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A generative approach to condition-aware score calibration for speaker verification

Published in:
IEEE/ACM Trans. Audio, Speech, Language Process., Vol. 31, 2023, pp. 891-901.

Summary

In speaker verification, score calibration is employed to transform verification scores to log-likelihood ratios (LLRs) which are statistically interpretable. Conventional calibration techniques apply a global score transform. However, in condition-aware (CA) calibration, information conveying signal conditions is provided as input, allowing calibration to be adaptive. This paper explores a generative approach to condition-aware score calibration. It proposes a novel generative model for speaker verification trials, each which includes a trial score, a trial label, and the associated pair of speaker embeddings. Trials are assumed to be drawn from a discrete set of underlying signal conditions which are modeled as latent Categorical random variables, so that trial scores and speaker embeddings are drawn from condition-dependent distributions. An Expectation-Maximization (EM) Algorithm for parameter estimation of the proposed model is presented, which does not require condition labels and instead discovers relevant conditions in an unsupervised manner. The generative condition-aware (GCA) calibration transform is then derived as the log-likelihood ratio of a verification score given the observed pair of embeddings. Experimental results show the proposed approach to provide performance improvements on a variety of speaker verification tasks, outperforming static and condition-aware baseline calibration methods. GCA calibration is observed to improve the discriminative ability of the speaker verification system, as well as provide good calibration performance across a range of operating points. The benefits of the proposed method are observed for task-dependent models where signal conditions are known, for universal models which are robust across a range of conditions, and when facing unseen signal conditions.
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Summary

In speaker verification, score calibration is employed to transform verification scores to log-likelihood ratios (LLRs) which are statistically interpretable. Conventional calibration techniques apply a global score transform. However, in condition-aware (CA) calibration, information conveying signal conditions is provided as input, allowing calibration to be adaptive. This paper explores a generative...

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Backdoor poisoning of encrypted traffic classifiers

Summary

Significant recent research has focused on applying deep neural network models to the problem of network traffic classification. At the same time, much has been written about the vulnerability of deep neural networks to adversarial inputs, both during training and inference. In this work, we consider launching backdoor poisoning attacks against an encrypted network traffic classifier. We consider attacks based on padding network packets, which has the benefit of preserving the functionality of the network traffic. In particular, we consider a handcrafted attack, as well as an optimized attack leveraging universal adversarial perturbations. We find that poisoning attacks can be extremely successful if the adversary has the ability to modify both the labels and the data (dirty label attacks) and somewhat successful, depending on the attack strength and the target class, if the adversary perturbs only the data (clean label attacks).
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Summary

Significant recent research has focused on applying deep neural network models to the problem of network traffic classification. At the same time, much has been written about the vulnerability of deep neural networks to adversarial inputs, both during training and inference. In this work, we consider launching backdoor poisoning attacks...

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Contingent routing using orbital geometry in proliferated low-earth-orbit satellite networks

Published in:
2022 IEEE Military Communications Conf., MILCOM, 28 November - 2 December 2022.
Topic:
R&D group:

Summary

Optimum adaptive routing in proliferated low-earth-orbit (pLEO) satellite networks requires intensive computation. The very small size, light weight, and low power of individual satellites in such networks makes a centralized, terrestrial, SDN-like approach to routing computation an attractive solution. However, it is highly desirable to have a distributed backup routing capability onboard each satellite that can maintain service if the central computational node(s) fail or lose their pathway(s) to upload routing data frequently to each satellite. This paper presents a routing algorithm based on orbital geometry that has a very low computation and storage requirements and is suitable as a backup routing capability in the event of failure of a centralized routing calculation node or nodes. Path failure rate, path latency, and link resource usage are simulated for a 360-satellite Walker Delta constellation with 4 inter-satellite link (ISL) terminals per satellite, and with up to 10% of the satellites having failed. For the fully intact satellite constellation, path failure rate is zero (identical to a shortest path routing algorithm), while mean latency and average link resource usage are shown to be approximately 12% and 13% higher, respectively, than with shortest path routing. With 10 random satellite failures in the constellation, the geometric algorithm has a path failure rate of less than 0.5%, while the mean latency and link resource usage are approximately 12% and 16% higher, respectively, than with shortest path routing.
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Summary

Optimum adaptive routing in proliferated low-earth-orbit (pLEO) satellite networks requires intensive computation. The very small size, light weight, and low power of individual satellites in such networks makes a centralized, terrestrial, SDN-like approach to routing computation an attractive solution. However, it is highly desirable to have a distributed backup routing...

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Failure resilience in proliferated low earth orbit satellite network topologies

Published in:
2022 IEEE Military Communications Conf., MILCOM, 28 November - 2 December 2022.
Topic:
R&D group:

Summary

The vision of continuous network connectivity for users located anywhere on Earth is increasingly being enabled by satellite constellations with hundreds to thousands of satellites operating in low altitude orbits (typically somewhere between a few hundred and two thousand km). These constellations are often referred to as proliferated Low Earth Orbit (pLEO) constellations. Potential military use of such constellations would require a high degree of resilience against various types of failures. This paper examines how resilience to satellite failures in particular is affected by topology and topology management for a moderate-sized constellation of 360 low-earth-orbit satellites providing 2X-redundant global coverage. We present simulations quantifying the effects of two vs. four inter-satellite links (ISLs) per satellite, and of dynamic post-failure topology reconfiguration vs static topology management. Simulations show differences of 65-80% in mission connectivity between 4-ISL topologies with dynamic topology reconfiguration and 2-ISL topologies with static topology using two different traffic scenarios.
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Summary

The vision of continuous network connectivity for users located anywhere on Earth is increasingly being enabled by satellite constellations with hundreds to thousands of satellites operating in low altitude orbits (typically somewhere between a few hundred and two thousand km). These constellations are often referred to as proliferated Low Earth...

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Rate control with autoregressive forecasting for high frequency communication

Published in:
2022 IEEE Military Communications Conf., MILCOM, 28 November - 2 December 2022.

Summary

This work introduces a data-driven framework for rate control and applies it to high frequency (HF) communication systems that propagate via the Earth’s ionosphere. The rate control approach uses statistical techniques to forecast channel state with an autoregressive (AR) model, which has previously been applied to different forms of wireless fading, including "medium" timescale fading at HF. The objective of rate control is to maximize the data rate while constraining the rate of packets decoded in error. We show that under ideal assumptions, the rate controller selects the rate by backing off from the forecast average signal-to-noise ratio (SNR) by a factor of sigmaQ^-1(Beta), where sigma^2 correlates with fading variance, Beta denotes a constraint on decoder errors, and Q(.) is the complementary cumulative distribution function of the Gaussian distribution. Simulation results on an HF channel model show that compared with naive schemes, AR forecasting provides a good balance between achieving high rate and ensuring reliability.
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Summary

This work introduces a data-driven framework for rate control and applies it to high frequency (HF) communication systems that propagate via the Earth’s ionosphere. The rate control approach uses statistical techniques to forecast channel state with an autoregressive (AR) model, which has previously been applied to different forms of wireless...

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Automated contact tracing assessment

Published in:
MIT Lincoln Laboratory Report TR-1287

Summary

The COVID-19 pandemic placed unprecedented demands on the global public health systems for disease surveillance and contact tracing. Engineers and scientists recognized that it might be possible to augment the efforts of public health teams, if a system for automated digital contact tracing could be quickly devised and deployed to the population of smartphones. The Private Automated Contact Tracing (PACT) protocol was one of several digital contact tracing proposals offered worldwide. PACT’s mission—to preserve individuals’ privacy and anonymity while enabling them to quickly alert even nearby strangers of a likely risky exposure—was adopted by Google and Apple and realized in the Exposure Notifications (EN) service and API for mobile application development. The Exposure Notifications system, like many digital proximity tools, is based on Bluetooth signal strength estimation, and keeps much of the necessary information and computation on the smartphones themselves. It implemented a decentralized approach to contact tracing: the public health authority, and other governmental authorities, cannot access the records of an individual’s encounters with others; nor is physical location used or shared by the service. Although the service is available on most modern iOS and Android devices, it is not enabled by default; the individual must opt in to use a particular region’s implementation of the service, either by installing the regional app or by enrolling through a menu of regions in the operating system settings. Likewise, individuals must affirm their consent before the service can share anonymized infection status with the regional public health authority, and alert recent close contacts. The widespread availability of Exposure Notifications through Apple and Google’s platforms has made it a de facto world standard. Determining its accuracy and effectiveness as a public health tool has been a subject of intense interest. In July 2020, CDC’s Innovative Technologies Team designated MIT LL and the PACT team as trusted technical advisors on the deployment of private automated contact tracing systems as part of its overall public health response to COVID-19. The Innovative Technologies Team sought to answer the following key question regarding automated contact tracing: Does automated contact tracing have sufficient public health value that it is worthwhile to integrate it at scale into existing and evolving manual contact tracing systems? Rapidly rising caseloads necessitated parallel-path assessment activities of most mature systems at the time. When access to the Google and Apple Exposure Notifications system became available, MIT LL focused the assessment efforts on the systems being built and deployed. There were two immediate and significant challenges to observing and quantifying the performance of the system as a whole: first, the privacy preserving design decisions of PACT and the system implementers denied access to system-level performance metrics, and second, obtaining accurate “ground truth” data about risky encounters in the population, against which to measure the detector performance, would require an unacceptable level of effort and intrusion. Therefore, MIT LL designed a set of parallel research activities to decompose the problem into components that could be assessed quantifiably (Bluetooth sensor performance, algorithm performance, user preferences and behaviors), components that could be assessed qualitatively (potential cybersecurity risks, potential for malicious use), and components that could be modeled based on current and emergent knowledge (population-level effects). The MIT LL research team conducted early assessments of the privacy and security aspects of new EN app implementations and closely reviewed the available system code exercised by the apps, before conducting a series of phone-to-phone data collections both in the laboratory and in simulated real-world conditions. The data from these experiments fed into models and visualization tools created to predict and understand the risk score output of candidate “weights and thresholds” configurations for EN, i.e., to predict the performance of the system as-built against ground truth data for distance and duration of “exposure”. The data and performance predictions from this effort helped to inform the global and local community of practice in making configuration decisions, and can help to predict the performance of future versions of similar tools, or alternative implementations of the current system. We conducted a human factors and usability review of early app user interfaces and messaging from public health, and designed a follow-on large-scale survey to investigate questions about user trust and system adoption decisions. The results of the human factors, user trust, and adoption studies were used by U.S. public health jurisdictions to make adjustments to public-facing communications, and were shared with Apple and Google to improve the user interface. Information gathered from public health experts enabled us to better understand conventional contact tracing workflows and data streams, and we incorporated that information into an agent-based model of “hybrid” contact tracing plus Exposure Notifications. We then combined it with emerging reports on vaccination, mask effectiveness, social interaction, variant transmissibility, and our own data on the sensitivity and specificity of the Bluetooth “dose” estimator, to predict system-level effects under various conditions. Finally, we helped to establish a network of Exposure Notifications “practitioners” in public health, who surfaced desirable system-level key performance indicators (implemented during 2021 and 2022, in the Exposure Notifications Private Analytics system, or ENPA). At the conclusion of the program, many of the initial conditions of the pandemic had changed. The Exposure Notifications service was available to most of the world, but had only been deployed by 28 U.S. states and territories, and had not been adopted by much of the population in those regions. High case rates during the Omicron surge (December 2021 – January 2022) and newly available ENPA data offered the first hints at calculating “real” state-level performance metrics, but those data belong to the states and many are cautious about publishing. Although Google and Apple have stated that Exposure Notifications was designed for COVID-19, and will not be maintained in its current form after the pandemic ends, the public health and engineering communities show clear interest in using the “lessons learned” from Exposure Notifications and other similar solutions to preserve the capabilities developed and prepare better systems for future public health emergencies. The intent of this report is to document the work that has been completed, as well as to inform where the work could be updated or adapted to meet future needs.
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Summary

The COVID-19 pandemic placed unprecedented demands on the global public health systems for disease surveillance and contact tracing. Engineers and scientists recognized that it might be possible to augment the efforts of public health teams, if a system for automated digital contact tracing could be quickly devised and deployed to...

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On randomization in MTD systems

Published in:
Proc. of the 9th ACM Workshop on Moving Target Defense, MTD ’22, 7 November 2022.

Summary

Randomization is one of the main strategies in providing security in moving-target-defense (MTD) systems. However, randomization has an associated cost and estimating this cost and its impact on the overall system is crucial to ensure adoption of the MTD strategy. In this paper we discuss our experience in attempting to estimate the cost of path randomization in a message transmission system that used randomization of paths in the network. Our conclusions are (i) the cost crucially depends on the underlying network control technology, (ii) one can reduce this cost by better implementation, and (iii) reducing one type of cost may result in increased costs of a different type, for example a higher device cost. These suggest that estimating the cost of randomization is a multivariable optimization problem that requires a full understanding of the system components.
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Summary

Randomization is one of the main strategies in providing security in moving-target-defense (MTD) systems. However, randomization has an associated cost and estimating this cost and its impact on the overall system is crucial to ensure adoption of the MTD strategy. In this paper we discuss our experience in attempting to...

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Predicting ankle moment trajectory with adaptive weighted ensemble of LSTM network

Published in:
2022 IEEE High Perf. Extreme Comp. Conf. (HPEC), 19-23 September 2022, DOI: 10.1109/HPEC55821.2022.9926370.

Summary

Estimations of ankle moments can provide clinically helpful information on the function of lower extremities and further lead to insight on patient rehabilitation and assistive wearable exoskeleton design. Current methods for estimating ankle moments leave room for improvement, with most recent cutting-edge methods relying on machine learning models trained on wearable sEMG and IMU data. While machine learning eliminates many practical challenges that troubled more traditional human body models for this application, we aim to expand on prior work that showed the feasibility of using LSTM models by employing an ensemble of LSTM networks. We present an adaptive weighted LSTM ensemble network and demonstrate its performance during standing, walking, running, and sprinting. Our result show that the LSTM ensemble outperformed every single LSTM model component within the ensemble. Across every activity, the ensemble reduced median root mean squared error (RMSE) by 0.0017-0.0053 N. m/kg, which is 2.7 – 10.3% lower than the best performing single LSTM model. Hypothesis testing revealed that most reductions in RMSE were statistically significant between the ensemble and other single models across all activities and subjects. Future work may analyze different trajectory lengths and different combinations of LSTM submodels within the ensemble.
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Summary

Estimations of ankle moments can provide clinically helpful information on the function of lower extremities and further lead to insight on patient rehabilitation and assistive wearable exoskeleton design. Current methods for estimating ankle moments leave room for improvement, with most recent cutting-edge methods relying on machine learning models trained on...

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Science applications of phased array radars

Summary

Phased array radars (PARs) are a promising observing technology, at the cusp of being available to the broader meteorological community. PARs offer near-instantaneous sampling of the atmosphere with flexible beam forming, multifunctionality, and low operational and maintenance costs and without mechanical inertia limitations. These PAR features are transformative compared to those offered by our current reflector-based meteorological radars. The integration of PARs into meteorological research has the potential to revolutionize the way we observe the atmosphere. The rate of adoption of PARs in research will depend on many factors, including (i) the need to continue educating the scientific community on the full technical capabilities and trade-offs of PARs through an engaging dialogue with the science and engineering communities and (ii) the need to communicate the breadth of scientific bottlenecks that PARs can overcome in atmospheric measurements and the new research avenues that are now possible using PARs in concert with other measurement systems. The former is the subject of a companion article that focuses on PAR technology while the latter is the objective here.
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Summary

Phased array radars (PARs) are a promising observing technology, at the cusp of being available to the broader meteorological community. PARs offer near-instantaneous sampling of the atmosphere with flexible beam forming, multifunctionality, and low operational and maintenance costs and without mechanical inertia limitations. These PAR features are transformative compared to...

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Multimodal physiological monitoring during virtual reality piloting tasks

Summary

This dataset includes multimodal physiologic, flight performance, and user interaction data streams, collected as participants performed virtual flight tasks of varying difficulty. In virtual reality, individuals flew an "Instrument Landing System" (ILS) protocol, in which they had to land an aircraft mostly relying on the cockpit instrument readings. Participants were presented with four levels of difficulty, which were generated by varying wind speed, turbulence, and visibility. Each of the participants performed 12 runs, split into 3 blocks of four consecutive runs, one run at each difficulty, in a single experimental session. The sequence of difficulty levels was presented in a counterbalanced manner across blocks. Flight performance was quantified as a function of horizontal and vertical deviation from an ideal path towards the runway as well as deviation from the prescribed ideal speed of 115 knots. Multimodal physiological signals were aggregated and synchronized using Lab Streaming Layer. Descriptions of data quality are provided to assess each data stream. The starter code provides examples of loading and plotting the time synchronized data streams, extracting sample features from the eye tracking data, and building models to predict pilot performance from the physiology data streams.
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Summary

This dataset includes multimodal physiologic, flight performance, and user interaction data streams, collected as participants performed virtual flight tasks of varying difficulty. In virtual reality, individuals flew an "Instrument Landing System" (ILS) protocol, in which they had to land an aircraft mostly relying on the cockpit instrument readings. Participants were...

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