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The tale of discovering a side channel in secure message transmission systems

Published in:
The Conf. for Failed Approaches and Insightful Losses in Cryptology, CFAIL, 13 August 2022.

Summary

Secure message transmission (SMT) systems provide information theoretic security for point-to-point message transmission in networks that are partially controlled by an adversary. This is the story of a research project that aimed to implement a flavour of SMT protocols that uses "path hopping" with the goal of quantifying the real-life efficiency of the system, and while failing to achieve this initial goal, let to the discovery a side-channel that affects the security of a wide range of SMT implementations.
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Summary

Secure message transmission (SMT) systems provide information theoretic security for point-to-point message transmission in networks that are partially controlled by an adversary. This is the story of a research project that aimed to implement a flavour of SMT protocols that uses "path hopping" with the goal of quantifying the real-life...

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Development and validation of the public-facing SimAEN web application

Summary

During a pandemic such as COVID-19, non-pharmaceutical interventions (NPIs) can help protect public health; however, it is not always clear which actions will have the greatest positive impact, or what the trade-offs are between different options. Exposure Notification (EN) was introduced as a prevention measure during the COVID-19 pandemic to supplement traditional contact tracing activities. To predict the estimated impacts of EN, a model for "simulation of automated exposure notification" (SimAEN) was developed by researchers at MIT Lincoln Laboratory (MIT LL) with CDC funding [2]. The model was published through an accessible web interface, available for use by the general public at https://SimAEN.philab.cdc.gov/.
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Summary

During a pandemic such as COVID-19, non-pharmaceutical interventions (NPIs) can help protect public health; however, it is not always clear which actions will have the greatest positive impact, or what the trade-offs are between different options. Exposure Notification (EN) was introduced as a prevention measure during the COVID-19 pandemic to...

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Self-supervised contrastive pre-training for time series via time-frequency consistency

Published in:
arXiv, June 16, 2022.

Summary

Pre-training on time series poses a unique challenge due to the potential mismatch between pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends, and long-range and short cyclic effects, which can lead to poor downstream performance. While domain adaptation methods can mitigate these shifts, most methods need examples directly from the target domain, making them suboptimal for pre-training. To address this challenge, methods need to accommodate target domains with different temporal dynamics and be capable of doing so without seeing any target examples during pre-training. Relative to other modalities, in time series, we expect that time-based and frequency-based representations of the same example are located close together in the time-frequency space. To this end, we posit that time-frequency consistency (TF-C) — embedding a time-based neighborhood of a particular example close to its frequency-based neighborhood and back—is desirable for pre-training. Motivated by TF-C, we define a decomposable pre-training model, where the self-supervised signal is provided by the distance between time and frequency components, each individually trained by contrastive estimation. We evaluate the new method on eight datasets, including electrodiagnostic testing, human activity recognition, mechanical fault detection, and physical status monitoring. Experiments against eight state-of-the-art methods show that TF-C outperforms baselines by 15.4% (F1 score) on average in one-to-one settings (e.g., fine-tuning an EEG-pretrained model on EMG data) and by up to 8.4% (F1 score) in challenging one-to-many settings (e.g., fine-tuning an EEG-pretrained model for either hand-gesture recognition or mechanical fault prediction), reflecting the breadth of scenarios that arise in real-world applications. The source code and datasets are available at https://anonymous.4open.science/r/TFC-pretraining-6B07.
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Summary

Pre-training on time series poses a unique challenge due to the potential mismatch between pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends, and long-range and short cyclic effects, which can lead to poor downstream performance. While domain adaptation methods can mitigate these shifts, most methods need...

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Axon tracing and centerline detection using topologically-aware 3D U-nets

Published in:
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022, pp. 238-242

Summary

As advances in microscopy imaging provide an ever clearer window into the human brain, accurate reconstruction of neural connectivity can yield valuable insight into the relationship between brain structure and function. However, human manual tracing is a slow and laborious task, and requires domain expertise. Automated methods are thus needed to enable rapid and accurate analysis at scale. In this paper, we explored deep neural networks for dense axon tracing and incorporated axon topological information into the loss function with a goal to improve the performance on both voxel-based segmentation and axon centerline detection. We evaluated three approaches using a modified 3D U-Net architecture trained on a mouse brain dataset imaged with light sheet microscopy and achieved a 10% increase in axon tracing accuracy over previous methods. Furthermore, the addition of centerline awareness in the loss function outperformed the baseline approach across all metrics, including a boost in Rand Index by 8%.
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Summary

As advances in microscopy imaging provide an ever clearer window into the human brain, accurate reconstruction of neural connectivity can yield valuable insight into the relationship between brain structure and function. However, human manual tracing is a slow and laborious task, and requires domain expertise. Automated methods are thus needed...

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Fun as a strategic advantage: applying lessons in engagement from commercial games to military logistics training

Summary

Digital games offer many elements to augment traditional classroom lectures and reading assignments. They enable players to explore concepts through repeat play in a low-risk environment, and allow players to integrate feedback given during gameplay and evaluate their own performance. Commercial games leverage a number of features to engage players and hold their attention. But do those engagement-improving methods have a place in instructional environments with a captive and motivated audience? Our experience building a logistics supply chain training game for the Marine Corps University suggests that yes; applying lessons in engagement from commercial games can both help improve player experience with the learning environment, and potentially improve learning outcomes.
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Summary

Digital games offer many elements to augment traditional classroom lectures and reading assignments. They enable players to explore concepts through repeat play in a low-risk environment, and allow players to integrate feedback given during gameplay and evaluate their own performance. Commercial games leverage a number of features to engage players...

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Impact of haptic cues and an active ankle exoskeleton on gait characteristics

Published in:
Hum. Factors, Vol. 0, No. 0, July 2022, p. 1-12.

Summary

Objective This study examined the interaction of gait-synchronized vibrotactile cues with an active ankle exoskeleton that provides plantarflexion assistance. Background An exoskeleton that augments gait may support collaboration through feedback to the user about the state of the exoskeleton or characteristics of the task. Methods Participants (N = 16) were provided combinations of torque assistance and vibrotactile cues at pre-specified time points in late swing and early stance while walking on a self-paced treadmill. Participants were either given explicit instructions (N = 8) or were allowed to freely interpret (N=8) how to coordinate with cues. Results For the free interpretation group, the data support an 8% increase in stride length and 14% increase in speed with exoskeleton torque across cue timing, as well as a 5% increase in stride length and 7% increase in speed with only vibrotactile cues. When given explicit instructions, participants modulated speed according to cue timing-increasing speed by 17% at cues in late swing and decreasing speed 11% at cues in early stance compared to no cue when exoskeleton torque was off. When torque was on, participants with explicit instructions had reduced changes in speed. Conclusion These findings support that the presence of torque mitigates how cues were used and highlights the importance of explicit instructions for haptic cuing. Interpreting cues while walking with an exoskeleton may increase cognitive load, influencing overall human-exoskeleton performance for novice users. Application Interactions between haptic feedback and exoskeleton use during gait can inform future feedback designs to support coordination between users and exoskeletons.
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Summary

Objective This study examined the interaction of gait-synchronized vibrotactile cues with an active ankle exoskeleton that provides plantarflexion assistance. Background An exoskeleton that augments gait may support collaboration through feedback to the user about the state of the exoskeleton or characteristics of the task. Methods Participants (N = 16) were...

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Impact of WSR-88D intra-volume low-level scans on sever weather warning performance

Published in:
Weather Forecast., Vol. 37, No. 7, July 2022, p. 1169-98.

Summary

The statistical relationship between supplemental adaptive intra-volume low-level scan (SAILS) usage on the Weather Surveillance Radar-1988 Doppler and National Weather Service severe storm warning performance during 2014–20 is analyzed. Results show statistically significant improvement in severe thunderstorm (SVR), flash flood (FF), and tornado (TOR) warning performance associated with SAILS-on versus SAILS-off. Within the three possible SAILS modes of one (SAILSx1), two (SAILSx2), and three (SAILSx3) additional base scans per volume, for SVR, SAILSx2 and SAILSx3 are associated with better warning performance compared to SAILSx1; for FF and TOR, SAILSx3 is associated with better warning performance relative to SAILSx1 and SAILSx2. Two severe storm cases (one that spawned a tornado, one that did not) are presented where SAILS usage helped forecasters make the correct TOR warning decision, lending real-life credence to the statistical results. Furthermore, a statistical analysis of automated volume scan evaluation and termination effects, parsed by SAILS usage and mode, yield a statistically significant association between volume scan update rate and SVR warning lead time.
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Summary

The statistical relationship between supplemental adaptive intra-volume low-level scan (SAILS) usage on the Weather Surveillance Radar-1988 Doppler and National Weather Service severe storm warning performance during 2014–20 is analyzed. Results show statistically significant improvement in severe thunderstorm (SVR), flash flood (FF), and tornado (TOR) warning performance associated with SAILS-on versus...

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Advances in cross-lingual and cross-source audio-visual speaker recognition: The JHU-MIT system for NIST SRE21

Summary

We present a condensed description of the joint effort of JHUCLSP/HLTCOE, MIT-LL and AGH for NIST SRE21. NIST SRE21 consisted of speaker detection over multilingual conversational telephone speech (CTS) and audio from video (AfV). Besides the regular audio track, the evaluation also contains visual (face recognition) and multi-modal tracks. This evaluation exposes new challenges, including cross-source–i.e., CTS vs. AfV– and cross-language trials. Each speaker can speak two or three languages among English, Mandarin and Cantonese. For the audio track, we evaluated embeddings based on Res2Net and ECAPA-TDNN, where the former performed the best. We used PLDA based back-ends trained on previous SRE and VoxCeleb and adapted to a subset of Mandarin/Cantonese speakers. Some novel contributions of this submission are: the use of neural bandwidth extension (BWE) to reduce the mismatch between the AFV and CTS conditions; and invariant representation learning (IRL) to make the embeddings from a given speaker invariant to language. Res2Net with neural BWE was the best monolithic system. We used a pre-trained RetinaFace face detector and ArcFace embeddings for the visual track, following our NIST SRE19 work. We also included a new system using a deep pyramid single shot face detector and face embeddings trained on Crystal loss and probabilistic triplet loss, which performed the best. The number of face embeddings in the test video was reduced by agglomerative clustering or weighting the embedding based on the face detection confidence. Cosine scoring was used to compare embeddings. For the multi-modal track, we just added the calibrated likelihood ratios of the audio and visual conditions, assuming independence between modalities. The multi-modal fusion improved Cprimary by 72% w.r.t. audio.
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Summary

We present a condensed description of the joint effort of JHUCLSP/HLTCOE, MIT-LL and AGH for NIST SRE21. NIST SRE21 consisted of speaker detection over multilingual conversational telephone speech (CTS) and audio from video (AfV). Besides the regular audio track, the evaluation also contains visual (face recognition) and multi-modal tracks. This...

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Advances in speaker recognition for multilingual conversational telephone speech: the JHU-MIT system for NIST SRE20 CTS challenge

Published in:
Speaker and Language Recognition Workshop, Odyssey 2022, pp. 338-345.

Summary

We present a condensed description of the joint effort of JHUCLSP/HLTCOE and MIT-LL for NIST SRE20. NIST SRE20 CTS consisted of multilingual conversational telephone speech. The set of languages included in the evaluation was not provided, encouraging the participants to develop systems robust to any language. We evaluated x-vector architectures based on ResNet, squeeze-excitation ResNets, Transformers and EfficientNets. Though squeeze-excitation ResNets and EfficientNets provide superior performance in in-domain tasks like VoxCeleb, regular ResNet34 was more robust in the challenge scenario. On the contrary, squeeze-excitation networks over-fitted to the training data, mostly in English. We also proposed a novel PLDA mixture and k-NN PLDA back-ends to handle the multilingual trials. The former clusters the x-vector space expecting that each cluster will correspond to a language family. The latter trains a PLDA model adapted to each enrollment speaker using the nearest speakers–i.e., those with similar language/channel. The k-NN back-end improved Act. Cprimary (Cp) by 68% in SRE16-19 and 22% in SRE20 Progress w.r.t. a single adapted PLDA back-end. Our best single system achieved Act. Cp=0.110 in SRE20 progress. Meanwhile, our best fusion obtained Act. Cp=0.110 in the progress–8% better than single– and Cp=0.087 in the eval set.
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Summary

We present a condensed description of the joint effort of JHUCLSP/HLTCOE and MIT-LL for NIST SRE20. NIST SRE20 CTS consisted of multilingual conversational telephone speech. The set of languages included in the evaluation was not provided, encouraging the participants to develop systems robust to any language. We evaluated x-vector architectures...

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AAM-Gym: Artificial intelligence testbed for advanced air mobility

Published in:
arXiv, 9 June 2022.

Summary

We introduce AAM-Gym, a research and development testbed for Advanced Air Mobility (AAM). AAM has the potential to revolutionize travel by reducing ground traffic and emissions by leveraging new types of aircraft such as electric vertical take-off and landing (eVTOL) aircraft and new advanced artificial intelligence (AI) algorithms. Validation of AI algorithms require representative AAM scenarios, as well as a fast time simulation testbed to evaluate their performance. Until now, there has been no such testbed available for AAM to enable a common research platform for individuals in government, industry, or academia. MIT Lincoln Laboratory has developed AAM-Gym to address this gap by providing an ecosystem to develop, train, and validate new and established AI algorithms across a wide variety of AAM use-cases. In this paper, we use AAM-Gym to study the performance of two reinforcement learning algorithms on an AAM use-case, separation assurance in AAM corridors. The performance of the two algorithms is demonstrated based on a series of metrics provided by AAM-Gym, showing the testbed’s utility to AAM research.
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Summary

We introduce AAM-Gym, a research and development testbed for Advanced Air Mobility (AAM). AAM has the potential to revolutionize travel by reducing ground traffic and emissions by leveraging new types of aircraft such as electric vertical take-off and landing (eVTOL) aircraft and new advanced artificial intelligence (AI) algorithms. Validation of...

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