Publications

WearableMil: An End-to-End Framework for Military Activity Recognition and Performance Monitoring

Under Review

Musculoskeletal injuries during military training significantly impact readiness, making prevention through activity monitoring crucial. While Human Activity Recognition (HAR) using wearable devices offers promising solutions, it faces challenges in processing continuous data streams and recognizing diverse activities without predefined sessions. This paper introduces an end-to-end framework for preprocessing, analyzing, and recognizing activities from wearable data in military training contexts. Using data from 135 soldiers wearing Garmin 55 smartwatches over six months, we develop a hierarchical deep learning approach that achieves 93.8\% accuracy in temporal splits and 83.8\% in cross-user evaluation. Our framework addresses missing data through physiologically-informed methods, reducing unknown sleep states from 40.38\% to 3.66\%. We demonstrate that while longer time windows (45-60 minutes) improve basic state classification, they present trade-offs in detecting fine-grained activities. Additionally, we introduce an intuitive visualization system that enables real-time comparison of individual performance against group metrics across multiple physiological indicators. This approach to activity recognition and performance monitoring provides military trainers with actionable insights for optimizing training programs and preventing injuries.

Exploring QUIC Dynamics: A Large-Scale Dataset for Encrypted Traffic Analysis

Under Review

QUIC, an increasingly adopted transport protocol, addresses limitations of TCP by offering improved security, performance, and features such as stream multiplexing and connection migration. However, these enhancements also introduce challenges for network operators in monitoring and analyzing web traffic, especially due to QUIC’s encryption. Existing datasets are inadequate—they are often outdated, lack diversity, anonymize critical information, or exclude essential features like SSL keys—limiting comprehensive research and development in this area. We introduce VisQUIC, a publicly available dataset of over 100,000 labeled QUIC traces with corresponding SSL keys, collected from more than 40,000 websites over four months. By generating visual representations of the traces, we facilitate advanced machine learning (ML) applications and in-depth analysis of encrypted QUIC traffic. To demonstrate the dataset’s potential, we estimate the number of HTTP/3 request-response pairs in a QUIC connection using only encrypted traffic, achieving up to 92\% accuracy. This estimation provides insights into server behavior, client-server interactions, and connection load—crucial for tasks like load balancing and intrusion detection. Our dataset enables comprehensive studies on QUIC and HTTP/3 protocols and supports the development of tools for encrypted traffic analysis. Currently under review.

Estimating the number of HTTP/3 Responses in QUIC Using Deep Learning

Under Review

QUIC, a new and increasingly used transport protocol, enhances TCP by offering improved security, performance, and stream multiplexing. These features, however, also impose challenges for network middle-boxes that need to monitor and analyze web traffic. This paper proposes a novel method to estimate the number of HTTP/3 responses in a given QUIC connection by an observer. This estimation reveals server behavior, client-server interactions, and data transmission efficiency, which is crucial for various applications such as designing a load balancing solution and detecting HTTP/3 flood attacks. The proposed scheme transforms QUIC connection traces into image sequences and uses machine learning (ML) models, guided by a tailored loss function, to predict response counts. Evaluations on more than seven million images—derived from 100,000 traces collected across 44,000 websites over four months—achieve up to 97% accuracy in both known and unknown server settings and 92\% accuracy on previously unseen complete QUIC traces. Currently under review.

Beyond the Alphabet: Deep Signal Embedding for Enhanced DNA Clustering

Under Review

The emerging field of DNA storage employs strands of DNA bases (A/T/C/G) as a storage medium for digital information to enable massive density and durability. The DNA storage pipeline includes: (1) encoding the raw data into sequences of DNA bases; (2) synthesizing the sequences as DNA strands that are stored over time as an unordered set; (3) sequencing the DNA strands to generate DNA reads; and (4) deducing the original data. The DNA synthesis and sequencing stages each generate several independent error-prone duplicates of each strand which are then utilized in the final stage to reconstruct the best estimate for the original strand. Specifically, the reads are first clustered into groups likely originating from the same strand (based on their similarity to each other), and then each group approximates the strand that led to the reads of that group.

Data-Driven Cellular Network Selector for Vehicle Teleoperations

Accepted as a full paper in 2024 15th International Conference on Network of the Future (NoF)

The effectiveness of video-based teleoperation systems is heavily influenced by the quality of the cellular network and, in particular, its packet loss rate and latency. To optimize these parameters, an autonomous vehicle can be connected to multiple cellular networks and determine in real time over which cellular network each video packet will be transmitted. We present an algorithm, called Active Network Selector (ANS), which uses a time series machine learning approach for solving this problem. We compare ANS to a baseline algorithm, which is used today in commercial systems, and show that ANS performs much better, with respect to both packet loss and packet latency.

Using Deep Reinforcement Learning for mmWave Real-Time Scheduling

Published in 2023 14th International Conference on Network of the Future (NoF), 2023

We study the problem of real-time scheduling in a multi-hop millimeter-wave (mmWave) mesh. We develop a model-free deep reinforcement learning algorithm called Adaptive Activator RL (AARL), which determines the subset of mmWave links that should be activated during each time slot and the power level for each link. The most important property of AARL is its ability to make scheduling decisions within the strict time frame constraints of typical 5G mmWave networks.

Recommended citation: @inproceedings{gahtan2023using, title={Using Deep Reinforcement Learning for mmWave Real-Time Scheduling}, author={Gahtan, Barak and Cohen, Reuven and Bronstein, Alex M and Kedar, Gil}, booktitle={2023 14th International Conference on Network of the Future (NoF)}, pages={71--79}, year={2023}, organization={IEEE} }
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Robusta

Can be sent upon request

This paper presents Robusta, a hybrid recoverable cache leveraging PMem and DRAM to get the best of the two: DRAM-like low latency for very frequent items, reduced tail latency due to Pmem’s large capacity, and warm start on fail- ure recovery. Robusta is implemented as a wrapper around Caffeine, a state-of-the-art Java cache that is integrated into a range of production systems, including HBase, Druid, Solr, and Cassandra.