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516
How Deep is Knowledge Tracing?
"... ABSTRACT In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, generalpurpose models whose parameters and representations are difficult to interpret. The former typically provide more ins ..."
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insight into cognition but the latter often perform better. This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to predicting students' performance as they work through a series of exercises-termed deep knowledge tracing or DKT -has demonstrated
Deep Knowledge Tracing
"... Knowledge tracing—where a machine models the knowledge of a student as they interact with coursework—is a well established problem in computer supported education. Though effectively modeling student knowledge would have high ed-ucational impact, the task has many inherent challenges. In this paper ..."
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Knowledge tracing—where a machine models the knowledge of a student as they interact with coursework—is a well established problem in computer supported education. Though effectively modeling student knowledge would have high ed-ucational impact, the task has many inherent challenges. In this paper
A High-level Programming Environment for Packet Trace Anonymization and Transformation
- In Proceedings of the ACM SIGCOMM Conference
, 2003
"... Packet traces of operational Internet traffic are invaluable to network research, but public sharing of such traces is severely limited by the need to first remove all sensitive information. Current trace anonymization technology leaves only the packet headers intact, completely stripping the conten ..."
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Cited by 108 (3 self)
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the contents; to our knowledge, there are no publicly available traces of any significant size that contain packet payloads. We describe a new approach to transform and anonymize packet traces. Our tool provides high-level language support for packet transformation, allowing the user to write short policy
Differentially Private Aggregation of Distributed Time-Series with Transformation and Encryption
"... We propose the first differentially private aggregation algorithm for distributed time-series data that offers good practical utility without any trusted server. This addresses two important challenges in participatory data-mining applications where (i) individual users wish to publish temporally co ..."
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Cited by 88 (3 self)
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correlated time-series data (such as location traces, web history, personal health data), and (ii) an untrusted thirdparty aggregator wishes to run aggregate queries on the data. To ensure differential privacy for time-series data despite the presence of temporal correlation, we propose the Fourier
Low-Energy Encoding for Deep-Submicron Address Buses
, 2001
"... In this paper, we introduce a new encoding scheme that explicitly targets the minimization of the bus energy due to the crosstalk capacitances between adjacent bus lines. The key transformation operated by the code consists of a permutation of the bus lines, implemented directly during physical desi ..."
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Cited by 11 (3 self)
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knowledge of the binary stream being transmitted. Therefore, the code can be effectively exploited in general-purpose computing systems. The proposed code works best on address buses � savings obtained for different address traces generated by two different processors are in the order of 26% with respect
SHALLOW REFLECTION DETECTION USING THE RADIAL TRACE TRANSFORM
"... ABSTRACT In most of seismic data acquisition, shallow reflection data will be masked with refraction and first arrival events and observation of this event is not usually easy. Radial trace transform is a simple transform for mapping x-t gathered data into apparent velocity and travel time domain. T ..."
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ABSTRACT In most of seismic data acquisition, shallow reflection data will be masked with refraction and first arrival events and observation of this event is not usually easy. Radial trace transform is a simple transform for mapping x-t gathered data into apparent velocity and travel time domain
Knowledge Tracing and Prediction of Future Trainee Performance
, 2006
"... Intelligent tutoring systems seek to optimize instruction and training by adapting and individualizing the learning experience on the basis of a student model (Shute, 1995). This model represents the system’s estimate of the student’s current knowledge or skill level, established from a performance ..."
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history. Knowledge tracing (Aleven & Koedinger, 2002; Anderson, Conrad, & Corbett, 1989) is a dynamic, Bayesian approach to updating the estimates of probability of skill mastery in the student model. A fundamental shortcoming of this approach is that it does not include a representation
Crosslanguage knowledge transfer using multilingual deep neural networks with shared hidden layers,” ICASSP
, 2013
"... In the deep neural network (DNN), the hidden layers can be considered as increasingly complex feature transformations and the final softmax layer as a log-linear classifier making use of the most abstract features computed in the hidden layers. While the loglinear classifier should be different for ..."
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Cited by 29 (7 self)
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In the deep neural network (DNN), the hidden layers can be considered as increasingly complex feature transformations and the final softmax layer as a log-linear classifier making use of the most abstract features computed in the hidden layers. While the loglinear classifier should be different
Using models of partial knowledge to test model transformations
- In ICMT
, 2012
"... Abstract. Testers often use partial knowledge to build test models. This knowl-edge comes from sources such as requirements, known faults, existing inputs, and execution traces. In Model-Driven Engineering, test inputs are models executed by model transformations. Modelers build them using partial k ..."
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Cited by 4 (2 self)
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Abstract. Testers often use partial knowledge to build test models. This knowl-edge comes from sources such as requirements, known faults, existing inputs, and execution traces. In Model-Driven Engineering, test inputs are models executed by model transformations. Modelers build them using partial
Deep Learning Methods and Applications
"... Deep Learning: Methods and Applications provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2) t ..."
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Cited by 9 (0 self)
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Deep Learning: Methods and Applications provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2
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