Many universities around the world were forced to lock down and students had to continue their learning in online environments in response to the COVID-19 pandemic. Teachers thus had to adopt effective and appropriate...
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Many universities around the world were forced to lock down and students had to continue their learning in online environments in response to the COVID-19 pandemic. Teachers thus had to adopt effective and appropriate online teaching pedagogy integrated with related educational technologies to help their students achieve satisfactory learning outcomes in these courses. In addition, the world-wide problems of high failure level and dropout rates in programming courses challenge both teachers and students. Aiming to develop students' practical programming skills, commitment to learning, and reduce learning disengagement, the researchers behind this study adopted two teaching approaches, integrating online partial pair programming (PPP) and socially shared metacognitive regulation (SSMR), to explore their effects on students' learning performance in an online programming course. A quasi-experiment was implemented to explore the effects of online PPP and SSMR. The participants comprised three classes of students, all from non-information or non-computer departments taking a compulsory course titled 'programming Design'. The experimental groups included the first class (G1) simultaneously receiving the online PPP and SSMR intervention and the second class (G2) receiving only the online SSMR intervention. The third class (G3) received a traditional teaching method (non-PPP and non-SSMR) delivered online and served as the control group. Both quantitative and qualitative data were collected and analyzed. Experimental results show that the SSMR group (G2) demonstrated significantly better development of programming skills and commitment to learning than the control group (G3). However, the expected effects of online PPP on improving students' learning were not found. The implications of designing pedagogies with PPP and SSMR in an online programming course for decision-makers in governments and universities, researchers, and teachers implementing online courses, particularly
Constraint logic programming emerged in the late 80's as a highly declarative class of programming languages based on first-order logic and theories with decidable constraint languages, thereby subsuming Prolog re...
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ISBN:
(纸本)9789819722990;9789819723003
Constraint logic programming emerged in the late 80's as a highly declarative class of programming languages based on first-order logic and theories with decidable constraint languages, thereby subsuming Prolog restricted to equality constraints over the Herbrand's term domain. This approach has proven extremely successful in solving combinatorial problems in the industry which quickly led to the development of a variety of constraint solving libraries in standard programming languages. Later came the design of a purely declarative front-end constraint-based modeling language, MiniZinc, independent of the constraint solvers, in order to compare their performances and create model benchmarks. Beyond that purpose, the use of a high-level modeling language such as MiniZinc to develop complete applications, or to teach constraint programming, is limited by the impossibility to program search strategies, or new constraint solvers, in a modeling language, as well as by the absence of an integrated development environment for both levels of constraint-based modeling and constraint solving. In this paper, we propose to solve those issues by taking Prolog with its constraint solving libraries, as a unified relation-based modeling and programming language. We present a Prolog library for high-level constraint-based mathematical modeling, inspired by MiniZinc, using subscripted variables (arrays) in addition to lists and terms, quantifiers and iterators in addition to recursion, together with a patch of constraint libraries in order to allow array functional notations in constraints. We show that this approach does not come with a significant computation time overhead, and presents several advantages in terms of the possibility of focussing on mathematical modeling, getting answer constraints in addition to ground solutions, programming search or constraint solvers if needed, and debugging models within a unique modeling and programming environment.
We propose a novel approach to measure student originality in computerprogramming. We collected two sets of programming problems in Java and Python, and their solutions submitted by multiple students. We parsed the s...
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Maximum-likelihood multiuser detection incurs a large computational complexity, and its low-complexity detection scheme suffers from a performance loss, where this tradeoff is inevitable and inherent in a classical co...
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Maximum-likelihood multiuser detection incurs a large computational complexity, and its low-complexity detection scheme suffers from a performance loss, where this tradeoff is inevitable and inherent in a classical computer. In this paper, we use the Grover adaptive search (GAS) to break the tradeoff, which is a quantum exhaustive search algorithm guaranteed to obtain the optimal solution, achieving a quadratic speedup. Specifically, we design two specific parameters of GAS to achieve the optimal performance with a reduced complexity: the initial threshold and the number of Grover rotations. The initial threshold of GAS can be optimized using a solution of semi-definite programming, and it is possible to calculate the distribution of the number of solutions smaller than the initial threshold in advance, which depends on instantaneous channel coefficients. In addition, we analyze the number of quantum gates required for GAS and show that the gate count can be reduced by bypassing the higher-order terms in the objective function, leading to a reduced circuit runtime. Our analysis and simulation results demonstrate that the proposed approach achieves the same performance as the optimal maximum-likelihood detection while reducing the query complexity of GAS, implying that the large constant overhead of quadratic speedup can be further reduced.
Dynamic programming based methods have been widely used in solving discrete-time nonlinear constrained optimal control problems. However, applying these methods in real-time is challenging because a large amount of me...
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Dynamic programming based methods have been widely used in solving discrete-time nonlinear constrained optimal control problems. However, applying these methods in real-time is challenging because a large amount of memory is needed and the associated computational cost is high. Here, a search space dimension reduction strategy is proposed for a class of nonlinear discrete-time systems that are control-affine and invertible. Specifically, a bio-inspired motion rule is combined with inverse dynamics to reduce the value iteration search space to one dimension. The corresponding suboptimal control algorithm is developed and its optimality is analysed. An adaptation rule is developed to estimate uncertainties and improve the base policy. The closed-loop system is proven to be asymptotically stable. The advantages of the algorithm including much smaller computational cost and significantly reduced memory usage are demonstrated with two simulation examples.
Return Oriented programming (ROP) is one of the most challenging threats to operating systems. Traditional detection and defense techniques for ROP such as stack protection, address randomization, compiler optimizatio...
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Return Oriented programming (ROP) is one of the most challenging threats to operating systems. Traditional detection and defense techniques for ROP such as stack protection, address randomization, compiler optimization, control flow integrity, and basic block thresholds have certain limitations inaccuracy or efficiency. At the same time, they cannot effectively detect ROP variant attacks, such as COP, COOP, JOP. In this paper, we propose a novel ROP and its variants detection approach that first filters the normal execution flow according to four strategies provided and then adopts Graph Matching Network (GMN) to determine whether there is ROP or its variant attack. Moreover, we developed a prototype named ROPGMN with shared memory to solve cross-language and cross-process problems. Using real-world vulnerable programs and constructed programs with dangerous function calls, we conduct extensive experiments with 6 ROP detectors to evaluate ROPGMN. The experimental results demonstrate the effectiveness of ROPGMN in discovering ROPs and their variant attacks with low performance overhead.
Input validation is a fairly universal programming practice that helps reduce the chances of producing protection-related vulnerabilities in software. In this paper, an experiment is conducted to specifically determin...
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Background and contextWe address the question of what computerscience students take the discipline to be. How students conceive the discipline can influence whether a student pursues computerscience, what particular...
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Background and contextWe address the question of what computerscience students take the discipline to be. How students conceive the discipline can influence whether a student pursues computerscience, what particular area within computerscience they focus on and whether they persist in the discipline. In this paper, we examine the epistemic practices - how knowledge is created and warranted - of students and teacher as a way to understand what computerscience is, as practiced by the *** goal of this study is to provide an account for how the students and teacher make and legitimize knowledge in the classroom and computer *** this case study, we use a combination of ethnographic and multimodal methods. The data collected include audio and video recordings and researcher field notes of classroom and lab sessions. The analysis of the data considers speech, marks on the blackboard and embodied actions including gesture and gaze *** both of these learning environments, CS knowledge in the form of general principles and programming practice are discussed by students and the instructor. However, the way in which such knowledge is warranted in these two settings is different. In the classroom, the instructor is the authority that warrants knowledge claims, whereas in the computer lab, the computer plays a key authority role to determine "what works".ImplicationsThis study provides insight into how instructors can design learning environments based on ways they intend knowledge to be created and warranted.
Design science is a methodology that addresses complex and evolving problems requiring a multidisciplinary approach. In this research, the methodology of design science is employed to construct a model for the process...
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Design science is a methodology that addresses complex and evolving problems requiring a multidisciplinary approach. In this research, the methodology of design science is employed to construct a model for the process of V gene annotation in vertebrates. The process entails the identification of genes within a genome, the characterization of their structural elements, and the classification of their associated data. The model standardizes the work of molecular biologists, reducing time and errors. The methodology followed in this project includes rigor, relevance, and design cycles, developed in collaboration with experts in molecular biology and information systems. The result of the research is a BPMN model, validated by experts and trainees from the Centro de Investigaci & oacute;n Sobre Enfermedades Infecciosas (CISEI) of the Instituto Nacional de Salud P & uacute;blica (INSP) in Cuernavaca, Mexico.
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