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September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
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Having a good defence attorney in criminal proceedings can largely determine whether a defendant retains their freedom, health and good name. Researchers at HSE University propose a method for predicting an attorney’s performance based on the outcomes of their previous cases. The methodology takes into account the severity of the charges, the complexity of the cases, and the most likely outcome, drawing on judicial statistics.

 

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KMHCR: A Key-Controlled Signal-Domain Transformation for 5G IoT Security

Journal of Signal Processing Systems. 2026. Vol. 98. Article 31.
Ronglin Z., Wei L., Jiahong C., Yuxiang C., Zulong D., Zhongming F., Jigang W., Naixue X., Xiaoyan C., Avdoshin S. M.

To address the need for lightweight and low-latency protection in massive resource-constrained 5G Internet of Things (IoT) systems, this paper proposes Key-Controlled Modulation Hopping and Constellation Rotation (KMHCR). KMHCR is designed as a physical-layer confidentiality-enhancement mechanism that avoids bit-wise full-payload encryption in the protection pipeline. It uses a shared key derived from channel-reciprocity secret key generation to drive a stateless, counter-based pseudo-random function, which determines a 5G-compliant modulation scheme and a quantized constellation rotation angle for each packet without requiring fragile per-packet state synchronization. For legitimate users, these operations introduce only lightweight signal-domain processing, whereas an unauthorized receiver must jointly infer the modulation type and the phase rotation from the observed signal. To evaluate the robustness of the proposed mechanism against intelligent attacks, we construct a 3GPP-compliant 5G adversarial dataset covering six modulation types, multiple signal-to-noise ratio regimes, and realistic channel impairments. Experimental results with multiple state-of-the-art deep learning-based AMC models show that KMHCR can substantially reduce the recoverable throughput of unauthorized receivers under the considered adversarial setting. These results support the use of KMHCR as a lightweight complementary protection mechanism for 5G IoT communications.

Research target: Computer Science
Language: English
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Keywords: Deep learningPhysical layer security5G IoTAutomatic modulation classificationConstellation rotation5G adversarial dataset
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