Machine Learning
The results are based on the measurements conducted on small drones and a bionic bird using a 60 GHz millimeter wave radar, analyzing their micro-Doppler characteristics in both time and frequency domain. The results are presented in .pkl format. The more detailed description of the data and how the authors processed it will be updated soon.
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This dataset presents real-world IoT device traffic captured under a scenario termed "Active," reflecting typical usage patterns encountered by everyday users. Our methodology emphasizes the collection of authentic data, employing rigorous testing and system evaluations to ensure fidelity to real-world conditions while minimizing noise and irrelevant capture.
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The rise of e-commerce in Latin America has been driven by the digital presence of the younger generations and the adaptation of retail businesses to online sales channels. The COVID-19 pandemic has further accelerated this shift, forcing businesses to enhance their online commerce strategies. Peru has witnessed a notable 131\% increase in online shoppers from 2019 to 2021. However, the absence of a unique global code for product identification negatively affects the Zero Moment of Truth (ZMOT) in customer decision-making.
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An AI-based Ancient Hebrew Language Translator aims to revive Ancient Hebrew by constructing a comprehensive dataset with contemporary and ancient Hebrew samples. Seamless integration of the Google Vision API facilitates Optical Character Recognition (OCR) for image processing. The translation process initiates in English through the model, leading to a multilingual interface. This initiative represents a crucial step in preserving ancient languages in the digital age.
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Weconsiderfivebenchmarkdatasets-Pokec-z,NBA,
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Standard dataset of the Tennessee–Eastman (TE) process.
The overall process consists of five operating units: reactor, condenser, vapor-liquid separator, recycle compressor and product stripper.
It has standard training and test data sets for soft sensor, fault detection and diagnosis, fault classification, etc. Each data set is under different operating conditions.
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Containerization has emerged as a revolutionary technology in the software development and deployment industry. Containers offer a portable and lightweight solution that allows for packaging applications and their dependencies systematically and efficiently. In addition, containers offer faster deployment and near-native performance with isolation and security drawbacks compared to Virtual Machines. To address the security issues, scanning tools that scan containers for preexisting vulnerabilities have been developed, but they suffer from false positives.
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This dataset contains the online appendix of the paper titled "The effectiveness of hidden dependence metrics in bug prediction"
Abstract:
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The rapid evolution of wireless technology has led to the proliferation of small, low-power IoT devices, often constrained by traditional battery limitations, resulting in size, weight, and maintenance challenges. In response, ambient radio frequency (RF) energy harvesting has emerged as a promising solution to power IoT devices using RF energy from the environment. However, optimizing the placement of energy harvesters is crucial for maximizing energy reception. This paper employs machine learning (ML) techniques to predict areas with high power intensity for RF energy harvesting.
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These datasets are gathered from an array of four gas sensors to be used for the odor detection and recognition system. The smell inspector Kit IX-16 used to create the dataset. each of 4 sensor has 16 channels of readings. Odors of different 12 samples are taken from these six sensors
1- Natural Air
2- Fresh Onion
3- Fresh Garlic
4- Black Lemon
5- Tomato
6- Petrol
7- Gasoline
8- Coffee
9- Orange
10- Colonia Perfume
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