Reviewerly/exHarmony huggingface dataset:
HAPNet: hierarchically aggregated pyramid network for real-time stereo matching

(2021). HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization: Vol. 9, Special Issue: AE-CAI 2020, pp. 219-224.
Subject Guided Eye Image Synthesis with Application to Gaze Redirection

We propose a method for synthesizing eye images from segmentation masks with a desired style. The style encompasses attributes such as skin color, texture, iris color, and personal identity. Our approach generates an eye image that is consistent with a given segmentation mask and has the attributes of the input style image. We apply our method to data augmentation as well as to gaze redirection. The previous techniques of synthesizing real eye images from synthetic eye images for data augmentation lacked control over the generated attributes. We demonstrate the effectiveness of the proposed method in synthesizing realistic eye images with given characteristics corresponding to the synthetic labels for data augmentation, which is further useful for various tasks such as gaze estimation, eye image segmentation, pupil detection, etc. We also show how our approach can be applied to gaze redirection using only synthetic gaze labels, improving the previous state of the art results. The main contributions of our paper are i) a novel approach for Style-Based eye image generation from segmentation mask; ii) the use of this approach for gaze-redirection without the need for gaze annotated real eye images.
Automatic identification of abnormal blood smear images using color and morphology variation of RBCS and central pallor

The anatomy of red blood cells (RBCs) in blood smear images plays an important role in the detection of several diseases. The automated image-based technique is fast and accurate for the analysis of blood cells morphology that can save time of both pathologists as well as that of patients. In this paper, we propose a novel method which segment and identify varied RBCs in a given blood smear images. In the proposed method, the central pallor and whole cell information are used, after using color processing followed by double thresholding of blood smear images. The shape and size variances of cells are calculated for the identification of abnormalities in peripheral blood smear images. We used cross-validation accuracy weighted probabilistic ensemble (CAWPE). It is a heterogeneous ensembling technique of nearly equivalent classifiers produced on averagely significant better classifiers (regarding errors and probability estimates) as compared to a wide range of potential parent classifiers. The proposed method is tested on 3 sets of images. The sets of images were prepared in a local government hospital by expert pathologists. Each image set has varied photographic conditions. The method was found accurate in term of results, closer to the ground truth. The average accuracy of the proposed method is 97% for the segmentation of single cells and 96% for overlapped cells. The variance (σ2) of accuracy is 3.5 and the deviation (σ) is 1.87.
Detecting Common Web Attacks Based on Machine Learning Using Web Log

SQL injection (SQLi), Cross-site Scripting (XSS) attacks have long been considered major threats to web-based applications and their users. These types of web attacks can cause serious damage to web applications and web users, ranging from bypassing authentication systems, stealing information from databases and users, to even taking control of server systems. To cope with web attacks, many measures have been researched and applied to protect web applications and users. Among them, the detection of web attacks is a promising approach in the defensive layers for web applications. However, some measures can only detect a single type of web attacks, while others require frequent updates to the detection rule sets, or require extensive computational power because of using complex detection methods. This paper proposes a web attack detection model based on machine learning using web log. The detection model is built using the inexpensive decision tree algorithm and it does not require frequent update. Our experiments on a labelled dataset and real web logs show that the proposed model is capable of detecting several types of web attacks effectively with the overall detection accuracy rate of 98.56%.
A Motif-Based Graph Neural Network to Reciprocal Recommendation for Online Dating

Recommender systems have been widely adopted in various large-scale Web applications. Among these applications, online dating application has attracted more and more research efforts. Essentially, online dating data is a bipartite graph with sparse reciprocal links. Reciprocal recommendations consider bi-directional interests of service and recommended users, not merely the service user's interest. This paper proposes a motif-based graph neural network (MotifGNN) for online dating recommendation task. We first define seven kinds of motifs and then design a motif based random walk algorithm to sample neighbor users to learn feature embeddings of each service user. At last, these learned feature embeddings are used to predict whether a reciprocal link exists or not. Experiments are evaluated on two real-world online dating datasets. The promising results demonstrate the superiority of the proposed approach against a number of state-of-the-art approaches.
Full- and Reduced-Order Observers for Image-Based Depth Estimation Using Concurrent Learning

In this brief, concurrent learning (CL)-based full- and reduced-order observers for a perspective dynamical system (PDS) are developed. The PDS is a widely used model for estimating the depth of a feature point from a sequence of camera images. Building on the current progress of CL for parameter estimation in adaptive control, state observers are developed for the PDS model where the inverse depth appears as a time-varying parameter in the dynamics. The data recorded over a sliding time window in the near past are used in the CL term to design the full- and reduced-order state observers. A Lyapunov-based stability analysis is carried out to prove the uniformly ultimately bounded (UUB) stability of the developed observers. Simulation results are presented to validate the accuracy and convergence of the developed observers in terms of convergence time, root mean square error (RMSE), and mean absolute percentage error (MAPE) metrics. Real-world depth estimation experiments are performed to demonstrate the performance of the observers using the aforementioned metrics on a 7-DoF manipulator with an eye-in-hand configuration.
Progressively volumetrized deep generative models for data-efficient contextual learning of MR image recovery

Magnetic resonance imaging (MRI) offers the flexibility to image a given anatomic volume under a multitude of tissue contrasts. Yet, scan time considerations put stringent limits on the quality and diversity of MRI data. The gold-standard approach to alleviate this limitation is to recover high-quality images from data undersampled across various dimensions, most commonly the Fourier domain or contrast sets. A primary distinction among recovery methods is whether the anatomy is processed per volume or per cross-section. Volumetric models offer enhanced capture of global contextual information, but they can suffer from suboptimal learning due to elevated model complexity. Cross-sectional models with lower complexity offer improved learning behavior, yet they ignore contextual information across the longitudinal dimension of the volume. Here, we introduce a novel progressive volumetrization strategy for generative models (ProvoGAN) that serially decomposes complex volumetric image recovery tasks into successive cross-sectional mappings task-optimally ordered across individual rectilinear dimensions. ProvoGAN effectively captures global context and recovers fine-structural details across all dimensions, while maintaining low model complexity and improved learning behavior. Comprehensive demonstrations on mainstream MRI reconstruction and synthesis tasks show that ProvoGAN yields superior performance to state-of-the-art volumetric and cross-sectional models.
Fast Class-wise Updating for Online Hashing

Online image hashing has received increasing research attention recently, which processes large-scale data in a streaming fashion to update the hash functions on-the-fly. To this end, most existing works exploit this problem under a supervised setting, i.e., using class labels to boost the hashing performance, which suffers from the defects in both adaptivity and efficiency: First, large amounts of training batches are required to learn up-to-date hash functions, which leads to poor online adaptivity. Second, the training is time-consuming, which contradicts with the core need of online learning. In this paper, a novel supervised online hashing scheme, termed Fast Class-wise Updating for Online Hashing (FCOH), is proposed to address the above two challenges by introducing a novel and efficient inner product operation. To achieve fast online adaptivity, a class-wise updating method is developed to decompose the binary code learning and alternatively renew the hash functions in a class-wise fashion, which well addresses the burden on large amounts of training batches. Quantitatively, such a decomposition further leads to at least 75 percent storage saving. To further achieve online efficiency, we propose a semi-relaxation optimization, which accelerates the online training by treating different binary constraints independently. Without additional constraints and variables, the time complexity is significantly reduced. Such a scheme is also quantitatively shown to well preserve past information during updating hashing functions. We have quantitatively demonstrated that the collective effort of class-wise updating and semi-relaxation optimization provides a superior performance comparing to various state-of-the-art methods, which is verified through extensive experiments on three widely-used datasets.
Safe Path Planning for Unmanned Aerial Vehicle under Location Uncertainty

For the autonomous operations of unmanned aerial vehicles (UAVs), path planning satisfying collision avoidance plays an essential role in navigating through dynamic and uncertain environments. To this end, a probabilistic collision-free planning method based on Rapidly-exploring Random Tree (RRT) is presented in this paper. This method employs chance constraints to find a trajectory satisfying desired safety requirement for the UAV under location uncertainties for both the UAV model itself or obstacles. By assuming Gaussian distributions, the chance constraints arising from those uncertainties are formulated through converting dynamic, probabilistic constraints into equivalent static, deterministic constraints, namely the probabilistic bound. Also, a method named relative uncertainty is introduced to reduce different forms of uncertainties into a common one. On this basis, a chance constrained RRT planning algorithm is developed through combining formulated chance constraints with RRT. The feasibility of our proposed path planning method without violating the prescribed risk bound is well validated through numerical trials.
SNEGAN: Signed Network Embedding by Using Generative Adversarial Nets

Network embedding (NE) aims to learn low-dimensional node representations of networks while preserving essential node structures and properties. Existing NE methods mainly preserve simple link structures in unsigned networks, neglecting conflicting relationships that widely exist in social media and Internet of things. In this paper, we propose a novel generative adversarial nets learning framework (called SNEGAN) for signed network embedding, which tries to preserve link structures signed by positive or negative labels. SNEGAN combines a generator with a signed random walker technique and a graph softmax function to generate fake links that are used to deceive discriminator. Moreover, it combines a discriminator with a tanh function to discriminate truths and signs of links sampled from the generator and real network structures. The generator and discriminator play a two-player minimax game, and they will eventually generate low-dimensional node representations of signed networks. Extensive experiments on both LFR benchmark and real-world signed networks show the superiority of SNEGAN over the state-of-the-art NE methods in tackling both link (sign) prediction and reconstruction tasks.
Profiling Bots and Fake News Spreaders at PAN’19 and PAN’20 : Bots and Gender Profiling 2019, Profiling Fake News Spreaders on Twitter 2020

This paper synthesizes our participation in the CLEF conference 2020 regarding the Profiling Fake News Spreaders on Twitter task, organized at the PAN lab on digital text forensics and stylometry. Our team obtained one of the two best performing results with an average accuracy of 0.7775 -0.7350 for English and 0.8200 for Spanish. In summary, we submit a Support Vector Machine (SVM) classifier trained with character and word n-gram to determine whether the author of a tweet feed is keen to be a spreader of fake news. Besides, we present our results as the best performing team in Bots and Gender Profiling 2019 with an average accuracy of 0.8805. Its goal was to determine if the author of a tweet feed was a bot or human, followed by identifying her/his gender in the case of humans.
The Detecting Cross-Site Scripting (XSS) Using Machine Learning Methods

This article discusses the problem of detecting cross-site scripting (XSS) using machine learning methods. XSS is an attack in which malicious code is embedded on a page to interact with an attacker's web server. The XSS attack ranks third in the ranking of key web application risks according to Open Source Foundation for Application Security (OWASP). This attack has not been studied for a long time. It was considered harmless. However, this is fallacious: the page or HTTP Cookie may contain very vulnerable data, such as payment document numbers or the administrator session token. Machine learning is a tool that can be used to detect XSS attacks. This article describes an experiment. As a result the model for detecting XSS attacks was created. Following machine learning algorithms are considered: the support vector method, the decision tree, the Naive Bayes classifier, and Logistic Regression. The accuracy of the presented methods is made a comparison.
Fundamental study of porcine drip bloodstains on fabrics: Blood droplet impact and wicking dynamics

The underlying physics in bloodstain formation on fabrics is not well understood, despite its importance in bloodstain pattern analysis (BPA). This paper presents a fundamental study of the formation of drip bloodstains on fabrics, by focusing on blood droplet impact and wicking dynamics. The bloodstains were created on plain woven fabric by the perpendicular impact of a single blood drop with seven different impact velocities. The whole droplet impact and wicking processes were captured by multiple cameras. Fabric properties were characterized in detail at different levels. The bloodstain formation process was classified into distinct stages, including the inertial impact, initial absorption, first wicking and second wicking stages. The subsequent wicking process greatly alters the impact-induced bloodstains, in terms of bloodstain area. The dimensionless impact-induced stain factor (βi,e) is strongly dependent on the impact velocity while the final stain factor (βf,e) after the second wicking stage is not. The contribution of the subsequent wicking in altering the stain factor (or stain area) is quantified and found to decrease with increasing impact velocity. The blood wicking dynamics on the fabric in the majority of the first wicking stage can be well described by a simple scaling: (βe-βi,e)/βi,e=C[t-ti/ti]1/2, where ti marks the end of the inertial impact stage. The wicking coefficient C, which represents the influence of droplet impact on the subsequent droplet wicking, is found to scale as C∼We-0.34. In the end, brief comments are provided regarding (1) the influence of the evaporation on the blood drop post-impact wicking dynamics and (2) the shape of bloodstains formed on fabrics, with a few suggested research directions for future work.
EarthCube Data Discovery Studio: A gateway into geoscience data discovery and exploration with Jupyter notebooks

Abstract EarthCube Data Discovery Studio (DDStudio) is a crossdomain geoscience data discovery and exploration portal. It indexes over 1.65 million metadata records harvested from 40+ sources and utilizes a configurable metadata augmentation pipeline to enhance metadata content, using text analytics and an integrated geoscience ontology. Metadata enhancers add keywords with identifiers that map resources to science domains, geospatial features, measured variables, and other characteristics. The pipeline extracts spatial location and temporal references from metadata to generate structured spatial and temporal extents, maintaining provenance of each metadata enhancement, and allowing user validation. The semantically enhanced metadata records are accessible as standard ISO 19115/19139 XML documents via standard search interfaces. A search interface supports spatial, temporal, and text‐based search, as well as functionality for users to contribute, standardize, and update resource descriptions, and to organize search results into shareable collections. DDStudio bridges resource discovery and exploration by letting users launch Jupyter notebooks residing on several platforms for any discovered datasets or dataset collection. DDStudio demonstrates how linking search results from the catalog directly to software tools and environments reduces time to science in a series of examples from several geoscience domains. URL: datadiscoverystudio.org
RETRACTED: The Poverty Alleviation Effect of Smart Financial Services Based on FPGA and Wireless Sensors

Poverty alleviation of micro-finance, ability and social security. It tends to be partitioned into four kinds, including market-based and the administration network association. Micro-credit, to lift the poor out of poverty, is an effective poverty reduction strategy. In previous research, financial services, lack of shows that are not available to anyone, and understand their need for several reasons (financial illiteracy), unfavorable regulatory environment, the stiffness It is a product structure and may be on it. Reduce poverty, some of the reform measures aimed at promoting financial inclusion have government and has been tried its development partners in the modern. Neediness decrease procedures, miniature account, capacity and government-backed retirement can be separated into four kinds: market-based and regularly the predominant network associations. A Field-Programmable Gate Array (FPGA), given the associated Configurable Logic Blocks (CLB), grid by programmable interconnect is a semiconductor gadget. FPGA is especially useful for creating an Application-Specific Integrated Circuit (ASIC) or a processor prototype. ASIC or processor design can be reprogrammed to an FPGA until no defects final, the actual production of the final ASIC starts. Intel itself is using an FPGA to create a new chip prototype. Micro-credit, to lift the poor out of poverty, is an effective poverty reduction strategy. Mitigation role of micro-finance: This study aims to explore the impact of the financial inclusion of poverty reduction. Data collection technology to be used is a self-filling questionnaire.
Synthetic algorithm for deinterleaving radar signals in a complex environment

The electronic support measure has a pivotal role in electronic warfare. Radar signal deinterleaving achieves complex radar pulse streams to deinterleave by analysing the intercepted radar emitter signals and estimating the pulse repetition interval (PRI) values. At present, most of the deinterleaving algorithms use the classical model of pre-sorting combined with the PRI deinterleaving algorithm, which is challenging to adapt to the current radar environment. The agility of pulse parameters increases the error rate; the existence of PRI jitter and pulse missing leads to the sub-harmonic problem and low estimation accuracy; the overall algorithm real time is mediocre. To overcome these disadvantages, the authors propose a synthetic algorithm incorporating the statistical histogram into pre-sorting, combining the improved sequence difference histogram algorithm with the multi-width PRI transform in PRI deinterleaving. The simulation results show that the algorithm can accurately estimate the PRI values in a short time and detect various radar signals. C language experiments validate the algorithm in real time.
Risk-Sensitive Motion Planning using Entropic Value-at-Risk

We consider the problem of risk-sensitive motion planning in the presence of randomly moving obstacles. To this end, we adopt a model predictive control (MPC) scheme and pose the obstacle avoidance constraint in the MPC problem as a distributionally robust constraint with a KL divergence ambiguity set. This constraint is the dual representation of the Entropic Value-at-Risk (EVaR). Building upon this viewpoint, we propose an algorithm to follow waypoints and discuss its feasibility and completion in finite time. We compare the policies obtained using EVaR with those obtained using another common coherent risk measure, Conditional Value-at-Risk (CVaR), via numerical experiments for a 2D system. We also implement the waypoint following algorithm on a 3D quadcopter simulation.
AdaGrad Gradient Descent Method for AI Image Management

In this study, we used AdaGrad gradient descent method in optimizer for image deep learning, and compare with Adam gradient descent methods. After processing over six thousand huge database of through silicon via images, AdaGrad has shown a fast convergence and less generalization errors than Adam. The results help Artificial Intelligence for making the management of image judgment more accurate and faster.
Reversible data hiding scheme for high dynamic range images based on multiple prediction error expansion

For sensitive areas that even the slight distortion in images is not tolerated, we propose a prediction error expansion-based reversible data hiding algorithm which can embed reversible watermark in high dynamic range (HDR) image with low distortion. On the one hand, considering unique floating-point storage format and perceptual characteristics of HDR image, the multiple carriers are generated with low distortion and various characteristics to improve the embedding capacity. On the other hand, multi-model predictor and multi-level embedding strength are adopted adaptively to realize multi-level reversible HDR image information hiding based on prediction error expansion. The experimental results show that the proposed algorithm has good invisibility by making full use of HDR image characteristics. Meanwhile, the multiple strategies of multiple carriers, multi-model predictor and multi-level embedding strength, which further improve the embedding capacity and security of the algorithm, can meet the needs of sensitive areas such as medical processing, judicial authentication, and so on.
A modified LSB image steganography method using filtering algorithm and stream of password

(2021). A modified LSB image steganography method using filtering algorithm and stream of password. Information Security Journal: A Global Perspective: Vol. 30, COVID-19 Pandemic Crisis and its Implications on Cybersecurity, pp. 359-370.