Development Mode of Urban Grassroots Libraries under the Background of Lifelong Learning:Inspiration from Idea Stores in London

The urban grass-roots library is an important part of the public cultural service system,and also a place to carry out national reading and lifelong learning,which is of great significance to the construction of a learning society.In this paper,the development and evolution of urban grassroots libraries in China are reviewed,and the current situation and usage issues of grassroots libraries in Beijing are analyzed.Moreover,the development strategy of idea stores in London,UK is studied,and characteristics are summarized,and possible references are sought.In the new era,urban grassroots libraries should integrate into communities with multiple functions and play a more sufficient role in public education,learning and training,and other aspects.

The Appeals,Dilemmas,and Pathways of Enabling Rural Teachers’Professional Development Through Emerging Technologies

With the rapid advancements in technology,especially in digitalization and intelligence,numerous modern technologies have poured into rural schools,effectively improving their informatization conditions.Nevertheless,these technologies remain detached from rural teachers,failing to significantly enhance the quality of education and teaching in rural areas.Rural education is a crucial aspect of ensuring balanced development in education.The question of how to enhance rural teachers’technological application abilities and fully leverage the positive role of technology in rural education and teaching has become a significant topic of current research on rural education issues.To better address this question,this study conducted a thorough examination of the specific appeals of rural teachers in the process of technology enablement.It was discovered that rural teachers generally face dilemmas such as insufficient technological application abilities,difficulties in obtaining quality teaching resources,and the lack of continuous technical support and update mechanisms.Based on these findings,specific pathways such as strengthening rural teacher training,optimizing the allocation of educational resources,and establishing mechanisms for continuous technical support and updates are proposed to aid in the high-quality development of rural education.

Deep Learning Driven Arabic Text to Speech Synthesizer for Visually Challenged People

Text-To-Speech(TTS)is a speech processing tool that is highly helpful for visually-challenged people.The TTS tool is applied to transform the texts into human-like sounds.However,it is highly challenging to accomplish the TTS out-comes for the non-diacritized text of the Arabic language since it has multiple unique features and rules.Some special characters like gemination and diacritic signs that correspondingly indicate consonant doubling and short vowels greatly impact the precise pronunciation of the Arabic language.But,such signs are not frequently used in the texts written in the Arabic language since its speakers and readers can guess them from the context itself.In this background,the current research article introduces an Optimal Deep Learning-driven Arab Text-to-Speech Synthesizer(ODLD-ATSS)model to help the visually-challenged people in the Kingdom of Saudi Arabia.The prime aim of the presented ODLD-ATSS model is to convert the text into speech signals for visually-challenged people.To attain this,the presented ODLD-ATSS model initially designs a Gated Recurrent Unit(GRU)-based prediction model for diacritic and gemination signs.Besides,the Buckwalter code is utilized to capture,store and display the Arabic texts.To improve the TSS performance of the GRU method,the Aquila Optimization Algorithm(AOA)is used,which shows the novelty of the work.To illustrate the enhanced performance of the proposed ODLD-ATSS model,further experi-mental analyses were conducted.The proposed model achieved a maximum accu-racy of 96.35%,and the experimental outcomes infer the improved performance of the proposed ODLD-ATSS model over other DL-based TSS models.

An Analysis of Polices in Terms of Lifelong Learning

Lifelong learning is a focused issue explored by many scholars.After having reviewed the practices in lifelong leaning policies adopted in many countries and organizations,this paper analyzes the current situation in lifelong learning policies in China,thus to satisfy people's need to live and develop,fulfill spiritual world and level up the quality of life.

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%.

Hand Gesture Recognition for Disabled People Using Bayesian Optimization with Transfer Learning

Sign language recognition can be treated as one of the efficient solu-tions for disabled people to communicate with others.It helps them to convey the required data by the use of sign language with no issues.The latest develop-ments in computer vision and image processing techniques can be accurately uti-lized for the sign recognition process by disabled people.American Sign Language(ASL)detection was challenging because of the enhancing intraclass similarity and higher complexity.This article develops a new Bayesian Optimiza-tion with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication(BODL-HGRSLC)for Disabled People.The BODL-HGRSLC technique aims to recognize the hand gestures for disabled people’s communica-tion.The presented BODL-HGRSLC technique integrates the concepts of compu-ter vision(CV)and DL models.In the presented BODL-HGRSLC technique,a deep convolutional neural network-based residual network(ResNet)model is applied for feature extraction.Besides,the presented BODL-HGRSLC model uses Bayesian optimization for the hyperparameter tuning process.At last,a bidir-ectional gated recurrent unit(BiGRU)model is exploited for the HGR procedure.A wide range of experiments was conducted to demonstrate the enhanced perfor-mance of the presented BODL-HGRSLC model.The comprehensive comparison study reported the improvements of the BODL-HGRSLC model over other DL models with maximum accuracy of 99.75%.

Deep Learning-Based Sign Language Recognition for Hearing and Speaking Impaired People

Sign language is mainly utilized in communication with people who have hearing disabilities.Sign language is used to communicate with people hav-ing developmental impairments who have some or no interaction skills.The inter-action via Sign language becomes a fruitful means of communication for hearing and speech impaired persons.A Hand gesture recognition systemfinds helpful for deaf and dumb people by making use of human computer interface(HCI)and convolutional neural networks(CNN)for identifying the static indications of Indian Sign Language(ISL).This study introduces a shark smell optimization with deep learning based automated sign language recognition(SSODL-ASLR)model for hearing and speaking impaired people.The presented SSODL-ASLR technique majorly concentrates on the recognition and classification of sign lan-guage provided by deaf and dumb people.The presented SSODL-ASLR model encompasses a two stage process namely sign language detection and sign lan-guage classification.In thefirst stage,the Mask Region based Convolution Neural Network(Mask RCNN)model is exploited for sign language recognition.Sec-ondly,SSO algorithm with soft margin support vector machine(SM-SVM)model can be utilized for sign language classification.To assure the enhanced classifica-tion performance of the SSODL-ASLR model,a brief set of simulations was car-ried out.The extensive results portrayed the supremacy of the SSODL-ASLR model over other techniques.

Efficient People Detection with Infrared Images

This work focuses on the problem of monitoring the coastline, which in Portugal’s case means monitoring 3007 kilometers, including 1793 maritime borders with the Atlantic Ocean to the south and west. The human burden on the coast becomes a problem, both because erosion makes the cliffs unstable and because pollution increases, making the fragile dune ecosystem difficult to preserve. It is becoming necessary to increase the control of access to beaches, even if it is not a popular measure for internal and external tourism. The methodology described can also be used to monitor maritime borders. The use of images acquired in the infrared range guarantees active surveillance both day and night, the main objective being to mimic the infrared cameras already installed in some critical areas along the coastline. Using a series of infrared photographs taken at low angles with a modified camera and appropriate filter, a recent deep learning algorithm with the right training can simultaneously detect and count whole people at close range and people almost completely submerged in the water, including partially visible targets, achieving a performance with F1 score of 0.945, with 97% of targets correctly identified. This implementation is possible with ordinary laptop computers and could contribute to more frequent and more extensive coverage in beach/border surveillance, using infrared cameras at regular intervals. It can be partially automated to send alerts to the authorities and/or the nearest lifeguards, thus increasing monitoring without relying on human resources.

Research on the influence of community public welfare education activities on residents’awareness of lifelong learning

The purpose of this study is to explore the impact of community public welfare education activities on residents’awareness of lifelong learning.Through the analysis of the connotation,form and mechanism of community public welfare education activities,the importance of improving the comprehensive quality of residents and promoting the harmonious development of society is revealed.At the same time,it analyzes the problems faced by current community public welfare education activities,including uneven allocation of resources,single content forms,weak teachers and insufficient capital investment,and puts forward corresponding solutions,in order to provide theoretical basis and practical guidance for optimizing community public welfare education activities and enhancing residents’awareness of lifelong learning.

Willingness of People of Different Ages to Learn Online——Taking Guizhou Cadre Online Learning School as an Example

The method of statistical analysis is employed in this paper to research the interests of online cadre learners, including learners from administrative organizations directly governed by the provincial government, Zunyi city and the state-owned enterprises directly governed by the provincial government in 2011 through the courseware of Guizhou Cadre Online Learning School. The difference in willingness to study in this manner between people of differing ages is examined through data analysis.

Gender Differences in Motivation and Learning Preferences of Pharmacists in Lifelong Learning

Objectives： To analyse motivation and preferences of pharmacists who participate in CE （continuing education） to develop suitable lifelong learning programmes for pharmacists. Methods： An online questionnaire, which explored the motivation and preferences of the pharmacists to lifelong learning, was sent to all members of the Royal Dutch Pharmaceutical Society （4321） in the Netherlands. The data were analysed using a non-hierarchical clustering technique. Key findings： Two clusters of pharmacists were discovered. Cluster A pharmacists （n = 474） were more motivated by credit points （63.5% vs. 47.2%）, personal interest （84.1% vs. 56.3%）, updating knowledge （73.8% vs. 56.8%） and topicality of CE courses （47.7% vs. 26.1%）. Cluster B pharmacists （n = 199） were predominantly motivated by the aspect ＂duty as a care-giver＂ （97.0% vs. 0 % in cluster A）. Pharmacists who belonged to cluster A tended to be women （60.5%）, often worked part-time （29.3%） and mostly preferred lectures （71.1%）. Cluster B pharmacists consisted of statistically significantly more male pharmacists （52.8%, p = 0.001）, worked more full time （77.4%, p = 0.009） and mostly preferred blended learning （62.3%, p = 0.047）. Conclusions： These results suggest the use of different education formats for different kinds of pharmacists to participate in CE activities.

Sign Language Recognition and Classification Model to Enhance Quality of Disabled People

Sign language recognition can be considered as an effective solution for disabled people to communicate with others.It helps them in conveying the intended information using sign languages without any challenges.Recent advancements in computer vision and image processing techniques can be leveraged to detect and classify the signs used by disabled people in an effective manner.Metaheuristic optimization algorithms can be designed in a manner such that it fine tunes the hyper parameters,used in Deep Learning(DL)models as the latter considerably impacts the classification results.With this motivation,the current study designs the Optimal Deep Transfer Learning Driven Sign Language Recognition and Classification(ODTL-SLRC)model for disabled people.The aim of the proposed ODTL-SLRC technique is to recognize and classify sign languages used by disabled people.The proposed ODTL-SLRC technique derives EfficientNet model to generate a collection of useful feature vectors.In addition,the hyper parameters involved in EfficientNet model are fine-tuned with the help of HGSO algorithm.Moreover,Bidirectional Long Short Term Memory(BiLSTM)technique is employed for sign language classification.The proposed ODTL-SLRC technique was experimentally validated using benchmark dataset and the results were inspected under several measures.The comparative analysis results established the superior performance of the proposed ODTL-SLRC technique over recent approaches in terms of efficiency.

Deep Learning Based Audio Assistive System for Visually Impaired People

Vision impairment is a latent problem that affects numerous people across the globe.Technological advancements,particularly the rise of computer processing abilities like Deep Learning(DL)models and emergence of wearables pave a way for assisting visually-impaired persons.The models developed earlier specifically for visually-impaired people work effectually on single object detection in unconstrained environment.But,in real-time scenarios,these systems are inconsistent in providing effective guidance for visually-impaired people.In addition to object detection,extra information about the location of objects in the scene is essential for visually-impaired people.Keeping this in mind,the current research work presents an Efficient Object Detection Model with Audio Assistive System(EODM-AAS)using DL-based YOLO v3 model for visually-impaired people.The aim of the research article is to construct a model that can provide a detailed description of the objects around visually-impaired people.The presented model involves a DL-based YOLO v3 model for multi-label object detection.Besides,the presented model determines the position of object in the scene and finally generates an audio signal to notify the visually-impaired people.In order to validate the detection performance of the presented method,a detailed simulation analysis was conducted on four datasets.The simulation results established that the presented model produces effectual outcome over existing methods.

Educational System for the Holy Quran and Its Sciences for Blind and Handicapped People Based on Google Speech API

There is a great need to provide educational environments for blind and handicapped people. There are many Islamic websites and applications dedicated to the educational services for the Holy Quran and Its Sciences (Quran Recitations, the interpretations, etc.) on the Internet. Unfortunately, blind and handicapped people could not use these services. These people cannot use the keyboard and the mouse. In addition, the ability to read and write is essential to benefit from these services. In this paper, we present an educational environment that allows these people to take full advantage of the scientific materials. This is done through the interaction with the system using voice commands by speaking directly without the need to write or to use the mouse. Google Speech API is used for the universal speech recognition after a preprocessing and post processing phases to improve the accuracy. For blind people, responses of these commands will be played back through the audio device instead of displaying the text to the screen. The text will be displayed on the screen to help other people make use of the system.

Phronesis and Transformative Learning： A Joint Challenge for Moral Philosophy and Educational Theory

Strengthening moral learning may become available to us by bringing phronesis and transformative learning in a common theoretical space. For both Aristotle and Mezirow, the exercise of morality, or rising to the standard of moral choice, decision, and action, is not the result of an intuitive achievement or a sudden understanding of a morally demanding situation but a lifelong affair. Our strategy here addresses three aims： Firstly, to invoke and reclaim the endemic bond between education in the broader sense of paideia and the significant role that reeds to be re-ascribed to moral education. This allows a turn towards qualitative features and makes room for an inclusion of moral education, or values education, within education. Secondly, to portray the exercise of autonomy, choice, and judgment as a result of paideutic development; both theories share the assumption that moral learning rests on constant reflection upon past experiences and the zetesis of future goals. Thirdly, to focus on the way one reclaims the right to exercise judgment, whenever this is required. A joint study of the two theories may enlighten the content of this lifelong reflective procedure.

Human Rights and Social Justice through Open Educational Resources and Lifelong Learning

A landmark in the realization of UNESCO’s Sustainability Goals,Education for All(SDG4),was passed when the organization’s Recommendation of Open Educational Resources(OER)was uniformly adopted in 2019.Now it is time to transfer from the consciousness of OER to their mainstream realization at all levels,micro,meso,and macro,including all stakeholders,such as governments,institutions,academics,teachers,administrators,librarians,students,learners,and the civil service.The OER Recommendation includes five areas:building capacity and utilizing OER;developing supportive policies;ensuring effectiveness;promoting the creation of sustainable OER models;promoting and facilitating international collaboration;monitoring and evaluation.OER are valued as a catalyst for innovation and the achievement of UNESCO’s SDG 4,education for all,lifelong learning,social justice,and human rights.The OER Recommendation will be a catalyst for the realization of several other SDGs.Because access to quality OER concerns human rights and social justice,this Recommendation is vital.In 2020,the effects of the worldwide COVID-19 pandemic clearly demonstrated the importance of opening up education and the access to internationally recognized,qualified learning resources.This article describes and discusses how the promise of resilient,sustainable quality open education can be fulfilled in the new normal and the next normal.

The Education of Students of Pedagogy With the Aim of Fostering Multiculturalism in a Community

The paper starts from the theoretical model of multiculturalism as a possibility for an individual to develop their own identity, to accept and respect the identity of other members of a community. The aim of this research is to determine how the students of pedagogy define the concept of multiculturalism and in how far they consider it to be current in contemporary social relationships. The survey was carried out using the descriptive method and the analysis of contents. It examined the attitudes of students who are preparing themselves for advisory work with children in elementary school and high school. The results of the research confirm that the pedagogy students define the concept of multiculturalism clearly. They recognize the need for upbringing that fosters multiculturalism in modern education, in order to gain extensive knowledge and use it in a particular situation.

Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System for Visually Impaired People

The problem of producing a natural language description of an image for describing the visual content has gained more attention in natural language processing(NLP)and computer vision(CV).It can be driven by applications like image retrieval or indexing,virtual assistants,image understanding,and support of visually impaired people(VIP).Though the VIP uses other senses,touch and hearing,for recognizing objects and events,the quality of life of those persons is lower than the standard level.Automatic Image captioning generates captions that will be read loudly to the VIP,thereby realizing matters happening around them.This article introduces a Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System(RDOAI-ICS)for Visually Impaired People.The presented RDOAI-ICS technique aids in generating image captions for VIPs.The presented RDOAIICS technique utilizes a neural architectural search network(NASNet)model to produce image representations.Besides,the RDOAI-ICS technique uses the radial basis function neural network(RBFNN)method to generate a textual description.To enhance the performance of the RDOAI-ICS method,the parameter optimization process takes place using the RDO algorithm for NasNet and the butterfly optimization algorithm(BOA)for the RBFNN model,showing the novelty of the work.The experimental evaluation of the RDOAI-ICS method can be tested using a benchmark dataset.The outcomes show the enhancements of the RDOAI-ICS method over other recent Image captioning approaches.

The International Mobility of Students Apprentices at the Heart of Building a Skills Economy: the Example of the Corsican Territory

At a time of exacerbated globalization,education,and more generally training,is a key factor for our society,at the heart of territories challenged to renew themselves in the face of the emergence around the globe of new centers of economic and demographic gravity,with their own models.Training,and its link with working life,is a real challenge to face,in the near future,the technological,economic,political and environmental revolutions that we are already facing.Beyond what is called sandwich training or continuing vocational training in Higher Education,the current challenge is indeed around lifelong learning,old concept but whose forms always call for an actualization in modernity.At the heart of a small island territory like Corsica,this challenge is all the more crucial to take up as it foreshadows its attractiveness in a context of glocalisation[8]now durably anchored.In an ever-changing global environment,with moving landmarks,increasingly complex personal and professional lives,and where everything that seemed well compartmentalized yesterday faces increasing porosity,the purpose of this contribution is to explain that the international mobility of students apprentices,at the heart of the construction of a skills economy,is a major strategic issue for the development and structuring of a small territory such as Corsica.