Publications
Discriminative PLDA for speaker verification with X-vectors
Summary
Summary
This paper proposes a novel approach to discrimina-tive training of probabilistic linear discriminant analysis (PLDA) for speaker verification with x-vectors. Model over-fitting is a well-known issue with discriminative PLDA (D-PLDA) forspeaker verification. As opposed to prior approaches which address this by limiting the number of trainable parameters, the proposed method...
Topological effects on attacks against vertex classification
Summary
Summary
Vertex classification is vulnerable to perturbations of both graph topology and vertex attributes, as shown in recent research. As in other machine learning domains, concerns about robustness to adversarial manipulation can prevent potential users from adopting proposed methods when the consequence of action is very high. This paper considers two...
Toward an autonomous aerial survey and planning system for humanitarian aid and disaster response
Summary
Summary
In this paper we propose an integrated system concept for autonomously surveying and planning emergency response for areas impacted by natural disasters. Referred to as AASAPS-HADR, this system is composed of a network of ground stations and autonomous aerial vehicles interconnected by an ad hoc emergency communication network. The system...
Automated discovery of cross-plane event-based vulnerabilities in software-defined networking
Summary
Summary
Software-defined networking (SDN) achieves a programmable control plane through the use of logically centralized, event-driven controllers and through network applications (apps) that extend the controllers' functionality. As control plane decisions are often based on the data plane, it is possible for carefully crafted malicious data plane inputs to direct the...
Safe predictors for enforcing input-output specifications [e-print]
Summary
Summary
We present an approach for designing correct-by-construction neural networks (and other machine learning models) that are guaranteed to be consistent with a collection of input-output specifications before, during, and after algorithm training. Our method involves designing a constrained predictor for each set of compatible constraints, and combining them safely via...
AI data wrangling with associative arrays [e-print]
Summary
Summary
The AI revolution is data driven. AI "data wrangling" is the process by which unusable data is transformed to support AI algorithm development (training) and deployment (inference). Significant time is devoted to translating diverse data representations supporting the many query and analysis steps found in an AI pipeline. Rigorous mathematical...
The JHU-MIT System Description for NIST SRE19 AV
Summary
Summary
This document represents the SRE19 AV submission by the team composed of JHU-CLSP, JHU-HLTCOE and MIT Lincoln Labs. All the developed systems for the audio and videoconditions consisted of Neural network embeddings with some flavor of PLDA/cosine back-end. Primary fusions obtained Actual DCF of 0.250 on SRE18 VAST eval, 0.183...
Graph matching via multi-scale heat diffusion
Summary
Summary
We propose a novel graph matching algorithm that uses ideas from graph signal processing to match vertices of graphs using alternative graph representations. Specifically, we consider a multi-scale heat diffusion on the graphs to create multiple weighted graph representations that incorporate both direct adjacencies as well as local structures induced...
Prototype and analytics for discovery and exploitation of threat networks on social media
Summary
Summary
Identifying and profiling threat actors are high priority tasks for a number of governmental organizations. These threat actors may operate actively, using the Internet to promote propaganda, recruit new members, or exert command and control over their networks. Alternatively, threat actors may operate passively, demonstrating operational security awareness online while...
Identification and detection of human trafficking using language models
Summary
Summary
In this paper, we present a novel language model-based method for detecting both human trafficking ads and trafficking indicators. The proposed system leverages language models to learn language structures in adult service ads, automatically select a list of keyword features, and train a machine learning model to detect human trafficking...