This article discusses the relationship of ICT with the need for lifelong learning and the development of the professional potential of the faculty of the Higher School. The goal of the paper is to ...present a mechanism for developing the professional potential of the faculty of the Higher School.
AIoT applications often encounter challenges such as terminal resource constraints, data drift, and data heterogeneity in real world, leading to problems such as catastrophic forgetting, low ...generalization ability, and low accuracy during model training. To address these challenges, we proposed CoLLaRS, a cloud–edge–terminal collaborative lifelong learning framework for AIoT applications. In the CoLLaRS framework, we alleviate the problem of terminal resource constraints by uploading terminal tasks at the edge. CoLLaRS uses continuous training at the edge to achieve lifelong learning training of the model and solve the problem of catastrophic forgetting. CoLLaRS employs federated optimization in the cloud to perform personalized aggregation of different edge models and solve the problem of weak model generalization ability. Finally, the model is fine-tuned at the terminal to further optimize its accuracy in local tasks. Our experiments on real-world datasets showed that CoLLaRS has an 8% improvement in accuracy and a 5% improvement in backward transfer(BWT) and forward transfer(FWT) compared to other baseline algorithms. The results of the ablation experiments further confirmed the effectiveness of CoLLaRS.
•A cloud–edge–terminal collaborative lifelong learning framework.•Higher accuracy and robust to catastrophic forgetting with similar training time.•Evaluated in real-world AIoT semantic segmentation datasets.
Abstract
Introduction
Military service is associated with a number of occupational stressors, including non-conducive sleeping environments, shift schedules, and extended deployments overseas. ...Service members who undergo combat deployments are at increased risk for mental health and sleep difficulties. Bidirectional associations between sleep and mental health difficulties are routinely observed, but the directional association of these difficulties from one deployment to the next has not been addressed. The purpose of this study was to examine whether residual sleep problems or mental health difficulties after a 12-month period of reset operations following an initial deployment were associated with changes in sleep and mental health following a subsequent deployment.
Methods
Data from 74 U.S. Soldiers were case-matched across three time points. Participants were assessed 6 months (T1) and 12 months (T2) following an initial deployment. Participants were then assessed 3 months (T3) following a subsequent deployment. Symptoms of PTSD, anxiety, depression, and sleep difficulties were assessed at all three time points.
Results
Cross-lagged hierarchical regression models revealed that residual sleep difficulties across the time points uniquely predicted later changes in PTSD and anxiety symptoms, but not depressive symptoms, following a subsequent deployment. Conversely, residual mental health difficulties were not unique predictors of later changes in sleep difficulties.
Conclusion
These findings suggest that higher levels of residual sleep difficulties 12 months following a prior deployment are associated with larger increases in mental health problems following a subsequent deployment. Moreover, and importantly, the converse association was not supported. Residual mental health difficulties prior to deployment were not associated with changes in sleep difficulties. These data provide a viable target for intervention during reset operations to mitigate mental health difficulties associated with combat deployments. They might also help inform return-to-duty decisions.
Support
N/A.
Learning continually from sequentially arriving data has been a long standing challenge in machine learning. An emergent body of deep learning literature suggests various solutions, through ...introduction of significant simplifications to the problem statement. As a consequence of a growing focus on particular tasks and their respective benchmark assumptions, these efforts are thus becoming increasingly tailored to specific settings. Whereas approaches that leverage Variational Bayesian techniques seem to provide a more general perspective of key continual learning mechanisms, they however entail their own caveats. Inspired by prior theoretical work on solving the prevalent mismatch between prior and aggregate posterior in deep generative models, we return to a generic variational auto-encoder based formulation and investigate its utility for continual learning. Specifically, we propose to adapt a two-stage training framework towards a context conditioned variant for continual learning, where we then formulate mechanisms to alleviate catastrophic forgetting through choices of generative rehearsal or well-motivated extraction of data exemplar subsets. Although the proposed generic two-stage variational auto-encoder is not tailored towards a particular task and allows for flexible amounts of supervision, we empirically demonstrate it to surpass task-tailored methods in both supervised classification, as well as unsupervised representation learning.
Online continual learning for image classification studies the problem of learning to classify images from an online stream of data and tasks, where tasks may include new classes (class incremental) ...or data nonstationarity (domain incremental). One of the key challenges of continual learning is to avoid catastrophic forgetting (CF), i.e., forgetting old tasks in the presence of more recent tasks. Over the past few years, a large range of methods and tricks have been introduced to address the continual learning problem, but many have not been fairly and systematically compared under a variety of realistic and practical settings.
To better understand the relative advantages of various approaches and the settings where they work best, this survey aims to (1) compare state-of-the-art methods such as Maximally Interfered Retrieval (MIR), iCARL, and GDumb (a very strong baseline) and determine which works best at different memory and data settings as well as better understand the key source of CF; (2) determine if the best online class incremental methods are also competitive in the domain incremental setting; and (3) evaluate the performance of 7 simple but effective tricks such as the ”review” trick and the nearest class mean (NCM) classifier to assess their relative impact. Regarding (1), we observe that iCaRL remains competitive when the memory buffer is small; GDumb outperforms many recently proposed methods in medium-size datasets and MIR performs the best in larger-scale datasets. For (2), we note that GDumb performs quite poorly while MIR – already competitive for (1) – is also strongly competitive in this very different (but important) continual learning setting. Overall, this allows us to conclude that MIR is overall a strong and versatile online continual learning method across a wide variety of settings. Finally for (3), we find that all tricks are beneficial, and when augmented with the “review” trick and NCM classifier, MIR produces performance levels that bring online continual learning much closer to its ultimate goal of matching offline training. Our codes are available at https://github.com/RaptorMai/online-continual-learning.
This study aimed to offer fresh insights into the analysis of attitudes towards learning and perceptions of lifelong learning affecting lifelong learning participation by exploring the differences in ...network structures between lifelong learning participants and non-participants and identifying the core items with the greatest impact on lifelong learning participation. This study utilised network analysis, a method that involves nodes representing each factor and edges indicating connectivity between nodes to reveal the relationships among these factors. Data were collected from a large-scale national survey in South Korea, with 9,973 respondents selected through systematic sampling. The main variables were attitudes towards learning and perceptions of lifelong learning. Network analyses were conducted separately for participants and non-participants. The study's findings revealed differences in network structures between participants and non-participants. Non-participants consistently reported perceiving learning primarily as an economic means. Network centrality analysis revealed that participants' attitudes towards learning and perceptions of lifelong learning constituted a multifactorial construct. Conversely, for non-participants, items related to workplace success exhibited significantly higher values, indicating distinct central factors influencing lifelong learning participation in each group. This study's findings can provide insights for enhancing lifelong learning participation rates, making it a reference for related research and policies.
Employability in Adult and Higher Education Boffo, Vanna; Melacarne, Claudio
New directions for adult and continuing education,
10/2019, Volume:
2019, Issue:
163
Journal Article
The chapters contained in this special issue are summarized here, highlighting core themes and future implications for research and practice for adult education and lifelong learning.
Our recently developed Synchronized Low Energy Electronically chopped Passive Infra-Red (SLEEPIR) sensor node enables the stationary occupancy detection capability of traditional Passive Infra-Red ...sensors. A Machine Learning (ML) algorithm reports occupancy based on a locally collected dataset from the sensor node. Though promising, the ML algorithm's detection accuracy depends on the diversity of the collected dataset- provided the dataset contains a wide variety of infrared noise and occupancy patterns. Thus, it is challenging to train a universal ML model that contains all possible patterns. We propose an efficient K-Nearest Neighbor (KNN) occupancy classifier that incrementally adapts to the novel data from the sensor. The proposed algorithm ensures that only the relevant noise and occupancy patterns are learned. The fact that training observations are gathered on the same sensor node where the inference is made, keeps the proposed classifier accurate even with the bounded size of the dataset. A small dataset and an architecture like KNN, both enable the training and inference to be executed on a resource-constrained Internet of Things device. Thus, the proposed On-Device Lifelong Learning approach eliminates the need for over-the-cloud ML model updates. The dataset was collected for two distinct floorplans over two months. Results indicate an average occupancy accuracy improvement of 20.8% compared to a statically trained Long Short-Term Memory (LSTM) model. The proposed KNN model delivers comparable detection accuracy while remaining orders of magnitude faster in terms of computational performance when compared to the LSTM-based occupancy detection algorithm.
Approach to nocturnal enuresis in children Ong, Li Ming; Chan, Joel Meng Fai; Koh, Gabrielle Eloise Ming Yen ...
Singapore medical journal,
04/2024, Volume:
65, Issue:
4
Journal Article
Rapid development of artificial intelligence applications and widespread digital transformation are driving the need for employees to learn digital skills. The body of research on digital skills that ...working professionals need to thrive in an uncertain and ever-evolving workforce is fast accumulating. Although there have been literature reviews on the nature and types of such digital skills, an integrative overview of how digital skills are acquired remains lacking. This systematic literature review seeks to close the gap by focusing on digital skilling. A total of 39 journal articles published between January 2010 and June 2022 were identified using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Based on thematic coding of the articles, eleven digital skilling approaches were identified and conceptually organized into four categories. Findings regarding the contextual factors affecting digital skilling and impacts of digital skilling indicate an emerging framework of digital skilling. Emerging interests and opportunities for future research related to virtual worlds, learning analytics, and blockchain are discussed.
•This literature review identified 11 digital skilling approaches in 4 categories•A digital skilling framework incorporating contextual factors and impacts emerged•Emerging digital skilling approaches such as Co-Skilling merit further examination•More research on the fit between digital skills and skilling approaches is needed•There are opportunities related to virtual worlds, learning analytics and blockchain
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