractiveness of incomplete-information multi-player game: case study using DouDiZhu

This paper explores the nature of DouDiZhu which is an incomplete-information multi-player game and the most popular card game in China. While there are many DouDiZhu-like card games in the world, standard DouDiZhu card game is particularly characterized by its harmonic combination of several factors including the number of players, player's roles such as landlord and peasant, the number of decks, and score system to determine the winner. However, a study on such factors in relation to DouDiZhu game sophistication and attractiveness is not yet established. As such, this paper conducts such study by building the artificial intelligence (AI) player of DouDiZhu game with different levels, conducting simulations of self-play among the AI players, and employing the game refinement measure to assess and identify the attractive settingsof this game. The results obtained show that standard DouDiZhu has provided the most sophisticated game setting for various AI players,which is analogous to actual human players of various levels.