Reviewerly/exHarmony huggingface dataset:
Research and Practice of Physical Education Teaching Methods based on OBE Concept

This article delves into the application and practice of the OBE concept in physical education courses. OBE is a learner centered educational method that focuses on learning outcomes and outcomes. Unlike traditional input-oriented education, OBE emphasizes the practical knowledge, skills, and attitudes that students acquire after completing their studies. In the field of physical education, this means not only paying attention to students' skill mastery, but also emphasizing the cultivation of their physical fitness, teamwork ability, and life attitude. The article first reviews the basic concepts and characteristics of OBE, and then provides a detailed introduction to its application methods in physical education courses, including clear learning outcomes setting, the use of formative evaluation, and the integration of cognition, skills, and attitudes. In addition, it also explored how to design and implement effective physical education teaching activities based on the OBE concept. Through the application and practice of OBE in physical education, this article aims to provide a new perspective to help educators better meet students' learning needs and expectations, and provide them with more meaningful and valuable learning experiences. Overall, the OBE concept provides an innovative way of thinking and practice for physical education, and is expected to guide physical education into a new era that is more humane, scientific, and effective.
Research and Application of Mixed Teaching Mode in C Language Course

The current situation of “C language programming” course in our school is characterized by wide teaching scope, more students in class, heavy teaching tasks and so on. In recent years, the curriculum reconstruction and teaching reform have been carried out in the aspects of teaching mode, platform selection, resource utilization and feasibility. the research results in the “C language programming” course have been adjusted and improved, and then applied to teaching. The application results show that the mixed teaching approach based on independent SPOC, which integrates online and offline platforms and multiple teaching methods, can achieve great teaching effect. The new teaching mode fully integrates the advantages of different platforms. It not only guarantees the teaching effect in such a special period, but also provides novel ideas and ways for the future teaching reform.
Design of Digital Culture Teaching for Chinese Language International Education

Digital culture reflects the psychological characteristics and national characteristics of a country and a nation, and is closely related to the religion, belief, philosophy and literature of a country and a nation. Students come from different countries and nationalities, and they have different understandings of numbers and the cultural information they carry, thus forming digital cultural taboos. Digital culture is rich and diverse, and in the teaching of Teaching Chinese as a foreign language, the diversity and complexity of digital culture is a difficult point for students 'learning and teachers' teaching. There are various problems in the teaching of Chinese as a foreign language. Therefore, the author takes the digital culture in the teaching of Chinese as a foreign language as the research object, based on the ontology research of digital culture, analyzes the problems of the digital culture in the teaching of Chinese as a foreign language, and puts forward the corresponding teaching strategies.
CHAT GPT Explore the Relevance with Higher Education Classroom

Chat GPT As the most representative achievement of today's artificial intelligence, it has attracted high attention from the society since its inception, which has not only caused a great shock in the science and technology community, but also caused a strong response in the academic community. Its great influence on colleges and universities solved Jobs' question and highlights the shortcomings of Chinese universities. Chat GPT The series of questions on universities has aroused the reflection on the philosophy of higher education: can the status of knowledge center of universities be maintained? Has the nature of college teachers' profession changed? Will there be new types of universities? These questions not only touch the pain points of China's higher education, but also involve the root of university survival. Where is the future of the university? Universities must make clear decisions: should they adhere to the traditional university development path or organically integrate it with artificial intelligence? No matter what choice they make, it is inevitable that artificial intelligence technology will enter universities. Only by uniting with the society can universities maintain their dominant position of knowledge and further play their functions.
Research on Student Self-education in Universities and Colleges

Through the analysis of self-education concepts and characteristics, the article analyzes the necessity of self -education from the essence of self-education, the level of students' physical and mental development, and school's educational goals. Educational ability, playing teachers 'dominance, creating a good self-education atmosphere, etc. to strengthen students' self -education and cultivation.
Reform and Exploration of Modular Teaching Methods in Higher Vocational Education

In the deepening of modular teaching talent cultivation mode, the traditional teaching concepts in vocational colleges are challenged. It is necessary to integrate the theoretical content, practical skills, and professional qualities of traditional teaching, emphasizing the shift from cultivating students' adaptability to cultivating students' participation ability, and emphasizing the cultivation of students' independent innovation ability. This puts higher requirements on the curriculum system that emphasizes application ability as the standard. This article takes the mechatronics integration major as an example, and combines the work tasks and processes of the relevant job groups in the professional enterprise. Through typical work tasks, the original course system has been reconstructed into 13 modules. Taking one of the modules as an example, this paper deeply analyzes the advantages and characteristics of the theoretical level, practical level and professional quality level, and integrates the three levels into each other to infiltrate, connect and match, and organically combine, ultimately achieving the development and improvement of students' comprehensive ability and quality.
Generating Fine-Grained Aspect Names from Movie Review Sentences Using Generative Language Model

This paper proposes a method for identifying an aspect highlighted in a sentence from a movie review, utilizing a generative language model. For example, the aspect “SFX Techniques” is identified for the sentence “The explosions in cosmic space were realistic.” Classically, aspects are commonly estimated in the field of opinion mining within product reviews with classification or extraction approaches. However, because the aspects of movie reviews are diverse and innumerable, they cannot be listed in advance. Thus, we propose a generation-based approach using a generative language model to identify the aspect of a review sentence. We adopt T5 (Text-to-Text Transfer Transformer), a modern generative language model, providing additional pre-training and fine-tuning to reduce the training data. To verify the effectiveness of the learning techniques thus adopted, we conducted an experiment incorporating reviews of Yahoo! movies. Manual labeling of the correctness and diversity of the aspect names generated shows that our method can generates a variety of fine-grained aspect names using little training data.
Towards a Unified Symbolic AI Framework for Mining High Utility Itemsets

This paper deals with the task of mining high utility itemsets. The proposed approach presents a unified framework for efficiently mining high utility patterns from transaction databases while handling effectively various condensed representations. In addition, this approach offers a way to integrate multiple constraints, including closedness, minimality, and maximality, while maintaining flexibility in the mining process. This allows to significantly enhance the efficiency and effectiveness of mining high utility patterns, making it a valuable tool for various data mining applications. Finally, we show through an extensive campaign of experiments on several popular real-life datasets the efficiency of our proposed approach.
Face to Face with Efficiency: Real-Time Face Recognition Pipelines on Embedded Devices

While real-time face recognition has become increasingly popular, its use in decentralized systems and on embedded hardware presents numerous challenges. One challenge is the trade-off between accuracy and inference-time on constrained hardware resources. While achieving higher accuracy is desirable, it comes at the cost of longer inference-time. We first conduct a comparative study on the effect of using different face recognition distance functions and introduce a novel inference-time/accuracy plot to facilitate the comparison of different face recognition models. Every application must strike a balance between inference-time and accuracy, depending on its focus. To achieve optimal performance across the spectrum, we propose a combination of multiple models with distinct characteristics. This allows the system to address the weaknesses of individual models and to optimize performance based on the specific needs of the application. We demonstrate the practicality of our proposed approach by utilizing two face detection models positioned at either end of the inference-time/accuracy spectrum to develop a multimodel face recognition pipeline. By integrating these models on an embedded device, we are able to achieve superior overall accuracy, reliability, and speed; improving the trade-off between inference-time and accuracy by striking an optimal balance between the performance of the two models, with the more accurate model being utilized when necessary and the faster model being employed for generating fast proposals. The proposed pipeline can be used as a guideline for developing real-time face recognition systems on embedded devices.
DISA - A Blockchain-Based Distributed Information Security Audit

Information security audits are essential for the assessment of enterprise cyber security maturity levels, both from a technical and organizational perspective. A common way of conducting such an assessment is to carry out a security audit based on the international security standard ISO 27001. However, modern organizations often have complex or even global supply chains, which are hard to secure. Verification of sufficient security levels across organizations is a non-trivial task and requires trust between all entities. This paper explores the feasibility of a blockchain-based distributed information security audit, highlights the encountered challenges, and contributes to the discussion of distributed security audits.
Movie Keyword Search Using Large-Scale Language Model with User-Generated Rankings and Reviews

The paper proposes a novel method for conducting keyword-based movie searches using user-generated rankings and reviews, by utilizing the BERT language model for task-specific fine-tuning. The model was trained on paired titles and reviews, enabling it to predict the likelihood of a movie appearing in a ranking that includes a particular keyword. An experiment using data from a reputable Japanese movie review site demonstrated that the method outperformed existing similarity-based approaches. However, some aspects, such as pooling methods, could be improved for accuracy.
Effects of Deep Generative AutoEncoder Based Image Compression on Face Attribute Recognition: A Comprehensive Study

Face Attribute Recognition (FAR) is a computer vision task that has attracted a lot of attention for applications ranging from security and surveillance to healthcare. In real-world scenarios, setting up a FAR system requires an important step, which is image compression because of computational, storage, and transmission constraints. However, severe face image compression not adapted to FAR tasks can affect the accuracy of these latter. In this paper, we investigate the impact of image compression based on deep generative models on face attribute recognition performance. In particular, we present a case study on smile and gender detection by face attribute classification of compressed images. For this purpose, we use QRes-VAE (Quantized ResNet Variational AutoEncoder) for image compression, which is, to the best of our knowledge, the most powerful and efficient VAE model for lossy image compression. Unlike prior studies, we quantify the impact of using deep generative autoencoder for image compression on FAR performance. We also study the impact of varying compression rates on the FAR performance. Results obtained from experiments on the CelebA dataset highlight the potential trade-off between image compression by deep generative autoencoder and FAR performance.
Character Entity Recognition Using Hybrid Binary-Particle Swarm Optimization and Conditional Random Field on Balinese Folklore Text

Identifying the character entities correctly in a story becomes extremely challenging since an entity can refer to a proper noun, a phrase, or a particular definition. This study proposes BPSO-CRF, a hybrid NER method to extract character entities in Balinese stories. In addition, we develop a training dataset for balinese character named entities recognition task. We compare the proposed method against three baseline methods. Overall, BPSO-CRF obtains a relatively better recognition rate compared to the baseline method. Furthermore, only a few numbers of contextual features are relevant to improve the performance of the baseline CRF model.
Integration of Knowledge Bases and External Information Sources via Magic Properties and Query-Driven Entity Linking

A knowledge base (KB) accumulates human knowledge in resource description framework (RDF), which forms a knowledge graph consisting of subject-predicate-object triples. Although RDF KBs are essential information sources for various knowledge processing, many non-RDF information sources exist. Therefore, integrating RDF KBs and external sources will be beneficial in supporting various applications. However, external sources have diverse access methods and input/output formats. In addition, their integration with the KB involves identifying correct entities which correspond to objects in the external sources, which is challenging. In this paper, we present an architecture for an integrated environment (named Knowledge Mediator) in which the user can query the KB as if external sources were an integral part using the Magic Properties of SPARQL. We also propose novel query-driven on-demand entity linking, to select correct entities in the KB for objects in the external sources.
RDF Data Partitioning for Efficient SPARQL Query Processing with Spark SQL

In the age of big data, the volume of RDF data has been exploding due to the growing demands for open data, including Linked Open Data (LOD), semantic data processing, and knowledge graphs. Large-scale RDF data may contain millions to hundreds of millions of triples, comprising subject, predicate, and object, making fast query processing on such datasets challenging. To address this issue, distributed parallel processing systems like Apache Spark has been successfully used. One of the key issues in such systems is to partition the data to maximize performance while balancing the load and minimizing communication between processing nodes by taking into account the dataset’s characteristics and the workload. In this study, we propose a method of RDF data partitioning for efficient query processing by Spark SQL. We exploit the statistics of RDF data and the workload information representing typical user queries, allowing us to group strongly related RDF triples into the same partition. Moreover, we employ indexes whereby only the necessary partitions are loaded for answering a query, reducing the amount of data to be processed and improving query processing performance. Our evaluation experiments showed that the proposed scheme outperformed the comparative methods in table load time and query time for most benchmark queries in a single-node setting.
ImputAnom: Anomaly Detection Framework Using Imputation Methods for Univariate Time Series

Anomaly detection plays a crucial role in various domains such as cybersecurity, fraud detection, and industrial asset condition monitoring. In these fields, identifying abnormal patterns or outliers is paramount for the business they support. This paper presents a new framework that utilizes imputation methods to effectively identify anomalies. To evaluate the performance of the proposed framework, experiments were conducted on different datasets that contain anomalies from different domains. Experimental results demonstrate the effectiveness of the framework in helping to detect anomalies. It provides improvements between 8.17% and 165.21% for all datasets. Experimental results also confirm the effectiveness of the proposed framework and its potential to be applied in real-world scenarios.
Buy Eye-Mask Instead of Alarm Clock!: Graph-Based Approach to Identify Functionally Equal Alternative Products

This paper proposes a method to analyze product reviews to identify other products that can achieve the product’s intended use. When people want to achieve the purpose of “getting up early in the morning,” they generally tend to look only for an “alarm clock.” In contrast, there are several products such as “smart curtains” that can directly achieve the purpose and “sleeping pills” that can indirectly achieve the purpose by replacing “getting up early in the morning” with “going to sleep early at night.” The proposed method constructs a bipartite graph comprising product and uses purpose information from product review data. Then, the random walk with restart technique is employed to rank other products that can satisfy the use purpose of the input product. The proposed method was evaluated in subjective experiments, and the results suggest that the method is both accurate and useful in terms of identifying alternative products that satisfy similar use purposes.
Tag2Seq: Enhancing Session-Based Recommender Systems with Tag-Based LSTM

A key problem of session-based recommendation (sequence pattern or neighborhood-based recommendation) is to predict items of interest based on anonymous user’s current interaction session or unavailable profile user. While recurrent neural networks (RNNs) have been applied to the recommender systems with remarkable success, little attention has been paid to the semantic and contextual information about tags. In this work we argue that long-short-term memory recurrent neural network (LSTM-RNN) incorporating pre-trained global vectors embeddings can be applied to capture semantic and contextual information about tags in session-based recommendation systems with remarkable results. Therefore, this paper introduces Tag2Seq, a novel recommendation system based on a recurrent neural network architecture that utilizes LSTM to leverage user’s tags. Our method is evaluated on the Movielens 20M dataset, Experimental results indicate that Tag2Seq outperforms state-of-the-art session-based recommendation methods on large real-world dataset.
Using Derived Sequential Pattern Mining for E-Commerce Recommendations in Multiple Sources

Existing multi-source E-Commerce recommendation algorithms, such as Multi-source Category Extension system (ECCF19), use item categories to improve the quality of the user-item rating matrix input to the collaborative filtering (CF) process for better recommendations. HPCRec18 model derives a consequential bond between clicks and purchases to predict preferences for users with no purchase history, whereas HSPRec19 uses sequential purchase patterns from historical data as well as consequential bond in mined patterns to improve the user-item matrix for CF. None of these systems use both the historical and item description data to address the CF limitations. This paper proposes a Multi-source Category Extended Historical Sequential Pattern Recommendation System (MCE-HSPRec), an extension of the HSPRec19 system to increase recommendation coverage and alleviate new item problem by enriching item category information. MCE-HSPRec derives enriched category-based user profiles by analyzing item categories that are frequently purchased together. Results show that HSPRec achieves 36.64% more prediction coverage compared to HSPRec. MCE-HSPRec also obtains high precision and recall values (0.94716, 0.94781) in comparison to HSPRec19 (0.8985, 0.90002).
Does Use of Blink Interface Affect Number of Blinks When Reading Paper Books?

Blinking is essential in maintaining eye health. The lack of blinking may lead to dry eyes, which usually happens when reading paper books. The previous blink acceleration system prompted users to blink effectively, but mandatory blinking made users uncomfortable. If users stop using the blink acceleration system, the number of blinks will reduce. Therefore, we proposed a new blink acceleration system using classical conditioning to prompt to blink when reading paper books unconsciously. Specifically, the number of blinks would increase while reading paper books because of the association between page-turning action and blinking by using the system that assigns blinking to the page-turning action when reading e-books. As a result of our evaluation experiment, the number of blinks increased for some participants with our system. There is a possibility of increasing the number of blinks while reading paper books by periodically using the proposed blink acceleration system.
Federated Learning for Collaborative Cybersecurity of Distributed Healthcare

Healthcare 4.0 is a new paradigm for providing healthcare services in highly distributed and complex settings. The distributed and heterogeneous nature of home-based medical devices, and their need to exchange data with external sources make Healthcare 4.0 solutions susceptible to cyberattacks and require decentralized solutions to protect sensitive local data. This work presents a collaborative approach to security incident detection for distributed healthcare utilizing federated learning. At this stage, the federated learning process has been facilitated and evaluated using simulation, training, and testing.
Multitask, Cross-Lingual Recipe Classification Using Joint Fine-Tuning Mechanisms

We explore solutions for text classification applied to online cooking recipes, in a multitask, multilingual approach. The main objective is designing a solution that ensures high accuracy on the prediction tasks from, but not constrained to, 6 European Languages, considering also the cross-lingual transferability. The challenges of the problem are structured on two main dimensions: (1) data driven - such as imbalance and noise in the training data, and (2) solution driven - such as multilingualism, or the need to easily extend the model to new languages. We propose a solution focused on the XLM-R architecture, fine-tuned jointly on all tasks. We apply self-supervised domain adaptation via additional pre-training and analyze the enhancements produced by performing a 0-shot evaluation for underrepresented languages. Compared to basic language modeling solutions, we obtained an increase of 1.32% and 2.42%, respectively for the two most difficult classification tasks. In the 0-shot context, the absolute improvements are of 16.71% and 7.83% respectively, on underrepresented languages.