An Undergraduate Consortium for Addressing the Leaky Pipeline to Computing Research

Despite an increasing number of successful interventions designed to broaden participation in computing research, there is still significant attrition among historically marginalized groups in the computing research pipeline. This experience report describes a first-of-its-kind Undergraduate Consortium (UC; https://aaai-uc.github.io/about) that addresses this challenge by empowering students with a culmination of their undergraduate research in a conference setting. The UC, conducted at the AAAI Conference on Artificial Intelligence (AAAI), aims to broaden participation in the AI research community by recruiting students, particularly those from historically marginalized groups, supporting them with mentorship, advising, and networking as an accelerator toward graduate school, AI research, and their scientific identity. This paper presents our program design, inspired by a rich set of evidence-based practices, and a preliminary evaluation of the first years that points to the UC achieving many of its desired outcomes. We conclude by discussing insights to improve our program and expand to other computing communities

Analytical and Numerical Comparisons of Biogeography-based Optimization and Genetic Algorithms.

We show that biogeography-based optimization (BBO) is a generalization of a genetic algorithm with global uniform recombination (GA/GUR). Based on the common features of BBO and GA/GUR, we use a previously-derived BBO Markov model to obtain a GA/GUR Markov model. One BBO characteristic which makes it distinctive from GA/GUR is its migration mechanism, which affects selection pressure (i.e., the probability of retaining certain features in the population from one generation to the next). We compare the BBO and GA/GUR algorithms using results from analytical Markov models and continuous optimization benchmark problems. We show that the unique selection pressure provided by BBO generally results in better optimization results for a set of standard benchmark problems. We also present comparisons between BBO and GA/GUR for combinatorial optimization problems, include the traveling salesman, the graph coloring, and the bin packing problems

Markov Models for Biogeography-based Optimization

Biogeography-based optimization (BBO) is a population-based evolutionary algorithm that is based on the mathematics of biogeography. Biogeography is the science and study of the geographical distribution of biological organisms. In BBO, problem solutions are analogous to islands, and the sharing of features between solutions is analogous to the migration of species. This paper derives Markov models for BBO with selection, migration, and mutation operators. Our models give the theoretically exact limiting probabilities for each possible population distribution for a given problem. We provide simulation results to confirm the Markov models

OPPOSITIONAL BIOGEOGRAPHY-BASED OPTIMIZATION

This dissertation outlines a novel variation of biogeography-based optimization (BBO), which is an evolutionary algorithm (EA) developed for global optimization. The new algorithm employs opposition-based learning (OBL) alongside BBO migration to create oppositional BBO (OB BO). Additionally, a new opposition method named quasi-reflection is introduced. Quasireflection is based on opposite numbers theory and we mathematically prove that it has the highest expected probability of being closer to the problem solution among all OBL methods that we explore. Performance of quasi-opposition is validated by mathematical analysis for a single-dimensional problem and by simulations for higher dimensions. Experiments are performed on benchmark problems taken from the literature as well as real-world optimization problems provided by the European Space Agency. Empirical results demonstrate that with the assistance of quasi-reflection, OB BO significantly outperforms BBO in terms of success rate and the number of fitness function evaluations required to find an optimal solution for a set of standard continuous domain benchmarks. The oppositional algorithm is further revised by the addition of fitness dependent quasi-reflection which gives a candidate solution that we call ̂xKr. In this algorithm, the amount of reflection is based on the fitness of the individual and can be non-uniform. We find that for small reflection weights, ̂xKr has a higher probability of being closer to the solution, but only by a negligible amount. As the reflection weight increases, ̂xKr gets closer (on average) to the solution of an optimization problem as the probability of being closer decreases. In addition, we extend the idea of opposition to combinatorial problems. We introduce two different methods of opposition to solve two types of combinatorial optimization problems. The first technique, open-path opposition, is suited for combinatorial problems where the final node in the graph does not have be connected to the firs

Oppositional Biogeography-Based Optimization for Combinatorial Problems

In this paper, we propose a framework for employing opposition-based learning to assist evolutionary algorithms in solving discrete and combinatorial optimization problems. To our knowledge, this is the first attempt to apply opposition to combinatorics. We introduce two different methods of opposition to solve two different type of combinatorial optimization problems. The first technique, open-path opposition, is suited for combinatorial problems where the final node in the graph does not have be connected to the first node, such as the graphcoloring problem. The latter technique, circular opposition, can be employed for problems where the endpoints of a graph are linked, such as the well-known traveling salesman problem (TSP). Both discrete opposition methods have been hybridized with biogeography-based optimization (BBO). Simulations on TSP benchmarks illustrate that incorporating opposition into BBO improves its performance. Index Terms—Biogeography-based optimization, opposition, combinatorics, discrete optimization, evolutionary algorithms, graph-coloring problem, traveling salesman problem

Visual Tracking of Objects via Rule-based Multiple Hypothesis Tracking

In this paper, one of the most crucial step of a visual surveillance system is presented. To track the multiple objects in the scene, multiple hypothesis tracking is combined with the fuzzy logic. Mixture of Gaussians method has been used to detect the moving objects in the video, which is taken from a static camera. Kalman filter has been utilized to estimate the next state of the objects. After the estimation, current measurements have been compared with the estimated features by utilizing fuzzy rules. The proposed method has been tested for both single and multiple camera configurations

3D Path Planning for Multiple UAVs for Maximum Information Collection

This paper addresses the problem of path planning for multiple UAVs. The paths are planned to maximize collected amount of information from Desired Regions (DR) while avoiding Forbidden Regions (FR) violation and reaching the destination. The approach extends prior study for multiple UAVs by considering 3D environment constraints. The path planning problem is studied as an optimization problem. The problem has been solved by a Genetic Algorithm (GA) with the proposal of novel evolutionary operators. The initial populations have been generated from a seed-path for each UAV. The seed-paths have been obtained both by utilizing the Pattern Search method and solving the multiple-Traveling Salesman Problem (mTSP). Utilizing the mTSP solves both the visiting sequences of DRs and the assignment problem of "which DR should be visited by which UAV". It should be emphasized that all of the paths in population in any generation of the GA have been constructed using the dynamical mathematical model of an UAV equipped with the autopilot and guidance algorithms. Simulations are realized in the MATLAB/Simulink environment. The path planning algorithm has been tested with different scenarios, and the results are presented in Section 6. Although there are previous studies in this field, this paper focuses on maximizing the collected information instead of minimizing the total mission time. Even though, a direct comparison of our results with those in the literature is not possible, it has been observed that the proposed methodology generates satisfactory and intuitively expected solutions

Refining the progressive multiple sequence alignment score using genetic algorithms

Given a set of N (N > 2) sequences, the Multiple Sequence Alignment (MSA) problem is to align these N sequences, possibly with gaps, that bring out the best score due to a given scoring criterion between characters. Multiple sequence alignment is one of the basic tools for interpreting the information obtained from bioinformatics studies. Dynamic Programming (DP) gives the optimal alignment of the two sequences for the given scoring scheme. But, in the case of multiple sequence alignment it requires enormous time and space to obtain the optimal alignment. The time and space requirement increases exponentially with the number of sequences. There are two basic classes of solutions except the DP method: progressive methods and iterative methods. In this study, we try to refine the alignment score obtained by using the progressive method due to given scoring criterion by using an iterative method. As an iterative method genetic algorithm (GA) has been used. The sum-of-pairs (SP) scoring system is used as our target of optimization. There are fifteen operators defined to refine the alignment quality by combining and mutating the alignments in the alignment population. The results show that the novel operators, sliding-window, local-alignment, which have not been used up to now, increase the score of the progressive alignment by amount of % 2

Anomaly Detection and Activity Perception Using Covariance Descriptor for Trajectories

In this work, we study the problems of anomaly detection and activity perception through the trajectories of objects in crowded scenes. For this purpose, we propose a novel representation for trajectories via covariance features. Representing trajectories via feature covariance matrices enables us to calculate the distance between the trajectories of different lengths. After setting this proposed representation and calculation of distances between trajectories, anomaly detection is achieved by sparse representations on nearest neighbors and activity perception is achieved by extracting the dominant motion patterns in the scene through the use of spectral clustering. Conducted experiments show that the proposed method yields results which are outperforming or comparable with state of the art

Online path planning for unmanned aerial vehicles to maximize instantaneous information

© The Author(s) 2021.In this article, an online path planning algorithm for multiple unmanned aerial vehicles (UAVs) has been proposed. The aim is to gather information from target areas (desired regions) while avoiding forbidden regions in a fixed time window starting from the present time. Vehicles should not violate forbidden zones during a mission. Additionally, the significance and reliability of the information collected about a target are assumed to decrease with time. The proposed solution finds each vehicle’s path by solving an optimization problem over a planning horizon while obeying specific rules. The basic structure in our solution is the centralized task assignment problem, and it produces near-optimal solutions. The solution can handle moving, pop-up targets, and UAV loss. It is a complicated optimization problem, and its solution is to be produced in a very short time. To simplify the optimization problem and obtain the solution in nearly real time, we have developed some rules. Among these rules, there is one that involves the kinematic constraints in the construction of paths. There is another which tackles the real-time decision-making problem using heuristics imitating human-like intelligence. Simulations are realized in MATLAB environment. The planning algorithm has been tested on various scenarios, and the results are presented