This paper addresses the implementation of a methodological approach that combines two widely recognized professional practices: Scrum and Design Sprint. This methodology is applied in the context of first-year comput...
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ISBN:
(纸本)9798350348729;9798350348736
This paper addresses the implementation of a methodological approach that combines two widely recognized professional practices: Scrum and Design Sprint. This methodology is applied in the context of first-year computer engineering students during their algorithm and programming course. The initiative aims to work with real-world situations, where students form development teams to tackle computer challenges presented by small and medium-sized businesses. This innovative approach not only enriches students' education but also prepares them to enter the workforce with a strong foundation of technical skills and practical experience. Ultimately, this methodology has allowed students to acquire programming skills in Python and Java, similar to those of junior and intermediate programmers, eliminating the need to complete an entire degree to face such challenges, thanks to the promotion of teamwork skills identified through AI.
The increased emphasis on competency management and learning objectives in higher education has led to a rise in Learning Analytics (LA) applications. These tools play a vital role in measuring and optimizing learning...
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Task-based programming models significantly improve the efficiency of parallel systems. The Sequential Task Flow (STF) model focuses on static task sizes within task graphs, but determining optimal granularity during ...
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ISBN:
(数字)9783031617638
ISBN:
(纸本)9783031617621;9783031617638
Task-based programming models significantly improve the efficiency of parallel systems. The Sequential Task Flow (STF) model focuses on static task sizes within task graphs, but determining optimal granularity during graph submission is tedious. To overcome this, we extend StarPU's STF recursive tasks model, enabling dynamic transformation of tasks into subgraphs. Early evaluations on homogeneous shared memory reveal that this just-in-time adaptation enhances performance.
We study the automatic generation of primal and dual bounds from decision diagrams in constraint programming. In particular, we expand the functionality of the Haddock system to optimization problems by extending its ...
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ISBN:
(数字)9783031332715
ISBN:
(纸本)9783031332708;9783031332715
We study the automatic generation of primal and dual bounds from decision diagrams in constraint programming. In particular, we expand the functionality of the Haddock system to optimization problems by extending its specification language to include an objective function. We describe how restricted decision diagrams can be compiled in Haddock similar to the existing relaxed decision diagrams. Together, they provide primal and dual bounds on the objective function, which can be seamlessly integrated into the constraint programming search. The entire process is automatic and only requires a high-level user model specification. We evaluate our method on the sequential ordering problem and compare the performance of Haddock to a dedicated decision diagram approach. The results show that Haddock achieves comparable results in similar time, demonstrating the viability of our automated decision diagram procedures for constraint optimization problems.
Packing a designated set of shapes on a regular grid is an important class of operations research problems that has been intensively studied for more than six decades. Representing a d-dimensional discrete grid as Zd,...
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Packing a designated set of shapes on a regular grid is an important class of operations research problems that has been intensively studied for more than six decades. Representing a d-dimensional discrete grid as Zd, we formalise the generalised regular grid (GRG) as a surjective function from Zd to a geometric tessellation in a physical space, for example, the cube coordinates of a hexagonal grid or a quasilattice. This study employs 0-1 integer linear programming (ILP) to formulate the polyomino tiling problem with adjacency constraints. Rotation & reflection invariance in adjacency are considered. We separate the formal ILP from the topology & geometry of various grids, such as Ammann-Beenker tiling, Penrose tiling and periodic hypercube. Based on cutting-edge solvers, we reveal an intuitive correspondence between the integer program (a pattern of algebraic rules) and the computer codes. Models of packing problems in the GRG have wide applications in production system, facility layout planning, and architectural design. Two applications in planning high-rise residential apartments are illustrated.
This poster presents the development and implementation of a 10-day remix-based summer camp curriculum designed to introduce high school students, particularly a multinational cohort of young women, to programming thr...
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Over time, power network equipment can face defects and must be maintained to ensure transmission network reliability. Once a piece of equipment is scheduled to be withdrawn from the network, it becomes unavailable an...
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ISBN:
(数字)9783031605970
ISBN:
(纸本)9783031605963;9783031605970
Over time, power network equipment can face defects and must be maintained to ensure transmission network reliability. Once a piece of equipment is scheduled to be withdrawn from the network, it becomes unavailable and can lead to power outages when other adjacent equipment fails. This problem is commonly referred to as a transmission maintenance scheduling (TMS) problem and remains a challenge for power utilities. Numerous combinatorial constraints must be satisfied to ensure the stability and reliability of the transmission network. While most of these constraints can be naturally formalized in constraint programming (CP), there are some complex constraints like transit-power limits that are challenging to model because of their continuous and nonlinear nature. This paper proposes a methodology based on active constraint acquisition to automatically approximate these constraints. The acquisition is carried out using a simulator developed by Hydro-Quebec (HQ), a power utility to compute the power-flow of its transmission network. The acquired constraints are then integrated into a CP model to solve the HQ network's TMS problem. Our experimental results show the relevance of the methodology to approximate transit-power constraints in an automated way. It allows HQ to automatically schedule a maintenance plan for an instance that remained intractable until now. To our knowledge, it is the first time that active constraint acquisition has been used successfully for the TMS problem in an industrial setting.
This paper addresses the robust two-machine permutation flow-shop scheduling problem considering non-deterministic operation processing times associated with an uncertainty budget. The objective is to minimize the mak...
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ISBN:
(纸本)9783031332708;9783031332715
This paper addresses the robust two-machine permutation flow-shop scheduling problem considering non-deterministic operation processing times associated with an uncertainty budget. The objective is to minimize the makespan of the schedule. Exact solution methods incorporated within the framework of a two-stage robust optimization are proposed to solve the problem. We first prove that under particular conditions the robust two-machine permutation flow-shop scheduling problem can be solved in polynomial time by the well-known Johnson's algorithm usually dedicated to the deterministic version. Then we tackle the general problem, for which we propose a column and constraint generation algorithm. We compare two versions of the algorithm. In the first version, a mixed-integer linear programming formulation is used for the master problem. In the second version, we use a constraint programming model for the master problem. To the best of our knowledge, the use of constraint programming for a master problem in a two-stage robust optimization problem is innovative. The experimental results show the very good performance of the method based on the constraint programming formulation. We also notice that Johnson's algorithm is surprisingly efficient for the robust version of the general problem.
The premise for the great advancement of molecular machine learning is dependent on a considerable amount of labeled data. In many real-world scenarios, the labeled molecules are limited in quantity or laborious to de...
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ISBN:
(纸本)9798350353013;9798350353006
The premise for the great advancement of molecular machine learning is dependent on a considerable amount of labeled data. In many real-world scenarios, the labeled molecules are limited in quantity or laborious to derive. Recent pseudo-labeling methods are usually designed based on a single domain knowledge, thereby failing to understand the comprehensive molecular configurations and limiting their adaptability to generalize across diverse biochemical context. To this end, we introduce an innovative paradigm for dealing with the molecule pseudo-labeling, named as Molecular Data programming (MDP). In particular, we adopt systematic supervision sources via crafting multiple graph labeling functions, which covers various molecular structural knowledge of graph kernels, molecular fingerprints, and topological features. Each of them creates an uncertain and biased labels for the unlabeled molecules. To address the decision conflicts among the diverse pseudo-labels, we design a label synchronizer to differentiably model confidences and correlations between the labeling functions, which yields probabilistic molecular labels to adapt for specific applications. These probabilistic molecular labels are used to train a molecular classifier for improving its generalization capability. On eight benchmark datasets, we empirically demonstrate the effectiveness of MDP on the weakly supervised molecule classification tasks, achieving an average improvement of 9.5%. The code is in: https://***/xinjuan1/MDP/.
The research purpose is to analyze how block-based programming, introduced in parallel with classical school education, will be an effective tool for developing environmental awareness among students. The research pro...
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The research purpose is to analyze how block-based programming, introduced in parallel with classical school education, will be an effective tool for developing environmental awareness among students. The research proposes a well-developed theoretical program on environmental literacy and a practical science, technology, engineering, and mathematics (STEM) project creating an eco-house model with a weather station and alternative energy sources in a regular secondary school in Shanghai. The sample involved school students of the seventh grade, equally divided into two groups: the control group (learnt only a theoretical program) and the experimental group (learnt both a theoretical program and a practical STEM project). At the end of the school year, students passed an environmental literacy test. The differences between the control and experimental groups were statistically significant. The experimental group has the advantage in all parameters: it has the highest scores and a large number of students who showed a high and very high level of knowledge. The results prove the methodology effectiveness of the experimental group in terms of developing sensitivity to the environment, cognitive abilities, ecological analysis, and preserving the environment. It was found that overall academic performance correlates significantly with both test scores and project scores. The highest scores were for the project, which indicates the effectiveness of block-based programming in the learning of the experimental group, their implementation of new ideas based on the acquired knowledge, success in decision-making, and experience that went beyond previous achievements.
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