Reviewerly/exHarmony huggingface dataset:
Network Education Evaluation System and Its Design Based on Decision Tree Classification Algorithm

Because education is a complex system, it should be based on long-term formative evaluation. Network teaching breaks the limitation of region and time and space, and realizes many functions, such as two-way interaction, real-time multi-point communication, wide dissemination, quick data information collection and so on. Therefore, on the basis of understanding the individual characteristics of learners, we must formulate learning strategies for individual learners according to certain teaching rules, allocate teaching resources and contents predictably, and build a personalized learning environment. Chinese traditional teaching mode is an exam oriented teaching mode with classroom teaching as the core. Its guiding ideology is book knowledge centered, classroom centered and teacher centered. The chip has the ability of hardware floating-point operation and portability, so the processor is selected to ensure the operation of the system. This paper mainly discusses the development of a new evaluation system – network education evaluation system by using network environment, and introduces the main structure and function of decision tree classification algorithm.
Construction and Application of English Education Platform Based on Big Data

The English education platform overcomes the limitations of traditional education on both sides of English teaching in terms of time, space and instant interaction, making learners more flexible and free in terms of learning content, learning form, learning time, and learning location. Fully mobilize the enthusiasm of learners. The research purpose of this article is to build and apply English education platform based on big data, mainly introduces the application research of data mining technology in assisting teachers to make decisions and analyze learner information. In view of the problem of the scattered information of the English education platform and it is difficult to unify, firstly, it is proposed to use the statistical analysis module to comprehensively grasp the learner's activities, which is conducive to the teacher's overall grasp of the learner's learning situation, and also helps the teacher understand the learning situation of this course. The application process of association rules in the analysis of learners' basic information is described in detail, and some potential associations hidden in students' basic information and student activities are discovered through association rules mining, so as to facilitate teachers to grasp the learning situation of learners and make appropriate Formative evaluation. On the basis of the system design, the main technology of the development platform of the English education platform based on big data is analyzed and selected, and the system implementation of the big data infrastructure module is introduced with emphasis, and the test results are analyzed. The experimental results show that under the condition of 800G data volume, the current system data loss rate is within 0.3%, which can ensure the accuracy of data analysis and meet the stability requirements.
Graph Convolutional Neural Networks with AM-Actor-Critic for Minimum Vertex Cover Problem

At present, deep reinforcement learning (DRL) methods are mostly used to solve the problems in Euclidean space. Typical evaluation environment of algorithm performance includes Atari pixel game with image as input, or single inverted pendulum system with specific data input shape, etc. However, most of the practical problems exist in non-Euclidean space, and the features of topological data need to be extracted. In this paper, a new framework combining DRL and graph neural network (GNN) is proposed, which aims to reduce the over-estimation problem, and obtain better performance results than before in solving NP-hard problems. NP-hard problem is a problem with high time complexity. The proposed method is used to solve a typical NP-hard problem named the Minimum Vertex Cover problem (MVC) and is compared with the common Actor-Critic-GNN_(AC-GNN) method, a heuristic strategy and a random strategy, the proposed algorithm has the best performance. At the same time, the experiment also show the superiority of our new method in generalization ability, which is a feature that the previous DRL methods do not have.
Electromagnetic compatibility of the on-board electrical complex as a key factor in ensuring the operational safety of vehicles

Abstract The issues of functional safety of the on-board electrical complex of vehicles under exposure of electromagnetic interference are considered in this paper. Besides, the modern practice of organizing the process of assessing the immunity of electrical systems of cars is shown.
A Software Defect Avoidance Technique for E-commerce Website Development

As per the literature study, the development of e-commerce websites is done by adopting different methodologies, which does not give due importance to the logical sequencing of the functionality of each module to be developed. This work proposes a technique that helps in producing defect-free e-commerce applications by developing a module dependency functionality matrix, mainly on ATG (Art Technology Group) framework, using rational unified process (RUP).
PENGEMBANGAN ARSITEKTUR INFORMASI DALAM MENUNJANG STRATEGI SI/TI MENGGUNAKAN PENDEKATAN BUSINESS SYSTEM PLANNING : STUDI KASUS YAYASAN CAHAYA SAKTI

In order to improve the quality of education and competitiveness, information systems provide the most important role in the business world. The development of information technology has caused role changes from the role of efficiency and effectiveness to become strategic roles. The preparation of the IS / IT planning strategy is a step taken by a company that wants a better corporate environment in the future. In the case of building a computer-based information system, a business plan and process can be defined so that the flow of information between business processes can be clearly described. Cahaya Sakti Foundation is an institute in the field of education. The writer uses the Business System Planning method which consists of four steps, namely defining Business Objectives, defining Business Processes, defining Data Classes and determining Information Architecture. The results of this research are information architecture where the aim is to integrate information needs, information components and supporting technologies, the final results of which are made of information architecture priorities.
Integrating Heterogeneous Data About Quebec Literature into an IFLA LRM Knowledge Base

In the process of translating cultural heritage data into linked open data, in particular from multiple sources, heterogeneity of data is a crucial issue. In this paper, our aim is to demonstrate the interest of linked open data and more precisely the IFLA LRM model for the digital representation of Quebec literature, based on data from various perspectives: authors, publishers, and books. We propose an IFLA LRM knowledge base to represent the aggregated data from these heterogeneous sources.
Quantum web of trust

Abstract Authentication is the foremost requirement for any online secure transaction. In post quantum era, public key encryption is considered no longer a safe option. The research is therefore, progressing toward providing a quantum resistant solution using both classical as well as quantum computing medium. The paper proposes a quantum based mutual authentication protocol for autonomous system, utilizing the properties of quantum mechanics for exchange, storage and validation of confidential data. The protocol so designed evolves into a chain of mutually trusted nodes to form quantum web of trust. The protocol cater for devices infected with malware by utilizing physically unclonable function inside trusted platform module. The mathematical expressions conveying the correctness of operations has been demonstrated on a quantum machine to verify the efficacy of the protocol. The security analysis demonstrates the ability of the proposed protocol to mitigate cyber attacks and eavesdropping in the network.
Studying web technologies to expand basic digital competences of school graduates

The article discusses the relevance of studying web technologies in basic and secondary schools. Revealed the relationship of the concepts of "web programming", "web development" and "web technology", clarified the concept of "web technology". The connection between the concepts of "competency", "educational competency", "digital competencies", "digital skills" has been investigated. The requirements for the results of studying web technologies at school are analyzed, which are contained in Federal State Educational Standards and in Approximate Basic Educational Programs of Basic and Secondary Education in informatics, as well as in educational and methodological kits of basic and secondary schools. On the basis of this analysis, the requirements for what a high school graduate should know, be able to and master at a basic level after studying the theme "Web technologies" are summarized. An attempt has been made to formulate and distinguish between basic, intermediate and advanced skills that are formed in school graduates.
The Concept of Lawful Behavior in the Digital Age

The global situation with cybercrime is worsening. The study aims to investigate the concept of lawful behavior in the context of challenges facing the IT sector and from the perspective of previous and modern theoretical and legal views concerning the formation of lawful behavior. The paper outlines factors that shape the lawful behavior of digital natives. The results of the literature review suggest that a digital shift of legal relations objectively reduces the role of the socio-hierarchical factor in the formation of lawful behavior. This research is of practical interest to specialists in legal and sociological fields.
Gamification of Applied Technical Disciplines

Three forms of interaction between teacher and students, which have become widespread, are described. The main purpose of the work is to study the fundamental differences between games and gamification in order to identify and modify the most effective methods for introducing active learning elements in the educational process. After decomposition of games and gamification, the following features of activities were revealed. Already at this stage, differences are noticeable between the goals of games where you need to win, and gamification – to learn. It is shown that the selection of optimal game mechanics, to improve the learning process, was carried out according to subjective criteria, and the result according to objective – academic performance, aimed at students of technical specialties. Thus, repeated studies for groups of other specialties can give different, but no less interesting situations. It was found that the use of game scenarios during a laboratory practice objectively increases academic performance, thereby increasing the formation of skills and abilities of professional competencies.
Encryption and Error Control in Satellite Images Using Linear Block Code

In general, images captured by satellite or aircraft contain errors when it is transmitted to a long distance. These errors are mainly due to various atmospheric problems and relative motions of a satellite etc. Now a days high-resolution satellite images are required for detail land cover analysis in many cases. As satellite images contain a lot of information, therefore it is the primary requirement to eliminate errors from those images to get accurate result. In this paper, we have proposed a novel method of removal of noise from satellite images using linear block coding. The selected images are encoded with using a generator matrix before long distance transmission. At the receiving end, the encoded images are decrypted using linear block code method. Each pixel of the transmitted images is checked separately. With the help of this system, errors of each pixel can be detected as well as corrected. This system can be used to remove noise from satellite images too.
On the Rate of Convergence of a Classifier Based on a Transformer Encoder

Pattern recognition based on a high-dimensional predictor is considered. A classifier is defined which is based on a Transformer encoder. The rate of convergence of the misclassification probability of the classifier towards the optimal misclassification probability is analyzed. It is shown that this classifier is able to circumvent the curse of dimensionality provided the a posteriori probability satisfies a suitable hierarchical composition model. Furthermore, the difference between the Transformer classifiers theoretically analyzed in this paper and the ones used in practice today is illustrated by means of classification problems in natural language processing.
Quantization of Deep Neural Network Models Considering Per-Layer Computation Complexity for Efficient Execution in Multi-Precision Accelerators

Quantization of parameters and activation data in deep neural network (DNN) models plays an important role in multi-precision DNN hardware accelerators where the per-layer bit-widths are dynamically adjusted to speed up the computation. In this paper, we present an efficient quantization algorithm which considers the supported types of bit-width in the multi-precision DNN hardware when quantizing activations and parameters from the pre-trained models. Furthermore, we consider various computation complexity in different DNN layers in order to minimize the execution time in the DNN hardware. Experimental results show that the proposed algorithm has better quantization results for more efficient computation in multi-precision DNN hardware accelerators.
Competency Training Ability of College Counselors Based on Intelligent Computing

College counselors are an important part of the faculty of colleges and universities, the backbone of the ideological and political education of college students, and the main force in the work of college students. The 21st century we live in is a highly intelligent era. With the continuous emergence of intelligent computing as a typical technical means in contemporary network technology, the relationship between intelligent computing and people's life and work is getting closer and closer. However, college counselors are also facing many new problems and challenges. Therefore, how to improve the use of intelligent computing and better improve the competence of counselors has become a difficult problem for colleges and universities. Based on this, this article launched a research on the competency training ability of college counselors based on intelligent computing. This article uses behavioral incident interview method, literature analysis method and questionnaire survey method to carry out research. Through interviews with counselors, the competency characteristics of counselors are obtained, and the competency model of counselors is established. By consulting the literature, compiling questionnaires, designing a complete set of questionnaires in line with the research, and issuing questionnaires to 60% of college counselors and teachers to obtain the most authentic data. Collect relevant data through questionnaire surveys, and then enter the collected data into the database for data analysis. By consulting related materials, using publicly published papers and works at home and abroad as references, we can obtain basic theoretical support and background materials, as well as the current status and development level of the research on the competence of counselors. The survey results show that counselors have a strong understanding of work. Generally, 45% are familiar with them, 18% are relatively familiar, and few are very familiar. 40% of counselors think they have average competence. 44 % of counselors voluntarily signed up and wanted to participate in training to improve themselves. 51% of counselors believe that the best time for a training session is half a day. Regarding the training goals, only a small number of instructors have clear training goals. It can be seen that the research on the competence training ability of college counselors is of extremely high value.
Bayesian Wishart Filter for Random Shape Tracking

In stochastic estimation problems, we aim to estimate an unknown state of interest given a set of measurements received from noisy sensory devices, such as radar, light detection and ranging, etc. A common model of the measurements’ random error is white Gaussian noise. This noise model is usually used to derive an estimator of the unknown state; accordingly, the state is described as a random variable with a Gaussian density. In some applications, the unknown state is known to take only positive values, e.g., estimating size or dimension. In such a case, the classical approaches based on Gaussian densities might fail, i.e., produce a negative value. In this article, a recursive Bayesian filter is proposed based on modeling the unknown state as Wishart distributed random matrix. This model ensures that the probability densities of the random variables are restricted to positive real values, even though the measurement’s noise is still modeled as white Gaussian noise. The feasibility of the proposed Bayesian–Wishart filter is demonstrated within the framework of extended target tracking. A target contour measurement model of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">symmetric star-convex</i> shape is presented and integrated in the proposed filter to track the target’s extent.
Against generalisation: Data-driven decisions need context to be human-compatible

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.
Application of Artificial Neural Network in Automated English Speech Scoring

short-paper Application of Artificial Neural Network in Automated English Speech Scoring Share on Author: Juntong Liang Shandong University of Political Science and Law, China Shandong University of Political Science and Law, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2480–2482https://doi.org/10.1145/3482632.3487454Online:22 November 2021Publication History 0citation8DownloadsMetricsTotal Citations0Total Downloads8Last 12 Months8Last 6 weeks3 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
A Simple Method for Unsupervised Anomaly Detection: An Application to Web Time Series Data

We propose a simple anomaly detection method that is applicable to unlabeled time series data and is sufficiently tractable, even for non-technical entities, by using the density ratio estimation based on the state space model. Our detection rule is based on the likelihood ratio estimated by the dynamic linear model, i.e. the ratio of likelihood in our model to that in an over-dispersed model that we will call the NULL model. Using the Yahoo S5 data set and the Numenta Anomaly Benchmark (NAB) data set, publicly available and commonly used benchmark data sets, we find that our method achieves better or comparable performance compared to the existing methods. The result implies that it is essential in time series anomaly detection to incorporate the specific information on time series data into the model. In addition, we apply the proposed method to unlabeled Web time series data, specifically, daily page view and average session duration data on an electronic commerce site that deals in insurance goods to show the applicability of our method to unlabeled real-world data. We find that the increase in page view caused by e-mail newsletter deliveries is less likely to contribute to completing an insurance contract. The result also suggests the importance of the simultaneous monitoring of more than one time series.
Zinc ore supplier evaluation and recommendation method based on nonlinear adaptive online transfer learning

&lt;p style='text-indent:20px;'&gt;Purchasing decisions determine the purchasing cost, which is the largest section of the production cost of zinc smelting enterprise(ZSE). An excellent supplier recommendation is significant for ZSE to reduce the cost. However, during the supplier recommendation process, the nonlinear demand feature of purchasing department varies with the production environment, and there are wrong samples that can affect the supplier recommendation effect. To handle these problems, the recommendation strategy based on a multiple-layer perceptron adaptive online transfer learning algorithm(AOTLMLP) are proposed. In this method, the original prediction function is modified based on MLP nonlinear projective function and adaptive loss function, which enables the AOTLMLP algorithm to tackle the nonlinear classification problems and efficiently follow the demand change of purchasing department, thereby improving the result of the recommendation. The performance of the AOTLMO algorithm is evaluated through a common dataset and a purchasing dataset from a zinc smelter that generated by a supplier evaluation model. It can be assumed that AOTLMLP can ignore the influence of wrong samples and provide an effective recommendation confronting the characteristic of zinc ore purchasing.&lt;/p&gt;
Design and Implementation of MD5 Hash Function Algorithm Using Verilog HDL

Over the past 20 years, the demand of computers and the Internet has been increasing and people have paid a growing attention to information and network security. In result, various encryption algorithms coming into being. Cryptographic algorithm has become one of the most essential features of embedded system design. Hash functions are one of the cryptographies that can be used in both security design applications and protocol suites. A few distinct applications of hash algorithms are digital signatures, digital time stamping and the message integrity verification. Among hash algorithms, MD5 is the most used hash function algorithm. This paper proposed iterative looping architecture. The architecture includes MD5 padding block, data path, and a controller. A general concept and implementation of the MD5 hash function is described. The MD5 hash function modelling was done using Verilog, compiled with a few targeted virtual Altera Quartus devices, and simulated using ModelSim. Its performance in terms of frequency and throughput is compared with other MD5 implementations. The maximum frequency achieved is 111.45 MHz, and the throughput of iterative looping design was increased significantly to 864.58 Mbps using family device of Arria II GX. The improved performance of the implementation is the main goal of the design presented herein.