Deep learning algorithm featuring continuous learning for modulation classifications in wireless networks

Although modulation classification based on deep neural network can achieve high Modulation Classification(MC)accuracies,catastrophic forgetting will occur when the neural network model continues to learn new tasks.In this paper,we simulate the dynamic wireless communication environment and focus on breaking the learning paradigm of isolated automatic MC.We innovate a research algorithm for continuous automatic MC.Firstly,a memory for storing representative old task modulation signals is built,which is employed to limit the gradient update direction of new tasks in the continuous learning stage to ensure that the loss of old tasks is also in a downward trend.Secondly,in order to better simulate the dynamic wireless communication environment,we employ the mini-batch gradient algorithm which is more suitable for continuous learning.Finally,the signal in the memory can be replayed to further strengthen the characteristics of the old task signal in the model.Simulation results verify the effectiveness of the method.

Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes

An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.

Relational Reinforcement Learning with Continuous Actions by Combining Behavioural Cloning and Locally Weighted Regression

Reinforcement Learning is a commonly used technique for learning tasks in robotics, however, traditional algorithms are unable to handle large amounts of data coming from the robot’s sensors, require long training times, and use dis-crete actions. This work introduces TS-RRLCA, a two stage method to tackle these problems. In the first stage, low-level data coming from the robot’s sensors is transformed into a more natural, relational representation based on rooms, walls, corners, doors and obstacles, significantly reducing the state space. We use this representation along with Behavioural Cloning, i.e., traces provided by the user;to learn, in few iterations, a relational control policy with discrete actions which can be re-used in different environments. In the second stage, we use Locally Weighted Regression to transform the initial policy into a continuous actions policy. We tested our approach in simulation and with a real service robot on different environments for different navigation and following tasks. Results show how the policies can be used on different domains and perform smoother, faster and shorter paths than the original discrete actions policies.

Implicit Continuous User Authentication for Mobile Devices based on Deep Reinforcement Learning

The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like,fingerprint or face.Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously confirming users’identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session.However,divergent issues remain unaddressed.This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called,Gaussian Weighted Cauchy Kriging-based Continuous Czekanowski’s(GWCK-CC).First,a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise pre-sent in the raw input face images.Cauchy Kriging Regression function is employed to reduce the dimensionality.Finally,Continuous Czekanowski’s Clas-sification is utilized for proficient classification between the genuine user and attacker.By this way,the proposed GWCK-CC method achieves accurate authen-tication with minimum error rate and time.Experimental assessment of the pro-posed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset.The results confirm that the proposed GWCK-CC method enhances authentication accuracy,by 9%,reduces the authen-tication time,and error rate by 44%,and 43%as compared to the existing methods.

A Deep Learning-Based Continuous Blood Pressure Measurement by Dual Photoplethysmography Signals

This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography(PPG)sensors and a deep learning(DL)that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators.The proposed platform measured the signal changes in PPG and converted them into physiological indicators,such as pulse transit time(PTT),pulse wave velocity(PWV),perfusion index(PI)and heart rate(HR);these indicators were then fed into the DL to calculate blood pressure.The hardware of the experiment comprised 2 PPG components(i.e.,Raspberry Pi 3 Model B and analog-todigital converter[MCP3008]),which were connected using a serial peripheral interface.The DL algorithm converted the stable dual PPG signals acquired from the strictly standardized experimental process into various physiological indicators as input parameters and finally obtained the systolic blood pressure(SBP),diastolic blood pressure(DBP)and mean arterial pressure(MAP).To increase the robustness of the DL model,this study input data of 100 Asian participants into the training database,including those with and without cardiovascular disease,each with a proportion of approximately 50%.The experimental results revealed that the mean absolute error and standard deviation of SBP was 0.17±0.46 mmHg.The mean absolute error and standard deviation of DBP was 0.27±0.52 mmHg.The mean absolute error and standard deviation of MAP was 0.16±0.40 mmHg.

Control Task for Reinforcement Learning with Known Optimal Solution for Discrete and Continuous Actions

The overall research in Reinforcement Learning (RL) concentrates on discrete sets of actions, but for certain real-world problems it is important to have methods which are able to find good strategies using actions drawn from continuous sets. This paper describes a simple control task called direction finder and its known optimal solution for both discrete and continuous actions. It allows for comparison of RL solution methods based on their value functions. In order to solve the control task for continuous actions, a simple idea for generalising them by means of feature vectors is presented. The resulting algorithm is applied using different choices of feature calculations. For comparing their performance a simple measure is

Squeezing More Past Knowledge for Online Class-Incremental Continual Learning

Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter sharing.In this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new classes.Moreover,prior knowledge is unavailable during training and evaluation.Existing works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory information.To better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous knowledge.Specifically,besides the previous raw samples,we store the corresponding logits and features in the memory.Furthermore,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the memory.With the same number of samples for replay,our method can use more past knowledge to prevent interference.We conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic memory.We further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.

A Culturally-Sensitive Model for CME Distance Learning Demonstratesa Way Forward： Kenya＇s Experience with ACME-IT （Advancing Continuing Medical Education Through Information Technology）

A distance learning Continuing Medical Education （CME） project based on live interactive presentations and a group participatory model demonstrated that important strides can be made in the quality of CME available to health care professionals in African rural settings. Implementers choose a communication model consistent with the fundamental orality of Kenyan and other sub-Saharan African countries. The project involved four hospitals and one training institution in rural Kenya. The testing of learners and focus group discussions with learners, facilitators and presenters indicated that the project＇s methodologies, that strove to be culturally and work place friendly, contributed to gains in knowledge, competencies including case management, the continuity of patient care, team work, staff morale and other issues of expressed importance to the hospital healthcare work force and hospital administrators. The learning system, known as Advancing Continuing Medical Education through Information Technology （ACME-IT）, was implemented by the Kenyan Ministry of Medical Services, EC Associates and the US-funded Aphia2 Western initiative implemented by PATH. The findings of this pilot suggests that the ACME-IT methodology that set up learning centers at the participating institutions is a promising viable alternative to the traditional and relatively expensive workshop training that characterizes much of CME in lower and middle income countries.

Practical Activities to Improve Transfer of Learning in the Nuclear-Related Continuing Professional Educations for Developing Countries in Korea

Much in-class education and training for developing countries have focused on how a learner absorbs knowledge and skills efficiently or effectively in the class,but are less interested in how the learners should transfer the knowledge and skills into their jobs in their workplace.In principle,in-class education and training have a difficulty with applying the learned knowledge and skills to learners’jobs in the workplace in comparison with any other practical-basis training.To overcome this difficulty,many educational stakeholders in the nuclear field have concentrated on how learners can transfer the knowledge and skills absorbed in the class into their jobs in their workplace.The action learning activity for learners can be one of the solutions to apply the knowledge and skills to their job in the workplace.The purpose of this study is to clarify how the transfer of learning has been implemented in the nuclear-related continuing professional educations and training for developing countries in Korea.To accomplish this purpose,this study is implemented as follows.The first is to define the concept of the“transfer of learning”clearly.The second is to clarify the core elements of the transfer of learning.Along with the clarification,the third is to show how the transfer of learning has been implemented in the continuing professional nuclear-related education and training for developing countries in Korea.The fourth is to present core problems in such education and training.As the fifth,this study suggests alternatives to overcome the core problems in the nuclear-related continuing professional education and training.

交互环境、质量支持对企业员工E-learning持续使用意愿的影响

员工对E-learning培训系统这一新兴培训方式的持续使用意愿是影响培训效果最重要的因素之一。以信息系统持续使用模型(ECM-IS)为基础,结合E-learning系统特有的体系结构,构建影响企业员工E-learning持续系统使用意愿的理论模型并进行实证研究。研究结果表明:交互环境、质量支持中的信息质量、服务质量正向影响感知有用性;系统质量、服务质量、交互环境正向影响期望确认度;感知有用性、期望确认度、满意度对企业员工Elearning持续使用意愿均具有显著的正向影响。

E-learning:互联网时代继续教育发展的新途径

互联网时代教育的观念和手段在不断更新,面对信息化社会对人的知识、素质和能力的新要求,继续教育领域提供了更多的机会和方式。采用E-learn ing系统开展继续教育工作将是一种新的尝试,它能够促进理念的更新、功能的优化、价值的拓展。总之,两者的合作与融合能够为继续教育的发展提供更为快速的平台,提供更为多元的途径。

医院图书馆如何拓展E-learning在医学继续教育中的作用

介绍了E-learning概念和特点,论述了图书馆以E-learning促进临床继续教育及科研教学工作的重要性,并探讨了医院图书馆拓展E-learning的途径。

浅论E-Learning环境下继续教育中师生角色的再定位

随着信息技术的发展,E-Learning将成为学生学习的主要方式之一,本文主要论述在E-Learning中,教师和学生各自应扮演的角色.

Creative Imitation: An Answer to the Fundamental Issue of L2 Learning

This paper reports on a study on the effects of reading-writing integrated tasks on vocabulary learning and explored the differential roles of creative construction and non-creative construction in promoting lexical learning. Participants were 90 first-year English majors, randomly assigned to two experimental groups(continuation and retelling) and one control group, with 30 students in each group. Results showed that the continuation group generated a substantial amount of creative construction and produced significantly more instances of creative imitation than the retelling group. The continuation group outperformed the retelling group for both receptive and productive vocabulary knowledge gain and retention, but differences were only significant in terms of productive vocabulary retention. Finally, productive vocabulary knowledge retention among the continuation group was significantly and positively correlated with creative imitation(meaning creation coupled with language imitation), but not with linguistic alignment per se. As productive vocabulary knowledge constitutes the learner ’s ability to use lexical knowledge to express ideas in dynamic contexts, the findings afforded evidence that creative imitation could be the answer to the fundamental issue of L2 learning(i.e., mapping static language onto dynamic idea expression). The pedagogical implications as well as future research directions are also discussed.

L2 Syntactic Alignment in the Reading-Writing Integrated Continuation Task: Evidence From Chinese EFL Learners’ Description of Motion Events

This study investigated how the mode in which the reading-writing integrated continuation task was conducted modulates the effects of second language(L2) syntactic alignment, through the English motion event construction with manner verbs. Ninety Chinese students were assigned to either of the two experimental groups or a control group, and they all experienced a pretest, an alignment phase and a posttest. In the alignment phase, the two experimental groups completed a reading-writing integrated continuation task but in different modes. For the multi-turn mode,participants reconstructed a picture story by continuing the episodes extracted from the story with one episode presented and continued at a time;for the single-turn mode, the first half of the same picture story was presented as a chunk, and then participants read and continued it. Results show that L2 learners aligned with the target structure in completing the story, and the alignment effect was retained in the posttest conducted after a delay of two weeks. Moreover, syntactic alignment was modulated by task mode with the multi-turn group exhibiting stronger immediate and longterm alignment effects. We conclude that the continuation task is a fruitful context for L2 structural alignment, and the magnitude of alignment effect hinges on interactive intensity.

Web-based learning in inflammatory bowel diseases:General truths and current specifics

In a field rapidly evolving over the past few years, the management of inflammatory bowel diseases(IBD), Crohn's disease and ulcerative colitis, is becoming in-creasingly complex, demanding and challenging. In the recent years, IBD quality measures aiming to improve patients' care have been developed, multiple new medical therapies have been approved, new treatment goals have been set with the "treat--to--target" concept and drug monitoring has been implemented into IBD clinical management. Moreover, patients are increasingly using Internet resources to obtain information about their health conditions. The healthcare professional with an interest in treating IBD patients should deal with all these challenges in everyday practice by establishing, enhancing and maintaining a strong core of knowledge and skills related to IBD. This is an ongoing process and traditionally these needs are covered with additional reading of textbook or journal articles, attendance at meetings or conferences, or at local rounds. Web--based learning resources expand the options for knowledge acquisition and save time and costs as well. In the new era of communications technology, web-based resources can cover the educational needs of both patients and healthcare professionals and can contribute to improvement of disease management and patient care. Healthcare professionals can individually visit and navigate regularly relevant websites and tailor choices for educational activities according to their existing needs. They can also provide their patients with a few certified suitable internet resources. In this review, we explored the Internet using PubMed and Startpage(Google), for web-based IBD--related educational resources aiming to provide a guide for those interested in obtaining certified knowledge in this subject.

Comparison of Spatio-Spectral Properties of Zen-Meditation and Resting EEG Based on Unsupervised Learning

This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of centroid frequency (BMFc). Zen practitioners simultaneously concentrate on the third ventricle, hypothalamus and corpora quadrigemina touniversalize all brain neurons to construct a detached brain and gradually change the normal brain traits, leading to the process of brain-neuroplasticity. During such tri-aperture concentration, EEG exhibits prominent diffuse high-frequency oscillations. Unsupervised self-organizing map (SOM), clusters the dataset of quantitative EEG by matching the input feature vector Fc and the output cluster center through the SOM network weights. Input dataset contains brain mappings of 30 centroid frequencies extracted from CWT (continuous wavelet transform) coefficients. According to SOM clustering results, resting EEG is dominated by global low-frequency (<14 Hz) activities, except channels T7, F7 and TP7 (>14.4 Hz);whereas Zen-meditation EEG exhibits globally high-frequency (>16 Hz) activities throughout the entire record. Beta waves with a wide range of frequencies are often associated with active concentration. Nonetheless, clinic report discloses that benzodiazepines, medication treatment for anxiety, insomnia and panic attacks to relieve mind/body stress, often induce beta buzz. We may hypothesize that Zen-meditation practitioners attain the unique state of mindfulness concentration under optimal body-mind relaxation.

Learn to Unlearn for Transformative Learning:An Epistemological Look

The present work corresponds to a reflection about several theories and approaches of the learning and their relevance in the training of social agents,reflection that emerges from the practice of the training,and attending to the fact that the meaning of what it implies to learn and how it is that is learned is not at the center of the discussion in universities in Latin America,which emphasizes more what to teach rather than how to teach.

Model identification of continuous stirred tank reactor based on QKLMS algorithm

The continuous stirred tank reactor(CSTR)is one of the typical chemical processes.Aiming at its strong nonlinear characteristics,a quantized kernel least mean square(QKLMS)algorithm is proposed.The QKLMS algorithm is based on a simple online vector quantization technology instead of sparsification,which can compress the input or feature space and suppress the growth of the radial basis function(RBF)structure in the kernel learning algorithm.To verify the effectiveness of the algorithm,it is applied to the model identification of CSTR process to construct a nonlinear mapping relationship between coolant flow rate and product concentration.In additiion,the proposed algorithm is further compared with least squares support vector machine(LS-SVM),echo state network(ESN),extreme learning machine with kernels(KELM),etc.The experimental results show that the proposed algorithm has higher identification accuracy and better online learning ability under the same conditions.

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.