We present a sound-based anomaly detection system to diagnose printer health. Also, we improve the model performance by using acoustic data augmentation. We first use the detector to extract the important acoustic information from the input printer sound. Second, we use principal component analysis to do feature extraction. Third, we feed the extracted features from the previous step into the two different anomaly detection models to evaluate the model performances. Finally, we go through the same system pipeline with different augmented training data to see whether or not acoustic data augmentation can improve the model performance.
Automatic detection of crowd congestion in high density crowds is a challenging problem, with substantial interest for safety and security applications. In this paper, we propose a method that can automatically identify and localize congested regions in crowded videos. Our proposed method is based on the notion that pedestrians in the congested region follow a particular behavior. Pedestrians in the congested areas cannot move freely due to space unavailability and tend to undergo lateral oscillations. In our method, we first extract trajectories by using particle advection technique and then compute oscillatory features for each trajectory. Trajectories with higher oscillation values and with less proximity are clustered, indicating the congested regions. We perform experiments on a diversity of challenging scenarios. From the experimental results, we show that our method provides precise localization of congested regions in crowd videos.
Cyber security has become an increasingly important topic in recent years. The increasing popularity of systems and devices such as computers, servers, smartphones, tablets and smart home devices is causing a rapidly increasing attack surface. In addition, there are a variety of security vulnerabilities in software and hardware that make the security situation more complex and unclear. Many of these systems and devices also process personal or secret data and control critical processes in the industry. The need for security is tremendously high. The owners and administrators of modern computer systems are often overwhelmed with the task of securing their systems as the systems become more complex and the attack methods increasingly intelligent. In these days a there are a lot of encryption and hiding techniques available. They are used to make the detection of malicious software with signature based scanning methods very difficult. Therefore, novel methods for the detection of such threats are necessary. This paper examines whether cyber threats can be detected using modern artificial intelligence methods. We develop, describe and test a prototype for windows systems based on neural networks. In particular, an anomaly detection based on autoencoders is used. As this approach has shown, it is possible to detect a wide range of threats using artificial intelligence. Based on the approach in this work, this research topic should be continued to be investigated. Especially cloud-based solutions based on this principle seem to be very promising to protect against modern threats in the world of cyber security.