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Found 14 papers, 4 papers with code
A Corpus-based Study of Corporate Image Represented in Corporate Social Responsibility Report: A Case Study of China Mobile and Vodafone
By examination of the high-frequency nouns, verbs, and keywords, the present study probes into the similarities and differences of corporate images represented in Corporate Social Responsibility (CSR) reports of China Mobile and Vodafone.
Careful at Estimation and Bold at Exploration
In prior work, it has been shown that policy-based exploration is beneficial for continuous action space in deterministic policy reinforcement learning(DPRL).
Model Extraction Attacks on Split Federated Learning
We show that under practical cases, the proposed ME attacks work exceptionally well for SFL.
LMD: A Learnable Mask Network to Detect Adversarial Examples for Speaker Verification
It utilizes score variation as an indicator to detect adversarial examples, where the score variation is the absolute discrepancy between the ASV scores of an original audio recording and its transformed audio synthesized from its masked complex spectrogram.
Symmetric Saliency-based Adversarial Attack To Speaker Identification
Adversarial attack approaches to speaker identification either need high computational cost or are not very effective, to our knowledge.
An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data
Sampling is an essential part of raw point cloud data processing such as in the popular PointNet++ scheme.
The Sufficiency of Off-Policyness and Soft Clipping: PPO is still Insufficient according to an Off-Policy Measure
The popular Proximal Policy Optimization (PPO) algorithm approximates the solution in a clipped policy space.
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning
While such a scheme helps reduce the computational load at the client end, it opens itself to reconstruction of raw data from intermediate activation by the server.
Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations
Here we show that a dynamical neural network, trained on a minimal amount of data, can predict the behavior of spintronic devices with high accuracy and an extremely efficient simulation time, compared to the micromagnetic simulations that are usually employed to model them.
Tsformer: Time series Transformer for tourism demand forecasting
Based on the Transformer, we proposed a time series Transformer (Tsformer) with Encoder-Decoder architecture for tourism demand forecasting.
Communication and Computation Reduction for Split Learning using Asynchronous Training
An added benefit of the proposed communication reduction method is that the computations at the client side are reduced due to reduction in the number of client model updates.
Phase Noise and Frequency Accuracy in Crystal-less Wireless Edge Nodes
This paper presents a fundamental analysis connecting phase noise and long-term frequency accuracy of oscillators and explores the possibilities and limitations in crystal-less frequency calibration for wireless edge nodes from a noise-impact perspective.
Revenue and Energy Efficiency-Driven Delay Constrained Computing Task Offloading and Resource Allocation in a Vehicular Edge Computing Network: A Deep Reinforcement Learning Approach
In this paper, we propose a joint task type and vehicle speed-aware task offloading and resource allocation strategy to decrease the vehicl's energy cost for executing tasks and increase the revenue of the vehicle for processing tasks within the delay constraint.
Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): Comparing multi-electrode recordings from simulated and biological mammalian cortical tissue
A VERTEX-based simulation successfully reproduced features of the LFPs from an in vitro multi-electrode array recording of macaque neocortical tissue.
