Reviewerly/exHarmony huggingface dataset:
Application of VR Technology in Interior Design

In the field of interior design,VR technology mainly takes the form of visual images and videos.From traditional 3D Max modeling to rendering two-dimensional images, to online platforms such as Kujiarle and Panorama IE generating panoramic roaming videos,VR technology is combined with interior design to transform interior design from form to interior.Allow innovation and development.The improvement and optimization of 5G network speed and VR technology improve the efficiency, cost and process of interior design, help interior design from design to intelligent experience of material selection, and expand the experience of VR technology in interior design.Taking the interior design of OFFICE HOME Apartment for young people in Suzhou as an example, this paper explains the embodiment of VR technology in the interior design process from three stages: concept of scheme design, scheme output and scheme report, and expounds the prospect direction of VR technology in the field of interior design [1].
A Study on the Discourse Construction of Foreign Mainstream Media’s China-Related Reports

From the perspective of DHA, this paper analyzes the discourse construction of foreign mainstream media's China-related reports based on quantitative corpus analysis.The study selects China-related reports of mainstream media in Japan and the United States as the corpus, and carries out a detailed excavation and analysis of news discourse from three aspects: discourse themes, discursive strategies and language representations.Furthermore, it is found that the media's China-related reports of the two countries cover many topics such as politics, economy and security, which reflects the attention of foreign mainstream media to China in all aspects.At the same time, in the process of reporting, the media of the two countries try to play up the "China threat theory" and stigmatize China through the strategies of nomination, predication, argumentation and perspectivization, thus affecting the positive spread of China's national image overseas.
An Analysis of Grid-Based Digitized and Intelligent Teaching System for Vocational Education Resources in Minority Regions

Restricted by many factor, it is difficult for minority regions to acquire the quality and abundant educational resources equivalent to what economically developed regions can have.This has significantly hindered the development of vocational education in minority regions.A digitized and intelligent teaching system was established.The system adopts a multi-layer architecture, and comprises the interaction layer, the content layer, the service layer and the data layer.It covers two sub-systems, namely the curriculum resources and the teaching resources, and opens up diversified teaching channels for the vocational education in minority regions.Through systematic simulation and test, the learning situations, the teaching results and the attention rates were compared and contrasted between the traditional teaching model and the digitized and intelligent teaching system for vocational education resources in minority regions, so as to analyze the rationality and necessity of the design on this teaching system.
Research on University Education Cost Accounting Under the Background of Big Data

With the development of digital technology and artificial intelligence, the era of big data has arrived.In the background of big data, data is highly shared and integrated, which provides a guarantee for using the operation cost method to accurately calculate the cost of university education.Taking S university as an example, this paper first analyzes the feasibility of accounting of education cost in universities under the background of big data, and then the process of using operation cost method is constructed.Finally, relevant suggestions are put forward from the two aspects of improving the professional level of accountant and strengthening the information construction to ensure the smooth implementation of the operation cost method under the background of big data.
A Model of Hybrid Teaching Innovation

To improve the teaching effectiveness of course, the paper created an innovative design for the "teaching pain points" in the teaching process.With computer technology and algorithm innovation, the paper established a hybrid online and offline teaching innovation model, innovated in five aspects -innovation of course concept, innovation of teaching design, innovation of teaching content, innovation of teaching method and innovation of evaluation method.The model of "BOPPPS teaching model + Chaoxing learning platform + rain classroom + PBL problem-oriented method + TBL team learning method" based on BLOOM teaching taxonomy was created and statistically analyzed by questionnaire and SPSS software.The results showed that 88.2% of the surveyed students were satisfied with the course in general, 98% of the students spoke up in class and completed the pre-study through Chaoxing learning platform, and 92% of the students preferred the rain classroom teaching or the combination of rain classroom and traditional teaching.Through teaching innovation, the course has achieved the reform results of high student motivation, strong learning interest, improved practical ability, and continuous optimization of the course.
Study on Artificial Intelligence (AI) Assisted English Linguistics Teaching System

Artificial Intelligence offers new solutions for English learning, and its emergence has broken the traditional way of learning English.This paper aims to investigate the Artificial Intelligence (AI) assisted English linguistics teaching system, conducting the numerical simulation to validate the performance.Based on intelligent English education and English linguistic theory, we have carried out three sets of models to test the influence of AI on English learning: (1) Face recognition algorithm is applied for the student concentration estimation.(2) Speech recognition is used to test students' pronunciation accuracy.(3) Cloud storage is applied to realize the efficient storage of the above algorithms.The findings from this research showed that we can design teaching content and methods in a customized chemical way by considering the students' unique English learning needs, and conduct comprehensive analysis through intelligent analysis of the students' background levels based on the artificial intelligence system.This research also contributes to expanding the development and application of Artificial Intelligence, as well as the improvement of front-line English teaching practice designs.
Research and Analysis of Student Behavior Portrait Based on Big Data

The application of big data in the Internet, cloud computing and other fields is becoming more and more extensive, with the continuous increase of the network environment and information resources of students' campuses, the network structure of university libraries has also undergone tremendous changes, and the amount of data storage has also become larger and larger.How to use massive data mining technology to effectively classify and analyze students' campus information has become an urgent problem to be solved.In this paper, we mainly study the modeling and profiling of college students' behavior characteristics based on big data analysis and predictive training set.Firstly, this paper studies the theoretical techniques related to big data, including data mining algorithms and behavioral profiling overviews.Secondly, the analysis of student behavior portraits was carried out; Finally, the fusion verification of the student behavior portrait model was carried out, and the experimental results were analyzed and summarized.
The House that Looked Like It Should Collapse. Natural Language Processing for Architectural Design

Machine learning (ML) has not only become increasingly popular in the fields of computer science and AI since the 1990s, it also has become increasingly used in architectural design in the past few years. While some experimentations deal with fabrication or engineering issues and performance prediction, others deal with layout generation. As in any process involving ML, layout generation necessitates a large database to train the system. These databases are currently in their majority 2D images or 3D-models. However, these databases are often either scarce or monolithic – they gather too few examples and/or examples that are very similar in regard to the vast diversity of architectural design. The Generative Pre-trained Transformer 3 (GPT-3) is a language model developed by Open AI to produce text answers to text prompts submitted to it. With 175 billion parameters, it is one of the largest models available. It has been trained on billions of online texts, coming from databases such as Common Crawl, Books2 or Wikipedia. The texts it generates are therefore based on a very large range of information, including about architecture. While only being word-based, the amount of information it has been trained with makes the suggestions relative to architecture GPT-3 outputs worthwhile to examine. What patterns relating to architectural design emerge when training a ML algorithm on such a database, much larger than what our field is currently used to manipulating? We have been testing and calibrating GPT-3 in order to obtain descriptions of buildings that could then be translated into drawings, i.e. design briefs. This calibration has also enabled us to assess the patterns identified by the system. We thus propose an evaluation of how design issues usually perceived by architects as key are handled by GPT-3 – location, climate, program, references, inhabitants and their peculiarities. This evaluation is based on an assessment method that can be applied to other general ML tools in architecture, including image generators. Upon performing these tests, some prompts generated particularly various and interesting answers, oscillating between rationality and fiction. The present paper uses these prompts as examples in order to deepen the assessment of GPT-3, to evaluate its potential as a design tool and to open a larger discussion about databases within ML use for architectural design.
“How Can Visual Arts Promote Social Integration?: A Study on Museum Visitors’ Participatory Use of VR &amp; AR

With the popularity of virtual reality technology, the interactive design has been widespread in the display process of immersion virtual museums.Based on the questionnaire analysis of museum visitors combined with in-depth interview of VR & AR technology users, this study analyzes the characteristics, functions and prospects of VR and AR technology in virtual museum display in China, explores the interactive design in the exhibition of immersive virtual museums with user experience and proposes a VR & AR-SI model based on Unity 3D and Unreal engine.The virtual interaction design represented by the application of VR & AR has changed the traditional display mode, broke the constraints of time and space, enhanced the enthusiasm of the visitors, and further enriched the museum's display form, which also pointed out that the interactive design realized through virtual technology has played a significant role in promoting social integration and would be the development direction of the future immersion virtual museum.
RSA Based Consensus Algorithm for Lightweight Private Blockchain Network

Consensus algorithms are essential for achieving agreement among nodes in blockchain systems. However, traditional consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS) can be resource-intensive and unsuitable for lightweight private blockchain applications. This paper proposes using the RSA (Rivest–Shamir–Adleman) encryption algorithm as a consensus algorithm for a lightweight private blockchain in the context of a college placement system. RSA offers several advantages over traditional consensus algorithms, including simplicity, efficiency, and security. Moreover, RSA can be implemented on resource-constrained nodes, making it a promising solution for lightweight blockchain applications. The resource constrained nodes are students, academic department, training and placement cell department, and placement cell coordinator. The company acts as a client. The movement of the student’s data to companies is recorded as transactions on the distributed ledger or blockchain, allowing the student to track its progress.
Exploring Snippets as a Dataset to Overcome Challenges in CLIR

Cross-lingual information retrieval (CLIR) is a challenging task that requires overcoming linguistic barriers to match user queries with relevant documents in different languages. One of the major challenges in CLIR is the lack of parallel corpora, which hinders the development of effective translation models. This challenge can be addressed using snippets as a dataset to train CLIR models. Snippets can be automatically extracted from various sources, such as search engine result pages and can provide a rich and diverse set of collections for cross-lingual information retrieval. This paper initially discusses the challenges in CLIR and then explores the use of snippets as a dataset which can lead towards the development or improvements in the techniques to improve the retrieval effectiveness and further discusses the advantages and limitations of using snippets dataset in CLIR.
The Standard Model Theory [May Be] a Wrong Theory

The Standard Model is the theory of Physics that describes the elementary particles of matter and the strong, weak and electromagnetic interactions, between them.The theory of the Standard Model does not include the description of the gravitational interactions.It is a very well founded theory that How to cite this paper:
Load Balancing and Server Consolidation for Energy Management in Cloud Data Center

Users worldwide can access utility-oriented computing services through cloud computing. It enables the pay-as-you-go model to host well-known consumer, educational, and business applications. However, cloud data centers encounter significant issues in effectively managing resources, ensuring optimal performance, and reducing energy usage as the demand for cloud computing services is increasing rapidly. A possible strategy to reduce energy consumption and operating expenses is the energy-efficient management of virtual machines (VMs) and server consolidation. In this paper, a VM migration and server consolidation algorithm is proposed, which is energy efficient and SLA aware. The proposed approach is evaluated using the CloudSim simulation kit and PlanetLab workload. The experiment result shows that the proposed approach has reduced energy consumption and SLA violation.
Improving Software Modularity Using Software Remodularization: Challenges and Opportunities

Long-period maintenance of software often causes the original program modularization to decay, resulting in a degraded quality of the software. Placement of software artifacts, namely code files or classes in a suboptimal manner among software packages is one of the most common causes of this decaying modularization of software. Software remodularization (remodularization) is an old reverse engineering approach that helps in alleviating this issue by improving the quality of software modularization. However, in literature, a multitude of software remodularization approaches exists leaving researchers and developers in a dilemma of choosing appropriate remodularization criteria. Therefore, this paper carries out a Systematic Literature Review (SLR) of the last two decades and presents concise information by answering different relevant research questions important to both developers and researchers of this field. This paper considers 54 research articles as primary studies.
Enhancing Performance of End-to-End Gujarati Language ASR using combination of Integrated Feature Extraction and Improved Spell Corrector Algorithm

A number of intricate deep learning architectures for effective End-to-End (E2E) speech recognition systems have emerged due to recent advancements in algorithms and technical resources. The proposed work develops an ASR system for the publicly accessible dataset on Gujarati language. The approach provided in this research combines features like Mel frequency Cepstral Coefficients (MFCC) and Constant Q Cepstral Coefficients (CQCC) at front-end feature extraction methodologies. Enhanced spell corrector with BERT-based algorithm and Gated Recurrent Units (GRU) based DeepSpeech2 architecture are used to implement the back end portion of the proposed ASR system. The proposed study shown that combining the MFCC features and CQCC features extracted from speech with the GRU-based DeepSpeech2 model and the upgraded or enhanced spell corrector improves the Word Error Rate (WER) by 17.46% when compared to the model without post processing.
Real-World Applications

This chapter explores the practical applications of GANs in the real world. As previously discussed, GANs are deep neural networks capable of generating new data resembling a target distribution, such as images and audio signals. GANs have garnered much attention in machine learning in recent years due to their versatile usage across various industries. This chapter will delve into the various ways in which GANs have been employed in real-world applications. They include but are not limited to the generation of synthetic data for training machine learning models, the creation of photorealistic images and videos, the generation of human faces, the production of fake videos, image-to-image translation, text-to-image translation, CycleGAN, enhancement of image resolution, semantic image inpainting, and text-to-speech. A comprehensive overview of the exciting possibilities of GANs and their impact on various industries will be provided. By the end of the chapter, the reader will clearly understand the practical applications of GANs and their potential to shape the future.
Isolated Word Sign Language Recognition Based on Improved SKResNet-TCN Network

This paper proposes an improved selective kernel network-temporal convolutional (SKResNet-TCN) network-based video recognition model for isolated word sign language with too large parameters, large computation, and difficult to extract effective features. SKResNet uses grouped convolution to save computational cost while dynamically selecting feature information of different perceptual fields to improve the feature extraction ability of the model for video frame images, and TCN introduces causal and inflation convolution to take full advantage of computer parallel computing and reduce memory overhead during computation. The introduction of causal convolution and dilation convolution allows the network to take full advantage of computer parallel computing and reduce memory overhead during computation, and it can capture the feature information between consecutive frames. In this paper, we design a hybrid SKResNet-TCN network model based on these two networks, and propose a solution of hybrid inflated convolution for the problem of losing information between data features in inflated convolution, using adaptive maximum pooling to preserve significant features of sign language instead of adaptive average pooling, and using Mish activation function to improve the generalization ability and accuracy of the model. The accuracy is 100% on the Argentine LSA64 dataset, and the experimental results show that the model in this paper has the advantages of fewer model parameters, smaller operations, and higher accuracy in sign language recognition compared with traditional 3D convolutional networks and long–short term memory, which effectively saves computational cost and time cost.
Machine learning based priors for Bayesian inversion in MR imaging

Abstract The Bayesian approach allows the incorporation of informative prior knowledge to effectively enable and improve the solution of inverse problems. Obtaining prior information in probabilistic terms is, however, a challenging task. Recently, machine learning has been applied for the training of generative models to facilitate the translation of historically or otherwise available data to a prior distribution. In this work, we apply this methodology to undersampled magnetic resonance imaging. In particular, we employ an autoencoder as part of a generative model to statistically regularise and solve the high-dimensional inverse problem using Bayesian inversion. Comparison with a classical Gaussian Markov random field prior is performed and numerical examples highlight the possible advantages of data-driven priors.
Using Instagram Stories to supplement a required self-care pharmacy course

Social media is widely used among Americans, with Instagram being especially popular in adults under age 30. There are few examples of the use of Instagram in pharmacy education and no reports of student perspectives on using Instagram to supplement self-care pharmacy coursework. This article describes the design, implementation, and evaluation of a teaching innovation using Instagram Stories to supplement a required self-care course.The instructors of Self-Care Therapeutics created an Instagram account to share content as an optional supplement to course topics. The account shares stories featuring real-time questions from friends and family of the instructors, product and device demonstrations, and discussion of current events or news related to over-the-counter products. An anonymous survey was sent to all students at the semester conclusion to gain student perceptions of the posted content. A focus group was conducted to further interpret the survey data.Of the 89 students enrolled, 51 completed the survey and 30 students followed the course account. Students agreed that the account was helpful for reinforcing information from class and provided information beyond what was discussed in class, but they had mixed perceptions of its utility for exam preparation and application to real life.Use of Instagram Stories as an alternative method to supplement content in a required self-care course is feasible and received well by students. Use of social media could improve perceived relevance of course topics to students.