Search Results for author: Arkady Zaslavsky

Found 5 papers, 0 papers with code

Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art

no code implementations16 Jan 2024 Alireza Nezhadettehad, Arkady Zaslavsky, Rakib Abdur, Siraj Ahmed Shaikh, Seng W. Loke, Guang-Li Huang, Alireza Hassani

Predicting the future location of mobile objects reinforces location-aware services with proactive intelligence and helps businesses and decision-makers with better planning and near real-time scheduling in different applications such as traffic congestion control, location-aware advertisements, and monitoring public health and well-being.


Deakin RF-Sensing: Experiments on Correlated Knowledge Distillation for Monitoring Human Postures with Radios

no code implementations24 May 2023 Shiva Raj Pokhrel, Jonathan Kua, Deol Satish, Philip Williams, Arkady Zaslavsky, Seng W. Loke, Jinho Choi

The proposed CKD framework transfers and fuses pose knowledge from a robust "Teacher" model to a parameterized "Student" model, which can be a promising technique for obtaining accurate yet lightweight pose estimates.

Knowledge Distillation

Reinforcement Learning Based Approaches to Adaptive Context Caching in Distributed Context Management Systems

no code implementations22 Dec 2022 Shakthi Weerasinghe, Arkady Zaslavsky, Seng W. Loke, Amin Abken, Alireza Hassani

This paper proposes a reinforcement learning based approach to adaptively cache context with the objective of minimizing the cost incurred by context management systems in responding to context queries.

Management reinforcement-learning +1

From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey

no code implementations21 Nov 2022 Shakthi Weerasinghe, Arkady Zaslavsky, Seng W. Loke, Alireza Hassani, Amin Abken, Alexey Medvedev

This paper presents a critical survey of the state-of-the-art in adaptive data caching with the objective of developing a body of knowledge in cost- and performance-efficient adaptive caching strategies.


Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey

no code implementations22 Feb 2022 Ngoc Dung Huynh, Mohamed Reda Bouadjenek, Imran Razzak, Kevin Lee, Chetan Arora, Ali Hassani, Arkady Zaslavsky

Indeed, Adversarial Artificial Intelligence (AI) which refers to a set of techniques that attempt to fool machine learning models with deceptive data, is a growing threat in the AI and machine learning research community, in particular for machine-critical applications.

Adversarial Attack BIG-bench Machine Learning +3

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