Reviewerly/exHarmony huggingface dataset:
A Novel Trusted Boot Model for Embedded Smart Device without TPM

Embedded smart devices are widely used in people’s life, and the security problems of embedded smart devices are becoming more and more prominent. Meanwhile lots of methods based on software have been presented to boot the system safely and ensure the security of the system execution environment. However, it is easy to attack and destroy the methods based on software, which will cause that the security of the system cannot be guaranteed. Trusted Computing Group proposed the method of using Trusted Platform Module (TPM) to authenticate the credibility of the platform, which can solve the disadvantages of using methods based on software to protect the system. However, due to the limited resource and volume of embedded smart devices, it is impossible to deploy TPM on embedded smart devices to ensure the security of the system operating environment. Therefore, a novel trusted boot model for embedded smart devices without TPM is proposed in this paper, in which a device with TPM provides trusted service to realize the trusted boot of embedded smart devices without TPM through the network and ensure the credibility of the system execution environment.
Swarmed Grey Wolf Optimizer

Background: The Particle Swarm Optimization (PSO) algorithm is amongst the utmost favourable optimization algorithms often employed in hybrid procedures by the researchers considering simplicity, smaller count of parameters involved, convergence speed and capability of searching global optima. The PSO algorithm acquires memory and the collaborative swarm interactions enhances the search procedure. The high exploitation ability of PSO which intends to locate the best solution within a limited region of the search domain gives PSO an edge over other optimization algorithms. Whereas, low exploration ability results in lack of assurance of proper sampling of the search domain and thus enhances the chances of rejecting a domain containing high quality solutions. A perfect harmony between exploration and exploitation abilities in the course of selection of best solution is needed. High exploitation capacity makes PSO get trapped in local minima when its initial location is far off from the global minima. OBJECTIVES: The intent of this study is to reform this drawback of PSO of getting trapped in local minima. With an objective to upgrade the potential of Particle Swarm Optimization (PSO) to exploit along with preventing PSO of getting trapped in local minima, we require an algorithm with a positive acceptable exploration capacity. METHODS: We utilized, the recently developed metaheuristic Grey Wolf Optimizer (GWO) emulating the seeking and hunting techniques of Grey wolves for this purpose. In our way, the GWO has been utilized to assist PSO in a manner to unite their strengths and lessen their weaknesses. The proposed hybrid has two driving parameters to adjust and assign the preference to PSO or GWO. RESULTS: To test the act of the proposed hybrid it has been examined in comparison with the PSO and GWO methods. For this, eleven benchmark functions involving different unimodal and multimodal functions have been taken. The PSO, GWO and SGWO pseudo codes were coded in visual basic. In all the functions parameters of PSO and GWO were chosen as: w = 0.7, c1 = c2 = 2, population size = 30, number of iterations = 30. Experiments were redone 25 times for each of the method and for each benchmark function. The methods were compared with regard to their best and worst values besides their average values and standard deviations. The obtained results revealed that in terms of average values and standard deviations our hybrid SGWO outperformed both PSO and GWO notably. CONCLUSION: The outcomes of the experiments reveals that the proposed hybrid is better in comparison to both PSO and GWO in the search ability. Though the SGWO algorithm refines result quality, the computational complexity also gets elevated. Thus, lowering the computational complexity would be another issue of future work. Moreover, we will apply the proposed hybrid in the field of water quality estimation and prediction.
Development of a computational and experimental method for assessment of the interference effects of lightning discharge radio emissions on the functioning of ground-based receiving stations of high-altitude satellite networks using domestic geoinformation systems

Problem statement . In the spring/summer season, operation of high-altitude satellite systems (HASS) is periodically hindered by disturbances in reception of information from space vehicles (SV), resulting from the interference effects produced by the electromagnetic emissions of lightning discharges (LD) on the receiving channels of ground-based stations (GBS), occurring due to thunderstorm activity. It is necessary to reliably assess the degree of influence such natural phenomena have upon functioning of HASS according to their intended purpose, with quantitative estimates made, and determine practicable ways to parry those phenomena. Objective . To develop a method for assessing LD radio emission effects, resulting from thunderstorm activity, for the purpose of making qualitative and quantitative estimates of the degree of their interference effect on the functioning of HASS’s GBS. Results . The paper proposes a computational and experimental method for assessing LD radio emission effects on the functioning of HASS’s GBS using a software complex for computation of electromagnetic field (EMF) and EMC parameters of communications electronics, developed on the basis of a domestic geoinformation system (GIS). Practical usefulness . A qualitative and quantitative estimate is drawn up as to the degree of interference effect produced by LD on the receiving lines of HASS’s GBS, and a method is proposed for compensation of losses in information reception by using spatially diverse GBS.
Thermodynamic cost of quantum transfers

We explain how thermodynamic cost can use for diagnosing optimal dense coding. We present a quantum channel where is included two initially uncorrelated thermal quantum systems to reveal the optimal dense coding using thermodynamic cost. The interest in dense coding brings into quantum correlation calculation. At first, the quantum Fisher information and spin squeezing are used to quantify the correlation dynamics over the system. The system reveals that the thermal evolution of quantum correlations depends crucially on specific energy and temperature. Also, they can be utilized as control parameters for optimal dense coding. Several interesting features of the variations of the energy cost and the dense coding capacity are obtained. It can keep its valid capacity value in a broad range of temperatures by increasing the energy value of excited states. Also, we can identify valid dense coding with the help of calculating the energy cost of the system. Using this approach, identifying a critical point of this model in dense coding capacity quality can be very effective.
Offline Text Recognition Using Hidden Markov Model

In this paper, we have proposed a methodology of implementing a system model based on Hidden Markov Model (HMM) that can effectively recognize digital textual material. The idea behind the model relies on the ease of implementing HMMs to predict the succeeding character depending on the observable of the present. A similar concept is then applied as a whole for complete word detection and recognition. The model is termed as H and relies on heavily pre-processed images of digital textual data-set. The training phase depends heavily on the vocabulary fed to the system in image format and a series of textual characters, sentences and non-sentimental phrases in text format. Evaluation of the model is expressed in terms of the likelihood of occurrence of testing data. The evaluation result is maintained as the final criterion for the model&#x0027;s ability to filter text from noisy text images. The applications of the project lie in the noise removal from text, clarification of text, scaling the model to operate on a huge amount of textual data and the scope of the project is limitless in image processing and natural language processing.
3D U-Net Based Semantic Segmentation of Kidneys and Renal Masses on Contrast-Enhanced CT

The accurate, automated detection and segmentation of renal tumors is of great interest for the imaging-based diagnosis, histologic subtyping, and management of suspected renal malignancy. The KiTS21 Grand Challenge provides 300 contrast enhanced CT images with kidney, tumors and cysts with corresponding manual annotation, to facilitate the development of robust segmentation algorithms for this task. In this work, we present an adaptation of the historically-successful 3D U-Net architecture, combined with deep supervision, foreground oversampling and large-scale image context, and trained on the majority-prediction segmentation masks. Our model achieved test-set performance of 97.0%, 85.1%, and 81.9% volumetric Dice score, and 93.7%, 72.0%, and 70.0% surface Dice score, on combined foreground, renal masses, and renal tumors, respectively, which tied for sixth place among challenge participants.
Comparative analysis of median and Laplacian filter to detect edges of non-contrast cardiac CT image

The incidence of heart disease is increasing every year. One of the preventive actions is through early detection of the disease to take further action. Computer-Aided Diagnosis (CAD) helps doctors and cardiologists detect heart abnormalities seen in non-contrast cardiac CT images faster than manual detection. This study aims to detect the edges of non-contrast cardiac CT images. The method used is the Median and Laplacian filter algorithm using Matlab. The result shows that there is an automatic edge detection of non-contrast cardiac CT images. The conclusion obtained by the Laplacian filter with -8 kernel at its centre is clearer to detect edges of non-contrast cardiac CT images than the Median and Laplacian filters with -4 kernel at the centre. The study results are useful for medical image processing to facilitate the subsequent image analysis stage.
Channel-Spatial Attention Network for Lunar Image Super-Resolution

High-resolution images of the lunar surface are generally used to study the lunar soil and terrain. Nonetheless, acquiring higher-resolution images involves greater memory and calculation power, which is a challenge for lunar landers or rovers. In this study, a deep convolution neural network single image super-resolution reconstruction method based on channel-space attention is proposed to achieve the mapping of low-resolution lunar surface images to high-resolution lunar surface images. An enhanced block named feature fusion block is used for feature extraction. Furthermore, a channel-spatial attention module including efficient channel attention module and enhanced spatial attention module can extract more discriminative channel features and critical spatial features. Last, the model utilizes local implicit image function to predict RGB values of the images. The images of lander terrain camera and rover panoramic camera carried by Chang'e 3 and Chang'e 4 are used to training and validating models, and the Apollo rover images are used to test. The experiment demonstrates the superiority of the proposed novel model over the comparative method by using images of the Apollo project as the test dataset. Compared with the traditional methods, the PSNR value of the proposed method is improved by about 0.26dB in the 4x super-resolution reconstruction experiment.
Application of graph calculation based on SparkGraphX in social circle

In social life, social circle has always been a concern of people, and it is also a hot research issue in computer and other disciplines. With more and more social network users, the amount of data on the network is growing rapidly, and the scale of graph data is becoming larger and larger, which leads to the problem of computing social networks more and more difficult. SparkGraphX is a very important component in the spark framework and has great advantages in distributed computing and iteration. Using the built-in graph calculation function in SparkGraphX, we can quickly find everyone's social circle and greatly improve the calculation efficiency.
From a Dynamic Image Annotation Process within the Humanities to a Canonical Workflow

Abstract One idea of the Canonical Workflow Framework for Research (CWFR) is to improve the reusability and automation in research. In this paper, we aim to deliver a concrete view on the application of CWFRs to a use case of the arts and humanities to enrich further discussions on the practical realization of canonical workflows and the benefits that come with it. This use case involves context dependent data transformation and feature extraction, ingests into multiple repositories as well as a “human-in-the-loop” workflow step, which introduces a certain complexity into the mapping to a canonical workflow.
Around the parameters of primitive BCH codes

Let <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{q}$</tex> be the finite field with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> elements, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n, \delta$</tex> are positive integers such that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\leqslant\delta\leqslant n-1$</tex> . The aim of this work, is to present the primitive BCH codes of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> and designed distance <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\delta$</tex> over the finite field <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{q}$</tex> , and to give an overview about the recent results on the parameters of these codes, which cover minimum weight, bose distance and dimension. As applications of some results presented in this work, we use the SageMath program.
De-speckle noise model for SAR images based on modified wavelet decomposition and partial differential equation

The removal of speckle noise in synthetic aperture radar (SAR) images is important for the subsequent processing and analysis of SAR images. In order to suppress the speckle noise and improve the equivalent number of looks (ENL) in SAR images, a de-speckle noise model based on modified wavelet decomposition and partial differential equation (PDE) is proposed. In this study, the de-speckle noise model based on modified wavelet decomposition and PDE is compared with wavelet transform (WT) model, integer order total variation (TV) model, fractional order total variation (FTV) model, and tight frame (TF) model. The experimental results show that the improved total variance model has better de-noising effect on real SAR images and retains edge details, which is of practical value.
Minimal σ-field for flexible sufficient dimension reduction

Sufficient Dimension Reduction (SDR) becomes an important tool for mitigating the curse of dimensionality in high dimensional regression analysis. Recently, Flexible SDR (FSDR) has been proposed to extend SDR by finding lower dimensional projections of transformed explanatory variables. The dimensions of the projections however cannot fully represent the extent of data reduction FSDR can achieve. As a consequence, optimality and other theoretical properties of FSDR are currently not well understood. In this article, we propose to use the σ-field associated with the projections, together with their dimensions to fully characterize FSDR, and refer to the σ-field as the FSDR σ-field. We further introduce the concept of minimal FSDR σ-field and consider FSDR projections with the minimal σ-field optimal. Under some mild conditions, we show that the minimal FSDR σ-field exists, attaining the lowest dimensionality at the same time. To estimate the minimal FSDR σ-field, we propose a two-stage procedure called the Generalized Kernel Dimension Reduction (GKDR) method and partially establish its consistency property under weak conditions. Extensive simulation experiments demonstrate that the GKDR method can effectively find the minimal FSDR σ-field and outperform other existing methods. The application of GKDR to a real life air pollution data set sheds new light on the connections between atmospheric conditions and air quality.
How to Build a Scalable Quantum Controller

We discuss quantum computers from the perspective of one of their major building blocks, the hardware controller, explaining how it fits into the computer and the requirements and challenges it poses for engineers and scientists.
Defending against Deep-Learning-Based Flow Correlation Attacks with Adversarial Examples

Tor is vulnerable to flow correlation attacks, adversaries who can observe the traffic metadata (e.g., packet timing, size, etc.) between client to entry relay and exit relay to the server will deanonymize users by calculating the degree of association. A recent study has shown that deep-learning-based approach called DeepCorr provides a high flow correlation accuracy of over 96%. The escalating threat of this attack requires timely and effective countermeasures. In this paper, we propose a novel defense mechanism that injects dummy packets into flow traces by precomputing adversarial examples, successfully breaks the flow pattern that CNNs model has learned, and achieves a high protection success rate of over 97%. Moreover, our defense only requires 20% bandwidth overhead, which outperforms the state-of-the-art defense. We further consider implementing our defense in the real world. We find that, unlike traditional scenarios, the traffic flows are “fixed” only when they are coming, which means we must know the next packet’s feature. In addition, the websites are not immutable, and the characteristics of the transmitted packets will change irregularly and lead to the inefficiency of adversarial samples. To solve these problems, we design a system to adapt our defense in the real world and further reduce bandwidth overhead.
A Comparative Study of Task Scheduling Metaheuristic Algorithms in Cloud Computing

Today, cloud computing is one of the fastest-growing IT areas. Cloud computing provides users with on-demand access to resources over the internet. User&#x2019;s requirements are met by using the internet to reorganize physical resources, platforms, and software applications. Cloud computing&#x2019;s capabilities are expanding by the day, making it a need for IT businesses. This paper solely puts forward a detailed study of these methods which are used in the more so advancing computing environment. In the cloud computing context, the adoption of excellent cloud scheduling algorithms is still an important aim. In the cloud computing era, task scheduling is crucial. During the procedure of task scheduling, users request tasks, and those are subsequently assigned to specified resources at a specific time. There are several algorithms that can provide high quality service and performance while also reducing execution time. The study&#x2019;s goal is to investigate the many scheduling algorithms that are utilized in cloud computing environments, as well as to identify certain results and research gaps that may be used to improve the quality of these algorithms.
More Efficient Parallel Integer Sorting

We present a more efficient CREW PRAM algorithm for integer sorting. This algorithm sorts [Formula: see text] integers in [Formula: see text] in [Formula: see text] time and [Formula: see text] operations. It also sorts [Formula: see text] integers in [Formula: see text] in [Formula: see text] time and [Formula: see text] operations. Previous best algorithm [15] on both cases has time complexity [Formula: see text] but operation complexity [Formula: see text].
Uav Based Visual Object Tracking

With the wide adoption of UAVs (unmanned aerial vehicles) in various industries by the government as well as private corporations for solving computer vision tasks it’s necessary that their potential is analyzed completely. Recent advances in Deep Learning have also left us with a plethora of algorithms to solve different computer vision tasks. This study provides a comprehensive survey on solving the Visual Object Tracking problem and explains the tradeoffs involved in building a real-time yet reasonably accurate object tracking system for UAVs by looking at existing methods and evaluating them on the aerial datasets. Finally, the best trackers suitable for UAV based applications are provided.
Investigate the impact of user’s state on the quality of authentication by keystroke dynamic

Abstract One of the most important issues in the field of information security has been and remains the issue a reliable user’s authentication. A special place among the possible authentication methods today is occupied by behavioural biometrics, which have a high degree of reliability. A keystroke dynamic, as a type of behavioural biometrics, is also capable of providing a high level of protection of information systems in the case of correctly selected characteristics. This article shows that external factors, including the psychophysiological state of a person, can also influence the authentication process by keystroke dynamics. In the paper, the state of the user was assessed in the process of collecting a sample of keystroke dynamics and a conclusion was made about the presence of such an influence.
Unbiased Variable Windows Size Impulse Noise Filter using Genetic Algorithm

This paper proposes an Unbiased Variable Windows Size Impulse noise filter (UVWS) using a genetic algorithm to effectively restore the corrupted images with high or slight noise densities. The method consists of three stages. First, all pixels are classified into noisy and noise-free categories based on their intensities. In the second stage, the noisy pixels are pushed into a descending priority list the priority associated with each pixel is the number of noise-free pixels in the neighbor’s local window. Finally, for each pixel in the list, a local weighted average is calculated so that the corresponding weight for each neighbor is optimized by the genetic algorithm (GA). The performance of the proposed method is evaluated on several benchmark images and compared with four methods from the literature. The results show that the proposed method performs better in terms of visual quality and PSNR especially when the noise density is very high.
Refining Word Embeddings for Funniness Predicting Sentimental Analysis Tasks

Sentiment analysis lies within one of the core fields for natural language processing. Concerning this field, word embeddings have been widely used as an effective way of representing words based on distributional semantics. In this paper, we focus on improving the GloVe embedding used for predicting the funniness of edited news headlines. The GloVe embedding of different dimensions is further tuned with the news headline dataset provided by SemEval, using the Continuous Bag of Words model and Skip-gram model, respectively. A recurrent neural network model is developed as the regression model for prediction task. A set of experiments are designed to evaluate the performance of various refined GloVe embeddings. We found out that original GloVe embedding and refined embeddings share similar performance in our current work. The prediction is more accurate for all refined embeddings when using 100 dimensions rather than 300 dimensions embeddings.