Explore the vast range of titles available.

This brief paper aims to indicate what the study of Chinese astronomy can add to our understanding of premodern astronomy in the wider world. Some aspects of Chinese astronomy enable us to perceive the interestingly contingent nature of much of what is often taken for granted about the way astronomy was done elsewhere, particularly in the premodern West. Two examples of East-West contact in astronomy are examined: exchanges with India, particularly in the Sui and Tang dynasties in the 7th and 8th centuries of the Common Era and contacts with astronomy from the 13 th century CE Islamicate world. Finally the issue of the sphericity of the Earth is considered in a comparative context.

The part of this special issue with the title of “Context-awareness and Nature of Computation and Communication: ICCASA and ICTCC 2021” edited by Prof. Phan Cong Vinh is presented to scientists, researchers, experts and students in the fields of context-awareness and nature of computation and communication. Hopefully, they will find this part stimulating their research related to the hot topics of context-awareness and nature of computation and communication and being useful for their future work.

Psychologically “wise” interventions can cause lasting improvement in key aspects of people’s lives, but where will they work, and where will they not work? We consider the psychological affordance of the social context: Does the context in which the intervention is delivered afford the way of thinking offered by the intervention? If not, treatment effects are unlikely to persist. Change requires planting good seeds (more adaptive perspectives) in fertile soil in which those seeds can grow (a context with appropriate affordances). We illustrate the role of psychological affordances in diverse problem spaces, including recent large-scale trials of growth-mind-set and social-belonging interventions designed specifically to investigate heterogeneity across contexts. We highlight how the study of psychological affordances can advance theory about social contexts and inform debates about replicability.

Recommender systems are software mechanisms whose usage is to offer suggestions for different types of entities like products, services, or contacts that could be useful or interesting for a specific user. Other ways have been explored in the field to enhance the power of these systems by integrating the context as an additional attribute. This inclusion tries to extract the user preferences more accurately taking into account multiple components such as temporal, spatial, or social ones. Notwithstanding the magnitude of context-awareness in this area, the research community is in agreement with the lack of framework for context information and how to integrate it into recommender systems. Under this premise, this paper focuses on a comprehensive systematic literature review of the state-of-the-art recommendation techniques and their characteristics to benefit from contextual information. The following survey presents the following contributions as outcomes of our study: (i) determine a framework where multiple aspects are taken into account to have a clear definition of context representation, (ii) the techniques used to incorporate context, and (iii) the evaluation of these methods in terms of reproducibility and effectiveness. Our review also covers some crucial topics about context integration, classification of the contexts, application domains, and evaluation of the used datasets, metrics, and code implementations, where we observed clear shiftings in algorithmic and evaluation trends towards Neural Network approaches and ranking metrics, respectively. Just as importantly, future research opportunities and directions are exposed as final closure, standing out the exploitation of various data sources and the scalability and customization of existing solutions.

Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work.

As the key products of ubiquitous computing, context-aware systems have been widely used in many fields such as digital home, smart healthcare and so on. However, in the face of the typical application environment formed by multiple sensors and intelligent devices, the inconsistency of contexts that hinders the normal operation of the systems has become an inevitable and urgent problem that needs to be resolved. In this paper, we propose a new quality of context (QoC) parameter relevance to enrich the comprehensive assessment of the context quality. Moreover, on this basis, we put forward novel context inconsistency elimination algorithms that use multiple QoC parameters and Dempster-Shafer theory to solve the inconsistency problem of sensed contexts and non-sensed contexts, respectively. Experimental analyses from multiple dimensions fully show that the proposed algorithms have obvious advantages over the other algorithms in terms of accuracy, stability, and robustness.

This study aimed at investigating on factors affecting the attitudes of grade 10 students towards learning EFL in Debremarkos Comprehensive Secondary School in Debre Markos town, Ethiopia. The researcher randomly selected 103 sample students (10%) out of the total population (1030) for the study. In order to gather data, a questionnaire was carefully and systematically adapted and designed. Nine sample students were also selected purposely for focus group discussion, and Grade 10 English teachers were selected for the interview. Then, the data were analyzed quantitatively and qualitatively. The findings of the study mainly showed that the attitudes of grade 10 students towards learning EFL is positive. There are social factors (e.g., English native speakers, peer groups and learners' parents) affecting students' attitudes positively. On the other hand, educational context factors like English language teachers, the English language learning situations (e.g., the classrooms, arrangements of seats and the physical learning environment) had negative impacts on students' attitude. However, the findings showed that target language learners have positive attitudes towards the other educational context factor that is the English textbook of grade 10 which means English as a foreign language teaching materials in the study's context affect students' attitudes positively. By lowering the psychological variables (i.e. affective filters) for the target language learners, it is possible to aid the language learning process. Thus, as the implication of this study considers, the physical learning environment should be improved, and to achieve this, the government should work in conjunction with the school principals, teachers and societies.

We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised representation pretraining. We pretrain an encoder by making predictions in the encoded representation space. The pretraining tasks include two tasks: masked representation prediction—predict the representations for the masked patches, and masked patch reconstruction—reconstruct the masked patches. The network is an encoder–regressor–decoder architecture: the encoder takes the visible patches as input; the regressor predicts the representations of the masked patches, which are expected to be aligned with the representations computed from the encoder, using the representations of visible patches and the positions of visible and masked patches; the decoder reconstructs the masked patches from the predicted encoded representations. The CAE design encourages the separation of learning the encoder (representation) from completing the pertaining tasks: masked representation prediction and masked patch reconstruction tasks, and making predictions in the encoded representation space empirically shows the benefit to representation learning. We demonstrate the effectiveness of our CAE through superior transfer performance in downstream tasks: semantic segmentation, object detection and instance segmentation, and classification. The code will be available at https://github.com/Atten4Vis/CAE.