Reviewerly/exHarmony huggingface dataset:
MEDIA PEMBELAJARAN SISTEM PERIODIK UNSUR DENGAN KONSEP JEMBATAN KELEDAI MENGGUNAKAN TEKNOLOGI AUGMENTED REALITY BERBASIS ANDROID

Augmented Reality is a technology that unites the real world view with the virtual world in real time. Augmented Reality has been widely used in various fields, one of which is education. Augmented Reality can be used by students to make it easier to learn the periodic system. The mnemonic concept is a concept that is used to help students remember vocabulary information more quickly and easily. In this study, a learning media application for the periodic system of elements was developed using the mnemonic concept that utilizes Augmented Reality technology that can help students understand and remember material related to the periodic system of chemical elements. This application is made with Blender 3D software to create 3D atomic structure objects and their uses, Vuforia SDK and Unity 3D for building applications and marker detection. The final result of making this learning media application is Augmented Reality which can display 3D objects from the atomic structure and uses of these elements as well as mnemonic concepts from groups IA to VIIIA. This application uses a Markerless Based Tracking technique. From the tests that have been carried out on users, it is concluded that the applications built are suitable for use
Pay attention to the hidden semanteme

With the capability of modeling lighter, MLP-based models like the pNLP-Mixer and the HyperMixer demonstrate the potential for diverse tasks in NLP. However, these linguistic models are not optimized for the regularity of textual hierarchical abstraction. Here, this paper proposes the hidden bias attention (HBA), a novel attention mechanism that is lighter than self-attention and focuses on extracting hidden (topic) semanteme. Additionally, this paper introduces a series of lightweight deep learning architectures, HBA-Mixer based on HBA and MHBA-Mixers based on multi-head HBA, which both outperforms pNLP-Mixer and HyperMixer in accuracy with fewer parameters on 3 tasks, including text classification, natural language inference, and sentiment analysis. Compared with large pre-trained models, MHBA-Mixers achieve over 90% of their accuracy with one-thousandth of the parameters.
A model-based mutation framework for IEC61131-3 manufacturing systems

Abstract This article presents a model-based mutation framework for creating mutant systems implemented in IEC61131-3 languages. The framework is based on a meta-model of the IEC61131-3 standard. It supports 21 mutation operations, such as the addition or removal of POUs or the mutation of Structured Text and Sequential Functional Charts. Moreover, operations can be combined to create higher-order changes, such as Type III code clones. The mutation framework can be used to evaluate and improve development tools for supporting the evolution of manufacturing systems, such as regression test suite generators or clone detectors.
A reinforcement learning algorithm acquires demonstration from the training agent by dividing the task space

Although reinforcement learning (RL) has made numerous breakthroughs in recent years, addressing reward-sparse environments remains challenging and requires further exploration. Many studies improve the performance of the agents by introducing the state-action pairs experienced by an expert. However, such kinds of strategies almost depend on the quality of the demonstration by the expert, which is rarely optimal in a real-world environment, and struggle with learning from sub-optimal demonstrations. In this paper, a self-imitation learning algorithm based on the task space division is proposed to realize an efficient high-quality demonstration acquire while the training process. To determine the quality of the trajectory, some well-designed criteria are defined in the task space for finding a better demonstration. The results show that the proposed algorithm will improve the success rate of robot control and achieve a high mean Q value per step. The algorithm framework proposed in this paper has illustrated a great potential to learn from a demonstration generated by using self-policy in sparse environments and can be used in reward-sparse environments where the task space can be divided.
On a Mismatch Between Design and Social Change

Social change projects, such as those undertaken under the banner of information & communication technologies for development (ICT4D), frequently address problems using a design-based approach and therefore inherit the values of what could be called “classical design.” One of those values, as we demonstrate through an examination of popular design textbooks, is to accommodate people’s needs, abilities, and behaviors. Accommodating people as they are, however, is very different from encouraging the internal changes in people – as either individuals or societies – that are required for meaningful social change. The inclinations of classical design are thus poorly matched to efforts intended to lead to social change. We flesh out this argument through an examination of classical design’s underlying ethos, an articulation of the difference between social good and social change, case studies of three ICT4D design efforts, and analysis of the implications of “giving people what they want.” We conclude with a call to draw a clear line between design for convenience or efficiency and innovation for social change in ICT4D pedagogy, research, and practice.
Design of online course management system based on Python

With the continuous progress and development of Internet technology, computer information management technology is also developing continuously, and all walks of life have developed their own management systems. The course management system uses educational technology and network technology to realize the online release of course resources, which is convenient for students to learn and assist teachers in teaching. The development of the visual programming environment based on Python language in this paper has gone through three stages: system requirements analysis, structure and coding design, detailed design and implementation of each module, and realized a complete and standardized course management system based on project teaching.
Research on Conceptual Model of the Remote Sensing Big Data Workflow

Remote sensing big data workflows are formed by combining workflow and the remote sensing data processing system, and have the capabilities of remote sensing data processing, analysis, and visualization. There are many issues such as numerous sensors, sophisticated processing algorithms, and lacking of a unified process management about the operation control of the remote sensing data processing system. Therefore, the concept and characteristics of remote sensing big data workflows is reanalysed and the metamodel of remote sensing big data workflows from different aspects is constructed in order to establish a conceptual framework of them, and thus establish concept framework of Remote Sensing Big Data Workflow.
Improvement on message passing of hyper-relational knowledge graph

Unlike traditional knowledge graphs (KGs), which represent real-world facts as entity-relationship-entity triples, hyper-relational knowledge graphs allow triples of traditional entities with additional relation-entity pairs (also known as qualifiers) to establish connections to deliver more complicated messages. Modelling triplet-qualifier relationships effectively for hyper-relational knowledge graphs to accomplishing prediction tasks is an existing challenge. In this paper, an improved hyper-relational knowledge graph completion method, STARE, is proposed by introducing two new methods: (1) Reconstructing the graph neural network module in the original STARE; (2) Introducing centralization and scaling to the model-the idea of shrinking to avoid over-smoothing. In our experiments on four benchmark datasets, the proposed method is always better than STARE, improving the prediction ability of the completion of HKGs.
Research on Course Personalized Evaluation Model Based on Recurrent Neural Network Algorithm

Recurrent Neural Network (RNN) is a kind of neural network with short-term memory ability. In the recurrent neural network, neurons can not only receive information from other neurons, but also receive information from themselves, forming a network structure with loops. This paper uses recurrent neural network to study the course personalized evaluation model based on intelligent optimization algorithm, and creates a set of effective evaluation modules in the course, which is conducive to the effective monitoring and quality management of the course, so as to improve the teaching quality. And it describes in detail the CNN (convolutional neural network) structure and (recurrent neural network) structure commonly used in current mainstream sentiment analysis algorithms. In the algorithm, the Bi-LSTM (long short-term memory network) structure is selected as the basic network. After innovation and improvement, it is tested for the specific data set in this paper. The construction of the Bi-LSTMA-CNNA algorithm model and the analysis of the course evaluation results provide a reference for subsequent related research.
Design and Development of a Platform System for Autonomous Foreign Language Learning

With the rapid development of information technology, information assisted teaching has been widely popularized. The society has increasingly high requirements for individual learning ability, and the ability of individual independent learning and team assisted cooperative learning has been comprehensively improved. In the network, rich learning resources, convenient interaction methods, and timely information have greatly changed people's learning concepts and learning methods, and web-based autonomous learning has also become an important means and an important learning method in real teaching. This paper analyzes the current common network autonomous learning, cooperative learning, network teaching and other modes, and puts forward the guiding ideology of RISCPA model on the basis of autonomous learning, cooperative cooperative learning, "dual master" teaching mode, constructivist learning theory and so on, making it the guiding theory for establishing the system platform, thus providing a theoretical basis for the establishment of the platform system model, the design and development of the system. A foreign language autonomous learning platform based on RISCPA model is designed and constructed. The platform has five roles and five subsystems corresponding to the roles. The system involves resource center, curriculum development, collaborative communication and question answering, two-way evaluation system and other as
Questionnaire Design of Digital Teaching Ability Measurement of College Tourism Management Teachers Based on Factor Analysis Method

In order to explore the digital teaching level of tourism management teachers under the background of digital cultural tourism, this paper uses the theoretical framework of TPACK and the factor analysis method to develop a digital teaching ability measurement questionnaire for tourism management teachers based on the background of digital cultural tourism from seven aspects: TK, PK, CK, TPK, PCK, PCK, and TPACK. The results of the preliminary questionnaire survey were used to revise the 36 questions of the questionnaire. Using factor analysis methods, a factor structure model with 28 items in seven dimensions was finally constructed. Through the analysis of 364 survey data, it is proved that the measurement questionnaire has good reliability and validity, and also indicates that the validity of the research model is better. This questionnaire can provide a reference for the evaluation of college tourism management teachers.
Improved Binary Particle Swarm Optimization Based on Multi-Exemplar and Forgetting Ability Applied to Feature Selection

Feature selection can eliminate irrelevant, redundant, and misleading features in the data set, and improve the classification performance of machine learning algorithms while reducing their computational consumption, effectively avoiding "dimensional disasters". The multi-exemplar particle swarm optimization algorithm with forgetting capability (XPSO) has achieved good performance in function optimization, but has not been applied to the feature selection problem of binary variables. In this paper, an XPSO based on binary encoding, named Binary XPSO (BXPSO) is proposed to solve the optimal feature subset. The algorithm also proposes a local search strategy to improve the forgetting ability of different particles, balancing the local exploitation and global exploration of the algorithm. To verify the effectiveness of the proposed algorithm, multiple sets of simulation experiments with different perspectives are conducted on the proposed method in this paper using classical datasets from the UCI machine learning repository constituting feature selection problems of different dimensions. The experimental results show that the algorithm has competitive advantages in terms of classification accuracy and computational performance.
A Study of A Growing Problem-based Teaching Model for the Development of Information-based Teaching Design Ability of Normal University Students under the Concept of Wisdom Education

The concept of wisdom education focuses more on "precision, individuality, optimization, collaboration, thinking, and creativity" than traditional education, and the era of information technology 2.0 requires the innovative integration of education and technology, the innovation and optimization of normal university students' training programs, and the strengthening of the training of normal university students' information technology teaching ability. As a compulsory public course for normal university students, Modern Educational Technology is responsible for improving the information-based teaching ability of students.However, the current training mode of most universities is mainly based on the basic operation of multimedia software taught by teachers, which is separated from theoretical knowledge and practical activities, and the teaching content and form are single.Therefore, this study hopes to break the traditional teaching mode, update the teaching cultivation objectives in accordance with the development of the times, introduce GPBL(Growing Problem-Based Learning) teaching method for advanced cognitive ability cultivation, and generate a set of teaching mode for the cultivation of information-based teaching ability of normal university students.
An improved MTCNN face detection algorithm

Abstract: In view of the low accuracy of face detection by MTCNN, an improved MTCNN face detection algorithm is proposed. The detection accuracy is improved by the following factors: the migration learning to the original network model, the fine-tuned to the model key parameters, selected the best effect of the parameters, and determine the threshold of face confidence. Experimental results show that the improved MTCNN algorithm greatly improves the detection accuracy.
The Design of Employment Recommendation System for College Students Based on Bilateral Matching Decision

This paper studies the bilateral matching problem between recruiting companies and college students, designs a single-objective matching model, and studies the optimal matching decision-making method based on college students' employment. It provides a way of thinking to promote the stable and effective matching of employment subjects, and on this basis, through in-depth analysis and research on the current recommendation technology and application, combined with the current deep learning technology, a college student employment based on deep neural network is proposed. Recommend an algorithm and verify the algorithm with an example.
Cloudlet Overload Prediction Based on Deep Learning

Cloudlet is a typical model of edge computing, which has received widespread attention as it can better meet users' latency-sensitive business needs compared with the traditional cloud computing models. Accurate prediction of cloudlet overload is an important prerequisite for designing a more effective cloudlet task migration strategy. In this paper, we first build a multi-cloudlet load model. By designing the basis for judgment of cloudlet overload and task migration strategy, a dataset that can be used to predict the load of cloudlet using supervised learning strategy is generated. Then a deep learning-based cloudlet load prediction model is designed and trained to predict whether the cloudlet will be overloaded by describing a series of parameters such as the arrival of user requests, the required network bandwidth, and computing resources. The experimental results validate the effectiveness of the model.
C++ Teaching Reform and Exploration Based on ACM/ICPC and Live Code

How to further increase the programming interest of the C++ course for all freshmen is still a very challenging problem for the time being. The International Collegiate Programming Contest (ICPC) is sponsored by the Association for Computing Machinery (ACM). It is an annual competition designed to showcase college students' innovative ability, team spirit and ability to write programs, and analyze and solve problems under pressure. Aiming at the main problems existing in traditional C++ curriculum teaching, a live code teaching method and an ACM/ICPC innovation model are proposed in this paper that highlights the "student-centered" teaching concept. By taking the "problem-solving" as the main line of teaching, the "live code" as the teaching method, and the informatization teaching as the means, the ACM/ICPC competition promotes the integration of the students' theory and practice. The results of more than ten years of practical teaching show that the proposed live code pedagogy and the ACM/ICPC-based reform have effectively cultivated the students' programming interest and computational thinking, and significantly improved their programming and innovation abilities. At present, the live code teaching method and the ACM/ICPC innovation model have been effectively promoted and applied in the three campuses of our university, as they have a certain leading and exemplary role in cultivating strong professional programming skills, computational thinking, global vision and the all-round development of top-notch innovative talents in the computer field.
Application analysis of quality evaluation model based on multi-source data mining

In order to improve the accuracy and efficiency of talent training quality evaluation, a design method of quality evaluation model based on multi-source data mining is proposed. The data processing technology is used to preprocess the talent training data, the multi-source data mining method is used to classify the information in the evaluation database, and the information in the quality evaluation database is mapped to the evaluation index system. According to the diversity and complexity of knowledge modules required by aviation service talents, the talent evaluation system is divided into knowledge literacy module, interpersonal communication ability module, work status module, foreign language ability module and service literacy module to obtain various evaluation indicators. Set the evaluation standard, and use Likert scale method and standard deviation method to calculate the weight of each evaluation index. Finally, use BP neural network to build the talent training quality evaluation model and obtain the final evaluation result. The experimental results show that the proposed method has obvious advantages in evaluation accuracy and efficiency.
Integration Path of Artificial Intelligence Technology and Education and Risk Avoidance

The development of artificial intelligence (AI) technology brings many challenges and opportunities to education. Integrating AI into modern education can cultivate professional and innovative talents and enable students to better integrate into the current intelligent era. Firstly, this paper analyzes AI and modern education theory, introduces the characteristics of learning behavior under the background of AI, and discusses the integration path of AI technology and education based on the possible educational risks in the era of AI, so as to effectively avoid the risk of "AI + education" and provide reference ideas for the development of education in the future.
Learning Sense Embeddings from Dictionary Definition

We introduce a method for learning to embed word senses as defined in a given set of dictionaries. In our approach, senses as definition triples, <word, pos, definition> are transformed into low-dimension vectors aimed at maximizing the probability of reconstructing the definitions in an autoencoder. The method involves automatically training sense autoencoder for encoding sense definitions, automatically aligning sense definitions, and automatically generating embeddings of arbitrary description. At run-time, queries from users are mapped to the embedding space and re-ranking is performed on the sense definition retrieved. We present a prototype sense definition embedding, SenseNet, that applies the method to two dictionaries. Blind evaluation on a set of real queries shows that the method significantly outperforms a baseline based on the Lesk algorithm. Our methodology clearly supports combining multiple dictionaries resulting in additional improvement in representing sense definitions of multiple dictionaries. Although there is no distinctive header, this is the abstract. This submission template allows authors to submit their papers for review to an ACM Conference or Journal without any output design specifications incorporated at this point in the process. The ACM manuscript template is a single column document that allows authors to type their content into the pre-existing set of paragraph formatting styles applied to the sample placeholder text here. Throughout the document you will find further instructions on how to format your text.
Academic Motivation and Learning Strategies of Students at Lectures

Motivation is considered one of the most important factors in the educational process, especially when it is about teaching university students in the time when online computer technologies are available, it helps to achieve better results in mastering new materials and learning procedure in general. The purpose of the work is to analyze the academic motivation of students and determine the relationship with educational strategies when working in lectures, that includes the practice of learning online with help of computer technologies. In order to increase academic motivation, it is necessary to improve the quality of education, as well as to find out the reasons that affect its level among students. To study the motivation of undergraduate students, we used the methodology of Gordeeva et al. As a result of the research, the predominance of intrinsic cognition and motivation of personal growth (14.61% and 14.31%), as well as motivation for self-respect and motivation of achievement (13.16% and 12.97%) as factors of high importance was confirmed. It was also discovered that students prefer to watch distant online lectures in the real time (simultaneously) more, than recorded ones (later watching), and motivation does not have any connection with this factor. Longhand writing is more common for students than writing notes in computer and the strategy to write with a pen/pencil has the direct connection with motivation for personal growth and motivation of self-respect. Also, younger students prefer to make notes during the lectures more than older learners. It was also found that younger students are more likely to make notes with colored markers and female students are more likely to make notes with colored markers than male students. Active participation on lectures has many connections with motivation to learn.