Reviewerly/exHarmony huggingface dataset:
Video Anomaly Detection Based on Convolutional Neural Network

The intelligent video surveillance technology, which can analyze and report the abnormal events that endanger the social order real time, has been widely used.As an important research direction of intelligent video surveillance technology, video anomaly recognition focuses on how to extract video features and process them and detect anomalies.The features used in traditional video anomaly detection algorithms are mainly motion features based on manual design.However, the characteristics of artificial design need some prior knowledge, which mainly depends on the monitoring target and is difficult to define in different applications.Based on the feature that convolutional neural network can automatically learn the features related to tasks during training, this paper proposes a video anomaly detection algorithm.In video prediction network, a self-coding structure is proposed to realize video feature extraction, timing modeling, and prediction frame generation.The 3DCNN and LSTM are combined to enhance the network's ability to model video actions, and the feature images are fused by stitching to reduce the information loss in the process of feature extraction.After that, a multi-task video prediction model is designed based on the action features extracted from the non-deep learning model.With the help of MOG2 to extract the foreground image and Farneback to extract the optical flow image, the network can predict the video frame and the action features of the video frame at the same time.In addition, according to the action features extracted from the deep learning model, another multi-task video prediction model is designed.With the help of Flownet2-SD, the optical flow graph is extracted , and the video prediction network is optimized by minimizing this difference.The effects of different auxiliary tasks on video prediction ability and video anomaly detection ability of different data sets are discussed.
Comparison of Value- and Reference-Based Memory Page Compaction in Virtualized Systems

The issue of copying values or references has historically been studied for managing memory objects, especially in distributed systems. In this paper, we explore a new topic on copying values v.s. references, for memory page compaction on virtualized systems. Memory page compaction moves target physical pages to a contiguous memory region at the operating system kernel level to create huge pages. Memory virtualization provides an opportunity to perform memory page compaction by copying the references of the physical pages. That is, instead of copying pages' values, we can move guest physical pages by changing the mappings of guest-physical to machine-physical pages. The goal of this paper is a quantitative comparison between value- and reference-based memory page compaction. To do so, we developed a software mechanism that achieves memory page compaction by appropriately updating the references of guest-physical pages. We prototyped the mechanism on Linux 4.19.29 and the experimental results show that the prototype's page compaction is up to 78% faster and achieves up to 17% higher performance on the memory-intensive real-world applications as compared to the default value-copy compaction scheme.
Accelerating LiNGAM Causal Discovery with Massive Parallel Execution on Supercomputer Fugaku

Statical causal discovery is an approach to infer the causal relationship between observed variables whose causalities are not revealed. LiNGAM (Linear Non-Gaussian Acyclic Model), an algorithm for causal discovery, can calculate the causal relationship uniquely if the independent components of variables are assumed to be non-Gaussian. However, use-cases of LiNGAM are limited because of its O(d3x) computational complexity, where dx is the number of variables. This paper shows two approaches to accelerate LiNGAM causal discovery: SIMD utilization for LiNGAM's mathematical matrixes operations and MPI parallelization. We evaluate the implementation with the supercomputer Fugaku. Using 96 nodes of Fugaku, our improved version can achieve 17,531 times faster than the original OSS implementation (completed in 17.7 hours).
Low-Latency Design and Implementation of the Squaring in Class Groups for Verifiable Delay Function Using Redundant Representation

A verifiable delay function (VDF) is a function whose evaluation requires running a prescribed number of sequential steps over a group while the result can be efficiently verified. As a kind of cryptographic primitives, VDFs have been adopted in rapidly growing applications for decentralized systems. For the security of VDFs in practical applications, it is widely agreed that the fastest implementation for the VDF evaluation, sequential squarings in a group of unknown order, should be publicly provided. To this end, we propose a possible minimum latency hardware implementation for the squaring in class groups by algorithmic and architectural level co-optimization. Firstly, low-latency architectures for large-number division, multiplication, and addition are devised using redundant representation, respectively. Secondly, we present two hardware-friendly algorithms which avoid time-consuming divisions involved in calculations related to the extended greatest common divisor (XGCD) and design the corresponding low-latency architectures. Besides, we schedule and reuse these computation modules to achieve good resource utilization by using compact instruction control. Finally, we code and synthesize the proposed design under the TSMC 28nm CMOS technology. The experimental results show that our design can achieve a speedup of 3.6x compared to the state-of-the-art implementation of the squaring in the class group. Moreover, compared to the optimal C++ implementation over an advanced CPU, our implementation is 9.1x faster.
Ricettari regionali e lessico gastronomico napoletano d’età borbonica

Abstract The paper aims to present some preliminary outcomes as part of the National Research Project Atlante della lingua e dei testi della cultura gastronomica italiana dall’età medievale all’Unità ( AtLiTeG ). The project’s main purpose is to reconstruct the history and geography of Italian culinary texts and their language from the medieval period to the Unification of Italy. The Unit of Naples «Federico II» created a corpus of interregional cookbooks printed between the 18 th and the 19 th centuries, namely before the turning point represented by Pellegrino Artusi’s Scienza in cucina (1891). After illustrating our digital database, together with other kinds of collected sources, we will deal with some historical-linguistic and lexical issues related to the Cuoco galante (1773) by Vincenzo Corrado, a very important gastronome and chef of Bourbon Naples.
Randezvous: Making Randomization Effective on MCUs

Internet-of-Things devices such as autonomous vehicular sensors, medical devices, and industrial cyber-physical systems commonly rely on small, resource-constrained microcontrollers (MCUs). MCU software is typically written in C and is prone to memory safety vulnerabilities that are exploitable by remote attackers to launch code reuse attacks and code/control data leakage attacks.
Formal Modeling and Security Analysis for Intra-level Privilege Separation

Privileged system software such as mainstream operating system kernels and hypervisors have an ongoing stream of vulnerabilities. Even the inflated secure world in Trusted Execution Environment (TEE) is no longer secure in complex real-world scenarios. Since higher privilege levels cannot always be stacked to provide protection, intra-level privilege separation has become a powerful way to build trustworthy systems. However, existing intra-level privilege separation systems lack sound security analysis and cannot give formal guarantees.
On the Initial Stage of the Formation of Cognitive Universal Educational Activities in First-Graders in Mathematics Lessons

The article is devoted to the propaedeutics of the formation of cognitive universal educational activities among first-graders. The features of logical operations and information actions that are mastered by children in the process of working with a variety of mathematical material are characterized. It is considered as the most relevant for achieving the goal. Examples of tasks that a teacher can use in mathematics lessons in the first grade are given, adapting the degree of their complexity in accordance with the level of readiness of students.
Adopting Enlightened Marketing And its Role In Enhancing the Entrepreneurial Orientation/An Exploratory Study of the Opinions of A Sample of Managers of Some Productive Organizations in the City of Mosul

The research dealt with the topic of adopting enlightened marketing and its role in enhancing entrepreneurial orientation.As the research tried to provide a clear vision in its theoretical aspect and clear answers in the field regarding its dimensions and the significance of its variables, the research adopted its field framework after enlightened marketing as an independent dimension represented by (customer orientation, customer value, innovative marketing, community marketing, marketing sense of the mission (mission)).The two researchers chose some production organizations in the city of Mosul as a sample for research.The research adopted the questionnaire as the main tool for collecting data and information on the field side, and the researchers distributed (56) forms to the managers of the researched production organizations, and (52) valid forms for analysis were retrieved, and the research variables were described and diagnosed, and the correlation and impact relationships for the research variables were clarified.The primary was using the statistical package for applications of social sciences (SPSS) and the following statistical methods were used (arithmetic mean, standard deviation, multiple simple correlation coefficient, simple and multiple regression).The research reached some conclusions, the most important of which was that, through the results of the description and diagnosis, the management of the surveyed organizations is not working to implement continuous programs related to social responsibility .In line with the content of the conclusions, the research reached some proposals, the most important of which is that organizations should enhance the societal marketing variable, especially concerning implementing ongoing programs related to social responsibility.
LANGUAGE ERROR FORM ANALYSIS IN THE ESSAY OF CLASS XI STUDENTS OF SMA NEGERI 1 MAKASSAR

This research aims to analyze the form of language errors in the essays of class XI students of SMA Negeri 1 Makassar. This type of research method, that is, a descriptive research method. The technology of data analysis used in this study is prescriptive. The type of data that will be used in this study is in the form of student essay text. The results showed that there were four forms of language errors, namely in the form of addition, subtraction, word choice, and arrangement.
Fault Tolerant Control of Actuator Failure in Distributed Drive Electric Vehicle

Research object of the paper is hub-drive electric vehicle. A vehicle body yaw stability control strategy based on sliding mode variable structure theory was proposed for distributed drive electric vehicles, state parameter monitoring and a fault tolerant control method for actuator failure based on parameter estimation and multi-agent system is designed. The existing motor failure control methods and vehicle state estimation algorithms are analyzed, and the real-time driving state of the vehicle is determined based on Unscented Kalman filter(UKF). The failure control rules and fault-tolerant coordinated control framework are constructed by defining the driving wheel agents, and the failure model is established. Combined with the parameter estimation algorithm, keeping the normal driving state of the vehicle after the actuator fails. The simulation results of line and curve failure driving conditions show the dynamic parameters of vehicle body can be stabilized in a controllable with this strategy. Which is further improvement the fault-tolerant control ability of vehicle actuators and vehicle handling stability.
A Comparative Study of Regression Machine Learning Algorithms: Tradeoff Between Accuracy and Computational Complexity

The computational complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers which is the determination of looking for the best methods to solve a problem. The accuracy of a regression model's predictions must be reported as an error. According to the researchers, the most problematic issue is the lack of a properly defined machine learning assessment. In this research, Various types of machine learning regression algorithms, namely, Linear Regression, Support Vector Regression, Random Forest Regression, and Multilayer Perceptron Neural Network have been used to process and analyze the collected data in terms of comparison of their accuracy and the computational complexity. The applied dataset was collected using IoT sensors seeking an appropriate algorithm that is the fittest to the collected data to design a model system that represents the goal of specific future applications. The result shows that the Random Forest regression has the highest computational complexity and highest accuracy depending on the calculated error metrics (Mean Square Error, Mean Absolute Error, and R Squared score) which are (0.0002, 0.005, and 0.995) respectively. Based on that, Random Forest Regression will be adapted and implemented with the structure of a planned design system.
Adjusting Level of Abstraction for Stylized Image Composition

Recently, many image stylization techniques have been proposed to convert photographs into illustrations, paintings, cartoons, etc. Artists sometimes use these techniques to efficiently generate background images for their own illustrations of foreground objects, such as cartoon characters. However, this approach often produces unnatural results because the level of abstraction between the foreground and the background images are different, resulting in inconsistent atmosphere between them. In this paper, we address this problem and propose a method for adjusting the abstraction level of the stylized background images. The effectiveness of our method is demonstrated by several examples with different levels of abstraction.
Did physicists create a wormhole in a quantum computer?

An unusual teleportation experiment uses ordinary quantum physics, but was inspired by tunnels in an exotic ‘toy universe’. An unusual teleportation experiment uses ordinary quantum physics, but was inspired by tunnels in an exotic ‘toy universe’.
Augmented Reality-Based Rapid Digital Verification of the Body-in-white for Intelligent Manufacturing

The body-in-white (BIW) is the structural assembly of the automobile after it is welded, which is the skeleton of the automobile. BIW design verification is an important part of the automobile development, not only to test the structural performance, but also to verify its manufacturability in subsequent production lines. Augmented reality, as an important tool for rapid digital verification, has been widely used in intelligent manufacturing systems. This paper presents an augmented reality-based method and associated system for rapid digital verification of BIW manufacturability during the BIW design phase. The system approach is described: 1) AR based auto BIW verification environment construction, 2) Bare hand interaction assisted AR verification, 3) Distance measurement and collision detection. Finally, an experimental scenario was built to illustrate how this AR system assist in verifying BIW structures on a production line and reveal the huge potential of AR in product design verification.
Research on Trajectory Prediction of Cricket System Based on OPENMV

Currently, the cricket system has attracted the attention of many scholars as an experimental platform for studying various control algorithms in the field of control. This paper designs a low-cost based on OPENMV cricket motion control system. In system design, OPENMV obtains ball position information, then the curve fitting of the ball motion is performed to use the least squares method for predicting the next position of the ball, and use Kalman filtering to optimize the ball prediction point, to improve prognostic accuracy. Finally, the PID algorithm is used to control the system to track this predicted coordinate. The system incorporates the prediction of the position of the ball at the next moment, eliminating the problem of lagging of the camera to obtain information about the position of the ball, making the system more resistant to interference and laying the foundation for the precise study of the control algorithm. The experimental results show that this system can predict the trajectory of the ball very well and achieve the expected results.
An Interference Suppression Method For Spaceborne Sar Image Via Space-Channel Attention Network

Spaceborne synthetic aperture radar (SAR) is becoming a widely used sensor in the universe because of its ability to acquire high-resolution radar images under harsh conditions such as clouds, fog, rain, and snow. However, the presence of electromagnetic interference in cosmic space can seriously affect the efficiency of information detection of spaceborne SAR. To address the problem of degradation of spaceborne SAR images, we present the Space-Channel Attention Network (SCANet). Our model structure consists of two branches, the channel and spatial transform branch (CSTB) and the detail restoration branch (DRB). Among them, CSTB, with attention mechanisms in both channel and spatial dimensions, greatly preserves the texture features of the original images while eliminating interference. In addition, DRB recovers precise details of CSTB output feature maps guided by ground truth. We have verified the feasibility of the method by adopting real Sentinel-I data. Compared with some denoising algorithms in field of computer vision, Our method attains the best interference suppression performance with high-resolution image output.
Finding the Best Performance of Bayesian and Naïve Bayes Models in Fraudulent Firms Classification through Varying Threshold

—Fraud detection is the first step to preventing fraud committed by both individuals and organizations. The development of a high-performance classification model to detect fraud is an interesting topic in machine learning modeling. A finding of the best Bayesian and Naive Bayes classification models is a crucial issue because both models are simple and easily applied models in the fields of life and social sciences. This study aims to obtain the best performance of classification models developed based on probability concepts, namely Bayesian and Naive Bayes models. Adding a threshold value to the decision-making criteria of the two models is an effort expected to can find models that perform superiorly. Data on the auditing of fraudulent firms containing of 775 firms from various business sectors in Australia is used as a case study. The testing data consisting of 100 instances were taken by cluster random sampling with a proportion of 61 non-fraudulent and 39 fraudulent firms and the remaining instances as the training data. The best Bayesian model has an average accuracy of 84% obtained at a threshold value of 0.22. While the best Naive Bayes model has an average accuracy of 94% which is obtained in the 15 threshold values. Adding the threshold value has a significant impact on the performance of the Bayesian model, which can increase the average accuracy from 36% to 84%. On the other hand, the average accuracy of the Naive Bayes model only increased by 1%, from 93% to 94%. Performance measures Sensitivity, Specificity, F1 score, and ROC curve of the Naive Bayes model are also superior to the Bayesian model.
An Ancient Chinese Painting Restoration Method Based on Improved Generative Adversarial Network

Abstract During the long-term preservation process of ancient Chinese paintings, there are various degrees of damage. Manual repair methods are inefficient, professional, and prone to secondary damage. Aiming at the above problems, a UGAN ancient painting restoration model based on an improved Generative Adversarial Network (GAN) is proposed. The model uses GAN as the overall framework. The generator in the framework adopts a U-shaped network (U-Net), and adds a dilated convolution-gated residual block (DCGR-Block) in the middle, which enhances the model’s ability to extract the shallow and deep feature information of the image. In the experiment, the test results are compared with the current mainstream methods. The comparison results show that when repairing the overall deletion, the peak signal-to-noise ratio (SPNR) and structural similarity (SSIM) of UGAN are improved by 8.14% and 4.79% on average compared with the global and local consistency image inpainting (GLCIC). When the rate is higher, the performance of the network model in this paper is also better than that of similar mainstream algorithms.
Security Factors affecting Internet of Things

Abstract: Internet of things (IoT) is the following huge thing in the networking field. The vision of IoT is to connect day by day used gadgets that have the capacity of sensing and actuation to the internet. This can or may additionally or might not contain human. In this paper we are able to go through all of the demanding situations of IOT and mainly focus on IOT safety undertaking. IoT entails adding net connectivity to a system of interrelated computing gadgets, mechanical and digital machines, items, animals and/or people. Each "component" is furnished a completely unique identifier and the capability to automatically switch statistics over a community. Allowing devices to connect with the internet opens them as much as some of severe vulnerabilities if they're now not nicely included.
High-Quality Virtual Reality (VR) Videos Using the Black Box Algorithm in Cloud Environment

Cloud computing is online-based and provides you with resources and information.So, cloud computing supports e-learning and virtual learning.Virtual learning needs high-resolution content.The paper will present the idea of creating a new service.It is a high-quality Virtual Reality (VR)video service across the black box in the cloud environment.This idea helps deal with a large number of scholars in dealing with high-quality Virtual Reality (VR) videos at the same time.The results of the paper are to create a new service in a computer cloud environment (Virtual Reality (VR)as-a-Service (VRaaS)), using the black box algorithm through which high-quality Virtual Reality (VR) videos pass through.Data obtained in high-quality Virtual Reality (VR) videos and tools used UML, HTML, JavaScript, and PHP.