Reviewerly/exHarmony huggingface dataset:
An Algorithm for Solving The Traveling Salesman Problem

The Traveling Salesman Problem is a typical combinatorial optimization problem, which has not been well solved until now. In this paper, The Genetic Algorithm is used to solve this problem, and the gene segment is regarded as an urban sequence. The introduction of the crossover rate and the mutation rate not only ensures the current good genes, but also produces better genes. In this way, the optimization of the problem solution is continuously realized. We experimentally validate The Genetic Algorithm with 100 cities, and for this problem, The Ant Colony Algorithm and The Particle Swarm Algorithm are used for experimental comparison. The experimental results show that The Genetic Algorithm has a strong global optimization ability and better overall performance under the premise of ensuring a faster operation speed.
Research on the Design of Music Learning Applications for Preschool Children Based on Tangible Interaction

Music learning is important for preschool children’s musical development, but preschool children are considered to have limited cognitive development and have difficulties understanding abstract musical concepts. Tangible interaction can introduce physical entities into the interaction, allowing children to use their daily life experiences to interact with the computer, reducing the cognitive load and therefore improving the effectiveness of music learning. Combining the cognitive characteristics and physical ability level of preschool children, a design model of a music learning system for preschool children based on tangible interaction is constructed with reference to existing research models, and a set of design recommendations based on this model is proposed. This paper also designs and develops an interactive prototype Music Magician for preschool children to learn music and completes the construction of an interactive action library and iteration of the prototype after pre-experiment feedback. The validity of the prototype is verified through experiments, and the rationality of the proposed design model and design recommendations is also validated.
A Concise Chinese Text Time Relationship Recognition Scheme

Temporal relationship recognition plays an important role in many areas of natural language processing. The current mainstream research aspect in the related field is guided by the approach of English relationship recognition, focusing on event-time-relationship recognition. In view of the high complexity presented by the event-time-relationship recognition task and the differences in the grammar of Chinese and English texts, this paper no longer adopts the idea of extracting events first and matching time later, and proposes a processing scheme for temporal slicing of Chinese documents. The paper first summarizes the extensive research on Chinese time marker recognition and normalization, and designs a more reliable set of rule-based solutions; then the documents are time-sliced through the identified time markers to obtain document time slices. The algorithm is tested on the data and achieves better results on news texts.
Research of Super-Resolution Reconstruction based on Zooming Image Sequence

Image super-resolution reconstruction based on zooming sequence is a good solution to image quality enhancement for low resolution video. This paper proposes an iterative back projection (IBP) algorithm based method to reconstruct a high resolution image from low resolution zooming image sequence. Firstly, we perform image registration based on scale-invariant feature transform (SIFT) with adaptive ratio to acquire precise matching points. Then, an ergodic method is adopted to yield the best reconstructed results from each iteration. Experiment results show that our proposed algorithm can not only enhance the details of image but also enables the reconstructed image to achieve better visual effect compared with bi-cubic interpolation.
Trusted Access Scheme Based on Blockchain

System information (SI) broadcast is an essential element in 5G, which acts as a bridge between user’s equipment (UE) and gNB, providing users with the critical information to access networks. However, as a typical security attack in ratio access network (RAN), the fake base station (FBS) attack precisely exploits SI’s characteristics in terms of unencrypted broadcast to tamper or replay it to lure users to join. Therefore, protecting SI from being exploited by FBS is still an important issue facing the field of wireless network security. Blockchain, as an emerging distributed ledger technology, has shown great potential with its unique security features in many domains. Therefore, in this paper, we leverage blockchain to secure SI and prove its effectiveness in securing information from an information theory perspective. Specifically, we propose a concise theoretical model to measure the effectiveness of blockchain in wireless network security by mapping blockchain security assurance mechanisms into a physical layer security model. Through numerical analysis, we prove the effectiveness of the proposed mechanism under the condition of many network parameter changes.
Design and Implementation of Intelligent Education Platform with Film and Television Features Centered on Textbooks

Film and television media are very commonly used in education and teaching. With the continuous development of the concept of "Internet & Education", the construction and application of a smart education platform featuring film and television is extremely important. This paper is based on the construction of a media-integrated learning platform “textbooks in film and television”, digital processing of textbook resources, and combined with data resources such as text, image, video, audio, 3D model and other data resources to build a knowledge base of course resources. Using Node.JS as the operating environment, the front-end uses the Vue3 framework, and the back-end uses Python & Flask technology to build a film and television characteristic smart education platform to help students master the content of knowledge points and related expansion, and improve their learning effects.
Fast Monte Carlo Method for Condition Number Estimation

This paper presents theoretical results on estimation the condition number with application in image restoration problems.
Personality model for key lower-secondary school managers meeting general education innovation requirements

Lower-secondary school managers play an important role in determining the effectiveness and efficiency of the educational system in schools. Training and developing the personality of lower-secondary school managers is an urgent and essential task of personnel development in order to create positive changes at schools, which, therefore, promotes the development of the secondary education system as a whole. In this article, the author identifies the personality model of key lower-secondary school managers meeting requirements of renovation of general education with the following characteristics: educators; administrators; leaders; social activists; counselors, supporter, organizer and advisors of lower-secondary school manager training activities.
A Novel Robust Color Tag for a Highly Efficient Soccer Robot Vision System

Soccer robot league is a competition on the cooperation between multi-robots in a highly dynamic environment. Getting accurate and precise information on time in a computationally constrained environment is challenging. In the small size league, each robot has a unique colored tag on the top for identification. Traditional color tag maximizes the colored pattern to give a stable recognition result, however, it might cause recognition confusion in the competitive game. This paper proposes a novel rotation-invariant tag to avoid the confusion without extra recognition constrains. A highly efficient vision system is constructed, each module is optimized to improve the recognition efficiency and robustness. The system focuses on the small size league but capable to be transferred to other leagues. The experiments evaluate the time consumption of each part and the whole system. The results demonstrate that recognizing the robot through our novel identity tag by the highly optimized system has achieved the real-time standard.
(POSTER) SmartTwins: Secure and Auditable DLT-based Digital Twins for the WoT

Digital Twins and the Internet of Things (IoT) are two of the most prominent recent concepts and technologies. The IoT supports many applications that merge the physical with the cyber world. This highlights the need for improved security. Here, we argue that digital twins can help in securing and strengthening the IoT by using them for interacting with the actual IoT devices. While most digital twins implementations are centralized, we propose to integrate Distributed Ledger Technologies (DLTs) and digital twins into the IoT to realize decentralized, secure, available, flexible, and auditable blockchain-based IoT services for IoT devices that follow the W3C Web of Things (WoT) standards. In this work, we present the design of SmartTwin, a blockchain-based digital twin framework for which we provide two different implementations using two different blockchains, we present the design trade-offs, and we discuss future research and development directions.
On feature selection in automatic detection of fitness exercises using LSTM models

As constantly stated by the World Health Organization, physical activity is extremely important for a healthy aging, but how exercises are made is as important as how much activity is made. A large variety of wearable devices capable of sensing people movement is available on the market. Automatic detection and classification of fitness activity is also possible, leveraging artificial intelligence (AI) algorithms. In this paper, some ideas on the impact of specific input features on AI model performance for fitness exercise recognition is reported and discussed. Then, a general classification of input features is proposed. Using a pre-recorded dataset composed of 9 exercise repetition sets performed by 7 volunteers, a LSTM network have been trained and validated using the Leave One Out Cross Validation approach. Finally, the same network has been re-trained several times, varying the input parameters. Differences in classification results due to such parameters have been evaluated through the precision, recall and accuracy metrics. In particular, the precision is between 97.8% and 63.8%, whereas recall is between 98.5% and 42.3%, in line with results in literature.
Competency-based evaluation of department deans at teacher training colleges/departments

In this paper, we focus on defining: 1) the role and significance of the competency-based evaluation of department deans at teacher training colleges/departments; 2) the reality of the competency-based evaluation of department deans at teacher training colleges/departments; 3) the methods of conducting the competency-based evaluation of department deans at teacher training colleges/departments.
Algebraic structure of cyclic and negacyclic codes over a finite chain ring alphabet and applications

Foundational and theoretical aspects of algebraic coding theory are discussed with the concentration in the classes of cyclic and negacyclic codes over finite chain rings. The significant role of finite rings as alphabets in coding theory is presented. We surveys results on both simple-root and repeated-root cases of such codes. Many directions in which the notions of cyclicity and negacyclicity have been generalized are also considered. The paper is devoted to giving an introduction to this area of applied algebra. We do not intend to be encyclopedic, the topics included are bounded to reflect our own research interest.
Administrative management of soft skill training for secondary school teachers meeting the requirements of educational innovation

Soft skill is an important component of human personality, especially in modern society. Training soft skill becomes a new goal and task in the 2018 general education programme. The vital factor that makes the implementation of the project successful is the teaching body of whom secondary school teachers must have sufficiently new competencies to fulfill new tasks. The article discusses the competences of training soft skill and the method of administrative management of soft skill training for secondary school teachers meeting the requirements of educational innovation.
Insight reality of natuaral science competence of junior secondary school students - the view from the teacher

The Draft of General Educational Curriculum in 2017 defined that natural science competence is one of the specialized capabilities in the core competence system that needs to be formed and developed for junior secondary school students. The results of a survey of teachers in Chemistry, Physics and Biology at 29 junior secondary schools show that teachers are initially interested in the structure of the natural science competences. However, the results also reveal that teachers have difficulties in determining the natural science competence and their manifestations. This contribution will be the basis for the proposal of specific manifestation system as well as for the development of a set of tools for assessing the competence system to improve the teaching and learning quality of natural sciences for teachers and students at junior secondary schools in the coming period.
Competency-based solutions to develop head teachers at junior secondary schools

Based on the confirmation of the important role of head teachers at junior secondary schools, the paper proposes five competency-based solutions to develop head teachers at junior secondary schools, including the planning of head teachers at such schools based on the needs for local education development and each teacher’s ability; to establish core head teachers at junior secondary schools; to foster head teachers at junior secondary schools under professional capacity framework; to innovate competency-based assessment methods for head teachers at junior secondary schools; and to create favorable environment for them to promote and develop their professional capacity.
Approaches to Teach Cataloguing Modules during Emergencies

With reference to COVID-19 pandemic, the study investigated the emergent approaches to teaching and learning cataloguing modules at the institutions of higher learning. Cataloguing modules require face to face interactions between instructors and students for the explanation of concepts. The module involves the practical use of manuals and the application of standards which are skills that students should master. The closure of higher education institutions (HEIs) due to the outbreak of COVID-19 meant that the traditional approach to teach the cataloguing modules was inhibited. Although in some countries and in distance institutions cataloguing modules were already taught online even before the pandemic, their programs were planned as online education was the nature of their business and was not mandated by the pandemic experiences. The face to face institutions had to drastically make changes in their programs in order to continue with their mandates. The chapter explores measures taken by institutions of higher learning in order to continue with teaching and learning amidst COVID-19 pandemic.
Development of the software to research the chemistry knowledge to support the development of chemistry self-study competence for high school students

According to the General Education Program 2018, it is determined that self-study and informatics competencies are two of the core competencies that need to be formed and developed for high school students. Developing chemistry knowledge search software with open source to help teachers and students update their knowledge in addition to the available knowledge is also a way to help students study effectively by themselves. With the design process from basic theory, application exercises, guided practice, and testing to assessing self-study results will help teachers and students implement the self-study process smoothly. The results of the pedagogical experiment show that the content and technical impacts of the software have fostered the chemistry self-study competence for high school students
Sack Detection and Counting Using Deep Learning

In this paper, a grain sack detection and counting system is developed to help the logistics management of a warehouse stock. As main objective is to construct an electronic device that performs the counting of sacks with great precision through the most advanced artificial vision techniques. The device will count the total number of sacks that make up a stowage, calculate the volume, and eventually estimate the amount of mass, these results are transferred to an Excel sheet for constant monitoring and easy handling for users. The detection model is carried out with Python, PyTorch and YOLOv3, obtaining as a result a mean Average Precision (mPA) of 0.92. In addition, a sacks stowage arrangement was established that allows the volume result to be as accurate as possible, likewise, a program that performs the calculation was developed.
QOE-based intelligent intrusion detection Use case: University video surveillance

A smart university is supposed to be a safe university. At this moment we observe multiple cameras in different locations in the Hall University and rooms to detect suspicious behavior such as violation, larceny or persons in a state of alcohol or drug intoxication. Samples of the video footage is monitored 24/7 by operators in control rooms. Currently the recorded videos are visual assessed after a suspicious event has occurred. There is a requirement for real-time surveillance with smart cameras which can detect, track and analyze suspicious behavior over place and time. The expanding number of cameras requires an enormous measure of observing operators. This paper proposes a distributed intelligent surveillance system based on smart cameras. We seek to improve the Quality of Experience QoE operator side or QoE-video surveillance expressed in function of i- resource availability constraints, ii- false detection of suspicious behavior, iii- define an optimal perimeter for intrusion detection (subset of cameras, network parameters required …).
Improvement of Attentional Mechanisms for Unsupervised Learning Models

Image recognition based on unsupervised learning is a worthy direction of research. Inspired by ImageNet and other open source image sets, more and more unsupervised learning methods have been applied to image recognition and achieved good results. Based on the unsupervised learning model based on momentum contrast, the attention mechanism module is introduced, and a new parameter adjustment strategy for learning rate is proposed for further optimization of the algorithm. In the module of attentional mechanism, self-SE attentional mechanism model is proposed by integrating self-attention and SENet models. Batch-size Warmup parameter adjustment strategy is proposed in this paper to further improve the performance of the model. In this paper, the proposed improved module and adjustment strategy are verified by several groups of experiments with CIFAR-100 as data set. Compared with the original method, the accuracy of the unsupervised learning image recognition method with improved modules and adjusted strategies has been improved by about 1.2%.
Nested Random Forest: A Personalized Imputation Method for Missing Data

In the field of data science, the imputation of missing data is a common and important problem. Accompanied by the development of artificial intelligence, the data analysis methods of machine learning have received unprecedented attention. As one kind of the important algorithms of machine learning, Random Forest has been sought after by researchers for its excellent practical effect and reasonable interpretation. In this paper, a Nested Random Forest imputation method is proposed to deal with the missing data problem. To ensure the authenticity of the filling data, this method not only considers the overall distribution of the data but also pays attention to the personalized characteristics of the data. From the experimental results, the Nested Random Forest algorithm achieves better effects than the seven mainstream baseline methods in three real-world datasets of different application fields.
Learning Graph Representation Based on Single Graph Frequent Pattern Mining

Graph representation learning is crucial in a variety of real-life domains, and thus has received much attention from researchers. The traditional graph kernel-based method, while effective, has the problem of poor generalization ability because it relies on handcrafted features. Inspired by a neural embedding model for text (Doc2Vec), we propose an innovative approach named Fsg2Vec to learn graph representation. We obtain the unique features of each graph by extracting the frequent subgraphs in a specific single graph. Following the neural document embedding model, we use a graph instead of a document in the document embedding method, and replace words in the document with frequent subgraphs that are mined from the graph via a traditional pattern mining technique. There are mainly three points in our module superior to the latest graph embedding methods: completely unsupervised learning graph embedding, making the most of label information for nodes and edges, and mining the mutational characteristics of each graph for easy differentiation. Experimental data indicate our proposed module significantly outperforms baseline methods in graph classification task for small and medium-sized graphs.
Segmentation Mask Resampling for MRI Prostate Localization Improvement

This study aimed to examine the effect of segmentation mask resampling on segmentation performance and to confirm whether attempt to convert anisotropic data to isotropic can improve results of the current state of the art segmentation technique. A total of 50 prostate cancer cases gathered at 4 different institutions were examined. Magnetic resonance images of different resolutions and spacing were input into both 2D and 3D nnU-Net networks. Dice similarity coefficient was used as a metric to compare segmentation results between different models validated with the use of k-fold cross validation. Models trained included 2D and 3D models on original data and 3D models on resampled data. Image resampling was performed by applying third order spline interpolation. Two techniques were used for binary segmentation mask resampling: nearest neighbor interpolation followed by morphological opening and closing and calculation of average shape to be inserted between two adjacent slices. The mean dice similarity coefficients were 0.8736±0.0480 and 0.8680±0.0552 for 2D and 3D models with original data respectively. The use of both resampling techniques resulted in better segmentation results than no resampling with 0.8787±0.0299 and 0.8965±0.0165 for models with segmentation masks resampled with morphological operations and average shape calculation respectively. Thus, the study confirmed that converting anisotropic data to isotropic can improve segmentation results as well as reduce room for error.