Reviewerly/exHarmony huggingface dataset:
The New Development Path of Sports industry is explored with the Case of Synchronized Rope Skipping

Pattern rope skipping as a form of a more unique and novel and lively movement, not subject to site restrictions, not only can improve the enthusiasm of students on physical education, promote students' physical and psychological health and perfect development, cultivate students' perseverance, but also can virtually cultivate students' coordination quality and comprehensive physical quality.This article is to try to use historical documentary, questionnaire investigation and integrated modern and mathematical statistics, random random sample survey by including provincial and municipal obviously by nearly 10 around the rope skipping school, for the normal pattern woman rope skipping competition project of the country's teaching and the status quo of the quality and management problems of a preliminary investigation and analysis of the survey and status quo Analysis.The preliminary analysis of household surveys by the above conclusion: figure rope skipping movement characteristics of projects in all elementary and middle schools reflects the city's current status quo level can be carried out practical teaching conditions and project implementation sustainable development path choice are not implemented truly reaches a certain height of athletics level of competitive ability, in the present to participate in this survey and the baseline survey Of the 10 county-level high schools in the city, only 2 county-level rural schools can realize the opening of pattern jump rope, and the rest of the schools will hardly popularize the content of pattern jump rope in the time of physical education.
The Path Exploration on English Teachers' Professional Development from the Perspective of Learning Community

Teachers' professional development, as an important role in educational reform, is getting more and more attention.Learning community is one of the most effective ways to promote teachers' professional development.Teachers' learning communities are beneficial to stimulating teachers' subject consciousness of professional development, providing resources for teachers' professional development, and improving teachers' practical wisdom.This paper aims to analyze learning community and try to explore the construction and path of English teachers' learning communities.Some strategies are put forward: develop a Shared Vision; create cooperative school culture; establish a developmental evaluation system.
Research on the Service of Normal Universities to Local Economic Development under the Background of "New Strengthening Normal University Project"

Under the development requirements of the "new strong teachers project", the requirements for schools and teachers' educational ability are also higher and more diverse.How to make normal colleges adapt to the needs of social and economic development is the focus of numerous scholars.For this reason, the research on Normal Colleges Serving Local Economic Development under the background of the "new strong teachers project" is proposed.Analyze the relationship between normal universities and local economic development under the background of the "new strong teachers project", analyze the current situation of normal universities serving local economic development, on this basis, explore the path to promote normal universities serving local economic development, and put forward corresponding development measures, including promoting the professional reform of Colleges around local industries, strengthening the cooperation between schools and enterprises to promote the integration of industry, University and research The construction of diversified teaching and cooperative teaching platform.
The Construction of College English Blended Learning Assessment Model under the Guidance of CSE

The blended teaching mode has gradually gained its popularity among teachers and students in the "Internet+" era.The new teaching mode leads to the significant changes in many aspects of teaching, including teaching objectives, teaching design, teaching methods, teaching activities, learning assessment, etc.The reform of learning assessment is the key to ensuring the high teaching quality in the blended teaching mode.This study discusses the principles and technical support of constructing a learning assessment model in the blended teaching mode under the guidance of China's Standards of English Language Ability (CSE), manages to construct an AAPP blended learning assessment model in the college English teaching, and examines its implementation and effect.The study concludes that the learning assessment model can make the student-centered, process-oriented and multiple assessment possible and integrate several items of learning ability, attitude, process and outcomes to make objective and comprehensive assessment.Its implementation has proven that the blended learning assessment model can promote students' learning enthusiasm, improve their learning ability, and make them to learn autonomously and efficiently.However, there still exist some problems.The learning assessment model needs to be further developed and optimized in the future teaching research and practices.
Research on Higher Education Evaluation Based on Decision Tree and AHP

Higher education is of great significance in a country, both for the value of the industry and for the economy.First, we use the decision tree and AHP to establish an evaluation model based on the weight of criteria layer, which can evaluate the health degree of any country.Meanwhile, we conduct sensitivity analysis on the weight results.Then, we apply our model in the United States, Japan and South Korea for evaluation, compare the evaluation results of the TOPSIS evaluation model, further verify the correctness of the model, and obtain the evaluation scores of higher education health of the three countries.In the process of modeling, the correlation between various factors is emphasized, and quantifiable values are used as the link as far as possible.Through the relationship between each layer in the decision tree, we combine it with the weight to ensure research direction of the problem and the predicted index.This research method helps us to determine the best research direction in the evaluation of the higher education system with many influencing factors, and eliminates the interference of too many factors, so that our analysis and proposed policies are more targeted.Intuitive graphs also help us analyze the problem more effectively.
Translation of Chinese, English and American Literary Works in College English Teaching under the Cultural Differences between China and the West

In the process of college English teaching, dealing with cultural differences is an important, complex and difficult issue.With the increasingly close cultural exchanges between China and foreign countries, the continuous advancement of economic globalization and the trend of cultural diversification, translation is not only one of the ways of information exchange and expression in English, but also an indispensable tool.Due to the huge differences in language background and other factors, in order to better deal with this situation, it is necessary to effectively communicate with the Chinese and Western ethnic groups and achieve a satisfactory result for both parties.This is a very necessary task, and it is also a an inevitable trend.By analyzing the English teaching under the cultural differences between China and the West, this paper effectively communicates and understands both Chinese and foreign languages, analyzes the cultural differences between China and the West, and proposes some translation strategies.
A Study on the Practical Path of “Three-All Education” in College English Translation Teaching Based on POA

The purpose of the study is to discuss the practical path of "Three-All Education" in college English translation teaching based on Production-Oriented Approach (POA).This paper explains the definition of "Three-All Education", which is an educational mode of all-staff-in-charge education, allprocess education and all-round education.About POA, this paper successively presents its teaching philosophies, namely Learning-centered Principle, Learning-using Integrated Principle, and Wholeperson Education Principle, its teaching hypotheses, namely Output-driven Hypothesis, Input-enabled Hypothesis, and Selective Learning Hypothesis, and its teaching procedures, namely motivating, enabling and assessing.This study combines the theoretical framework of POA and the connotation of "Three-All Education" to construct the practical path of "Three-All Education" in college English translation teaching based on POA.
Metadata mapping in Tainacan: new functionality for digital museums linked to the Brazilian Institute of Museums

The Brazilian Institute of Museums – Ibram, in partnership with the Federal University of Goiás and the University of Brasília, has been building a new service for searching and retrieving digital objects from federal museum collections, an aggregator of cultural data called Brasiliana Museums. The process for aggregating museological data includes different steps. One of which is essential is mapping metadata from the museums of origin to a single metadata standard, enabling the interoperability of information between museums. Ibram's museums make their collections available through the Tainacan software. Thus, this article presents a new functionality for Tainacan, entitled "Tainacan Mappers IBRAM INBCM", which aims to allow users to map their collections to the descriptive elements proposed by Ibram, listed in the National Inventory of Museum Cultural Assets – INBCM. The research is qualitative, exploratory and descriptive, in the form of a case study. The results presented the script developed using the programming language Hypertext Preprocessor - PHP and its benefits for data aggregation. The new functionality is in use in the Brasiliana Museus prototype. As future works, it is intended to implement more advanced operations, such as transformation, concatenation and division of values.
Bilateral personalized dialogue generation with contrastive learning

Generating personalized responses is one of the major challenges in natural human–robot interaction. Current studies in this field mainly focus on generating responses consistent with the robot’s pre-assigned persona, while ignoring the user’s persona. Such responses may be inappropriate or even offensive, which may lead to the bad user experience. Therefore, we propose a bilateral personalized dialogue generation (BPDG) method for dyadic conversation, which integrates user and robot personas into dialogue generation via designing a dynamic persona-aware fusion method. To bridge the gap between the learning objective function and evaluation metrics, the conditional mutual information maximum (CMIM) criterion is adopted with contrastive learning to select the proper response from the generated candidates. Moreover, a bilateral persona accuracy metric is designed to measure the degree of bilateral personalization. Experimental results demonstrate that, compared with several state-of-the-art methods, the proposed method achieves the improvement on the random personalized test set of 23.99 in bilateral persona accuracy, 1.1 in BLEU, 0.83 in F1, 0.02 in distinct score, and the improvement on the biased personalized test set of 5.56 in bilateral persona accuracy, 7.51 in BLEU, 2.12 in F1, 0.02 in distinct score. On the manual evaluations, the proposed method can generate more fluency, bilateral persona-consistent, and context-consistent responses compared with other state-of-the-art methods.
Development of Mobile Application for Water Concessionaire towards Efficient Account Monitoring and Service Delivery

Metro Roxas Water District (MRWD) is a Category B water district and Government Owned and Controlled Corporation in Capiz that provides safe and potable water for its concessionaires. The study aimed to develop an android mobile application for concessionaires of MRWD and evaluate its acceptability based on ISO 25010: 2011criteria. This utilized descriptive and developmental methods. Data were gathered using an evaluation form to evaluate the level of acceptability of the mobile application in terms of functional suitability, performance efficiency, compatibility, usability, reliability, maintainability, portability, and security. The android mobile application is a concessionaire centered app to deliver the services of MRWD to its clients anytime and anywhere. Viewing of the current bill feature was based on the existing paper water bill of MRWD. As soon as a bill is created via MRWD's current billing and collection system, the functionality that reminds users of their bill due dates automatically sends them alerts. Viewing of payment history feature shows the last 30 payment transactions of the user. In other incident features, the user is required to type a description, remarks, and details about the incident. The reinstallation and reconnection notification feature is a way of informing the user that a job order will be performed with their account. The link to payment apps feature is to open directly the GCash and PayMaya apps in accepting payments. The respondents evaluated the MRWD concessionaires’ android mobile application as very acceptable.
ReDDLE-Net: Reflectance Decomposition for Directional Light Estimation

The surfaces of real objects can visually appear to be glossy, matte, or anywhere in between, but essentially, they display varying degrees of diffuse and specular reflectance. Diffuse and specular reflectance provides different clues for light estimation. However, few methods simultaneously consider the contributions of diffuse and specular reflectance for light estimation. To this end, we propose ReDDLE-Net, which performs Reflectance Decomposition for Directional Light Estimation. The primary idea is to take advantage of diffuse and specular clues and adaptively balance the contributions of estimated diffuse and specular components for light estimation. Our method achieves a superior performance advantage over state-of-the-art directional light estimation methods on the DiLiGenT benchmark. Meanwhile, the proposed ReDDLE-Net can be combined with existing calibrated photometric stereo methods to handle uncalibrated photometric stereo tasks and achieve state-of-the-art performance.
Experimental Demonstration of Swift Analytical Universal Control Over Nearby Transitions

Along with the scaling of dimensions in quantum systems, transitions between the system's energy levels would become close in frequency, which are conventionally resolved by weak and lengthy pulses. Here, we extend and experimentally demonstrate analytically based swift quantum control techniques on a four-level trapped ion system, where we perform individual or simultaneous control over two pairs of spectrally nearby transitions with tailored time-varied drive, achieving operational fidelities ranging from 99.2(3)% to 99.6(3)%. We achieve approximately an order of magnitude speed up compared with the case of a weak square pulse for a general control. Therefore, our demonstration may be beneficial to a broad range of quantum systems with crowded spectrum, for spectroscopy, quantum information processing, and quantum simulation.
Virtual Reality Application for Co-Bot Training

The direction of robot technology development is now towards human-centric robots where co-bots are starting to be in demand as assistants for human work. Working together means needing to get used to each other’s behavior so as to create collaborative interactions between each other. To create that sense of familiarity, Co-Bot Interaction Virtual Training (CBIVT) was developed. CBIVT is used in wireless all-in-one VR devices thereby increasing user-friendliness. As a simulation scenario of the interaction between the Co-Bot and the user, the furniture assembly procedure was chosen. And, with the help of the path finding algorithm, the behavior of the Co-Bot AI to follow and approach the user is also created. To assess the importance and satisfaction of CBIVT, 32 participants were assigned to complete scenarios and then complete a questionnaire. Using the PIECES framework to assess the above parameters, we get results of 3.95 out of 5 for importance level and 3.99 out of 5 for satisfaction level.
Optimal Scheduling of Data Centers Considering Renewable Energy Consumption and Temporalspatial Load Characteristics

With the large-scale integration of renewable energy into the power grid, the demand for flexible resources on the demand side of the power grid continues to increase. As a demand-side resource with both temporal and spatial load regulation potential, the data center can absorb local new energy by adjusting the load. Therefore, this paper proposes an optimal scheduling mode for data centers that considers new energy consumption and temporal-spatial load characteristics. First, the power consumption model of the data center under various load regulation methods, new energy system model and energy storage system model are established; secondly, the load optimal scheduling of the data center is carried out with the minimum cost of the data center, and the relevant constraints are considered to absorb local renewable energy; Finally, a case study is set to evaluate indicators including renewable energy utilization and data center operating costs. It was shown that the optimal scheduling method from result can significantly decrease the data center' operating cost and improve the utilization rate of renewable energy.
A Decision Procedure for Guarded Separation Logic Complete Entailment Checking for Separation Logic with Inductive Definitions

We develop a doubly exponential decision procedure for the satisfiability problem of guarded separation logic —a novel fragment of separation logic featuring user-supplied inductive predicates, Boolean connectives, and separating connectives, including restricted (guarded) versions of negation, magic wand, and septraction. Moreover, we show that dropping the guards for any of the preceding connectives leads to an undecidable fragment. We further apply our decision procedure to reason about entailments in the popular symbolic heap fragment of separation logic. In particular, we obtain a doubly exponential decision procedure for entailments between (quantifier-free) symbolic heaps with inductive predicate definitions of bounded treewidth ( SL btw )—one of the most expressive decidable fragments of separation logic. Together with the recently shown 2ExpTime -hardness for entailments in said fragment, we conclude that the entailment problem for SL btw is 2ExpTime -complete—thereby closing a previously open complexity gap.
Watermarking and Encryption for Holographic Communication

Holographic communication is a three-dimensional (3D) video communication technology based on computer-generated holograms (CGHs) which has the potential to give users a more realistic visual perception. As this is an emerging field, the encrypted encoding and decoding methods in holographic communication have not been widely studied. In this work, a watermarking and encryption method for holographic communication is proposed. A watermark is inserted into the original image using the discrete cosine transform before the calculation of the CGH, while a secret key is employed to produce the encrypted CGH during the holographic calculation. Through the proposed watermarking and encryption method, the signal of holographic communication is difficult to decrypt. Even if the signal is decrypted, the source of the leak is easy to trace due to the existence of the watermark. The watermarking and encryption method can provide a practical solution for the privacy protection and copyright protection of 3D video communication.
A Novel Framework of Federated and Distributed Machine Learning for Resource Provisioning in 5G and Beyond Using Mobile-Edge SCBS

The future needs of the telecommunication system lie in deploying a heterogeneous ultra-dense network with varied topographical use cases. However, this increase in ultra-denseness in 5g and beyond poses several challenges in resource allocation, requiring an accurate learning-based prediction. This paper proposes a novel framework using Federated Learning (FL) and Distributed Machine Learning (DML) for Mobile Edge based resource provisioning to User Equipment (UEs). This work formulates the correlation-based novel procedures between UEs in applying Federated and Distributed Machine Learning through Kolmogorov tests for predicting SNR. The correlations of the distribution obtained through the Kolmogorov test check the extent of Independent and Identically Distributed (IID) - ness between modelled data and evaluate the global model for resource provisioning accuracy. Further, correlation-based DML is also employed to balance the computational load of a mobile edge, which acts as a small cell base station and a computational node. In this approach, we account for correlation-based resource predictive model training to balance the uniform computational load by data distribution methods among the neighbouring mobile Edge SCBS nodes for computation. Together with both DML and FL, we create a novel Framework for resource prediction with minimal time for achieving high accuracy without over-fitting.
Ubiquitous Machinery Monitoring - A Field Study on Manufacturing Workers' User Experience of Mobile and Wearable Monitoring Apps

Fueled by ongoing digitization efforts, manufacturing is currently undergoing a transformational process towards interconnected machinery and workforce, which enables a wide range of interactive monitoring and controlling applications. Whereas existing user-centered work addressed remote monitoring from office workplaces, it remains unclear how manufacturing workers experience and adopt machinery monitoring apps on mobile and wearable devices. To close this gap, we conducted a four-week field study in a running factory to study workers' overall user experience and acceptance of such monitoring apps, the subjective impact on their work routines, and their preferred device type. Under productive operation, 11 manufacturing workers used functional application prototypes on smartphones and smartwatches to receive notifications of machine incidents. In 22 individual interviews and two focus groups, we collected the participants' impressions and assessments. Based on these results, we derive a set of recommendations for designing and deploying machinery monitoring apps for manufacturing workers.
SEE-3D: Sentiment-driven emotion-cause pair extraction based on 3D-CNN

As an emotional cause detection task, Emotion-Cause Pair Extraction (ECPE) provides technical support for intelligent psychological counseling, empty-nest elderly care, and other fields. Current approaches mainly focus on extracting by recognizing causal relationships between clauses. Different from these existing methods, this paper further considers the influence of sentimental intensity to improve extraction accuracy. To address this issue, we propose an extraction model based on sentiment analysis and 3D Convolutional Neural Networks (3D-CNN), named SEE-3D. First, to prepare fundamental data for sentiment analysis, emotion clauses are clustered into six emotion domains according to six emotion types in the ECPE dataset. Then, a pre-trained sentiment analysis model is introduced to compute emotional similarity, which provides a reference for identifying emotion clauses. In the extraction process, similar features of adjacent documents in the same batch of samples are fused as input of 3D-CNN. The 3D-CNN enhances the macro semantic understanding ability of the model, thereby improving the extraction performance. The results of experiments show that the accuracy of ECPE can be effectively improved by the SEE-3D model.