Tracking the Functions of AI as Paradata & Pursuing Archival Accountability

While a familiar term in fields like social science research and digital cultural heritage, \u27paradata\u27 has not yet been introduced conceptually into the archival realm. In response to an increasing number of experiments with machine learning and artificial intelligence, the InterPARES Trust AI research group proposes the definition of paradata as \u27information about the procedure(s) and tools used to create and process information resources, along with information about the persons carrying out those procedures.\u27 The utilization of this concept in archives can help to ensure that AI-driven systems are designed from the outset to honor the archival ethic, and to aid in the evaluation of off-the-shelf automation solutions. An evaluation of current AI experiments in archives highlights opportunities for paradata-conscious practice

Doing More, With More: Academic Libraries, Digital Services, and Revenue Generation

This issue brief will address program development, financial principles of the program and their relationship to the institution’s budgetary practices, challenges encountered, risk assessment, and ongoing operations oversight. Fiscal management of the DDS program shares some foundational principles with responsibility center management, such as proximity, community, and transparency, and is made possible under the aegis of the University of Maryland’s encouragement of entrepreneurial initiatives fostered in campus units; while these environmental conditions help the program succeed, they are not inherently required for the initiation and advancement of the program. With careful strategic planning and financial sustainability, such a model can translate to many other research libraries with in-house technological expertise in systems management, software development, preservation, digitization, or research data management. Furthermore, such a revenue-generating model may have potential for adoption outside of technology departments--at UMD Libraries, the DDS team has been consulted about establishing fee-based services in public-facing units. Drawing from three years of data regarding project success, cost-benefit analysis, and assessment of the program, the authors will share best practices, valuation, and opportunities for growth and change. Ultimately, negotiating the tension between revenue generation and the altruistic mission of academic libraries is a challenging and reflective practice, and requires transparency, reflection, and compelling evidence of support for our mission to enable the intellectual inquiry and learning required to meet the education, research and community outreach mission of the university.The axiom to “do more with less” in university research libraries is increasingly untenable, as budgets continue to shrink and demand for novel services continues to rise. The impacts of such existential uncertainties are self-evident and widely discussed in the literature--staff burnout, lowered morale and increased toxicity, weakened local collections, and limited capacity for ambitious and genuinely innovative work. In response to calls for entrepreneurial initiatives from campus and library leadership, the Digital Systems and Stewardship (DSS) division of the University of Maryland Libraries has been engaged since 2015 in developing a revenue generation program known as Digital Data Services. This initiative tackles the challenging financial landscape of higher education and furthers our institutional mission by offering fee-based technological services to the campus community, to affiliated partners, and to the commercial sector. Conceived of as a means to generate steady revenue to support and sustain library initiatives, the program currently represents a significant source of income for the Libraries DSS division after three years of growth, and is envisioned to contribute to other divisions in the Libraries, as well. More than standard cost recovery programs, the Digital Data Services program generates returns that can be reinvested in staffing or equipment for the Libraries, and DDS projects represent unique opportunities to cultivate talent and expand expertise to benefit other library initiatives. While a large-scale revenue generating program may initially appear contrary to traditional models of library services, this program has enabled the Libraries to expand both our capacity and aptitude to improve many of our mission-driven services over time

High-Level Requirements for a Bit Preservation System University of Maryland Libraries

The mission of the high-level requirements for a bit preservation system at the University of Maryland Libraries is to provide a plan for digital content management services in all phases of content’s lifecycle, including selection, creation, acquisition, and disposition

Adaptive planning and scheduling in dynamic task domains

An algorithm is proposed which performs problem solving on line in order to obtain new information about the availability of resources in its local surroundings. The algorithm performs partial planning followed by partial execution, in order to take immediate advantage of resources which become available and remain available for a short period of time. A model of dynamicity is also introduced. Furthermore, theoretical and empirical analyses of the proposed algorithm for a routing problem in the proposed dynamic model are given

B.: Face Animation: A Case Study for Multimedia Modeling and Specification Languages

This chapter will discuss the multimedia modeling and specification methods, especially in the context of face animation. Personalized Face Animation is and/or can be a major User Interface component in modern multimedia systems. After reviewing the related works in this area, we present the ShowFace streaming structure. This structure is based on most widely accepted industry standards in multimedia presentation like MPEG-4 and SMIL, and extends them by providing a higher level Face Modeling Language (FML) for modeling and control purposes, and also by defining image transformations required for certain facial movements. ShowFace establishes a comprehensive framework for face animation consisting of components for parsing the input script, generating and splitting the audio and video “behaviors”, creating the required images and sounds, and eventually displaying or writing the data to files. This component-based design and scripted behavior make the framework suitable for many purposes including web-based applications

Opportunistic behavior and its automatic adjustment in dynamic task domains

In many dynamic application domains, the environment changes during problem solving. A problem solver, in these applications, does not have complete information about the task and resources, a priori. The problem solver is required to use up-to-date information that becomes available on line. It must use this information to avoid producing solutions that are obsolete by the time they are to be executed. The problem solver has to be opportunistic, in order to take immediate advantage of resources that become available and remain available for a short period of time. How opportunistic the algorithm should be depends on the degree of dynamicity in the environment. In this paper, we propose an algorithm which performs problem solving on line in order to obtain new information about the availability of resources in the system. The proposed algorithm adjusts itself automatically to adapt to the degree of dynamicity in the environment. We introduce a model of dynamicity in a graph representation of a task. We provide theoretical and empirical analyses of our algorithm for a routing problem in the proposed dynamic model. Our theoretical analyses demonstrate the correctness and completeness properties of our algorithm. Results of our performance-comparison experiments show that the proposed algorithm performs as well as the best of the candidate algorithms under a wide range of experiment parameters. The results also show that the proposed algorithm is capable of automatically adapting to the degree of dynamicity in the environment

Dynamic scheduling strategies for shared-memory multiprocessors

Efficiently scheduling parallel tasks on to the processors of a shared-memory multiprocessor is critical to achieving high performance. Given perfect information at compile-time, a static scheduling strategy can produce an assignment of tasks to processors that ideally balances the load among the processors while minimizing the run-time scheduling overhead and the average memory referencing delay. Since perfect information is seldom available, however, dynamic scheduling strategies distribute the task assignment function to the processors by having idle processors allocate work to themselves from a shared queue. While this approach can improve the load balancing compared to static scheduling, the time required to access the shared work queue adds directly to the overall execution time. To overlap the time required to dynamically schedule tasks with the execution of the tasks, we examine a class of Self-Adjusting Dynamic Scheduling (SADS) algorithms that centralizes the assignment of tasks to processors. These algorithms dedicate a single processor of the multiprocessor to perform a novel on-line branch-and-bound technique that dynamically computes partial schedules based on the loads of the other processors and the memory locality (affinity) of the tasks and the processors. Our simulation results show that this centralized scheduling outperforms self-scheduling algorithms even when using only a small number of processors