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Found 29 papers, 3 papers with code
Minimize Control Inputs for Strong Structural Controllability Using Reinforcement Learning with Graph Neural Network
We find that the number of input nodes is determined by the average degree of the network and the input nodes tend to select nodes with low in-degree and avoid high-degree nodes.
Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surface
Whilst a legitimate RIS can yield beneficial impacts including increased channel randomness to enhance physical layer secret key generation (PL-SKG), malicious RIS can poison legitimate channels and crack most of existing PL-SKGs.
Federated Transformed Learning for a Circular, Secure, and Tiny AI
Deep Learning (DL) is penetrating into a diverse range of mass mobility, smart living, and industrial applications, rapidly transforming the way we live and work.
Two-Stage Violence Detection Using ViTPose and Classification Models at Smart Airports
This study introduces an innovative violence detection framework tailored to the unique requirements of smart airports, where prompt responses to violent situations are crucial.
Securing IoT Communication using Physical Sensor Data -- Graph Layer Security with Federated Multi-Agent Deep Reinforcement Learning
This paper presents a Federated multi-agent Deep reinforcement learning-assisted Distributed Key generation scheme (FD2K), which fully exploits the common features of physical dynamics to establish secret key between legitimate users.
Vision-based Conflict Detection within Crowds based on High-Resolution Human Pose Estimation for Smart and Safe Airport
Where the model falls short is against ambiguous behaviour such as a hug or losing track of a subject in the frame.
Adversarial Reconfigurable Intelligent Surface Against Physical Layer Key Generation
The development of reconfigurable intelligent surfaces (RIS) has recently advanced the research of physical layer security (PLS).
A Transistor Operations Model for Deep Learning Energy Consumption Scaling Law
At a general computational energy model level, there is both strong dependency to both the hardware architecture (e. g. generic processors with different configuration of inner components- CPU and GPU, programmable integrated circuits - FPGA), as well as different interacting energy consumption aspects (e. g., data movement, calculation, control).
Revealing the Excitation Causality between Climate and Political Violence via a Neural Forward-Intensity Poisson Process
The causal mechanism between climate and political violence is fraught with complex mechanisms.
Reconfigurable Intelligent Surface-induced Randomness for mmWave Key Generation
In this paper, for mmWave secret key generation of physical layer security, we use a reconfigurable intelligent surface (RIS) to induce randomness directly in wireless environments, without adding complexity to transceivers.
Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Space
We believe that this approach of exploiting general data distribution knowledge form neural networks can be applied to a wide range of scarce data open challenges.
The Rising Entropy of English in the Attention Economy
We present evidence that the word entropy of American English has been rising steadily since around 1900, contrary to predictions from existing sociolinguistic theories.
Regions of Attraction Estimation using Level SetMethod for Complex Network System
This approach is insufficient for dynamic networks with changing equilibrium and estimating the Region of Attraction(ROA) is needed.
Social and Information Networks
Automatic Quantification of Settlement Damage using Deep Learning of Satellite Images
Humanitarian disasters and political violence cause significant damage to our living space.
Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms
The linear inverse problem is fundamental to the development of various scientific areas.
Scalable Partial Explainability in Neural Networks via Flexible Activation Functions
In this paper, we achieve partially explainable learning model by symbolically explaining the role of activation functions (AF) under a scalable topology.
Graph Layer Security: Encrypting Information via Common Networked Physics
Leveraging this, we propose for the first time, graph layer security (GLS), by exploiting the dependency in physical dynamics among network nodes for information encryption and decryption.
Neural Network Approximation of Graph Fourier Transforms for Sparse Sampling of Networked Flow Dynamics
Infrastructure monitoring is critical for safe operations and sustainability.
Explainable Artificial Intelligence (XAI) for 6G: Improving Trust between Human and Machine
As the 5th Generation (5G) mobile networks are bringing about global societal benefits, the design phase for the 6th Generation (6G) has started.
Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge
To scale learning across geographic areas, a spatial transfer learning scheme is proposed to further promote the learning efficiency of distributed DRL entities by exploiting the traffic demand correlations.
Graph hierarchy and spread of infections
Trophic levels and hence trophic coherence can be defined only on networks with well defined sources, trophic analysis of networks had been restricted to the ecological domain until now.
Physics and Society Combinatorics
Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes
Here, we design a feature embedding (FE) kernel for a GP model to forecast traffic demand with extreme values.
Simulating Imperial Dynamics and Conflict in the Ancient World
The development of models to capture large-scale dynamics in human history is one of the core contributions of the cliodynamics field.
Physics and Society Digital Libraries
Deep Learning for Bridge Load Capacity Estimation in Post-Disaster and -Conflict Zones
Many post-disaster and -conflict regions do not have sufficient data on their transportation infrastructure assets, hindering both mobility and reconstruction.
Sequential Bayesian Detection of Spike Activities from Fluorescence Observations
Extracting and detecting spike activities from the fluorescence observations is an important step in understanding how neuron systems work.
High-dimensional Metric Combining for Non-coherent Molecular Signal Detection
If the channel is unknown, we cannot easily achieve traditional coherent channel estimation and cancellation, and the impact of ISI will be more severe.
Robust Fuzzy-Learning For Partially Overlapping Channels Allocation In UAV Communication Networks
This allows the system to achieve a smoother and more robust performance by optimizing in an alternate space.
Model-Free Information Extraction in Enriched Nonlinear Phase-Space
We benchmark the astonishing accuracy of our method on three diverse and challenging problems in: biology, medicine, and engineering.
Combining Heterogeneous User Generated Data to Sense Well-being
In this paper we address a new problem of predicting affect and well-being scales in a real-world setting of heterogeneous, longitudinal and non-synchronous textual as well as non-linguistic data that can be harvested from on-line media and mobile phones.
