*.csv; *.txt

Microarchitectural attacks have become more threatening the society than before with the increasing diversity of attacks such as Spectre and Meltdown. Vendor patches cannot keep up with the pace of the new threats, which makes the dynamic anomaly detection tools more evident than before. Unfortunately, hardware performance counters (HPCs) utilized in previous works lead to high performance overhead and detection of a few microarchitectural attacks due to the small number of counters that can be profiled concurrently.


These files are associated with the manuscript "Stability of Power Supply Bootstrapped Unity-Gain Buffers" and help reproduce and explore the figures from the paper, which show the main results. First, the results from simulation are imported and graphed as in Fig. 6. Next, the equations from the manuscript are implemented to show that they output correct predictions reproducing the Nyquist diagram from Fig. 7. Finally, the experimentally recorded step responses are imported and graphed as in Fig. 9.


Dataset haiving the curvature and spline values for the roundabout


These datasets report data of 64 Force Sensing Resistors at multiple voltages. It was foun that the input voltage can be used to trim sensors' sensitivity and ultimately to reduce dispersion. The DMAIC cycle was used to reduce process variability on the basis of the Six Sigma Methodology. The zip folder contains:

1) a Matlab file for loading the data

2) four .txt files with the experimental data of Force Sensing resistors


This dataset was produced as a part of my PhD research on Android malware detection using Multimodal Deep Learning. It contains raw data (DEX grayscale images), static analysis data (Android Intents & Permissions), and dynamic analysis data (system call sequences). For the conference research paper, please refer to https://sbic.org.br/eventos/cbic_2021/cbic2021-32/



The Dada dataset is associated with the paper “Debiasing Android Malware Datasets: How can I trust your results if your dataset is biased?”. The goal of this dataset is to provide a new updated dataset of goodware/malware applications that can be used by other researchers for performing experiments, for example, detection or classification algorithms. The dataset contains the applications hashes and some characteristics.


Simple text file obtained from manually scraping the web for the question "What is Machine Learning?".

The files contain the first paragraph/ page on the website's approach to answer the question. This data is not used for commercial purposes and is available to all.

This data is used in TAES to show how it can be used for plagiarism checking. The text files (*.txt) contain plain text and need no preprocessing to use. Simply read the file and assign the data to a string object. 


This dataset is taken from 20 subjects over a duration of 1 hour where experiments were done on the upper body bio-impedance with the following objectives:

a)     Evaluate the effect of externally induced perturbance at the SE interface caused by motion, applied pressure, temperature variation and posture change on bio-impedance measurements.

b)     Evaluate the degree of distortion due to artefact at multiple frequencies (10kHz-100kHz) in the bio-impedance measurements.


The PD-BioStampRC21 dataset provides data from a wearable sensor
accelerometry study conducted for studying activity, gait, tremor, and
other motor symptoms in individuals with Parkinson's disease (PD).  In
addition to individuals with PD, the dataset also includes data for
controls that also went through the same study protocol as the PD
participants.  Data were acquired using lightweight MC 10 BioStamp RC
sensors (MC 10 Inc, Lexington, MA), five of which were attached to
each participant for gathering data over a roughly two day


Amidst the COVID-19 pandemic, cyberbullying has become an even more serious threat. Our work aims to investigate the viability of an automatic multiclass cyberbullying detection model that is able to classify whether a cyberbully is targeting a victim’s age, ethnicity, gender, religion, or other quality. Previous literature has not yet explored making fine-grained cyberbullying classifications of such magnitude, and existing cyberbullying datasets suffer from quite severe class imbalances.