Institutional Repositories, Policies, and Disruption

For many librarians, institutional repositories (IRs) promised significant change for academic libraries. We envisioned enlarging collection development scope to include locally produced scholarship and an expansion of library services to embrace scholarly publication and distribution. However, at the University of Rochester, as at many other institutions, this transformational technology was introduced in the conservative, controlled manner associated with stereotypical librarian culture, and so these expected changes never materialized. In this case study, we focus on the creation of our institutional repository (a potentially disruptive technology) and how its success was hampered by our organizational culture, manifested as a lengthy and complicated set of policies. In the following pages, we briefly describe our repository project, talk about our original policies, look at the ways those policies impeded our project, and discuss the disruption of those policies and the benefits in user uptake that resulted.

OpenBU: Institutional Repository Procedures Manual

(adapted from the DSpace Procedures Manual developed by Kalamazoo College Digital Archive)

An Institutional Approach to Donor Control: From Dyadic Ties to a Field‐Level Analysis

Literature on the nonprofit sector focuses on charities and their interactions with clients or governmental agencies; donors are studied less often. Studies on philanthropy do examine donors but tend to focus on microlevel factors to explain their behavior. This study, in contrast, draws on institutional theory to show that macrolevel factors affect donor behavior. It also extends the institutional framework by examining the field‐level configurations in which donors and fundraisers are embedded. Employing the case of workplace charity, this new model highlights how the composition of the organizational field structures fundraisers and donors alike, shaping fundraisers’ strategies of solicitation and, therefore, the extent of donor control.

Exploiting Phonological Constraints for Handshape Inference in ASL Video

Handshape is a key articulatory parameter in sign language, and thus handshape recognition from signing video is essential for sign recognition and retrieval. Handshape transitions within monomorphemic lexical signs (the largest class of signs in signed languages) are governed by phonological rules. For example, such transitions normally involve either closing or opening of the hand (i.e., to exclusively use either folding or unfolding of the palm and one or more fingers). Furthermore, akin to allophonic variations in spoken languages, both inter- and intra- signer variations in the production of specific handshapes are observed. We propose a Bayesian network formulation to exploit handshape co-occurrence constraints, also utilizing information about allophonic variations to aid in handshape recognition. We propose a fast non-rigid image alignment method to gain improved robustness to handshape appearance variations during computation of observation likelihoods in the Bayesian network. We evaluate our handshape recognition approach on a large dataset of monomorphemic lexical signs. We demonstrate that leveraging linguistic constraints on handshapes results in improved handshape recognition accuracy. As part of the overall project, we are collecting and preparing for dissemination a large corpus (three thousand signs from three native signers) of American Sign Language (ASL) video. The video have been annotated using SignStream® [Neidle et al.] with labels for linguistic information such as glosses, morphological properties and variations, and start/end handshapes associated with each ASL sign.

Stochastic Refinement of the Visual Hull to Satisfy Photometric and Silhouette Consistency Constraints

An iterative method for reconstructing a 3D polygonal mesh and color texture map from multiple views of an object is presented. In each iteration, the method first estimates a texture map given the current shape estimate. The texture map and its associated residual error image are obtained via maximum a posteriori estimation and reprojection of the multiple views into texture space. Next, the surface shape is adjusted to minimize residual error in texture space. The surface is deformed towards a photometrically-consistent solution via a series of 1D epipolar searches at randomly selected surface points. The texture space formulation has improved computational complexity over standard image-based error approaches, and allows computation of the reprojection error and uncertainty for any point on the surface. Moreover, shape adjustments can be constrained such that the recovered model's silhouette matches those of the input images. Experiments with real world imagery demonstrate the validity of the approach.

Automated Placement of Cameras in a Floorplan to Satisfy Task-Specific Constraints

In many multi-camera vision systems the effect of camera locations on the task-specific quality of service is ignored. Researchers in Computational Geometry have proposed elegant solutions for some sensor location problem classes. Unfortunately, these solutions utilize unrealistic assumptions about the cameras' capabilities that make these algorithms unsuitable for many real-world computer vision applications: unlimited field of view, infinite depth of field, and/or infinite servo precision and speed. In this paper, the general camera placement problem is first defined with assumptions that are more consistent with the capabilities of real-world cameras. The region to be observed by cameras may be volumetric, static or dynamic, and may include holes that are caused, for instance, by columns or furniture in a room that can occlude potential camera views. A subclass of this general problem can be formulated in terms of planar regions that are typical of building floorplans. Given a floorplan to be observed, the problem is then to efficiently compute a camera layout such that certain task-specific constraints are met. A solution to this problem is obtained via binary optimization over a discrete problem space. In preliminary experiments the performance of the resulting system is demonstrated with different real floorplans.

Lightweight Modeling of Java Virtual Machine Security Constraints using Alloy

The Java programming language has been widely described as secure by design. Nevertheless, a number of serious security vulnerabilities have been discovered in Java, particularly in the component known as the Bytecode Verifier. This paper describes a method for representing Java security constraints using the Alloy modeling language. It further describes a system for performing a security analysis on any block of Java bytecodes by converting the bytes into relation initializers in Alloy. Any counterexamples found by the Alloy analyzer correspond directly to insecure code. Analysis of a real-world malicious applet is given to demonstrate the efficacy of the approach.