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Title: Improving Abilityy to verify Audio CAPTCHA's (Semester Unknwon) IPRO 316: ImprovingAbilityToVerifyAudioIPRO316EthicsSp10
Creator: Klansky, Gabriel, Schneider, Erick, Kaim, Maxwell, Ciarkowski, Adam, Fabian, Michael, Wallace, Sean, Kipp, Daniel
Date: 2010, 2010-05
Description: CAPTCHAs (Completely Automated Public Turing Test to Tell Computers and Humans Apart) are used to prevent automated access to sensitive...
Show moreCAPTCHAs (Completely Automated Public Turing Test to Tell Computers and Humans Apart) are used to prevent automated access to sensitive information online. In its usual format, users are presented with distorted text and asked to enter the displayed text in an answer box. If successful, humans, but not computers, will be able to interpret the distorted text. Another format of CAPTCHAs asks users to identify audio information (usually a string of digits or phrase of words) that has been distorted or placed against a background of noise (\white" noise, reversed speech, etc.). Users type the words they hear into an answer box. The audio format is intended to be accessible to blind and low-vision users who cannot use the visually-based format. Unfortunately, audio CAPTCHAs are di cult for humans to use (Bigham and Cavendar 2009) but relatively easy for computers to solve (Tam et al. 2008), which is exactly the opposite outcome desired. To take two extreme examples, in one recent study (Sauer et al. 2008), users were able to solve only 46 percent of audio CAPTCHAs, while in another study (Burztein and Bethard 2009), a computer program was able to break 75 percent of audio CAPTCHAs. At issue is whether audio CAPTCHAs can be designed so that users can easily solve them but computers cannot. Yan and Ahmad (2008) propose testing di erent kinds of background noise to determine which is the most e ective at blocking computers but admitting humans. To this end, Tam et al. (2008) suggest using other human voices as background noise (to thwart computers) but familiar phrases as the string to decode (to aid listeners). The current project will focus on selecting from a set of potential solutions to test.
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Title: Improving Abilityy to verify Audio CAPTCHA's (Semester Unknwon) IPRO 316: ImprovingAbilityToVerifyAudioIPRO316FinalReportSp10
Creator: Klansky, Gabriel, Schneider, Erick, Kaim, Maxwell, Ciarkowski, Adam, Fabian, Michael, Wallace, Sean, Kipp, Daniel
Date: 2010, 2010-05
Description: CAPTCHAs (Completely Automated Public Turing Test to Tell Computers and Humans Apart) are used to prevent automated access to sensitive...
Show moreCAPTCHAs (Completely Automated Public Turing Test to Tell Computers and Humans Apart) are used to prevent automated access to sensitive information online. In its usual format, users are presented with distorted text and asked to enter the displayed text in an answer box. If successful, humans, but not computers, will be able to interpret the distorted text. Another format of CAPTCHAs asks users to identify audio information (usually a string of digits or phrase of words) that has been distorted or placed against a background of noise (\white" noise, reversed speech, etc.). Users type the words they hear into an answer box. The audio format is intended to be accessible to blind and low-vision users who cannot use the visually-based format. Unfortunately, audio CAPTCHAs are di cult for humans to use (Bigham and Cavendar 2009) but relatively easy for computers to solve (Tam et al. 2008), which is exactly the opposite outcome desired. To take two extreme examples, in one recent study (Sauer et al. 2008), users were able to solve only 46 percent of audio CAPTCHAs, while in another study (Burztein and Bethard 2009), a computer program was able to break 75 percent of audio CAPTCHAs. At issue is whether audio CAPTCHAs can be designed so that users can easily solve them but computers cannot. Yan and Ahmad (2008) propose testing di erent kinds of background noise to determine which is the most e ective at blocking computers but admitting humans. To this end, Tam et al. (2008) suggest using other human voices as background noise (to thwart computers) but familiar phrases as the string to decode (to aid listeners). The current project will focus on selecting from a set of potential solutions to test.
Deliverables
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Title: Improving Abilityy to verify Audio CAPTCHA's (Semester Unknwon) IPRO 316
Creator: Klansky, Gabriel, Schneider, Erick, Kaim, Maxwell, Ciarkowski, Adam, Fabian, Michael, Wallace, Sean, Kipp, Daniel
Date: 2010, 2010-05
Description: CAPTCHAs (Completely Automated Public Turing Test to Tell Computers and Humans Apart) are used to prevent automated access to sensitive...
Show moreCAPTCHAs (Completely Automated Public Turing Test to Tell Computers and Humans Apart) are used to prevent automated access to sensitive information online. In its usual format, users are presented with distorted text and asked to enter the displayed text in an answer box. If successful, humans, but not computers, will be able to interpret the distorted text. Another format of CAPTCHAs asks users to identify audio information (usually a string of digits or phrase of words) that has been distorted or placed against a background of noise (\white" noise, reversed speech, etc.). Users type the words they hear into an answer box. The audio format is intended to be accessible to blind and low-vision users who cannot use the visually-based format. Unfortunately, audio CAPTCHAs are di cult for humans to use (Bigham and Cavendar 2009) but relatively easy for computers to solve (Tam et al. 2008), which is exactly the opposite outcome desired. To take two extreme examples, in one recent study (Sauer et al. 2008), users were able to solve only 46 percent of audio CAPTCHAs, while in another study (Burztein and Bethard 2009), a computer program was able to break 75 percent of audio CAPTCHAs. At issue is whether audio CAPTCHAs can be designed so that users can easily solve them but computers cannot. Yan and Ahmad (2008) propose testing di erent kinds of background noise to determine which is the most e ective at blocking computers but admitting humans. To this end, Tam et al. (2008) suggest using other human voices as background noise (to thwart computers) but familiar phrases as the string to decode (to aid listeners). The current project will focus on selecting from a set of potential solutions to test.
Deliverables
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Title: Improving Abilityy to verify Audio CAPTCHA's (Semester Unknwon) IPRO 316: ImprovingAbilityToVerifyAudioIPRO316PosterSp10
Creator: Klansky, Gabriel, Schneider, Erick, Kaim, Maxwell, Ciarkowski, Adam, Fabian, Michael, Wallace, Sean, Kipp, Daniel
Date: 2010, 2010-05
Description: CAPTCHAs (Completely Automated Public Turing Test to Tell Computers and Humans Apart) are used to prevent automated access to sensitive...
Show moreCAPTCHAs (Completely Automated Public Turing Test to Tell Computers and Humans Apart) are used to prevent automated access to sensitive information online. In its usual format, users are presented with distorted text and asked to enter the displayed text in an answer box. If successful, humans, but not computers, will be able to interpret the distorted text. Another format of CAPTCHAs asks users to identify audio information (usually a string of digits or phrase of words) that has been distorted or placed against a background of noise (\white" noise, reversed speech, etc.). Users type the words they hear into an answer box. The audio format is intended to be accessible to blind and low-vision users who cannot use the visually-based format. Unfortunately, audio CAPTCHAs are di cult for humans to use (Bigham and Cavendar 2009) but relatively easy for computers to solve (Tam et al. 2008), which is exactly the opposite outcome desired. To take two extreme examples, in one recent study (Sauer et al. 2008), users were able to solve only 46 percent of audio CAPTCHAs, while in another study (Burztein and Bethard 2009), a computer program was able to break 75 percent of audio CAPTCHAs. At issue is whether audio CAPTCHAs can be designed so that users can easily solve them but computers cannot. Yan and Ahmad (2008) propose testing di erent kinds of background noise to determine which is the most e ective at blocking computers but admitting humans. To this end, Tam et al. (2008) suggest using other human voices as background noise (to thwart computers) but familiar phrases as the string to decode (to aid listeners). The current project will focus on selecting from a set of potential solutions to test.
Deliverables
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Title: Improving Abilityy to verify Audio CAPTCHA's (Semester Unknwon) IPRO 316: ImprovingAbilityToVerifyAudioIPRO316ProjectPlanSp10
Creator: Klansky, Gabriel, Schneider, Erick, Kaim, Maxwell, Ciarkowski, Adam, Fabian, Michael, Wallace, Sean, Kipp, Daniel
Date: 2010, 2010-05
Description: CAPTCHAs (Completely Automated Public Turing Test to Tell Computers and Humans Apart) are used to prevent automated access to sensitive...
Show moreCAPTCHAs (Completely Automated Public Turing Test to Tell Computers and Humans Apart) are used to prevent automated access to sensitive information online. In its usual format, users are presented with distorted text and asked to enter the displayed text in an answer box. If successful, humans, but not computers, will be able to interpret the distorted text. Another format of CAPTCHAs asks users to identify audio information (usually a string of digits or phrase of words) that has been distorted or placed against a background of noise (\white" noise, reversed speech, etc.). Users type the words they hear into an answer box. The audio format is intended to be accessible to blind and low-vision users who cannot use the visually-based format. Unfortunately, audio CAPTCHAs are di cult for humans to use (Bigham and Cavendar 2009) but relatively easy for computers to solve (Tam et al. 2008), which is exactly the opposite outcome desired. To take two extreme examples, in one recent study (Sauer et al. 2008), users were able to solve only 46 percent of audio CAPTCHAs, while in another study (Burztein and Bethard 2009), a computer program was able to break 75 percent of audio CAPTCHAs. At issue is whether audio CAPTCHAs can be designed so that users can easily solve them but computers cannot. Yan and Ahmad (2008) propose testing di erent kinds of background noise to determine which is the most e ective at blocking computers but admitting humans. To this end, Tam et al. (2008) suggest using other human voices as background noise (to thwart computers) but familiar phrases as the string to decode (to aid listeners). The current project will focus on selecting from a set of potential solutions to test.
Deliverables
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Title: POWER PROFILING, ANALYSIS, LEARNING, AND MANAGEMENT FOR HIGH-PERFORMANCE COMPUTING
Creator: Wallace, Sean
Date: 2017, 2017-05
Description: As the field of supercomputing continues its relentless push towards greater speeds and higher levels of parallelism the power consumption of...
Show moreAs the field of supercomputing continues its relentless push towards greater speeds and higher levels of parallelism the power consumption of these large scale systems is steadily transitioning from a burden to a serious problem. While the machines are highly scaleable, the buildings, power supplies, etc. are not. Even the most power efficient systems today consume one to two megawatts per peata op/s. Multiplying that by 1,000 to reach the next generation of supercomputer (i.e., exascale) and the power necessary just to turn the machine on is simply impractical. Thus, power has become a primary design constraint for future supercomputing system designs. As such, it has become a matter of paramount importance to understand exactly how current generation systems utilize power and what implications this has on future systems. As the saying goes, you can't manage what you don't measure. This work addresses several large hurdles in fully understanding the power consumption of current systems and making actionable decisions based on this understanding. First, by leveraging environmental data collected from runs of real leadership class applications we analyze power consumption and temperature as it pertains to scale on a production IBM Blue Gene/Q supercomputer. Then, through development of a new power monitoring library, MonEQ, we quantitatively studied how power is consumed in major portions of the system (e.g., CPU, memory, etc.) through profiling of microbenchmarks. Expanding on this, we then studied how scale and network topology affect power consumption for several well-known benchmarks. Wanting to increase the effectiveness of our power monitoring library, we extended it to work with many of the most common classes of hardware available in today's HPC landscape. In doing so, we provided an in-depth analysis of what data is obtainable, what the process of obtaining it is like, and how data from different systems compares. Next, utilizing the knowledge gained from these experiences, we developed a new scheduling approach which utilizing power data can effectively keep a production system's power consumption under a user-specified power cap without modification to the applications running on the system. Finally, we extend this scheduling approach to be applicable to more than just one objective. In doing so, the scheduler can now optimize on multiple criteria instead of simply considering system utilization.
Ph.D. in Computer Science, May 2017
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