Human interaction in natural language with computer systems has been a prime focus of research, and the field of conversational agents (including chatbots and Interactive Voice Response (IVR) systems) has evolved significantly since 2009, with a major boost in 2016, especially for industrial solutions. Emergency systems are crucial elements of today’s societies that can benefit from the advantages of intelligent human–computer interaction systems. In this paper, we present two solutions for human-to-computer emergency systems with critical deadlines that use a multi-layer FreeSwitch IVR solution and the Botpress chatbot platform. We are the pioneers in Romania who designed and implemented such a solution, which was evaluated in terms of performance and resource management concerning Quality of Service (QoS). Additionally, we assessed our Proof of Concept (PoC) with real data as part of the system for real-time Romanian transcription of speech and recognition of emotional states within emergency calls. Based on our feasibility research, we concluded that the telephony IVR best fits the requirements and specifications of the national 112 system, with the presented PoC ready to be integrated into the Romanian emergency system.

Ground and infrastructure stability are important for our technologically based civilization. Infrastructure projects take into consideration the risk posed by ground displacement (e.g., seismicity, geological conditions and geomorphology). To address this risk, earth scientists and civil engineers employ a range of measurement technologies, such as optical/laser leveling, GNSS and, lately, SAR interferometry. Currently there is a rich source of measurement information provided in various formats that covers most of the industrialized world. Integration of this information becomes an issue that will only increase in importance in the future. This work describes a practical approach to address and validate integrated stability measurements through the development of a platform that could be easily used by a variety of groups, from geoscientists to civil engineers and also private citizens with no training in this field. The platform enables quick cross-validation between different data sources, easy detection of critical areas at all scales (from large-scale individual buildings to small-scale tectonics) and can be linked to end-users from various monitoring fields and countries for automated notifications. This work is closing the gap between the specialized monitoring work and the general public, delivering the full value of technology for societal benefits in a free and open manner. The platform is calibrated and validated by an application of SAR interferometry data to specific situations in the general area of the Romanian Carpathians and their foreland. The results demonstrate an interplay between anthropogenically induced changes and high-amplitude active tectono–sedimentary processes creating rapid regional and local topographic variations.

The evolution of Natural Language Processing technologies transformed them into viable choices for various accessibility features and for facilitating interactions between humans and computers. A subset of them consists of speech processing systems, such as Automatic Speech Recognition, which became more accurate and more popular as a result. In this article, we introduce an architecture built around various speech processing systems to enhance Romanian emergency services. Our system is designed to help the operator evaluate various situations with the end goal of reducing the response times of emergency services. We also release the largest high-quality speech dataset of more than 150 h for Romanian. Our architecture includes an Automatic Speech Recognition model to transcribe calls automatically and augment the operator’s notes, as well as a Speech Recognition model to classify the caller’s emotions. We achieve state-of-the-art results on both tasks, while our demonstrator is designed to be integrated with the Romanian emergency system.

Network Intrusion Detection Systems (NIDS) are a critical component in any security system and their role is to monitor traffic on a network, looking for suspicious activity (e.g. attacks, unauthorized activities). Neural networks have become an increasingly popular solution for implementing network intrusion detection. Their ability to identify complex patterns and behaviours have made them a suitable solution for differentiating between normal and malicious traffic. One of the major problems encountered in implementing such neural networks is that they are computational and memory expensive and they can cause massive overhead in small embedded devices. This paper proposes a deep autoencoded dense neural network for detecting network attacks, trained and evaluated on NSL-KDD dataset that can satisfy the memory and real time constraints of such systems. The hyperparameters of the model are tuned, and three variants with different sizes and accuracies are proposed in order to produce flexibility in choosing the correct architecture for the desired platform.

This paper analyzes a through-the-wall synthetic aperture radar (SAR) imaging system designed for indoor use with imprecise positioning systems. Due to uncertainties in transceiver positions at scanning timestamps and the defocusing effects caused by multi-medium (air-wall-air) wave propagation, an autofocus algorithm is essential to achieve high-quality SAR imaging for further detection purposes. We propose a fast and accurate autofocus procedure integrated with the back-projection algorithm, outperforming conventional SAR autofocus algorithms (e.g., map drift algorithm, phase gradient algorithm). Furthermore, we present extensive simulated and experimental results for two indoor positioning systems: a stereoscopic camera and a radio frequency localization system based on the off-the-shelf Pulson P440 transceiver. The primary contributions of this paper include a novel SAR autofocus algorithm based on a prominent scatterer located in front of or behind the wall, along with a fast, recursive routine for solving the nonlinear system of equations associated with the 3D localization system.