Low rank optimization for efficient deep learning:making a balance between compact architecture and fast training

Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power cost.In this paper,we focus on low-rank optimization for efficient deep learning techniques.In the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network parameters.In the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast convergence.The model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,respectively.With a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and storage.In addition to summary of recent technical advances,we have two findings for motivating future works.One is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network compression.The other is a spatial and temporal balance for tensorized neural networks.For accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.

Exploring the Application Effect of Flipped Classroom Combined with Problem-Based Learning Teaching Method in Clinical Skills Teaching of Standardized Training for Resident Doctors of Traditional Chinese Medicine

Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese Medicine. Methods: The study used the experimental control method. The study lasted from September to November 2022. The subjects of this study were 49 students of standardized training for resident doctors of traditional Chinese Medicine from grades 2020, 2021 and 2022 of Dazhou integrated TCM & Western Medicine Hospital. They were randomly divided into experiment group (25) and control group (24). The experiment group adopted flipped classroom combined with problem-based learning teaching method, and the control group adopted traditional teaching method. The teaching content was 4 basic clinical skill projects, including four diagnoses of traditional Chinese Medicine, cardiopulmonary resuscitation, dressing change procedure, acupuncture and massage. The evaluation method was carried out by comparing the students’ performance and a self-designed questionnaire was used to investigate the students’ evaluation of the teaching method. Results: The test scores of total scores in the experimental group (90.12 ± 5.89) were all higher than those in the control group (81.47 ± 7.96) (t = 4.53, P P Conclusions: The teaching process of the flipped classroom combined with problem-based learning teaching method is conducive to improving the efficiency of classroom teaching, cultivating students’ self-learning ability, and enhancing students’ willingness to learn.

Research on the Reform of the Course“Reading of Concrete Structure Plan and Construction Drawings”Under the Background of“Promoting Teaching and Learning Through Competitions”

The inherent teaching approach can no longer meet the demands of society.In this paper,current issues within the teaching landscape of architectural engineering technology in higher vocational colleges as well as the policies and teaching demands that formed the basis of this model were analyzed.The study shows the importance of the implementation of the teaching model“promoting teaching and learning through competitions.”This model puts emphasis on the curriculum and teaching resources,while also integrating the teaching process and evaluation with competition.These efforts aim to drive education reform in order to better align with the objectives of vocational education personnel training,while also acting as a reference for similar courses.

Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning

The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.

Person-Dependent Handwriting Verification for Special Education Using DeepLearning

Individuals with special needs learn more slowly than their peers and they need repetitions to be permanent.However,in crowded classrooms,it is dif-ficult for a teacher to deal with each student individually.This problem can be overcome by using supportive education applications.However,the majority of such applications are not designed for special education and therefore they are not efficient as expected.Special education students differ from their peers in terms of their development,characteristics,and educational qualifications.The handwriting skills of individuals with special needs are lower than their peers.This makes the task of Handwriting Recognition(HWR)more difficult.To over-come this problem,we propose a new personalized handwriting verification sys-tem that validates digits from the handwriting of special education students.The system uses a Convolutional Neural Network(CNN)created and trained from scratch.The data set used is obtained by collecting the handwriting of the students with the help of a tablet.A special education center is visited and the handwrittenfigures of the students are collected under the supervision of special education tea-chers.The system is designed as a person-dependent system as every student has their writing style.Overall,the system achieves promising results,reaching a recognition accuracy of about 94%.Overall,the system can verify special educa-tion students’handwriting digits with high accuracy and is ready to integrate with a mobile application that is designed to teach digits to special education students.

M-learning结合CBL在消化科规培教学中的探讨及应用

目的探究移动学习平台(M-learning,ML)结合案例教学(Case-based Learning,CBL)在消化科住院医师规范化培训(简称规培)教学中的应用效果。方法选取2021年1月—2023年1月于上海市徐汇区中心医院消化科参加规培学习的80名医师作为研究对象,将其按照随机数表法分为研究组和对照组,每组40名。对照组给予传统讲授式教学法,研究组给予M-learning结合CBL教学法,对比两组医师的理论考试成绩、实践技能考试成绩和学习满意度。结果研究组的理论成绩和实践技能考试成绩均高于对照组,差异具有统计学意义(P均<0.05);研究组的学习满意度明显高于对照组,差异具有统计学意义(P<0.05)。结论将Mlearning结合CBL教学法应用于消化科规培教学中,不仅能够提升医师的理论考试成绩和实践技能考试成绩,还能够有效提高医师学习满意度。

Application of Machine-Learning-Based Objective Correction Method in the Intelligent Grid Maximum and Minimum Temperature Predictions

Post-processing correction is an effective way to improve the model forecasting result. Especially, the machine learning methods have played increasingly important roles in recent years. Taking the meteorological observational data in a period of two years as the reference, the maximum and minimum temperature predictions of Shenyang station from the European Center for Medium-Range Weather Forecasts (ECMWF) and national intelligent grid forecasts are objectively corrected by using wavelet analysis, sliding training and other technologies. The evaluation results show that the sliding training time window of the maximum temperature is smaller than that of the minimum temperature, and their difference is the largest in August, with a difference of 2.6 days. The objective correction product of maximum temperature shows a good performance in spring, while that of minimum temperature performs well throughout the whole year, with an accuracy improvement of 97% to 186%. The correction effect in the central plains is better than in the regions with complex terrain. As for the national intelligent grid forecasts, the objective correction products have shown positive skills in predicting the maximum temperatures in spring (the skill-score reaches 0.59) and in predicting the minimum temperature at most times of the year (the skill-score reaches 0.68).

Multimodal teaching,learning and training in virtual reality:a review and case study

It is becoming increasingly prevalent in digital learning research to encompass an array of different meanings,spaces,processes,and teaching strategies for discerning a global perspective on constructing the student learning experience.Multimodality is an emergent phenomenon that may influence how digital learning is designed,especially when employed in highly interactive and immersive learning environments such as Virtual Reality(VR).VR environments may aid students'efforts to be active learners through consciously attending to,and reflecting on,critique leveraging reflexivity and novel meaning-making most likely to lead to a conceptual change.This paper employs eleven industrial case-studies to highlight the application of multimodal VR-based teaching and training as a pedagogically rich strategy that may be designed,mapped and visualized through distinct VR-design elements and features.The outcomes of the use cases contribute to discern in-VR multimodal teaching as an emerging discourse that couples system design-based paradigms with embodied,situated and reflective praxis in spatial,emotional and temporal VR learning environments.

Effect of behavior training on learning,memory and the expression of NR2B,GluR1 in hippocampus of rats offspring with fetal growth restriction

Objective： To study effects of behavior training on learning, memory and the expression of NR2B, GluR1 in hippocampus of rat＇ s offspring with fetal growth restriction（FGR）. Methods： The rat model of FGR was established by passive smoking method. The rats offspring were divided into the FGR group and the control group, then randomly divided into the trained and untrained group, respectively. Morris water maze test was proceeded on postnatal month（PM2/4） as a behavior training method, then the learning-memory of rats was detected through dark-avoidance and step-down tests. The expressions of NR2B and GluR1 subunits in hippocampal CA1 and CA3 areas were detected by immunohistochemical method. Results： In the dark-avoidance and step-down tests, the performance record of rats with FGR was worse than that of control rats, and the behavior-trained rats was better than the untrained rats, when the FGR model and training factors were analyzed singly. The model factor and training factor had significant interaction（P 〈 0.05）. The expressions of NR2B and GluR1 subunits in hippocampal CA1 and CA3 areas of rats with FGR reduced. In contrast, the expressions of GluR1 and NR2B subunits in CA1 area of behavior-trained rats increased, when the FGR model and training factors were analyzed singly. Conclusion： These findings suggested that the effect of behavior training on the expressions of NR2B and GluR1 subunits in CA1 area should be the mechanistic basis for the training-induced improvement in learning-memory abilities.

An Application of Machine Learning Methods to Detect Mango Varieties

The mango, a fruit of immense economic and dietary significance in numerous tropical and subtropical regions, plays a pivotal role in our agricultural landscape. Accurate identification is not just a necessity, but a crucial step for effective classification, sorting, and marketing. This study delves into the potential of machine learning for this task, comparing the performance of four models: MobileNetV2, Xception, VGG16, and ResNet50V2. These models were trained on a dataset of annotated mango images, and their performance was evaluated using precision, accuracy, F1 score, and recall, which are standard metrics for image classification. The Xception model, with its exceptional performance, outshone the other models on all performance indicators. It achieved a staggering accuracy of 99.47%, an F1 score of 99.43%, and a recall of 99.43%, showcasing its remarkable ability to accurately identify mango varieties. MobileNetV2 followed closely with performances of 98.95% accuracy, 98.85% F1 score, and 98.86% recall. ResNet50V2 also delivered satisfactory results with 97.39% accuracy, 97.08% F1 score, and 97.17% recall. VGG16, however, was the least effective, with a precision rate of 83.25%, an F1 score of 83.25%, and a recall of 85.47%. These results confirm the superiority of the Xception model in detecting mango varieties. Its advanced architecture allows it to capture more distinguishing features of mango images, leading to greater precision and reliability. Xception’s robustness in identifying true positives is another advantage, minimizing false positives and contributing to more accurate classification. This study highlights the promising potential of machine learning, particularly the Xception model, for accurately identifying mango varieties.

Gamification and virtual reality for digital twin learning and training:architecture and challenges

Background Digital Twins are becoming increasingly popular in a variety of industries to manage complex systems.As digital twins become more sophisticated,there is an increased need for effective training and learning systems.Teachers,project leaders,and tool vendors encounter challenges while teaching and training their students,co-workers,and users.Methods In this study,we propose a new method for training users in using digital twins by proposing a gamified and virtual environment.We present an overall architecture and discuss its practical realization.Results We propose a set of future challenges that we consider critical to enabling a more effective learning/training approach.

EU Including: Development of Radiological and Nuclear Training Learning Objectives

Worldwide, about 20 million consignments of radioactive material are transported annually on public roads, railways, aircraft, and ships. About 95% of radioactive consignments are not related to nuclear power. In 2016, a total of 143 incidents of nuclear or other radioactive materials were found to be outside of regulatory control, which occurred in 19 countries. On an international level risk assessment has to account for the potential threats due to millions of radioactive sources in use worldwide and hundreds of tons of military grade U/Pu not under IAEA safeguards. The European Union (EU) has tasked the INCLUDING project consortium, connecting 15 partners from 10 EU Member States, to address this issue and create an innovative cluster for radiological and nuclear (RN) emergencies. The project is coordinated by the Italian Agency for the New Technologies, Energy and Sustainable Economic Development (ENEA). INCLUDING will provide comprehensive training in the RN security sector. Thereby, know-how is enhanced for practitioners in this sector. An important part in this endeavor is the development of radiological- and nuclear training learning objectives. INCLUDING partners involved in this task (Work Package 4) represent companies, organisations and government agencies from Austria, Greece, Italy, Lithuania, Hungary and Portugal. The task has four main objectives: 1) Harmonisation of RN education/training for EU first responders: 2) Identification of main problems in setting norms;3) Developing a training matrix using revised Bloom’s taxonomy;4) Use of the methodology developed for Joint Actions and its application at INCLUDING Cluster Facilities in different EU Member States. The INCLUDING Work Package 4 members have analyzed the EU EDEN Training Matrix and identified gaps in accordance with NATO CBRN training standards related to civil-military cooperation. Furthermore, they analyzed 5 EUHORIZON 2020- and 9 EUFP7-SECURITY projects, and 97 RN training courses offered to the international community by NATO, 6 EU organisations, Qatar, US military- and civilian organisations, and the International Atomic Energy Agency (IAEA). This paper will present these results, which are being used to develop the basic structure for the Learning Objective Catalogue (LOC), comprised of multiple RN-related Learning Objectives for different threat scenarios.

Training and Development Strategies for Senior and Middle Level Managers with the Purpose of Learning Organizations

In the presence of dynamic organizational environment and a growing supply of‘knowledgeable employees’which require more professional managers to address their fast changing and increasing needs,senior and middle level managers are now required to keep up with the dynamic and learning environment more than ever.In order to train senior and middle level managers,the article has recommended four perspectives to encourage the development of learning manager.The first aspect for senior and middle level mangers is to integrate learning talents into their practices.The second point is to encourage managers to provide strong support for individuals and teams to develop a learning organization.The third point encourages learning managers and organizations to be composed into the culture of the organization.The last point advocates for more open and free dissemination of information and knowledge to be allowed within an organization.

The Application of E-learning and Distance Learning Technologies to Crew Education and Training in the Russian Federation

The article examines the world experience of e-learning as well as distance education technologies within the education process organization on higher and post-higher education programs.There have been listed the results of the most popular e-learning platforms analysis.Furthermore,there have been looked through the core legislative background of the development of the mentioned technologies in Russia and worldwide among the universities,specialized in seafarers training.There have been also drawn up the points of the Admiral Makarov State University of Maritime and Inland Shipping(Admiral Makarov SUMIS)design of the distance education system LMS"FARWATER"in compliance with the International Convention on Standards of Training,Certification and Watchkeeping for Seafarers(STCW Convention).The practical application of distance education system to the advanced professional training has been discussed in the article.

Effect of behavior training on learning and memory of young rats with fetal growth restriction

Objective: To investigate the effect of behavior training on the learning and memory of young rats with fetal growth restriction (FGR). Methods: The model of FGR was established by passive smoking method to pregnant rats. The new-born rats were divided into FGR group and normal group, and then randomly subdivided into trained and untrained group respectively. Morris water maze behavior training was performed on postnatal months 2 and 4, then learning and memory abilities of young rats were measured by dark-avoidance testing and step-down testing. Results: In the dark-avoidance and step-down testing, the young rats’ performance of FGR group was worse than that of control group, and the trained group was better than the untrained group significantly. Conclusion: FGR young rats have descended learning and memory abilities. Behavior training could improve the young rats’ learning and memory abilities, especially for the FGR young rats.

Learning Strategy Training in English Teaching

This paper mainly deals with the comprehensive knowledge system of learning strategy.Then it tries to probe the steps of strategy training and it's significance to English teaching.

An Investigation and Analysis in English Learning Strategy of SFLS Students

There is a close relationship between English learning strategy and language proficiency. This study is aimed to investigate how frequently students in our school use different English learning strategies. With the detailed analysis of the questionnaire data, it is hoped that strategy awareness can be gradually cultivated in students' mind.

Learning Vector Coding Methods of ART1 and Their Applications

As one of the unsupervised learning models, ART1 has been widely used in data mining or other fields, while coding of it’s learning vector is very important. Their input vector coding methods and learning vector coding methods are described in detail. The corresponding applications are given.

Adversarial Training Against Adversarial Attacks for Machine Learning-Based Intrusion Detection Systems

Intrusion detection system plays an important role in defending networks from security breaches.End-to-end machine learning-based intrusion detection systems are being used to achieve high detection accuracy.However,in case of adversarial attacks,that cause misclassification by introducing imperceptible perturbation on input samples,performance of machine learning-based intrusion detection systems is greatly affected.Though such problems have widely been discussed in image processing domain,very few studies have investigated network intrusion detection systems and proposed corresponding defence.In this paper,we attempt to fill this gap by using adversarial attacks on standard intrusion detection datasets and then using adversarial samples to train various machine learning algorithms(adversarial training)to test their defence performance.This is achieved by first creating adversarial sample based on Jacobian-based Saliency Map Attack(JSMA)and Fast Gradient Sign Attack(FGSM)using NSLKDD,UNSW-NB15 and CICIDS17 datasets.The study then trains and tests JSMA and FGSM based adversarial examples in seen(where model has been trained on adversarial samples)and unseen(where model is unaware of adversarial packets)attacks.The experiments includes multiple machine learning classifiers to evaluate their performance against adversarial attacks.The performance parameters include Accuracy,F1-Score and Area under the receiver operating characteristic curve(AUC)Score.

Prediction of Outcomes in Mini-Basketball Training Program for Preschool Children with Autism Using Machine Learning Models

In recent years evidence has emerged suggesting that Mini-basketball training program(MBTP)can be an effec-tive intervention method to improve social communication(SC)impairments and restricted and repetitive beha-viors(RRBs)in preschool children suffering from autism spectrum disorder(ASD).However,there is a considerable degree if interindividual variability concerning these social outcomes and thus not all preschool chil-dren with ASD profit from a MBTP intervention to the same extent.In order to make more accurate predictions which preschool children with ASD can benefit from an MBTP intervention or which preschool children with ASD need additional interventions to achieve behavioral improvements,further research is required.This study aimed to investigate which individual factors of preschool children with ASD can predict MBTP intervention out-comes concerning SC impairments and RRBs.Then,test the performance of machine learning models in predict-ing intervention outcomes based on these factors.Participants were 26 preschool children with ASD who enrolled in a quasi-experiment and received MBTP intervention.Baseline demographic variables(e.g.,age,body,mass index[BMI]),indicators of physicalfitness(e.g.,handgrip strength,balance performance),performance in execu-tive function,severity of ASD symptoms,level of SC impairments,and severity of RRBs were obtained to predict treatment outcomes after MBTP intervention.Machine learning models were established based on support vector machine algorithm were implemented.For comparison,we also employed multiple linear regression models in statistics.Ourfindings suggest that in preschool children with ASD symptomatic severity(r=0.712,p<0.001)and baseline SC impairments(r=0.713,p<0.001)are predictors for intervention outcomes of SC impair-ments.Furthermore,BMI(r=-0.430,p=0.028),symptomatic severity(r=0.656,p<0.001),baseline SC impair-ments(r=0.504,p=0.009)and baseline RRBs(r=0.647,p<0.001)can predict intervention outcomes of RRBs.Statistical models predicted 59.6%of variance in post-treatment SC impairments(MSE=0.455,RMSE=0.675,R2=0.596)and 58.9%of variance in post-treatment RRBs(MSE=0.464,RMSE=0.681,R2=0.589).Machine learning models predicted 83%of variance in post-treatment SC impairments(MSE=0.188,RMSE=0.434,R2=0.83)and 85.9%of variance in post-treatment RRBs(MSE=0.051,RMSE=0.226,R2=0.859),which were better than statistical models.Ourfindings suggest that baseline characteristics such as symptomatic severity of 144 IJMHP,2022,vol.24,no.2 ASD symptoms and SC impairments are important predictors determining MBTP intervention-induced improvements concerning SC impairments and RBBs.Furthermore,the current study revealed that machine learning models can successfully be applied to predict the MBTP intervention-related outcomes in preschool chil-dren with ASD,and performed better than statistical models.Ourfindings can help to inform which preschool children with ASD are most likely to benefit from an MBTP intervention,and they might provide a reference for the development of personalized intervention programs for preschool children with ASD.