Reviewerly/exHarmony huggingface dataset:
A Comprehensive Method for Judging the Operation State of High-Voltage Relay Protection Equipment Based on Association Rules and Cloud Model

In order to further improve the operational reliability of high-voltage relay protection equipment and reduce equipment maintenance costs, a comprehensive judgment method for the operation status of high-voltage relay protection equipment based on association rules and cloud model was proposed. By analyzing and sorting out the offline and online data of the relay protection equipment, the evaluation index system is constructed from the three aspects of the device body, the secondary circuit and the channel condition. The association rule confidence is introduced to calculate the weight of evaluation indicators, and the relative importance of evaluation indicators can be objectively quantified. The membership degree of the evaluation index is determined based on the cloud model, and the comprehensive judgment result of the operation state of the relay protection equipment is obtained according to the principle of the maximum membership degree. The example analysis shows that the proposed method can effectively improve the identification accuracy of the operating state of the relay protection equipment.
Research on Evaluation of Power Grid Digitization Project Based on Grey Relational Analysis

Aiming at the problem of how to reasonably evaluate the power grid digitization project, the evaluation indexes of the digitization project are initially screened from three dimensions, and the gray relational analysis method is used to further select the indexes included in the digitization benefit to establish an evaluation index system. Then set the index normalization standard, use the Delphi method to determine the weight of the three categories of indicators, use the combination weighting method combining the analytic hierarchy process and the entropy weight method to determine the weight of each indicator, and establish a comprehensive scoring model according to the weight of each indicator. Finally, taking the digital project of a provincial power grid company as an example, the evaluation scores of the digital project in the past five years are calculated, which verifies the rationality and scientific of the evaluation model.
Encryption Algorithm of Power Equipment Nameplate Based on Quadratic Convolution

Image encryption technology hides the original image information by disrupting the pixel ordering and other methods, so as to improve the security of the image. It is widely used in military, medical and life fields. As a reference for power operation and maintenance personnel, the security of power equipment nameplate information also needs to be further guaranteed, so it has gradually become the main encryption object of equipment supply enterprises. In order to improve the security of power equipment nameplate, combined with the irregular characteristics of white noise image, this paper presents an encryption algorithm for secondary convolution of power equipment nameplate. The encrypted image is obtained by primary self convolution of the original image, and then secondary convolution of the self convolution output value based on the white noise image. The experimental results show that the power equipment nameplate encryption algorithm based on quadratic convolution presented in this paper can effectively encrypt, prevent violent attacks and other attacks, and ensure a certain degree of security.
Research on Legal Protection of "Digital Inadvantaged Group"

The world is currently experiencing rapid Internet technology development, marking the onset of the "digital age".As this transition unfolds, human rights protection in the digital age is increasingly prominent, with the rights of "digital vulnerable groups" coming under threat.The primary threats to "digital vulnerable groups" are twofold: first, information resource asymmetry that leaves their rights invisible and vulnerable, and second, strong digital infringement concealment that often makes it difficult to identify members of "digital vulnerable groups".Protecting the rights of "digital vulnerable groups" should focus on two key aspects: first, redistributing information resources to benefit these groups, and second, reversing the burden of proof so that the tort subject bears the burden of proof.
Transparent Object Detection with Simulation Heatmap Guidance and Context Spatial Attention

The texture scarcity properties make transparent object localization a challenging task in the computer vision community. This paper addresses this task in two aspects. (i) Additional guidance cues: we propose a Simulation Heatmap Guidance (SHG) to improve the localization ability of the model. Concretely, the target’s extreme points and inference centroids are used to generate simulation heatmaps to offer additional position guides. A high recall is rewarded even in extreme cases. (ii) Enhanced attention: we propose a Context Spatial Attention (CSA) combined with a unique backbone to build dependencies between feature points and to boost multi-scale attention fusion. CSA is a lightweight module and brings apparent perceptual gain. Experiments show that our method achieves more accurate detection for cluttered transparent objects in various scenarios and background settings, outperforming the existing methods.
Social Relation Graph Generation on Untrimmed Video

For a more intuitive understanding of videos, we demonstrate SRGG-UnVi, a social relation graph generation system for untrimmed videos. Given a video, the demonstration can combine existing knowledge to build a dynamic relation graph and a static multi-relation graph. SRGG-UnVi integrates various multimodal technologies, including Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), face recognition and clustering, multimodal video relation extraction, etc. The system consists of three modules: (1) The video process engine takes advantage of parallelization, efficiently providing multimodal information to other modules. (2) The relation recognition module utilize multimodal information to extract the relationship between characters in each scene. (3) The graph generation module generates social relation graph for users.
Peculiarities of developing text materials of modern media publications to increase their rating and reach a wider readership

The transition to digital technologies and the remoteness of workplaces put forward new requirements in the textual materials of the mass media in order to increase the competitive advantages of individual online companies (editions) and the perception of readers. Therefore, the purpose of the study is to evaluate modern means of forming the perception and expressiveness of texts in an online format in order to maintain the loyalty of readers and website visitors. The article proves the need to improve the perception and expressiveness of text formats of online publications as a basic factor in increasing the competitiveness of media companies due to the expansion of the circle of readers and their retention as potential consumers of information products. It was noted that site visitors psychologically evaluate text materials on an emotional basis. Therefore, this aspect is fundamental in the formation of the relationship "consumer of information - emotional content of the text". The effectiveness of modern means of expressiveness of the Internet media regarding the presentation of text materials in Internet publications and stylistic design is noted. It is indicated that the further development of texts from the standpoint of emotional content is possible due to the use of innovative technologies based on artificial intelligence. Further research in the formation of textual materials should focus on the "editor-reader" relationship.
Supplementary Data from Accurate Outcome Prediction in Neuroblastoma across Independent Data Sets Using a Multigene Signature

&lt;p&gt;Supplementary Figures S1-S2; Supplementary Tables S1 and S3-S5.&lt;/p&gt;
Data from Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer

&lt;div&gt;AbstractPurpose:&lt;p&gt;The presence of a high degree of tumor-infiltrating lymphocytes (TIL) has been proven to be associated with outcome in patients with non–small cell lung cancer (NSCLC). However, recent evidence indicates that tissue architecture is also prognostic of disease-specific survival and recurrence. We show a set of descriptors (spatial TIL, SpaTIL) that capture density, and spatial colocalization of TILs and tumor cells across digital images that can predict likelihood of recurrence in early-stage NSCLC.&lt;/p&gt;Experimental Design:&lt;p&gt;The association between recurrence in early-stage NSCLC and SpaTIL features was explored on 301 patients across four different cohorts. Cohort D&lt;sub&gt;1&lt;/sub&gt; (&lt;i&gt;n&lt;/i&gt; = 70) was used to identify the most prognostic SpaTIL features and to train a classifier to predict the likelihood of recurrence. The classifier performance was evaluated in cohorts D&lt;sub&gt;2&lt;/sub&gt; (&lt;i&gt;n&lt;/i&gt; = 119), D&lt;sub&gt;3&lt;/sub&gt; (&lt;i&gt;n&lt;/i&gt; = 112), and D&lt;sub&gt;4&lt;/sub&gt; (&lt;i&gt;n&lt;/i&gt; = 112). Two pathologists graded each sample of D&lt;sub&gt;1&lt;/sub&gt; and D&lt;sub&gt;2&lt;/sub&gt;; intraobserver agreement and association between manual grading and likelihood of recurrence were analyzed.&lt;/p&gt;Results:&lt;p&gt;SpaTIL was associated with likelihood of recurrence in all test sets (log-rank &lt;i&gt;P&lt;/i&gt; &lt; 0.02). A multivariate Cox proportional hazards analysis revealed an HR of 3.08 (95% confidence interval, 2.1–4.5, &lt;i&gt;P&lt;/i&gt; = 7.3 × 10&lt;sup&gt;−5&lt;/sup&gt;). In contrast, agreement among expert pathologists using tumor grade was moderate (Kappa = 0.5), and the manual TIL grading was only prognostic for one reader in D&lt;sub&gt;2&lt;/sub&gt; (&lt;i&gt;P&lt;/i&gt; = 8.0 × 10&lt;sup&gt;−3&lt;/sup&gt;).&lt;/p&gt;Conclusions:&lt;p&gt;A set of features related to density and spatial architecture of TILs was found to be associated with a likelihood of recurrence of early-stage NSCLC. This information could potentially be used for helping in treatment planning and management of early-stage NSCLC.&lt;/p&gt;&lt;p&gt;&lt;i&gt;See related commentary by Peled et al., p. 1449&lt;/i&gt;&lt;/p&gt;&lt;/div&gt;
Using Deep Neural Network Learning Technology to Design of Intelligent Network Crawler on Big Data Platform

The paper first analyzes the working principle and technology analysis of intelligent network crawler based on big data platform. In particular, the working mechanism and improvement method of distributed network crawler are deeply discussed. The search strategy for the network crawler is optimized. This paper discusses the integration method of deep learning and convolutional neural network technology. The paper presents using deep neural network learning technology to design of intelligent network crawler on big data platform.
Optimization Strategy of Machine Translation Algorithm for English Long Sentences Based on Semantic Relations

In order to improve the quality of long sentence machine translation results, this paper studies the optimization strategies of long sentence translation algorithms from the perspective of semantic relations. Firstly, this paper analyzes the current situation of machine translation for English long sentences, points out the main problems of existing algorithms, and introduces the connotation of semantic relations. Secondly, based on semantic relations, the algorithm is modeled and an optimized machine translation algorithm is proposed. Through research, the optimized algorithm gives full play to the role of semantic relations and can translate long English sentences more accurately, so it has higher application value.
Blur image detection and classification using resnet-50

Blur classification is important for blind image restoration. It is difficult to detect blur in a single image without knowing any additional information. This paper uses edge detection methods and a deep learning convolutional neural network called Resnet-50 to classify blurry-type images. The Resnet model effectively reduces the gradient vanishing problem and uses connection skipping to train the network. Typically, images are subject to defocus blur and motion blur, which are caused by the incorrect depth of field and the movement of objects during capture. The dataset used here is the blur dataset from Kaggle, which consists of sharp images, images with blur, and motion blur. In this paper, edge detection methods are applied to images using Laplace, Sobel, Prewitt, and Roberts filters and derived features such as mean, variance, and maximum signal-to-noise ratio, which are used to train a classification algorithm for image classification.
Research on Power Material Supply Method Based on Smart Contract System

The current traditional centralized power supply process has problems such as time-consuming, unreasonable distribution, and low efficiency. To solve the aforementioned problems, the paper designes a smart contract-based power supply method. The smart contract of the blockchain has the advantages of autonomy, credibility, strong robustness, and fast processing speed. The model in this paper takes advantage of the autonomy and fast processing speed of smart contracts to improve the efficiency and security of the power supply process. First, a block database system and the corresponding smart contract update process are designed; then, the smart contract for the supply of power materials in emergency scenarios is proposed, including the smart contract for the supply and demand of power materials and the smart contract for the balance management of supply and demand in the whole network; finally Through simulation, the effectiveness of the method in this paper is verified.
Data Poisoning Attacks on Traffic State Estimation and Prediction (TSEP)

Transportation has been and will continue to be under rapid transformations. One transformation is that the transportation system is increasingly driven by massive datasets, including vehicular data (e.g., from GPS, camera and LiDAR) and infrastructure-generated data (e.g., from loop detectors and travel time sensors). However, the growing reliance on data poses potential cybersecurity issues to the transportation system, among which the so-called “data poisoning” attacks by adversaries are becoming increasingly critical. Data poisoning attacks aim to compromise a system’s performance by adding malicious noises, perturbations, or deviations to the dataset used by the system. Extensive studies have demonstrated the vulnerability of vehicular data to data poisoning attacks, while research on the infrastructure side is sparse. TSEP, e.g., is one of the most important and extensively studied areas in traffic modeling. It largely relies on data collected at the infrastructure side and produces traffic measures (travel times, volumes, queue lengths, etc.), which are important both in their own merit (e.g., for performance evaluation) and as a critical input to control and optimization applications (e.g., traffic control). However, research on data poisoning-related attacks (and defense) on TSEP is sparse: such attacks have not been formally defined and very few TSEP methods currently consider the risks of data attacks. To narrow the gap, this study develops a generic data poisoning attack model for traffic state estimation and prediction (TSEP) applications that primally rely on infrastructure-generated data. Specifically, we first formulate data poisoning attacks as a general sensitivity analysis of parameterized optimization problems over parameter changes (i.e., data perturbations) and study the Lipschitz continuity property of the solution with the presence of general (equality and inequality) constraints. Then, we develop attack models that fit a broader spectrum of applications by extending optimization problems (with no or equality constraints, widely used in the cybersecurity field) to those expressed as variation inequality (VI) with general constraints. The generalized implicit function theorem for VI problems is utilized to compute the semi-derivatives that express how the TSEP solution responds to data perturbations. The semi-derivatives then enable us to evaluate TSEP models’ vulnerability (at each data point) and design a generic attack model. Finally, we demonstrate the generality and effectiveness of the proposed method with applications on common TSEP models.
Backdoor poisoning attacks against few-shot classifiers based on meta-learning

Few-shot classification is a classification made on the basis of very few samples, and meta-learning methods (also called “learning to learn”) are often employed to accomplish it. Research on poisoning attacks against meta-learning-based few-shot classifier has recently started to be investigated. While poisoning attacks aimed at disrupting the availability of the classifier during meta-testing have been studied in Xu et al. [1] and Oldewage et al. [2], backdoor poisoning in meta-testing has only been briefly explored by Oldewage et al. [2] under limited conditions. We formulate a backdoor poisoning attack on meta-learning-based few-shot classification in this study. We show that the proposed backdoor poisoning attack is effective against the few-shot classification using model-agnostic meta-learning (MAML) [3] through experiments.
CHIEFS: Corneal-Specular Highlights Imaging for Enhancing Fake-Face Spotter

This paper presents a novel Machine Learning (ML)-based DeepFake detection technology named CHIEFS (Corneal-Specular Highlights Imaging for Enhancing Fake-Face Spotter). We focus on the most reflective area of a human face, the eyes, upon the hypothesis that the existing DeepFake creation methods fail to coordinate their counterfeits with the reflective components. In addition to the traditional checking of the reflection shape similarity (RSS), we detect various corneal-specular highlights features, such as color components and textures, to find corneal-specular highlights consistency (CHC). Furthermore, we inspect the ensemble of the highlights with the surrounding environmental factors (SEF), including the light settings, directions, and strength. We designed and built them as modular features and have conducted extensive experiments with different combinations of the components using various input parameters and Deep Neural Network (DNN) architectures on Generative Adversarial Network (GAN)-based DeepFake datasets. The empirical results show that CHIEFS with three modules improves the accuracy from 86.05% (with the RSS alone) to 99.00% with the ResNet-50-V2 architecture.
Research on Security Encryption Algorithm of Marketing Service Data in Rural Areas of State Grid Corporation

The marketing service data information of State Grid Corporation in rural areas is private and confidential and cannot be disclosed, so it needs to be encrypted. In order to achieve the purpose of encryption, this paper will study the security encryption algorithm, first introduced the rural power grid marketing service data information security protection status and the main risk types, then put forward the data information security encryption algorithm application way, finally carried out a simulation test. Through the research, the data information security encryption algorithm adopted in this paper can effectively improve the information security level of grid marketing service data, and the algorithm has achieved ideal results in the simulation test.
Trusted Identity Access Authentication Based on Spa Single Packet Authentication Technology

Trusted identity authentication is an application service of power grid service identification. It adopts top-down and bottom-up bidirectional identification methods to scan and identify the scene. The method of cross classification is used to divide the power grid business scenarios and identity authentication functions. The principle is to extract the individual features and common features of the power grid business trusted identity authentication requirements. Combined with the needs of power grid core business, the authentication technology based on biometric identification will be deeply studied, and the multi-factor authentication technology combining explicit authentication factor and implicit authentication factor will be explored to establish the security model of different authentication factor combination. Combined with the core business of power grid, explore the research of spa single-package authentication technology, and realize the policy combination of different authentication factors according to the security level of the business. Design multi-factor based identity authentication system protocol, and realize the research and development of multi-level trusted multi-factor identity authentication system prototype based on big data and behavior analysis.
Big Data Analytics in Healthcare: COVID-19 Indonesia Clustering

The rapid growth of the Internet and Technology produced a massive amount of data that resulted a phenomenon called Big Data.To process such a complex kind of massive amount of data, an advanced approach and tool is needed that is able to quickly produce results.This approach to analyzing massive amount of data is known as Big Data Analytics.Big data analytics is widely used in various sectors, not to mention the health sector.In the healthcare sector, recently there has been a study that is often carried out in dealing with crisis situations, namely research on implementing big data analytics to provide technological solutions to help deal with pandemics.In this article, we analyze and visualize the data collected from Indonesia.The data analyzed starts from the first case of COVID-19 in Indonesia to present.The proposed solution is to classify the regional case data into a group that can represent the situation of the area.As a result, it is determined based on the data that there are three groups consisting of areas with low risk, moderate risk, and high risk.In addition, this article proposes combining big data analytics technology with cloud technology to facilitate the dissemination of information to citizens to increase awareness about the spread of the COVID-19 virus.
Cybersecurity for Online Safety Enhancement: Female Participation

Female participation in cybersecurity is low across the globe, as they represent below a quarter (about 11%) of global cybersecurity professionals. Cybersecurity being a crucial aspect of IT ought to involve women in the deliberation and proffering of security mechanisms and solutions; however, their representation is very minimal. Via a qualitative approach, this chapter discusses emphatically on the urgent need for female participation in cybersecurity. Involving females in cybersecurity has the potential of enhancing online safety, especially for children, as women naturally are concerned about the safety of their wards both online and offline. Thus, it is imperative to engage more females in the conceptualization, design, deliberation, development, up till the implementation of cybersecurity solutions as this can help to enhance online safety. Apparently, to achieve an effective participation of females in cybersecurity, it is reasonable to immerse cybersecurity knowledge alongside awareness into young female children early enough so as to break the gender disparity as well as establish a long-lasting interest of cybersecurity in females, thus contributing immensely to online safety enhancement. The review revealed that female IT employees’ financial awareness can be impacted by investment decision relationships. Novel cybersecurity solutions should include special consideration for the female gender that can ensure that they are aware of female-targeted cyberattacks. Personal factors such as personality traits, abilities and interests, self-efficacy, gender identity, among others linked to cybersecurity, if negatively manifested, can result from exposure dearth to computing mentors and role models as well as mastery of experiential knowledge.