Reviewerly/exHarmony huggingface dataset:
An Enhanced Security in Medical Image Encryption Using Dynamic Chaotic Fuzzy Based Technique

As IoT and cloud computing have grown in popularity, medical images are now often transmitted between devices or accessed directly from the cloud. With this, the security is always a concern as these images are prone to many types of attack. We have proposed a proven method that is efficient in terms of security, time complexity, and integrity in order to be cloud-friendly so that it may be launched into the cloud and made accessible to users at any time. The goal of the work is to create a dynamic key that, depending on fuzzy values, alters the reproduction rate parameters with each repetition. By applying the last chaotic value created from the previous iteration, the fuzzy triangular membership function has been used in this manner to generate the reproduction rate parameter. The uniqueness and major benefit of the suggested strategy are that it can increase the security of the algorithm that makes use of a chaotic map and a static key. The method has been put forth when designing algorithms so that it should not only demonstrate security against different attacks but also provide efficiency towards computational complexity. The technique has been tested against a set of images and an existing algorithm using a variety of security metrics, including the correlation coefficient, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), and entropy. It has been determined from the comparative analysis that the proposed approach can make the existing algorithm more secure.
Design of a Knowledge Graph-based Interactive English Teaching Platform for College Students

Abstract In this paper, we first studied the construction technology of knowledge graphs, used the BERT model for named entity recognition, extracted the Relationship between different entities through semantic analysis, and stored the database based on table structure and graph structure. Then, the knowledge graph technology is utilized to construct the English interactive teaching platform, and modules such as online classroom, operation and maintenance management, and database are designed to clarify the process of English interactive teaching. Finally, a comparative analysis was conducted to study the effects of English interactive teaching on students’ performance, teaching satisfaction, and classroom behavior. The results show that chi-squared=18.276, df=3, p-value=0.000656, p&lt;0.001, the difference in students’ achievement after using interactive teaching is obvious. The mean value of classroom silence behavior is distributed below 20%, which indicates that interactive teaching is effective. This study has important reference significance for the design and development of interactive teaching platforms in colleges and universities.
Professional identity of grid workers based on SPSS 22.0

Abstract The professional identity of grid workers is a significant expression of social service consciousness, and this paper utilizes statistical analysis methods to study its related factors. Under the framework of SPSS 22.0, the non-robust paradigm square of traditional PCA is replaced by a robust measure, and the AWL-RPCA model is constructed by combining the introduced data adaptive learning. Meanwhile, to address the data smoothness problem of quantile regression, the PDQR model was proposed by using the method of constructing panel data to optimize the control of individual heterogeneity. Taking the gridmen in L street of a city as the research object, the related factors of occupational identity were mined based on the AWL-RPCA model, and then the regression equation was obtained by the PDQR model. The degree of influence of the independent variables on the occupational identity of the gridmen from the highest to the lowest is the working status, the level of education, the pressure of helping and teaching, the appraisal satisfaction, the support and coordination satisfaction, and the propaganda satisfaction, and the regression coefficients are 8.36, 3.14, 5.08, 3.60, -4.35, and 3.32, respectively. Under the guidance of the statistical analysis of the SPSS, the construction of the gridmen team can be optimized in a better way.
Policy Design of Retail Central Bank Digital Currencies: Embedding AML/CFT Compliance

This paper provides a foundation on how to ensure effective and efficient Anti-Money Laundering (AML) and Combating the Financing of Terrorism (CFT) compliance measures in the Central Bank Digital Currency (CBDC) system, focusing particularly on the retail CBDC in a two-tiered system. By examining the existing AML/CFT practices and those implemented in newly launched CBDC projects, we build a policy framework of AML/CFT measures that should be embedded in the CBDC system to enhance compliance. The most common enforcement of AML/CFT measures associated with CBDC balances assumes the form of transaction amount or holding amount limits based on the level of Know-Your-Customer (KYC) findings. In this paper, we examine practices beyond the initial KYC measures and explore the processes of on-boarding and on-going customer due diligence, transaction analysis and evaluation, decision making and reporting to the Financial Investigation Unit (FIU).
The Construction of a Grammatical System for Japanese Linguistics in the Context of Big Data

Abstract This paper first outlines the concept of teaching Japanese grammar according to the Japanese language talent cultivation standard and the current dilemma in teaching Japanese language in colleges and universities. Secondly, on the basis of big data technology, considering the effective availability of the initial data of Japanese grammar, it is necessary to preprocess the language grammar learning data and, at the same time, according to the fuzzy comprehensive evaluation, determine the Japanese grammar evaluation affiliation function. Then, for the problem of an incomplete and inaccurate traditional fuzzy comprehensive evaluation, they proposed to construct a comprehensive evaluation model of the Japanese linguistics grammar system based on the bat algorithm and carried out research and analysis on the Japanese linguistics grammar system. The results show that on the evaluation model, the comprehensive evaluation score of the Japanese language grammar system is C=2.65, which belongs to the E2 level. On the statistical analysis, the mean value of all the scores of the concise class was higher than that of the standard Japanese class, and all of them showed significant differences (p=0.002, 0.000, and 0.047, all less than 0.05).
The Construction of a Precision Education Model of English in Colleges and Universities Based on Regression Modelling

Abstract This paper first clarifies the goal of English precision teaching, constructs a big data precision teaching model by combining it with Internet technology, and designs a precision teaching flow chart. Then the regression model is used to construct the assessment model of the precise teaching program, portraying the relationship between the teaching effect and students’ characteristics and behavioral performance under the teaching program. Then, the parameters of the assessment model were evaluated using a similar least squares method to optimize the model. Finally, the assessment model was used to analyze the level and impact of English precision teaching. The results show that there is a significant improvement in students’ performance after precision teaching p=0.00&lt;00.05. In terms of the level of teaching, there is 78.76% of classroom teaching conformity, 69.68% of precision optimization teaching conformity, and 66.67% of precision diagnosis teaching conformity, which is close to the level of comparative conformity. This study has important reference value for English teaching in colleges and universities.
Research on English Intercultural Teaching Mode in Colleges and Universities under the Background of Industry-Teaching Integration

Abstract In order to improve the practical ability of English in colleges and universities, this paper puts forward the concept of AKEE teaching based on English intercultural communicative competence under the background of integration of industry and education and designs the intercultural teaching mode of college English based on AKEE teaching under this concept, and constructs the teaching quality evaluation index system of the intercultural teaching mode of English in colleges and universities in the light of the teaching content. On this basis, the English intercultural teaching model was constructed based on the entropy weight-TOPSIS method, and the selected indicators were tested for validity using principal component analysis, and the final evaluation results were obtained by calculating the entropy weights. The results show that in the order of comprehensive evaluation, teaching effect&gt;teacher quality&gt;teaching content&gt;teaching resources&gt;teaching quality feedback, with the mean values of 0.811, 0.804, 0.803, 0.799 and 0.798, respectively. Intercultural teaching of English in colleges and universities can improve the teaching effect as well as the quality of teachers.
A Comparison of Chinese-English News Metaphors for Corpus Analysis in the Context of Big Data Technology

Abstract In this paper, firstly, the Chinese-English news corpus is constructed using crawler technology, and a distributed retrieval system is designed to search the Chinese-English data in the corpus. Secondly, the potential semantic analysis algorithm is improved to extract semantic information from the Chinese-English corpus and realize the semantic search data source. Finally, by designing the experimental environment as well as the samples of the Chinese-English news corpus, the performance of the corpus as well as the distribution retrieval performance are analyzed, and the comparison of Chinese-English news metaphors is explored. The results show that the system’s word-level accuracy is 96.32% when the training corpus size reaches 100,000 word-level, the F-value is 96.08, and the latency of 1Mb/s is higher than the latency of 3Mb/s and the latency time of 5M/s, and the corresponding latency is always stabilized within 1.0. The proportion of conceptual metaphors in Chinese news is above 0.5, while in English news, the proportion is mostly around 0.3. Most of the Chinese news is conceptual metaphors, while most English news is interpersonal metaphors.
Exploration and Practice of Online-Offline Hybrid Teaching Mode in Teaching Higher Education Courses under the Background of Internet+

Abstract In this paper, a Bayesian approach and a recommendation algorithm are used to construct a method for evaluating students’ competence in a hybrid teaching model. The Bayesian method is used to create a mathematical description of the student’s ability level. The purpose of classification is achieved by extracting features of students’ learning results and preference directions using the simple Bayesian method. According to the recommendation algorithm, the recommended objects’ content features are retrieved, and teaching resources with high matching degrees are recommended for students. The recommendation algorithm in this paper can effectively determine the ability level of students, as demonstrated by the results. The probability of the highest P1 mastery rate among A-E students is 0.3179 for B students, and the probability of the highest P2 mastery rate is 0.1409 for D students. The average score of the experimental class that implements blended teaching is 76.631, while the average score of the control class is 73.7841, and the experimental class is 2.8469 points higher than the control class. 2.8469 points. This study promotes the development and progress of blended teaching to a certain extent.
Application of Recurrent Neural Networks Based on Attentional Mechanisms in Classification Error Correction in English Teaching

Abstract The way teachers correct errors in English teaching can cause problems such as psychological pressure, and deep learning technology offers the possibility of automatic error correction. In this paper, the final states of left and right texts are computed by constructing two attention mechanisms, target word-independent and related, and the merged obtained vectors are inputted into the RNN model for grammatical error recognition. In collocation error recognition, a Rank-based word candidate set ranking method is added, and error correction for verb usage is semantically encoded using RNN. The study was analyzed and tested in terms of grammar, collocation, and verbs. The ATT-RNN model accuracy is 3.62 percentage points higher than CAMB, and the difference in recall and F 0.5 -value is not more than 0.5 percentage points, which indicates that the algorithmic model has some research value.
Influencing Factors and Realization Strategies of Teachers’ Professional Development in Higher Vocational Colleges and Universities Based on the Background of Deep Learning

Abstract The fuzzy comprehensive evaluation algorithm is employed in this paper to evaluate teachers’ professional competence and improve the traditional evaluation system to construct a comprehensive evaluation system. According to the Zagan theory, twenty-three influencing factors of teachers’ professional development were screened, and five main influencing factors were derived after exploratory factor analysis and structural equation test. Countermeasures and suggestions were proposed after analyzing the factors that affect the professional development of teachers in higher vocational colleges. The results show that the model KMO value is 0.923, Bartlett’s spherical test chi-square value is 2712.752, and the significance probability value P=0.000&lt;0.005 is statistically significant. The correlation coefficients between the five main factors are in line with the condition of correlation attribute defined among the factor analysis variables, with a range of 0.37 to 1.00. The optimization countermeasures proposed in this paper have important theoretical significance and practical significance for the professional development of teachers in higher vocational colleges.
Applying Optimisation Theory in English Language Teaching in Practice

Abstract This paper firstly researches the optimization of the teaching process and teaching language according to the English teaching objectives and teaching contents and summarizes the optimization strategy of university English teachers’ language so as to improve the efficiency of English teaching. Secondly, on the basis of applying the optimization theory, the formal description of English utterances and word splitting is carried out, followed by obtaining the features of English utterances through the selection function and further constructing the English utterance alignment model by using the particle swarm optimization algorithm. Then, we study the application of English utterance alignment in English teaching, which is mainly embodied in the construction of the English teaching corpus, word frequency list, and core word lexicon, followed by the analysis of the practical application in English teaching. The results show that in terms of algorithm performance, the correct rate of utterance alignment based on the particle swarm optimization algorithm is 0.82, and the correct rate of utterance alignment is also improved to a great extent. On speaking teaching, self-efficacy showed a significant correlation with cognitive strategies, metacognitive strategies, affective strategies, communicative strategies, compensatory strategies, and memory strategies, and their corresponding coefficient values were 0.652, 0.716, 0.741, 0.732, 0.718, and 0.748, which indicated that the better self-efficacy was, the higher the scores of speaking memory strategies were.
Confusion and Countermeasures of College Students’ Career Guidance Work Based on Deep Learning Models

Abstract In this paper, we identify teaching signals and employment factors by designing a college student employment guidance work model. The deep learning model is used to identify the given feature vectors, find the word sequence with the highest probability among them, generate the probability of the corresponding acoustic feature vectors, and model the college students’ employment guidance work model to model and calculate them. The teaching signal feature distribution is used to create the description, and the output probability is adjusted to it. The number of college graduates in 2020 will be 6.3 million, an increase of 190,000 compared to last year, and the initial employment rate is 91.07%. The deep learning model can effectively identify college students’ employment confusion, propose effective countermeasures and improve the employment rate.
Using Time Series Analysis to Study the Changing Trend of Business English Teaching Effectiveness in Colleges and Universities

Abstract This paper analyzes the characteristics and changes of influence on college business English teaching in the context of the new era, constructs a multimodal college business English teaching model based on vision and hearing, and carries out a multimodal fusion of graphics, text and audio through deep learning. To solve this problem, the time series analysis problem is easily affected by noise. On the basis of the basic time series algorithm, the GBDT-KF algorithm based on the Kalman filter is designed. It also predicts the effectiveness of college business English teaching. The results show that listening ability is improved by 0.3, speaking ability by 0.25, writing ability by 0.21, and reading ability by 0.18; the effect of college business English teaching has also been improved in different degrees.
EasyComVi: A Tool for the Interactive Learning of Computer Vision in Distance Education

This article presents the EasyComVi Tool, an interactive application developed to facilitate the study of Computer Vision for students enrolled in an Engineering Master degree with a Distance Learning methodology. The main objective of this tool is to enable students in subjects related to Computer Vision with a distance learning methodology, to acquire a better understanding of different concepts about the subject studied in a theoretical way, through its interactive visual illustration, in an autonomous way. This tool offers the students the possibility to visualize the results of applying different image processing operations to various example images selecting the value of a set of input parameters. The usefulness and applicability of the tool has been evaluated by means of a questionnaire disseminated among the students of a subject on Computer Vision in a Master's degree in Industrial Engineering. This paper presents the preliminary results obtained in the study carried out, which are very promising and encourage further research in this work.
Message from the ISMAR 2023 Science and Technology Conference Program Chairs

We are delighted to welcome you to the 22nd IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2023) Conference Program. Building on the success of 2022, this year’s venue once again featured a separate conference paper track, ensuring a clear distinction between the contributions of the journal and conference papers. The Science & Technology program of ISMAR 2023 collates together the journal and conference papers, presenting them equally in terms of presentation time and sessions. This supports a clear collegial vision of the contributions within ISMAR 2023 and showcases cutting-edge research in our field. We are confident that this program will inspire further research while provoking discussions and creating a platform for co-creation of future work.
Eye-Tracking Pilot Software Description

The processing of eye-tracking signals could be relevant in multiple fields. The report presents a description of pilot software for eye-tracking analysis based on python libraries. The outlines the various applications of the software, including its processing of input data of eye-tracking studies, variety of visualization tools, as well as features evaluation for assessment of eye-tracking signals. Overview of the basic pipeline was presented, which includes data calibration, demonstration of key visualizations graphs as well as features extraction in table format. Furthermore, the software was used to process data of pilot study involving healthy subjects and patients after a hemispheric stroke. Research has shown that when comparing target hit statistics, as well comparing as extracted data from static and dynamic tests for a group of healthy subjects and patients, the parameters are significantly different for the two groups, in particular amount of fixation events and misses of target is lower for healthy subjects, while successful hit of target is higher. Based on the extracted data, it can be noted that for the observed group of data, simple statistics of hitting the target, using the angular velocity algorithm to divide into fixations and saccades may be enough to divide the subjects into healthy and patients. Presented results revealed multiple informative features to discriminate subjects from different groups. Future development of the presented software includes increase of the extracted features to make it possible not only in binary classification but also for a multi-class tasks to distinguish patients with different pathologies.
Pixel-based Visual Secret Sharing Using Lorenz System

(n, n)-Visual Secret Sharing (VSS) allows a user to send an image in the form of shares to different participants. Every share can not reveal the secret alone, and only all shares together can reveal the secret with fast recovery. This paper proposes a pixel-based (n, n)-VSS system, where to share a pixel from the secret image, (n − 1) random pixels are generated from the Lorenz chaotic system for a varying set of (n − 1) shares. Then, the nth pixel is calculated for a random share using the secret pixel and the generated (n − 1) random pixels. The system is efficient, lossless, implemented for grayscale and color images, and has a simple XOR-based recovery scheme. It passed several security analysis tests and is robust against noise attacks. Moreover, performance analysis and comparisons with other VSS systems are presented, showing good results.
Chemical Life: Knowledge-based Personality, Emotion and Action Cues in Educational Games

This paper proposes an educational game design framework named the Knowledge-based Personality, Emotion, and Action Cues (KPEAC). It aims to naturally enhance the user's intrinsic motivation and learning effectiveness by employing these novel Anthropomorphic Design Cues (ADC). As an example of this framework, we developed Chemical Life, a chemistry education game, where existing atoms and molecules act as characters with assigned personality traits, emotions, and reactions according to chemical rules. For instance, sodium atoms with unstable properties exhibit neuroticism, while oxygen molecules, which readily engage in various reactions, demonstrate openness. These characters engage in dialogues generated with the assistance of a large language model (LLM). Players initially interact with these chemical characters, immersing themselves in their personalities and emotions, before guiding them to perform the appropriate chemical reactions. Preliminary experimental results indicate that this framework stimulates users’ intrinsic motivation and facilitates knowledge acquisition in a proactively goal-driven manner. It also provides new directions for the application of ADC and LLMs in the field of educational games.