Automatic object segmentation from calibrated images

This paper addresses the problem of automatically obtaining the object/background segmentation of a rigid 3D object observed in a set of images that have been calibrated for camera pose and intrinsics. Such segmentations can be used to obtain a shape representation of a potentially texture-less object by computing a visual hull. We propose an automatic approach where the object to be segmented is identified by the pose of the cameras instead of user input such as 2D bounding rectangles or brush-strokes. The key behind our method is a pairwise MRF framework that combines (a) foreground/background appearance models, (b) epipolar constraints and (c) weak stereo correspondence into a single segmentation cost function that can be efficiently solved by Graph-cuts. The segmentation thus obtained is further improved using silhouette coherency and then used to update the foreground/background appearance models which are fed into the next Graph-cut computation. These two steps are iterated until segmentation convergences. Our method can automatically provide a 3D surface representation even in texture-less scenes where MVS methods might fail. Furthermore, it confers improved performance in images where the object is not readily separable from the background in colour space, an area that previous segmentation approaches have found challenging. © 2011 IEEE.

A quest for helpful feedback to programming coursework

The 2011 National Student Survey (NSS) revealed that 40% of full-time students in England do not think that the feedback on their work has been helpful, even though 66% of these students agreed that the feedback was detailed and 62% of them agreed that the feedback has been prompt. Detailed feedback that is not considered helpful by students means a waste of tutors' time while students continue to struggle with their learning. What do students consider as helpful feedback? What are the qualities of helpful feedback? What are the preferred forms of feedback? How should tutors write feedback so that students will find it helpful? Can ICT help to improve the quality of feedback? In our ongoing search for answers to the above questions, we have trialled the use of a novel online application (eCAF) to assess programming coursework from engineering, mathematics and computing students and, through a survey, have collected their views on the feedback received. The survey reveals that most students prefer electronic feedback as given through eCAF, with verbal feedback ranked second and hand-written feedback ranked even lower. The survey also indicates that the feedback from some tutors is considered more helpful than others. We report on the detailed findings of the survey. By comparing the kinds of feedback given by each tutor who took part in the trial, we explore ways to improve the helpfulness of feedback on programming coursework in a bid to promote learning amongst engineering students.

Integer Programming Approach to HP Folding

One of the most widely studied protein structure prediction models is the hydrophobic-hydrophilic (HP) model, which explains the hydrophobic interaction and tries to maximize the number of contacts among hydrophobic amino-acids. In order to find a lower bound for the number of contacts, a number of heuristics have been proposed, but finding the optimal solution is still a challenge. In this research, we focus on creating a new integer programming model which is capable to provide tractable input for mixed-integer programming solvers, is general enough and allows relaxation with provable good upper bounds. Computational experiments using benchmark problems show that our formulation achieves these goals.

Understanding Computing through Defining its Lexicon

Бойко Банчев - Понятието пресмятане в широкия му смисъл и неговото присъствие в природата, науката, технологията и други области е свързано с редица неизяснени въпроси. Дори в по-тесните рамки на програмирането и използването на компютри то не е адекватно разбрано. Представяме тезата, че един начин за приближаване към такова разбиране е разкриването на дълбинните структури и отношения, свързани с пресмятането, и на тази основа построяване на речник на пресмятането.

Application of Service-Oriented Architecture in Software Quality Management

ACM Computing Classification System (1998): D.2.5, D.2.9, D.2.11.

A Mixed Integer Quadratic Programming Model for the Low Autocorrelation Binary Sequence Problem

In this paper the low autocorrelation binary sequence problem (LABSP) is modeled as a mixed integer quadratic programming (MIQP) problem and proof of the model’s validity is given. Since the MIQP model is semidefinite, general optimization solvers can be used, and converge in a finite number of iterations. The experimental results show that IQP solvers, based on this MIQP formulation, are capable of optimally solving general/skew-symmetric LABSP instances of up to 30/51 elements in a moderate time. ACM Computing Classification System (1998): G.1.6, I.2.8.

Programming and Testing a Two-Tree Algorithm

ACM Computing Classification System (1998): G.2.2, F.2.2.

Hausdorff Approximation of Functions Different from Zero at One Point - Implementation in Programming Environment Mathematica

ACM Computing Classification System (1998): G.1.2.

Reproducible computing with Tzar and Docker

The potential for sharing environmental data and models is huge, but can be challenging for experts without specific programming expertise. We built an open-source, cross-platform framework (‘Tzar’) to run models across distributed machines. Tzar is simple to set up and use, allows dynamic parameter generation and enhances reproducibility by accessing versioned data and code. Combining Tzar with Docker helps us lower the entry barrier further by versioning and bundling all required modules and dependencies, together with the database needed to schedule work.

Revisiting goal-oriented models for self-aware systems-of-systems

Systems-of-systems (SoS) are systems resulted from the interaction among other independent constituent systems that collaborate to offer new functionalities towards accomplishing global missions. Each of these constituent systems accomplishes its individual missions and is able to contribute to the achievement of the global missions of the SoS, both being viewed as a set of associated goals. In the perspective of self-aware systems, SoS need to exhibit goal-awareness, i.e., They need to be aware of their own goals and of how their constituent systems contribute to their accomplishment. In this paper, we revisit goal-oriented concepts aiming at identifying and modeling goals at both SoS level and the constituent systems level. Moreover, we take advantage of such goal-oriented models to express the relationship among goals at these levels as well as to define how each constituent system can contribute to the accomplishment of global goals of an SoS. In addition, we shed light on important issues related to goal modeling in self-aware SoS to be addressed in future research.

Client QoE-oriented segment selection for DASH

A segment selection method controlled by Quality of Experience (QoE) factors for Dynamic Adaptive Streaming over HTTP (DASH) is presented in this paper. Current rate adaption algorithms aim to eliminate buffer underrun events by significantly reducing the code rate when experiencing pauses in replay. In reality, however, viewers may choose to accept a level of buffer underrun in order to achieve an improved level of picture fidelity or to accept the degradation in picture fidelity in order to maintain the service continuity. The proposed rate adaption scheme in our work can maximize the user QoE in terms of both continuity and fidelity (picture quality) in DASH applications. It is shown that using this scheme a high level of quality for streaming services, especially at low packet loss rates, can be achieved. Our scheme can also maintain a best trade-off between continuity-based quality and fidelity-based quality, by determining proper threshold values for the level of quality intended by clients with different quality requirements. In addition, the integration of the rate adaptation mechanism with the scheduling process is investigated in the context of a mobile communication network and related performances are analyzed.

On the Equivalence between Assumption-Based Argumentation and Logic Programming

Postprint

Studying Recommender Systems to Enhance Distributed Computing Schedulers

Distributed Computing frameworks belong to a class of programming models that allow developers to

launch workloads on large clusters of machines. Due to the dramatic increase in the volume of

data gathered by ubiquitous computing devices, data analytic workloads have become a common

case among distributed computing applications, making Data Science an entire field of

Computer Science. We argue that Data Scientist's concern lays in three main components: a dataset,

a sequence of operations they wish to apply on this dataset, and some constraint they may have

related to their work (performances, QoS, budget, etc). However, it is actually extremely

difficult, without domain expertise, to perform data science. One need to select the right amount

and type of resources, pick up a framework, and configure it. Also, users are often running their

application in shared environments, ruled by schedulers expecting them to specify precisely their resource

needs. Inherent to the distributed and concurrent nature of the cited frameworks, monitoring and

profiling are hard, high dimensional problems that block users from making the right

configuration choices and determining the right amount of resources they need. Paradoxically, the

system is gathering a large amount of monitoring data at runtime, which remains unused.

In the ideal abstraction we envision for data scientists, the system is adaptive, able to exploit

monitoring data to learn about workloads, and process user requests into a tailored execution

context. In this work, we study different techniques that have been used to make steps toward

such system awareness, and explore a new way to do so by implementing machine learning

techniques to recommend a specific subset of system configurations for Apache Spark applications.

Furthermore, we present an in depth study of Apache Spark executors configuration, which highlight

the complexity in choosing the best one for a given workload.

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction.

High-performance parallel systems for data-intensive computing

Thesis (Ph.D.)--University of Washington, 2016-08