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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.

The demand for graduates with exposure in Cloud Computing is on the rise. For many educational institutions, the challenge is to decide on how to incorporate appropriate cloud-based technologies into their curricula. In this paper, we describe our design and experiences of integrating Cloud Computing components into seven third/fourth-year undergraduate-level information system, computer science, and general science courses that are related to large-scale data processing and analysis at the University of Queensland, Australia. For each course, we aimed at finding the best-available and cost-effective cloud technologies that fit well in the existing curriculum. The cloud related technologies discussed in this paper include open-source distributed computing tools such as Hadoop, Mahout, and Hive, as well as cloud services such as Windows Azure and Amazon Elastic Computing Cloud (EC2). We anticipate that our experiences will prove useful and of interest to fellow academics wanting to introduce Cloud Computing modules to existing courses. (Contains 3 tables and 11 figures.)

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]

Within existing ICT degrees there is a widely-held belief that content must be tailored for different "kinds" of students--often two differing student groups: a technical group requiring detailed Computer Science knowledge and a separate group requiring less technical, more strategic ICT knowledge and skills. Our institution has produced a combined degree that contains both technical and non-technical content taught to a single cohort of students and thus requires a deeper insight into the needs of this diverse group of learners. This paper reports on an assessment of learning styles across our first year students, in order to inform our teaching delivery practices. Our findings inform the development of teaching and assessment materials that better support the diverse needs of students, regardless of their self-selected discipline.

In a Finnish study of four different academic disciplines, Ylijoki (2000) found that in Computer Science there was a disparity between the conceptions held by undergraduate students and staff about their discipline; students viewed it as being far more pragmatic and results focused than did their instructors. Not surprisingly, here at our Australian university where the undergraduate Computer Science program emphasizes programming and problem solving skills, the authors had noticed a similar inconsistency between staff and student beliefs. This paper reports on an effort to realign these conceptions and broaden student experience using an assessment task. Centered on solutions to the popular "Sudoku" puzzle (Sudoku, 2005), the task was designed and introduced into an Intelligent Systems course, a final year elective of a Computer Science degree. The goal was to expose students to some of the "pure" rather than applied aspects of the Computer Science discipline (Becher & Trowler, 2001), by using assessment to encourage experimental learning (Kolb & Fry, 1975). The assessment specification instructed students to design and conduct several "in silica" Computer Science experiments to solve and/or create Sudoku puzzles. Importantly, students were asked to keep a Research Diary documenting their thoughts, attempts, backtracking and progresses as they attempted the assignment. Most unique from a student's perspective was that "failure" to solve the given problem by experimentation was a viable option; their efforts would be rewarded given they conducted themselves "scientifically" in their attempt. The inclusion of a Research Diary as part of the assessment proved beneficial to both students and staff, particularly as the Diaries focused students on the doing, rather than on the results achieved. For staff, these journals allowed the privilege of seeing what students were thinking and doing as they engaged in the problem. An early analysis of Diary entries shows evidences of students "thinking scientifically", that is, understanding and thinking about the problem before formalizing an approach to a solution. In addition, Diary entries also show evidences of students "behaving scientifically" as they reported conducting experiments and analyzing their results, before concluding about their experiences. For students, regardless of whether they able to complete the task, the Research Diaries were valuable testaments to their efforts and experimentations. (Contains 1 figure and 2 tables.)

Teaching future knowledge engineers, the necessary skills for designing and implementing intelligent software solutions required by business, industry and research today, is a very tall order. These skills are not easily taught in traditional undergraduate computer science lectures; nor are the practical experiences easily reinforced in laboratory sessions. In an attempt to address this issue, a software development project, designed to take students through a complete process of knowledge engineering, was introduced in an undergraduate Intelligent Systems subject. In this project, students were required to act as domain experts, knowledge engineers, programmers, end users and project manager in the production of a game-playing expert system. The paper describes the project, its objectives and development, as well as some of the benefits. (Contains 3 figures and 1 table.)

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]

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]

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]

Objectives: We quantify spatial turnover in communities of 1939 plant and 59 mammal species at 2.5 km resolution across a topographically heterogeneous region in south-eastern Australia to identify distributional breaks and low turnover zones where multiple species distributions overlap. Environmental turnover is measured to determine how climate, topography and geology influence biotic turnover differently across a variety of biogeographic breaks and overlaps. We identify the genera driving turnover and confirm the versatility of this approach across spatial scales and locations. Methods: Directional moving window analyses, rotated through 360°, were used to measure spatial turnover variation in different directions between gridded cells containing georeferenced plant and mammal occurrences and environmental variables. Generalised linear models were used to compare taxic turnover results with equivalent analyses for geology, regolith weathering, elevation, slope, solar radiation, annual precipitation and annual mean temperature, both uniformly across the entire study area and by stratifying it into zones of high and low turnover. Identified breaks and transitions were compared to a conservation bioregionalisation framework widely used in Australia. Results/Significance: Detailed delineations of plant and mammal turnover zones with gradational boundaries denoted subtle variation in species assemblages. Turnover patterns often diverged from bioregion boundaries, though plant turnover adhered most closely. A prominent break zone contained either comparable or greater numbers of unique genera than adjacent overlaps, but these were concentrated in a small subsection relatively under-protected by conservation reserves. The environmental correlates of biotic turnover varied for different turnover zones in different subsections of the study area. Topography and temperature showed much stronger relationships with plant turnover in a topographically complex overlap, relative to a lowland overlap where weathering was most predictive. This method can quantify transitional turnover patterns from small to broad extents, at different resolutions for any location, and complements broad-scale bioregionalisation schemes in conservation planning. [ABSTRACT FROM AUTHOR]