Reviewerly/exHarmony huggingface dataset:
A New Code Based Signature Scheme for Blockchain Technology

Blockchain is a method of recording information that makes it not feasible for the system to be replaced, attacked, or manipulated. A blockchain is equipped with a notebook that copies and processes the various procedures across the network of computers participating in the blockchain. Digital signature algorithm is one of the cryptographic protocols used by the blockchain. In this work, we introduce a new digital signature scheme based on error correcting codes. In the scheme constructed on a [n, k, d]− code over 𝔽q, which is d ≥ 2t + 1, and the size of the signature length is n − k. The signature verification is based on the bounded distance decoding of the code. Since the verification space is 𝔽qn, the proposed scheme has an improved performance in terms of working in a wider space.
Algorithm for Safety Decisions in Social Media Feeds Using Personification Patterns

For safety decisions in social media applications, it is necessary to classify personification patterns. The paper proposes using video material to apply machine learning to select, and extract significant feature qualities and grasp the semantics of feature space connection to comprehend the personification of a certain user. The feature traits are based on a computer vision-based approach and a natural language-based approach. A strong belief is calculated from language descriptions and persona traits. These traits are then used to determine the overlap of feature space using various ML algorithms to deduce the intrinsic relationships. The proposed goal is validated by this algorithm and user personification is an important aspect that can be captured through video analytics. Using this personification-based method, better decisions can be made in the given domain space.
Machine learning based data classification methods in cloud security using cloudlightning framework

With the increase in the usage of Cloud services and applications, securing the data naturally becomes a major concern. There are numerous methods in which cloud data can be secured and numerous more ways that are being currently researched. In this paper, we present the approach of data classification and encryption to secure the data. It is incorrect to use security methods without understanding the requirements of that data and hence we use the K-NN classification method to divide the data with respect to their needs followed by encrypting the sensitive data to prevent any security hazards. We used the CloudLightning simulator to test the framework. The framework is broad in scope, but it also aims to enable cloud services for high-performance computing. Infrastructure-as-a-service. The core use case is service provision. However, we believe that genomics, oil and gas exploration, and ray tracing are three downstream use cases that will benefit from the proposed architecture.
A Robust Visual SLAM Method for Additive Manufacturing of Vehicular Parts Under Dynamic Scenes

Additive manufacturing has significant advantages in complex parts of the vehicle manufacturing.As additive manufacturing is a kind of precise production activity, different components of manufacturing instruments need to be located in appropriate positions to ensure accuracy.The visual Simultaneous Localization and Mapping (SLAM) can be considered to be a practical means for this purpose.Considering dynamic characteristics of additive manufacturing scenarios, this paper constructs a deep learning-enhanced robust SLAM approach for production monitoring of additive manufacturing.The proposed method combines the semantic segmentation technique with the motion-consistency detection algorithm together.Firstly, the Transformer-based backbone network is used to segment the images to establish the a prior semantic information of dynamic objects.Next, the feature points of dynamic objects are projected by the motion-consistency detection algorithm.Then, the static feature points are adopted for feature matching and position estimation.In addition, we conducted a couple of experiments to test function of the proposed method.The obtained results show that the proposal can have excellent performance to promote realistic additive manufacturing process.As for numerical results, the proposal can improve image segmentation effect about 10% to 15% in terms of scenarios of visual SLAM-based additive manufacturing.
Reliable and Secure X2V Energy Trading Framework for Highly Dynamic Connected Electric Vehicles

Vehicle-to-vehicle energy trading has become one of the most popular charge-sharing systems nowadays. It allows energy transfer between electric vehicles without being necessarily relied on infrastructure-based charging stations. However, the demands-offers matching and energy transportation between the vehicles remain challenging issues while considering vehicles– space and temporal location, their dynamicity, availability, and reliability. This paper addresses these issues by proposing a framework of energy trading based on blockchain and smart contracts. The energy transfer between vehicles is performed via a distributed coalition of unmanned aerial vehicles transporting the electric energy from selected sellers to a needy requester vehicle. The selection mechanism of sellers aims to maximize the service availability and fault-tolerance and minimize both the energy transportation latency and overhead. We modeled the selection process by a 0-1 knapsack problem, which we relaxed using a dynamic protocol of energy negotiation, and then developed a linear approach for its resolution. The seller reliability assessment is addressed by the proposition of a trust management approach, which evaluates over time the quality of participants regarding their history of transactions. We conducted intensive simulations with a comparison to the exact solution of resolution. The obtained results show a reduction of 42% of charging latency, an improvement of 24% of service availability, a 96% of approximation from the exact resolution, and an increase of up to 62% of robustness against unfulfilled commitments.
Examining Teachers’ Perception On The Impact Of Ict On Basic School Students’ Academic Performance: A Study Of Afienya Circuit

Schooling is crucial to a country’s socioeconomic progress. No nation can progress without education, so every nation must provide a quality education so its population can participate in development. Raising the population’s education level is key to economic growth. The researchers were interested in the topic since using computers at a fundamental level can lead to better schooling. ICT in education has three purposes: teaching, administration, and personal (Kellenberger &amp; Hendricks, 2000). A descriptive survey research design was used. Afienya circuits. The survey questionnaire analysis complemented the interview and document analysis. Using computers in primary schools in Mobole-Afienya Circuit encourages open learning, removes distance and time between teachers and students, supports the practice of previously taught concepts, and helps learners acquire concepts and skills like word recognition and vocabulary building. Most teachers in the Mobole-Afienya circuit believe that computers aid in content production, and research may be used for pedagogical activities and increase successful teaching.Most respondents feel computers may improve teaching and learning in elementary schools. Insufficient computers, a shortage of electricity and unpredictable power supply, poor internet connectivity, and insufficient teacher training and refresher courses were the primary problems facing computer use in public elementary schools, according to the report.
PECULIARITIES OF THE USE OF INNOVATIVE TECHNOLOGIES IN THE WORK OF A CONCERTMASTER IN VOCAL AND CHORAL DISCIPLINES IN THE CONDITIONS OF DISTANCE LEARNING

The article outlines the main directions of the accompanist's work and analyzes the features of the use of innovative technologies in distance learning. It is noted that it is advisable to involve software for recording musical material during the rehearsal process for further monitoring. The possibility of using programs that provide the ability to perform video and audio chats and implement the idea of conducting rehearsals online is analysed. Emphasis is placed on software that can be used for group and individual classes: Zoom, Skype, Google Meet, Microsoft Teams. The use of information technologies is extremely useful for a music teacher, accompanist, and will also make professional activities more diverse. The necessity of Internet research is outlined to invent musical examples of interesting interpretive versions and new methods. It is specified that there exists a need to combine the traditional and innovative approach, which is associated with the development of opportunities provided by information technology. Nonetheless, the authors of the article believe that the use of information technology should not replace traditional approaches in the work of a music teacher, accompanist.According to the authors, the use of information technology significantly changes the structure and organization of educational activities and provides endless opportunities for creative ideas of teachers and graduates, changing the role of the latter from a passive listener to an active, creative and independent participant of the process. Finally, we would like to note that the possibility of using information technology by a music teacher, an accompanist is really comprehensive, it contains methods of working with Internet technologies in search of special literature and various methods of using educational video programs.
Augmented Reality-Based System for Skill Transfer of Workpiece Fixturing in Turning Operations

For machining operations, preparation work called a “setting operation” is always required in advance. The setting operation, which affects the lead time and machining accuracy, strongly depends on the skill level of the operator. Therefore, to improve the quality of machining operations, skill transfer is necessary by extracting and generalizing the skills related to the setting operation. In addition, a variety of accidents often occur during the setting operation. This can lead to machine tool failure or a serious incident involving the operator. Thus, skill transfer to an unskilled operator is also important for work safety. On the other hand, augmented reality (AR) is a promising human-computer interaction technology to support skill transfer at the manufacturing site. An AR technology generally overlays virtual images on the real-world environment. In this study, an AR-based system is developed to demonstrate a recommended workpiece fixturing method in turning operations for assisting unskilled operators as the first step of skill transfer. In turning operations, two types of fixturing are usually assumed: outer diameter clamping and inner diameter clamping. The dimensions of the targeted product shape are detected, and the workpiece shape is obtained. The removal volume to be machined is calculated as the difference between the targeted product shape and workpiece shape. The fixturing method is determined to avoid contact between the removal volume and the assumed jaw. The results of a case study show that the developed AR-based system is effective for skill transfer of workpiece fixturing by demonstrating the recommended fixturing method using skills acquired from operators.
Dis/similarities in the design and development of legal and algorithmic normative systems: the case of <i>Perspective API</i>

For several decades now, legal scholars and other social scientists have been interested in conceiving of technologies as regulatory media and comparing their normative affordances to law’s regulatory characteristics. Recently, scholars have also started to explore the normative nature of Machine Learning and Artificial Intelligence systems. Most of this scholarship, however, adopts a largely theoretical perspective. This article takes a different approach and attempts to provide the discussion with a more factual grounding. It does so by investigating the construction of one particular Machine Learning system, the content moderation system Perspective API developed by Google. Its open-source development and a voluminous trove of publicly available documentation render Perspective API a virtually unique resource to study the inner logics of Machine Learning systems development. Based on an in-depth analysis of these logics, the article fleshes out similarities and dissimilarities concerning the normative structure of algorithmic and legal systems regarding four different subjects: practical constraints, evaluative diversity, modes of evolution and standards of evaluation. The article then relates the case of Perspective to the European Union’s proposal for an Artificial Intelligence Act and shows how the study’s insights might help in readying the Act for the realities of contemporary multi-party AI development.
The Interaction Selection Method and Application Cases for AR Platform

AR technology is an essential technology to provide closer interaction with users in the metaverse and digital twin environments. Representative interaction methods for displaying content on the AR platform include image recognition, QR code recognition, NFC tag recognition, GPS signal, and Beacon recognition. In applying these various input methods, it is common for most developers to select and develop an input method empirically. In this paper, we examine the advantages and disadvantages of these input methods and implementation methods to reduce trial and error in the initial development of AR applications. In addition, we studies are presented to help find suitable AR interactions cases.
A Robust Tightly Coupled Method for SINS/USBL Integrated Navigation

The integrated navigation of strapdown inertial navigation system (SINS)/ ultra-short baseline (USBL) has been widely used for the high precision navigation and positioning technology of Autonomous Underwater Vehicle (AUV). The tightly coupled method that directly uses the original information measured by USBL has high accuracy, which reduces the error in the process of position calculation in loosely coupled method. However, affected by the complex underwater acoustic environment, the statistical characteristics of USBL measurement are unknown or time-varying, which also contain irregular outliers. To solve the above problems, a robust Kalman filter based on Huber M and variational Bayes (VB) are introduced into the tightly coupled method. To improve the accuracy of statistical characteristics with outliers, the chi-square test is introduced and the simulation results verify the effectiveness of the robust filter and the simulation results verify the effectiveness of the introduced algorithm.
Construction of Online Education Model of Marketing Specialty Based on Cloud Computing

In the process of constructing the online education model for marketing major, the actual application requirements are not considered, and the problems of low functional success rate and poor operation performance exist. In order to provide auxiliary work for the teaching work of marketing major, the cloud computing technology is used to realize the optimization design of the online education model for marketing major. Install cloud servers, modify cloud storage, communication networks and other hardware devices as the operation support of the model. Determine the online education mode and teaching content of marketing specialty. Use the web crawler algorithm to collect the online education resources of marketing specialty, and complete the sharing of online education resources according to the principle of cloud computing technology. Finally, through the design of online education activities and interactive functions, the construction of online education model is realized. Through the model test experiment, it is concluded that the functional success rate of the design model reaches 98.6%, and the response time and memory occupation meet the test requirements.
Covid-19 and the effectiveness of ERM frameworks

event held on Monday 15 August 2022.The Moderator (Mr P
Study on Rainfall Distribution in Yunnan Province Based on ARIMA Model by Big Data Computation

Taking the monthly rainfall data of 16 prefectures and cities in Yunnan Province from 2012 to 2020 as the research object, an ARIMA time series model is established based on big data calculation.First, we use the cluster analysis method to divide all regions into three categories according to the distribution characteristics of rainfall and select a representative city from each category to study the rainfall distribution.Then, according to the results of the ADF test, the method using phase and seasonal differences is used to eliminate the non-stationary and seasonal trends of the series.Finally, the ARIMA prediction model of rainfall distribution in Kunming, Dali, and Pu'er is obtained by combining the sequence autocorrelation and partial autocorrelation analysis diagram to determine the values of various parameters in the ARIMA model.The prediction accuracy of the model is high, and the residual sequence is a white noise sequence, which has a good fitting effect.It effectively predicts the fluctuation law of rainfall and provides an early warning mechanism for drought, flood, debris flow, and other disasters.
Trade Openness, Financial Openness and Income Inequality: Empirical Evidence for MIST Countries/ Ticari Açıklık, Finansal Açıklık ve Gelir Eşitsizliği İlişkisi: MIST Ülkeleri için Ampirik Bulgular

Since the 1980s, income inequality has increased significantly in developed and developing countries. The literature has also seen studies that attempt to explain this rising trend in income inequality. Some studies explaining the increase in income inequality in various ways have focused on economic globalization. Questions on how the steps taken towards liberalization in international trade and finance and increasing market integration affect income inequality have been at the center of the discussions in these studies. Uncertainty about the direction and extent of the link between globalization and inequality persists primarily because of the conflicting empirical findings in the literature. This study aims to contribute to the literature by conducting an empirical analysis of MIST countries with data covering 1987-2019. The study's findings, which used panel data analysis to assess the data, showed that while trade and financial openness reduce income inequality for MIST countries over the analyzed period, foreign direct investments do the opposite. The results also showed that the effect of financial openness on income inequality is greater than the effect of trade openness. It is also discovered that the rate of per capita income increase has no statistically significant impact on income inequality.
What is... an Equivariant Neural Network?

We explain equivariant neural networks, a notion underlying breakthroughs in machine learning from deep convolutional neural networks for computer vision [KSH12] to AlphaFold 2 for protein structure prediction [JEP + 21], without assuming knowledge of equivariance or neural networks.The basic mathematical ideas are simple but are often obscured by engineering complications that come with practical realizations.We extract and focus on the mathematical aspects, and limit ourselves to a cursory treatment of the engineering issues at the end.We also include some materials with machine learning practitioners in mind.Let and be sets, and ∶ → a function.If a group acts on both and , and this action commutes with the function : ( ⋅ ) = ⋅ () for all ∈ , ∈ , then we say that is -equivariant.The special case where acts trivially on is called -invariant.Linear equivariant maps are well-studied in representation theory and continuous equivariant maps are well-studied in topology.The novelty of equivariant neural networks is that they
Geological Hazard Susceptibility Assessment Based on RS and PSO-SVR Model

It is crucial to undertake thorough study and data mining on factors that determine the occurrence of geohazards to prevent and anticipate their occurrence.A small-sample learning method with a strong theoretical underpinning, the classical support vector machine model provides considerable expressive power in dealing with the interaction of nonlinear characteristics and lessens reliance on the entire data set.However, its excellent generalization capabilities result in an excessively large optimal search space, which impacts the search for kernel parameters and reduces the model's accuracy.The particle swarm algorithm, which has a robust search capability, is thus introduced to improve it.This paper's research area is Chian Town, China.The prediction study of geological hazard susceptibility in Chian town was validated using Gaofen 2 remote sensing imagery in conjunction with a support vector machine regression model enhanced by the particle swarm algorithm.The findings show that the high-risk zone comprises 17% of the overall area and has an 88.88% prediction accuracy.The results imply that integrating high-resolution remote sensing imagery-based and optimized machine learning algorithm models has prospective applications in investigating geological hazard susceptibility with small samples, numerous feature factors, and large-scale data.
Yaozhou Kiln Museum e-commerce applet mindstream experience design practice

In this paper, we design and practice the e-commerce applet platform of Yaozhou Kiln Museum to help the development of Yao porcelain culture.Based on the heart flow theory and the PAT model, we analyze the factors that generate the heart flow experience in the e-commerce applet of Yaozhou Kiln Museum, summarize the factors from the five elements of user experience and then classify them, and build a design model for the e-commerce applet of Yaozhou Kiln Museum.According to the results of user satisfaction survey, it can be seen that the heart flow experience of Yaozhou Kiln Museum e-commerce applet has good satisfaction in three perspectives of visual experience, interactive experience and emotional perception experience, which can enhance users' willingness to use the platform to stimulate heart flow.Therefore, the introduction of mind flow theory into the design of e-commerce applet of Yaozhou Kiln Museum can strengthen the link between users and the platform and explore more possibilities for the establishment of e-commerce applet of Yaozhou Kiln Museum from the perspective of optimal experience.
Image denoising based on a new anisotropic mean curvature model

A number of variational models for image denoising have been proposed in the last few years in order to advance the denoising performance. To improve the denoising quality, it is very significant to describe the local structure of image in the proposed models. To this end, this paper proposes a novel denoising model which combines the gradient operator $ \nabla $ with the adaptive weighted matrix $ W $ in the mean curvature regularized term such that the proposed model can describe the local features in image efficiently. Since the proposed model is a high-order nonlinear and nonconvex optimization problem, we need to use the operator splitting method to tansform it into a multi-variable optimization problem and then the alternating direction method of multipliers (ADMM) can be applied to solve it. Numerical experiments demonstrate that the proposed model yields good performance compared with other well-known gradient-based models.