Learning Systems
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Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Mahdi Bohlouli ◽  
Omed Hassan Ahmed ◽  
Ali Ehsani ◽  
Marwan Yassin Ghafour ◽  
Hawkar Kamaran Hama ◽  

PurposeMany people have been dying as a result of medical errors. Offering clinical learning can lead to better medical care. Clinics have conventionally incorporated direct modality to teach personnel. However, they are now starting to take electronic learning (e-learning) mechanisms to facilitate training at work or other suitable places. The objective of this study is to identify and prioritize the medical learning system in developing countries. Therefore, this paper aims at describing a line of research for developing medical learning systems.Design/methodology/approachNowadays, organizations face fast markets' changing, competition strategies, technological innovations and accessibility of medical information. However, the developing world faces a series of health crises that threaten millions of people's lives. Lack of infrastructure and trained, experienced staff are considered essential barriers to scaling up treatment for these diseases. Promoting medical learning systems in developing countries can meet these challenges. This study identifies multiple factors that influence the success of e-learning systems from the literature. The authors have presented a systematic literature review (SLR) up to 2019 on medical learning systems in developing countries. The authors have identified 109 articles and finally selected 17 of them via article choosing procedures.FindingsThe paper has shown that e-learning systems offer significant advantages for the medical sector of developing countries. The authors have found that executive, administrative and technological parameters have substantial effects on implementing e-learning in the medical field. Learning management systems offer a virtual method of augmented and quicker interactions between the learners and teachers and fast efficient instructive procedures, using computer and Internet technologies in learning procedures and presenting several teaching-learning devices.Research limitations/implicationsThe authors have limited the search to Scopus, Google Scholar, Emerald, Science Direct, IEEE, PLoS, BMC and ABI/Inform. Many academic journals probably provide a good picture of the related articles, too. This study has only reviewed the articles extracted based on some keywords such as “medical learning systems,” “medical learning environment” and “developing countries.” Medical learning systems might not have been published with those specific keywords. Also, there is a requirement for more research with the use of other methodologies. Lastly, non-English publications have been removed. There could be more potential related papers published in languages other than English.Practical implicationsThis paper helps physicians and scholars better understand the clinical learning systems in developing countries. Also, the outcomes can aid hospital managers to speed up the implementation of e-learning mechanisms. This research might also enable the authors to have a role in the body of knowledge and experience, so weakening the picture of the developing country's begging bowl is constantly requesting help. The authors hoped that their recommendations aid clinical educators, particularly in developing countries, adopt the trends in clinical education in a changing world.Originality/valueThis paper is of the pioneers systematically reviewing the adoption of medical learning, specifically in developing countries.

2021 ◽  
Shengyi Wu ◽  
Tommy Blanchard ◽  
Emily Meschke ◽  
Richard N. Aslin ◽  
Ben Hayden ◽  

Normative learning theories dictate that we should preferentially attend to informative sources, but only up to the point that our limited learning systems can process their content. Humans, including infants, show this predicted strategic deployment of attention. Here we demonstrate that rhesus monkeys, much like humans, attend to events of moderate surprisingness over both more and less surprising events. They do this in the absence of any specific goal or contingent reward, indicating that the behavioral pattern is spontaneous. We suggest this U-shaped attentional preference represents an evolutionarily preserved strategy for guiding intelligent organisms toward material that is maximally useful for learning.

E-Management ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 28-36
A. A. Dashkov ◽  
Yu. O. Nesterova

In the XXI century, “trust” becomes a category that manifests itself in a variety of ways and affects many areas of human activity, including the economy and business. With the development of information and communication technologies and end-to-end technologies, this influence is becoming more and more noticeable. A special place in digital technologies is occupied by human trust when interacting with artificial intelligence and machine learning systems. In this case, trust becomes a potential stumbling block in the field of further development of interaction between artificial intelligence and humans. Trust plays a key role in ensuring recognition in society, continuous progress and development of artificial intelligence.The article considers human trust in artificial intelligence and machine learning systems from different sides. The main objectives of the research paper are to structure existing research on this subject and identify the most important ways to create trust among potential consumers of artificial intelligence products. The article investigates the attitude to artificial intelligence in different countries, as well as the need for trust among users of artificial intelligence systems and analyses the impact of distrust on business. The authors identified the factors that are crucial in the formation of the initial level of trust and the development of continuous trust in artificial intelligence.

2021 ◽  
Vol 4 ◽  
Emilia Viaene ◽  
Lenneke Kuijer ◽  
Mathias Funk

Smart home technologies with the ability to learn over time promise to adjust their actions to inhabitants’ unique preferences and circumstances. For example, by learning to anticipate their routines. However, these promises show frictions with the reality of everyday life, which is characterized by its complexity and unpredictability. These systems and their design can thus benefit from meaningful ways of eliciting reflections on potential challenges for integrating learning systems into everyday domestic contexts, both for the inhabitants of the home as for the technologies and their designers. For example, is there a risk that inhabitants’ everyday lives will reshape to accommodate the learning system’s preference for predictability and measurability? To this end, in this paper we build a designer’s interpretation on the Social Practice Imaginaries method as developed by Strengers et al. to create a set of diverse, plausible imaginaries for the year 2030. As a basis for these imaginaries, we have selected three social practices in a domestic context: waking up, doing groceries, and heating/cooling the home. For each practice, we create one imaginary in which the inhabitants’ routine is flawlessly supported by the learning system and one that features everyday crises of that routine. The resulting social practice imaginaries are then viewed through the perspective of the inhabitant, the learning system, and the designer. In doing so, we aim to enable designers and design researchers to uncover a diverse and dynamic set of implications the integration of these systems in everyday life pose.

2021 ◽  
David Miralles ◽  
Guillem Garrofé ◽  
Calota Parés ◽  
Alejandro González ◽  
Gerard Serra ◽  

Abstract The cognitive connection between the senses of touch and vision is probably the best-known case of cross-modality. Recent discoveries suggest that the mapping between both senses is learned rather than innate. These evidences open the door to a dynamic cross-modality that allows individuals to adaptively develop within their environment. Mimicking this aspect of human learning, we propose a new cross-modal mechanism that allows artificial cognitive systems (ACS) to adapt quickly to unforeseen perceptual anomalies generated by the environment or by the system itself. In this context, visual recognition systems have advanced remarkably in recent years thanks to the creation of large-scale datasets together with the advent of deep learning algorithms. However, such advances have not occurred on the haptic mode, mainly due to the lack of two-handed dexterous datasets that allow learning systems to process the tactile information of human object exploration. This data imbalance limits the creation of synchronized multimodal datasets that would enable the development of cross-modality in ACS during object exploration. In this work, we use a multimodal dataset recently generated from tactile sensors placed on a collection of objects that capture haptic data from human manipulation, together with the corresponding visual counterpart. Using this data, we create a cross-modal learning transfer mechanism capable of detecting both sudden and permanent anomalies in the visual channel and still maintain visual object recognition performance by retraining the visual mode for a few minutes using haptic information. Here we show the importance of cross-modality in perceptual awareness and its ecological capabilities to self-adapt to different environments.

2021 ◽  
Vol 28 ◽  
Jannis Born ◽  
Matteo Manica

: It is more pressing than ever to reduce the time and costs for developing lead compounds in the pharmaceutical industry. The co-occurrence of advances in high-throughput screening and the rise of deep learning (DL) have enabled the development of large-scale multimodal predictive models for virtual drug screening. Recently, deep generative models have emerged as a powerful tool for exploring the chemical space and raising hopes to expedite the drug discovery process. Following this progress in chemocentric approaches for generative chemistry, the next challenge is to build multimodal conditional generative models that leverage disparate knowledge sources when biochemical mapping properties to target structures. Here, we call the community to bridge drug discovery more closely with systems biology when designing deep generative models. Complementing the plethora of reviews on the role of DL in chemoinformatics, we herein specifically focus on the interface of predictive and generative modeling for drug discovery. Through a systematic publication keyword search on PubMed and a selection of preprint servers (arXiv, biorXiv, chemRxiv, and medRxiv), we quantify trends in the field and find that molecular graphs and VAEs have become the most widely adopted molecular representations and architectures in generative models, respectively. We discuss progress on DL for toxicity, drug-target affinity, and drug sensitivity prediction and specifically focus on conditional molecular generative models that encompass multimodal prediction models. Moreover, we outline prospects in the field and identify challenges such as the integration of deep learning systems into experimental workflows in a closed-loop manner or the adoption of federated machine learning techniques to overcome data sharing barriers. Other challenges include, but are not limited to interpretability in generative models, more sophisticated metrics for the evaluation of molecular generative models, and, following up on that, community-accepted benchmarks for both multimodal drug property prediction and property-driven molecular design.

Marta Nola ◽  
Cecilia Guiot ◽  
Stefano Damiani ◽  
Natascia Brondino ◽  
Roberta Milani ◽  

AbstractDuring the CoViD-19 pandemic, University students may have suffered from increased anxiety due to interferences in their relationships and in academic requirements, as didactic activities have moved to distance learning systems. However, being surrounded by supportive relationships and being motivated to cultivate personal interests might have decreased anxiety. In this pilot study, we collected the responses of 174 students from Italian University merit colleges to an online questionnaire, investigating their perceived anxiety, the quality of surrounding relationships, whether they were cultivating any personal interests and whether they had spent the period of lockdown in college or at home. Regression analyses indicated that both quality of relationships and personal interests predicted low levels of anxiety (p < 0.001). However, simple slope analyses showed that personal interests were negatively related to anxiety only at medium and high quality of relationships (p < 0.001), while no association was found at low quality of relationships. No differences were found between students who stayed in college or at home. These results suggest that Universities should promote accessibility to relationships and cultivation of personal interests to protect students’ mental health during mass emergencies such as the current pandemic, in the perspective of improving community resilience.

2021 ◽  
Vol 19 (1) ◽  
Diana Sarakbi ◽  
Nana Mensah-Abrampah ◽  
Melissa Kleine-Bingham ◽  
Shams B. Syed

Abstract Introduction Transforming a health system into a learning one is increasingly recognized as necessary to support the implementation of a national strategic direction on quality with a focus on frontline experience. The approach to a learning system that bridges the gap between practice and policy requires active exploration. Methods This scoping review adapted the methodological framework for scoping studies from Arksey and O’Malley. The central research question focused on common themes for learning to improve the quality of health services at all levels of the national health system, from government policy to point-of-care delivery. Results A total of 3507 records were screened, resulting in 101 articles on strategic learning across the health system: health professional level (19%), health organizational level (15%), subnational/national level (26%), multiple levels (35%), and global level (6%). Thirty-five of these articles focused on learning systems at multiple levels of the health system. A national learning system requires attention at the organizational, subnational, and national levels guided by the needs of patients, families, and the community. The compass of the national learning system is centred on four cross-cutting themes across the health system: alignment of priorities, systemwide collaboration, transparency and accountability, and knowledge sharing of real-world evidence generated at the point of care. Conclusion This paper proposes an approach for building a national learning system to improve the quality of health services. Future research is needed to validate the application of these guiding principles and make improvements based on the findings.

Chandana EP

Now-a-days, in the world of enterprise, machine learning workloads have become mainstream. However, there is an abundance of choices that can be made around multi-cloud infrastructure and machine learning toolkits, making it complex to balance their costs and performance. Microservices architecture has been the preferred architecture style for a few years now and there’s been rapid growth in its adoption, never failing to provide exceptionally testable & maintainable services. To have a lot more simplified services management, deployment and to orchestrate tools, Kubernetes is recommended. Kubeflow, a known and widely adopted open source container management platform that manages machine learning stack on Kubernetes. This paper discusses the development and validation of Kubeflow components such as PyTorch, TensorFlow, & Notebook Servers. It includes PodDefault functionalities for notebooks and container builder API to build docker images using Kaniko. Using Helm, Kubeflow upgrade operation is performed to enhance the configured resources whenever required for the distributed training jobs & workloads. Hence, providing data scientists a scalable platform to run machine learning workloads without having to worry about resources, costs, time, and portability.

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