In recent decades, the demand for STEM professionals has increased, and consequently, the effort of educational institutions to sponsor their STEM degrees. In the context of a university program promoting STEM careers...
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ISBN:
(纸本)9783031616877;9783031616884
In recent decades, the demand for STEM professionals has increased, and consequently, the effort of educational institutions to sponsor their STEM degrees. In the context of a university program promoting STEM careers among high-school students, we designed a workshop for middle and high-school students to show them a different perspective of computing as a pragmatic, social, and people-centered profession. We focus on teaching basic concepts of Human-computer Interaction (HCI) as part of a problem-solving strategy to offer creative solutions to real problems of real people. The workshop proposes a list of well-known problems in urban environments, like social integration, elderly loneliness, or recycling, and asks the students to solve them envisioning an innovative mobile app by following a design thinking approach and applying several design artifacts, like scenario boards, user personas, thematic cards, and paper-based sketches. The results show that with little or no experience in designing and programming, participants engage in fruitful brainstorming sessions, offer creative solutions, and even implement low-level prototypes.
We analyse the search behaviour of genetic programming (GP) for symbolic regression (SR) in search spaces that are small enough to allow exhaustive enumeration, and use an improved exhaustive symbolic regression algor...
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ISBN:
(纸本)9783031700545;9783031700552
We analyse the search behaviour of genetic programming (GP) for symbolic regression (SR) in search spaces that are small enough to allow exhaustive enumeration, and use an improved exhaustive symbolic regression algorithm to generate the set of semantically unique expression structures, which is orders of magnitude smaller than the original SR search space. The efficiency of GP and a hypothetical random search in this set of unique expressions is compared, whereby the efficiency is quantified via the number of function evaluations performed until a given error threshold is reached, and the percentage of unique expressions evaluated during the search after simplification to a canonical form. The results for two real-world datasets with a single input variable show that GP in such limited search space explores only a small fraction of the search space, and evaluates semantically equivalent expressions repeatedly. GP has a smaller success probability than the idealised random search for such small search spaces.
As computerscience is integrated into a wider variety of fields, block-based programming languages like Snap!, which assemble code with visual blocks rather than text syntax, are increasingly used to teach computatio...
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ISBN:
(纸本)9781450394338
As computerscience is integrated into a wider variety of fields, block-based programming languages like Snap!, which assemble code with visual blocks rather than text syntax, are increasingly used to teach computational thinking (CT) to students from diverse backgrounds. Although automated evaluators (autograders) for programming assignments usually focus on runtime efficiency and output accuracy, effective evaluation of a student's CT skills requires assessing coding best practices, such as decomposition, abstraction, and algorithm design. While autograders are commonplace for text languages like Python, we present a machine learning approach to assess how effectively block-based code demonstrates understanding of CT fundamentals. Our dataset consists of Snap! programs written by students new to coding and evaluated by instructors using a CT rubric. We explore how to best transform these programs into low-dimensional features to allow encapsulation and repetition patterns to emerge. Experimentation involves comparing the effectiveness of a suite of clustering models and similarity metrics by analyzing how directly automated feedback correlates to the course staff's manual evaluation. Lastly, we demonstrate the practical application of the autograder in a classroom setting and discuss scalability and feasibility in other domains of CS education.
As we increasingly rely on artificial intelligence systems, we must ensure that those systems are reliable and need to know how much we can rely on them. In software quality assurance, testing is a useful method to hi...
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ISBN:
(纸本)9783031808883;9783031808890
As we increasingly rely on artificial intelligence systems, we must ensure that those systems are reliable and need to know how much we can rely on them. In software quality assurance, testing is a useful method to highlight and fix issues during development to avoid unexpected behavior after the system has been deployed. Artificial intelligence engineers are increasingly becoming aware of quality assurance as a requirement. Previous results in the area of answer set programming suggest that a high proportion of errors can be found when testing a program against a small scope, i.e. by inputs from a small domain. However, these results are based on assumptions that may be impractical for testing. To find out whether small scopes remain sufficient in practice, we evaluate several benchmarks against actual test oracles. Our findings suggest that small scopes can indeed find a high proportion of errors, but results depend on the observed benchmark and appropriate test oracles are required to achieve reliable scores.
Serverless computing, also known as the Function-as-a-Service (FaaS) paradigm, has become a cornerstone of modern cloud-based applications, enabling developers to build and execute workflows by composing serverless fu...
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This paper mainly studies the problem of determining the assembly plan of underwater vehicles under different constraints. Firstly, the dynamic programming model is established, and the total cost is determined as the...
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This study explores the impact of free choice of examination format on student performance in the ‘programming of Mobile Applications’ (PMA) course at the Saxon University of Cooperative Education. The PMA course, o...
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Stream processing systems must often cope with workloads varying in content, format, size, and input rate. The high variability and unpredictability make statically fine-tuning them very challenging. Our work addresse...
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ISBN:
(纸本)9783031695827;9783031695834
Stream processing systems must often cope with workloads varying in content, format, size, and input rate. The high variability and unpredictability make statically fine-tuning them very challenging. Our work addresses this limitation by providing a new framework and run-time system to simplify implementing and assessing new self-adaptive algorithms and optimizations. We implement a prototype on top of MPI called MPR and show its functionality. We focus on horizontal scaling by supporting the addition and removal of processes during execution time. Experiments reveal that MPR can achieve performance similar to that of a handwritten static MPI application. We also assess MPR's adaptation capabilities, showing that it can readily re-configure itself, with the help of a self-adaptive algorithm, in response to workload variations.
For a given undirected graph G = (V, E) with a non-negative weight function w : E -> R+ and subsets G(1),..., G(k) of V, the Group Steiner Tree (GST) problem consists of constructing a tree T = (V-T, E-T) with mini...
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ISBN:
(纸本)9783031629112;9783031629129
For a given undirected graph G = (V, E) with a non-negative weight function w : E -> R+ and subsets G(1),..., G(k) of V, the Group Steiner Tree (GST) problem consists of constructing a tree T = (V-T, E-T) with minimal cost, where V-T. subset of V, E-T subset of E, and T spans at least one node from each of the groups. We develop a VNS-based metaheuristics approach for solving the GST problem. Our main contribution is that we propose a new problem-specific node release strategy that mimics the steps of a VNS-based heuristic. Instead of exploring different neighborhoods by combinatorially enumerating neighboring solutions, as in classical local search, we use a provably good Integer Linear programming (ILP) formulation to solve a sequence of subproblems of the original problem. Our approach leads to an improvement over the state-of-theart Gurobi solver both in terms of quality and runtime of the instances available in the literature.
computerscience and engineering students in college get introduced to high-level language programming (Java, C++, Python) early in their first year and later to computer organization and architecture courses. Most st...
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ISBN:
(纸本)9781450394338
computerscience and engineering students in college get introduced to high-level language programming (Java, C++, Python) early in their first year and later to computer organization and architecture courses. Most students lack a clear understanding of the architecture of a computer before learning how to write code for the first time. This deficiency is due to the lack of courses focused on computer architecture and organization early in high school. Even though introductory computerscience courses are now offered from 6th to 12th grade, in some schools, the curriculum lacks emphasis on the fundamentals of computer architecture. This work presents an educational framework suitable for K-12 and undergraduate college students to learn computer architecture by building custom processors, exploring computer subsystems, and observing how programs are simulated in real-time.
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