Computational and complexity theory are core components of the computer science curriculum, and in the vast majority of cases are taught using decision problems as the main paradigm. For experienced practitioners, decision problems are the best tool. But for undergraduates encountering the material for the first time, we present evidence that non-decision problems (such as optimization problems and search problems) are preferable. In addition, we describe technical definitions and pedagogical strategies that have been used successfully for teaching the theory course using non-decision problems as the central concept.

Many computer science programs have capstone experiences or project courses that allow students to integrate knowledge from the full breadth of their major. Such capstone projects may be student-designed, instructor-designed, designed in conjunction with outside companies, or integrated with ongoing free and open source (FOSS) projects. The literature shows that the FOSS approach has attracted a great deal of interest, in particular when implemented with projects that have humanitarian goals (HFOSS). In this article, we describe five unique models from five distinct types of institutions for incorporating sustained FOSS or HFOSS (alternatively H/FOSS) project work into capstone experiences or courses. The goal is to provide instructors wishing to integrate open source experiences into their curriculum with additional perspectives and resources to help in adapting this approach to the specific needs and goals of their institution and students. All of the models presented are based on sustained engagement with H/FOSS projects that last at least one semester and often more. Each model is described in terms of its characteristics and how it fits the needs of the institution using the model. Assessment of each model is also presented. We then discuss the themes that are common across the models, such as project selection, team formation, mentoring, and student assessment. We examine the choices made by each model, as well as the challenges faced. We end with a discussion how the models have leveraged institutional initiatives and collaborations with outside organizations to address some of the challenges associated with these projects.

Computational and complexity theory are core components of the computer science curriculum, and in the vast majority of cases they are taught using decision problems as the main paradigm. For experienced practitioners, decision problems are the best tool. But for undergraduates encountering the material for the first time, non-decision problems (such as optimization problems and search problems) may be preferable. This lightning talk will give a brief pointer to some new technical definitions and pedagogical strategies that have been used successfully for teaching the theory course using non-decision problems as the central concept. For example, instead of the familiar complexity classes P and NP, we can define analogous classes of non-decision problems, Poly and NPoly. The key question behind this lightning talk is to ask whether the new definitions and strategies are actually beneficial. Anecdotal evidence and certain theories of learning suggest the new approach should result in superior learning outcomes for students. We are seeking ideas, feedback, and collaborators interested in investigating this hypothesis and obtaining stronger evidence for it. To summarize, our central question is: how can we investigate whether students gain a superior grasp of computational and complexity theory when they are taught primarily using non-decision problems?

An analysis of data from 259 CS1 students is performed to compare the performance of students who were paired by demonstrated ability to that of students who were paired randomly or worked alone. The results suggest that when given individual programming tasks to complete, lowest-quartile students who were paired by ability perform better than those who were paired randomly and those who worked alone.

Kinesis is a novel data placement model for distributed storage systems. It exemplifies three design principles: structure (division of servers into a few failure-isolated segments), freedom of choice (freedom to allocate the best servers to store and retrieve data based on current resource availability), and scattered distribution (independent, pseudo-random spread of replicas in the system). These design principles enable storage systems to achieve balanced utilization of storage and network resources in the presence of incremental system expansions, failures of single and shared components, and skewed distributions of data size and popularity. In turn, this ability leads to significantly reduced resource provisioning costs, good user-perceived response times, and fast, parallelized recovery from independent and correlated failures. This article validates Kinesis through theoretical analysis, simulations, and experiments on a prototype implementation. Evaluations driven by real-world traces show that Kinesis can significantly outperform the widely used Chain replica-placement strategy in terms of resource requirements, end-to-end delay, and failure recovery.

Fault-tolerant, replicated file systems are a crucial component of todaypsilas data centers. Despite their huge complexity, these systems are typically specified only in brief prose, which makes them difficult to reason about or verify. This paper describes the authorspsila experience using formal methods to improve our understanding of and confidence in the behavior of replicated file systems. We wrote formal specifications for three real-world fault-tolerant file systems and used them to: (1) expose design similarities and differences; (2) clarify and mechanically verify consistency properties; and (3) evaluate design alternatives. Our experience showed that formal specifications for these systems were easy to produce, useful for a deep understanding of system functions, and valuable for system comparison.

Continuously-Adaptive Discretization for Message-Passing (CAD-MP) is a new message-passing algorithm employing adaptive discretization. Most previous message-passing algorithms approximated arbitrary continuous probability distributions using either: a family of continuous distributions such as the exponential family; a particle-set of discrete samples; or a fixed, uniform discretization. In contrast, CAD-MP uses a discretization that is (i) non-uniform, and (ii) adaptive. The non-uniformity allows CAD-MP to localize interesting features (such as sharp peaks) in the marginal belief distributions with time complexity that scales logarithmically with precision, as opposed to uniform discretization which scales at best linearly. We give a principled method for altering the non-uniform discretization according to information-based measures. CAD-MP is shown in experiments on simulated data to estimate marginal beliefs much more precisely than competing approaches for the same computational expense.

The task of consistently and reliably replicating data is fundamental in distributed systems, and numerous existing protocols are able to achieve such replication efficiently. When called on to build a large-scale enterprise storage system with built-in replication, we were therefore surprised to discover that no existing protocols met our requirements. As a result, we designed and deployed a new replication protocol called Niobe . Niobe is in the primary-backup family of protocols, and shares many similarities with other protocols in this family. But we believe Niobe is significantly more practical for large-scale enterprise storage than previously published protocols. In particular, Niobe is simple, flexible, has rigorously proven yet simply stated consistency guarantees, and exhibits excellent performance. Niobe has been deployed as the backend for a commercial Internet service; its consistency properties have been proved formally from first principles, and further verified using the TLA + specification language. We describe the protocol itself, the system built to deploy it, and some of our experiences in doing so.

We propose an algorithm for estimating disparity and occlusion in stereo video sequences. The algorithm defines a prior on sequences of disparity maps using a 3D Markov random field, and approximately computes the MAP estimate for the disparity sequence using loopy belief propagation. In contrast to previous work on temporal stereo, the algorithm (i) correctly models half-occlusions - scene points visible in one camera but not the other - and (ii) enforces the so-called "monotonicity constraint" on the boundary of half-occluded regions. The algorithm is also able to exploit temporal coherence more appropriately than many previous approaches to temporal stereo, by employing additional states in the Markov random field. These additional states permit rudimentary motion estimation to be performed as part of the belief propagation, thus improving the quality of temporal inference. Parameters of the algorithm are learned from the ground truth disparities of a real stereo sequence. Qualitative results are shown on real sequences, including comparisons with competing approaches, and the performance of the algorithm is assessed quantitatively using the ground truth data.

Coliseum is a multiuser immersive remote teleconferencing system designed to provide collaborative workers the experience of face-to-face meetings from their desktops. Five cameras are attached to each PC display and directed at the participant. From these video streams, view synthesis methods produce arbitrary-perspective renderings of the participant and transmit them to others at interactive rates, currently about 15 frames per second. Combining these renderings in a shared synthetic environment gives the appearance of having all participants interacting in a common space. In this way, Coliseum enables users to share a virtual world, with acquired-image renderings of their appearance replacing the synthetic representations provided by more conventional avatar-populated virtual worlds. The system supports virtual mobility--participants may move around the shared space--and reciprocal gaze, and has been demonstrated in collaborative sessions of up to ten Coliseum workstations, and sessions spanning two continents. This paper summarizes the technology, and reports on issues related to its performance.

Blob trackers have become increasingly powerful in recent years largely due to the adoption of statistical appearance models which allow effective background subtraction and robust tracking of deforming foreground objects. It has been standard, however, to treat background and foreground modelling as separate processes-background subtraction is followed by blob detection and tracking-which prevents a principled computation of image likelihoods. This paper presents two theoretical advances which address this limitation and lead to a robust multiple-person tracking system suitable for single-camera real-time surveillance applications. The first innovation is a multi-blob likelihood function which assigns directly comparable likelihoods to hypotheses containing different numbers of objects. This likelihood function has a rigorous mathematical basis: it is adapted from the theory of Bayesian correlation, but uses the assumption of a static camera to create a more specific background model while retaining a unified approach to background and foreground modelling. Second we introduce a Bayesian filter for tracking multiple objects when the number of objects present is unknown and varies over time. We show how a particle filter can be used to perform joint inference on both the number of objects present and their configurations. Finally we demonstrate that our system runs comfortably in real time on a modest workstation when the number of blobs in the scene is small.

The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. Effective models of human dynamics can be learned from motion capture data using switching linear dynamic system (SLDS) models. We present results for human motion synthe(cid:173) sis, classification, and visual tracking using learned SLDS models. Since exact inference in SLDS is intractable, we present three approximate in(cid:173) ference algorithms and compare their performance. In particular, a new variational inference algorithm is obtained by casting the SLDS model as a Dynamic Bayesian Network. Classification experiments show the superiority of SLDS over conventional HMM's for our problem domain.

Tracking multiple targets whose models are indistinguishable is a challenging problem. Simply instantiating several independent I-body trackers is not an adequate solution, because the independent trackers can coalesce onto the best-fitting target. This paper presents an observation density for tracking which solves this problem by exhibiting a probabilistic exclusion principle. Exclusion arises naturally from a systematic derivation of the observation density, without relying on heuristics. Another important contribution of the paper is the presentation of partitioned sampling, a new sampling method for multiple object tracking. Partitioned sampling avoids the high computational load associated with fully coupled trackers, while retaining the desirable properties of coupling.

Maximisation of cross correlation is a commonly used principle for intensity based object localization that gives a single estimate of location. However, to facilitate sequential inference (e.g. over time or scale) and to allow the representation of ambiguity, it is desirable to represent an entire probability distribution for object location. Although the cross correlation itself (or some function of it) has sometimes been treated as a probability distribution, this is not generally justifiable. Bayesian correlation achieves a consistent probabilistic treatment by combining several developments. The first is the interpretation of correlation matching functions in probabilistic terms, as observation likelihoods. Second, probability distributions of filter bank responses are learned from training examples. Inescapably, response learning also demands statistical modelling of background intensities, and there are links here with image coding and Independent Component Analysis. Lastly, multi scale processing is achieved in a Bayesian context by means of a new algorithm, layered sampling, for which asymptotic properties are derived.

A method of localising objects in images is proposed. Possible configurations are evaluated using the contour discriminant, a likelihood ratio which is derived from a probabilistic model of the feature detection process. We treat each step in this process probabilistically, including the occurrence of clutter features, and derive the observation densities for both correct "target" configurations and incorrect "clutter" configurations. The contour discriminant distinguishes target objects from the background even in heavy clutter, making only the most general assumptions about the form that clutter might take. The method generates samples stochastically to avoid the cost of processing an entire image, and promises to be particularly suited to the task of initialising contour trackers based on sampling methods.