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Diversity, Equity, Inclusion and Accessibility at the University Library
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Showing 241–280 of 54,802 items
  • The Robbins Eagle, 15 May 1954 - Page 5
    Digitized Newspapers
    Date
    1954
  • Farm, Field, and Fireside, 3 February 1906 - Page 30
    Digitized Newspapers
    Date
    1906
  • Daily Illini, 17 May 1973 - Page 4
    Digitized Newspapers
    Date
    1973
  • Utility-driven optimization and placement framework for Visual IoT analytics over edge-cloud environments
    Scholarship
    Creator
    Elgamal, Tarek
    Description
    Internet of Things (IoT) applications generate massive amounts of real-time data. A large amount of this data is visual data that comes from cameras. Reports by Information Handling Services (IHS) indicate that 245 million professionally installed surveillance cameras are operating worldwide as of 2015. We refer to the data coming from such cameras as Visual IoT data. Recent advances in computer vision and neural networks have made it possible for more visual IoT data to be automatically searched and analyzed by algorithms rather than humans. This happens in parallel with advances in Edge computing and Serverless computing. Edge computing, has emerged to allow analyzing visual IoT data closer to where it is generated, and hence avoiding sending vast amounts of visual data streams to be analyzed in one remote location. On the other hand, serverless computing facilitates the analysis of such streams by allowing users to deploy individual analysis functions in user-owned edge devices or public cloud infrastructure. In this dissertation, we argue that the current video analytics systems are not keeping up with such advances. For example, video encoders have been designed for a long time to please human viewers and be agnostic of the downstream analysis tasks (e.g., object detection). Moreover, existing video analytics systems fail to leverage pipeline parallelism when distributing the analysis across edge and cloud devices. Existing systems also do not address several challenges associated with deploying analytics functions on public cloud infrastructure. Such challenges include performing hybrid edge and cloud analytics in a price-efficient manner as well as protecting the privacy and confidentiality of users' sensitive data against misuse by the edge/cloud provider. We address the above challenges by: (1) building a framework for processing visual data streams across edge and cloud compute resources, (2) developing algorithms that identify the best placement of computations across edge and cloud resources to optimize various utilities (e.g., latency, bandwidth, price, and privacy), and (3) building the systems that validate the effectiveness of the optimization algorithms and their ability to control the tradeoff between different utilities. The framework and the algorithms optimize various utilities and address the tradeoffs between them. The first algorithm focuses on optimizing the bandwidth by detecting the events of interest in videos closer to where the video is generated. To achieve this, we develop a Semantic Video Encoding technique in which we redesign the video compression algorithms at the camera to be aware of the edge-based downstream analysis tasks. This allows compressed videos to be easily analyzed by algorithms rather than humans because the downstream tasks can search the compressed video for the parts that are relevant to the overall analysis goals. The second algorithm focuses on optimizing the application's end-to-end latency. To achieve this, we develop an Operator Placement algorithm that is given a processing job expressed in the form of a Directed Acyclic Graph (DAG) of operators/functions, it finds which operators to place on an edge device and which operators to place on a remote cloud server. The third algorithm is a price optimization algorithm which optimizes the price of deploying visual IoT analytics applications in serverless computing platforms (e.g. AWS Lambda). The fourth algorithm focuses on optimizing the end-to-end latency of computation while maintaining data privacy. To achieve this, we leverage trusted execution environments (e.g., Intel-SGX) which allow users to execute machine learning predictions on visual IoT data while maintaining data confidentiality. To speed up the machine learning predictions, we develop a technique to find the best partitioning of neural networks computation across multiple trusted execution environments.
  • Weekly Gettysburgian, 30 March 1995 - Page 11
    Digitized Newspapers
    Date
    1995
  • American Eagle, 20 March 1970 - Page 7
    Digitized Newspapers
    Date
    1970
  • Index of congressional committee hearings (not confidential in character) : for Eighty-second Congress (January 3, 1951,...
    HathiTrust  ·   Library Catalog
    Date
    1955
  • Weekly Gettysburgian, 2 October 1997 - Page 2
    Digitized Newspapers
    Date
    1997
  • Daily Illini, 15 November 1974 - Page 27
    Digitized Newspapers
    Date
    1974
  • Weekly Gettysburgian, 4 March 2004 - Page 16
    Digitized Newspapers
    Date
    2004
  • Ithacan, 4 May 1967 - Page 14
    Digitized Newspapers
    Date
    1967
  • The Daily National Hotel Reporter, 4 August 1920 - Page 7
    Digitized Newspapers
    Date
    1920
  • The Day Book, 13 April 1917 - Page 11
    Digitized Newspapers
    Date
    1917
  • Daily Illini, 22 November 1974 - Page 1
    Digitized Newspapers
    Date
    1974
  • Daily Illini, 30 August 1973 - Page 1
    Digitized Newspapers
    Date
    1973
  • Farm, Field, and Fireside, 15 November 1902 - Page 23
    Digitized Newspapers
    Date
    1902
  • The Forum, 23 March 1907 - Page 3
    Digitized Newspapers
    Date
    1907
  • Ithacan, 3 May 1973 - Page 6
    Digitized Newspapers
    Date
    1973
  • Conservation priorities and collaboration in the Upper Midwest and Great Lakes Landscape Conservation Cooperative
    Scholarship
    Creator
    Girod, Lyndsey
    Date
    2013
    Description
    Large-scale drivers of environmental change, such as invasive species, climate change, and human land use, are prompting conservation planning and action at a landscape or regional scale. Historically, natural resource agencies worked within individual jurisdictional boundaries – both geographical boundaries and those boundaries created by differing missions. Yet the shift towards regional-scale conservation is prompting cross-jurisdictional coordination and collaboration among the various natural resource management agencies and conservation organizations within a region. Landscape Conservation Cooperatives (LCCs) were established in 2009 by the US Department of the Interior in response to large-scale environmental drivers. Twenty-two LCCs were established in North America. They were provided a structure for operation as well as funding and asked to create a cooperative of conservation players that would work together on shared issues. These regional bodies are made up of representatives from federal, state, and tribal governmental agencies, inter-governmental commissions and joint ventures, and non-governmental organizations. LCCs provide an opportunity to study two facets of regional conservation – priority setting and collaboration. A Shared Conservation Priorities Assessment was conducted for the Upper Midwest and Great Lakes LCC in 2012 to aid them in setting conservation priorities for future work. A mixed methodology approach was used to assess the perspectives of LCC members and help them arrive at a set of shared priorities. The iterative and sequential process ensured that results from one phase were used to help create subsequent phases. The sequential process provided time for relationship-building between the researchers and participants and between the participants themselves. Semi-structured interviews, a document analysis, a Q-sort, and a workshop were used in the assessment. Results show that the mixed methodology approach used to assess conservation priorities provided three overarching benefits. First, qualitative data gathered in preliminary phases of the assessment helped establish a foundational understanding of participant perspectives regarding the preferred role of the LCC in the Upper Midwest and Great Lakes region. That data were carried through and incorporated in subsequent phases and provided a road map for the LCC’s future activities. Second, the different methodologies produced several kinds of data, allowing the researchers to present the data in multiple ways. Interview data was presented through quotes highlighting strongly-articulated perspectives, as well grouped into themes for a more quantitative understanding of the prevalence of perspectives. Word maps were created to visually assess how subgroups within the LCC were articulating conservation priorities. Background document data and Q-sort data were presented in tables and charts. The ability to display the data in numerous ways provided for a more holistic understanding of the data and is a benefit of mixed methods. Third, results from the interview and Q-sort phases of the assessment were kept confidential by the research team, which ensured that results were less biased and that each participant was represented equally. Many assessments of this nature found in the literature and undertaken by other LCCs do not use confidential results in their assessment processes. The push for increased collaboration among conservation groups is due to the widespread and complex nature of many natural resource issues today, which require increasingly new skillsets. In addition, the push for more collaboration stems from declining agency budgets and reduced manpower. To better understand conservation collaboration in a regional setting, a conceptual framework based on natural resource collaboration literature was developed. This framework identifies three “spaces” where barriers to effective collaboration may arise, including within individual agencies, between agencies, and external to a collaboration. This framework was used to assess barriers to effective conservation collaboration and best strategies for collaboration from the perspectives of natural resource professionals engaged in the LCC. Participants provided new perspectives about barriers to effective conservation collaboration, but many of their thoughts built on ideas framed in existing literature. For example, constrained resources, stagnant agency culture, and a lack of trust were some of the barriers identified by participants that are also echoed in the literature. New barriers were also articulated, including having too many people in a group, individual personalities that aren’t collaborative, and having a large number of existing collaboration initiatives in a region. The conceptual framework used to examine barriers to effective collaboration provided a new way to understand where barriers are occurring and the potential for addressing them. This study provides insight to LCCs and other regional conservation initiatives regarding priority assessment methodologies and a new way to examine barriers to effective collaboration.
  • True Republican, 8 May 1942 - Page 8
    Digitized Newspapers
    Date
    1942
  • Weekly Gettysburgian, 31 October 1983 - Page 5
    Digitized Newspapers
    Date
    1983
  • The Daily National Hotel Reporter, 7 August 1920 - Page 7
    Digitized Newspapers
    Date
    1920
  • American Eagle, 7 February 1969 - Page 8
    Digitized Newspapers
    Date
    1969
  • Farmer's Wife, 1 September 1931 - Page 23
    Digitized Newspapers
    Date
    1931
  • Daily Illini, 30 January 1975 - Page 1
    Digitized Newspapers
    Date
    1975
  • The Daily National Hotel Reporter, 5 August 1920 - Page 7
    Digitized Newspapers
    Date
    1920
  • F&M College Reporter, 5 May 1970 - Page 2
    Digitized Newspapers
    Date
    1970
  • Lancaster Farming, 5 May 1979 - Page 126
    Digitized Newspapers
    Date
    1979
  • Daily Illini, 21 May 1954 - Page 8
    Digitized Newspapers
    Date
    1954
  • Sangamo Journal / Illinois State Journal, 13 June 1860 - Page 4
    Digitized Newspapers
    Date
    1860
  • The Arsenal Record, 16 July 1918 - Page 2
    Digitized Newspapers
    Date
    1918
  • Sangamo Journal / Illinois State Journal, 5 June 1860 - Page 4
    Digitized Newspapers
    Date
    1860
  • Farmers' Weekly Review, 17 August 2006 - Page 4
    Digitized Newspapers
    Date
    2006
  • Daily Illini, 24 March 1960 - Page 8
    Digitized Newspapers
    Date
    1960
  • Daily Illini, 6 December 1969 - Page 4
    Digitized Newspapers
    Date
    1969
  • Daily Illini, 14 October 1966 - Page 11
    Digitized Newspapers
    Date
    1966
  • Hoya, 6 February 1976 - Page 3
    Digitized Newspapers
    Date
    1976
  • The Daily National Hotel Reporter, 3 August 1920 - Page 7
    Digitized Newspapers
    Date
    1920
  • Daily Illini, 17 October 1973 - Page 10
    Digitized Newspapers
    Date
    1973
  • Daily Illini, 20 November 1970 - Page 11
    Digitized Newspapers
    Date
    1970