Arabic Syntactic Diacritics Restoration Using BERT Models.

The Arabic syntactic diacritics restoration problem is often solved using long short-term memory (LSTM) networks. Handcrafted features are used to augment these LSTM networks or taggers to improve performance. A transformer-based machine learning technique known as bidirectional encoder representations from transformers (BERT) has become the state-of-the-art method for natural language understanding in recent years.

A Multimodal Approach to Improve Performance Evaluation of Call Center Agent.

The paper proposes three modeling techniques to improve the performance evaluation of the call center agent. The first technique is speech processing supported by an attention layer for the agent's recorded calls. The speech comprises 65 features for the ultimate determination of the context of the call using the Open-Smile toolkit.

Countering DDoS Attacks in SIP Based VoIP Networks Using Recurrent Neural Networks.

Many companies have transformed their telephone systems into Voice over IP (VoIP) systems. Although implementation is simple, VoIP is vulnerable to different types of attacks. The Session Initiation Protocol (SIP) is a widely used protocol for handling VoIP signaling functions.