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Found 9 papers, 2 papers with code
Comparing AI Algorithms for Optimizing Elliptic Curve Cryptography Parameters in Third-Party E-Commerce Integrations: A Pre-Quantum Era Analysis
This paper presents a comparative analysis between the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), two vital artificial intelligence algorithms, focusing on optimizing Elliptic Curve Cryptography (ECC) parameters.
GeXSe (Generative Explanatory Sensor System): An Interpretable Deep Generative Model for Human Activity Recognition in Smart Spaces
The standard approach is based on a CNN model, which our MLP model outperforms. GeXSe offers two types of explanations: sensor-based activation maps and visual domain explanations using short videos.
Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor Streams
We show that by learning a representation specifically with the segmentation objective based on maximum mean discrepancy (MMD), our algorithm can robustly detect time-series events across different applications.
A Review of the Non-Invasive Techniques for Monitoring Different Aspects of Sleep
In this paper, we present a comprehensive survey of the latest research works (2015 and after) conducted in various categories of sleep monitoring including sleep stage classification, sleep posture recognition, sleep disorders detection, and vital signs monitoring.
RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning
We introduce a new semi-supervised, time series anomaly detection algorithm that uses deep reinforcement learning (DRL) and active learning to efficiently learn and adapt to anomalies in real-world time series data.
SECRET: Semantically Enhanced Classification of Real-world Tasks
SECRET performs classifications by fusing the semantic information of the labels with the available data: it combines the feature space of the supervised algorithms with the semantic space of the NLP algorithms and predicts labels based on this joint space.
Time Series Segmentation through Automatic Feature Learning
As such, we have seen many recent IoT data sets include annotations with a human expert specifying states, recorded as a set of boundaries and associated labels in a data sequence.
Get More With Less: Near Real-Time Image Clustering on Mobile Phones
In this paper, we show that by combining the computing power distributed over a number of phones, judicious optimization choices, and contextual information it is possible to execute the end-to-end pipeline entirely on the phones at the edge of the network, efficiently.
Sensor-Type Classification in Buildings
One important aspect of normalization is to differentiate sensors by the typeof phenomena being observed.
