Publications
Tagged As
Sparse-coded net model and applications
Summary
Summary
As an unsupervised learning method, sparse coding can discover high-level representations for an input in a large variety of learning problems. Under semi-supervised settings, sparse coding is used to extract features for a supervised task such as classification. While sparse representations learned from unlabeled data independently of the supervised task...
Apprenticeship scheduling: learning to schedule from human experts
Summary
Summary
Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale...
Balancing security and performance for agility in dynamic threat environments
Summary
Summary
In cyber security, achieving the desired balance between system security and system performance in dynamic threat environments is a long-standing open challenge for cyber defenders. Typically an increase in system security comes at the price of decreased system performance, and vice versa, easily resulting in systems that are misaligned to...
Feedback-based social media filtering tool for improved situational awareness
Summary
Summary
This paper describes a feature-rich model of data relevance, designed to aid first responder retrieval of useful information from social media sources during disasters or emergencies. The approach is meant to address the failure of traditional keyword-based methods to sufficiently suppress clutter during retrieval. The model iteratively incorporates relevance feedback...
Recommender systems for the Department of Defense and intelligence community
Summary
Summary
Recommender systems, which selectively filter information for users, can hasten analysts' responses to complex events such as cyber attacks. Lincoln Laboratory's research on recommender systems may bring the capabilities of these systems to analysts in both the Department of Defense and intelligence community.
Assessing functional neural connectivity as an indicator of cognitive performance
Summary
Summary
Studies in recent years have demonstrated that neural organization and structure impact an individual's ability to perform a given task. Specifically, individuals with greater neural efficiency have been shown to outperform those with less organized functional structure. In this work, we compare the predictive ability of properties of neural connectivity...
Multimodal sparse coding for event detection
Summary
Summary
Unsupervised feature learning methods have proven effective for classification tasks based on a single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities. The shared representations are applied to multimedia event detection (MED) and evaluated in comparison to unimodal counterparts, as well as other feature...
A spectral framework for anomalous subgraph detection
Summary
Summary
A wide variety of application domains is concerned with data consisting of entities and their relationships or connections, formally represented as graphs. Within these diverse application areas, a common problem of interest is the detection of a subset of entities whose connectivity is anomalous with respect to the rest of...
Simulation based evaluation of a code diversification strategy
Summary
Summary
Periodic randomization of a computer program's binary code is an attractive technique for defending against several classes of advanced threats. In this paper we describe a model of attacker-defender interaction in which the defender employs such a technique against an attacker who is actively constructing an exploit using Return Oriented...
Temporal and multi-source fusion for detection of innovation in collaboration networks
Summary
Summary
A common problem in network analysis is detecting small subgraphs of interest within a large background graph. This includes multi-source fusion scenarios where data from several modalities must be integrated to form the network. This paper presents an application of novel techniques leveraging the signal processing for graphs algorithmic framework...