Artificial Intelligence

Dataset was created for the purposes of exploring time distortion with non-ideal near-field conditions. A 90 Hz square wave is played at 100dBA through a bookshelf speaker with the port removed. The recordings were captured at 5 separate axial distances (From 2" to 17", following inverse square law), and at three levels of resistive loading (No added resistance, 1.5 ohm, 3 ohm). The DC resistance of the speaker was measured at 6.9 ohms. To avoid overtraining, captures were recorded on a moving dynamic microphone.  

Gramatika is a syntectic GEC dataset for Indonesian. The Gramatika dataset has a total of 1.5 million sentences with 4,666,185 errors. Of all sentences, only 30,000 (2%) are correct sentences with no mistakes. Each sentence has a maximum of 6 errors, and there can only be 2 of the same error type in each sentence.We also split the dataset into three splits: train, dev, and test splits, with the proportion of 8:1:1 (with the size of 1,199,705, 150,171, and 150,124 sentences, respectively).

This paper presents an enhanced methodology for network anomaly detection in Industrial IoT (IIoT) systems using advanced data aggregation and Mutual Information (MI)-based feature selection. The focus is on transforming raw network traffic into meaningful, aggregated forms that capture crucial temporal and statistical patterns. A refined set of 150 features including unique IP counts, TCP acknowledgment patterns, and ICMP sequence ratios was identified using MI to enhance detection accuracy.

This paper explores the cryptanalysis of the ASCON algorithm, a lightweight cryptographic method designed for applications like the Internet of Things (IoT). We utilize deep learning techniques to identify potential vulnerabilities within ASCON's structure. First, we provide an overview of how ASCON operates, including key generation and encryption processes.