Reviewerly/exHarmony huggingface dataset:
Design of application evaluation platform for cryptography in IoT system

In order to improve network security protection capabilities, according to the requirements of the classified protection 2.0 of cybersecurity, the network is divided into five grades. Networks that involve national security and social public interests must be graded and evaluated. In the level evaluation stage, the cryptographic products used on the network need to be approved and comply with the regulations of State Cryptography Administration Office of Security Commercial Code Administration (OSCCA). The use of commercial cryptographic technology in the network must meet the standardization requirements. Based on this, the evaluation platform of commercial cryptography application was designed. The functional design of the platform is based on the cryptographic technology of the existing information system, summarizing the application characteristics of the mainstream commercial cryptographic technology in the network, including 7 types of testing items, and testing the cryptographic functions of the system according to the cryptographic industry standards and national standards. Verify the authenticity of the information source, the integrity of the data, the confidentiality of the information, and the non-repudiation of the behavior. At present, the main functions of the platform have been designed and built, and all functions such as user management, database services, and core testing have been verified. The later will be further improved to promote the standardization and correctness of commercial cryptographic system applications. The system self-inspection and self-inspection monitoring methods derived from the evaluation platform of commercial cryptography application are provided to the system builders and operators, providing effective commercial cryptographic system testing tools for cryptographic evaluation agencies, and powerful monitoring methods for supervision and inspection agencies.
Self-residual Embedding for Click-Through Rate Prediction

In the Internet, categorical features are high-dimensional and sparse, and to obtain its low-dimensional and dense representation, the embedding mechanism plays an important role in the click-through rate prediction of the recommendation system. Prior works have proved that residual network is helpful to improve the performance of deep learning models, but there are few works to learn and optimize the embedded representation of raw features through residual thought in recommendation systems. Therefore, we designed a self-residual embedding structure to learn the distinction between the randomly initialized embedding vector and the ideal embedding vector by calculating the self-correlation score, and applied it to our proposed SRFM model. Extensive experiments on four real datasets show that the SRFM model can achieve satisfactory performance compared with the superior model. Also, the self-residual embedding mechanism can improve the prediction performance of some existing deep learning models to a certain extent.
Defeating the Credit Card Scams Through Machine Learning Algorithms

Credit card fraud is a significant problem that is not going to go away. It is a growing problem and surged during the Covid-19 pandemic since more transactions are done without cash in hand now. Credit card frauds are complicated to distinguish as the characteristics of legitimate and fraudulent transactions are very similar. The performance evaluation of various Machine Learning (ML)-based credit card fraud recognition schemes are significantly pretentious due to data processing, including collecting variables and corresponding ML mechanism being used. One possible way to counter this problem is to apply ML algorithms such as Support Vector Machine (SVM), K nearest neighbor (KNN), Naive Bayes, and logistic regression. This research work aims to compare the ML as mentioned earlier models and its impact on credit card scam detection, especially in situations with imbalanced datasets. Moreover, we have proposed state of the art data balancing algorithm to solve data unbalancing problems in such situations. Our experiments show that the logistic regression has an accuracy of 99.91%, and naive bays have an accuracy of 97.65%. K nearest neighbor has an accuracy is 99.92%, support vector machine has an accuracy of 99.95%. The precision and accuracy comparison of our proposed approach shows that our model is state of the art.
A Portable Network Isolation Apparatus for Substation Maintenance Based on Firewall

research-article A Portable Network Isolation Apparatus for Substation Maintenance Based on Firewall Share on Authors: Chen Li Wenzhou Power Supply Company of SGCC, Substation Maintenance Center, China Wenzhou Power Supply Company of SGCC, Substation Maintenance Center, ChinaView Profile , Chen Qiongliang Wenzhou Power Supply Company of SGCC, Substation Maintenance Center, China Wenzhou Power Supply Company of SGCC, Substation Maintenance Center, ChinaView Profile , Xia Renyi Wenzhou Power Supply Company of SGCC, Substation Maintenance Center, China Wenzhou Power Supply Company of SGCC, Substation Maintenance Center, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 148Pages 1–5https://doi.org/10.1145/3469213.3470351Online:18 August 2021Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Cable Bracket Tilt Detection Based on YOLOv3

Aiming at the problems of large number of brackets, small targets and overlapping perspectives in bracket tilt detection in cable tunnels, an improved YOLOv3 algorithm is proposed. Optimize the Darknet-53 feature extraction network; improve the target detection layer; weight the center coordinate loss and confidence loss according to the location information; use the K-Means clustering algorithm to eliminate interference data. The experimental results show that, compared with the traditional YOLOv3 algorithm, the average accuracy of cable bracket recognition has increased from 82.34% to 86.73%, the detection frame rate has increased from 47 FPS to 54 FPS, and the cable bracket tilt recognition accuracy has reached 92%.
A Distributed Large-Scale Agile Software Development for Multiple Products and Its Practical Evaluation

In agile software development, there are several well-known frameworks by integrating rules and methods. Among them, Scrum-based agile frameworks for large organizations need much knowledge and experience to start it, although Scrum has fewer rules and is easy to start. Among other known frameworks, Scaled Agile Framework (SAFe), which has complicated rules, has lots of case studies than others. However, Large Scale Scrum (LeSS) and LeSS Huge are also known and have lots of case studies in Japan, so enhancing LeSS and LeSS Huge framework is one of the well-developed ways to solve the organization's issues quickly. This paper aims to propose a new method, named Multi-Products Agile Method, based on LeSS Huge for developing multiple products in parallel at a large organization. We applied the proposed method to three products based in Japan. We had been tracking the velocity that is a total number of about 10 teams for one year. Our results prove that the proposed method is an effective way to scale agile organizations for multiple products. The method also makes it possible to use the knowledge and experiences of LeSS or LeSS Huge. We conclude Multi-Products Agile Method is an extended framework of LeSS and LeSS Huge.
Android Application for Academic Record

In Today’s advancing world of Technology, android is rapidly growing segment of the global mobile market. The Android Studio provides tools and APIs that are effective to develop application on the Android platform using programming language like JAVA. Android owners can remodify the source code of their own android devices. This allows users who want the flexibility to change the way their mobile devices run. The objective of this project is to provide Android application for Academic Record to the Institutes. Now days due to improper interaction between teachers and students effective and cooperative learning can’t achieved. Hence to overcome this issue we have come up with application which will record all academic contents of last 3 years. All the study material is stored in Google Drive. Using Firebase Server authentication each student can access this app contents by its Email ID. After signed in student can easily find its exam material and projects list of its final year semester and he can download if required. All branch can have different study material according to their academic syllables.
Privacy protection method of power metering data in clustering based on differential privacy

Power companies can use the power grid big data platform to cluster analysis of power metering data, which can improve the personalized service quality of power grid companies for different users and discover the power stealing behavior of users to protect the interests of power grid companies. However, in the cluster analysis of power measurement data, the privacy information of power users may also be disclosed. To defend the privacy information of power users, the article applies differential privacy technology to cluster analysis of power metering data to avoid power users’ privacy leakage. First, the article presents the attack model that exists in the cluster analysis of power metering data. Then, the article add Laplacian noise to the power metering data to defend against attacks in the cluster analysis of attackers. Next, to enhance the data availability of noise-added power measurement data in cluster analysis, the article limits noise distance based on the results of the cluster analysis. Experiments show that method proposed in article can guarantee the privacy information of power data during the cluster analysis of power metering data, and ensure the data quality of the power metering data after privacy protection.
Remote Phase Sensing by Coherent Single Photon Addition

We propose a remote phase sensing scheme inspired by the high sensitivity of the entanglement produced by coherent multimode photon addition on the phase set in the remote heralding apparatus. By exploring the case of delocalized photon addition over two modes containing identical coherent states, we derive the optimal observable to perform remote phase estimation from heralded quadrature measurements. The technique is experimentally tested with calibration measurements and then used for estimating a remote phase with a sensitivity that is found to scale with the intensity of the (local) coherent states, which never interacted with the sample.
Sentencing Decisions Around Quantity Thresholds: Theory and Experiment

To limit sentencing disparities, criminal codes divide offenses into subsections with specific sentencing ranges. To do so, they often use quantity thresholds (e.g. drug amount). We study the effects of such quantity thresholds on sentencing decisions and argue that the threshold effect can be decomposed into two opposing mechanisms: the severity and the reference mechanisms. We conduct a vignette experiment with Czech prosecutors and find that quantity thresholds drive substantial increases in sentences and thus lead to a new source of sentencing disparities. In the most striking case, the quantity threshold increases the average recommended sentence between two marginally different cases by 55%. Finally, using a new parametric measure of (in)justice, we quantify the effect of thresholds on the probability of a just sentence for a given level of tolerance.
Key Characteristics to Create Optimized Blockchain Consensus Algorithms

Blockchain is a fairly new technology and still in its infancy. As a result, many research papers are creating optimized consensus algorithms. Therefore, a need for key characteristics to create optimized blockchain consensus algorithms has been identified. This research paper presents the results of a systematic literature on identifying the main blockchain consensus algorithms and their associated advantages and disadvantages. Papers from four different databases were retrieved and after exclusion criteria were applied, 44 papers were ultimately included in the review. Results indicated that the five main consensus algorithms were Proof-of-Work (PoW), Proof-of-Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Delegated Proof-of-Stake. The results further indicated that efficiency was the main advantage of the PoS, PBFT, PoA and hybrid consensus algorithms. The main disadvantage was "energy wastage" and was attributed to the PoW algorithm. Security concerns were the main disadvantage of the PoS algorithm. These findings were used to present key characteristics that future researchers can have in mind when creating optimized blockchain consensus algorithms.
Controlled GAN-Based Creature Synthesis via a Challenging Game Art Dataset - Addressing the Noise-Latent Trade-Off

The state-of-the-art StyleGAN2 network supports powerful methods to create and edit art, including generating random images, finding images "like" some query, and modifying content or style. Further, recent advancements enable training with small datasets. We apply these methods to synthesize card art, by training on a novel Yu-Gi-Oh dataset. While noise inputs to StyleGAN2 are essential for good synthesis, we find that coarse-scale noise interferes with latent variables on this dataset because both control long-scale image effects. We observe over-aggressive variation in art with changes in noise and weak content control via latent variable edits. Here, we demonstrate that training a modified StyleGAN2, where coarse-scale noise is suppressed, removes these unwanted effects. We obtain a superior FID; changes in noise result in local exploration of style; and identity control is markedly improved. These results and analysis lead towards a GAN-assisted art synthesis tool for digital artists of all skill levels, which can be used in film, games, or any creative industry for artistic ideation.
Research on the Application of Computer Network Technology in the Design of Basketball Course System

Abstract The rapid iterative expansion of computer information tech makes its application in the field of education gradually deepens. The basketball course teaching system based on computer network tech can realize distributed basketball teaching and maximize the use of computer network resources, so it has important research value. Based on this, this paper first analyses the concept and application of computer network tech, then studies the design of basketball curriculum system on account of computer network tech, and finally gives the design &amp; use of basketball curriculum system on account of computer network tech.
Fast fringe enhancement by improved bidimensional sinusoids-assisted empirical mode decomposition

Fringe enhancement is necessary for optical measurement, which includes the elimination of noise and background from a fringe pattern. Bidimensional empirical mode decomposition (BEMD) algorithms have been used for fringe enhancement, but they usually decompose a fringe pattern into many bidimensional intrinsic mode functions (BIMFs), which is not only inefficient but also leads to difficulties for classifying the BIMFs to distinguish noise, the useful signal and background. In this paper, the bidimensional sinusoids-assisted empirical mode decomposition (BSEMD) algorithm is firstly studied in depth, and a key factor is found in deciding the results. Then an improved BSEMD (iBSEMD) algorithm is proposed, where the key factor is estimated easily and automatically. The proposed method largely reduces the number of BIMFs resulted, making the fringe enhancement become faster and have a more robust effect. Some optimized strategies are given additionally to implement the iBSEMD algorithm by compute unified device architecture (CUDA) for practical applications. Comparison experiments are conducted with state-of-the-art methods to prove the efficiency and effectiveness of our method. • The key factor that determines the results of BSEMD is found and analyzed. • A new method of determining this key factor is given. • An efficient iBSEMD algorithm is proposed for fringe pattern processing. • The iBSEMD based algorithm for fringe pattern processing is optimized and implemented with CUDA.
Relative entropy-regularized optimal transport on a graph: a new algorithm and an experimental comparison

The present work investigates a new relative entropy-regularized algorithm for solving the optimal transport on a graph problem within the randomized shortest paths formalism. More precisely, a unit flow is injected into a set of input nodes and collected from a set of output nodes with specified marginals, while minimizing the expected transportation cost, together with a paths-based relative entropy regularization term, providing a randomized routing policy. The main advantage of this new formulation is the fact that it can easily accommodate edge flow capacity constraints which commonly occur in real-world problems. The resulting optimal routing policy, i.e., the probability distribution of following an edge in each node, is Markovian and is computed after constraining the input and output flows to the prescribed marginal probabilities. In addition, experimental comparisons with other recently developed techniques show that the distance measure between nodes derived from the introduced model provides competitive results on semi-supervised classification tasks.
Generative Adversarial Networks Using Neural Architecture Search for Semantic Image Segmentation

Semantic image segmentation is a crucial task in various fields that use computer-vision based applications. Generative Adversarial Networks (GANs) are attracting widespread interest in the data science community for their prowess in image feature recognition due to their adversarial nature of training. Neural Architecture Search (NAS) is known as the process of obtaining a neural architectural schema that performs the best for a particular task. NAS has been applied in GANs, and it achieved striking success compared to human-designed architectures in conditional and unconditional image generation and GAN-compression. Our research was inspired by the success of NAS applied in GANs. This paper proposes a novel approach for NAS in GANs for semantic image segmentation. After extensive research on related works, the architecture of the Pix2Pix GAN variant was selected for the proposed approach. The architecture of the Pix2Pix GAN consists of a U-Net as the Generator and a PatchGAN classifier as the Discriminator. The NAS component is searched for U-Net architectures using PASCAL VOC 2012 dataset. The NAS component is adapted from using the NAS-Unet research proposed by Weng et al. in 2019. The NAS searched architecture was used as the Generator of the proposed GAN by transferring the searched architecture from the PASCAL VOC 2012 dataset to the Cityscapes dataset. To determine the success of the proposed approach, quantitative analysis was performed with Mean Pixel Accuracy (MPA) and mean Intersection over Union (mIoU) metrics. Several experiments were done on the Cityscape validation set and achieved 81.73 MPA and 71.91 mIoU. The proposed approach outperformed several NAS in semantic segmentation approaches and GANs in semantic segmentation approaches. This study is a preliminary attempt to apply NAS for semantic segmentation using GANs. Further, this research has raised many possible areas in need of further investigation.
Diagnosis of the Problem of the Class Teaching System and Analysis of the Direction of Reform

Class lecture production is the most basic form of education organization in modern schools. While it makes important contributions to China's economic and social development and human resources training, it is also inappropriate due to the increasing demand for high-quality and high-level human resources are training in modern society. By diagnosing the problems of the classroom teaching system and analyzing its problems in such aspects as teacher-led, teacher-passed-student, collective teaching and hierarchical differentiation, it is proposed that the reform of the classroom teaching system should make use of modern technological means, gradually implement hierarchical teaching, appropriately compress the class size, scientifically and rationally set up the curriculum and improve the psychological adaptation ability, so as to make some useful explorations for the educational reform actions of the relevant education departments, and ultimately better perform the function of training talents and better serve the cause of modern socialist education.
bwsample: Processing Best-Worst Scaling data

bwsample is a Python package that provides algorithms to sample best-worst scaling sets (BWS sets), extract and count pairwise comparisons from user-evaluated BWS sets, and compute rankings and scores.
An End-to-End Framework for Image Super Resolution and Denoising of SAR Images

Single image super resolution (or upscaling) has become very efficient because of the powerful application of generative adversarial networks (GANs). However, the presence of noise in the input image often produces undesired artifacts in the resultant output image. Denoising an image and then upscaling introduces more chances of these artifacts due to the accumulation of errors in the prediction. In this work, we propose a single shot upscaling and denoising of SAR images using GANs. We have compared the quality of the output image with the two-step denoising and upscaling network. To evaluate our standing with respect to the state-of-the-art, we compare our results with other denoising methods without super resolution. We also present a detailed analysis of experimental findings on the publicly available COWC dataset, which come with context information for object classification.
Usage Features of Semantic Query and Rule Languages of Semantic Web in the Intelligent Systems, Based on Conceptual Graphs Technologies

The article considers some problems related to applying languages of semantic query and rules, oriented towards their application within Semantic Web, in the information systems, based on conceptual graphs techniques and knowledge models of the appropriate type. At this, special attention is paid to the analysis of given languages interpretation correctness, as well as technological aspects of organizing for processing chosen-class knowledge models.