Reviewerly/exHarmony huggingface dataset:
Design on Interactive Virtual Display Based on User Experience

In the course of the continuous development of science and technology, enterprises are giving increasing priority to the virtual display of products as a result that the way of picture display, text description, plane publicity posters, and others have been unable to meet the growing cultural needs of consumers.Most enterprises have realized that only by selecting the way of product display from the perspective of user experience can the communication and interaction between enterprises and users be realized so that users can better experience the features and functions of enterprise products.In this way, this paper realizes the design of interactive virtual displays based on user experience.
Research on Strengthening the Construction of Young Teachers in Colleges and Universities in the New Era

Teachers are the engineers of human soul.Young teachers in colleges and universities are the important force of college teachers and play a vital role in the development of colleges and universities.In the new era, strengthening the construction of young teachers in colleges and universities is of great significance to the construction of high-quality education and teaching level.This paper puts forward reasonable suggestions for strengthening the construction of young teachers in colleges and universities in the new era, so as to promote the healthy and rapid growth of the construction of young teachers in colleges and universities in the new era.Such as strengthening the ideological and political quality and ethics of young teachers in the new era, and enhancing their sense of responsibility and mission; Strengthening the training of the professional ability of young teachers in the new era and improving their education and teaching level; Strengthening the cultivation and support of young teachers in the new era and ensuring the healthy and rapid development of young teachers and other aspects.
Learning Shape Distributions from Large Databases of Healthy Organs: Applications to Zero-Shot and Few-Shot Abnormal Pancreas Detection

We propose a scalable and data-driven approach to learn shape distributions from large databases of healthy organs. To do so, volumetric segmentation masks are embedded into a common probabilistic shape space that is learned with a variational auto-encoding network. The resulting latent shape representations are leveraged to derive zero-shot and few-shot methods for abnormal shape detection. The proposed distribution learning approach is illustrated on a large database of 1200 healthy pancreas shapes. Downstream qualitative and quantitative experiments are conducted on a separate test set of 224 pancreas from patients with mixed conditions. The abnormal pancreas detection AUC reached up to $$65.41\%$$ in the zero-shot configuration, and $$78.97\%$$ in the few-shot configuration with as few as 15 abnormal examples, outperforming a baseline approach based on the sole volume.
Rapid virtual training and field deployment for COVID-19 surveillance officers: experiences from Ethiopia

Rapid scale-up of surveillance activities is the key to successful coronavirus disease 2019 (COVID-19) pandemic prevention and mitigation. Ethiopia did not have a sufficient number of active surveillance officers for the public health COVID-19 response. Training of surveillance officers was needed urgently to fill the gap in the workforce needed. Subject-matter experts from the United States and Ethiopia developed applicable training modules including background on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), contact investigation, and communications. The training modules were delivered live in real-time via web-based virtual presentation. Seventy-seven health surveillance officers were hired, trained, and deployed in two weeks to assist with surveillance activities in Ethiopia. Electronic capacity building is needed in order to improve Web-based training in resource-limited settings where internet access is limited or unreliable. Web-based synchronously delivered course was an effective platform for COVID-19 surveillance training. However, strengthening public and private information technology capacity, literacy, and internet availability will improve Web-based education platforms in resource-limited countries.
Balkan financial development and its impact on economic growth: Granger causality

This paper examines the relationship between financial development and economic growth for the six countries of the Western Balkan (Albania, Montenegro, North Macedonia, Kosovo, Bosnia and Herzegovina, and Serbia) for the period 2005–2019. To determine the direction of the causality between economic growth and financial development, we employed the vector autoregression VAR approach. Findings indicate evidence for the supply leading theory (Hurlin &amp; Venet, 2008; McKinnon, 1973; Patrick, 1966; Shaw, 1973): financial development causes economic growth overall, especially when private credit was used as the proxy for it. Yet, we observed bi-directional links when financial development was proxied by broad money. Furthermore, interest spread affected economic growth. The findings also indicate a positive relationship between broad money and private credit taken together to GDP growth, but only in the first lag; in the second lag, the inverse effect of broad money and private credit on GDP growth became evident. On a comparative scale, private credit was found to have a bigger impact on GDP than broad money. We also observe that the banking system intermediaries have a significant role in spurring economic growth in the region.
The Teaching Mode of Combining Theory with Practice in Automobile Inspection and Maintenance Specialty

With the continuous development of society, the auto industry has also been rapid rise, as the leader of auto repair professional vocational colleges, the principle of real integration teaching is an effective teaching mode, is a kind of teaching concept of sublimation, mechanics professional real integration teaching of "rationality" is a task that around the maintenance enterprise, under the guidance of teachers, students as the leading factor, the organic combination of theory teaching and practice teaching, teaching theory, in practice the use of technical secondary school, solve the problem of theory teaching and practice teaching, help to improve the quality of teaching and the cultivation of high-quality talents
A Study on the Teaching Strategies of English Translation Course in Colleges and Universities under the Network Environment

With the development of computer technology, the teaching environment of various disciplines is gradually becoming networked.English translation is the basic discipline of English majors, which is of great significance to their subsequent learning.Therefore, this paper studies the teaching strategies of English translation courses in Colleges and Universities under the network environment.Firstly, it clarifies the current situation of translation teaching.Secondly, it expounds the reasonable teaching strategies according to the existing problems, which has made a certain contribution to the optimization of College English teaching.
Research on the strategy of cultivating students' subject literacy in physical education in colleges and universities

This paper mainly uses the literature method, questionnaire survey method, survey interview method, logical analysis method and other research methods to study the core literacy training strategies of students in college physical education teaching.New strategies and clear operating procedures have strong practical value for promoting physical education in colleges and universities.
College Student Management System Based on K-means Clustering Algorithm

Student management is a problem that every university needs to solve.The purpose of this paper is to design a college student management system based on K-mean clustering algorithm.The purpose of this campus project analysis and research is to help teachers fully understand, master and adapt to students' skills, achieve the purpose of teaching, and write and develop optimized K-means algorithms.Firstly, through the analysis of the student management data, the system function module is added to the student management system.Then the number of K clusters is selected to set the total range, and the optimal value of the number of K clusters is selected by calculating the ratio of the inner and outer distances.Use the K-means method to analyse the optimization of the algorithm to analyse the performance on student management.The optimized K-mean algorithm has completed the classification of students well, and the average score of Pmoral Learning is 17.54 for the first category.College student management is a very important innovation, which has played a certain role in promoting the development of student management.
Translation of Japanese Literature Language and Natural Language Environment Understanding Based on Artificial Neural Network

Literary translation encompasses not only the communication of ideas across cultures but also the conversion of one word into another. The translation of Japanese literature will, therefore, undoubtedly convey this foreign culture. This study examines the semantic expression of words, the NN model for common sense reasoning, the method of building a common sense knowledge base, and the translation of Japanese literary language with an emphasis on the semantic expression and reasoning methods of natural language based on NN. The network structure, parameter processing, learning algorithm, and learning samples are all discussed in detail at the same time as the integrated NN model is presented. According to simulation results, this algorithm can achieve an accuracy of about 95% and a recall rate of about 96.5%. In addition to greatly enhancing the model’s flexibility and popularity, this approach successfully addresses the issue of information loss. It can offer some references for both the translation of literary language and the understanding of natural language.
Potensi Pengembangan Circulamodo untuk Meningkatkan Keaktifan Peserta Didik

This study aims to develop Circulamodo learning media in increasing the active potential of students. The research method used is the ADDIE development method through analysis, design, development, implementation, and evaluation stages. The results showed that 1) the average score of Circulamodo's media assessment was 77.4% and had good qualifications; 2) the active potential of students is 76% and is categorized as very good; 3) students' cognitive learning outcomes increased by 39.8%. In conclusion, the development of Circulamodo media is proven to increase student activity potential.&#x0D; Keywords: ADDIE, Cognitive Learning Outcomes, Circulamodo Media, Activity Potential
Memory Transformers for Full Context and High-Resolution 3D Medical Segmentation

Transformer models achieve state-of-the-art results for image segmentation. However, achieving long-range attention, necessary to capture global context, with high-resolution 3D images is a fundamental challenge. This paper introduces the Full resolutIoN mEmory (FINE) transformer to overcome this issue. The core idea behind FINE is to learn memory tokens to indirectly model full range interactions while scaling well in both memory and computational costs. FINE introduces memory tokens at two levels: the first one allows full interaction between voxels within local image regions (patches), the second one allows full interactions between all regions of the 3D volume. Combined, they allow full attention over high resolution images, e.g. 512 $$\,\times \,$$ 512 $$\,\times \,$$ 256 voxels and above. Experiments on the BCV image segmentation dataset shows better performances than state-of-the-art CNN and transformer baselines, highlighting the superiority of our full attention mechanism compared to recent transformer baselines, e.g. CoTr, and nnFormer.
Aggregative Self-supervised Feature Learning from Limited Medical Images

Limited training data and annotation shortage are challenges for developing automated medical image analysis systems. As a potential solution, self-supervised learning (SSL) causes an increasing attention from the community. The key part in SSL is its proxy task that defines the supervisory signals and drives the learning toward effective feature representations. However, most SSL approaches usually focus on a single proxy task, which greatly limits the expressive power of the learned features and therefore deteriorates the network generalization capacity. In this regard, we hereby propose two strategies of aggregation in terms of complementarity of various forms to boost the robustness of self-supervised learned features. We firstly propose a principled framework of multi-task aggregative self-supervised learning from limited medical samples to form a unified representation, with an intent of exploiting feature complementarity among different tasks. Then, in self-aggregative SSL, we propose to self-complement an existing proxy task with an auxiliary loss function based on a linear centered kernel alignment metric, which explicitly promotes the exploring of where are uncovered by the features learned from a proxy task at hand to further boost the modeling capability. Our extensive experiments on 2D and 3D medical image classification tasks under limited data and annotation scenarios confirm that the proposed aggregation strategies successfully boost the classification accuracy.
Open Sharing Management of Artificial Intelligence Laboratory Based on Deep Fusion of Big Data

In order to improve the efficiency of information management of open laboratories in colleges and universities and reduce the cost of information management of open laboratories in colleges and universities, this paper proposes the open sharing management of artificial intelligence laboratories based on the deep integration of big data. The infrastructure layer of the system transmits the information management data of the university open laboratory to the cloud platform layer. The cloud platform layer completes the data management, cloud computing, and access control and transmits the processed data to the application layer. The application layer realizes the information management of the university open laboratory according to the data fed back by the cloud platform layer. Among them, in the process of data transfer in open laboratories of colleges and universities, pyramid technology is used to realize data transfer, and the optimization calculation of particle swarm optimization algorithm is used to complete the transfer of multidimensional and complex data transmission information, so as to improve the efficiency of data transfer. The experimental results show that the system has high accuracy in transferring multidimensional and complex data in university open laboratories, the average resource utilization rate of the system is as high as 92%, the average response of the system is as fast as 8.5 ms, and the management cost is effectively reduced. Conclusion. The system designed in this paper can reduce the information management cost of open laboratories in colleges and universities.
SEARCH ON TEACHING MODE AND EVALUATION OF FOREIGN LANGUAGE WISDOM EDUCATION BASED ON BIG DATA

The development of modern information technology and the deepening integration of education and teaching provide excellent support for the upgrading and popularization of foreign language wisdom education platform. The foreign language wisdom education platform based on the theoretical framework of foreign language educational technology conforms to the law of foreign language teaching, and provides teachers with an intelligent teaching assistant system based on big data analysis, so as to truly realize autonomous learning and improve learning efficiency; To provide big data decision analysis for teaching administrators, so as to truly realize intelligent teaching management and comprehensively improve teaching quality. Under the background of big data, the concept of wisdom education has brought systematic changes to foreign language education. Data mining and learning analysis technology are adopted to construct personalized learning content and multiinteractive teaching evaluation mechanism, so as to improve students' learning efficiency and learning autonomy, thus realizing intelligent and personalized education.
Human Activity Recognition Using Wearable and Inertial Sensors

A wide range of applications and issues in the virtual reality, health and medical, entertainment, and security industries may be addressed thanks to advancements in human activity recognition (HAR) systems. In this research, we have used two data sets consisting of information about various activities performed by participants using their smartphones which allow us to explore various human activities. Two different deep learning techniques, i.e., DNN and LSTM, were implemented. Performance analysis was conducted, wherein we achieved an accuracy of 76% and 97%, respectively. Furthermore, to extend the scope of the data set, we integrated the KU-HAR dataset which consists of complex activities. We utilized the decision tree and random forest methods to analyze this data set. The precision and efficiency of the model’s actions were assessed to be 77% and 90%, respectively.
Application of Punitive Damages in Intellectual Property Law in Complex Network Environment

Many IP (intellectual property) will also be infringed, and it will become more and more serious, causing huge economic losses to people or groups who own IP. If punitive damages can be made for intentional but intentional infringement of IP, it will help to stop intentional infringement of IP, and it can also give due compensation to the obligee’s loss. Based on this, this paper analyzes the introduction and implementation of punitive damages in IP law. In this paper, a relatively efficient text classification method is realized by using the method of deep learning and model design. Similar texts in the training set are grouped into a group, and each group is considered as a common text vector. Then, a threshold is set to deal with clusters higher than the threshold, and the training set is reformed. Through the experimental results, the feature extraction effect of this model is improved, the classification accuracy is constantly improved, and the final convergence rate of this model reaches 95%. It has been verified that the system can realize the automatic classification of basic Chinese patent texts.
An Evaluation Model for the Influence Factors of Interest in Literature Courses Based on Data Analysis and Association Rules in a Small-Sample Environment

The primary tools for developing pupils' creativity, capacity for verbal expression, and spiritual growth are literary reading and writing. Literature is a sort of art that elicits feelings and expresses the author's comprehension and outlook on social life via the use of language. Reading and writing literary works helps students develop their aesthetic sensibilities and capacity to create compelling images, as well as their spirituality and wisdom. This study suggests a data mining-based optimal design approach for analyzing association rules of influencing aspects of interest in literary courses. To increase the frequency and accuracy of data mining, association rules are used to obtain the association mapping relationship between data sets of influencing factors of interest in literature courses. Rough set theory is then used to distinguish between the feature sets of data sets in the same subspace and different subspaces. To identify the most prevalent factor that affects the interest of the curriculum's literary components and then to conduct simulation testing and analysis. The proposed arithmetic has a particular accuracy, which is 8.25% greater than the conventional arithmetic, according to simulation findings. This outcome demonstrates in full how the enhanced arithmetic decreases the amount of records in the scanning database by grouping and compressing the database, hence lowering the scanning time and pruning before the connection process of Liu arithmetic. Classical literary works are without a doubt the most valuable resources to feed, edify, forge, and grow the spirit, soul, and personality of contemporary individuals throughout history and in all nations. Education through literature develops one's character, spirit, emotions, and aesthetic sense. The importance of reaffirming the significant place of literature education in contemporary national basic education cannot be overstated.
Design and Research of Campus Culture Application Based on AR and Metaverse Technology

Under the background of closed campus management of Chinese universities, the combination of Metaverse technology and campus exhibition is advantageous. Through Metaverse technology, the overall campus scene is constructed by reverse modeling of unmanned aerial vehicles through three-dimensional point cloud scanning, which integrates campus spirit and cultural elements. Then, the campus culture APP is designed and developed. The campus culture APP based on the metaverse innovates a new idea of virtual integration and interaction on campus and has a positive impact on improving the campus culture environment, enriching the manifestations of campus culture, and expanding the construction of campus culture.
Echo State Network Optimization Based on Improved Fireworks Algorithm

First, the standard firework algorithm (Firework Algorithm, FWA) is improved, and the initial firework population is generated by an adversarial learning strategy. At the same time, according to the displacement method of the explosion spark in the original FWA, the explosion spark generation method and an inertia weight factor in the enhanced firework algorithm are introduced. Then the improved FWA algorithm WOFWA (Weight Opposition Firework Algorithm, WOFWA) is combined with Echo State Networks (ESN) to construct a WOFWA-ESN model. Four classical complex nonlinear benchmark functions are selected as test functions to verify the superiority of the WOFWA algorithm, and the improved FWA-ESN model is used to predict three time series data sets. The experimental results show that the accuracy of the WOFWA-ESN prediction model proposed in this paper is significantly improved.
Development of College English Personalized Learning Support System Based on Data Mining Technology

With the popularity of big data, artificial intelligence, and distance education, the teaching and learning of college English have changed significantly. In particular, personalized college English learning based on data mining technology has emerged as a key topic at this stage. In this context, we first analyzed the overall scheme of the college English personalized learning support system and then expatiated the core implementation methods of the college English personalized learning support system. The modeling methods and the generation countermeasures of the college English personalized learning scheme are included. The idea of personalized education is integrated into college English with big data to establish the main position of students in learning, improve the effectiveness and pertinacity of education, and maintain the dynamic balance between students' personalized development and social development.