Reviewerly/exHarmony huggingface dataset:
Brain Extraction From Brain MRI Images Based on Wasserstein GAN and O-Net

Brain extraction is an essential pre-processing step for neuroimaging analysis.It is difficult to achieve high-precision extraction from low-quality brain MRI images with artifacts and gray inconsistencies which often result in irregular hole regions in the extracted brain tissues.In addition, the U-Net based brain extraction methods trend to output over-smoothed brain boundary.To remove those irregular holes in the extracted mask, we proposed a new U-Net based model for brain extraction named O-Net.O-Net replaces the skip-connection path in the U-Net with a dual shortcut paths including an attention module to form an Oshaped network, which uses deep semantic information to highlight the target area while retaining more image details.O-Net effectively reduces the impact of intensity differences resulted from artifacts or gray inconsistencies in the brain MRI images on the extraction results.To obtain more precise brain boundary, we designed a new GAN based brain extraction method, which used above O-Net as the segmentation network.The discriminant network of the proposed GAN model adopts the residual structure to enhance the nonlinear expression ability of the network to balance the adversarial training of the two networks.To speed up the convergence of the proposed model, we added a segmentation loss to the adversarial loss to supervise the feature learning of the segmentation network.This method was compared with other popular brain extraction methods on two public datasets (IBSR18 and LPBA40).The mean dice similarity coefficients obtained by the proposed method were 97.26% and 98.29% on IBSR18 and LPBA40 respectively.The results of the proposed method are the best on the two public datasets.Experimental results prove that the proposed model can stably output high-precision brain tissue and reduce the influence of artifacts and gray inconsistencies.
EasySpec: Automatic Specular Reflection Detection and Suppression From Endoscopic Images

The outcome of endoscopic tasks can be significantly affected by the presence of specular reflections. Although numerous methods have been proposed for specular reflection detection and suppression from endoscopic images, they are inefficient, usually require tedious empirical parameters selection, and perform poorly when handling large specular regions. To this end, we propose a robust and efficient deep learning framework termed EasySpec for identifying and suppressing specular reflections from endoscopic images. Our proposed EasySpec consists of two stages: a detection stage and a suppression stage. The former stage is performed using a lightweight UNet-variant dubbed Scaled-UNet, which is trained exploiting a novel hybrid strategy that integrates the advantages of both transfer learning and weakly supervised techniques. The latter stage is achieved utilizing the concept of deep image inpainting. Specifically, a new and fast end-to-end approach for inpainting multi-size specular reflection regions in endoscopic images is developed. Our proposed approach termed GatedResUNet, takes advantage of the gated convolution to accurately differentiate specular pixels from non-specular pixels, and the U-Net architecture to learn multi-level semantic representative features, which helps reconstruct more realistic and semantically plausible images. Extensive qualitative and quantitative experiments on several endoscopic datasets acquired from different MIS scenes reveal that our proposed EasySpec outperforms state-of-the-art endoscopic specular reflection suppression approaches. Specifically, EasySpec generalizes well on various MIS scenes, and can properly reconstruct blood vessels and fine details in a reasonable processing time, which validates its practical significance.
Revisiting convolutional neural network on graphs with polynomial approximations of Laplace–Beltrami spectral filtering

This paper revisits spectral graph convolutional neural networks (graph-CNNs) given in Defferrard (2016) and develops the Laplace-Beltrami CNN (LB-CNN) by replacing the graph Laplacian with the LB operator. We define spectral filters via the LB operator on a graph and explore the feasibility of Chebyshev, Laguerre, and Hermite polynomials to approximate LB-based spectral filters. We then update the LB operator for pooling in the LB-CNN. We employ the brain image data from Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) to demonstrate the use of the proposed LB-CNN. Based on the cortical thickness of two datasets, we showed that the LB-CNN slightly improves classification accuracy compared to the spectral graph-CNN. The three polynomials had a similar computational cost and showed comparable classification accuracy in the LB-CNN or spectral graph-CNN. The LB-CNN trained via the ADNI dataset can achieve reasonable classification accuracy for the OASIS dataset. Our findings suggest that even though the shapes of the three polynomials are different, deep learning architecture allows us to learn spectral filters such that the classification performance is not dependent on the type of the polynomials or the operators (graph Laplacian and LB operator).
Delay-Limited Computation Offloading for MEC-Assisted Mobile Blockchain Networks

The proof-of-work (PoW) mining process requires a large amount of intensive computing, which leads to some plights such as heavy equipment and fixed access nodes in traditional blockchain networks. A novel mobile blockchain network with the help of a mobile edge computing (MEC) server is presented, where all mobile users participate in the PoW mining process. The traditional Bitcoin network adjusts the target difficulty value to ensure a stable block time. However, for MEC-assisted mobile blockchain networks, the adjusted difficulty value needs to be broadcast to all mobile users, which results in expensive communication costs. To maintain a stable block time of mobile blockchain networks, we formulate the delay-limited computation offloading strategy of the PoW-based mining task as a non-cooperative game that maximizes an individual revenue in the MEC-assisted mobile blockchain network. Specifically, the non-cooperative game problem can be divided into multiple sub-game optimization problems to obtain final solutions for all users. We analyze the sub-game optimization problem and prove the existence of Nash equilibrium (NE) of the non-cooperative game. Moreover, we design an alternating iterative algorithm based on the continuous relaxation and greedy rounding (CRGR) to achieve the NE of this game. Given the sub-optimal delay-limited computation offloading results, we also derive the optimal transmit power for an individual user within the maximum mining delay range. From the analytical results, we can see that the proposed CRGR-based alternating iterative algorithm can efficiently attain the sub-optimal delay-limited computation offloading strategies of all mobile users in the polynomial time. The individual transmit power increases accordingly with the delay-limited computation offloading strategies of all users. Numerical results demonstrate that the proposed CRGR-based alternating iterative algorithm has fast convergence and good stability.
A Blockchain Architecture Design that Takes into Account Privacy Protection and Regulation

The privacy protection technology on blockchain provides participants with good anonymity, but it also brings great difficulties to regulators. At the same time, for the permission chain, anonymity can not play the role of privacy protection. In this paper, a new framework is designed to meet the dual requirements of privacy protection and data supervision in the context of multi organizations and multi participants. The architecture can not only satisfy the privacy protection and supervision of transaction data, but also protect the data privacy under the condition of compliance with the supervision.
How to Design the Perfect Prompt: A Linguistic Approach to Prompt Design in Automotive Voice Assistants – An Exploratory Study

In-vehicle voice user interfaces (VUIs) are becoming increasingly popular while needing to handle more and more complex functions. While many guidelines exist in terms of dialog design, a methodical and encompassing approach to prompt design is absent in the scientific landscape. The present work closes this gap by providing such an approach in form of linguistic-centered research. By extracting syntactical, lexical, and grammatical parameters from a German contemporary grammar, we examine how their respective manifestations affect users' perception of a given system output across different prompt types. Through exploratory studies with a total of 1,206 participants, we provide concrete best practices to optimize and refine the design of VUI prompts. Based on these best practices, three superordinate user needs regarding prompt design can be identified: a) a suitable level of (in)formality, b) a suitable level of complexity/simplicity, and c) a suitable level of (im)mediacy.
An Improved SRGAN Based Ambiguity Suppression Algorithm for SAR Ship Target Contrast Enhancement

Due to the specific characteristics of synthetic aperture radar (SAR), there will be ambiguity interference in SAR images, resulting in low contrast of the ship target to the clutter. This letter proposes an improved super-resolution generative adversarial network (ISRGAN) based ambiguity suppression algorithm for SAR ship target contrast enhancement. The proposed ISRGAN is the first attempt of using GAN for SAR ambiguity suppression. As a post-processing procedure, it does not need prior information of SAR systems, so it can be applied to various observation scenes and different acquisition modes. The generator of ISRGAN embeds the residual dense network (RDN) to optimally fuse the global and local features of the image, and it effectively improves the completeness of the feature information used for SAR ship target contrast enhancement. The superiority of ISRGAN on ambiguity suppression is validated on the Chinese Gaofen-3 imagery.
Residual 3-D Scene Flow Learning With Context-Aware Feature Extraction

Scene flow estimation is the task to predict the point-wise or pixel-wise 3-D displacement vector between two consecutive frames of point clouds or images, which has important application in fields such as service robots and autonomous driving. Although many previous works have explored greatly on scene flow estimation based on point clouds, there are two problems that have not been noticed or well solved before: 1) points of adjacent frames in repetitive patterns tend to be wrongly associated due to similar spatial structure in their neighborhoods and 2) scene flow between adjacent frames of point clouds with long-distance movement tends to be inaccurately estimated. To solve the first problem, a novel context-aware set convolution layer is proposed in this article to exploit contextual structure information of Euclidean space and learn soft aggregation weights for local point features. This design is inspired by human perception of contextual structure information during scene understanding with repetitive patterns. For the second problem, an explicit residual flow learning structure is proposed in the residual flow refinement layer to cope with long-distance movement. The experiments and ablation study on FlyingThings3D and Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) scene flow datasets demonstrate the effectiveness of each proposed component. The qualitative results show that the problems of ambiguous interframe association and long-distance movement estimation are well handled. Quantitative results on both FlyingThings3D and KITTI scene flow datasets show that the proposed method achieves state-of-the-art performance, surpassing all previous works to the best of our knowledge by at least 26.22% on FlyingThings3D dataset and 25.42% on KITTI scene flow dataset for 3-D endpoint error (EPE3D) metric. The source codes are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/IRMVLab/CARFlow</uri> .
Graph Fourier Transform Based Audio Zero-Watermarking

The frequent exchange of multimedia information in the present era projects an increasing demand for copyright protection. In this work, we propose a novel audio zerowatermarking technology based on graph Fourier transform for enhancing the robustness with respect to copyright protection. In this approach, the combined shift operator is used to construct the graph signal, upon which the graph Fourier analysis is performed. The selected maximum absolute graph Fourier coefficients representing the characteristics of the audio segment are then encoded into a feature binary sequence using K-means algorithm. Finally, the resultant feature binary sequence is XORed with the watermark binary sequence to realize the embedding of the zero-watermark. The experimental studies show that the proposed approach performs more effectively in resisting common or synchronization attacks than the existing state ofthe-art methods.
Model of Learning Management System Based on Artificial Intelligence in Team-Based Learning Framework

The presence of various innovations and technology of Artificial Intelligence (AI) has now begun to be adopted by Indonesia in many aspects including in the industrial sector. The utilization of AI also can offer a new paradigm in learning and teaching strategies. The development of learning strategies continues also performed to obtain the variations of learning that involve student activity. Team-Based Learning (TBL) is one of the teaching strategies that can improve the quality of the learning process and student activity in groups. By starting to integrate digital-based learning resources in the learning and teaching process, the TBL strategy can potentially be an alternative to new learning strategies that are relevant to be applied in the era of the educational revolution 4.0. The main focus of this research is to develop and evaluate the application of AI in a digital-based TBL strategy that will be implemented in the form of personalized learning about student learning styles. This research was developed based on the learning strategy development of the research method. However, this article more focuses on developing the AI application design that will be applied. The application design was developed based on the agile development method, especially SCRUM. The result of this study is an additional feature that was implemented on a Learning Management System (LMS) based on the Modular Object-Oriented Dynamic Learning Environment (MOODLE).
Recent advancement in VM task allocation system for cloud computing: review from 2015 to2021

Cloud computing is new technology that has considerably changed human life at different aspect over the last decade. Especially after the COVID-19 pandemic, almost all life activity shifted into cloud base. Cloud computing is a utility where different hardware and software resources are accessed on pay per user ground base. Most of these resources are available in virtualized form and virtual machine (VM) is one of the main elements of visualization.VM used in data center for distribution of resource and application according to benefactor demand. Cloud data center faces different issue in respect of performance and efficiency for improvement of these issues different approaches are used. Virtual machine play important role for improvement of data center performance therefore different approach are used for improvement of virtual machine efficiency (i-e) load balancing of resource and task. For the improvement of this section different parameter of VM improve like makespan, quality of service, energy, data accuracy and network utilization. Improvement of different parameter in VM directly improve the performance of cloud computing. Therefore, we conducting this review paper that we can discuss about various improvements that took place in VM from 2015 to 20,201. This review paper also contain information about various parameter of cloud computing and final section of paper present the role of machine learning algorithm in VM as well load balancing approach along with the future direction of VM in cloud data center.
Forecasting Bitcoin realized volatility by measuring the spillover effect among cryptocurrencies

This paper studies whether the volatility spillover effect among cryptocurrencies matters for forecasting Bitcoin realized volatility. Our results show that Bitcoin volatility models considering the linkage effect have better in-sample explanatory power and significantly improve the performance for short-term forecasts.
Visual FUDGE: Form Understanding via Dynamic Graph Editing

We address the problem of form understanding: finding text entities and the relationships/links between them in form images. The proposed FUDGE model formulates this problem on a graph of text elements (the vertices) and uses a Graph Convolutional Network to predict changes to the graph. The initial vertices are detected text lines and do not necessarily correspond to the final text entities, which can span multiple lines. Also, initial edges contain many false-positive relationships. FUDGE edits the graph structure by combining text segments (graph vertices) and pruning edges in an iterative fashion to obtain the final text entities and relationships. While recent work in this area has focused on leveraging large-scale pre-trained Language Models (LM), FUDGE achieves the same level of entity linking performance on the FUNSD dataset by learning only visual features from the (small) provided training set. FUDGE can be applied on forms where text recognition is difficult (e.g. degraded or historical forms) and on forms in resource-poor languages where pre-training such LMs is challenging. FUDGE is state-of-the-art on the historical NAF dataset.
Differentially Private ADMM Algorithms for Machine Learning

In this paper, we study efficient differentially private alternating direction methods of multipliers (ADMM) via gradient perturbation for many centralized machine learning problems. For smooth convex loss functions with (non)-smooth regularization, we propose the first differentially private ADMM (DP-ADMM) algorithm with the performance guarantee of (ϵ,δ)-differential privacy ((ϵ,δ)-DP). From the viewpoint of theoretical analysis, we use the Gaussian mechanism and the conversion relationship between Rényi Differential Privacy (RDP) and DP to perform a comprehensive privacy analysis for our algorithm. Then we establish a new criterion to prove the convergence of the proposed algorithms including DP-ADMM. We also give the utility analysis of our DP-ADMM. Moreover, we propose a new accelerated DP-ADMM (DP-AccADMM) algorithm with the Nesterov’s acceleration technique. Finally, we conduct numerical experiments on many real-world datasets to show the privacy-utility tradeoff of the two proposed algorithms, and all the comparative analysis shows that DP-AccADMM converges faster and has a better utility than DP-ADMM, when the privacy budget ϵ is larger than a threshold.
Long Multi-digit Number Recognition from Images Empowered by Deep Convolutional Neural Networks

Abstract Scanning images and converting the scanned information into digital format is an active research area. Scanning is an automated, fast and efficient process as compared to the traditional data entry, and the resultant converted data is more accurate. Recognizing digits from the scanned images is a challenging task. To address this issue, most of the existing techniques perform multiple individual steps that are localization, segmentation and recognition. Some researchers also focused on adopting a unified approach that combined these three steps for multi-digit recognition of up to five digits. To cope with the modern requirements, a unified multi-digit recognition technique capable of recognizing more than five digits is the need of the hour. Considering this necessity, a unified multi-digit recognition approach is presented in the current study that can recognize sequences up to 18 digits long. The proposed technique is based on a deep convolutional neural network algorithm that performs two basic functions. First, it localizes and extracts the region of interest in the image, and then it performs multi-digit recognition. The proposed algorithm recognizes sequences of up to 18 characters that makes it one of the preferred recognition techniques among the existing algorithms. The proposed technique is compared with state-of-the-art techniques and is proved to be superior and robust. The experiments are performed on two datasets, and overall accuracy up to 98% is achieved.
Shape-Biased Domain Generalization via Shock Graph Embeddings

There is an emerging sense that the vulnerability of Image Convolutional Neural Networks (CNN), i.e., sensitivity to image corruptions, perturbations, and adversarial attacks, is connected with Texture Bias. This relative lack of Shape Bias is also responsible for poor performance in Domain Generalization (DG). The inclusion of a role of shape alleviates these vulnerabilities and some approaches have achieved this by training on negative images, images endowed with edge maps, or images with conflicting shape and texture information. This paper advocates an explicit and complete representation of shape using a classical computer vision approach, namely, representing the shape content of an image with the shock graph of its contour map. The resulting graph and its descriptor is a complete representation of contour content and is classified using recent Graph Neural Network (GNN) methods. The experimental results on three domain shift datasets, Colored MNIST, PACS, and VLCS demonstrate that even without using appearance the shape-based approach exceeds classical Image CNN based methods in domain generalization.
Grayscale-inversion and rotation invariant image description with sorted LBP features

Local binary pattern (LBP) is sensitive to inverse grayscale changes. To overcome this problem, several methods map each LBP code and its complement to the minimum one. However, without distinguishing LBP codes and their complements, these methods show limited description ability. In this paper, we introduce a generic histogram sorting method which exploits pattern transition rules to preserve the distribution information of LBP codes and their complements. Based on this method, we develop a series of sorted LBP (SLBP) descriptors, including pairwise sorted ones and fully sorted ones, which are all invariant to grayscale inversion and image rotation. Since SLBP focuses on encoding difference-sign information, it is further generalized to embed difference-magnitude LBP features to obtain complementary representations. We also propose an invariant pyramid pooling strategy to aggregate SLBP features into a pyramid image representation. Experiments on several benchmark texture databases and one newly collected image database (grayscale-inversion images, GII) demonstrate the effectiveness of our descriptors for image classification under (linear or nonlinear) grayscale-inversion and rotation changes. The source code will be available at https://github.com/stc-cqupt/slbp.
Assessment of the Performance of the Solar Power Plant with a Capacity 150W

The article presents the results of experimental studies on electrical energy operations of solar power with a capacity 150 W, with which experimental studies were carried out in the form of a change in the angle of inclination of the solar panel relative to the surface of the earth, during sunny weather in summer. In the course of these experimental studies, we used:(computing and measuring equipment, polycrystalline solar panel 150 W, charge controller, inverter and energy storage system in the form of a battery with a capacity of 75 Ah). The results of these studies were addressed by analyzing the resulting curves and the impact of the resulting values on the external parameters in the form of regression equation depending on the programs (Microsoft Office Excel - Mathcad Professional).