Reviewerly/exHarmony huggingface dataset:
A Typology of Civil Society: Type of discourse and Practice of public interest action

This study construct a typological framework from the viewpoint of public interest actions as a cognitive tool to comprehensively and systematically figure out the flow and current situation of Korean civil society. Through the review of theoretical resources, it was discerned two key issues that are important to the typology of civil society from the perspective of public interest actions; ‘relationships’ between civil society and other realms, and ‘roles’ performed by civil society. While considering the main factors affecting the discourse and practice of public interest actions in civil society, a typological framework was constructed using two dimensions, the relationship and role of civil society, which contain different perspectives. Regarding relationships, the view that civil society should actively ‘involved’ in other areas such as government and market, versus the view that it should establish an ‘independent’ area from other areas; Regarding roles, the view of ‘conflictive’ that raise issues and pursue social changes, versus the view of ‘functional’ that complements roles are distinguished from roles of government and market. Then, by crossing two dimensions, set four type sub-sector of civil society: ‘opposition’ (involvement-conflict), ‘autonomy’ (independence-conflict), ‘charity’ (independence-function), and ‘resolution’ (involvement-function), and looked at researches and discourses on each sub-sectors. Finally, In order to verify whether the typological framework can adequately explain related phenomena, I applied this framework to the historical practice of civil society and public interest actions in Korea. As a result of the discussion, this typological framework is verified to have an explanatory power about the discourse and situation of civil society and public interest actions in Korea.
A Study on the Perspective of Confucian Classicsby Park Jang hyun

Although Park Jang-hyun lived for a short time, he stood out in the field of Confucianism and history and left many related writings. In the midst of the loss of his country, he thought about the role of intellectuals and diagnosed that his country was annexed to Japan because previous Confucian scholars did not understand the reality properly, but he saw that it was not a matter of Confucianism itself. Confucianism was not a study to be discarded, but rather an object to be restored. The problem was the attitude or interpretation of Confucian Classics. He saw that although Confucian Classics emphasizes universal truths, on the other hand, its interpretation and application should vary depending on reality and the situation of the times.&#x0D; Representative sentences on the study of Park Jang-hyun's Confucian Classics abroad include “Gyeonghakdogbon” and “Samgyeongsurok”. The three Confucian Classics mentioned in “Samgyeongsurok” are “The Book of Songs”, “The Book of History” and “The Book of Changes”. These are included in the sentences of “Doksisugi”, “Doksiseogi”and “Juyeokhokmun”. Through the analysis of these three sentences, this paper attempts to analyze Park Jang-hyun's views on Confucian Classics and its interpretation.
Persona Design: Representativeness and Empathy through Cultural Integration

Abstract Persona design aims to increase students’ ability to understand their target users and address their needs. Yet, there is a lack of conceptual frameworks that help students systematically conceptualise user needs, specifically the two key requirements of persona design: representativeness and empathy. In this study, we find an alternative method using cultural dimensions to ensure that students conceptualise personas by reflecting representativeness and empathy in a systematic way. We justify cultural dimensions and engagement aspects and suggest a classified table for representative and empathetic persona design. In a design course, we analyse personas created by students in two different groups with the table (Group 1, n = 16) and without the table (Group 2, n = 17) through comparative thematic analysis to evaluate the qualities of representativeness and empathy. As a result, the cognitive aspect of engagement is predominated in Group 2, whereas the cognitive, emotional, behavioural, and social aspects of engagement are evenly distributed in Group 1. 11 cultural dimensions are identified in Group 2, whereas 20 cultural dimensions are identified in Group 1. In Group 2, a particular dimension is predominant (44.4% of individualism), whereas, in Group 1, the rate of the most used dimension is 12.2% (femininity and collectivism). The study results indicate that the method allows students to diversify and deepen their understanding of user needs and thereby conceptualising personas in in‐depth and analytical ways. From instructional perspectives, it can be used by educators to help students systematically conceptualise user needs in design activities.
Background and Impact Analysis of the 10-Year Fishing Ban and Fishing Culture

In January 2020, the Ministry of Agriculture and Rural Affairs issued a notice on the official website on the scope and timing of the fishing ban in key waters of the Yangtze River Basin, announcing that the ten-year fishing ban plan for the Yangtze River will be implemented from 00:00 on January 1, 2020.According to the notice, the natural waters of the main stream and important tributaries of the Yangtze River, except for aquatic organisms nature reserves and aquatic germplasm resources protection areas, will implement a provisional 10-year year-round ban on fishing from 0:00 on January 1, 2021 at the latest.Productive fishing of natural fishery resources is prohibited.In order to deeply study the background and possible impact of the ten-year fishing ban policy and fishin g culture, this article is based on the study of articles related to the ten-year fishing ban and fishing culture.The t hesis is divided into layers to summarize the practical significance of the ten-year fishing ban policy and the infl uencing factors of fishing culture.Over the past year since the implementation of the policy, the biodiversity and ecological environment in the Yangtze River Basin have been significantly restored, and the number of fish populations has increased signi ficantly.Protecting and inheriting innovative fishing culture is conducive to the smooth advancement of the tenyear fishing ban, the inheritance and innovation of Yangtze River culture, the promotion of the construction of a cultural power, the overall layout of ecological civilization construction, the rural revitalization strategy of the Y angtze River Economic Belt, and the richness of Chinese cultural connotations.
Optimization and Learning With Randomly Compressed Gradient Updates

Abstract Gradient descent methods are simple and efficient optimization algorithms with widespread applications. To handle high-dimensional problems, we study compressed stochastic gradient descent (SGD) with low-dimensional gradient updates. We provide a detailed analysis in terms of both optimization rates and generalization rates. To this end, we develop uniform stability bounds for CompSGD for both smooth and nonsmooth problems, based on which we develop almost optimal population risk bounds. Then we extend our analysis to two variants of SGD: batch and mini-batch gradient descent. Furthermore, we show that these variants achieve almost optimal rates compared to their high-dimensional gradient setting. Thus, our results provide a way to reduce the dimension of gradient updates without affecting the convergence rate in the generalization analysis. Moreover, we show that the same result also holds in the differentially private setting, which allows us to reduce the dimension of added noise with “almost free” cost.
Self-adaptive graph convolution networks with application to industrial soft sensor modeling

In recent years, deep learning has been widely studied in soft sensor modeling. However, the prediction of the deep learning model is difficult to explain, and it is hard to incorporate prior experience into the model. These shortcomings of deep learning prevent its application in real industrial processes. In this article, we propose a self-adaptive graph convolution networks (SAGCN) for industrial soft sensor modeling. This model uses the graph convolution network to introduce prior knowledge and construct the displayed nonlinear relationship among variables. And the graph convolution network can aggregate information to extract features from data. Because it is difficult and highly subjective to rely on prior knowledge and mechanisms to obtain the graph structure, this article proposes a graph structure self-learning method to realize the joint learning of the nonlinear relationship among auxiliary variables and the regression relationship between auxiliary variables and quality variables. The proposed method is verified through the CO<sub>2</sub> absorption column process from a real ammonia synthesis process. Based on the results, SAGCN demonstrates high accuracy and a certain capacity to discover knowledge.
Improving Fault Tolerance and Tackling Broker Failure in MQTT through Blockchain

MQTT is a widely used Publisher-Subscriber protocol in IoT and Edge Computing. Its lightweight, minimal overhead nature has aided real-time data exchange between the Publishers and the Subscribers. As of MQTT 5, the protocol has fault-tolerance and fault detection implemented through QoS Levels 1 and 2 in the form of ‘Retained Messages,’ ‘Persistent Sessions,’ and ‘LWT’ (‘Last Will’ and Testament’). Many researchers have dived deep into securing this protocol by using Blockchain, using multiple Fog and Mist Computing infrastructures, and they have accomplished faster, more efficient, and more secure implementations of the MQTT protocol. Often, the less explored scope of optimization is the fault tolerance of MQTT, which has its limitations. The current specification of MQTT allows the broker to only store one retained message per client ID, which becomes a limitation in the application use cases where the newly subscribed devices are supposed to get an array of data points instead of one. The current specification of MQTT does not implement fault tolerance on the broker end, which can disrupt the network on broker failure. The paper explores how we can use Blockchain Technology to overcome the limitations of ‘Retained Messages' in MQTT and improve the Dead Letter Exchanges in QoS levels 1 and 2 through Blockchain Technology. In addition, the paper explores and implements dynamic load balancing using Blockchain Naming Services to tackle broker failure in the MQTT protocol, which will allow brokers to be hosted under decentralized domain names on the Blockchain, monitored by a load balancer.
An exploratory study on how release management practices minimize the impact factors affecting software releases

Software delivery is always challenging without any issues. During this process of software delivery, release management team plays a significant role in planning, communication, coordination, and implementation. This paper covers exploratory study some of the factors that influence software releases and how release management strategies and practices will help in minimizing those factors to have smoother software releases on time and within budget.
Transfer Learning with EfficientNet for Computer Vision based Automatic Gender Classification

The past several years have seen a lot of activity in the field of gender classification research. For a wide range of tasks including surveillance, computer vision, human-computer interaction, and traffic monitoring, gender classification is essential. The traditional approach used to classify gender has a number of drawbacks and is insufficient when used with live images. However, gender classification is just one of several computer vision applications where deep learning algorithms have demonstrated their potential. CNNs are capable of automatic feature generation and provide data insight which is used for efficient classification. In this paper, a special class of pre-trained CNN, called EfficientNet is proposed for automatic gender classification from input image. The experiment is carried out using a dataset with 58,700 images categorized into male and female. The performance of EfficientNet-B0 is also compared with other benchmark pre-trained CNN models i.e. VGG16, ResNet50 and DenseNet. The Precision, Recall and F1-Score values achieved for EfficientNet-BO are 96.80%, 96.60% and 96.88% respectively.
End-to-End Pre-Training With Hierarchical Matching and Momentum Contrast for Text-Video Retrieval

Lately, video-language pre-training and text-video retrieval have attracted significant attention with the explosion of multimedia data on the Internet. However, existing approaches for video-language pre-training typically limit the exploitation of the hierarchical semantic information in videos, such as frame semantic information and global video semantic information. In this work, we present an end-to-end pre-training network with Hierarchical Matching and Momentum Contrast named HMMC. The key idea is to explore the hierarchical semantic information in videos via multilevel semantic matching between videos and texts. This design is motivated by the observation that if a video semantically matches a text (can be a title, tag or caption), the frames in this video usually have semantic connections with the text and show higher similarity than frames in other videos. Hierarchical matching is mainly realized by two proxy tasks: Video-Text Matching (VTM) and Frame-Text Matching (FTM). Another proxy task: Frame Adjacency Matching (FAM) is proposed to enhance the single visual modality representations while training from scratch. Furthermore, momentum contrast framework was introduced into HMMC to form a multimodal momentum contrast framework, enabling HMMC to incorporate more negative samples for contrastive learning which contributes to the generalization of representations. We also collected a large-scale Chinese video-language dataset (over 763k unique videos) named CHVTT to explore the multilevel semantic connections between videos and texts. Experimental results on two major Text-video retrieval benchmark datasets demonstrate the advantages of our methods. We release our code at https://github.com/cheetah003/HMMC.
Video-Based Sentiment Analysis of International Chinese Education Online Class

In the era of COVID-19, online teaching has gradually become an indispensable part of the education systems in countries around the world and a major direction of research for educators. This emerging educational approach has both advantages and disadvantages. In the absence of observing students’ learning status and emotions, the assessment of students’ learning emotions as well as class efficiency have become a major challenge. This paper used Chinese international education class videos as the data source and adopted a semi-automatic method to construct a student emotion data-set, which can overcome the difficulty of insufficient class teaching video resources and fill the gap of student emotion data in international Chinese education online class. Also, this paper creatively used a segmentation method to research students’ emotions within each teaching behavior segment, which would provide a reference for future research on students’ emotion analysis and teaching behavior.
Fake News Detection Approach Based on Logistic Regression in Machine Learning

Today, social media is allowing everyone to spread information without any cost, and only a few people will do the small investigation and fewer filters than before. Political events are one of the increases in the popularity and spread of fake news. The increasing of fake news is the major problem of today and will negatively impact the world. To avoid this type of bogus information spreading worldwide, we can use artificial intelligence and machine learning. Recently, deep learning techniques used complex natural language processing also to providing the solution for counterfeit news detection. Facebook is using AI techniques to filter the fake news stories from the user feeds. Now it is possible using the Naïve Bayes classifier to differentiate between fake news and real news.
Hypergraph Modeling

Abstract Hypergraph modeling is the fundamental task in hypergraph computation, which targets on establishing a high-quality hypergraph structure to accurately formulate the high-order correlation among data. In this section, we introduce different hypergraph modeling methods to show how to build hypergraphs using various pieces of information, such as features, attributes, and/or graphs. These methods are organized into two broad categories, depending on whether these correlations are explicit or implicit, to distinguish the similarities and differences. We then further discuss different hypergraph structure optimization and generation methods, such as adaptive hypergraph modeling, generative hypergraph modeling, and knowledge hypergraph generation.
Natural Language Processing Techniques for Sentiment Analysis in Social Media

Natural Language Processing Techniques for Sentiment Analysis inSocial Media
The role of physical education professionals in post-covid-19 rehabilitation: experience report

Hospitalized patients affected by COVID-19 have neuromuscular and cardiorespiratory sequelae. Thus, this study was carried out with the objective of reporting the performance of the physical education professional in a functional rehabilitation program, offered to post-COVID-19 patients. The reported experience took place in the Municipal Health Network in the city of Campo Grande/MS, with interventions developed through Unique Therapeutic Projects. The physical education professional participates in the entire rehabilitation process, from reception to discharge of the patient. The approach is aimed at promoting functional autonomy, control of risk factors for comorbidities, postural correction and maintenance of ideal weight. The service includes aerobic, resistance and flexibility training, health education actions and the continuity of the practice of physical activities after the end of the Project sessions. We conclude that the intervention of this professional was essential to improve the functional autonomy and well-being of patients. With this, we recommend that the experience be replicated, either within the Unified Health System or in other national public health systems.
Fall Detection and Elderly Monitoring System Using the CNN

Fall detection has become a critical concern in the medical and healthcare fields due to the growing population of the elderly people. The research on fall and movement detection using wearable devices has made strides. Accurately recognizing the fall behavior in surveillance video and providing the early feedback can significantly minimize the fall-related injury and death of elderly people. However, the fall event is highly dynamic, impairing categorization accuracy. The current study sought to construct a fall detection architecture based on deep learning to predict falls and the Activities of Daily Living (ADLs). This paper proposes an efficient method for representing extracted features as RGB images and a CNN model for learning the features needed for accurate fall detection. Additionally, the proposed CNN model is used to test for and locate the target in video using threshold-based categorization. The suggested CNN model was evaluated on the SisFall dataset and was found to be capable of detecting falls prior to impact with a sensitivity of 100%, a specificity of 96.48%, and a response time of 223ms. The experimental findings attained an overall accuracy of 97.43%.
Blockchain and Sustainable Energy

Blockchain is a relatively new technology that can provide security, transparency, and integrity to digital data. These properties are particularly important in sustainable energy developments. In this chapter, we first examine the sustainability of blockchain technology. We then explore applications of this technology in sustainable oil and gas and the broader energy sector. Furthermore, a case study is provided to showcase how securing emission data using blockchain could lead to the development of differentiated and premium fuels. We interviewed several experts involved or knowledgeable of this project. Excerpts from those interviews are included in this chapter to get their perspective and understand the significance and limitations of this new development. In the end, the challenges and risks of applying blockchain for sustainable energy applications are discussed.
Exploring Zero and Few-shot Techniques for Intent Classification

Conversational NLU providers often need to scale to thousands of intent-classification models where new customers often face the cold-start problem. Scaling to so many customers puts a constraint on storage space as well. In this paper, we explore four different zero and few-shot intent classification approaches with this low-resource constraint: 1) domain adaptation, 2) data augmentation, 3) zero-shot intent classification using descriptions large language models (LLMs), and 4) parameter-efficient fine-tuning of instruction-finetuned language models. Our results show that all these approaches are effective to different degrees in low-resource settings. Parameter-efficient fine-tuning using T-few recipe (Liu et al., 2022) on Flan-T5 (Chang et al., 2022) yields the best performance even with just one sample per intent. We also show that the zero-shot method of prompting LLMs using intent descriptions