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  1. Sparse Coding for N-Gram Feature Extraction and Training for File Fragment Classification

    File fragment classification is an important step in the task of file carving in digital forensics. In file carving, files must be reconstructed based on their content as a result of their fragmented storage on disk or in memory. Existing methods for classification of file fragments typically use hand-engineered features such as byte histograms or entropy measures. In this paper, we propose an approach using sparse coding that enables automated feature extraction. Sparse coding, or sparse dictionary learning, is an unsupervised learning algorithm, and is capable of extracting features based simply on how well those features can be used tomore » reconstruct the original data. With respect to file fragments, we learn sparse dictionaries for n-grams, continuous sequences of bytes, of different sizes. These dictionaries may then be used to estimate n-gram frequencies for a given file fragment, but for significantly larger n-gram sizes than are typically found in existing methods which suffer from combinatorial explosion. To demonstrate the capability of our sparse coding approach, we used the resulting features to train standard classifiers such as support vector machines (SVMs) over multiple file types. Experimentally, we achieved significantly better classification results with respect to existing methods, especially when the features were used in supplement to existing hand-engineered features.« less
  2. From Steganographic Payload Location to Message Extraction.

    Abstract not provided.
  3. A Model-Based Approach to Finding Tracks in SAR CCD Images.

    Abstract not provided.
  4. A Model-Based Approach to Finding Tracks in SAR CCD Images.

    Abstract not provided.
  5. A Model-Based Approach to Finding Tracks in SAR CCD Images.

    Abstract not provided.
  6. Scalable Track Detection in SAR CCD Images

    Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images ta ken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are often too simple to capture natural track features such as continuity and parallelism. We present a simple convolutional network architecture consisting of a series of 3-by-3 convolutions to detect tracks. The network is trained end-to-end to learn natural track features entirely from data. The network is computationally efficient and improves the F-score on a standard dataset to 0.988,more » up fr om 0.907 obtained by the current state-of-the-art method.« less
  7. Vehicle track detection in CCD imagery via conditional random field.

    Abstract not provided.
  8. The Energy Scaling Advantages of RRAM Crossbars.

    Abstract not provided.
  9. Energy Scaling Advantages of Resistive Memory Crossbar Based Computation.

    Abstract not provided.
  10. The Energy Scaling Advantages of RRAM Crossbars.

    Abstract not provided.
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"Quach, Tu-Thach"

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