This work in progress innovative practice paper describes a tentative study on Computer Science students' pseudocode comprehension, aiming to explore intermediate steps in their understanding of reading and writing pseudocodes. As problem-solving skills are integral to undergraduate studies, mastering pseudocode formulation becomes fundamental for these students. In advanced computer science courses, solutions are often presented in pseudocode format, requiring students to express their solutions likewise. Proficiency in comprehending and creating pseudocode becomes crucial. This study aims to evaluate students' aptitude in comprehending pseudocode from written sources and in devising solutions using pseudocode. The investigation is designed to be conducted in multiple phases, employing Think-Pair-Share and many other active learning strategies to enhance pseudocode understanding. This paper presents promising preliminary survey/quiz findings, which were conducted in an advanced algorithm course. The subsequent phases will delve deeper into students' metacognition regarding pseudocode reading and composition, aiming to gain a deeper understanding of learning processes and identify effective instructional strategies. In the realm of Computer Science education, a profound understanding of students' cognitive processes and cognitive frameworks becomes essential in refining instructional techniques and teaching strategies. The objective of this study is to gain a deeper understanding of student's learning processes and to investigate which actions by the instructors will help them the most with regard to pseudocode reading and writing. This encompasses a comprehensive development journey, including theoretical understanding, practical application, and hands-on experiences that collectively contribute to their adeptness in addressing complex problems and solutions within the field of Computer Science. We aim to examine how students understand problems and their solutions when presented in pseudocode format, particularly when they need to represent the solution as pseudocode instead of writing code in a high-level language. In Phase-I of the study, the objective is to assess the effectiveness of using an active learning strategy, such as Think-Pair-Share, in a classroom setting to teach pseudocode as opposed to traditional lecture-based methods. Phase-II of the research will further explore students' metacognitive processes during their engagement with pseudocode, focusing specifically on problem-solving contexts.

We present a transformative approach to histopathological cancer detection and grading by introducing a very powerful feature extraction method based on the latest topological data analysis tools. By analyzing the evolution of topological patterns in different color channels, we discovered that every tumor class leaves its own topological footprint in histopathological images, allowing to extract feature vectors that can be used to reliably identify tumor classes.Our topological signatures, even when combined with traditional machine learning methods, provide very fast and highly accurate results in various settings. While most DL models work well for one type of cancer, our model easily adapts to different scenarios, and consistently gives highly competitive results with the state-of-the-art models on benchmark datasets across multiple cancer types including bone, colon, breast, cervical (cytopathology), and prostate cancer. Unlike most DL models, our proposed Topo-ML model does not need any data augmentation or pre-processing steps and works perfectly on small datasets. The model is computationally very efficient, with end-to-end processing taking only a few hours for datasets consisting of thousands of images.

Melanomas are the most aggressive form of skin cancer. Due to observer bias, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion areas in the dermoscopy images. In this paper, we present a deep learning method for automatic skin lesion segmentation. We use a subset of the International Skin Imaging Collaboration (ISIC) Archive dataset, which contains dermoscopic images paired with their corresponding lesion binary masks, provided by IEEE International Symposium on Biomedical Imaging (ISBI) 2017 challenge for Skin Lesion Analysis Towards Melanoma Detection, and compare against the benchmark results submitted by other participants. The experimental results show that our proposed method can outperform the submissions in terms of segmentation accuracy.

An ongoing challenge with game and simulation design is immersion. Users want to feel as though the avatars that they see on screen are themselves and that the experiences of their avatars are their own. To that end, much work has been done in the area of 3D facial reconstruction for the purpose of inserting one's own likeness into a game or simulation. However, most existing methods require a minimum of two images and complex computations to produce the 3D head mesh. In this paper we propose a simple method for 3D facial reconstruction that renders the face mesh as a terrain using a single input image and minimal computation. This results in a fast and lightweight facial reconstruction that is highly portable and has a wide variety of applications.

This video illustrates the k-LinkSolver software for computing k-link shortest paths in weighted regions. The k-LinkSolver implements methods to find paths of length at most (1+e) times the length of a shortest k-link path, for any fixed e>0, and having at most 2k−1 links. The methods implemented are an improvement over the previously known (1+e)-approximation algorithms, which guarantee at most 5k−2 links.

Abstract We consider the problem of finding themost sustainable path(MSP) between two nodes in a wireless mobilead hocnetwork (MANET). A MSP between two nodes is a path that maximizes the probability of path existence under some probability model. We show how to exploit geometric properties in computing the reliability of a path in the resulting mobility graphG, prove that simple shortest path algorithms such as Dijkstra's algorithm cannot be directly applied in this scenario, and propose an algorithm for computing a single pair MSP in a modified representation ofG. To improve reliability in routing, we also discuss the computation ofkmost sustainable paths under the link metric and under the standard shortest path formulation. Copyright © 2006 John Wiley & Sons, Ltd.

Given a polyhedral terrain T with n vertices, the two-watchtower problem for T calls for finding two vertical segments, called watchtowers, of smallest common height, whose bottom endpoints (bases) lie on T, and whose top endpoints guard T, in the sense that each point on T is visible from at least one of them. In this paper we present the following results for the two-watchtower problem in R2 and R3: (1) We show that the discrete two-watchtowers problem in R2, where the bases are constrained to lie at vertices of T, can be solved in O(n2 log4n) time, significantly improving previous solutions. The algorithm works, without increasing its asymptotic running time, even if, one of the towers is allowed to be placed anywhere on T. (2) We show that the continuous two-watchtower problem in R2, where the bases can lie anywhere on T, can be solved in O(n3α(n)log3n) time, again significantly improving previous results. (3) Still in R2, we show that the continuous version of the problem of guarding a finite set P ⊂ T of m points by two watchtowers of smallest height can be solved in O(mn log4n) time. (4) The discrete version of the two-watchtower problem in R3 can be solved in O(n11/3 polylog(n)) time; this is the first nontrivial result for this problem in R3.

We illustrate the Link Solver software for computing 1-link shortest paths in weighted regions. The Link Solver implements a prune-and-search method that can be used to approximate an optimal solution within a user specified precision. The theoretical foundation of the method is a result stating that an optimal solution goes through a vertex of the subdivision. This result provides a way to discretize the problem with respect to vertices of interest which in turn leads to efficient algorithms.

In this paper we consider the problem of planning the execution of a set of tasks, where each task has associated some processing time and has to be transported to some destination. The problem arises in various applications in production planning and scheduling. We address several variants of the problem and discuss implementations of some of the proposed solutions. The objective is to compute a processing-and-delivery schedule so as to minimize the sum of delivery completion times. All the variants we consider are NP-hard. We experimentally evaluate the performance of some heuristics and show that the shortest processing time (SPT) heuristic consistently yields good schedules. We also briefly discuss results on scheduling with transportation in planar weighted subdivisions, that specifically address the computation of transportation times.

We present efficient algorithms for solving the problem of computing an optimal penetration (a ray or a line segment) among weighted regions in 2-D and 3-D spaces.This problem finds applications in several areas, such as radiation therapy, geological exploration, and environmental engineering.Our algorithms are based on a combination of geometric techniques and optimization methods.Our geometric analysis shows that the optimal penetration problem in d-D (d = 2,3) can be reduced to solving O(n2td-l)) instances of certain special types of nonlinear optimization problems, where n is the total number of vertices of the regions.We also give implementation results of our 2-D algorithms. IntroductionIn this paper, we study the following geometric optimization problem (called optimal penetration problem): Given a subdivision R with a total of n vertices in 2-D or 3-D space, divided in m regions R..i, i = 1,2,. . ., m, find a ray L such that L

We present efficient algorithms for solving polygonal-path approximation problems in three and higher dimensions.Given an n-vertex polygonal curve P in EL', d 2 3, we approximate P by another polygonal curve P' of m 5 n vertices in IR! such that the vertex sequence of P' is an ordered subsequence of the vertices of P. The goal is to either minimize the size m of P' for a given error tolerance E (called the min-# problem), or to minimize the deviation error E between P and P' for a given size m of P' (called the min-.sproblem).Our techniques enable us to develop efficient nearquadratic-time algorithms in 3-D and sub-cubictime algorithms in 4-D for solving the mm-# and mine problems.We discuss extensions of our solutions to d-dimensional space, where d > 4.

The following problem is studied in this paper: Given a scene with an n-vertex simple polygon and a trajectory path in the plane, construct a data structure for reporting the perspective view from a moving point along the trajectory. We present conceptually simple algorithms for the cases of this problem in which the trajectory path consists of several line segments or of a conic curve that contains the polygon. Our algorithms take O(n log n) time and O(n) space. We also prove that the problem of reporting perspective views from successive points along a trajectory path takes n log n) time in the worst case in the algebraic computation tree model. Our data structure reports the view from any query point on the trajectory in O(k + log n) time for a view of size k. Keywords: Algorithms, visibility, simple polygon, trajectory, topology change, shortest path. 1 Introduction In this paper, we study the following problem: Given a scene with an n-vertex simple polygon P and a trajectory ...