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In this paper, we are interested in the notion of multiple ways of thinking, knowing and transforming life, namely an increasing capacity to intervene in "life" as a "molecular biopolitics," and the changing ways in which "life" can be understood computationally. We identify and speculate on the ways different ideas of life, drawing from humanities, social, life and computing sciences, may impact the ways we think about and know education policy. We focus on key aspects of education policy around self-responsibilisation and choice, new forms of network governance and the move to education data science. The paper examines what is required to understand how advancements in the life and computing sciences may be implemented within educational institutions and organisations, and what political and ethical issues might pertain to the challenges these new advancements bring to educational institutions.

In this paper we have examined the position and roles of Computer Science in curricula in the light of recent calls for curriculum change and we have proposed principles and issues to consider in curriculum design as well as identifying priority areas for further research. The paper is based on discussions within and beyond the International Federation of Information Processing (IFIP) Education Community since 2012 as well as an analysis of curriculum developments in five different countries. Emerging themes have been discussed with reference to important perspectives from curriculum theory including "powerful knowledge" as a key element of entitlement and management of the growth of expertise. Based on this analysis we have identified areas of consensus as well as constraints, risks and issues that are still subject to controversy. There is an emerging consensus of the importance of Computer Science and the nature of its "powerful knowledge". Furthermore current understanding of the opportunities and benefits for starting to learn Computer Science early in primary schools has identified this early start as an entitlement and equity issue. There is a strong consensus that teacher professional development in Computer Science Education is critical for supporting curriculum change and is currently a major challenge in many countries. Other key issues include understanding how the growth of expertise affects potential structure and sequencing in the curriculum and the balance of content. Further considerations include how new technological opportunities interact with pedagogical approaches and can provide new potential for the growth of expertise.

Computer science has been a discipline for some years, and its position in the school curriculum has been contested differently in several countries. This paper looks at its role in three countries to illustrate these differences. A reconsideration of computer science as a separate subject both in primary and secondary education is suggested. At EDUsummIT 2015 it was argued that the major rationales for including computer science as a subject in the K-12 curriculum are economic, social and cultural. The paper explores these three rationales and also a beneficence matrix to assist curriculum designers. It also argues computer science is rapidly becoming critical for generating new knowledge, and should be taught as a distinct subject or content area, especially in secondary schools. The paper concludes by looking at some of the key questions to be considered when implementing computer science in the school curriculum, and at ways its role might change in the future.

This study aims to analyse the work done in the field of Night Vision Robots using IR and Object Detection from 2011 to 2021, using the bibliometric methods. This paper presents a Scopus database review on "Night Vision Bot using Dynamic IR and Object Detection". The necessity for doing this bibliometric survey is that to know how the technology in the field of mobile robotics and night vision, as well as to object detection, has evolved over the years. This paper shows the importance of Night Vision Robot from the year 2011 and continued up to 2021 April. The database analysis for the robot is done through Scopus and VOSviewer Version 1.6.16. Through this database survey, it is revealed that the maximum number of publications are Conference Paper and most of them are from the field of Engineering and Computer Science, India has the greatest number of publications followed by China. Methods: The Scopus database was used to obtain the articles for the above topic. The research papers were considered from the year 2011 to 2021. The Scopus analyzer can be used for the analysis of the database with different categories like Source, Subject Area, Country, etc. The analysis such as co-authorship, co-occurrences, citation analysis etc. is done by using VOSviewer Version 1.6.16. Results: In the study, a total of 69 articles on Night Vision Robot were obtained between the years 2011 and 2021. The statistical analysis and network analysis shows that the maximum number of papers were published in the year 2020. India is the highest contributor followed by China and Australia. Conclusion: The outcome of the Scopus database is 69 articles with the English Language having the largest number of articles. The Statistic Analysis helps to understand the potential of topic. It is done for Authors, Documents, Country, affiliations, funding sponsors. The Network Analysis indicates the interconnections between different parameters such as Coauthor, sources. It indicates that this is a new concept, and the research has been done mostly in the last year, so there is lot of future potential and scope for development. [ABSTRACT FROM AUTHOR]

Graduate employability has become an issue since there are broad mismatches between the acquired graduate skills from university and the required skills by employers. While previous researches have outlined the salient skills that need to be embedded in graduate education, to date no studies have attempted to methodically identify and synthesize the literature on graduate attributes. In this paper a total of 39 relevant studies on graduate skills and attributes in the subject areas of business and management, accounting, and computer science were extracted from Scopus® (database). This revealed a total of 53 graduate attributes, with some being highly used, such as communication, teamwork, problem solving, technological skills, creativity, interpersonal skills, leadership skills, self-management and flexibility/adaptability. The majority of studies used a quantitative survey method to collect and rank graduate attributes, and Australia emerged as the most active country in researching the domain.

The challenges and solutions for inclusive engineering education are discussed in this paper. We propose remote experimentation as the practicaloriented basis to train engineers with disabilities in the fields of Computer Science and Information Technologies. The structure and the functionality of international GOLDi network that unites partner universities from Germany, Australia, Ukraine, Armenia and Georgia is given. The possibilities of REIoT complex for studying the features of embedded systems design and Internet of Things technologies as well as an overview of ISRT laboratory for embedded software development and testing are given. The presented Remote Laboratories are successfully used to improve educational services quality and accessibility as well as to strengthen the practical component of the learning process. [ABSTRACT FROM AUTHOR]

Graphs are commonly used to represent objects, such as images and text, for pattern classification. In a dynamic world, an object may continuously evolve over time, and so does the graph extracted from the underlying object. These changes in graph structure with respect to the temporal order present a new representation of the graph, in which an object corresponds to a set of time-variant graphs. In this paper, we formulate a novel time-variant graph classification task and propose a new graph feature, called a graph-shapelet pattern, for learning and classifying time-variant graphs. Graph-shapelet patterns are compact and discriminative graph transformation subsequences. A graph-shapelet pattern can be regarded as a graphical extension of a shapelet—a class of discriminative features designed for vector-based temporal data classification. To discover graph-shapelet patterns, we propose to convert a time-variant graph sequence into time-series data and use the discovered shapelets to find graph transformation subsequences as graph-shapelet patterns. By converting each graph-shapelet pattern into a unique tokenized graph transformation sequence, we can measure the similarity between two graph-shapelet patterns and therefore classify time-variant graphs. Experiments on both synthetic and real-world data demonstrate the superior performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Despite the fact that great progress has been made on single image deraining tasks, it is still challenging for existing models to produce satisfactory results directly, and it often requires a single or multiple refinement stages to gradually improve the quality. However, in this paper, we demonstrate that existing image-level refinement with a stage-independent learning design is problematic with the side effect of over/under-deraining. To resolve this issue, we for the first time propose the mechanism of learning to carry out refinement on the unsatisfactory features, and propose a novel attentive feature refinement (AFR) module. Specifically, AFR is designed as a two-branched network for simultaneous rain-distribution-aware attention map learning and attention guided hierarchy-preserving feature refinement. Guided by task-specific attention, coarse features are progressively refined to better model the diversified rainy effects. By using a separable convolution as the basic component, our AFR module introduces little computation overhead and can be readily integrated into most rainy-to-clean image translation networks for achieving better deraining results. By incorporating a series of AFR modules into a general encoder-decoder network, AFR-Net is constructed for deraining and it achieves new state-of-the-art results on both synthetic and real images. Furthermore, by using AFR-Net as a teacher model, we explore the use of knowledge distillation to successfully learn a student model that is also able to achieve state-of-the-art results but with a much faster inference speed (i.e., it only takes 0.08 second to process a $512\times 512$ rainy image). Code and pre-trained models are available at $\langle $ https://github.com/RobinCSIRO/AFR-Net $\rangle $. [ABSTRACT FROM AUTHOR]

Aim The aim of this study was to provide an introduction to nutrition informatics, including a brief history and a rationale of its importance for Australian dietetics. Methods The study method used was a narrative review informed by a literature review and expert consultation with the Academy of Nutrition and Dietetics. Results Nutrition informatics has demonstrated potential to improve efficiencies, reduce costs, support research and ultimately enhance patient care. Governments and organisations have identified this as an emerging priority area. The Dietitians Association of Australia has launched initiatives to advocate and provide strategic advice on dietetic involvement in biomedical informatics and specifically nutrition informatics. Conclusions If nutrition standards and processes are not integrated into information systems, dietitians may inherit technologies that do not support dietetic work practices, and opportunities to enhance nutrition services may be missed. It is recommended that dietitians be aware of the opportunities and potential benefits of nutrition informatics, become familiar and involved in technology initiatives, and take the lead in guiding the development and implementation of technologies that may impact patient nutritional care. [ABSTRACT FROM AUTHOR]

The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user–user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen–Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics. [ABSTRACT FROM AUTHOR]

Computer science, like technology in general, is seen as a masculine field and the under-representation of women an intransigent problem. In this paper, we argue that the cultural belief in Australia that computer science is a domain for men results in many girls and women being chased away from that field as part of a border protection campaign by some males – secondary school teachers, boys and men playing games online and young men on campus at university. We draw on American feminist philosopher, Iris Marion Young’s analysis of the ‘five faces’ of oppression to suggest strategies whereby Australian universities could support women in computer science and educate men about respectful behaviour and gender equity. [ABSTRACT FROM PUBLISHER]

A novel canonical duality theory (CDT) is presented for solving general bilevel mixed integer nonlinear optimization governed by linear and quadratic knapsack problems. It shows that the challenging knapsack problems can be solved analytically in term of their canonical dual solutions. The existence and uniqueness of these analytical solutions are proved. NP-hardness of the knapsack problems is discussed. A powerful CDT algorithm combined with an alternative iteration and a volume reduction method is proposed for solving the NP-hard bilevel knapsack problems. Application is illustrated by benchmark problems in optimal topology design. The performance and novelty of the proposed method are compared with the popular commercial codes. [ABSTRACT FROM AUTHOR]