Reviewerly/exHarmony huggingface dataset:
Research on Information Construction of New Campus Based on Computer Intelligent Campus

Abstract Smart campus is a new concept of education in our country at this stage. Its emergence is the inevitable process of a nation’s educational reform. In order to carry out the construction of intelligent campus with informatization in the new campus, we should explore it from the perspective of building a new information school on the basis of clarifying its connotation [1] . In order to carefully carry out the construction of intelligent new campus information, we need to consider three main levels. They are network infrastructure, data support platform and intelligent application services. In order to ensure the stable operation of the network information of the new campus, we should also consider the specific steps and safeguard measures in the construction process.
Dynamic Features Spaces and Machine Learning: Open Problems and Synthetic Data Sets

Dynamic feature spaces appear when different records or instances in databases are defined in terms of different features. This is in contrast with usual (static) feature spaces in standard databases, where the schema of the database is known and fixed. Then, all records in the database have the same set of variables, attributes or features. Dynamic feature mining algorithms are to extract knowledge from data on dynamic feature spaces. As an example, spam detection methods have been developed from a dynamic feature space perspective. Words are taken as features and new words appearing in new emails are, therefore, considered new features. In this case, the problem of spam detection is represented as a classification problem (a supervised machine learning problem). The relevance of dynamic feature spaces is increasing. The large amounts of data currently available or received by systems are not necessarily described using the same feature spaces. This is the case of distributed databases with data about customers, providers, etc. Industry 4.0, Internet of Things, and RFIDs are and will be a source of data in dynamic feature spaces. New sensors added in an industrial environment, new devices connected into a smart home, new types of analysis and new types of sensors in healthcare, all are examples of dynamic feature spaces. Machine learning algorithms are needed to deal with these type of scenarios. In this paper we motivate the interest for dynamic feature mining, we give some examples of scenarios where these techniques are needed, we review some of the existing solutions and its relationship with other areas of machine learning and data mining (e.g., incremental learning, concept drift, topic modeling), we discuss some open problems, and we discuss synthetic data generation for this type of problem.
On Random-Forest-Based Prediction Intervals

In the context of predicting continuous variables, many proposals in the literature exist dealing with point predictions. However, these predictions have inherent errors which should be quantified. Prediction intervals (PI) are a great alternative to point predictions, as they permit measuring the uncertainty of the prediction. In this paper, we review Quantile Regression Forests and propose five new alternatives based on them, as well as on classical random forests and linear and quantile regression, for the computation of PIs. Moreover, we perform several numerical experiments to evaluate the performance of the reviewed and proposed methods and extract some guidelines on the method to choose depending on the size of the data set and the shape of the target variable.
Improvement of College Teachers’ Teaching Ability Under the Background of the Development of Artificial Intelligence Platform

With the advent of the “Internet + Education” era, educational artificial intelligence platforms are playing an increasingly important role in higher education. It provides rich learning resources and convenient learning methods for college students, and is generally welcomed by college students. As an important element of higher education talent training, college teachers are facing a severe impact from educational artificial intelligence platforms. Facing the development of artificial intelligence platforms, college teachers must take proactive measures to continuously enhance their informationization level, improve the teaching ability of using artificial intelligence platforms, and cultivate outstanding talents for society. Based on the realistic background of the development of educational artificial intelligence platform, this article will analyze the problems existing in the course teaching of the era of artificial intelligence in China’s college teachers from the perspective of supply side, and seek solutions to the problems.
Opportunities of internet technologies in solving educational tasks

The article examines the process of using and adapting Internet technologies to solve educational problems, highlights the possibilities of the educational model of training, identifies ways to develop the education system in these conditions and identifies factors that determine the effectiveness of training, considers Internet technology as an innovative pedagogical technology and as a means of mass communication of training.
Capturing User Emotions in Interactive Stories: Comparing a Diegetic and a Non-diegetic Approach to Self-reporting Emotion

Capturing the player’s emotions in an interactive story can serve many purposes, such as to understand player response to a story or to alter the story’s path. The concept of diegesis defines the boundary between the story world and the real world, and has been used to explore different ways of conceptualizing user interface (UI) elements in interactive media. This paper explores methods of capturing players’ emotions during an interactive story and the concept of diegesis. Specifically, we posit that whether emotion capture exists in a diegetic versus a non-diegetic space can affect its accuracy when capturing player emotion. We developed two interactive stories with two emotion capture methods, one using a diegetic approach, and the other using a non-diegetic approach. We conducted a study with 64 participants to compare the two emotion capture approaches. Our results show that a diegetic approach leads to a better story experience, but that a non-diegetic approach leads to the player’s emotion being captured more accurately. We discuss the implications of the study results for integrating emotion capture in the design of interactive stories.
Application Scenarios Of Knowledge Graph In IT Operation

Abstract Since Google optimized its search service by using knowledge graph, the application of knowledge graph has developed rapidly, especially in the aspect of information system operation and maintenance. CMDB (configuration management) built based on knowledge graph plays an excellent role in fault location of information system.However, the operation and maintenance data generated during the operation of the information system are diverse, not limited to configuration, correlation, and alarm information, and include all data generated during the operation and maintenance of the system. The knowledge map technology is used to effectively correlate various types of operation and maintenance data, which is more conducive to identifying core application scenarios of the information system, thereby further improving the global control ability in the operation and maintenance process. Therefore, this paper explores the rich application scenarios and prospects of knowledge graph application in the field of information operation and maintenance.
Application of analytic hierarchy process in performance appraisal system

Abstract Based on the KPI and BSC assessment method commonly used in management, combined with the analytic hierarchy process in mathematics, the core algorithm of the assessment system is updated, the weight of each assessment element is scientifically measured, and the requirement of fairness and justice in the assessment function of the system is guaranteed, which provides scientific basis for the final performance appraisal results. Zero client B/S architecture is adopted, and UML modeling is used for system analysis. Modeling methods such as timing diagrams are used in the system design, and combined with JavaWeb technology to complete the construction of system pages and interaction with the server. It realizes the design and development of the performance appraisal system, and makes human resource management more scientific and convenient.
FEATURES OF LESSON QUALITY ASSESSMENT IN THE CONTEXT OF DIGITAL EDUCATIONAL RESOURCES AND DISTANCE LEARNING TECHNOLOGIES

Statement of the problem. The variety of new models, forms and methods of organizing school lessons in the context of ICT actualizes the problem of finding objective and affordable ways to assess the quality of a modern lesson. The purpose of the article is to substantiate an expert-criteria approach to assessment of the quality of lessons, which use elements of e-learning and distance educational technologies. The idea of ​​designing a methodology for assessing the quality of lessons is to create expert questionnaires that include weighted criteria quality indicators, as well as questionnaires and test materials. Research methodology is the analysis and synthesis of regulatory documents of basic general education, research works of Russian and foreign scientists, experience in teaching students computer science at different levels of general education. Research results. The paper proposes and substantiates an expert-criteria procedural scheme for evaluating the quality of modern lessons that use elements of e-learning and distance learning technologies. The method of assessing the quality of lessons consists in creating expert questionnaires that include weight criteria for quality, as well as questionnaire and test materials. Conclusion. Currently, the performance of testing of test tasks for evaluating each type of educational results (personal, subject, and meta-subject) is being tested and a pedagogical experiment is being carried out in Secondary School No. 9 of Krasnoyarsk to conduct lessons in Computer Science of four types: 1) without the use of the digital educational resources (DER); 2) with partial use of the DER; 3) with long-term use of the DER; 4) mega-lessons (the use of the DER throughout the lesson). The procedural scheme for collecting and processing expert indicators is formalized and provides for the possibility of automating statistical data processing and subsequent improvement of criteria based on the “constructor” principle. The proposed method can be used by school administrations to make decisions on the management of the educational process.
Research on the Construction of Practice Teaching System of Business Administration Specialty Based on Artificial Intelligence

Abstract With the development of the times, the progress of society, and the continuous improvement of human science and technology and culture, artificial intelligence technology has been widely used in people’s daily production and life. In today’s era, the practice teaching system of Business Administration major has always been the focus of attention of universities and relevant departments. Although some domestic college students have certain theoretical knowledge, their experience in the field of professional practice teaching is basically blank or relatively weak. Therefore, this paper starts from the basic construction of practice teaching basic system of advanced business administration major, and integrates the practicality of professional artificial intelligence technology into the structure of practical teaching curriculum foundation system of advanced business administration specialty, so as to deeply discuss the basic construction of practice course and teaching system of advanced business management specialty. Through the analysis, it is found that the introduction of artificial intelligence technology into the construction of practical teaching system of business administration specialty can effectively improve the depth of research on the construction of practice teaching system of Business Administration major.
RolNE: Improving the Quality of Network Embedding with Structural Role Proximity

The structural role of a node is an essential network structure information, which provides a better perspective to understand the network structure. In recent years, network embedding learning methods have been widely used in node classification, link prediction, and visualization tasks. Most network embedding learning algorithms attempt to preserve the neighborhood information of nodes. However, these methods are hard to recognize the structural role proximity of nodes. We propose a novel method, RolNE, which learns structural role proximity of nodes through clustering the degree vector of nodes and uses an aggregation function to learn node embedding that contains both neighborhood information and structural role proximity. Experiments on multiple datasets show that our algorithm outperforms other state-of-the-art baselines on downstream tasks.
Design of Personalized Intelligent Learning Assistant System Under Artificial Intelligence Background

In the context of the era of artificial intelligence, integration and innovation of big data, cloud computing, Internet of Things, and 5G and other cutting-edge technologies in the field of higher education have the development of smart education. It provides a valuable opportunity for fully implementing adaptive and personalized learning based on teaching according to aptitude. This article is driven by AI and student-centered, starting from the personalized needs of learning. Analyze human-computer interaction technology founded on natural language processing, and design anthropomorphic learning assistance and communication functions. Collect and process educational big data, mining quantitative data on learning behavior characteristics, learning status and learning effects, and build a staged student portrait model. Intelligently generate personalized learning programs and plans, recommend high-quality learning resources, and implement adaptive personalized learning program updates based on learning effect feedback. Through the research and design of the framework and hierarchical functions of the intelligent learning assistant system, we are committed to providing students with anthropomorphic learning assistance functions and an ecological learning environment, promoting students’ personalized learning and sustainable development, and realizing the function of intelligent tutoring.
Integrity of E-Health Record Ensured With Context-Based Merkle Tree Through Temporal Shadow in Blockchain

The patient's health record is sensitive and confidential information. The sharing of health information is a first venture to make health services more productive and improve the quality of healthcare services. Decentralized online ledgers with blockchain-based platforms were already proposed and in use to address the interoperability and privacy issues. However, other challenges remain, in particular, scalability, usability, and accessibility as core technical challenges. The paper focuses on ensuring the integrity of the health record with context-based Merkle tree (CBMT) through temporal shadow. In this system, two ledgers were used to ensure the integrity of eHealth records like general public ledger (GPL) and personalized micro ledger (PML). The context-based Merkle tree (CBMT) is used to aggregates all the transactions at a particular time. The context means it depends on time, location, and identity. This is ensured without the help of a third party.
[Quantitative Evaluation of Human Health Risk of Heavy Metals in Soils Based on Positive Matrix Factorization Model and Geo-statistics].

The aim of this study was to quantitatively assess the human health risks derived from different exposure paths of heavy metals in the soil. Zhangqiu county was selected as the study area, and 425 soil samples were collected to measure the As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn contents. A descriptive statistical method was used to assess the heavy metal pollution status of the soils, and the quantitative sources for human health were then determined based on positive matrix factorization (PMF) and geo-statistical techniques. The results show that the contents of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in the soils exceed background values, but do not exceed the risk screening values, indicating that there is slight heavy metals pollution in the soil. The sources of heavy metals in the soil can be divided into three categories. The spatial distribution of Cr and Ni is approximately the same, is similar to the spatial distribution trend of parent materials, and belongs to natural sources. Cd, Cu, and Zn are controlled by transportation. The spatial distribution is significantly affected by the location of road lines. The hot spot areas of Hg, Pb, and As correspond to the locations of the industrial park and the urban area. Industrial emissions and coal combustion increase the accumulation of Hg in the soil, and As, Pb, and Hg are classified as industrial sources. The contribution rate of industrial source is 41.85%, with transportation sources and natural sources being 33.79% and 24.36%, respectively. The non-carcinogenic and carcinogenic risks under the exposure paths of hand, breathing, and skin are within the acceptable level. For children, the sources of heavy metals with the largest carcinogenic (36.53%) and non-carcinogenic (36.01%) risks are industrial sources. However, transportation is the largest source of carcinogenic (34.98%) and non-carcinogenic (37.06%) risk for adults. Differential avoidance of heavy metal sources and exposure pathways is vital to reducing human health risks.
Insights into Fairness through Trust: Multi-scale Trust Quantification for Financial Deep Learning.

The success of deep learning in recent years have led to a significant increase in interest and prevalence for its adoption to tackle financial services tasks. One particular question that often arises as a barrier to adopting deep learning for financial services is whether the developed financial deep learning models are fair in their predictions, particularly in light of strong governance and regulatory compliance requirements in the financial services industry. A fundamental aspect of fairness that has not been explored in financial deep learning is the concept of trust, whose variations may point to an egocentric view of fairness and thus provide insights into the fairness of models. In this study we explore the feasibility and utility of a multi-scale trust quantification strategy to gain insights into the fairness of a financial deep learning model, particularly under different scenarios at different scales. More specifically, we conduct multi-scale trust quantification on a deep neural network for the purpose of credit card default prediction to study: 1) the overall trustworthiness of the model 2) the trust level under all possible prediction-truth relationships, 3) the trust level across the spectrum of possible predictions, 4) the trust level across different demographic groups (e.g., age, gender, and education), and 5) distribution of overall trust for an individual prediction scenario. The insights for this proof-of-concept study demonstrate that such a multi-scale trust quantification strategy may be helpful for data scientists and regulators in financial services as part of the verification and certification of financial deep learning solutions to gain insights into fairness and trust of these solutions.
Evaluation of Deep Learning Algorithms for Traffic Sign Detection to Implement on Embedded Systems

Nowadays, machine learning algorithms are trendy and are used to solve different problems of autonomous vehicles obtaining good results. Among these algorithms, deep learning has emerged as an excellent alternative to improve the results of the state-of-the-art in machine vision applications. An essential task in autonomous vehicles is the detection of traffic signs. Some metrics used for these detectors focus on assessing precision and recall. However, it is necessary to consider other factors, such as the implementation of these models on an embedded system. In this work, we implement deep learning algorithms on an embedded system to evaluate two different detection algorithms: Faster R-CNN and Single Shot Multibox Detector (SSD) with two feature extractors, ResNet V1 101 and MobileNet V1 to determine the location of traffic signs within the observed scenario. The contribution of this work focuses on evaluating the implementation of traffic sign detection systems based on deep learning algorithms on embedded systems. The experiments were achieved on the experimental embedded system board Nvidia Jetson Nano. The inference time and memory consumption of these detection systems were evaluated; they delivered good performance (81–98%) measure by average precision for each superclass (prohibitory, warning, and mandatory).
Testing pre-trained transformer models for Lithuanian news clustering

A recent introduction of Transformer deep learning architecture made breakthroughs in various natural language processing tasks. However, non-English languages could not leverage such new opportunities with the English text pre-trained models. This changed with research focusing on multilingual models, where less-spoken languages are the main beneficiaries. We compare pre-trained multilingual BERT, XLM-R, and older learned text representation methods as encodings for the task of Lithuanian news clustering. Our results indicate that publicly available pre-trained multilingual Transformer models can be fine-tuned to surpass word vectors but still score much lower than specially trained doc2vec embeddings.
Innovation Research of Network Technology in Music Teaching Environment

Abstract Internet multimedia has brought great convenience to people’s life.The teaching method of vocal music is also challenged by multimedia with the development of network.Universities as an important base of vocal music talents training in China provide a large number of high-quality and high-level vocal talents for the society every year.According to the actual situation of vocal music teaching in domestic colleges and universities, there is still a certain lag in teaching methods and teaching concepts.The network era has brought abundant teaching resources and broadened the way of knowledge transmission. The application of modern information technology is changing the traditional teaching methods in Colleges and universities. This paper analyzes the problems and innovation strategies in vocal music teaching in Colleges and universities, expounds the specific reform measures of college vocal music teaching innovation, and puts forward that we should make full use of network technology, expand vocal music course content, change vocal music teaching methods, enrich vocal music teaching practice, promote the construction of vocal music curriculum database, and constantly improve students’ vocal music skills and comprehensive artistic quality.
Attribute-efficient learning of Boolean functions from Post closed classes

Abstract We consider exact attribute-efficient learning of functions from Post closed classes using membership queries and obtain bounds on learning complexity.