Offline algorithm configuration methods search for fixed parameter values for a given set of problem instances. For each parameter, such methods perform an equivalent to a constant regression, since the parameter value remains constant for any problem instance. However, optimal parameter values may depend on instance features, such as the instance size. In this paper, we represent parameters by non-constant models, which set the parameter values according to the instance size. Instead of searching for parameter values directly, the configuration process calibrates such models. In particular, we propose a simple yet effective linear model, which approximates linear relations between instance size and optimal parameter values. For modeling nonlinear relations, we propose piecewise and log-log linear models. The evaluation of the proposed methods on four configuration scenarios show good performance gains in comparison to traditional instance-independent algorithm configuration with comparable tuning effort.

In this work we present AutoBQP, a heuristic solver for binary optimization problems. It applies automatic algorithm design techniques to search for the best heuristics for a given optimization problem. Experiments show that the solver can find algorithms which perform better than or comparable to state-of-the-art methods, and can even find new best solutions for some instances of standard benchmark sets.

This paper presents a set of capping methods to speed-up the automated configuration of optimization algorithms. These methods use known previous executions to compute a performance envelope, which is used to evaluate new executions and early stop those with unsatisfactory performance. Preliminary experiments on six scenarios show that the capping methods save up to 78% of the configuration effort, while finding configurations of the same quality.

The Internet of Things (IoT) entails new challengesfor the development of wireless transceivers. Radio-Frequency building blocks require complex design flows in order to address very different sets of specifications. This paper presents a three-step design methodology dedicated to the synthesis of RF building blocks. Based on the Inversion Coefficient, the proposed design methodology allows the optimization of the circuit performance for any CMOS process and any application. It is illustrated through the implementation of inductorless Low-Noise Amplifiers (LNA) in three different CMOS nodes: 28 nm Fully-Depleted Silicon-On-Insulator (FDSOI), 65 nm and 130 nm Bulk. These LNAs are dedicated to two cases of IoT applications: multi-standard and ultra low power. The proposed circuits achieve state of the art performance for all nodes and applications. Furthermore, the methodology enables an insightful comparison of the capabilities of the different CMOS nodes in terms of power consumption, noise and bandwidth.

A way of minimizing the opportunity of cheating in exams is to assign different tests to students. The likelihood of cheating then depends on the proximity of the students' desks, and the similarity of the tests. The test-assignment problem is to find an assignment of tests to desks that minimizes that total likelihood of cheating. The problem is a variant of a graph coloring problem and is NP-hard. We propose a new heuristic solution for this problem. Our approach differs from the usual way of designing heuristics in two ways. First, we reduce test-assignment to the more general unconstrained binary quadratic programming. Second, we search for a good heuristic using an automatic algorithm configuration tool that evolves heuristics in a space of algorithms built from known components for binary quadratic programming. The best hybrid heuristics found repeatedly recombine elements of a population of elite solutions and improve them by a tabu search. Computational tests suggest that the resulting algorithms are competitive with existing heuristics that have been designed manually.

This Research Paper presents the evaluation of an instrument to identify the impact of motivation and engagement factors in undergraduate students in computing. Although researches indicate a direct impact of motivation and engagement on student performance and retention, few studies have been found that address which factors are relevant in this process. The instrument is a questionnaire based on the compilation of several works of the literature containing 48 items divided into 6 groups: personal and demographic data, general perception about motivation, perception about the university, student behavior, perception about program and perception about classes/teachers. The questionnaire evaluation is based on a case study with 112 undergraduate students in Software Engineering. As a result, we found that the questionnaire can be considered reliable (Cronbach's alpha = .8904). Considering the validity of constructs, we found an acceptable degree of correlation between the most pair of items in each group (averaging 63%). We also found that the item-total correlation coefficient was only not adequate for one factor group, indicating satisfactory correlation for all other items. Finally, we found that the number of factors is coherent, but there are several items from different groups strongly correlated, indicating the need for a reorganization.

This Research to Practice Paper presents the results of the evaluation of pre-university factors that impact the initial motivation of undergraduate students in computing. Although there are studies in the literature that have investigated some previous factors, this paper replicates a previous work that aims to consolidate several pre-university factors and, as the main differential, uses the AMS (Academic Motivation Scale), a scale already consolidated in the literature to measure students' initial motivation, and evaluate the relation between motivation and candidate factors. We applied a questionnaire to 159 students from different computing programs in ten universities, which evaluates 20 factors divided into 4 groups: personal and demographic data, taste and knowledge of the program and area, computing experience, and school performance. To evaluate the correlation between factors and motivation, we used Spearman's coefficient, t-student test, and ANOVA to evaluate the correlation between factors and motivation. As main results, we found significant variation in the initial motivation according to following factors: taste for programming and technology, knowledge about the undergraduate program content, correct perception about computing professionals, knowledge and experience in computer programming, and general school performance.