Index to Library Trends Volume 38

published or submitted for publicatio

Abstract State Machines 1988-1998: Commented ASM Bibliography

An annotated bibliography of papers which deal with or use Abstract State Machines (ASMs), as of January 1998.Comment: Also maintained as a BibTeX file at http://www.eecs.umich.edu/gasm

Recent Developments in Cultural Heritage Image Databases: Directions for User-Centered Design

published or submitted for publicatio

Interpretation at the controller's edge: designing graphical user interfaces for the digital publication of the excavations at Gabii (Italy)

This paper discusses the authors’ approach to designing an interface for the Gabii Project’s digital volumes that attempts to fuse elements of traditional synthetic publications and site reports with rich digital datasets. Archaeology, and classical archaeology in particular, has long engaged with questions of the formation and lived experience of towns and cities. Such studies might draw on evidence of local topography, the arrangement of the built environment, and the placement of architectural details, monuments and inscriptions (e.g. Johnson and Millett 2012). Fundamental to the continued development of these studies is the growing body of evidence emerging from new excavations. Digital techniques for recording evidence “on the ground,” notably SFM (structure from motion aka close range photogrammetry) for the creation of detailed 3D models and for scene-level modeling in 3D have advanced rapidly in recent years. These parallel developments have opened the door for approaches to the study of the creation and experience of urban space driven by a combination of scene-level reconstruction models (van Roode et al. 2012, Paliou et al. 2011, Paliou 2013) explicitly combined with detailed SFM or scanning based 3D models representing stratigraphic evidence. It is essential to understand the subtle but crucial impact of the design of the user interface on the interpretation of these models. In this paper we focus on the impact of design choices for the user interface, and make connections between design choices and the broader discourse in archaeological theory surrounding the practice of the creation and consumption of archaeological knowledge. As a case in point we take the prototype interface being developed within the Gabii Project for the publication of the Tincu House. In discussing our own evolving practices in engagement with the archaeological record created at Gabii, we highlight some of the challenges of undertaking theoretically-situated user interface design, and their implications for the publication and study of archaeological materials

Physical Representation-based Predicate Optimization for a Visual Analytics Database

Querying the content of images, video, and other non-textual data sources requires expensive content extraction methods. Modern extraction techniques are based on deep convolutional neural networks (CNNs) and can classify objects within images with astounding accuracy. Unfortunately, these methods are slow: processing a single image can take about 10 milliseconds on modern GPU-based hardware. As massive video libraries become ubiquitous, running a content-based query over millions of video frames is prohibitive. One promising approach to reduce the runtime cost of queries of visual content is to use a hierarchical model, such as a cascade, where simple cases are handled by an inexpensive classifier. Prior work has sought to design cascades that optimize the computational cost of inference by, for example, using smaller CNNs. However, we observe that there are critical factors besides the inference time that dramatically impact the overall query time. Notably, by treating the physical representation of the input image as part of our query optimization---that is, by including image transforms, such as resolution scaling or color-depth reduction, within the cascade---we can optimize data handling costs and enable drastically more efficient classifier cascades. In this paper, we propose Tahoma, which generates and evaluates many potential classifier cascades that jointly optimize the CNN architecture and input data representation. Our experiments on a subset of ImageNet show that Tahoma's input transformations speed up cascades by up to 35 times. We also find up to a 98x speedup over the ResNet50 classifier with no loss in accuracy, and a 280x speedup if some accuracy is sacrificed.Comment: Camera-ready version of the paper submitted to ICDE 2019, In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE 2019

MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.Environmental Economics and Policy,

Science-based restoration monitoring of coastal habitats, Volume One: A framework for monitoring plans under the Estuaries and Clean Waters Act of 2000 (Public Law 160-457)

Executive Summary: The Estuary Restoration Act of 2000 (ERA), Title I of the Estuaries and Clean Waters Act of 2000, was created to promote the restoration of habitats along the coast of the United States (including the US protectorates and the Great Lakes). The NOAA National Centers for Coastal Ocean Science was charged with the development of a guidance manual for monitoring plans under this Act. This guidance manual, titled Science-Based Restoration Monitoring of Coastal Habitats, is written in two volumes. It provides technical assistance, outlines necessary steps, and provides useful tools for the development and implementation of sound scientific monitoring of coastal restoration efforts. In addition, this manual offers a means to detect early warnings that the restoration is on track or not, to gauge how well a restoration site is functioning, to coordinate projects and efforts for consistent and successful restoration, and to evaluate the ecological health of specific coastal habitats both before and after project completion (Galatowitsch et al. 1998). The following habitats have been selected for discussion in this manual: water column, rock bottom, coral reefs, oyster reefs, soft bottom, kelp and other macroalgae, rocky shoreline, soft shoreline, submerged aquatic vegetation, marshes, mangrove swamps, deepwater swamps, and riverine forests. The classification of habitats used in this document is generally based on that of Cowardin et al. (1979) in their Classification of Wetlands and Deepwater Habitats of the United States, as called for in the ERA Estuary Habitat Restoration Strategy. This manual is not intended to be a restoration monitoring “cookbook” that provides templates of monitoring plans for specific habitats. The interdependence of a large number of site-specific factors causes habitat types to vary in physical and biological structure within and between regions and geographic locations (Kusler and Kentula 1990). Monitoring approaches used should be tailored to these differences. However, even with the diversity of habitats that may need to be restored and the extreme geographic range across which these habitats occur, there are consistent principles and approaches that form a common basis for effective monitoring. Volume One, titled A Framework for Monitoring Plans under the Estuaries and Clean Waters Act of 2000, begins with definitions and background information. Topics such as restoration, restoration monitoring, estuaries, and the role of socioeconomics in restoration are discussed. In addition, the habitats selected for discussion in this manual are briefly described. (PDF contains 116 pages