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Found 6 papers, 2 papers with code
An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge
In this paper, we present an energy-aware approach for the design and deployment of self-adaptive AI-based applications that can balance application objectives (e. g., accuracy in object detection and frames processing rate) with energy consumption.
Homogenization of Existing Inertial-Based Datasets to Support Human Activity Recognition
The final aim of our work is the definition and implementation of a platform that integrates datasets of inertial signals in order to make available to the scientific community large datasets of homogeneous signals, enriched, when possible, with context information (e. g., characteristics of the subjects and device position).
Cloud Failure Prediction with Hierarchical Temporal Memory: An Empirical Assessment
Hierarchical Temporal Memory (HTM) is an unsupervised learning algorithm inspired by the features of the neocortex that can be used to continuously process stream data and detect anomalies, without requiring a large amount of data for training nor requiring labeled data.
FILO: FIx-LOcus Localization for Backward Incompatibilities Caused by Android Framework Upgrades
Mobile operating systems evolve quickly, frequently updating the APIs that app developers use to build their apps.
Software Engineering
Personalization in Human Activity Recognition
In the recent years there has been a growing interest in techniques able to automatically recognize activities performed by people.
UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones
Nowadays, publicly available data sets are few, often contain samples from subjects with too similar characteristics, and very often lack of specific information so that is not possible to select subsets of samples according to specific criteria.
