Reviewerly/exHarmony huggingface dataset:
The Research on the Challenges Confronted by the Combination of Virtual Reality Technology and Educational Games

As one of the key technologies of the 21st century, virtual reality technology has been widely used in the field of education, and educational games based on this technology are becoming increasingly mature. This article adopts a case study approach to investigate examples of combining virtual reality technology with educational games. The article focuses on the advantages, necessity and development of virtual reality technology in the field of educational games as well as its characteristics, and presents the problems and challenges in the process of combining virtual reality technology with educational games to provide reference and reference for empirical research in this field.
Investigation and Study of Teachers’ Teaching Development Centers in Higher Institutions in Hunan Province

In recent years, colleges and universities in Hunan Province have set up teachers’ teaching development centers. In order to deeply understand their organizational structure and work situation, a questionnaire survey was conducted on the staff of teaching development institutions of ordinary colleges and universities in Hunan Province. According to the survey, the work of teaching development centers in Hunan Province has been widely recognized by the majority of teachers, but there are still problems such as imperfect organization, inadequate condition construction and lacking of academic professionalism. The solution of these problems requires the university to put forward the understanding and top-level design at the school level, strengthen the centers’ cooperation and exchanges between different colleges and universities, and jointly promote the level of teaching quality and faculty development.
Research on the Virtual Teaching of History Subject in the Process of Meta-universe

Meta-universe is a kind of Internet form that integrates many new technologies, and it has great development potential in the education industry. This paper points out the present situation of the application of Metauniverse in education and the problems in history teaching, and discusses how to carry out virtual learning of history course in Metauniverse. Virtual reality technology can overcome the problem of abstract knowledge in history teaching by creating virtual scenes, combining visual, auditory, tactile and other sensory systems, and increase learners’ immersive feeling, thus improving learners’ learning effect.
SATCN: An Improved Temporal Convolutional Neural Network with Self Attention Mechanism for Knowledge Tracing

With the rapid expansion of E-education, knowledge tracing (KT) has become a fundamental mission which traces the formation of learners’ knowledge states and predicts their performance in future learnng activates. Knowledge states of each learner are simulated by estimating their behavior in historical learning activities. There are often numerous questions in online education systems while researches in the past fails to involve massive data together with negative historical data problems, which is mainly limited by data sparsity issues and models. From the model perspective, previous models can hardly capture the long-term dependency of learner historical exercises, and model the individual learning behavior in a consistent manner is also hard to accomplish. Therefore, in this paper, we develop an Improved Temporal Convolutional Neural Network with Self Attention Mechanism for Knowledge Tracing (SATCN). It can take the historical exercises of each learner as input and model the individual learning in a consistent manner that means it can realize personalized knowledge tracking prediction without extra manipulations. Moreover, with the self attention mechanism our model can adjust weights adaptively, thus to intelligently weaken the influence of those negative historical data, and highlight those historical data that have greater impact on the prediction results. We also take attempt count and answer time two features into account, considering proficiency and forgetting of the learners to enrich the input features. Empirical experiments on three widely used real-world public datasets clearly demonstrate that our framework outperforms the presented state-of-the-art models.
Sentiment Classification of Chinese Commodity-Comment Based on EMCCNN Model

Targeting at the great commercial and social values of sentiment analysis of online product reviews, this paper puts forward an emotion-semantics enhanced multi-channel convolutional neural network (EMCCNN) approach to dig automatically out the emotional position of the reviewer. This EMCCNN model is featured firstly with a completed construction of emotional symbols datasets which are composed of Chinese emotion words, degree adverbs, negative words, common Internet slang and emoticons, secondly with three input channels containing a relatively independent text sequence and emotional symbol sequence in each, thirdly with weighted pooling which summarizes different channel’s convolution result with various weight, then joins with max pooling to form the final emotion vectors, which are subsequently classified with softmax function. The experiment result shows that, after the adjustment of super parameters, the F1 value of EMCCNN has been improved by 3.2% compared with the traditional CNN one.
Research on Food Recommendation Method Based on Knowledge Graph

Food recommendation is a crucial task in the field of food computing, which aims to match a user's preferences with appropriate food options by uncovering their latent preferences. However, traditional content-based and collaborative filtering methods may not effectively capture these preferences, as they overlook the rich information contained in food data. To address this issue, this paper explores the use of a Knowledge Graph-based approach to food recommendation. Specifically, we construct a food knowledge graph that contains abundant relational information and data on food products. We propose a novel recommendation model called the Knowledge-Aware Attention Graph Convolutional Network, which aggregates neighbor information using knowledge-aware attention and captures higher-order neighbor information by stacking multiple layers. We also employ different aggregation methods for user entities and internal entities of the knowledge graph. We conducted experiments on a large food recommendation dataset, and our results show that our proposed model significantly outperforms the benchmark approach.
On English-Chinese Machine Translation Evaluation in Translating Attributive Clauses in Scientific Texts

As a formal style, English for Science and Technology (EST) has characteristic of preciseness and terseness. For this reason, various clauses, especially attributive clauses, are often used in EST texts. Attributive clauses account for a large proportion of long sentences in EST, and cause difficulties in EST translation. The new generation of neural machine translation (NMT) has been greatly improved in translation quality, but it still has problems in translating English attributive clauses into Chinese. As a new branch of linguistics, language typology aims at describing and explaining the commonness and structural diversity of languages. It provides a new perspective to evaluate the quality of machine translation (MT) of attributive clauses in EST. This paper selects a certain proportion of examples from EST textbooks to make a comparative analysis between Google MT and manual translation under the guidance of linguistic typology. The goal of this paper is to enrich the existing research on EST translation in theory, to inspire translators to predict and correct possible errors in the output, and finally to improve the accuracy and efficiency of MT.
A Research on Wisdom Classroom Teaching Supported by Virtual Reality

The rapid development of emerging technologies will profoundly change the demand for talents and teaching forms, and the future society needs more innovative and intelligent talents. Relying on artificial intelligence, Internet of Things and virtual reality, the smart classroom came into being and was widely practiced in teaching. With the development of the concept of smart classroom, how to construct smart classroom under the environment of emerging technology has become a research hotspot. This paper analyzes the supporting role of virtual reality technology in the intelligent classroom, and puts forward the application model of scientific intelligent classroom in primary schools from three stages: before, during and after class, so as to give consideration to the advantages of intelligent classroom and virtual reality technology, and provide theoretical and practical reference for promoting the reform of teaching methods.
Adaptive Exercise Recommendation Based on Cognitive Level and Collaborative Filtering

Adaptive learning is inseparable from adaptive testing, and adaptive testing needs to rely on students cognitive level for personalized recommendations, and recommending exercises that match students’ cognitive level can enhance students’ learning interest and efficiency. Some of the existing exercise recommendation methods only recommend based on the similarity between students or exercises, while others only focus on students knowledge level for recommendation. The former ignores students’ cognitive level and tends to recommend exercises that are too difficult or too easy. The latter ignores the group nature among students, and the recommendation results are too homogeneous. To tackle these problems, in this work, we propose an adaptive exercise recommendation based on cognitive level and collaborative filtering (ACLCE). Learning students’ cognitive level by a deep knowledge tracking model, and then recommends exercises by a neural collaborative filtering algorithm combining students groupness. Extensive experiments on two-real world datasets show the effectiveness of ACLCE by significantly boosting the recommendation. Compared with the advanced baseline method, ACLCE achieves a significant improvement in recommendation effectiveness with the Six to eight percent performance gain.
Privacy-Preserving Educational Credentials Sharing Based on Blockchain and Proxy Re-Encryption

The educational credential is an important reference for enterprises to screen students. However, the educational credentials are normally stored in a centralized cloud server with improper maintenance and inadequate supervision, and students’ credentials are easily tampered with by server administrators. In addition, students’ credentials are also sensitive information and therefore need to be encrypted and stored in the cloud server, but traditional encryption schemes face challenges because of private key sharing. The paper designs a privacy-preserving educational credentials sharing scheme based on blockchain and proxy re-encryption. The proposed approach addresses the problems of centralized storage and encryption sharing. Students’ sensitive information is encrypted and stored in a cloud server, and the hash index is stored in a blockchain. Enterprise is able to match students’ public information and request private credentials by proxy re-encryption. We also realize the prototype of the system and evaluate the performance of the encryption scheme.
Detecting Medical Insurance Fraud Using a Heterogeneous Information Network with a Multi-behavior Pattern

Medical insurance frauds impose huge losses on the medical insurance funds. Hence, efficient detection of medical insurance frauds is essential for sustainable medical insurance funds and affects the quality and affordability of health care. The existing detection methods determine fraudulent cases by statistical analyses of the patients’ characteristics using graph neural network models. Nevertheless, such methods ignore the potential relationship between the graph nodes which represents the degree of similarity between the patients’ characteristics. To address this issue, we propose a medical insurance fraud detection model based on a heterogeneous information network with a multi- behavior pattern, namely $$\mathrm{BPGAN}$$ . In our proposed approach, we analyze the characteristics of different behavior patterns among patients and design an attentional mechanism for embedded learning of patient nodes in the medical insurance data network. It enables capturing potential relationships between the patients’ nodes and obtaining the corresponding weight coefficients of different behavior patterns in the process of embedded learning. Experimental results on real medical insurance data sets confirm that the proposed method effectively identifies medical insurance fraudsters and overperforms existing fraud detection methods.
Tracing Knowledge State with Individualized Ability and Question Difficulty

Knowledge tracing(KT) refers to the task of modeling students’ evolving knowledge state according to their historical learning trajectories. Although many methods have been proposed to solve KT task, most of them ignore the difference of students and questions, i.e., the students’ learning ability are different from each other. To this end, in this paper, a learning ability estimation module is proposed to extract students’ learning ability according to their learning history and a novel method to obtain questions’ representation is designed. Besides, a knowledge state estimation module is proposed to estimation students’ knowledge state which takes both students’ learning ability and their learning interaction into consideration when modeling. Extensive experiments demonstrate that the proposed model could improve thr results of knowledge tracing through modeling individualized students’ learning ability and questions’ difficulty in learning process.
Using Deep Ontologies in Formal Software Engineering

Isabelle/DOF is an ontology framework on top of Isabelle. It allows for the formal development of ontologies as well as continuous conformity-checking of integrated documents annotated by ontological data. An integrated document may contain text, code, definitions, proofs, and user-programmed constructs supporting a wide range of formal methods Isabelle/DOF is designed to leverage traceability in integrated documents by supporting navigation in Isabelle’s IDE as well as the document generation process. In this paper, we extend Isabelle/DOF with annotations of $$\lambda $$ -terms, a pervasive data-structure underlying Isabelle used to syntactically represent expressions and formulas. Rather than introducing an own programming language for meta-data, we use Higher-order Logic (HOL) for expressions, data-constraints, ontological invariants, and queries via code-generation and reflection. This allows both for powerful query languages and logical reasoning over ontologies in, for example, ontological mappings. Our application examples cover documents targeting formal certifications such as CENELEC 50128, or Common Criteria.
Study on the Challenges of Implementing Industry 4.0 in UAE Using Analytical Hierarchy Process AHP Method

Intelligent manufacturing, or the fourth industrial revolution, can significantly reduce the environmental impacts of products and processes by reducing resource consumption such as energy, water, and other resources. However, many challenges may face the implementation of Industry 4.0. The current study tries to prioritize these challenges using sequential steps. A structured literature review was conducted to identify the main challenges, and then a questionnaire specially designed to use the AHP method was distributed to the relevant industrial experts. Three iterations of revising the AHP results have been done to get a consistency ratio of less than 10%. The results show that the main challenges were the cost of infrastructure followed by cybersecurity.
Encoding $$\textrm{TLA}^{+}$$ Proof Obligations Safely for SMT

The $$\textrm{TLA}^{+}$$ Proof System (TLAPS) allows users to verify proofs with the support of automated provers, including SMT solvers. To better ensure the soundness of TLAPS, we revisited the encoding of $$\textrm{TLA}^{+}$$ into SMT-LIB, whose implementation had become too complex. Our approach is based on a first-order axiomatization with E-matching patterns. The new encoding is available with TLAPS and achieves performances similar to the previous version, despite its simpler design.
Visual Question Answering Using Convolutional and Recurrent Neural Networks

This paper presents a methodology that deals with the task of generating answers corresponding to the respective questions which are based on the input images in the dataset. The model proposed in this methodology constitutes two major components and then integration of analysis results and features from these components to form a combination in order to predict the answers. We have created a pipeline that first preprocesses the dataset and then encodes the question string and answer string. Using NLP techniques like tokenization and stemming, text data is processed to form a vocabulary set. Yet another experiment with modification in model and approach was performed using easy-VQA dataset which is available publically. This model used the bag of words technique to turn a question into a vector. This approach considered two components separately for text and image feature extraction and merged it to form analysis and generate an answer. Merge is done by using element-wise multiplication. In these approaches, we have used the softmax activation function in the output layer to generate output or answer to the question. When compared to existing methodologies this approach seems comparable and gives decent results.
Determination of the characteristic function of discrete-time conditional linear random process and its application

The discrete-time conditional linear random process is defined, and its properties in the context of application for mathematical modelling of information signals in energy and medicine are analyzed. The relation to the continuous-time counterpart is considered on the basis of time sampling and aggregation. One-dimensional and multidimensional characteristic functions of discrete-time conditional linear random process are obtained using conditional characteristic function approach. The conditions for the investigated model to be strict sense stationary are justified.
Objectionable Image Content Classification Using CNN-Based Semi-supervised Learning

Due to the increased online activity, people of all ages, especially adolescents, may get exposed to objectionable image content such as internet pornography. These images are spread quickly and widely over the internet, which causes serious social problems. Many researchers have proposed objectionable image content classification models by utilizing deep neural networks to prevent such images from being retrieved while surfing the web. The performance of such models can be enhanced by the semi-supervised learning method by effectively utilizing the image data from an overwhelming number of unlabeled objectionable samples. For many such unlabeled objectionable images, this paper proposes a semi-supervised image content classification framework using a balanced sample inclusion mechanism based on a higher class probability outcome to include the pseudo labels effectively in the existing model. The proposed framework fully utilizes semi-supervised learning and gradually improves model classification accuracy and reliability.
Face Mask Detection Based on YSK Neural Network for Smart Campus

In this paper, a new YSK neural network is proposed to detect face mask wearing to prevent COVID-19 or other infectious diseases. And the aim is not only to detect the face mask wearing person, but also can detect, track and warn the not-wearing face mask person. The technique we applied is referred to as object detection based on deep learning. Several experiments were made to test the performance of the proposed model and it showed that it has better performance than other common detection models and the result is excellent.
Artificial Hummingbird Algorithm with Parallel Compact Strategy

This paper improves the Artificial Hummingbird Algorithm (AHA). First, we introduce a compact scheme to reduce computer storage capacity and speed up computation. Second, the parallel strategy is added to improve the optimization ability of the algorithm. Third, we improve the original algorithm’s territorial and migration foraging strategies. The purpose of enhancing the territorial foraging strategy is to optimize the algorithm to be more directional. We removed the migration-foraging strategy, which is more suitable for combining with the compact scheme. Finally, we tested the improved algorithm on the cec2013 test set, which showed good performance.
Path Planning Method of UAV Cluster Against Forgery Attack Under Differential Boundary Constraint

With the development of UAV ad hoc network and networking, various attacks in network environment pose the more and more increasing threats to UAV cluster system. To solve this problem, this paper selects a forgery attack as the research object and proposes an ant colony algorithm based on octree to carry out the path planning task of a UAV cluster in a complex environment. Firstly, the attack detection algorithm based on differential constraint and the processing method of converting the attacked UAV into static obstacles are proposed, so that the UAV cluster can detect and process forgery attacks in real time; secondly, on this basis, the UAV cluster defense model is established, and the algorithm based on octree is proposed to improve the efficiency of solving the model. The simulation results show that the Oc-ACO algorithm can solve the problem of UAV cluster encountering forgery attack safely and quickly.
A Hybrid Orthogonal Learning and QUATRE Algorithm Based on PPE Algorithm

Combining the characteristics of PPE, QUATRE algorithm, and orthogonal learning, this paper proposes a hybrid Orthogonal Learning and QUATRE algorithm based on the PPE algorithm (OLQTPPE). This algorithm takes the PPE algorithm as the main body and uses the QUATRE algorithm to search deeper. The purpose of using the QUATRE algorithm is to prevent the algorithm from falling into local optimization. After this, orthogonal learning is used to optimize the whole, to find better particles in a small area. The algorithm is tested on CEC2014 and compared with PSO, PPSO, BA, and PPE. The results show that the algorithm is superior to the four algorithms. In particular, it is the proposed OLQTPPE algorithm that has high performance and effectiveness compared to PSO, PPSO, and BA algorithms.
Research on Gannet Optimization Algorithm and Its Application in Traveling Salesman Problem

With the high level of information technology in modern society, a series of intelligent optimization algorithms have emerged to solve classic multi-combinatorial optimization applications. The origin of intelligent algorithms is the intelligent behavior and physical phenomenon of biological communities in nature, and a large number of intelligent optimization algorithms are widely used in various combinatorial optimization problems. Gannet optimization algorithm (GOA) is a newly proposed intelligent optimization algorithm, which is applied to large-scale constrained optimization problems with the advantages of high convergence and high-quality solutions. For the traveling salesman optimization problem (TSP), the original traditional way is very difficult to calculate. The calculation difficulty increases exponentially with the increase in the number of cities and is rarely used in real life. In this paper, we use the GOA to optimize the TSP problem. Experiments are carried out through two TSP instances, it can be seen from the experimental results that GOA can find a better solution with less computation time.