Opportunity++ is a precisely annotated dataset designed to support AI and machine learning research focused on the multimodal perception and learning of human activities (e.g. short actions, gestures, modes of locomotion, higher-level behavior).

Instructions: 

Complete documentation is provided in the readme.

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Human intention is an internal, mental characterization for acquiring desired information. Frominteractive interfaces, containing either textual or graphical information, intention to perceive desiredinformation is subjective and strongly connected with eye gaze. In this work, we determine such intention byanalyzing real-time eye gaze data with a low-cost regular webcam.

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This dataset is composed by both real and sythetic images of power transmission lines, which can be fed to deep neural networks training and applied to line's inspection task. The images are divided into three distinct classes, representing power lines with different geometric properties. The real world acquired images were labeled as "circuito_real" (real circuit), while the synthetic ones were identified as "circuito_simples" (simple circuit) or "circuito_duplo" (double circuit). There are 290 total images for each class, 232 inteded for training and 58 aimed for validation/testing.

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Any work using this dataset should cite the following paper:

Nirmalya Thakur, Saumick Pradhan, and Chia Y. Han, “Investigating the impact of COVID-19 on Online Learning-based Web Behavior”, Proceedings of the 7th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications (IHIET-AI 2022), Lausanne, Switzerland, April 21-23, 2022 (Submitted)

Abstract

Instructions: 

For details on instructions on how to use the dataset, the above mentioned paper may be studied.

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Any work using this dataset should cite the following paper:

Nirmalya Thakur, Isabella Hall, and Chia Y. Han, “Investigating the Emergence of Online Learning in Different Countries using the 5 W’s and 1 H Approach”, Proceedings of the 7th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications (IHIET-AI 2022), Lausanne, Switzerland, April 21-23, 2022

Abstract

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Any work using this dataset should cite the following paper:

 Nirmalya Thakur and Chia Y. Han, "An open access dataset of tweets related to exoskeletons and 100 research questions, " arXiv [cs.CY], 2021.

 

Abstract

 

Instructions: 

Please refer to the paper

Nirmalya Thakur and Chia Y. Han, "An open access dataset of tweets related to exoskeletons and 100 research questions", arXiv [cs.CY], 2021.

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The dataset contains temperature measurements taken with an 8x8 infrared array (Panasonic Grid-EYE) over a period of three weeks during 2018 in Bucharest, Romania, in an educational facility.

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Device fingerprints are extremely attractive hardware security functions for anti-counterfeiting, device identification and authentication, two-factor authentication and so on. Given the ubiquity of memory in commodity electronic devices, fingerprinting memory is a compelling proposition, especially for low-end Internet of Things (IoT) devices where cryptographic modules are often unavailable.

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Opportunity++ is a precisely annotated dataset designed to support AI and machine learning research focused on the multimodal perception and learning of human activities (e.g. short actions, gestures, modes of locomotion, higher-level behavior).

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This repository contains code and instruction to reproduce the experiments presented in the paper
"A Methodology and Simulation-based Toolchain for Estimating Deployment Performance of Intelligent Collective Services at the Edge"
by Roberto Casadei, Giancarlo Fortino, Danilo Pianini, Andrea Placuzzi, Claudio Savaglio, and Mirko Viroli.

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