Detection of weapon possession and fire in Public Safety surveillance cameras

The employment of video surveillance cameras by public safety agencies enables incident detection in monitored cities by using object detection for scene description, enhancing the protection to the general public. Object detection has its drawbacks, such as false positives. Our work aims to enhance object detection and image classification by employing IoU (Intersection over Union) to minimize the false positives and identify weapon holders or fire in a frame, adding more information to the scene.

Incident detection and classification in renewable energy news using pre-trained language models on deep neural networks

The surge of renewable energy systems can lead to increasing incidents that negatively impact economics and society, rendering incident detection paramount to understand the mechanism and range of those impacts. In this paper, a deep learning framework is proposed to detect renewable energy incidents from news articles containing accidents in various renewable energy systems. The pre-trained language models like Bidirectional Encoder Representations from Transformers (BERT) and word2vec are utilized to represent textual inputs, which are trained by the Text Convolutional Neural Networks (TCNNs) and Text Recurrent Neural Networks. Two types of classifiers for incident detection are trained and tested in this paper, one is a binary classifier for detecting the existence of an incident, the other is a multi-label classifier for identifying different incident attributes such as causal-effects and consequences, etc. The proposed incident detection framework is implemented on a hand-annotated dataset with 5 190 records. The results show that the proposed framework performs well on both the incident existence detection task (F1-score 91.4%) and the incident attributes identification task (micro F1-score 81.7%). It is also shown that the BERT-based TCNNs are effective and robust in detecting renewable energy incidents from large-scale textual materials.

Predicting of cyber attacks on critical information infrastructure

According to modern statistics and analytical reviews, targeted computer attacks (cyber attacks) are becoming more and more numerous. Attackers began to use non-standard schemes for implementing attacks, using employees of organizations as intermediaries, which reduces the efficiency of detecting violations. At the same time, the targets of attackers are increasingly critical information infrastructure (CII) objects. The number of cyberattacks on the critical infrastructure of the Russian Federation increased by 150%. Successful attacks on CII are associated with a lack of software updates for industrial equipment, personnel errors, incorrect configuration of protection tools and can potentially lead to disasters. Prediction of computer attacks on CII based on a comprehensive analysis of the characteristics of incidents and system users can significantly increase the efficiency of incident detection, since it is obvious that technical and anthropogenic characteristics in this case should be taken into account together. It is difficult to classify computer incidents due to the volume and heterogeneity of the data about them. The paper proposes approaches that provide for the initial systematization of system log data and user characteristics, an assessment of their informativeness. This will reduce the complexity of further data processing and increase the performance of the computer attack forecasting system by excluding some uninformative data from a single secure storage. The second important task is to create test systems based on available platforms for analyzing and detecting computer incidents in order to train future information security specialists in big data analysis technologies.

A Cost-Benefit Analysis of the Installation of an Automatic Incident Detection System in Vilnius

A well-functioning transport network is a key element of a successful economy. One of the biggest problems in transportation is the large number of vehicles, which leads to congestion. Traffic congestion negatively influences the social and economic environment: it creates pollution and causes many accidents. A variety of innovative technologies are being applied in all areas. Different countries use Intelligent Transport Systems to create a safer, more efficient, and sustainable transport system to monitor and manage traffic flow. The application of Intelligent Transport Systems helps solve problems in the transport sector: Intelligent Transport Systems helps manage traffic flows, reduce accident rates, environmental pollution, and inform drivers and passengers about the traffic situation. Consequently, Intelligent Transport System improves the efficiency of the transport system, the quality of services, increases mobility, reduces energy consumption, and reduces the negative impact of vehicles on the environment. As reported by the Department of Statistics data in Lithuania, the number of road vehicles in the Vilnius region increases every year. Traffic accidents remain constant, and long-lasting traffic jams occur. Due to these reasons, Vilnius and its residents incur economic costs, and the transport system is used inefficiently. This article examines the importance of Intelligent Transport System application in solving congestion, pollution and accidents in Vilnius. A cost-benefit analysis of the Automatic Incident Detection System installation in Vilnius is performed.

Anti-HPV16 Antibody Titers Prior to an Incident Cervical HPV16/31 Infection

The goal of this study was to investigate the serological titers of circulating antibodies against human papillomavirus (HPV) type 16 (anti-HPV16) prior to the detection of an incident HPV16 or HPV31 infection amongst vaccinated participants. Patients were selected from a prospective post-HPV vaccine longitudinal cohort at Mount Sinai Adolescent Health Center in Manhattan, NY. We performed a nested case–control study of 43 cases with incident detection of cervical HPV16 (n = 26) or HPV31 (n = 17) DNA who had completed the full set of immunizations of the quadrivalent HPV vaccine (4vHPV). Two control individuals whom had received three doses of the vaccine (HPV16/31-negative) were selected per case, matched on age at the first dose of vaccination and follow-up time in the study: a random control, and a high-risk control that was in the upper quartile of a sexual risk behavior score. We conducted an enzyme-linked immunosorbent assay (ELISA) for the detection of immunoglobulin G (IgG) antibodies specific to anti-HPV16 virus-like particles (VLPs). The results suggest that the average log antibody titers were higher among high-risk controls than the HPV16/31 incident cases and the randomly selected controls. We show a prospective association between anti-HPV16 VLP titers and the acquisition of an HPV16/31 incident infection post-receiving three doses of 4vHPV vaccine.

VANET-Based Traffic Monitoring and Incident Detection System: A Review

<span>As a component of intelligent transport systems (ITS), vehicular ad hoc network (VANET), which is a subform of manet, has been identified. It is established on the roads based on available vehicles and supporting road infrastructure, such as base stations. An accident can be defined as any activity in the environment that may be harmful to human life or dangerous to human life. In terms of early detection, and broadcast delay. VANET has shown various problems. The available technologies for incident detection and the corresponding algorithms for processing. The present problem and challenges of incident detection in VANET technology are discussed in this paper. The paper also reviews the recently proposed methods for early incident techniques and studies them.</span>

The Local Government’s Position in the Polish Cybersecurity System

This article discusses the local government’s position in the national cybersecurity system. It refers to the status of the local government administration in cyberspace, including the duties and responsibilities ensuring cybersecurity. In Poland, the local government is considered the basic form of decentralisation of public power, as a result of which the legislator has entrusted it with a significant portion of public duties. The list of such duties also encompasses telecommunication responsibilities carried out in cyberspace. In general practice, cyberspace is also used to carry out other responsibilities. The local government has the most extensive knowledge on the matters concerning a given (local or regional) community, referring also to cybersecurity; however, the legislator has not awarded this entity with any special status. It is merely one of the many entities forming the national cybersecurity system. Inter alia, the local government is obliged to carry out a range of activities aimed at incident detection, incident cause analysis, and corrective actions. It is also expected to ensure the appropriate incident management which includes, inter alia, incident handling and eliminating incident causes.