Reviewerly/exHarmony huggingface dataset:
Fake News Classification using BLSTM with Glove Embedding

Considering the extensive utilization of social media platforms, false information is more likely to spread rapidly among users. With the surge of user-generated content, a significant amount of the information being shared is in-accurate and has no connection to reality. It can be chal-lenging to automatically identify and verify the accuracy of textual content. This research project aims to employ B-LSTM deep learning model with dynamic and static pre-trained embeddings to classify news datasets utilizing three fake news benchmarking datasets.
Recommendation System for Human Resource Management by the Use of Apache Spark Cluster

In this paper, there has been built and tested recommendation systems for recommending new positions for employees on Apache Spark by the use of a cluster of physical servers. The cluster has been built in the university, and the code has been executed both on a cluster, google colab, and a standalone mode. It was shown that the current cluster spends more time on the execution in comparison with colab, but as new servers get added to the cluster and the size of dataset is to increase, the performance will be much faster in comparison with colab. It can be valuable in HR departments which wish to use their own servers for the security purposes.
Discovering Kazakh facial phenotype

This article discusses the potential applications of face-morphing technology, which has become popular in the field of Computer Vision. In this work, we focus on finding the average face of the Kazakh nation by combining different photos of Kazakh men and women separately. We believe that having an average face for our nation will help other nations quickly identify Kazakh people and can provide insight into its culture and characteristics. The project involves finding all face points and placing them in their initial perfect position with the help of face detection. We use OpenCV as a computational platform and the methods of face detection and face morphing by computer vision to accomplish our goal.
A Matrix Factorization-based Collaborative Filtering Framework for Course Recommendations in Higher Education

In higher education, students are often faced with a plethora of elective courses, making it challenging to determine which courses align with their academic and career goals. Recommender systems offer a potential solution to this problem by utilizing data on students' past academic performance, interests, and goals to provide personalized course recommendations. This paper proposes a personalized course recommendation system based on collaborative filtering with matrix factorization. The proposed method analyzes data from 603 students and provides third-year course recommendations for each student based on their individual preferences and past academic performance. The system has the potential to assist students in selecting the most appropriate elective courses, improving their academic performance, and enhancing their overall educational experience.
Exploration on Effective Mechanism of University-enterprise Joint Training of Electronic Information Professional Master Degree

Based on the analysis of the current training mode and development status of master's degree in electronic information, this article focuses on how to build a training mode for master's degree personnel in electronic information with a deep integration of "production, education and research", aiming at the problems on campus, such as lack of mentor project experience, lack of guidance experience for enterprise mentors, lack of system support for practice base construction and imperfect evaluation system. In this paper, a talent training mode which is suitable for the training objectives of electronic information major graduate students in local universities has been established from the aspects of building a team of double tutors, perfecting the management mechanism of practice base and establishing the quality supervision system.
Equipment Maintenance based in BOPPPS Mode Course Research and Exploration

This paper is based on the BOPPPS teaching model, Taking the professional course of the construction and work of high and low machines in the equipment maintenance course as an example, The classroom teaching is divided into six teaching links: introduction, goal, pre-test, participatory learning, post-test and summary, Overall, more attention is paid to the students' participation in the class, Through the mutual feedback between faculty and trainees, To achieve that both teachers and students can have a deeper understanding of the teaching process and teaching objectives, So as to continuously improve the teaching effect, therefore, The application of this teaching model, Thinking for the teaching reform in the information age, It plays an important practical significance to the research of talent training construction.
An Introduction to Education Informatization for Educational Equity: An Example of Online Education Platform

Education informatization is based on modern information technology, and the main feature is the wide application of information technology in education, which organically combines modern information technology with subject teaching, so that various teaching resources can be shared and students can interact, interact and develop together. With the deep integration of information technology and education practice, the in-depth use of information technology makes China's education more fair and just, and education informatization is an important way to improve education equity, improve teaching quality and promote education modernization. This paper conducts this study on educational informatization for educational equity by introducing educational informatization, educational equity and online education platforms, describing the concept and challenges of educational equity, describing the theoretical framework and empirical research on online education platforms for educational equity, discussing the opportunities and challenges of online education platforms for educational equity, and providing some strategies and suggestions for promoting online education platforms for educational equity.
The Enlightenment of the Educational Thought of "The Analects of Confucius" to International Chinese Teachers

"The Analects of Confucius" is a classic Confucian work in my country, compiled by disciples of Confucius and his disciples, it embodies Confucius' rich political views, ethical thoughts, moral concepts and educational principles. The educational thoughts include educational purpose, teaching principle, teaching method, teaching content, etc. have had a profound impact on today's education. The International Chinese Language Education as a booming industry, while constantly innovating teaching, we should also learn from some ancient teaching concepts. As a classic pedagogical work, “The Analects of Confucius” is feasible and powerful to provide guidance and help for the development of international Chinese language teachers.
Establishing a 'Dual-Teacher' Teaching Team as an Implementation Path for Cultivating High-Quality Technical and Skill Talent within the Visionary Talent Development Framework

In the construction of "Double High School", the integrated teacher-student development has been the internal requirements for establishing a team of high-level double-position teachers and cultivating talents with high-quality technical skills. In the study, a high-level structured teaching team is built with the individualized training of "one teacher, one case" as the starting point; the integrated teacher-student development is promoted by creating a "golden course", establishing a research interest, determining a social service direction, setting up an innovation team of students; “one teacher, one case” cultivation system and a "golden chain" of integrated teacher-student development are formed, which is characterized by "golden teachers, golden courses and golden students". It's hoped to provide references for higher vocational colleges to cultivate high-level double-position teachers and high-quality talents with technical skills, to provide valuable experience for the construction of high-level "double-position teacher" teams and the cultivation of high-quality technical and skilled talents in vocational colleges nationwide.
Negative Transfer Influence of Chongqing Dialect on Second Language Acquisition

There is a phenomenon of language transfer in language learning, which will directly affect the efficiency of second language acquisition. In order to explore the negative transfer of Chongqing dialect to English learning, this paper firstly discusses the negative transfer of Chongqing dialect to second language acquisition based on the theory of language transfer, then focuses on the negative transfer of Chongqing dialect’s initial pronunciation system to second language English learners and its causes. Finally, some suggestions are put forward to overcome the negative transfer caused by Chongqing dialect.
Exploration on the Integration of E-book Bags and Primary School Chinese Classroom Teaching

With the continuous application and popularization of information technology, e-books have gradually entered primary school Chinese language classrooms and become an important force in promoting teaching mode reform and achieving the development of information-based teaching. At the same time, the conflicts and contradictions between e-books and traditional teaching modes in classroom teaching have gradually emerged. Taking the practice of primary school Chinese language teaching as an example, starting from the substitution effect of e-books, the design and selection of information based teaching content, and the interaction of classroom teaching factors, this paper elaborates on the problems in the integration of e-books and traditional teaching models, and explores corresponding development strategies for the problems, in order to provide suggestions for promoting the information based reform of primary school Chinese language teaching.
Research on the Analysis of Classroom Teaching Behavior Based on Artificial Intelligence technology

The study of classroom teaching behavior is usually the primary concern of teaching research, and the early methods of analyzing classroom teaching behavior require a lot of human and material resources, but with the development of information technology, artificial intelligence technology is more and more widely used in education, and the use of artificial intelligence technology can quickly and easily achieve the analysis of classroom teaching behavior, and the application of artificial intelligence technology in the analysis of classroom teaching behavior It plays an important role in the professional development of teachers and the improvement of teaching quality. At this stage, most of the research on analyzing classroom teaching behavior uses computer vision technology and face recognition technology; analyzing classroom teaching behavior requires a large amount of data, and the smart classroom provides a data source and support platform for this research. Artificial intelligence technology involves a very large number of technologies, and how other related technologies can be integrated into various aspects of education and teaching to achieve a deep integration of artificial intelligence and education is a question that should be considered in future research.
Visually Guided Network Reconstruction Using Multiple Embeddings

Embeddings are powerful tools for transforming complex and unstructured data into numeric formats suitable for computational analysis tasks. In this paper, we extend our previous work on using multiple embeddings for text similarity calculations to the field of networks. The embedding ensemble approach improves network reconstruction performance compared to single-embedding strategies. Our visual analytics methodology is successful in handling both text and network data, which demonstrates its generalizability beyond its originally presented scope.
Query-driven Edge Node Selection in Distributed Learning Environments

Computing nodes in Edge Computing environments share unlimited data. Such data are exploited to locally build Machine Learning (ML) models for applications such as predictive analytics, exploratory analysis, and smart applications. This edge node-centric local learning reduces the need for data transfer and centralization, which is affected by different factors such as data privacy, data size, communication overhead, and computing resource limitations. Therefore, a collaborative learning fashion at the network edge has appeared as a promising paradigm that enables multiple distributed (edge) nodes to train and deploy ML models cooperatively without infringement of data privacy. Nevertheless, the variety, distribution and quality of data vary between edge nodes. Hence, selecting unsuitable edge nodes can have a negative impact on the ML model performances. We have devised (i) an intelligent node selection mechanism per analytics query based on the range of the availability of required training data at the edge and (ii) variants of collaborative learning processes engaging the most suitable nodes for models training and inference. We evaluate the efficiency of our selection mechanism and collaborative learning and provide a comparative assessment with other methods found in the literature using real data. The results showcase that our mechanism significantly outperforms baseline approaches and existing node selection mechanisms in distributed computing environments.
Personalized Semantic Matching for Web Search

In recent years, a wide variety of neural networks have been introduced for Web search to calculate the semantic relevance between search queries and Web pages. Although fairly good performance has been achieved, a severe drawback which impedes the existing models delivering superior performance is their one size fits all fashion: the neural networks treat the information needs from different users exactly in the same way. In order to obtain better relevance estimation between search queries and Web pages for each individual user, one novel approach is proposed in this paper to insert a user component for user embedding into the neural network for personalization and three strategies are introduced based on the insert location. These strategies incrementally add a user component to non-personalized neural networks and result in highly personalized relevance computation. The personalized neural networks do not only provide quality calibrated result for the chosen users but also guarantee no side-effect to the others. Comprehensive experiments have been carried out on a large-scale data set of a major commercial search engine to evaluate the proposed method. Experimental results verify the validity of neural network personalization and the superiority of proposed strategies.
Parallel Assemblies Plot, a visualization tool to explore categorical and quantitative data: application to digital mobility outcomes

This paper presents a tool enabling the visual analysis of multivariate heterogeneous data. Large amounts of measured and contextual data are being gathered for a large number of applications, increasing connectivity across different data types. While measured data are often quantitative, contextual data tend to be categorical. This results in datasets containing multivariate data with heterogeneous properties. Difference in the natures of these properties raises challenges when combining them for analysis. This paper presents the design of a tool that enables the exploration of multivariate heterogeneous data by combining the strengths of Parallel Coordinates and Parallel Sets. The design relied on the application domain of real-life mobility monitoring that is particularly affected by the challenge mentioned above. To validate the suggested approach this paper presents the result of a usability evaluation, which confirms that the presented design is as efficient as other exiting tools while providing more features for correlation analysis.
The Future of Eating Together: Exploring the Promises and Challenges of Digital Commensality in the Metaverse

In this talk, we review our evolving relationship with food in the digital world, with a focus on digital commensality, which describes the sharing of food and drink experiences through digital technologies, such as social media, video conferencing, and virtual reality. With the rise of social media and the promises of the metaverse, there has been a growing interest in digital commensality as a way to maintain social connections and a sense of community through food. On the one hand, the metaverse helps us elevate, reconnect with others and with our food practices and create new culinary experiences together. However, the metaverse also presents various challenges from transforming our relationship with food and with the physical world to data collection, integration and processing, privacy and data security. Here, we will examine the opportunities and challenges associated with the development of a food metaverse.
A Visual Analytics Inspired Approach to Correlate and Understand Multiple Mechanical Tensor Fields

We develop an interactive approach for analyzing multi-field tensor data from simulations in close collaboration with domain scientists. Our approach is based on extensive application analysis and built around a multi-field clustering addressing multiple user-defined quantities which were required by the domain scientists. Established techniques like linked views complement the approach to support reasoning while offering an overview and detailed insight into the multi-field tensor data. Further, we include an evaluation containing a real-world use case and a user study with domain scientists to demonstrate the usefulness compared to existing tools.
Understanding 3D Data Videos: From Screens to Virtual Reality

Data storytelling explores how to communicate data insights to the general public engagingly and effectively. It combines the power of data visualizations and storytelling techniques and is popular in various media such as newspapers, interactive websites, and videos. Recently, virtual reality has brought new opportunities to enhance data storytelling with an incomparable sense of immersion. However, there exists a limited understanding of data stories in virtual reality (VR) as they are still in the early stage. In this paper, we investigated the idea of VR data videos by drawing inspiration from popular 3D data videos and studying how to transfer them from screens to VR. We systematically analyzed 100 highly-watched 3D data videos from Youtube and Tiktok channels to derive their design space. We then conducted a user study with 12 participants to explore the effects of four design factors on user experience, including varying camera angles, showing chart overview, animation, and using anchors. Specifically, participants watched 3D data videos in desktop and VR environments. We collected and analyzed their quantitative and qualitative feedback regarding the story’s understandability, memorability, engagement, and emotional effects. Results suggested that data videos in VR were significantly more appreciated than on desktops. We concluded with design implications for future applications and research on VR data videos.
Toward Reproducible Visual Analysis Results

In visualization research, reproducibility is often not taken into account as a design goal. Reproducibility is desirable beyond ensuring scientific rigor: In the long-term, it helps advance sustainable visualization research, and in the short-term, it supports domain experts using the visualizations in their daily work. Designing for and ensuring reproducibility introduces costs, such as as storage cost or infrastructure maintenance. We propose a typology of reproducibility aspects that can be considered during visualization design. We then propose practical strategies to improve reproducibility and discuss their initial and running costs, their limits, and their tradeoffs. Finally, we discuss three concrete visualization approaches in the context of our typology and strategies.
An Empirical Performance Comparison between Matrix Multiplication Join and Hash Join on GPUs

Recent advances in Graphic Processing Units (GPUs) have facilitated a significant performance boost for database operators, in particular, joins. It has been intensively studied how conventional join implementations, such as hash joins, benefit from the massive parallelism of GPUs. With the proliferation of machine learning, more databases have started to provide native support for the basic building blocks of ML algorithms, i.e., linear algebra operators such as matrix multiplication (MM). Despite the recent increasing interest in processing relational joins using matrix multiplication (MM-join), two crucial questions still remain open: i) how efficient are current MM-join implementations compared to the GPU-based join algorithms; ii) how should practitioners choose among MM-join and conventional GPU-based joins given different data characteristics.In this paper, we compare the execution time, and memory I/O of MM-join against multiple GPU hash joins. An empirical analysis of our experimental results reveals that the state-of-the-art hash join implementation shows substantial scalability for various data characteristics. In contrast, MM-join outperforms the SOTA hash join in low join selectivity and low table cardinality but shows unsatisfactory scalability due to synchronous data movement and computation.
NCARVis: No-Code Visualization Creation System based on Free-hand

A low-code development platform (LCDP) is an efficient way to reduce the learning curve for programming languages. It provides a graphical user interface and configurable environment for application building. Users can focus on the design and functionality parts rather than the technical details of programming languages. This can lead to a faster and more efficient development process. However, most of the low-code platforms are mainly used on web pages through traditional interactive devices like mouse and keyboard. To the best of our knowledge, we found there are currently few tools for creating visual interfaces using free-hand manipulation in AR. Interactions in current AR are mostly limited to the two-dimensional display space of screens. Thus, we propose NCARVis, a LCDP that is a novel visualization interface-creating system, it can help users create and show visualization without any programming skills. To provide an environment for creating visualization in NCARVis, we have proposed an initial prototype (MARLP) that changes the interaction method of only touching the 2D screen in the traditional AR scene and provides more interaction methods in AR. Users can freely explore virtual worlds through it, interacting in different directions, and have more creative space to place all diagrams than a 2D scene. We present two tasks for five users without any programming skills to evaluate the usability and effectiveness of NCARVis in visualization creation and design. And we compare NCARVis with a graphical low-code development platform to evaluate the advantages of NCARVis on learning cost. We plan to explore more interesting interactive experiences and visualization design in the future.