Analysis of community question-answering issues via machine learning and deep learning:State-of-the-art review

Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic interest.Scholars have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs engender.To clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature review.The goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)users.The final review included 133 articles.Dominant research themes include question quality,answer quality,and expert identification.In terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack Overflow.The scope of most articles was confined to just one platform with few cross-platform investigations.Articles with ML outnumber those with DL.Nonetheless,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.

Analysis of the Relationship between Eating and Swallowing Function and Lifestyle of the Elderly Living in the Community-A Secondary Publication

Objectives:The relationship between eating and swallowing function,and lifestyle among community-dwelling elderly people has not been extensively studied.This study aimed to analyze the characteristics of eating and swallowing function and their association with the lifestyle among the elderly.Methods:A self-administered questionnaire survey was conducted on 419 elderly people who participated in the oral function improvement project operated by the Community Comprehensive Support Center.A total of 288 valid responses(58 males,230 females,average age 73.6 years)were analyzed.The survey items included basic demographics,health status,lifestyle,and eating and swallowing functions.The chi-square(χ2)test was used to compare for a difference in the risk of dysphagia.Results:72 patients(25.0%)were judged to be at risk for dysphagia,and 216(75.0%)were judged to be not at risk for dysphagia using the revised dysphagia risk assessment scale.The mean score for oral preparatory dysphagia was the highest,while the mean score for pharyngeal dysphagia was the lowest.The group at risk of dysphagia had significant difficulty in chewing and had bad sleep quality as compared to the group that was not at risk.Conclusion:Concerning the risk of dysphagia,there is a need to maintain and improve masticatory function.In addition,improving the swallowing function of the elderly may prevent insomnia and improve sleep quality.

Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers

Contactless verification is possible with iris biometric identification,which helps prevent infections like COVID-19 from spreading.Biometric systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses,replayed the video,and print attacks.The work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris liveness.Seven assorted feature creation ways are studied in the presented solutions,and these created features are explored for the training of eight distinct machine learning classifiers and ensembles.The predicted iris liveness identification variants are evaluated using recall,F-measure,precision,accuracy,APCER,BPCER,and ACER.Three standard datasets were used in the investigation.The main contribution of our study is achieving a good accuracy of 99.18%with a smaller feature vector.The fragmental coefficients of Haar transformed iris image of size 8∗8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size(64 features).Random forest gave 99.18%accuracy.Additionally,conduct an extensive experiment on cross datasets for detailed analysis.The results of our experiments showthat the iris biometric template is decreased in size tomake the proposed framework suitable for algorithmic verification in real-time environments and settings.

Deep Transfer Learning Driven Automated Fall Detection for Quality of Living of Disabled Persons

Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.

LC-NPLA: Label and Community Information-Based Network Presentation Learning Algorithm

Many network presentation learning algorithms(NPLA)have originated from the process of the random walk between nodes in recent years.Despite these algorithms can obtain great embedding results,there may be also some limitations.For instance,only the structural information of nodes is considered when these kinds of algorithms are constructed.Aiming at this issue,a label and community information-based network presentation learning algorithm(LC-NPLA)is proposed in this paper.First of all,by using the community information and the label information of nodes,the first-order neighbors of nodes are reconstructed.In the next,the random walk strategy is improved by integrating the degree information and label information of nodes.Then,the node sequence obtained from random walk sampling is transformed into the node representation vector by the Skip-Gram model.At last,the experimental results on ten real-world networks demonstrate that the proposed algorithm has great advantages in the label classification,network reconstruction and link prediction tasks,compared with three benchmark algorithms.

The Practical Path to Cultivating Innovative Talents:Implementing Cross-Disciplinary Learning Communities in Environmental Design Education

In response to the national strategy of“vigorously cultivating interdisciplinary talents and actively promoting interdisciplinary integration,”this article focuses on the nationally recognized Environmental Design program at Hezhou University’s College of Design,leveraging local industry advantages to engage in interdisciplinary integration through educational practices.Using the“Construction of the Panoramic Virtual Nature Museum of the Guizhou Crocodile Lizard at Mount Dagui”as a case study,we aim to establish a professional and interdisciplinary learning community,incorporate student-centered interactive teaching methods,boost student motivation,enhance teaching quality,nurture forward-thinking versatile innovative talents,and provide a guideline for interdisciplinary educational reform.

Enhancing Students’ Learning about Healthy Living through Community Participation

Introduction: The Health Enhancement Module (HEM) is taught as a core curriculum for all medical students at Monash University since 2002. In 2012 we moved the year three content of the program into a community setting, calling it the Health Enhancement Carnival (HEC). At the carnival, our undergraduates interacted with school students, their teachers, and their parents, involving them in a mix of discussions, poster presentations, and video presentations. In this paper we present our experience with the HEC. Specifically, we looked at the following two measures: how did the HEC influence the knowledge, attitude, and practice of healthy living among medical students? And, what were the learning experiences of the students during the HEC? Methods: Five themes (exercise, food, healthy sleep, workplace stress and ageing) were divided among students. They were asked to develop those themes with the help of posters, power point presentations, community talks as well as video presentations. The carnival was held in the setting of two nearby children’s schools. Students were evaluated by a panel of examiners with regards to learning objectives as well as preparation and presentation. As part of evaluation, we developed 2 questionnaires. The HEP Healthy Living Questionnaire provided feedback on how the program had improved students’ knowledge, attitudes, and practice of healthy living. The HEP Learning Style Questionnaire covered twelve areas, including collegiality, environment, leadership, community interaction and other facets of learning style. Analyses were performed using the IBM SPSS Statistics version 20 software in the Clinical School Johor Bahru. Results: 1) Influence of HEC on the knowledge, attitude, and practice of healthy living among medical students. From the interviews, the judges gave the students mean ratings of 4.0/5. We also received 77 out of 127 feedback questionnaires (response rate: 60.6%) from the students. Most students (range: 49.35% to 55.84%) were “satisfied/totally satisfied”, “achieved/totally achieved”, or “improved/totally improved” to 5 questions of the Healthy Living Questionnaire. Correlation coefficients between knowledge of healthy living, attitude towards healthy living, and practice of healthy living were large (exceeding 0.8) suggesting that these three measures were highly and positively inter-correlated. Most students (range: 60.28% to 71.43%) scored “a lot/almost all”, to 5 questions regarding achievement of learning objectives. 2) Learning experiences of the students during the HEC. Responding to the HEP Learning Style Questionnaire, most students (range: 66.24% to 85.72%) agreed or strongly agreed that the program provided an optimal environment for learning, encouraging students to assume leadership responsibilities and promoting self-directed learning. A correlation matrix of the 12 items showed medium to large correlations between all twelve variables. Conclusions: The Health Enhancement Program (HEP) is an innovative approach that has enabled students to learn about healthy living within the context of the local community.

Innovation in Adult Language Learning Programs: A Case Study of "Livemocha" Online Language Learning Community

Due to the increasing broadband Internet access, VOIP adoption as well as the Web 2.0 concepts, online language learning communities have gained great popularity among adult language learners. Based upon Malcolm Knowles' adult learning theory, this paper analyzes the innovation that online language learning communities have brought to adult language learning programs. At the same time, the limitations of such programs have been pointed out.

从学习社区到学习共同体——教育技术学领域中的“learning community”辨析

"社区"一词在汉语中侧重于"地域性",而教育技术学领域中的"learning community"更强调"共同归属感",翻译为"学习共同体"更为合适。"学习共同体"应具备"活动场"、"一定数量固定的人群与频繁的互动"、"共同的社会心理基础"三个基本要素,根据不同的标准可以分为多种不同类型。

Learning Community视阈下地方师范院校教师资源整合

在对Learning Community介绍的基础上,分析了其在中国实现的可能性,在LearningCommunity视阈下,以创新的专门用途英语教学实现专业教师和外语教师的教师和教学资源整合为例,探讨地方师范院校教师资源整合。

An Introduction to Learning Community

Learning community, a theory that has gained popularity in the USA and many European countries over the past twenty years, has made a tremendous influence upon each aspect of education. There is no universal definition of it, but there is a general consensus that learning community develops through the cooperative efforts of the group members within the environment that al - lows all the members to achieve shared goals. Learning is not merely based on individual's efforts, but mainly on the collective ex- periences of learning community.

Applying Learning Community Theory to Oral English Teaching in Sport Institutes

Since traditional English teaching method, which merely focuses on language teaching but ignores communicative competence, severely impedes the development of students' oral ability. It is high time that English teachers took measures to find a workable and valuable teaching method which can improve students' speaking proficiency effectively. Learning community theory provides a broad space for this, for it regards learning as a process which takes place in a community where the learners are sharing their experience towards knowledge building in an interactive and cooperative way.

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes.These sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during emergencies.Falling is one of the most important problems confronted by older people and people with movement disabilities.Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people.But,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor environments.Currently,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements.Against this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)model.The presented IWODL-FDDP model aims to identify the fall events to assist disabled people.The presented IWODLFDDP model applies an image filtering approach to pre-process the image.Besides,the EfficientNet-B0 model is utilized to generate valuable feature vector sets.Next,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall events.Finally,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the work.The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

Delineation of an Urban Community Life Circle Based on a Machine-Learning Estimation of Spatiotemporal Behavioral Demand

Delineating life circles is an essential prerequisite for urban community life circle planning. Recent studies combined the environmental contexts with residents’ global positioning system(GPS) data to delineate the life circles. This method, however, is constrained by GPS data, and it can only be applied in the GPS surveyed communities. To address this limitation, this study developed a generalizable delineation method without the constraint of behavioral data. According to previous research, the community life circle consists of the walking-accessible range and internal structure. The core task to develop the generalizable method was to estimate the spatiotemporal behavioral demand for each plot of land to acquire the internal structure of the life circle, as the range can be delineated primarily based on environmental data. Therefore, behavioral demand estimation models were established through logistic regression and machine learning techniques, including decision trees and ensemble learning. The model with the lowest error rate was chosen as the final estimation model for each type of land. Finally, we used a community without GPS data as an example to demonstrate the effectiveness of the estimation models and delineation method. This article extends the existing literature by introducing spatiotemporal behavioral demand estimation models, which learn the relationships between environmental contexts, population composition and the existing delineated results based on GPS data to delineate the internal structure of the community life circle without employing behavioral data. Furthermore, the proposed method and delineation results also contributes to facilities adjustments and location selections in life circle planning, people-oriented transformation in urban planning, and activity space estimation of the population in evaluating and improving the urban policies.

A Qualitative Study on What It Means for Patients with Schizophrenia Living in the Community to Remain on Medication

Background: Little is known about what the experience of “taking antipsychotics” means in a patient’s life. Therefore, this study aims to identify what it means for patients with schizophrenia living in the community to remain on medication. Methods: The participants were five residents of communities, who had been discharged from a psychiatric hospital, but were currently visiting a private psychiatric hospital. In this study, we used participants’ narratives as data and analyzed them according to the procedures described in “An Application of Phenomenological Method in Psychology” (Giorgi, 1975), and “Practice of analyzing materials describing experiences” (Giorgi, 2004). Results: The study results are as follows. 1) The drug may be effective, but Subject (below, S) still wants to take it as little as possible. Meanwhile, S has people who care about S and a person who S can rely on nearby, to manage S’s life. The people above tell S to take medicine, and S takes it. 2) S does not know what kind of medication S is consuming, but recently S has been having a hard time walking;S has people who care for S’s foot and look after S. S thinks taking medicine is for living. 3) S feel some drugs is ineffective. However, S met some people S could trust who passionately recommended the medication to S. S started being careful in remembering to take it. 4) S does not think drugs are necessary for S, but S can interact with people and spend S’s days. S has people who accept S as S is. S continues living in the community while taking medicine that a doctor offers. 5) S was skeptical about the drugs. However, S has a person S can trust, who recommended a way to take the medication in a way that S does not feel overwhelmed. S thinks that it may be a good idea to take it. Conclusions: Based on the analysis of the narratives of each of the five participants, the essential structure was read from the perspective of a third party regarding participants’ medication adherence. A generalized reading of the structure common to the above five essential structures reveals a structure that includes the following three opportunities: 1) Patients realize the importance of people;2) They sometimes entrust themselves to people or follow people’s opinions when taking actions;3) They have come to terms with their initial negative feelings about antipsychotic drugs, subsequently continuing to take antipsychotic drugs. This suggests that the following are important attitudes of supporters of patients with schizophrenia who continue to live in the community: To accept what is happening to the patients, to talk to them with encouragement and compassion, and to be there for them. It is also important for supporters to make patients feel comfortable in opening up while the patients reside in the community and to support patients in making decisions.

Topic Sentiment Analysis in Online Learning Community from College Students

Purpose:Opinion mining and sentiment analysis in Online Learning Community can truly reflect the students’learning situation,which provides the necessary theoretical basis for following revision of teaching plans.To improve the accuracy of topic-sentiment analysis,a novel model for topic sentiment analysis is proposed that outperforms other state-of-art models.Methodology/approach:We aim at highlighting the identification and visualization of topic sentiment based on learning topic mining and sentiment clustering at various granularitylevels.The proposed method comprised data preprocessing,topic detection,sentiment analysis,and visualization.Findings:The proposed model can effectively perceive students’sentiment tendencies on different topics,which provides powerful practical reference for improving the quality of information services in teaching practice.Research limitations:The model obtains the topic-terminology hybrid matrix and the document-topic hybrid matrix by selecting the real user’s comment information on the basis of LDA topic detection approach,without considering the intensity of students’sentiments and their evolutionary trends.Practical implications:The implication and association rules to visualize the negative sentiment in comments or reviews enable teachers and administrators to access a certain plaint,which can be utilized as a reference for enhancing the accuracy of learning content recommendation,and evaluating the quality of their services.Originality/value:The topic-sentiment analysis model can clarify the hierarchical dependencies between different topics,which lay the foundation for improving the accuracy of teaching content recommendation and optimizing the knowledge coherence of related courses.

Significance of Live Streaming in Shaping Business: A Critical Review and Analytical Study

With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between businesses and their customers. This critical literature review paper explores the impact of live streaming on businesses, focusing on its role in attracting and satisfying consumers by promoting products tailored to their needs and wants. It emphasizes live streaming’s crucial role in engaging customers, a key to business growth. The study also provides viable strategies for businesses to leverage live streaming for growth and customer engagement, underscoring its importance in the business landscape.

Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem.Meanwhile, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.

Professional Learning Communities(PLCs)of Chemistry Teachers

The models of Professional Learning Communities(PLCs)are based on principles of learning that emphasize the co-construction of knowledge by learners,who in this case are the teachers themselves.Teachers in a PLC meet regularly to explore their practices and the learning outcomes of their students,analyze their teaching and their students’learning processes,draw conclusions,and make changes in order to improve their teaching and the learning of their students.It was found that participation in a PLC influences teaching practice,so teachers become more student-centered.Moreover,the teaching culture improves as the community increases the degree of cooperation among teachers,and focuses on the processes of learning rather than the accumulation of knowledge.This enables students to be innovative,creative,and critical.In addition,trust is developed among the participants,which enables them to discuss and analyze their students’cognitive and affective problems,misconceptions,and learning outcomes.

Service Learning at National Taipei College of Business, 2012-2013＂ New Perspectives on Community, Peer-Based Education

Service learning （also known as peer learning） has received increased attention in tertiary education. By linking learning with authentic volunteer work experience, service learning bridges the gap between theoretical study and practical reality. This practice can increase students＇ professional knowledge, educational development, self-esteem, and awareness of social responsibility. Service learning became a mandatory requirement for all students at National Taipei College of Business （NTCB; now National Taipei University of Business） in 2011, and this is the focus of this study, extending into the 2012 school year. This course-based program allows undergraduate language majors to put their language skills into practice by teaching peers, and also through other volunteer work. This program has greatly benefitted undergraduate students at the school. This study will examine course data, student reports, and interviews with student teachers. The researchers have documented the implementation of the peer-mentor program at NTCB, and the learning gains experienced among students and the professors who assisted in these processes.